"Authors","Author full names","Author(s) ID","Title","Year","Source title","Volume","Issue","Art. No.","Page start","Page end","Page count","Cited by","DOI","Link","Affiliations","Authors with affiliations","Abstract","Author Keywords","Index Keywords","Molecular Sequence Numbers","Chemicals/CAS","Tradenames","Manufacturers","Funding Details","Funding Texts","References","Correspondence Address","Editors","Publisher","Sponsors","Conference name","Conference date","Conference location","Conference code","ISSN","ISBN","CODEN","PubMed ID","Language of Original Document","Abbreviated Source Title","Document Type","Publication Stage","Open Access","Source","EID"
"Mosconi G.; Randall D.; Karasti H.; Aljuneidi S.; Yu T.; Tolmie P.; Pipek V.","Mosconi, Gaia (57194540455); Randall, Dave (7202208815); Karasti, Helena (55912546300); Aljuneidi, Saja (57904842900); Yu, Tong (58067660500); Tolmie, Peter (6602706528); Pipek, Volkmar (8541973000)","57194540455; 7202208815; 55912546300; 57904842900; 58067660500; 6602706528; 8541973000","Designing a Data Story: A Storytelling Approach to Curation, Sharing and Data Reuse in Support of Ethnographically-driven Research","2022","Proceedings of the ACM on Human-Computer Interaction","6","CSCW2","289","","","","0","10.1145/3555180","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144542350&doi=10.1145%2f3555180&partnerID=40&md5=f5bd1e533106f2cbc8681b48bddec931","University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; IT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen, 2300, Denmark; OFFIS-Institute for Information Technology, Escherweg 2, Oldenburg, 26121, Germany","Mosconi G., University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; Randall D., University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; Karasti H., IT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen, 2300, Denmark; Aljuneidi S., OFFIS-Institute for Information Technology, Escherweg 2, Oldenburg, 26121, Germany; Yu T., University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; Tolmie P., University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; Pipek V., University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany","In this paper, we introduce an innovative design concept for the curation of data, which we call 'Data Story'. We view this as an additional resource for data curation, aimed specifically at supporting the sharing of qualitative and ethnographic data. The Data Story concept is motivated by three elements: 1.The increased attention of funding agencies and academic institutions on Research Data Management and Open Science; 2. our own work with colleagues applying ethnographic research methods; and 3. existing literature that has identified specific challenges in this context. Ongoing issues entailed in dealing with certain contextual factors that are inherent to qualitative research reveal the extent to which we still lack technical design solutions that can support meaningful curation and sharing. Data Story provides a singular way of addressing these issues by integrating traditional data curation approaches, where research data are treated as 'objects' to be curated and preserved according to specific standards, with a more contextual, culturally-nuanced and collaborative organizing layer that can be thought of as a ""Story"". The concept draws on existing literature on data curation, digital storytelling and Critical Data Studies (CDS). As a possible design solution for Research Data Management and data curation, Data Story offers: 1) a collaborative workflow for data curation; 2) a story-like format that can serve as an organizing principle; 3) a means of enhancing and naturalizing curation practices through storytelling. Data Story is currently being developed for deployment and evaluation.  © 2022 ACM.","conceptual design; data curation; data reflexivity; data storytelling; open science; qualitative data sharing; qualitative research; research collaboration","Information management; Curation; Data curation; Data reflexivity; Data Sharing; Data storytelling; Open science; Qualitative data; Qualitative data sharing; Qualitative research; Research collaborations; Conceptual design","","","","","","","Antes A.L., Walsh H.A., Strait M., Hudson-Vitale C.R., Dubois J.M., Examining Data Repository Guidelines for Qualitative Data Sharing, J. Empir. Res. Hum. Res. Ethics, 13, 1, pp. 61-73, (2018); Barrett H., Researching and Evaluating Digital Storytelling as a Deep Learning Tool, Soc. Inf. Technol. Teach. Educ. Int. Conf, 2006, 1, pp. 647-654, (2006); Bartling S., Friesike S., Sönke Bartling & Sascha Friesike, (2014); Van Den Berg H., Reanalyzing qualitative interviews from different angles: The risk of decontextualization and other problems of sharing qualitative data, Hist. Soc. Res, 6, 1, pp. 179-192, (2008); Birnholtz J.P., Bietz M.J., Data at Work: Supporting Sharing in Science and Engineering, Proc. Siggr. Conf. Support. Gr. Work, pp. 339-348, (2003); Bishop L., Ethical Sharing and Reuse of Qualitative Data, Aust. J. Soc. Issues, 44, 3, pp. 255-272, (2009); Bishop L., Using archived qualitative data for teaching: Practical and ethical considerations, Int. J. Soc. Res. Methodol, 15, 4, pp. 341-350, (2012); Bishop L., Re-using Qualitative Data: A Little Evidence, On-going Issues and Modest Reflections, Stud. Socjol, 3, 214, pp. 167-176, (2014); Blomberg J., Karasti H., Reflections on 25 Years of Ethnography in CSCW, Comput. Support. Coop. Work, 22, 4-6, pp. 373-423, (2013); Borgman C.L., Scharnhorst A., Golshan M.S., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, J. Assoc. Inf. Sci. Technol, 70, 8, pp. 888-904, (2019); Burge J.E., Carroll J.M., McCall R., Mistrik I., Rationale-based Software Engineering, (2008); Dalton C.M., Thatcher J., What does a critical data studies look like, and why do we care? Seven points for a critical approach to ?big data, Soc. Sp, 29, (2014); Dalton C.M., Taylor L., Thatcher J., Critical Data Studies: A dialog on data and space, Big Data Soc, 3, (2016); Aull Davies C., Reflexive Ethnography : A Guide to Researching Selves and Others, (2008); Demian P., Fruchter R., Effective visualisation of design versions: Visual storytelling for design reuse, Res. Eng. des, 19, 4, pp. 193-204, (2009); Denning S., Effective storytelling: Strategic business narrative techniques, Strateg. Leadersh, 34, 1, pp. 42-48, (2006); Dourish P., Gomez Cruz E., Datafication and data fiction: Narrating data and narrating with data, Big Data Soc, 5, (2018); Duarte N., Data Story : Explain Data and Inspire Action Through Story, (2019); Eberhard I., Kraus W., Der Elefant im Raum. Ethnographisches Forschungsdatenmanagement als Herausforderung för Repositorien, Mitteilungen der Vereinigung Österreichischer Bibl. und Bibl, 71, 1, pp. 41-52, (2018); H2020 Programme Guidelines on FAIR Data Management in Horizon, (2020); Van Karin E.S., Schafer M.T., The Datafied Society. Studying Culture Through Data, (2017); Faniel I.M., Jacobsen T.E., Reusing Scientific Data, Computer Supported Cooperative Work, pp. 9117-9118, (2010); Feger S.S., Wozniak P.W., Lischke L., Schmidt A., Yes, i comply!"" Motivations and Practices around Research Data Management and Reuse across Scientific Fields, Proceedings of the ACM on Human-Computer Interaction, pp. 1-26, (2020); Fenlon K., Modeling Digital Humanities Collections as Research Objects, ACM/ IEEE Joint Conference on Digital Libraries (JCDL, pp. 138-147, (2019); Game A., Metcalfe A., Passionate Sociology, (1996); Lisa Gitelman, Raw Data Is An Oxymoron, (2013); Heaton J., Secondary analysis of qualitative data: An overview, Hist. Soc. Res, 33, 3, pp. 33-45, (2008); Iliadis A., Russo F., Critical data studies: An introduction, Big Data Soc, 3, (2016); Karasti H., Baker K.S., Bowker G.C., Ecological storytelling and collaborative scientific activities, ACM Siggr. Bull, 23, 2, pp. 29-30, (2002); Kervin K., Cook R.B., Michener W.K., The Backstage Work of Data Sharing, Proceedings of the 18th International Conference on Supporting Group Work-GROUP ?14, pp. 152-156, (2014); Kitchin R., The Data Revolution. Big Data, Open Data, Data Infrastructures & Their Consequences, (2014); Kitchin R., Data Lives: How Data Are Made and Shape Our World, (2021); Kitchin R., Lauriault T.P., Towards critical data studies: Charting and unpacking data assemblages and their work, Thinking Big Data in Geography: New Regimes, New Research, (2014); Knaflic C.N., Storytelling with Data: A Data Visualization Guide for Business Professionals, (2015); Lee J., Design rationale systems: Understanding the issues, IEEE Expert. Syst.Their Appl, 12, 3, pp. 78-85, (1997); Linde C., Narrative and social tacit knowledge, J. Knowl. Manag, 5, 2, pp. 160-171, (2001); Mannheimer S., Pienta A., Kirilova D., Elman C., Wutich A., Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges, Am. Behav. 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Knowledge and preparedness among qualitative researchers, IRB Members, and data repository curators, IASSIST, 43, (2020); Neale B., Adding Time into the Mix: Stakeholder Ethics in Qualitative Longitudinal Research, Methodol. Innov. Online, 8, 2, pp. 6-20, (2013); Nelson S.D., Simek J.W., Data Dumps: The Bane of E-Discovery, OR. ST. B. BULL, 71, 2011, pp. 36-36, (2011); Ojo A., Heravi B., Patterns in award winning data storytelling: Story types, enabling tools and competences, Digit. Journal, 6, 6, pp. 693-718, (2017); Rawson K., Munoz T., Against cleaning, Curating Menus, 6, 2016, pp. 1-14, (2016); Restrepo E., Davis L., Storytelling: Both Art and Therapeutic Practice, Int. J. Hum. Caring, 7, 1, pp. 43-48, (2003); Ruggiano N., Perry T.E., Conducting secondary analysis of qualitative data: Should we, can we, and how?, Qual. Soc. Work, 18, 1, pp. 81-97, (2019); Ryen A., Ethics and qualitative research, Qual. Res, 3, 2011, pp. 416-238, (2011); Sacks Harvey., Lectures on Conversation, 1, (1992); Sole D., Gray Wilson D., Storytelling in Organizations: The power and traps of using stories to share knowledge in organizations, LILA, Harvard, Grad. Sch. Educ, 2002, pp. 1-12, (2002); Sturm B.W., Beth Nelson S., With Our Own Words: Librarians? Perceptions of the Values of Storytelling in Libraries, Storytell. Self, Soc, 12, 1, pp. 4-23, (2016); Thomer A.K., Wickett K.M., Relational data paradigms: What do we learn by taking the materiality of databases seriously?, Big Data Soc, 7, (2020); Treloar A., Data management and the curation continuum: How the Monash experience is informing repository relationships, VALA2008 Proc, 2008, (2008); Tsai A.C., Kohrt B.A., Matthews L.T., Betancourt T.S., Lee J.K., Papachristos A.V., Weiser S.D., Dworkin S.L., Promises and pitfalls of data sharing in qualitative research, Soc. Sci. 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"Niso G.; Botvinik-Nezer R.; Appelhoff S.; De La Vega A.; Esteban O.; Etzel J.A.; Finc K.; Ganz M.; Gau R.; Halchenko Y.O.; Herholz P.; Karakuzu A.; Keator D.B.; Markiewicz C.J.; Maumet C.; Pernet C.R.; Pestilli F.; Queder N.; Schmitt T.; Sójka W.; Wagner A.S.; Whitaker K.J.; Rieger J.W.","Niso, Guiomar (55774526600); Botvinik-Nezer, Rotem (57201064006); Appelhoff, Stefan (57192913960); De La Vega, Alejandro (55925533200); Esteban, Oscar (6601965136); Etzel, Joset A. (14022702000); Finc, Karolina (57194075074); Ganz, Melanie (35172848700); Gau, Rémi (23093525700); Halchenko, Yaroslav O. (6503870081); Herholz, Peer (55216559100); Karakuzu, Agah (56529447700); Keator, David B. (7801381750); Markiewicz, Christopher J. (57190371018); Maumet, Camille (35746407900); Pernet, Cyril R. (16070297800); Pestilli, Franco (6506525986); Queder, Nazek (57221477540); Schmitt, Tina (57338339100); Sójka, Weronika (57917163500); Wagner, Adina S. (57194276814); Whitaker, Kirstie J. (24774205600); Rieger, Jochem W. (12798625100)","55774526600; 57201064006; 57192913960; 55925533200; 6601965136; 14022702000; 57194075074; 35172848700; 23093525700; 6503870081; 55216559100; 56529447700; 7801381750; 57190371018; 35746407900; 16070297800; 6506525986; 57221477540; 57338339100; 57917163500; 57194276814; 24774205600; 12798625100","Open and reproducible neuroimaging: From study inception to publication","2022","NeuroImage","263","","119623","","","","8","10.1016/j.neuroimage.2022.119623","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139338259&doi=10.1016%2fj.neuroimage.2022.119623&partnerID=40&md5=5ada4b69de58bc7b50c5bfa0024d4725","Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States; Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain; Instituto Cajal, CSIC, Madrid, Spain; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States; Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany; Department of Psychology, University of Texas at Austin, Austin, TX, United States; Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Psychology, Stanford University, Stanford, CA, United States; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States; Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland; Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark; Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium; Montreal Neurological Institute-Hospital, McGill University, Montréal, QC, Canada; Biomedical Engineering Institute, Polytechnique Montréal, Montréal, QC, Canada; Montréal Heart Institute, Montréal, QC, Canada; Department of Psychiatry and Human Behavior, University of California, Irvine, CA, United States; Inria, Univ Rennes, CNRS, Inserm – IRISA UMR 6074, Empenn ERL U 1228, Rennes, France; Department of Psychology, The University of Texas at Austin, Austin, TX, United States; Department of Neurobiology and Behavior, University of California, Irvine, CA, United States; Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland; Institute for Neuroscience and Medicine, Research Centre Juelich, Germany; The Alan Turing Institute, British Library, London, United Kingdom; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany","Niso G., Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States, Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain, Instituto Cajal, CSIC, Madrid, Spain; Botvinik-Nezer R., Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States; Appelhoff S., Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany; De La Vega A., Department of Psychology, University of Texas at Austin, Austin, TX, United States; Esteban O., Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, Department of Psychology, Stanford University, Stanford, CA, United States; Etzel J.A., Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States; Finc K., Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland; Ganz M., Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark; Gau R., Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium; Halchenko Y.O., Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States; Herholz P., Montreal Neurological Institute-Hospital, McGill University, Montréal, QC, Canada; Karakuzu A., Biomedical Engineering Institute, Polytechnique Montréal, Montréal, QC, Canada, Montréal Heart Institute, Montréal, QC, Canada; Keator D.B., Department of Psychiatry and Human Behavior, University of California, Irvine, CA, United States; Markiewicz C.J., Department of Psychology, Stanford University, Stanford, CA, United States; Maumet C., Inria, Univ Rennes, CNRS, Inserm – IRISA UMR 6074, Empenn ERL U 1228, Rennes, France; Pernet C.R., Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Pestilli F., Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States, Department of Psychology, The University of Texas at Austin, Austin, TX, United States; Queder N., Montreal Neurological Institute-Hospital, McGill University, Montréal, QC, Canada, Department of Neurobiology and Behavior, University of California, Irvine, CA, United States; Schmitt T., Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Sójka W., Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland; Wagner A.S., Institute for Neuroscience and Medicine, Research Centre Juelich, Germany; Whitaker K.J., The Alan Turing Institute, British Library, London, United Kingdom; Rieger J.W., Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany, Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany","Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development. © 2022","EEG; MEG; MRI; Open science; PET; Reproducibility","ecosystem; electroencephalogram; human; neuroimaging; nuclear magnetic resonance imaging; reproducibility; review","","","","","International Neuroinformatics Coordinating Facility; NIDM-Terms; NIH NIMH, (1R01MH126699); NIH-NIBIB, (P41 EB019936); TransMedTech Institute, (RF1 MH120021); US-France Data Sharing Proposal; Unifying Neuroscience and Artificial Intelligence - Québec; Weizmann Institute of Science -Israel National Postdoctoral Award Program for Advancing Women in Science; National Science Foundation, NSF, (BCS 1734853, IIS 1636893, IIS 1912270); National Institutes of Health, NIH; National Institute of Mental Health, NIMH, (1RF1MH120021, R01MH096906, R37MH066078, RF1MH121867); National Institute of Biomedical Imaging and Bioengineering, NIBIB, (1R01EB029272, 5R01MH109682, ANR-20-NEUC-0004-01, R01 EB030896); Fanconi Anemia Research Fund, FARF; California Department of Fish and Game, DFG, (390895286, EXC 2177/1, INST 184/216-1); McGill University, MGU; Fondation Brain Canada; Alan Turing Institute, ATI, (EP/N510129/1); UK Research and Innovation, UKRI; Engineering and Physical Sciences Research Council, EPSRC, (EP/T001569/1); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (185872); Elsass Fonden, (18-3-0147, 1P41EB019936-01A1); Novo Nordisk Fonden, NNF, (NNF20OC0063277); Canada First Research Excellence Fund, CFREF; Narodowa Agencja Wymiany Akademickiej, NAWA, (PPN/BEK/2020/1/00279/U/00001)","Funding text 1: G.N. was supported by the AXA Research Fund and by NIH CRCNS: US-France Data Sharing Proposal (NIBIB (USA) R01 EB030896 and ANR-20-NEUC-0004-01). R.B-N. is an Awardee of the Weizmann Institute of Science -Israel National Postdoctoral Award Program for Advancing Women in Science. A.d.l.V was supported by NIH grant 5R01MH109682. O.E. was supported by the Swiss National Science Foundation (SNSF) Project 185872 and NIMH grant (RF1MH121867, O.E.). J.A.E was supported by NIH grant R37MH066078 to Todd S. Braver. K.F. was supported by the Polish National Agency for Academic Exchange (the Bekker programme; PPN/BEK/2020/1/00279/U/00001). M.G. was supported by the Elsass foundation (18-3-0147). Y.O.H. was supported by NIH grant 1P41EB019936-01A1. P.H. was supported in parts by funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative, the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 (ReproNim), the National Institute Of Mental Health of the NIH under Award Number R01MH096906, a research scholar award from Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative, as well as an Excellence Scholarship from Unifying Neuroscience and Artificial Intelligence - Québec. A.K. was supported by the TransMedTech Institute Postdoc Fellowship. D.B.K. was supported by the National Institute of Mental Health under grant RF1 MH120021 (PI: Keator), and acknowledges the International Neuroinformatics Coordinating Facility (INCF). C.R.P. was supported by the Novo Nordisk Fonden NNF20OC0063277. F.P. is supported by NSF grants IIS 1636893, IIS 1912270, and BCS 1734853, NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant 1R01EB029272, NIH NIMH 1R01MH126699, and a Microsoft Investigator Fellowship. N.Q. acknowledges the NIDM-Terms grant (National Institutes of Mental Health (NIMH) grant 1RF1MH120021 PI: David B Keator). K.J.W. was supported by The UKRI Strategic Priorities Fund under EPSRC Grant EP/T001569/1, particularly the ""Tools, Practices and Systems"" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. J.W.R. was supported by the DFG Device Center Grant INST 184/216-1 “Tools and infrastructure for open and reproducible neuroimaging”, and the DFG grant 390895286 of the Excellence Strategy (EXC 2177/1, Hearing4All). ; Funding text 2: G.N. was supported by the AXA Research Fund and by NIH CRCNS: US-France Data Sharing Proposal (NIBIB (USA) R01 EB030896 and ANR-20-NEUC-0004-01). R.B-N. is an Awardee of the Weizmann Institute of Science -Israel National Postdoctoral Award Program for Advancing Women in Science. A.d.l.V was supported by NIH grant 5R01MH109682. O.E. was supported by the Swiss National Science Foundation (SNSF) Project 185872 and NIMH grant (RF1MH121867, O.E.). J.A.E was supported by NIH grant R37MH066078 to Todd S. Braver. K.F. was supported by the Polish National Agency for Academic Exchange (the Bekker programme; PPN/BEK/2020/1/00279/U/00001). M.G. was supported by the Elsass foundation (18-3-0147). Y.O.H. was supported by NIH grant 1P41EB019936-01A1. P.H. was supported in parts by funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative, the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 (ReproNim), the National Institute Of Mental Health of the NIH under Award Number R01MH096906, a research scholar award from Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative, as well as an Excellence Scholarship from Unifying Neuroscience and Artificial Intelligence - Québec. A.K. was supported by the TransMedTech Institute Postdoc Fellowship. D.B.K. was supported by the National Institute of Mental Health under grant RF1 MH120021 (PI: Keator), and acknowledges the International Neuroinformatics Coordinating Facility (INCF). C.R.P. was supported by the Novo Nordisk Fonden NNF20OC0063277. F.P. is supported by NSF grants IIS 1636893, IIS 1912270, and BCS 1734853, NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant 1R01EB029272, NIH NIMH 1R01MH126699, and a Microsoft Investigator Fellowship. N.Q. acknowledges the NIDM-Terms grant (National Institutes of Mental Health (NIMH) grant 1RF1MH120021 PI: David B Keator). K.J.W. was supported by The UKRI Strategic Priorities Fund under EPSRC Grant EP/T001569/1, particularly the ""Tools, Practices and Systems"" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. J.W.R. was supported by the DFG Device Center Grant INST 184/216-1 “Tools and infrastructure for open and reproducible neuroimaging”, and the DFG grant 390895286 of the Excellence Strategy (EXC 2177/1, Hearing4All).","Abraham A., Pedregosa F., Eickenberg M., Gervais P., Mueller A., Kossaifi J., Gramfort A., Thirion B., Varoquaux G., Machine learning for neuroimaging with scikit-learn, Front. 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Neurosci., 17, 11, pp. 1510-1517, (2014); Poldrack R.A., Huckins G., Varoquaux G., Establishment of best practices for evidence for prediction: a review, JAMA Psychiatry, 77, 5, pp. 534-540, (2020); Poldrack R.A., Kittur A., Kalar D., Miller E., Seppa C., Gil Y., Parker D.S., Sabb F.W., Bilder R.M., The cognitive atlas: toward a knowledge foundation for cognitive neuroscience, Front. Neuroinf., 5, September, (2011); Poldrack R.A., Whitaker K., Kennedy D., Introduction to the special issue on reproducibility in neuroimaging, Neuroimage, 218, September, (2020)","G. Niso; Psychological & Brain Sciences, Indiana University, Bloomington, United States; email: guiomar.niso@ctb.upm.es; R. Botvinik-Nezer; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, United States; email: rotemb9@gmail.com; J.W. Rieger; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany; email: jochem.rieger@uni-oldenburg.de","","Academic Press Inc.","","","","","","10538119","","NEIME","36100172","English","NeuroImage","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139338259"
"Wagner M.; Henzen C.","Wagner, Michael (57200105179); Henzen, Christin (55976278000)","57200105179; 55976278000","Quality Assurance for Spatial Research Data","2022","ISPRS International Journal of Geo-Information","11","6","334","","","","1","10.3390/ijgi11060334","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132070048&doi=10.3390%2fijgi11060334&partnerID=40&md5=ec90ffbc9610519c37767ec3875f3b49","Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Helmholtzstr. 10, Dresden, 01062, Germany; Geoinformatics, Technische Universität Dresden, Helmholtzstr. 10, Dresden, 1062, Germany","Wagner M., Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Helmholtzstr. 10, Dresden, 01062, Germany; Henzen C., Geoinformatics, Technische Universität Dresden, Helmholtzstr. 10, Dresden, 1062, Germany","In Earth System Sciences (ESS), spatial data are increasingly used for impact research and decision-making. To support the stakeholders’ decision, the quality of the spatial data and its assurance play a major role. We present concepts and a workflow to assure the quality of ESS data. Our concepts and workflow are designed along the research data life cycle and include criteria for openness, FAIRness of data (findable, accessible, interoperable, reusable), data maturity, and data quality. Existing data maturity concepts describe (community-specific) maturity matrices, e.g., for meteorological data. These concepts assign a variety of maturity metrics to discrete levels to facilitate evaluation of the data. Moreover, the use of easy-to-understand level numbers enables quick recognition of highly mature data, and hence fosters easier reusability. Here, we propose a revised maturity matrix for ESS data including a comprehensive list of FAIR criteria. To foster the compatibility with the developed maturity matrix approach, we developed a spatial data quality matrix that relates the data maturity levels to quality metrics. The maturity and quality levels are then assigned to the phases of the data life cycle. With implementing openness criteria and matrices for data maturity and quality, we build a quality assurance (QA) workflow that comprises various activities and roles. To support researchers in applying this workflow, we implement an interactive questionnaire in the tool RDMO (research data management organizer) to collaboratively manage and monitor all QA activities. This can serve as a blueprint for use-case-specific QA for other datasets. As a proof of concept, we successfully applied our criteria for openness, data maturity, and data quality to the publicly available SPAM2010 (crop distribution) dataset series. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","data maturity; FAIR; maturity matrix; quality assurance; spatial data quality","","","","","","Bundesministerium für Bildung und Forschung, BMBF, (16QK04A); Bundesministerium für Bildung und Forschung, BMBF","Funding: The workflow for quality assurance for spatial research data was developed in the projects GeoKur and NFDI4Earch. We thank the BMBF (Federal Ministry of Education and Research) granting GeoKur under number 16QK04A.","Devillers R., Stein A., Bedard Y., Chrisman N., Fisher P., Shi W., Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities: Thirty Years of Research on Spatial Data Quality, Transactions in GIS, 14, pp. 387-400, (2010); Peng G., Lacagnina C., Ivanova I., Downs R.R., Ramapriyan H., Ganske A., Jones D., Bastin L., Wyborn L., Bastrakova I., Et al., International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets, (2021); Nightingale J., Boersma K., Muller J.-P., Compernolle S., Lambert J.-C., Blessing S., Giering R., Gobron N., De Smedt I., Coheur P., Et al., Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications, Remote Sensing, 10, (2018); Recommendations for Sustainable Research in the Digital Turn, (2020); Ruegg J., Gries C., Bond-Lamberty B., Bowen G.J., Felzer B.S., McIntyre N.E., Soranno P.A., Vanderbilt K.L., Weathers K.C., Completing the Data Life Cycle: Using Information Management in Macrosystems Ecology Research, Frontiers in Ecology and the Environment, 12, pp. 24-30, (2014); Cai L., Zhu Y., The Challenges of Data Quality and Data Quality Assessment in the Big Data Era, CODATA, 14, (2015); Hassenstein M.J., Vanella P., Data Quality—Concepts and Problems, Encyclopedia, 2, pp. 498-510, (2022); Geographic Information-Data Quality-Part 1: General Requirements; GeoDCAT-AP-Version 2.0.0; International Organization for Standardization Geographic Information – Data Quality (ISO 19157:2013), (2013); International Organization for Standardization Quality Management Systems – Fundamentals and Vocabulary (ISO 9000:2015), (2015); Henzen C., GeoKur-Curation and Quality Assurance of Environmental Research Data for the Use Case of Global Land Use Data, Zenodo, pp. 1-10, (2021); Home of the Spatial Production Allocation Model; Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, (2019); Yu Q., You L., Wood-Sichra U., Ru Y., Joglekar A.K.B., Fritz S., Xiong W., Lu M., Wu W., Yang P., A Cultivated Planet in 2010 – Part 2: The Global Gridded Agricultural-Production Maps, Earth Syst. Sci. Data, 12, pp. 3545-3572, (2020); Agricultural Producer Prices (Global-National-Annual/Monthly-FAOSTAT); Peng G., Lacagnina C., Downs R.R., Ganske A., Ramapriyan H.K., Ivanova I., Wyborn L., Jones D., Bastin L., Shie C., Et al., Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets, Data Science Journal, 21, (2022); 5-Star Open Data; About CC Licenses; Wilkinson M.D., Dumontier M., Aalbersberg Ij.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Sci Data, 3, (2016); Research Data Alliance FAIR Data Maturity Model Working Group FAIR Data Maturity Model: Specification and Guidelines, Zenodo, pp. 1-47, (2020); Devaraju A., Huber R., Mokrane M., Herterich P., Cepinskas L., de Vries J., L'Hours H., Davidson J., White A., FAIRsFAIR Data Object Assessment Metrics, Zenodo, pp. 1-25, (2020); Lacagnina C., Doblas-Reyes F., Larnicol G., Buontempo C., Obregon A., Costa-Suros M., San-Martin D., Bretonniere P.-A., Polade S.D., Romanova V., Et al., Quality Management Framework for Climate Datasets, CODATA, 21, (2022); Peng G., The State of Assessing Data Stewardship Maturity – An Overview, Data Science Journal, 17, (2018); National Research Council Environmental Data Management at NOAA: Archiving, Stewardship, and Access, (2007); Sadin S.R., Povinelli F.P., Rosen R., The NASA Technology Push towards Future Space Mission Systems, Acta Astronautica, 20, pp. 73-77, (1989); Technology Readiness Levels (TRLs); Bates J.J., Privette J.L., A Maturity Model for Assessing the Completeness of Climate Data Records, Eos Trans. AGU, 93, pp. 441-441, (2012); Bates J.J., Privette J.L., Kearns E.J., Glance W., Zhao X., Sustained Production of Multidecadal Climate Records: Lessons from the NOAA Climate Data Record Program, Bulletin of the American Meteorological Society, 97, pp. 1573-1581, (2016); Schulz J., System Maturity Assessment, (2015); Peng G., Privette J.L., Kearns E.J., Ritchey N.A., Ansari S., A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets, Data Sci. J, 13, pp. 231-252, (2015); Hock H., Toussaint F., Thiemann H., Fitness for Use of Data Objects Described with Quality Maturity Matrix at Different Phases of Data Production, Data Science Journal, 19, (2020); Yang X., Blower J.D., Bastin L., Lush V., Zabala A., Maso J., Cornford D., Diaz P., Lumsden J., An Integrated View of Data Quality in Earth Observation, Phil. Trans. R. Soc. A, 371, (2013); Data on the Web Best Practices: Data Quality Vocabulary; International Organization for Standardization Space Data and Information Transfer Systems – Open Archival Information System (OAIS) – Reference Model (ISO 14721:2012), (2012); International Organization for Standardization Geographic Information – Metadata – Part 1: Fundamentals (ISO 19115-1:2014), (2014); Hunter G.J., Wachowicz M., Bregt A.K., Understanding Spatial Data Usability, Data Sci. J, 2, pp. 79-89, (2003)","M. Wagner; Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden, Helmholtzstr. 10, 01062, Germany; email: michael.wagner@tu-dresden.de","","MDPI","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85132070048"
"Queralt-Rosinach N.; Kaliyaperumal R.; Bernabé C.H.; Long Q.; Joosten S.A.; van der Wijk H.J.; Flikkenschild E.L.A.; Burger K.; Jacobsen A.; Mons B.; Roos M.","Queralt-Rosinach, Núria (35766634600); Kaliyaperumal, Rajaram (55097102600); Bernabé, César H. (57204685650); Long, Qinqin (57641597900); Joosten, Simone A. (6601983679); van der Wijk, Henk Jan (8919468700); Flikkenschild, Erik L.A. (57638765200); Burger, Kees (55190905200); Jacobsen, Annika (57189102708); Mons, Barend (7004074338); Roos, Marco (35240924600)","35766634600; 55097102600; 57204685650; 57641597900; 6601983679; 8919468700; 57638765200; 55190905200; 57189102708; 7004074338; 35240924600","Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic","2022","Journal of Biomedical Semantics","13","1","12","","","","2","10.1186/s13326-022-00263-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128838996&doi=10.1186%2fs13326-022-00263-7&partnerID=40&md5=b562abca7e9ea2fa069ebbfa11439db9","Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands; Department of IT&DI, Leiden University Medical Center, Leiden, Netherlands; GO FAIR Foundation, Leiden, Netherlands; CODATA, Paris, France; Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands","Queralt-Rosinach N., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Kaliyaperumal R., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Bernabé C.H., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Long Q., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Joosten S.A., Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands; van der Wijk H.J., Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands; Flikkenschild E.L.A., Department of IT&DI, Leiden University Medical Center, Leiden, Netherlands; Burger K., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Jacobsen A., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Mons B., GO FAIR Foundation, Leiden, Netherlands, CODATA, Paris, France, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Roos M., Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands","Background: The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. Results: In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Conclusions: Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery. © 2022, The Author(s).","FAIR; Hospital; Ontologies; Open science; Patient data; Research data management","COVID-19; Hospitals; Humans; Metadata; Pandemics; Semantic Web; epidemiology; hospital; human; metadata; pandemic; semantic web","","","","","Leiden University Fund; Horizon 2020 Framework Programme, H2020, (EJP RD COFUND-EJP N 825575)","N. Queralt-Rosinach, R. Kaliyaperumal, C. Bernabé, Q. Long, A. Jacobsen and M. Roos are supported by funding from the European Union’s Horizon 2020 research and innovation program under the EJP RD COFUND-EJP N 825575. We would also like to thank to the EJP RD, the GO FAIR VODAN, and the ZonMW Health Holland under the Trusted World of Corona, for supporting the research on FAIR data stewardship that was reused here. We would like to acknowledge that work in the BEAT-COVID project was partly funded by the Wake Up To Corona crowdfunding initiated by the Leiden University Fund (LUF). ∘ ","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci Data, 3, (2016); (2021); Zonmw. 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Karim R., Dumontier M., Decker S., da Silva S., Dekker A., Distributed analytics on sensitive medical data: The personal health train, Data Intell, 2, pp. 96-107, (2020); Landi A., Thompson M., Giannuzzi V., Bonifazi F., Labastida I., da Silva Santos L.O.B., Roos M., The “A” of FAIR – As Open as Possible, as Closed as Necessary, Data Intell, 2, 1-2, pp. 47-55, (2020); Jacobsen A., Kaliyaperumal R., da Silva S., Mons B., Schultes E., Roos M., Thompson M., A generic workflow for the data fairification process, Data Intell, 2, pp. 56-65, (2020); Groenen K.H.J., Jacobsen A., Kersloot M.G., Vieira B., van Enckevort E., Kaliyaperumal R., Arts DL, ‘t Hoen PAC, Cornet R, Roos M, Kool LS. 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Digital Personalized Health and Medicine, - Proceedings of MIE 2020, Medical Informatics Europe, pp. 392-396, (2020); Guidelines for implementing FAIR Open Data policy in health research., (2019); Training Portal, (2020); Ammenwerth E., Horbst A., Hayn D., Schreier G., eHealth2014 - Health Informatics Meets eHealth, Studies in Health Technology and Informatics, (2014); Baker D.B., Knoppers B.M., Phillips M., van Enckevort D., Kaufmann P., Lochmuller H., Taruscio D., Privacy-preserving linkage of genomic and clinical data sets, IEEE/ACM Trans Comput Biol Bioinforma, 16, 4, pp. 1342-1348, (2019); (2021); FAIR, 4Health. Fair4health., (2019); Health- R.I., FAIR Principles., (2019); Walonoski J., Kramer M., Nichols J., Quina A., Moesel C., Hall D., Duffett C., Dube K., Gallagher T., McLachlan S., Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, J Am Med Inf Assoc, 25, 3, pp. 230-238, (2017); (2021); Malone J., Holloway E., Adamusiak T., Kapushesky M., Zheng J., Kolesnikov N., Zhukova A., Brazma A., Parkinson H., Modeling sample variables with an Experimental Factor Ontology, Bioinformatics, 26, 8, pp. 1112-1118, (2010); Jacobsen A., de Miranda Azevedo R., Juty N., Batista D., Coles S., Cornet R., Courtot M., Crosas M., Dumontier M., Evelo C.T., Goble C., Guizzardi G., Hansen K.K., Hasnain A., Hettne K., Heringa J., Hooft R.W.W., Imming M., Jeffery K.G., Kaliyaperumal R., Kersloot M.G., Kirkpatrick C.R., Kuhn T., Labastida I., Magagna B., McQuilton P., Meyers N., Montesanti A., van Reisen M., Rocca-Serra P., Pergl R., Sansone S.-A., da Silva Santos L.O.B., Schneider J., Strawn G., Thompson M., Waagmeester A., Weigel T., Wilkinson M.D., Willighagen E.L., Wittenburg P., Roos M., Mons B., Schultes E., FAIR Principles: Interpretations and Implementation Considerations, Data Intell, 2, 1-2, pp. 10-29, (2020); Gruninger M., Fox M.S., Methodology for the Design and Evaluation of Ontologies, International Joint Conferences on Artificial Intelligence (IJCAI), Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, Canada, April 13, 1995, (1995); Queralt-Rosinach N., Stupp G.S., Li T.S., Mayers M., Hoatlin M.E., Might M., Good B.M., Su A.I., Structured reviews for data and knowledge-driven research, Database, 2020, (2020); VODAN in a Box: The All in One Solution for Easy Instalment of VODAN FAIR Data Points, (2020); Health- R.I., Personal Health Train, (2019); Proof of Concept developed by VODAN Africa and Asia, (2020); Allegrograph Webview (Lumc.Nl), (2020); Neo, 4J. 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Graphdb Homepage., (2015); Wilkinson M.D., Dumontier M., Sansone S.A., Et al., Evaluating fair maturity through a scalable, automated, community-governed framework, Sci Data, 174, 6, (2019); Knight S.R., Ho A., Pius R., Buchan I., Carson G., Drake T.M., Dunning J., Fairfield C.J., Gamble C., Green C.A., Gupta R., Halpin S., Hardwick H.E., Holden K.A., Horby P.W., Jackson C., McLean K.A., Merson L., Nguyen-Van-tam J.S., Norman L., Noursadeghi M., Olliaro P.L., Pritchard M.G., Russell C.D., Shaw C.A., Sheikh A., Solomon T., Sudlow C., Swann O.V., Turtle L.C., Openshaw P.J., Baillie J.K., Semple M.G., Docherty A.B., Harrison E.M., Risk stratification of patients admitted to hospital with covid-19 using the isaric who clinical characterisation protocol: Development and validation of the 4c mortality score, BMJ, (2020)","M. Roos; Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; email: M.Roos@lumc.nl","","BioMed Central Ltd","","","","","","20411480","","","35468846","English","J. Biomed. Semant.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128838996"
"Murillo A.P.","Murillo, Angela P. (55552303100)","55552303100","Data matters: how earth and environmental scientists determine data relevance and reusability","2022","Collection and Curation","41","3","","77","86","9","1","10.1108/CC-11-2018-0023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071638126&doi=10.1108%2fCC-11-2018-0023&partnerID=40&md5=9a6c305a2af34c0d4dc8ecc27bd741a3","School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States","Murillo A.P., School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States","Purpose: The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers. Design/methodology/approach: This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2. Findings: This study identifies earth and environmental scientists’ information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information. Research limitations/implications: While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields. Practical implications: The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data. Social implications: By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse. Originality/value: While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record. © 2019, Emerald Publishing Limited.","Data curation; Data repositories; Data reuse; Data sharing; Research data management; Scientific data","","","","","","","","Baker B., The science of team science, BioScience, 65, 7, pp. 639-644, (2015); Baru C., Sharing and caring of eScience data, International Journal on Digital Libraries, 7, 1-2, pp. 113-116, (2007); Beran B., van Ingen C., Fatland D.R., SciScope: a participatory geoscientific web application, Concurrency and Computation: Practice and Experience, 22, 17, pp. 2300-2312, (2010); Blumenthal D., Campbell E.G., Gokhale M., Yucel R., Clarridge B., Hilgartner S., Data withholding in genetics and the other life sciences: prevalences and predictors, Academic Medicine, 81, 2, pp. 137-145, (2006); Borgman C.L., Research data: who will share what, with whom, when, and why?, Fifth China – North America Library Conference 2010, (September), (2010); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brown C.M., The changing face of scientific discourse: analysis of genomic and proteomic database usage and acceptance, Journal of the American Society for Information Science and Technology, 54, 10, pp. 926-938, (2003); Browne G.L., Pitts M.G., Wetherbe J.C., Cognitive stopping rules for terminating information search in online tasks, MIS Quarterly, 31, 1, pp. 88-104, (2007); Ceci S.J., Scientists’ attitudes toward data sharing, Science, Technology, and Human Values, 13, 1-2, pp. 45-52, (1988); Cohen J., Share and share alike isn’t always the rule in science, Science (New York, N.Y.), 268, 5218, pp. 1715-1718, (1995); Constant D., Kiesler S., Sproull L., What’s mine is ours, or is it? A study of attitudes about information sharing, Information Systems Research, 5, 4, pp. 400-421, (1994); Benefits of becoming a member node | DataONE, (2013); What is DataONE? | DataONE, (2013); Dryad, (2019); Eppler M.J., Mengis J., The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines, The Information Society, 20, 5, pp. 325-344, (2004); Faniel I.M., Jacobsen T.E., Reusing scientific data: how earthquake engineering researchers assess the reusability of colleagues’ data, Computer Supported Cooperative Work (Cscw), 19, 3-4, pp. 355-375, (2010); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, Plos One, 10, 2, (2015); Hall K.L., Vogel A.L., Huang G.C., Serrano K.J., Rice E.L., Tsakraklides S.P., Fiore S.M., The science of team science: a review of the empirical evidence and research gaps on collaboration in science, American Psychologist, 73, 4, pp. 532-548, (2018); Hank C., Wildemuth B.M., Quasi-experimental studies, Applications of Social Research Methods to Questions in Information and Library Science, pp. 93-104, (2009); Joo Y.K., Kim Y., Engineering researchers’ data reuse behaviours: a structural equation modelling approach, The Electronic Library, 35, 6, pp. 1141-1161, (2017); Lifschitz S., Gomes L., Rehen S.K., Dealing with reusability and reproducability for scientific workflows, Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 625-632, (2011); Liu B., Grossman R., Zhai Y., Mining data records in web pages, available at:, pp. 601-606, (2003); Lord P., Macdonald A., e-Science Curation Report: Data Curation for e-Science in the UK: An Audit to Establish Requirements for Future Curation and Provision, pp. 1-84, (2003); McCain K.W., Mandating sharing: journal policies in the natural sciences, Science Communication, 16, 4, pp. 403-431, (1995); Marcial L.H., Hemminger B.M., Scientific data repositories on the web: an initial survey, Journal of the American Society for Information Science and Technology, 61, 10, pp. 2029-2048, (2010); Murillo A.P., Examining data sharing and data reuse in the DataONE environment, Proceedings of Association for Information Science and Technology (ASIS&T) Annual Meeting, 51, 1, (2014); Final NIH statement on sharing research data, (2003); NIH data sharing policy, (2007); Dissemination and sharing of research results, (2010); Neuendorf K.A., The Content Analysis Guidebook, (2002); Noor M.A.F., Zimmerman K.J., Teeter K.C., Data sharing: how much doesn’t get submitted to genBank?, PLoS Biology, 4, 7, (2006); Piwowar H.A., Who shares? Who doesn’t? Factors associated with openly archiving raw research data, PLoS One, 6, 7, (2011); Piwowar H.A., Chapman W.W., Public sharing of research datasets: a pilot study of associations, Journal of Informetrics, 4, 2, pp. 148-156, (2010); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: understanding the motivation to publish research data, Government Information Quarterly, 30, pp. S19-S13, (2013); Si L., Li Y., Zhuang X., Xing W., Hua X., Li X., Xin J., An empirical study on the performance evaluation of scientific data sharing platforms in China, Library Hi Tech, 33, 2, pp. 211-229, (2015); Sieber J.E., Data sharing: defining problems and seeking solutions, Law and Human Behavior, 12, 2, pp. 199-206, (1988); Someren M.W., van Barnard Y.F., Sandberg J., The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes, (1994); Spurgin K.M., Wildemuth B.M., Content analysis, Applications of Social Research Methods to Questions in Information and Library Science, pp. 189-198, (2009); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos One, 10, 8, (2015); USGIN; DMPTool, (2019); Yoon A., Role of communication in data reuse, Proceedings of the Association for Information Science and Technology, 54, 1, pp. 463-471, (2017); Yoon A., Jeng W., Curty R., Murillo A., In between data sharing and reuse: shareability, availability and reusability in diverse contexts, Proceedings of the Association for Information Science and Technology, 54, 1, pp. 606-609, (2017); Zimmerman A.S., (2003); Zimmerman A.S., New knowledge from old data: the role of standards in the sharing and reuse of ecological data, Science, Technology, & Human Values, 33, 5, pp. 631-652, (2008)","A.P. Murillo; School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, United States; email: apmurill@iu.edu","","Emerald Group Holdings Ltd.","","","","","","25149326","","","","English","Collect. Curation","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85071638126"
"Naseema S.; Sevukan R.","Naseema, S. (57859333400); Sevukan, R. (56768085400)","57859333400; 56768085400","Global Research Trends in Research Data Management (Rdm) – A Scientometric view","2022","International Journal of Information Science and Management","20","4","","117","135","18","0","10.22034/ijism.2022.698421","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145826933&doi=10.22034%2fijism.2022.698421&partnerID=40&md5=ca4ee8faebb0083360bc2eb298ec5fdb","Department of Library and Information Science, Pondicherry University, Puducherry,Kalapet, India","Naseema S., Department of Library and Information Science, Pondicherry University, Puducherry,Kalapet, India; Sevukan R., Department of Library and Information Science, Pondicherry University, Puducherry,Kalapet, India","The study focuses on Research Data Management (RDM), aiming to demonstrate how RDM is evolving globally. It provides a systematic mapping of the current literature to aid in identifying core coverage and reflecting on potential trends using the scientometric technique. For detailed thematic analysis, performance analysis, network representation, science mapping, and scientific collaboration, this study used bibliometric tools such as the R package Biblioshiny, ScientoPy, and VOSviewer. Two premier indexing databases, SCOPUS and Web of Science, which extensively cover RDM literature, are appropriately considered. Furthermore, all retrieved documents are refined by language, document type, and irrelevant keywords for practical analysis. This study covers the Research Data Management (RDM) literature from 1926 to 2020, with 6263 documents published from 1666 sources and 15,545 authors. The most common domains discovered in RDM research are Computer Science, Library and Information Science. RDM is primarily familiar and cultivated in nations such as the United States, China, the United Kingdom, Canada, Germany, and Australia are pioneers in RDM research. The findings are vital for researchers working on RDM projects and policy formulation. This research helps to identify the literature's strengths and potential gaps. This secondary data contributes significantly to the scientific landscape in scientific production, network architecture, source clustering, and international collaboration, the evolution of dominant subjects and countries, and science mapping of productive word frequency © 2022, International Journal of Information Science and Management. All Rights Reserved.","Data centre; Data management; Research data; Research life cycle; Rstudio; Science mapping; Scientific collaboration","","","","","","","","Alonso J. 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D., Perception of researchers & academicians of parul university towards research data management system & role of library: A study, Desidoc Journal Of Library & Information Technology, 40, 3, pp. 139-146, (2020); Chawinga W. D., Zinn S., Research data management at an African medical university: Implications for academic librarianship, Journal Of Academic Librarianship, 46, 4, (2020); Chawinga W. D., Zinn S., Research data management in universities: A comparative study from the perspectives of librarians and management, International Information and Library Review, 53, 2, pp. 97-111, (2020); Chiware E. R. T., Data librarianship in South African academic and research libraries: a survey, Library Management, 41, 6-7, pp. 401-416, (2020); Cho J., Study of Asian RDR based on re3data, Electronic Library, 37, 2, pp. 302-313, (2019); Choi M. S., Lee S., Research data management status of science and technology research institutes in Korea, Data Science Journal, 19, 29, pp. 1-11, (2020); Corrall S., Kennan M. A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A. M., Tam W. W. T., A critical analysis of lifecycle models of the research process and research data management, Aslib Journal Of Information Management, 70, 2, pp. 142-157, (2018); Cyberinfrastructure vision for 21st century discovery, (2007); Fusco F., Marsilio M., Guglielmetti C., Co-production in health policy and management: A comprehensive bibliometric review, BMC Health Services Research, 20, 1, (2020); Gunjal B., Gaitanou P., Research data management: A proposed framework to boost research in higher educational institutes, IASSIST Quarterly, 41, 1 -4, (2017); Herbst K., Juvekar S., Bhattacharjee T., Bangha M., Patharia N., Tei T., Gilbert B., Sankoh O., The INDEPTH Data Repository: An International Resource for Longitudinal Population and Health Data From Health and Demographic Surveillance Systems, Journal of Empirical Research on Human Research Ethics (JERHRE), 10, 3, pp. 324-333, (2015); Janik A., Ryszko A., Szafraniec M., The scientific landscape of smart and sustainable cities literature: A bibliometric analysis, Sustainability, 12, 3, (2020); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship and Information Science, 52, 3, pp. 633-646, (2020); Koltay T., Research 2.0 and research data services in academic and research libraries: priority issues, Library Management, 38, 6-7, pp. 345-353, (2017); Kun W., Tong L., Xiaodan X., Application of big data technology in scientific research data management of military enterprises, Procedia Computer Science, 147, (2019); Late E., Kekalainen J., Use and users of a social science research data archive, PLOS ONE, 15, 8, (2020); Liao Y. P., Ma T. J., Institutional repositories: A bibliometric study of the social sciences citation index (SSCI), Electronic Library, 36, 3, pp. 504-517, (2018); Liu G., Zotoo I. K., Su W., Research data management policies in USA, U.K. and Australia universities: An online survey, Malaysian Journal of Library & Information Science, 25, 2, pp. 21-42, (2020); Liu H., Peng K., Li W., Cao Y., Investigation on the trends and characteristics of articles on submerged macrophytes: perception from bibliometrics between 1991 and 2018, Journal of Freshwater Ecology, 34, 1, pp. 703-713, (2019); Liu X., Ding N., Research data management in universities of central China: Practices at Wuhan University Library, Electronic Library, 34, 5, pp. 808-822, (2016); Marlina E., Purwandari B., Strategy for research data management services in Indonesia, Procedia Computer Science, 161, pp. 788-796, (2019); Mushi G. E., Pienaar H., Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, 1, pp. 1-9, (2020); Onyancha O. B., Open research data in Sub-Saharan Africa: A bibliometric study using the data citation index, Publishing Research Quarterly, 32, 3, pp. 227-246, (2016); Payal, Awasthi S., Tripathi M., A selective review of literature on research data management in academic libraries, DESIDOC Journal of Library and Information Technology, 39, 6, pp. 338-345, (2019); What is data management?, (2014); R: A language and environment for statistical computing, (2018); Redkina N. S., Current trends in research data management, Scientific And Technical Information Processing, 46, 2, pp. 53-58, (2019); Roche D. G., Granados M., Austin C. C., Wilson S., Mitchell G. M., Smith P. A., Cooke S. J., Bennett J. R., Open government data and environmental science: A federal Canadian perspective, Facets, 5, 1, pp. 942-962, (2020); Ruiz-Rosero J., Ramirez-Gonzalez G., Williams J. M., Liu H., Khanna R., Pisharody G., Internet of things: A scientometric review, Symmetry, 9, 12, pp. 1-34, (2017); Saeed S., Ali P. M. N., Research data management and data sharing among research scholars of life sciences and social sciences, DESIDOC Journal Of Library & Information Technology, 39, 6, pp. 290-299, (2019); Secinaro S., Brescia V., Calandra D., Biancone P., Employing bibliometric analysis to identify suitable business models for electric cars, Journal of Cleaner Production, 264, (2020); Surkis A., Read K., Research data management, Journal of the Medical Library Association, 103, 3, pp. 154-156, (2015); Tripathi M., Chand M., Sonkar S. K., Jeevan V. K. J., A brief assessment of researchers' perceptions towards research data in India, IFLA journal, 43, 1, pp. 22-39, (2017); Tripathi M., Shukla A., Sonker S. K., Research data management practices in university libraries: A study, DESIDOC Journal of Library and Information Technology, 37, 6, pp. 417-424, (2017)","R. Sevukan; Department of Library and Information Science, Pondicherry University, Puducherry,Kalapet, India; email: rsevukan.lis@pondiuni.edu.in","","Regional Inform. Center for Sci. and Technol.","","","","","","20088302","","","","English","Int. J. Inf. Sci. Manage.","Article","Final","","Scopus","2-s2.0-85145826933"
"Yun-Chi C.; Li-Fei K.; Hsu-Chun H.; Wei J.","Yun-Chi, Chang (57931895700); Li-Fei, Kung (57932455000); Hsu-Chun, Hsiao (57932033900); Wei, Jeng (57929247100)","57931895700; 57932455000; 57932033900; 57929247100","“Prove It!” A User-centric Client for the Blockchain-Based Research Lifecycle Transparency Framework","2022","Proceedings of the Association for Information Science and Technology","59","1","","636","638","2","0","10.1002/pra2.674","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140097901&doi=10.1002%2fpra2.674&partnerID=40&md5=fe191eec5d56a36a8f1a04a4cc1d0323","National Taiwan University, Taiwan","Yun-Chi C., National Taiwan University, Taiwan; Li-Fei K., National Taiwan University, Taiwan; Hsu-Chun H., National Taiwan University, Taiwan; Wei J., National Taiwan University, Taiwan","This work develops a user-centric interface for an existing blockchain-based research transparency framework. As researchers in different disciplines may have different research data management and reproducibility needs, we first conducted an exploratory study to comprehend their motivations and expectations when managing research records. We analyzed data from a design thinking workshop with 17 participants and conducted six interviews. We have three critical components in a user-centric interface design: 1) user-decided autonomy, 2) a progress dashboard, and 3) research-team management. In the future, we would like to build an scholarly information ecosystem that fosters safe research lifecycle transparency with the assistance of technology. Thus, we plan to accumulate more interview data from different fields to improve the design of the user-centric client.  Annual Meeting of the Association for Information Science & Technology | Oct. 29 – Nov. 1, 2022 | Pittsburgh, PA. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.","blockchain; open science; reproducibility; research lifecycle transparency; research management tool; user-centered design","Blockchain; Human resource management; Information management; Life cycle; User centered design; Block-chain; Management tool; Open science; Reproducibilities; Research data managements; Research lifecycle transparency; Research management; Research management tool; User centric interface; User-centric; Transparency","","","","","Center for Research in Econometric Theory and Applications, (111 L900204); Universities and Colleges Humanities and Social Sciences Benchmarking Project, (111L9A002); Ministry of Education, MOE; Ministry of Science and Technology, Taiwan, MOST, (111‐2636‐H‐002‐004, MOST 111‐2634‐F‐002‐018)","This work was financially supported by the Ministry of Science and Technology (MOST) in Taiwan, under MOST 111‐2636‐H‐002‐004‐ and MOST 111‐2634‐F‐002‐018‐, and the Center for Research in Econometric Theory and Applications (Grant no. 111 L900204) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project, and the Universities and Colleges Humanities and Social Sciences Benchmarking Project (Grant no. 111L9A002) by the Ministry of Education (MOE) in Taiwan. Please address all correspondence to Wei Jeng ( wjeng@ntu.edu.tw ).","Baker M., First results from psychology's largest reproducibility test, Nature, 30, (2015); Jeng W., Wang S.H., Chen H.W., Huang P.W., Chen Y.J., Hsiao H.C., A decentralized framework for cultivating research lifecycle transparency, PLoS One, 15, 11, (2020); Norman D.A., Some Observations on Mental Models, Mental Models, 1, pp. 1-8, (1983); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); UNESCO Recommendation on Open Science, (2021)","C. Yun-Chi; National Taiwan University, Taiwan; email: r09126026@ntu.edu.tw; K. Li-Fei; National Taiwan University, Taiwan; email: r10126022@ntu.edu.tw; H. Hsu-Chun; National Taiwan University, Taiwan; email: hchsiao@csie.ntu.edu.tw; J. Wei; National Taiwan University, Taiwan; email: wjeng@ntu.edu.tw","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85140097901"
"Ryu S.-H.; Yoon H.; Kim D.; Choi S.-H.","Ryu, Shin-Hye (58097065600); Yoon, Heewon (58097230900); Kim, Daewuk (58097065700); Choi, Seon-Hwa (57208393811)","58097065600; 58097230900; 58097065700; 57208393811","A Study on Establishing the Strategies for Integrated Management and Utilization of Disaster & Safety Research Data","2022","Korean Journal of Remote Sensing","38","6","","1789","1803","14","1","10.7780/kjrs.2022.38.6.3.4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147766171&doi=10.7780%2fkjrs.2022.38.6.3.4&partnerID=40&md5=2507dec36c1caf07073fa51f346f442c","Disaster Information Research Division, National Disaster Management Research Institute, Ulsan, South Korea; STADT Co., Daegu, South Korea","Ryu S.-H., Disaster Information Research Division, National Disaster Management Research Institute, Ulsan, South Korea; Yoon H., Disaster Information Research Division, National Disaster Management Research Institute, Ulsan, South Korea; Kim D., STADT Co., Daegu, South Korea; Choi S.-H., Disaster Information Research Division, National Disaster Management Research Institute, Ulsan, South Korea","With the increase of data and the development of AI technology, the strategies and policies related to integrated data are being actively established to increase the usability of data all over the world. Recently, in the research field, infrastructure projects and management systems are being prepared to utilize research data at the initiative of the government. Also, in Korea, platforms for searching and sharing research data are being actively developed. The National Disaster Management Research Institute (NDMI) has been conducting extensive research on disaster & safety as a national institute, but data-oriented management and utilization are insufficient. Because it still lacks consistent data management systems, metadata for outcomes of research, experts on data and policies for utilization of data to research. In order to move to the data-based research paradigm, we defined the master plans and verified a target model for the integrated management and utilization of disaster & safety research data. In this study, we found out the need to establish differentiated data governance, such as data standardization and unification of the data management system, and dedicated organization for managing data, based on the necessity and actual demands of NDMI. In order to verify the effectiveness of the target model reflecting the derived implications, we intend to establish a pilot mode. In the future, major improvement measures to establish a disaster & safety research data management system will be implement. © 2022 The authors.","Disaster and safety research data; Integrated management; utilization of research data","","","","","","OSTP; Office of Science and Technology Policy; National Science Foundation, NSF","2007년 4월 OECD에서 ‘정부 지원 연구데이터 접근 에 대한 원칙과 지침’을 발표함에 따라 연구데이터 공 유에 관한 관심이 증대되었다. 더불어 데이터 중심 연 구인 4세대 연구 패러다임이 주목받고 있다. 세계 선진국들은 데이터의 증가, AI 기술의 발전 등 에 의해 데이터 활용을 국제 경쟁력의 핵심으로 삼고, 통합적 데이터 전략 수립과 정책적 움직임을 서두르고 있다(KISTI, 2018; KISTI, 2021; Korea Data Agency, 2021; NIA, 2019). 미국은 2010년 ‘미국 경쟁력 강화법(America Competes Reauthorization Act)’에 의한 과학데이터 관리원칙 규정 을 수립한 바 있다. 미국의 과학데이터 공유활용 정책은 과학기술정책사무국(Office of Science and Technology Policy, OSTP)이 주관하여 연방 내 관리 계획에 따라 연 구데이터를 수집하고, 연구데이터의 생산·관리·공개 및 보존은 미국과학재단(National Science Foundation, NSF)에서 관리한다. 2015년 공공·연구·민간 분야 간 협업을 촉진하기 위해 250여개 이상의 대학, 재단, 기업 등의 참여를 통해 사회 문제를 해결하는 Big Data Regional Innovation Hubs (BD Hubs) 프로젝트가 본격 적으로 추진되었다. 이를 기반으로 미국과학재단은 저 비용, 고속, 고용량 네트워크 제공이 가능한 개방저장네 트워크(Open Storage Network, OSN)를 구축하는 방안 을 발표하고 2020년까지 개발 사업에 총 180만 달러(약 20억 원)을 투자하기로 하였다. 유럽연합은 2020년 ‘유럽데이터전략(European Data","Korean New Deal 2.0, pp. 1-107, (2021); Construction of Disaster and safety information analysis sharing platform, (2020); Establishing a system for sharing and disseminating research data, (2018); Development of a Disaster and Safety Information Sharing Platform, (2020); A Development on the Scientific Data Sharing and Utilizing System, (2021); Global News Trends in the USA, Korea Data Agency – Data Economy, 7, pp. 1-17, (2021); Development of Disaster and Safety Information Integrated Platform, (2019); A Study on Development of Foundation Technologies and Archiving for Disaster Safety Information, (2020); Global AI Insight, 20, pp. 1-8, (2019); Development of cause information analysis and utilization technology in disaster archive, (2020); Classification Scheme for Development of Integrated Metadata for Disaster and Safety Information, (2018); Metadata for Management and Sharing of Disaster and Safety Information, (2019)","S.-H. Choi; Disaster Information Research Division, National Disaster Management Research Institute, Ulsan, South Korea; email: shchoi33@korea.kr","","Korean Society of Remote Sensing","","","","","","12256161","","","","Korean","Kor. J. Remote Sens.","Article","Final","","Scopus","2-s2.0-85147766171"
"Feger S.S.; Pertiwi C.; Bonaiuti E.","Feger, Sebastian S. (56893703100); Pertiwi, Cininta (56423686300); Bonaiuti, Enrico (57220747026)","56893703100; 56423686300; 57220747026","Research Data Management Commitment Drivers: An Analysis of Practices, Training, Policies, Infrastructure, and Motivation in Global Agricultural Science","2022","Proceedings of the ACM on Human-Computer Interaction","6","CSCW2","322","","","","0","10.1145/3555213","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146372052&doi=10.1145%2f3555213&partnerID=40&md5=6e27ed4f2f98564cf098cc49f9585d28","LMU Munich AndICARDA, Munich, Germany; ICARDA, Beirut, Lebanon","Feger S.S., LMU Munich AndICARDA, Munich, Germany, ICARDA, Beirut, Lebanon; Pertiwi C., ICARDA, Beirut, Lebanon; Bonaiuti E., ICARDA, Beirut, Lebanon","Scientists largely acknowledge the value of research data management (RDM) to enable reproducibility and reuse. But, RDM practices are not sufficiently rewarded within the traditional academic reputation economy. Recent work showed that emerging RDM tools can offer new incentives and rewards. But, the design of such platforms and scientists' commitment to RDM is contingent on additional factors, including policies, training, and several types of personal motivation. To date, studies focused on investigating single or few of those RDM components within a given environment. In contrast, we conducted three studies within a global agricultural science organization, to provide a more complete account of RDM commitment drivers: one survey study (n = 23) and two qualitative explorations of regulatory frameworks (n = 17), as well as motivation, infrastructure, and training components (n = 13). Based on the sum of findings, we contribute to the triangulation of a recent RDM commitment evolution model. In particular, we find that strong support and suitable tools help develop RDM commitment, while policy conflicts, unclear data standards, and multi-platform sharing, lead to unexpected negotiation processes. We expect that these findings will help to better understand RDM commitment drivers, refine the RDM commitment evolution model, and benefit its application in science.  © 2022 ACM.","data management commitment; data-processing science; human data interventions; motivation; reproducibility; research data management; reuse","Agriculture; Data handling; Information management; Agricultural science; Data management commitment; Data-processing science; Evolution modeling; Human data; Human data intervention; Management practises; Reproducibilities; Research data managements; Reuse; Motivation","","","","","","","Artifact Review and Badging, (2018); Akers K.G., Doty J., Disciplinary Differences in Faculty Research Data Management Practices and Perspectives, (2013); Baker M., 1,500 scientists lift the lid on reproducibility, Nature 533, 7604 2016, pp. 452-454, (2016); Bechhofer S., Buchan I., De Roure D., Missier P., Ainsworth J., Bhagat J., Couch P., Cruickshank D., Delderfield M., Dunlop I., Gamble M., Michaelides D., Owen S., Newman D., Sufi S., Goble C., Why linked data is not enough for scientists, Future Generation Computer Systems, 29, 2, pp. 599-611, (2013); Glenn Begley C., Ellis L.M., Drug development: Raise standards for preclinical cancer research, Nature, 483, 7391, pp. 531-533, (2012); Belhajjame K., Zhao J., Garijo D., Hettne K., Palma R., Corcho O., Gomez-Perez J., Bechhofer S., Klyne G., Goble C., The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web, (2014); Bell G., Hey T., Szalay A., Beyond the Data Deluge, Science, 323, 5919, pp. 1297-1298, (2009); Bishoff C., Johnston L., Approaches to Data Sharing: An Analysis of NSF Data Management Plans from a Large Research University, Journal of Librarianship & Scholarly Communication 3, 2, 2015, (2015); Wade Bishop B., Borden R.M., Scientists? 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Motivations and Practices around Research Data Management and Reuse across Scientific Fields, Proceedings of the ACM on Human-Computer Interaction 4, CSCW2, pp. 1-26, (2020); Stefan Feger S., Wozniak P.W., Niess J., Schmidt A., Tailored Science Badges: Enabling New Forms of Research Interaction, Designing Interactive Systems Conference, 2021, pp. 576-588, (2021); Folinas D., Manikas I., Manos B., Traceability Data Management for Food Chains, (2006); The FAIR Data Principles. 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Data and Information Management 1, Ahead-of-print, 2019, (2019); Thielen J., Nichols Hess A., Advancing research data management in the social sciences: Implementing instruction for education graduate students into a doctoral curriculum, Behavioral & Social Sciences Librarian, 36, 1, pp. 16-30, (2017); Trisovic A., Mika K., Boyd C., Feger S., Crosas M., Repository approaches to improving the quality of shared data and code, Data 6, 2, 2021, (2021); Vertesi J., Dourish P., The value of data: Considering the context of production in data economies, Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work. ACM, pp. 533-542, (2011); Wallis J.C., Rolando E., Borgman C.L., If We Share Data Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology, PLoS ONE, 8, (2013); Whyte A., Tedds J., Making the case for research data management, Digital Curation Centre, (2011); Wilkinson M.D., Dumontier M., Jan Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Boiten J., Da Silva B., Santos L., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray G.A.J., Groth P., Goble C., Grethe J.S., Heringa J., Hoen T.C.P.A., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 2016, (2016); Wilson J.A.J., Martinez-Uribe L., Fraser M.A., Jeffreys P., An Institutional Approach to Developing Research Data Management Infrastructure, (2011); Wittenburg P., Van De Sompel H., Vigen J., Bachem A., Romary L., Marinucci M., Andersson T., Genova F., Best C., Los W., Et al., Riding the wave: How Europe can gain from the rising tide of scientific data, 2010 Final Report of the High Level Expert Group on Scientific Data. A Submission to the European Commission, (2010)","","","Association for Computing Machinery","","","","","","25730142","","","","English","Proc. ACM Hum. Comput. Interact.","Article","Final","","Scopus","2-s2.0-85146372052"
"Rafiq M.; Ameen K.","Rafiq, Muhammad (56684582000); Ameen, Kanwal (23468838600)","56684582000; 23468838600","Research data management and sharing awareness, attitude, and behavior of academic researchers","2022","Information Development","38","3","","391","405","14","2","10.1177/02666669211048491","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117436852&doi=10.1177%2f02666669211048491&partnerID=40&md5=9aa5fbaf8b62ba74b4aaeb0f405253f5","University of the Punjab, 66906, Pakistan; University of Home Economics, Lahore, 66906, Pakistan","Rafiq M., University of the Punjab, 66906, Pakistan; Ameen K., University of Home Economics, Lahore, 66906, Pakistan","This study assesses the research data management (RDM) awareness, attitude, practices, and behaviors of Pakistan's academic researchers. By using an internationally designed structured questionnaire as a data collection instrument. Quantitative survey research method was opted to meet the research objectives and data was collected from academicians and researchers of four premier universities of Pakistan. The study reveals used and produced data file formats, data acquisition sources, data storage patterns, metadata and tagging practices, data sharing patterns, RDM awareness, attitude, and behavior of the respondents by investigating the self-opinion of respondents on extensive sets of structured questionnaire items. It is a comprehensive assessment of the phenomenon from a developing country's perspective where research data management policies are absent at national and institutional level. The findings have theoretical implications for researchers and practical implications for policymakers, university administrators, university library administrators, and educational trainers. © The Author(s) 2021.","data literacy; data management practices; data management skills; data management training; data sharing behaviors; higher education; metadata behaviors; Pakistan; tagging behaviors","","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: Findings from a focus group in an academic health sciences library, Journal of Web Librarianship, 6, 4, pp. 274-287, (2012); Berghmans S., Cousijn H., Deakin G., Et al., (2017); (2003); Burnette M.H., Williams S.C., Imker H.J., From plan to action: Successful data management plan implementation in a multidisciplinary project, Journal of EScience Librarianship, 5, 1, (2016); Cheah P.Y., Tangseefa D., Somsaman A., Et al., Perceived benefits, harms, and views about how to share data responsibly: A qualitative study of experiences with and attitudes toward data sharing among research staff and community representatives in Thailand, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 278-289, (2015); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); (2018); Donnelly M., (2017); Fecher B., Friesike S., Hebing M., Et al., A reputation economy: Results from an empirical survey on academic data sharing, arXiv preprint arXiv:1503.00481, (2015); Federer L.M., Lu Y.L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association: JMLA, 104, 1, pp. 52-57, (2016); (2016); Houtkoop B.L., Chambers C., Macleod M., Et al., Data sharing in psychology: A survey on barriers and preconditions, Advances in Methods and Practices in Psychological Science, 1, 1, pp. 70-85, (2018); Mclure M., Level A.V., Cranston C.L., Et al., Data curation: A study of researcher practices and needs introduction and research questions, Portal Libraries and the Academy, 14, 2, pp. 139-164, (2014); Mallasvik M.L., Martins J.T., Research data sharing behaviour of engineering researchers in Norway and the UK: Uncovering the double face of Janus, Journal of Documentation, 77, 2, pp. 576-593, (2020); (2018); (2013); (2007); Piracha H.A., Ameen K., Research data management practices of faculty members, Pakistan Journal of Information Management and Libraries, 20, pp. 60-75, (2018); Piwowar H.A., Who shares? Who doesn’t? Factors associated with openly archiving raw research data, PLoS ONE, 6, 7, pp. 1-13, (2011); (2018); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, International Journal of Digital Curation, 6, 2, pp. 232-244, (2011); Schmidt B., Gemeinholzer B., Treloar A., Open data in global environmental research: The Belmont Forum's Open data survey, PloS One, 11, 1, (2016); (2018); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); (2016); (2018); Van den Eynden V., Bishop L., (2014); Van den Eynden V., Corti L., The importance of managing and sharing research data, Managing and Sharing Research Data, pp. 1-32, (2020); Van Panhuis W.G., Paul P., Emerson C., Et al., A systematic review of barriers to data sharing in public health, BMC Public Health, 14, 1, pp. 1-9, (2014); Waard A.D., Rotman D., Lauruhn M., (2014); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Wolff-Eisenberg C., Rod A.B., Schonfeld R.C., (2016)","M. Rafiq; University of the Punjab, 66906, Pakistan; email: dr.rafiqm@gmail.com","","SAGE Publications Ltd","","","","","","02666669","","","","English","Inf. Dev.","Article","Final","","Scopus","2-s2.0-85117436852"
"Kanza S.; Knight N.J.","Kanza, Samantha (57194278449); Knight, Nicola J. (56522701700)","57194278449; 56522701700","Behind every great research project is great data management","2022","BMC Research Notes","15","1","20","","","","1","10.1186/s13104-022-05908-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123570727&doi=10.1186%2fs13104-022-05908-5&partnerID=40&md5=5192f724dfba8b88c2a2489c3476736b","Department of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom","Kanza S., Department of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom; Knight N.J., Department of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom","Research data management (RDM) is the cornerstone of a successful research project, and yet it often remains an underappreciated art that gets overlooked in the hustle and bustle of everyday project management even when required by funding bodies. If researchers are to strive for reproducible science that adheres to the principles of FAIR, then they need to manage the data associated with their research projects effectively. It is imperative to plan your RDM strategies early on, and setup your project organisation before embarking on the work. There are several different factors to consider: data management plans, data organisation and storage, publishing and sharing your data, ensuring reproducibility and adhering to data standards. Additionally it is important to reflect upon the ethical implications that might need to be planned for, and adverse issues that may need a mitigation strategy. This short article discusses these different areas, noting some best practices and detailing how to incorporate these strategies into your work. Finally, the article ends with a set of top ten tips for effective research data management. © 2022, The Author(s).","Data ethics; Data management plans; Data organisation; Data sharing; FAIR data; Reproducibility; Research data management","Data Management; Humans; Publishing; Reproducibility of Results; Research Design; Research Personnel; human; information processing; methodology; personnel; publishing; reproducibility","","","","","Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery; Physical Sciences Data science Service; Engineering and Physical Sciences Research Council, EPSRC","This work was funded by EPSRC through grants EP/S000356/1-AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) and EP/S020357/1-PSDS (Physical Sciences Data science Service). ","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, 1, pp. 1-9, (2016); Kanza S., Knight N., Failed It to Nailed It! Getting Data Sharing Right: Event 1 Report - Dealing with Data: Tips and Tricks, (2020); Stark I., (2020); Kanza S., AI3SD video: Collaborative data management; Github: Where the World Builds Software; Kanza S., Knight N., Failed It to Nailed It! Getting Data Sharing Right: Event 3 report—responsible Data Management, (2020); McNutt M., Reproducibility, Science, 343, 6168, (2014); Miyakawa T., No raw data, no science: another possible source of the reproducibility crisis, Mol Brain, 13, 1, (2020); Craigon P., AI3SD video: Intro to ethics, (2021); Kanza S., AI3SD Video: Writing an Ethics Application, (2021); Kanza S., Knight N., Failed it to nailed it! Getting data sharing right: Event 2 report - data standards, University of Southampton, (2020)","S. Kanza; Department of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, University Road, SO17 1BJ, United Kingdom; email: s.kanza@soton.ac.uk","","BioMed Central Ltd","","","","","","17560500","","","35063017","English","BMC Res. Notes","Note","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85123570727"
"Bona J.; Kemp A.S.; Cox C.; Nolan T.S.; Pillai L.; Das A.; Galvin J.E.; Larson-Prior L.; Virmani T.; Prior F.","Bona, Jonathan (36156141500); Kemp, Aaron S. (7202027667); Cox, Carli (57467938500); Nolan, Tracy S. (25632859500); Pillai, Lakshmi (57202118208); Das, Aparna (57235284000); Galvin, James E. (14071228600); Larson-Prior, Linda (6602160212); Virmani, Tuhin (6507315898); Prior, Fred (7004715742)","36156141500; 7202027667; 57467938500; 25632859500; 57202118208; 57235284000; 14071228600; 6602160212; 6507315898; 7004715742","Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)","2022","Frontiers in Artificial Intelligence","4","","649970","","","","5","10.3389/frai.2021.649970","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125291956&doi=10.3389%2ffrai.2021.649970&partnerID=40&md5=9897f9e05b85fb89186bb70bd22d7921","Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Neurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Department of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Miami, FL, United States; Department of Radiology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States","Bona J., Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Kemp A.S., Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States, Neurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Cox C., Neurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Nolan T.S., Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Pillai L., Department of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Das A., Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Galvin J.E., Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Miami, FL, United States; Larson-Prior L., Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States, Neurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States, Department of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Virmani T., Department of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States; Prior F., Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States, Department of Radiology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States","Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings). These unique capabilities ensure greater reproducibility of scientific findings across large-scale research projects. The current investigation was conducted with three collaborating teams who are using ARIES in a project focusing on neurodegeneration. Datasets included magnetic resonance imaging (MRI) data as well as non-imaging data obtained from a variety of assessments designed to measure neurocognitive functions (performance scores on neuropsychological tests). We integrate and manage these data with semantic representations based on axiomatically rich biomedical ontologies. These instantiate a knowledge graph that combines the data from the study cohorts into a shared semantic representation that explicitly accounts for relations among the entities that the data are about. This knowledge graph is stored in a triple-store database that supports reasoning over and querying these integrated data. Semantic integration of the non-imaging data using background information encoded in biomedical domain ontologies has served as a key feature-engineering step, allowing us to combine disparate data and apply analyses to explore associations, for instance, between hippocampal volumes and measures of cognitive functions derived from various assessment instruments. Copyright © 2022 Bona, Kemp, Cox, Nolan, Pillai, Das, Galvin, Larson-Prior, Virmani and Prior.","imaging informatics; knowledge representation; neuroinformatics; ontologies (artificial intelligence); semantic web","","","","","","National Institutes; National Institutes of Health, NIH, (16X011, HHSN261200800001E); National Cancer Institute, NCI; National Center for Advancing Translational Sciences, NCATS; University at Buffalo, UB; Tropical Resources Institute, TRI, (UL1 TR003107); Translational Research Institute, University of Arkansas for Medical Sciences, TRI, UAMS","Funding text 1: This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Under this contract the University of Arkansas is funded by Leidos Biomedical Research subcontract 16X011. Funding was also provided by U24CA215109. This project is supported in part by the UAMS Translational Research Institute (TRI), grant UL1 TR003107 through the National Center for Advancing Translational Sciences of the National Institutes; Funding text 2: The authors wish to thank the creators of the Neuropsychological Testing Ontology, and in particular Alexander Diehl and Alexander Cox (University at Buffalo), for their input on our use of the NPT within ARIES.","Arp R., Smith B., Spear A.D., Building ontologies with basic formal ontology, Mit Press, (2015); Baader F., Calvanese D., McGuinness D., Patel-Schneider P., Nardi D., The description logic handbook: Theory, implementation and applications, (2003); Baghal A., Zozus M., Al-Shukri S., Prior F., Factors associated with increased adoption of a research data warehouse, In: ITCH, pp. 31-35, (2019); Banker K., MongoDB in Action, (2011); Bennett W., Smith K., Jarosz Q., Nolan T., Bosch W., Reengineering workflow for curation of DICOM datasets, J Digit Imaging, 31, pp. 783-791, (2018); Benton A.L., Hamsher K., Sivan A.B., Multilingual Aphasia Examination, (1983); Benton A.L., Varney N.R., Hamsher K.S., Visuospatial judgment, Archives of Neurol, 35, pp. 364-367, (1978); Blobel B., Yang B., The role of axiomatically-rich ontologies in transforming medical data to knowledge, pp. 249-238, (2018); Brandt J., The Hopkins Verbal Learning Test: Development of a new memory test with six equivalent forms, Clin Neuropsychol, 5, pp. 125-142, (1991); Brochhausen M., Bona J., Blobel B., The Role of Axiomatically-Rich Ontologies in Transforming Medical Data to Knowledge, Stud Health Technol Inform, 249, pp. 38-49, (2018); Camicioli R., Moore M.M., Kerr D., Kaye J., Dementia in Parkinson's disease is associated with hippocampal atrophy, Neurology, 52, (1999); Camicioli R., Moore M.M., Kinney A., Corbridge E., Glassberg K., Kaye J.A., Parkinson's disease is associated with hippocampal atrophy, Movement Disorders, 18, pp. 784-790, (2003); Clark K., Vendt B., Smith K., The cancer imaging archive (TCIA): maintaining and operating a public information repository, J Digit Imaging, 26, pp. 1045-1057, (2013); Cox A.P., Jensen M., Ruttenberg A., Szigeti K., Diehl A.D., Measuring Cognitive Functions: Hurdles in the Development of the NeuroPsychological Testing Ontology, In ICBO, pp. 78-83, (2013); Doty R.L., Shaman P., Kimmelman C.P., Dann M.S., University of pennsylvania smell identification test: A rapid quantitative olfactory function test for the clinic, The Laryngoscope, 94, pp. 176-178, (1984); Eickhoff S.B., Nichols T.E., Van Horn J.D., Turner J.A., Sharing the wealth: neuroimaging data repositories, Neuroimage, 124, pp. 1-8, (2016); Fonov V., Evans A., Botteron K., Almli C., McKinstry R., Collins D., Unbiased average age-appropriate atlases for pediatric studies, Neuroimage, 54, pp. 313-327, (2011); Gold J.M., Carpenter C., Randolph C., Goldberg T.E., Weinberger D.R., Auditory working memory and Wisconsin Card Sorting Test performance in schizophrenia, Archives of General Psychiat, 54, pp. 159-165, (1997); Greenlief C.L., Margolis R.B., Erker G.J., Application of the Trail Making Test in differentiating neuropsychological impairment of elderly persons, Percept Mot Skills, 61, pp. 1283-1289, (1985); Han J., Haihong E., Le G., Du J., Survey on NoSQL database, 2011, 6th. 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ACM transactions on database systems (TODS), ACM J, 34, pp. 1-45, (2009); Randolph C., Tierney M.C., Mohr E., Chase T.N., The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity, J Clin Exp Neuropsychol, 20, pp. 310-319, (1998); Rohloff K., Dean M., Emmons I., Ryder D., Sumner J., An Evaluation of Triple-Store Technologies for Large Data Stores, On the Move to Meaningful Internet Systems 2007: OTM Workshops, pp. 1105-1114, (2007); Rosen W.G., Verbal fluency in aging and dementia, J Clin Neuropsychol, 2, pp. 135-146, (1980); Sharma A., Tarbox L., Kurc T., PRISM: a platform for imaging in precision medicine, JCO Clinical Cancer Informat, pp. 491-499, (2020); Smith A., The symbol-digit modalities test: A neuropsychologic test of learning and other cerebral disorders, Learning disorders, (1968); Smith B., Ashburner M., Rosse C., Bard J., Bug W., Ceusters W., Et al., Foundry: coordinated evolution of ontologies to support biomedical data integration, Nature Biotechnol, 25, pp. 1251-1255, (2007); Smith S.M., Jenkinson M., Woolrich M.W., Beckmann C.F., Behrens T., Johansen-Berg H., Et al., Functional and structural MR image analysis and implementation as FSL, NeuroImage, 23, pp. 208-219, (2004); Stroop J., Studies of interference in serial verbal reactions, J Exp Psychol Gen, 18, pp. 643-662, (1935); Syed S., Syed M., Syeda H., API Driven On-Demand Subject ID Pseudonymization in Heterogeneous Multi-Study Research, (2020); Woolrich M.W., Jbabdi S., Patenaude B., Chappell M., Makni S., Behrens T., Et al., Bayesian analysis of neuroimaging data in FSL, NeuroImage, 45, pp. S173-S186, (2009); Yildiz D., Erer S., Zarifoglu M., Hakyemez B., Bakar M., Karli N., Et al., Impaired cognitive performance and hippocampal atrophy in Parkinson disease, Turk J Med Sci, 45, pp. 1173-1177, (2015)","J. Bona; Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, United States; email: jbona@uams.edu; A.S. Kemp; Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, United States; email: askemp@uams.edu","","Frontiers Media S.A.","","","","","","26248212","","","","English","Frontier. Artif. Intell.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85125291956"
"Löbe M.; Ulrich H.; Beger C.; Bender T.; Bauer C.; Sax U.; Ingenerf J.; Winter A.","Löbe, Matthias (55938448500); Ulrich, Hannes (56347799300); Beger, Christoph (57195720120); Bender, Theresa (57204583786); Bauer, Christian (57200871605); Sax, Ulrich (8956991900); Ingenerf, Josef (6603276740); Winter, Alfred (57720778200)","55938448500; 56347799300; 57195720120; 57204583786; 57200871605; 8956991900; 6603276740; 57720778200","Improving Findability of Digital Assets in Research Data Repositories Using the W3C DCAT Vocabulary","2022","Studies in Health Technology and Informatics","290","","","61","65","4","0","10.3233/SHTI220032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131496124&doi=10.3233%2fSHTI220032&partnerID=40&md5=50506bdab1c25e72286620b6fa4690b8","Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, 04107, Germany; Institut für Medizinische Informatik, University of Lübeck, Lübeck, Germany; University Medical Center Göttingen, Göttingen, Germany; Campus Institute Data Science (CIDAS), Gottingen, Germany","Löbe M., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, 04107, Germany; Ulrich H., Institut für Medizinische Informatik, University of Lübeck, Lübeck, Germany; Beger C., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, 04107, Germany; Bender T., University Medical Center Göttingen, Göttingen, Germany; Bauer C., University Medical Center Göttingen, Göttingen, Germany; Sax U., University Medical Center Göttingen, Göttingen, Germany, Campus Institute Data Science (CIDAS), Gottingen, Germany; Ingenerf J., Institut für Medizinische Informatik, University of Lübeck, Lübeck, Germany; Winter A., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, 04107, Germany","Research data management requires stable, trustworthy repositories to safeguard scientific research results. In this context, rich markup with metadata is crucial for the discoverability and interpretability of the relevant resources. SEEK is a web-based software to manage all important artifacts of a research project, including project structures, involved actors, documents and datasets. SEEK is organized along the ISA model (Investigation-Study-Assay). It offers several machine-readable serializations, including JSON and RDF. In this paper, we extend the power of RDF serialization by leveraging the W3C Data Catalog Vocabulary (DCAT). DCAT was specifically designed to improve interoperability between digital assets on the Web and enables cross-domain markup. By using community-consented gold standard vocabularies and a formal knowledge description language, findability and interoperability according to the FAIR principles are significantly improved. © 2022 International Medical Informatics Association (IMIA) and IOS Press.","Data Management; Health Information Systems; Semantic Web","Data Management; Metadata; Research Design; Software; Vocabulary; Health; Interoperability; Medical informatics; Resource Description Framework (RDF); Data repositories; Digital assets; Health information systems; Interpretability; Research data; Research data managements; Research results; Scientific researches; Semantic-Web; Trustworthy repositories; conference paper; FAIR principles; gold standard; human; human experiment; language; medical information system; semantic web; vocabulary; information processing; metadata; methodology; software; Information management","","","","","NFDI4Health DFG, (442326535); Deutsche Forschungsgemeinschaft, DFG, (IN 50/3-2, SA 1009/3-2, WI 1605/10-2); Bundesministerium für Bildung und Forschung, BMBF, (031L0026)","Funding from the German Research Foundation (DFG NMDR grants IN 50/3-2, SA 1009/3-2, WI 1605/10-2 and NFDI4Health DFG grant 442326535) as well as the German Federal Ministry of Education and Research (BMBF Grant 031L0026) is acknowledged.","Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P.A.C.T., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); De Smedt K., Koureas D., Wittenburg P., FAIR digital objects for science: From data pieces to actionable knowledge units, Publications, 8, (2020); FAIR Data Maturity Model: Specification and Guidelines, Research Data Alliance, (2020); Witt M., 2, 000 data repositories and science Europe's framework for discipline-specific research data management, DataCite, (2018); Haendel M., Su A., McMurry J., Et al., Fair-Tlc: Metrics to Assess Value of Biomedical Digital Repositories: Response to Rfi Not-Od-16-133, Zenodo, (2016); Perego A., Browning D., Albertoni R., Cox S., Winstanley P., Gonzalez Beltran A., Data Catalog Vocabulary (DCAT)-Version 2, (2020); Wolstencroft K., Owen S., Krebs O., Nguyen Q., Stanford N.J., Golebiewski M., Weidemann A., Bittkowski M., An L., Shockley D., Snoep J.L., Mueller W., Goble C., SEEK: A systems biology data and model management platform, BMC Syst Biol, 9, (2015); Wolstencroft K., Krebs O., Snoep J.L., Stanford N.J., Bacall F., Golebiewski M., Kuzyakiv R., Nguyen Q., Owen S., Soiland-Reyes S., Straszewski J., Van Niekerk D.D., Williams A.R., Malmstrom L., Rinn B., Muller W., Goble C., FAIRDOMHub: A repository and collaboration environment for sharing systems biology research, Nucleic Acids Res, 45, pp. D404-D407, (2017); Schmidt C.O., Darms J., Shutsko A., Lobe M., Nagrani R., Lindstadt B., Golebiewski M., Koleva S., Bender T., Sax U., Hu X., Lieser M., Junker V., Lehne M., Zeleke A., Pigeot I., Fluck J., Facilitating study and item level browsing for clinical and epidemiological COVID-19 studies, Studies in Health Technology and Informatics, (2021); Parciak M., Bender T., Sax U., Bauer C.R., Applying fairness: Redesigning a biomedical informatics research data management pipeline, Methods Inf Med, 58, pp. 229-234, (2019); Sansone S.-A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hofmann O., Fang H., Neumann S., Tong W., Amaral-Zettler L., Begley K., Booth T., Bougueleret L., Burns G., Chapman B., Clark T., Coleman L.-A., Copeland J., Das S., De Daruvar A., De Matos P., Dix I., Edmunds S., Evelo C.T., Forster M.J., Gaudet P., Gilbert J., Goble C., Griffin J.L., Jacob D., Kleinjans J., Harland L., Haug K., Hermjakob H., Ho Sui S.J., Laederach A., Liang S., Marshall S., McGrath A., Merrill E., Reilly D., Roux M., Shamu C.E., Shang C.A., Steinbeck C., Trefethen A., Williams-Jones B., Wolstencroft K., Xenarios I., Hide W., Toward interoperable bioscience data, Nat Genet, 44, pp. 121-126, (2012); Meineke Frank A., Matthias L., Sebastian S., Introducing technical aspects of research data management in the leipzig health atlas, Studies in Health Technology and Informatics, 247, pp. 426-430, (2018); Wichmann G., Rosolowski M., Krohn K., Kreuz M., Boehm A., Reiche A., Scharrer U., Halama D., Bertolini J., Bauer U., Holzinger D., Pawlita M., Hess J., Engel C., Hasenclever D., Scholz M., Ahnert P., Kirsten H., Hemprich A., Wittekind C., Herbarth O., Horn F., Dietz A., Loeffler M., The role of HPV RNA transcription, immune response-related gene expression and disruptive TP53 mutations in diagnostic and prognostic profiling of head and neck cancer, Int J Cancer, 137, pp. 2846-2857, (2015); Murphy S.N., Weber G., Mendis M., Gainer V., Chueh H.C., Churchill S., Kohane I., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), J Am Med Inform Assoc, 17, pp. 124-130, (2010)","M. Löbe; Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, 04107, Germany; email: matthias.loebe@imise.uni-leipzig.de","Otero P.; Scott P.; Martin S.Z.; Huesing E.","IOS Press BV","","18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021","2 October 2021 through 4 October 2021","Virtual, Online","179966","09269630","978-164368264-8","","35672971","English","Stud. Health Technol. Informatics","Conference paper","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85131496124"
"Broneske D.; Wolff I.; Köppen V.; Schäler M.","Broneske, David (55510437000); Wolff, Ian (58034778800); Köppen, Veit (34877053100); Schäler, Martin (56811189600)","55510437000; 58034778800; 34877053100; 56811189600","Exploiting Views for Collaborative Research Data Management of Structured Data","2022","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13636 LNCS","","","360","376","16","0","10.1007/978-3-031-21756-2_28","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145006282&doi=10.1007%2f978-3-031-21756-2_28&partnerID=40&md5=f6de6bbd9208b042e7a7b69f82ce2ce9","DZHW, Hannover, Germany; University of Magdeburg, Magdeburg, Germany; Zentral- und Landesbibliothek Berlin, Berlin, Germany; Salzburg University, Salzburg, Austria","Broneske D., DZHW, Hannover, Germany; Wolff I., University of Magdeburg, Magdeburg, Germany; Köppen V., Zentral- und Landesbibliothek Berlin, Berlin, Germany; Schäler M., Salzburg University, Salzburg, Austria","Data-driven analysis plays a vital role in research projects, and sharing data with collaborators inside or outside a project is supposed to be daily scientific work. There are various tools for research data management, which offer features like storing data, meta-data indexing, and provide options to share data. However, currently, none of them offers capabilities for sharing data in different levels of detail without excessive data duplication. Naturally, sharing data by duplication is a tedious process, as preparing data for sharing typically involves changing temporal resolution (i.e., aggregation) or anonymization, e.g., to ensure privacy. In this paper, instead of re-inventing the wheel, we ask whether the concept of views, a well-established concept in relational databases, fulfills the above requirement. Conducting a case study for a project employing sharing of learning analytics data, we propose a framework that allows for fine-granular configuration of shared content based on the concept of views. In the case study, we a) analyze a data reuse scenario based on the FAIR principles, b) suggest a concept for using views for data sharing, and c) demonstrate its feasibility with a proof-of-concept. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Data sharing; Provenance; Research data management","Case-studies; Collaborative research; Data duplication; Data Sharing; Data-driven analysis; Level-of-detail; Provenance; Research data managements; Structured data; Temporal resolution; Information management","","","","","Bundesministerium für Bildung und Forschung, BMBF, (16DHB 3008)","Information. German Federal Ministry of Education and Research [16DHB 3008].","Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univ. Access Inf. Soc., 16, 4, pp. 851-862, (2016); Bloemers M., Montesanti A., The FAIR funding model: Providing a framework for research funders to drive the transition toward FAIR data management and stewardship practices, Data Intell, 2, 1-2, pp. 171-180, (2020); Codd E.F., A relational model of data for large shared data banks, Commun. ACM, 26, 1, pp. 64-69, (1983); Devarakonda R., Palanisamy G., Green J., Wilson B., Data sharing and retrieval using OAI-PMH, Earth Sci. Inf., 4, 1, pp. 1-5, (2011); Dewey M., Dewey decimal classification and relative index. 2, Schedules 000-599. OCLC, Library, (1989); Dietrich A., Liascript: A domain-specific-language for interactive online courses, Multi Conference on Computer Science and Information Systems, P, (2019); Drachsler H., Greller W., Privacy and analytics: It’s a delicate issue a checklist for trusted learning analytics, Proceedings of the Sixth International Conference on Learning Analytics and Knowledge, LAK 2016, pp. 89-98, (2016); Garcia-Molina H., Ullman J.D., Widom J., Database Systems: The Complete Book, (2008); Gray J., Reuter A., Transaction Processing: Concepts and Techniques, (1992); Grolinger K., Higashino W.A., Tiwari A., Capretz M.A.M., Data management in cloud environments: NoSQL and NewSQL data stores, J. Cloud Comput. Adv. Syst. Appl., 2, 1, pp. 1-24, (2013); Hildt E., Laas K., Informed consent in digital data management, Codes of Ethics and Ethical Guidelines. TILELT, 23, pp. 55-81, (2022); Jorn Nielsen H., Hjorland B., Curating research data: The potential roles of libraries and information professionals, J. Doc., 70, 2, pp. 221-240, (2014); Kim Y., Zhang P., Understanding data sharing behaviors of stem researchers: The roles of attitudes, norms, and data repositories, Libr. Inf. Sci. Res., 37, 3, pp. 189-200, (2015); Kimball R., Slowly Changing Dimensions. Unlike OLTP Systems, Data Warehouse Systems Cab Track Historical Data, DBMS Online, 9, 4, (1996); Linnemann V., Et al., Design and Implementation of an Extensible Database Management System Supporting User Defined Data Types and Functions, pp. 294-305, (1988); Michener W.K., Ten simple rules for creating a good data management plan, Plos Comput. Biol., 11, 10, pp. 1-9, (2015); Motro A., An access authorization model for relational databases based on algebraic manipulation of view definitions, ICDE Fifth International Conference on Data Engineering, pp. 339-347, (1989); Obionwu V., Broneske D., Hawlitschek A., Koppen V., Saake G., SQLValidator-an online student playground to learn SQL, Datenbank-Spektrum, (2021); Pampel H., Et al., Making research data repositories visible: The re3data.org registry, Plos ONE, 8, 11, pp. 1-10, (2013); Pardo A., Siemens G., Ethical and privacy principles for learning analytics, Br. J. Edu. Technol., 45, 3, pp. 438-450, (2014); Pasquetto I.V., Randles B.M., Borgman C.L., On the reuse of scientific data, Data Sci. J., (2017); Pouchard L., Revisiting the data lifecycle with big data curation, Int. J. Digit. Curation, (2015); Pyrounakis G., Nikolaidou M., Hatzopoulos M., Building digital collections using open source digital repository software: A comparative study, Int. J. Digital Libr. Syst. (IJDLS), 4, 1, pp. 10-25, (2014); Rizvi S., Mendelzon A., Sudarshan S., Roy P., Extending query rewriting techniques for fine-grained access control, Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, SIGMOD 2004, pp. 551-562, (2004); Scheffel M., Drachsler H., Slavi S., Specht M., Quality indicators for learning analytics, Educ. Technol. Soc., 17, 4, pp. 117-132, (2014); Smith M., Et al., DSpace: an open source dynamic digital repository, D-Lib Mag, 9, 1, (2003); Space data and information transfer systems-Open archival information system (OAIS)-Reference model. International Organization for Standardization, Vernier, Geneva, Switzerland, ISO 14721:2012-09 Edn, (2012); Sweeney L., K-anonymity: A model for protecting privacy, Int. J. Uncertain. Fuzziness Knowledge-Based Syst., 10, 5, pp. 557-570, (2002); Treloar A., Klump J., Updating the data curation continuum, IJDC, 14, 1, pp. 87-101, (2019); Viberg O., Hatakka M., Balter O., Mavroudi A., The current landscape of learning analytics in higher education, Comput. Hum. Behav., 89, pp. 98-110, (2018); Wilkinson M.D., Et al., The fair guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016)","D. Broneske; DZHW, Hannover, Germany; email: broneske@dzhw.eu","Tseng Y.-H.; Katsurai M.; Nguyen H.N.","Springer Science and Business Media Deutschland GmbH","","24th International Conference on Asia-Pacific Digital Libraries, ICADL 2022","30 November 2022 through 2 December 2022","Hanoi","287629","03029743","978-303121755-5","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85145006282"
"Weber S.; Zimmermann R.T.; Bremer J.; Abel K.L.; Poppitz D.; Prinz N.; Ilsemann J.; Wendholt S.; Yang Q.; Pashminehazar R.; Monaco F.; Cloetens P.; Huang X.; Kübel C.; Kondratenko E.; Bauer M.; Bäumer M.; Zobel M.; Gläser R.; Sundmacher K.; Sheppard T.L.","Weber, Sebastian (57220041302); Zimmermann, Ronny T. (57214091482); Bremer, Jens (57191859820); Abel, Ken L. (57201720148); Poppitz, David (55967373400); Prinz, Nils (57208393617); Ilsemann, Jan (57203527625); Wendholt, Sven (57218407279); Yang, Qingxin (57219125132); Pashminehazar, Reihaneh (57188958988); Monaco, Federico (57196116047); Cloetens, Peter (7004115610); Huang, Xiaohui (57216756779); Kübel, Christian (6701623681); Kondratenko, Evgenii (7003539910); Bauer, Matthias (57201583106); Bäumer, Marcus (7005432099); Zobel, Mirijam (55286063000); Gläser, Roger (7202586559); Sundmacher, Kai (55147531700); Sheppard, Thomas L. (56672199600)","57220041302; 57214091482; 57191859820; 57201720148; 55967373400; 57208393617; 57203527625; 57218407279; 57219125132; 57188958988; 57196116047; 7004115610; 57216756779; 6701623681; 7003539910; 57201583106; 7005432099; 55286063000; 7202586559; 55147531700; 56672199600","Digitization in Catalysis Research: Towards a Holistic Description of a Ni/Al2O3 Reference Catalyst for CO2 Methanation","2022","ChemCatChem","14","8","e202101878","","","","10","10.1002/cctc.202101878","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125999878&doi=10.1002%2fcctc.202101878&partnerID=40&md5=0201efd5372e64b3331631a0e623c91d","Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, Karlsruhe, 76131, Germany; Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Chair for Process Systems Engineering, Institute of Process Engineering, Otto-von-Guericke University Magdeburg, Universitätplatz 2, Magdeburg, 39106, Germany; Department Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, Magdeburg, 39106, Germany; Institute of Chemical Technology, Universität Leipzig, Linnéstraße 3, Leipzig, 04103, Germany; Institute of Crystallography, RWTH Aachen University, Jägerstraße 17–19, Aachen, 52066, Germany; Institute of Applied and Physical Chemistry and MAPEX Center for Materials and Processes, University of Bremen, Leobener Straße 6, Bremen, 28359, Germany; Faculty of Science and Center for Sustainable Systems Design (CSSD), Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany; Leibniz-Institut für Katalyse e.V., Albert-Einstein-Straße 29a, Rostock, 18059, Germany; ESRF – the European Synchrotron, Grenoble, 38043, France; Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Department of Materials and Earth Sciences, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, Darmstadt, 64287, Germany; Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany","Weber S., Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, Karlsruhe, 76131, Germany, Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Zimmermann R.T., Chair for Process Systems Engineering, Institute of Process Engineering, Otto-von-Guericke University Magdeburg, Universitätplatz 2, Magdeburg, 39106, Germany; Bremer J., Department Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, Magdeburg, 39106, Germany; Abel K.L., Institute of Chemical Technology, Universität Leipzig, Linnéstraße 3, Leipzig, 04103, Germany; Poppitz D., Institute of Chemical Technology, Universität Leipzig, Linnéstraße 3, Leipzig, 04103, Germany; Prinz N., Institute of Crystallography, RWTH Aachen University, Jägerstraße 17–19, Aachen, 52066, Germany; Ilsemann J., Institute of Applied and Physical Chemistry and MAPEX Center for Materials and Processes, University of Bremen, Leobener Straße 6, Bremen, 28359, Germany; Wendholt S., Faculty of Science and Center for Sustainable Systems Design (CSSD), Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany; Yang Q., Leibniz-Institut für Katalyse e.V., Albert-Einstein-Straße 29a, Rostock, 18059, Germany; Pashminehazar R., Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, Karlsruhe, 76131, Germany; Monaco F., ESRF – the European Synchrotron, Grenoble, 38043, France; Cloetens P., ESRF – the European Synchrotron, Grenoble, 38043, France; Huang X., Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Department of Materials and Earth Sciences, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, Darmstadt, 64287, Germany; Kübel C., Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Department of Materials and Earth Sciences, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, Darmstadt, 64287, Germany, Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Kondratenko E., Leibniz-Institut für Katalyse e.V., Albert-Einstein-Straße 29a, Rostock, 18059, Germany; Bauer M., Faculty of Science and Center for Sustainable Systems Design (CSSD), Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany; Bäumer M., Institute of Applied and Physical Chemistry and MAPEX Center for Materials and Processes, University of Bremen, Leobener Straße 6, Bremen, 28359, Germany; Zobel M., Institute of Crystallography, RWTH Aachen University, Jägerstraße 17–19, Aachen, 52066, Germany; Gläser R., Institute of Chemical Technology, Universität Leipzig, Linnéstraße 3, Leipzig, 04103, Germany; Sundmacher K., Chair for Process Systems Engineering, Institute of Process Engineering, Otto-von-Guericke University Magdeburg, Universitätplatz 2, Magdeburg, 39106, Germany, Department Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, Magdeburg, 39106, Germany; Sheppard T.L., Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, Karlsruhe, 76131, Germany, Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany","There is considerable motivation in the catalysis community and chemical industry to envision a future where rational catalyst design and targeted chemical process optimization become standard. Achieving this goal for heterogeneous catalysis requires a cultural shift centered around effective research data management. The core elements of modern catalysis research are synthesis, characterization, and testing, while all can be elevated by effective collection, correlation, interoperation, and exploitation of data between disciplines and stakeholders. Here, first steps are made towards a holistic picture of an industrial Ni/Al2O3 reference catalyst for CO2 methanation. A range of conventional and advanced characterization tools are applied to probe metal particle size and pore characteristics of the support, selected as crucial parameters for catalyst performance. Challenges are shown with respect to current reporting of characterization data and metadata, which ultimately influences the development and reliability of digital twins in catalysis research. Furthermore, the cooperation and combined expertise of diverse research groups from different fields is recognized as essential to deliver meaningful progress towards the digital future of catalysis research. © 2022 The Authors. ChemCatChem published by Wiley-VCH GmbH.","characterization; digitization; methanation; nickel; reference catalyst","Aluminum compounds; Carbon dioxide; Catalysis; Catalysts; Chemical industry; Information management; Methanation; Nickel; Nickel compounds; Optimization; Particle size; Catalysis research; Catalyst designs; Characterization; Chemical process; Core elements; Cultural shift; Digitisation; Process optimisation; Reference catalyst; Research data managements; Hydrogenation","","","","","Gef?rdert durch die Deutsche Forschungsgemeinschaft, (ZO 369/2-1); International Max Planck Research School; Deutsche Forschungsgemeinschaft, DFG, (406914011, BA 1710/31‐1, KO 2261/10‐1); China Scholarship Council, CSC; Helmholtz Association, (CH-5805, I-20200385)","Funding text 1: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) – 406914011, KO 2261/10‐1, ZO 369/2‐1, BA 1710/31‐1 funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 406914011, KO 2261/10‐1, ZO 369/2‐1, BA 1710/31‐1. We acknowledge discussion of the results with Jan‐Dierk Grunwaldt in the frame of the SPP2080 project by the DFG. Generous supply of SPP2080‐IMRC by our industrial partner is gratefully acknowledged. We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III beamline P21.1. Beamtime was allocated for proposal I‐20200385. Thanks to Philipp Glaevecke, Soham Banerjee, Olof Gutowski, Linda Klag and Srashtasrita Das for support during beamtime. The holographic X‐ray computed tomography experiments were performed on beamline ID16A at the European Synchrotron Radiation Facility (ESRF), Grenoble, France in the frame of experiment CH‐5805. This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMF), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT), which provided access to FIB and TEM instruments via proposal 2020‐023‐028494. Thanks to Mariam Schulte for performing catalytic experiments of the TEM sample. Thanks to Sabine Schlabach for support during FIB sample preparation. Ronny Zimmermann is also affiliated with the International Max Planck Research School (IMPRS) for Advanced Methods in Process and Systems Engineering, Magdeburg, Germany. Xiaohui Huang acknowledges the China Scholarship Council for providing a PhD scholarship. Open Access funding enabled and organized by Projekt DEAL. ; Funding text 2: Gef?rdert durch die Deutsche Forschungsgemeinschaft (DFG) ? 406914011, KO 2261/10-1, ZO 369/2-1, BA 1710/31-1 funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) ? 406914011, KO 2261/10-1, ZO 369/2-1, BA 1710/31-1. We acknowledge discussion of the results with Jan-Dierk Grunwaldt in the frame of the SPP2080 project by the DFG. Generous supply of SPP2080-IMRC by our industrial partner is gratefully acknowledged. We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III beamline P21.1. Beamtime was allocated for proposal I-20200385. Thanks to Philipp Glaevecke, Soham Banerjee, Olof Gutowski, Linda Klag and Srashtasrita Das for support during beamtime. The holographic X-ray computed tomography experiments were performed on beamline ID16A at the European Synchrotron Radiation Facility (ESRF), Grenoble, France in the frame of experiment CH-5805. This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMF), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT), which provided access to FIB and TEM instruments via proposal 2020-023-028494. Thanks to Mariam Schulte for performing catalytic experiments of the TEM sample. Thanks to Sabine Schlabach for support during FIB sample preparation. Ronny Zimmermann is also affiliated with the International Max Planck Research School (IMPRS) for Advanced Methods in Process and Systems Engineering, Magdeburg, Germany. Xiaohui Huang acknowledges the China Scholarship Council for providing a PhD scholarship. Open Access funding enabled and organized by Projekt DEAL.; Funding text 3: In the current work, we present early efforts towards thorough characterization of a target catalyst and reaction system, with the aim to demonstrate how collection and interpretation of characterization data relies on a diverse and interdisciplinary approach. The work is presented in the context of the collaborative research program ‘DynaKat – Catalysts and Reactors under Dynamic Conditions for Energy Storage and Conversion’. Here we select methanation of CO over an industrial standard Ni/AlO catalyst (labelled SPP2080‐IMRC) as a target material and process. SPP2080‐IMRC in this respect stands for “Industrial Methanation Reference Catalyst” of the SPP2080 priority program of the German Research Foundation (DFG). In addition to being a useful starting point as a relatively facile gas‐phase heterogeneously catalyzed reaction, CO methanation is also an important topic in the context of chemical energy storage, future energy scenarios based on hydrogen economy and decentralized energy supply, and carbon management (where CO is derived from waste emissions rather than direct capture). The chosen catalyst is intended as a reference point and benchmark between the diverse research consortia represented by the co‐authors within the SPP2080 project. In terms of characterization, we selected two properties regarded as crucial for effective catalytic performance and which can be probed to a certain extent using multiple characterization methods. These are firstly the size distribution of active metal species, and secondly the pore system characteristics of the catalyst support. While the active sites are fundamentally responsible for catalytic reaction and turnover, the support properties govern the delivery of reactant molecules to, and product molecules from, these sites. The above parameters and their related characterization data are therefore ideal for an exploration into consistency of data and representativity of different characterization methods with respect to the catalytic active sites and support phase. This work highlights challenges associated with reporting information from catalyst characterization and performance testing. Tackling these are essential steps towards a “digital twin” of the catalyst. While no clear answer exists yet on how to overcome the challenges, the aim is to stimulate the whole catalysis community to find solutions together with other disciplines, i. e., engineering, physics, material science, data science, mathematics, among others. 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"Goldman J.; Trepanowski N.F.","Goldman, Julie (57651093600); Trepanowski, Nevada F. (57942576100)","57651093600; 57942576100","Reflection and Analysis of Implementing a Free Asynchronous MOOC to Build Competence in Biomedical Research Data Management","2022","College and Research Libraries","83","4","","669","691","22","0","10.5860/crl.83.4.669","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133723365&doi=10.5860%2fcrl.83.4.669&partnerID=40&md5=b6cabd942adf92910153f88e33938072","Countway Research Data Services Librarian, Harvard Medical School, United States; Simmons University, United States","Goldman J., Countway Research Data Services Librarian, Harvard Medical School, United States; Trepanowski N.F., Simmons University, United States","This article reports on the development and evaluation of a massive open online course (MOOC) that provides instruction on best practices in research data management (RDM). The course was developed in response to the growing need for data management professional development for LIS professionals and to promote data management to researchers. In just 18 months of the course launch, the course reached more than 1,000 people from across the world and was effective in building student competency in RDM. The success of this course illustrates the value and utility of free online professional development as a tool for both library and research staff. © 2022 Julie Goldman and Nevada F. Trepanowski.","biomedical data; library and information science; massive open online course; professional development; research data management","","","","","","Francis A. Countway Library of Medicine at the Harvard Medical School; National Institutes of Health, NIH, (R25LM012284)","Funding text 1: Through funding from the NIH BD2K Initiative Research Education MOOC on Data Management for Biomedical Big Data, the NECDMC curriculum was transformed from static documents into an open online course.26 To convert these fixed materials into dynamic online; Funding text 2: Traditional in-person continuing education is a great resource for professional development. However, the time and expense associated with in-person education can pose barriers to many Library and Information Science (LIS) professionals looking to increase their knowledge of data services. Massive Open Online Courses (MOOCs) offer flexibility and affordability via asynchronous instruction to ensure the LIS professionals can build the skills required to become effective research data management (RDM) partners. In 2015, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) Initiative to address data science challenges, including lack of appropriate tools, poor data accessibility, and insufficient training. As a result, multiple groups received grant funding1 to expand research education: Georgetown University, to develop a MOOC focused on Big Data; Rutgers, to create open educational resources (OER) “Enabling Data Science in Biology”; Johns Hopkins University, to build OER to “Facilitate Sharing of Next Generation Sequencing Data;”2 and New York University (NYU) School of Medicine, to establish online training for “Medical Librarians to Understand and Teach Research Data Management.”3 The research presented in this article focuses on the outcomes of a course funded by one of these grants, a library-developed MOOC focusing on comprehensive training for managing biomedical data for a broad research audience. The developed course, Best Practices for","NIH Big Data to Knowledge (BD2K) Grants; Wright Robert A., Developing a Suite of Online Learning Modules on the Components of Next-Generation Sequencing Projects, Medical Reference Services Quarterly, 39, 1, pp. 90-99, (2020); Read Kevin B., Et al., A Two-Tiered Curriculum to Improve Data Management Practices for Researchers, PloS One, 14, 5, (2019); Best Practices for Biomedical Research Data Management; Final NIH Statement On Sharing Research Data; Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans, (2010); Anderson Nicholas R., Et al., Issues in Biomedical Research Data Management and Analysis: Needs and Barriers, Journal of the American Medical Informatics Association, 14, 4, pp. 478-488, (2007); Bardyn Tania P., Et al., Health Sciences Libraries Advancing Collaborative Clinical Research Data Management in Universities, Journal of eScience Librarianship, 7, 2, (2018); Kafel Donna, Creamer Andrew, Martin Elaine, Building the New England Collaborative Data Management Curriculum, Journal of eScience Librarianship, 3, 1, pp. 60-66, (2014); Ishida Mayu, The New England Collaborative Data Management Curriculum Pilot at the University of Manitoba: A Canadian Experience, Journal of eScience Librarianship, 3, 1, pp. 80-85, (2014); Ishida, The New England Collaborative Data Management Curriculum Pilot at the University of Mani-toba”; Christie Peters and Porcia Vaughn, “Initiating Data Management Instruction to Graduate Students at the University of Houston Using the New England Collaborative Data Management Curriculum, Journal of eScience Librarianship, 3, 1, pp. 86-99, (2014); Henkel Heather, Et al., DataONE Education Modules, Data Observation Network for Earth; Soyka Heather, Et al., Using Peer Review to Support Development of Community Resources for Research Data Management, Journal of eScience Librarianship, 6, 2, (2017); Rice Robin, New MOOC! Research Data Management and Sharing, Edinburgh Research Data Blog, (2016); Duda Stephany, Harris Paul, Data Management for Clinical Research; Van Der Volgen Jessica, Zhao Shirley, Building a National Research Data Management Course for Health Information Professionals, Journal of eScience Librarianship, 8, 1, (2019); RDMLA; Reich Justin, Ruiperez-Valiente Jose A., The MOOC Pivot: From Teaching the World to Online Professional Degrees, Science, 363, 6423, pp. 130-131, (2019); Adam Taskeen, Digital Neocolonialism and Massive Open Online Courses (MOOCs): Colonial Pasts and Neoliberal Futures, Learning, Media and Technology, 44, 3, pp. 365-380, (2019); Koller Daphne, Et al., Retention and Intention in Massive Open Online Courses: In Depth, Educause Review, 48, pp. 62-63, (2013); Koutropoulos Apostolos, Hogue Rebecca, How to Succeed in a Massive Online Open Course (MOOC), Learning Solutions Magazine, (2012); Liyanagunawardena Tharindu, Williams Shirley, Adams Andrew, The Impact and Reach of MOOCs: A Developing Countries’ Perspective, eLearning Papers, 33, (2013); How to Succeed in a Massive Online Open Course; Macleod Hamish, Et al., Emerging Patterns in MOOCs: Learners, Course Designs and Directions, Tech-Trends, 59, pp. 56-63, (2014); Douglas Kerrie A., Et al., Meaningful Learner Information for MOOC Instructors Examined through a Contextualized Evaluation Framework, International Review of Research in Open and Distributed Learning, 20, 1, (2019); Goldman Julie, Pitfalls and Positives: Developing a Massive Open Online Course, North Atlantic Health Science Libraries 2017; Ishida, The New England Collaborative Data Management Curriculum Pilot at the University of Mani-toba”; Peters and Vaughn, “Initiating Data Management Instruction to Graduate Students at the University of Houston Using the New England Collaborative Data Management Curriculum; Douglas, Et al., Meaningful Learner Information for MOOC Instructors Examined Through a Contextualized Evaluation Framework; Douglas, Et al., Meaningful Learner Information for MOOC Instructors Examined Through a Contextualized Evaluation Framework; MacNeill Sheila, Campbell Lorna M., Hawksey Martin, Analytics for Education, Journal of Interactive Media in Education, 1, (2014); Koller, Et al., Retention and Intention in Massive Open Online Courses”; Kerrie A. Douglas et al., “Board #32: NSF PRIME Project: Contextualized Evaluation of Advanced STEM MOOCs, ASEE Annual Conference & Exposition, (2017); Goldman Julie, Trepanowski Nevada, Data from Reflection and Analysis of Implementing a Free Asynchronous MOOC to Build Competence in Biomedical Research Data Management, Open Science Framework, (2020); Data from Reflection and Analysis of Implementing a Free Asynchronous MOOC to Build Competence in Biomedical Research Data Management; Liyanagunawardena Tharindu, Williams Shirley, Adams Andrew, The Impact and Reach of MOOCs; How to Succeed in a Massive Online Open Course; Goldman Julie, Martin Elaine, Biomedical Research Data Management Open Online Education: Challenges & Lessons Learned; Burton Matt, Et al., Shifting to Data Savvy: The Future of Data Science in Libraries, (2018); Data from Reflection and Analysis of Implementing a Free Asynchronous MOOC to Build Competence in Biomedical Research Data Management","","","Association of College and Research Libraries","","","","","","00100870","","","","English","Coll. Res. Libr.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85133723365"
"Jäckel D.; Helbig K.; Odebrecht C.","Jäckel, Denise (57225015065); Helbig, Kerstin (57188698727); Odebrecht, Carolin (57192208762)","57225015065; 57188698727; 57192208762","Desiderata on research data management 2013 and 2022; [Desiderata sur la gestion des données de recherche 2013 et 2022]; [Desiderate zum Forschungsdatenmanagement 2013 und 2022]","2022","Information-Wissenschaft und Praxis","73","5-6","","265","276","11","0","10.1515/iwp-2022-2239","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141049988&doi=10.1515%2fiwp-2022-2239&partnerID=40&md5=630a88449c0171d1bf793968f3a96230","Humboldt-Universität zu Berlin, Computer- und Medienservice, Unter den Linden 6, Berlin, 10099, Germany","Jäckel D., Humboldt-Universität zu Berlin, Computer- und Medienservice, Unter den Linden 6, Berlin, 10099, Germany; Helbig K., Humboldt-Universität zu Berlin, Computer- und Medienservice, Unter den Linden 6, Berlin, 10099, Germany; Odebrecht C., Humboldt-Universität zu Berlin, Computer- und Medienservice, Unter den Linden 6, Berlin, 10099, Germany","Research data management has become a part of good scientific practice since the first requirements of the Deutschen Forschungsgemeinschaft were introduced in 2015. Universities are thus required to provide researchers with the best possible support. Since 2013, surveys have been conducted throughout Germany to identify desiderates in infrastructure and services. However, an evaluation of the needs has hardly taken place so far. The article summarises developments and issues-based on two needs surveys conducted at Humboldt-Universität zu Berlin.  © 2022 Walter de Gruyter GmbH, Berlin/Boston.","Empirical study; Humboldt-Universität zu Berlin; Information needs; Research data management; Service","","","","","","","","Grundsätze zum Umgang mit Forschungsdaten, (2010); Cremer F., Engelhardt C., Neuroth H., Embedded Data Manager-Integriertes Forschungsdatenmanagement: Praxis, Perspektiven und Potentiale, Bibliothek Forschung und Praxis, 39, pp. 13-31, (2015); Helmkamp K., Glitsch S., Nutzerbefragung an der SUB Göttingen: Zielgruppenspezifische Angebotsentwicklung, (2015); Helbig K., Aust P., Kein Königsweg-die Vermittlung von Forschungsdatenkompetenz auf allen universitären Ebenen. obib, Das offene Bibliotheksjournal/Herausgeber VDB, 4, pp. 108-116, (2017); Higgins S., Digital curation: the development of a discipline within information science, Journal of Documentation, 74, pp. 1318-1338, (2018); Wie Hochschulleitungen die Entwicklung des Forschungsdatenmanagements steuern können. Orientierungspfade, Handlungsoptionen, Szenarien, (2016); Hornig C., Walker A., Nutzerumfrage 2017. Ausführlicher Abschlussbericht, (2018); Grundsätze zum Umgang mit Forschungsdaten an der Humboldt-Universität zu Berlin, (2014); Satzung der Humboldt-Universität zu Berlin zur Sicherung guter wissenschaftlicher Praxis und zum Umgang mit Vorwürfen wissenschaftlichen Fehlverhaltens, (2014); Kindling M., Schirmbacher P., Die digitale Forschungswelt"" als Gegenstand der Forschung, Information - Wissenschaft & Praxis, 64, pp. 127-136, (2013); Simukovic E., Kindling M., Schirmbacher P., Umfrage zum Umgang mit digitalen Forschungsdaten an der Humboldt-Universität zu Berlin, (2013); Neuroth H., Putnings M., Neumann J., Praxishandbuch Forschungsdatenmanagement, (2021); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, pp. 1-9, (2016); Simukovic E., Kindling M., Schirmbacher P., Umfrage zum Umgang mit digitalen Forschungsdaten an der Humboldt-Universität zu Berlin, (2013); Jackel D., Helbig K., Odebrecht O., Umfrage Forschungsdatenmanagement 2021 HU-Berlin [Data set], (2022)","D. Jäckel; Humboldt-Universität zu Berlin, Computer- und Medienservice, Berlin, Unter den Linden 6, 10099, Germany; email: denise.jaeckel@hu-berlin.de","","De Gruyter Saur","","","","","","14344653","","","","German","Inf.-Wiss. Prax.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85141049988"
"Rabasa T.A.; Abrizah A.","Rabasa, Talatu Adamu (58145020900); Abrizah, A. (6504290111)","58145020900; 6504290111","Academic librarians’ roles and competencies in research partnership: A qualitative study","2022","Malaysian Journal of Library and Information Science","27","3","","69","95","26","0","10.22452/mjlis.vol27no3.4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150287964&doi=10.22452%2fmjlis.vol27no3.4&partnerID=40&md5=596d0893a9115eb2ca1865c72f9bea89","Ramat Library, University of Maiduguri, Borno State, Maiduguri, Nigeria; Department of Library & Information Science, Faculty of Arts & Social Sciences Universiti Malaya, Kuala Lumpur, Malaysia","Rabasa T.A., Ramat Library, University of Maiduguri, Borno State, Maiduguri, Nigeria, Department of Library & Information Science, Faculty of Arts & Social Sciences Universiti Malaya, Kuala Lumpur, Malaysia; Abrizah A., Department of Library & Information Science, Faculty of Arts & Social Sciences Universiti Malaya, Kuala Lumpur, Malaysia","In the past decades, academic librarianship has been preoccupied with changes that include academic librarians as research partners due to the changing landscape in scholarly communication services, evolving technologies, and institutional missions. Thus, the idea of academic librarians being a partner in research rather than mere research supporters is a new development in academic librarianship that requires attention, especially on what academic librarians can do to partner with the research community successfully. Therefore, this study employed a qualitative approach to explore academic librarians’ roles as research partners and the competencies required for better collaborations. Data were generated through face-to-face interviews with 14 academic librarians purposively sampled from a major research university in Nigeria. Ten themes emerged from the findings regarding academic librarians' role as research partners. These include information discovery and provision, information use and evaluation; grants application; articulation of research topic; literature and reference management; research data management, systematic review, authoring a manuscript, scholarly publishing, and research dissemination. Similarly, eleven themes emerged as the competencies required to be analysed under the dimensions of knowledge, skill, and attitude. These include research methodology and research data management as the knowledge needed in a research partnership. While digital scholarship, systematic review, reference / citation management, data curation and preservation, bibliometrics/information evaluation and communication emerged as the skills required for the effective discharge of their responsibilities. Professionalism, patience, and cordiality were found to be the attitude required. This paper is limited to providing academic librarians with the necessary authority to enhance their performance in a research partnership, and offering them the approach to guide their practice in a research partnership. Thus, the study recommended, among others, the need for enlightenment programmes and activities for academic librarians that cut across training, workshops, and organising conferences in research areas in enhancing their service performance to ensure successful research partnerships © 2022, Malaysian Journal of Library and Information Science.All Rights Reserved.","Academic librarians; Competencies; Embedded librarianship; Research partnership; Roles; Scholarly communication","","","","","","","","Adegbaye S. I., Okorie N. C., Wagwu V., Ajiboye B. A., Workload as correlate of publication output of academic librarians in universities, UNIZIK Journal of Research in Library and Information Science, 4, 1, pp. 68-83, (2019); Alabi A. O., Bridging the great divide: Librarian-faculty collaboration in selected higher institutions in Lagos State Nigeria, The Journal of Academic Librarianship, 44, 4, pp. 459-467, (2018); Andrikopoulou A., Rowley J., Walton G., Research data management (RDM) and the evolving identify of academic libraries and librarians: A literature review, New Review of Academic Librarianship, 28, 4, pp. 349-365, (2021); Astrom F., Hansson J., How implementation of bibliometric practice affects the role of academic libraries, Journal of Librarianship and Information Science, 45, 4, pp. 316-322, (2013); Austria R.M., Cabonero D.A., Research knowledge and skills of academic librarians in Northern Philippines, Library Philosophy and Practice (e-journal), (2020); Ayoku O. A., Okafor V. N., ICT skills acquisition and competencies of librarians: Implications for the digital and electronic environment in Nigerian universities libraries, The Electronic Library, 33, 3, pp. 502-523, (2015); Bayley, Phipps D., Batac M., Stevens E., Development of a framework for knowledge mobilization and impact competencies, A Journal of Research Debate Practice, 14, 4, pp. 725-738, (2018); Bedi S., Walde C., Transforming roles: Canadian academic librarians embedded in faculty research projects, College & Research Libraries, 78, 3, pp. 314-327, (2017); Borrego A., Anglada L., Research support services in Spanish academic libraries: An analysis of their strategic plans and an opinion survey administered to their directors, Publications, 6, 4, pp. 1-10, (2018); Borrego A., Ardanuy J., Urbano C., Librarians as research partners: Their contribution to the scholarly endeavor beyond the library and information science, The Journal of Academic Librarianship, 44, 5, pp. 663-670, (2018); Borrego A., Pinfield S., Librarians publishing in partnership with other researchers: Roles, motivations, benefits, and challenges, Portal: Libraries and the Academy, 20, 4, pp. 655-675, (2020); Bowles-Terry M., Sobel K., Librarians as faculty developers: Competencies and recommendations, The Journal of Academic Librarianship, 48, pp. 1-6, (2022); Calarco P., Shearer K., Schmidt B., Tate D., Librarians' competencies profile for scholarly communication and open access, Joint Task Force on Librarian's Competencies in Support of E-research and Scholarly Communication, pp. 1-6, (2016); Cary S., Faculty rank, status, and tenure for librarians: Current trends, College & Research Libraries News, 62, 5, pp. 510-520, (2019); Chang Y.-W., A comparison of researcher-practitioner collaborations in library and information science, education, and sociology, Journal of Librarianship and Information Science, 51, 1, pp. 20-36, (2017); Chawner B., Oliver G., A New Zealand academic reference librarians survey: Current and future skills and competencies, Australian Academic & Research Libraries, 44, 1, pp. 29-39, (2013); Chigwada J. P., The role of the librarian in the research life cycle: Research collaboration among the library and faculty, Cooperation and collaboration initiatives for libraries and related institutions: 335-346, pp. 335-346, (2020); Cox A., Gadd E., Petersohn S., Sbaffi L., Competencies for bibliometrics, Journal of Librarianship and Information Science, 51, 3, pp. 746-762, (2019); Crampsie C., Neville T., Henry D., Academic librarian publishing productivity: an analysis of skills and behaviors leading to success, College Research Libraries UK, 81, 2, (2020); Creswell J. W., Creswell J. D., Research design: Qualitative, quantitative, and mixed methods approach, (2017); Delaney G., Bates J., Envisioning the academic library: a reflection on roles, relevancy, and relationships, New Review of Academic Librarianship, 21, 1, (2015); Diaz J. O., Mandernach M. A., Relationship building one step at a time: Case studies of successful faculty-librarian partnerships, Portal: Libraries the Academy, 17, 2, pp. 273-282, (2017); Ducas A., Michaud-Oystryk N., Speare M., Reinventing ourselves: New and emerging roles of academic librarians in Canadian research-intensive universities, College & Research Libraries, 81, 1, (2020); Enakrire R.T., Chista C.T., Adeyinka T., Partnership among librarians: Reflection on observations, interviews and research reports from three universities in Nigeria and Zimbabwe, International Journal of Higher Education, 9, 5, pp. 338-345, (2020); Fagan J. C., Ostermiller H., Price E., Sapp L. J., Librarian, faculty, and student perceptions of academic librarians: Study introduction and literature review, New Review of Academic Librarianship, 27, 1, pp. 38-75, (2021); Federer L., Defining data librarianship: a survey of competencies, skills, and training, Journal of the Medical Library Association: JMLA, 106, 3, (2018); Fontan J.M., Bussiere D., Evaluating the research partnership process, Knowledge, democracy and action: Community-university research partnerships in global perspectives, pp. 77-88, (2019); Furfuri I. M., ILO P. I., The changing roles of librarians for research support services in evolving landscape of higher education in Nigeria, Library Information Science Digest, 12, 1, pp. 73-81, (2019); Gbaje E. S., Yani S. D., Odigie I. O., Assessing the competencies and skills of academic librarians in Nigeria for scholarly communication [Paper presented at the International Federation of Library Association (IFLA) World Library and Information Congress (WLIC) 2018 - Kuala Lumpur, Malaysia - Transform Libraries, Transform Societies in Session 163 - Serials and Other Continuing Resources.], pp. 1-16, (2018); Grgic I. H., Zivkovic D., Core competencies for academic reference librarians in Croatia, Qualitative and Quantitative Methods in Libraries (QQML), 1, 3, pp. 247-256, (2017); Grguric E., Davis H., Davidson B., Supporting the modern research workflow, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-4, (2016); Inuwa S., Abrizah A., Embedded librarianship in research in Nigerian Universities: Practices and sources of practice knowledge, The Journal of Academic Librarianship, 44, 6, pp. 738-746, (2018); Johnson S.C., Bausman M., Ward S., Fostering information literacy: A call for collaboration between academic librarians and MSW, Instructors. Advances in Social Work, 21, 1, pp. 1-25, (2021); Johnson G., Raju J., Knowledge and skills competencies for humanities librarians supporting postgraduate students, Libri, 68, 4, pp. 331-344, (2018); Kaari J., Librarians are interested in finding research collaborators, Evidence Based Library and Information Practice, 16, 4, pp. 141-143, (2021); Kennan M., Corrall S., Afzal W., Making space in practice and education: Research support services in academic, Library Management, 35, 8, (2014); Ketlhoilwe B. K., Molatedi S. K., An evaluation of the University of Botswana Reference Services staff: current and future skills and competencies, pp. 1-12, (2015); King M., Digital scholarship librarian: What skills and competencies are needed to be a collaborative librarian, International Information of Library Review, 50, 1, (2018); Lan N. T., Tuamsuk K., Factors influencing the collaboration between faculty and librarians at the universities: A literature review, TLA Research Journal, 11, 2, pp. 78-90, (2018); Lewis V., McColgan K., New skills for the academic library workforce - A Canadian experience, Proceedings of the IATUL Conferences, (2017); Liman Y. A., Jain P., Grand B., Mutshewa A., Skills and competencies required by academic librarians in an internet-driven environment, Mousaion, 35, 4, (2017); Malik A., Sheikh A., Mahmood K., Assessing the perceived research competencies of academic librarians in Pakistan: Implications for work performance, Journal of Librarianship and Information Science, (2022); Mathews B., Metko S., Tomlin P., Empowerment, experimentation, engagement: embracing partnership models in libraries, EDUCAUSE Review, 53, 3, pp. 52-53, (2018); Mazure E. S., Alpi K. M., Librarian readiness for research partnerships, Journal of the Medical Library Association: JMLA, 103, 2, (2015); Inyang E., Mngutyo J., Required skills and competences of librarians for effective software application and use in contemporary libraries in Nigeria, Library Philosophy and Practice (e-journal), (2018); Monroe-Gulick A., O'Brien M. S., White G. W., Librarians as partners: Moving from research supporters to research partners, ACRL 16th National Conference Proceeding, ""Imagine, Innovate, Inspire"", 4, 13, pp. 382-388, (2013); Mushi C., Mwantimwa K., Wema K., Librarians Competencies for Implementing Embedded Librarianship University Libraries, College & Research Libraries, 80, 1, pp. 32-43, (2022); Narin F., Moll J.K., Bibliometrics, Annual Review of Information Science and Technology, 12, pp. 35-58, (1977); Nolen D S., Kathuria S., Peacock E., Quantifying interdisciplinary: Subject librarians as research collaborators, The Journal of Academic Librarianship, 47, 5, (2021); Nguyen L., Thi K., Roles of the faculty and librarian in the collaborative relationships at Vietnamese Universities: A qualitative methodology, Journal of Information Science Theory and Practice, 8, 1, pp. 33-44, (2020); Oberbichler S., Boroc E., Doucet A., Marjanen J., Pfanzelter E., Rautiainen J., Toivonen H., Tolonen M., Integrated interdisciplinary workflows for research on historical newspapers: Perspectives from humanities scholars, computer scientists, and librarians, Journal of the Association for Information Science Technology, 73, 2, pp. 225-239, (2022); Ocholla L., Mutsvunguma G., Hadebe Z., The impact of new information services on teaching, learning, and research at the University of Zululand Library, South African Journal of Libraries, 82, 2, pp. 11-19, (2016); Oladokun O., Mooko N, Academic libraries and the need for continuing professional development in Botswana, IFLA Journal, O, (2020); Otter M.L.E., Wright J.M., King N.V., Developing the librarians’ role in supporting grant applications and reducing waste in research: Outcomes from a literature review and survey in the NIHR research design service, New Review of Academic Librarianship, 23, 2-3, pp. 258-274, (2017); Sahabi M K., Unobe S E., Exploring the interaction between genuine collaboration and research productivity of academic librarians in Kaduna State University (Kasu), Kaduna, Library Philosophy and Practice (e-journal), (2021); Ridley M., Academic librarians and the Ph.D, The Canadian Journal of Library Information Practice Research, 13, 1, pp. 6-15, (2018); Schmidt B., Calarco P., Kuchma I., Shearer K., Time to adopt: Librarians' new skills and competency profiles, Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 1-9, (2016); Semeler A. R., Pinto A. L., Rozados H. B. F., Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, pp. 771-780, (2017); Shao Z., Li Y., Wu K., Guo Y., Feng F., Hui F., Niu N., Zheng Y., How do academic librarians get involved and contribute in research activities of universities? A systematic demonstration in practice through comparative studies of research productivities and research impacts, The Journal of Academic Librarianship, 44, 6, pp. 805-815, (2018); Shearer B., Schmidt K., Librarians competencies profile for research data management, Joint Task Force on Librarians' Competencies in Support of EResearch and Scholarly Communication, pp. 1-7, (2016); Stenholt L., Petersen L. J., Skrubbeltrang C., Initial experiences of embedded librarianship at a Danish University Hospital. European Association for Health Information and Libraries, Journal, 14, 2, pp. 18-22, (2018); Tran N.Y., Chan E.K., Supporting scholarly research: Current and new opportunities for academic libraries, Choice, (2020); Waller M., Tebbe H. J., The nuts and bolts of supporting change and transformation for research librarians, Roll with the Times, or the Times Roll Over You: Charleston Conference Proceedings, 2016, pp. 389-393, (2017); Weng C., Murray D. C., Faculty perceptions of librarians and library services: Exploring the impact of librarian faculty status and beyond, The Journal of Academic Librarianship, 46, 5, (2020); White H.D, McCain K.W., Bibliometrics, Annual Review of Information Science and Technology, 24, pp. 119-186, (1989); Yatim N., Nasharudin N., Samsudin N. F., Said S. M., Tarsik N. F., Recognizing the personal competencies of future information professionals, Act Informatics Malaysia, 3, 1, pp. 21-23, (2019); Zhan M., Widen G., Understanding big data in librarianship, Journal of Librarianship and Information Science, 51, 2, pp. 561-576, (2019)","A. Abrizah; Department of Library & Information Science, Faculty of Arts & Social Sciences Universiti Malaya, Kuala Lumpur, Malaysia; email: abrizah@um.edu.my","","Faculty of Computer Science and Information Technology","","","","","","13946234","","","","English","Malays. J. Libr. Inf. Sci.","Article","Final","","Scopus","2-s2.0-85150287964"
"Shih Y.-R.; Yang Y.-J.; Jeng W.","Shih, Yi-Ru (58109384100); Yang, Yu-Ju (58109080200); Jeng, Wei (37023332900)","58109384100; 58109080200; 37023332900","Factors Influencing Criminologists' Open Research Data Practices: Trust, Contract, and Value","2022","Proceedings of the Association for Information Science and Technology","59","1","","794","796","2","0","10.1002/pra2.729","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148422890&doi=10.1002%2fpra2.729&partnerID=40&md5=32fff0502d691fbb5a5af86dfd48f9c0","National Yang Ming Chiao Tung University, Taiwan; Carnegie Mellon University, United States; National Taiwan University, Taiwan","Shih Y.-R., National Yang Ming Chiao Tung University, Taiwan; Yang Y.-J., Carnegie Mellon University, United States; Jeng W., National Taiwan University, Taiwan","This study investigated how researchers in criminology dealt with their research data and explored the potential factors that would influence their willingness to share and reuse others' research data. Our findings unveiled three factors, trust, contract, and value, deeply rooted in researchers' attitudes and their data sharing and reuse patterns and behaviors. Through comprehending their concern behind sharing and reusing research data, we seek crucial insights that could be beneficial in developing a criminological research data repository, further consolidating the research data infrastructure in social science. Sharing and reusing data can advance scientific research and provide innovative possibilities for practice in criminology (e.g., new correctional methods, new crime prevention policies), providing sustained and thriving energy for the field.  Annual Meeting of the Association for Information Science & Technology | Oct. 29 – Nov. 1, 2022 | Pittsburgh, PA. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.","Data in Criminology; Open research data; Research data infrastructure; Research data management","Data in criminology; Data infrastructure; Data practices; Open research data; Research data; Research data infrastructure; Research data managements; Reuse; Willingness to share; Information management","","","","","Ministry of Science and Technology, Taiwan, MOST, (111‐2636‐H‐002‐004)","This work was financially supported by the Ministry of Science and Technology (MOST) in Taiwan, under MOST 111‐2636‐H‐002‐004‐. Please address all correspondence to Wei Jeng ( wjeng@ntu.edu.tw ).","Hey T., Tansley S., Tolle K.M., The fourth paradigm: data intensive scientific discovery, (2009); Jeng W., He D., Surveying research data-sharing practices in US social sciences: a knowledge infrastructure-inspired conceptual framework, Online Information Review., (2022); Lyon L., Jeng W., Mattern E., Developing the tasks-toward-transparency (T3) model for research transparency in open science using the lifecycle as a grounding framework, Library & Information Science Research, 42, 1, (2020); Murillo A.P., Curty R.G., Jeng W., He D., Confronting the Challenges of Computational and Social Perspectives of the Data Continuum, Data and Information Management, 4, 2, pp. 119-126, (2020); Thanos C., Global Research Data Infrastructures: The GRDI2020 Vision. Report of the GRDI2020 project funded under the 7th Framework Programme, Capacities-GEANT & eInfrastructures, (2011); Yoon A., Lee Y.Y., Factors of trust in data reuse, Online Information Review, 43, 7, pp. 1245-1262, (2019); Zuiderwijk A., Shinde R., Jeng W., What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption, PLoS One, 15, 9, (2020)","Y.-R. Shih; National Yang Ming Chiao Tung University, Taiwan; email: yiru840321@gmail.com; Y.-J. Yang; Carnegie Mellon University, United States; email: yujuy@andrew.cmu.edu; W. Jeng; National Taiwan University, Taiwan; email: wjeng@ntu.edu.tw","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85148422890"
"Raboudi A.; Allanic M.; Balvay D.; Hervé P.-Y.; Viel T.; Yoganathan T.; Certain A.; Hilbey J.; Charlet J.; Durupt A.; Boutinaud P.; Eynard B.; Tavitian B.","Raboudi, Amel (57205574619); Allanic, Marianne (56453543200); Balvay, Daniel (12645617900); Hervé, Pierre-Yves (8501617400); Viel, Thomas (57194530952); Yoganathan, Thulaciga (57204149940); Certain, Anais (57215408583); Hilbey, Jacques (57217252651); Charlet, Jean (57207604191); Durupt, Alexandre (24447666100); Boutinaud, Philippe (56400681400); Eynard, Benoît (8582074700); Tavitian, Bertrand (56214031300)","57205574619; 56453543200; 12645617900; 8501617400; 57194530952; 57204149940; 57215408583; 57217252651; 57207604191; 24447666100; 56400681400; 8582074700; 56214031300","The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology","2022","Journal of Biomedical Informatics","127","","104007","","","","0","10.1016/j.jbi.2022.104007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124697234&doi=10.1016%2fj.jbi.2022.104007&partnerID=40&md5=13152f1080870eb3ad10b581ec8dd914","Fealinx, 37 rue Adam Ledoux, Courbevoie, 92400, France; Université de Paris, PARCC, INSERM, Paris, F-75006, France; Université de Technologie de Compiègne, Roberval, Compiègne, France; Althenas, Nantes, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), UMRS_1142, Paris, France; APHP, Hôpital européen Georges Pompidou, Radiology department, Paris, 75015, France","Raboudi A., Fealinx, 37 rue Adam Ledoux, Courbevoie, 92400, France, Université de Paris, PARCC, INSERM, Paris, F-75006, France, Université de Technologie de Compiègne, Roberval, Compiègne, France; Allanic M., Althenas, Nantes, France; Balvay D., Université de Paris, PARCC, INSERM, Paris, F-75006, France; Hervé P.-Y., Fealinx, 37 rue Adam Ledoux, Courbevoie, 92400, France; Viel T., Université de Paris, PARCC, INSERM, Paris, F-75006, France; Yoganathan T., Université de Paris, PARCC, INSERM, Paris, F-75006, France; Certain A., Université de Paris, PARCC, INSERM, Paris, F-75006, France; Hilbey J., Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), UMRS_1142, Paris, France; Charlet J., Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), UMRS_1142, Paris, France; Durupt A., Université de Technologie de Compiègne, Roberval, Compiègne, France; Boutinaud P., Fealinx, 37 rue Adam Ledoux, Courbevoie, 92400, France; Eynard B., Université de Technologie de Compiègne, Roberval, Compiègne, France; Tavitian B., Université de Paris, PARCC, INSERM, Paris, F-75006, France, APHP, Hôpital européen Georges Pompidou, Radiology department, Paris, 75015, France","Biomedical research data reuse and sharing is essential for fostering research progress. To this aim, data producers need to master data management and reporting through standard and rich metadata, as encouraged by open data initiatives such as the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. This helps data re-users to understand and reuse the shared data with confidence. Therefore, dedicated frameworks are required. The provenance reporting throughout a biomedical study lifecycle has been proposed as a way to increase confidence in data while reusing it. The Biomedical Study - Lifecycle Management (BMS-LM) data model has implemented provenance and lifecycle traceability for several multimodal-imaging techniques but this is not enough for data understanding while reusing it. Actually, in the large scope of biomedical research, a multitude of metadata sources, also called Knowledge Organization Systems (KOSs), are available for data annotation. In addition, data producers uses local terminologies or KOSs, containing vernacular terms for data reporting. The result is a set of heterogeneous KOSs (local and published) with different formats and levels of granularity. To manage the inherent heterogeneity, semantic interoperability is encouraged by the Research Data Management (RDM) community. Ontologies, and more specifically top ontologies such as BFO and DOLCE, make explicit the metadata semantics and enhance semantic interoperability. Based on the BMS-LM data model and the BFO top ontology, the BioMedical Study - Lifecycle Management (BMS-LM) core ontology is proposed together with an associated framework for semantic interoperability between heterogeneous KOSs. It is made of four ontological levels: top/core/domain/local and aims to build bridges between local and published KOSs. In this paper, the conversion of the BMS-LM data model to a core ontology is detailed. The implementation of its semantic interoperability in a specific domain context is explained and illustrated with examples from small animal preclinical research. © 2022 Elsevier Inc.","Data annotation; Data sharing; Heterogeneous data; Local terminologies; Provenance; Research Data Management","Animals; Biological Ontologies; Biomedical Research; Data Curation; Metadata; Research Design; Semantics; Interoperability; Knowledge organization; Knowledge organization system (KOS); Life cycle; Metadata; Ontology; Open Data; Semantics; Data annotation; Data Sharing; Heterogeneous data; Knowledge organization systems; Lifecycle management; Local terminology; Ontology's; Provenance; Research data managements; Semantic interoperability; article; data interoperability; medical research; metadata; multimodal imaging; nomenclature; ontology; practice guideline; preclinical study; semantics; animal; biological ontology; information processing; medical research; methodology; Terminology","","","","","BMS-LM; COMUE Sorbonne Paris City University; Fealinx Company, (2018- PSPC- 07, ANR 18-RHUS-0014); Association Nationale de la Recherche et de la Technologie, ANRT, (216/1649)","Funding text 1: Amel Raboudi is supported by the ANRT CIFRE scholarship n°216/1649. The DRIVE-SPC project is supported by the COMUE Sorbonne Paris City University and the Fealinx Company. The work presented in this paper was partially supported by the PACIFIC (BPI n°2018- PSPC- 07) and PsyCARE (ANR 18-RHUS-0014) projects. ; Funding text 2: The authors wish to express their sincere appreciation to the first key users of the BMS-LM early versions: Drs. Gwennhael Autret, Caterina Facchin, Anikitos Garofalakis, Mailyn Perez-Liva, and Joevin Sourdon for their feedback and time.","Rance B., Canuel V., Countouris H., Laurent-Puig P., Burgun A., Integrating Heterogeneous Biomedical Data for Cancer Research: the CARPEM infrastructure, Applied Clinical Informatics., 7, 2, pp. 260-274, (2016); Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in biomedical research data management and analysis: Needs and barriers, Journal of the American Medical Informatics Association., 14, 4, pp. 478-488, (2007); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology., 63, 6, pp. 1059-1078, (2012); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data., 3, 1, (2016); Miksa T., Simms S., Mietchen D., Jones S., Ouellette F., Ten principles for machine-actionable data management plans, PLOS Computational Biology., 15, 3, (2019); Sansone S.-A., McQuilton P., Rocca-Serra P., Gonzalez-Beltran A., Izzo M., Lister A.L., Thurston M., FAIRsharing as a community approach to standards, repositories and policies, Nature Biotechnology., 37, 4, pp. 358-367, (2019); (2018); Hodge G., Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files, (2000); Whetzel P.L., Noy N.F., Shah N.H., Alexander P.R., Nyulas C., Tudorache T., Musen M.A., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications, Nucleic Acids Research., 39, pp. W541-W545, (2011); Souza R.R., Tudhope D., Almeida A.M.B., Towards a taxonomy of KOS: Dimensions for classifying Knowledge Organization Systems, Knowledge organization., 39, 3, pp. 179-192, (2012); Bergman M.; Daniel-Le Bozec C., Steichen O., Dart T., Jaulent M.-C., The role of local terminologies in electronic health records. 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Raboudi; Fealinx, Courbevoie, 37 rue Adam Ledoux, 92400, France; email: amel.raboudi@utc.fr","","Academic Press Inc.","","","","","","15320464","","JBIOB","35124236","English","J. Biomed. Informatics","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85124697234"
"Ismail M.I.; Jaafar C.R.C.; Azmi N.A.; Makhtar M.M.Z.; Samsuddin S.F.; Abrizah A.","Ismail, Mohd Ikhwan (57179420600); Jaafar, Cik Ramlah Che (58078597800); Azmi, Noor Adilah (58079358400); Makhtar, Muaz Mohd Zaini (57539873100); Samsuddin, Samsul Farid (56674406100); Abrizah, A. (6504290111)","57179420600; 58078597800; 58079358400; 57539873100; 56674406100; 6504290111","Eliciting Researchers’ Behaviour as the Foundation of Research Data Management Service Development","2022","Libres","32","1","","44","63","19","0","10.32655/LIBRES.2022.1.4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146844593&doi=10.32655%2fLIBRES.2022.1.4&partnerID=40&md5=8dd1a1565e18c5228dadd84ec6ff4e2a","Department of Library & Information Science, Faculty of Arts and Social Sciences, Universiti Malaya, Malaysia; Hamzah Sendut Library, Universiti Sains Malaysia, Malaysia; School of Industrial Technology and Centre for Global Sustainability Studies, Universiti Sains Malaysia, Malaysia","Ismail M.I., Department of Library & Information Science, Faculty of Arts and Social Sciences, Universiti Malaya, Malaysia, Hamzah Sendut Library, Universiti Sains Malaysia, Malaysia; Jaafar C.R.C., Hamzah Sendut Library, Universiti Sains Malaysia, Malaysia; Azmi N.A., Hamzah Sendut Library, Universiti Sains Malaysia, Malaysia; Makhtar M.M.Z., School of Industrial Technology and Centre for Global Sustainability Studies, Universiti Sains Malaysia, Malaysia; Samsuddin S.F., Department of Library & Information Science, Faculty of Arts and Social Sciences, Universiti Malaya, Malaysia; Abrizah A., Department of Library & Information Science, Faculty of Arts and Social Sciences, Universiti Malaya, Malaysia","Background. Research data management (RDM) has become an important activity in universities, for researchers to fulfil funding agencies’ and journal publication requirements, and to promote open science practices. Academic libraries have been identified as the locations to base RDM services. However, to develop effective RDM services, an understanding of RDM from the researchers’ perspectives is needed, including how researchers manage their research data. Objectives. This study aims to discover researchers’ behaviours and practices in RDM, and propose how the library can incorporate RDM into the research services offered. Methods. This case study, carried out at a research university in Malaysia, involved both quantitative and qualitative data gathering, focusing on three aspects of RDM: data creation, data storage and preservation, and data sharing. Quantitative data were collected via a survey of 113 researchers, and qualitative data were gathered through semi-structured interviews with 12 researchers. Results. It was found that the researchers had been generating research data irrespective of format and types. Most of the researchers managed their research data based on their own perspectives and practices, without following proper guidelines and standards. The researchers used personal solutions for research data storage and preservation, and utilized less than 10 gigabytes of storage for the short term. The researchers also did not share their research data due to privacy and confidentiality issues. Contributions. Researchers need support for organizing, archiving and preserving research data for future use, and libraries can provide this important service. The study reflects the library’s transformative role starting with conducting needs assessment of the academic research community, and establishing an RDM service. © 2022 The Authors. All rights reserved.","","","","","","","","","Abd Rahman N., The need for open science, Journal of Research Management & Governance, 2, 1, pp. 22-30, (2019); Abduldayan F. J., Abifarin F. P., Oyedum G. U., Alhassan J. A., Research data management practices of chemistry researchers in federal universities of technology in Nigeria, Digital Library Perspectives, 37, 1, pp. 70-90, (2021); Abrizah A., Malaysian researchers on open science readiness: Call for action, (2019); Adika F. O., Kwanya T., Research data management literacy amongst lecturers at Strathmore University, Kenya, Library Management, 41, 6-7, pp. 447-466, (2020); Akers K. 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W., Institutional readiness for data stewardship: Findings and recommendations from the research data assessment, Georgia Institute of Technology, pp. 1-32, (2013); Rowlands I., Your research data management needs: Research Data Management Survey results, (2018); Syn S. Y., Kim S., Professional and institutional support for RDM: A case of the National Institutes of Health (NIH), Proceedings of the Association for Information Science and Technology, 56, 1, pp. 776-777, (2019); Tenopir C., Allard S., Douglass K., Aydinoglu A. U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, pp. 1-21, (2011); Thelwall M., Munafo M., Mas-Bleda A., Stuart E., Makita M., Weigert V., Keene C., Khan N., Drax K., Kousha K., Is useful research data usually shared? An investigation of genome-wide association study summary statistics, PLoS ONE, 15, 2, pp. 1-11, (2020); Tripathi M., Shukla A., Sonker S. K. K., Research data management practices in university libraries: A study, DESIDOC Journal of Library and Information Technology, 37, 6, pp. 417-424, (2017); Vela K., Shin N., Establishing a research data management service on a health sciences campus, Journal of EScience Librarianship, 5, 1, (2019); Verbaan E., Cox A. M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, The Journal of Academic Librarianship, 40, 3-4, pp. 211-219, (2014); Vilar P., Zabukovec V., Research data management and research data literacy in Slovenian science, Journal of Documentation, 75, 1, pp. 24-43, (2019); Wallis J. C., Rolando E., Borgman C. L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 5, 7, (2013); Whitmire A. L., Boock M., Sutton S. C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program, 49, 4, pp. 382-407, (2015)","M.I. Ismail; Department of Library & Information Science, Faculty of Arts and Social Sciences, Universiti Malaya, Malaysia; email: ikhwanismail@usm.my; A. Abrizah; Department of Library & Information Science, Faculty of Arts and Social Sciences, Universiti Malaya, Malaysia; email: abrizah@um.edu.my","","Curtin University of Technology","","","","","","10586768","","","","English","Libres","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85146844593"
"Higgins S.G.; Nogiwa-Valdez A.A.; Stevens M.M.","Higgins, Stuart G. (56545137700); Nogiwa-Valdez, Akemi A. (22954456000); Stevens, Molly M. (7402243195)","56545137700; 22954456000; 7402243195","Considerations for implementing electronic laboratory notebooks in an academic research environment","2022","Nature Protocols","17","2","","179","189","10","10","10.1038/s41596-021-00645-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122865003&doi=10.1038%2fs41596-021-00645-8&partnerID=40&md5=e84e7875fd3faf38e47ae357a01c9e46","Department of Materials, Imperial College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London, United Kingdom; Institute of Biomedical Engineering, Imperial College London, London, United Kingdom","Higgins S.G., Department of Materials, Imperial College London, London, United Kingdom, Department of Bioengineering, Imperial College London, London, United Kingdom, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom; Nogiwa-Valdez A.A., Department of Materials, Imperial College London, London, United Kingdom, Department of Bioengineering, Imperial College London, London, United Kingdom, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom; Stevens M.M., Department of Materials, Imperial College London, London, United Kingdom, Department of Bioengineering, Imperial College London, London, United Kingdom, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom","As research becomes predominantly digitalized, scientists have the option of using electronic laboratory notebooks to record and access entries. These systems can more readily meet volume, complexity, accessibility and preservation requirements than paper notebooks. Although the technology can yield many benefits, these can be realized only by choosing a system that properly fulfills the requirements of a given context. This review explores the factors that should be considered when introducing electronic laboratory notebooks to an academically focused research group. We cite pertinent studies and discuss our own experience implementing a system within a multidisciplinary research environment. We also consider how the required financial and time investment is shared between individuals and institutions. Finally, we discuss how electronic laboratory notebooks fit into the broader context of research data management. This article is not a product review; it provides a framework for both the initial consideration of an electronic laboratory notebook and the evaluation of specific software packages. © 2021, Springer Nature Limited.","","Laboratories; adult; interdisciplinary research; investment; review; software; laboratory","","","","","UK Regenerative Medicine Platform ‘Acellular/Smart Materials - 3D Architecture, (MR/R015651/1); Wellcome Trust, WT, (098411/Z/12/Z); Royal Academy of Engineering, RAENG, (CIET2021\94); Cancer Research UK, CRUK, (C71717/A30035)","S.G.H. acknowledges support from a Cancer Research UK award (C71717/A30035). S.G.H., A.A.N.-V. and M.M.S. acknowledge support from a Wellcome Trust Senior Investigator Award (098411/Z/12/Z). M.M.S. acknowledges support from the UK Regenerative Medicine Platform ‘Acellular/Smart Materials - 3D Architecture’ (MR/R015651/1). M.M.S. acknowledges support from the Royal Academy of Engineering under the Chairs in Emerging Technologies scheme (CIET2021\94). The authors acknowledge the feedback and suggestions provided by the reviewers and editorial team, which helped improve the quality and breadth of the manuscript.","White K., Publications Output: U.S. Trends and International Comparisons, (2019); Waldo W.H., Barnett E.H., An electronic computer as a research assistant, Ind. Eng. Chem., 50, pp. 1641-1643, (1958); Borman S.A., Scientific software, Anal. Chem., 57, pp. 983A-994A, (1985); Gilbert W.A., RS/1: an electronic laboratory notebook, BioScience, 35, pp. 588-590, (1985); Schapira M., Harding R.J., Open laboratory notebooks: good for science, good for society, good for scientists, F1000Res., 8, (2019); Williamson A.E., Et al., Open source drug discovery: highly potent antimalarial compounds derived from the Tres Cantos Arylpyrroles, ACS Cent. Sci., 2, pp. 687-701, (2016); Adams R., Digital notebooks—productivity tools for researchers—event materials, Zenodo, (2018); Downie A., Electronic Lab Notebooks—for Prospective Users, (2020); Douglas S., ELN Vendor; Electronic Lab Notebooks, (2021); Ritt, S. & Paul Scherrer Institute, ELOG Linux Demo, (2021); Biosoftware T.; Dessy R., Electronic lab notebooks, Anal. Chem., 67, pp. 428A-433A, (1995); Butler D., A new leaf, Nature, 436, pp. 20-21, (2005); Kanza S., Gibbins N., Frey J.G., Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies, J. Cheminform., 11, (2019); Fowler J., (1994); BS EN ISO 15189:2012: Medical Laboratories. Requirements for Quality and Competence, (2012); BS EN ISO/IEC 17025:2017: General Requirements for the Competence of Testing and Calibration Laboratories, (2017); Code of Federal Regulations Title 21 Part 11, Code of Federal Regulations, 21, (2021); Gorry G.A., A virtual notebook for biomedical work groups, Bull. Med. Libr. Assoc., 76, pp. 256-267, (1988); Hansen J.D., Reich J., Democratizing education? Examining access and usage patterns in massive open online courses, Science, 350, pp. 1245-1248, (2015); Bromfield Lee D., Implementation and student perceptions on Google Docs as an electronic laboratory notebook in organic chemistry, J. Chem. Educ., 95, pp. 1102-1111, (2018); CARPi N., Minges A., Piel M., eLabFTW: an open source laboratory notebook for research labs, J. Open Source Softw., 2, (2017); Tremouilhac P., Et al., Chemotion ELN: an Open Source electronic lab notebook for chemists in academia, J. Cheminform., 9, (2017); Shipman F.M., Chaney R.J., Gorry G.A., Distributed hypertext for collaborative research: the virtual notebook system, HYPERTEXT ’89: Proceedings of the Second Annual ACM Conference on Hypertext, pp. 129-135, (1989); Bauch A., Et al., openBIS: a flexible framework for managing and analyzing complex data in biology research, BMC Bioinforma., 12, (2011); Guerrero S., Et al., Analysis and implementation of an electronic laboratory notebook in a biomedical research institute, PLoS One, 11, (2016); Eur. Union, 2016, 679, (2016); Managing Sensitive Data, (2021); Gotthardt M., The Labomator: A Flexible Electronic Lab Notebook (ELN) with Microsoft Onenote, (2015); ANSI X9.95-2016—Trusted Time Stamp Management and Security, (Accredited Standards Committee X9, (2016); Foster E.D., Deardorff A., Open Science Framework (OSF), J. Med. Libr. Assoc., 105, pp. 203-206, (2017); Rees I., Langley E., Chiu W., Ludtke S.J., EMEN2: an object oriented database and electronic lab notebook, Microsc. Microanal., 19, pp. 1-10, (2013); Vaas L.A.I., Et al., Electronic laboratory notebooks in a public–private partnership, PeerJ Comput. Sci., 2, (2016); Dirnagl U., Przesdzing I., A pocket guide to electronic laboratory notebooks in the academic life sciences, F1000Res., 5, (2016); Magid A., Is it worth it? Implementation of electronic lab notebook software among the STEM community at an American university in the UAE, In 2018 ASEE Annual Conference & Exposition (American Society for Engineering Education, (2018); Kotov S., Tremouilhac P., Jung N., Brase S., Chemotion-ELN part 2: adaption of an embedded Ketcher editor to advanced research applications, J. Cheminform., 10, (2018); Neylon C., Open Notebook Science: perspectives from a newbie, Nat. Prec., (2007); Lysakowski R., The CENB Advantage, Nat. Biotechnol., 13, pp. 347-348, (1995); Talbott T., Peterson M., Schwidder J., Myers J.D., Adapting the electronic laboratory notebook for the semantic era, Proceedings of the 2005 International Symposium on Collaborative Technologies and Systems, 2005 136–143, (2005); Switters J., Osimo D., Electronic Laboratory Notebooks, (2019); Consortium F.O.S.T.E.R.; Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Bartling S., Blockchain for Science and Knowledge Creation https://www.blockchainforscience.com/2017/02/23/blockchain-for-open-science-the-living-document/ (Blockchain for, Science, (2017); Wickham H., Et al., Welcome to the Tidyverse, J. Open Source Softw., 4, (2019); Muller K., Here: A Simpler Way to Find Your Files, (2020); Sievert C., Interactive Web-Based Data Visualization with R, Plotly, and Shiny, (2020); Vaidyanathan R., Htmlwidgets: HTML Widgets for R, (2020); Bowman S., Pfeiffer N., Pfeiffer N., Example Project for Demonstration: Hydroxymethylfurfural Oxidation over Supported Metal Catalysts, (2019); Rubacha M., Rattan A.K., Hosselet S.C., A review of electronic laboratory notebooks available in the market today, J. Assoc. Lab. Autom., 16, pp. 90-98, (2011)","M.M. Stevens; Department of Materials, Imperial College London, London, United Kingdom; email: m.stevens@imperial.ac.uk","","Nature Research","","","","","","17542189","","","35031789","English","Nat. Protoc.","Review","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85122865003"
"Azeroual O.; Schöpfel J.; Pölönen J.; Nikiforova A.","Azeroual, Otmane (57201378256); Schöpfel, Joachim (14619562900); Pölönen, Janne (56244719000); Nikiforova, Anastasija (57204202481)","57201378256; 14619562900; 56244719000; 57204202481","Putting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRness","2022","International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings","3","","","63","71","8","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146200844&partnerID=40&md5=0eb5a4b42b6a8b2aa111df859ae0a85c","German Centre for Higher Education Research and Science Studies (DZHW), Schützenstraße 6A, Berlin, 10117, Germany; GERiiCO Laboratory, University of Lille, Villeneuve-d’Ascq, 59653, France; Federation of Finnish Learned Societies, Snellmaninkatu 13, Helsinki, 00170, Finland; Institute of Computer Science, University of Tartu, Narva mnt 18, Tartu, 51009, Estonia; European Open Science Cloud (EOSC) Task Force “FAIR Metrics and Data Quality”, Brussels, 1050, Belgium","Azeroual O., German Centre for Higher Education Research and Science Studies (DZHW), Schützenstraße 6A, Berlin, 10117, Germany; Schöpfel J., GERiiCO Laboratory, University of Lille, Villeneuve-d’Ascq, 59653, France; Pölönen J., Federation of Finnish Learned Societies, Snellmaninkatu 13, Helsinki, 00170, Finland; Nikiforova A., Institute of Computer Science, University of Tartu, Narva mnt 18, Tartu, 51009, Estonia, European Open Science Cloud (EOSC) Task Force “FAIR Metrics and Data Quality”, Brussels, 1050, Belgium","Digitization in the research domain refers to the increasing integration and analysis of research information in the process of research data management. However, it is not clear whether it is used and, more importantly, whether the data are of sufficient quality, and value and knowledge could be extracted from them. FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication, they have rapidly proliferated and have become part of (inter-)national research funding programs. A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for research, is the subject of the paper. Supporting the call for the need for a”one-stop-shop and register-once-use-many approach”, we argue that CRIS is a key component of the research infrastructure landscape, directly targeted and enabled by operational application and the promotion of FAIR principles. We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","CRIS; Data Management; FAIR; Findability; Information Management; Interoperability; Knowledge; Open Science; Research Data Management; Research Information System","Information systems; Information use; Interoperability; Reusability; Sounding apparatus; 'current; Current research information system; Digitisation; FAIR; Findability; Knowledge; Open science; Research data managements; Research information systems; Information management","","","","","","","Arefolov A., Adam L., Brown S., Budovskaya Y., Chen C., Das D., Farhy C., Ferguson R., Huang H., Kanigel K., Lu C., Polesskaya O., Staton T., Tajhya R., Whitley M., Wong J., Zeng X., McCreary M., Implementation of the FAIR Data Principles for Exploratory Biomarker Data from Clinical Trials, Data Intelligence, 3, 4, pp. 631-662, (2021); Azeroual O., Schopfel J., Trustworthy or not? Research data on COVID-19 in data repositories, Libraries, Digital Information, and COVID, Chandos Digital Information Review, pp. 169-182, (2021); Azeroual O., Schopfel J., Ivanovic D., Nikiforova A., Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS, CRIS2022: 15th International Conference on Current Research Information Systems, In CRIS2022: 15th International Conference on Current Research Information Systems, (2022); Bryant R., Jan Fransen P. d. C., Helmstutler B., Scherer D., Research Information Management in the United States: Part 1—Findings and Recommendations, (2021); Commission E., Exploitation & Open Science in Horizon Europe, (2020); Commission E., Facts and Figures for open research data, (2020); Commission E., Research and innovation, (2020); Commission E., Open Science, (2021); Corte-Real N., Ruivo P., Oliveira T., Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?, Information & Management, 57, (2020); Danowski P., Ferus A., Hikl A.-L., McNeill G., Miniberger C., Reding S., Zarka T., Zojer M., ”Empfehlung” für die weitere Vorgangsweise für das Open-Access-Monitoring. Deliverable des AT2OA-Teilprojekts TP1-B, Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare, 73, pp. 278-284, (2020); Donohue T., Mennielli M., Wilcox D., DuraSpace for FAIRness and Data Protection: DSpace and Fedora, CRIS2018: 14th International Conference on Current Research Information Systems, (2018); Engelman A., Enkvist C., Pettersson K., A FAIR archive based on the CERIF model, Procedia Computer Science, 146, pp. 190-200, (2019); Europe S., Science Europe Position Statement on Research Information Systems, (2016); Cost-benefit analysis for FAIR research data: cost of not having FAIR research data, (2019); Towards a reform of the research assessment system: scoping report, (2021); Practical solutions for the use of FAIR principles throughout the data life cycle, (2022); Ferraris A., Mazzoleni A., Devalle A., Couturier J., Big data analytics capabilities and knowledge management: impact on firm performance, Management Decision. Management Decision, 57, pp. 1923-1936, (2018); Flores J. R., Brodeur J. J., Daniels M. G., Nicholls N., Turnator E., Libraries and the research data management landscape, The process of discovery: The CLIR postdoctoral fellowship program and the future of the academy, 2010, pp. 82-102, (2015); Assessing the FAIRness of data, (2020); Guba E., Lincoln Y., Fourth generation evaluation, (1989); Hasselbring W., Carr L., Hettrick S., Packer H., Tiropanis T., From FAIR research data toward FAIR and open research software, it - Information Technology, 62, 1, pp. 39-47, (2020); Insider L., Research Information definition, (2022); Ivanovic D., Ivanovic L., Layfield C., FAIRness at University of Novi Sad - Discoverability of PhD research results for Non-Serbian scientific community–, Procedia Computer Science, 146, pp. 3-10, (2019); Jetten M., Simons E., Research data management incorporated in a Research Information Management system. A case study on archiving data sets and writing Data Management Plans at Radboud University, the Netherlands, EUNIS19: 25th EUNIS Annual Congress, (2019); Jetten M., Simons E., Rijnders J., The role of CRIS’s in the research life cycle. A case study on implementing a FAIR RDM policy at Radboud University, the Netherlands, 14th International Conference on Current Research Information Systems, CRIS 2018, FAIRness of Research Information, Umeå, Sweden, June 14-16, 2018, volume 146 of Procedia Computer Science, pp. 156-165, (2018); Landi A., Thompson M., Giannuzzi V., Bonifazi F., Labastida I., da Silva Santos L. O. B., Roos M., The “A” of FAIR – As Open as Possible, as Closed as Necessary, Data Intelligence, 2, 1-2, pp. 47-55, (2020); Lindelow C., FAIR already? Principles of reusability and research output – evaluation at a national level, Spring 2019 euroCRIS Membership Meeting, (2019); Masuzzo P. C., Open Science & FAIR data: infinite possibilities, (2022); Mayer G., Muller W., Schork K., Uszkoreit J., Weidemann A., Wittig U., Rey M., Quast C., Felden J., Glockner F. O., Lange M., Arend D., Beier S., Junker A., Scholz U., Schuler D., Kestler H. A., Wibberg D., Puhler A., Twardziok S., Eils J., Eils R., Hoffmann S., Eisenacher M., Turewicz M., Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases, Briefings in Bioinformatics, 22, 5, (2021); Miniberger C., Reding S., From data collection to FAIR use in CRIS. The case of University of Vienna, euroCRIS Strategic Membership Meeting Autumn 2017, (2017); Mornati S., Enhancing interoperability: the implementation of OpenAIRE Guidelines and COAR NGR Recommendations in CRIS/RIMS, OR19 Workshop on Repository/CRIS Interoperability, (2019); Mornati S., Bollini A., Pascarelli L., How to make research information FAIR: DSpace-CRIS and best practices in open research information, CRIS2018: 14th International Conference on Current Research Information Systems, (2018); Mustajoki H., Polonen J., Gregory K., Ivanovic D., Brasse V., Kesaniemi J., Koivisto E., Pylvanainen E., Making FAIReR assessments possible. Final report of EOSC Co-Creation projects:”European overview of career merit systems” and”Vision for research data in research careers”, (2021); Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, (2009); Nguyen G. T., Dlugolinsky S., Bobak M., Tran V. D., Garcia A. L., Heredia I., Malik P., Hluchy L., Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey, Artificial Intelligence Review, 52, pp. 77-124, (2018); Nikiforova A., Definition and evaluation of data quality: User-oriented data object-driven approach to data quality assessment, Baltic Journal of Modern Computing, 8, pp. 391-432, (2020); Norway U., NOR-CAM – A toolbox for recognition and rewards in academic careers, (2016); O'Carroll C., Vandevelde K., McAllister D., Metcalfe J., Maas K., Rentier B., Kaunismaa E., Esposito F., Cabello Valdes C., Evaluation of research careers fully acknowledging Open Science practices: rewards, incentives and/or recognition for researchers practicing Open Science, (2017); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Good practice in researcher evaluation, (2020); Paic A., Open Science - Enabling Discovery in the Digital Age, (2021); Society M. P., Principles for the Handling of Research Data, (2010); Tammaro A. M., Matusiak K. K., Sposito F. A., Casarosa V., Data Curator’s Roles and Responsibilities: An International Perspective, Libri, 69, 2, pp. 89-104, (2019); Tatum C., Brown J., Principles and Pragmatics of “as open as possible”: persistent identifiers as the interface between research information commons and closed systems, CRIS2018: 14th International Conference on Current Research Information Systems, (2018); Terheggen C., Simons E., Towards a Model for FAIR Data Information Infrastructures, euroCRIS Strategic Membership Meeting Autumn 2016, (2016); UNESCO Recommendation on Open Science, (2021); Vancauwenbergh S., Research Information Systems as Leverage for Open Science, EARMA Digital Conference 2021, (2021); Wachtler T., Bauer P., Denker M., Grun S., Hanke M., Klein J., Oeltze-Jafra S., Ritter P., Rotter S., Scherberger H., Stein A., Witte O. W., NFDINeuro: building a community for neuroscience research data management in Germany, Neuroforum, 27, 1, pp. 3-15, (2021); Wilkinson M. D., Dumontier M., Aalbersberg I. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, 1, pp. 1-9, (2016); Wittenburg P., Large Research Infrastructure Building using FAIR Digital Objects, Autumn 2019 euroCRIS Strategic Membership Meeting, (2019)","","Bernardino J.; Masciari E.; Rolland C.; Filipe J.","Science and Technology Publications, Lda","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","14th International Conference on Knowledge Management and Information Systems, KMIS 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022","24 October 2022 through 26 October 2022","Valletta","184235","21843228","978-989758614-9","","","English","International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings","Conference paper","Final","","Scopus","2-s2.0-85146200844"
"Spichtinger D.","Spichtinger, Daniel (57496609300)","57496609300","Data Management Plans in Horizon 2020: What beneficiaries think and what we can learn from their experience","2022","Open Research Europe","1","","42","","","","1","10.12688/openreseurope.13342.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141998245&doi=10.12688%2fopenreseurope.13342.2&partnerID=40&md5=4d64632fb1dca4edec4719361a46c47d","University Library, University of Vienna, Vienna, 1010, Austria","Spichtinger D., University Library, University of Vienna, Vienna, 1010, Austria","Background: Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU's Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance. Methods: As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs. Results & Conclusions: We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being 'just' an European Commission (EC) requirement. DMPs are most prominently developed within a project's Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach. The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk. If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs. The study identifies IP and licensing arrangements for DMPs as promising areas for further research. © 2022 Spichtinger D.","Data management; Data management plans; Horizon 2020; Research data management","","","","","","University of Vienna Library; University of Vienna Library Gerda McNeill; University of Vienna Library IT Service; Horizon 2020 Framework Programme, H2020, (777541)","I wish to thank the volunteers of the OpenAIRE Advance RDM taskforce and the University of Vienna Library IT Service for their support in establishing the DMP white list. I also wish to thank my qualitative interview partners and those that filled in the quantitative survey as well as my supervisor at the University of Vienna Library Gerda McNeill. I also wish to thank the University of Vienna Library for additional funding to revise this article based on the peer review comments (which were obtained after project funding ended). Finally, my thanks also go to the reviewers for their thorough work and insightful suggestions.","Regulating the internet giants. The world's most valuable resource is no longer oil, but data, (2017); A European strategy for data, (2020); Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on European data governance (Data Governance Act), (2020); Open Data Maturity in Europe. Report 2020, (2020); Leonelli S., Why the Current Insistence on Open Access to Scientific Data? Big Data, Knowledge Production, and the Political Economy of Contemporary Biology, Bull Sci Technol Soc, 33, 1-2, pp. 6-11, (2013); Spichtinger D., Siren J., The Development of Research Data Management Policies in Horizon 2020, pp. 11-23, (2018); Data Management; Horizon 2020 DMPs what beneficiaries think and what we can learn from their experience ('DMP Use Case Project'), PHAIDRA; ANNEX 1: ERC PEER REVIEW EVALUATION PANELS (ERC PANELS); Establishing a collection of 841 publicly available Horizon 2020 Data Management Plans, (2021); DMP Use Case Project, PHAIDRA; Grootveld M., Leenarts E., Jones S., Et al., OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans, Zenodo, (2018); Guides for Researchers How to identify and assess Research Data Management (RDM) costs; Neylon C., Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers, Res Ideas Outcomes, 3, (2017)","D. Spichtinger; University Library, University of Vienna, Vienna, 1010, Austria; email: daniel.spichtinger@univie.ac.at","","F1000 Research Ltd","","","","","","27325121","","","","English","Open. Res. Eur.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85141998245"
"Kühbach M.; London A.J.; Wang J.; Schreiber D.K.; Mendez Martin F.; Ghamarian I.; Bilal H.; Ceguerra A.V.","Kühbach, Markus (55538643800); London, Andrew J. (56223665200); Wang, Jing (55523101773); Schreiber, Daniel K. (22635599200); Mendez Martin, Francisca (24169506600); Ghamarian, Iman (56041464900); Bilal, Huma (55901464000); Ceguerra, Anna V. (15130624900)","55538643800; 56223665200; 55523101773; 22635599200; 24169506600; 56041464900; 55901464000; 15130624900","Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy","2022","Microscopy and Microanalysis","28","4","","1038","1053","15","1","10.1017/S1431927621012241","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111422969&doi=10.1017%2fS1431927621012241&partnerID=40&md5=dc3ee0c31045283ad7136952b628382f","Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Straβe 1, DÜsseldorf, D-40237, Germany; United Kingdom Atomic Energy Authority, Culham Centre for Fusion Energy, Culham Science Centre, Oxon, Abingdon, OX14 3DB, United Kingdom; Pacific Northwest National Laboratory, Energy and Environment Directorate, 902 Battelle Boulevard, Richland, 99352, WA, United States; Department of Materials Science, Montanuniversität Leoben, Franz Josef-Straβe 18, Leoben, A-8700, Austria; Department of Materials Science and Engineering, University of Michigan, 2300 Hayward St, Ann Arbor, 48109-2117, MI, United States; School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, 7019-1052, OK, United States; Australian Centre for Microscopy & Microanalysis, School of Aerospace Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, 2006, NSW, Australia","Kühbach M., Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Straβe 1, DÜsseldorf, D-40237, Germany; London A.J., United Kingdom Atomic Energy Authority, Culham Centre for Fusion Energy, Culham Science Centre, Oxon, Abingdon, OX14 3DB, United Kingdom; Wang J., Pacific Northwest National Laboratory, Energy and Environment Directorate, 902 Battelle Boulevard, Richland, 99352, WA, United States; Schreiber D.K., Pacific Northwest National Laboratory, Energy and Environment Directorate, 902 Battelle Boulevard, Richland, 99352, WA, United States; Mendez Martin F., Department of Materials Science, Montanuniversität Leoben, Franz Josef-Straβe 18, Leoben, A-8700, Austria; Ghamarian I., Department of Materials Science and Engineering, University of Michigan, 2300 Hayward St, Ann Arbor, 48109-2117, MI, United States, School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, 7019-1052, OK, United States; Bilal H., Australian Centre for Microscopy & Microanalysis, School of Aerospace Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, 2006, NSW, Australia; Ceguerra A.V., Australian Centre for Microscopy & Microanalysis, School of Aerospace Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, 2006, NSW, Australia","Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results. Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America.","Atom probe microscopy; FAIR; research data management; software development; uncertainty quantification","","","","","","","","Ankerst M., Breuning M.M., Kriegel H.P., Sander J., OPTICS: Ordering Points to Identify the Clustering Structure, pp. 49-60, (1999); Baier C., Katoen J.P., Principles of Model Checking, (2008); Barton D., Hornbuckle B., Darling K., Thompson G., The influence of isoconcentration surface selection in quantitative outputs from proximity histograms, Microsc Microanal, 25, pp. 401-409, (2019); Bas P., Bostel A., Deconihout B., Blavette D., A general protocol for the reconstruction of 3D atom probe data, Appl Surf Sci, 87, 8, pp. 298-304, (1995); Breen A.J., Babinsky K., Day A.C., Eder K., Oakman C.J., Trimby P.W., Primig S., Cairney J.M., Ringer S.P., Correlating atom probe crystallographic measurements with transmission Kikuchi diffraction data, Microsc Microanal, 23, pp. 279-290, (2017); 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Ward L., Blaiszik B., NUCAPT publication manager, (2021); Wei Y., Varanasi R.S., Schwarz T., Gomell L., Zhao H., Larson D.J., Sun B., Liu G., Chen H., Raabe D., Gault B., Machine-learning-enhanced time-of-flight mass spectrometry analysis, Patterns, 2, (2021); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., Da Silva S.L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.G., Groth P., Goble C., Grethe J.S., Heringa J., T'Hoen P.A., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S.A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Zhou X., Mianroodi J.R., Da Silva A.K., Koenig T., Thompson G.B., Shanthraj P., Ponge D., Gault B., Svendsen B., Raabe D., The hidden structure dependence of the chemical life of dislocations, Sci Adv, 7, pp. 1-9, (2021)","M. Kühbach; Max-Planck-Institut für Eisenforschung GmbH, DÜsseldorf, Max-Planck-Straβe 1, D-40237, Germany; email: kuehbach@fhi-berlin.mpg.de","","Cambridge University Press","","","","","","14319276","","MIMIF","","English","Microsc. Microanal.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85111422969"
"Curdt C.; Dierkes J.; Kloppenburg S.","Curdt, Constanze (36681871300); Dierkes, Jens (56727998600); Kloppenburg, Sonja (58031956600)","36681871300; 56727998600; 58031956600","RDM in a Decentralised University Ecosystem—A Case Study of the University of Cologne","2022","Data Science Journal","21","1","20","1","8","7","0","10.5334/dsj-2022-020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144823458&doi=10.5334%2fdsj-2022-020&partnerID=40&md5=e70f44bceb118478472632a74b9291cc","Regional Computing Centre, University of Cologne, Germany; University and City Library Cologne, University of Cologne, Germany; Department Research Management, University of Cologne, Germany","Curdt C., Regional Computing Centre, University of Cologne, Germany; Dierkes J., University and City Library Cologne, University of Cologne, Germany; Kloppenburg S., Department Research Management, University of Cologne, Germany","The University of Cologne (UoC) has historically grown in highly decentralised structures. This is reflected by a two-layered library structure as well as by a number of decentralised research data management (RDM) activities established on the faculty and research consortium level. With the aim to foster networking, cooperation, and synergies between existing activities, a university-wide RDM will be established. A one-year feasibility study was commissioned by the Rectorate in 2016 and carried out by the department research management, library and computing centre. One study outcome was the adoption of a university-wide research data guideline. Based on a comprehensive RDM service portfolio, measures were developed to put a central RDM into practice. The challenges have been to find the right level of integration and adaptation of existing and established decentralised structures and to develop additional new structures and services. We will report on first steps to map out central RDM practices at the UoC and to develop a structure of cooperation between loosely coupled information infrastructure actors. Central elements of this structure are a competence center, an RDM expert network, a forum for exchange about RDM and associated topics as well as the faculties with their decentralized, domain-specific RDM services. The Cologne Competence Center for Research Data Management (C3RDM) was founded at the end of 2018 and is still in its development phase. It provides a one-stop entry point for all questions regarding RDM. The center itself provides basic and generic RDM services, such as training, consulting, and data publication support, and acts as a hub to the decentral experts, information infrastructure actors, and resources. © 2022 The Author(s).","information infrastructure; institutional solution; research data management; university-wide strategy","Case-studies; Data management services; Decentralised; Decentralized structures; Information infrastructures; Institutional solution; Management activities; Research consortium; Research data managements; University-wide strategy; Information management","","","","","","","Adler R., Navigating continual disruption: A report of the 2014 Aspen Institute roundtable on institutional innovation, (2015); Akers KG, Sferdean FC, Nicholls NH, Green JA., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Calton JM, Payne SL., Coping with paradox: Multistakeholder learning dialogue as a pluralist sensemaking process for addressing messy problems, Business & Society, 42, 1, pp. 7-42, (2003); Childs S, McLeod J., Tackling the Wicked Problem of ERM: Using the Cynefin Framework as a Lens, Records Management Journal, 23, 3, pp. 191-227, (2013); Cox AM, Kennan MA, Lyon L, Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox AM, Pinfield S, Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Curdt C., Supporting the interdisciplinary, long-term research project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services, Data Science Journal, 18, 5, pp. 1-9, (2019); Dierkes J, Wuttke U., The Göttingen eResearch Alliance: A case study of developing and establishing institutional support for research data management, ISPRS International Journal of Geo-Information, 5, 8, (2016); Dierkes J, Curdt C., Von der Idee zum Konzept—Forschungsdatenmanagement an der Universität zu Köln. O-bib, Das Offene Bibliotheksjournal/Herausgegeben vom VDB, 5, 2, pp. 28-46, (2018); Dierkes J, Helling P., Forschungsdatenmanagement als gemeinsame Herausforderung am Beispiel C3RDM und DCH an der Universität zu Köln, Bausteine Forschungsdatenmanagement, 1, pp. 77-85, (2020); Fitt A, Rouse R, Taylor S., RDM: An approach from a modern university with a growing research portfolio, International Journal of Digital Curation, 10, 1, pp. 154-162, (2015); Hagel J, Brown JS, Davison L., The power of pull: How small moves, smartly made, can set big things in motion, (2012); Hagel J, Durchslag S, Brown JS., Orchestrating loosely coupled business processes: The secret to successful collaboration, (2002); Empfehlung der 16. Mitgliederversammlung der HRK am 13. Mai 2014 in Frankfurt am Main. Management von Forschungsdaten–eine zentrale strategische Herausforderung für Hochschulleitungen, (2014); Wie Hochschulleitungen die Entwicklung des Forschungsdatenmanagements steuern können, (2015); Mathiesen J, Jamtveit B, Sneppen K., Organizational structure and communication networks in a university environment, Physical Review E, 82, 1, (2010); Plomp E, Dintzner N, Teperek M, Dunning A., Cultural obstacles to research data management and sharing at TU Delft, Insights, 32, 1, (2019); Savage JL, Cadwallader L., Establishing, developing, and sustaining a community of data champions, Data Science Journal, 18, 1, (2019); Stamnas E, Lammert A, Winkelmann V, Lang U., The HD(CP)<sup>2</sup> data archive for atmospheric measurement data, ISPRS International Journal of Geo-Information, 5, 7, (2016); Symanski U., Uni, wie tickst Du? Eine exemplarische Erhebung von organisationskulturellen Merkmalen an Universitäten im Zeitalter der Hochschulreform, (2012); Verbaan E, Cox AM., Occupational Sub-Cultures, Jurisdictional Struggle and Third Space: Theorising Professional Service Responses to Research Data Management, The Journal of Academic Librarianship, 40, 3, pp. 211-219, (2014)","C. Curdt; Regional Computing Centre, University of Cologne, Germany; email: c.curdt@uni-koeln.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85144823458"
"Weigl D.M.; Vanderhart C.; Pescoller M.; Rammler D.; Grassl M.; Trümpi F.; Goebl W.","Weigl, David M. (55359633400); Vanderhart, Chanda (57850043400); Pescoller, Matthäus (57850519300); Rammler, Delilah (57850519400); Grassl, Markus (57850758600); Trümpi, Fritz (23398382300); Goebl, Werner (6603194269)","55359633400; 57850043400; 57850519300; 57850519400; 57850758600; 23398382300; 6603194269","The Vienna Philharmonic Orchestra's New Year's Concerts: Building a FAIR Data Corpus for Musicology","2022","ACM International Conference Proceeding Series","","","","36","40","4","0","10.1145/3543882.3543892","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136219750&doi=10.1145%2f3543882.3543892&partnerID=40&md5=b78ff2f8ff4b36aa9b5c09a82e58386f","University of Music and Performing Arts Vienna, Austria","Weigl D.M., University of Music and Performing Arts Vienna, Austria; Vanderhart C., University of Music and Performing Arts Vienna, Austria; Pescoller M., University of Music and Performing Arts Vienna, Austria; Rammler D., University of Music and Performing Arts Vienna, Austria; Grassl M., University of Music and Performing Arts Vienna, Austria; Trümpi F., University of Music and Performing Arts Vienna, Austria; Goebl W., University of Music and Performing Arts Vienna, Austria","The Vienna Philharmonic Orchestra's New Year's Concert is an annual, live-broadcast New Year's Day staple for a vast international audience, with an alternating line-up of star conductors and an ever-changing repertoire that incorporates the same favourites - most notably, the Blue Danube Waltz and the Radetzky March - year after year. We are gathering, digitizing, and aligning the concert recordings of this series with audio features, score encodings, records of historical discourse, and other ephemera, interconnecting this multimodal music information and making it available as a digital corpus of linked open data following the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) research data management. Here, we raise musicological research questions motivating our work; describe the approach to assembling our corpus and developing associated editorial and analytical tooling, building on and extending recently established semantic music information workflows; and, provide insight into ongoing digital musicology research incorporating this data. Our work is motivated both by the pursuit of our own research interests in musicology and performance science, but also by a desire to provide a useful and reusable dataset for the wider digital music research community, bringing publication practices between these fields into further dialogue.  © 2022 Owner/Author.","Digital Libraries; Digital Musicology; Linked Open Data","Audio acoustics; Information management; Linked data; Music; Open Data; Semantics; Audio features; Digital musicology; Encodings; Linked open datum; Multi-modal; Music information; Research data managements; Research interests; Research questions; Work-flows; Digital libraries","","","","","Austrian Science Fund, FWF, (P 34664-G)","This research was funded in whole, or in part, by the Austrian Science Fund (FWF) (P 34664-G). For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.","Bengtsson I., Gabrielsson A., Methods for analyzing performance of musical rhythm, Scand. J. Psychol., 21, 1, pp. 257-268, (1980); Montero Bravo S., Territorial art, design & architecture, VIS-Nordic Journal for Artistic Research, 7, (2022); Buffa M., Cabrio E., Fell M., Gandon F., Giboin A., Hennequin R., Michel F., Pauwels J., Pellerin G., Tikat M., Et al., The WASABI dataset: Cultural, lyrics and audio analysis metadata about 2 million popular commercially released songs, European Semantic Web Conference, pp. 515-531, (2021); Chew E., Notating disfluencies and temporal deviations in music and arrhythmia, Music & Science, 1, (2018); Choi K., Stephen Downie J., A trend analysis on concreteness of popular song lyrics, 6th International Conference on Digital Libraries for Musicology, pp. 43-52, (2019); De Roure D., Page K.R., Fields B., Crawford T., Stephen Downie J., Fujinaga I., An e-Research approach to Web-scale music analysis, Philosophical Transactions of the Royal Society A, 369, 1949, (2011); Dieman-Dichtl K., Wiens goldener Klang: Geschichten um die Wiener Philharmoniker und ihr Neujahrskonzert, Amalthea, (1996); Sheridan Dodds P., Danforth C.M., Measuring the happiness of large-scale written expression: Songs, blogs, and presidents, Journal of happiness studies, 11, 4, pp. 441-456, (2010); Goebl W., Weigl D.M., Alleviating the last mile of encoding: The mei-friend package for the Atom text editor, Music Encoding Conference, (2021); Hankinson A., Roland P., Fujinaga I., The Music Encoding Initiative as a Document-Encoding Framework, Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), pp. 293-298, (2011); Hofmann A., Miksa T., Knees P., Bakos A., Salam H., Ahmedaja A., Yimwadsana B., Chan C., Rauber A., Enabling FAIR use of Ethnomusicology Data-Through Distributed Repositories, Linked Data and Music Information Retrieval, Empirical Musicology Review, 16, 1, pp. 47-64, (2021); Refsum Jensenius A., Best versus Good Enough Practices for Open Music Research, Empirical Musicology Review, 16, 1, (2021); Kos W., Rapp C., Alt-Wien: die Stadt, die niemals war; [Historisches Museum der Stadt (Wien) Wien Museum im Künstlerhaus, 25 Nov 2004-28. März 2005], Czernin, (2004); Lewis D., Page K., Dreyfus L., Narratives and Exploration in a Musicology App: Supporting Scholarly Argument with the Lohengrin TimeMachine, 8th International Conference on Digital Libraries for Musicology (Virtual Conference, GA, USA) (DLfM '21), pp. 50-58, (2021); Lewis D., Weigl D.M., Bullivant J., Page K.R., Publishing musicology using multimedia digital libraries: creating interactive articles through a framework for linked data and MEI, Proceedings of the 5th International Conference on Digital Libraries for Musicology, pp. 21-25, (2018); Lewis R.J., Crawford T., Lewis D., Exploring information retrieval, semantic technologies and workflows for music scholarship: the Transforming Musicology project, Early Music, 43, 4, pp. 635-647, (2015); Lisena P., Troncy R., Todorov K., Achichi M., Modeling the complexity of music metadata in semantic graphs for exploration and discovery, Proceedings of the 4th InternationalWorkshop on Digital Libraries for Musicology, pp. 17-24, (2017); Mauch M., MacCallum R.M., Levy M., Leroi A.M., The evolution of popular music: USA 1960-2010, Royal Society open science, 2, 5, (2015); Mayer-Hirzberger A., Szabo-Knotik C., Österreichs Tor in die Welt""-Musik als Mittel der Kulturpropaganda nach '45, Österr. Musikz., 60, 4, (2005); Muller M., Ozer Y., Krause M., Pratzlich T., Driedger J., Sync Toolbox: A Python Package for Efficient, Robust, and Accurate Music Synchronization, Journal of Open Source Software, 6, 64, (2021); Nussbaumer M., Musikstadt Wien: Die Konstruktion eines Images, Rombach Wissenschaften-Edition Parabasen, 6, (2007); Organisciak P., Stephen Downie J., Research access to in-copyright texts in the humanities, Information and Knowledge Organisation in Digital Humanities, pp. 157-177, (2021); Page K.R., Bechhofer S., Fazekas G., Weigl D.M., Wilmering T., Realising a Layered Digital Library: Exploration and Analysis of the Live Music Archive through Linked Data, Proceedings of the 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL 2017), pp. 1-10, (2017); Parmer T., Ahn Y., Evolution of the informational complexity of contemporary western music, (2019); Pugin L., Zitellini R., Roland P., Verovio: A library for Engraving MEI Music Notation into SVG, Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), pp. 107-112, (2014); Holger Rutz H., Pirro D., Anemone Actiniaria by Hanns Holger Rutz, (2022); Sandler M., De Roure D., Benford S., Page K., Semantic web technology for new experiences throughout the music productionconsumption chain, Proceedings of the 2019 International Workshop on Multilayer Music Representation and Processing (MMRP). IEEE, pp. 49-55, (2019); Schipper C., Research catalogue-society for artistic research, (2022); Serra X., Creating Research Corpora for the Computational Study of Music: the case of the CompMusic Project, Proceedings of the AES 53rd International Conference: Semantic Audio. AES, pp. 1-9, (2014); Serra Julia J., Corral A., Boguna M., Haro Berois M., Lluis Arcos J., Measuring the evolution of contemporary western popular music, Scientific reports., 2012, 2, (2012); Rata Stutzbach A., Digital Media Reviews: MusicBrainz, 68, 1, pp. 147-151, (2011); Trumpi F., The Political Orchestra: The Vienna and Berlin Philharmonics during the Third Reich, (2016); Weigl D.M., Rehearsal encodings with a social life, Music Encoding Conference, (2020); Weigl D.M., Goebl W., Baker D.J., Crawford T., Zubani F., Gkiokas A., Gutierrez N.F., Porter A., Santos P., Notes on the Music: A social data infrastructure for music annotation, 8th International Conference on Digital Libraries for Musicology, pp. 23-31, (2021); Weigl D.M., Goebl W., Crawford T., Gkiokas A., Gutierrez N.F., Porter A., Santos P., Karreman C., Vroomen I., Liem C.C.S., Et al., Interweaving and enriching digital music collections for scholarship, performance, and enjoyment, Proceedings of the 6th International Conference on Digital Libraries for Musicology (DLfM'19), pp. 84-88, (2019); Wilkinson M.D., Dumontier M., Jan Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Boiten J., Bonino Da Silva Santos L., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, 1, pp. 1-9, (2016)","","","Association for Computing Machinery","","9th International Conference on Digital Libraries for Musicology, DLfM 2022","28 July 2022","Virtual, Online","181512","","978-145039668-4","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-85136219750"
"Schmidt C.; Hanne J.; Moore J.; Meesters C.; Ferrando-May E.; Weidtkamp-Peters S.","Schmidt, Christian (57039461300); Hanne, Janina (57974230800); Moore, Josh (23091520300); Meesters, Christian (57198147010); Ferrando-May, Elisa (6603443621); Weidtkamp-Peters, Stefanie (14520642400)","57039461300; 57974230800; 23091520300; 57198147010; 6603443621; 14520642400","Research data management for bioimaging: The 2021 NFDI4BIOIMAGE community survey","2022","F1000Research","11","","638","","","","0","10.12688/f1000research.121714.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141936166&doi=10.12688%2ff1000research.121714.2&partnerID=40&md5=686b9e61285532093ebb22d4bdcb2f15","Enabling Technology, German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany; Bioimaging Center, University of Konstanz, Konstanz, Germany; German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany; Open Microscopy Environment Consortium, University of Dundee, Dundee, United Kingdom; High Performance Computing, Johannes Gutenberg University Mainz, Mainz, Germany; Center for Advanced Imaging, Heinrich Heine University Dusseldorf, Dusseldorf, Germany","Schmidt C., Enabling Technology, German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany, Bioimaging Center, University of Konstanz, Konstanz, Germany; Hanne J., German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany; Moore J., German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany, Open Microscopy Environment Consortium, University of Dundee, Dundee, United Kingdom; Meesters C., High Performance Computing, Johannes Gutenberg University Mainz, Mainz, Germany; Ferrando-May E., Enabling Technology, German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany, Bioimaging Center, University of Konstanz, Konstanz, Germany, German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany; Weidtkamp-Peters S., German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany, Center for Advanced Imaging, Heinrich Heine University Dusseldorf, Dusseldorf, Germany","Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap. © 2022 Schmidt C et al.","Bioimage analysis; Bioimaging; FAIR-principles; Microscopy; OMERO; Research data management","Data Management; Humans; Information Dissemination; Metadata; Surveys and Questionnaires; Workflow; adoption; adult; article; biomedicine; career; FAIR principles; female; Germany; health survey; human; human experiment; image processing; legal aspect; male; metadata; microscopy; practice guideline; questionnaire; standardization; summer; workflow; information dissemination; information processing; metadata","","","","","","","Ouyang W., Zimmer C., The imaging tsunami: Computational opportunities and challenges, Curr. Opin. Syst. Biol, 4, pp. 105-113, (2017); Driscoll M.K., Zaritsky A., Data science in cell imaging, J. Cell Sci, 134, 7, (2021); Moen E., Et al., Deep learning for cellular image analysis, Nat. Methods, 16, 12, pp. 1233-1246, (2019); Andreev A., Koo D.E.S., Practical Guide to Storage of Large Amounts of Microscopy Data, Micros. Today, 28, 4, pp. 42-45, (2020); Wallace C.T., Croix C.M.S., Watkins S.C., Data management and archiving in a large microscopy-and-imaging, multi-user facility: Problems and solutions, Mol. Reprod. Dev, 82, 9, pp. 630-634, (2015); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Berman H.M., Et al., The Protein Data Bank, Nucleic Acids Res, 28, 1, pp. 235-242, (2000); Crystallography: Protein Data Bank, Nat. New Biol, 233, 42, pp. 223-223, (1971); Jones D.T., Thornton J.M., The impact of AlphaFold2 one year on, Nat. Methods, 19, 1, pp. 15-20, (2022); Swedlow J.R., Et al., A global view of standards for open image data formats and repositories, Nat. Methods, 18, 12, pp. 1440-1446, (2021); Goldberg I.G., Et al., The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging, Genome Biol, 6, 5, (2005); Linkert M., Et al., Metadata matters: access to image data in the real world, J. Cell Biol, 189, 5, pp. 777-782, (2010); Li S., Et al., Metadata management for high content screening in OMERO, Methods, 96, pp. 27-32, (2016); Burel J.M., Et al., Publishing and sharing multi-dimensional image data with OMERO, Mamm. Genome, 26, 9-10, pp. 441-447, (2015); Cuellar L.K., Et al., A data management infrastructure for the integration of imaging and omics data in life sciences, BMC Bioinformatics, 23, (2022); Barillari C., Et al., openBIS ELN-LIMS: an open-source database for academic laboratories, Bioinformatics, 32, 4, pp. 638-640, (2016); Wittig U., Et al., Data management and data enrichment for systems biology projects, J. Biotechnol, 261, pp. 229-237, (2017); Guerrero S., Et al., Analysis and Implementation of an Electronic Laboratory Notebook in a Biomedical Research Institute, PLoS One, 11, 8, (2016); Neuroth H., Et al., Aktives Forschungsdatenmanagement, J ABI Technik, 38, pp. 55-64, (2018); Boehm U., Et al., QUAREP-LiMi: a community endeavor to advance quality assessment and reproducibility in light microscopy, Nat. Methods, 18, 12, pp. 1423-1426, (2021); Nelson G., Et al., QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy, J. Microsc, 284, 1, pp. 56-73, (2021); Ferrando-May E., Et al., Advanced light microscopy core facilities: Balancing service, science and career, Microsc. Res. Tech, 79, 6, pp. 463-479, (2016); Ellenberg J., Et al., A call for public archives for biological image data, Nat. Methods, 15, 11, pp. 849-854, (2018); Hartley M., Et al., The BioImage Archive - home of life-sciences microscopy data, J. Mol. Biol, 434, 11, (2022); Williams E., Et al., The Image Data Resource: A Bioimage Data Integration and Publication Platform, Nat. Methods, 14, 8, pp. 775-781, (2017); Sarkans U., Et al., REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology, Nat. Methods, 18, 12, pp. 1418-1422, (2021); Moore J., Et al., OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies, Nat. Methods, 18, 12, pp. 1496-1498, (2021); Rigano A., Et al., Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications, Nat. Methods, 18, 12, pp. 1489-1495, (2021); Kunis S., Et al., MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community, Nat. 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Tech, 30, 3, pp. 36-44, (2019); Kos-Braun I.C., Gerlach B., Pitzer C., A survey of research quality in core facilities, elife, 9, (2020); Miura K., A Survey on Bioimage Analysis Needs, (2015); Schmidt C., Et al., Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 1 - Supplementary Information and Supplementary Figures, (2022); Schmidt C., Et al., Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 2 - Questionnaire, (2022); Schmidt C., Et al., Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries, (2022); Schmidt C., Et al., Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 4 - Analysis Data Sheet, (2022); Allan C., Et al., OMERO: flexible, model-driven data management for experimental biology, Nat. 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Tissue-based map of the human proteome, Science, 347, 6220, (2015); Iudin A., Et al., EMPIAR: a public archive for raw electron microscopy image data, Nat. Methods, 13, 5, pp. 387-388, (2016); Europe S., Practical Guide to the International Alignment of Research Data Management - Extended Edition, (2021); Simons N., Et al., The State of Open Data 2021, Digital Science, (2021); Hammer M., Et al., Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model, Nat. Methods, 18, 12, pp. 1427-1440, (2021); Ryan J., Et al., MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text, Nat. Methods, 18, 12, pp. 1414-1416, (2021)","C. Schmidt; Enabling Technology, German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany; email: christian01.schmidt@dkfz-heidelberg.de; J. Hanne; German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany; email: janina.hanne@gerbi-gmb.de","","F1000 Research Ltd","","","","","","20461402","","","36405555","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85141936166"
"Kvale L.H.; Darch P.","Kvale, Live Håndlykken (57195713528); Darch, Peter (35766212500)","57195713528; 35766212500","Privacy protection throughout the research data life cycle","2022","Information Research","27","3","938","","","","0","10.47989/IRPAPER938","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142283877&doi=10.47989%2fIRPAPER938&partnerID=40&md5=9dd91a84a56f5ac240f17568f5a0cb6c","University of Oslo Library, Norway; School of Information Sciences, University of Illinois at Urbana-Champaign, United States","Kvale L.H., University of Oslo Library, Norway; Darch P., School of Information Sciences, University of Illinois at Urbana-Champaign, United States","Introduction. The sharing and reuse of research data is gradually becoming best practice in research. However, multiple frictions exist between realising stakeholders’ ambitions for research and research data sharing and addressing legal, social and cultural imperatives for protecting data subjects’ privacy. Through identifying and addressing frictions between personal privacy and research, our paper offers advice to research data management services on how to approach personal privacy in research data, sharing using the research data life cycle as the context. Method. A three-phase Delphi study on a population comprising twenty-four stakeholders involved in research data curation in Norway. Data were collected during three consecutive rounds over fourteen months. Analysis. The data were analysed qualitatively using themes following exploratory sequential design methods. After three rounds of data collection, the entire corpus of data were connected and analysed thematically according to integrated analysis. Results. The findings show multiple tensions between maintaining research subjects’ right to privacy and advancing research through data sharing. This paper identifies and analyses three particular sources of tension: 1) maintaining trust with the research participants, 2) managing divergent views of privacy in international and intercultural research collaborations and 3) interpreting and applying policy. The divergent motivations and perspectives on privacy held by different stakeholders complicate these tensions. Conclusions. Researchers, research data management support staff and data organizations must reconcile these motivations and resolve tensions throughout the data life cycle, from collection to archiving and eventual sharing. Through dialogue and negotiation, all stakeholders involved in data sharing should aim to respect the research subjects’ own understandings of privacy. © 2022, University of Boras. All rights reserved.","","","","","","","","","Alcala J.C., Star S. 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C., Memory practices in the sciences, (2005); Budin-Ljosne I., Teare H.J.A., Kaye J., Beck S., Bentzen H.B., Caenazzo L., Collett C., D'Abramo F., Felzmann H., Finlay T., Javaid M.K., Jones E., Katic V., Simpson A., Mascalzoni D., Dynamic consent: a potential solution to some of the challenges of modern biomedical research</a>, BMC Medical Ethics, 18, 1, (2017); cOAlition S., Accelerating the transition to full and immediate open access to scientific publications, (2019); Corti L., Van der Eynden V., Bishop L., Woollard M., Managing and sharing research data: a guide to good practice, (2014); Creswell J.W., Plano Clark V.L., Designing and conducting mixed methods research, (2018); Darch P.T., Borgman C.L., Traweek S., Cummings R.L., Wallis J.C., Sands A.E., What lies beneath? Knowledge infrastructures in the subseafloor biosphere and beyond, International Journal on Digital Libraries, 16, 1, pp. 61-77, (2015); de Koning M., Meyer B., Moors A., Pels P., Guidelines for anthropological research: data management, ethics, and integrity, Ethnography, 20, 2, pp. 170-174, (2019); Dilger H., Pels P., Sleeboom-Faulkner M., Ethnography, 20, 1, pp. 3-7, (2019); Elias P., A European perspective on research and big data analysis, Privacy, big data, and the public good: frameworks for engagement, pp. 173-191, (2014); Eschenfelder K.R., Shankar K., Of seamlessness and frictions: transborder data flows of European and US social science data, Sustainable Digital Communities: 15th International Conference, iConference 2020, Boras, Sweden, March 23–26, 2020, Proceedings, 12051, pp. 695-702, (2020); Ess C., Hard af Segerstad Y., Everything old is new again, Designs for experimentation and inquiry: approaching learning and knowing in digital transformation, pp. 179-196, (2020); (2016); CORDIS: Projects and results, (2020); (2017); Protocols for Native American archival materials, (2007); Floridi L., The ontological interpretation of informational privacy, Ethics and Information Technology, 7, 4, pp. 185-200, (2005); Floridi L., The ethics of information, (2013); CARE principles for indigenous data governance, (2019); Guillemin M., Barnard E., Allen A., Stewart P., Walker H., Rosenthal D., Gillam L., Journal of Empirical Research on Human Research Ethics, 13, 3, pp. 285-294, (2018); Hardy L.J., Hughes A., Hulen E., Schwartz A. L., Implementing qualitative data management plans to ensure ethical standards in multi-partner centers, Journal of Empirical Research on Human Research Ethics, 11, 2, pp. 191-198, (2016); Havemann J., Bezuidenhout L., Harnessing the open science infrastructure for an efficient African response to COVID-19</a>, (2020); Hofstede G., Culture’s consequences: international differences in work-related values, 5, (1984); Hofstede G., Hofstede G.J., Minkov M., Cultures and organizations, (2010); Inness J. 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"Chew A.W.; Oo C.Z.; Wong A.L.H.; Gladding J.","Chew, Adrian W (57208592786); Oo, Cherry Zin (57210290202); Wong, Adeline LH (57222387567); Gladding, Joanne (57222383523)","57208592786; 57210290202; 57222387567; 57222383523","An initial evaluation of research data management online training at the University of New South Wales","2022","IFLA Journal","48","4","","510","522","12","1","10.1177/03400352211054120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120500884&doi=10.1177%2f03400352211054120&partnerID=40&md5=30a09edc21b53da2b0abd6c538d8a126","School of Education, University of New South Wales, Australia; Department of Educational Psychology, Yangon University of Education, Myanmar; Research Technology Services, University of New South Wales, Australia; School of Psychology, University of New South Wales, Australia","Chew A.W., School of Education, University of New South Wales, Australia; Oo C.Z., Department of Educational Psychology, Yangon University of Education, Myanmar; Wong A.L.H., Research Technology Services, University of New South Wales, Australia; Gladding J., School of Psychology, University of New South Wales, Australia","In response to low research data management engagement at the University of New South Wales, Australia, an introductory research data management online training was developed and rolled out to all newly enrolled Higher Degree Research candidates. This article outlines the development process of the research data management online training and provides an initial evaluation of the training from the perspectives of the candidates and the university. As such, this article joins up with existing literature on research data management training to assist institutions and research data management stakeholders with the development of research data management training to help researchers and research students enact research data management best practices. Overall, the majority of the candidates (n = 643) were satisfied with the quality of the training and found it helpful. The benefits the training brought to the university are mapped out by linking institutional research data management problems, the research data management online training’s design and findings into a coherent narrative. © The Author(s) 2021.","academic development; Data management training; evaluation; participatory design approach; research students","","","","","","Cecilia Stenstrom; Luc Betbeder-Matibet; UNSW Division of Research","We like to thank UNSW Division of Research and other business units across the University for assisting with the development and rollout of the Research Data Management online Training (RDMoT). Special thanks are extended to Cecilia Stenstrom (Director Researcher Development) and Luc Betbeder-Matibet (Director Research Technology Services) for their support in our project. The authors received no financial support for the research, authorship and/or publication of this article.","Awre C., Baxter J., Clifford B., Et al., Research data management as a ‘wicked problem’, Library Review, 64, 4-5, pp. 356-371, (2015); Balka E., ACTION for health: Influencing technology design, practice and policy through participatory design, Routledge International Handbook of Participatory Design, pp. 257-280, (2013); Bamber V., Stefani L., Taking up the challenge of evidencing value in educational development: From theory to practice, International Journal of Academic Development, 21, 3, pp. 242-254, (2016); Braa J., Sahay S., Health information systems programme: Participatory design within the HISP network, Routledge International Handbook of Participatory Design, pp. 235-256, (2013); Bryman A., Social Research Methods, (2012); Clark R.C., Mayer R.E., E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, (2016); Clement R., Blau A., Abbaspour P., Et al., Team-based data management instruction at small liberal arts colleges, IFLA Journal, 43, 1, pp. 105-118, (2017); Corti L., Van den Eynden V., Bishop L., Et al., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library and Information Science Research, 38, 4, pp. 319-326, (2016); Creswell J., Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, (2012); Creswell J., Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, (2015); Di Cresce R., King J., Developing collaborative best practices for digital humanities data collection: A case study, College and Undergraduate Libraries, 24, 2-4, pp. 226-237, (2017); DiSalvo B., Bonsignore E., Yip J., Et al., Participatory Design for Learning: Perspectives from Practice and Research, (2017); Fitzpatrick J.L., Sanders J.R., Worthen B.R., Program Evaluation: Alternative Approaches and Practical Guidelines, (2011); Hattie J., Timperley H., The power of feedback, Review of Educational Research, 77, 1, pp. 81-112, (2007); 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(2018); Newcomer K.E., Hatry H.P., Wholey J.S., Handbook of Practical Program Evaluation, (2015); Oo C.Z., Chew A.W., Wong A.L.H., Et al., Delineating the successful features of research data management training: A systematic review, International Journal for Academic Development, (2021); Petters J.L., Brooks G.C., Smith J.A., Et al., The impact of targeted data management training for field research projects – a case study, Data Science Journal, 18, 1, pp. 1-7, (2019); Redkina N.S., Current trends in research data management, Scientific and Technical Information Processing, 46, pp. 53-58, (2019); Simonsen J., Robertson T., Routledge International Handbook of Participatory Design, (2013); Spinuzzi C., The methodology of participatory design, Technical Communication, 52, 2, pp. 163-174, (2005); Thielen J., Nichols Hess A., Advancing research data management in the social sciences: Implementing instruction for education graduate students into a doctoral curriculum, Behavioral and Social Sciences Librarian, 36, 1, pp. 16-30, (2017); Thoegersen J.L., Yeah, I guess that’s data’: Data practices and conceptions among humanities faculty, portal, 18, 3, pp. 491-504, (2018); Trigg R., Ishimaru K., Integrating participatory design into everyday work at the Global Fund for Women, Routledge International Handbook of Participatory Design, pp. 213-234, (2013); Van der Kleij F.M., Feskens R.C.W., Eggen T.J.H.M., Effects of feedback in a computer-based learning environment on students’ learning outcomes: A meta-analysis, Review of Educational Research, 85, 4, pp. 475-511, (2015); Vilar P., Zabukovec V., Research data management and research data literacy in Slovenian science, Journal of Documentation, 75, 1, pp. 24-43, (2018); Voss V., Hamrin G., Quadcopters or linguistic corpora: Establishing RDM services for small-scale data producers at big universities, LIBER Quarterly, 28, 1, pp. 1-58, (2018); Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); Yu F., Deuble R., Morgan H., Designing research data management services based on the research lifecycle – a consultative leadership approach, Journal of the Australian Library and Information Association, 66, 3, pp. 287-298, (2017)","C.Z. Oo; Department of Educational Psychology, Yangon University of Education, Myanmar; email: cherryzinn@gmail.com","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85120500884"
"Senagi K.; Tonnang H.E.Z.","Senagi, Kennedy (56928434400); Tonnang, Henri E. Z. (25422913500)","56928434400; 25422913500","A Novel Tightly Coupled Information System for Research Data Management","2022","Electronics (Switzerland)","11","19","3196","","","","0","10.3390/electronics11193196","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139868482&doi=10.3390%2felectronics11193196&partnerID=40&md5=918d9ed12e211e7934ccfb6cfe67197a","International Centre of Insect Physiology and Ecology, Nairobi, 30772-00100, Kenya","Senagi K., International Centre of Insect Physiology and Ecology, Nairobi, 30772-00100, Kenya; Tonnang H.E.Z., International Centre of Insect Physiology and Ecology, Nairobi, 30772-00100, Kenya","Most research projects are data driven. However, many organizations lack proper information systems (IS) for managing data, that is, planning, collecting, analyzing, storing, archiving, and sharing for use and re-use. Many research institutions have disparate and fragmented data that make it difficult to uphold the FAIR (findable, accessible, interoperable, and reusable) data management principles. At the same time, there is minimal practice of open and reproducible science. To solve these challenges, we designed and implemented an IS architecture for research data management. Through it, we have a centralized platform for research data management. The IS has several software components that are configured and unified to communicate and share data. The software components are, namely, common ontology, data management plan, data collectors, and the data warehouse. Results show that the IS components have gained global traction, 56.3% of the total web hits came from news users, and 259 projects had metadata (and 17 of those also had data resources). Moreover, the IS aligned the institution’s scientific data resources to universal standards such as the FAIR principles of data management and at the same time showcased open data, open science, and reproducible science. Ultimately, the architecture can be adopted by other organizations to manage research data. © 2022 by the authors.","common data model; data engineering; data management; information systems; research data; software engineering","","","","","","Fund International Agricultural Research, (18.7860.2-001.00); German Federal Ministry for Economic Cooperation and Development; Styrelsen för Internationellt Utvecklingssamarbete, Sida; Direktion für Entwicklung und Zusammenarbeit, DEZA; Government of the Republic of Kenya, GoK; Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung, BMZ; Deutsche Gesellschaft für Internationale Zusammenarbeit, GIZ","The authors gratefully acknowledge the financial support for this research by the following organizations and agencies: the German Federal Ministry for Economic Cooperation and Development (BMZ), commissioned by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) through the Fund International Agricultural Research (FIA), grant number: 18.7860.2-001.00; the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Federal Democratic Republic of Ethiopia; and the Government of the Republic of Kenya. The views expressed herein do not necessarily reflect the official opinion of the donors.","A World that Counts: Mobilising the Data Revolution for Sustainable Development. The United Nations Secretary General, (2014); Kanza S., Knight N.J., Behind every great research project is great data management, BMC Res. Notes, 15, (2022); Freitas A., Curry E., Big Data Curation, New Horizons for a Data-Driven Economy, (2016); Ralph M., Reynolds G.W., Principles of Information Systems A Managerial Approach, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. 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Senagi; International Centre of Insect Physiology and Ecology, Nairobi, 30772-00100, Kenya; email: ksenagi@icipe.org","","MDPI","","","","","","20799292","","","","English","Electronics (Switzerland)","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85139868482"
"Senft M.; Stahl U.; Svoboda N.","Senft, Matthias (57920754700); Stahl, Ulrike (57920829900); Svoboda, Nikolai (55625249600)","57920754700; 57920829900; 55625249600","Research data management in agricultural sciences in Germany: We are not yet where we want to be","2022","PLoS ONE","17","9 September","e0274677","","","","1","10.1371/journal.pone.0274677","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139535973&doi=10.1371%2fjournal.pone.0274677&partnerID=40&md5=c41e0930c94f716dfdd9be4023450f03","Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany; Julius Kühn Institute (JKI), Federal Research Centre for Cultivated Plants, Quedlinburg, Germany; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany","Senft M., Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany; Stahl U., Julius Kühn Institute (JKI), Federal Research Centre for Cultivated Plants, Quedlinburg, Germany; Svoboda N., Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany","To meet the future challenges and foster integrated and holistic research approaches in agricultural sciences, new and sustainable methods in research data management (RDM) are needed. The involvement of scientific users is a critical success factor for their development. We conducted an online survey in 2020 among different user groups in agricultural sciences about their RDM practices and needs. In total, the questionnaire contained 52 questions on information about produced and (re-)used data, data quality aspects, information about the use of standards, publication practices and legal aspects of agricultural research data, the current situation in RDM in regards to awareness, consulting and curricula as well as needs of the agricultural community in respect to future developments. We received 196 (partially) completed questionnaires from data providers, data users, infrastructure and information service providers. In addition to the diversity in the research data landscape of agricultural sciences in Germany, the study reveals challenges, deficits and uncertainties in handling research data in agricultural sciences standing in the way of access and efficient reuse of valuable research data. However, the study also suggests and discusses potential solutions to enhance data publications, facilitate and secure data re-use, ensure data quality and develop services (i.e. training, support and bundling services). Therefore, our research article provides the basis for the development of common RDM, future infrastructures and services needed to foster the cultural change in handling research data across agricultural sciences in Germany and beyond. © 2022 Senft et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Agriculture; Curriculum; Data Management; Germany; agricultural science; article; awareness; data quality; education; Germany; human; human experiment; information service; landscape; legal aspect; questionnaire; uncertainty; agriculture; curriculum; Germany; information processing","","","","","","","Foley JA., Can we feed the world and sustain the planet?, SciAm, 305, 5, pp. 60-65, (2011); Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2014: Mitigation of Climate Change, (2014); Dudley N, Alexander S., Agriculture and biodiversity: a review, Biodiversity, 18, 2-3, (2017); Gomiero T., Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge, Sustainability, 8, 3, (2016); Iglesias A, Quiroga S, Moneo M, Garrote L., From climate change impacts to the development of adaptation strategies: Challenges for agriculture in Europe, Clim Change, 112, pp. 143-168, (2012); Snyder CS, Bruulsema TW, Jensen TL, Fixen PE., Review of greenhouse gas emissions from crop production systems and fertilizer management effects, Agric Ecosyst Environ, 133, 3-4, pp. 247-266, (2009); Howden MS, Soussana J-F, Tubiello FN, Chhetri N, Dunlop M, Meinke H., Adapting agriculture to climate change, Proc. 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Senft; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany; email: matthias.senft@julius-kuehn.de","","Public Library of Science","","","","","","19326203","","POLNC","36178887","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139535973"
"Strömert P.; Hunold J.; Castro A.; Neumann S.; Koepler O.","Strömert, Philip (57567348200); Hunold, Johannes (57200397408); Castro, André (57385001500); Neumann, Steffen (18038199000); Koepler, Oliver (6507094492)","57567348200; 57200397408; 57385001500; 18038199000; 6507094492","Ontologies4Chem: the landscape of ontologies in chemistry","2022","Pure and Applied Chemistry","94","6","","605","622","17","2","10.1515/pac-2021-2007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127911093&doi=10.1515%2fpac-2021-2007&partnerID=40&md5=e7467ee86d5ad503349aa1c5ab745e0a","TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, 30167, Germany; Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle, 06120, Germany","Strömert P., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, 30167, Germany; Hunold J., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, 30167, Germany; Castro A., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, 30167, Germany; Neumann S., Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle, 06120, Germany; Koepler O., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, 30167, Germany","For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.  © 2022 IUPAC & De Gruyter. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. For more information, please visit: http://creativecommons.org/licenses/by-nc-nd/4.0/.","Cheminformatics; FAIR data; linked data; ontology; research data; terminology","Application programs; Information management; Linked data; Semantics; Cheminformatics; Data informations; FAIR data; Heterogeneous data sources; Holistic approach; Knowledge graphs; Ontology's; Pubchem; Research data; Retrospective processing; Ontology","","","","","Deutsche Forschungsgemeinschaft, DFG","Research funding: The presented work was conducted as part of the NFDI4Chem project (DFG project no. 441958208). The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for funding and support.","Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., T'Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., Sci. 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Strömert; TIB - Leibniz Information Centre for Science and Technology, Hannover, Welfengarten 1 B, 30167, Germany; email: philip.stroemert@tib.eu","","De Gruyter Open Ltd","","","","","","00334545","","PACHA","","English","Pure Appl. Chem.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85127911093"
"Mozgova I.; Altun O.; Sheveleva T.; Castro A.; Oladazimi P.; Koepler O.; Lachmayer R.; Auer S.","Mozgova, I. (27067881500); Altun, O. (57221950364); Sheveleva, T. (57221965627); Castro, A. (57672591600); Oladazimi, P. (57385463000); Koepler, O. (6507094492); Lachmayer, R. (6602616454); Auer, S. (23391879500)","27067881500; 57221950364; 57221965627; 57672591600; 57385463000; 6507094492; 6602616454; 23391879500","Knowledge Annotation within Research Data Management System for Oxygen-Free Production Technologies","2022","Proceedings of the Design Society","2","","","525","532","7","0","10.1017/pds.2022.54","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131380270&doi=10.1017%2fpds.2022.54&partnerID=40&md5=d7d63d3a55358b40d027695c8b918588","Leibniz Universität, Hannover, Germany; Leibniz Information Centre of Science and Technology', University Library, Germany","Mozgova I., Leibniz Universität, Hannover, Germany; Altun O., Leibniz Universität, Hannover, Germany; Sheveleva T., Leibniz Information Centre of Science and Technology', University Library, Germany; Castro A., Leibniz Information Centre of Science and Technology', University Library, Germany; Oladazimi P., Leibniz Information Centre of Science and Technology', University Library, Germany; Koepler O., Leibniz Information Centre of Science and Technology', University Library, Germany; Lachmayer R., Leibniz Universität, Hannover, Germany; Auer S., Leibniz Information Centre of Science and Technology', University Library, Germany","The comprehensive implementation of digital technologies in product manufacturing leads to changes in engineering processes and requires new approaches to data management. An important role belongs to the processes of organizing the collection, storage and reuse of research data obtained and used in the process of product, system or technology development, taking into account the FAIR data principles. This article describes a Research Data Management System for the organization of documentation and measurement requests in the research and development of new oxygen-free production technologies.  © The Author(s), 2022.","design support system; FAIR data principles; knowledge management; research data management; semantic modelling","Digital storage; Knowledge organization; Semantics; Data management system; Design support systems; Digital technologies; Engineering process; FAIR data principle; Knowledge annotations; Product manufacturing; Production technology; Research data managements; Semantic modelling; Oxygen","","","","","Deutsche Forschungsgemeinschaft, DFG, (394563137 – SFB 1368)","Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 394563137 – SFB 1368","Altun O., Scheveleva T., Castro A., Et al., Integration eines digitalen Maschinenparks in ein Forschungsdatenmanagementsystem, Proceedings of the 32nd Symposium Design for X (DFX2021)., (2021); Amorim R.C., Castro J.A., Rocha Da Silva J., Et al., A comparison of research datamanagement platforms: Architecture, flexible metadata and interoperability, Univ. Access Inf Soc, 16, (2017); Bolser D.M., Et al., MetaBase-the Wiki-Database of Biological Databases, Nucleic Acids Research, 40, pp. D1250-D1254, (2012); Cariaso M., Lennon G., SNPedia: A wiki supporting personal genome annotation, interpretation and analysis, Nucleic Acids Research, 40, D1, pp. D1308-D1312, (2012); Ckanext-dcat; Claus F., Kirchmeyer S., Muller M.S., Richtering W., Das INF-Projekt Im SFB 985. Funktionelle Mikrogele Und Mikrogelsysteme, Bausteine Forschungsdatenmanagement, 2, pp. 104-111, (2019); Gronewald M., Niekamp R., CRC/TRR 270 Z-INF-Inside a multidisciplinary joint project (1. 0), HeFDI Plenary 2020, (2020); Herzig D.M., Ell B., Semantic MediaWiki in Operation: Experiences with Building a Semantic Portal, The Semantic Web-ISWC 2010. ISWC 2010. Lecture Notes in Computer Science 6497, pp. 114-128, (2010); Huss J.W., Et al., A Gene Wiki for Community Annotation of Gene Function, PLOS Biologue, 6, 7, pp. 1398-1402, (2008); Krotzsch M., Vrandei D., Volkel M., Semantic MediaWiki, The Semantic Web-ISWC 2006. ISWC 2006. Lecture Notes in Computer Science 4273, pp. 935-942, (2006); Lagoze C., Sompel H.V., The Open Archives Initiative Protocol for Metadata Harvesting Protocol, Computer Science, (2002); Maier H.J., Et al., Towards Dry Machining of Titanium-Based Alloys: A New Approach Using an Oxygen-Free Environment, Metals, 10, (2020); MediaWiki contributors. Extension: Graph; MediaWiki contributors. Extension: LinkedWiki; MediaWiki contributors. Extention: Cargo; Mozgova I., Koepler O., Kraft A., Lachmayer R., Auer S., Research Data Management System for a large Collaborative Project, DS 101: Proceedings of NordDesign 2020, (2020); Smith M., Et al., DSpace: An Open Source Dynamic Digital Repository, D-Lib Magazine, 9, 1, (2003); Szafarska M., Gustus R., Maus-Friedrichs W., Sauerstofffreier Transport, Präparation und Transfer von Materialproben für die Oberflächenanalytik, Tagungsband 4. Symposium Materialtechnik, pp. 829-839, (2021); Tansley R., Harnad S., Eprints. org Software for Creating Institutional and Individual Open Archives, D-Lib Magazine, 6, 10, (2000); Ckanext-Semantic Media Wiki, (2021); Vrandei D., Krotzsch M., Wikidata: A free collaborative knowledgebase, Communications of the ACM, 57, 10, pp. 78-85, (2014); Willmes C., Viehberg F., Lopez S.E., Bareth G., CRC806-KB: A Semantic MediaWiki Based Collaborative Knowledge Base for an Interdisciplinary Research Project, Data, 3, 4, (2018); Wilkinson M., Dumontier M., Aalbersberg I., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016)","I. Mozgova; Leibniz Universität, Hannover, Germany; email: mozgova@ipeg.uni-hannover.de","","Cambridge University Press","","17th International Design Conference, DESIGN 2022","23 May 2022 through 26 May 2022","Virtual, Online","179641","2732527X","","","","English","Proc. Des. Soc.","Conference paper","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85131380270"
"Rantasaari J.","Rantasaari, Jukka (57813554300)","57813554300","Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research","2022","LIBER Quarterly","32","1","","","","","0","10.53377/lq.11726","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134565044&doi=10.53377%2flq.11726&partnerID=40&md5=21c8ce441c05856290202e961f69a4c3","University of Turku, Åbo Akademi University, Finland","Rantasaari J., University of Turku, Åbo Akademi University, Finland","To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers. In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback. Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising. Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage. Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria. This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research. Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM. © 2022, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Competencies; Doctoral students; Early career researchers; PhD students; Postdoc researchers; Research data management; Training","","","","","","Uppsala Universitet","I would like to thank my supervisors, Professor Gunilla Widén at the Åbo Akademi University and Professor Isto Huvila at the Uppsala University, who read and commented on several drafts of this study; Biostatistician Eliisa Löyttyniemi at the University of Turku for discussions on statistical analyses; and Information Specialist Päivi Kanerva for acting as a responsible teacher of the BRDM course.","Aalto S., Ahokas M., Friman J., Fuchs S., Korhonen T., Kuusniemi M. E., Laakso K., Lennes M., Manninen S., Ojanen M., Rantasaari J., Virtanen M. E., Xu Q., Finnish DMP evaluation guidance. [Working paper], (2021); The Academy of Finland’s funding terms and conditions 2019–2020, 1, (2019); Adamick J., Reznik-Zellen R. C., Sheridan M., Data management training for graduate students at a large research university, Journal of eScience Librarianship, 1, 3, pp. 180-188, (2013); Akers K. G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); In Wikipedia, (2016); Borghi J., Abrams S., Lowenberg D., Simms S., Chodacki J., Support your data: A research data management guide for researchers, Research Ideas and Outcomes, 4, (2018); Borycz J., Carroll A. J., COVID-19 as an opportunity to expand the instructional portfolio of STEM librarians, Issues in Science and Technology Librarianship, 98, (2021); Briney K., Data management for researchers: Organize, maintain and share your data for research success, (2015); Bryant A., Charmaz K., The SAGE Handbook of grounded theory, (2016); Byatt D., Scott M., Beale G., Cox S. J., White W., Developing researcher skills in research data management: Training for the future-a DataPool project report, (2013); Data carpentry: Building communities teaching universal data literacy; Cassidy A., Vermin, victims and disease: UK framings of badgers in and beyond the bovine TB controversy, Journal of the European Society for Rural Sociology, 52, 2, pp. 192-214, (2012); Castle C., Getting the central RDM message across: A case study of central versus discipline-specific research data services (RDS) at the University of Cambridge, Libri, 69, 2, pp. 105-116, (2019); Cerny M., Digital Competences of students of library studies: Comparison of research results for 2018-2020, Education Sciences, 11, 11, pp. 1-13, (2021); Chew A. W., Oo C. Z., Wong A. L., Gladding J., An initial evaluation of research data management online training at the University of New South Wales, IFLA Journal, (2021); Chiarelli A., Loffreda L., Johnson R., The art of publishing reproducible research outputs: Supporting emerging practices through cultural and technological innovation, (2021); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at small liberal arts colleges, IFLA Journal, 43, 1, pp. 105-118, (2017); Cole G., Evans J., University of Exeter research data management and open access training for staff, ALISS Quarterly, 10, 1, pp. 22-25, (2014); Corti L., Van den Eynden V., Learning to manage and share data: Jumpstarting the research methods curriculum, International Journal of Social Research Methodology, 18, 5, pp. 545-559, (2015); Cox A. M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Doucette L., Fyfe B., Drowning in research data: Addressing data management literacy of graduate students, Imagine, Innovate, Inspire: The Proceedings of the ACRL 2013 Conference, pp. 165-171, (2013); OSPP-REC: Open science policy platform recommendations, (2018); Commission recommendation (EU) 2018/790 of 25 April 2018 on access to and preservation of scientific information, Official journal of the European Union L, 134, pp. 12-18, (2018); Towards full open access in 2020: Aims and recommendations for university leaders and national rectors’ conferences, (2017); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services & Use, 36, 1-2, pp. 35-43, (2016); Goben A., Griffin T., In aggregate: Trends, needs, and opportunities from research data management surveys, College and Research Libraries, 80, 7, pp. 903-924, (2019); Griffin T. M., Centering graduate students’ research projects in data management education: A pilot program, Journal of Librarianship and Scholarly Communication, 8, 1, (2020); Johnston L., Jeffryes J., Civil engineering/ graduate students, Data Information Literacy Case Study Directory, 3, 1, (2015); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship and Information Science, 52, 3, pp. 633-646, (2020); Kafel D., Creamer A.T, Martin E.R., Building the New England Collaborative Data Management Curriculum, Journal of EScience Librarianship, 3, 1, pp. 60-66, (2014); Kowalczyk S. T., Modelling the research data lifecycle, International Journal of Digital Curation, 12, 2, pp. 331-361, (2017); Krahe M. A., Toohey J., Wolski M., Scuffham P. A., Reilly S., Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia, Health Information Management Journal, 49, 2–3, pp. 108-116, (2020); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Lefebvre A., Schermerhorn E., Spruit M., How research data management can contribute to efficient and reliable science, ECIS 2018 Proceedings, pp. 1-15, (2018); Maienschein J., Parker J. N., Laubichler M., Hackett E. J., Data management and data sharing in science and technology studies, Science Technology and Human Values, 44, 1, pp. 143-160, (2019); Mithun S., Luo X., Design and evaluate the factors for flipped classrooms for data management courses, 2020 IEEE Frontiers in Education Conference (FIE), 2020, pp. 1-8, (2020); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data management pilot course at an academic library, Journal of eScience Librarianship, 3, 1, pp. 67-73, (2014); Mustajoki H., Finnish open science online resource; Grant proposal guide (NSF 11-1), (2011); Oliver J. C., Bioinformatic training needs at a health sciences campus, PLoS One, 12, 6, (2017); Oo C. Z., Chew A. W., Wong A. L. H., Gladding J., Stenstrom C., Delineating the successful features of research data management training: A systematic review, International Journal for Academic Development, (2021); Pascuzzi P. E., Sapp Nelson M. R., Integrating data science tools into a graduate level data management course, Journal of eScience Librarianship, 7, 3, (2018); Pasek J. E., Mayer J., Education needs in research data management for science-based disciplines: Self-assessment surveys of graduate students and faculty at two public universities, Issues in Science and Technology Librarianship, 92, (2019); Perrier L., Blondal E., Ayala A. P., Dearborn D., Kenny T., Lightfoot D., Reka R., Thuna M., Trimble L., MacDonald H., Research data management in academic institutions: A scoping review, PLoS One, 12, 5, (2017); Perrier L., Blondal E., MacDonald H., The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis, PLoS One, 15, 2, (2020); Peters C., Vaughn P., Initiating data management instruction to graduate students at the University of Houston Using the New England Collaborative Data Management Curriculum, Journal of eScience Librarianship, 3, 1, pp. 86-89, (2014); Petters J. L., Brooks G. C., Smith J. A., Haas C. A., The impact of targeted data management training for field research projects-A case study, Data Science Journal, 18, 1, (2019); Piorun M. E., Kafel D., Leger-Hornby T., Najafi S., Martin E.R., Colombo P., LaPelle N. R., Teaching research data management: an undergraduate/ graduate curriculum, Journal of eScience Librarianship, 1, 1, pp. 46-50, (2012); Qin J., D'ignazio J., The central role of metadata in a science data literacy course, Journal of Library Metadata, 10, 2–3, pp. 188-204, (2010); Rantasaari J., Doctoral students’ educational needs in research data management: Perceived importance and current competencies, International Journal of Digital Curation, 16, 1, pp. 1-36, (2021); Rantasaari J., Kokkinen H., Closing the skills gap: The basics of the research data management (BRDM) course: Case University of Turku, The fortieth IATUL conference, (2019); Rantasaari J., Loyttyniemi E., Cooper H., Fredriksson M., Henriksson B., Huttunen S., Wilson L., Basics of the Research Data Management (BRDM) Course: Course Structure and Learning Objectives 2019-22 [Presentation], (2021); Read K. B., Adapting data management education to support clinical research projects in an academic medical center, Journal of the Medical Library Association, 107, 1, pp. 89-97, (2019); Read K. B., Larson C., Gillespie C., Oh S. Y., Surkis A., A two-tiered curriculum to improve data management practices for researchers, PLoS One, 14, 5, (2019); MANTRA: Research data management training; Revez J., Opening the heart of science: A review of the changing roles of research libraries, Publications, 6, 1, pp. 1-13, (2018); Rieser A., How to handle big data, before it handles you, Industry Week, (2018); Schmidt L., Holles J., A graduate class in research data management, Chemical Engineering Education, 52, 1, pp. 52-59, (2018); Scholtens S., Anbeek P., Bohmer J., Brullemans-Spansier M., van der Geest M., Jetten M., Staiger C., Slouwerhof I., van Gelder C. W. G., Life sciences data steward function matrix. [Project deliverable], (2019); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the University of Lille (France)-LIBREAS, Library Ideas. Libreas: Library Ideas, 29, pp. 98-112, (2016); Shadbolt A., Konstantelos L., Lyon L., Guy M., Delivering innovative RDM training: The immersive informatics pilot programme, International Journal of Digital Curation, 9, 1, pp. 313-323, (2014); Shearer K., Research data: Unseen opportunities, (2009); Southall J., Scutt C., Training for research data management at the Bodleian Libraries: National contexts and local implementation for researchers and librarians, New Review of Academic Librarianship, 23, 2–3, pp. 303-322, (2017); Surkis A., LaPolla F. W. Z., Contaxis N., Read K. B., Data day to day: Building a community of expertise to address data skills gaps in an academic medical center, Journal of the Medical Library Association, 105, 2, pp. 185-191, (2017); Thielen J., Samuel S. M., Carlson J., Moldwin M., Developing and teaching a two-credit data management course for graduate students in climate and space sciences, Issues in Science and Technology Librarianship, 86, (2017); Thielen J., Hess A. N., Advancing research data management in the social sciences: Implementing instruction for education graduate students into a doctoral curriculum, Behavioral and Social Sciences Librarian, 36, 1, pp. 1-15, (2017); Timonen V., Foley G., Conlon C., Challenges when using grounded theory: A pragmatic introduction to doing GT research, International Journal of Qualitative, 17, 1, pp. 1-10, (2018); Open science and data: Action programme for the Finnish scholarly community, (2016); Verbaan E., Cox A. M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, 3–4, pp. 211-219, (2014); Wang M., Fong B. L., Embedded data librarianship: A case study of providing data management support for a science department, Science & Technology Libraries, 34, 3, pp. 228-240, (2015); Data, software and materials management and sharing policy, (2017); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Whitmire A. L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 1-22, (2015); Wiley C. A., Kerby E. E., Managing research data: Graduate student and postdoctoral researcher perspectives, Issues in Science and Technology Librarianship, 89, (2018); Wiley C. A., Mischo W. H., Schlembach M. C., Imker H. J., An integrated data management instructional program, ASEE Annual Conference and Exposition, (2017); Wiljes C., Cimiano P., Teaching research data management for students, Data Science Journal, 18, 1, pp. 1-9, (2019); Wittenberg J., Elings M., Building a research data management service at the university of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); Wright S. J., Andrews C., Developing a for-credit course to teach data information literacy skills: A case study in natural resources, Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, pp. 73-99, (2015); Yu H. H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017)","J. Rantasaari; University of Turku, Åbo Akademi University, Finland; email: jukka.rantasaari@utu.fi","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85134565044"
"Courtot M.; Gupta D.; Liyanage I.; Xu F.; Burdett T.","Courtot, Mélanie (12777834700); Gupta, Dipayan (57221545708); Liyanage, Isuru (57424753200); Xu, Fuqi (57424682100); Burdett, Tony (25932075500)","12777834700; 57221545708; 57424753200; 57424682100; 25932075500","BioSamples database: FAIRer samples metadata to accelerate research data management","2022","Nucleic Acids Research","50","D1","","D1500","D1507","7","3","10.1093/nar/gkab1046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123389163&doi=10.1093%2fnar%2fgkab1046&partnerID=40&md5=f9308e32a1bab9c5862150d7f2bb66ac","European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom","Courtot M., European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom; Gupta D., European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom; Liyanage I., European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom; Xu F., European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom; Burdett T., European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom","The BioSamples database at EMBL-EBI is the central institutional repository for sample metadata storage and connection to EMBL-EBI archives and other resources. The technical improvements to our infrastructure described in our last update have enabled us to scale and accommodate an increasing number of communities, resulting in a higher number of submissions and more heterogeneous data. The BioSamples database now has a valuable set of features and processes to improve data quality in BioSamples, and in particular enriching metadata content and following FAIR principles. In this manuscript, we describe how BioSamples in 2021 handles requirements from our community of users through exemplar use cases: increased findability of samples and improved data management practices support the goals of the ReSOLUTE project, how the plant community benefits from being able to link genotypic to phenotypic information, and we highlight how cumulatively those improvements contribute to more complex multi-omics data integration supporting COVID-19 research. Finally, we present underlying technical features used as pillars throughout those use cases and how they are reused for expanded engagement with communities such as FAIRplus and the Global Alliance for Genomics and Health. © 2022 The Author(s).","","COVID-19; Databases, Factual; Gene Expression Profiling; Genomics; Host-Pathogen Interactions; Humans; Metadata; Phenotype; Plant Physiological Phenomena; SARS-CoV-2; Article; controlled study; coronavirus disease 2019; data base; FAIR principles; genotyping; information processing; metadata; multiomics; phenotype; technology; factual database; gene expression profiling; genetics; genomics; host pathogen interaction; human; metadata; physiology; plant physiology; virology","","","BioSamples","","ELIXIR; Wellcome Trust, WT, (201535/Z/16/Z, GA4GH); Innovative Medicines Initiative, IMI, (802750)","EMBL-EBI Core Funds; Wellcome Trust [GA4GH award number 201535/Z/16/Z].M.C., T.B. and F.X. were funded by FAIRplus [IMI grant agreement 802750]; M.C. and I.L. acknowledge funding from the research infrastructure for life science data ELIXIR. Funding for open access charge: EMBL-EBI Core Funds.","Arita M., Karsch-Mizrachi I., Cochrane G., The international nucleotide sequence database collaboration, Nucleic Acids Res, 49, pp. D121-D124, (2021); Courtot M., Cherubin L., Faulconbridge A., Vaughan D., Green M., Richardson D., Harrison P., Whetzel P.L., Parkinson H., Burdett T., BioSamples database: an updated sample metadata hub, Nucleic Acids Res, 47, pp. D1172-D1178, (2019); Durinx C., McEntyre J., Appel R., Apweiler R., Barlow M., Blomberg N., Cook C., Gasteiger E., Kim J.-H., Lopez R., Et al., Identifying ELIXIR core data resources, F1000Research, 5, (2017); Hendler J., Data integration for heterogenous datasets, Big Data, 2, pp. 205-215, (2014); Le Sueur H., Bruce I.N., Geifman N., The challenges in data integration - heterogeneity and complexity in clinical trials and patient registries of Systemic Lupus Erythematosus, BMC Med. Res. Methodol, 20, (2020); Lin L., Yee S.W., Kim R.B., Giacomini K.M., SLC transporters as therapeutic targets: emerging opportunities, Nat. Rev. Drug Discov, 14, (2015); Brazma A., Minimum Information About a Microarray Experiment (MIAME)-successes, failures, challenges, Scientific World Journal, 9, pp. 420-423, (2009); Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M., Et al., NCBI GEO: archive for functional genomics data sets-update, Nucleic Acids Res, 41, pp. D991-D995, (2012); Athar A., Fullgrabe A., George N., Iqbal H., Huerta L., Ali A., Snow C., Fonseca N.A., Petryszak R., Papatheodorou I., Et al., Array Express update - from bulk to single-cell expression data, Nucleic Acids Res, 47, pp. D711-D715, (2018); Bairoch A., The cellosaurus, a cell-line knowledge resource, J. Biomol. Tech, 29, pp. 25-38, (2018); Papoutsoglou E.A., Faria D., Arend D., Arnaud E., Athanasiadis I.N., Chaves I., Coppens F., Cornut G., Costa B.V., Cwiek-Kupczynska H., Et al., Enabling reusability of plant phenomic datasets with MIAPPE 1.1, New Phytol, 227, pp. 260-273, (2020); Pommier C., Michotey C., Cornut G., Roumet P., Duchene E., Flores R., Lebreton A., Alaux M., Durand S., Kimmel E., Et al., Applying FAIR principles to plant phenotypic data management in GnpIS, Plant Phenomics, 2019, (2019); Leinonen R., Akhtar R., Birney E., Bower L., Cerdeno-Tarraga A., Cheng Y., Cleland I., Faruque N., Goodgame N., Gibson R., Et al., The european nucleotide archive, Nucleic Acids Res, 39, pp. D28-D31, (2011); North A.M.K., The global alliance for genomics and health: towards international sharing of genomic and clinical data, Pathology, 47, pp. S28-S29, (2015); Johnson D., Batista D., Cochrane K., Davey R.P., Etuk A., Gonzalez-Beltran A., Haug K., Izzo M., Larralde M., Lawson T.N., Et al., ISA API: An open platform for interoperable life science experimental metadata, GigaScience, 10, (2021); Harrison P.W., Ahamed A., Aslam R., Alako B.T.F., Burgin J., Buso N., Courtot M., Fan J., Gupta D., Haseeb M., Et al., The european nucleotide archive in 2020, Nucleic Acids Res, 49, pp. D82-D85, (2021); Mapping the human genetic architecture of COVID-19, Nature, (2021); Ballestar E., Farber D.L., Glover S., Horwitz B., Meyer K., Nikolic M., Ordovas-Montanes J., Sims P., Shalek A., Et al., Single cell profiling of COVID-19 patients: an international data resource from multiple tissues, medRxiv, (2020); Papatheodorou I., Fonseca N.A., Keays M., Tang Y.A., Barrera E., Bazant W., Burke M., Fullgrabe A., Fuentes A.M.-P., George N., Et al., Expression Atlas: gene and protein expression across multiple studies and organisms, Nucleic Acids Res, 46, pp. D246-D251, (2018); Barrett T., Clark K., Gevorgyan R., Gorelenkov V., Gribov E., Karsch-Mizrachi I., Kimelman M., Pruitt K.D., Resenchuk S., Tatusova T., Et al., BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata, Nucleic Acids Res, 40, pp. D57-D63, (2012); Harrison P.W., Lopez R., Rahman N., Allen S.G., Aslam R., Buso N., Cummins C., Fathy Y., Felix E., Glont M., Et al., The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing, Nucleic Acids Res, 49, pp. W619-W623, (2021); Schoch C.L., Ciufo S., Domrachev M., Hotton C.L., Kannan S., Khovanskaya R., Leipe D., McVeigh R., O'Neill K., Robbertse B., Et al., NCBI Taxonomy: a comprehensive update on curation, resources and tools, Database, 2020, (2020); Sunagawa S., Acinas S.G., Bork P., Bowler C., Tara Oceans C., Eveillard D., Gorsky G., Guidi L., Iudicone D., Karsenti E., Et al., Tara Oceans: towards global ocean ecosystems biology, Nat. Rev. Microbiol, 18, pp. 428-445, (2020); Norlin L., Fransson M.N., Eriksson M., Merino-Martinez R., Anderberg M., Kurtovic S., Litton J.-E., A minimum data set for sharing biobank samples, information, and data: MIABIS, Biopreserv. Biobank, 10, pp. 343-348, (2012); Regev A., Teichmann S.A., Lander E.S., Amit I., Benoist C., Birney E., Bodenmiller B., Campbell P., Carninci P., Clatworthy M., Et al., The human cell atlas, Elife, 6, (2017); Lappalainen I., Almeida-King J., Kumanduri V., Senf A., Spalding J.D., Ur-Rehman S., Saunders G., Kandasamy J., Caccamo M., Leinonen R., Et al., The European Genome-phenome Archive of human data consented for biomedical research, Nat. Genet, 47, pp. 692-695, (2015); Griffiths E.J., Timme R.E., Page A.J., Alikhan N.-F., Fornika D., Maguire F., Mendes C.I., Tausch S.H., Black A., Connor T.R., Et al., The PHA4GE SARS-CoV-2 contextual data specification for open genomic epidemiology, (2020); Cezard T., Cunningham F., Hunt S.E., Koylass B., Kumar N., Saunders G., Shen A., Silva A.F., Tsukanov K., Venkataraman S., Et al., The European Variation Archive: a FAIR resource of genomic variation for all species, Nucleic Acids Res, (2021)","M. Courtot; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom; email: mcourtot@gmail.com","","Oxford University Press","","","","","","03051048","","NARHA","34747489","English","Nucleic Acids Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85123389163"
"Brandt N.; Garabedian N.T.; Schoof E.; Schreiber P.J.; Zschumme P.; Greiner C.; Selzer M.","Brandt, Nico (57219502886); Garabedian, Nikolay T. (57196318990); Schoof, Ephraim (56422042200); Schreiber, Paul J. (57200792226); Zschumme, Philipp (57221981540); Greiner, Christian (22634049000); Selzer, Michael (24503304900)","57219502886; 57196318990; 56422042200; 57200792226; 57221981540; 22634049000; 24503304900","Managing FAIR Tribological Data Using Kadi4Mat","2022","Data","7","2","15","","","","2","10.3390/data7020015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123915134&doi=10.3390%2fdata7020015&partnerID=40&md5=a5345ef8e7989912cf7809c67f922b5c","Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; KIT IAM-CMS MicroTribology Center (µTC), Straße am Forum 5, Karlsruhe, 76131, Germany; Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU), Helmholtzstraße 11, Ulm, 89081, Germany; Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Brandt N., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Garabedian N.T., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, KIT IAM-CMS MicroTribology Center (µTC), Straße am Forum 5, Karlsruhe, 76131, Germany; Schoof E., Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU), Helmholtzstraße 11, Ulm, 89081, Germany; Schreiber P.J., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, KIT IAM-CMS MicroTribology Center (µTC), Straße am Forum 5, Karlsruhe, 76131, Germany; Zschumme P., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Greiner C., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, KIT IAM-CMS MicroTribology Center (µTC), Straße am Forum 5, Karlsruhe, 76131, Germany; Selzer M., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","The ever-increasing amount of data generated from experiments and simulations in engineering sciences is relying more and more on data science applications to generate new knowledge. Comprehensive metadata descriptions and a suitable research data infrastructure are essential pre-requisites for these tasks. Experimental tribology, in particular, presents some unique challenges in this regard due to the interdisciplinary nature of the field and the lack of existing standards. In this work, we demonstrate the versatility of the open source research data infrastructure Kadi4Mat by managing and producing FAIR tribological data. As a showcase example, a tribological experiment is conducted by an experimental group with a focus on comprehensiveness. The result is a FAIR data package containing all produced data as well as machine-and user-readable metadata. The close collaboration between tribologists and software developers shows a practical bottom-up approach and how such infrastructures are an essential part of our FAIR digital future. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Digitisation; FAIR data; Materials science; Open source; Research data management; Tribology","Engineering research; Information management; Open source software; Research and development management; Tribology; Data infrastructure; Digitisation; Engineering science; FAIR data; Material science; Open-source; Pre-requisites; Research data; Research data managements; Science applications; Metadata","","","","","Ministry of Science Baden-Württemberg, (57); State Governments; Alexander von Humboldt-Stiftung, AvH; Horizon 2020 Framework Programme, H2020, (771237); European Research Council, ERC; Deutsche Forschungsgemeinschaft, DFG, (390874152, 391128822); Bundesministerium für Bildung und Forschung, BMBF, (03XP0174E)","Funding: This work was supported by the Federal Ministry of Education and Research (BMBF) in the project FestBatt (project number 03XP0174E), the German Research Foundation (DFG) in the projects POLiS (project number 390874152) and SuLMaSS (project number 391128822), the Ministry of Science Baden-Württemberg in the project MoMaF—Science Data Center, with funds from the state digitization strategy digital@bw (project number 57), the Federal Ministry of Education and Research (BMBF) and the Ministry of Science Baden-Württemberg as part of the Excellence Strategy of the Federal Government and the State Governments in the Kadi4X project, the Cx project, which is funded as part of the National High-Performance Computing (NHR) initiative, the European Research Council (ERC) under Grant No. 771237 (TriboKey) and the Alexander von Humboldt Foundation for awarding a postdoctoral fellowship to Nikolay Garabedian.","Hey T., Trefethen A., The Data Deluge: An e-Science Perspective, Wiley Series in Communications Networking & Distributed Systems, pp. 809-824, (2003); Hey A.J.G., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Sandfeld S., Dahmen T., Fischer F.O.R., Eberl C., Klein S., Selzer M., Moller J., Mucklich F., Engstler M., Diebels S., Et al., Strategiepapier—Digitale Transformation in der Materialwissenschaft und Werkstofftechnik, (2018); Kimmig J., Zechel S., Schubert U.S., Digital Transformation in Materials Science: A Paradigm Change in Material’s Development, Adv. Mater, 33, (2021); Heidorn P.B., Shedding Light on the Dark Data in the Long Tail of Science, Libr. Trends, 57, pp. 280-299, (2008); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Guidelines for Safeguarding Good Research Practice Code of Conduct, (2019); Devaraju A., Huber R., Mokrane M., Herterich P., Cepinskas L., de Vries J., L'Hours H., Davidson J., White A., FAIRsFAIR Data Object Assessment Metrics, Zenodo, 10, (2020); Jain A., Ong S.P., Hautier G., Chen W., Richards W.D., Dacek S., Cholia S., Gunter D., Skinner D., Ceder G., Et al., Commentary: The Materials Project: A materials genome approach to accelerating materials innovation, APL Mater, 1, (2013); Draxl C., Scheffler M., NOMAD: The FAIR concept for big data-driven materials science, MRS Bull, 43, pp. 676-682, (2018); Hill J., Mulholland G., Persson K., Seshadri R., Wolverton C., Meredig B., Materials science with large-scale data and informatics: Unlocking new opportunities, MRS Bull, 41, pp. 399-409, (2016); Zenodo, (2013); CARPi N., Minges A., Piel M., eLabFTW: An open source laboratory notebook for research labs, J. Open Source Softw, 2, (2017); Jalili V., Afgan E., Gu Q., Clements D., Blankenberg D., Goecks J., Taylor J., Nekrutenko A., The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update, Nucleic Acids Res, 48, pp. W395-W402, (2020); Santner E., Computer support in tribology—Experiments and database, Tribotest, 2, pp. 267-280, (1996); Rumble J., Sibley L., Towards a Tribology Information System, (1987); Woydt M., Modern methods to retrieve innovative material solutions for tribosystems, Lubr. Eng, 56, pp. 26-30, (2000); Vellore A., Romero Garcia S., Johnson D.A., Martini A., Ambient and Nitrogen Environment Friction Data for Various Materials & Surface Treatments for Space Applications, Tribol. Lett, 69, (2021); Schembera B., Iglezakis D., EngMeta: Metadata for Computational Engineering, Int. J. Metadata Semant. Ontol, 14, (2020); Medina-Smith A., Becker C.A., Plante R.L., Bartolo L.M., Dima A., Warren J.A., Hanisch R.J., A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery, Data Sci. J, 20, (2021); Kugler P., Marian M., Schleich B., Tremmel S., Wartzack S., tribAIn—Towards an Explicit Specification of Shared Tribological Understanding, Appl. Sci, 10, (2020); Brandt N., Griem L., Herrmann C., Schoof E., Tosato G., Zhao Y., Zschumme P., Selzer M., Kadi4Mat: A Research Data Infrastructure for Materials Science, Data Sci. J, 20, (2021); IAM-CMS/kadi: Kadi4Mat. Zenodo, (2021); Garabedian N.T., Schreiber P., Li Y., Blatter I., Dollmann A., Haug C., Kummel D., Meyer F., Morstein C., Rau J., Et al., FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment, (2021); Garabedian N.T., Schreiber P.J., Brandt N., Zschumme P., Blatter I.L., Dollmann A., Haug C., Kummel D., Li Y., Meyer F., Et al., Generating FAIR Research Data in Experimental Tribology, (2022); Fielding R.T., Taylor R.N., Architectural Styles and the Design of Network-Based Software Architectures, 7, (2000); IAM-CMS/kadi-apy: Kadi4Mat API Library, Zenodo, (2021); Manske M., Crocker L.D., MediaWiki, (2021); Musen M.A., The protégé project: A look back and a look forward, AI Matters, 1, pp. 4-12, (2015); Garabedian N., TriboDataFAIR Ontology, (2021); Bechhofer S., Van Harmelen F., Hendler J., Horrocks I., McGuinness D.L., Patel-Schneider P.F., Stein L.A., OWL Web Ontology Language Reference, W3C Recomm, 10, pp. 1-53, (2004); Weber M., Garabedian N., SurfTheOWL. Zenodo, (2021); Lamy J.B., Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies, Artif. Intell. Med, 80, pp. 11-28, (2017); Django—The Web Framework for Perfectionists with Deadlines, (2005); Bitter R., Mohiuddin T., Nawrocki M., LabVIEW: Advanced Programming Techniques, (2007); Brandt N., FAIR Tribological Data Helper Scripts, Zenodo, (2021); Ellson J., Gansner E., Koutsofios L., North S.C., Woodhull G., Graphviz— Open Source Graph Drawing Tools, Graph Drawing, 2265, pp. 483-484, (2002); Lamprecht A.L., Garcia L., Kuzak M., Martinez C., Arcila R., Martin Del Pico E., Dominguez Del Angel V., van de Sandt S., Ison J., Martinez P.A., Et al., Towards FAIR principles for research software, Data Sci, 3, pp. 37-59, (2020); Open Archives Initiative Protocol for Metadata Harvesting, (2015); Cyganiak R., Wood D., Lanthaler M., RDF 1.1 Concepts and Abstract Syntax, W3C Recommendation, (2014); Sporny M., Longley D., Kellogg G., Lanthaler M., Champin P.A., Lindstrom N., JSON-LD 1.1 A JSON-Based Serialization for Linked Data, (2020)","N. Brandt; Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Karlsruhe, Straße am Forum 7, 76131, Germany; email: nico.brandt@kit.edu","","MDPI","","","","","","23065729","","","","English","Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85123915134"
"Foster E.D.; Whipple E.C.; Rios G.R.","Foster, Erin D. (57627023000); Whipple, Elizabeth C. (26028224900); Rios, Gabriel R. (57627023100)","57627023000; 26028224900; 57627023100","Implementing an institution-wide electronic lab notebook initiative","2022","Journal of the Medical Library Association","110","2","","222","227","5","4","10.5195/jmla.2022.1407","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128793014&doi=10.5195%2fjmla.2022.1407&partnerID=40&md5=26320532962e0a36eabfd89df2d4963e","Research Data Management (RDM), Program Service Lead (Research IT/ UC Berkeley Library), University of California Berkeley, Berkeley, CA, United States; Ruth Lilly Medical Library, Indiana University School of Medicine, Indianapolis, IN, United States","Foster E.D., Research Data Management (RDM), Program Service Lead (Research IT/ UC Berkeley Library), University of California Berkeley, Berkeley, CA, United States; Whipple E.C., Ruth Lilly Medical Library, Indiana University School of Medicine, Indianapolis, IN, United States; Rios G.R., Ruth Lilly Medical Library, Indiana University School of Medicine, Indianapolis, IN, United States","Background: To strengthen institutional research data management practices, the Indiana University School of Medicine (IUSM) licensed an electronic lab notebook (ELN) to improve the organization, security, and shareability of information and data generated by the school’s researchers. The Ruth Lilly Medical Library led implementation on behalf of the IUSM’s Office of Research Affairs. Case Presentation: This article describes the pilot and full-scale implementation of an ELN at IUSM. The initial pilot of the ELN in late 2018 involved fifteen research labs with access expanded in 2019 to all academic medical school constituents. The Ruth Lilly Medical Library supports researchers using the electronic lab notebook by (1) delivering trainings that cover strategies for adopting an ELN and a hands-on demo of the licensed ELN, (2) providing one-on-one consults with research labs or groups as needed, and (3) developing best practice guidance and template notebooks to assist in adoption of the ELN. The library also communicates availability of the ELN to faculty, students, and staff through presentations delivered at department meetings and write-ups in the institution's newsletter as appropriate. Conclusion: As of August 2021, there are 829 users at IUSM. Ongoing challenges include determining what support to offer beyond the existing training, sustaining adoption of the ELN within research labs, and defining “successful” adoption at the institution level. By leading the development of this service, the library is more strongly integrated and visible in the research activities of the institution, particularly as related to information and data management. © 2022, Medical Library Association. All rights reserved.","electronic lab notebook; information management; institutional collaborations","Electronics; Humans; Schools, Medical; adoption; article; case report; clinical article; human; Indiana; medical school; security; electronics; medical school","","","","","","","Electronic lab notebook comparison matrix; Kloeckner F, Farkas R, Franken T, Schmitz-Rode T., Development of a prediction model on the acceptance of electronic laboratory notebooks in academic environments, Biomed Tech (Berl), 59, 2, pp. 95-102, (2014); Machina HK, Wild DJ., Electronic laboratory notebooks progress and challenges in implementation, J Lab Autom, 18, 4, pp. 264-268, (2013); Marvin MC., Microsoft OneNote provides continuity for undergraduate biochemistry lab during a pandemic, Biochem Mol Biol Educ, 48, 5, pp. 523-552, (2020); Voegele C, Bouchereau B, Robinot N, McKay J, Damiecki P, Alteyrac L., A universal open-source electronic laboratory notebook, Bioinforma Oxf Engl, 29, 13, pp. 1710-1712, (2013); Guerrero S, Dujardin G, Cabrera-Andrade A, Paz-y-Mino C, Indacochea A, Ingles-Ferrandiz M, Nadimpalli HP, Collu N, Dublanche Y, De Mingo I, Camargo D., Analysis and implementation of an electronic laboratory notebook in a biomedical research institute, PLOS One, 11, 8, (2016); Badiola KA, Bird C, Brocklesby WS, Casson J, Chapman RT, Coles SJ, Cronshaw JR, Fisher A, Frey JG, Gloria D, Grossel MC, Hibbert DB, Knight N, Mapp LK, Marazzi L, Matthews B, Milsted A, Minns RS, Mueller KT, Murphy K, Parkinson T, Quinnell R, Robinson JS, Robertson MN, Robins M, Springate E, Tizzard G, Todd MH, Williamson AE, Willoughby C, Yang E, Ylioja PM., Experiences with a researcher-centric ELN, Chem Sci, 6, 3, pp. 1614-1629, (2015); Kanza S, Willoughby C, Gibbins N, Whitby R, Frey JG, Erjavec J, Zupancic K, Hren M, Kovac K., Electronic lab notebooks: can they replace paper?, J Cheminformatics, 9, 1, (2017); Wright JM., Make it better but don’t change anything, Autom Exp, 1, (2009); Argento N., Institutional ELN/LIMS deployment: highly customizable ELN/LIMS platform as a cornerstone of digital transformation for life sciences research institutes, EMBO Rep, 21, 3, (2020); Rudolphi F, Goossen LJ., Electronic laboratory notebook: the academic point of view, J Chem Inf Model, 52, 2, pp. 293-301, (2012); Sayre F, Bakker C, Kelly J, Kocher M, Lafferty M., Support for electronic lab notebooks at top American research universities, J EScience Librariansh, 7, 2, (2018); Grynoch C., Finding connections in policies covering electronic laboratory notebook retention and transferal, J EScience Librariansh, 10, 1, (2021); Nosek B., Strategy for culture change","","","Medical Library Association","","","","","","15365050","","JMLAC","35440896","English","J. Med. Libr. Assoc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128793014"
"Zeunert M.; Schneemann C.","Zeunert, Miriam (57423838000); Schneemann, Carsten (57424289600)","57423838000; 57424289600","Forschungsdatenmanagement als Arbeitsschwerpunkt für Informationswissenschaftler/innen","2022","Information-Wissenschaft und Praxis","73","2","","103","112","9","0","10.1515/iwp-2021-2191","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123350180&doi=10.1515%2fiwp-2021-2191&partnerID=40&md5=98d180ba6ac08369db53c4c407e2c09e","Universität Potsdam, Universitätsbibliothek, Am Neuen Palais, Potsdam, 14469, Germany; Fachhochschule Potsdam, Fachbereich Informationswissenschaften HA3 / 004, Kiepenheuerallee 5, Potsdam, 14469, Germany","Zeunert M., Universität Potsdam, Universitätsbibliothek, Am Neuen Palais, Potsdam, 14469, Germany; Schneemann C., Fachhochschule Potsdam, Fachbereich Informationswissenschaften HA3 / 004, Kiepenheuerallee 5, Potsdam, 14469, Germany","Research data management is a topic within the information sciences whose importance is continuously increasing. In order to locate the focal points of work for information scientists, a current collection of job advertisements relating to research data management was evaluated and located in an extended research data life cycle. This representation of work practice was compared with a literature analysis, which deals with requirements and competencies in research data management. The final merging and evaluation show a congruence of key areas between theory and practice.  © 2022 Walter de Gruyter GmbH, Berlin/Boston.","Information Science; Job Advertisement; Research data management","","","","","","","","Biernacka Katarzyna, Et al., Metadata Schema for Research Data Management Training Materials, (2020); Blask Katarina, Et al., Forschungsprozessspezifische Kompetenzmatrix für die Einführung des Forschungsdatenmanagements (FDM), (2019); Borgman Christine L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Buttner Stephan, Et al., Informationswissenschaftler im Forschungsdatenmanagement, Handbuch Forschungsdatenmanagement, pp. 203-218, (2011); Research Data Management, Handbuch Forschungsdatenmanagement, pp. 13-25, (2011); Herausgeber. Leitlinien zur Sicherung guter wissenschaftlicher Praxis: Kodex, (2019); Empfehlungen zur gesicherten Auf-bewahrung und Bereitstellung digitaler Forschungsprimärdaten, (2009); Leitlinien zum Umgang mit Forschungsdaten, (2015); Bund-Länder-Vereinbarung zu Aufbau und Förderung einer Nationalen Forschungsdateninfrastruktur (NFDI) vom 26, (2018); Hahnel Mark, Et al., The State of Open Data 2020, Digital Science, 2020, (2020); Halbherr Verena, Brauchen wir überhaupt eine/n Data Steward?: Klar, an großen Einrichtungen macht das Sinn, aber für doch nicht für uns Kleine?!, DINI/nestor-Workshop: Data Stewardship im Forschungsdatenmanagement, (2020); Heidrich Jens, Et al., Future Skills: Ansätze zur Vermittlung von Data Literacy in der Hochschulbildung, Arbeitspapier, Arbeitspapier, 37, (2018); Hochschulen sind wichtige Akteure innerhalb der Infrastrukturen für das Forschungsdatenmanagement. Spürbare Impulse von Bund und Ländern sind unverzichtbar: Gemeinsame Erklärung von Hochschulleitungen, die am Workshop der HRK zum Forschungsdatenmanagement am 16.12.2016 teilgenommen haben, Hochschulrektorenkonferenz (HRK), (2016); Kindling Maxi, Schirmbacher Peter, Die digitale For-schungswelt""als Gegenstand der Forschung, Information-Wissenschaft & Praxis, 64, 2-3, pp. 127-136, (2013); Klar Jochen, Et al., UAG Schulungen/Fortbildungen, (2020); Kuhlen Rainer, Et al., Herausgeber. Grundlagen der praktischen Information und Dokumentation, 6, (2013); Leyh Georg, Der Bibliothekar und sein Beruf, Handbuch der Bibliothekswissenschaft, 2, pp. 1-112, (1961); Lindstadt Birte, Schmitz Jasmin, Das Management von Forschungsdaten als Handlungsfeld wissenschaftlicher Bibliotheken: Forschungsunterstützung am Beispiel ZB MED-Informationszentrum Lebenswissenschaften, Bibliothek Forschung und Praxis, 43, 1, pp. 42-48, (2019); Ludwig Jens, Enke Harry, Herausgeber. 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Das offene Bibliotheksjournal, 2, 4, pp. 65-72, (2015); Neuroth Heike, Et al., Digitales Datenmanagement als neue Aufgabe für wissenschaftliche Bibliotheken, Bibliothek Forschung und Praxis, 43, 3, pp. 421-431, (2019); Petras Vivien, Et al., Digitales Datenmanagement als Berufsfeld im Kontext der Data Literacy, ABI Technik, 39, 1, pp. 26-33, (2019); Plappert Rainer, Einleitung zum Themenschwerpunkt ‚Berufsbild wissenschaftliche/r Bibliothekar/in'"". o-bib, Das offene Bibliotheksjournal, 2, 3, pp. 1-3, (2015); Datendienste nachhaltig gestalten: Ein Diskussionsimpuls zur Weiterentwicklung von Forschungsdateninfrastrukturen, (2020); Digital Competencies-Urgently Needed! Rat für Informationsinfrastrukturen, (2019); Herausgeber. Leistung aus Vielfalt: Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland, (2016); Ridsdale Chantel, Et al., Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report, (2015); Ritze Dominique, Et al., Forschungsdaten"". (Open) Linked Data in Bibliotheken, pp. 122-138, (2013); Schneemann Carsten, Et al., Rahmendaten zu FDM-Bun-deslandinitiativen, (2020); Tammaro Anna Maria, Et al., Data Curator's Roles and Responsibilities: An In-ternational Perspective, Libri, 69, 2, pp. 89-104, (2019); Tenopir Carol, Et al., Research Data Services in European Academic Research Libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); First Draft of the UNESCO Recommendation on Open Science. first draft, The General Conference of the United Nations Educational, Scientific and Cultural Organization (UNESCO), (2021); Wandt Julia, Der Datenlebenszyklus: Stationen des Forschungsdatenmanagements, (2020); Whyte Angus, Et al., D7.3: Skills and Capability Framework. V1.0, The European Open Science Cloud for Research Pilot Project (EOSCpilot), (2018); Wiljes Cord, Was bin ich?, DINI/nestor-Workshop: Data Stewardship im Forschungsdatenmanagement, (2020); Winkler-Nees Stefan, Vorwort, Handbuch Forschungsdatenmanagement, pp. 5-6, (2011); Wuttke Ulrike, Et al., Umfeldanalyse zum Aufbau einer neuen Datenkultur in Brandenburg (FDM-BB), (2021); Zeunert Miriam, Schneemann Carsten, Analysematrix von Stellenausschreibungen mit Forschungsdaten(management)-Schwerpunkt in Deutschland 2020, (2021); Stellenkorpus-Forschungsdaten(management) 2020.01.03-2020.11.22. 11, (2021); Forschungsdatenmanagement: Arbeitsschwerpunkte für Informationswissenschaftler∗innen, (2021)","M. Zeunert; Universität Potsdam, Universitätsbibliothek, Potsdam, Am Neuen Palais, 14469, Germany; email: miriam.zeunert@uni-potsdam.de","","De Gruyter Saur","","","","","","14344653","","","","German","Inf.-Wiss. Prax.","Article","Final","","Scopus","2-s2.0-85123350180"
"Elberskirch L.; Binder K.; Riefler N.; Sofranko A.; Liebing J.; Minella C.B.; Mädler L.; Razum M.; van Thriel C.; Unfried K.; Schins R.P.F.; Kraegeloh A.","Elberskirch, Linda (57210912912); Binder, Kunigunde (57397680900); Riefler, Norbert (6504223629); Sofranko, Adriana (57217204462); Liebing, Julia (56781463000); Minella, Christian Bonatto (57397305400); Mädler, Lutz (6602708276); Razum, Matthias (25825498000); van Thriel, Christoph (6602736631); Unfried, Klaus (6602781841); Schins, Roel P. F. (7004555639); Kraegeloh, Annette (8850685200)","57210912912; 57397680900; 6504223629; 57217204462; 56781463000; 57397305400; 6602708276; 25825498000; 6602736631; 6602781841; 7004555639; 8850685200","Digital research data: from analysis of existing standards to a scientific foundation for a modular metadata schema in nanosafety","2022","Particle and Fibre Toxicology","19","1","1","","","","3","10.1186/s12989-021-00442-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122289487&doi=10.1186%2fs12989-021-00442-x&partnerID=40&md5=9694fd793fcbe5e3e2c4abae9ad1ad23","INM - Leibniz Institute for New Materials, Campus D2 2, Saarbrücken, 66123, Germany; FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76133, Germany; IWT - Leibniz-Institut für Werkstofforientierte Technologien, Badgasteiner Str. 3, Bremen, 28359, Germany; IUF - Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, Düsseldorf, 40225, Germany; IfADo - Leibniz Research Centre for Working Environment and Human Factors, Ardeystraße 67, Dortmund, 44139, Germany","Elberskirch L., INM - Leibniz Institute for New Materials, Campus D2 2, Saarbrücken, 66123, Germany; Binder K., FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76133, Germany; Riefler N., IWT - Leibniz-Institut für Werkstofforientierte Technologien, Badgasteiner Str. 3, Bremen, 28359, Germany; Sofranko A., IUF - Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, Düsseldorf, 40225, Germany; Liebing J., IfADo - Leibniz Research Centre for Working Environment and Human Factors, Ardeystraße 67, Dortmund, 44139, Germany; Minella C.B., FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76133, Germany; Mädler L., IWT - Leibniz-Institut für Werkstofforientierte Technologien, Badgasteiner Str. 3, Bremen, 28359, Germany; Razum M., FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76133, Germany; van Thriel C., IfADo - Leibniz Research Centre for Working Environment and Human Factors, Ardeystraße 67, Dortmund, 44139, Germany; Unfried K., IUF - Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, Düsseldorf, 40225, Germany; Schins R.P.F., IUF - Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, Düsseldorf, 40225, Germany; Kraegeloh A., INM - Leibniz Institute for New Materials, Campus D2 2, Saarbrücken, 66123, Germany","Background: Assessing the safety of engineered nanomaterials (ENMs) is an interdisciplinary and complex process producing huge amounts of information and data. To make such data and metadata reusable for researchers, manufacturers, and regulatory authorities, there is an urgent need to record and provide this information in a structured, harmonized, and digitized way. Results: This study aimed to identify appropriate description standards and quality criteria for the special use in nanosafety. There are many existing standards and guidelines designed for collecting data and metadata, ranging from regulatory guidelines to specific databases. Most of them are incomplete or not specifically designed for ENM research. However, by merging the content of several existing standards and guidelines, a basic catalogue of descriptive information and quality criteria was generated. In an iterative process, our interdisciplinary team identified deficits and added missing information into a comprehensive schema. Subsequently, this overview was externally evaluated by a panel of experts during a workshop. This whole process resulted in a minimum information table (MIT), specifying necessary minimum information to be provided along with experimental results on effects of ENMs in the biological context in a flexible and modular manner. The MIT is divided into six modules: general information, material information, biological model information, exposure information, endpoint read out information and analysis and statistics. These modules are further partitioned into module subdivisions serving to include more detailed information. A comparison with existing ontologies, which also aim to electronically collect data and metadata on nanosafety studies, showed that the newly developed MIT exhibits a higher level of detail compared to those existing schemas, making it more usable to prevent gaps in the communication of information. Conclusion: Implementing the requirements of the MIT into e.g., electronic lab notebooks (ELNs) would make the collection of all necessary data and metadata a daily routine and thereby would improve the reproducibility and reusability of experiments. Furthermore, this approach is particularly beneficial regarding the rapidly expanding developments and applications of novel non-animal alternative testing methods. © 2021, The Author(s).","Data re-use; Metadata; Minimum information standard; Nanomaterial; Nanoparticle; Quality criteria; Research data management; Toxicology","Databases, Factual; Metadata; Reproducibility of Results; Research Design; nanomaterial; data analysis; information processing; interpersonal communication; medical expert; metadata; ontology; practice guideline; Review; safety; standard; workshop; factual database; methodology; reproducibility","","","","","Bundesministerium für Bildung und Forschung, BMBF, (FKZ 16QK07)","This work is part of the NanoS-QM project and has received funding by the German Federal Ministry of Education and Research (BMBF) under FKZ 16QK07. 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ALTEX Edition, 36, pp. 682-699, (2019)","A. Kraegeloh; INM - Leibniz Institute for New Materials, Saarbrücken, Campus D2 2, 66123, Germany; email: annette.kraegeloh@leibniz-inm.de","","BioMed Central Ltd","","","","","","17438977","","","34983569","English","Part. Fibre Toxicol.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85122289487"
"Sheffield M.; Burton K.B.","Sheffield, Megan (57195064722); Burton, Karen B. (58093125900)","57195064722; 58093125900","Research Data Management needs assessment of Clemson University","2022","Journal of Librarianship and Scholarly Communication","10","1","eP13970","","","","0","10.31274/jlsc.13970","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147495005&doi=10.31274%2fjlsc.13970&partnerID=40&md5=82e91d2485293fa557acc5dc0450b93c","Clemson University, United States; University of South Carolina School of Medicine Greenville, United States","Sheffield M., Clemson University, United States; Burton K.B., University of South Carolina School of Medicine Greenville, United States","Research data management (RDM) is a growing field of practice within academic librarianship and information management. Research data are generated by researchers investigating and describing new information; often, the data that are generated are digital in nature, for example, in spreadsheets or computer code. Researchers are experts in their fields but may not possess the same skillset as librarians and other information professionals when it comes to organizing, preserving, and sharing information. As a field, RDM encompasses a wide range of activities that include documenting and managing research data during a research project as well as sharing and preserving data after the research project is completed. Academic libraries can offer a variety of services that support researchers during the research life cycle; these services vary among institutions. The faculty, staff, and graduate students at Clemson University were surveyed by the library about their RDM needs in the spring of 2021. The survey was based on previous surveys from 2012 and 2016 to allow for comparison, but language was updated, and additional questions were added because the field of RDM has evolved. Survey findings indicated that researchers are overall more likely to back up and share their data, but the process of cleaning and preparing the data for sharing was an obstacle. Few researchers reported including metadata when sharing or consulting the library for help with writing a Data Management Plan (DMP). Researchers want RDM resources; offering and effectively marketing those resources will enable libraries to both support researchers and encourage best practices. Understanding researcher needs and offering time-saving services and convenient training options makes following RDM best practices easier for researchers. Outreach and integrated partnerships that support the research life cycle are crucial next steps for ensuring effective data management. © 2022 The Author(s).","data management; data repository; data services; environmental scan; research data management; research life cycle","","","","","","","","Bishop B. W., Borden R. M., Scientists’ research data management questions: lessons learned at a data help desk, Portal: Libraries & the Academy, 20, 4, (2020); Bull J., Schultz T., Harvesting the academic landscape: streamlining the ingestion of professional scholarship metadata into the institutional repository, Journal of Librarianship and Scholarly Communication, 6, 1, (2018); Choudhury S., Data management and preservation of digital research data, Against the Grain, 29, 5, pp. 30-34, (2017); Coates H. L., Carlson J., Ryan C., Henderson M., Johnston L. R., Shorish Y., How are we measuring up? Evaluating research data services in academic libraries, Journal of Librarianship and Scholarly Communication, 6, (2018); Cousijn H., Lammey R., Why data citation matters to publishers and data repositories, Crossref, (2018); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science & Technology, 68, 9, pp. 2182-2200, (2017); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Faniel I. M., Connaway L. S., Librarians’ perspectives on the factors influencing research data management programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Hickson S., Connor M., Poulton K. A., Richardson J., Wolski M., Modifying researchers’ data management practices, IFLA Journal, 42, 4, pp. 253-265, (2016); Johnston L., Curating research data, (2017); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship & Information Science, 52, 3, pp. 633-646, (2020); Kenyon J., Attebury R., Doney J., Godfrey B., Martinez J., Seiferle-Valencia M., Help-seeking behaviors in research data management, Issues in Science & Technology Librarianship, 96, pp. 1-1, (2020); Khan N., Thelwall M., Kousha K., Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics, Scientometrics, 126, 4, pp. 3621-3639, (2021); Kouper I., Fear K., Ishida M., Kollen C., Williams S. C., Research data services maturity in academic libraries, Curating Research Data: Practical Strategies for Your Digital Repository, 1, pp. 153-170; Mannheimer S., Ready, engage! Outreach for library data services, Bulletin of the Association for Information Science & Technology, 41, 1, pp. 42-44, (2014); Morgan A., Duffield N., Walkley Hall L., Research data management support: sharing our experiences, Journal of the Australian Library & Information Association, 66, 3, pp. 299-305, (2017); Nicholson S. W., Bennett T. B., The good news about bad news: communicating data services to cognitive misers, Journal of Electronic Resources Librarianship, 29, 3, pp. 151-158, (2017); Perrier L., Barnes L., Developing research data management services and support for researchers: a mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, 13, 1, pp. 1-23, (2018); Ricardo M., Urban S., Databases Illuminated, (2015); Rinehart A., Finding the connection: research data management and the office of research, Bulletin of the Association for Information Science & Technology, 43, 1, pp. 28-30, (2016); Schwartz M., Charleston conference report, Library Journal, 139, 20, pp. 1-1, (2014); Shen Y., Strategic planning for a data-driven, shared-access research enterprise: Virginia Tech Research Data Assessment and Landscape study, College and Research Libraries (Online), 77, 4, (2016); Singh A., Enabling researchers to make their data count, (2019); Vannan S., Downs R. R., Meier W., Wilson B. E., Gerasimov I. V., Data sets are foundational to research. Why don’t we cite them?, Eos: Science News by AGU, (2020); Whitmire A. L., Boock M., Sutton S. C., Variability in academic research data management practices, Program, 49, 4, pp. 382-407, (2015); Wiley C., Metadata use in research data management, Bulletin of the Association for Information Science & Technology, 40, 6, pp. 38-40, (2014); Wright S., Whitmire A., Zilinski L., Minor D., Collaboration and tension between institutions and units providing data management support, Bulletin of the Association for Information Science & Technology, 40, 6, pp. 18-21, (2014); Yu H. H., The role of academic libraries in research data service (RDS) provision, Electronic Library, 35, 4, pp. 783-797, (2017); Yu F., Deuble R., Morgan H., Designing research data management services based on the research lifecycle—a consultative leadership approach, Journal of the Australian Library & Information Association, 66, 3, pp. 287-298, (2017)","","","Iowa State University Digital Press","","","","","","21623309","","","","English","J. Librariansh. Sch. Commun.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85147495005"
"Giaretta D.; Redondo T.; Martinez A.G.; Fuertes M.","Giaretta, David (58101303700); Redondo, Teofilo (58101807800); Martinez, Antonio G. (58101282900); Fuertes, Maria (58101635400)","58101303700; 58101807800; 58101282900; 58101635400","LABDRIVE, a Petabyte scalable, OAIS/ISO 16363 conformant, for scientific research organisations to preserve documents, processed data, and software","2022","Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022","","","","2515","2521","6","0","10.1109/BigData55660.2022.10020648","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147897762&doi=10.1109%2fBigData55660.2022.10020648&partnerID=40&md5=c85da66f0400d97605aad3050c382c33","Ceo, Giaretta Associates Ltd, Sherborne, United Kingdom; Head of RandD, Libnova Srl, Madrid, Spain; Ceo, Libnova Srl, Madrid, Spain; Head of Marketing, Libnova Srl, Madrid, Spain","Giaretta D., Ceo, Giaretta Associates Ltd, Sherborne, United Kingdom; Redondo T., Head of RandD, Libnova Srl, Madrid, Spain; Martinez A.G., Ceo, Libnova Srl, Madrid, Spain; Fuertes M., Head of Marketing, Libnova Srl, Madrid, Spain","Vast amounts of scientific, cultural, social, business and government, and other, information is being created every day. There are billions of objects, in a multitude of formats, semantics and associated software. Much of this information is transitory but there is still an immense amount which should be preserved for the medium and long term, even indefinitely.Preservation requires that the information continues to be usable, not simply to be printed or displayed. Of course, the digital objects (the bits) must be preserved, as must the 'metadata' which enables the bits to the understood which includes the software.Before LABDRIVE no system could adequately preserve such information, especially in such gigantic volume and variety.In this paper we describe the development of LABDRIVE and its ability to preserve and to scale up to tens or hundreds of Petabytes in a way which is conformant to the OAIS Reference Model and capable of being ISO 16363 certified. © 2022 IEEE.","ARCHIVER Project; digital preservation; OAIS; Research Data Management; scalable services for archives","Digital storage; Information management; ARCHIVER project; Associated softwares; Digital preservation; OAIS; Petabytes; Research data managements; Research organization; Scalable service for archive; Scientific researches; Social business; Semantics","","","","","Amazon Web Services, AWS; Consejo Superior de Investigaciones Científicas, CSIC; Universitat de Barcelona, UB; Instituto de Física de Cantabria, IFCA","We would like to acknowledge the ARCHIVER project under Grant agreement ID: 824516, and our colleagues in the LIBNOVA consortium: CSIC – IFCA, University of Barcelona, Amazon Web Services (AWS), Voxility and Bidaidea.","Labdrive Support for Oais Conformance; Exporting Archival Information Packages; Preservation Activities; Iso 16363 Certification Guide; Reproducing Research; Preserving Complex Software; Software As Part of the Rin; Labdrive Support for FAIRness; Archiver Project Fact Sheet, H2020, Cordis; Pcp; Caspar Project; Parse Insight; Prelida Project; SCIDIP-ES Project; Aparsen Project; Updated Oais Reference Model; Ica ISAD(G): General International Standard Archival Description Second Edition or Later; International Standard Archival Authority Record (Corporate Bodies, Persons, Families) (ISAAR)-2nd Edition, (2003); International Standard for Describing Institutions with Archival Holdings (ISDIAH)-1st Edition, (2008); International Standard for Describing Functions (ISDF)-1st Edition, (2007); Model Requirements for Records Systems; Information and Documentation-Principles and Functional Requirements for Records in Electronic Office Environments Iso 16175-1: 2010, Iso 16175-2:2011 and Iso 16175-3: 2010, (2001); The Fair Guiding Principles for Scientific Data Management and Stewardship","D. Giaretta; Ceo, Giaretta Associates Ltd, Sherborne, United Kingdom; email: david@giaretta.org","Tsumoto S.; Ohsawa Y.; Chen L.; Van den Poel D.; Hu X.; Motomura Y.; Takagi T.; Wu L.; Xie Y.; Abe A.; Raghavan V.","Institute of Electrical and Electronics Engineers Inc.","Ankura; et al.; Hitachi; KPMG Consulting Co., Ltd.; NTT Data Intellilink Corporation; Think in Data Initiative, Association Inc","2022 IEEE International Conference on Big Data, Big Data 2022","17 December 2022 through 20 December 2022","Osaka","186390","","978-166548045-1","","","English","Proc. - IEEE Int. Conf. Big Data, Big Data","Conference paper","Final","","Scopus","2-s2.0-85147897762"
"Chigwada J.P.","Chigwada, Josiline Phiri (57193754137)","57193754137","Management and maintenance of research data by researchers in Zimbabwe","2022","Global Knowledge, Memory and Communication","71","4-5","","193","207","14","3","10.1108/GKMC-06-2020-0079","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106047214&doi=10.1108%2fGKMC-06-2020-0079&partnerID=40&md5=1c9232c9df2ff628be2b0fd50e3b1d82","Department of Library, Chinhoyi University of Technology, Chinhoyi, Zimbabwe","Chigwada J.P., Department of Library, Chinhoyi University of Technology, Chinhoyi, Zimbabwe","Purpose: The concept of research data management (RDM) is new in Zimbabwe and other developing countries. Research institutions are developing research data repositories and promoting the archiving of research data. As a way of creating awareness to researchers on RDM, the purpose of this paper is to determine how researchers are managing their research data and whether they are aware of the developments that are taking place in RDM. Design/methodology/approach: A survey using a mixed method approach was done and an online questionnaire was administered to 100 researchers in thirty research institutions in Zimbabwe. Purposive sampling was done by choosing participants from the authors of articles published in journals indexed by Google Scholar, Scopus and Web of Science. Interviews were done with five top researchers. The data was analysed using NVIVO. The results were presented thematically. The questionnaire was distributed using the research offices of the selected 30 research institutions. There was a 75% response rate. Findings: The findings indicated that all the researchers are aware of the traditional way of managing research data. A total of 70% of the respondents are not aware of the current trends in RDM services, as they are keeping their data on machines and external hard drives, while 97.3% perceive RDM services as useful, as it is now a requirement when applying for research grants. Librarians have a bigger role to play in creating awareness on RDM among researchers and hosting the data repositories for archiving research data. Practical implications: Research institutions can invest in research data services and develop data repositories. Librarians will participate in educating researchers to come up with data management plans before they embark on a research project. This study also helps to showcase the strategies that can be used in awareness creation campaigns. The findings can also be used in teaching RDM in library schools and influence public policy both at institutional and national level. Social implications: This study will assist in building capacity among stakeholders about RDM. Based on the findings, research institutions should prioritise research data services to develop skills and knowledge among librarians and researchers. Originality/value: Few researches on RDM practices in Zimbabwe were done previously. Most of the papers that were published document the perception of librarians towards RDM, but this study focused mainly on researchers’ awareness and perception. The subject is still new and people are beginning to research on it and create awareness amongst the stakeholders in Zimbabwe. © 2021, Emerald Publishing Limited.","Awareness and perception; FAIR data principles; Research data awareness; Research data management; Research data perception; Research data services; Research data sharing","","","","","","","","Baker K.S., Yarmey L., Data stewardship: environmental data curation and a web-of-repositories, International Journal of Digital Curation, 4, 2, pp. 12-27, (2009); Bezuidenhout L., Chakauya E., Hidden concerns of sharing research data by low/middle income country scientists, Global Bioethics, 29, 1, pp. 39-54, (2018); Buys C.M., Shaw P.L., Data management practices across an institution: survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Chigwada J.P., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, (2017); Chigwada J.P., Hwalima T., Kwangwa N., Proposed framework for research data management services in research institutions in Zimbabwe, Research Data Access and Management in Modern Libraries, pp. 23-53, (2019); Chiparausha B., Chigwada J.P., Accessibility of research data at academic institutions in Zimbabwe, Research Data Access and Management in Modern Libraries, pp. 81-89, (2019); Chiware E., Mathe Z., Academic libraries’role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2015); Survey of research data management: results Kathleen Shearer and Filipe Furtado, confederation of open access repositories (COAR), (2017); Cook-Deegan R., Ankeny R.A., Jones K.M., Sharing data to build a medical information commons: from Bermuda to the global alliance, Annual Review of Genomics and Human Genetics, 18, 1, pp. 389-415, (2017); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, 70, (2012); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Erway R., Starting the Conversation: University-Wide Research Data Management Policy, (2013); Guidelines on data management in horizon 2020, (2013); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the research data management landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy (2015), pp. 82-102, (2015); Harvey R., Digital Curation: A How to Do It Manual, (2010); Johnson M., Ahlfeldt J., Research Libraries and Research Data Management within the Humanities and Social Sciences, (2015); Jones K.M., Ankeny R.A., Cook-Deegan R., The Bermuda triangle: the pragmatics, policies, and principles for data sharing in the history of the human genome project, Journal of the History of Biology, 51, 4, pp. 693-805, (2018); Kennan M.A., Markauskaite L., Research data management practices: a snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Majid S., Foo S., Zhang X., Research data management by academics and researchers: perceptions, knowledge and practices, Maturity and innovation in digital libraries: 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Hamilton, New Zealand, November 19-22, 2018, Proceedings, pp. 166-178, (2018); Marchionini G., Research Data Stewardship at UNC: Recommendations for Scholarly Practice and Leadership, (2012); Getting to the bottom line: 20 cost questions for digital preservation, (2015); Mosha N.F., Luhanga E.T., Mosha M.V., Marwa J.J., Research data management among researchers in higher learning institutions of Sub-Saharan Africa, Handbook of Research on Connecting Research Methods for Information Science Research, (2019); Ndhlovu P., The state of preparedness for digital curation and preservation: a case study of a developing country academic library, IASSIST Quaterly/International Association for Social Science Information Service and Technology, 42, 3, pp. 1-22, (2018); Ndhlovu P., Ngwenya S., Research data management services: an investigation of research data management practices at the national university of science and technology, 7th Annual International Conference on Communication and Information Science, Caribbea Bay, Kariba, (2017); Nhendodzashe N., Pasipamire N., Research data management services: are academic libraries in Zimbabwe ready? The case of university of Zimbabwe library, (2017); Registry of research data repositories, (2019); Rolando L., Doty C., Hagenmaier W., Valk A., Parham S.W., Institutional readiness for data stewardship: findings and recommendations from the research data assessment, (2013); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of eScience Librarianship, 1, 2, (2012); Brussels declaration on STM publishing, (2007); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Dalton E.D., Allard S., Frame M., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Tenopir C., Christian L., Allard S., Borycz J., Research data sharing: practices and attitudes of geophysicists, Earth and Space Science, 5, 12, pp. 891-902, (2018); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Introduction to research data management, (2019); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, 7, (2013); Sharing data during Zika and other global health emergencies, (2016); Office of science and technology policy, (2013); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: implications for data services development from a faculty survey, Program: electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Handbook for Cancer Research in Africa, (2013); Yoon A., Data reusers’ trust development, Journal of the Association for Information Science and Technology, 68, 4, pp. 946-956, (2017); Zhou Q., Academic libraries in research data management service: perceptions and practices, Open Access Library Journal, 5, (2018); Research data management: good practice note, (2017)","J.P. Chigwada; Department of Library, Chinhoyi University of Technology, Chinhoyi, Zimbabwe; email: josyphiri@gmail.com","","Emerald Group Holdings Ltd.","","","","","","25149342","","","","English","Glob. Knowl.,  Mem. Commun.","Article","Final","","Scopus","2-s2.0-85106047214"
"Garkov D.; Muller C.; Braun M.; Weiskopf D.; Schreiber F.","Garkov, Dimitar (57215128373); Muller, Christoph (57202189811); Braun, Matthias (57213768032); Weiskopf, Daniel (6603960393); Schreiber, Falk (7102481723)","57215128373; 57202189811; 57213768032; 6603960393; 7102481723","Research Data Curation in Visualization : Position Paper","2022","Proceedings - 2022 IEEE 9th Workshop on Evaluation and Beyond - Methodological Approaches to Visualization, BELIV 2022","","","","56","65","9","0","10.1109/BELIV57783.2022.00011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145770957&doi=10.1109%2fBELIV57783.2022.00011&partnerID=40&md5=81d7ce80f66bba1fa49ebbe8173f4fc3","University of Konstanz, Department of Computer and Information Science, Germany; University of Stuttgart, Visualization Research Center (VISUS), Germany; University of Stuttgart, Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC), Germany","Garkov D., University of Konstanz, Department of Computer and Information Science, Germany; Muller C., University of Stuttgart, Visualization Research Center (VISUS), Germany; Braun M., University of Stuttgart, Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC), Germany; Weiskopf D., University of Stuttgart, Visualization Research Center (VISUS), Germany; Schreiber F., University of Konstanz, Department of Computer and Information Science, Germany","Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher's compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.  © 2022 IEEE.","Human-centered computing - ; Visualization - ; Visualization design and evaluation methods","Data visualization; Information management; Curation; Data curation; Design and evaluation methods; Human-centered computing; Human-centered computing -; Position papers; Research data; Visualization -; Visualization design and evaluation method; Visualization designs; Visualization","","","","","National Science Foundation, NSF; HORIZON EUROPE Framework Programme; Deutsche Forschungsgemeinschaft, DFG, (251654672, EXC 2120/1 – 390831618)","Funding text 1: The authors wish to thank all members of SFB/Transregio 161, and in particular Thomas Ertl, Karsten Klein, Dietmar Saupe, Sabine Storandt, Oliver Deussen, Michael Aichem, Florian Frieß, and Sab-rina Jaeger-Honz, who contributed to our guidelines. This work was partially funded by Deutsche Forschungsgemeinschaft (DFG) as part of SFB/Transregio 161 (Project ID 251654672) and under Germany’s Excellence Strategy – EXC 2120/1 – 390831618.; Funding text 2: Research data management and curation are essential for research repeatability. Provisioning reliable storage, guidance, and open access can bolster long-term outreach and trust in the scientific field. Therefore, funding bodies such as the EU with its Horizon Europe funding program and the National Science Foundation (NSF) in the US insist on planning data management ahead and require it to be implemented in the projects they fund. In this context, we cannot help but notice the visualization community is lagging behind on this trend, at least compared to some other scientific disciplines. In this paper, we want to shed light on what we believe to be reasons for this discrepancy and what we can do to resolve it. We report on the general background of research data management (RDM) and data management plans (DMP), data curation and research data repositories and try to isolate the factors that make data curation in visualization different—and possibly harder—than in other fields.","Abdelaal M., Amtsberg F., Becher M., Estrada R.D., Kannenberg F., Calepso A.S., Wagner H.J., Reina G., Sedlmair M., Menges A., Weiskopf D., Visualization for architecture, engineering, and construction: Shaping the future of our built world, IEEE Computer Graphics and Applications, 42, 2, pp. 10-20, (2022); Adadi A., Berrada M., Peeking inside the black-box: A survey on explainable artificial intelligence (XAI), IEEE Access, 6, pp. 52138-52160, (2018); (2022); Baker M., 1, 500 scientists lift the lid on reproducibility, Nature, 533, 7604, (2016); Barba L.A., Terminologies for reproducible research, (2018); Benson D.A., Cavanaugh M., Clark K., Karsch-Mizrachi I., Lipman D.J., Ostell J., Sayers E.W., GenBank, Nucleic Acids Research, 41, D1, pp. D36-D42, (2012); Brainard J., Rethinking retractions, (2018); Brecher C., Buchmeiser M.R., Burkert A., Busemeyer M.R., Conermann S., Ertl T., Friedrich M., Helmig R., Hohmann V., Johnston A.J., Kollmeier B., Larkum M., Louis J., Menges A., Morgner U., Muller J., Niessen C., Ohlberger M., Schaffner W., Schmidt P., Schmitz D., Seeger W., Stammer D., Thomas A., Traninger A., Wegener M., Colomb J., Hermann S., Kopsch-Xhema J., Range J., Flemisch B., Commitment zu aktivem Daten-und Softwaremanagement in grosen Forschungsverbünden: Commitment to active data and software management in large research alliances, Bausteine Forschungsdatenmanagement, pp. 121-123, (2022); Brickley D., Burgess M., Noy N., Google dataset search: Building a search engine for datasets in an open web ecosystem, The World Wide Web Conference, pp. 1365-1375, (2019); Buckheit J.B., Donoho D.L., Wavelab and reproducible research, Wavelets and Statistics, pp. 55-81, (1995); Claerbout J.F., Karrenbach M., Electronic documents give reproducible research a new meaning, 1992 SEG Technical Program Expanded Abstracts, pp. 601-604, (1992); Colavizza G., Hrynaszkiewicz I., Staden I., Whitaker K., McGillivray B., The citation advantage of linking publications to research data, PloS One, 15, 4, (2020); Collins F.S., Morgan M., Patrinos A., The human genome project: lessons from large-scale biology, Science, 300, 5617, pp. 286-290, (2003); Correll M., What do we actually learn from evaluations in the ""heroic era"" of visualization?: Position paper, 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV), pp. 48-54, (2020); Cranmer K., Heinrich L., Jones R., South D.M., Analysis preservation in ATLAS, Journal of Physics: Conference Series, 664, 3, (2015); DaRUS: The Data Repository of the University of Stuttgart, (2022); Donoho D.L., Maleki A., Rahman I.U., Shahram M., Stodden V., Reproducible research in computational harmonic analysis, Computing in Science Engineering, 11, 1, pp. 8-18, (2009); Drummond C., Replicability is not reproducibility: nor is it good science, Proceedings of the Evaluation Methods for Machine Learning Workshop at the 26th ICML, (2009); Editorial, Data sharing comes to structural biology, Nature Methods, 13, 5, (2016); Enke N., Thessen A., Bach K., Bendix J., Seeger B., Gemeinholzer B., The user's view on biodiversity data sharing-investigating facts of acceptance and requirements to realize a sustainable use of research data, Ecological Informatics, 11, pp. 25-33, (2012); European Organization For Nuclear Research and OpenAIRE, Zenodo, (2013); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PloS One, 10, 2, (2015); Federer L.M., Belter C.W., Joubert D.J., Livinski A., Lu Y.-L., Snyders L.N., Thompson H., Data sharing in PLoS One: An analysis of data availability statements, PloS One, 13, 5, (2018); Feger S.S., Wozniak P.W., Lischke L., Schmidt A., Yes, I comply!' 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Beyond - Methodol. Approaches Vis., BELIV","Conference paper","Final","","Scopus","2-s2.0-85145770957"
"Herres-Pawlis S.; Bach F.; Bruno I.J.; Chalk S.J.; Jung N.; Liermann J.C.; McEwen L.R.; Neumann S.; Steinbeck C.; Razum M.; Koepler O.","Herres-Pawlis, Sonja (9277407800); Bach, Felix (55695210800); Bruno, Ian J. (7003982568); Chalk, Stuart J. (6603873609); Jung, Nicole (57641098800); Liermann, Johannes C. (24376894100); McEwen, Leah R. (56270423600); Neumann, Steffen (18038199000); Steinbeck, Christoph (7003655166); Razum, Matthias (25825498000); Koepler, Oliver (6507094492)","9277407800; 55695210800; 7003982568; 6603873609; 57641098800; 24376894100; 56270423600; 18038199000; 7003655166; 25825498000; 6507094492","Minimum Information Standards in Chemistry: A Call for Better Research Data Management Practices","2022","Angewandte Chemie - International Edition","61","51","e202203038","","","","1","10.1002/anie.202203038","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141586079&doi=10.1002%2fanie.202203038&partnerID=40&md5=87c3373e53390a6f91f050c70b033356","Institut für Anorganische Chemie, RWTH Aachen University, Landoltweg 1A, Aachen, 52074, Germany; E-Research, FIZ Karlsruhe—Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, United Kingdom; Department of Chemistry, University of North Florida, 1 UNF Drive, Jacksonville, 32224, FL, United States; Institute of Biological and Chemical Systems (IBCS), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Johannes Gutenberg University Mainz, Department of Chemistry, Duesbergweg 10–14, Mainz, 55128, Germany; Cornell University Library, 293 Clark Hall, Ithaca, 14853-2501, NY, United States; Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle, 06120, Germany; Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessingstr. 1, Jena, 07743, Germany; Lab Linked Scientific Knowledge, TIB—Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hannover, 30173, Germany","Herres-Pawlis S., Institut für Anorganische Chemie, RWTH Aachen University, Landoltweg 1A, Aachen, 52074, Germany; Bach F., E-Research, FIZ Karlsruhe—Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Bruno I.J., Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, United Kingdom; Chalk S.J., Department of Chemistry, University of North Florida, 1 UNF Drive, Jacksonville, 32224, FL, United States; Jung N., Institute of Biological and Chemical Systems (IBCS), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Liermann J.C., Johannes Gutenberg University Mainz, Department of Chemistry, Duesbergweg 10–14, Mainz, 55128, Germany; McEwen L.R., Cornell University Library, 293 Clark Hall, Ithaca, 14853-2501, NY, United States; Neumann S., Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle, 06120, Germany; Steinbeck C., Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessingstr. 1, Jena, 07743, Germany; Razum M., E-Research, FIZ Karlsruhe—Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Koepler O., Lab Linked Scientific Knowledge, TIB—Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hannover, 30173, Germany","Research data management (RDM) is needed to assist experimental advances and data collection in the chemical sciences. Many funders require RDM because experiments are often paid for by taxpayers and the resulting data should be deposited sustainably for posterity. However, paper notebooks are still common in laboratories and research data is often stored in proprietary and/or dead-end file formats without experimental context. Data must mature beyond a mere supplement to a research paper. Electronic lab notebooks (ELN) and laboratory information management systems (LIMS) allow researchers to manage data better and they simplify research and publication. Thus, an agreement is needed on minimum information standards for data handling to support structured approaches to data reporting. As digitalization becomes part of curricular teaching, future generations of digital native chemists will embrace RDM and ELN as an organic part of their research. © 2022 Wiley-VCH GmbH.","Data Publication; ELN; Minimum Information; Repositories; Research Data Management (RDM)","Data Management; Laboratories; Information management; Taxation; Data publications; Electronic lab; Electronic lab notebook; Information standards; Management IS; Management practises; Minimum information; Repository; Research data management; Research data managements; information processing; laboratory; Data handling","","","","","Deutsche Forschungsgemeinschaft, DFG, (441958208)","F.B., S.H.P., N.J., J.C.L., S.N., M.R., O.K., and C.S. acknowledge funding by the DFG for NFDI4Chem under project number 441958208. ","Hey A.J.G., Tansley S., Tolle K.M., Et al., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Luckenbach R., J. Chem. Inf. Comput. 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Biotechnol., 35, pp. 406-409, (2017); Wikidata,” can be found under; Waagmeester A., Stupp G., Burgstaller-Muehlbacher S., Good B.M., Griffith M., Griffith O.L., Hanspers K., Hermjakob H., Hudson T.S., Hybiske K., Keating S.M., Manske M., Mayers M., Mietchen D., Mitraka E., Pico A.R., Putman T., Riutta A., Queralt-Rosinach N., Schriml L.M., Shafee T., Slenter D., Stephan R., Thornton K., Tsueng G., Tu R., Ul-Hasan S., Willighagen E., Wu C., Su A.I., eLife, 9, (2020); Des Europäischen Parlaments und des Rates vom 20, (2019); Meyers F., WorldFAIR: Global cooperation on FAIR data policy and practice,” can be found under, (2022)","S. Herres-Pawlis; Institut für Anorganische Chemie, RWTH Aachen University, Aachen, Landoltweg 1A, 52074, Germany; email: sonja.herres-pawlis@ac.rwth-aachen.de; S. Neumann; Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle, Weinberg 3, 06120, Germany; email: sneumann@ipb-halle.de; C. Steinbeck; Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Lessingstr. 1, 07743, Germany; email: christoph.steinbeck@uni-jena.de; O. Koepler; Lab Linked Scientific Knowledge, TIB—Leibniz Information Centre for Science and Technology, Hannover, Welfengarten 1B, 30173, Germany; email: oliver.koepler@tib.eu","","John Wiley and Sons Inc","","","","","","14337851","","ACIEF","36347644","English","Angew. Chem. Int. Ed.","Article","Final","","Scopus","2-s2.0-85141586079"
"Färber M.; Lamprecht D.","Färber, Michael (55890962100); Lamprecht, David (57447844900)","55890962100; 57447844900","The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models","2022","CEUR Workshop Proceedings","3254","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142663478&partnerID=40&md5=e22b23a7abd20e785d70996874af96ae","Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany","Färber M., Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany; Lamprecht D., Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany","Modeling AI systems’ characteristics of energy consumption and their sustainability level as an extension of the FAIR data principles has been considered only rudimentarily. In this paper, we propose the Green AI Ontology for modeling the energy consumption and other environmental aspects of AI models. We evaluate our ontology based on competency questions. Our ontology is available at https://w3id.org/Green-AI-Ontology and can be used in a variety of scenarios, ranging from comprehensive research data management to strategic controlling of institutions and environmental efforts in politics. © 2022 Copyright for this paper by its authors.","Energy Consumption; Green AI; Machine Learning; Ontology Engineering","Information management; Machine learning; Ontology; Sustainable development; AI systems; Comprehensive research; Energy-consumption; Environmental aspects; Green AI; Machine-learning; Ontology engineering; Ontology's; Ontology-based; System characteristics; Energy utilization","","","","","","","Wilkinson M. D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, pp. 2052-4463, (2016); Nguyen V. B., Svatek V., Rabby G., Corcho O., Ontologies Supporting Research-Related Information Foraging Using Knowledge Graphs: Literature Survey and Holistic Model Mapping, Proc. of EKAW, pp. 88-103, (2020); Nguyen V. B., Svatek V., Ontology for informatics research artifacts, Proceedings of the 18th Extended Semantic Web Conference, ESWC’21, pp. 126-130, (2021); Nguyen A., Weller T., Farber M., Sure-Vetter Y., Making neural networks FAIR, Proceedings of the Second Iberoamerican Conference and First Indo-American Conference on Knowledge Graphs and Semantic Web, KGSWC’20, pp. 29-44, (2020); Lacoste A., Luccioni A., Schmidt V., Dandres T., Quantifying the Carbon Emissions of Machine Learning, (2019); Henderson P., Hu J., Romoff J., Brunskill E., Jurafsky D., Pineau J., Towards the systematic reporting of the energy and carbon footprints of machine learning, (2020)","M. Färber; Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany; email: michael.faerber@kit.edu","Dimou A.; KU Leuven - Leuven.AI - FlandersMake, Jan Pieter de Nayerlaan 5, Sint-Katelijne-Waver; Haller A.; Gentile L.; Ristoski P.","CEUR-WS","","2022 International Semantic Web Conference Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2022","23 October 2022 through 27 October 2022","Virtual, Hangzhou","183867","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85142663478"
"Sales L.F.; Sayão L.F.","Sales, Luana Farias (25646168600); Sayão, Luís Fernando (7801523487)","25646168600; 7801523487","Proposal for a model of research data management service; [Proposta de modelo de serviço de gestão de dados de pesquisa]","2022","AtoZ","11","","","","","","0","10.5380/atoz.v11i0.85765","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146265030&doi=10.5380%2fatoz.v11i0.85765&partnerID=40&md5=5dfee792bcddcdb4a8b896a9962564b7","Instituto Brasileiro de Informação em Ciência e Tecnologia – IBICT, Universidade Federal do Rio de Janeiro, Programa de Pós-graduação em Ciência da Informação – PPGCI, RJ, Rio de Janeiro, Brazil; Comissão Nacional de Energia Nuclear – CNEN, Universidade Federal do Rio de Janeiro, Programa de Pós-graduação em Ciência da Informação – PPGCI, RJ, Rio de Janeiro, Brazil","Sales L.F., Instituto Brasileiro de Informação em Ciência e Tecnologia – IBICT, Universidade Federal do Rio de Janeiro, Programa de Pós-graduação em Ciência da Informação – PPGCI, RJ, Rio de Janeiro, Brazil; Sayão L.F., Comissão Nacional de Energia Nuclear – CNEN, Universidade Federal do Rio de Janeiro, Programa de Pós-graduação em Ciência da Informação – PPGCI, RJ, Rio de Janeiro, Brazil","The planning, development, implementation, and operation of research data management platforms involves many variables that need to be properly addressed. The design of these platforms constitutes a complex and multifaceted problem, of which the solution needs to be manifested in the form of services that meet the needs of research flows in specific disciplinary domains, informational, technological and scientific parameters, varied professional profiles, political, legal and ethical constraints, and economic and temporal sustainability. In this multifaceted environment of contemporary science, the question we intend to answer is: What types of data management services can be offered based on the workflows of the scientific environment, considering the peculiarities of the disciplinary domains and serving as a support mechanism for the development of research and encouraging the sharing and reuse of scientific data? As an answer to the question, theoretical-exploratory research was developed that took as a methodological basis the analysis of the literature in the area, with special emphasis on articles, reports, manuals and data infrastructure projects, prepared by researchers and research institutions, giving prominence to the observation points of the actors most closely involved in the issue. From the interrelationship of the diverse variables raised, the result is the description of a set of services that can be offered, organized in a service model. It is hoped that this model can serve as a guide for research institutions that wish to implement data management platforms closer to their communities and that follow the standards and traditions of sharing and reusing scientific data from their researchers. © 2022 Sales & Sayão.","FAIR data; Research data management services; Scientific knowledge management","","","","","","Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, (430553/2018-8 O)","O(s) autor(es) declara(m) que esta pesquisa recebeu financiamento conforme dados indicados a seguir e o documento comprobatório foi anexado como documento suplementar: Chamada: Universal 2018 – Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) – no 430553/2018-8 O(s) autor(es) concorda(m) em interagir diretamente com pareceristas responsáveis pela avaliação do manuscrito, dessa forma tornando a revisão por pares aberta.","Borgman C. L., Data: the input and output of scholarship, Scholarship in the digital age: Information, infrastructure, and the internet, (2007); Choudhury S., Cowles E., Croft H. R., Estlund K., Fary M., Faustino G., Menard K., Research data cura-tion: A framework for an institution-wide services approach, EDUCAUSE Working Group on Data Curation, (2018); Coates H. L., Building data services from the ground up: Strategies and resources, Journal of eScience Librarianship, 3, 1, pp. 52-59, (2014); Erway R., Horton L., Nurnberger A., Otsuji R., Rushing A., Building blocks: laying the foundation for a research data management program, (2016); Six recommendations to implementation of fair practices, (2020); Fearon D., Gunia B., Pralle B. E., Lake S., Sallans A. L., Spec kit 334: Research data management services, (2013); Gil A. C., Como elaborar projetos de pesquisa, 4, (2002); Graaf M., Waaijers L., A surfboard for riding the wave: towards a four country action programme on research data, (2011); Jones S., Prior G., White A., How to develop research data management services – a guide for heis, (2013); Kouper I., Fear K., Ishida M., Kollen C., Williams S. C., Research data services maturity in academic libraries, Curating research data: practical strategies for your digital repository, 1, pp. 153-170, (2017); Leonardi P. M., Digital materiality? how artifacts without matter, matter, First monday, 15, 6-7, (2010); Marin-Arraiza P., Vidotti S., Et al., Implementação de serviços institucionais de administração de dados, Liinc em Revista, 15, 2, (2019); Mayernik M. S., Choudhury G. S., DiLauro T., Metsger E., Pralle B., Rippin M., Duerr R., The data conservancy instance: Infrastructure and organizational services for research data curation, D-Lib Magazine, 18, 9, (2012); Mons B., Neylon C., Velterop J., Dumontier M., da Silva Santos L. O. B., Wilkinson M. D., Cloudy, increasingly fair; revisiting the fair data guiding principles for the european open science cloud, Information services & use, 37, 1, pp. 49-56, (2017); Mushi G. E., Pienaar H., Deventer M., Identifying and implementing relevant research data management services for the library at the university of do- doma, tanzania, Data Science Journal, 19, 1, pp. 1-9, (2020); Preparing the work-force for digital curation, (2015); Reed R. B., Diving into data: planning a research data management event, Journal of escience Librarianship, 4, 1, (2015); Sayao L. F., Modelos teóricos em ciên-cia da informação-abstração e método científico, Ciên-cia da informação, 30, 1, pp. 82-91, (2001); Sayao L. F., Sales L. F., Afinal, o que é dado de pesquisa?, Biblos, 34, 2, (2020); Solomonides A., Research data governance, roles, and infrastructure, Clinical research informatics, pp. 291-310, (2019); Strasser C., Research data management, (2015); Tang R., Hu Z., Providing research data management (rdm) services in libraries: preparedness, roles, chal-lenges, and training for rdm practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future, (2012); Wilkinson M. D., Dumontier M., Aalbersberg I. J., Appleton G., Axton M., Baak A., Bourne P. E., The fair guiding principles for scientific data management and stewardship, Scientific data, 3, 1, pp. 1-9, (2016); Wilson J. A., Martinez-Uribe L., Fraser M. A., Jeffreys P., An institutional approach to developing research data management infrastructure, The International Journal of Digital Curation, 6, 2, (2011)","L.F. Sales; Instituto Brasileiro de Informação em Ciência e Tecnologia – IBICT, Universidade Federal do Rio de Janeiro, Programa de Pós-graduação em Ciência da Informação – PPGCI, Rio de Janeiro, RJ, Brazil; email: luanasales@ibict.br","","Programa de Pos-Graduacao em Gestao da Informacao, Universidade Federal do Parana","","","","","","2237826X","","","","Portuguese","AtoZ.","Article","Final","","Scopus","2-s2.0-85146265030"
"Arpin S.M.; Kambesis P.N.","Arpin, Sarah M. (57219872386); Kambesis, Patricia N. (6508236207)","57219872386; 6508236207","Exploring best practices in data management: examples from cave and karst research and resource management","2022","Carbonates and Evaporites","37","3","53","","","","0","10.1007/s13146-022-00772-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133652709&doi=10.1007%2fs13146-022-00772-7&partnerID=40&md5=55c024c66fb4227020d31fff43f2e341","Kentucky Geological Survey, University of Kentucky, 228 Mining and Mineral Resources Bldg., Lexington, KY, United States; Department of Earth, Environmental and Atmospheric Studies, Center for Human and Environmental Studies, Western Kentucky University, Bowling Green, 42101, KY, United States","Arpin S.M., Kentucky Geological Survey, University of Kentucky, 228 Mining and Mineral Resources Bldg., Lexington, KY, United States; Kambesis P.N., Department of Earth, Environmental and Atmospheric Studies, Center for Human and Environmental Studies, Western Kentucky University, Bowling Green, 42101, KY, United States","In August 2020, researchers and resource managers from around the world gathered virtually for Conservation of Fragile Karst: A Workshop on Sustainability and Community, in support of UNESCO science programs. The purpose of the workshop was to enhance communication and the sharing of ideas and resources between major international conservation and science programs that protect, study, or manage cave and karst resources. As part of this meeting, a workshop was held to help resource managers and researchers consider data from a data management perspective. The goal was to familiarize participants with best practices in data management, provide resources for following these practices, and promote an understanding and appreciation of the benefits of good data management. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Data curation; Data preservation; FAIR principles; Natural resource management; Open science; Research data management","cave; conservation management; data management; karst; research; resource management; sustainability; UNESCO","","","","","","","Belter C.W., Measuring the value of research data: A citation analysis of oceanographic data sets, PLoS ONE, 9, 3, (2014); Borghi J., Abrams S., Lowenberg D., Simms S., Chodacki J., Support your data: A research data management guide for researchers, Res Ideas Outcomes, 4, (2018); Borgman C., Wallis J., Enyedy N., Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries, Int J Digit Libr, 7, pp. 17-30, (2007); Carlson J., The use of lifecycle models in developing and supporting data services, Research data management: Practical strategies for information professionals, (2014); Chen Z., Auler A.S., Bakalowicz M., Drew D., Griger F., Hartmann J., Jiang G., Moosdorf N., Richts A., Stevanovic Z., Veni G., Goldscheider N., The World Karst Aquifer Mapping project: concept, mapping procedure and map of Europe, Hydrogeol J, 25, pp. 771-785, (2017); Chen Z., Goldscheider N., Auler A., Bakalowicz M., Broda S., Drew D., Hartmann J., Jiang G., Moosdorf N., Richts A., Stevanovic Z., Veni G., Dumont A., Aureli A., Clos P., Krombholz M., World Karst Aquifer Map (WHYMAP WOKAM), (2017); Chung Y., Servan-Schrieber S., Zgraggen E., Kraska T., Towards quantifying uncertainty in data analysis & exploration, Bull IEEE Comput Soc Tech Comm Data Eng, 41, pp. 15-27, (2018); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib J Inf Manag, 70, 2, pp. 142-157, (2018); Faundeen J.L., Burley T.E., Carlino J.A., Govoni D.L., Henkel H.S., Holl S.L., Hutchison V.B., Martin E., Montgomery E.T., Ladino C.C., Tessler S., Zolly L.S., The United States Geological Survey Science Data Lifecycle Model: U.S, Geological Survey Open-File Report, 2013-1265, (2013); Federer L.M., Belter C.W., Joubert D.J., Livinski A., Lu Y.-L., Snyders L.N., Thompson H., Data sharing in PLOS ONE: an analysis of data availability statements, PLoS ONE, 13, 5, (2018); Gil Y., David C.H., Demir I., Essawy B.T., Fulweiler R.W., Goodall J.L., Karlstrom L., Lee H., Mills H.J., Oh J., Pierce S.A., Pope A., Tzeng M.W., Villamizar S.R., Yu X., Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance, Earth Sp Sci, 3, pp. 388-415, (2016); Goldstein J.C., Mayernik M.S., Ramapriyan H.K., Identifiers for earth science data sets: where we have been and where we need to go, Data Sci J, 16, 23, pp. 1-12, (2017); Goodman A., Pepe A., Blocker A.W., Borgman C.L., Cranmer K., Crosas M., Di Stefano R., Gil Y., Groth P., Hedstrom M., Hogg D.W., Kashyap V., Mahabal A., Siemiginowska A., Slavkovic A., Ten simple rules for the care and feeding of scientific data, PLoS Comput Biol, 10, 4, (2014); Guo H., Big Earth data: A new frontier in Earth and information sciences, Big Earth Data, 1, 1-2, pp. 4-20, (2017); Hauselmann P., UIS mapping grades, Int J Speleol, 40, 2, (2011); Helf K.L., Moore W., Wells B., Monitoring cave aquatic biota at selected parks in the Cumberland Piedmont Network: Data quality standards—version 1.0, Natural Resource Report. NPS/CUPN/NRR—2018/1696, (2018); Holdren J.P., Memorandum for the Heads of Executive Departments and Agencies: Increasing Access to the Results of Federally Funded Scientific Research (Office of Science and Technology Policy)., (2013); Factsheet: Key Facts on Digital Object Identifier System, International DOI Foundation, (2020); Irwin A., No PhDs needed: How citizen science is transforming research, Nature, 562, pp. 480-482, (2018); Klump J., Huber R., Diepenbroek M., DOI for geoscience data—how early practices shape present perceptions, Earth Sci Inform, 9, 1, pp. 123-136, (2016); McKinley D.C., Miller-Rushing A.J., Ballard H.L., Bonney R., Brown H., Cook-Patton S.C., Evans D.M., French R.A., Parrish J.K., Phillips T.B., Ryan S.F., Shanley L.A., Shirk J.L., Stepenuck K.F., Weltzin J.F., Wiggins A., Boyle O.D., Briggs R.D., Chapin S.F., Hewitt D.A., Preuss P.W., Soukup M.A., Citizen science can improve conservation science, natural resource management, and environmental protection, Biol Conserv, 208, pp. 15-28, (2017); Open science by design: realizing a vision for 21st century research, (2018); Olarinoye T., Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater, Figshare., (2019); Olarinoye T., Gleeson T., Marx V., Seeger S., Adinehvand R., Allocca V., Andreo B., Apaestegui J., Apolit C., Arfib B., Auler A., Barbera J.A., Batiot-Guilhe C., Bechtel T., Binet S., Bittner D., Blatnik M., Bolger T., Brunet P., Charlier J.-P., Chen Z., Chiogna G., Coxon G., De Vita P., Doummar J., Epting J., Fournier M., Goldscheider N., Gunn J., Guo F., Guyot J.L., Howden N., Huggenberger P., Hunt B., Jeannin P.-Y., Jiang G., Jones G., Jourde H., Karmann I., Koit O., Kordilla J., Labat D., Ladouche B., Liso I.S., Liu Z., Massei N., Mazzilli N., Mudarra M., Parise M., Pu J., Ravbar N., Sanchez L.H., Santo A., Sauter M., Sivelle V., Skoglund R.O., Stevanovic Z., Wood C., Worthington S., Hartmann A., Global karst springs hydrograph dataset for research and management of the world's fastest-flowing groundwater, Sci Data, 7, 59, pp. 1-9, (2020); Rentmeester S., Regional Guidance on Metadata for Environmental Data., (2010); Stall S., Yarmey L.R., Boehm R., Cousiijn H., Cruse P., Cutcher-Gershenfeld J., Dasler R., de Waard A., Duerr R., Elger K., Fenner M., Glaves H., Hanson B., Hausman J., Heber J., Hills D.J., Hoebelheinrich N., Hou S., Kinkade D., Koskela R., Martin R., Lehnert K., Murphy F., Nosek B., Parsons M.A., Petters J., Plante R., Robinson E., Samors R., Servilla M., Ulrich R., Witt M., Wyborn L., Advancing FAIR Data in Earth, space, and environmental science, Eos, (2018); Stall S., Yarmey L., Cutcher-Gershenfeld J., Hanson B., Lehnert K., Nosek B., Parsons M., Robinson E., Wyborn L., Make all scientific data FAIR, Nature, 570, pp. 27-29, (2019); Stevens L.E., Springer A.E., Ledbetter J.D., Springs ecosystem inventory protocols, (2016); Vannan S., Downs R.R., Meier W., Wilson B.E., Gerasimov I.V., Data sets are foundational to research. Why don’t we cite them?, Eos, (2020); Volk C.J., Lucero Y., Barnas K., Why is data sharing in collaborative natural resource efforts so hard and what can we do to improve it?, Env Manag, 53, 5, pp. 883-893, (2014); Weary D.J., Doctor D.H., Karst in the United States: A digital map compilation and database, U.S. Geological Survey Open-File Report, pp. 2014-1156, (2014); Wilkinson M., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Boiten J., Bonino de Silva Santos L., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Rv S., Sansone S., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Zheng F., Tao R., Maier H.R., See L., Savic D., Zhang T., Chen Q., Assumpcao T.H., Yang P., Heidari B., Rieckermann J., Minsker B., Bi W., Cai X., Solomatine D., Popescu I., Crowdsourcing methods for data collection in geophysics: state of the art, issues, and future directions, Rev Geophys, 56, pp. 698-740, (2018)","S.M. Arpin; Kentucky Geological Survey, University of Kentucky, Lexington, 228 Mining and Mineral Resources Bldg., United States; email: sarah.arpin@uky.edu","","Springer Science and Business Media Deutschland GmbH","","","","","","08912556","","","","English","Carbonates Evaporites","Article","Final","","Scopus","2-s2.0-85133652709"
"Arend D.; Psaroudakis D.; Memon J.A.; Rey-Mazón E.; Schüler D.; Szymanski J.J.; Scholz U.; Junker A.; Lange M.","Arend, Daniel (55531371500); Psaroudakis, Dennis (57222339538); Memon, Junaid Altaf (57733010400); Rey-Mazón, Elena (57731256200); Schüler, Danuta (56289878700); Szymanski, Jedrzej Jakub (57206429428); Scholz, Uwe (57732513900); Junker, Astrid (35792080900); Lange, Matthias (36028279400)","55531371500; 57222339538; 57733010400; 57731256200; 56289878700; 57206429428; 57732513900; 35792080900; 36028279400","From data to knowledge – big data needs stewardship, a plant phenomics perspective","2022","Plant Journal","111","2","","335","347","12","0","10.1111/tpj.15804","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131557654&doi=10.1111%2ftpj.15804&partnerID=40&md5=c643b4fc0815f4cf54bac8f8ec858888","Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany","Arend D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Psaroudakis D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Memon J.A., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Rey-Mazón E., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Schüler D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Szymanski J.J., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Scholz U., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Junker A., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany; Lange M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, OT Gatersleben, Seeland, D-06466, Germany","The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research. © 2022 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.","data stewardship; FAIR data; plant phenomics; research data","Concrete; Data; Farm Crops; Life Cycle; Management; Phases; Plants; Research; Big Data; Phenomics; Plants; Big data; Information management; Life cycle; Metadata; Data life cycle; Data publishing; Data stewardship; Experimental biology; FAIR data; Integral part; Plant phenomic; Plant phenotyping; Project planning; Research data; genetics; plant; Crops","","","","","Horizon 2020 Framework Programme, H2020; Deutsche Forschungsgemeinschaft, DFG, (HE 9114/1‐1); Bundesministerium für Bildung und Forschung, BMBF, (FKZ 031B0770A); Horizon 2020, (862201, 862613)","This work was supported by grants from the German Federal Ministry of Education and Research to Matthias Lange (AVATARS: FKZ 031B0770A) and from European Union's Horizon 2020 Research and Innovation Program to Matthias Lange (AGENT project: grant agreement no. 862613; CAPITALISE project: grant agreement no. 862201). Furthermore the work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – HE 9114/1‐1. The authors thank the NPZ Innovation GmbH for fruitful discussions. Open Access funding enabled and organized by Projekt DEAL.","Andres-Hernandez L., Halimi R.A., Mauleon R., Mayes S., Baten A., King G.J., Challenges for FAIR-compliant description and comparison of crop phenotype data with standardized controlled vocabularies, Database, 2021, (2021); Araus J.L., Kefauver S.C., Zaman-Allah M., Olsen M.S., Cairns J.E., Translating high-throughput phenotyping into genetic gain, Trends in Plant Science, 23, 5, pp. 451-466, (2018); Arend D., Junker A., Scholz U., Schuler D., Wylie J., Lange M., PGP repository: a plant phenomics and genomics data publication infrastructure, Database, 2016, (2016); Arend D., Konig P., Junker A., Scholz U., Lange M., The on-premise data sharing infrastructure e!DAL: foster FAIR data for faster data acquisition, GigaScience, 9, 10, (2020); Arend D., Lange M., Chen J., Colmsee C., Flemming S., Hecht D., Et al., e!DAL - a framework to store, share and publish research data, BMC Bioinformatics, 15, 1, (2014); Arend D., Colmsee C., Knupffer H., Oppermann M., Scholz U., Schuler D., Et al., Data management experiences and best practices from the perspective of a plant research institute, Data integration in the life sciences, 8574, pp. 41-49, (2014); Aubry S., The future of digital sequence information for plant genetic resources for food and agriculture, Frontiers in Plant Science, 10, (2019); Baker K.S., Yarmey L., Data stewardship: environmental data curation and a web-of-repositories, International Journal of Digital Curation, 4, 2, pp. 12-27, (2009); Baker M., 1,500 scientists lift the lid on reproducibility, Nature, 533, 7604, pp. 452-454, (2016); Bierwirth M., Glockner F.O., Grimm C., Schimmler S., Boehm F., Busse C., Et al., Leipzig-Berlin-Erklärung zu NFDI-Querschnittsthemen der Infrastrukturentwicklung, (2020); Plant MIAPPE, (2022); (2022); Bolger A.M., Poorter H., Dumschott K., Bolger M.E., Arend D., Osorio S., Et al., Computational aspects underlying genome to phenome analysis in plants, The Plant Journal, 97, 1, pp. 182-198, (2019); Boudry C., Chartron G., Availability of digital object identifiers in publications archived by PubMed, Scientometrics, 110, 3, pp. 1453-1469, (2017); Brous P., Janssen M., Vilminko-Heikkinen R., Coordinating decision-making in data management activities: a systematic review of data governance principles, Electronic government, 9820, pp. 115-125, (2016); About the Nagoya protocol, (2015); Chen J., Chen Y., Du X., Li C., Lu J., Zhao S., Et al., Big data challenge: a data management perspective, Frontiers in Computational Science, 7, 2, pp. 157-164, (2013); de Almeida A.V., Borges M.M., Roque L., The European Open Science Cloud: A New Challenge for Europe, Proceedings of the 5th international conference on technological ecosystems for enhancing multiculturality, pp. 1-4, (2017); Open-Data-Strategie der Bundesregierung, (2021); Guidelines for safeguarding good research practice, (2019); Digman J.M., Personality structure: emergence of the five-factor model, Annual Review of Psychology, 41, 1, pp. 417-440, (1990); Downs R.R., Duerr R., Hills D.J., Ramapriyan H.K., Data stewardship in the earth sciences, Lib Magazine, 21, 7-8, (2015); (2022); Digital skills for FAIR and Open Science: report from the EOSC Executive Board Skills and Training Working Group. 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Arend; Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Corrensstraße 3, OT Gatersleben, D-06466, Germany; email: arendd@ipk-gatersleben.de","","John Wiley and Sons Inc","","","","","","09607412","","PLJUE","35535481","English","Plant J.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85131557654"
"Donaldson D.R.; Koepke J.W.","Donaldson, Devan Ray (36141752900); Koepke, Joshua Wolfgang (57746051600)","36141752900; 57746051600","A focus groups study on data sharing and research data management","2022","Scientific Data","9","1","345","","","","3","10.1038/s41597-022-01428-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132127507&doi=10.1038%2fs41597-022-01428-w&partnerID=40&md5=1ccf3ca46c215192739ee146213aaa07","Department of Information and Library Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States","Donaldson D.R., Department of Information and Library Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States; Koepke J.W., Department of Information and Library Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States","Data sharing can accelerate scientific discovery while increasing return on investment beyond the researcher or group that produced them. Data repositories enable data sharing and preservation over the long term, but little is known about scientists’ perceptions of them and their perspectives on data management and sharing practices. Using focus groups with scientists from five disciplines (atmospheric and earth science, computer science, chemistry, ecology, and neuroscience), we asked questions about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans. Participants identified metadata quality control and training as problem areas in data management. Additionally, participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. We present their desired repository features as a rubric for the research community to encourage repository utilization. Future directions for research are discussed. © 2022, The Author(s).","","Data Management; Focus Groups; Humans; Information Dissemination; Metadata; Research Personnel; human; information dissemination; information processing; metadata; personnel","","","","","Institute of Museum and Library Services, IMLS, (RE-37-19-0082-19)","This material is based on work supported by the Institute of Museum and Library Services under grant number RE-37-19-0082-19. ","Curty R.G., Crowston K., Specht A., Grant B.W., Dalton E.D., Attitudes and norms affecting scientists’ data reuse, PLOS ONE, 12, (2017); Vuong Q.H., Author’s corner: Open data, open review and open dialogue in making social sciences plausible, Scientific Data Updates, (2017); Duke C.S., Porter J.H., The ethics of data sharing and reuse in biology, BioScience, 63, pp. 483-489, (2013); Perrino T., Et al., Advancing science through collaborative data sharing and synthesis, Perspect Psychol Sci, 8, pp. 433-444, (2013); Pisani E., Et al., Beyond open data: realising the health benefits of sharing data, BMJ, 355, (2016); Vuong Q.H., The (ir)rational consideration of the cost of science in transition economies, Nat Hum Behav, 2, (2018); Ukwoma S.C., Dike V.W., Academics’ attitudes toward the utilization of institutional repositories in Nigerian universities, portal, 17, pp. 17-32, (2017); Bagdasarian N., Cross G.B., Fisher D., Rapid publications risk the integrity of science in the era of COVID-19, BMC Med, 18, (2020); Vuong Q.H., Et al., Covid-19 vaccines production and societal immunization under the serendipity-mindsponge-3D knowledge management theory and conceptual framework, Humanit Soc Sci Commun, 9, (2022); Bezuidenhout L., To share or not to share: incentivizing data sharing in life science communities, Developing World Bioeth, 19, pp. 18-24, (2019); Borgmandata C.L.B.D., No Data: Scholarship in the Networked World, (2016); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, IJDC, 8, pp. 5-26, (2013); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Phil. Trans. R. Soc. A., 368, pp. 4023-4038, (2010); Pryor G., Attitudes and aspirations in a diverse world: the Project StORe perspective on scientific repositories, IJDC, 2, pp. 135-144, (2008); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, pp. 467-482, (2014); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries, Int J Digit Libr, 7, pp. 17-30, (2007); Cragin M.H., Shankar K., Scientific data collections and distributed collective practice, Comput Supported Coop Work, 15, pp. 185-204, (2006); Polydoratou P., Use and linkage of source and output repositories and the expectations of the chemistry research community about their use, Digital Libraries: Achievements, Challenges and Opportunities (Eds. Sugimoto, S., Hunter, J., Rauber, A. & Morishima, A.) 4312 429–438 (Springer Berlin Heidelberg, (2006); Edwards P.N., A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming., (2013); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Gil Y., Et al., Toward the geoscience paper of the future: best practices for documenting and sharing research from data to software to provenance, Earth and Space Science, 3, pp. 388-415, (2016); Tenopir C., Et al., Data sharing by scientists: practices and perceptions, PLOS ONE, 6, (2011); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLOS ONE, 10, (2015); Waide R.B., Brunt J.W., Servilla M.S., Demystifying the landscape of ecological data repositories in the United States, BioScience, 67, pp. 1044-1051, (2017); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library and Information Systems, 49, pp. 382-407, (2015); Hampton S.E., Et al., Big data and the future of ecology, Frontiers in Ecology and the Environment, 11, pp. 156-162, (2013); Tenopir C., Christian L., Allard S., Borycz J., Research data sharing: practices and attitudes of geophysicists, Earth and Space Science, 5, pp. 891-902, (2018); De Biagi L.D., Saccone M., Trufelli L., Puccinelli R., Research product repositories: strategies for data and metadata quality control, Grey Journal (TGJ), 8, pp. 83-94, (2012); Schriml L.M., Et al., COVID-19 pandemic reveals the peril of ignoring metadata standards, Sci Data, 7, (2020); Cyberinfrastructure, data, and libraries, part 2: Libraries and the data challenge: Roles and actions for libraries, D-Lib Magazine, (2007); MacMillan D., Data sharing and discovery: what librarians need to know, The Journal of Academic Librarianship, 40, pp. 541-549, (2014); Borgman C.L., The conundrum of sharing research data, JASIST, 63, pp. 1059-1078, (2012); Thematic Analysis.; Donaldson D.R., Focus groups on data sharing and research data management with scientists from five disciplines, figshare https, (2022); Krueger R.A., Casey M.A., Focus Groups: A Practical Guide for Applied Research, (SAGE Publications, (2015); Then K.L., Rankin J.A., Ali E., Focus group research: what is it and how can it be used?, Can J Cardiovasc Nurs, 24, pp. 16-22, (2014); Wallace R., Goodyear-Grant E., Bittner A., Harnessing technologies in focus group research, Can J Pol Sci, 54, pp. 335-355, (2021); Kim Y., Stanton J., Institutional and individual influences on scientists’ data sharing practices, JOCSE, 3, pp. 47-56, (2012)","D.R. Donaldson; Department of Information and Library Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, United States; email: drdonald@indiana.edu","","Nature Research","","","","","","20524463","","","35715445","English","Sci. Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85132127507"
"Carmona-Pírez J.; Poblador-Plou B.; Poncel-Falcó A.; Rochat J.; Alvarez-Romero C.; Martínez-García A.; Angioletti C.; Almada M.; Gencturk M.; Sinaci A.A.; Ternero-Vega J.E.; Gaudet-Blavignac C.; Lovis C.; Liperoti R.; Costa E.; Parra-Calderón C.L.; Moreno-Juste A.; Gimeno-Miguel A.; Prados-Torres A.","Carmona-Pírez, Jonás (57202163168); Poblador-Plou, Beatriz (35622277100); Poncel-Falcó, Antonio (55043233700); Rochat, Jessica (57194129754); Alvarez-Romero, Celia (57210788267); Martínez-García, Alicia (55937333600); Angioletti, Carmen (57205739691); Almada, Marta (51160963300); Gencturk, Mert (53063547700); Sinaci, A. Anil (36905158800); Ternero-Vega, Jara Eloisa (57192702213); Gaudet-Blavignac, Christophe (57090883300); Lovis, Christian (55046580400); Liperoti, Rosa (9940135200); Costa, Elisio (7402527214); Parra-Calderón, Carlos Luis (24332533000); Moreno-Juste, Aida (57194234154); Gimeno-Miguel, Antonio (57201724319); Prados-Torres, Alexandra (57200124667)","57202163168; 35622277100; 55043233700; 57194129754; 57210788267; 55937333600; 57205739691; 51160963300; 53063547700; 36905158800; 57192702213; 57090883300; 55046580400; 9940135200; 7402527214; 24332533000; 57194234154; 57201724319; 57200124667","Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm","2022","International Journal of Environmental Research and Public Health","19","4","2040","","","","1","10.3390/ijerph19042040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124273798&doi=10.3390%2fijerph19042040&partnerID=40&md5=dae91ea198bb93c37bfcd23afb680a13","EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain; Delicias-Sur Primary Care Health Centre, Aragon Health Service (SALUD), Zaragoza, 50009, Spain; Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain; Aragon Health Service (SALUD), Zaragoza, 50017, Spain; Division of Medical Information Sciences, Geneva University Hospitals, Geneva, 1205, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1205, Switzerland; Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, 41013, Spain; Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Rome, 00168, Italy; Ucibio Requimte, Faculty of Pharmacy University of Porto, Porto4Ageing, Porto, 4050-313, Portugal; SRDC Software Research & Development and Consultancy Corporation, Ankara, 06800, Turkey; Internal Medicine Department, Virgen del Rocío University Hospital, Seville, 41013, Spain","Carmona-Pírez J., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain, Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain, Delicias-Sur Primary Care Health Centre, Aragon Health Service (SALUD), Zaragoza, 50009, Spain, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain; Poblador-Plou B., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain, Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain; Poncel-Falcó A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain, Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain, Aragon Health Service (SALUD), Zaragoza, 50017, Spain; Rochat J., Division of Medical Information Sciences, Geneva University Hospitals, Geneva, 1205, Switzerland, Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1205, Switzerland; Alvarez-Romero C., Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, 41013, Spain; Martínez-García A., Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, 41013, Spain; Angioletti C., Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Rome, 00168, Italy; Almada M., Ucibio Requimte, Faculty of Pharmacy University of Porto, Porto4Ageing, Porto, 4050-313, Portugal; Gencturk M., SRDC Software Research & Development and Consultancy Corporation, Ankara, 06800, Turkey; Sinaci A.A., SRDC Software Research & Development and Consultancy Corporation, Ankara, 06800, Turkey; Ternero-Vega J.E., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, 41013, Spain; Gaudet-Blavignac C., Division of Medical Information Sciences, Geneva University Hospitals, Geneva, 1205, Switzerland, Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1205, Switzerland; Lovis C., Division of Medical Information Sciences, Geneva University Hospitals, Geneva, 1205, Switzerland, Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1205, Switzerland; Liperoti R., Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Rome, 00168, Italy; Costa E., Ucibio Requimte, Faculty of Pharmacy University of Porto, Porto4Ageing, Porto, 4050-313, Portugal; Parra-Calderón C.L., Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, 41013, Spain; Moreno-Juste A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain, Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain, Aragon Health Service (SALUD), Zaragoza, 50017, Spain; Gimeno-Miguel A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain, Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain; Prados-Torres A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain, Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, Madrid, 28029, Spain, Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), ISCIII, Madrid, 28029, Spain","The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","FAIR principles; Mortality; Multimorbidity; Pathfinder case study; Privacy-preserving distributed data mining; Research data management","Algorithms; Data Management; Electronic Health Records; Multimorbidity; Privacy; Europe; algorithm; data management; data mining; data set; morbidity; mortality risk; research work; age; aged; algorithm; anemia; anxiety; Article; atrial fibrillation; bibliographic database; body weight loss; chronic kidney failure; comorbidity; controlled study; data privacy; depression; diabetes mellitus; disease association; ethnicity; FAIR4Health solution; female; frequent pattern growth association algorithm; functional status assessment; gender; Groningen Frailty Index; hearing disorder; heart failure; human; hyperlipidemia; hypertension; institutionalization; ischemic heart disease; major clinical study; male; memory disorder; mobile application; mortality rate; mortality risk; obesity; observational study; polypharmacy; prescription; probability; retrospective study; smoking; very elderly; visual disorder; workflow; algorithm; clinical trial; electronic health record; information processing; multicenter study; multiple chronic conditions; privacy","","","","","Carlos III National Institute of Health, (IMP/00019, PT20/00088); Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health, (CM19/00164); RICAPPS, (RD21/0016/0019); Horizon 2020 Framework Programme, H2020, (824666); Federación Española de Enfermedades Raras, FEDER; European Regional Development Fund, ERDF; Red de Investigación en Servicios de Salud en Enfermedades Crónicas, REDISSEC, (RD16/0001/0005)","Funding text 1: Funding: This study was performed in the framework of FAIR4Health, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’, and by REDISSEC (RD16/0001/0005) and RICAPPS (RD21/0016/0019) from Carlos III National Institute of Health. This work was also supported by Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health [Río Hortega Program, grant number CM19/00164].; Funding text 2: Acknowledgments: This work was supported by the FAIR4Health project [8], which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’. Special acknowledgements to the clinical researchers of the project, coming from the health research performing organizations that are part of the FAIR4Health consortium: Université de Genève (Switzerland), University Hospitals of Geneva (Switzerland), Università Cattolica del Sacro Cuore (Italy), Universidade do Porto (Portugal), Instituto Aragonés de Ciencias de la Salud (Spain), Institut Za Plucne Bolesti Vojvodine (Serbia), and Servicio Andaluz de Salud (Spain).","WHO Global Strategy and action Plan on Aging and Health, (2017); Prados-Torres A., Calderon-Larranaga A., Hancco-Saavedra J., Poblador-Plou B., van den Akker M., Multimorbidity patterns: A systematic review, J. Clin. Epidemiol, 67, pp. 254-266, (2014); Barnett K., Mercer S.W., Norbury M., Watt G., Wyke S., Guthrie B., Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study, Lancet, 380, pp. 37-43, (2012); Masnoon N., Shakib S., Kalisch-Ellett L., Caughey G.E., What is polypharmacy? A systematic review of definitions, BMC Geriatr, 17, (2017); Bradley M.C., Motterlini N., Padmanabhan S., Cahir C., Williams T., Fahey T., Hughes C.M., Potentially inappropriate prescribing among older people in the United Kingdom, BMC Geriatr, 14, (2014); Muth C., van den Akker M., Blom J.W., Mallen C.D., Rochon J., Schellevis F.G., Becker A., Beyer M., Gensichen J., Kirchner H., Et al., The Ariadne principles: How to handle multimorbidity in primary care consultations, BMC Med, 12, (2014); Palmer K., Marengoni A., Forjaz M.J., Jureviciene E., Laatikainen T., Mammarella F., Muth C., Navickas R., Prados-Torres A., Rijken M., Et al., Multimorbidity care model: Recommendations from the consensus meeting of the Joint Action on Chronic Diseases and Promoting Healthy Ageing across the Life Cycle (JA-CHRODIS), Health Policy, 122, pp. 4-11, (2018); FAIR4Health FAIR4Health Project; Wilkinson M.D., Dumontier M., Aalbersberg Ij.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., Comment: The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016); Sinaci A.A., Nunez-Benjumea F.J., Gencturk M., Jauer M.L., Deserno T., Chronaki C., Cangioli G., Cavero-Barca C., Rodriguez-Perez J.M., Perez-Perez M.M., Et al., From Raw Data to FAIR Data: The FAIRification Workflow for Health Research, Methods Inf. Med, 59, pp. E21-E32, (2020); Data Curation Tool; Gencturk M., Teoman A., Alvarez-Romero C., Martinez-Garcia A., Parra-Calderon C.L., Poblador-Plou B., Lobe M., Sinaci A.A., End user evaluation of the FAIR4Health data curation tool, Public Health and Informatics; Proc. MIE 2021, pp. 8-12, (2021); FAIR4Health Project Data Privacy Tool; Onder G., Carpenter I., Finne-Soveri H., Gindin J., Frijters D., Henrard J., Nikolaus T., Topinkova E., Tosato M., Liperoti R., Et al., Assessment of nursing home residents in Europe: The Services and Health for Elderly in Long TERm care (SHELTER) study, BMC Health Serv. Res, 12, (2012); Onder G., Liperoti R., Fialova D., Topinkova E., Tosato M., Danese P., Gallo P.F., Carpenter I., Finne-Soveri H., Gindin J., Et al., Polypharmacy in nursing home in Europe: Results from the SHELTER study, J. Gerontol.-Ser. A Biol. Sci. Med. Sci, 67 A, pp. 698-704, (2012); Midao L., Sa C., Marques E., Duarte M., Paul C., Viana J., Costa E., EHEALTH ON FRAILTY: FRAILSURVEY, A RELIABLE SMARTPHONE APPLICATION FOR SELF-ASSESSMENT OF FRAILTY, Innov. Aging, 3, (2019); Prados-Torres A., Poblador-Plou B., Gimeno-Miguel A., Calderon-Larranaga A., Poncel-Falco A., Gimeno-Feliu L.A., Gon-zalez-Rubio F., Laguna-Berna C., Marta-Moreno J., Clerencia-Sierra M., Et al., Cohort Profile: The Epidemiology of Chronic Diseases and Multimorbidity. The EpiChron Cohort Study, Int. J. Epidemiol, 47, pp. 382-384, (2018); FAIR4Health Project FAIR4Health Common Data Model; HL7_FHIR HL7 FHIR; Report on the Demonstrators Performance; FAIR4Health Consortium, (2021); Han J., Pei J., Yin Y., Mining FrequentPatterns without Candidate Generation, SIGMOD, 29, pp. 1-12, (2000); Busija L., Lim K., Szoeke C., Sanders K.M., McCabe M.P., Do replicable profiles of multimorbidity exist? Systematic review and synthesis, Eur. J. Epidemiol, 34, pp. 1025-1053, (2019); Ioakeim-Skoufa I., Poblador-Plou B., Carmona-Pirez J., Diez-Manglano J., Navickas R., Gimeno-Feliu L.A., Gonzalez-Rubio F., Jureviciene E., Dambrauskas L., Prados-Torres A., Et al., Multimorbidity Patterns in the General Population: Results from the EpiChron Cohort Study, Int. J. Environ. Res. Public Health, 17, (2020); Carmona-Pirez J., Poblador-Plou B., Diez-Manglano J., Morillo-Jimenez M.J., Marin Trigo J.M., Ioakeim-Skoufa I., Gimeno Miguel A., Prados-Torres A., Multimorbidity networks of chronic obstructive pulmonary disease and heart failure in men and women: Evidence from the EpiChron Cohort, Mech. Ageing Dev, 193, (2021); Carmona-Pirez J., Poblador-Plou B., Ioakeim-Skoufa I., Gonzalez-Rubio F., Gimeno-Feliu L.A., Diez-Manglano J., Laguna Berna C., Marin J.M., Gimeno-Miguel A., Prados-Torres A., Multimorbidity clusters in patients with chronic obstructive airway diseases in the EpiChron Cohort, Sci. Rep, 11, (2021)","J. Carmona-Pírez; EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; email: jcarmona@iisaragon.es","","MDPI","","","","","","16617827","","","35206230","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124273798"
"Maurya A.; Subaveerapandiyan A.","Maurya, Anuradha (57267267000); Subaveerapandiyan, A. (57221391744)","57267267000; 57221391744","Research Data Preservation Practices of Library and Information Science Faculties","2022","DESIDOC Journal of Library and Information Technology","42","4","","259","264","5","0","10.14429/djlit.42.4.17538","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134364285&doi=10.14429%2fdjlit.42.4.17538&partnerID=40&md5=1129c9fdf54991214c8219ace2573044","Department of Library and Information Science, University of Delhi, New Delhi, 110 007, India; DMI-St. Eugene University, Lusaka, Zambia","Maurya A., Department of Library and Information Science, University of Delhi, New Delhi, 110 007, India; Subaveerapandiyan A., DMI-St. Eugene University, Lusaka, Zambia","Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries. © 2022, DESIDOC.","Research data; Research data awareness and practices; Research data curation; Research data management; Research data preservation","","","","","","","","Barczak G., Hopp C., Kaminski J., Piller F., Pruschak G., How Open is innovation research? – An empirical analysis of data sharing among innovation scholars, Industry and Innovation, 29, 2, pp. 186-218, (2022); Donaldson D.R., Koepke J.W., A focus groups study on data sharing and research data management, Scientific Data, 9, 1, pp. 1-7, (2022); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, PLOS ONE, 12, 3, (2017); Taylor S., Wright S., Narlock M.R., Habermann T., Think Globally, Act Locally: The Importance of Elevating Data Repository Metadata to the Global Infrastructure, pp. 1-46, (2022); Amorim R.C., Castro J. A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Universal Access Inf. Soc, 16, 4, pp. 851-862, (2017); Perrier L., Barnes L., Developing research data management services and support for researchers: Amixed methods study, Partnership: Can. J. Libr. Inf. Pract. Res, 13, 1, pp. 1-23, (2018); Soomro Z.A, Shah M.H., Ahmed J., Information security management needs more holistic approach: A literature review, Int. J. Inf. Manage, 36, 2, pp. 215-225, (2016); Pm N.A., Saeed, Research data management and data sharing among research scholars of life sciences and social sciences, DESIDOC J. Libr. Inf. Technol, 39, 6, pp. 290-299, (2019); Noonan D., Chute T., Data curation and the University archives, The Am. Archivist, 77, 1, pp. 201-240, (2014); Why manage research data? University of Leeds; Unal Y., Chowdhury G., Kurbanoglu S., Boustany J., Walton G., Research data management and data sharing behaviour of university researchers, Inf. Res.: An Int. Electron. J, 24, 1, (2019); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: Integrated findings to develop research data services, J. eSci. Libr, 6, 1, (2017); Schumacher J., VandeCreek D., Intellectual capital at risk: Data management practices and data loss by faculty members at five american universities, Int. J. Digital Curation, 10, 2, pp. 96-109, (2015); Van Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon University, J. Libr. Scholarly Commun, 3, 3, (2015); Mancilla H.A., Teperek M., van Dijck J., den Heijer K., Eggermont R., Plomp E., Turkyilmaz-van der Velden Y, Kurapati S., On a quest for cultural change-Surveying research data management practices at Delft University of Technology, LIBER Q.: The J. Assoc. Eur. R. Libr, 29, 1, pp. 1-27, (2019); Johnson K.A., Steeves V., Research data management among life sciences faculty: Implications for library service, J. Sci. Libr, 8, 1, pp. 1-23, (2019); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: implications for data services development from a faculty survey, Program: Electron. Libr. Inf. Syst, 49, 4, pp. 382-407, (2015); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, J. Assoc. Inf. Sci. Technol, 68, 9, pp. 2182-2200, (2017); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research data services in european academic research libraries, LIBER Q.: The J. Assoc. Eur. Res. Libr, 27, 1, pp. 23-44, (2017); Mohammed M.S., Ibrahim R., Challenges and practices of research data management in selected Iraq universities, DESIDOC J. Libr. Inf. Technol, 39, pp. 308-314, (2019); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership: The Canadian J. Libr. Inf. Pract. Res, 13, 1, (2018); Abdullahi K.A., Exploring the motives behind research data sharing: Nigerian scholars’ perspective, MiddleBelt J. Libr. Inf. Sci, 18, pp. 41-51, (2020); Adika F.O., Kwanya T., Research data management literacy amongst lecturers at Strathmore University, Kenya, Library Management, 41, pp. 447-466, (2020); Chiware E.R.T., Becker D.A., Research data management services in Southern Africa: A readiness survey of academic and research libraries, Af. J. Libr., Archives Inf. Sci, 28, 1, pp. 1-16, (2018); Marlina E., Purwandari B., Strategy for research data management services in Indonesia, Procedia Comput. Sci, 161, pp. 788-796, (2019); Pasek J., Mayer J., Education needs in research data management for science-based disciplines: Self-assessment surveys of graduate and faculty at two public universities, ISTL: Issues Sci. Technol. Libr, 88, 92, (2019); Abduldayan F.J., Abifarin F.P., Oyedum G.U., Alhassan J.A., Research data management practices of chemistry researchers in federal universities of technology in Nigeria, Digital Libr. Perspect, 37, 4, pp. 328-348, (2021); Chawinga W.D., Zinn S., Research data management at a public university in Malawi: The role of “three hands.”, Library Management, 41, pp. 467-485, (2020); Chigwada J.P., Management and maintenance of research data by researchers in Zimbabwe, Global Knowl., Memory Commun, 71, pp. 193-207, (2022)","A. Maurya; Department of Library and Information Science, University of Delhi, New Delhi, 110 007, India; email: connectanu2net@gmail.com","","Defense Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85134364285"
"Torino E.; Brito J.F.; Trevisan G.L.; Vidotti S.A.B.G.","Torino, Emanuelle (57216039217); Brito, Jean Fernandes (57222167423); Trevisan, Gustavo Lunardelli (57216040010); Vidotti, Silvana Aparecida Borsetti Gregorio (55382353300)","57216039217; 57222167423; 57216040010; 55382353300","Research data management infrastructure and services: an assessment within the scope of São Paulo State University (Unesp); [INFRAESTRUTURA E SERVIÇOS DE GESTÃO DE DADOS DE PESQUISA: UMA AVALIAÇÃO NO ÂMBITO DA UNIVERSIDADE ESTADUAL PAULISTA (Unesp)]","2022","Encontros Bibli","27","","","","","","0","10.5007/1518-2924.2022.e85188","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133822198&doi=10.5007%2f1518-2924.2022.e85188&partnerID=40&md5=bdadcde1d227fd2ef702e24c2d341040","Programa de Pós-Graduação em Ciência da Informação, Universidade Estadual Paulista (UNESP), Marília, Brazil; Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil; Universidade Estadual Paulista-Unesp, Programa de Pós-Graduação em Ciência da Informação, Marília, Brazil; Universidade Estadual Paulista-Unesp, Departamento de Ciência da Informação, Programa de Pós-Graduação em Ciência da Informação, Marília, Brazil","Torino E., Programa de Pós-Graduação em Ciência da Informação, Universidade Estadual Paulista (UNESP), Marília, Brazil, Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil, Universidade Estadual Paulista-Unesp, Programa de Pós-Graduação em Ciência da Informação, Marília, Brazil; Brito J.F., Programa de Pós-Graduação em Ciência da Informação, Universidade Estadual Paulista (UNESP), Marília, Brazil, Universidade Estadual Paulista-Unesp, Programa de Pós-Graduação em Ciência da Informação, Marília, Brazil; Trevisan G.L., Programa de Pós-Graduação em Ciência da Informação, Universidade Estadual Paulista (UNESP), Marília, Brazil, Universidade Estadual Paulista-Unesp, Programa de Pós-Graduação em Ciência da Informação, Marília, Brazil; Vidotti S.A.B.G., Programa de Pós-Graduação em Ciência da Informação, Universidade Estadual Paulista (UNESP), Marília, Brazil, Universidade Estadual Paulista-Unesp, Departamento de Ciência da Informação, Programa de Pós-Graduação em Ciência da Informação, Marília, Brazil","Objective: Understand the process of infrastructure self-assessment and the provision of research data management services, through the diagnosis of the São Paulo State University, using the Research Infrastructure Self-Evaluation Framework. Methods: Exploratory, applied and qualitative research, which consisted of the self-assessment carried out at the São Paulo State University, using the Research Infrastructure Self-Evaluation, generating a spider chart, in order to assess the infrastructure and the offer of services of research data management. Data collection consisted of applying the Research Infrastructure Self-Evaluation, resulting in the mapping of research data management practices in the Institution. Results: Based on the mapping, it was possible to analyze the strengths and needs to improve the infrastructure to support research data management at São Paulo State University, enabling the Institution to verify aspects that require investments, in order to improve the infrastructure and support services for researchers. Conclusions: Starting from the self-assessment and analysis of aspects related to research data management, it was possible to identify the points highlighted, among which institutional attention to policies stands out, which, although general, can be applied to research data management. However, for the infrastructure and research data management services to be properly structured, it is necessary to have a global look by São Paulo State University for all aspects that make up the management of research data, as structured in the Research Infrastructure Self-Evaluation model and through the As a result of self-assessment, invest in filling existing gaps and remedying them for the benefit of the researcher, their research results, the scientific community, society and the institution itself. © 2022, Universidade Federal de Santa Catarina. All rights reserved.","Research Data; Research Data Management; Research Infrastructure Self-Evaluation; RISE","","","","","","Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, (311936/2016-4)","Bolsa de Produtividade em Pesquisa PQ do CNPq – Processo nº 311936/2016-4.","AKERS K. G., DOTY J., Research data management practices and perspectives: Differences among the arts and humanities, social sciences, medical sciences, and basic sciences, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); BHIDE A., Como os empreendedores constroem estratégias que dão certo, Harvard Business Review. Empreendedorismo e estratégia, (2002); (2018); Altera, atualiza e consolida a legislação sobre direitos autorais e dá outras providências, (1998); Ministério da Saúde. Resolução nº 466, de 12 de dezembro de 2012. Aprovar as seguintes diretrizes e normas regulamentadoras de pesquisas envolvendo seres humanos; (2016); HEDSTROM M., Digital preservation: a time bomb for digital libraries, Computers and the Humanities, 31, pp. 189-202, (1998); Gestão de Dados, (2020); GILLILAND A. J., Setting the stage, Introduction to metadata, pp. 1-19, (2008); HIGMAN R., BANGERT D., JONES S., Three camps, one destination: the intersections of research data management, FAIR and Open, Insights, 32, 1, (2019); JONES S., Et al., Data management planning: How requirements and solutions are beginning to converge, Data Intelligence, 2, 1-2, pp. 208-219, (2020); MARDERO ARELLANO M. A., Preservação de documentos digitais, Ciência da Informação, Brasília, DF, 33, 2, pp. 15-27, (2004); OECD principles and guidelines for access to research data from public funding, (2007); RANS J., WHYTE A., Using RISE the Research Infrastructure Self-Evaluation Framework, (2017); SALES L., SAYAO L. F., Uma proposta de taxonomia para dados de pesquisa, Conhecimento em Ação, 4, 1, (2019); SANTOS E. A., SILVA C. E., Os modelos de plano de negócios e sua relevância para sustentabilidade das micro e pequenas empresas, Revista Brasileira de Administração Científica, Aquidabã, 3, 1, (2012); SAYAO L. F., Uma outra face dos metadados: informações para a gestão da preservação digital, Encontros Bibli: Revista Eletrônica de Biblioteconomia e Ciência da Informação, Florianópolis, 15, 30, pp. 1-31, (2010); SAYAO L. F., SALES L., Guia de gestão de dados de pesquisa para bibliotecários e pesquisadores, (2015); Evaluate your RDM Offering, (2020); STRASSER C, COOK R., MICHENER W., BUDDEN A., Primer on data management: what you always wanted to know, (2012); TORINO E., ROA-MARTINEZ S. M., VIDOTTI S. A. B. G., Dados de pesquisa: disponibilização ou publicação?, Tópicos sobre dados abertos para editores científicos, pp. 183-201, (2020); WHYTE A., TEDDS J., Making the case for research data management, DCC Briefing Papers, (2020)","","","Universidade Federal de Santa Catarina","","","","","","15182924","","","","Portuguese","Encontro. Bibl.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85133822198"
"Range J.; Halupczok C.; Lohmann J.; Swainston N.; Kettner C.; Bergmann F.T.; Weidemann A.; Wittig U.; Schnell S.; Pleiss J.","Range, Jan (57211256726); Halupczok, Colin (57387254600); Lohmann, Jens (57386881900); Swainston, Neil (6507079993); Kettner, Carsten (56218961600); Bergmann, Frank T. (57197963162); Weidemann, Andreas (7004164137); Wittig, Ulrike (6603347010); Schnell, Santiago (7006138397); Pleiss, Jürgen (7004236379)","57211256726; 57387254600; 57386881900; 6507079993; 56218961600; 57197963162; 7004164137; 6603347010; 7006138397; 7004236379","EnzymeML—a data exchange format for biocatalysis and enzymology","2022","FEBS Journal","289","19","","5864","5874","10","4","10.1111/febs.16318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121821483&doi=10.1111%2ffebs.16318&partnerID=40&md5=b0aae9898d07092849c00e41fd1ba9c2","Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; Beilstein-Institut, Frankfurt am Main, Germany; BioQUANT/COS, Heidelberg University, Germany; Heidelberg Institute for Theoretical Studies, Germany; Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, United States; Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States","Range J., Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany; Halupczok C., Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany; Lohmann J., Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany; Swainston N., Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom; Kettner C., Beilstein-Institut, Frankfurt am Main, Germany; Bergmann F.T., BioQUANT/COS, Heidelberg University, Germany; Weidemann A., Heidelberg Institute for Theoretical Studies, Germany; Wittig U., Heidelberg Institute for Theoretical Studies, Germany; Schnell S., Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, United States, Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States; Pleiss J., Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany","EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK. © 2021 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.","biocatalysis; bioinformatics; data exchange; enzymology; FAIR data principles; Python; research data management; Systems Biology Markup Language; XML","Biocatalysis; Databases, Factual; Software; application programming interface; Article; biocatalysis; data base; data exchange format; data flow; data processing; documentation; electronic spreadsheet; enzyme kinetics; enzymology; feasibility study; flow; kinetic modeling; model; protocol compliance; publication; simulation; software; standard; biocatalysis; factual database; software","","","","","Michael Hucka; California Institute of Technology, CIT; Biotechnology and Biological Sciences Research Council, BBSRC, (BB/S004955/1); University of Liverpool, UoL; Deutsche Forschungsgemeinschaft, DFG, (EXC2075, EXC310); Bundesministerium für Bildung und Forschung, BMBF, (031A540, 031L0104A); Klaus Tschira Stiftung, KTS; Universität Stuttgart","Funding text 1: The authors acknowledge Michael Hucka (California Institute of Technology) for inspiring discussions and constructive comments during the meetings of the EnzymeML Development Team and Patrick Buchholz (University of Stuttgart) for his support with BioCatNet. JP acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, grants EXC310 and EXC2075). NS acknowledges funding from the Biotechnology and Biological Sciences Research Council (BBSRC) under grant “GeneORator: a novel and high-throughput method for the synthetic biology-based improvement of any enzyme” (BB/S004955/1) and from the University of Liverpool. AW and UW acknowledge funding from the Klaus Tschira Foundation and the German Federal Ministry of Education and Research within de.NBI (031A540). FTB acknowledges funding from the German Federal Ministry of Education and Research within de.NBI (031L0104A). We are grateful for the support of Beilstein-Institut zur Förderung der Chemischen Wissenschaften by supporting discussions through its Beilstein Enzymology Symposia and STRENDA Commission Meetings. Open access funding enabled and organized by ProjektDEAL.; Funding text 2: The authors acknowledge Michael Hucka (California Institute of Technology) for inspiring discussions and constructive comments during the meetings of the EnzymeML Development Team and Patrick Buchholz (University of Stuttgart) for his support with BioCatNet. JP acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, grants EXC310 and EXC2075). NS acknowledges funding from the Biotechnology and Biological Sciences Research Council (BBSRC) under grant “GeneORator: a novel and high‐throughput method for the synthetic biology‐based improvement of any enzyme” (BB/S004955/1) and from the University of Liverpool. AW and UW acknowledge funding from the Klaus Tschira Foundation and the German Federal Ministry of Education and Research within de.NBI (031A540). FTB acknowledges funding from the German Federal Ministry of Education and Research within de.NBI (031L0104A). We are grateful for the support of Beilstein‐Institut zur Förderung der Chemischen Wissenschaften by supporting discussions through its Beilstein Enzymology Symposia and STRENDA Commission Meetings. Open access funding enabled and organized by ProjektDEAL. ","Pellis A., Cantone S., Ebert C., Gardossi L., Evolving biocatalysis to meet bioeconomy challenges and opportunities, N Biotechnol, 40, pp. 154-169, (2018); Decoene T., De Paepe B., Maertens J., Coussement P., Peters G., De Maeseneire S.L., Et al., Standardization in synthetic biology: an engineering discipline coming of age, Crit Rev Biotechnol, 38, pp. 647-656, (2018); Lapatas V., Stefanidakis M., Jimenez R.C., Via A., Schneider M.V., Data integration in biological research: an overview, J Biol Res, 22, pp. 1-16, (2015); Kettner C., Cornish-Bowden A., Quo Vadis, enzymology data? 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Pleiss; Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany; email: juergen.pleiss@itb.uni-stuttgart.de","","John Wiley and Sons Inc","","","","","","1742464X","","FJEOA","34890097","English","FEBS J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85121821483"
"Schmieg B.; Brandt N.; Schnepp V.J.; Radosevic L.; Gretzinger S.; Selzer M.; Hubbuch J.","Schmieg, Barbara (56674570600); Brandt, Nico (57219502886); Schnepp, Vera J. (57866678800); Radosevic, Luka (57220836075); Gretzinger, Sarah (55033686800); Selzer, Michael (24503304900); Hubbuch, Jürgen (6602622772)","56674570600; 57219502886; 57866678800; 57220836075; 55033686800; 24503304900; 6602622772","Structured Data Storage for Data-Driven Process Optimisation in Bioprinting","2022","Applied Sciences (Switzerland)","12","15","7728","","","","0","10.3390/app12157728","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136939752&doi=10.3390%2fapp12157728&partnerID=40&md5=fda0cf383682c268fefd77102fb69725","Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany; Institute of Engineering in Life Sciences—Section IV: Molecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany; Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany; Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Karlsruhe, 76133, Germany","Schmieg B., Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany, Institute of Engineering in Life Sciences—Section IV: Molecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany; Brandt N., Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany; Schnepp V.J., Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany; Radosevic L., Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany; Gretzinger S., Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany, Institute of Engineering in Life Sciences—Section IV: Molecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany; Selzer M., Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany, Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Karlsruhe, 76133, Germany; Hubbuch J., Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany, Institute of Engineering in Life Sciences—Section IV: Molecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany","Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats. © 2022 by the authors.","bioprinting; data filtering; data-driven process development; digitisation; electronic lab notebook; open source; research data management; systematic data storage","","","","","","Karlsruhe Institute of Technology, KIT; Bundesministerium für Bildung und Forschung, BMBF, (03XP0435D, 13XP5071B)","We would like to acknowledge the German Federal Ministry of Education and Research (BMBF) for its financial support within the project SOP_BioPrint, under the grant number 13XP5071B, and within the project FB2 TheoDat, under the grant number 03XP0435D. We acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology.","Xu F., Celli J., Rizvi I., Moon S., Hasan T., Demirci U., A three-dimensional in vitro ovarian cancer coculture model using a high-throughput cell patterning platform, Biotechnol. J, 6, pp. 204-212, (2011); Vermeulen N., Haddow G., Seymour T., Faulkner-Jones A., Shu W., 3D bioprint me: A socioethical view of bioprinting human organs and tissues, J. Med. Ethics, 43, pp. 618-624, (2017); Kilian D., Sembdner P., Bretschneider H., Ahlfeld T., Mika L., Lutzner J., Holtzhausen S., Lode A., Stelzer R., Gelinsky M., 3D printing of patient-specific implants for osteochondral defects: Workflow for an MRI-guided zonal design, Bio-Des. 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Cybern, 49, pp. 4042-4050, (2019); Gretzinger S., Beckert N., Gleadall A., Lee-Thedieck C., Hubbuch J., 3D bioprinting—Flow cytometry as analytical strategy for 3D cell structures, Bioprinting, 11, (2018); Schmieg B., Nguyen M., Franzreb M., Simulative Minimization of Mass Transfer Limitations Within Hydrogel-Based 3D-Printed Enzyme Carriers, Front. Bioeng. Biotechnol, 8, (2020); IAM-CMS/kadi-apy: Kadi4Mat API Library (0.21.0); IAM-CMS/kadi: Kadi4Mat (kadi-v0.25.1)","J. Hubbuch; Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany; email: juergen.hubbuch@kit.edu","","MDPI","","","","","","20763417","","","","English","Appl. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85136939752"
"Milewska A.; Wiśniewska N.; Cimoszko P.; Rusakow J.","Milewska, Agnieszka (57520029200); Wiśniewska, Natalia (55935834600); Cimoszko, Paulina (57258924400); Rusakow, Jakub (57258834000)","57520029200; 55935834600; 57258924400; 57258834000","A survey of medical researchers indicates poor awareness of research data management processes and a role for data librarians","2022","Health Information and Libraries Journal","39","2","","132","141","9","4","10.1111/hir.12403","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114941493&doi=10.1111%2fhir.12403&partnerID=40&md5=0293fd33b48b76c5835408f928fdd5cc","Medical University of Gdansk Main Library, Gdansk, Poland; Faculty of Biology, University of Gdansk, Gdansk, Poland","Milewska A., Medical University of Gdansk Main Library, Gdansk, Poland; Wiśniewska N., Medical University of Gdansk Main Library, Gdansk, Poland, Faculty of Biology, University of Gdansk, Gdansk, Poland; Cimoszko P., Medical University of Gdansk Main Library, Gdansk, Poland; Rusakow J., Medical University of Gdansk Main Library, Gdansk, Poland","Background: The European Parliament's directive on open data indicates the direction to follow for all public institutions in Europe. The portal Polish Platform of Medical Research (PPM) required more information about researcher attitudes and training requirements for strategic planning. Objectives: The aim was to assess (1) the status of knowledge about research data management among medical researchers in Poland, and (2) their attitudes towards data sharing. This knowledge may help to inform a training program and adapt PPM to the requirements of researchers. Methods: The authors circulated an online survey and received responses from 603 researchers representing medical sciences and related disciplines. The survey was conducted in 2019 at seven Polish medical universities and at the Nofer Institute of Occupational Medicine. Analysis used descriptive statistics. Results: Data sharing was not widespread (55.7% only shared with their research team, 9.8% had shared data on an open access basis). Many cited possible benefits of research data sharing but were concerned about drawbacks (e.g. fraud, plagiarism). Discussion: Polish medical scientists, like many researchers, are not aware of the processes required for safe data preparation for sharing. Academic libraries should develop roles for data librarians to help train researchers. Conclusion: Fears about the dangers of data sharing need to be overcome before researchers are willing to share their own research data. © 2021 Health Libraries Group.","academic; central; data management; education and training; Europe; institutional repositories; libraries; libraries; medical; research data; research support; surveys","Data Management; Humans; Information Dissemination; Librarians; Research Personnel; Surveys and Questionnaires; article; awareness; education; financial management; fraud; human; librarian; library; medical research; occupational medicine; plagiarism; Poland; information dissemination; information processing; personnel; questionnaire","","","","","","","Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Carr D., Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak, (2020); Cox A., Kennan M., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, pp. 1432-1462, (2019); Directive 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast), Official Journal of European Union, 62, L 172, pp. 56-83, (2019); Federer L.M., Lu Y.-L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Fenrich W., Siewicz K., Szprot J., Towards open research data in Poland, (2016); Goben A., Griffin T., In aggregate: Trends, needs, and opportunities from research data management surveys, College and Research Libraries, 80, 7, pp. 903-924, (2019); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Kupferschmidt K., ‘A completely new culture of doing research.’ Coronavirus outbreak changes how scientists communicate, (2020); Read K.B., Adapting data management education to support clinical research projects in an academic medical center, Journal of the Medical Library Association, 107, 1, pp. 89-97, (2019); São Paulo Statement on Open Access, (2019); Practical guide to the international alignment of research data management, (2018); Surkis A., LaPolla F.W.Z., Contaxis N., Read K.B., Data Day to Day: Building a community of expertise to address data skills gaps in an academic medical center, Journal of the Medical Library Association, 105, 2, pp. 185-191, (2017); Tenopir C., Birch B., Allard S., Academic libraries and research data services. Current practices and plans for the future, (2012); Tenopir C., Kaufman J., Sandusky R., Pollock D., Research data services in academic libraries: Where are we today?, Choice White Papers, (2019); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Dane badawcze - usługi europejskich bibliotek akademickich, EBIB - Electronic Bulletin for Librarians, 177, 7, pp. 1-17, (2017); Terry R.F., Littler K., Olliaro P.L., Sharing health research data – The role of funders in improving the impact, F1000Research, 7, (2018); Walek A., Is data management a new ‘digitisation’? A change of the role of librarians in the context of changing academic libraries’ tasks, IFLA WLIC 2018 Kuala Lumpur, Malaysia – Transform Libraries, Transform Societies, pp. 1-11, (2018); Waleszko M., Dane badawcze w nowej dyrektywie unijnej, (2019); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 5-21, (2017)","J. Rusakow; Medical University of Gdansk Main Library, Gdansk, Poland; email: jakubrusakow@gumed.edu.pl","","John Wiley and Sons Inc","","","","","","14711834","","","34532974","English","Health Inf. Libr. J.","Article","Final","","Scopus","2-s2.0-85114941493"
"Wagner G.; Lukyanenko R.; Paré G.","Wagner, Gerit (57189003821); Lukyanenko, Roman (37007689800); Paré, Guy (35262553100)","57189003821; 37007689800; 35262553100","Artificial intelligence and the conduct of literature reviews","2022","Journal of Information Technology","37","2","","209","226","17","13","10.1177/02683962211048201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116780866&doi=10.1177%2f02683962211048201&partnerID=40&md5=66544d8e6211b060522e774a484a51df","Department of Information Technologies, HEC Montréal, Montréal, QC, Canada","Wagner G., Department of Information Technologies, HEC Montréal, Montréal, QC, Canada; Lukyanenko R., Department of Information Technologies, HEC Montréal, Montréal, QC, Canada; Paré G., Department of Information Technologies, HEC Montréal, Montréal, QC, Canada","Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual steps of the literature review process. Considering that the use of AI in this context is in an early stage of development, we propose a comprehensive research agenda for AI-based literature reviews (AILRs) in our field. With this agenda, we would like to encourage design science research and a broader constructive discourse on shaping the future of AILRs in research. © Association for Information Technology Trust 2021.","Artificial intelligence; automation; data infrastructure; literature review; machine learning; natural language processing; research data management","Data handling; Information management; Learning algorithms; Machine learning; Comprehensive research; Data infrastructure; Information system research; Literature reviews; Meta-data; Recent trends; Research agenda; Research data managements; Review process; Science disciplines; Natural language processing systems","","","","","","","Adams C.E., Polzmacher S., Wolff A., Systematic reviews: work that needs to be done and not to be done, Journal of Evidence-Based Medicine, 6, 4, pp. 232-235, (2013); Alter S., Architecture of Sysperanto: a model-based ontology of the is field, Communications of the Association for Information Systems, 15, 1, pp. 1-40, (2005); Alvesson M., Sandberg J., Generating research questions through problematization, Academy of Management Review, 36, 2, pp. 247-271, (2011); Al-Zubidy A., Carver J.C., Hale D.P., Et al., Vision for SLR tooling infrastructure: prioritizing value-added requirements, Information and Software Technology, 91, pp. 72-81, (2017); Anderson M.R., Antenucci D., Bittorf V., Et al., Brainwash: a data system for feature engineering, (2013); Antons D., Breidbach C.F., Big data, big insights? 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Paré; Department of Information Technologies, HEC Montréal, Montréal, Canada; email: guy.pare@hec.ca","","SAGE Publications Ltd","","","","","","02683962","","","","English","J. Inf. Technol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85116780866"
"Singh R.K.; Bharti S.; Madalli D.P.","Singh, Ranjeet Kumar (57221852042); Bharti, Sneha (57765393200); Madalli, Devika P. (6507016815)","57221852042; 57765393200; 6507016815","Evaluation of Research Data Management (RDM) services in academic libraries of India: A triangulation approach","2022","Journal of Academic Librarianship","48","6","102586","","","","2","10.1016/j.acalib.2022.102586","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135104738&doi=10.1016%2fj.acalib.2022.102586&partnerID=40&md5=fd5697ca634aff0d90b1ec234b413a4f","Documentation Research and Training Centre, Indian Statistical Institute, 8th Mile Mysore Road, RVCE Post, Bangalore, 560059, India; Department of Library and Information Science, University of Calcutta, West Bengal, Kolkata, 700073, India","Singh R.K., Documentation Research and Training Centre, Indian Statistical Institute, 8th Mile Mysore Road, RVCE Post, Bangalore, 560059, India, Department of Library and Information Science, University of Calcutta, West Bengal, Kolkata, 700073, India; Bharti S., Documentation Research and Training Centre, Indian Statistical Institute, 8th Mile Mysore Road, RVCE Post, Bangalore, 560059, India, Department of Library and Information Science, University of Calcutta, West Bengal, Kolkata, 700073, India; Madalli D.P., Documentation Research and Training Centre, Indian Statistical Institute, 8th Mile Mysore Road, RVCE Post, Bangalore, 560059, India","Research is a planned and scientific method of increasing knowledge that is typically funded by a country's government or funding agencies. Research activity produces valuable data. Research Data Services (RDS) or Research Data Management (RDM) services are considered vital services provided by an academic library and are focused on the management, archiving, processing, and reuse of critical research data. This study evaluates the current status of the adaptation of RDS or RDM services in Indian academic libraries (which includes a total of 186 institutions, including all of India's Central Universities (54) and Institutes of National Importance (132)). A method triangulation approach was used for the data collection, including a literature survey, library website study, online survey, and telephonic interview with LIS professionals from Indian academic libraries. Academic libraries in India are yet to keep up with those in developed countries in adopting RDM services owing to a lack of RDM policy, institutional support, and technological challenges, according to the data. The study also presents suggestions to decision-makers, higher authorities of academic institutions, and the government to develop a strong RDM policy at both the institutional and national levels defining the role and duties of the libraries in RDM. © 2022 Elsevier Inc.","Academic libraries; Library services; RDM; RDS; Research Data Management; Research Data Services; Triangulation","","","","","","","","ACRL, ACRL research planning and review committee. Top ten trends in academic libraries. 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Tiwari A., Madalli D.P., Maturity models in LIS study and practice, Library & Information Science Research, 43, 1, (2021); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers' perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Tripathi M., Shukla A., Sonkar S.K., Research data management practices in university libraries: A study, DESIDOC Journal of Library & Information Technology, 37, 6, (2017); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, 7, (2013); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College & Research Libraries, 78, 7, (2017); Yu H.H., The role of academic libraries in research data service (RDS) provision, The Electronic Library, 35, 4, pp. 783-797, (2017)","R.K. Singh; Documentation Research and Training Centre, Indian Statistical Institute, 8th Mile Mysore Road, RVCE Post, Bangalore-560059, India & Department of Library and Information Science, University of Calcutta, Kolkata, West Bengal, India; email: ranjeet@drtc.isibang.ac.in","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85135104738"
"Gualandi B.; Pareschi L.; Peroni S.","Gualandi, Bianca (57719380000); Pareschi, Luca (57213134751); Peroni, Silvio (35183805200)","57719380000; 57213134751; 35183805200","What do we mean by “data”? A proposed classification of data types in the arts and humanities","2022","Journal of Documentation","79","7","","51","71","20","0","10.1108/JD-07-2022-0146","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144043601&doi=10.1108%2fJD-07-2022-0146&partnerID=40&md5=9f5e2230410c5344d9799f2652cf9626","Research Services Coordination Unit, Research Services Division (ARIC), University of Bologna, Bologna, Italy; School of Economics, University of Rome Tor Vergata, Roma, Italy; Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy","Gualandi B., Research Services Coordination Unit, Research Services Division (ARIC), University of Bologna, Bologna, Italy; Pareschi L., School of Economics, University of Rome Tor Vergata, Roma, Italy; Peroni S., Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy","Purpose: This article describes the interviews the authors conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word “data” in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term. Design/methodology/approach: The authors invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into five main research areas: philology and literary criticism, language and linguistics, history of art, computer science and archival studies. The interviews were transcribed and analysed using a grounded theory approach. Findings: A list of 13 research data types has been compiled thanks to the information collected from participants. The term “data” does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported. Originality/value: Our findings confirm that “data” within the FAIR framework should include all types of inputs and outputs humanities research work with, including publications. Also, the participants of this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues. © 2022, Bianca Gualandi, Luca Pareschi and Silvio Peroni.","FAIR principles; Grounded theory approach; Humanities; Research data management; Survey","","","","","","","","Akers K.J., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Armeni K., Brinkman L., Carlsson R., Eerland A., Fijten R., Fondberg R., Heininga V.E., Heunis S., Wei Qi Koh, Masselink M., Moran N., O Baoill A., Sarafoglou A., Schettino A., Schwamm H., Sjoerds Z., Teperek M., van den Akker O.R., van't Veer A., Zurita-Milla R., Towards wide-scale adoption of open science practices: the role of open science communities, Science and Public Policy, 48, 5, pp. 605-611, (2021); Avanco K., Balula A., Blaszczynska M., Buchner A., Caliman L., Clivaz C., Costa C., Franczak M., Gatti R., Giglia E., Gingold A., Jarmelo S., Padez M.J., Leao D., Maryl M., Melinscak Zlodi I., Mojsak K., Morka A., Mosterd T., Nury E., Plag C., Schafer V., Silva M., Stojanovski J., Szleszynski B., Szulinska A., Toth-Czifra E., Wcislik P., Wieneke L., Future of scholarly communication. Forging an inclusive and innovative research infrastructure for scholarly communication in Social Sciences and Humanities, (2021); Avanco K., Giglia E., Gingold A., OPERAS-P Deliverable D6.3: report on innovative approach to SSH FAIR data and publications (DRAFT), (2021); Baumer E.P., Mimno D., Guha S., Quan E., Gay G.K., Comparing grounded theory and topic modeling: extreme divergence or unlikely convergence?, Journal of the Association for Information Science and Technology, 68, 6, pp. 1397-1410, (2017); Boyd C., Data as assemblage, Journal of Documentation, 78, 6, pp. 1338-1352, (2022); Brown P.O., Cabell D., Chakravarti A., Cohen B., Delamothe T., Eisen M., Grivell L., Guedon J.-C., Hawley R.S., Johnson R.K., Kirschner M.W., Lipman D., Lutzker A.P., Marincola E., Roberts R.J., Rubin G.M., Schloegl R., Siegel V., So A.D., Suber P., Varmus H.E., Velterop J., Walport M.J., Watson L., Bethesda statement on open access publishing, (2003); Chan L., Cuplinskas D., Eisen M., Friend F., Genova Y., Guedon J.-C., Hagemann M., Harnad S., Johnson R., Kupryte R., La Manna M., Rev I., Segbert M., de Souza S., Suber P., Velterop J., Budapest open access initiative, (2002); Giglia E., CO-OPERAS- SSH FAIR data - wksp report - Brussels 03032020, (2020); Co-Operas A., Toth-Czifra E., (2020); Curtain C., QualCoder version 2.8, software heritage, (2022); Horizon Europe, open science. 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Interview Transcripts, (2022); Hansson K., Dahlgren A., Open research data repositories: practices, norms, and metadata for sharing images, Journal of the Association for Information Science and Technology, 73, 2, pp. 303-316, (2021); Harrower N., Maryl M., Biro T., Immenhauser B., Sustainable and FAIR data sharing in the Humanities: recommendations of the ALLEA working group E-Humanities, Digital Repository of Ireland, (2020); Hofelich Mohr A., Bishoff J., Bishoff C., Braun S., Storino C., Johnston L.R., When data is a dirty word: a survey to understand data management needs across diverse research disciplines, Bulletin of the Association for Information Science and Technology, 42, 1, pp. 51-53, (2015); Academic disciplines for Italian University research and teaching; Berlin declaration on open access to knowledge in the sciences and humanities, (2003); CO-OPERAS, (2022); Poole A.H., A greatly unexplored area': digital curation and innovation in digital humanities, Journal of the Association for Information Science and Technology, 68, 7, pp. 1772-1781, (2017); Prost H., Malleret C., Schopfel J., Hidden treasures: opening data in PhD dissertations in social sciences and humanities, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Senseney M., Dickson Koehl E., Nay L., Collaboration, consultation, or transaction: modes of team research in humanities scholarship and strategies for library engagement, College and Research Libraries, 80, 6, pp. 787-804, (2019); Thoegersen J.L., Yeah, I guess that's data': data practices and conceptions among humanities faculty, Libraries and the Academy, 18, 3, pp. 491-504, (2018); Toth-Czifra E., The risk of losing thick description: data management challenges Arts and Humanities face in the evolving FAIR data ecosystem, (2019); Toth-Czifra E., Truan N., Creating and analyzing multilingual parliamentary corpora: research data management workflows volume 1, (2021); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Hofelich Mohr A., Bishoff J., Johnston L.R., Braun S., Storino C., Bishoff C., Data management needs assessment – surveys in CLA, AHC, CSE, and CFANS, University of Minnesota Digital Conservancy, (2015); Poole A.H., Garwood D.A., Digging into data management in public-funded, international research in digital humanities, Journal of the Association for Information Science and Technology, 71, 1, pp. 84-97, (2020)","B. Gualandi; Research Services Coordination Unit, Research Services Division (ARIC), University of Bologna, Bologna, Italy; email: bianca.gualandi4@unibo.it","","Emerald Publishing","","","","","","00220418","","","","English","J. Doc.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85144043601"
"Aronson M.; Hayden G.; Peterson E.","Aronson, Michael (26034518600); Hayden, Gabriele (57980964300); Peterson, Elizabeth (57191197124)","26034518600; 57980964300; 57191197124","Local Cinema History at Scale: Data and Methods for Comparative Exhibition Studies","2022","Iluminace","34","2","","73","100","27","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142611760&partnerID=40&md5=a78812ed8e6fa9b05cea5d1194680bc7","University of Oregon, United States; Research Data Management and Reproducibility at the University of Oregon., United States","Aronson M., University of Oregon, United States; Hayden G., Research Data Management and Reproducibility at the University of Oregon., United States; Peterson E., University of Oregon, United States","Digital tools and digitized sources have expanded our ability to research and present regional film histories, along with the hope of conducting comparative work across both place and time. Along-side these projects are increasing calls for more deliberate coordination of tools, methods, and sources to create more meaningful comparisons. However, it remains difficult for researchers to know what digital projects exist for comparative work, and the methods, points of comparison, data structure, and sources used all considerably vary. Utilizing research data management principles, we conducted an exploratory survey of local film exhibition digital projects to document the current historiographic landscape, and to assess existing coverage of geography, time, sources, data struc-tures, metadata schema, data accessibility and reproducibility. The dataset from the survey results can be shared by researchers to better discover each other’s work, but also to serve as a guide to best practices going forward. © 2022, National Film Archive. All rights reserved.","data management; data reproducibility; digital humanities; film exhibition; New Cinema History","","","","","","","","Acland C., Hoyt E., The Arclight Guidebook to Media History and the Digital Humanities (Reframe Books, (2016); Sustainable and FAIR Data Sharing in the Humanities: Recommendations of the ALLEA Working Group E-Humanities. Edited by Natalie Harrower, Maciej Maryl, Beat Im-Menhauser, and Timea Biro, (2020); Aronson M., Peterson E., Oregon Theater Project Database, (2022); Preserving GIS, (2021); Australian Cinemas Map: A Map of Film Weekly Motion Picture Directory Cinema Data, 1948– 1971, Flinders Institute for Research in the Humanities, (2011); Author_Dataset_Readmetemplate.Txt, (2021); Baptist V., Noordegraaf J., Oort T.V., A Digital Toolkit to Detect Cinema Audiences of the Silent Era: Scalable Perspectives on Film Exhibition and Consumption in Amsterdam Neighbourhoods (1907–1928), Studies in European Cinema, 18, 3, pp. 252-273, (2021); Barats C., Schafer V., Fickers A., Fading Away… The Challenge of Sustaina-bility in Digital Studies, Digital Humanities Quarterly, 14, 3, (2020); Biltereyst D., Maltby R., Meers P., The Routledge Companion to New Cinema History, (2019); Daniel B., Meers P., New Cinema History and the Comparative Mode: Reflections on Comparing Historical Cinema Cultures, Alphaville: Journal of Film and Screen Media, 11, pp. 13-32, (2016); (2021); (2021); (2021); Cinema Context RDF, (2021); Cinema Context RDF Documentation, (2021); (2021); (2021); (2021); Colavizza G., Hrynaszkiewicz I., Staden I., Whitaker K., McGillivray B., The Citation Advantage of Linking Publications to Research Data, Plos One, 15, 4, (2020); Dacos M., Manifeste Des Digital Humanities, (2022); Transformations I Cinema and Media Studies Research Meets Digital Humanities Tools, DH Cinema Projects, (2021); Dibbets K., Cinema Context: Film in Nederland Vanaf 1896: Een Encyclopedie van de Filmcul-tuur, DANS, (2018); Quinn D., The Directory Paradox, Debates in Digital Humanities: Institutions, Infra-Structures at the Interstices; Dressel W., Research Data Management Instruction for Digital Humanities, Journal of Esci-Ence Librarianship, 6, 2, (2017); Drucker J., Svensson P.B.O., The Why and How of Middleware, Digital Humanities Quarterly, 10, 2, (2016); Dutch National Centre of Expertise and Repository for Research Data, (2021); “The Early Cinema in Scotland: Research Project, (2021); FAIR Principles, (2021); Film in the Netherlands from 1896, (2021); Frymus A., Black Women & Cinemas in Harlem, Black Cinemagoing, (2020); Going to the Show: Mapping Moviegoing in North Carolina, (2008); Golub K., Kamal A.M., Vekselius J., Information and Knowledge Organisation in Digital Humanities, Knowledge Organisation for Digital Hu-Manities: An Introduction, pp. 5-6; Hayden G., Peterson E., Aronson M., Replication Data for: Local Cinema History at Scale: Data and Methods for Comparative Exhibition Studies, Harvard Dataverse, (2022); A History of Film Exhibition and Reception in Colonial Hong Kong (1897–1925), (2021); HoMER Network, Homer Projects, (2021); International Organization for Standardization, ISO 3166 Country Codes, (2021); Klenotic J., Mapping Flat, Deep, and Slow: On the ‘Spirit of Place’ in New Cinema History, TMG Journal for Media History, 23, 1-2, pp. 1-34, (2020); Klenotic J., Mapping Movies, (2021); Krautli F., Chen E., Valleriani M., Linked Data Strategies for Conserving Digital Research Outputs: The Shelf Life of Digital Humanities, Information and Knowledge Organisation in Digital Humanities, pp. 206-224; Creative Commons, License Chooser, (2021); Listed Cinemas, Cinema Theatre Association, (2021); Maltby R., New Cinema Histories, Explorations in New Cinema History: Approaches and Case Studies, pp. 1-40, (2011); Making Your Code Citable, Github Guides, (2016); Mapping Desmet; Naud D., Canadian Movie Theaters Spatial Distribution, Cinematographic Atlas, (2012); Orcid; Pafort-Overduin C., Sedgwick J., Van de Vijver L., Identifying Cinema Cultures and Audience Preferences: A Comparative Analysis of Audience Choice and Popularity in Three Medium-sized Northern European Cities in the Mid-1930s, TMG Journal for Media History, 21, 1, pp. 102-118, (2018); Piwowar H.A., Vision T.J., Data Reuse and the Open Data Citation Advantage, Peerj, (2013); Projects, European Association for Digital Humanities, (2021); Regester C., From the Buzzard‘s Roost: Black Movie-Going in Durham and Other North Carolina Cities During the Early Period of American Cinema, Film History, 17, 1, pp. 113-124, (2005); “Sources, Hiding in Plain Sight, Lost Cinemas, (2021); SPARQL Endpoint for Cinema Context, (2021); Sperberg-McQueen C.M., Dubin D., “Data Representation, Digital Humanities Data Cura-Tion (Blog, (2017); Jacqueline Najuma S., Migrating to the Movies: Cinema and Black Urban Modernity, (2005); TMG Journal for Media History, 23, 1-2, (2020); Turner A., README.md: Geoiq/Gc_data, Github Repository, (2022); van Oort T., Industrial Organization of Film Exhibitors in the Low Countries: Comparing the Netherlands and Belgium, 1945–1960, Historical Journal of Film, Radio and Television, 37, 3, pp. 475-498, (2016); Oort, Whitehead J.L., Common Ground: Comparative Histories of Cinema Audiences, TMG Journal for Media History, 23, 1-2, pp. 1-11, (2020); Verhoeven D., New Cinema History and the Computational Turn, In beyond Art, beyond Hu-Manities: A New Creativity (World Congress of Communication and the Arts Conference Pro-Ceedings, (2012); Verhoeven D., Visualising Data in Digital Cinema Studies: More than Just Going through the Motions?, Alphaville: Journal of Film and Screen Media, 11, pp. 92-104, (2016); CERN Data Centre & Invenio, (2021)","","","National Film Archive","","","","","","0862397X","","","","English","Iluminace","Article","Final","","Scopus","2-s2.0-85142611760"
"Medina J.; Ziaullah A.W.; Park H.; Castelli I.E.; Shaon A.; Bensmail H.; El-Mellouhi F.","Medina, Johanne (57214653244); Ziaullah, Abdul Wahab (57389327900); Park, Heesoo (57200370569); Castelli, Ivano E. (25821783000); Shaon, Arif (36091877800); Bensmail, Halima (9238553100); El-Mellouhi, Fedwa (8435468300)","57214653244; 57389327900; 57200370569; 25821783000; 36091877800; 9238553100; 8435468300","Accelerating the adoption of research data management strategies","2022","Matter","5","11","","3614","3642","28","0","10.1016/j.matt.2022.10.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144248526&doi=10.1016%2fj.matt.2022.10.007&partnerID=40&md5=24e5109fae322550d8f99ca9d1fa2841","College of Science and Engineering, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar; Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar; Centre for Material Science and Nanotechnology, Department of Chemistry, University of Oslo, Oslo, Norway; Department of Energy Conversion and Storage, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; Qatar National Library, Qatar Foundation, P.O. Box 5825, Doha, Qatar; Qatar Computing Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar","Medina J., College of Science and Engineering, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar; Ziaullah A.W., Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar; Park H., Centre for Material Science and Nanotechnology, Department of Chemistry, University of Oslo, Oslo, Norway; Castelli I.E., Department of Energy Conversion and Storage, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; Shaon A., Qatar National Library, Qatar Foundation, P.O. Box 5825, Doha, Qatar; Bensmail H., Qatar Computing Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar; El-Mellouhi F., Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar","The need for good research data management (RDM) practices is becoming more recognized as a critical part of research. This may be attributed to the 5V challenge in big data: volume, variety, velocity, veracity, and value. The materials science community is no exception to these challenges as it heralds its new paradigm of data-driven science, which uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, preserve, and disseminate these data in a way that is findable, accessible, interoperable, and reusable (FAIR). To understand the current state of data-driven materials science and learn about the challenges faced with RDM, we gather user stories of researchers from small- and large-scale projects. This enables us to provide relevant recommendations within the data-driven research life cycle to develop and/or procure an effective RDM system following the FAIR guiding principles. © 2022 Elsevier Inc.","batteries; data-driven research; FAIR guiding principles; MAP5: Improvement; materials science; perovskite crystal structure; photovoltaic devices; research data management","Crystal structure; Electric batteries; Information management; Life cycle; Battery; Data driven; Data-driven research; Findable, accessible, interoperable, and reusable guiding principle; Guiding principles; MAP5: improvement; Material science; Perovskite crystal structure; Photovoltaic devices; Research data managements; Perovskite","","","","","AIPAM; Office of the Hamad Bin Khalifa University, (VPR-TG01-006); Qatar National Research Fund, QNRF, (GSRA8-L-2-0503-21026, NPRP12S-0209-190063); Horizon 2020 Framework Programme, H2020; Norges Forskningsråd, (257653); Horizon 2020, (957189, 957213)","Funding text 1: Low-hanging fruits for effective data management could be achieved by implementing some of the relevant principles at a work package or a small project level that can be quickly and easily adopted even for projects with a modest budget. Research projects with considerable financial support within large consortia should consider the following: ; Funding text 2: This work is supported by the Qatar National Research Fund (QNRF) through the Graduate Sponsorship Research Award Cycle 8 (GSRA8-L-2-0503-21026) and the National Priority Research Program Cycle 12 (NPRP12S-0209-190063). F.E.-M. acknowledges support (AIPAM) from the Office of the Hamad Bin Khalifa University Vice President of Research under grant number VPR-TG01-006. I.E.C. acknowledges support from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 957189 (BIG-MAP) and no. 957213 (BATTERY 2030+). H.P. acknowledges the support of MoZEES, a Norwegian Centre for Environment-friendly Energy Research (FME), co-sponsored by the Research Council of Norway (project no. 257653). Conceptualization, J.M. A.S. and F.E.-M.; investigation and resources, J.M. A.W.Z. H.P. I.E.C. and F.E.-M.; writing – original draft, J.M. A.Z. H.P. I.E.C. and A.S.; writing – editing and revisions, J.M. A.W.Z. A.S. and F.E.-M.; visualization, J.M.; supervision and funding acquisition, A.S. H.B. F.E.-M. 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Energy, 12, (2021); Xu H., Thakur K., Kamruzzaman A.S., Ali M.L., Applications of cryptography in database: a review, 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1-6, (2021); Del Valle E., Sharing My Loss to Protect Your Data: A Story of Unexpected Data Loss and How to Do Real Preservation, 9, (2017); (2022); Where Discoveries Begin; Accelerating the Discovery of Materials and Molecules Needed for a Sustainable Future; The FAIRmat Consortium; Musen M.A., Without appropriate metadata, data-sharing mandates are pointless, Nature, 609, (2022)","J. Medina; College of Science and Engineering, Hamad Bin Khalifa University, Doha, P.O. Box 34110, Qatar; email: jmedina@hbku.edu.qa; F. El-Mellouhi; Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, P.O. Box 34110, Qatar; email: felmellouhi@hbku.edu.qa","","Cell Press","","","","","","25902393","","","","English","Matter","Review","Final","","Scopus","2-s2.0-85144248526"
"Zibani P.; Rajkoomar M.; Naicker N.","Zibani, Patiswa (57238817200); Rajkoomar, Mogiveny (57219251817); Naicker, Nalindren (57198431553)","57238817200; 57219251817; 57198431553","A systematic review of faculty research repositories at higher education institutions","2022","Digital Library Perspectives","38","2","","237","248","11","1","10.1108/DLP-04-2021-0035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113892727&doi=10.1108%2fDLP-04-2021-0035&partnerID=40&md5=a7dc6e62d113a0af9887f2426a8a3de7","Durban University of Technology, Durban, South Africa; Department of Information Systems, Durban University of Technology, Durban, South Africa","Zibani P., Durban University of Technology, Durban, South Africa; Rajkoomar M., Department of Information Systems, Durban University of Technology, Durban, South Africa; Naicker N., Durban University of Technology, Durban, South Africa","Purpose: This study aims to evaluate faculty research repositories used in higher education institutions, their different levels and functions with regard to research information management. This is revealed through the selected studies reviewed. Design/methodology/approach: A systematic literature search of journal article studies on research repositories in higher education institutions was carried out on several databases, namely, Ebscohost, Emerald Insight, Science Direct, Sage, Google Scholar, SA e-Publications and citation databases such as Scopus and Web of Science. The systematic review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses guidelines. The time frame for the analysis was 2015 to 2021. Findings: The findings are presented on the motives for developing faculty research repositories the services provided and benefits derived from faculty research repositories and what is the utilization of faculty research repositories. Originality/value: The results show that the development of research repositories at the faculty level enhances sharing, analysis, evaluation and preservation of scholarly research produced. © 2021, Patiswa Zibani, Mogiveny Rajkoomar and Nalindren Naicker.","Cloud-based; PRISMA; Repositories; Research data; Research data management; Research platforms","Design/methodology/approach; Google scholar; Higher education institutions; Journal articles; Literature search; Scholarly research; Systematic Review; Web of Science; Information management","","","","","Durban University of Technology, DUT","Thanks to the Durban University of Technology for providing resources and funding for this research study.","Allison-Cassin S., Scott D., Wikidata: a platform for your library’s linked open data, Code4Lib Journal, (2018); Armbruster C., Romary L., Comparing repository types: challenges and barriers for subject-based repositories, research repositories, national repository systems and institutional repositories in serving scholarly communication, International Journal of Digital Library Systems, 1, 4, pp. 62-63, (2010); Brouwer D., Du Plessis J.L., Occupational hygiene-related research in South Africa: development of a research repository, Occupational Health Southern Africa, 22, 4, (2016); Deng S., Expanding the metadata librarian horizon: reflections on the metadata practices in the web and digital repositories, presented at the 2018 ALA Midwinter Meeting, (2018); Sharing research data, (2019); Fienberg S.E., Martin M.E., Straf M.L., Sharing Research Data, (1985); Glaeser P.S., Scientific and Technical Data in a New Era, (1990); Henderson S., The OnCoRe blueprint: the art and science of repository creation, Proceedings of 8th Annual MERLOT International Conference, (2008); Jernigan T.L., Brown T.T., Hagler Jr D.J., Akshoomoff N., Bartsch H., Newman E., Thompson W.K., Bloss C.S., Murray S.S., Schork N., Kennedy D.N., The pediatric imaging, neurocognition, and genetics (PING) data repository, Neuroimage, 124, pp. 1149-1154, (2016); Jettena M., Simons E., Rijnders J., The role of CRIS’s in the research life cycle. A case study on implementing a FAIR RDM policy at radboud university, Netherlands, Procedia Computer Science, 146, 2019, pp. 156-165, (2019); Johnson R.K., Institutional repositories: partnering with faculty to enhance scholarly communication, D-Lib Magazine, 8, 11, (2002); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: how we shape up, Australian Academic and Research Libraries, 45, 4, pp. 296-308, (2014); Kanngieser A., Neilson B., Rossiter N., What is a research platform? Mapping methods, mobilities and subjectivities, Media, Culture and Society, 36, 3, pp. 302-318, (2014); KemEnya Z., Richard, Beregia J.R., Erdos G., Nacsa J., The MTA SZTAKI smart factory: platform for research and project-oriented skill development in higher education, Procedia CIRP, 54, pp. 53-58, (2016); Kim J., Warga E., Moen W., Competencies required for digital curation: an analysis of job advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Leng C.B., Ali K.M., Hoo C.E., Open access repositories on open educational resources: feasibility of adopting the Japanese model for academic libraries, Asian Association of Open Universities Journal, 11, 1, pp. 35-49, (2016); Matkin G.W., Learning object repositories: problems and promise, (2002); Mi X., Bernardy R., Schmidt L., Building an archaeological data repository: a digital library and digital humanities collaboration at the university of South Florida, International Journal on Digital Libraries, 22, 1, pp. 135-145, (2021); Moher D., Liberati A., Tetzlaff J., Altman D.G., Group P., Preferred reporting items for systematic reviews and meta-analyses: the prisma statement, Annals of Internal Medicine, 151, 4, pp. 264-269, (2009); Meta-Analysis of artificial intelligence works in ubiquitous learning environments and technologies, International Journal of Advanced Computer Science and Applications, 11, 9, (2020); Schumacher J., Vande Creek D., Intellectual capital at risk: data management practices and data loss by faculty members at five American universities, International Journal of Digital Curation, 10, 2, pp. 96-109, (2015); Shearer K., Haigh S., Whitehead M., Supporting Canadian innovation through shared expertise and stewardship of research data, Paper Presented on the 81st IFLA General Conference and Assembly, pp. 15-21, (2015); Sweeper D., Ramsden K., Establishing and promoting an institutional repository and research information management system, Library Hi Tech News, 37, 7, pp. 9-12, (2020); Data repositories, (2020); Tekian A., Stapleton G., Online resources for healthcare SIMULATION, The Clinical Teacher, 9, 6, pp. 417-419, (2012); Learning object repository, (2015); Walters T., Skinner K., New roles for new times: digital curation for preservation, (2011); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship. Scientific data, (2016); Xu H., Faculty use of a learning object repository in higher education, VINE Journal of Information and Knowledge Management Systems, 46, 4, pp. 469-478, (2016); Yoon A., Data reuse and users' trust judgments: toward trusted data curation, (2015); Manu T.R., Viral A., Madesh G., Shashikumara A.A., Panna C., Prasanna K., Analysis of research data repositories in India, Knowledge Organisation in Academic Libraries, (2018); O'Neill B., Et al., Supporting nursing faculty with a digital repository of simulation resources, Teaching and Learning in Nursing, 15, 3, pp. 175-180, (2020); Tsabedze V., A framework for the management of E-records in higher education institutions: a case study of the institute of development management, Mousaion: South African Journal of Information Studies, 37, 3, (2019)","N. Naicker; Durban University of Technology, Durban, South Africa; email: nalindrenn@dut.ac.za","","Emerald Group Holdings Ltd.","","","","","","20595816","","","","English","Digit. Library Perspect.","Review","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85113892727"
"Klappe E.S.; Cornet R.; Dongelmans D.A.; de Keizer N.F.","Klappe, Eva S. (57217125028); Cornet, Ronald (57201825238); Dongelmans, Dave A. (6508168536); de Keizer, Nicolette F. (35557831100)","57217125028; 57201825238; 6508168536; 35557831100","Inaccurate recording of routinely collected data items influences identification of COVID-19 patients","2022","International Journal of Medical Informatics","165","","104808","","","","2","10.1016/j.ijmedinf.2022.104808","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132871262&doi=10.1016%2fj.ijmedinf.2022.104808&partnerID=40&md5=c56956d0e94b17c052b3132bcbc68427","Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; Amsterdam UMC, University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, Netherlands","Klappe E.S., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; Cornet R., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; Dongelmans D.A., Amsterdam UMC, University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, Netherlands; de Keizer N.F., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands","Background: During the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients. Methods: The following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled ‘COVID-19′ if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled ‘non-COVID-19′ if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F1 score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences. Results: The total dataset consisted of 402 patients: 196 ‘COVID-19′ and 206 ‘non-COVID-19′ patients. F1 scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F1 scores ranged between 0.73 and 0.99. F1 scores showed no clear pattern in variability between the two time periods. Conclusions: Our study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse. © 2022 The Author(s)","COVID-19; Data accuracy; Electronic Health Records; Problem list; Real-time data extraction; Routinely collected data","COVID-19; Humans; Pandemics; Retrospective Studies; Routinely Collected Health Data; SARS-CoV-2; Data mining; Decision making; Diagnosis; Information management; Intensive care units; Polymerase chain reaction; Records management; Data accuracy; Data extraction; Data items; Electronic health; Electronic health record; Health records; Problem list; Real-time data; Real-time data extraction; Routinely collected data; Article; computer assisted tomography; controlled study; coronavirus disease 2019; data accuracy; data extraction; diagnostic accuracy; diagnostic test accuracy study; electronic health record; gold standard; health care personnel; hospital admission; hospitalization; human; ICD-10; intensive care unit; intensivist; major clinical study; retrospective study; World Health Organization; diagnosis; epidemiology; pandemic; COVID-19","","","","","Amsterdam University Medical Centers, AUMC, (2019-AMC-JK-7)","This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This study was funded by Amsterdam UMC 2019-AMC-JK-7. Amsterdam UMC did not have any role in the study design, collection, analysis, interpretation of the data, writing the report and the decision to submit the report for publication. 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Med., 36, 8, pp. 2532-2535, (2021); Juyal D., Kumar A., Pal S., Thaledi S., Jauhari S., Thawani V., Medical certification of cause of death during COVID-19 pandemic–a challenging scenario, Journal of Family Medicine and Primary Care., 9, 12, (2020); Liao N., Kasick R., Allen K., Bode R., Macias C., Lee J., Ramachandran S., Erdem G., Pediatric Inpatient Problem List Review and Accuracy Improvement, Hosp Pediatr., 10, 11, pp. 941-948, (2020); Owen R.K., Conroy S.P., Taub N., Jones W., Bryden D., Pareek M., Et al., Comparing associations between frailty and mortality in hospitalised older adults with or without COVID-19 infection: a retrospective observational study using electronic health records, Age ageing., 50, 2, pp. 307-316, (2021); Yu B., Li X., Chen J., Ouyang M., Zhang H., Zhao X., Tang L., Luo Q., Xu M., Yang L., Huang G., Liu X., Tang J., Evaluation of variation in D-dimer levels among COVID-19 and bacterial pneumonia: a retrospective analysis, J thromb thrombolys., 50, 3, pp. 548-557, (2020); Lekpa F.K., Njonnou S.R.S., Balti E., Luma H.N., Choukem S.P., Negative antigen RDT and RT-PCR results do not rule out COVID-19 if clinical suspicion is strong, Lancet Infect Dis, 21, 9, (2021); Wang Q.Q., Kaelber D.C., Xu R., Volkow N.D., COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States, Mol psychiatry., 26, 1, pp. 30-39, (2021); Taquet M., Husain M., Geddes J.R., Luciano S., Harrison P.J., Cerebral venous thrombosis: a retrospective cohort study of 513,284 confirmed COVID-19 cases and a comparison with 489,871 people receiving a COVID-19 mRNA vaccine, (2021); Martin P.M., Sbaffi L., Electronic Health Record and Problem Lists in Leeds, United Kingdom: Variability of general practitioners’ views, Health Inform J., pp. 1-14, (2019); Hose B.-Z., Hoonakker P., Wooldridge A., Brazelton III T., Dean S., Eithun B., Fackler J., Gurses A., Kelly M., Kohler J., McGeorge N., Ross J., Rusy D., Carayon P., Physician perceptions of the electronic problem list in pediatric trauma care, Appl Clin Inform., 10, 1, pp. 113-122, (2019); Wright A., McCoy A.B., Hickman T.-T., Hilaire D.S., Borbolla D., Bowes W.A., Dixon W.G., Dorr D.A., Krall M., Malholtra S., Bates D.W., Sittig D.F., Problem list completeness in electronic health records: a multi-site study and assessment of success factors, Int J Med Inform., 84, 10, pp. 784-790, (2015); Wright A., Feblowitz J., Maloney F.L., Henkin S., Ramelson H., Feltman J., Bates D.W., Increasing patient engagement: patients’ responses to viewing problem lists online, Appl Clin Inform., 5, 4, pp. 930-942, (2014); Chen E., Garcia-Webb M., An analysis of free-text alcohol use documentation in the electronic health record, Appl Clin Inform., 5, 2, pp. 402-415, (2014); Holmes C., The problem list beyond meaningful use: part I: the problems with problem lists, J AHIMA., 82, 2, pp. 30-33, (2011); Wright A., Pang J., Feblowitz J.C., Maloney F.L., Wilcox A.R., Ramelson H.Z., Et al., A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record, J Am Med Inform Assoc., 18, 6, pp. 859-867, (2011); Wright A., Pang J., Feblowitz J.C., Maloney F.L., Wilcox A.R., McLoughlin K.S., Et al., Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial, J Am Med Inform Assoc., 19, 4, pp. 555-561, (2012); Kaplan D.M., Clear writing, clear thinking and the disappearing art of the problem list, J. Hosp. Med., 2, 4, pp. 199-202, (2007); Holmes C., Brown M., St Hilaire D., Wright A., Healthcare provider attitudes towards the problem list in an electronic health record: a mixed-methods qualitative study, BMC Med Inform Decis Mak., 12, 127, (2012); Wright A., Feblowitz J., Maloney F.L., Henkin S., Bates D.W., Use of an electronic problem list by primary care providers and specialists, J. Gen. Intern. Med., 27, 8, pp. 968-973, (2012); Wang E.-C.-H., Wright A., Characterizing outpatient problem list completeness and duplications in the electronic health record, J. Am. Med. Inform. Assoc., 27, 8, pp. 1190-1197, (2020); Ancker J.S., Kern L.M., Edwards A., Nosal S., Stein D.M., Hauser D., Kaushal R., How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use, J. Am. Med. Inform. Assoc., 21, 6, pp. 1001-1008, (2014); Gundlapalli A.V., Lavery A.M., Boehmer T.K., Beach M.J., Walke H.T., Sutton P.D., Anderson R.N., Death Certificate-Based ICD-10 Diagnosis Codes for COVID-19 Mortality Surveillance—United States, January–December 2020, Morb. Mortal. Wkly Rep., 70, 14, pp. 523-527, (2021); Ioannidis J., Over-and under-estimation of COVID-19 deaths, Eur. J. Epidemiol., 36, 6, pp. 581-588, (2021); Harteloh P., De Bruin K., Kardaun J., The reliability of cause-of-death coding in The Netherlands, Eur. J. Epidemiol., 25, 8, pp. 531-538, (2010); Yin A.L., Guo W.L., Sholle E.T., Rajan M., Alshak M.N., Choi J.J., Goyal P., Jabri A., Li H.A., Pinheiro L.C., Wehmeyer G.T., Weiner M., Safford M.M., Campion T.R., Cole C.L., Comparing Automated vs, Int. J. Med. Informatics, 157, (2022); (2022); Klappe E.S., van Putten F.J., de Keizer N.F., Cornet R., Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality, BMC Med Inform Decis Mak., 21, 1, pp. 1-17, (2021); Holmes C., The problem list beyond meaningful use: part 2: fixing the problem list, J AHIMA., 82, 3, pp. 32-35, (2011)","E.S. Klappe; Amsterdam UMC, Department of Medical Informatics, Amsterdam, Netherlands; email: e.s.klappe@amsterdamumc.nl","","Elsevier Ireland Ltd","","","","","","13865056","","IJMIF","35767912","English","Int. J. Med. Informatics","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85132871262"
"Mulongo J.; Wambiri D.; Gwademba G.; Sanya O.","Mulongo, Johnson (57981367700); Wambiri, Daniel (56951088800); Gwademba, Goudian (57980867300); Sanya, Otuoma (57981367800)","57981367700; 56951088800; 57980867300; 57981367800","Submission Template for ACM Papers","2022","ACM International Conference Proceeding Series","","","","288","300","12","0","10.1145/3560107.3560153","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142659269&doi=10.1145%2f3560107.3560153&partnerID=40&md5=9c00b3f64471017ed0900dd24e16bafc","University of Embu, Kenya; Mama Ngina University, Kenya; Kenyatta University, Kenya; Karatina University, Kenya","Mulongo J., University of Embu, Kenya; Wambiri D., Mama Ngina University, Kenya; Gwademba G., Kenyatta University, Kenya; Sanya O., Karatina University, Kenya","A major development in academic libraries has been their recognition of the need to support research data services (RDS) to advance knowledge and science, augment novel solutions to social and economic impediments, and amplify immense potentials for competitiveness, productivity, and livability. This study investigated research data service maturity in academic libraries of developed countries with the intent to provide valuable insights and implications for other academic libraries. The maturity model informed the study. The study used in-depth web analysis and literature review to examine research data services in top-rated 100 university libraries of developed countries. Study findings showed a clear and well-developed landscape of research data services in these academic libraries; 81 (94%) of the investigated universities offered a wide array of both informational and technical services. The most common services consisted research data curation and storage service 81 (94%), research data management and training service 81 (94%), research data management reference service 80 (93%), research data introduction service 80 (93%) and data management plan and guidance 79 (92%) respectively. The least common service was the resource recommendation service 64 (74%). Study findings, therefore, suggest that the current services are extensive, robust and mature, signifying an overall maturity of the RDS landscape in academic libraries of developed countries.  © 2022 ACM.","academic libraries; developed Countries; Research data management","Digital storage; Information services; Libraries; Academic libraries; Data services; Developed countries; Economic impediments; Literature reviews; Maturity model; Novel solutions; Research data; Research data managements; Web analysis; Information management","","","","","","","Cox A., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, pp. 299-316, (2014); Cox A., Verbaan E., Sen B., A Spider, an Octopus, or an Animal Just Coming into Existence Designing a Curriculum for Librarians to Support Research Data Management, Journal of eScience Librarianship, 3, (2014); Cox Andrew M., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Crowston K., Qin J., A Capability Maturity Model for Scientific Data Management 1 A Capability Maturity Model for Scientific Data Management, Proceedings of the American Society for Information Science and Technology, 47, pp. 1-2, (2010); Regional Economic Outlook, (2021); Johnson Mulongo M., Jing C., Daniel Wambiri M., Researchers' Perceptions of Research Data Management Activities at an Academic Library in a Developing Country, International Journal of Library and Information Services (IJLIS), 10, 2, pp. 1-17, (2021); Lewis M.J., Libraries and the management of research data, (2010); Lyon L., Librarians in the Lab: Toward Radically Re-Engineering Data Curation Services at the Research Coalface, New Review of Academic Librarianship, 22, 4, pp. 391-409, (2016); Masinde J., Chen J., Wambiri D., Mumo A., Research Librarians' Experiences of Research Data Management Activities at an Academic Library in a Developing Country, Data and Information Management, (2021); Hanisch R., Research Data and Open Science at NIST, (2020); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Sonkar M.T.A.S.S.K., Research Data Management Practices in University libraries: A study, Journal of Library & Information Technology, 37, 6, pp. 417-424, (2017); Tenopir C., Allard S., Baird L., Sandusky R.J., Lundeen A., Hughes D., Pollock D., Academic Librarians and Research Data Services: Attitudes and Practices, Information Technology and Libraries Journal, 1, pp. 24-37, (2019); Tenopir C., Rice N., Allard S., Baird L., Borycz J., Christian L., Sandusky R., Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide, PLoS ONE, (2020); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research Data Services in European Academic Research Libraries, Liber Quarterly, 27, pp. 23-44, (2017); Woeber C., Towards best practice in research data management in the humanities, (2017); Yoon A.S., Teresa, Research Data Management Services in Academic Libraries in the US: A Content Analysis of Libraries'Websites, College & Research Libraries, 78, 7, pp. 920-933, (2017)","","Amaral L.; Soares D.; Zheng L.","Association for Computing Machinery","Norte Portugal Regional Operational Programme (NORTE 2020)","15th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2022","4 October 2022 through 7 October 2022","Guimaraes","184471","","978-145039635-6","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85142659269"
"Miksa T.; Oblasser S.; Rauber A.","Miksa, Tomasz (55260160000); Oblasser, Simon (57387939200); Rauber, Andreas (57074846700)","55260160000; 57387939200; 57074846700","Automating Research Data Management Using Machine-Actionable Data Management Plans","2022","ACM Transactions on Management Information Systems","13","2","18","","","","1","10.1145/3490396","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127565497&doi=10.1145%2f3490396&partnerID=40&md5=ca869181b59acbb4aeb5787ce5c59569","Tu Wien, Sba Research, Vienna, Austria","Miksa T., Tu Wien, Sba Research, Vienna, Austria; Oblasser S., Tu Wien, Sba Research, Vienna, Austria; Rauber A., Tu Wien, Sba Research, Vienna, Austria","Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks.In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs. © 2021 Copyright held by the owner/author(s).","automation; business processes; Data management plan; enterprise architecture; FAIR; funder template; machine-actionable; RDA; RDM; requirements engineering","Decision making; Information management; Interoperability; Reusability; Business Process; Data management plan; Enterprise Architecture; Funder template; Machine-actionable; Management plans; RDA; RDM; Requirement engineering; Research data managements; Automation","","","","","FAIR; Vienna Business Agency; WAW; Österreichische Forschungsförderungsgesellschaft, FFG; Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF","This research was carried out in the context of the Austrian COMET K1 program and publicly funded by the Austrian Research Promotion Agency (FFG) and the Vienna Business Agency (WAW). This work was also supported by the FAIR Data Austria project funded by the Austrian Federal Ministry of Education, Science and Research. Authors’ addresses: T. Miksa, TU Wien & SBA Research, Vienna, Austria; email: tmiksa@sba-research.org; S. Oblasser and A. Rauber, TU Wien, Vienna, Austria; emails: simon.oblasser@student.tuwien.ac.at, rauber@ifs.tuwien.ac.at.","Abiteboul S., Stoyanovich J., Transparency, fairness, data protection, neutrality: Data management challenges in the face of new regulation, Journal of Data and Information Quality, 11, 3, (2019); Active Data Management Plans: A Metadata-Driven Model for Data Management Plans (Draft White Paper); Amorim R.C., Castro J.A., Rocha Da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, fexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Fwf Data Management Plan Template (DMP)-Guide (01/2019), (2019); Ball A., How to License Research Data. Dcc How-to Guides, (2014); Boukhari I., Jean S., Ait-Sadoune I., Bellatreche L., The role of user requirements in data repository design, International Journal on Software Tools for Technology Transfer, 20, 1, pp. 19-34, (2018); Cardoso J., Jones S., Miksa T., Hasan A., Praetzellis M., Lieby P., Papadopoulou E., Gierend K., Mapping of MaDMPs to Funder Templates, (2020); H2020 Programme Guidelines on Fair Data Management in Horizon 2020. Technical Report Version 3.0, (2016); Cox A.M., Kennan M.A., Lyon L., Pinfeld S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Checklist for a Data Management Plan, (2013); H2020 Templates: Data Management Plan, (2018); Feger S.S., Wozniak P.W., Lischke L., Schmidt A., Yes, i comply!': Motivations and practices around research data management and reuse across scientifc felds, Proceedings of the Acm on Human-Computer Interaction 4, CSCW2, (2020); Grandon Gill T., Hevner A.R., A ftness-utility model for design science research, Acm Transactions on Management Information Systems, 4, 2, (2013); Gregory A., Data Management Planning and the Data Documentation Initiative (DDI), (2013); Grootveld M., Leenarts E., Jones S., Hermans E., Fankhauser E., OpenAIRE and Fair Data Expert Group Survey about Horizon 2020 Template for Data Management Plans, (2018); Harrison K.A., Wright D.G., Trembath P., Implementation of a workfow for publishing citeable environmental data: Successes, challenges and opportunities from a data centre perspective, International Journal on Digital Libraries, 18, 2, pp. 133-143, (2017); Hooft R., Kuzak M., Suchanek M., Pergl R., Data stewardship wizard"": A tool bringing together researchers, data stewards, and data experts around data management planning, Data Science Journal, 18, 1, (2019); Jones S., How to Develop a Data Management and Sharing Plan. Dcc How-to Guides, (2011); Jones S., Pergl R., Hooft R., Miksa T., Samors R., Ungvari J., Davis R.I., Lee T., Data management planning: How requirements and solutions are beginning to converge, Data Intelligence, 2, 1-2, pp. 208-219, (2020); Khan H.R., Chang H.-C., Kim J., Unfolding research data services: An information architecture perspective, Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL'18), pp. 353-354, (2018); Lee Y.-H., Jen-Hwa Hu P., Tu C.-Y., Ontology-based mapping for automated document management: A concept-based technique for word mismatch and ambiguity problems in document clustering, Acm Transactions on Management Information Systems, 6, 1, (2015); Maltese V., Giunchiglia F., Search and analytics challenges in digital libraries and archives, Journal of Data and Information Quality, 7, 3, (2016); Michener W.K., Ten simple rules for creating a good data management plan, Plos Computational Biology, 11, 10, (2015); Miksa T., Ashley K., Workshop Report-Research Data Lifecycle and Machine-Actionable Data Management Plans, (2017); Miksa T., Cardoso J., Luis Borbinha J., Framing the scope of the common data model for machine-actionable data management plans, Proceedings of the Ieee International Conference on Big Data (Big Data'18)., pp. 2733-2742, (2018); Miksa T., Neish P., Walk P., Rauber A., Defning requirements for machine-actionable data management plans, Proceedings of the 15th International Conference on Digital Preservation (iPRES'18), (2018); Miksa T., Rauber A., Ganguly R., Budroni P., Information integration for machine actionable data management plans, International Journal of Digital Curation, 12, 1, pp. 22-35, (2017); Miksa T., Simms S., Mietchen D., Jones S., Ten principles for machine-actionable data management plans, Plos Computational Biology, 15, 3, (2019); Miksa T., Walk P., Neish P., Rda Dmp Common Standard for Machine-actionable Data Management Plans, (2020); Nikolov D., Tuna E., A lightweight framework for research data management, Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning) (PEARC'19), (2019); Data Management Plans Guidance for Principal Investigators, (2018); Nunamaker J.F., Briggs R.O., Toward a broader vision for information systems, Acm Transactions on Management Information Systems, 2, 4, (2012); Oblasser S., Machine-Actionable Dmp Application (DMap), (2019); Oblasser S., Designing An Architecture for Machine-Actionable Research Data Management Planning in An Institutional Context, (2020); Oblasser S., Oblassers/dmap-mockups: DMap Mockups, (2020); Oblasser S., Miksa T., Bpmn Processes for Machine-Actionable DMPs, (2019); Oblasser S., Miksa T., Kitamoto A., Findinga repository with the help of machine-actionable DMPs: Opportunities and challenges, International Journal of Digital Curation, 15, 1, pp. 1-11, (2020); O'Donoghue J., Herbert J., Data management within mHealth environments: Patient sensors, mobile devices, and databases, Journal of Data and Information Quality, 4, 1, (2012); Wg Dmp Common Standards Case Statement, (2017); Practical Guide to the International Alignment of Research Data Management (Extended Edition), (2021); Simms S., Jones S., Next-generation data management plans: Global, machine-actionable, FAIR, International Journal of Digital Curation, 12, 1, pp. 36-45, (2017); Simms S., Jones S., Mietchen D., Miksa T., Machine-actionable data management plans (maDMPs), Research Ideas and Outcomes, 3, (2017); Simms S., Jones S., Miksa T., Mietchen D., Simons N., Unsworth K., A landscape survey of ActiveDMPs, International Journal of Digital Curation, 13, 1, pp. 204-214, (2018); Simms S., Strong M., Jones S., Ribeiro M., The future of data management planning: Tools, policies, and players, International Journal of Digital Curation, 11, 1, pp. 208-217, (2016); Simons E., Jetten M., Messelink M., Van Berchum M., Schoonbrood H., Wittenberg M., The important role of CRIS's for registering and archiving research data. the RDS-project at Radboud University (The Netherlands) in cooperation with data-archive dans, Procedia Computer Science, 106, C, pp. 321-328, (2017); Smale N., Unsworth K., Denyer G., Barr D., The history, advocacy and efcacy of data management plans, International Journal of Digital Curation, 15, pp. 1-30, (2020); Tuzhilin A., Knowledge management revisited: Old dogs, new tricks, Acm Transactions on Management Information Systems, 2, 3, (2011); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Et al., The FAIR guiding principles for scientifc data management and stewardship, Scientifc Data, 3, (2016)","","","Association for Computing Machinery","","","","","","2158656X","","","","English","ACM Trans. Manage. Inf. Syst.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85127565497"
"Gossler H.; Riedel J.; Daymo E.; Chacko R.; Angeli S.; Deutschmann O.","Gossler, Hendrik (56737061300); Riedel, Johannes (57876822100); Daymo, Eric (15845121000); Chacko, Rinu (57877017500); Angeli, Sofia (55901978400); Deutschmann, Olaf (7004044947)","56737061300; 57876822100; 15845121000; 57877017500; 55901978400; 7004044947","A New Approach to Research Data Management with a Focus on Traceability: Adacta","2022","Chemie-Ingenieur-Technik","94","11","","1798","1807","9","0","10.1002/cite.202200064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137330897&doi=10.1002%2fcite.202200064&partnerID=40&md5=1a0794a715e865641430a376b1efbbcf","omegadot software & consulting GmbH, Mühlweg 40, Limburgerhof, 67117, Germany; Karlsruhe Institute of Technology, Institute for Chemical Technology and Polymer Chemistry, Engesserstraße 20, Karlsruhe, 76131, Germany; Tonkomo LLC, Gilbert, 85297, AZ, United States","Gossler H., omegadot software & consulting GmbH, Mühlweg 40, Limburgerhof, 67117, Germany; Riedel J., omegadot software & consulting GmbH, Mühlweg 40, Limburgerhof, 67117, Germany, Karlsruhe Institute of Technology, Institute for Chemical Technology and Polymer Chemistry, Engesserstraße 20, Karlsruhe, 76131, Germany; Daymo E., Tonkomo LLC, Gilbert, 85297, AZ, United States; Chacko R., Karlsruhe Institute of Technology, Institute for Chemical Technology and Polymer Chemistry, Engesserstraße 20, Karlsruhe, 76131, Germany; Angeli S., Karlsruhe Institute of Technology, Institute for Chemical Technology and Polymer Chemistry, Engesserstraße 20, Karlsruhe, 76131, Germany; Deutschmann O., Karlsruhe Institute of Technology, Institute for Chemical Technology and Polymer Chemistry, Engesserstraße 20, Karlsruhe, 76131, Germany","Traceability between samples, devices and data is of great importance to the catalysis community. Adacta is a new research data management (RDM) system designed to create a traceable digital twin of a testing environment, not only storing data, but also creating a readily retrievable time-accurate record of the critical components used to measure catalyst performance. Future developments include extending Adacta to interact with electronic laboratory notebooks and to spawn simulations directly using data and measurements stored in the database. © 2022 The Authors. Chemie Ingenieur Technik published by Wiley-VCH GmbH.","Digital catalysis; FAIR principles; Research data management; Software; Traceability","Information management; Critical component; Data management system; Digital catalyse; FAIR principle; New approaches; Research data managements; Software; Testing environment; Time-accurate; Traceability; Catalysis","","","","","Deutsche Forschungsgemeinschaft, DFG, (426888090 – SFB 1441, 670389‐NFDI 2/1)","The development of the concept for the software Adacta and its application at laboratory setups at KIT was supported by the NFDI4Cat project, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) with the project number 670389‐NFDI 2/1. Please contact the corresponding author for information on obtaining Adacta. The authors also acknowledge Dr. P. Lott, M. Borchers and N. Ruf for their contribution and valuable feedback during the development of the software. Additionally, S. Angeli and O. Deutschmann acknowledge funding by the TrackAct project, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project‐ID 426888090 – SFB 1441 for the implementation of the RDM software. Open access funding enabled and organized by Projekt DEAL.","Raptis T.P., Passarella A., Conti M., IEEE Access., 7, pp. 97052-97093, (2019); Wilkinson M.D., Et al., Sci. Data, 3, 1, (2016); Schlogl R., ChemCatChem., 9, 4, pp. 533-541, (2017); Wulf C., Et al., ChemCatChem., 13, 14, pp. 3223-3236, (2021); Weber S., Et al., ChemCatChem., 14, 8, (2022); Trunschke A., Et al., Top. Catal., 63, 19-20, pp. 1683-1699, (2020); Gossler H., Maier L., Angeli S., Tischer S., Deutschmann O., Phys. Chem. Chem. Phys., 20, 16, pp. 10857-10876, (2018); Gossler H., Maier L., Angeli S., Tischer S., Deutschmann O., Catalysts, 9, 3, (2019); Deutschmann O., Et al., (2020); Nieva de la Hidalga A., Decarolis D., Xu S., Matam S., Yesid Hernandez Enciso W., Goodall J., Matthews B., Catlow C.R.A., Data Intell., 4, 2, pp. 455-470, (2022); Nieva de la Hidalga A., Goodall J., Anyika C., Matthews B., Catlow C.R.A., Catal. Commun., 162, (2022); Nfdi4cat – NFDI for Catalysis-Related Sciences, (2021); Generating Knowledge with Myhte - Data Management and Evaluation; Futter C., Modern Applications of High Throughput R&D in Heterogeneous Catalysis, (2014)","H. Gossler; omegadot software & consulting GmbH, Limburgerhof, Mühlweg 40, 67117, Germany; email: h.gossler@omegadot.software; O. Deutschmann; Karlsruhe Institute of Technology, Institute for Chemical Technology and Polymer Chemistry, Karlsruhe, Engesserstraße 20, 76131, Germany; email: deutschmann@kit.edu","","John Wiley and Sons Inc","","","","","","0009286X","","CITEA","","English","Chem Ing Tech","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85137330897"
"Chen D.","Chen, Di (57984142900)","57984142900","Practice on the Data Service of University Scientific Research Management Based on Cloud Computing","2022","World Automation Congress Proceedings","2022-October","","","424","428","4","0","10.23919/WAC55640.2022.9934710","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142796582&doi=10.23919%2fWAC55640.2022.9934710&partnerID=40&md5=136e7691b5767ce11c029754ba70f9bb","Minjiang University, Fujian, 350100, China","Chen D., Minjiang University, Fujian, 350100, China","With the continuous development of computer technology, the coverage of informatization solutions covers all walks of life and all fields of society. For colleges and universities, teaching and scientific research are the basic tasks of the school. The scientific research ability of the school will affect the level of teachers and the training of students. The establishment of a good scientific research environment has become a more important link in the development of universities. SR(Scientific research) data is a prerequisite for SR activities. High-quality SR management data services are conducive to ensuring the quality and safety of SRdata, and further assisting the smooth development of SR projects. Therefore, this article mainly conducts research and practice on cloud computing-based scientific research management data services in colleges and universities. First, analyze the current situation of SR data management in colleges and universities, and the results show that the popularity of SR data management in domestic universities is much lower than that of universities in Europe and the United States, and the data storage awareness of domestic researchers is relatively weak. Only 46% of schools have developed SR data management services, which is much lower than that of European and American schools. Second, analyze the effect of CC(cloud computing )on the management of SR data in colleges and universities. The results show that 47% of SR believe that CC is beneficial to the management of SR data in colleges and universities to reduce scientific research costs and improve efficiency, the rest believe that CC can speed up data storage and improve security by acting on SR data management in colleges and universities.  © 2022 TSI Enterprises.","Cloud Computing; Scientific Research Data; Scientific Research Data Services; Universities","Digital storage; Information management; Personnel training; Cloud-computing; Colleges and universities; Data services; Research data managements; Research management; Scientific research data service; Scientific research datum; Scientific researches; University; Cloud computing","","","","","","","Wang X., Yu D., Zhang F., Et al., Research on the control system and risk management based on internet big data and cloud computing, Journal of Physics Conference Series, 1952, 4, (2021); Merle P., Seinturier L., Paraiso F., SoCloud: A service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds, Computing, 98, 5, pp. 539-565, (2016); Zhang J., Zhu W., Wu X., Et al., Traffic state detection based on multidimensional data fusion system of internet of things, Wireless Communications and Mobile Computing, 2021, 1, pp. 1-12, (2021); Cala J., Marei E., Xu Y., Et al., Scalable and efficient whole-exome data processing using workflows on the cloud, Future Generation Computer Systems, 65, pp. 153-168, (2016); Hu Z., Yan Q., Luo J., ATME: Accurate traffic matrix estimation in both public and private datacenter networks, IEEE Transactions on Cloud Computing, 6, 1, pp. 60-73, (2018); Keshavarzi A., Haghighat A.T., Bohlouli M., Adaptive resource management and provisioning in the cloud computing: a survey of definitions, standards and research roadmaps, Ksii Transactions on Internet & Information Systems, 11, 9, pp. 4280-4300, (2017); Wang J., Zhang L., Duan L., Et al., A new paradigm of cloud-based predictive maintenance for intelligent manufacturing, Journal of Intelligent Manufacturing, 28, 5, pp. 1125-1137, (2017); Hong J., Yao X., Yong C., Correlation-aware QoS modeling and manufacturing cloud service composition, Journal of Intelligent Manufacturing, 28, 8, pp. 1947-1960, (2017); Contractor D., Patel D., Trust management framework for attenuation of application layer DDoS attack in cloud computing, International Federation for Information Processing -Publications- Ifip, 374, pp. 201-208, (2017); Porwal S., Nair S., Dimitrakos T., Regulatory impact of data protection and privacy in the cloud, Ifip Advances in Information & Communication Technology, 358, pp. 290-299, (2017); Tian J., Xu D., Cui M., Research on big data analysis platform of power grid enterprise accounting based on cloud computing, IOP Conference Series: Materials Science and Engineering, 677, 4, (2019); Chen W., Jia Z., Qin L., Design & research of legal affairs information service platform based on UIMA and semantics, International Journal of Future Generation Communication and Networking, 9, 3, pp. 1-14, (2016); Kaur T., Need to use fog computing with IoT, International Journal of Scientific Research in Computer Science Engineering and Information Technology, pp. 596-601, (2021); Kan J.G., Lee J.Y., Na K.S., Et al., A study on the implementation of 'IT outsourcing management system' based on open PaaS, Journal of Advanced Research in Dynamical and Control Systems, 10, 1, pp. 242-249, (2018); Chun Y.H., A study on design approaches for HTML 5-based smart learning systems in a cloud computing environment, Journal of Advanced Research in Dynamical and Control Systems, 10, 2, pp. 94-98, (2018)","D. Chen; Minjiang University, Fujian, 350100, China; email: cd520king@126.com","","IEEE Computer Society","","2022 World Automation Congress, WAC 2022","11 October 2022 through 15 October 2022","San Antonio","184152","21544824","979-888831444-9","","","English","World Autom. Congress Proc.","Conference paper","Final","","Scopus","2-s2.0-85142796582"
"Heinemann B.; Ehlenz M.; Görzen S.; Schroeder U.","Heinemann, Birte (57204048897); Ehlenz, Matthias (57202986128); Görzen, Sergej (57866776700); Schroeder, Ulrik (24434093700)","57204048897; 57202986128; 57866776700; 24434093700","xAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects","2022","International journal of online and biomedical engineering","18","14","","99","113","14","1","10.3991/ijoe.v18i14.35079","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143194178&doi=10.3991%2fijoe.v18i14.35079&partnerID=40&md5=685989ebf40d9896cdbc0526688b5c04","Learning Technologies Research Group, RWTH Aachen University, Aachen, Germany","Heinemann B., Learning Technologies Research Group, RWTH Aachen University, Aachen, Germany; Ehlenz M., Learning Technologies Research Group, RWTH Aachen University, Aachen, Germany; Görzen S., Learning Technologies Research Group, RWTH Aachen University, Aachen, Germany; Schroeder U., Learning Technologies Research Group, RWTH Aachen University, Aachen, Germany","Learning Analytics provides plenty of pedagogical uses. However, the integration of learning analytics must be accompanied by different perspectives: technical, organizational, and pedagogical. At this point, there are still gaps, e.g., the need to connect the various stakeholders and support the systematic, structured, and sustainable process. This paper presents different approaches to making the learning data standard xAPI for interdisciplinary projects easier by working on other starting points. Starting with a basic infrastructure to support the interdisciplinary collection of definitions for the standardized data format, it continues with a graphical user interface supporting different stakeholders. A modular tool for quickly connecting programming IDEs with the vocabulary is also presented. Last, a connector is shown for easier multi-modal data management using virtual reality as an example. © 2022,International journal of online and biomedical engineering. All Rights Reserved.","Definition registry; Educational virtual reality; Interdisciplinary research; Lab-based learning; Learning analytics; Open science; Research data management; Research infrastructure; Scientific collaboration; Xapi","","","","","","Fabian Dünzer; LeBiAC, (01JA1813); Bundesministerium für Bildung und Forschung, BMBF, (16DHB2114)","Funding text 1: We thank the Federal Ministry of Education and Research of Germany (BMBF) for supporting the participating research projects DigiLab4U (grant no. 16DHB2114) and LeBiAC (grant no. 01JA1813). We also thank the developers and researchers who provided feedback and the engaged students who contributed, especially Lars Florian Meiendresch and Fabian Dünzer.; Funding text 2: We thank the Federal Ministry of Education and Research of Germany (BMBF) for supporting the participating research projects DigiLab4U (grant no. 16DHB2114) and LeBiAC (grant no. 01JA1813). We also thank the developers and researchers who provided feedback and the engaged students who contributed, especially Lars Florian Meiendresch and Fabian Dünzer","Alves G. R., Marques A., Bento da Silva J., Lab-based Education, Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’21), pp. 533-535, (2021); Abulrub A.-H. G., Attridge A., Williams M. A., Virtual Reality in Engineering Education: The Future of Creative Learning, International Journal of Emerging Technologies in Learning (iJET), 6, 4, pp. 4-11, (2011); Feisel L. D., Rosa A. J., The Role of the Laboratory in Undergraduate Engineering Education, Journal of Engineering Education, 94, pp. 121-130, (2005); Criteria for Accrediting Engineering Technology Programs, 2022–2023, Accreditation Board for Engineering and Technology, (2021); Worner S., Kuhn J., Scheiter K., The Best of Two Worlds: A Systematic Review on Combining Real and Virtual Experiments in Science Education, Review of Educational Research, (2022); Kammerlohr V., Pfeiffer A., Uckelmann D., Digital Laboratories for Educating the IoT-Generation Heatmap for Digital Lab Competences, Cross Reality and Data Science in Engineering, 1231, pp. 3-20, (2021); Milgram P., Takemura H., Utsumi A., Kishino F., Augmented Reality: A Class of Displays on the Reality-Virtuality Continuum, Telemanipulator and Telepresence Technologies, 2351, pp. 282-292, (1995); Siemens G., 1st International Conference on Learning Analytics and Knowledge 2011, (2011); Worsley M., Multimodal Learning Analytics’ Past, Present, and Potential Futures, the Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18), (2018); Wilkinson M. D., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, 1, (2016); IEEE to Standardize xAPI v2.0 as an International Standard, Advanced Distributed Learning Initiative News, (2021); xAPI-Specification, Advanced Distributed Learning (ADL) Initiative; Sicherung guter wissenschaftlicher Praxis, Sicherung Guter Wissenschaftlicher Praxis, pp. 1-109, (2013); Briney K. A., Coates H. L., Goben A., Foundational Practices of Research Data Management, (2020); Amorim R. C., Castro J. A., Rocha da Silva J., Ribeiro C., A Comparison of Research Data Management Platforms: Architecture, Flexible Metadata and Interoperability, Univ Access Inf Soc, 16, 4, pp. 851-862, (2017); Strasser C., Research Data Management. As Those Components Could be Further Discussed and Improved as the Community of Scientific Users of the Infrastructure Grows, to Keep its Core Promise, (2015); Ehlenz M., Heinemann B., Leonhardt T., Ropke R., Lukarov V., Schroeder U., Eine for-schungspraktische Perspektive auf xAPI-Registries, the DELFI 2020, (2020); Heinemann B., Ehlenz M., Schroeder U., Human-Centered Learning Analytics in Interdisc iplinary Projects: Co-designing Data Collection, Companion Proceedings of the 12th International Conference on Learning Analytics & Knowledge LAK22, Online Conference, (2022); Prieto-Alvarez C. G., Martinez-Maldonado R., Anderson T. D., Co-designing Learning Analytics Tools with Learners, Learning Analytics in the Classroom, pp. 93-110, (2018); Mulders M., Buchner J., Kerres M., A Framework for the Use of Immersive Virtual Reality in Learning Environments, International Journal of Emerging Technologies in Learning (iJET), 15, 24, (2020); Asad M. M., Naz A., Churi P., Tahanzadeh M. M., Virtual Reality as Pedagogical Tool to Enhance Experiential Learning: A Systematic Literature Review, Education Research International, 2021, pp. 1-17, (2021); Heinemann B., Gorzen S., Schroeder U., Systematic Design of Effective Learning Sce narios for Virtual Reality, the 2022 International Conference on Advanced Learning Technologies (ICALT), (2022); Mesa J. A., Esparragoza I., Maury H., Modular Architecture Principles – MAPs: A Key Factor in the Development of Sustainable Open Architecture Products, International Journal of Sustainable Engineering, 13, 2, pp. 108-122, (2020); Eifert T., Schilling U., Bauer H.-J., Kramer F., Lopez A., Infrastructure for Research Data Management as a Cross-University Project, Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration, Cham, pp. 493-502, (2017); Nielsen J., Enhancing the Explanatory Power of Usability Heuristics, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 152-158, (1994); Temmen K., Nofen B., Wehebrink M., Lecture Meets Laboratory - Experimental Experiences for Large Audiences: Concept and Implementation, International Journal of Engineering Pedagogy (iJEP), 4, 4, (2014); Pardo A., Jovanovic J., Dawson S., Gasevic D., Mirriahi N., Using Learning Analytics to Scale the Provision of Personalised Feedback, British Journal of Educational Technology, 50, 1, pp. 128-138, (2019); Dourado R. A., Rodrigues R. L., Ferreira N., Mello R. F., Gomes A. S., Verbert K., A Teacher-facing Learning Analytics Dashboard for Process-oriented Feedback in Online Learning, LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 482-489, (2021); Nehiri N., Aknin N., A Proposed Learner’s Data Model: Integrating Informal Learning and Enhancing Personalization and Interoperability, International Journal of Emerging Technologies in Learning (iJET), 16, pp. 173-187, (2021); Judel S., Schroeder U., EXCALIBUR LA - An Extendable and Scalable Infrastructure Build for Learning Analytics, 2022 International Conference on Advanced Learning Technologies (ICALT), pp. 155-157, (2022); Borghi J., Abrams S., Lowenberg D., Simms S., Chodacki J., Support Your Data: A Research Data Management Guide for Researchers, Research Ideas and Outcomes, 4, pp. 1-13, (2018)","B. Heinemann; Learning Technologies Research Group, RWTH Aachen University, Aachen, Germany; email: heinemann@cs.rwth-aachen.de","","International Association of Online Engineering","","","","","","26268493","","","","English","Int. J. Online. Biomed. Eng.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85143194178"
"Wieder P.; Nolte H.","Wieder, Philipp (14034873100); Nolte, Hendrik (57670317900)","14034873100; 57670317900","Toward data lakes as central building blocks for data management and analysis","2022","Frontiers in Big Data","5","","945720","","","","1","10.3389/fdata.2022.945720","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137862759&doi=10.3389%2ffdata.2022.945720&partnerID=40&md5=e0b7d6078a65fedb714076d4f329e0a4","Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), Göttingen, Germany","Wieder P., Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), Göttingen, Germany; Nolte H., Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), Göttingen, Germany","Data lakes are a fundamental building block for many industrial data analysis solutions and becoming increasingly popular in research. Often associated with big data use cases, data lakes are, for example, used as central data management systems of research institutions or as the core entity of machine learning pipelines. The basic underlying idea of retaining data in its native format within a data lake facilitates a large range of use cases and improves data reusability, especially when compared to the schema-on-write approach applied in data warehouses, where data is transformed prior to the actual storage to fit a predefined schema. Storing such massive amounts of raw data, however, has its very own challenges, spanning from the general data modeling, and indexing for concise querying to the integration of suitable and scalable compute capabilities. In this contribution, influential papers of the last decade have been selected to provide a comprehensive overview of developments and obtained results. The papers are analyzed with regard to the applicability of their input to data lakes that serve as central data management systems of research institutions. To achieve this, contributions to data lake architectures, metadata models, data provenance, workflow support, and FAIR principles are investigated. Last, but not least, these capabilities are mapped onto the requirements of two common research personae to identify open challenges. With that, potential research topics are determined, which have to be tackled toward the applicability of data lakes as central building blocks for research data management. Copyright © 2022 Wieder and Nolte.","big data; data analytics; data lake; FAIR; provenance; research data management","","","","","","","","Amstutz P., Crusoe M.R., Tijanic N., Common Workflow Language. v1. 0, (2016); Armbrust M., Das T., Sun L., Yavuz B., Zhu S., Murthy M., Et al., Delta lake: high-performance acid table storage over cloud object stores, Proc. VLDB Endowment, 13, pp. 3411-3424, (2020); Armbrust M., Ghodsi A., Xin R., Zaharia M., Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics,, Proceedings of CIDR, (2021); Armbrust M., Xin R.S., Lian C., Huai Y., Liu D., Bradley J.K., Et al., Spark sql: relational data processing in spark,, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1383-1394, (2015); Aundhkar A., Guja S., A review on enterprise data lake solutions, J. Sci. Technol, 6, pp. 11-14, (2021); Batyuk A., Voityshyn V., Apache storm based on topology for real-time processing of streaming data from social networks,, 2016 IEEE First International Conference on Data Stream Mining and Processing (DSMP), pp. 345-349, (2016); Bechhofer S., De Roure D., Gamble M., Goble C., Buchan I., Research objects: toward exchange and reuse of digital knowledge, Nat. Preced, (2010); Beheshti A., Benatallah B., Nouri R., Chhieng V.M., Xiong H., Zhao X., Coredb: a data lake service,, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2451-2454, (2017); Beheshti A., Benatallah B., Nouri R., Tabebordbar A., Corekg: a knowledge lake service, Proc. VLDB Endowment, 11, pp. 1942-1945, (2018); Beheshti S.-M.-R., Motahari-Nezhad H.R., Benatallah B., Temporal provenance model (TPM): model and query language, arXiv preprint arXiv:1211.5009, (2012); Beheshti S.-M.-R., Tabebordbar A., Benatallah B., Nouri R., On automating basic data curation tasks,, Proceedings of the 26th International Conference on World Wide Web Companion, pp. 165-169, (2017); Belhajjame K., B'Far R., Cheney J., Coppens S., Cresswell S., Gil Y., Et al., Prov-dm: The prov data model, (2013); Bhardwaj A., Bhattacherjee S., Chavan A., Deshpande A., Elmore A.J., Madden S., Et al., Datahub: collaborative data science and dataset version management at scale, arXiv preprint arXiv:1409.0798, (2014); Bingert S., Kohler C., Nolte H., Alamgir W., An API to include HPC resources in workflow systems,, INFOCOMP 2021, The Eleventh International Conference on Advanced Communications and Computation, pp. 15-20, (2021); Borges K.A., Laender A.H., Davis C.A., Spatial data integrity constraints in object oriented geographic data modeling,, Proceedings of the 7th ACM International Symposium on Advances in Geographic Information Systems, pp. 1-6, (1999); Borthakur D., The hadoop distributed file system: architecture and design, Hadoop Project Website, 11, (2007); Chakraborty J., Jimenez I., Rodriguez S.A., Uta A., LeFevre J., Maltzahn C., Skyhook: towards an arrow-native storage system, arXiv preprint arXiv:2204.06074, (2022); Chang F., Dean J., Ghemawat S., Hsieh W.C., Wallach D.A., Burrows M., Et al., Bigtable: a distributed storage system for structured data, ACM Trans. Comput. Syst, 26, pp. 1-26, (2008); Chavan A., Huang S., Deshpande A., Elmore A., Madden S., Parameswaran A., Towards a unified query language for provenance and versioning,, 7th USENIX Workshop on the Theory and Practice of Provenance (TaPP 15), (2015); Cockcroft S., A taxonomy of spatial data integrity constraints, Geoinformatica, 1, pp. 327-343, (1997); de Oliveira D., Ogasawara E., Oca na K., Bai ao F., Mattoso M., An adaptive parallel execution strategy for cloud-based scientific workflows, Concurrency Comput, 24, pp. 1531-1550, (2012); Dean J., Ghemawat S., Mapreduce: simplified data processing on large clusters, Commun. ACM, 51, pp. 107-113, (2008); Devlin B.A., Murphy P.T., An architecture for a business and information system, IBM Syst. J, 27, pp. 60-80, (1988); Diamantini C., Giudice P.L., Musarella L., Potena D., Storti E., Ursino D., A new metadata model to uniformly handle heterogeneous data lake sources,, European Conference on Advances in Databases and Information Systems, pp. 165-177, (2018); Dibowski H., Schmid S., Svetashova Y., Henson C., Tran T., Using semantic technologies to manage a data lake: data catalog, provenance and access control,, SSWS@ ISWC, pp. 65-80, (2020); Dixon J., Pentaho, Hadoop, and Data Lakes, (2010); Elmasri R., Navathe S., (1994); El-Sappagh S.H.A., Hendawi A.M.A., El Bastawissy A.H., A proposed model for data warehouse ETL processes, J. King Saud Univer. Comput. Inf. Sci, 23, pp. 91-104, (2011); Fagin R., Lotem A., Naor M., Optimal aggregation algorithms for middleware, J. Comput. Syst. Sci, 66, pp. 614-656, (2003); Giebler C., Groger C., Hoos E., Eichler R., Schwarz H., Mitschang B., The data lake architecture framework: a foundation for building a comprehensive data lake architecture,, Proceedings der 19. Fachtagung für Datenbanksysteme für Business, Technologie und Web (BTW 2021), (2021); Giebler C., Groger C., Hoos E., Schwarz H., Mitschang B., Modeling data lakes with data vault: practical experiences, assessment, and lessons learned,, International Conference on Conceptual Modeling, pp. 63-77, (2019); Giebler C., Groger C., Hoos E., Schwarz H., Mitschang B., A zone reference model for enterprise-grade data lake management,, Proceedings of the 24th IEEE Enterprise Computing Conference (EDOC 2020), (2020); Golec D., Data lake architecture for a banking data model,, ENTRENOVA-ENTerprise REsearch InNOVAtion, Vol. 5, pp. 112-116, (2019); Gorelik A., The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science, (2019); Hai R., Geisler S., Quix C., Constance: an intelligent data lake system,, Proceedings of the 2016 International Conference on Management of Data, pp. 2097-2100, (2016); Hai R., Quix C., Jarke M., Data lake concept and systems: a survey, arXiv preprint arXiv:2106.09592, (2021); Hai R., Quix C., Zhou C., Query rewriting for heterogeneous data lakes,, European Conference on Advances in Databases and Information Systems, pp. 35-49, (2018); Halevy A., Korn F., Noy N.F., Olston C., Polyzotis N., Roy S., Et al., Goods: organizing google's datasets,, Proceedings of the 2016 International Conference on Management of Data, pp. 795-806, (2016); Halevy A.Y., Korn F., Noy N.F., Olston C., Polyzotis N., Roy S., Et al., Managing google's data lake: an overview of the goods system, IEEE Data Eng. Bull, 39, pp. 5-14, (2016); Hartig O., Zhao J., Publishing and consuming provenance metadata on the web of linked data,, International Provenance and Annotation Workshop, pp. 78-90, (2010); Hasani Z., Kon-Popovska M., Velinov G., Lambda architecture for real time big data analytic,, ICT Innovations, pp. 133-143, (2014); Hitzler P., Krotzsch M., Ehrig M., Sure Y., What is ontology merging?, American Association for Artificial Intelligence, (2005); Hukkeri T.S., Kanoria V., Shetty J., A study of enterprise data lake solutions,, International Research Journal of Engineering and Technology (IRJET), Vol. 7, (2020); Inmon B., Data Lake Architecture: Designing the Data Lake and Avoiding the Garbage Dump, (2016); Inmon W.H., Building the Data Warehouse, (2005); Ives Z.G., Zhang Y., Dataset relationship management,, Proceedings of Conference on Innovative Database Systems Research (CIDR 19), (2019); Khine P.P., Wang Z.S., Data lake: a new ideology in big data era, ITM Web Conf, (2018); Kurtzer G.M., Sochat V., Bauer M.W., Singularity: Scientific containers for mobility of compute, PLoS ONE, 12, (2017); Li J., Design of real-time data analysis system based on impala,, 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pp. 934-936, (2014); Lindstedt D., Graziano K., Super Charge Your Data Warehouse: Invaluable Data Modeling Rules to Implement Your Data Vault, (2011); Maccioni A., Torlone R., Crossing the finish line faster when paddling the data lake with kayak, Proc. VLDB Endowment, 10, pp. 1853-1856, (2017); Maccioni A., Torlone R., Kayak: a framework for just-in-time data preparation in a data lake,, International Conference on Advanced Information Systems Engineering, pp. 474-489, (2018); Madera C., Laurent A., The next information architecture evolution: the data lake wave,, Proceedings of the 8th International Conference on Management of Digital Ecosystems, pp. 174-180, (2016); Madsen M., How to Build an Enterprise Data Lake: Important Considerations Before Jumping in, (2015); Mathis C., Data lakes, Datenbank Spektrum, 17, pp. 289-293, (2017); Miao H., Chavan A., Deshpande A., Provdb: Lifecycle management of collaborative analysis workflows,, Proceedings of the 2nd Workshop on Human-in-the-Loop Data Analytics, pp. 1-6, (2017); Miao H., Deshpande A., Provdb: provenance-enabled lifecycle management of collaborative data analysis workflows, IEEE Data Eng. Bull, 41, pp. 26-38, (2018); Miller G.A., Wordnet: a lexical database for english, Commun. ACM, 38, pp. 39-41, (1995); Miloslavskaya N., Tolstoy A., Big data, fast data and data lake concepts, Procedia Comput. Sci, 88, pp. 300-305, (2016); Missier P., Belhajjame K., Cheney J., The W3C PROV family of specifications for modelling provenance metadata,, Proceedings of the 16th International Conference on Extending Database Technology, pp. 773-776, (2013); Missier P., Ludascher B., Bowers S., Dey S., Sarkar A., Shrestha B., Et al., Linking multiple workflow provenance traces for interoperable collaborative science,, The 5th Workshop on Workflows in Support of Large-Scale Science, pp. 1-8, (2010); Munappy A.R., Bosch J., Olsson H.H., Data pipeline management in practice: challenges and opportunities,, Product-Focused Software Process Improvement, pp. 168-184, (2020); Munshi A.A., Mohamed Y.A.R.I., Data lake lambda architecture for smart grids big data analytics, IEEE Access, 6, pp. 40463-40471, (2018); Navigli R., Ponzetto S.P., Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network, Artif. 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ACM, 59, pp. 56-65, (2016); Zhang Y., Ives Z.G., Juneau: data lake management for jupyter, Proc. VLDB Endowment, 12, (2019); Zikopoulos P., Big Data Beyond the Hype: A Guide to Conversations for Today's Data Center, (2015)","P. Wieder; Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), Göttingen, Germany; email: philipp.wieder@gwdg.de","","Frontiers Media S.A.","","","","","","2624909X","","","","English","Frontiers. Big. Data.","Review","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85137862759"
"Sheveleva T.; Wawer M.L.; Oladazimi P.; Koepler O.; Nürnberger F.; Lachmayer R.; Auer S.; Mozgova I.","Sheveleva, Tatyana (57221965627); Wawer, Max Leo (57889314100); Oladazimi, Pooya (57385463000); Koepler, Oliver (6507094492); Nürnberger, Florian (8561645900); Lachmayer, Roland (6602616454); Auer, Sören (23391879500); Mozgova, Iryna (27067881500)","57221965627; 57889314100; 57385463000; 6507094492; 8561645900; 6602616454; 23391879500; 27067881500","Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering","2022","IFAC-PapersOnLine","55","10","","2030","2035","5","0","10.1016/j.ifacol.2022.10.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144492269&doi=10.1016%2fj.ifacol.2022.10.006&partnerID=40&md5=0eacc2744505a876b18618a1268e79dd","TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hanover, 30167, Germany; Institute of Product Development, Leibniz University Hannover, An der Universität 1, Garbsen, 30823, Germany; Institut für Werkstoffkunde (Materials Science), Leibniz University Hannover, An der Universität 2, Garbsen, 30823, Germany","Sheveleva T., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hanover, 30167, Germany; Wawer M.L., Institute of Product Development, Leibniz University Hannover, An der Universität 1, Garbsen, 30823, Germany; Oladazimi P., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hanover, 30167, Germany; Koepler O., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hanover, 30167, Germany; Nürnberger F., Institut für Werkstoffkunde (Materials Science), Leibniz University Hannover, An der Universität 2, Garbsen, 30823, Germany; Lachmayer R., Institute of Product Development, Leibniz University Hannover, An der Universität 1, Garbsen, 30823, Germany; Auer S., TIB - Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hanover, 30167, Germany; Mozgova I., Institute of Product Development, Leibniz University Hannover, An der Universität 1, Garbsen, 30823, Germany","The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology. Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license.","Knowledge Management in Production; Research Data Management; Semantic Annotation","Knowledge based systems; Semantics; Data repositories; Dataflow; Knowledge management in production; Knowledge management system; Knowledge spaces; Process chain; Processes documentation; Research data; Research data managements; Semantic annotations; Knowledge management","","","","","Deutsche Forschungsgemeinschaft, DFG","The authors gratefully acknowledge the support from the CRC 1153 Process Chain for Manufacturing Hybrid High Performance Components by Tailored Forming, Project number 252662854 (INF), funded by the German Research Foundation (DFG).","Altun O., Sheveleva T., Castro A., Oladazimi P., Koepler O., Mozgova I., Lachmayer R., Auer S., Integration eines digitalen Maschinenparks in ein Forschungsdatenmanagementsystem, Proceedings of the 32nd Symposium Design for X (DFX2021), 32, 23, (2021); Brockmoller T., Mozgova I., Lachmayer R., An Approach to Analyse the Potential of Tailored Forming by TRIZ Reverse, Proceedings of the 21st International Conference on Engineering Design (ICED 17), 4: Design Methods and Tools, pp. 445-452, (2017); CKAN - The Open Source Data Portal Software; Aluminium und Aluminiumlegierungen - Begriffe - Teil 1: Allgemeine Begriffe; Dreisprachige Fassung EN 12258-1:2012, (2012); Effertz E., The Funder's Perspective: Data Management in Coordinated Programmes of the German Research Foundation (DFG), Proceedings of the Data Management Workshop, (2010); Hartl N., Wossner E., Sure-Vetter Y., Nationale Forschungsdateninfrastruktur (NFDI), Informatik Spektrum, 44, pp. 370-437, (2021); Krotsch M., Vrandecic D., Volkel M., Semantic MediaWiki, The Semantic Web, 4273, pp. 935-942, (2006); Mozgova I., Koepler O., Kraft A., Lachmayer R., Auer S., Research data management system for a large collaborative project, Proceedings of NordDesign 2020, (2020); Mozgova I., Jagusch G., Freund J., Kraft A., Gluck T., Herrmann K., Knochelmann M., Lachmayer R., Product Life Cycle Oriented Data Management Planning with RDMO at the Example of Research Field Data, E-Science-Tage 2021: Share Your Research Data, (2022); Sandfeld S., Dahmen T., Fischer F.O.R., Eberl C., Klein S., Selzer M., Nestler B., Moller J., Mucklich F., Engstler M., Diebels S., Tschuncky R., Prakash A., Steinberger D., Kubel C., Herrmann H.-G., Schubotz R., Strategiepapier Digitale Transformation in der Materialwissenschaft und Werkstofftechnik, Deutsche Gessekschaft für Materialkunde, (2018); Semantic MediaWiki; Siqueira R., Bibani M., Duran D., Mozgova I., Lachmayer R., Behrens B.-A., An Adapted Case-based Reasoning for Design and Manufacturing of Tailored Forming Multimaterial Components, International Journal for Interactive Design and Manufacturing, 13, 3, (2019); Ckanext- Semantic Media Wiki, (2021); Thurer S., Peddinghaus J., Heimes N., Bayram F.C., Bal B., Uhe J., Behrens B.-A., Maier H.J., Klose C., Lateral Angular Co-Extrusion: Geometrical and Mechanical Properties of Compound Profiles, Metals, 10, 9, (2020); Wellner K., B 6 Ontologien, Grundlagen der praktischen Information und Dokumentation: Handbuch in die Informationswissenschaft und -praxis, pp. 207-218, (2013); Wilkinson M.D., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","T. Sheveleva; TIB - Leibniz Information Centre for Science and Technology, Hanover, Welfengarten 1B, 30167, Germany; email: tatyana.sheveleva@tib.eu; ; ; ","Bernard A.; Dolgui A.; Benderbal H.H.; Ivanov D.; Lemoine D.; Sgarbossa F.","Elsevier B.V.","","10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022","22 June 2022 through 24 June 2022","Nantes","148818","24058963","","","","English","IFAC-PapersOnLine","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85144492269"
"Nieva de la Hidalga A.; Goodall J.; Anyika C.; Matthews B.; Catlow C.R.A.","Nieva de la Hidalga, Abraham (43861497700); Goodall, Josephine (25626260700); Anyika, Corinne (57393726300); Matthews, Brian (16643340700); Catlow, C. Richard A. (57218594176)","43861497700; 25626260700; 57393726300; 16643340700; 57218594176","Designing a data infrastructure for catalysis science aligned to FAIR data principles","2022","Catalysis Communications","162","","106384","","","","2","10.1016/j.catcom.2021.106384","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122108845&doi=10.1016%2fj.catcom.2021.106384&partnerID=40&md5=dd7d8b7cb4b2c69ed0b8b5f47261a56e","UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, R92 Harwell, Oxfordshire, OX11 0FA, United Kingdom; School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff, CF10 3AT, United Kingdom; Scientific Computing Department STFC, Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, United Kingdom; Department of Chemistry, University College London, 20 Gordon Street, London, WC1E 6BT, United Kingdom","Nieva de la Hidalga A., UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, R92 Harwell, Oxfordshire, OX11 0FA, United Kingdom, School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff, CF10 3AT, United Kingdom; Goodall J., UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, R92 Harwell, Oxfordshire, OX11 0FA, United Kingdom, School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff, CF10 3AT, United Kingdom; Anyika C., UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, R92 Harwell, Oxfordshire, OX11 0FA, United Kingdom, School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff, CF10 3AT, United Kingdom; Matthews B., Scientific Computing Department STFC, Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, United Kingdom; Catlow C.R.A., UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, R92 Harwell, Oxfordshire, OX11 0FA, United Kingdom, School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff, CF10 3AT, United Kingdom, Department of Chemistry, University College London, 20 Gordon Street, London, WC1E 6BT, United Kingdom","The UK Catalysis Hub (UKCH) is designing and implementing an infrastructure to facilitate the management of research data produced by researchers, the Catalysis Data Infrastructure (CDI). The CDI is proposed to encompass the presentation of research outputs (publications and data) in a digital repository that brings together an array of heterogeneous data types. The CDI is designed to hold references to research outputs, maintains links between them and promotes publishing and sharing of data. The proposal is to create persistent relationships between the different types of data and publications complying with FAIR data principles (findability, accessibility, interoperability, and reuse). In this paper, we will discuss how the elicited requirements for data management are being incorporated in the design of the CDI. The prototype has been used in discussion with researchers and in presentations to the UKCH community, generating increased interest and providing ideas for further development. Additionally, the CDI prototype and its code are publicly available for further analysis. © 2021 The Authors","Catalysis research data; FAIR data principles; Prototyping; Research data management","Information management; Catalyse research data; Catalysis research; Data infrastructure; Digital repository; FAIR data principle; Heterogeneous data; Prototyping; Research data; Research data managements; Research outputs; Catalysis","","","","","Engineering and Physical Sciences Research Council, EPSRC, (EP/M013219/1, EP/R026645/1, EP/R026815/1, EP/R026939/1, EP/R027129/1)","UK Catalysis Hub is kindly thanked for resources and support provided via our membership of the UK Catalysis Hub Consortium and funded by EPSRC grant: EP/R026939/1 , EP/R026815/1 , EP/R026645/1 , EP/R027129/1 or EP/M013219/1 (biocatalysis)). We also thank the the reviewers for their thoughtful comments and efforts towards improving this paper.","Arnold O., Bilheux J.C., Borreguero J.M., Buts A., Campbell S.I., Chapon L., Lynch V.E., Mantid—Data analysis and visualization package for neutron scattering and μ SR experiments, Nuclear Inst. Methods Phys. Res. Sect. A., 764, pp. 156-166, (2014); Basham M., Filik J., Wharmby M.T., Chang P.C.Y., El Kassaby B., Gerring M., Aishima J., Levik K., Pulford B.C.A., Sikharulidze I., Et al., Data analysis WorkbeNch (DAWN), J. Synchrotron Radiat., 22, (2015); Chamberlain S., Habanero Python Client Api for Crossref, (2021); Chamberlain S., Serrano Ruby Client Api for Crossref, (2021); Clements P.C., Active Reviews for Intermediate Designs (CMU/SEI-2000-TN-009). 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Instrum., 91, 1, (2020); Kapyaho M., Kauppinen M., Agile requirements engineering with prototyping: a case study,  International requirements engineering conference (RE), pp. 334-343, (2015); Klampanos I.A., Martin P., Atkinson M., Consistency and Collaboration for Fine-Grained Scientific Workflow Development: The dispel4py Information Registry, (2019); Leong S.H., Stadler H.C., Chang M.C., Dorsch J.P., Aliaga T., Ashton A.W., SELVEDAS: A data and compute as a service workflow demonstrator targeting supercomputing ecosystems, 2020 IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies (SuperCompCloud), pp. 7-13, (2020); Messinis A.M., Luckham S.L., Wells P.P., Gianolio D., Gibson E.K., O'Brien H.M., Bedford R.B., The highly surprising behaviour of diphosphine ligands in iron-catalysed Negishi cross-coupling, Nat. Catal., 2, 2, pp. 123-133, (2019); Nature Research, The State of Open Data 2018: Global Attitudes towards Open Data, (2018); Neil T., Mobile Design Pattern Gallery: UI Patterns for Smartphone Apps, (2014); Newville M., Larch: an analysis package for XAFS and related spectroscopies, Int. J. Phys. Conf. Ser., 430, (2013); Nieva de la Hidalga A., Hardisty A., Making heterogeneous specimen data ‘FAIR’: implementing a digital specimen repository, Biodiv. Inform. Sci. Stand., 3, (2019); Pappas L., Whitman L., Riding the technology wave: effective dashboard data visualization, Human Interface and the Management of Information. Interacting with Information. Human Interface 2011. Lecture Notes in Computer Science, 6771, (2011); Ravel B., Newville M., (2005) ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT, J. Synchrotron Radiat., 12, pp. 537-541, (2005); Scalfani V., (2017) RDA CRDIG Open Meeting, (2017); Sutcliffe A.G., User-Centered Requirements Engineering, (2002); Teixeira L., Saavedra V., Ferreira C., Simoes J., Santos B.S., Requirements engineering using mockups and prototyping tools: developing a healthcare web-application, In International Conference on Human Interface and the Management of Information, pp. 652-663, (2014); Thielen J., Li Y., Profiling common types of research data and methods published by organic synthesis chemists at the University of Michigan. Paper presented at the SLA 2015 Annual Conference & Info Expo, Boston, MA, (2015); Xu S., Cibin G., Gianolio D., Celorrio V., Parry S., Gibson E.K., Richard C., Catlow A., High throughput XAS reactor system for operando spectroscopy study, Oral Presentation, UK Catalysis Conference 2021, 2021-01-08, (2021); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Womack R.P., Research data in core journals in biology, chemistry, mathematics, and physics, PLoS One, 10, 12, (2015)","A. Nieva de la Hidalga; UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, Oxfordshire, R92 Harwell, OX11 0FA, United Kingdom; email: nievadelahidalgaa@cardiff.ac.uk","","Elsevier B.V.","","","","","","15667367","","CCAOA","","English","Catal. Commun.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85122108845"
"Kirsten T.; Meineke F.A.; Loeffler-Wirth H.; Beger C.; Uciteli A.; Stäubert S.; Löbe M.; Hänsel R.; Rauscher F.G.; Schuster J.; Peschel T.; Herre H.; Wagner J.; Zachariae S.; Engel C.; Scholz M.; Rahm E.; Binder H.; Loeffler M.","Kirsten, Toralf (8986863700); Meineke, Frank A. (6508217583); Loeffler-Wirth, Henry (57192961924); Beger, Christoph (57195720120); Uciteli, Alexandr (26536918400); Stäubert, Sebastian (36648060200); Löbe, Matthias (55938448500); Hänsel, René (57209717731); Rauscher, Franziska G. (55626252700); Schuster, Judith (57224206913); Peschel, Thomas (57198188242); Herre, Heinrich (7004478721); Wagner, Jonas (57220773548); Zachariae, Silke (53464562000); Engel, Christoph (57720769800); Scholz, Markus (7202888704); Rahm, Erhard (6603724515); Binder, Hans (7202460187); Loeffler, Markus (57893058500)","8986863700; 6508217583; 57192961924; 57195720120; 26536918400; 36648060200; 55938448500; 57209717731; 55626252700; 57224206913; 57198188242; 7004478721; 57220773548; 53464562000; 57720769800; 7202888704; 6603724515; 7202460187; 57893058500","The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online","2022","Methods of Information in Medicine","61","1","","E103","E115","12","0","10.1055/a-1914-1985","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136156537&doi=10.1055%2fa-1914-1985&partnerID=40&md5=82350e22238794b929c6d6178a54872e","Department of Medical Data Science, Leipzig University, Medical Center, Leipzig, Germany; Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany; Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany; LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany; Department of Computer Sciences, Leipzig University, Leipzig, Germany; Anhalt University of Applied Sciences, Köthen, Germany","Kirsten T., Department of Medical Data Science, Leipzig University, Medical Center, Leipzig, Germany, Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Meineke F.A., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Loeffler-Wirth H., LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Beger C., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Uciteli A., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Stäubert S., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Löbe M., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Hänsel R., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Rauscher F.G., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Schuster J., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Peschel T., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Herre H., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Wagner J., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Zachariae S., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Engel C., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Scholz M., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Rahm E., Department of Computer Sciences, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Binder H., LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany; Loeffler M., Institute for Medical Informatics Statistics, and Epidemiology, Leipzig University, Leipzig, Germany, Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany, LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany, Anhalt University of Applied Sciences, Köthen, Germany","Background  Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. Objective  The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. Methods  Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. Results  The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany. © 2022 Georg Thieme Verlag. All rights reserved.","biomathematical models; clinical trials; data semantics; FAIR; metadata; omics data; ontology; research data management; risk prediction models","Germany; Prospective Studies; Germany; prospective study","","","","","","","Homepage Dryad Digital Repository; Edgar R., Domrachev M., Lash A.E., Gene Expression Omnibus: NCBI gene expression and hybridization array data repository, Nucleic Acids Res, 30, 1, pp. 207-210, (2002); Homepage Gene Expression Omnibus; European Council. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation); Wilkinson M.D., Dumontier M., Aalbersberg I.J., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Herre H., Towards a new foundational ontology of properties, attributives and data, pp. 194-210, (2019); Space Data and Information Transfer Systems-Open Archival Information System (OAIS)-Reference Model; Operational Data Model (ODM); Kirsten T., Kiel A., Wagner J., Ruhle M., Loffler M., Selecting, Packaging, and Granting Access for Sharing Study Data, pp. 1381-1392, (2017); Semler S.C., Wissing F., Heyder R., German Medical Informatics Initiative, Methods Inf Med, 57, pp. e50-e56, (2018); Loeffler M., Engel C., Ahnert P., The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany, BMC Public Health, 15, (2015); Murphy S., Churchill S., Bry L., Instrumenting the health care enterprise for discovery research in the genomic era, Genome Res, 19, 9, pp. 1675-1681, (2009); LIFE Datenportal; Wolstencroft K., Owen S., Krebs O., SEEK: a systems biology data and model management platform, BMC Syst Biol, 9, (2015); Schriml L.M., Arze C., Nadendla S., Disease Ontology: a backbone for disease semantic integration, Nucleic Acids Res, 40, pp. D940-D946, (2012); Schriml L.M., Mitraka E., The Disease Ontology: fostering interoperability between biological and clinical human disease-related data, Mamm Genome, 26, pp. 584-589, (2015); Schriml L.M., Mitraka E., Munro J., Human Disease Ontology 2018 update: classification, content and workflow expansion, Nucleic Acids Res, 47, pp. D955-D962, (2019); Whetzel P.L., Noy N.F., Shah N.H., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications, Nucleic Acids Res, 39, pp. W541-W5, (2011); Ong E., Xiang Z., Zhao B., Ontobee: a linked ontology data server to support ontology term dereferencing, linkage, query and integration, Nucleic Acids Res, 45, pp. D347-D352, (2017); Nelson B., Data sharing: empty archives, Nature, 461, pp. 160-163, (2009); Evans D.G., Lalloo F., Cramer A., Addition of pathology and biomarker information significantly improves the performance of the Manchester scoring system for BRCA1 and BRCA2 testing, J Med Genet, 46, 12, pp. 811-817, (2009); Kast K., Rhiem K., Wappenschmidt B., Prevalence of BRCA1/2 germline mutations in 21 401 families with breast and ovarian cancer, J Med Genet, 53, 7, pp. 465-471, (2016); Barnetson R.A., Tenesa A., Farrington S.M., Identification and survival of carriers of mutations in DNA mismatch-repair genes in colon cancer, N Engl J Med, 354, 26, pp. 2751-2763, (2006); Kastrinos F., Steyerberg E.W., Mercado R., The PREMM(1,2,6) model predicts risk of MLH1, MSH2, and MSH6 germline mutations based on cancer history, Gastroenterology, 140, 1, pp. 73-81, (2011); Kastrinos F., Uno H., Ukaegbu C., Development and validation of the PREMM <sub>5</sub>model for comprehensive risk assessment of Lynch syndrome, J Clin Oncol, 35, 19, pp. 2165-2172, (2017); Baniasadi N., Rauscher F.G., Li D., Norms of interocular circumpapillary retinal nerve fiber layer thickness differences at 768 retinal locations, Transl Vis Sci Technol, 9, 9, (2020); Wang M., Elze T., Li D., Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness, J Biomed Opt, 22, 12, pp. 1-19, (2017); Li D., Rauscher F.G., Choi E.Y., Sex-specific differences in circumpapillary retinal nerve fiber layer thickness, Ophthalmology, 127, 3, pp. 357-368, (2020); Peschel T., Wang M., Kirsten T., Rauscher F.G., Elze T., A cloud-based infrastructure for interactive analysis of RNFLT data; Kiel A., Wagner J., Ruhle M., Twrdik A., Lens - The system behind the LIFE Data Portal; Wagner J., Softwaregestützte Bereitstellung Von Epidemiologischen Forschungsdaten [Master's thesis], (2016); Uciteli A., Beger C., Wagner J., Ontological modelling and execution of phenotypic queries in the Leipzig Health Atlas, Stud Health Technol Inform, 278, pp. 66-74, (2021); Uciteli A., Beger C., Kirsten T., Meineke F.A., Herre H., Ontological representation, classification and data-driven computing of phenotypes, J Biomed Semantics, 11, 1, (2020); Loeffler-Wirth H., Reikowski J., Hakobyan S., Wagner J., Binder H., oposSOM-Browser: an interactive tool to explore omics data landscapes in health science, BMC Bioinformatics, 21, 1, (2020); Wirth H., Loffler M., Von Bergen M., Binder H., Expression cartography of human tissues using self organizing maps, BMC Bioinformatics, 12, (2011); Loffler-Wirth H., Kalcher M., Binder H., oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor, Bioinformatics, 31, 19, pp. 3225-3227, (2015); Schmidt M., Hopp L., Arakelyan A., The human blood transcriptome in a large population cohort and its relation to aging and health, Front Big Data, 3, (2020); Loeffler-Wirth H., Schmidt M., Binder H., Covid-19 transmission trajectories-monitoring the pandemic in the worldwide context, Viruses, 12, 7, (2020); Schmidt M., Arshad M., Bernhart S.H., The evolving faces of the SARS-CoV-2 genome, Viruses, 13, 9, (2021); Leipzig Health Atlas; FHIR Release 4 (Technical Correction #1) (v4.0.1); Observational Health Data Sciences and Informatics; PCORnet Common Data Model; ICD-10: International Statistical Classification of Diseases and Related Health Problems: Tenth Revision; Logical Observation Identifiers Names and Codes (LOINC); SNOMED International. Systematized Nomenclature Of Medicine Clinical Terms (SNOMED CT); HITS gGmbH. FAIRDOMHub; Comprehensive Knowledge Archive Network (CKAN); King G., Homepage Dataverse; King G., An Introduction to the Dataverse Network as an infrastructure for data sharing, Sociol Methods Res, 36, 2, pp. 173-199, (2007); Homepage openBIS; Bauch A., Adamczyk I., Buczek P., openBIS: a flexible framework for managing and analyzing complex data in biology research, BMC Bioinformatics, 12, (2011); Homepage onedata","T. Kirsten; Department of Medical Data Science, Leipzig University, Leipzig, Härtelstraβe 16-18, 4107, Germany; email: toralf.kirsten@medizin.uni-leipzig.de","","Georg Thieme Verlag","","","","","","00261270","","MIMCA","35915977","English","Methods Inf. Med.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85136156537"
"Oyelude A.A.","Oyelude, Adetoun A. (57199578179)","57199578179","Innovatively doing data design in libraries in the fourth industrial revolution (4IR)","2022","Library Hi Tech News","39","10","","5","6","1","0","10.1108/LHTN-10-2022-0115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141874351&doi=10.1108%2fLHTN-10-2022-0115&partnerID=40&md5=2471663e2153e67b6fa38d7c13dccef2","The Kenneth Dike Library, University of Ibadan, Ibadan, Nigeria","Oyelude A.A., The Kenneth Dike Library, University of Ibadan, Ibadan, Nigeria","Purpose: The purpose of the paper is to review ways to innovatively do data design in the Fourth Industrial Revolution. Design/methodology/approach: Data design helps in data research management (a core library service), to avoid chaotic service delivery. Therefore, data design is important to equip library and information services professionals with skills for the 4IR. Furthermore, 4IR entails a transformation of humankind, and industries are now compelled to reconsider business transactions in the new economic order. Desk research was used for the study. Findings: Findings indicated that although online resources are used in data design, and for software developments, elements of artificial intelligence are hardly included. It is recommended that data design and research data management be used more innovatively to accommodate the 4IR. Originality/value: New technologies used for delivering library services and using data literacy are pointed out as well as the challenges faced in using them, recommending enhanced use of information and communication technology in providing library services in the digital era. © 2022, Emerald Publishing Limited.","4IR; Data design; Data management; Data visualisation; Fourth industrial revolution; Libraries","","","","","","","","Brown J.L., Blockchain in the library? Researchers explore potential applications, (2018); Florczykowska D., Let's create a database design for a library system!, (2022); Herbert A., Industry 4.0: the fourth industrial revolution is now, (2020); Data modelling, (2020); Jana S., How data designing is helping software engineering?, (2021); Database design basics, (2021); The four skills young people need to find work in the fourth industrial revolution, (2019); (2021); Ndung'u N., Signe N., The fourth industrial revolution and digitization will transform Africa into a global powerhouse, (2020)","A.A. Oyelude; The Kenneth Dike Library, University of Ibadan, Ibadan, Nigeria; email: toyelude@yahoo.com","","Emerald Publishing","","","","","","07419058","","","","English","Libr. Hi Tech News","Review","Final","","Scopus","2-s2.0-85141874351"
"Rhode W.; Elsässer D.","Rhode, Wolfgang (7006626313); Elsässer, Dominik (26435065200)","7006626313; 26435065200","Research data management","2022","Discovery in Physics","","","","145","150","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147905801&partnerID=40&md5=7f129b7c8639fddc839c5a04d5fb498c","TU Dortmund University, Department of Physics, Ruhr University of Bochum, CRC 876, Germany; TU Dortmund University, Department of Physics, Astroparticle Physics Group, Germany","Rhode W., TU Dortmund University, Department of Physics, Ruhr University of Bochum, CRC 876, Germany; Elsässer D., TU Dortmund University, Department of Physics, Astroparticle Physics Group, Germany","Astroparticle and particle physics analyses usually rely on processing a large number of observables or features that are recorded and processed at a high frequency. The data may have been either recorded experimentally or calculated usingMonte Carlo methods. The data may contain information that is scientifically irrecoverable either because the triggering event is unique (such as a supernova explosion) or because the experiments no longer exist in the same form. Thus, regardless of their nature, the data also represents a large material value. In this chapter, the path of data through data analysis is discussed keeping in mind that appropriate means of access must be found, depending on the particular purpose of the data analysis: from real-time analysis during data collection to precision analysis in the weeks and months after data collection and possibly to re-analysis many years after data collection. © 2023 the author(s), published by De Gruyter. All rights reserved.","","","","","","","","","","W. Rhode; TU Dortmund University, Department of Physics, Ruhr University of Bochum, CRC 876, Germany; email: wolfgang.rhode@tu-dortmund.de","","De Gruyter","","","","","","","978-311078596-8; 978-311078595-1","","","English","discov. in phys.","Book chapter","Final","","Scopus","2-s2.0-85147905801"
"Williams T.B.; Schmidtke C.; Roessger K.; Dieffenderfer V.; Garza M.; Zozus M.","Williams, Tremaine Brueon (57195233283); Schmidtke, Carsten (55337480200); Roessger, Kevin (55369726500); Dieffenderfer, Vicki (57968575800); Garza, Maryam (57192102881); Zozus, Meredith (56744770800)","57195233283; 55337480200; 55369726500; 57968575800; 57192102881; 56744770800","Informing training needs for the revised certified clinical data manager (CCDMTM) exam: Analysis of results from the previous exam","2022","JAMIA Open","5","1","ooac010","","","","0","10.1093/jamiaopen/ooac010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142024766&doi=10.1093%2fjamiaopen%2fooac010&partnerID=40&md5=6627e96a2163788045ebc06cece1c3d4","Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, AR, United States; Department of Adult and Lifelong Learning, University of Arkansas, Fayetteville, AR, United States; Department of Population Health Sciences, Joe R. and Teresa Lozano Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX, United States","Williams T.B., Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Schmidtke C., Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, AR, United States; Roessger K., Department of Adult and Lifelong Learning, University of Arkansas, Fayetteville, AR, United States; Dieffenderfer V., Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, AR, United States; Garza M., Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Zozus M., Department of Population Health Sciences, Joe R. and Teresa Lozano Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX, United States","Objective: To inform training needs for the revised Certified Clinical Data Manager (CCDMTM) Exam. Introduction: Clinical data managers hold the responsibility for processing the data on which research conclusions and regulatory decisions are based, highlighting the importance of applying effective data management practices. The use of practice standards such as the Good Clinical Data Management Practices increases confidence in data, emphasizing that the study conclusions likely hold much more weight when utilizing standard practices. Methods: A quantitative, descriptive study, and application of classic test theory was undertaken to analyze past data from the CCDMTM Exam to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis. Results: Competency domain-level analysis identified training needs in 4 areas: design tasks; data processing tasks; programming tasks; and coordination and management tasks. Conclusions: Analysis of past CCDMTM Exam results using classic test theory identified training needs reflective of exam takers. Training in the identified areas could benefit CCDMTM Exam takers and improve their ability to apply effective data management practices. While this may not be reflective of individual or organizational needs, recommendations for assessing individual and organizational training needs are provided.  © 2022 The Author(s).","clinical research data management; competency; informatics; training","article; clinical research; human; information science; manager; quantitative analysis; theoretical study","","","","","National Institutes of Health, NIH, (KL2 TR003108, TR003107); National Center for Advancing Translational Sciences, NCATS","This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health grant numbers TR003107 and KL2 TR003108. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health nor Society for Clinical Data Management.","Krishnankutty B, Naveen Kumar B, Moodahadu L, Bellary S., Data management in clinical research: an overview, Indian J Pharmacol, 44, 2, (2012); Kahn M, Weng C., Clinical research informatics: a conceptual perspective, J Am Med Inform Assoc, 19, e1, pp. 36-42, (2012); Embi P, Payne P., Clinical research informatics: challenges, opportunities and definition for an emerging domain, J Am Med Inform Assoc, 16, 3, pp. 316-327, (2009); Zozus MN., Letter from the editor, Data Basics, 26, 2, pp. 6-17, (2020); Edwards I, Aronson J., Adverse drug reactions: definitions, diagnosis, and management, The Lancet, 356, 9237, pp. 1255-1259, (2000); Preventable adverse drug reactions: a focus on drug interactions; Abouelenein S, Williams T, Baldner J, Zozus MN., Analysis of professional competencies for the clinical research data management profession, Stud Health Technol Inform, 270, pp. 1199-1200, (2020); Zozus M, Lazarov A, Smith L, Et al., Analysis of professional competencies for the clinical research data management profession: implications for training and professional certification, J Am Med Inform Assoc, 24, 4, pp. 737-745, (2017); Allen M, Yen W., Introduction to Measurement Theory, (1979); De Champlain A., A primer on classical test theory and item response theory for assessments in medical education, Med Educ, 44, 1, pp. 109-117, (2010); DeVellis R., Classical test theory, Med Care, 44, pp. 50-59, (2006); Kunovskaya I, Cude B, Alexeev N., Evaluation of a financial literacy test using classical test theory and item response theory, J Fam Econ Iss, 35, 4, pp. 516-531, (2014); Kehoe J., Basic item analysis for multiple-choice tests, Practical Assessment, Research, and Evaluation, 4, 10, pp. 1-3, (1994); Crocker L, Algina J., Introduction to Classical and Modern Test Theory, (1986); LeBlanc V, Cox M., Interpretation of the point-biserial correlation coefficient in the context of a school examination, TQMP, 13, 1, pp. 46-56, (2017); Cronbach L., Coefficient alpha and the internal structure of tests, Psychometrika, 16, 3, pp. 297-334, (1951); Salkind N., Statistics for People Who (Think They) Hate Statistics, (2016); Fleiss J, Levin BL, Paik M., Statistical Methods for Rates and Proportions, (2003); Wittkuhn K., Understanding performance improvement, Perf Improv, 55, 6, pp. 13-18, (2016); Rothwell W., Beyond Training and Development: The Groundbreaking Classic on Human Performance Enhancement, (2005)","T.B. Williams; Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 4301 West Markham St., Slot #782, 72205, United States; email: Tbwilliams@uams.edu","","Oxford University Press","","","","","","25742531","","","","English","JAMIA Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85142024766"
"Chen X.; Dommermuth E.; Benner J.G.; Kuglitsch R.; Lewis A.B.; Marsteller M.R.; Mika K.; Young S.","Chen, Xiaoju (56368665100); Dommermuth, Emily (57211682137); Benner, Jessica G. (36981314600); Kuglitsch, Rebecca (56527421700); Lewis, Abbey B. (57201434113); Marsteller, Matthew R. (56574371400); Mika, Katherine (57222045840); Young, Sarah (12646072300)","56368665100; 57211682137; 36981314600; 56527421700; 57201434113; 56574371400; 57222045840; 12646072300","Understanding Research Data Practices of Civil and Environmental Engineering Graduate Students","2022","Issues in Science and Technology Librarianship","2022","100","","","","","0","10.29173/istl2678","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136300628&doi=10.29173%2fistl2678&partnerID=40&md5=176be4986f937c565b3672a755223ce4","Carnegie Mellon University, Pittsburgh, PA, United States; University of Colorado, Boulder, CO, United States; Harvard University, Cambridge, MA, United States","Chen X., Carnegie Mellon University, Pittsburgh, PA, United States; Dommermuth E., University of Colorado, Boulder, CO, United States; Benner J.G., Carnegie Mellon University, Pittsburgh, PA, United States; Kuglitsch R., University of Colorado, Boulder, CO, United States; Lewis A.B., University of Colorado, Boulder, CO, United States; Marsteller M.R., Carnegie Mellon University, Pittsburgh, PA, United States; Mika K., Harvard University, Cambridge, MA, United States; Young S., Carnegie Mellon University, Pittsburgh, PA, United States","Research data management is essential for high-quality reproducible research, yet relatively little is known about how research data management is practiced by graduate students in Civil and Environmental Engineering (CEE). Prior research suggests that faculty in CEE delegate research data management to graduate students, prompting this investigation into how graduate students practice data management. This study uses semi-structured interviews and qualitative content analysis to explore how CEE graduate students work with data and practice data management in their research, as well as what resources and support would meet their needs. Many respondents touched on data collection, data management, disseminating research outputs, and collaboration and learning in their interviews. Several themes emerged from the interviews: data quality as a concern, as many CEE graduate students rely on secondary data for research; a gap between values and enacted practices; a connection between disseminating data and reproducibility; and a reliance on peer and self-directed learning for data management education. Based on these themes, the study recommends strategies for librarians and others on campus to better support CEE graduate student research data practices. © 2022, Association of College and Research Libraries. All rights reserved.","Data management; Engineering; Graduate students; Qualitative","","","","","","","","Antell K., Foote J. B., Turner J., Shults B., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Beaver D. Deb, Reflections on scientific collaboration (and its study): Past, present, and future, Scientometrics, 52, 3, pp. 365-377, (2001); Cai L., Zhu Y., The challenges of data quality and data quality assessment in the big data era, Data Science Journal, 14, pp. 1-10, (2015); Carlson J., Fosmire M., Miller C. C., Nelson M. S., Determining data information literacy needs: A study of students and research faculty, portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Stowell-Bracke M., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality field station, portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); (2020); Chen X., Benner J., Young S., Marsteller M. R., Understanding the research practices and service needs of civil and environmental engineering researchers – A grounded theory approach [Paper presentation], 2019 ASEE Annual Conference & Exposition, (2019); Cho J. Y., Lee E.-H., Reducing confusion about grounded theory and qualitative content analysis: Similarities and differences, The Qualitative Report, 19, 32, pp. 1-20, (2014); Choudhury S., Case study in data curation at Johns Hopkins University, Library Trends, 57, 2, pp. 211-220, (2008); Cooper D., Springer R., Benner J., Bloom D., Carrillo E., Carroll A., Chang B., Chen X., Daix E., Dommermuth E., Figueriredo R., Haas J., Hafner C., Henshilwood A., Krogman A., Kuglitsch R., Lanteri S., Lewis A., Li L., Yu S. H., Supporting the changing research practices of civil and environmental engineering scholars, Ithaka S+R, (2019); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A. M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); The FAIR data principles, (2014); Gardner S. K., “I heard it through the grapevine”: Doctoral student socialization in chemistry and history, Higher Education, 54, 5, pp. 723-740, (2007); Ivey S. S., Best R. M., Camp C. V., Palazolo P. J., Transforming a civil engineering curriculum through GIS integration [Paper presentation], 2012 ASEE Annual Conference & Exposition, (2012); Jahnke L., Asher A. D., Keralis S. D. C., Henry C., The problem of data, (2012); Johnston L., Jeffryes J., Data management skills needed by structural engineering students: Case study at the University of Minnesota, Journal of Professional Issues in Engineering Education and Practice, 140, 2, pp. 1-8, (2014); Kim J., Data sharing and its implications for academic libraries, New Library World, 114, 11, pp. 494-506, (2013); Kuglitsch R., Dommermuth E., Lewis A., Research practices of civil and environmental engineering scholars, (2018); Lage K., Losoff B., Maness J., Receptivity to library involvement in scientific data curation: A case study at the University of Colorado Boulder, portal: Libraries and the Academy, 11, 4, pp. 915-937, (2011); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Montgomery J. L., Harmon T., Kaiser W., Sanderson A., Hass C. N., Hooper R., Minsker B., Schnoor J., Clesceri N., Graham W., Brezonik P., The WATERS Network: An integrated environmental observatory network for water research, Environmental Science & Technology, 41, 19, pp. 6642-6647, (2007); Newton M. P., Miller C. C., Bracke M. S., Librarian roles in institutional repository data set collecting: Outcomes of a research library task force, Collection Management, 36, 1, pp. 53-67, (2010); Pasek J. E., Mayer J., Education needs in research data management for science-based disciplines: Self-assessment surveys of graduate students and faculty at two public universities, Issues in Science and Technology Librarianship, 92, (2019); Pejsa S., Song C., Publishing earthquake engineering research data [Paper presentation], Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, (2013); Petters J. L., Brooks G. C., Smith J. A., Haas C. A., The Impact of targeted data management training for field research projects – A case study, Data Science Journal, 18, 43, pp. 1-7, (2019); Pinfield S., Cox A. M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, pp. 1-28, (2014); Radecki J., Springer R., Research data services in US higher education: A web-based inventory, (2020); Rolando L., Carlson J., Hswe P., Parham S. W., Westra B., Whitmire A. L., Data management plans as a research tool, Bulletin of the Association for Information Science and Technology, 41, 5, pp. 43-45, (2015); Sadiq S., Indulska M., Open data: Quality over quantity, International Journal of Information Management, 37, 3, pp. 150-154, (2017); Sapp Nelson M., Data strategies: The research says, (2015); Satheesan S. P., Alameda J., Bradley S., Dietze M., Galewsky B., Jansen G., Kooper R., Kumar P., Lee J., Marciano R., Marini L., Minsker B. S., Navarro C. M., Schmidt A., Slavenas M., Sullivan W. C., Zhang B., Zhao Y., Zharnitsky I., McHenry K., Brown Dog: Making the digital world a better place, a few files at a time [Paper presentation], Proceedings of the Practice and Experience on Advanced Research Computing, (2018); Schroder W., Nickel S., Research data management as an integral part of the research process of empirical disciplines using landscape ecology as an example, Data Science Journal, 19, 26, pp. 1-14, (2020); Shahi A., Haas C. T., West J. S., Akinci B., Workflow-based construction research data management and dissemination, Journal of Computing in Civil Engineering, 28, 2, pp. 244-252, (2014); Sharma S., Qin J., Data management: Graduate student’s awareness of practices and policies, Proceedings of the American Society for Information Science and Technology, 51, 1, pp. 1-3, (2014); Tang R., Hu Z., Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Trisovic A., Mika K., Boyd C., Feger S., Crosas M., Repository approaches to improving the quality of shared data and code, Data, 6, 2, (2021); Open science: Making science more accessible, inclusive and equitable for the benefit of all, (2020); (2020); Best engineering schools ranked in 2021, (2021); Valentino M., Boock M., Data management for graduate students: A case study at Oregon State University, Practical Academic Librarianship, 5, 2, pp. 77-91, (2015); Wiley C. A., Kerby E. E., Managing research data: Graduate student and postdoctoral researcher perspectives, Issues in Science and Technology Librarianship, 89, (2018); Witt M., Co-designing, co-developing, and co-implementing an institutional data repository service, Journal of Library Administration, 52, 2, pp. 172-188, (2012)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85136300628"
"Feser M.; König P.; Fiebig A.; Arend D.; Lange M.; Scholz U.","Feser, Manuel (58037230300); König, Patrick (57204672703); Fiebig, Anne (53871213300); Arend, Daniel (55531371500); Lange, Matthias (36028279400); Scholz, Uwe (57732513900)","58037230300; 57204672703; 53871213300; 55531371500; 36028279400; 57732513900","On the way to plant data commons - a genotyping use case","2022","Journal of integrative bioinformatics","19","4","","","","","0","10.1515/jib-2022-0033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145242449&doi=10.1515%2fjib-2022-0033&partnerID=40&md5=0160d8a7aaffc31bd3efaf51777758d3","Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany","Feser M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany; König P., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany; Fiebig A., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany; Arend D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany; Lange M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany; Scholz U., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany","Over the last years it has been observed that the progress in data collection in life science has created increasing demand and opportunities for advanced bioinformatics. This includes data management as well as the individual data analysis and often covers the entire data life cycle. A variety of tools have been developed to store, share, or reuse the data produced in the different domains such as genotyping. Especially imputation, as a subfield of genotyping, requires good Research Data Management (RDM) strategies to enable use and re-use of genotypic data. To aim for sustainable software, it is necessary to develop tools and surrounding ecosystems, which are reusable and maintainable. Reusability in the context of streamlined tools can e.g. be achieved by standardizing the input and output of the different tools and adapting to open and broadly used file formats. By using such established file formats, the tools can also be connected with others, improving the overall interoperability of the software. Finally, it is important to build strong communities that maintain the tools by developing and contributing new features and maintenance updates. In this article, concepts for this will be presented for an imputation service. © 2022 the author(s), published by De Gruyter, Berlin/Boston.","biodiversity; cloud computing; imputation; plants; research data commons","Computational Biology; Ecosystem; Genotype; Software; biology; ecosystem; genotype; software","","","","","","","","","","NLM (Medline)","","","","","","16134516","","","36065132","English","J Integr Bioinform","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85145242449"
"Xu Z.; Zhou X.; Kogut A.; Clough M.","Xu, Zhihong (57204631412); Zhou, Xuan (57395705900); Kogut, Ashlynn (57205744961); Clough, Michael (12778971000)","57204631412; 57395705900; 57205744961; 12778971000","Effect of online research data management instruction on social science graduate students’ RDM skills","2022","Library and Information Science Research","44","4","101190","","","","2","10.1016/j.lisr.2022.101190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138811339&doi=10.1016%2fj.lisr.2022.101190&partnerID=40&md5=3b20cca6aca110cced94368239855788","Department of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, 77843, TX, United States; Department of Educational Psychology, Texas A&M University, College Station, 77843, TX, United States; Department of Teaching, Learning, and Culture, Texas A&M University, College Station, 77843, TX, United States","Xu Z., Department of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, 77843, TX, United States; Zhou X., Department of Educational Psychology, Texas A&M University, College Station, 77843, TX, United States; Kogut A., Department of Teaching, Learning, and Culture, Texas A&M University, College Station, 77843, TX, United States; Clough M., Department of Teaching, Learning, and Culture, Texas A&M University, College Station, 77843, TX, United States","Prior research has suggested the value of and the need to provide consistent research data management (RDM) instruction specifically for graduate students. However, there is a lack of RDM instruction that is tailored for the social science disciplines. The effect of a four-hour, online RDM instruction intervention, designed based on the research data life cycle, on the RDM knowledge of graduate students in social science disciplines was investigated. A total of 84 students completed both pre/post knowledge assessments with 40 students randomly assigned into the intervention group receiving online instruction and 44 in the control group. A one-way ANCOVA was used for the data analysis. Results indicated that social science graduate students who received online RDM instruction had a significantly higher score in RDM knowledge than students in the control group. Moreover, the effect of the instruction on participants' RDM skills varied by their disciplines. © 2022","Data literacy; Graduate students; Intervention; Online instruction; Research data management","","","","","","Texas A&M Triads for Transformation, (1762); Texas A and M University, TAMU","This work was supported by T3: Texas A&M Triads for Transformation [grant numbers 1762 , 2020 ], Texas A&M University Institutional Funding . 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Gurat M.G., College students’ data management skills in a private university in the Philippines, International Journal of Social Sciences & Educational Studies, 5, 2, (2018); Johnson A.M., Bresnahan M.M., DataDay!: Designing and assessing a research data workshop for subject librarians, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Johnson B., Christensen L., Educational research: Quantitative, qualitative, and mixed approaches, (2008); Johnston L., Jeffryes J., Civil Engineering/ Graduate Students/ Johnston & Jeffryes/ University of Minnesota/ 2012, Data Information Literacy Case Study Directory, 3(1), Article 1, (2015); Johnston L.R., Jeffryes J., Data management skills needed by structural engineering students: Case study at the University of Minnesota, Journal of Professional Issues in Engineering Education and Practice, 140, 2, (2014); Lund B.D., Wang T., An analysis of research methods utilized in five top, practitioner-oriented LIS journals from 1980 to 2019, Journal of Documentation, 77, pp. 1196-1208, (2021); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data management pilot course at an academic library, Journal of eScience Librarianship, 3, 1, (2015); Oo C.Z., Chew A.W., Wong A.L.H., Gladding J., Stenstrom C., Delineating the successful features of research data management training: A systematic review, International Journal for Academic Development, (2021); Pascuzzi P., Nelson M.R.S., Integrating data science tools into a graduate level data management course, Journal of eScience Librarianship, 7, 3, (2018); Pasek J.E., Mayer J., Education needs in research data management for science-based disciplines: Self-assessment surveys of graduate and faculty at two public universities, Issues in science and technology librarianship, 92, (2019); Perrier L., Blondal E., MacDonald H., Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies, Library & Information Science Research, 40, pp. 173-183, (2018); Phillips G.A., Miller R.S., Miller C.F., Understanding graduate students’ knowledge about research data management: Workflows, challenges, and the role of the library, The Proceedings of the Association of College and Research Librarians 2019 Conference, (2019); Pierce C.A., Block R.A., Aguinis H., Cautionary note on reporting eta-squared values from multifactor ANOVA designs, Educational and Psychological Measurement, 64, pp. 916-924, (2004); Rantasaari J., Kokkinen H., Closing the skills gap-the basics of the research data management (BRDM) course: Case University of Turku, Proceedings of the IATUL conferences, (2019); Raszewski R., Goben A.H., Bergren M.D., Jones K., Ryan C., Steffen A.D., Vonderheid S.C., A survey of current practices in data management education in nursing doctoral programs, Journal of Professional Nursing, 37, pp. 155-162, (2021); Read K.B., Larson C., Gillespie C., Oh S.Y., Surkis A., A two-tiered curriculum to improve data management practices for researchers, PLoS One, 14, 5, (2019); Schmidt L., Holles J., Graduate class in research data management, Chemical Engineering Education, 52, 1, pp. 52-59, (2018); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, pp. 417-449, (2015); Southall J., Planning ahead: Delivering research data management support for the social sciences, ALISS Quarterly, 14, 1, pp. 18-21, (2018); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Sandusky R., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Thielen J., Hess A.N., Advancing research data management in the social sciences: Implementing instruction for education graduate students into a doctoral curriculum, Behavioral & Social Sciences Librarian, 36, pp. 16-30, (2017); Thielen J., Samuel S.M., Carlson J., Moldwin M., Developing and teaching a two-credit data management course for graduate students in climate and space science, Issues in science and technology librarianship, 86, (2017); VanScoy A., Fontana C., How reference and information service is studied: Research approaches and methods, Library & Information Science Research, 38, pp. 94-100, (2016); Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Whyte A., Tedds J., Making the case for research data management, DCC briefing papers, (2011); Williams S.C., Gathering feedback from early-career faculty: Speaking with and surveying agricultural faculty members about research data, Journal of eScience Librarianship, 2, 2, (2013); Xu Z., Research data management practice in academic libraries, Journal of Librarianship and Scholarly Communication, 10, 1, (2022); Xu Z., Chen Z., Eutsler L., Geng Z., Kogut A., A scoping review of digital game-based technology on English language learning, Educational Technology Research and Development, 68, pp. 877-904, (2020); Xu Z., Zhou X., Kogut A., Watts J., A scoping review: Synthesizing evidence on research data management instruction in academic libraries, The Journal of Academic Librarianship, 43, (2022)","Z. Xu; Department of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, 77843, United States; email: xuzhihong@tamu.edu","","Elsevier Ltd","","","","","","07408188","","LISRD","","English","Libr. Inf. Sci. Res.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85138811339"
"Boté-Vericad J.-J.; Healy S.","Boté-Vericad, Juan-José (53363101000); Healy, Sharon (57205036443)","53363101000; 57205036443","ACADEMIC LIBRARIES AND RESEARCH DATA MANAGEMENT: A SYSTEMATIC REVIEW; [UPRAVLJANJE AKADEMSKIM KNJIŽNICAMA I ISTRAŽIVAČKIM PODACIMA: PREGLED]","2022","Vjesnik Bibliotekara Hrvatske","65","3","","171","193","22","0","10.30754/vbh.65.3.1016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145374965&doi=10.30754%2fvbh.65.3.1016&partnerID=40&md5=4a9378b7e3be5b7fa597064ec9531074","Facultat d’Informació i Mitjans Audiovisuals, Centre de Recerca en Informació, Comunicació i Cultura, Universitat de Barcelona, Spain; Maynooth University, Ireland","Boté-Vericad J.-J., Facultat d’Informació i Mitjans Audiovisuals, Centre de Recerca en Informació, Comunicació i Cultura, Universitat de Barcelona, Spain; Healy S., Maynooth University, Ireland","Purpose. Open Science entails research reproducibility, with an emphasis on data sharing and reuse. Hence, research data management (RDM) is an essential asset in research institutions for supporting open science. This study offers a systematic review of the landscape of research data management in academic libraries. It further examines the influence academic libraries can have if they are involved in the research lifecycle process, and how this benefits research institutions that have started implementing research data management, especially in the data-intensive disciplines. Methodology. In this study, the authors analysed Web of Science and Scopus data-bases, searching for papers connecting research data management and academic libra-ries. The authors found a total of N=387 articles. After removing duplicates and applying the exclusion and inclusion criteria process, the authors finally analysed N=32 articles, n=20 case studies, and n=12 research papers at both national and international levels. Limitations. This study has some limitations. Although the authors retrieved as many papers as possible for the analysis, it should not be considered as an exhaustive analysis, as varying studies may also be missing from the sample. The authors observed that there are more case studies focused on one institution rather than research papers involving different institutions at the international or national level. Therefore, more research studies would enrich the literature and show best practices in RDM. Results. The results show that research data management has some services implemented in different countries at the local or the international level. The authors argue that research data management generates new opportunities for academic libraries and librarians to acquire new skills as a part of the research data lifecycle. Originality/value. This study reports the current state of research data management at the international level in academic libraries and the influence libraries can have if they are involved in the research lifecycle process. © 2022, Hrvatsko Knjiznicarsko Drustvo. All rights reserved.","Academic libraries; librarian skills; open science; research data mana-gement; systematic review","","","","","","Ministerio de Ciencia, Innovación y Universidades, MCIU, (RTI2018-094360-B-I00)","This work was supported by the Spanish Ministerio de Innovación, Ciencia y Universidades [grant ref RTI2018-094360-B-I00]. Authors thank the Ministry for funding the project.","Abadal E., Ciencia abierta: un modelo con piezas por encajar, Arbor, 197, 799, (2021); Ashiq M., Usmani M. H., Naeem M., A systematic literature review on research data management practices and services, Global Knowledge, Memory and Communication, (2020); Ayris P., Brexit: and its potential impact for open access in the UK, Insights: the UKSG Journal, 30, 1, pp. 4-10, (2017); Ball J., Research data management for libraries: getting started, Insights, 26, 3, pp. 256-260, (2013); Bardyn, Et al., Bardyn, T.; E. Patridge; M. Moore and J. Koh. Health sciences libraries advancing collaborative clinical research data management in universities, Journal of EScience Librarianship, 7, (2018); Bishop, Et al., Bishop, B. W.; H. R. Collier; A. M. Orehek and M. Ihli. Potential roles for science librarians in research data management: a gap analysis, Issues in Science and Technology Librarianship, 98, (2021); Blumesberger S., Repositorien als Tools für ein umfassendes Forschungsda-tenmanagement: am Beispiel von PHAIDRA an der Universitätsbibliothek Wien, Bibliothek Forschung und Praxis, 44, 3, pp. 503-511, (2020); Bote J.-J., Termens M., Reusing data: technical and ethical challenges, DE-SIDOC Journal of Library and Information Technology, 39, 6, pp. 329-337, (2019); Bradley-Ridout G., Preferred but not required: examining research data management roles in health science librarian positions, Journal of the Canadian Health Libraries Association, 39, 3, pp. 138-145, (2018); Chiware E. R. T., Mathe Z., Academic libraries’ role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, (2015); Chu C. M., Ortiz-Repiso Jimenez V., Slavic A., Talavera-Ibarra A. M., Zakaria S., IFLA Guidelines for Professional LIS Education Programmes, (2022); Clements A., Research information meets research data management … in the library?, Insights, 26, 3, pp. 298-304, (2013); Corrall S., Kennan M. A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A. M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox, Et al., Cox, A. M.; M. A. Kennan; L. Lyon and S. Pinfield. Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox, Et al., Cox, A. M.; M. A. Kennan; L. Lyon; S. Pinfield and L. Sbaffi. Matu-ring research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Cox A. M., Pinfield S., Smith J., Moving a brick building: UK libraries co-ping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Open Science, (2019); Faniel I. M., Connaway L. S., Librarians’ perspectives on the factors influen-cing research data management programs, College and Research Libraries, 79, 1, pp. 100-119, (2018); Hansen, Et al., Hansen, J. S.; S. Gadegaard; K. K. Hansen; A. V. Larsen; S. Møller; G. S. Thomsen and K. F. Holmstrand. Research data management challenges in ci-tizen science projects and recommendations for library support services: a scoping review and case study, Data Science Journal, 20, 1, (2021); Healy, Et al., Healy, S.; H. Byrne; K. Schmid; N. Bingham; O. Holownia; M. Kurzmeier and R. Jansma. 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Y., Practical considerations for a library’s research data management services: the case of the National Institutes of Health Library, Journal of the Medical Library Association, 109, 3, pp. 450-458, (2021); Komiyama Y., Yamaji K., Nationwide research data management service of Japan in the Open Science era, IN: 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 129-133, (2017); Kruse F., Thestrup J. B., Research libraries’ new role in research data mana-gement: current trends and visions in Denmark, LIBER Quarterly: The Journal of the Association of European Research Libraries, 23, 4, pp. 310-335, (2014); Lefebvre A., Spruit M., Laboratory forensics for Open Science readiness: an investigative approach to research data management, Information Systems Frontiers, (2021); Lindstadt B., Schmitz J., Das Management von Forschungsdaten als Hand-lungsfeld wissenschaftlicher Bibliotheken: Forschungsunterstützung am Beispiel ZB MED – Informationszentrum Lebenswissenschaften, Bibliothek Forschung und Praxis, 43, 1, pp. 42-48, (2019); Muellenbach J. M., A Pilot to initiate research data management services within academic libraries helps librarians to learn about, engage with, and enhance skills within their research communities, Evidence Based Library and Information Practi-ce, 16, 1, pp. 104-106, (2021); Mushi G. E., Pienaar H., van Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodo-ma, Tanzania, Data Science Journal, 19, 1, (2020); Nie H., Luo P., Fu P., Research data management implementation at Peking University Library: foster and promote Open Science and Open Data, Data Intelli-gence, 3, 1, pp. 189-204, (2021); Page M. J., McKenzie J. E., Bossuyt P. M., Boutron I., Hoff-mann T. C., Mulrow C. D., Shamseer L., Et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews, BMJ, 372, (2021); Pinfield S., Cox A. M., Smith J., Research data management and libraries: relationships, activities, drivers and influences, PLOS ONE, 9, 12, (2014); Read K. B., Koos J., Miller R. S., Miller C. F., Phillips G. A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, Journal of the Medical Library Association, 107, 3, pp. 432-441, (2019); Read K., Surkis A., Research data management teaching toolkit [Data set], (2018); Rehwald S., Stegemann J., Roadmap zur Servicestelle für Forschungsdaten-management am Beispiel der Universitätsbibliothek Duisburg-Essen, Information – Wissenschaft & Praxis, 72, 4, pp. 194-203, (2021); Rice R., Supporting research data management and Open Science in academic libraries: a data librarian’s view, VOEB-Mitteilungen, 72, 2, pp. 263-273, (2019); Searle S., Using scenarios in introductory research data management workshops for library staff, D-Lib Magazine, 21, (2015); Shelly M., Jackson M., Research data management compliance: is there a bigger role for university libraries?, Journal of the Australian Library and Information Association, 67, 4, pp. 394-410, (2018); Shipman J. P., Tang R., The collaborative creation of a Research Data Management Librarian Academy (RDMLA), Information Services and Use, 39, 3, pp. 243-247, (2019); Slavnic Z., Research and data-sharing policy in Sweden: neoliberal courses, forces and discourses, Prometheus, 35, 4, pp. 249-266, (2017); Steiner K., Forschungsdatenmanagement und Informationskompetenz: Neue Entwicklungen an Hochschulbibliotheken Neuseelands = Research data management and information literacy: new developments at New Zealand university libra-ries, Information –Wissenschaft & Praxis, 66, 4, pp. 230-236, (2015); Strauch A., Forschungsdatenmanagement an der Stiftung Universität Hildeshe-im: praktische Unterstützung für Forschende und Studierende durch die Universität-sbibliothek, Information – Wissenschaft & Praxis, 70, pp. 259-263, (2019); Strauch A., “To begin, at the beginning […]“: Bibliotheken als „Player“ im pro-fessionellen Forschungsdatenmanagement. Universitätsbibliothek Hildesheim, Bi-bliothek Forschung und Praxis, 44, 2, pp. 166-169, (2020); Tenopir, Et al., Tenopir, C.; R. J. Sandusky; S. Allard and B. Birch. Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Traub I. D., Solis B. S., Budroni P., Forschungsdaten und zeitgemäße Au-farbeitung durch Policies – 2. Internationaler LEARN Workshop zum Thema‚ Fors-chungsdatenmanagement‘ (Wien, 6. April 2016), Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare, 69, 1, pp. 142-150, (2016); Truter V. Z., Research data management and sharing practices in the digital humanities with a focus on publisher support: a case study in the field of Web archive studies: MA thesis, (2021); Research Data Management, (2022); UNESCO recommendations for Open Science, (2021); Working Groups Aarhus University; Wilkinson, Et al., Wilkinson, M. D.; M. Dumontier; Ij. J. Aalbersberg; G. Apple-ton; M. Axton; A. Baak; N. Blomberg et al. The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Wu M., Chen X., Library service design based on the needs of chemistry research data management and sharing survey, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-4, (2016); Yoon A., Schultz T., Research data management services in academic libraries in the US: a content analysis of libraries’ websites, College & Research Libra-ries, (2017)","","","Hrvatsko Knjiznicarsko Drustvo","","","","","","05071925","","","","English","Vjesn. Bibl. Hrvat.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85145374965"
"Mwinami N.V.; Dulle F.W.; Mtega W.P.","Mwinami, Nolasko Victory (58024870600); Dulle, Frankwell W. (6507812934); Mtega, Wulystan Pius (55661585000)","58024870600; 6507812934; 55661585000","Data preservation practices for enhancing agricultural research data usage among agricultural researchers in Tanzania","2022","Journal of Librarianship and Information Science","","","","","","","2","10.1177/09610006221138110","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144363294&doi=10.1177%2f09610006221138110&partnerID=40&md5=5ece38d26796d7d4800255fae2e19617","Sokoine University of Agriculture, Tanzania","Mwinami N.V., Sokoine University of Agriculture, Tanzania; Dulle F.W., Sokoine University of Agriculture, Tanzania; Mtega W.P., Sokoine University of Agriculture, Tanzania","The objective of this study was to investigate the role of research data preservation for enhanced data usage among agricultural researchers in Tanzania. Specifically, the study aimed to examine the data preservation methods used by agriculture researchers, find out how long agriculture researchers preserve their agriculture research data, and determine factors that influence agriculture researchers on their choice of data preservation methods for use. The study employed a cross-sectional research design. The study employed both qualitative and quantitative approaches. A survey was conducted to collect data in 11 research institutions. A simple random sampling technique was used to select 204 respondents from the study area while purposive sampling techniques were used to select 11 agriculture research institutions including 10 Tanzanian Agricultural Research Institution (TARI) centers, and Sokoine University of Agriculture (SUA). Also, 12 respondents were selected purposively for an in-depth interview as key informants. The study adopted Data Curation Centre (DCC) Lifecycle Model to explain data preservation process. Findings indicated that a majority of more than 90% of researchers preferred to preserve their data using different storage devices such as field notebooks, computers, and institutional libraries. Moreover, findings indicated that about 74% of agricultural researchers preferred to preserve their data for more than 6 years after the end of the project. Findings also indicated factors that influence researchers in the choice of data preservation methods to be easy to reach, cost-effective storage devices, support to use the devices, adequate infrastructure for data preservation, and reliable power supply. It can be concluded that there is yet a great role of research data preservation in enhancing data usage among researchers in Tanzania. It is recommended that the government should establish an agricultural research data bank to guarantee permanent availability of data at all times when needed. © The Author(s) 2022.","Agricultural research data; data preservation; data reuse; data sharing; data storage","","","","","","","","Adika F.O., Kwanya T., Research data management literacy amongst lecturers at Strathmore University Kenya, Library Management, 41, 6-7, pp. 447-466, (2020); Adrian A.M., Emison B., Musker R., Et al., Open access and open data at push universities in partnership with global open data for agriculture (GODAN), (issue June 2018), (2018); Agrawal A., Data roadmaps for sustainable development: Assessment and lessons learned, (2017); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Barakabitze A.A., Kitindi E.J., Sanga C., Et al., New technologies for disseminating and communicating agriculture knowledge and information: Challenges for agricultural research institutes in Tanzania, The Electronic Journal of Information Systems in Developing Countries, 70, 1, pp. 1-22, (2015); Bhatia V., Stout S., Baldwin B., Et al., Results data initiatives: Findings from Tanzania development gateway, (2016); Brouder S., Eagle A., Fukagawa N.K., Et al., Enabling Open-source Data Networks in Public Agricultural Research, (2019); Chidi C., Investigation of Factors Influencing Data Management in District Health Planning Process: A Case Study of Kibaha Town Council, (2017); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, (2017); Dileepkumar G., Improving research data management and sharing: Experiences from ICRISAT, (2014); Sokoine University of Agriculture Research Regulations and Guidelines, Sokoine University of Agriculture, 4th edn. Morogoro: Sokoine University of Agriculture, (2019); Eckes A.H., Gubala T., Nowakowski P., Et al., Introducing the brassica information portal: Towards integrating genotypic and phenotypic brassica crop data, F1000 Research, 6, pp. 465-516, (2017); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, International Journal of Bioassays, 44, 4, pp. 281-299, (2018); Country programming framework for united republic of Tanzania 2017–2020, prepared by the government of the united republic of Tanzania, ministry of agriculture, livestock and fisheries in collaboration with the food and agriculture organization of the united nations (FAO), (2017); Frederick C., Blumzon I., Panescu A.T., Data Storage, (2019); Funk B.Y.C., Parker K., Calazza T., Women and men in stem at odd over workplace equity: Perceived inequalities are especially common among women in science, technology, engineering and math jobs who work mostly with men, (2018); Granda P., Blasczyk E., Data dissemination: Springer reference 688–713, (2011); Hawkins E., Fulton J., Lee J., Et al., Data fundamentals: Agricultural and biosystems engineering, (2018); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Katabalwa A.S., Bates J., Abbott P., Potential opportunities and risks of sharing agricultural research data in Tanzania, IASSIST Quarterly/International Association for Social Science Information Service and Technology, 45, pp. 3-4, (2021); Kijazi A., Data availability, exchange and gaps: A case study of Tanzania, (2018); Kirub A., Agricultural Research Data Management: Principles, Policy, and Practice, (2016); Kruse F., Thestrup J.B., Research libraries’ new role in research data management, current trends and visions in Denmark, Liber Quarterly, 23, 4, pp. 310-335, (2014); Maru A., ICT/ICM in agricultural research and development: Status in sub-Saharan Africa, (2004); Mboera L.E.G., The Management of Health and Biomedical Data in Tanzania, Need for a National Scientific Data Policy, Directorate of Information Technology, (2015); Methew B., Ross L., Research Methods: A Practical Guide for the Social Sciences, (2010); Mulder N., Adebamowo C.A., Adebamowo S.N., Et al., Genomic research data generation, analysis, and sharing – Challenges in the African setting, Data Science Journal, 16, pp. 1-15, (2017); Mushi G.E., Pienaar H., van Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, (2020); Musker R., Schaap B., Global open data in agriculture and nutrition (Godan) initiative partner network analysis [version 1; peer review: 2 approved with reservations], F1000 Research, 7, 5, (2018); Dissemination and Pricing Policy: The NBS Recognizes That Timely Dissemination of Data to Specialized Users for Research Purposes and Other Decision-Makers is Beneficial, (2010); Ng'eno E.J., Mutula S., Research data management in Kenya’s agricultural research institutes. Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (Information Studies) in the School of Social Sciences, College of Humanities, University of KwaZulu-Natal, Pietermaritzburg: South Africa, Inkanyiso, Journal of Humanities and Social Sciences, 1, (2018); Ng'eno E.J., Mutula S., Research data management (RDM) in agricultural research institutes: A literature review, submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (Information Studies) in the School of Social Sciences, College of Humanities, University of KwaZulu-Natal, Pietermaritzburg, South Africa, Inkanyiso, Journal of Humanities and Social Sciences, 10, 1, pp. 28-50, (2018); Tanzania agricultural research institute (TARI) act no 10 of 2016, Dodoma: Tanzania, (2016); Plooy-Cilliers A., Vavis C., Bezuidenhout R., Research Matters, (2014); Pouchard L., Revisiting the data lifecycle with Big Data curation, International Journal of Digital Curation, 10, 2, pp. 176-192, (2016); Sielemann K., Hafner A., Pucker B., The reuse of public datasets in the life sciences: Potential risks and rewards, Peer Journal, 8, (2020); Smirnova L., Mergen P., Groom Q.J., Et al., Data sharing tools adopted by the European Biodiversity Observation Network Project, Research Ideas and Outcomes, 2, (2016); Tenopir C., Rice N.M., Allard S., Et al., Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide, PLoS One, 15, 3, (2020); Tanzania meteorological authority (TMA) agrometeorological database, (2020); Tripathi M., Shukla A., Sonkar S.K., Research data management practices in university libraries: A study, DESIDOC Journal of Library & Information Technology, 37, 6, pp. 417-424, (2017); Tanzania Agricultural Research Institute (TARI) act no 10 of 2016, (2016); Information communication technology (ICT) policy for national bureau of statistics, (2017); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Yamane T., Statistics: An Introductory Analysis, (1967); Zhao H., Wang J., Development and current situation of agricultural scientific data sharing in China, IFIP Advances in Information and Communication Technology, 452, pp. 80-86, (2015)","N.V. Mwinami; Sokoine University of Agriculture, Tanzania; email: mwinaminolasko@yahoo.com","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Article","Article in press","","Scopus","2-s2.0-85144363294"
"Wheeler T.R.; Delgado D.; Albert P.J.; Ben Maamar S.; Oxley P.R.","Wheeler, Terrie R. (57194779102); Delgado, Diana (55440789000); Albert, Paul J. (26031291500); Ben Maamar, Sarah (57209386142); Oxley, Peter R. (57217450944)","57194779102; 55440789000; 26031291500; 57209386142; 57217450944","Transforming and extending library services by embracing technology and collaborations: A case study","2022","Health Information and Libraries Journal","39","3","","294","298","4","0","10.1111/hir.12439","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132436541&doi=10.1111%2fhir.12439&partnerID=40&md5=2cf9a26f731d002e47d25768e3d8f7cb","Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, NY, United States; Information, Education and Clinical Services, Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, NY, United States; Information Technologies & Services, Weill Cornell Medicine, New York, NY, United States","Wheeler T.R., Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, NY, United States; Delgado D., Information, Education and Clinical Services, Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, NY, United States; Albert P.J., Information Technologies & Services, Weill Cornell Medicine, New York, NY, United States; Ben Maamar S., Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, NY, United States; Oxley P.R., Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, NY, United States","Technology advances and collaborations with information technology and computer science groups have enabled library services to expand into new domains. Listening to user needs, eliminating administrative burden and saving users time remain strong foundations on which to build new library services enabled by technology. Examples of what is now possible is described, including service to user groups, successes, failures and challenges. Although technology advances have enabled library service enhancements to all user groups, special emphasis on new library services in support of the research enterprise is discussed. As Lindberg and Humphreys predicted in 2015, the research enterprise's need for responsible curation of research data has created new opportunities for library services and examples of those services are discussed. As technology continues to advance, new library services are expected to emerge. These may include regulatory and compliance services. By developing these services with user feedback to save users time and expedite their work, and in collaboration with technology experts, libraries can expect to offer sustainable and valued services for years to come. © 2022 The Authors Health Information and Libraries Journal published by John Wiley & Sons Ltd on behalf of Health Libraries Group.","academic; artificial intelligence (AI); bibliometrics; clinical decision making; consumer health information; information services; libraries; libraries; library services; medical; research data (management); research support","Humans; Information Science; Libraries, Medical; Library Services; Technology; human; information science; library; technology","","","","","","","Albert P.J., Dutta S., Lin J., Zhu Z., Bales M., Johnson S.B., Mansour M., Wright D., Wheeler T.R., Cole C.L., ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions, PLoS ONE, 16, 4, (2021); Bik E.M., Casadevall A., Fang F.C., Sibley L.D., The prevalence of inappropriate image duplication in biomedical research publications, mBio, 7, 3, pp. e00809-e00816, (2016); DeRosa A.P., Baltich Nelson B., Delgado D., Mages K.C., Martin L., Stribling J.C., Involvement of information professionals in patient- and family-centered care initiatives: A scoping review, Journal of the Medical Library Association, 107, 3, pp. 314-322, (2019); Dutta S., ReCiter machine learning/analysis—A suite of scripts and tools for retrieving and analyzing data from ReCiter, (2022); Dutta S., ReCiter: An enterprise open source author disambiguation system for academic institutions, (2022); Dutta S., WCMC-ITS/VIVO-Docker, (2022); Harrison H., Griffin S.J., Kuhn I., Usher-Smith J.A., Software tools to support title and abstract screening for systematic reviews in healthcare: An evaluation, BMC Medical Research Methodology, 20, 1, (2020); Hurst E.J., Educational technologies in health sciences libraries: Teaching technology skills, Medical Reference Services Quarterly, 33, 1, pp. 102-108, (2014); Hutchins B.I., Yuan X., Anderson J.M., Santangelo G.M., Relative citation ratio (RCR): A new metric that uses citation rates to measure influence at the article level, PLoS Biology, 14, 9, (2016); Lindberg D.A., Humphreys B.L., 2015—The future of medical libraries, The New England Journal of Medicine, 352, 11, pp. 1067-1070, (2005); Nevius A.M., Ettien A., Link A.P., Sobel L.Y., Library instruction in medical education: A survey of current practices in the United States and Canada, Journal of the Medical Library Association, 106, 1, pp. 98-107, (2018); Final NIH policy for data management and sharing, (2020); Norton H.F., Tennant M.R., Edwards M.E., Pomputius A., Use of annual surveying to identify technology trends and improve service provision, Journal of the Medical Library Association, 106, 3, pp. 320-329, (2018); Oxley P.R., Ruffing J., Campion T.R., Wheeler T.R., Cole C.L., Design and implementation of a secure computing environment for analysis of sensitive data at an academic medical center, (2018); Patterson B., Casucci T., Hull B.E., Lombardo N.T., Library as the technology hub for the health sciences, Medical Reference Services Quarterly, 37, 4, pp. 341-356, (2018); Ranganathan S.R., The five laws of library science, (1931); Rodrigues G., Hoshino A., Kenific C.M., Matei I.R., Steiner L., Freitas D., Kim H.S., Oxley P.R., Scandariato I., Casanova-Salas I., Dai J., Badwe C.R., Gril B., Tesic Mark M., Dill B.D., Molina H., Zhang H., Benito-Martin A., Bojmar L., Lyden D., Tumour exosomal CEMIP protein promotes cancer cell colonization in brain metastasis, Nature Cell Biology, 21, 11, pp. 1403-1412, (2019); Network; Data catalog, (2022); Library scientific software hub, (2022); WCM data core, (2022); Wheeler T.R., Oxley P.R., Developing a library bioinformatics program fully integrated into a medical research institution, Medical Reference Services Quarterly, 37, 4, pp. 413-421, (2018)","T.R. Wheeler; Weill Cornell Medicine Samuel J. Wood Library and C.V. Starr Biomedical Information Center, New York, United States; email: tew2004@med.cornell.edu","","John Wiley and Sons Inc","","","","","","14711834","","","35734785","English","Health Inf. Libr. J.","Article","Final","","Scopus","2-s2.0-85132436541"
"Alvarez-Romero C.; Martinez-Garcia A.; Vega J.T.; Díaz-Jimènez P.; Jimènez-Juan C.; Nieto-Martín M.D.; Villarán E.R.; Kovacevic T.; Bokan D.; Hromis S.; Malbasa J.D.; Beslać S.; Zaric B.; Gencturk M.; Sinaci A.A.; Baturone M.O.; Parra Calderón C.L.","Alvarez-Romero, Celia (57210788267); Martinez-Garcia, Alicia (55937333600); Vega, Jara Ternero (57782597700); Díaz-Jimènez, Pablo (57218660835); Jimènez-Juan, Carlos (57219847292); Nieto-Martín, María Dolores (35795876200); Villarán, Esther Román (57782597800); Kovacevic, Tomi (56205406300); Bokan, Darijo (57195593453); Hromis, Sanja (32867618500); Malbasa, Jelena Djekic (57208734534); Beslać, Suzana (57783095100); Zaric, Bojan (16403676100); Gencturk, Mert (53063547700); Sinaci, A. Anil (36905158800); Baturone, Manuel Ollero (57207620440); Parra Calderón, Carlos Luis (24332533000)","57210788267; 55937333600; 57782597700; 57218660835; 57219847292; 35795876200; 57782597800; 56205406300; 57195593453; 32867618500; 57208734534; 57783095100; 16403676100; 53063547700; 36905158800; 57207620440; 24332533000","Predicting 30-Day Readmission Risk for Patients with Chronic Obstructive Pulmonary Disease through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study","2022","JMIR Medical Informatics","10","6","e35307","","","","2","10.2196/35307","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133535592&doi=10.2196%2f35307&partnerID=40&md5=1c9b32f78776868e9e8cdcd7de06447c","Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia; Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Software Research & Development and Consultancy Corporation, Ankara, Turkey","Alvarez-Romero C., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Martinez-Garcia A., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Vega J.T., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Díaz-Jimènez P., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Jimènez-Juan C., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Nieto-Martín M.D., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Villarán E.R., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Kovacevic T., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Bokan D., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia; Hromis S., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Malbasa J.D., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Beslać S., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia; Zaric B., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Gencturk M., Software Research & Development and Consultancy Corporation, Ankara, Turkey; Sinaci A.A., Software Research & Development and Consultancy Corporation, Ankara, Turkey; Baturone M.O., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Parra Calderón C.L., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain","Background: Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. Objective: The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). Methods: The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. Results: Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. Conclusions: Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles. ©Celia Alvarez-Romero, Alicia Martinez-Garcia, Jara Ternero Vega, Pablo Díaz-Jimènez, Carlos Jimènez-Juan, María Dolores Nieto-Martín, Esther Román Villarán, Tomi Kovacevic, Darijo Bokan, Sanja Hromis, Jelena Djekic Malbasa, Suzana Beslać, Bojan Zaric, Mert Gencturk, A Anil Sinaci, Manuel Ollero Baturone, Carlos Luis Parra Calderón.","chronic obstructive pulmonary disease; clinical validation; early predictive model; FAIR principles; privacy-preserving distributed data mining; research data management","","","","","","Carlos III National Institute of Health, (IMP/00019, PT20/00088); European Regional Development Fund Fondo Europeo de Desarrollo Regional; European Union’s (EU) Horizon 2020 research and innovation program; Institut Za Plucne Bolesti Vojvodine; Instituto Aragonés de Ciencias de la Salud; Spanish National Health System Industrial Capacities; Horizon 2020 Framework Programme, H2020, (824666); Università Cattolica del Sacro Cuore, UCSC; Hôpitaux Universitaires de Genève, HUG; Universidade do Porto, U.Porto; Servicio Andaluz de Salud, SAS","Funding text 1: This work was supported by The FAIR4Health project [10], which received funding from The European Union’s Horizon 2020 Research and Innovation Program under grant 824666. This research has also been cosupported by The Carlos III National Institute of Health through The Programa de Ciencia de Datos de la Infraestructura de Medicina de Precisión Asociada a la Ciencia y la Tecnología Program (IMPaCT-Data, code IMP/00019) and through The Platform for Dynamization and Innovation of the Spanish National Health System Industrial Capacities and their effective transfer to the productive sector (code PT20/00088), both cofunded by The European Regional Development Fund Fondo Europeo de Desarrollo Regional “A way of making Europe.” The authors would like to thank the clinical researchers of the project, coming from the organizations that are part of the FAIR4Health Consortium: Universite De Geneve (Switzerland), University Hospitals of Geneva (Switzerland), Università Cattolica Del Sacro Cuore (Italy), Universidade Do Porto (Portugal), Instituto Aragonés de Ciencias de la Salud (Spain), Institut Za Plucne Bolesti Vojvodine (Serbia), and Servicio Andaluz de Salud (Spain).; Funding text 2: This work was supported by the FAIR4Health project [10], which received funding from the European Union’s Horizon 2020 research and innovation program under grant 824666. This research has also been cosupported by the Carlos III National Institute of Health through the Programa de Ciencia de Datos de la Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología program (IMPaCT-Data, code IMP/00019) and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both cofunded by the European Regional Development Fund Fondo Europeo de Desarrollo Regional “A way of making Europe.” The authors would like to thank the clinical researchers of the project, coming from the organizations that are part of the FAIR4Health Consortium: Universite De Geneve (Switzerland), University Hospitals of Geneva (Switzerland), Università Cattolica Del Sacro Cuore (Italy), Universidade Do Porto (Portugal), Instituto Aragonés de Ciencias de la Salud (Spain), Institut Za Plucne Bolesti Vojvodine (Serbia), and Servicio Andaluz de Salud (Spain).; Funding text 3: FAIR4Health is a project that received funding from the European Union’s (EU) Horizon 2020 research and innovation program under grant 824666. This project started in December 2018 and ended in November 2021. The main objective of this European project was to promote and encourage the EU health research community to apply the Findable, Accessible, Interoperable, and Reusable (FAIR) principles [1] in their data sets derived from publicly funded research initiatives through the implementation of an effective outreach strategy at the EU level, the production of a set of guidelines to set the foundations for a FAIR data certification road map, the development of an intuitive platform, and the demonstration of the potential impact on health research and health outcomes through the validation of 2 pathfinder case studies. At a high level, this project aimed to facilitate health research data sharing and reuse. This project brought together expertise from the key stakeholders involved in properly addressing this main objective: health research, data managers, medical informatics, software developers, standards, and lawyers. The FAIR4Health Consortium accounted for 17 partners from 11 EU and non-EU countries.","Wilkinson M, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, pp. 160018-160019, (2016); Parra-Calderon CL, Sanz F, McIntosh LD., The challenge of the effective implementation of FAIR principles in biomedical research, Methods Inf Med, 59, 4-5, pp. 117-118, (2020); Delgado J, Llorente S., Security and privacy when applying FAIR principles to genomic information, Stud Health Technol Inform, 275, pp. 37-41, (2020); Dijkers MP., A beginner's guide to data stewardship and data sharing, Spinal Cord, 57, 3, pp. 169-182, (2019); Couture JL, Blake RE, McDonald G, Ward CL., A funder-imposed data publication requirement seldom inspired data sharing, PLoS One, 13, 7, (2018); Almada M, Midao L, Portela D, Dias I, Nunez-Benjumea FJ, Parra-Calderon CL, Et al., A new paradigm in health research: FAIR data (Findable, Accessible, Interoperable, Reusable)], Acta Med Port, 33, 12, pp. 828-834, (2020); Holub P, Kohlmayer F, Prasser F, Mayrhofer MT, Schlunder I, Martin GM, Et al., Enhancing reuse of data and biological material in medical research: from FAIR to FAIR-health, Biopreserv Biobank, 16, 2, pp. 97-105, (2018); Mello MM, Lieou V, Goodman SN., Clinical trial participants' views of the risks and benefits of data sharing, N Engl J Med, 378, 23, pp. 2202-2211, (2018); Rios R, Zheng KI, Zheng MH., Data sharing during COVID-19 pandemic: what to take away, Expert Rev Gastroenterol Hepatol, 14, 12, pp. 1125-1130, (2020); Inau E, Sack J, Waltemath D, Zeleke AA., Initiatives, concepts, and implementation practices of FAIR (findable, accessible, interoperable, and reusable) data principles in health data stewardship practice: protocol for a scoping review, JMIR Res Protoc, 10, 2, (2021); FAIR4Health key outputs for the scientific community; Sinaci A, Nunez-Benjumea FJ, Gencturk M, Jauer ML, Deserno T, Chronaki C, Et al., From raw data to FAIR data: the FAIRification workflow for health research, Methods Inf Med, 59, pp. e21-e32, (2020); The FAIR data principles; EOSC Declaration; European Open Science Cloud (EOSC) Strategic Implementation Plan; Adeloye D, Chua S, Lee C, Basquill C, Papana A, Theodoratou E, Global and regional estimates of COPD prevalence: systematic review and meta-analysis, J Glob Health, 5, 2, (2015); Mannino D, Gagnon R, Petty T, Lydick E., Obstructive lung disease and low lung function in adults in the United States: data from the National Health and Nutrition Examination Survey, 1988-1994, Arch Intern Med, 160, 11, pp. 1683-1689, (2000); Baty F, Putora P, Isenring B, Blum T, Brutsche M., Comorbidities and burden of COPD: a population based case-control study, PLoS One, 8, 5, (2013); Divo M, Cote C, de Torres JP, Casanova C, Marin JM, Pinto-Plata V, Et al., Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 186, 2, pp. 155-161, (2012); Divo MJ, Celli BR, Poblador-Plou B, Calderon-Larranaga A, de-Torres JP, Gimeno-Feliu LA, Chronic Obstructive Pulmonary Disease (COPD) as a disease of early aging: evidence from the EpiChron Cohort, PLoS One, 13, 2, (2018); Anecchino C, Rossi E, Fanizza C, De Rosa M, Tognoni G, Romero M, Prevalence of chronic obstructive pulmonary disease and pattern of comorbidities in a general population, Int J Chron Obstruct Pulmon Dis, 2, 4, pp. 567-574, (2007); Holguin F, Folch E, Redd SC, Mannino DM., Comorbidity and mortality in COPD-related hospitalizations in the United States, 1979 to 2001, Chest, 128, 4, pp. 2005-2011, (2005); Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, 9859, pp. 2095-2128, (2012); Gershon A, Thiruchelvam D, Aaron S, Stanbrook M, Vozoris N, Tan W, Et al., Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: a population-based cohort study, PLoS One, 14, 5, (2019); Global Initiative for Chronic Obstructive Lung Disease; Coventry P, Gemmell I, Todd C., Psychosocial risk factors for hospital readmission in COPD patients on early discharge services: a cohort study, BMC Pulm Med, 11, (2011); Jiang W, Siddiqui S, Barnes S, Barouch LA, Korley F, Martinez DA, Et al., Readmission risk trajectories for patients with heart failure using a dynamic prediction approach: retrospective study, JMIR Med Inform, 7, 4, (2019); Brand C, Sundararajan V, Jones C, Hutchinson A, Campbell D., Readmission patterns in patients with chronic obstructive pulmonary disease, chronic heart failure and diabetes mellitus: an administrative dataset analysis, Intern Med J, 35, 5, pp. 296-299, (2005); Kelly M., Self-management of chronic disease and hospital readmission: a care transition strategy, J Nursing Healthcare Chronic Illness, 3, 1, pp. 4-11, (2011); Reducing COPD readmissions—a personal and political priority, Lancet Respiratory Med, 1, 5, (2013); Jencks SF, Williams MV, Coleman EA., Rehospitalizations among patients in the medicare fee-for-service program, N Engl J Med, 360, 14, pp. 1418-1428, (2009); Vos T, Allen C, Arora M, Barber R, Bhutta Z, Brown A, Et al.; Chan F, Wong F, Yam C, Cheung W, Wong E, Leung M, Et al., Risk factors of hospitalization and readmission of patients with COPD in Hong Kong population: analysis of hospital admission records, BMC Health Serv Res, 11, (2011); Jacobs DM, Noyes K, Zhao J, Gibson W, Murphy TF, Sethi S, Et al., Early hospital readmissions after an acute exacerbation of chronic obstructive pulmonary disease in the nationwide readmissions database, Annals ATS, 15, 7, pp. 837-845, (2018); Prados-Torres A, Poblador-Plou B, Gimeno-Miguel A, Calderon-Larranaga A, Poncel-Falco A, Gimeno-Feliu LA, Et al., Cohort profile: the epidemiology of chronic diseases and multimorbidity. The Epichron cohort study, Int J Epidemiol, 47, 2, pp. 382-34, (2018); GO FAIR; HL7 FHIR® Based Secure Data Repository; FAIR4Health data curation and validation tool, GitHub; FAIR4Health data privacy tool, GitHub; Gencturk M, Teoman A, Alvarez-Romero C, Martinez-Garcia A, Parra-Calderon CL, Poblador-Plou B, Et al., End user evaluation of the FAIR4Health data curation tool, Stud Health Technol Inform, 281, pp. 8-12, (2021); GitHub; Andaur Navarro CL, Damen JA, Takada T, Nijman SW, Dhiman P, Ma J, Et al., Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review, BMJ, 375, (2021); Kinsella C, Santos PD, Postigo-Hidalgo I, Folgueiras-Gonzalez A, Passchier TC, Szillat KP, Et al., Preparedness needs research: how fundamental science and international collaboration accelerated the response to COVID-19, PLoS Pathog, 16, 10, (2020); Besancon L, Peiffer-Smadja N, Segalas C, Jiang H, Masuzzo P, Smout C, Et al., Open science saves lives: lessons from the COVID-19 pandemic, BMC Med Res Methodol, 21, 1, pp. 117-118, (2021)","C. Alvarez-Romero; Computational Health Informatics Group Institute of Biomedicine of Seville, Virgen del Rocío University Hospital Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Avda Manuel Siurot s/n, Spain; email: celia.alvarez@juntadeandalucia.es","","JMIR Publications Inc.","","","","","","22919694","","","","English","JMIR Med. Inform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85133535592"
"Adorjan A.; Vargas-Solar G.; Motz R.","Adorjan, Alejandro (56021800000); Vargas-Solar, Genoveva (14632835700); Motz, Regina (55901539200)","56021800000; 14632835700; 55901539200","Towards a human-in-the-loop curation: A qualitative perspective","2022","Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA","2022-December","","","","","","0","10.1109/AICCSA56895.2022.10017577","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146978144&doi=10.1109%2fAICCSA56895.2022.10017577&partnerID=40&md5=eb3068c4669b46389b42588f37093bfd","Universidad Ort Uruguay, Facultad de Ingeniería, Montevideo, Uruguay; Cnrs, Univ Lyon, Insa Lyon, Ucbl, Liris, UMR5205, Lyon, France; Universidad de la República, Facultad de Ingeniería, Montevideo, Uruguay","Adorjan A., Universidad Ort Uruguay, Facultad de Ingeniería, Montevideo, Uruguay; Vargas-Solar G., Cnrs, Univ Lyon, Insa Lyon, Ucbl, Liris, UMR5205, Lyon, France; Motz R., Universidad de la República, Facultad de Ingeniería, Montevideo, Uruguay","This paper proposes a data curation environment to record, maintain, and enrich research data using quantitative and qualitative methodologies. The research addresses the following research questions: What is research data curation from a hybrid perspective? and What software tools are adapted for hybrid research projects? The paper also proposes a hybrid research workflow representing the phases of projects adopting this kind of methodology towards a human-in-the-loop approach. It introduces a view model to represent the data produced across its stages, which should be curated. Finally, proposes a set of operators to manage and explore the different versions of curated data and their associated knowledge.  © 2022 IEEE.","data curation; Hybrid research; semantic enrichment","Curation; Data curation; Human-in-the-loop; Hybrid research; Is researches; Qualitative methodologies; Quantitative methodology; Research data; Research questions; Semantic enrichment; Semantics","","","","","Agencia Nacional de Investigación e Innovación, ANII","Supported by the National Agency for Research and Innovation (ANII), Uruguay.","Romeo C.G., El graffiti minado, analisis cuantitativo aplicado, (2021); Dragut E., Li Y., Popa L., Vucetic S., Data science with human in the loop, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 4123-4124, (2021); Choudhury G.S., Huang C., Palmer C.L., Updating the dcc curation lifecycle model, Int. J. Digit. Curation, 15, pp. 1-12, (2020); Woods M., MacKlin R., Lewis G.K., Researcher reflexivity: Exploring the impacts of caqdas use, International Journal of Social Research Methodology, 19, 4, pp. 385-403, (2016); (2022); (2022); V. Software, (2022); NVivo, (2022); Chen N.-C., Drouhard M., Kocielnik R., Suh J., Aragon C.R., Using machine learning to support qualitative coding in social science: Shifting the focus to ambiguity, ACM Transactions on Interactive Intelligent Systems (TiiS), 8, 2, pp. 1-20, (2018); Evers J.C., Current issues in qualitative data analysis software (qdas): A user and developer perspective, The Qualitative Report, 23, 13, pp. 61-73, (2018); Karcher S., Kirilova D.D., Page C., Weber N., How data curation enables epistemically responsible reuse of qualitative data, The Qualitative Report, 26, 6, pp. 1996-2010, (2021); (2022); (2022); (2022); Lafia S., Thomer A., Bleckley D., Akmon D., Hemphill L., Leveraging machine learning to detect data curation activities, 2021 IEEE 17th International Conference on eScience (eScience), pp. 149-158, (2021); Terrizzano I.G., Schwarz P.M., Roth M., Colino J.E., Data wrangling: The challenging yourney from the wild to the lake, CIDR, (2015); Huang C., Lee J.-S., Palmer C.L., Dcc curation lifecycle model 2.0: Literature review and comparative analysis, Journal of Digital Curation, 15, 1, (2020); Palmer C., Weber N.M., Renear A.H., Munoz T., Foundations of data curation: The pedagogy and practice of purposeful work with research data, Archives Journal, 3, (2013); Zorich D.M., Data management: Managing electronic information: Data curation in museums, Museum Management and Curatorship, 14, 4, pp. 430-432, (1995); Lyngdoh A., What we leave behind: The future of data curation, Trends, Discovery, and People in the Digital Age, pp. 153-165, (2013); Zouari F., Ghedira-Guegan C., Kabachi N., Boukadi K., Towards an adaptive curation services composition based on machine learning, 2021 IEEE International Conference on Web Services (ICWS), pp. 73-78, (2021); Thirumuruganathan S., Tang N., Ouzzani M., Doan A., Data curation with deep learning [vision], (2018); Sposito F.A., What do data curators care about? data quality, user trust, and the data reuse plan, (2022); Arocena P.C., Glavic B., Mecca G., Miller R.J., Papotti P., Santoro D., Benchmarking data curation systems, IEEE Data Eng. Bull, 39, 2, pp. 47-62, (2016); Garat D., Wonsever D., Automatic curation of court documents: Anonymizing personal data, Information, 13, 1, (2022); Freitas A., Curry E., Big data curation, New horizons for a data-driven economy, pp. 87-118, (2016); Shang Z., Zgraggen E., Buratti B., Kossmann F., Eichmann P., Chung Y., Binnig C., Upfal E., Kraska T., Democratizing data science through interactive curation of ml pipelines, Proceedings of the 2019 international conference on management of data, pp. 1171-1188, (2019); Weber N.M.J., Karcher S., Open source tools for scaling data curation at QDR, The code4lib journal, (2020); Vargas-Solar G., Kemp G., Hernandez-Gallegos I., Espinosa-Oviedo J., Da Silva C., Ghodous P., Exploring and curating data collections with curare, Proceeding of the 35eme Conference sur la Gestion de Donnees-Principes, Technologies et Applications, (2019); Vargas-Solar G., Zechinelli-Martini J.-L., Espinosa-Oviedo J.A., Enacting data science pipelines for exploring graphs: From libraries to studios, ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium, pp. 271-280, (2020); Jobst A., Atzberger D., Cech T., Scheibel W., Trapp M., Dollner J., Efficient github crawling using the graphql api, International Conference on Computational Science and Its Applications, pp. 662-677, (2022)","","","IEEE Computer Society","","19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022","5 December 2022 through 7 December 2022","Abu Dhabi","186154","21615322","979-835031008-5","","","English","Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl., AICCSA","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85146978144"
"Ferenz S.; Ofenloch A.; Penaherrera Vaca F.; Wagner H.; Werth O.; Breitner M.H.; Engel B.; Lehnhoff S.; Nieße A.","Ferenz, Stephan (57192541159); Ofenloch, Annika (57202256319); Penaherrera Vaca, Fernando (57581039100); Wagner, Henrik (57303645500); Werth, Oliver (57202494661); Breitner, Michael H. (6602910157); Engel, Bernd (37099800600); Lehnhoff, Sebastian (23135379900); Nieße, Astrid (6503972035)","57192541159; 57202256319; 57581039100; 57303645500; 57202494661; 6602910157; 37099800600; 23135379900; 6503972035","An Open Digital Platform to Support Interdisciplinary Energy Research and Practice—Conceptualization","2022","Energies","15","17","6417","","","","0","10.3390/en15176417","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137763472&doi=10.3390%2fen15176417&partnerID=40&md5=c7f569e66dbcbaf4d9ff3b38b251d9d2","Department of Computer Science, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany; Energy Division, OFFIS—Institute for Information Technology, Oldenburg, 24105, Germany; Elenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Information Systems Institute, Leibniz University Hannover, Hannover, 30167, Germany","Ferenz S., Department of Computer Science, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany, Energy Division, OFFIS—Institute for Information Technology, Oldenburg, 24105, Germany; Ofenloch A., Energy Division, OFFIS—Institute for Information Technology, Oldenburg, 24105, Germany; Penaherrera Vaca F., Department of Computer Science, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany, Energy Division, OFFIS—Institute for Information Technology, Oldenburg, 24105, Germany; Wagner H., Elenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Werth O., Information Systems Institute, Leibniz University Hannover, Hannover, 30167, Germany; Breitner M.H., Information Systems Institute, Leibniz University Hannover, Hannover, 30167, Germany; Engel B., Elenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Lehnhoff S., Department of Computer Science, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany, Energy Division, OFFIS—Institute for Information Technology, Oldenburg, 24105, Germany; Nieße A., Department of Computer Science, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany, Energy Division, OFFIS—Institute for Information Technology, Oldenburg, 24105, Germany","Energy research itself is changing due to digitalization and the trend to open science. While this change enables new research, it also increases the amount of, and need for, available data and models. Therefore, a platform for open digital energy research and development is required to support researchers and practitioners with their new needs and to enable FAIR (findable, accessible, interoperable and reusable) research data management in energy research. We present a functional and technological concept for such a platform based on six elements: Competence to enable researchers and practitioners to find suitable partners for their projects, Methods to give an overview on the diverse possible research methods within energy research, Repository to support finding data and models for simulation of energy systems, Simulation to couple these models and data to create user-defined simulation scenarios, Transparency to publish results and other content relevant for the different stakeholder in energy research, and Core to interconnect all elements and to offer a unified entry point. We discuss the envisioned use of the outlined platform with use cases addressing three relevant stakeholder groups. © 2022 by the authors.","digital platform; energy research; research data management","Research and development management; Simulation platform; Digital platforms; Energy research; Energy research and development; Energy system simulations; Modeling for simulations; Open science; Project methods; Research data managements; Research method; Technological concept; Information management","","","","","Center for Digital Innovations; ZDIN; Niedersächsische Ministerium für Wissenschaft und Kultur, (11-76251-13-3/19–ZN3488); Volkswagen Foundation","This research was funded by the Lower Saxony Ministry of Science and Culture under grant number 11-76251-13-3/19–ZN3488 (ZLE) within the Lower Saxony “Vorab“ of the Volkswagen Foundation. It was supported by the Center for Digital Innovations (ZDIN).","Pfenninger S., Hawkes A., Keirstead J., Energy systems modeling for twenty-first century energy challenges, Renew. Sustain. Energy Rev, 33, pp. 74-86, (2014); Niesse A., Troschel M., Sonnenschein M., Designing dependable and sustainable Smart Grids—How to apply Algorithm Engineering to distributed control in power systems, Environ. Model. Softw, 56, pp. 37-51, (2014); Pfenninger S., DeCarolis J., Hirth L., Quoilin S., Staffell I., The importance of open data and software: Is energy research lagging behind?, Energy Policy, 101, pp. 211-215, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., Santos L., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. 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Sci, 9, (2019); Oppermann L., Hirzel S., Guldner A., Heiwolt K., Krassowski J., Schade U., Lange C., Prinz W., Finding and analysing energy research funding data: The EnArgus system, Energy AI, 5, (2021); Uslar M., Specht M., Rohjans S., Trefke J., Vasquez Gonzalez J.M., The IEC Common Information Model, The Common Information Model CIM: IEC 61968/61970 and 62325—A Practical Introduction to the CIM, pp. 75-106, (2012); Zeng M.L., Qin J., Metadata, (2016); Wierling A., Schwanitz V.J., Altinci S., Balazinska M., Barber M.J., Biresselioglu M.E., Burger-Scheidlin C., Celino M., Demir M.H., Dennis R., Et al., FAIR Metadata Standards for Low Carbon Energy Research—A Review of Practices and How to Advance, Energies, 14, (2021); Reder K., Stappel M., Hofmann C., Forster H., Emele L., Hulk L., Glauer M., Identification of user requirements for an energy scenario database, Int. J. Sustain. Energy Plan. Manag, 25, pp. 95-108, (2020); Peffers K., Tuunanen T., Rothenberger M.A., Chatterjee S., A Design Science Research Methodology for Information Systems Research, J. Manag. Inf. Syst, 24, pp. 45-77, (2007); Hevner A.R., March S.T., Park J., Ram S., Design science in information systems research, MIS Q, 28, pp. 75-105, (2004)","S. Ferenz; Department of Computer Science, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany; email: stephan.ferenz@uol.de","","MDPI","","","","","","19961073","","","","English","Energies","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85137763472"
"Xiao S.; Ng T.Y.; Yang T.T.","Xiao, SiZhe (56921144400); Ng, Tsz Yan (57394816300); Yang, Tao T. (26028468800)","56921144400; 57394816300; 26028468800","Research data stewardship at the University of Hong Kong","2022","Library Management","43","1-2","","128","147","19","0","10.1108/LM-09-2021-0079","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122161631&doi=10.1108%2fLM-09-2021-0079&partnerID=40&md5=19df319f0c4cc6c9d073bbaba2565420","The University of Hong Kong, Hong Kong","Xiao S., The University of Hong Kong, Hong Kong; Ng T.Y., The University of Hong Kong, Hong Kong; Yang T.T., The University of Hong Kong, Hong Kong","Purpose: The purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library. Design/methodology/approach: The research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step. Findings: The HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines. Originality/value: This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation. © 2021, Emerald Publishing Limited.","Data librarianship; Data management plan; Research data management; Research data services","","","","","","","","Creating a Data Management Framework, (2018); Beitz A., Groenewegen D., Harboe-Ree C., Macmillan W., Searle S., Case study 3: Monash University, a strategic approach, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 163-189, (2014); Borchert M., Young J., Coordinated Research Support Services at Queensland University of Technology, (2010); Burgelman J.-C., Pascu C., Szkuta K., Von Schomberg R., Karalopoulos A., Repanas K., Schouppe M., Open science, open data, and open scholarship: european policies to make science Fit for the Twenty-first century [perspective], Frontiers in Big Data, 2, 43, (2019); Burrows T., Croker K., Supporting Research in an Era of Data Deluge: Developing a New Service Portfolio within Information Services at the University of Western Australia, (2012); DMPTool, (2021); Cheng M., Cobb P.J., Hu X., Lou V.W., Pan N., Woo M.W., Xiao J., Supporting Data for “Intergenerational Participatory Co-design Project, (2021); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Corti L., Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data : A Guide to Good Practice, (2020); Cox A., Kennan M., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Delasalle J., Research Data Management at the University of Warwick: Recent Steps towards a Joined-Up Approach at a UK University, 23, (2013); Overview of Funders' Data Policies, (2021); Turning FAIR into Reality, (2018); Riding the Wave: How Europe can Gain from the Rising Tide of Scientific Data, (2010); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS ONE, 10, 2, (2015); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the University of Bristol: charting a course from project to service, Program: Electronic Library and Information Systems, 49, 4, pp. 475-493, (2015); Jansen P., van den Berg L., van Overveld P., Boiten J.-W., Research data stewardship for healthcare professionals, Fundamentals of Clinical Data Science, pp. 37-53, (2019); Jones S., Bringing it All Together: A Case Study on the Improvement of Research Data Management at Monash University. DCC RDM Services Case Studies, (2013); Jones S., The range and components of RDM infrastructure and services, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 89-114, (2014); Kouper I., Fear K., Ishida M., Kollen C., Williams S.C., Research data services maturity in academic libraries, Curating Research Data, pp. 153-170, (2015); Lyon L., Brenner A., Bridging the data talent gap – positioning the iSchool as an agent for change, International Journal of Digital Curation, 10, 1, pp. 111-122, (2015); Lyon L., Pink C., University of Bath Roadmap for EPSRC: Compliance with Research Data Management Expectations, (2012); Berlin declaration on open access to knowledge in the sciences and humanities, (2021); Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, (2009); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Data Availability, (2021); Qin J., Crowston K., Kirkland A., A Capability Maturity Model for Research Data Management, (2014); Data Policies, Scientific Data, (2021); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program: Electronic Library and Information Systems, 49, 4, pp. 440-460, (2015); Sullivan I., DeHaven A., Mellor D., Open and reproducible research on open science framework, Current Protocols Essential Laboratory Techniques, 18, 1, (2019); Tenopir C., Pollock D., Allard S., Hughes D., Research data services in european and north american libraries: current offerings and plans for the future, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Science as an Open Enterprise, (2012); Research Data and records management policy Approved by senate, research services, (2015); Quick stats, (2021); Research data and records management, (2021); Summary of funding for on-going research grant projects 2018/19 - 2019/20, (2021); Wilkinson M., Research data services at university College London, LIBER Case Study, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Wilson J., University of Oxford research data management infrastructure, LIBER Case Study, (2014); Browse data sharing requirements by federal agency, (2021)","S. Xiao; The University of Hong Kong, Hong Kong; email: szxiao@hku.hk","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","","Scopus","2-s2.0-85122161631"
"Rossenova L.; Duchesne P.; Blümel I.","Rossenova, Lozana (57671915400); Duchesne, Paul (57777643800); Blümel, Ina (26421912700)","57671915400; 57777643800; 26421912700","Wikidata and Wikibase as complementary research data management services for cultural heritage data","2022","CEUR Workshop Proceedings","3262","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142336187&partnerID=40&md5=c271d679861acc8e9203acdcdbc31b07","TIB, Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hannover, 30167, Germany","Rossenova L., TIB, Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hannover, 30167, Germany; Duchesne P., TIB, Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hannover, 30167, Germany; Blümel I., TIB, Leibniz Information Centre for Science and Technology, Welfengarten 1B, Hannover, 30167, Germany","The NFDI (German National Research Data Infrastructure) consortia are associations of various institutions within a specific research field, which work together to develop common data infrastructures, guidelines, best practices and tools that conform to the principles of FAIR data [1, 2]. Within the NFDI, a common question is: What is the potential of Wikidata to be used as an application for science and research [3]? In this paper, we address this question by tracing current research use-cases and applications for Wikidata, its relation to standalone Wikibase instances [4], and how the two can function as complementary services to meet a range of research needs. This paper builds on lessons learned through the development of open data projects and software services within the Open Science Lab at TIB, Hannover, in the context of NFDI4Culture - the consortium including participants across the broad spectrum of the digital libraries, archives, and museums field, and the digital humanities [5, 6]. © 2022 Copyright for this paper by its authors.","cultural heritage; NFDI; NFDI4Culture; open science; research data management; Wikibase; Wikidata","Information management; Open Data; Cultural heritages; Data infrastructure; Data management services; NFDI; Nfdi4culture; Open science; Research data; Research data managements; Wikibase; Wikidata; Digital libraries","","","","","Deutsche Forschungsgemeinschaft, DFG, (441958017)","NFDI4Culture is funded by the Deutsche Forschungsgemeinschaft (DFG) under grant no. 441958017.","Homepage, (2022); Wilkinson Mark, Dumontier Michel, Aalbersberg IJsbrand Jan, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Blumel Ina, Duchesne Paul, Rossenova Lozana, Sack Harald, NFDI InfraTalk: Wikibase - knowledge graphs for RDM in NFDI4Culture, (2022); (2022); Open Science Lab, Homepage, (2022); (2022); Vrandecic Denny, Krotzsch Markus, Wikidata: A Free Collaborative Knowledgebase, Communications of the ACM, 57, 10, pp. 78-85, (2014); Stats Homepage, (2022); Homepage, (2022); Waagmeester Andra, Et al., Science Forum: Wikidata as a knowledge graph for the life sciences, eLife, 9, (2020); Piscopo Andrea, Structuring the World's Knowledge: Socio-Technical Processes and Data Quality in Wikidata, (2019); Homepage, (2022); Scholia, Homepage, (2022); Thornton Katherine, Kenneth Seals-Nutt, Euan Cochrane, Carl Wilson, Wikidata for Digital Preservation, Proceedings of iPRES'18, (2018); Kapsalis Effie, Wikidata: Recruiting the Crowd to Power Access to Digital Archives, Journal of Radio & Audio Media, 26, pp. 134-142, (2019); Pintscher Lydia, Heintze Silvan, Ontology issues in Wikidata, Data Quality Days, (2021); Pham Mike, Et al., Scaling Wikidata Query Service - unlimited access to all the world's knowledge for everyone is hard, WikidataCon 2021, (2021); Mietchen Daniel, Hagedorn Gregor, Willighagen Egon, Et al., Enabling Open Science: Wikidata for Research (Wiki4R), Research Ideas and Outcomes, 1, (2015); Zeinstra Martin, Returning Commons Community Metadata Additions and Corrections to Source, (2019); The problem with Wikipedia, (2010); Protection Policy, (2022); Requests for permissions, (2022); Roth Philip, An Open Letter to Wikipedia, New Yorker, (2012); WikiProject Books, Homepage, (2022); Functional Requirements for Bibliographic Records, (2009); Bibliographic Framework Initiative, (2022); Fairbairn Natasha, Pimpinelli Maria Assunta, Ross Thelma, The FIAF Moving Image Cataloguing Manual, International Federation of Film Archives, (2016); Mediawiki, EntitySchema Extension, (2021); Shigapov Renat, RaiseWikibase: Towards fast data import into Wikibase, 2nd Workshop on Wikibase in Knowledge Graph based Research Data Management (NFDI) Projects, (2021); Pintscher Lydia, Et al., Strategy for the Wikibase Ecosystem, (2019); Strategy 2021: Wikibase ecosystem, (2021); Rossenova Lozana, ArtBase Archive-Context and History: Discovery Phase and User Research 2017-2019, (2020); Rossenova Lozana, Sohmen Lucia, Using OpenRefine with arbitrary Wikibase instances, WikidataCon 2021, (2021); Derveaux Alexander, Demo of upload process for a Wikibase instance, WikidataCon 2021, (2021); Training, (2022); Fauconnier Sandra, Espenschied Dragan, Moulds Lyndsey, Rossenova Lozana, Many Faces of Wikibase: Rhizome's Archive of Born-Digital Art and Digital Preservation, Wikimedia Blog, (2018); Rhizome, Welcome to the ArtBase Query Service: Federation and Advanced Queries, (2021); Diefenbach Dennis, de Wilde Max, Alipio Samantha, Wikibase as an Infrastructure for Knowledge Graphs: the EU Knowledge Graph, ISWC 2021, (2021); (2022); Fichtmueller David, Using Wikibase as a Platform to Develop a Semantic Biodiversity Standard, 1st NFDI Wikibase Workshop, (2021); Emilio Labra Gayo Jose, Et al., Representing the Luxembourg Shared Authority File based on CIDOC-CRM in Wikibase, SWIB 2021, (2021); Homepage, (2022); Wikibase RDF Extension, (2022); Homepage, (2022); Blumel Ina, Sohmen Lucia, Casties Nils, Integration of Wikidata 4OpenGLAM into data and information science curricula, WikidataCon 2021, (2021); DigADigAMus goes Wikidata, (2021); Homepage, (2022); Commons: Copyright rules, (2021); WikiProject Digital projects of museums, (2022); Wuttke Ulrike, Here be dragons: Open Access to Research Data in the Humanities, (2019); Toth-Czifra Erzsebet, Wuttke Ulrike, Loners, Pathfinders, or Explorers?, How are the Humanities Progressing in Open Science?, (2019); Homepage, (2022); Corpus der barocken Deckenmalerei in Deutschland, (2021); Data Model, (2022); Sanderson Rob, Ciccarese Paolo, Young Benjamin, Web Annotation Data Model: W3C Recommendation 23 February 2017, (2017); Getty Vocabularies; Homepage, (2022); Wikibase Release Pipeline, (2022); Waagmeester Andra, Email conversation with the author Lozana Rossenova, (2022)","","Kaffee L.-A.; Razniewski S.; Amaral G.; Saad Alghamdi K.","CEUR-WS","","3rd Wikidata Workshop, Wikidata 2022","24 October 2022","Virtual, Hanghzou","184047","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85142336187"
"Ashiq M.; Usmani M.H.; Naeem M.","Ashiq, Murtaza (57221973297); Usmani, Muhammad Haroon (57220042022); Naeem, Muhammad (57220033696)","57221973297; 57220042022; 57220033696","A systematic literature review on research data management practices and services","2022","Global Knowledge, Memory and Communication","71","8-9","","649","671","22","14","10.1108/GKMC-07-2020-0103","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096511485&doi=10.1108%2fGKMC-07-2020-0103&partnerID=40&md5=5fbef2db01e828faef2c53124988c9e2","Islamabad Model College for Boys, Islamabad, Pakistan; University of the Punjab, Quaid-i-Azam Campus, Lahore, Pakistan; Government College University, Lahore, Pakistan","Ashiq M., Islamabad Model College for Boys, Islamabad, Pakistan; Usmani M.H., University of the Punjab, Quaid-i-Azam Campus, Lahore, Pakistan; Naeem M., Government College University, Lahore, Pakistan","Purpose: Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it. Design/methodology/approach: A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis. Findings: RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues. Research limitations/implications: The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted. Originality/value: RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing. © 2020, Emerald Publishing Limited.","Research data management; Research data management practices; Research data services; Systematic literature review","","","","","","","","ACRL research planning and review committee. Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 75, 6, pp. 294-302, (2014); Ali I., Warraich N.F., The relationship between mobile self-efficacy and mobile-based personal information management practices, Library Hi Tech, (2020); Ashiq M., Rehman S.U., Mujtaba G., Future challenges and emerging role of academic libraries in Pakistan: a phenomenology approach, Information Development, pp. 1-16, (2020); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: integrated findings to develop research data services, Journal of eScience Librarianship, 6, 1, (2017); Borghi J.A., Van Gulick A.E., Data management and sharing in neuroimaging: practices and perceptions of MRI researchers, Plos One, 13, 7, pp. 1-18, (2018); Brochu L., Burns J., Librarians and research data management – a literature review: commentary from a senior professional and a new professional librarian, New Review of Academic Librarianship, 25, 1, pp. 49-58, (2019); Burgi P.Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: national efforts to guarantee the sustainability of research outputs, IFLA Journal, 43, 1, pp. 5-21, (2017); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chiware E.R., Becker D.A., Research data management services in Southern Africa: a readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, (2018); Corrall S., Designing libraries for research collaboration in the network world: an exploratory study, LIBER Quarterly, 24, 1, pp. 17-48, (2014); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib Journal of Information Management, 70, 2, pp. 42-157, (2018); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: an exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Faniel I.M., Connaway L.S., Librarians' perspectives on the factors influencing research data management programs, College and Research Libraries, 79, 1, pp. 100-119, (2018); Fuhr J., How do I do that? A literature review of research data management skill gaps of Canadian health sciences information professionals, Journal of the Canadian Health Libraries Association/Journal de L'association Des Bibliothèques de la Santé du Canada, 40, 2, pp. 51-60, (2019); Grant R., Recordkeeping and research data management: a review of perspectives, Records Management Journal, 27, 2, pp. 159-174, (2017); Hamad F., Al-Fadel M., Al-Soub A., Awareness of research data management services at academic libraries in Jordan: roles, responsibilities and challenges, New Review of Academic Librarianship, (2019); Hswe P., Holt A., Joining in the enterprise of response in the wake of the NSF data management planning requirement, Research Library Issues, 274, pp. 11-17, (2011); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship and Information Science, 52, 3, (2019); Koltay T., Are you ready? Tasks and roles for academic libraries in supporting research 2.0, New Library World, 117, 1-2, pp. 94-10, (2016); Mahmood K., Reliability and validity of self-efficacy scales assessing students’ information literacy skills: a systematic review, The Electronic Library, 35, 5, pp. 1035-1051, (2017); Mohammed M.S., Ibrahim R., Challenges and practices of research data management in selected Iraq universities, DESIDOC Journal of Library and Information Technology, 39, 6, (2019); Moher D., Liberati A., Tetzlaff J., Altman D.G., Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, Journal of Clinical Epidemiology, 62, 10, pp. 1006-1012, (2009); Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A., Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement, Systematic Reviews, 4, 1, pp. 1-25, (2015); Ng'eno E., Mutula S., Research data management (RDM) in agricultural research institutes: a literature review, Inkanyiso: Journal of Humanities and Social Sciences, 10, 1, pp. 28-50, (2018); Pasek J.E., Mayer J., Education needs in research data management for science-based disciplines: self-assessment surveys of graduate and faculty at two public universities, ISTL: Issues in Science and Technology Librarianship, 92, (2019); Perrier L., Barnes L., Developing research data management services and support for researchers: a mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, 13, 1, (2018); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2014); Rafique G.M., Mahmood K., Relationship between knowledge sharing and job satisfaction: a systematic review, Information and Learning Science, 119, 5-6, pp. 295-312, (2018); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA Journal, 43, 1, pp. 51-64, (2017); Safdar M., Batool S.H., Mahmood K., Relationship between self-efficacy and knowledge sharing: systematic review, Global Knowledge, Memory and Communication, ahead-of-print, ahead-of-print, (2020); Sanjeeva M., Research data management: a new role for academic/research librarians, (2018); Stamatopols A., Neville T., Henry D., Analyzing the data management environment in a master’s-level institution, The Journal of Academic Librarianship, 42, 2, pp. 154-160, (2016); Tang R., Hu Z., Providing research data management (RDM) services in libraries: preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Vela K., Shin N., Establishing a research data management service on a health sciences campus, Journal of eScience Librarianship, 8, 1, (2019); Vilar P., Zabukovec V., Research data management and research data literacy in Slovenian science, Journal of Documentation, 75, 1, (2019); Piracha H.A., Ameen K., Policy and planning of research data management in university libraries of Pakistan, Collection and Curation, 38, 2, pp. 39-44, (2019)","M. Ashiq; Islamabad Model College for Boys, Islamabad, Pakistan; email: gmurtazaashiq00@gmail.com","","Emerald Publishing","","","","","","25149342","","","","English","Glob. Knowl.,  Mem. Commun.","Review","Final","","Scopus","2-s2.0-85096511485"
"Yeo-Teh N.S.L.; Tang B.L.","Yeo-Teh, Nicole Shu Ling (56764404700); Tang, Bor Luen (57894094800)","56764404700; 57894094800","Research data mismanagement–from questionable research practice to research misconduct","2022","Accountability in Research","","","","","","","1","10.1080/08989621.2022.2157268","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146325112&doi=10.1080%2f08989621.2022.2157268&partnerID=40&md5=26832d74db8c7c17f5d1e2f41ac16e84","Research Compliance and Integrity Office, National University of Singapore, Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore, Singapore","Yeo-Teh N.S.L., Research Compliance and Integrity Office, National University of Singapore, Singapore, Singapore; Tang B.L., Department of Biochemistry, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore, Singapore","Good record keeping practice and research data management underlie responsible research conduct and promote reproducibility of research findings in the sciences. In many cases of research misconduct, inadequate research data management frequently appear as an accompanying finding. Findings of disorganized or otherwise poor data archival or loss of research data are, on their own, not usually considered as indicative of research misconduct. Focusing on the availability of raw/primary data and the replicability of research based on these, we posit that most, if not all, instances of research data mismanagement (RDMM) could be considered a questionable research practice (QRP). Furthermore, instances of RDMM at their worst could indeed be viewed as acts of research misconduct. Here, we analyze with postulated scenarios the contexts and circumstances under which RDMM could be viewed as a significant misrepresentation of research (ie. falsification), or data fabrication. We further explore how RDMM might potentially be adjudicated as research misconduct based on intent and consequences. Defining how RDMM could constitute QRP or research misconduct would aid the formulation of relevant institutional research integrity policies to mitigate undesirable events stemming from RDMM. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","questionable research practices; Research data management; research ethics; research integrity; research misconduct","","","","","","","","Brumfiel G., Physicist found guilty of misconduct, (2002); RePair Consensus Guidelines: Responsibilities of Publishers, Agencies, Institutions, and Researchers in Protecting the Integrity of the Research Record, Research Integrity and Peer Review, 3, 1, (2018); Culina A., Adriaensen F., Bailey L.D., Burgess M.D., Charmantier A., Cole E.F., Eeva T., Matthysen E., Nater C.R., Sheldon B.C., Et al., Connecting the Data Landscape of Long-Term Ecological Studies: The SPI-Birds Data Hub, The Journal of Animal Ecology, 90, 9, pp. 2147-2160, (2021); den Boer S., Huser F., Wildgaard L., Rasmussen L., Drachen T., Larsen A., Dorch B., Sandoe P., Research Data Management, RCR–A Danish Handbook for Courses in Responsible Conduct of Research, pp. 55-68, (2020); Dresser R., Defining Scientific Misconduct. The Relevance of Mental State, JAMA, 269, 7, pp. 895-897, (1993); Enserink M., Final Report: Stapel Affair Points to Bigger Problems in Social Psychology, (2012); Enserink M., Misconduct allegations fly in spat over paper on microplastics and fish larvae, (2016); Fiedler K., Schwarz N., Questionable Research Practices Revisited, Social Psychological and Personality Science, 7, 1, pp. 1-8, (2015); Gross C., Scientific Misconduct, Annual Review of Psychology, 67, 1, pp. 693-711, (2016); John L.K., Loewenstein G., Prelec D., Measuring the Prevalence of Questionable Research Practices with Incentives for Truth Telling, Psychological Science, 23, 5, pp. 524-532, (2012); Joshi M., Krag S.S., Issues in Data Management, Science and Engineering Ethics, 16, 4, pp. 743-748, (2010); McCook A., Cancer researcher at The Ohio State University resigns following multiple misconduct findings, (2018); Milewska A., Wisniewska N., Cimoszko P., Rusakow J., A Survey of Medical Researchers Indicates Poor Awareness of Research Data Management Processes and a Role for Data Librarians, Health Information & Libraries Journal, 39, 2, pp. 132-141, (2022); Definition of Research Misconduct, (2000); Schiermeier Q., Data Management Made Simple, Nature, 555, 7696, pp. 403-405, (2018); Sims J.M., A Brief Review of the Belmont Report, Dimensions of Critical Care Nursing, 29, 4, pp. 173-174, (2010); Urbano F., Cagnacci F., Data Management and Sharing for Collaborative Science: Lessons Learnt from the Euromammals Initiative, Frontiers in Ecology and Evolution, 2021, 9; Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, 1, (2016); Yeo-Teh N.S.L., Tang B.L., A Research Misconduct Severity Matrix That Could Serve to Harmonize Adjudication of Findings, Accountability in Research, 29, 5, pp. 279-293, (2022)","N.S.L. Yeo-Teh; Research Compliance and Integrity Office, National University of Singapore, Singapore, Singapore; email: dprysln@nus.edu.sg; B.L. Tang; Department of Biochemistry, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore, Singapore; email: bchtbl@nus.edu.sg","","Taylor and Francis Ltd.","","","","","","08989621","","","","English","Account. Res.","Article","Article in press","","Scopus","2-s2.0-85146325112"
"Farinelli C.; Zigoni A.","Farinelli, Chiara (57311244400); Zigoni, Alberto (58036775700)","57311244400; 58036775700","Extending the value of a CRIS with Research Data Management","2022","Procedia Computer Science","211","C","","187","195","8","0","10.1016/j.procs.2022.10.190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145239322&doi=10.1016%2fj.procs.2022.10.190&partnerID=40&md5=97fb0a1ebb90fcfab346c0372710b026","Elsevier B.V., Radarweg 29, Amsterdam, 1043 NX, Netherlands","Farinelli C., Elsevier B.V., Radarweg 29, Amsterdam, 1043 NX, Netherlands; Zigoni A., Elsevier B.V., Radarweg 29, Amsterdam, 1043 NX, Netherlands","In this paper we aim to analyse the adoption of Current Research Information Systems (CRIS) for Research Data Management (RDM). We show how CRISs hold a key role in facilitating the management and reporting of an institution's research activities and outputs - not only do they offer extensive functionality for researchers and research administrators to effectively manage all aspects of their research information, but are also integrating more and more with specialized RDM tools, Institutional Repositories (IR), and other external systems. This paper provides an overview of how CRISs have evolved and integrated to become a crucial part of the RDM chain, including the interoperability, registration, linking, and archiving of data.  © 2022 The Author(s).","CRIS Aggregation; Current Research Information Systems (CRIS); Data Integration; Data Quality; FAIR data principles; Institutional Repositories (IR); Open Science; OpenAire; Pure; Research Data Management (RDM); Research Information Management (RIM); Use Cases","Data integration; Information services; Information systems; Information use; Sounding apparatus; 'current; Current research information system; Current research information system aggregation; Data quality; FAIR data principle; Institutional repositories; Institutional repository; Open science; Openaire; Pure; Research data management; Research data managements; Research information management; Research information systems; Use case; Information management","","","","","","","Wilkinson M.D., Dumontier M., Ijj A., Appleton G., Axton M., Baak A., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, pp. 1-9, (2016); Azeroual O., Schopfel J., Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries, Publications, 7, (2019); Dvorak J., Bollini A., Remy L., Schirrwagen J., OpenAIRE Guidelines for CRIS Managers 1.1 2018; OpenAIRE Guidelines for CRIS Managers - OpenAIRE Guidelines Documentation; Jetten M., Simons E., Rijnders J., The role of CRIS's in the research life cycle. A case study on implementing a FAIR RDM policy at Radboud University, the Netherlands, Procedia Computer Science, 146, pp. 156-165, (2019); Guillaumet A., Garcia F., Cuadron O., Analyzing a CRIS: From data to insight in university research, Procedia Computer Science, 146, pp. 230-240, (2019)","C. Farinelli; Elsevier B.V., Amsterdam, Radarweg 29, 1043 NX, Netherlands; email: c.farinelli@elsevier.com","Sicilia M.-A.; De-Castro P.; Vancauwenbergh S.; Simons E.; Ognjen O.","Elsevier B.V.","","15th International Conference on Current Research Information Systems, CRIS 2022","12 May 2022 through 14 May 2022","Dubrovnik","148668","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85145239322"
"Wiley C.","Wiley, Chris (58023396800)","58023396800","Research Data Management: A Case Study Examining Aerospace, Industrial and Mechanical Science Engineering Faculty Research Practices","2022","Science and Technology Libraries","","","","","","","0","10.1080/0194262X.2022.2153780","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144251152&doi=10.1080%2f0194262X.2022.2153780&partnerID=40&md5=5bb714fc476249b981e3e9eb593bf15b","Engineering and Physical Sciences Research Data Services, Grainger Engineering Library, University of Illinois Urbana-Champaign, Champaign, IL, United States","Wiley C., Engineering and Physical Sciences Research Data Services, Grainger Engineering Library, University of Illinois Urbana-Champaign, Champaign, IL, United States","This study explores data management perspectives of aerospace, industrial and mechanical science engineering faculty affiliated with (University of Illinois Urbana-Champaign (UIUC)). The author conducted fourteen semi-structured interviews and analyzed them using a qualitative inductive coding method. This study builds upon previous work and seeks to explore how the elements of data management planning align with researchers’ workflow, challenges, and awareness to research data services. Overall, the goal of this study is to gain a better understanding of these researcher’s data management practices and enhance the research data services provided to faculty and research groups. All these responses inform existing research data management services. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.","academic libraries; Research data management","Engineering education; Engineering research; Industrial research; Libraries; Research and development management; Academic libraries; Case-studies; Coding methods; Data services; Engineering faculty; Management planning; Mechanical; Research data; Research data managements; Semi structured interviews; article; awareness; Illinois; library; semi structured interview; space; workflow; Information management","","","","","","","Barsky E., Three UBC Research Data Management (RDM) surveys: Science and engineering humanities, and social sciences, and health sciences: Summary report, (2017); Bishop B.W., Nobles R., Collier H., Research integrity officers’ responsibilities and perspectives on data management compliance and evaluation, The Journal of Research Administration, 52, 1, pp. 76-101, (2021); Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Coates H.L., Carlson J., Clement R., Henderson M., Johnston L.R., Shorish Y., How are we measuring up? Evaluating research data services in academic libraries, Journal of Librarianship & Scholarly Communication, 6, 1, pp. 1-33, (2018); Cox A.M., Kennan L., Lyon S., Pinfield L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Goben A., Griffin T., In aggregate: Trends, needs, and opportunities from research data management surveys, College & Research Libraries, 80, 7, pp. 903-924, (2019); Holles J.H., Schmidt L., Graduate research data management course content: Teaching the Data Management Plan (DMP), ASEE Annual Conference & Exposition, (2018); Imker H.J., Luong H., Mischo W.H., Schlembach M.C., Wiley C.A., An “Examination of Data Reuse Practices within Highly Cited Articles of Faculty at a Research University, The Journal of Academic Librarianship, 47, 4, (2021); Kurata K., Mamiko M., Mine S., Identifying the complex position of research data and data sharing among researchers in natural science, SAGE Open, 7, 3, (2017); Mischo W.H., Schlembach M.C., Imker H.J., An integrated data management plan instructional program, ASEE Annual Conference & Exposition, (2017); Payal A.S., Tripathi M., A selective review of literature on research data management in academic libraries, DESIDOC Journal of Library and Information Technology, 39, 6, pp. 338-345, (2019); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership: The Canadian Journal of Librarian and Information Practice and Research, 13, 1, (2018); Poole A.H., Garwood D.A., Digging into data management in public‐funded, international research in digital humanities, Journal of the Association for Information Science and Technologies, 71, 1, pp. 84-97, (2019); Van Loon J.E., Akers K.G., Hudson C., Sarkozy A., Quality evaluation of data management plans at a research university, IFLA Journal, 43, 1, pp. 98-104, (2017); Van Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon University, Journal of Librarianship and Scholarly Communication, 3, 3, (2015); Vilar P., Zabukovec V., Research data management and research data literacy in Slovenian science, Journal of Documentation, 75, 1, pp. 24-43, (2019); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices, Program: Electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Burnett M.H., Assessing data management support needs of bioengineering and biomedical research faculty, Journal of E-Science Librarianship, 8, 1, (2019); Mischo W.H., Data management practices and perspectives of atmospheric scientists & engineering faculty. Issues of Science and Technology, No. 85, Fall 2016, (2016)","C. Wiley; Engineering and Physical Sciences Research Data Services, University of Illinois Urbana-Champaign, Grainger Engineering Library, Urbana, 1301 Springfield Ave, 61801, United States; email: cawiley@illinois.edu","","Routledge","","","","","","0194262X","","STELD","","English","Sci Technol Libr","Article","Article in press","","Scopus","2-s2.0-85144251152"
"Gonzaleznvelez H.; Dobre C.; Sancheznsolis B.; Antinucci G.; Feenan D.; Gheorghe D.","Gonzaleznvelez, Horacio (58101976700); Dobre, Ciprian (24437773100); Sancheznsolis, Barbara (58101629500); Antinucci, Giulia (58101092900); Feenan, Dave (58101798100); Gheorghe, Dana (58101976800)","58101976700; 24437773100; 58101629500; 58101092900; 58101798100; 58101976800","Open Science and Research Data Management: A FAIR European Postgraduate Programme","2022","Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022","","","","2522","2531","9","0","10.1109/BigData55660.2022.10020931","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147937578&doi=10.1109%2fBigData55660.2022.10020931&partnerID=40&md5=7f8666e4dc603c6a3b4a67572f98266d","National College of Ireland, Ireland; Ici Bucureti, Romania; Technischen Universität, Wien, Austria; Università Degli Studi di Roma ""la Sapienza"", Italy; Digital Technology Skills Ltd, Ireland; Universitatea Politehnica Din Bucureti, Romania","Gonzaleznvelez H., National College of Ireland, Ireland; Dobre C., Ici Bucureti, Romania; Sancheznsolis B., Technischen Universität, Wien, Austria; Antinucci G., Università Degli Studi di Roma ""la Sapienza"", Italy; Feenan D., Digital Technology Skills Ltd, Ireland; Gheorghe D., Universitatea Politehnica Din Bucureti, Romania","Open Science is widely regarded as a culture that is characterised by the transparency and broad accessibility of scholarly work, where researchers share openly artefacts almost immediately and with a very wide audience. The overarching aim of this paper is to document the systematic development of a European postgraduate programme on Open Science and Research Data Management developed by the TRAINRDM project. TRAINRDM is a 30-month European Union funded project, which aims to develop a training network around Open Science and Research Data Management. We have applied a comprehensive survey collecting 239 responses from researchers across Europe, representative of 2.58 million individuals i.e. the total number of researchers employed in the EU-27 region. We then mapped out existing skills and offerings at different TRAINRDM partner institutions to produce a fully-online postgraduate programme with micro-credentials, fully distributed delivery, and compliance to FAIR principles to address academic and industrial research needs. The main outputs of the project are a training programme for Early Career Researchers delivered in Summer 2022, and a the postgraduate programme (Master degree) to be fully validated under the European Qualifications Framework at Level 7 and delivered in 2023. The TRAINRDM curricula, teaching materials, data, and software are openly released under CC BY 4.0 and GPL licenses. © 2022 IEEE.","data carpentry; data management; FAIR principles; open data; open science; open source","Industrial research; Open Data; Open source software; Open systems; Data carpentry; European union; FAIR principle; Open datum; Open science; Open-source; Postgraduate programs; Research data managements; Scholarly works; Science-data; Information management","","","","","European Commission Erasmus+; European level","TRAINRDM is a 30-month project funded by the European Commission Erasmus+ Programme, designed to increase capacity and professionalism in OS and RDM at European level. The TRAINRDM Consortium is built on a 6-strong solid partnership composed of four Higher Education Institutions (HEIs) namely Universitatea Politehnica din Bucures,ti (UPB), Tech-nischen Universität Wien (TUW), National College of Ireland (NCI), and Università degli Studi di Roma “La Sapienza” (SAPIENZA); ICI Bucures,ti (ICI), a national institute for research, development, and innovation in information and communications technology; and Digital Technology Skills Ltd (DTSL), an industrial organisation with proven expertise in the management and delivery of large skills projects.","Vicente-Saez R., Martinez-Fuentes C., Open science now: A systematic literature review for an integrated definition, Journal of Business Research, 88, pp. 428-436, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, pp. 1-9, (2016); Jaber S., Gheorghe D., Ciobanu R., Dobre C., Riccio A., Negru C., Weise M., Miksa T., Moser M., Tsepelakis S., Bohan J., Ayala-Rivera V., Bradford M., Antinucci G., Riccio A., Ciaccia P., Di Giovancarlo C., Serafini I., Materials of Erasmus+ TrainRDM Open Science 'Early stage Researchers' Training Week, (2022); Computing Curricula 2020: Paradigms for Global Computing Education, (2020); Clear A., Parrish A., Zhang M., Van Der Veer G.C., CC2020: A vision on computing curricula, Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, ser. SIGCSE '17, pp. 647-648, (2017); Computing Competencies for Undergraduate Data Science Curricula, (2021); Schopfel J., Azeroual O., Rewarding research data management, Companion Proceedings of the Web Conference 2021, ser. WWW '21, pp. 446-450, (2021); Ray J.M., Introduction to research data management, Research Data Management: Practical Strategies for Information Professionals, ser. Charleston Insights in Library, Archival, and Information Sciences, pp. 1-22, (2014); Jacobsen A., De Miranda A.R., Juty N., Batista D., Et al., FAIR principles: Interpretations and implementation considerations, Data Intelligence, 2, 1-2, pp. 10-29, (2020); Ayris P., Smolders A., Implementing Open Science: Challenges and Opportunities for research-intensive universities in LERU, (2020); Foster E.D., Deardorff A., Open science framework (OSF), Journal of the Medical Library Association, 105, 2, pp. 203-206, (2017); Ayris P., De San Roman A.L., Maes K., Labastida I., Open Science and its role in universities: A roadmap for cultural change, (2018); OCarroll C., Hyllseth B., Van Den Berg R., Kohl U., Kamerlin C.L., Brennan N., ONeill G., Providing researchers with the skills and competencies they need to practise Open Science, (2017); Michener W.K., Ten simple rules for creating a good data management plan, PLOS Computational Biology, 11, 10, pp. 1-9, (2015); McGill M.M., Sexton S., Peterfreund A., Praetzellis M., Efficient, effective, and ethical education research data management and sustainability, Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, ser. SIGCSE '21, (2021); Schwab S., Janiaud P., Dayan M., Et al., Ten simple rules for good research practice, PLOS Computational Biology, 18, 6, pp. 1-14, (2022); Thompson C.A., Building data expertise into research institutions: Preliminary results, Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community, ser. ASIST '15, (2015); Fransson J., Lagunas P.T., Kjellberg S., Toit M.D., Developing integrated research data management support in close relation to doctoral students' research practices, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-4, (2016); Wilson G., Software carpentry: Lessons learned, F1000Research, 3, 62, pp. 1-24, (2016); Fecher B., Friesike S., Open science: One term, five schools of thought, Opening Science: The Evolving Guide on How the Internet is Changing Research, pp. 17-47, (2014); Beaudry J.L., Kaufman J., Johnstone T., Given L., Swinburne Open Science Survey, (2019); Beaudry J.L., Chen D.T., Cook B.G., Et al., The open scholarship survey, (2020); Nash S.S., Rice W., Moodle 4 E-Learning Course Development: The definitive guide to creating great courses in Moodle 4.0 using instructional design principles, (2022)","","Tsumoto S.; Ohsawa Y.; Chen L.; Van den Poel D.; Hu X.; Motomura Y.; Takagi T.; Wu L.; Xie Y.; Abe A.; Raghavan V.","Institute of Electrical and Electronics Engineers Inc.","Ankura; et al.; Hitachi; KPMG Consulting Co., Ltd.; NTT Data Intellilink Corporation; Think in Data Initiative, Association Inc","2022 IEEE International Conference on Big Data, Big Data 2022","17 December 2022 through 20 December 2022","Osaka","186390","","978-166548045-1","","","English","Proc. - IEEE Int. Conf. Big Data, Big Data","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85147937578"
"Xu Z.; Zhou X.; Kogut A.; Watts J.","Xu, Zhihong (57204631412); Zhou, Xuan (57395705900); Kogut, Ashlynn (57205744961); Watts, John (57222059360)","57204631412; 57395705900; 57205744961; 57222059360","A Scoping Review: Synthesizing Evidence on Data Management Instruction in Academic Libraries","2022","Journal of Academic Librarianship","48","3","102508","","","","7","10.1016/j.acalib.2022.102508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126023531&doi=10.1016%2fj.acalib.2022.102508&partnerID=40&md5=cb04179181d458ddb63860ee41505402","Texas A&M University, United States","Xu Z., Texas A&M University, United States; Zhou X., Texas A&M University, United States; Kogut A., Texas A&M University, United States; Watts J., Texas A&M University, United States","The present scoping review examines empirical RDM instruction-related studies in academic libraries between 2010 and 2021. We searched three databases (LISTA, ERIC, and MEDLINE Complete) and two journals (Journal of eScience Librarianship and International Journal of Digital Curation) and identified 124 articles for inclusion. Cohen's kappa indicated a strong to perfect inter-rater reliability on the coding between the authors. Overall, the findings indicate an increasing trend in empirical research regarding the topic of RDM instruction across many countries and regions after 2010. Also, faculty, researchers, and librarians in the higher education field were the primary audiences for the RDM instruction with few studies addressing RDM instruction for the undergraduate and graduate levels. In terms of the RDM aspects, RDM needs assessments were investigated the most among the reviewed studies, followed by data sharing and data management plans. Additionally, the face-to-face learning context was the most popular for RDM instruction, followed by online and hybrid contexts. However, few studies used an intervention research design while delivering instruction to the target audience. This study highlights the substantial characteristics and methodological designs of the RDM instruction empirical research and provides implications for approaches and techniques used to study RDM instruction in academic libraries. © 2022","Academic libraries; Data literacy; Instruction; Research data management; Scoping review; Training","","","","","","Texas A and M University, TAMU","The rapid advance of research technologies combined with an ever-shifting landscape of scholarly communication provides researchers with immense opportunities to harvest, analyze, and produce large and complex datasets. The output of these advances often drives the research questions of today that were hard to imagine even ten years ago. With this ability to mine, analyze, and produce data has come the expectation to foster greater scientific transparency and acceleration by means of data sharing. In order to advance research, this current data environment requires researchers to equip themselves with the data management skills and knowledge necessary to harness the capabilities and possibilities of data. Research data management (RDM) is now a key concern for funding agencies charged with making all outputs of their funded projects findable and reusable for the purposes of expanding knowledge and maintaining transparency. ","Abduldayan F.J., Abifarin F.P., Oyedum G.U., Alhassan J.A., Research data management practices of chemistry researchers in federal universities of technology in Nigeria, Digital Library Perspectives, 37, 4, (2021); Agogo D., Anderson J., “The data shuffle”: Using playing cards to illustrate data management concepts to a broad audience, Journal of Information Systems Education, 30, 2, pp. 84-96, (2019); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Anggawira D., Mayesti N., The Indonesian National Scientific Repository: A case study of research data sharing, Preservation, Digital Technology & Culture, 49, 1, pp. 14-25, (2020); Atwood T.P., Condon P.B., Goldman J., Hohenstein T., Mills C.V., Painter Z.W., Grassroots professional development via the New England research data management roundtables, Journal of eScience Librarianship, 6, 2, (2017); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech., 35, 2, pp. 271-289, (2017); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: Findings from a focus group in an academic health sciences library, Journal of Web Librarianship, 6, 4, pp. 274-287, (2012); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: Integrated findings to develop research data services, Journal of eScience Librarianship, 6, 1, (2017); Bishop B.W., Borden R.M., Scientists’ research data management questions: Lessons learned at a data help desk, Portal: Libraries & the Academy, 20, 4, pp. 677-692, (2020); Borghi J.A., Van Gulick A.E., Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers, PLoS One, 13, 7, (2018); Borgman C.L., Golshan M.S., Sands A.E., Wallis J.C., Cummings R.L., Darch P.T., Randles B.M., Data management in the long tail: Science, software, and service, International Journal of Digital Curation, 11, 1, pp. 128-149, (2016); Boyd C., Use of optional data curation features by users of Harvard dataverse repository, Journal of eScience Librarianship, 10, 2, (2021); Bresnahan M.M., Johnson A.M., Assessing scholarly communication and research data training needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Burnette M., Williams S., Imker H., From plan to action: Successful data management plan implementation in a multidisciplinary project, Journal of eScience Librarianship, 5, 1, (2016); Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Carlson J.R., Opportunities and barriers for librarians in exploring data: Observations from the data curation profile workshops, Journal of eScience Librarianship, 2, 2, pp. 17-33, (2013); Chawinga W.D., Zinn S., Research data management at a public university in Malawi: The role of “three hands”, Library Management, 41, 6-7, pp. 467-485, (2020); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chiware E.R., Becker D.A., Research data management services in southern Africa: A readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Chiware E.R.T., Data librarianship in South African academic and research libraries: A survey, Library Management., 41, 6-7, pp. 401-416, (2020); Clairoux N., En Français S'il Vous Plaît: Translation and adaptation of the New England collaborative data management Curriculum's introductory module, Journal of eScience Librarianship, 4, 1, (2015); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at small liberal arts colleges, IFLA Journal, 43, 1, pp. 105-118, (2017); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Coombs P.E., Malinowski C., Nurnberger A., Skills, standards, and Sapp Nelson's matrix: Evaluating research data management workshop offerings, Journal of eScience Librarianship, 8, 1, (2019); 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"Koya K.; Chowdhury G.","Koya, Kushwanth (55849924700); Chowdhury, Gobinda (7006058701)","55849924700; 7006058701","A quality and popularity based ranking method for research datasets","2022","ACM International Conference Proceeding Series","","","","103","110","7","0","10.1145/3512353.3512368","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126568833&doi=10.1145%2f3512353.3512368&partnerID=40&md5=5a1f1cf36b902e5e99cf45f6105dd9d0","ISchool, Department of Finance, Accounting and Business Systems, College of Business, Technology and Engineering, Sheffield Hallam University, United Kingdom; ISchool, Department of Computer and Information Sciences, Faculty of Science, University of Strathclyde, United Kingdom","Koya K., ISchool, Department of Finance, Accounting and Business Systems, College of Business, Technology and Engineering, Sheffield Hallam University, United Kingdom; Chowdhury G., ISchool, Department of Computer and Information Sciences, Faculty of Science, University of Strathclyde, United Kingdom","Research outputs are the final products in the scientific research process and their quality is progressively being evaluated by various methods such as altmetrics, bibliometrics, impact factors and citation count etc. However, a significant component of scientific research involves creating/collecting/curating research datasets and globally, funding agencies and governments are mandating an open access policy on research datasets. Though repositories exist to store the datasets, there is no metricised guidance, indicating the quality of datasets for researchers wishing to reuse. We propose a novel method for ranking and visualising research datasets based on their quality and popularity, constructed through a normalised citation count since the year of origin, total cites and the impact factor of the journals which publish the articles citing the dataset. Additionally, we present the process flow for a proposed digital information system for the access of datasets according to their discipline and rank based on the variables. The proposed method is expected to assist researchers, globally, to choose the right datasets for their research, encourage researchers to share their datasets and promote interdisciplinary research.  © 2022 ACM.","Research data; research data management; research data quality","Data quality; Impact factor; Ranking methods; Research data; Research data managements; Research data quality; Research outputs; Research process; Scientific researches; Information management","","","","","","","Allen L., Jones C., Dolby K., Lynn D., Walport M., Looking for landmarks: The role of expert review and bibliometric analysis in evaluating scientific publication outputs, PlosOne, 4, 6, (2009); Waltman L., Calero-Medina C., Kosten J., Noyons E.C.M., Tijssen R.J.W., Van Eck N.J., Van Leeuwen T.N., Van Raan A.F.J., Visser M.S., Wouters P., The Leiden Ranking 2011/2012: Data collection, indicators, and interpretation, Journal of the American Society for Information Science and Technology, 63, 12, pp. 2419-2432, (2012); Bornmann L., Mutz R., Neuhaus C., Daniel H., Citation counts for research evaluation: Standards of good practice for analyzing bibliometric data and presenting and interpreting results, Ethics in Science and Environmental Politics, 8, 1, pp. 93-102, (2008); Moed H.F., The future of research evaluation rests with an intelligent combination of advanced metrics and transparent peer review, Science and Public Policy, 34, 8, pp. 575-583, (2007); Abramo G., Andrea D'Angelo C., Di Costa F., National research assessment exercises: A comparison of peer review and bibliometrics rankings, Scientometrics, 89, 3, pp. 929-941, (2011); Van Raan A., Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises, Scientometrics, 36, 3, pp. 397-420, (1996); Belter C.W., Measuring the value of research data: A citation analysis of oceanographic data sets, PlosOne, 9, 3, (2014); Costello M.J., Motivating online publication of data, BioScience, 59, 5, pp. 418-427, (2009); Parsons M.A., Duerr R., Minster J., Data citation and peer review, Eos, Transactions American Geophysical Union, 91, 34, pp. 297-298, (2010); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); About Scientific Data, (2016); About Datacite., (2016); Reuters T., Data Citation Index, (2016); Ball A., Duke M., How to Track the Impact of Research Data with Metrics, DCC How-to Guides. Edinburgh: Digital Curation Centre, (2015); Bertino E., Data trustworthiness-approaches and research challenges, Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance, pp. 17-25, (2014); Case Statement of the RDA-WDS Publishing Data Interest Group, (2016); Tenopir C., Allard S., Douglass K., Umur Aydinoglu A., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PlosOne, 6, 6, (2011); Diepenbroek M., Grobe H., Uwe Schindler., Research Data Enters Scholarly Communication-toward An Infrastructure for Data Publication in the Empirical Sciences., (2013); Wand Y., Wang R.Y., Anchoring data quality dimensions in ontological foundations, Communications of the ACM, 39, 11, pp. 86-95, (1996); Chowdhury G., Koya K., Philipson P., Measuring the impact of research: Lessons from the UK's Research Excellence Framework 2014, PlosOne, 11, 6, (2016); Waltman L., A review of the literature on citation impact indicators, Journal of Informetrics, 10, 2, pp. 365-391, (2016); Vieira E.S., Cabral J.A.S., Anf Gomes J., How good is a model based on bibliometric indicators in predicting the final decisions made by peers?, Journal of Informetrics, 8, 2, pp. 390-405, (2014); Jayasinghe W.U., Herbert M.W., Bond N., Peer review in the funding of research in higher education: The Australian experience, Educational Evaluation and Policy Analysis, 23, 4, pp. 343-364, (2001); Abramo G., Andrea D'Angelo C., Caprasecca A., Allocative efficiency in public research funding: Can bibliometrics help?, Research Policy, 38, 1, pp. 206-215, (2009); Nelson M.R., Building an open cloud, Science, 324, 5935, pp. 1656-1657, (2009); Hey T., Trefethen A.E., Cyberinfrastructure for e-Science, Science, 308, 5723, pp. 817-821, (2005); Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov Ch P., Mark R.G., Mietus J.E., Moody G.B., Peng C., Eugene Stanley H., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, 101, 23, pp. e215-e220, (2000); Scope and Benefits of EPSRC policy framework on research data, EPSRC, (2016); Merrill S., Olson S., Measuring the Impacts of Federal Investments in Research: A Workshop Summary, (2011); About the REF, (2014); What Is ORCID?, (2016); About ISO, (2016); 2016 the European Open Science Cloud: EOSC Infoday; Jordan C., Esteva M., Walling D., Urban T., Kulasekaran S., Responses to Data Management Requirements at the National Scale, Research Data Management, 64, (2013)","","","Association for Computing Machinery","","4th Asia Pacific Information Technology Conference, APIT 2022","14 January 2022 through 16 January 2022","Virtual, Online","177694","","978-145039557-1","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85126568833"
"Gadelha L.; Hohmuth M.; Zulfiqar M.; Schone D.; Samuel S.; Sorokina M.; Steinbeck C.; Konig-Ries B.","Gadelha, Luiz (24921373000); Hohmuth, Martin (57210219511); Zulfiqar, Mahnoor (57457856900); Schone, David (57814426600); Samuel, Sheeba (57194280683); Sorokina, Maria (57217798619); Steinbeck, Christoph (7003655166); Konig-Ries, Birgitta (55864942100)","24921373000; 57210219511; 57457856900; 57814426600; 57194280683; 57217798619; 7003655166; 55864942100","Toward a Framework for Integrative, FAIR, and Reproducible Management of Data on the Dynamic Balance of Microbial Communities","2022","Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022","","","","443","449","6","0","10.1109/eScience55777.2022.00080","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145442188&doi=10.1109%2feScience55777.2022.00080&partnerID=40&md5=64cbed189d23de407c6d28ab821506e8","Friedrich-Schiller University Jena, Jena, 07743, Germany; Research and Development, Bayer Ag, Berlin, 13353, Germany","Gadelha L., Friedrich-Schiller University Jena, Jena, 07743, Germany; Hohmuth M., Friedrich-Schiller University Jena, Jena, 07743, Germany; Zulfiqar M., Friedrich-Schiller University Jena, Jena, 07743, Germany; Schone D., Friedrich-Schiller University Jena, Jena, 07743, Germany; Samuel S., Friedrich-Schiller University Jena, Jena, 07743, Germany; Sorokina M., Research and Development, Bayer Ag, Berlin, 13353, Germany; Steinbeck C., Friedrich-Schiller University Jena, Jena, 07743, Germany; Konig-Ries B., Friedrich-Schiller University Jena, Jena, 07743, Germany","The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often called a Fourth Paradigm for scientific research based on the exploration of large datasets. Current scientific research is often interdisciplinary, making data integration a critical technique for combining data from different scientific domains. Research data management is a critical part of this paradigm, through the proposition and development of methods, techniques, and practices for managing scientific data through their life cycle. Research on microbial communities follows the same pattern of production of large amounts of data obtained, for instance, from sequencing organisms present in environmental samples. Data on microbial communities can come from a multitude of sources and can be stored in different formats. For example, data from metagenomics, metatranscriptomics, metabolomics, and biological imaging are often combined in studies. In this article, we describe the design and current state of implementation of an integrative research data management framework for the Cluster of Excellence Balance of the Microverse aiming to allow for data on microbial communities to be more easily discovered, accessed, combined, and reused. This framework is based on research data repositories and best practices for managing workflows used in the analysis of microbial communities, which includes recording provenance information for tracking data derivation.  © 2022 IEEE.","computational reproducibility; data integration; microbial communities; research data management","Information management; Large dataset; Life cycle; Microorganisms; 'current; Computational infrastructure; Computational reproducibility; Critical technique; Dynamic balance; High-throughput; Large datasets; Microbial communities; Research data managements; Scientific researches; Data integration","","","","","Deutsche Forschungsgemeinschaft, DFG, (239748522, 390713860, SFB 1127)","Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2051 – Project-ID 390713860 and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 239748522, SFB 1127 ChemBioSys.","Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Miller R.J., Open data integration, Proceedings of the VLDB Endowment, 11, 12, pp. 2130-2139, (2018); Mons B., Data Stewardship for Open Science: Implementing FAIR Principles, (2018); Bechhofer S., Buchan I., De Roure D., Missier P., Ainsworth J., Bhagat J., Couch P., Cruickshank D., Delderfield M., Et al., Why linked data is not enough for scientists, Future Generation Computer Systems, 29, 2, pp. 599-611, (2013); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva S.L.B., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Herschel M., Diestelkamper R., Lahmar H.B., A survey on provenance: What for? What form? What from?, The VLDB Journal, 26, 6, pp. 881-906, (2017); Kyrpides N.C., Eloe-Fadrosh E.A., Ivanova N.N., Microbiome data science: Understanding our microbial planet, Trends in Microbiology, 24, 6, pp. 425-427, (2016); Jurburg S.D., Konzack M., Eisenhauer N., Heintz-Buschart A., The archives are half-empty: An assessment of the availability of microbial community sequencing data, Communications Biology, 3, 1, (2020); Chamanara J., Gaikwad J., Gerlach R., Algergawy A., Ostrowski A., Konig-Ries B., BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data, Biodiversity Data Journal, 9, (2021); Allan C., Burel J.-M., Moore J., Blackburn C., Linkert M., Loynton S., MacDonald D., Moore W.J., Neves C., Et al., OMERO: Flexible, model-driven data management for experimental biology, Nature Methods, 9, 3, pp. 245-253, (2012); Samuel S., Konig-Ries B., A collaborative semantic-based provenance management platform for reproducibility, PeerJ Computer Science, 8, (2022); Mondelli M.L., Samuel S., Konig-Ries B., Gadelha L.M.R., Capturing and semantically describing provenance to tell the story of r scripts, 2021 IEEE 17th International Conference on eScience (eScience). IEEE, pp. 283-288, (2021); Soiland-Reyes S., Sefton P., Crosas M., Castro L.J., Coppens F., Fernandez J.M., Garijo D., Gruning B., La Rosa M., Groth P., Goble C., Et al., Packaging research artefacts with RO-Crate, Data Science, pp. 1-42, (2022); Freire J., Chirigati F., Provenance and the different flavors of computational reproducibility, Bulletin of the Technical Committee on Data Engineering, 41, 1, pp. 15-26, (2018); Samuel S., Konig-Ries B., Understanding experiments and research practices for reproducibility: An exploratory study, PeerJ, 9, (2021); Pergl R., Hooft R., Suchanek M., Knaisl V., Slifka J., Data Stewardship Wizard"": A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning, Data Science Journal, 18, (2019); Vangay P., Burgin J., Johnston A., Beck K.L., Berrios D.C., Blumberg K., Canon S., Chain P., Chandonia J.-M., Et al., Microbiome metadata standards: Report of the national microbiome data collaborative's workshop and follow-on activities, mSystems, 6, 1, (2021); Field D., Amaral-Zettler L., Cochrane G., Cole J.R., Dawyndt P., Garrity G.M., Gilbert J., Glockner F.O., Hirschman L., Et al., The genomic standards consortium, PLOS Biology, 9, 6, (2011); Jackson R., Matentzoglu N., Overton J.A., Vita R., Balhoff J.P., Buttigieg P.L., Carbon S., Courtot M., Diehl A.D., Et al., OBO Foundry in 2021: Operationalizing open data principles to evaluate ontologies, Database, 2021, (2021); Yilmaz P., Kottmann R., Field D., Knight R., Cole J.R., Amaral-Zettler L., Gilbert J.A., Karsch-Mizrachi I., Johnston A., Et al., Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications, Nature biotechnology, 29, 5, pp. 415-420, (2011); Mayer G., Muller W., Schork K., Uszkoreit J., Weidemann A., Wittig U., Rey M., Quast C., Felden J., Et al., Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases, Briefings in Bioinformatics, (2021); Eloe-Fadrosh E.A., Anubhav F.A., Babinski M., Baumes J., Borkum M., Bramer L., Canon S., Christianson D.S., Corilo Y.E., Et al., The national microbiome data collaborative data portal: An integrated multi-omics microbiome data resource, Nucleic Acids Research, 50, D1, pp. D828-D836, (2022); Cernava T., Rybakova D., Buscot F., Clavel T., McHardy A.C., Meyer F., Meyer F., Overmann J., Stecher B., Et al., Metadata harmonization-Standards are the key for a better usage of omics data for integrative microbiome analysis, Environmental Microbiome, 17, 1, (2022); Oliveira W., Oliveira D.D., Braganholo V., Provenance analytics for workflow-based computational experiments, ACM Computing Surveys, 51, 3, pp. 1-25, (2018); Missier P., Belhajjame K., Cheney J., The W3C PROV family of specifications for modelling provenance metadata, Proceedings of the 16th International Conference on Extending Database Technology - EDBT '13, (2013); Goldberg I.G., Allan C., Burel J.-M., Creager D., Falconi A., Hochheiser H., Johnston J., Mellen J., Sorger P.K., Swedlow J.R., The open microscopy environment (ome) data model and xml file: Open tools for informatics and quantitative analysis in biological imaging, Genome biology, 6, 5, (2005); Kunis S., Hansch S., Schmidt C., Wong F., Weidtkamp-Peters S., OMERO.mde in a use case for microscopy metadata harmonization: Facilitating FAIR principles in practical application with metadata annotation tools, (2021); Steinbeck C., Koepler O., Bach F., Herres-Pawlis S., Jung N., Liermann J., Neumann S., Razum M., Baldauf C., Et al., NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany, Research Ideas and Outcomes, 6, (2020); Tremouilhac P., Nguyen A., Huang Y.-C., Kotov S., Lutjohann D.S., Hubsch F., Jung N., Brase S., Chemotion ELN: An Open Source electronic lab notebook for chemists in academia, Journal of Cheminformatics, 9, 1, (2017); Glockner F.O., Diepenbroek M., NFDI4BioDiversity: Biodiversity, ecology and environmental data, Biodiversity Information Science and Standards, 3, (2019); Reimer L.C., Forstner K.U., Overmann J., Besser forschen durch offene und FAIRe Daten, BIOspektrum, 28, 2, pp. 223-223, (2022); Schmidt C., Ferrando-May E., NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data, E-Science-Tage 2021. 339 Share Your Research Data, (2022); Hu B., Canon S., Eloe-Fadrosh E.A., Anubhav M.B., Corilo Y., Davenport K., Duncan W.D., Fagnan K., Et al., Challenges in bioinformatics workflows for processing microbiome omics data at scale, Frontiers in Bioinformatics, 1, (2022); Clark R.D., A path to next-generation reproducibility in cheminformatics, Journal of Cheminformatics, 11, 1, (2019); Samuel S., Konig-Ries B., End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach, Journal of Biomedical Semantics, 13, 1, (2022); Goble C., Cohen-Boulakia S., Soiland-Reyes S., Garijo D., Gil Y., Crusoe M.R., Peters K., Schober D., FAIR computational workflows, Data Intelligence, 2, 1-2, pp. 108-121, (2020); Perez-Riverol Y., Moreno P., Scalable data analysis in proteomics and metabolomics using biocontainers and workflows engines, PROTEOMICS, 20, 9, (2020); Gruening B., Sallou O., Moreno P., Da Veiga L.F., Menager H., Sondergaard D., Rost H., Sachsenberg T., O'Connor B., Perez-Riverol Y., Et al., Recommendations for the packaging and containerizing of bioinformatics software, F1000Research, 7, (2019); Crusoe M.R., Abeln S., Iosup A., Amstutz P., Chilton J., Tijanic N., Menager H., Soiland-Reyes S., Gavrilovic B., Goble C., Methods included: Standardizing computational reuse and portability with the common workflow language, Communications of the ACM, 65, 6, pp. 54-63, (2022); Ison J., Kalas M., Jonassen I., Bolser D., Uludag M., McWilliam H., Malone J., Lopez R., Pettifer S., Rice P., EDAM: An ontology of bioinformatics operations, types of data and identifiers, topics and formats, Bioinformatics, 29, 10, pp. 1325-1332, (2013); Gray A., Goble C., Jimenez R., Bioschemas: From potato salad to protein annotation, Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th International Semantic Web Conference (ISWC 2017), (2017); Mondelli M.L., Magalhaes T., Loss G., Wilde M., Foster I., Mattoso M., Katz D., Barbosa H., De Vasconcelos A.T.R., Ocana K., Gadelha L.M., BioWorkbench: A high-performance framework for managing and analyzing bioinformatics experiments, PeerJ, 6, (2018); Babuji Y., Woodard A., Li Z., Katz D.S., Clifford B., Kumar R., Lacinski L., Chard R., Wozniak J.M., Foster I., Wilde M., Chard K., Parsl: Pervasive parallel programming in python, 28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), (2019); Mondelli M.L., Peterson A.T., Gadelha L.M.R., Exploring reproducibility and fair principles in data science using ecological niche modeling as a case study, Advances in Conceptual Modeling. ER 2019. Lecture Notes in Computer Science, pp. 23-33, (2019); Joachimiak M.P., Hegde H., Duncan W.D., Reese J.T., Cappelletti L., Thessen A.E., KG-Microbe: A reference knowledge-graph and platform for harmonized microbial information, The 12th International Conference on Biomedical Ontologies (ICBO 2021), (2021)","L. Gadelha; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: luiz.gadelha@uni-jena.de; M. Hohmuth; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: martin.hohmuth@uni-jena.de; M. Zulfiqar; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: mahnoor.zulfiqar@uni-jena.de; D. Schone; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: david.schone@uni-jena.de; S. Samuel; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: sheeba.samuel@uni-jena.de; C. Steinbeck; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: christoph.steinbeck@uni-jena.de; B. Konig-Ries; Friedrich-Schiller University Jena, Jena, 07743, Germany; email: birgitta.konig-ries@uni-jena.de","","Institute of Electrical and Electronics Engineers Inc.","IEEE; IEEE Computer Society; IEEE Technical Committee on Parallel Processing (TCPP); IEEE�s Technical Committee on High Performance Computing (TCHPC)","18th IEEE International Conference on e-Science, eScience 2022","10 October 2022 through 14 October 2022","Salt Lake City","185105","","978-166546124-5","","","English","Proc. - IEEE Int. Conf. e-Sci., eScience","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85145442188"
"Barnes H.L.","Barnes, Heather L. (58041906400)","58041906400","Digital Curation and Contemporary Documentary Filmmaking","2022","Preservation, Digital Technology and Culture","51","4","","141","154","13","0","10.1515/pdtc-2022-0021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145612621&doi=10.1515%2fpdtc-2022-0021&partnerID=40&md5=048e16a392cf8cc02ccfaae62c4ee902","Wake Forest University, ZSR Library, 1834 Wake Forest Rd, Winston-Salem, 27109, NC, United States","Barnes H.L., Wake Forest University, ZSR Library, 1834 Wake Forest Rd, Winston-Salem, 27109, NC, United States","Documentary films have evolved considerably since 1922s Nanook of the North. Fans of nonfiction now stream multi-episode documentaries on platforms like Netflix or catch a feature at one of many documentary-centered film festivals around the world. Inexpensive video cameras and internet distribution have expanded the documentary film universe exponentially. From 1-min films to feature-length theater releases, moviegoers around the world have embraced this diverse and growing genre. To the benefit of aspiring filmmakers, documentaries can now be filmed on a wide array of digital video devices, including smartphones, and edited inexpensively. Given this abundance, it may seem counterintuitive that, from a preservation perspective, the documentary film genre faces substantial risks. Research indicates that independent filmmakers lack access to resources that would ensure the long-term stewardship of their works (Academy of Motion Picture Arts and Sciences 2012). This research project examines documentary film production through the lens of digital curation. It describes filmmakers' data practices and proposes a data curation model designed to guide filmmakers and film archives in developing data management plans similar to those currently used by researchers in the sciences. The proposed data curation model reflects the influence of the growing research data management field and integrates components related to digital storage, copyright, publishing, context, and file organization.  © 2022 Walter de Gruyter GmbH, Berlin/Boston.","data management; digital curation; digital preservation; documentary filmmaking","","","","","","","","The Digital Dilemma, (2007); The Digital Dilemma 2, (2012); Almeida A., Alvelos H., An Interactive Documentary Manifesto, Interactive Storytelling, pp. 123-128, (2010); Antoniazzi L., Digital Preservation and the Sustainability of Film Heritage, Information, Communication & Society, 24, 11, pp. 1658-1673, (2021); Aufderheide P., Interactive Documentaries: Navigation and Design, Journal of Film and Video, 67, 3-4, pp. 69-78, (2015); Bartliff Z., Kim Y., Hopfgartner F., Baxter G., Leveraging Digital Forensics and Data Exploration to Understand the Creative Work of a Filmmaker: A Case Study of Stephen Dwoskin's Digital Archive, Information Processing & Management, 57, 6, (2020); Besser H., Archiving Aggregates of Individually Created Digital Content: Lessons from Archiving the Occupy Movement, Preservation, Digital Technology & Culture, 42, pp. 31-37, (2013); Bordwell D., Pandora's Digital Box: Films, Files, and the Future of Movies, (2013); Boutard G., Towards Mixed Methods Digital Curation: Facing Specific Adaptation in the Artistic Domain, Archival Science, 15, pp. 169-189, (2015); Brand B., Artist as Archivist in the Digital Transition, The Moving Image: The Journal of the Association of Moving Image Archivists, 12, pp. 92-95, (2012); Capra R.G., Lee C.A., Marchionini G., Russell T., Shah C., Stutzman F., Selection and Context Scoping for Digital Video Collections: An Investigation of YouTube and Blogs, Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 211-220, (2008); De Jong W., Knudsen E., Rothwell J., Creative Documentary: Theory and Practice, (2014); Dekker A., Enjoying the Gap: Comparing Contemporary Documentation Strategies, Preserving and Exhibiting Media Art: Challenges and Perspectives, pp. 150-169, (2013); Del Barco M., The Documentary Is In - And Enjoying - An 'Undeniable Golden Age', (2019); Dinmore S., The Real Online: Database Documentary in the Knowledge Space, Journal of Media Practice, 15, pp. 123-132, (2014); Dixon L.J., The Influence of the World Wide Web on Documentary Form, Distribution, and Audience Relations: The Cases of Sin by Silence and This is Not a Conspiracy Theory, (2015); Dovey J., Rose M., This Great Mapping of Ourselves': New Documentary Forms Online, The Documentary Film Book, pp. 366-375, (2013); Fauconnier S., Fromme R., Capturing Unstable Media, Archives & Museum Informatics, 2, pp. 5-9, (2004); Giannachi G., Documenting the User Experience, Performing Documentation in the Conservation of Contemporary Art, (2015); Gifreu-Castells A., Mapping Trends in Interactive Non-Fiction Through the Lenses of Interactive Documentary, Interactive Storytelling, pp. 156-163, (2014); Haskins E., Between Archive and Participation: Public Memory in a Digital Age, Rhetoric Society Quarterly, 37, pp. 401-422, (2007); Hodges J.A., Transcoding Authenticity: Preserving Unreleased Gaming Software Outside of Memory Institutions, Journal of Documentation, 78, 2, pp. 320-333, (2022); Innocenti P., McHugh A., Ross S., Ruusalepp R., Assessing Long Term Preservation of Audiovisual Digital Contents with DRAMBORA, Axmedis, 45, (2008); Juhasz A., Ceding the Activist Digital Documentary, New Documentary Ecologies: Emerging Platforms, Practices and Discourses, pp. 33-49, (2014); Lee C.A., Tibbo H.R., Capturing the Moment: Strategies for Selection and Collection of Web-Based Resources to Document Important Social Phenomena, Archiving Proceedings, pp. 300-305, (2008); Lee J.H., Clarke R.I., Perti A., Empirical Evaluation of Metadata for Video Games and Interactive Media, Journal of the Association for Information Science and Technology, 66, pp. 2609-2625, (2015); Lozano-Hemmer R., #Best Practices for Conservation of Media Art from an Artist's Perspective, (2019); Marchionini G., Tibbo H., Lee C.A., Jones P., Capra R., Geisler G., Russell T., Shah C., Sheble L., Jorda S., Song Y., Howard D.E., Clemens R., Hill B., VidArch. Preserving Video Objects and Context, Final Report, (2009); Maron N.L., Pickle S., Sustaining the Digital Humanities Host Institution Support Beyond the Start-Up Phase, (2014); Molloy L., Digital Curation Skills in the Performing Arts - An Investigation of Practitioner Awareness and Knowledge of Digital Object Management and Preservation, International Journal of Performance Arts and Digital Media, 10, pp. 7-20, (2014); Mons B., Data Stewardship for Open Science: Implementing FAIR Principles, (2018); Nichols B., Representing Reality: Issues and Concepts in Documentary, (1991); Nichols B., Introduction to Documentary, (2017); Ocak E., New Media Documentary: Playing With Documentary Film Within the Database Logic and Culture, A Digital Janus, (2014); O'Flynn S., Documentary's Metamorphic Form: Webdoc, Interactive, Transmedia, Participatory and Beyond, Studies in Documentary Film, 6, pp. 141-157, (2012); Pietrobruno S., YouTube and the Social Archiving of Intangible Heritage, New Media & Society, 15, pp. 1259-1276, (2013); Rollason-Cass S., Reed S., Living Movements, Living Archives: Selecting and Archiving Web Content During Times of Social Unrest, New Review of Information Networking, 20, pp. 241-247, (2015); Rossenova L., Beyond the Screenshot: Interface Design and Data Protocols in the Net Art Archive, The Networked Image in Post-Digital Culture, pp. 208-228, (2022); Sabharwal A., Digital Humanities and the Emerging Framework for Digital Curation, College & Undergraduate Libraries, 24, pp. 238-256, (2017); Shah C., Marchionini G., Preserving 2008 US Presidential Election Videos, 7th International Web Archiving Workshop (IWAW07), (2007); Tammaro A.M., Heritage Curation in the Digital Age: Professional Challenges and Opportunities, International Information & Library Review, 48, pp. 122-128, (2016); Tedone G., Tracing Networked Images: An Emerging Method for Online Curation, Journal of Media Practice, 18, pp. 51-62, (2017); Tenopir C., Sandusky R.J., Allard S., Birch B., Research Data Management Services in Academic Research Libraries and Perceptions of Librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Thorson K., Driscoll K., Ekdale B., Edgerly S., Thompson L.G., Schrock A., Swartz L., Vraga E.K., Wells C., YouTube, Twitter and the Occupy Movement: Connecting Content and Circulation Practices, Information, Communication & Society, 16, pp. 421-451, (2013); Varanda P., Digital-Born Artworks and Interactive Experience: Documentation and Archiving, Dance Data, Cognition, and Multimodal Communication, pp. 89-98, (2022); Welburn W., Zanoni J., Downing K., Welburn J., Rivera A., # But How Long Will They Matter? Preserving Digital Artefacts of Acts of Remembrance and Resistance, Innovation: Journal of Appropriate Librarianship and Information Work in Southern Africa, 2016, pp. 56-72, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, (2016); Winston B., Vanstone G., Chi W., The Act of Documenting: Documentary Film in the 21st Century, (2017)","H.L. Barnes; Wake Forest University, ZSR Library, Winston-Salem, 1834 Wake Forest Rd, 27109, United States; email: 4storyfilms@gmail.com","","De Gruyter Open Ltd","","","","","","21952965","","","","English","Preser. Digital Tech. Cult.","Article","Final","","Scopus","2-s2.0-85145612621"
"Khan N.; Thelwall M.; Kousha K.","Khan, Nushrat (57200319120); Thelwall, Mike (57527841900); Kousha, Kayvan (55933111000)","57200319120; 57527841900; 55933111000","Are data repositories fettered? A survey of current practices, challenges and future technologies","2022","Online Information Review","46","3","","483","502","19","1","10.1108/OIR-04-2021-0204","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113755336&doi=10.1108%2fOIR-04-2021-0204&partnerID=40&md5=e31e4b4e26a10e3bf0fd24f2aaf76c2e","School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom; Great Ormond Street Institute of Child Health, University College London, London, United Kingdom; School of Mathematics and Computing, University of Wolverhampton, Wolverhampton, United Kingdom","Khan N., School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom; Thelwall M., School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom; Kousha K., School of Mathematics and Computing, University of Wolverhampton, Wolverhampton, United Kingdom","Purpose: The purpose of this study is to explore current practices, challenges and technological needs of different data repositories. Design/methodology/approach: An online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey. Findings: In total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%). Originality/value: This study identifies the current challenges and needs for improving data repository functionalities and user experiences. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204. © 2021, Emerald Publishing Limited.","Data repository; Data reuse; Impact measure; re3data","Automation; Information services; Interoperability; Managers; Surveys; User experience; Design/methodology/approach; Future technologies; Information professionals; Institutional repositories; Perceived usefulness; Repository systems; Research data managements; Technical frameworks; article; FAIR principles; human; informatician; librarian; manager; metadata; peer review; software; Information management","","","","","University of Wolverhampton","Funding: This study was funded by the University of Wolverhampton.","Assante M., Candela L., Castelli D., Tani A., Are scientific data repositories coping with research data publishing?, Data Science Journal, 15, (2016); Bishop L., Kuula-Luumi A., Revisiting qualitative data reuse: a decade on, Sage Open, 7, 1, (2017); Chamanara J., Kraft A., Auer S., Koepler O., Towards semantic integration of federated research data, Datenbank Spektrum, 19, pp. 87-94, (2019); Coady S.A., Mensah G.A., Wagner E.L., Goldfarb M.E., Hitchcock D.M., Giffen C.A., Use of the national heart, lung, and blood institute data repository, New England Journal of Medicine, 376, 19, pp. 1849-1858, (2017); Colavizza G., Hrynaszkiewicz I., Staden I., Whitaker K., McGillivray B., The citation advantage of linking publications to research data, PloS One, 15, 4, (2020); Costas R., Meijer I., Zahedi Z., Wouters P., The value of research data–metrics for datasets from a cultural and technical point of view, (2013); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368, 1926, pp. 4023-4038, (2010); Faniel I.M., Yakel E., Practices do not make perfect: disciplinary data sharing and reuse practices and their implications for repository data curation, Curating Research Data, Volume One: Practical Strategies for Your Digital Repository, 1, pp. 103-126, (2017); Faniel I.M., Kriesberg A., Yakel E., Social scientists' satisfaction with data reuse, Journal of the Association for Information Science and Technology, 67, 6, pp. 1404-1416, (2016); Federer L.M., Lu Y.-L., Joubert D.J., Welsh J., Brandys B., Biomedical data sharing and reuse: attitudes and practices of clinical and scientific research staff, PLoS ONE, 10, 6, (2015); Fenner M., Crosas M., Grethe J.S., Kennedy D., Hermjakob H., Rocca-Serra P., Durand G., Berjon R., Karcher S., Martone M., Clark T., A data citation roadmap for scholarly data repositories, Scientific Data, 6, 1, pp. 1-9, (2019); Fink A., How to Design Survey Studies, (2003); Goldstein S., The evolving landscape of federated research data infrastructures, (2017); Hripcsak G., Heitjan D.F., Measuring agreement in medical informatics reliability studies, Journal of Biomedical Informatics, 35, 2, pp. 99-110, (2002); Ivanovic D., Schmidt B., Grim R., Dunning A., FAIRness of repositories and their data: a report from LIBER's research data management working group, (2019); Khan N., Pink C.J., Thelwall M., Identifying data sharing and reuse with Scholix: potentials and limitations, Patterns, 1, 1, (2020); Khan N., Thelwall M., Kousha K., Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics, Scientometrics, 126, 4, pp. 3621-3639, (2021); Kim Y., Yoon A., Scientists' data reuse behaviors: a multilevel analysis, Journal of the Association for Information Science and Technology, 68, 12, pp. 2709-2719, (2017); Konkiel S., Assessing the impact and quality of research data using altmetrics and other indicators, Scholarly Assessment Reports, 2, 1, (2020); Kratz J.E., Strasser C., Making data count, Scientific Data, 2, 1, pp. 1-5, (2015); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Goebelbecker H.J., Gundlach J., Schirmbacher P., Dierolf U., Making research data repositories visible: the re3data.org registry, PloS One, 8, 11, (2013); Pasquetto I.V., Randles B.M., Borgman C.L., On the reuse of scientific data, Data Science Journal, 16, (2017); Patel D., How Google's dataset search engine work, (2019); Pinfield S., Cox A.M., Smith J., Research data management and libraries: relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Robinson-Garcia N., Jimenez-Contreras E., Torres-Salinas D., Analyzing data citation practices using the data citation index, Journal of the Association for Information Science and Technology, 67, 12, pp. 2964-2975, (2016); Shearer K., Furtado F., COAR survey of research data management: results, (2017); Thelwall M., Munafo M., Mas-Bleda A., Stuart E., Makita M., Weigert V., Keene C., Khan N., Drax K., Kousha K., Is useful research data usually shared? An investigation of genome-wide association study summary statistics, Plos One, 15, 2, (2020); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PloS One, 8, 7, (2013); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Bouwman J., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1, pp. 1-9, (2016); Yoon A., Red flags in data: learning from failed data reuse experiences, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Burton A., Koers H., Manghi P., Stocker M., Fenner M., Aryani A., La Bruzzo S., Diepenbroek M., Schindler U., The Scholix framework for interoperability in data-literature information exchange, D-Lib Magazine, 23, pp. 1-2, (2017)","N. Khan; School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom; email: n.j.khan@wlv.ac.uk","","Emerald Group Holdings Ltd.","","","","","","14684527","","OIRNA","","English","Online Info. Rev.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85113755336"
"Weisweiler N.; Kloska G.","Weisweiler, Nina (58171683700); Kloska, Gabriele (57193792667)","58171683700; 57193792667","Community-Driven Open Reference for Research Data Repositories (COREF) - A Project for Further Development of re3data","2022","Research Data Sharing and Valorization: Developments, Tendencies, Models","","","","197","209","12","0","10.1002/9781394163410.ch11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151735250&doi=10.1002%2f9781394163410.ch11&partnerID=40&md5=15e4ecba2ec455a2ed4feba2e862b9e9","Helmholtz Open Science Office am Deutschen GeoForschungsZentrum GFZ, Potsdam, Germany; Karlsruhe Institute of Technology (KIT), Germany","Weisweiler N., Helmholtz Open Science Office am Deutschen GeoForschungsZentrum GFZ, Potsdam, Germany; Kloska G., Karlsruhe Institute of Technology (KIT), Germany","re3data is a global registry for research data repositories and covers research data repositories from all different academic disciplines. It includes repositories that enable permanent storage of and access to data, for researchers and funding bodies, for publishers and also for scholarly institutions. Besides the typical search and discovery function, the re3data metadata is often used to monitor research infrastructures, for example in the European Open Science Monitor. The goal of the project is to further develop the re3data service and its functionalities, in line with the latest developments in the field of research data management. A larger project for this year, in collaboration with DataCite, is the implementation of a repository search function in DataCite Commons. The goal is for that the service to continue to be sustainable and to be developed as an open and effective substrate upon which other applications and services can be integrated and built by others. © ISTE Ltd 2022.","Data Cite; re3data; research data management; research data repositories; research infrastructures","","","","","","","","Gohain R.R., Status of global research data repository: An exploratory study, Library Philosophy and Practice, 2021, pp. 1-13, (2021); Kim S., Choi M.-S., Registry metadata quality assessment by the example of re3data.org schema, International Journal of Knowledge Content Development and Technology, 7, 2, pp. 41-51, (2017); Kindling M., Pampel H., van de Sandt S., Rucknagel J., Vierkant P., Kloska G., Witt M., Schirmbacher P., Bertelmann R., Scholze F., The landscape of research data repositories in 2015: A re3data analysis, D-Lib Magazine, 23, 3-4, (2017); Pampel H., Vierkant P., Current status and future plans of re3data.org - Registry of research data repositories, GeoBerlin2015: Dynamic Earth from Alfred Wegener to Today and Beyond; Abstracts, Annual Meeting of DGGV and DMG, (2015); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Goebelbecker H.-J., Gundlach J., Schirmbacher P., Dierolf U., Making research data repositories visible: The re3data.org registry, PLoS ONE, 8, 11, (2013); von der Heyde M., Ulrich R., Kloska G., Open Research Data (ORD): Landscape and cost analysis of data repositories currently used by the Swiss research community, and requirements for the future, (2019); von der Heyde M., Ulrich R., Kloska G., International open data repository survey: Description of collection, collected data, and analysis methods, (2019); Witt M., Stall S., Duerr R., Plante R., Fenner M., Dasler R., Cruse P., Hou S., Ulrich R., Kinkade D., Connecting researchers to data repositories in the earth, space, and environmental sciences, Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, (2019)","","","wiley","","","","","","","978-139416341-0; 978-178945073-6","","","English","Research Data Shar. and Valorization: Developments, Tendencies, Models","Book chapter","Final","","Scopus","2-s2.0-85151735250"
"Dorst T.; Gruber M.; Vedurmudi A.P.; Hutzschenreuter D.; Eichstädt S.; Schütze A.","Dorst, Tanja (57211160279); Gruber, Maximilian (57218342871); Vedurmudi, Anupam P. (57063513200); Hutzschenreuter, Daniel (57200627584); Eichstädt, Sascha (35317195000); Schütze, Andreas (55991031400)","57211160279; 57218342871; 57063513200; 57200627584; 35317195000; 55991031400","PROVIDING FAIR AND METROLOGICALLY TRACEABLE DATA SETS - A CASE STUDY","2022","IMEKO TC6 International Conference on Metrology and Digital Transformation","","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143201434&partnerID=40&md5=f6fb68b3b6d82d2c2ad55f204cb1a06a","ZeMA - Zentrum für Mechatronik und Automatisierungstechnik GGmbH, Saarbrücken, Germany; Physikalisch-Technische Bundesanstalt, Braunschweig, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany; Universität des Saarlandes, Lehrstuhl für Messtechnik, Saarbrücken, Germany","Dorst T., ZeMA - Zentrum für Mechatronik und Automatisierungstechnik GGmbH, Saarbrücken, Germany; Gruber M., Physikalisch-Technische Bundesanstalt, Braunschweig, Germany, Physikalisch-Technische Bundesanstalt, Berlin, Germany; Vedurmudi A.P., Physikalisch-Technische Bundesanstalt, Braunschweig, Germany, Physikalisch-Technische Bundesanstalt, Berlin, Germany; Hutzschenreuter D., Physikalisch-Technische Bundesanstalt, Braunschweig, Germany, Physikalisch-Technische Bundesanstalt, Berlin, Germany; Eichstädt S., Physikalisch-Technische Bundesanstalt, Braunschweig, Germany, Physikalisch-Technische Bundesanstalt, Berlin, Germany; Schütze A., ZeMA - Zentrum für Mechatronik und Automatisierungstechnik GGmbH, Saarbrücken, Germany, Universität des Saarlandes, Lehrstuhl für Messtechnik, Saarbrücken, Germany","In recent years, data science and engineering have faced many challenges concerning the increasing amount of data. In order to ensure findability, accessability, interoperability and reusability (FAIRness) of digital resources, digital objects as a synthesis of data and metadata with persistent and unique identifiers should be used. In this context, the FAIR data principles formulate requirements that research data and, ideally, also industrial data should fulfill to make full use of them, particularly when Machine Learning or other data-driven methods are under consideration. In this contribution, the process of providing scientific data of an industrial testbed in a traceable and FAIR manner is documented as an example. © 2022 IMEKO TC6 International Conference on Metrology and Digital Transformation. All rights reserved.","data set; digital SI; FAIR digital objects; research data management; traceability","Industrial research; Information management; Metadata; Research and development management; Accessability; Case-studies; Data set; Digital Objects; Digital resources; Digital SI; FAIR digital object; Research data managements; Science and engineering; Traceability; Reusability","","","","","Horizon 2020 Framework Programme, H2020; European Metrology Programme for Innovation and Research, EMPIR; European Commission, EC; Bundesministerium für Bildung und Forschung, BMBF, (01IS18078)","Funding text 1: Research for this paper has received funding within the projects 17IND12 Met4FoF and 17IND02 SmartCom from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program and from the Federal Ministry of Education and Research (BMBF) project FAMOUS (01IS18078).; Funding text 2: Research for this paper has received funding within the projects 17IND12 Met4FoF and 17IND02 SmartCom from the EMPIR program co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation program and from the Federal Ministry of Education and Research (BMBF) project FAMOUS (01IS18078).","Wilkinson M. D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Stall S., Et al., Make scientific data FAIR, Nature, 570, pp. 27-29, (2019); Hutzschenreuter D., Et al., SmartCom Digital System of Units (D-SI), (2020); Helwig N., Zustandsbewertung industrieller Prozesse mittels multivariater Sensordatenanalyse am Beispiel hydraulischer und elektromechanischer Antriebssysteme, (2018); Eichstadt S., Et al., Toward Smart Traceability for Digital Sensors and the Industrial Internet of Things, Sensors, 21, 6, (2021); Dorst T., Et al., Automated ML Toolbox for Cyclic Sensor Data, Mathematical and Statistical Methods for Metrology, (2021); Le Système international d'unités (SI), (2019); Groth P., Et al., FAIR Data Reuse - The Path through Data Citation, Data Intelligence, 2, 1-2, pp. 78-86, (2020); DCMI Metadata Terms, (2020); QUDT CATALOG - Quantities, Units, Dimensions and Data Types Ontologies, (2022); RDF Schema 1.1. Tech. rep, (2014); Semantic Sensor Network Ontology, (2021); Dorst T., Et al., Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit), (2021); CERN Data Centre & Invenio; Physikalisch-Technische Bundesanstalt; Liu J., Digital Object Identifier (DOI) and DOI Services: An Overview, Libri, 71, 4, pp. 349-360, (2021); Bahim C., Et al., The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments, Data Science Journal, 19, 1, pp. 1-7, (2020)","T. Dorst; ZeMA - Zentrum für Mechatronik und Automatisierungstechnik GGmbH, Saarbrücken, Germany; email: tdorst@zema.de","","IMEKO-International Measurement Federation Secretariat","","IMEKO TC6 1st International Conference on Metrology and Digital Transformation, M4Dconf 2022","19 September 2022 through 21 September 2022","Berlin","184483","","978-171386223-9","","","English","IMEKO TC6 Int. Conf. Metrol. Digit. Transform.","Conference paper","Final","","Scopus","2-s2.0-85143201434"
"Syn S.Y.; Kim S.","Syn, Sue Yeon (22836779000); Kim, Soojung (15848471000)","22836779000; 15848471000","Characterizing the research data management practices of NIH biomedical researchers indicates the need for better support at laboratory level","2022","Health Information and Libraries Journal","39","4","","347","356","9","1","10.1111/hir.12433","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128747644&doi=10.1111%2fhir.12433&partnerID=40&md5=8cc2b59d0079c092d2dfd62e423b1c9d","Department of Library and Information Science, The Catholic University of America, Washington, DC, United States; Department of Library and Information, Science, Jeonbuk National University, Jeonju, South Korea","Syn S.Y., Department of Library and Information Science, The Catholic University of America, Washington, DC, United States; Kim S., Department of Library and Information, Science, Jeonbuk National University, Jeonju, South Korea","Background: The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. Objectives: This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. Methods: Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. Results: The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. Discussion: Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. Conclusion: Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level. © 2022 Health Libraries Group.","research data (management); research support; research, biomedical; research, qualitative","Biomedical Research; Data Management; Humans; National Institutes of Health (U.S.); Research Personnel; United States; article; financial management; human; informatician; semi structured interview; education; information processing; medical research; national health organization; personnel; United States","","","","","National Institutes of Health, NIH","The authors would to like express gratitude to the NIH's Office of Research Services who provided feedback on the research. This paper was proofread by the Writing Center at Jeonbuk National University in April 2021.","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in biomedical research data management and analysis: Needs and barriers, Journal of the American Medical Informatics Association, 14, 4, pp. 478-488, (2007); Borghi J.A., Van Gulick A.E., Data management and sharing: Practices and perceptions of psychology researchers, PLoS ONE, 16, 5, (2021); Chen X., Wu M., Survey on the needs of chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); da Costa M.P., Lima Leite F.C., Factors influencing research data communication on Zika virus: A grounded theory, Journal of Documentation, 75, 5, pp. 910-926, (2019); Fecher B., Friesike S., Hebing M., What drives acacdemic data sharing?, PLoS ONE, 10, 2, (2015); Federer L.M., Lu Y.-L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Federer L.M., Lu Y.-L., Joubert D.J., Welsh J., Brandys B., Biomedical data sharing and reuse: Attitudes and practices of clinical and scientific research staff, PLoS One, 10, 6, (2015); Goben A., Griffin T., In aggregate: Trends, needs and opportunities from research data management surveys, College & Research Libraries, 80, 7, pp. 903-924, (2019); Hunt S.L., Bakker C.J., A qualitative analysis of the information science needs of public health researchers in an academic setting, Journal of the Medical Library Association, 106, 2, pp. 184-197, (2018); Jahnke L.M., Asher A., The problem of data: Data management and curation practices among university researchers, The problem of data, pp. 3-31, (2012); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship and Information Science, 52, 3, pp. 633-646, (2019); Kim S., Syn S.Y., Practical considerations for a library's research data management services: The case of the National Institutes of Health library, Journal of the Medical Library Association, 109, 3, pp. 450-458, (2021); Kim Y., Fostering scientists' data sharing behaviors via data repositories, journal supplements, and personal communication methods, Information Processing & Management, 53, 4, pp. 871-885, (2017); Kim Y., Kim S., Institutional motivational, and resource factors influencing health scientists' data-sharing behaviors, Journal of Scholarly Publishing, 46, 4, pp. 366-389, (2015); Krahe M.A., Toohey J., Wolski M., Scuffham P.A., Reilly S., Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia, Health Information Management Journal, 49, 2-3, pp. 108-116, (2020); McGuire L.A., Bakker C.J., Hunt S., Chew K.V., Brown S.J., Expanding research data management education for librarians & health sciences affiliates at the University of Minnesota Duluth, (2019); Milewska A., Wisniewska N., Cimoszko P., Rusakow J., A survey of medical researchers indicates poor awareness of research data management processes and a role for data librarians, Health Information and Libraries Journal, (2021); Milia N., Congiu A., Anagnostou P., Montinaro F., Capocasa M., Sanna E., Bisol G.D., Mine, yours, ours? Sharing data on human genetic variation, PLoS One, 7, 6, (2012); About NIH; Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS ONE, 10, 8, (2015); Vela K., Shin N., Establishing a research data management service on a health sciences campus, Journal of eScience Librarianship, 8, 1, (2019); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, 7, (2013); Wiley C.A., Burnette M.H., Assessing data management support needs of bioengineering and biomedical research faculty, Journal of eScience Librarianship, 8, 1, (2019); Yu F., Deuble R., Morgan H., Designing research data management services based on the research lifecycle – A consultative leadership approach, Journal of the Australian Library and Information Association, 66, 3, pp. 287-298, (2017)","S. Kim; Department of Library and Information Science, Jeonbuk National University, Jeonju-si, Jeollabuk-do, South Korea; email: kimsoojung@jbnu.ac.kr","","John Wiley and Sons Inc","","","","","","14711834","","","35472824","English","Health Inf. Libr. J.","Article","Final","","Scopus","2-s2.0-85128747644"
"Walther M.; Wagner M.","Walther, Marcus (57194548668); Wagner, Michael (58037004700)","57194548668; 58037004700","FAIR research data integration in CRIS at FAU Erlangen-Nürnberg","2022","Procedia Computer Science","211","C","","246","250","4","0","10.1016/j.procs.2022.10.198","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145179166&doi=10.1016%2fj.procs.2022.10.198&partnerID=40&md5=f309160a929b60655262f6eb4893d90d","FAU Competence Unit for Research Data and Information, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstraße 3, Erlangen, 91058, Germany","Walther M., FAU Competence Unit for Research Data and Information, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstraße 3, Erlangen, 91058, Germany; Wagner M., FAU Competence Unit for Research Data and Information, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstraße 3, Erlangen, 91058, Germany","CRIS systems are an important resource about the university's scientists, research projects and publications. The functionality of FAU's CRIS was constantly extended by several additional entity types in the last years, such as affiliations, research infrastructure, internal applications and more. Most of them go beyond standards like CERIF or ""Kerndatensatz Forschung"", but follow their general principles. In this paper, we present the current state of our approach in supporting FAIR research data management in our CRIS. We achieve synergy effects between research data and research information, since some parts of research data's meta data are already contained in the CRIS.  © 2022 The Author(s).","Converis; CRIS; datacite; FAIR; meta data; research data management; workflow","Data integration; Information management; Converis; CRIS; Datacite; Entity-types; FAIR; Meta-data; Research data; Research data managements; Research infrastructure; Work-flows; Metadata","","","","","","","Melsheimer B., Walther M., Introducing CRIS at FAU, Procedia Computer Science, 106, pp. 239-244, (2016); Asserson A., Jefery K.G., Lopatenko A., CERIF: Past, Present and Future: An Overview, Proceedings of the 6th International Conference on Current Research Information Systems, pp. 33-40, (2002); Walther M., Melsheimer B., Automated author afliation processing using Scopus data, Procedia Computer Science, 146, pp. 53-59, (2019); Fenner M., Aryani A., Introducing the PID Graph (Version 1.0), (2019); Wilkinson M., Dumontier M., Aalbersberg I., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016)","M. Walther; FAU Competence Unit for Research Data and Information, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Martensstraße 3, 91058, Germany; email: marcus.walther@fau.de","Sicilia M.-A.; De-Castro P.; Vancauwenbergh S.; Simons E.; Ognjen O.","Elsevier B.V.","","15th International Conference on Current Research Information Systems, CRIS 2022","12 May 2022 through 14 May 2022","Dubrovnik","148668","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85145179166"
"Samuel S.; König-Ries B.","Samuel, Sheeba (57194280683); König-Ries, Birgitta (55864942100)","57194280683; 55864942100","A collaborative semantic-based provenance management platform for reproducibility","2022","PeerJ Computer Science","8","","e921","","","","2","10.7717/PEERJ-CS.921","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128316011&doi=10.7717%2fPEERJ-CS.921&partnerID=40&md5=2fb80af309962821549daa713c8cc648","Michael Stifel Center Jena, Jena, Germany; Heinz Nixdorf Chair for Distributed Information Systems, Friedrich-Schiller Universität Jena, Thuringia, Jena, Germany","Samuel S., Michael Stifel Center Jena, Jena, Germany, Heinz Nixdorf Chair for Distributed Information Systems, Friedrich-Schiller Universität Jena, Thuringia, Jena, Germany; König-Ries B., Michael Stifel Center Jena, Jena, Germany, Heinz Nixdorf Chair for Distributed Information Systems, Friedrich-Schiller Universität Jena, Thuringia, Jena, Germany","Scientific data management plays a key role in the reproducibility of scientific results. To reproduce results, not only the results but also the data and steps of scientific experiments must be made findable, accessible, interoperable, and reusable. Tracking, managing, describing, and visualizing provenance helps in the understandability, reproducibility, and reuse of experiments for the scientific community. Current systems lack a link between the data, steps, and results from the computational and non-computational processes of an experiment. Such a link, however, is vital for the reproducibility of results. We present a novel solution for the end-to-end provenance management of scientific experiments. We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility), which allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational data and steps in an interoperable way. CAESAR integrates the REPRODUCE-ME provenance model, extended from existing semantic web standards, to represent the whole picture of an experiment describing the path it took from its design to its result. ProvBook, an extension for Jupyter Notebooks, is developed and integrated into CAESAR to support computational reproducibility. We have applied and evaluated our contributions to a set of scientific experiments in microscopy research projects. © Copyright 2022 Samuel and König-Ries","Jupyter notebooks; Ontology; Provenance; Reproducibility; Research data management platform; Scientific experiments; Semantic web; Visualization","Data visualization; Information management; Collaborative environments; Jupyter notebook; Management platforms; Ontology's; Provenance; Reproducibilities; Research data management platform; Research data managements; Scientific experiments; Semantic-Web; Semantic Web","","","","","Jo?o Pimentel; Deutsche Forschungsgemeinschaft, DFG","Funding text 1: This research is supported by the Deutsche Forschungsgemeinschaft (DFG) in Project Z2 of the CRC/TRR 166 High-end light microscopy elucidates membrane receptor function -ReceptorLight. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; Funding text 2: We would like to thank all the participants who took part in this evaluation. We thank Christoph Biskup, Kathrin Groeneveld, Tom Kache, Teresa Langenst?ck from University Hospital Jena, Germany, and our colleagues: Martin B?cker, Frank Taubert and Daniel Walther for providing the requirements to develop the proposed approach and validating the system. We thank Jo?o Pimentel and the two anonymous reviewers for their helpful comments on the drafts of this manuscript.","Allan C, Burel J-M, Moore J, Blackburn C, Linkert M, Loynton S, MacDonald D, Moore WJ, Neves C, Patterson A, Porter M, Tarkowska A, Loranger B, Avondo J, Lagerstedt I, Lianas L, Leo S, Hands K, Hay RT, Patwardhan A, Best C, Kleywegt GJ, Zanetti G, Swedlow JR., OMERO: flexible, model-driven data management for experimental biology, Nature Methods, 9, 3, pp. 245-253, (2012); Altintas I, Berkley C, Jaeger E, Jones MB, Ludascher B, Mock S., Kepler: an extensible system for design and execution of scientific workflows, Proceedings ofthe 16th international conference on scientific and statistical database management (SSDBM 2004), pp. 423-424, (2004); Amstutz P, Crusoe MR, Tijanic N, Chapman B, Chilton J, Heuer M, Kartashov A, Leehr D, Menager H, Nedeljkovich M, Scales M, Soiland-Reyes S, Stojanovic L., Common workflow language, (2016); Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G., Gene ontology: tool for the unification ofbiology, Nature genetics, 25, 1, (2000); Baker M., 1,500 scientists lift the lid on reproducibility, Nature News, 533, 7604, (2016); Belhajjame K, Zhao J, Garijo D, Gamble M, Hettne K, Palma R, Mina E, Corcho O, Gmez-Prez JM, Bechhofer S, Klyne G, Goble C., Using a suite of ontologies for preserving workflow-centric research objects, Web Semantics: Science, Services and Agents on the World Wide Web, 32, pp. 16-42, (2015); BEXIS2 UserDevConf workshop on fostering reproducible science, (2017); Brank J, Grobelnik M, Mladenic D., A survey of ontology evaluation techniques, Proceedings ofthe conference on data miningand data warehouses (SiKDD 2005), pp. 166-170, (2005); Bruggemann S, Bereta K, Xiao G, Koubarakis M., Ontology-based data access for maritime security, The Semantic Web. 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VLDB Endow, 14, 6, pp. 1093-1101, (2021); Mayer R, Rauber A, Neumann MA, Thomson J, Antunes G., Preserving scientific processes from design to publications, Theory and practice ofdigital libraries, pp. 113-124, (2012); McPhillips T, Song T, Kolisnik T, Aulenbach S, Belhajjame K, Bocinsky RK, Cao Y, Cheney J, Chirigati F, Dey S, Freire J, Jones C, Hanken J, Kintigh KW, Kohler TA, Koop D, Macklin JA, Missier P, Schildhauer M, Schwalm C, Wei Y, Bieda M, Ludscher B., YesWorkflow: a user-oriented, language-independent tool for recovering workflow information from scripts, (2015); Murta L, Braganholo V, Chirigati F, Koop D, Freire J., noWorkflow: capturing and analyzing provenance of scripts, International provenance and annotation workshop, pp. 71-83, (2014); Noy NF, McGuinness DL., Ontology development 101: a guide to creating your first ontology, (2001); Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock MR, Wipat A, Li P., Taverna: a tool for the composition and enactment ofbioinformatics workflows, Bioinformatics, 20, 17, pp. 3045-3054, (2004); Oliveira W, Oliveira DD, Braganholo V., Provenance analytics for workflow-based computational experiments: a survey, 51, 3, (2018); Schema documentation for ome.xsd, (2016); Petricek T, Geddes J, Sutton C., Wrattler: reproducible, live and polyglot notebooks, 10th USENIX workshop on the theory and practice ofprovenance, TaPP 2018, (2018); Pimentel JaF, Murta L, Braganholo V, Freire J., noWorkflow: a tool for collecting, analyzing, and managing provenance from python scripts, Proc. 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Samuel; Michael Stifel Center Jena, Jena, Germany; email: sheeba.samuel@uni-jena.de","","PeerJ Inc.","","","","","","23765992","","","","English","PeerJ Comput. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128316011"
"Sarntivijai S.; Blomberg N.; Lauer K.B.; Briggs K.; Steger-Hartmann T.; van der Lei J.; Sauer J.-M.; Liwski R.; Mourby M.; Camprubi M.","Sarntivijai, Sirarat (25646223300); Blomberg, Niklas (6603560379); Lauer, Katharina B. (56600132300); Briggs, Katharine (23979276700); Steger-Hartmann, Thomas (6603592872); van der Lei, Johan (56592306600); Sauer, John-Michael (57196827179); Liwski, Richard (56893528000); Mourby, Miranda (57200407222); Camprubi, Montse (57696291400)","25646223300; 6603560379; 56600132300; 23979276700; 6603592872; 56592306600; 57196827179; 56893528000; 57200407222; 57696291400","eTRANSAFE: Building a sustainable framework to share reproducible drug safety knowledge with the public domain","2022","F1000Research","11","","287","","","","0","10.12688/f1000research.74024.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130266576&doi=10.12688%2ff1000research.74024.1&partnerID=40&md5=30fda02523301a4e5973f693680c935f","ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom; Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom; Bayer AG, Research and Development, Pharmaceuticals, Investigational Toxicology, Berlin, 13342, Germany; Department of Medical Informatics, Erasmus University Rotterdam, EUR - Erasmus Medical Center (MC), Rotterdam, Netherlands; Predictive Safety Testing Consortium, Critical Path Institute, Tucson, Arizona, 85718, United States; Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, OX2 7DD, United Kingdom; Synapse Research Management Partners S.L., C. Diputació 237, Atic 3a, Barcelona, 08007, Spain; UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom","Sarntivijai S., ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Blomberg N., ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Lauer K.B., ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Briggs K., Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Steger-Hartmann T., Bayer AG, Research and Development, Pharmaceuticals, Investigational Toxicology, Berlin, 13342, Germany, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; van der Lei J., Department of Medical Informatics, Erasmus University Rotterdam, EUR - Erasmus Medical Center (MC), Rotterdam, Netherlands, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Sauer J.-M., Predictive Safety Testing Consortium, Critical Path Institute, Tucson, Arizona, 85718, United States, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Liwski R., Predictive Safety Testing Consortium, Critical Path Institute, Tucson, Arizona, 85718, United States, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Mourby M., Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, OX2 7DD, United Kingdom, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom; Camprubi M., Synapse Research Management Partners S.L., C. Diputació 237, Atic 3a, Barcelona, 08007, Spain, UK Research and Innovation, Polaris House, North Star Avenue, Swindon, SN2 1FL, United Kingdom","Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combination to aid meaningful interpretation and analysis of such data. The eTRANSAFE Consortium's effort to drive integrative drug safety research at a large scale hereby present the lessons learnt and the proposal of solution at the guidelines in practice at this Innovative Medicines Initiative (IMI) project. Recommendations in these guidelines were compiled from feedback received from key stakeholders in regulatory agencies, EFPIA companies, and academic partners. The research reproducibility guidelines presented in this study lay the foundation for a comprehensive data sharing and knowledge management plans accounting for research data management in the drug safety space - FAIR data sharing guidelines, and the model verification guidelines as generic deliverables that best practices that can be reused by other scientific community members at large. FAIR data sharing is a dynamic landscape that rapidly evolves with fast-paced technology advancements. The research reproducibility in drug safety guidelines introduced in this study provides a reusable framework that can be adopted by other research communities that aim to integrate public and private data in biomedical research space. © 2022 Sarntivijai S et al.","Data sharing; Drug Safety; ETRANSAFE; FAIR Data; Interoperability; Model validation; Research reproducibility","Biomedical Research; Information Dissemination; Metadata; Public Sector; Reproducibility of Results; Article; clinical outcome; data processing; decision making; disease registry; drug industry; drug manufacture; drug safety; electronic health record; feedback system; health insurance; human; knowledge management; medical informatics; medical research; peer review; personalized medicine; practice guideline; quality control; reproducibility; stakeholder engagement; information dissemination; medical research; metadata; public sector","","","","","","","Cases M., Et al., The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction, Int. J. Mol. Sci, 15, pp. 21136-21154, (2014); Pastor M., Quintana J., Sanz F., Development of an Infrastructure for the Prediction of Biological Endpoints in Industrial Environments. Lessons Learned at the eTOX Project, Front. Pharmacol, 9, (2018); Bjerregaard T.G., Et al., BioCelerate Toxicology Data Sharing initiative: Development of a centralized, searchable Preclinical Data Repository for the Biopharmaceutical Industry, Toxicol. Lett, 295, (2018); Cave A., Brun N.C., Sweeney F., Et al., Big Data - How to Realize the Promise, Clinical Pharmacology & Therapeutics; FDA's Predictive Toxicology Roadmap, Updated December, (2017); (2019); Bycroft C., Et al., The UK Biobank resource with deep phenotyping and genomic data, Nature, 562, pp. 203-209, (2018); Hrabovszki G., EMA annual report 2018 published, (2019); (2019); Woodcock J., Woosley R., The FDA Critical Path Initiative and Its Influence on New Drug Development, Annu. Rev. Med, 59, pp. 1-12, (2008); Wood F., Guinter T., Evolution and Implementation of the CDISC Study Data Tabulation Model (SDTM), Pharmaceutical Programming, 1, pp. 20-27, (2008); Malone J., Parkinson H., Reference and Application Ontologies, Ont. Dent, (2010); Moss G.P., Smith P.A.S., Tavernier D., Glossary of class names of organic compounds and reactivity intermediates based on structure (IUPAC Recommendations 1995), Pure Appl. Chem, 67, pp. 1307-1375, (1995); Willighagen E.L., Et al., The ChEMBL database as linked open data, J. Cheminform, 5, (2013); Smith B., Et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration, Nat. Biotechnol, 25, pp. 1251-1255, (2007); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Orr M.S., Goodsaid F., Amur S., Et al., The experience with voluntary genomic data submissions at the FDA and a vision for the future of the voluntary data submission program, Clin. Pharmacol. Ther, 81, pp. 294-297, (2007); Alterovitz G., Et al., Enabling precision medicine via standard communication of HTS provenance, analysis, and results, PLoS Biol, 16, (2018); Elisabet B., Et al., Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making, Environ. Health Perspect, 123, pp. 1232-1240, (2015); Raies A.B., Bajic V.B., In silico toxicology: computational methods for the prediction of chemical toxicity, WIREs Comput. Mol. Sci, 6, pp. 147-172, (2016); Sanz F., Et al., Legacy data sharing to improve drug safety assessment: the eTOX project, Nat. Rev. Drug Discov, 16, pp. 811-812, (2017); Mackey E., Elliot M., Understanding the Data Environment, XRDS, 20, pp. 36-39, (2013); Parla V.E., Pescatore B.H., Champagne T.S., Multiple application containerization in a single container, (2015); Newbury A.D., Stackable container for use in a containerization system, (1985); Turnbull J., The Docker Book: Containerization Is the New Virtualization, (2014); Ball R., Robb M., Anderson S.A., Et al., The FDA's sentinel initiative A comprehensive approach to medical product surveillance, Clinical Pharmacology & Therapeutics, 99, pp. 265-268, (2016); Sarntivijai S., Blomberg N., Lauer K.B., Et al., Suggestion for the eTRANSAFE data sharing and knowledge management guidelines that support long-term sustainability and research reproducibility [version 1; not peer reviewed], F1000Res, 10, (2021)","S. Sarntivijai; ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom; email: sirarat.sarntivijai@elixir-europe.org","","F1000 Research Ltd","","","","","","20461402","","","35602243","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130266576"
"Fu P.; Blackson M.; Valentino M.","Fu, Ping (57859332800); Blackson, Maurice (57222198623); Valentino, Maura (57858253700)","57859332800; 57222198623; 57858253700","Developing research data management services in a regional comprehensive university: The case of Central Washington University","2022","IFLA Journal","","","","","","","0","10.1177/03400352221116923","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136585022&doi=10.1177%2f03400352221116923&partnerID=40&md5=1fe7079f3c38cc2dd2e7606ca071a19d","University Libraries, Central Washington University, United States","Fu P., University Libraries, Central Washington University, United States; Blackson M., University Libraries, Central Washington University, United States; Valentino M., University Libraries, Central Washington University, United States","This study aims to analyze the needs of researchers in a regional comprehensive university for research data management services; discuss the options for developing a research data management program at the university; and then propose a phased three-year implementation plan for the university libraries. The method was to design a survey to collect information from researchers and assess and evaluate their needs for research data management services. The results show that researchers’ needs in a regional comprehensive university could be quite different from those of researchers in a research-intensive university. Also, the results verify the hypothesis that researchers in the regional comprehensive university would welcome the libraries offering managed data services for the research community. Therefore, this study suggests a phased three-year implementation plan. The significance of the study is that it can give some insights and helpful information for regional comprehensive universities that are planning to develop a research data management program. © The Author(s) 2022.","needs assessment and evaluation; regional comprehensive university; Research data management services","","","","","","","","Aaronson S.A., Leblond P., Another digital divide: The rise of data realms and its implications for the WTO, Journal of International Economic Law, 21, 2, pp. 245-272, (2018); Antell K., Foote J.B., Turner J., Et al., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries Institutions, College and Research Libraries, 75, 4, pp. 557-574, (2014); Bervell B., Umar I.N., Blended learning or face-to-face? Does tutor anxiety prevent the adoption of learning management systems for distance education in Ghana?, Open Learning, 35, 2, pp. 159-177, (2020); Borghi J.A., Van Gulick A.E., Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers, PloS ONE, 13, 7, (2018); Bornmann L., Haunschild R., Mutz R., Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases, Humanities and Social Sciences Communications, 8, (2021); Conrad S., Shorish Y., Whitmire A.L., Et al., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Cortes Sanchez J.D., Mission statements of universities worldwide: Text mining and visualization, Intangible Capital, 14, 4, pp. 584-603, (2018); Costello L., Survey of Canadian academic librarians outlines integration of traditional and emerging services, Evidence-Based Library and Information Practice, 15, 3, pp. 184-186, (2020); Cox A.M., Kennan M.A., Lyon L., Et al., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Davis H.M., Cross W.M., Using a data management plan review service as a training ground for librarians, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Dietrich D., Adamus T., Miner A., Et al., De-mystifying the data management requirements of research funders, Issues in Science and Technology Librarianship, (2012); Ducas A., Michaud-Oystryk N., Speare M., Reinventing ourselves: New and emerging roles of academic librarians in Canadian research-intensive universities, College and Research Libraries, 81, 1, pp. 43-65, (2020); Ellis E.M., Faculty participation in the Pennsylvania State University world campus: Identifying barriers to success, Open Learning, 15, 3, pp. 233-242, (2000); Fan W., Yan Z., Factors affecting response rates of the web survey: A systematic review, Computers in Human Behavior, 26, 2, pp. 132-139, (2010); Flannery M., Budden D.M., Mendes A., Flex DM: Simple, parallel and fault-tolerant data mining using WEKA, Source Code for Biology and Medicine, 10, 1, pp. 13-17, (2015); Grajek S., Research and data services for higher education information technology: Past, present, and future, EDUCAUSE Review, 46, 6, pp. 46-60, (2011); Hey T., Trefethen A., The data deluge: An e-science perspective, Grid Computing: Making the Global Infrastructure a Reality, pp. 809-903, (2003); Hinrichs U., Alex B., Clifford J., Et al., Trading consequences: A case study of combining text mining and visualization to facilitate document exploration, Digital Scholarship in the Humanities, 30, pp. i50-i75, (2015); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship and Information Science, 52, 3, pp. 633-646, (2020); Joo S., Schmidt G.M., Research data services from the perspective of academic librarians, Digital Library Perspectives, 37, 3, pp. 242-256, (2021); Kantardzic M., Data Mining: Concepts, Models, Methods, and Algorithms, (2011); Kim J., Determining research data services maturity: The role of library leadership and stakeholder involvement, Library and Information Science Research, 43, 2, (2021); Koltay T., Research 2.0 and research data services in academic and research libraries: Priority issues, Library Management, 38, 6-7, pp. 345-353, (2017); Lembinen L., Lupold A., Johnston L., Optimizing library services—research data supporting services that libraries can offer based on the experience of the University of Tartu Library, 29, 1, (2017); Mancilla H.A., Teperek M., Van Dijck J., Et al., On a quest for cultural change: Surveying research data management practices at Delft University of Technology, LIBER Quarterly, 29, 1, pp. 1-27, (2019); Mannheimer S., Pienta A., Kirilova D., Et al., Qualitative data sharing: Data repositories and academic libraries as key partners in addressing challenges, American Behavioral Scientist, 63, 5, pp. 643-664, (2019); Medina-Smith A., Becker C.A., Plante R.L., Et al., A controlled vocabulary and metadata schema for materials science data discovery, Data Science Journal, 20, 1, pp. 18-28, (2021); Muellenbach J.M., A pilot to initiate research data management services within academic libraries helps librarians to learn about, engage with, and enhance skills within their research communities, Evidence-Based Library and Information Practice, 16, 1, pp. 104-106, (2021); Murray M., O'Donnell M., Laufersweiler M.J., Et al., A survey of the state of research data services in 35 US academic libraries, or “Wow, what a sweeping question, Research Ideas and Outcomes, 5, (2019); Memorandum for the heads of executive departments and agencies: Increasing access to the results of federally funded scientific research, (2013); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership, 13, 1, pp. 1-23, (2018); Perrier L., Blondal E., Ayala A.P., Et al., Research data management in academic institutions: A scoping review, PloS ONE, 12, 5, (2017); Si L., Xing W., Zhuang X., Et al., Investigation and analysis of research data services in university libraries, Electronic Library, 33, 3, pp. 417-449, (2015); Si L., Zeng Y., Guo S., Et al., Investigation and analysis of research support services in academic libraries, Electronic Library, 37, 2, pp. 281-301, (2019); Tenopir C., Dalton E.D., Allard S., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PloS ONE, 10, 8, (2015); Tenopir C., Sandusky R.J., Allard S., Et al., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Et al., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Tsai W.-H., Chen J.-Y., The new version of Government Research Bulletin (GRB) website applying visualization and text-mining tools, Procedia Computer Science, 146, pp. 94-101, (2019); Vines T.H., Albert A.Y.K., Andrew R.L., Et al., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Wachowicz E., Wharton Research Data Services (WRDS), Journal of Business and Finance Librarianship, 25, 3-4, pp. 184-187, (2020); Watson R., EU pilot project promotes sharing of research data, BMJ, 348, (2014); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices, Program, 49, 4, pp. 382-407, (2015); Willaert T., Cottyn J., Kenens U., Et al., Research data management and the evolutions of scholarship: Policy, infrastructure and data literacy at KU Leuven, LIBER Quarterly, 29, 1, pp. 1-19, (2019); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College and Research Libraries, 78, 7, pp. 920-933, (2017); Yu H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, Electronic Library, 35, 4, pp. 783-797, (2017)","P. Fu; University Libraries, Central Washington University, United States; email: Ping.fu@cwu.edu","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Article in press","All Open Access; Green Open Access","Scopus","2-s2.0-85136585022"
"","","","International Symposium on Grids and Clouds 2021, ISGC 2021","2021","Proceedings of Science","378","","","","","370","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118971222&partnerID=40&md5=774769c3a0b751c6a98b2c68ebe5523a","","","The proceedings contain 25 papers. The topics discussed include: a possible solution for hep processing on network secluded computing nodes; enabling HPC systems for HEP: the INFN-CINECA experience; HPC-cloud-big data convergent architectures and research data management: the LEXIS approach; deep learning fast inference on FPGA for CMS Muon level-1 trigger studies; architecture of job scheduling simulator for demand response-based resource provisioning; scalable computing in Java with PCJ library. Improved collective operations; a big data platform for heterogeneous data collection and analysis in large-scale data centers; and reinforcement learning for smart caching at the CMS experiment.","","","","","","","","","","","","Sissa Medialab Srl","","2021 International Symposium on Grids and Cloud, ISGC 2021","22 March 2021 through 26 March 2021","Taipei","173412","18248039","","","","English","Proc. Sci.","Conference review","Final","","Scopus","2-s2.0-85118971222"
"Zieliński T.; Hay J.; Romanowski A.; Nenninger A.; Mccormick A.; Millar A.J.","Zieliński, Tomasz (56181435000); Hay, Johnny (57211125119); Romanowski, Andrew (24471921100); Nenninger, Anja (8833388200); Mccormick, Alistair (8351094400); Millar, Andrew J. (7201856684)","56181435000; 57211125119; 24471921100; 8833388200; 8351094400; 7201856684","SynBio2Easy- A biologist-friendly tool for batch operations on SBOL designs with Excel inputs","2022","Synthetic Biology","7","1","ysac002","","","","1","10.1093/synbio/ysac002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125181188&doi=10.1093%2fsynbio%2fysac002&partnerID=40&md5=babc482858117e9177f657cdfe4da765","SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; EPCC, University of Edinburgh, Edinburgh, United Kingdom","Zieliński T., SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; Hay J., EPCC, University of Edinburgh, Edinburgh, United Kingdom; Romanowski A., SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; Nenninger A., SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; Mccormick A., SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; Millar A.J., SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom","Practical delivery of Findable, Accessible, Reusable and Interoperable principles for research data management requires expertise, time resource, (meta)data standards and formats, software tools and public repositories. The Synthetic Biology Open Language (SBOL2) metadata standard enables FAIR sharing of the designs of synthetic biology constructs, notably in the repository of the SynBioHub platform. Large libraries of such constructs are increasingly easy to produce in practice, for example, in DNA foundries. However, manual curation of the equivalent libraries of designs remains cumbersome for a typical lab researcher, creating a barrier to data sharing. Here, we present a simple tool SynBio2Easy, which streamlines and automates operations on multiple Synthetic Biology Open Language (SBOL) designs using Microsoft Excel® tables as metadata inputs. The tool provides several utilities for manipulation of SBOL documents and interaction with SynBioHub: For example, generation of a library of plasmids based on an original design template, bulk deposition into SynBioHub, or annotation of existing SBOL component definitions with notes and authorship information. The tool was used to generate and deposit a collection of 3661 cyanobacterium Synechocystis plasmids into the public SynBioHub repository. In the process of developing the software and uploading these data, we evaluated some aspects of the SynBioHub platform and SBOL ecosystem, and we discuss proposals for improvement that could benefit the user community. With software such as SynBio2Easy, we aim to deliver a user-driven tooling to make FAIR a reality at all stages of the project lifecycle in synthetic biology research. © 2021 The Author(s). Published by Oxford University Press.","research data management; SBOL; software tools; spreadsheet; SynBioHub; synthetic biology","Computer software reusability; Information management; Libraries; Life cycle; Metadata; Batch operation; Excel; Language design; Meta-data; Open languages; Research data managements; Synbiohub; Synthetic biology; Synthetic biology open language; Synthetic biology","","","","","","","Wilkinson M.D., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N, Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Guiding Principles for Findable, Accessible, Interoperable and Re-usable Data Publishing version b1.0, (2020); McLaughlin J.A., Myers C.J., Zundel Z., Msrl G., Zhang M., Ofiteru I.D., Goni-Moreno A., Wipat A., SynBioHub: A standards-enabled design repository for synthetic biology, ACS Synth. Biol, 7, pp. 682-688, (2018); ICE Documentation. Inventory of Composable Elements, (2021); Urquiza-Garcia U., Zielinski T., Millar A.J., Better research by efficient sharing: Evaluation of free management platforms for synthetic biology designs, Synth. Biol, 4, (2019); RDF 1.1 Concepts and Abstract Syntax, (2014); SPARQL 1.1 Query Language. SPARQL 1.1 Query Language, (2013); The Synthetic Biology Open Language, The Synthetic Biology Open Language, (2021); GenBank Overview; Pearson W.R., Lipman D.J., Improved tools for biological sequence comparison, Proc. Natl Acad. Sci. USA, 85, pp. 2444-2448, (1988); Kaneko T., Structural analysis of four large plasmids harboring in a unicellular cyanobacterium, Synechocystis sp; Lea-Smith D.J., Vasudevan R., Howe C.J., Generation of marked and markerless mutants in model cyanobacterial species, J. Vis. Exp, 111, pp. 54001-540013, (2016); (2021); Zhang M., McLaughlin J.A., Wipat A., Myers C.J., SBOLDesigner 2: An intuitive tool for structural genetic design, ACS Synth. Biol, 6, pp. 1150-1160, (2017); Crowther M., Grozinger L., Pocock M., Taylor C.P.D., McLaughlin J.A., Msrl G., Bartley B.A., Beal J., Goni-Moreno A., Wipat A., ShortBOL, (2021); Microsoft Excel Spreadsheet Software, (2021); SynBioDex/Excel-to-SBOL, (2021); Java Software; Zielinski T., Hay J., BioRDM/sbol2easy, (2021); Zielinski T., Hay J., BioRDM/synbio2easy, (2021); (2020); Zhang Z., Nguyen T., Roehner N., Misirli G., Pocock M., Oberortner E., Samineni M., Zundel Z., Beal J., Clancy K., Et al., libSBOLj 2.0: A Java library to support SBOL 2.0, IEEE Life Sci. Lett, 1, pp. 34-37, (2015); Spring Boot; Maven A., (2021); (2021); SynBioHub API Documentation; ComponentDefinition Class Reference; McCormick A., cyano_coda_km_collection, (2021); Ho T.Y.H., Shao A., Lu Z., Savilahti H., Menolascina F., Wang L., Dalchau N., Wang B., A systematic approach to inserting split inteins for Boolean logic gate engineering and basal activity reduction, Nat. Commun, 12, (2021); Ho T.Y.H., Intein_assisted_Bisection_Mapping, (2020); SnapGene; ModuleDefinition Class Reference; The iGEM Registry Team Collections; Lee J.H., Skowron P.M., Rutkowska S.M., Hong S.S., Kim S.C., Sequential amplification of cloned DNA as tandem multimers using class-IIS restriction enzymes, Genet. Anal, 13, pp. 139-145, (1996); Vasudevan R., Gale G.A.R., Schiavon A.A., Puzorjov A., Malin J., Gillespie M.D., Vavitsas K., Zulkower V., Wang B., Howe C.J., Et al., CyanoGate: A modular cloning suite for engineering cyanobacteria based on the plant MoClo syntax, Plant Physiol, 180, pp. 39-55, (2019); Representational state transfer, (2021); OpenAPI Specification-Version 3.0.3","A.J. Millar; SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; email: Andrew.Millar@ed.ac.uk","","Oxford University Press","","","","","","19397267","","","","English","Synth. Biol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85125181188"
"Garcia P.O.; Berberi L.; Candela L.; Van Nieuwerburgh I.; Lazzeri E.; Czuray M.","Garcia, Paula Oset (57210153631); Berberi, Lisana (57844279900); Candela, Leonardo (14008282700); Van Nieuwerburgh, Inge (57223042953); Lazzeri, Emma (57907936000); Czuray, Marie (57907338200)","57210153631; 57844279900; 14008282700; 57223042953; 57907936000; 57907338200","Developing the EOSC-Pillar RDM Training and Support Catalogue","2022","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13541 LNCS","","","274","281","7","0","10.1007/978-3-031-16802-4_22","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138781819&doi=10.1007%2f978-3-031-16802-4_22&partnerID=40&md5=45454d1d9464df279871a7e5575ae582","Ghent University, Ghent University Library, Rozier 9, Ghent, 9000, Belgium; Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, Karlsruhe, 76128, Germany; Institute of Information Science and Technologies “Alessandro Faedo”, via G. Moruzzi 1, Pisa, 56124, Italy; Consortium GARR, Via dei Tizii, 6, Roma, 00185, Italy; Vienna University, Vienna University Library and Archive Services, Universitätsring 1, Wien, 1010, Austria","Garcia P.O., Ghent University, Ghent University Library, Rozier 9, Ghent, 9000, Belgium; Berberi L., Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, Karlsruhe, 76128, Germany; Candela L., Institute of Information Science and Technologies “Alessandro Faedo”, via G. Moruzzi 1, Pisa, 56124, Italy; Van Nieuwerburgh I., Ghent University, Ghent University Library, Rozier 9, Ghent, 9000, Belgium; Lazzeri E., Consortium GARR, Via dei Tizii, 6, Roma, 00185, Italy; Czuray M., Vienna University, Vienna University Library and Archive Services, Universitätsring 1, Wien, 1010, Austria","Today’s many research infrastructures and European projects offer training catalogues to store and list multiple forms of learning materials. In EOSC-Pillar project we propose a web application catalogue, which consists of training materials as well as day-to-day operational resources with the aim to support data stewards and other RDM (research data management), FAIR data (findable, accessible, interoperable, reusable) and open science actors. In this paper we briefly describe the scope and technical implementation of the EOSC-Pillar RDM Training and Support Catalogue and how we are addressing current challenges such as metadata standards, controlled vocabularies, curation, quality checking and sustainability. © 2022, Springer Nature Switzerland AG.","Catalogue; EOSC; FAIR; Open science; Research data management; Training material; Virtual research environment","Computer software reusability; E-learning; Information management; Catalog; EOSC; FAIR; Management support; Management training; Open science; Research data managements; Research infrastructure; Training material; Virtual research environment; Sustainable development","","","","","Horizon 2020 Framework Programme, H2020, (857650)","Acknowledgments. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under EOSC-Pillar project (grant agreement No. 857650).","Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Directorate-General for Research and Innovation, Digital Skills for FAIR and Open Science: Report from the EOSC Executive Board Skills and Training Working Group. Publications Office, (2021); Directorate-General for Research and Innovation, Realising the European Open Science Cloud: First Report and Recommendations of the Commission High Level Expert Group on the European Open Science Cloud. Publications Office, (2016); Horizon Europe, (2021); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Directorate-General for Research and Innovation. Turning FAIR into Reality: Final Report and Action Plan From the European Commission Expert Group on FAIR Data. Publications, Office, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Candela L., Castelli D., Pagano P., Virtual research environments: An overview and a research agenda, Data Sci. J., 12, 175, (2013); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Assante M., Et al., Enacting open science by D4Science, Futur. Gener. Comput. Syst., 101, pp. 555-563, (2019); Cazenave N., Candela L., Berberi L., van Wezel J., Hashibon A., Le Franc Y., Eosc-Pillar D5.1 FAIR Research Data Management Tool Set, (2020); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Candela L., Frosini L., Le Franc Y., Mangiacrapa F., Cazenave N., Eosc-Pillar D5.2 FAIR Research Data Management Workbench Operation Report, (2021); Newbold E., Et al., D6, 2, (2020); Oset Garcia P., Lazzeri E., van Nieuwerburgh I., von Hartrott P., Geistberger J., Eosc-Pillar D5, 4, (2021); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Hoebelheinrich N., Recommendations for a Minimal Metadata Set to Aid Harmonised Discovery of Learning Resources, (2022); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Leenarts E., OSF Workshop Resources RDM Training and Support Catalogues (21 September 2021), Zenodo, (2021); Newbold E., Kayumbi G., Whyte A., Lazzeri E., Summary Report: Workshop on Harmonising Training Resource Metadata for EOSC Communities, (2021); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Whyte A., D7.3: Skills and Capability Framework, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018)","P.O. Garcia; Ghent University, Ghent University Library, Ghent, Rozier 9, 9000, Belgium; email: Paula.OsetGarcia@UGent.be","Silvello G.; Corcho O.; Manghi P.; Di Nunzio G.M.; Golub K.; Ferro N.; Poggi A.","Springer Science and Business Media Deutschland GmbH","","26th International Conference on Theory and Practice of Digital Libraries, TPDL 2022","20 September 2022 through 23 September 2022","Padua","283349","03029743","978-303116801-7","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85138781819"
"Kalu C.O.; Chidi-Kalu E.I.; Mafe T.A.","Kalu, Chidi Onuoha (57221591478); Chidi-Kalu, Esther Ihechiluru (57221590277); Mafe, Titilola Abigail (57573347700)","57221591478; 57221590277; 57573347700","Research data management in an academic library","2021","Handbook of Research on Information and Records Management in the Fourth Industrial Revolution","","","","38","55","17","1","10.4018/978-1-7998-7740-0.ch003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128039509&doi=10.4018%2f978-1-7998-7740-0.ch003&partnerID=40&md5=bd02a1cfb1eecc431024d381eeb42630","Library Department, National Institute of Construction Technology and Management, Uromi, Nigeria; Nigerian Library Association, Nigeria; National Library of Nigeria Headquarters, Abuja, Nigeria","Kalu C.O., Library Department, National Institute of Construction Technology and Management, Uromi, Nigeria; Chidi-Kalu E.I., Nigerian Library Association, Nigeria; Mafe T.A., National Library of Nigeria Headquarters, Abuja, Nigeria","Academic libraries need to store, preserve, and manage scholars' intellectual output, hence the importance of research data management in academic libraries. This chapter focuses on research data management in academic libraries, and it aims at examining the concept of research data, which is referred to as the evidence used to inform or support research conclusions, while data management, on the other hand involves planning for and creating data, organizing, structuring, and documenting data, backing up and storing data, and preparing data for analysis to share with others or to preserve for the long-term. © 2021, IGI Global.","","","","","","","","","Top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 75, 6, pp. 294-308, (2014); Australian code for responsible research, (2015); Ball J., Research data management for libraries: Getting started, Insights, 26, 3, pp. 256-260, (2013); Vision and mission, (2015); Carlson J.R., Garritano J.R., E-science, cyber-infrastructure and the changing face of scholarship: Organizing for new models of research support at Purdue University Libraries, Libraries Research Publications, 137, (2010); Chabot L., Bivens-Tatum W., Coates H., Kern M., Top trends in academic libraries A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 77, 6, pp. 274-281, (2016); Chiware E., Mathe Z., Academic libraries' role in research data management services: A South African perspective, South African Journal of Library and Information Science, 81, 2, pp. 1-10, (2016); Constanze C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DB project database program, Electronic Library and Information System, 49, 4, pp. 494-512, (2015); Corti L., van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2011); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Davidson J., Jones S., Molloy L., Kejser U.B., Emerging good practice in managing research data and research information in UK Universities, Procedia Computer Science, 33, pp. 215-222, (2014); Gaietto M., What is data management? Thought Leadership, (2018); Halbert M., Prospects for research data management, Research data management: Principles, practices, and prospects, (2013); Hey T., Tansley S., Tolle K.M., The fourth paradigm: Data intensive scientific discovery (point of view), Proceedings of the IEEE, 99, 8, pp. 1334-1337, (2011); Hoare C.A.R., Communicating sequential processes, (2004); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Knight M., What is data management?, (2017); Koontz H., Weihrich H., Essential of management: An international perspective, (2010); Lewis M.J., Libraries and the management of research data, Envisioning future academic library services, (2010); University M., Research data management policy, (2013); Research data management policy, (2016); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Rice R., Final report of the DISC-UK DataShare project, (2009); Sanjeeva M.,  Sept. 2014, (2018); Stedman C., Vaughan J., What is data management and why is it important?, (2019); Surkis A., Read K., Research data management, Journal of the Medical Library Association: JMLA, 103, 3, pp. 154-156, (2015); Tammaroa A.M., Casarosab V., Research data management in the curriculum: An interdisciplinary approach. 10th Italian Research Conference on Digital Libraries, IRCDL, Procedia Computer Science, 38, pp. 138-142, (2014); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future: An ACRL white paper, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-81, (2013); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tripathi M., Shukla A., Sonker S.K., Research data management practices in university libraries: A study, DESIDOC Journal of Library and Information Technology, 37, 6, pp. 417-424, (2017); University of Leeds research data management policy, (2016); The university of Manchester research data management policy, (2016); van Berchum M., Grootveld M., Research data management. An overview of recent developments in the Netherland, (2017); Whyte A., Tedds J., Making the case for research data management. DCC Briefing Papers, (2011); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College & Research Libraries, 78, 7, pp. 920-933, (2017); University Y., Benefits of research data management, (2021); Yusuf F., Iwu J., Use of academic library: A case study of Covenant University, Nigeria, Chinese Librarianship: An International. The Electricity Journal, (2010)","","","IGI Global","","","","","","","978-179987742-4; 978-179987740-0","","","English","Handb. of Res. on Inf. and Rec. Manag. in the Fourth Ind. Revolut.","Book chapter","Final","","Scopus","2-s2.0-85128039509"
"Yadav A.K.S.","Yadav, Akhilesh K. S. (57188718086)","57188718086","An Evaluation of Library and Information Science Curricula and Professional Perspectives in India","2022","International Information and Library Review","54","3","","242","254","12","0","10.1080/10572317.2021.1988393","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117188432&doi=10.1080%2f10572317.2021.1988393&partnerID=40&md5=05cd3b09451a6278eadae1759c48b331","Centre for Library and Information Management Studies (CLIMS), SDTM Library, Tata Institute of Social Sciences, Mumbai, India","Yadav A.K.S., Centre for Library and Information Management Studies (CLIMS), SDTM Library, Tata Institute of Social Sciences, Mumbai, India","The purpose of this research study is to identify the core, advanced, specialized, and soft skills courses offered as part of the various library and information science programs. The course content of the Master of Library and Information Science (MLIS), offered in 2019–2020, was collected and analyzed from 10 LIS schools in India. Course contents were analyzed to understand whether the LIS schools are preparing graduates for the digital environment. A survey was conducted among 42 experts in library and information science on required courses in the MLIS program. Research finds that the LIS schools do not a have separate course module on digital library and that it is, instead, integrated and taught with the information and communication technology (ICT) course. There are also variations among the LIS schools in the allocation of topics and credits in each course. The experts opined that schools should allocate more credits to the advanced course, introduce new courses, and update the curricula for the future job market. New courses like ‘research data management’ and ‘data and visual literacy’ and courses on soft skills should be introduced in the MLIS program with 2–3 credits. There is a mismatch between the skills required in the job market and the skills imparted by the Indian LIS schools. The LIS schools should fill in the gap by introducing advanced, specialized, and soft courses in the MLIS program. The research findings have practical implications for the library and information science programs and educators. The LIS schools in India offer courses to meet the local job market demands, but they should design and develop courses for meeting the global standards and needs. © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.","Content analysis; course contents; course curricula; library and information science; LIS syllabi","","","","","","MS-LIS; Research Council of Tata Institute of Social Sciences","Funding text 1: The proliferation of library and information science education became noticeable during the 1980s. In the late 1980s, many universities came up with correspondence courses at various levels of LIS education. One such great initiative was the Indira Gandhi National Open University, New Delhi, introducing BLIS in 1989. The university has played an important role in expanding the LIS education all over the country. It expanded later to MLIS, PhD, and PGDLAN (one-year postgraduate diploma) and is well-known for maintaining a high-quality print course material (Singh & Babbar, ). The number of universities engaged in LIS education has gone up in recent years. A total of 234 institutions offer LIS education in India, of which, 175 offer courses in the regular mode, and 59 offer courses through the distance mode. The courses are offered at various levels, vis., certificate, diploma, post graduate diploma (Post Graduate Diploma in Digital Library and Information Management, Post Graduate Diploma in Digital Library and Data Management, Post Graduate Diploma in Library Automation and Networking, etc.), master’s degree (MLIS, M.Sc. (LIS), MS-LIS, MIS, etc.), and research degree (M.Phil., Ph.D., Postdoctoral Fellowship, and D.Litt. in Library and Information Science) (Singh & Babbar, ). ; Funding text 2: This research was supported by the Research Council of Tata Institute of Social Sciences, Mumbai.","Al-Shwabkah Y., Hamad F., Taha N., Al-Fadel M., The integration of ICT in library and information science curriculum analytical study of students’ perception in Jordanian Universities, Library Review, 65, 6-7, pp. 461-478, (2016); Ameen K., Erdelez S., Instructing usability evaluation in LIS curriculum: A case of the US, Pakistan Journal of Information Management and Libraries, 12, pp. 1-7, (2011); Bronstein J., An exploration of the library and information science professional skills and personal competencies: An Israeli perspective, Library & Information Science Research, 37, 2, pp. 130-138, (2015); Chandrashekara M., Ramasesh C.P., (2009); Dodson M., On target or missing the mark? Instruction courses in LIS graduate programs, Public Services Quarterly, 16, 2, pp. 83-94, (2020); Gokhale P., Library and information science education in Maharashtra: A perspective, DESIDOC Journal of Library & Information Technology, 30, 5, pp. 48-55, (2010); Hallam G., Calvert P., LIS education, Global library and information science, pp. 288-304, (2009); Hider P., Kennan M.A., Hay L., McCausland S., Qayyum A., Moving from LIS to IS + L: Curriculum renewal at Charles Sturt University, The Australian Library Journal, 60, 3, pp. 205-217, (2011); Hussain F., Ansari M.N., Professionals’ satisfaction and dissatisfaction with LIS curricula in Pakistan, Journal of History and Social Sciences, 8, 2, pp. 81-108, (2017); Hussain F., Ansari M.N., Content analysis of library & information science (LIS) curricula in Pakistani Universities, Journal of History and Social Sciences, 9, 1, pp. 1-32, (2018); Kamba M.A., ICT competency framework for library and information science schools in Nigeria: The need for model curriculum, International Journal of Library and Information Science, 3, 4, pp. 68-80, (2011); Kennan M.A., Carroll M., Thompson K.M., Letting go, holding on, or re-envisioning? Challenges and opportunities for LIS education in Australia, Re-envisioning the MLS: Perspectives on the future of library and information science education (Advances in Librarianship, Vol. 44A), pp. 161-176, (2018); Kumar K., Sharma J., Library and information science education in India: A historical perspective, DESIDOC Journal of Library & Information Technology, 30, 5, pp. 3-8, (2010); Kumari N., Mishra S., Comparing digital library job requirements and digital library course curriculum: An Indian perspective, SRELS Journal of Information Management, 57, 6, pp. 297-308, (2020); Maceli M.G., Making the future makers: Makerspace curriculum in library and information science graduate programs and continuing education, Library Hi Tech, 37, 4, pp. 781-793, (2019); Malik A., Ameen K., Library/information education programs in Pakistan: A comparison with IFLA Guidelines, Library Review, 66, 4-5, pp. 297-309, (2017); Oh S., Yang S., Pomerantz J.P., Wildemuth B.M., Fox E.A., Results of a digital library curriculum field test, International Journal on Digital Libraries, 17, 4, pp. 273-286, (2016); Raju J., Knowledge and skills for the digital era academic library, The Journal of Academic Librarianship, 40, 2, pp. 163-170, (2014); Rehman S., Marouf L., MLIS program at Kuwait University: Perceptions and reflections, Library Review, 57, 1, pp. 13-24, (2008); Salubi O.G., Library and Information Science education and training curriculum at institutions of higher education in Nigeria: A content analysis, Journal of Social Sciences, 51, 1-3, pp. 79-86, (2017); Satija M.P., Doctoral research in library and information science in India: Some observations and comments, Libri, 49, 4, pp. 236-242, (1999); Saunders L., Professional perspectives on library and information science education, The Library Quarterly, 85, 4, pp. 427-453, (2015); Saunders L., Core and more: Examining foundational and specialized content in library and information science, Journal of Education for Library and Information Science, 60, 1, pp. 3-34, (2019); Siddiqui S., Walia P.K., A comparative analysis of Library and Information Science post graduate education in India and UK. Library Philosophy & Practice (e-journal, (2013); Singh S.P., Babbar P., Doctoral research in library and information science in India: Trends and issues, DESIDOC Journal of Library & Information Technology, 34, 2, pp. 170-180, (2014); Singh V., Mehra B., Strengths and weaknesses of the information technology curriculum in library and information science graduate programs, Journal of Librarianship and Information Science, 45, 3, pp. 219-231, (2013); Varalakshmi R.S.R., Educating 21st century LIS professionals-Needs and expectations: A survey of Indian LIS professionals and alumni, Journal of Education for Library and Information Science, 47, 3, pp. 181-199, (2006); Warraich N., MLIS curriculum at Punjab University: Perception and reflections. Library Philosophy and Practice, (2010)","A.K.S. Yadav; Centre for Library and Information Management Studies (CLIMS), SDTM Library, Tata Institute of Social Sciences, Mumbai, India; email: akhilesh.yadav@tiss.edu","","Taylor and Francis Ltd.","","","","","","10572317","","","","English","Int. Inf. Libr. Rev.","Article","Final","","Scopus","2-s2.0-85117188432"
"Fabry C.; Pittner A.; Hirthammer V.; Rethmeier M.","Fabry, Cagtay (57202460930); Pittner, Andreas (25121704900); Hirthammer, Volker (57205731648); Rethmeier, Michael (25121854300)","57202460930; 25121704900; 57205731648; 25121854300","Recommendations for an Open Science approach to welding process research data","2021","Welding in the World","65","9","","1661","1669","8","0","10.1007/s40194-021-01151-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114223640&doi=10.1007%2fs40194-021-01151-x&partnerID=40&md5=6d51f68780bb4362f8cb84b59a345179","Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, 12205, Germany; Institute of Machine Tools and Factory Management, Technical University of Berlin, Berlin, 10623, Germany; Fraunhofer Institute of Production Systems and Design Technology (IPK), Berlin, 10587, Germany","Fabry C., Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, 12205, Germany; Pittner A., Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, 12205, Germany; Hirthammer V., Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, 12205, Germany; Rethmeier M., Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, 12205, Germany, Institute of Machine Tools and Factory Management, Technical University of Berlin, Berlin, 10623, Germany, Fraunhofer Institute of Production Systems and Design Technology (IPK), Berlin, 10587, Germany","The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research. © 2021, The Author(s).","Data exchange; FAIR; File format; Open Science; Open Source; Research data management; Welding; WelDX","Knowledge management; Open Data; Domain knowledge; File formats; Fusion welding; Knowledge exchange; Research communities; Research data; Science community; Welding process; Welding","","","","","Bundesministerium für Bildung und Forschung, BMBF, (16QK12)","Open Access funding enabled and organized by Projekt DEAL. The authors received the support from the Federal Ministry of Education and Research of Germany in the framework of WelDX under project number 16QK12. ","Baker M., Reproducibility crisis, Nature, 533, 26, pp. 353-366, (2016); Estimating the reproducibility of psychological science, Science, 349, 6251, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The fair guiding principles for scientific data management and stewardship, Sci Data, 3, 1, pp. 1-9, (2016); Franke S., Paulet L., Schafer J., O'Connell D., Becker M.M., Plasma-mds, a metadata schema for plasma science with examples from plasma technology, Sci Data, 7, 1, pp. 1-11, (2020); Fabry C., Digitalization and Open Science in Welding Research, (2019); Kuznetsova A., Rom H., Alldrin N., Uijlings J., Krasin I., Pont-Tuset J., Kamali S., Popov S., Malloci M., Kolesnikov A., The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale, (2018); Yu F., Chen H., Wang X., Xian W., Chen Y., Liu F., Madhavan V., Darrell T., Bdd100k: A diverse driving dataset for heterogeneous multitask learning, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2636-2645, (2020); Automotive radar dataset for deep learning based 3d object detection. In: 2019 16th european radar conference (EuRAD), Pp 129–132, (2019); Vasudevan M., Murugananth M., Bhaduri A., Raj B., Rao K.P., Bayesian neural network analysis of ferrite number in stainless steel welds, Sci Technol Weld Join, 9, 2, pp. 109-120, (2004); Ghanty P., Paul S., Roy A., Mukherjee D.P., Pal N.R., Vasudevan M., Kumar H., Bhaduri A.K., Fuzzy rule based approach for predicting weld bead geometry in gas tungsten arc welding, Sci Technol Weld Join, 13, 2, pp. 167-175, (2008); Fabry C., Pittner A., Rethmeier M., Design of neural network arc sensor for gap width detection in automated narrow gap GMAW, Weld World, 62, 4, pp. 819-830, (2018); Schmitt R.H., Anthofer V., Auer S., Baskaya S., Bischof C., Bronger T., Claus F., Cordes F., Demandt E., Eifert T., Flemisch B., Fuchs M., Fuhrmans M., Gerike R., Gerstner E.M., Hanke V., Heine I., Huebser L., Iglezakis D., Jagusch G., Klinger A., Krafczyk M., Kraft A., Kuckertz P., Kusters U., Lachmayer R., Langenbach C., Mozgova I., Muller M.S., Nestler B., Pelz P., Politze M., Preuss N., Przybylski-Freund M.D., Rissler-Pipka N., Robinius M., Schachtner J., Schlenz H., Schwarz A., Schwibs J., Selzer M., Sens I., Stacker T., Stemmer C., Stille W., Stolten D., Stotzka R., Streit A., Strotgen R., Wang W.M., Nfdi4ing - the national research data infrastructure for engineering sciences, Zenodo, (2020); Ayris P., Berthou J.Y., Bruce R., Lindstaedt S., Monreale A., Mons B., Murayama Y., Sodergard C., Tochtermann K., Wilkinson R., Realising the European Open Science Cloud. 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Fabry; Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, 12205, Germany; email: cagtay.fabry@bam.de","","Springer Science and Business Media Deutschland GmbH","","","","","","00432288","","WDWRA","","English","Weld. World","Review","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85114223640"
"Roche D.G.; Berberi I.; Dhane F.; Lauzon F.; Soeharjono S.; Dakin R.; Binning S.A.","Roche, Dominique G. (25623797000); Berberi, Ilias (57209799447); Dhane, Fares (57694237700); Lauzon, Félix (57211986892); Soeharjono, Sandrine (57285910000); Dakin, Roslyn (26029708700); Binning, Sandra A. (24376188000)","25623797000; 57209799447; 57694237700; 57211986892; 57285910000; 26029708700; 24376188000","Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution","2022","Proceedings of the Royal Society B: Biological Sciences","289","1975","20212780","","","","5","10.1098/rspb.2021.2780","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130134340&doi=10.1098%2frspb.2021.2780&partnerID=40&md5=32a482b08893a2a5f08e8d06a0280e9f","Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, ON, Canada; Département de Science Biologiques, Université de Montréal, Montréal, H3C 3J7, Canada; Institut de Biologie, Université de Neuchâtel, Neuchâtel, 2000, Switzerland; Department of Biology, McGill University, Montréal, H3A 1B1, Canada","Roche D.G., Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, ON, Canada, Département de Science Biologiques, Université de Montréal, Montréal, H3C 3J7, Canada, Institut de Biologie, Université de Neuchâtel, Neuchâtel, 2000, Switzerland; Berberi I., Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, ON, Canada; Dhane F., Département de Science Biologiques, Université de Montréal, Montréal, H3C 3J7, Canada; Lauzon F., Département de Science Biologiques, Université de Montréal, Montréal, H3C 3J7, Canada, Department of Biology, McGill University, Montréal, H3A 1B1, Canada; Soeharjono S., Département de Science Biologiques, Université de Montréal, Montréal, H3C 3J7, Canada; Dakin R., Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, ON, Canada; Binning S.A., Département de Science Biologiques, Université de Montréal, Montréal, H3C 3J7, Canada","Many leading journals in ecology and evolution now mandate open data upon publication. Yet, there is very little oversight to ensure the completeness and reusability of archived datasets, and we currently have a poor understanding of the factors associated with high-quality data sharing. We assessed 362 open datasets linked to first-or senior-Authored papers published by 100 principal investigators (PIs) in the fields of ecology and evolution over a period of 7 years to identify predictors of data completeness and reusability (data archiving quality). Datasets scored low on these metrics: 56.4% were complete and 45.9% were reusable. Data reusability, but not completeness, was slightly higher for more recently archived datasets and PIs with less seniority. Journal open data policy, PI gender and PI corresponding author status were unrelated to data archiving quality. However, PI identity explained a large proportion of the variance in data completeness (27.8%) and reusability (22.0%), indicating consistent interindividual differences in data sharing practices by PIs across time and contexts. Several PIs consistently shared data of either high or low archiving quality, but most PIs were inconsistent in how well they shared. One explanation for the high intra-individual variation we observed is that PIs often conduct research through students and postdoctoral researchers, who may be responsible for the data collection, curation and archiving. Levels of data literacy vary among trainees and PIs may not regularly perform quality control over archived files. Our findings suggest that research data management training and culture within a PI?s group are likely to be more important determinants of data archiving quality than other factors such as a journal?s open data policy. Greater incentives and training for individual researchers at all career stages could improve data sharing practices and enhance data transparency and reusability.  © 2022 The Author(s.","Data sharing; Fair data; Metascience; Open science; Public data archiving; Reproducibility","Data Accuracy; Data Collection; Ecology; Humans; Information Dissemination; data quality; data set; management practice; record; training; ecology; human; information dissemination; information processing; measurement accuracy","","","","","Horizon 2020 Framework Programme, H2020; Natural Sciences and Engineering Research Council of Canada, NSERC, (UIF-537860-2018); Canada Research Chairs; Horizon 2020, (838237)","This study was supported by the Natural Sciences and Engineering Research Council of Canada (grant no. UIF-537860-2018) and the Canada Research Chair program. D.G.R. was supported by the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 838237-OPTIMISE. Acknowledgements ","Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Soeharjono S., Roche D.G., Reported individual costs and benefits of sharing open data among Canadian faculty members in ecology and evolution, BioScience, 71, pp. 750-756, (2021); Culina A., Van Den Berg I., Evans S., Sanchez-Tojar A., Low availability of code in ecology: A call for urgent action, PLoS Biol, 18, (2020); Costello M.J., Motivating online publication of data, BioScience, 59, pp. 418-427, (2009); Parr C.S., Cummings M.P., Data sharing in ecology and evolution, Trends Ecol. Evol, 20, pp. 362-363, (2005); Roche D.G., Lanfear R., Binning S.A., Haff T.M., Schwanz L.E., Cain K.E., Kokko H., Jennions M.D., Kruuk L.E., Troubleshooting public data archiving: Suggestions to increase participation, PLoS Biol, 12, (2014); Westoby M., Falster D.S., Schrader J., Motivating data contributions via a distinct career currency, Proc. R. Soc B, 288, (2021); Xu B., Et al., Epidemiological data from the COVID-19 outbreak, real-Time case information, Sci. Data, 7, (2020); Asc H., Sutherland W.J., The data-index: An author-level metric that values impactful data and incentivizes data sharing, Ecol. Evol, 11, pp. 14344-14350, (2021); Buxton R., Et al., Avoiding wasted research resources in conservation science, Conserv. Sci. Prac, 3, (2021); Roche D.G., Odea R.E., Kerr K.A., Rytwinski T., Schuster R., Nguyen V.M., Young N., Bennett J.R., Cooke S.J., Closing the knowledge-Action gap in conservation with open science, Conserv. Biol, 3, (2021); Mills J.A., Et al., Archiving primary data: Solutions for long-Term studies, Trends Ecol. Evol, 30, pp. 581-589, (2015); Tedersoo L., Et al., Data sharing practices and data availability upon request differ across scientific disciplines, Sci. Data, 8, (2021); Tenopir C., Rice N.M., Allard S., Baird L., Borycz J., Christian L., Grant B., Olendorf R., Sandusky R.J., Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide, PLoS ONE, 15, (2020); Simons N., Et al., The state of open data 2021, Digital Science, (2021); Moore A.J., McPeek M.A., Rausher M.D., Rieseberg L., Whitlock M.C., The need for archiving data in evolutionary biology, J. Evol. Biol, 23, pp. 659-660, (2010); Berberi I., Roche D.G., Living Database of Journal Data Policies in E&E. Open Science Framework, (2021); Vines T.H., Et al., Mandated data archiving greatly improves access to research data, FASEB J, 27, pp. 1304-1308, (2013); Federer L.M., Belter C.W., Joubert D.J., Livinski A., Lu Y.-L., Snyders L.N., Thompson H., Data sharing in PLOS ONE: An analysis of data availability statements, PLoS ONE, 13, (2018); Roche D.G., Et al., Paths towards greater consensus building in experimental biology, J. Exp. Biol, 225, (2022); Odea R.E., Et al., Towards open, reliable, and transparent ecology and evolutionary biology, BMC Biol, 19, (2021); Couture J.L., Blake R.E., McDonald G., Ward C.L., A funder-imposed data publication requirement seldom inspired data sharing, PLoS ONE, 13, (2018); Vines T.H., Scientific community journals must boost data sharing, Nature, 508, (2014); Roche D.G., Kruuk L.E., Lanfear R., Binning S.A., Public data archiving in ecology and evolution: How well are we doing?, PLoS Biol, 13, (2015); Towse J., Ellis D.A., Towse A.S., Opening Pandora?s Box: Peeking inside psychology?s data sharing practices, and recommendations for change, Beh. Res. Meth, 53, pp. 1455-1468, (2020); Hardwicke T.E., Et al., Data availability, reusability, and analytic reproducibility evaluating the impact of a mandatory open data policy at the journal Cognition, R. Soc. 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Softw, 33, pp. 1-22, (2010); Dingemanse N.J., Dochtermann N.A., Quantifying individual variation in behaviour: Mixed-effect modelling approaches, J. Anim. Ecol, 82, pp. 39-54, (2013); Nakagawa S., Schielzeth H., Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists, Biol. Rev, 85, pp. 935-956, (2010); Araya-Ajoy Y.G., Mathot K.J., Dingemanse N.J., An approach to estimate short-Term, long-Term and reaction norm repeatability, Methods Ecol. Evol, 6, pp. 1462-1473, (2015); Roche D.G., Berberi I., Dhane F., Lauzon F., Soeharjono S., Dakin R., Binning S.A., Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution, Figshare, (2022)","D.G. Roche; Department of Biology, Carleton University, Ottawa, 1125 Colonel By Drive, K1S 5B6, Canada; email: dominique.roche@mail.mcgill.ca","","Royal Society Publishing","","","","","","09628452","","PRLBA","35582791","English","Proc. R. Soc. B Biol. Sci.","Article","Final","","Scopus","2-s2.0-85130134340"
"Ashiq M.; Warraich N.F.","Ashiq, Murtaza (57221973297); Warraich, Nosheen Fatima (26029582000)","57221973297; 26029582000","Librarian’s perception on data librarianship core concepts: a survey of motivational factors, challenges, skills and appropriate trainings platforms","2022","Library Hi Tech","","","","","","","4","10.1108/LHT-12-2021-0487","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130176004&doi=10.1108%2fLHT-12-2021-0487&partnerID=40&md5=3b3b7fac47e93fbd1764398c54787216","Islamabad Model College for Boys, Islamabad, H-9, Pakistan; Institute of Information Management, University of the Punjab, Lahore, Pakistan","Ashiq M., Islamabad Model College for Boys, Islamabad, H-9, Pakistan, Institute of Information Management, University of the Punjab, Lahore, Pakistan; Warraich N.F., Institute of Information Management, University of the Punjab, Lahore, Pakistan","Purpose: Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services. Design/methodology/approach: A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data. Findings: LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills. Research limitations/implications: The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others. Originality/value: This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services. © 2022, Emerald Publishing Limited.","Academic and research libraries; Data librarianship; Data-driven librarianship; Pakistan; Research data management","","","","","","","","Allchin O., Collins A., Cox A., Lewis J., Scott C., Realizing our role in research data management, CILIP Update, 12, 3, pp. 36-38, (2013); Ashiq M., Usmani M.H., Naeem M., A systematic literature review on research data management practices and services, Global Knowledge, Memory and Communication, (2020); Ashiq M., Saleem Q.U.A., Asim M., The perception of library and information science (LIS) professionals about research data management services in university libraries of Pakistan, Libri, (2021); Ashiq M., Rehman S.U., Mujtaba G., Future challenges and emerging role of academic libraries in Pakistan: a phenomenology approach, Information Development, 37, 1, pp. 158-173, (2021); Bryant R., Lavoie B., Malpas C., A Tour of the Research Data Management (RDM) Service Space. The Realities of Research Data Management, (2017); Chiware E.R.T., Data librarianship in South African academic and research libraries: a survey, Library Management, 41, 7, pp. 401-416, (2020); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Faniel I.M., Connaway L.S., Librarians' perspectives on the factors influencing research data management programs, College and Research Libraries, 79, 1, (2018); Federer L., Defining data librarianship: a survey of competencies, skills, and training, Journal of the Medical Library Association: JMLA, 106, 3, (2018); Federer L., Foster E.D., Glusker A., Henderson M., Read K., Zhao S., The Medical Library Association Data Services Competency: a framework for data science and open science skills development, Journal of the Medical Library Association: JMLA, 108, 2, (2020); Hamad F., Al-Fadel M., Al-Soub A., Awareness of research data management services at academic libraries in Jordan: roles, responsibilities and challenges, New Review of Academic Librarianship, 27, 1, pp. 1-21, (2019); Huang Y., Cox A.M., Sbaffi L., Research data management policy and practice in Chinese university libraries, Journal of the Association for Information Science and Technology, 72, 4, pp. 493-506, (2021); Kellam L., Thompson K., Introduction to Data Librarianship: The Academic Data Librarian in Theory and Practice, (2016); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2017); Mahmood K., Rehman S.U., Ashiq M., Measuring library service quality of the college libraries in Pakistan: an analysis of the LibQUAL comments, Library Philosophy and Practice, pp. 1-24, (2020); Mahmood K., Ahmad S., Ur Rehman S., Ashiq M., Evaluating library service quality of college libraries: the perspective of a developing country, Sustainability, 13, 5, (2021); Mani N.S., Cawley M., Henley A., Triumph T., Williams J.M., Creating a data science framework: a model for academic research libraries, Journal of Library Administration, 61, 3, pp. 281-300, (2021); McBurney J., Kubas A., Limitations to success in academic data reference support, Journal of Librarianship and Information Science, (2021); Mohammed M.S., Ibrahim R., Challenges and practices of research data management in selected Iraq universities, DESIDOC Journal of Library and Information Technology, 39, 6, pp. 308-314, (2019); Ohaji I.K., Chawner B., Yoong P., The role of data librarian in academic and research libraries, IR Information Research, 24, 4, pp. 1-14, (2019); Piracha H.A., Ameen K., Policy and planning of research data management in university libraries of Pakistan, Collection and Curation, 38, 2, pp. 39-44, (2019); Rice R., Southall S., The Data Librarian's Handbook, (2016); Semeler A., Pinto A., Data Librarianship Venn Diagram Handbook (Beta Version), (2019); Semeler A.R., Pinto A.L., Rozados H.B.F., Data science in data librarianship: core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2019); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Soehner C., Steeves C., Ward J., Science and Data Support Services: A Study of ARL Member Institutions, (2010); Starr J., Willett P., Federer L., Horning C., Bergstrom M., A collaborative framework for data management services: the experience of the University of California, Journal of eScience Librarianship, 1, 2, pp. 109-114, (2012); Tang R., Hu Z., Needs assessment of library data services: establishing a curriculum framework for RDMLA, Proceedings of the Association for Library and Information Science Education Annual Conference: ALISE 2019, (2019); Taufiq M., Rehman S.U., Ashiq M., User satisfaction with resources and services of public libraries of Lahore, Pakistan, Library Philosophy and Practice, pp. 1-30, (2020); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers' perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Virkus S., Garoufallou E., Data science from a library and information science perspective, Data Technologies and Applications, 53, 4, pp. 422-441, (2019); Xia J., Wang M., Competencies and responsibilities of social science data librarians: an analysis of job descriptions, College and Research Libraries, 75, 3, pp. 362-388, (2014); Yoon A., Donaldson D.R., Library capacity for data curation services: a US national survey, Library Hi Tech, 37, 4, pp. 811-828, (2019); Zaidi S.A., Rehman S.U., Ashiq M., Workplace motivation and stress on job satisfaction of librarians working in public sector universities of Lahore, Pakistan, International Journal of Information Science and Management (IJISM), 19, 2, pp. 181-195, (2021)","M. Ashiq; Islamabad Model College for Boys, Islamabad, H-9, Pakistan; email: gmurtazaashiq00@gmail.com","","Emerald Group Holdings Ltd.","","","","","","07378831","","","","English","Libr. Hi Tech","Article","Article in press","","Scopus","2-s2.0-85130176004"
"Odukoya O.; Nenrot D.; Adelabu H.; Katam N.; Christian E.; Holl J.; Okonkwo A.; Kocherginsky M.; Kim K.-Y.; Akanmu S.; Abdulkareem F.B.; Anorlu R.; Musa J.; Lesi O.; Hawkins C.; Okeke O.; Adeyemo W.L.; Sagay S.; Murphy R.; Hou L.; Ogunsola F.T.; Wehbe F.H.","Odukoya, O. (55356642300); Nenrot, D. (57271207400); Adelabu, H. (57204784379); Katam, N. (55370812500); Christian, E. (57220003146); Holl, J. (7006110122); Okonkwo, A. (57271207500); Kocherginsky, M. (6507231424); Kim, K.-Y. (13410621400); Akanmu, S. (57294759700); Abdulkareem, F.B. (35618512700); Anorlu, R. (6603460519); Musa, J. (15022919200); Lesi, O. (6507169522); Hawkins, C. (9941211000); Okeke, O. (57271759200); Adeyemo, W.L. (6506703422); Sagay, S. (24170022900); Murphy, R. (35373811100); Hou, L. (8532926200); Ogunsola, F.T. (6603110324); Wehbe, F.H. (8322102200)","55356642300; 57271207400; 57204784379; 55370812500; 57220003146; 7006110122; 57271207500; 6507231424; 13410621400; 57294759700; 35618512700; 6603460519; 15022919200; 6507169522; 9941211000; 57271759200; 6506703422; 24170022900; 35373811100; 8532926200; 6603110324; 8322102200","Application of the research electronic data capture (REDCap) system in a low- and middle income country– experiences, lessons, and challenges","2021","Health and Technology","11","6","","1297","1304","7","1","10.1007/s12553-021-00600-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115713288&doi=10.1007%2fs12553-021-00600-3&partnerID=40&md5=70266364bb627c8e83e4af08cdfe187a","Department of Community Health and Primary Care, College of Medicine, University of Lagos, Lagos State, Nigeria; College of Health Sciences, University of Jos, Plateau state, Nigeria; AIDS Prevention Initiative of Nigeria, Lagos University Teaching Hospital, Lagos State, Nigeria; Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States; Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Department of Neurology and Center for Healthcare Delivery Science and Innovation, Biological Sciences Division, University of Chicago, Chicago, IL, United States; Research Management Office, College of Medicine, University of Lagos, Lagos, Nigeria; Department of Preventive Medicine, Division of Biostatistics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Department of Anatomic & Molecular Pathology, College of Medicine, University of Lagos, Lagos State, Nigeria; Department of Obstetrics and Gynaecology, College of Medicine, University of Lagos, Lagos State, Nigeria; Department of Medicine, College of Medicine, University of Lagos, Lagos State, Nigeria; Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Department of Maxillofacial Surgery, College of Medicine, University of Lagos, Lagos State, Nigeria; Department of Medical Microbiology and Parasitology, College of Medicine, University of Lagos, Lagos State, Nigeria","Odukoya O., Department of Community Health and Primary Care, College of Medicine, University of Lagos, Lagos State, Nigeria; Nenrot D., College of Health Sciences, University of Jos, Plateau state, Nigeria; Adelabu H., AIDS Prevention Initiative of Nigeria, Lagos University Teaching Hospital, Lagos State, Nigeria; Katam N., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States; Christian E., Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Holl J., Department of Neurology and Center for Healthcare Delivery Science and Innovation, Biological Sciences Division, University of Chicago, Chicago, IL, United States; Okonkwo A., Research Management Office, College of Medicine, University of Lagos, Lagos, Nigeria; Kocherginsky M., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States, Department of Preventive Medicine, Division of Biostatistics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Kim K.-Y., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States, Department of Preventive Medicine, Division of Biostatistics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Akanmu S., AIDS Prevention Initiative of Nigeria, Lagos University Teaching Hospital, Lagos State, Nigeria; Abdulkareem F.B., Department of Anatomic & Molecular Pathology, College of Medicine, University of Lagos, Lagos State, Nigeria; Anorlu R., Department of Obstetrics and Gynaecology, College of Medicine, University of Lagos, Lagos State, Nigeria; Musa J., College of Health Sciences, University of Jos, Plateau state, Nigeria, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Lesi O., Department of Medicine, College of Medicine, University of Lagos, Lagos State, Nigeria; Hawkins C., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States, Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Okeke O., College of Health Sciences, University of Jos, Plateau state, Nigeria; Adeyemo W.L., Department of Maxillofacial Surgery, College of Medicine, University of Lagos, Lagos State, Nigeria; Sagay S., College of Health Sciences, University of Jos, Plateau state, Nigeria; Murphy R., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States, Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Hou L., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Ogunsola F.T., Department of Medical Microbiology and Parasitology, College of Medicine, University of Lagos, Lagos State, Nigeria; Wehbe F.H., Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States","The challenges of reliably collecting, storing, organizing, and analyzing research data are critical in low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa where several healthcare and biomedical research organizations have limited data infrastructure. The Research Electronic Data Capture (REDCap) System has been widely used by many institutions and hospitals in the USA for data collection, entry, and management and could help solve this problem. This study reports on the experiences, challenges, and lessons learned from establishing and applying REDCap for a large US-Nigeria research partnership that includes two sites in Nigeria, (the College of Medicine of the University of Lagos (CMUL) and Jos University Teaching Hospital (JUTH)) and Northwestern University (NU) in Chicago, Illinois in the United States. The largest challenges to this implementation were significant technical obstacles: the lack of REDCap-trained personnel, transient electrical power supply, and slow/ intermittent internet connectivity. However, asynchronous communication and on-site hands-on collaboration between the Nigerian sites and NU led to the successful installation and configuration of REDCap to meet the needs of the Nigerian sites. An example of one lesson learned is the use of Virtual Private Network (VPN) as a solution to poor internet connectivity at one of the sites, and its adoption is underway at the other. Virtual Private Servers (VPS) or shared online hosting were also evaluated and offer alternative solutions. Installing and using REDCap in LMIC institutions for research data management is feasible; however, planning for trained personnel and addressing electrical and internet infrastructural requirements are essential to optimize its use. Building this fundamental research capacity within LMICs across Africa could substantially enhance the potential for more cross-institutional and cross-country collaboration in future research endeavors. © 2021, The Author(s).","Data capture; LMIC; Redcap; Research collaboration","adoption; article; basic research; human; Illinois; Internet; middle income country; Nigeria; power supply; university hospital","","","","","National Institutes of Health, NIH, (U54 CA221205); National Cancer Institute, NCI; Fogarty International Center, FIC, (D43TW009575, K43TW010704)","Funding text 1: REDCap was selected as the uniform data collection system for this ongoing collaborative study funded by the National Cancer Institute, National Institutes of Health (U54 CA221205). Our goal is to understand the epigenetic determinants of two common HIV-associated cancers, hepatocellular carcinoma (HCC) and cervical cancer, in Nigeria. The project seeks to collect clinical data from HIV-positive and negative patients with or without liver and cervical cancer including: patients’ socio-demographic information, clinical and health history, laboratory results, and specimen tracking status. The bioinformatics team uses the information collected in REDCap to create datasets that are used for project analysis. Northwestern University (NU) in Chicago, Illinois, USA serves as the lead institution and is joined by two Nigerian partners, the College of Medicine of the University of Lagos (CMUL) and Jos University Teaching Hospital (JUTH). ; Funding text 2: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number U54CA221205, and by the Fogarty International Center of the National Institutes of Health under award number D43TW009575. The protected time for the contribution of Oluwakemi Odukoya towards the research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under the Award Number K43TW010704. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ","Morel T., Maher D., Nyirenda T., Olesen O.F., Strengthening health research capacity in sub-Saharan Africa: Mapping the 2012–2017 landscape of externally funded international postgraduate training at institutions in the region, Global Health, 14, 1, (2018); Uthman O.A., Wiysonge C.S., Ota M.O., Et al., Increasing the value of health research in the WHO African Region beyond 2015-reflecting on the past, celebrating the present and building the future: a bibliometric analysis, BMJ Open, 5, 3, (2015); Missinou M.A., Olola C.H., Issifou S., Et al., Short report: Piloting paperless data entry for clinical research in Africa, Am J Trop Med Hyg, 72, 3, pp. 301-303, (2005); Taylor D.M., Hodkinson P.W., Khan A.S., Simon E.L., Research skills and the data spreadsheet: A research primer for low- and middle-income countries, Afr J Emerg Med, 10, pp. S140-S144, (2020); Redcap: About. Vanderbilt; Harris P.A., Taylor R., Minor B.L., Et al., The REDCap consortium: Building an international community of software platform partners, J Biomed Inform, 95, (2019); (2020); (2020)","O. Odukoya; Department of Community Health and Primary Care, College of Medicine, University of Lagos, Lagos State, Nigeria; email: drolukemiodukoya@yahoo.com","","Springer Science and Business Media Deutschland GmbH","","","","","","21907188","","","","English","Health Technol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85115713288"
"Bridges P.G.; Akhavan Z.; Wheeler J.; Al-Azzawi H.; Albillar O.; Faustino G.","Bridges, Patrick G. (13404528300); Akhavan, Zeinab (57211944600); Wheeler, Jonathan (56819699600); Al-Azzawi, Hussein (56369442300); Albillar, Orlando (57338360000); Faustino, Grace (57337911400)","13404528300; 57211944600; 56819699600; 56369442300; 57338360000; 57337911400","SAMPRA: Scalable analysis, management, protection of research artifacts","2021","Proceedings - IEEE 17th International Conference on eScience, eScience 2021","","","","177","185","8","0","10.1109/eScience51609.2021.00028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119088219&doi=10.1109%2feScience51609.2021.00028&partnerID=40&md5=43faaebdce25ae734f714f9655347da2","University of New Mexico, United States","Bridges P.G., University of New Mexico, United States; Akhavan Z., University of New Mexico, United States; Wheeler J., University of New Mexico, United States; Al-Azzawi H., University of New Mexico, United States; Albillar O., University of New Mexico, United States; Faustino G., University of New Mexico, United States","This paper describes SAMPRA, a framework for supporting effective research on sensitive data being deployed at the University of New Mexico. SAMPRA and its associated implementation are designed to support the needs of a diverse set of use-cases from researchers across different disciplines at UNM, including from clinical neurosciences, forensic anthropology, and community health. From these use-cases, we identified a set of common unaddressed demands when handling data with privacy/protection requirements, particularly collaborative research projects, interfacing with scientific instruments, and full-lifecycle management of sensitive data. To properly address and accelerate research projects with these needs, SAMPRA a) integrates privacy-preserving storage and data transfer systems with data-centric virtual environments, and b) supports effective researcher use of the system through active collaboration between local IT personnel, campus enterprise IT service providers, and campus data librarians by defining clear roles with associated personnel. By doing so, SAMPRA seeks to meet the needs of research on sensitive data across the entire data lifecycle and avoid the pitfalls of generic 'one-size-fits-all' services. © 2021 IEEE.","Controlled Unclassified Information; Cyber-infrastructure; High-performance Computing; Research Data Management; Virtualization","Data transfer; Information management; Life cycle; Personnel; Virtual reality; Controled unclassified information; Cyberinfrastructure; High-performance computing; Performance computing; Research artefacts; Research data managements; Scalable analysis; Sensitive datas; University of New Mexico; Virtualizations; Virtualization","","","","","National Science Foundation, NSF, (OAC-1840069)","This paper is based upon work supported in part by the National Science Foundation under Grant No. OAC-1840069.","U. Of Florida Research Computing; Curating Research Data, 1, (2017); DCMI Metadata Terms, (2020); (2010); Foster I., Globus online: Accelerating and democratizing science through cloud-based services, IEEE Internet Computing, 15, 3, pp. 70-73, (2011); Winters K.D., Cowan M.A., George G.E., Gonzalez M.E., Priest B., Morris O., Landrum J., Analysis of Ers Use Cases for Irods, (2020); Edgar H.J.H., Daneshvari Berry S., Moes E., Adolphi N.L., Bridges P.G., Nolte K.B., New Mexico Decedent Image Database, (2020); Fischer J., Tuecke S., Foster I., Stewart C.A., Jetstream: A distributed cloud infrastructure for underresourced higher education communities, Proceedings of the 1st Workshop on the Science of Cyberinfrastructure: Research, Experience, Applications and Models, pp. 53-61, (2015); Strande S.M., Cai H., Cooper T., Flammer K., Irving C., Von Laszewski G., Majumdar A., Mishin D., Papadopoulos P., Pfeiffer W., Et al., Comet: Tales from the long tail: Two years in and 10,000 users later, Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, pp. 1-7, (2017); Towns J., Cockerill T., Dahan M., Foster I., Gaither K., Grimshaw A., Hazlewood V., Lathrop S., Lifka D., Peterson G.D., Roskies R., Scott J.R., Wilkins-Diehr N., Xsede: Accelerating scientific discovery, Computing in Science Engineering, 16, 5, pp. 62-74, (2014); Nystrom N.A., Levine M.J., Roskies R.Z., Scott J.R., Bridges: A uniquely flexible hpc resource for new communities and data analytics, Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure, pp. 1-8, (2015); Ricci R., Eide E., Introducing cloudlab: Scientific infrastructure for advancing cloud architectures and applications, Login:: The Magazine of USENIX & SAGE, 39, 6, pp. 36-38, (2014); (2015); Cybersecurity Maturity Model Certification (CMMC), (2020); Badia R.M., Ejarque J., Lordan F., Lezzi D., Conejero J., Cid-Fuentes J.A., Becerra Y., Queralt A., Workflow environments for advanced cyberinfrastructure platforms, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp. 1720-1729, (2019); Hong W., Moon J., Seok W., Chung J., Enhancing data transfer performance utilizing a DTN between cloud service providers, Symmetry, 10, 4, (2018); Liu Y., Liu Z., Kettimuthu R., Rao N.S., Chen Z., Foster I., Data Transfer between Scientific Facilities-bottleneck Analysis, Insights, and Optimizations, (2019); Roode D., A Vision for Research Cyberinfrastructure at Uci, Version 4.7e, (2016)","","","Institute of Electrical and Electronics Engineers Inc.","","17th IEEE International Conference on eScience, eScience 2021","20 September 2021 through 23 September 2021","Virtual, Online","173214","","978-166540361-0","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","","Scopus","2-s2.0-85119088219"
"Huang Y.-C.; Tremouilhac P.; Nguyen A.; Jung N.; Bräse S.","Huang, Yu-Chieh (57195808011); Tremouilhac, Pierre (13103602300); Nguyen, An (57195803328); Jung, Nicole (15064507300); Bräse, Stefan (7005396290)","57195808011; 13103602300; 57195803328; 15064507300; 7005396290","ChemSpectra: a web-based spectra editor for analytical data","2021","Journal of Cheminformatics","13","1","8","","","","4","10.1186/s13321-020-00481-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100954820&doi=10.1186%2fs13321-020-00481-0&partnerID=40&md5=f5351553ffe6e8fcb257f86169fb0494","Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany","Huang Y.-C., Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Tremouilhac P., Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Nguyen A., Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Jung N., Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany; Bräse S., Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany","ChemSpectra, a web-based software to visualize and analyze spectroscopic data, integrating solutions for infrared spectroscopy (IR), mass spectrometry (MS), and one-dimensional 1H and 13C NMR (proton and carbon nuclear magnetic resonance) spectroscopy, is described. ChemSpectra serves as web-based tool for the analysis of the most often used types of one-dimensional spectroscopic data in synthetic (organic) chemistry research. It was developed to support in particular processes for the use of open file formats which enable the work according to the FAIR data principles. The software can deal with the open file formats JCAMP-DX (IR, MS, NMR) and mzML (MS) proposing these data file types to gain interoperable data. ChemSpectra can be extended to read also other formats as exemplified by selected proprietary mass spectrometry data files of type RAW and NMR spectra files of type FID. The JavaScript-based editor can be integrated with other software, as demonstrated by integration into the Chemotion electronic lab notebook (ELN) and Chemotion repository, demonstrating the implementation into a digital work environment that offers additional functionality and sustainable research data management options. ChemSpectra supports different functions for working with spectroscopic data such as zoom functions, peak picking and automatic peak detection according to a default or manually defined threshold. NMR specific functions include the definition of a reference signal, the integration of signals, coupling constant calculation and multiplicity assignment. Embedded into a web application such as an ELN or a repository, the editor can also be used to generate an association of spectra to a sample and a file management. The file management supports the storage of the original spectra along with the last edited version and an automatically generated image of the spectra in png format. To maximize the benefit of the spectra editor for e.g. ELN users, an automated procedure for the transfer of the detected or manually chosen signals to the ELN was implemented. ChemSpectra is released under the AGPL license to encourage its re-use and further developments by the community. © 2021, The Author(s).","Analysis; IR; JCAMP-DX; Mass spectrometry; NMR; Spectroscopy","","","","","","Ministry of Science, Research and Arts Baden-W?; Karlsruhe Institute of Technology, KIT; Deutsche Forschungsgemeinschaft, DFG, (266379491); Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, MWK","Funding text 1: We acknowledge the support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology. This work was supported by the Helmholtz program Biointerfaces in Technology and Medicine (BIFTM) and by bwUniCluster, bwFORCluster. For computational resources we acknowledge the bwCloud ( https://www.bw-cloud.org ), funded by the Ministry of Science, Research and Arts Baden-Württemberg (Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg). ; Funding text 2: Open Access funding enabled and organized by Projekt DEAL. This project has been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, 266379491) and the Ministry of Science, Research and Arts Baden-Württemberg (Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg) through the Science Data Center MoMaF. We acknowledge the support of the VirtMat research consortium at the KIT. ; Funding text 3: We acknowledge the support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology. This work was supported by the Helmholtz program Biointerfaces in Technology and Medicine (BIFTM) and by bwUniCluster, bwFORCluster. For computational resources we acknowledge the bwCloud (https://www.bw-cloud.org), funded by the Ministry of Science, Research and Arts Baden-W?rttemberg (Ministerium f?r Wissenschaft, Forschung und Kunst Baden-W?rttemberg).","(2019); Lancashire R.J., The JSpecView Project: an Open Source Java viewer and converter for JCAMP-DX, and XML spectral data files, Chem Cent J, 1, (2007); (2019); Mohamed A., Nguyen C.H., Mamitsuka H., NMRPro: an integrated web component for interactive processing and visualization of NMR spectra, Bioinformatics, 32, pp. 2067-2068, (2016); Xia J., Mandal R., Sinelnikov I.V., Broadhurst D., Wishart D.S., MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis, Nucleic Acids Res, 40, pp. W127-W133, (2012); Tulpan D., Leger S., Belliveau L., Culf A., Cuperlovic-Culf M., MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures, BMC Bioinf, 12, (2011); Zhang F., Bruschweiler R., Robust deconvolution of complex mixtures by covariance TOCSY spectroscopy, Angew Chem Int Ed, 46, pp. 2639-2642, (2007); Vosegaard T., jsNMR: an embedded platform-independent NMR spectrum viewer, Magn Reson Chem, 53, pp. 285-290, (2015); Beisken S., Conesa P., Haug K., Salek R.M., Steinbeck C., SpeckTackle: JavaScript charts for spectroscopy, J Cheminform, 7, (2015); Wishart D.S., Jewison T., Guo A.C., Et al., HMDB 3.0—the human metabolome database in 2013, Nucleic Acids Res, 41, pp. D801-D807, (2012); Wishart D.S., Feunang Y.D., Guo A.C., Et al., DrugBank 5.0: a major update to the DrugBank database for 2018, Nucleic Acids Res, 46, D1, pp. D1074-D1082, (2017); Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., van Der Walt S.J., Brett M., Wilson J., Millman K.J., Mayorov N., Nelson A.R.J., Jones E., Kern R., Larson E., Carey C.J., Polat I., Feng Y., Moore E.W., Vanderplas J., Laxalde D., Perktold J., Cimrman R., Henriksen I., Quintero E.A., Harris C.R., Archibald A.M., Ribeiro A.H., Pedregosa F., van Mulbregt P., SciPy 1.0—fundamental algorithms for scientific computing in Python, (2019); Tremouilhac P., Nguyen A., Huang Y.-C., Kotov S., Lutjohann D.S., Hubsch F., Jung N., Brase S., Chemotion ELN: an Open Source electronic lab notebook for chemists in academia, J Cheminform, 9, (2017); Tremouilhac P., Lin C.-L., Huang P.-C., Huang Y.-C., Nguyen A., Jung N., Bach F., Neumair B., Streit A., Brase S., The repository chemotion: infrastructure for sustainable research in chemistry, ChemRxiv, (2020); Potthoff J., Tremouilhac P., Hodapp P., Neumair B., Brase S., Jung N., Procedures for systematic capture and management of analytical data in academia, Anal Chim Acta, 1, (2019); (2019); (2019); Deutsch E.W., Mass spectrometer output file format mzML, Methods Mol Biol, 604, pp. 319-331, (2010); Cobas J.C., Constantino-Castillo V., Martin-Pastor M., del Rio-Portilla F., A two-stage approach to automatic determination of 1H NMR coupling constants, Magn Reson Chem, 43, 10, pp. 843-848, (2005); Chambers M.C., MacLean B., Burke R., Amode D., Ruderman D.L., Neumann S., Gatto L., Fischer B., Pratt B., Egertson J., Hoff K., Kessner D., Tasman N., Shulman N., Frewen B., Baker T.A., Brusniak M.Y., Paulse C., Creasy D., Flashner L., Kani K., Moulding C., Seymour S.L., Nuwaysir L.M., Lefebvre B., Kuhlmann F., Roark J., Rainer P., Detlev S., Hemenway T., Huhmer A., Langridge J., Connolly B., Chadick T., Holly K., Eckels J., Deutsch E.W., Moritz R.L., Katz J.E., Agus D.B., MacCoss M., Tabb D.L., Mallick P., A cross-platform toolkit for mass spectrometry and proteomics, Nat Biotechnol, 30, pp. 918-920, (2012); (2019); Kosters M., Leufken J., Schulze S., Sugimoto K., Klein J., Zahedi R.P., Hippler M., Leidel S.A., Fufezan C., pymzML v2.0: introducing a highly compressed and seekable gzip format, Bioinformatics, 34, pp. 2513-2514, (2018); Rost H.L., Schmitt U., Aebersold R., Malmstrom L., pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library, Proteomics, 14, 1, pp. 74-77, (2014); (2020); (2019)","N. Jung; Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, 76344, Germany; email: nicole.jung@kit.edu; S. Bräse; Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, 76344, Germany; email: stefan.braese@kit.edu","","BioMed Central Ltd","","","","","","17582946","","","","English","J. Cheminformatics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85100954820"
"Proulx M.; Ross L.; Macdonald C.; Fitzsimmons S.; Smit M.","Proulx, MaryJane (57226445765); Ross, Lydia (57226451502); Macdonald, Christina (57226435195); Fitzsimmons, Shayla (57512629000); Smit, Michael (22235288500)","57226445765; 57226451502; 57226435195; 57512629000; 22235288500","Indigenous Traditional Ecological Knowledge and Ocean Observing: A Review of Successful Partnerships","2021","Frontiers in Marine Science","8","","703938","","","","5","10.3389/fmars.2021.703938","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111601389&doi=10.3389%2ffmars.2021.703938&partnerID=40&md5=1490b4a23f44a0519c522addfc0a7cbe","Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada; The Canadian Canoe Museum, Peterborough, ON, Canada; Coastal and Ocean Information Network - Atlantic (COINAtlantic), Halifax, NS, Canada; School of Information Management, Dalhousie University, Halifax, NS, Canada","Proulx M., Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada, The Canadian Canoe Museum, Peterborough, ON, Canada; Ross L., Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada, Coastal and Ocean Information Network - Atlantic (COINAtlantic), Halifax, NS, Canada; Macdonald C., Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada, Coastal and Ocean Information Network - Atlantic (COINAtlantic), Halifax, NS, Canada; Fitzsimmons S., Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada; Smit M., Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada, School of Information Management, Dalhousie University, Halifax, NS, Canada","Understanding and management of the marine environment requires respect for, and inclusion of, Indigenous knowledge, cultures, and traditional practices. The Aha Honua, an ocean observing declaration from Coastal Indigenous Peoples, calls on the ocean observing community to “formally recognize the traditional knowledge of Indigenous peoples,” and “to learn and respect each other’s ways of knowing.” Ocean observing systems typically adopt open data sharing as a core principle, often requiring that data be Findable, Accessible, Interoperable, and Reusable (FAIR). Without modification, this approach to Traditional Ecological Knowledge (TEK) would mean disregarding historical and ongoing injustices and imbalances in power, and information management principles designed to address these wrongs. Excluding TEK from global ocean observing is not equitable or desirable. Ocean observing systems tend to align with settler geography, but their chosen regions often include Indigenous coastal-dwelling communities that have acted as caretakers and stewards of the land and ocean for thousands of years. Achieving the call of Aha Honua will require building relationships that recognize Indigenous peoples play a special role in the area of ocean stewardship, care, and understanding. This review examines the current understanding of how Indigenous TEK can be successfully coordinated or utilized alongside western scientific systems, specifically the potential coordination of TEK with ocean observing systems. We identify relevant methods and collaborative projects, including cases where TEK has been collected, digitized and the meta(data) has been made open under some or all the FAIR principles. This review also highlights enabling factors that notably contribute to successful outcomes in digitization, and mitigation measures to avoid the decontextualization of TEK. Recommendations are primarily value- and process-based, rather than action-based, and acknowledge the key limitation that this review is based on extant written knowledge. In cases where examples are provided, or local context is necessary to be concrete, we refer to a motivating example of the nascent Atlantic Regional Association of the Canadian Integrated Ocean Observing System and their desire to build relationships with Indigenous communities. © Copyright © 2021 Proulx, Ross, Macdonald, Fitzsimmons and Smit.","CARE; FAIR; indigenous knowledge; ocean observing systems; research data management; traditional ecological knowledge; two-eyed seeing","","","","","","Marine Institute at Memorial University of Newfoundland; Marine Environmental Observation Prediction and Response Network, MEOPAR; Hakai Institute; Fisheries and Oceans Canada, DFO; Trent University; Dalhousie University; Ocean Frontier Institute, OFI; Canada First Research Excellence Fund, CFREF","Funding text 1: Research funding was provided by the Ocean Frontier Institute, through an award from the Canada First Research Excellence Fund. Funding for this Seed Fund grant was provided by Canada’s Ocean Supercluster. CIOOS is supported by funding partners Fisheries and Oceans Canada, MEOPAR, and the Hakai Institute. CIOOS Atlantic is additionally supported by Dalhousie University, the Ocean Frontier Institute, the Ocean Tracking Network, COINAtlantic, and the Marine Institute at Memorial University of Newfoundland.; Funding text 2: We would like to not only acknowledge, but thank the Indigenous caretakers of the Atlantic seaboard for their continued stewardship and resilience in preserving our lands and oceans. We thank Trent University for connections with knowledge keepers and Elders who provided a clearer understanding of respectful and reciprocal research protocols. We thank the individuals and organizations who contributed time and knowledge through informative conversations. MP offers personal thanks to family, friends, and resilient Nishnaabeg matriarchs for continuing to pass down knowledge and stories despite the many challenges faced. Chi?miigwech. Funding. Research funding was provided by the Ocean Frontier Institute, through an award from the Canada First Research Excellence Fund. Funding for this Seed Fund grant was provided by Canada?s Ocean Supercluster. CIOOS is supported by funding partners Fisheries and Oceans Canada, MEOPAR, and the Hakai Institute. CIOOS Atlantic is additionally supported by Dalhousie University, the Ocean Frontier Institute, the Ocean Tracking Network, COINAtlantic, and the Marine Institute at Memorial University of Newfoundland.","Adams M., Carpenter J., Housty J.A., Neasloss D., Paquet P.C., Service C.N., Et al., De-centering the university from community-based research: a framework for engagement between academic and indigenous collaborators in conservation and natural resource research,” in, Toolbox on the Research Principles in an Aboriginal Context: Ethics, Respect, Equity, Reciprocity, Collaboration and Culture, pp. 7-17, (2015); Aikenhead G.S., Michell H., Bridging Cultures: Scientific and Indigenous Ways of Knowing Nature, (2011); Facebook, (2020); First Nations Ethics First Nations Ethics Guide on Research and Aboriginal Traditional Knowledge; OCAP. (Ownership), Control, Access and Possession: First Nations Inherent Right to Govern First Nations Data, (2007); Barber M., Jackson S., ‘Knowledge Making’: issues in modelling local and indigenous ecological knowledge, Hum. Ecol, 43, pp. 119-130, (2015); Bickel R., Dupont S., Indigitization, KULA, 2, 11, (2018); Carroll S.R., Garba I., Figueroa-Rodriguez O.L., Holbrook J., Lovett R., Materechera S., Et al., The CARE Principles for Indigenous Data Governance, Data Sci. J, 19, 43, (2020); Carroll S.R., Herczog E., Hudson M., Russell K., Stall S., Operationalizing the CARE and FAIR Principles for Indigenous data futures, Sci. Data, 8, 108, (2021); Castleden H., Morgan V., Lamb C., I spent the first year drinking tea”: exploring Canadian university researchers’ perspectives on community-based participatory research involving Indigenous peoples, Can. Geogr, 56, pp. 160-179, (2012); Cochran P., Marshall C., Garcia-Downing C., Kendall E., Cook D., McCubbin L., Et al., Indigenous ways of knowing: implications for participatory research and community, Am. J. Public Health, 98, pp. 22-27, (2008); Nunavut Coastal Resource Inventory, (2013); Duarte M., Vigil-Hayes M., Littletree S., Belarde-Lewis M., Of Course, Data Can Never Fully Represent Reality”: assessing the Relationship between “Indigenous Data” and “Indigenous Knowledge,” “Traditional Ecological Knowledge,” and “Traditional Knowledge”, Hum. Biol, 91, pp. 163-178, (2017); Engler N., Scassa T., Taylor D., Mapping traditional knowledge: digital cartography in the Canadian North, Cartographica, 48, pp. 189-199, (2013); Ermine W., Sinclair R., Jeffrey B., The ethics of Research Involving Indigenous Peoples. Report of the Indigenous Peoples’ Health Research Centre to the Interagency Advisory Panel on Research Ethics, (2004); Failing L., Gregory R., Harstone M., Integrating science and local knowledge in environmental risk management: a Decision-focused approach, Ecol. Econ, 64, pp. 47-60, (2007); Fidel M., Kliskey A., Alessa L., Sutton P., Walrus harvest locations reflect adaptation: a contribution from a community-based observation network in the Bering Sea, Polar Geogr, 37, pp. 48-68, (2014); Welcome to Nunaliit: Nunaliit Map Makers, (2020); Hatcher A., Bartlett C., Marshall A., Marshall M., Two-eyed seeing in the classroom environment: concepts, approaches, and challenges, Can. J. Sci. Math. Technol. Educ, 9, pp. 141-153, (2009); Holkup P.A., Tripp-Reimer T., Salois E.M., Weinert C., Community-based participatory research: an approach to intervention research with a Native American community, ANS Adv. Nurs. Sci, 27, pp. 162-175, (2004); Indian Act and Elected Chief and Band Council System, (2015); Aha Honua Coastal Indigenous Peoples’ Declaration at OceanObs’19”, (2019); Indigitization: Tools for Digitizing and Sustaining Indigenous Knowledge, (2020); Two-Eyed Seeing, (2004); IFLA Statement on Indigenous Traditional Knowledge, (2019); Kirkness V.J., Barnhardt R., First nations and higher education: the Four R’s – respect, relevance, reciprocity, responsibility, Knowl. Across Cultures, 30, pp. 1-18, (2001); Kitikmeot Place Names Atlas, (2020); Liggins L., Hudson M., Anderson J., Creating space for Indigenous perspectives on access and benefit-sharing: encouraging researcher use of the Local Contexts Notices, Mol. Ecol, 30, pp. 2477-2482, (2021); ALVA. Living Lands, (2013); Mackenzie K., Siabato W., Reitsma F., Claramunt C., Spatio-temporal visualisation and data exploration of traditional ecological knowledge/indigenous knowledge, Conserv. Soc, 15, pp. 41-58, (2017); Mazzocchi F., Western science and traditional knowledge: despite their variations, different forms of knowledge can learn from each other, EMBO Rep, 7, pp. 463-466, (2006); Memorial University’s Proposed Policy on Research Impacting Indigenous Groups: Principles for Engagement, (2020); Mi’kmaw Research Principles and Protocols Conducting Research with/or Among Mi’kmaw People, (2000); Miller K., What does Indigenous participatory democracy look like? Kahnawà:ke’s community decision making process, Revi. Constit. Stud, 18, pp. 111-132, (2013); Inuit Land Use and Occupancy Project: A Report, (1976); Development and Contribution of Mukurtu, (2020); Ocean Frontier Institute’s Indigenous Engagement Guide, (2021); An Inuit Strategy for the Future of Pikialasorsuaq, (2019); CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance, (2019); Sieber R., Public participation geographic information systems: a literature review and framework, Ann. Assoc. Am. Geogr, 96, pp. 491-507, (2006); Overview of the Inuit Sea Ice Use and Occupancy Project, (2017); Simonds V., Christopher S., Adapting Western research methods to indigenous ways of knowing, Am. J. Public Health, 103, pp. 2185-2192, (2013); Simpson L., Anticolonial Strategies for the Recovery and Maintenance of Indigenous Knowledge, Am. Indian Q, 28, pp. 373-384, (2004); Simpson L., Land as pedagogy: nishnaabeg intelligence and rebellious transformation, Decolonization, 3, pp. 1-25, (2014); Singh G.G., Harden-Davies H., Allison E.H., Cisneros-Montemayor A.M., Swartz W., Crosman K.M., Et al., Opinion: will understanding the ocean lead to “the ocean we want”?, Proc. Nat. Acad. Sci, 118, e2100205118, (2021); Smit M., Kelly R., Fitzsimmons S., Bruce S., Bulger C., Covey B., Et al., Canadian Integrated Ocean Observing System: Cyberinfrastructure Investigative Evaluation, (2017); Stewart A., DeYoung B., Smit M., Donaldson K., Reedman A., Bastien A., Et al., The development of a Canadian integrated ocean observing system (CIOOS), Front. Mar. Sci, 6, 431, (2019); Strand K.J., Cutforth N., Stoecker R., Marullo S., Donohue P., Community-Based Research and Higher Education: Principles and Practices, (2003); Tanhua T., McCurdy A., Fischer A., Appeltans W., Bax N., Currie K., Et al., What we have learned from the framework for ocean observing: evolution of the global ocean observing system, Front. Mar. Sci, 6, 471, (2019); Teixeira J., Martins A., Pinheiro H., Secchin N., De Moura R., Bastos A., Traditional Ecological Knowledge and the mapping of benthic marine habitats, J. Environ. Manage, 115, 241, (2013); Tesar C., Dahl P.E., Aporta C., Picturing Pikialasorsuaq: ethics and effectiveness of representing Inuit knowledge in an online atlas, J. Ocean Technol, 14, pp. 13-21, (2019); Turner N., Gregory R., Brooks C., Failing L., Satterfield T., From invisibility to transparency: identifying the implications, Ecol. Soc, 13, 7, (2008); (2007); Whaanga H., Bainbridge D., Anderson M., Scrivener K., Cader P., Roa T., Et al., He Matapihi Mā Mua, Mō Muri: the Ethics, Processes, and Procedures Associated with the Digitization of Indigenous Knowledge-The Pei Jones Collection, Cat. Classif. Q, 53, pp. 520-547, (2015); Wilkinson M., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Withers P., Sipekne’katik May Seek UN Peacekeepers for Contentious N.S. Fishery Relaunch. CBC News, (2021); Witze A., How the Fight Over a Hawaii Mega Telescope Could Change Astronomy: Thirty Meter Telescope Controversy is Forcing Scientists to Grapple with How Their Research Affects Indigenous Peoples, (2020); Wiwchar D., Genetic Researcher Uses Nuu-chah-nulth Blood for Unapproved Studies in Genetic Anthropology, (2000)","M. Proulx; Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, Canada; email: mj.proulx@canoemuseum.ca; M. Smit; Canadian Integrated Ocean Observing System - Atlantic, Ocean Frontier Institute, Dalhousie University, Halifax, Canada; email: mike.smit@dal.ca","","Frontiers Media S.A.","","","","","","22967745","","","","English","Front. Mar. Sci.","Review","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85111601389"
"Zulauf B.; Knipprath N.","Zulauf, Bert (57926303700); Knipprath, Nina (57218483272)","57926303700; 57218483272","Experiential report from research data management with regard to electronic laboratory books; [Erfahrungsbericht aus dem Forschungsdatenmanagement in Bezug auf elektronische Laborbücher]","2022","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-326","","","1359","1363","4","0","10.18420/inf2022_116","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139744312&doi=10.18420%2finf2022_116&partnerID=40&md5=ff1c5c680fb1bb8a86daeaf814736407","Heinrich-Heine-Universität-Düsseldorf, Zentrum für Informations- und Medientechnologie, Universitätsstraße 1, Düsseldorf, 40225, Germany","Zulauf B., Heinrich-Heine-Universität-Düsseldorf, Zentrum für Informations- und Medientechnologie, Universitätsstraße 1, Düsseldorf, 40225, Germany; Knipprath N., Heinrich-Heine-Universität-Düsseldorf, Zentrum für Informations- und Medientechnologie, Universitätsstraße 1, Düsseldorf, 40225, Germany","[No abstract available]","Digitalisierung; ELB; elektronische Laborbücher; ELN; Forschungsdatenmanagement; Forschungspraxis; Naturwissenschaften","","","","","","","","Adam B., Lindstadt B., Elektronische Laborbücher im Kontext von Forschungsdatenmanagement und guter wissenschaftlicher Praxis - ein Wegweiser für die Lebenswissenschaften, (2019); ELB.nrw AG zu Elektronischen Laborbüchern; Hewera M., Et al., An inexpensive and easy-to-implement approach to a Quality Management System for an academic research lab, F1000Research, 9, (2020); Knipprath N., Schlussbericht FoDaKo - Forschungsdatenmanagement in Kooperation, (2020); Zulauf B., Knipprath N., Electronic Lab Notebooks - early research practice in teaching, 12th International Conference of Education, Research and Innovation (ICERI2019)","","Demmler D.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg; Krupka D.; Gesellschaft fur Informatik, Anna-Louisa-Karsch-Strasse 2, Berlin; Federrath H.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg","Gesellschaft fur Informatik (GI)","Adesso SE; et al.; Genua GmbH; Google Deutschland GmbH; Hamburger Informatik Technologie Center (HITEC); SAP SE","2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022","26 September 2022 through 30 September 2022","Hamburg","183150","16175468","978-388579720-3","","","German","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85139744312"
"Borycz J.; Carroll A.J.","Borycz, Joshua (57207788260); Carroll, Alexander J. (57188970110)","57207788260; 57188970110","Covid-19 as an opportunity to expand the instructional portfolio of stem librarians","2021","Issues in Science and Technology Librarianship","98","","","","","","1","10.29173/ISTL2609","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119616898&doi=10.29173%2fISTL2609&partnerID=40&md5=126b7053d5f8ead161fbc70ee0fc05d2","Librarian for STEM Research, Vanderbilt University, United States","Borycz J., Librarian for STEM Research, Vanderbilt University, United States; Carroll A.J., Librarian for STEM Research, Vanderbilt University, United States","The pivot to online teaching caused by the COVID-19 pandemic enabled science and engineering librarians at Vanderbilt University to expand their teaching roles within graduate-level courses in biomedical engineering, chemistry, and physics. In addition to addressing traditional information literacy skills related to information retrieval and resource evaluation, these new lessons addressed important science process skills such as academic reading, responsible conduct of research, and research data management. A facility with cloud-based teaching tools such as Zoom breakout rooms and Excel for Microsoft 365 allowed for engaging instructional experiences, even within synchronous online instructional environments. By integrating these topics into the graduate curricula, these guest lectures supported the professional development of early career graduate students and deepened relationships with the course instructors of record. © 2021, Association of College and Research Libraries. All rights reserved.","","","","","","","","","Assor A., Kaplan H., Roth G., Choice is good, but relevance is excellent: Autonomy-enhancing and suppressing teacher behaviours predicting students’ engagement in schoolwork, British Journal of Educational Psychology, 72, 2, pp. 261-278, (2002); Auckland M., Re-skilling for research an investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Bakkalbasi N., Rockenbach B., Tancheva K., Vine R., ARL Library Liaison Institute: What we learned about needs and opportunities for reskilling, College & Research Libraries News, 77, 3, pp. 118-121, (2016); Bandini J., Mitchell C., Epstein-Peterson Z.D., Amobi A., Cahill J., Peteet J., Balboni T., Balboni M.J., Student and faculty reflections of the hidden curriculum: How does the hidden curriculum shape students’ medical training and professionalization?, American Journal of Hospice and Palliative Medicine, 34, 1, pp. 57-63, (2015); Blakeslee S., The CRAAP test, LOEX Quarterly, 31, 3, (2004); Blanco M.A., Capello C.F., Dorsch J.L., Perry G., Zanetti M.L., A survey study of evidence-based medicine training in US and Canadian medical schools, Journal of the Medical Library Association, 102, 3, pp. 160-168, (2014); Borycz J., Implementing data management workflows in research groups through integrated library consultancy, Data Science Journal, 20, 1, (2021); Borycz J., Carroll A.J., Vanderbilt Science and Engineering Library information literacy instruction lectures 2020-2021, (2020); Bruff D., Resources for just-in-time online teaching, (2020); Bruff D., Active learning in hybrid and physically distanced classrooms, (2020); Calarco J.M., A Field Guide to Grad School: Uncovering the Hidden Curriculum, (2020); Carroll A.J., Thinking and reading like a scientist: Librarians as facilitators of primary literature literacy, Medical Reference Services Quarterly, 39, 3, pp. 295-307, (2020); Carroll A.J., Borycz J.D., Vernon J., Work in progress: Integrating information literacy into a multidisciplinary first-year engineering program, 2020 ASEE Virtual Annual Conference & Exposition Proceedings, (2020); Coil D., Wenderoth M.P., Cunningham M., Dirks C., Teaching the process of science: Faculty perceptions and an effective methodology, CBE: Life Sciences Education, 9, 4, pp. 524-535, (2010); Eskridge H.N., Carroll A.J., Why do we need an engineering library?”: Designing team-based liaison services for STEM educators and researchers, portal: Libraries and the Academy, 20, 4, pp. 565-584, (2020); Greer K., Hess A.N., Kraemer E.W., The librarian leading the machine: A reassessment of library instruction methods, College & Research Libraries, 77, 3, pp. 286-301, (2016); Handelsman J., Ebert-May D., Beichner R., Bruns P., Chang A., DeHaan R., Gentile J., Lauffer S., Stewart J., Tilghman S.M., Wood W.B., Scientific teaching, Science, 304, 5670, pp. 521-522, (2004); Herring C., Walther J., Academic help-seeking as a stand-alone, metacognitive action: An empirical study of experiences and behaviors in undergraduate engineering students, 2016 ASEE Annual Conference & Exposition Proceedings, (2016); Inda M., Rodriguez C., Pena J.V., Gender differences in applying social cognitive career theory in engineering students, Journal of Vocational Behavior, 83, 3, pp. 346-355, (2013); Klem M.L., Weiss P.M., Evidence-based resources and the role of librarians in developing evidence-based practice curricula, Journal of Professional Nursing, 21, 6, pp. 380-387, (2005); Klipfel K.M., Authentic engagement: Assessing the effects of authenticity on student engagement and information literacy in academic library instruction, Reference Services Review, 42, 2, pp. 229-245, (2014); Maggio L.A., Kung J.Y., How are medical students trained to locate biomedical information to practice evidence-based medicine? A review of the 2007–2012 literature, Journal of the Medical Library Association, 102, 3, pp. 184-191, (2014); Nathan M.J., Koedinger K.R., Alibali M.W., Expert blind spot: When content knowledge eclipses pedagogical content knowledge, Proceedings of the Third International Conference on Cognitive Science, pp. 644-648, (2001); Payakachat N., Gubbins P.O., Ragland D., Norman S.E., Flowers S.K., Stowe C.D., DeHart R.M., Pace A., Hastings J.K., Academic help-seeking behavior among student pharmacists, American Journal of Pharmaceutical Education, 77, 1, (2013); Raso A., Marchetti A., D'Angelo D., Albanesi B., Garrino L., Dimonte V., Piredda M., Marinis M.G.D., The hidden curriculum in nursing education: A scoping study, Medical Education, 53, 10, pp. 989-1002, (2019); Turner K.H., Building community in a virtual course, Teaching/Writing: The Journal of Writing Teacher Education, 9, 1, (2020); Technology and the virtual university: Opportunities and challenges, (1997); VanScoy A., Oakleaf M.J., Evidence vs. anecdote: Using syllabi to plan curriculum-integrated information literacy instruction, College & Research Libraries, 69, 6, pp. 566-575, (2008); Villanueva I., Gelles L., Di Stefano M., Smith B., Tull R., Lord S., Benson L., Hunt A., Riley D., Ryan G., What does hidden curriculum in engineering look like and how can it be explored?, 2018 ASEE Annual Conference & Exposition Proceedings, (2018); Vogt C.M., Hocevar D., Hagedorn L.S., A social cognitive construct validation: Determining women’s and men’s success in engineering programs, The Journal of Higher Education, 78, 3, pp. 337-364, (2007); Wallace M.C., Shorten A., Crookes P.A., McGurk C., Brewer C., Integrating information literacies into an undergraduate nursing programme, Nurse Education Today, 19, 2, pp. 136-141, (1999); Wiederhold B.K., Connecting through technology during the coronavirus disease 2019 pandemic: Avoiding “Zoom Fatigue, Cyberpsychology, Behavior, and Social Networking, 23, 7, pp. 437-438, (2020); Woolston C., PhDs: The tortuous truth, Nature, 575, 7782, pp. 403-406, (2019)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85119616898"
"Persaud B.D.; Dukacz K.A.; Saha G.C.; Peterson A.; Moradi L.; O'Hearn S.; Clary E.; Mai J.; Steeleworthy M.; Venkiteswaran J.J.; Kheyrollah Pour H.; Wolfe B.B.; Carey S.K.; Pomeroy J.W.; DeBeer C.M.; Waddington J.M.; Van Cappellen P.; Lin J.","Persaud, Bhaleka D. (57214107890); Dukacz, Krysha A. (57376174100); Saha, Gopal C. (57531191100); Peterson, Amber (57189216787); Moradi, Laleh (57376025800); O'Hearn, Stephen (57376025900); Clary, Erin (57193403186); Mai, Juliane (55699749600); Steeleworthy, Michael (55798242400); Venkiteswaran, Jason J. (6507911199); Kheyrollah Pour, Homa (55339245900); Wolfe, Brent B. (7201861594); Carey, Sean K. (7103028819); Pomeroy, John W. (24492458800); DeBeer, Chris M. (25633608900); Waddington, James M. (7102476841); Van Cappellen, Philippe (7003453108); Lin, Jimmy (56824507200)","57214107890; 57376174100; 57531191100; 57189216787; 57376025800; 57376025900; 57193403186; 55699749600; 55798242400; 6507911199; 55339245900; 7201861594; 7103028819; 24492458800; 25633608900; 7102476841; 7003453108; 56824507200","Ten best practices to strengthen stewardship and sharing of water science data in Canada","2021","Hydrological Processes","35","11","e14385","","","","2","10.1002/hyp.14385","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121349428&doi=10.1002%2fhyp.14385&partnerID=40&md5=f3e9767f8931eb5ce30baa2549be4e47","Water Institute, University of Waterloo, Waterloo, ON, Canada; School of Earth, Environment & Society, Master University, Hamilton, ON, Canada; Library, Wilfrid Laurier University, Waterloo, ON, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; Digital Research Alliance of Canada, Ottawa, ON, Canada; Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada; Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada","Persaud B.D., Water Institute, University of Waterloo, Waterloo, ON, Canada; Dukacz K.A., School of Earth, Environment & Society, Master University, Hamilton, ON, Canada; Saha G.C., Library, Wilfrid Laurier University, Waterloo, ON, Canada; Peterson A., Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; Moradi L., Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; O'Hearn S., Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; Clary E., Digital Research Alliance of Canada, Ottawa, ON, Canada; Mai J., Water Institute, University of Waterloo, Waterloo, ON, Canada; Steeleworthy M., Library, Wilfrid Laurier University, Waterloo, ON, Canada; Venkiteswaran J.J., Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada; Kheyrollah Pour H., Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada; Wolfe B.B., Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada; Carey S.K., School of Earth, Environment & Society, Master University, Hamilton, ON, Canada; Pomeroy J.W., Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada; DeBeer C.M., Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada, Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada; Waddington J.M., School of Earth, Environment & Society, Master University, Hamilton, ON, Canada; Van Cappellen P., Water Institute, University of Waterloo, Waterloo, ON, Canada; Lin J., Water Institute, University of Waterloo, Waterloo, ON, Canada","Water science data are a valuable asset that both underpins the original research project and bolsters new research questions, particularly in view of the increasingly complex water issues facing Canada and the world. Whilst there is general support for making data more broadly accessible, and a number of water science journals and funding agencies have adopted policies that require researchers to share data in accordance with the findable, accessible, interoperable, reusable (FAIR) principles, there are still questions about effective management of data to protect their usefulness over time. Incorporating data management practices and standards at the outset of a water science research project will enable researchers to efficiently locate, analyse and use data throughout the project lifecycle, and will ensure the data maintain their value after the project has ended. Here, some common misconceptions about data management are highlighted, along with insights and practical advice to assist established and early career water science researchers as they integrate data management best practices and tools into their research. Freely available tools and training opportunities made available in Canada through Global Water Futures, The Gordon Foundation DataStream, the Digital Research Alliance of Canada Portage Network, Compute Canada, and university libraries, among others are compiled. These include webinars, training videos, and individual support for the water science community that together enable researchers to protect their data assets and meet the expectations of journals and funders. The perspectives shared here have been developed as part of the Global Water Futures programme's efforts to improve data management and promote the use of common data practices and standards in the context of water science in Canada. Ten best practices are proposed that may be broadly applicable to other disciplines in the natural sciences and can be adopted and adapted globally. © 2021 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.","best practices; Canada; data management plan; data repositories; FAIR principles; metadata; research data management; water science","Canada; Digital libraries; Life cycle; Best practices; Canada; Data management plan; Data repositories; Findable, accessible, interoperable, reusable principle; Management plans; Research data managements; Research questions; Science-data; Water science; best management practice; data management; metadata; research work; water resource; Information management","","","","","Digital Research Alliance of Canada; Canada First Research Excellence Fund, CFREF","Funding text 1: This work was supported by the Canada First Research Excellence Fund's Global Water Futures Programme and is a product of its Data Management Core Team in collaboration with the Digital Research Alliance of Canada. Thanks to Julie Grant from the Water Institute and Alyssa Graham for their assistance with graphical abstract and Figure 1 . We would also like to thank the two reviewers whose suggestions improved this article. ; Funding text 2: Within academia, water science research in Canada builds on a rich legacy that continues to grow (Sandford et al., 2018 ; Woo, 2019 ). The Global Water Futures Programme (GWF), a 7‐year, multi‐university led project supported by the Canada First Research Excellence Fund (CFREF), aims to position Canada as a global leader in water science and help Canadians manage the risks of uncertain water futures and extreme hydroclimatic events. GWF, in line with the current national government and RDM frameworks, works to identify and address the challenges of accessing and managing water science research data in Canada to protect and extend this legacy. GWF's programme includes 18 universities, where over 189 investigators conduct research at 60 water observatories in different physiographic settings and ecological regions across Canada's major river basins. Many of the sites build on a legacy of previous research which has produced extensive and long‐term hydrometric, hydrometeorological, ecological, and geophysical datasets. The scope and scale of this long‐term observational network is unprecedented for a university‐led water research program and is a fundamental program strength. This also supports and links to global research initiatives. 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Retrieved April 12, 2021, from, (2016); Guide to instruments and methods of observation, (2018); Planning of water quality monitoring systems (Technical report series No. 03). Geneva: WMO. Retrieved January 21, 2021, from, (2013); Guidelines on homogenization. World Meteorological Organization, (2020); The guide to hydrological practices. World Meteorological Organization, (2008); Hydrological information systems for integrated water resources management, (2005); Woo M., Cryohydrology in Canada: A brief history, Hydrological Processes, 33, 26, pp. 3407-3411, (2019); Water Quality Exchange—WQX| the exchange network, (2020); Young D., Shumway L., DataStream (Producer) & DataStream (Director). Dive into data: Standardizing water quality data [DVD] YouTube, (2020); Zingel A., Suspending water quality monitoring during pandemic a 'serious oversight,' says expert, (2020)","B.D. Persaud; Water Institute, University of Waterloo, Waterloo, Canada; email: bd2persa@uwaterloo.ca","","John Wiley and Sons Ltd","","","","","","08856087","","HYPRE","","English","Hydrol. Processes","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85121349428"
"Bishop B.W.; Collier H.R.; Orehek A.M.; Ihli M.","Bishop, Bradley Wade (24469564000); Collier, Hannah Rose (57226268725); Orehek, Ashley Marie (57219600826); Ihli, Monica (56946866300)","24469564000; 57226268725; 57219600826; 56946866300","Potential roles for science librarians in research data management: A gap analysis","2021","Issues in Science and Technology Librarianship","","98","","","","","1","10.29173/ISTL2602","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120311737&doi=10.29173%2fISTL2602&partnerID=40&md5=ff00dbd15ac452c4a64b6a4d0e894161","School of Information Sciences, University of Tennessee, Knoxville, TN, United States; Katie Murrell Library, Lindsey Wilson College, Columbia, KY, United States; ORNL University Libraries, University of Tennessee, Knoxville, TN, United States","Bishop B.W., School of Information Sciences, University of Tennessee, Knoxville, TN, United States; Collier H.R., School of Information Sciences, University of Tennessee, Knoxville, TN, United States; Orehek A.M., Katie Murrell Library, Lindsey Wilson College, Columbia, KY, United States; Ihli M., ORNL University Libraries, University of Tennessee, Knoxville, TN, United States","As many sciences move to be more data-intensive, some science librarians are offering more research data services and perform research data management roles. Job analyses provide insight and context to the tasks employees actually do versus what their job descriptions depict or employers assume. Two separate job analyses studies investigated the roles and responsibilities of data services librarians and research integrity officers among the top 10 private and top 10 public higher education institutions. The focus of these interviews was research data management support roles. Comparing these two groups’ responses indicates that the role-based responsibilities for research data services are not always clear within institutions and are predominantly placed on individual researchers or research teams, but science librarians may provide some solutions to address this gap. This paper presents a model for the potential roles of science librarians in research data management. © 2021, Association of College and Research Libraries. All rights reserved.","","","","","","","","","Core values of librarianship, (2019); Bishop B.W., Data management plan compliance and evaluation [Data Set], (2020); Bishop B.W., Data services librarians, (2020); Bishop B.W., Hank C., Digital curation, International Encyclopedia of Human Geography, pp. 323-328, (2020); Bishop B.W., Nobles R., Collier H., Research integrity officers' responsibilities and perspectives on data management plan compliance and evaluation, Journal of Research Administration, 52, 1, pp. 76-101, (2021); Bishop B.W., Orehek A.M., Eaker C., Smith P., Data services librarians’ responsibilities and perspectives on research data management: Discussion of the results, challenges, and opportunities, Journal of eScience Librarianship; Bishop B.W., Orehek A.M., Eaker S., Smith P., Data services librarians’ responsibilities and perspectives on research data management: The context and data collection, Journal of eScience Librarianship; Brandenburg M.D., Anderson Cordell S., Joque J., MacEachern M.P., Song J., Interdisciplinary collaboration: Librarian involvement in grant projects, College & Research Libraries, 78, 3, pp. 272-282, (2017); Carlson J., Johnston L.R., Westra B., Developing the data information literacy project, Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, pp. 35-50, (2015); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, LIBER Quarterly, 24, 1, pp. 17-48, (2014); Cox A., Verbaan E., Exploring Research Data Management, (2018); Glaser B.G., Strauss A.L., The Discovery of Grounded Theory: Strategies for Qualitative Research, (1967); Gola C.H., Martin L., Creating an emotional intelligence community of practice: A case study for academic libraries, Journal of Library Administration, 60, 7, pp. 752-761, (2020); Gunderman H., Lesson plans for teaching spatial data management in academic libraries through a lens of popular culture [Institutional Repository], (2020); Herr M., Responding to research misconduct: A primer for LIS professionals, Science & Technology Libraries, 38, 3, pp. 272-287, (2019); Jaguszewski J., Williams K., New roles for new times: Transforming liaison roles in research libraries [Report], (2013); Kalichman M., Survey study of research integrity officers’ perceptions of research practices associated with instances of research misconduct, Research Integrity and Peer Review, 5, (2020); Koltay T., Accepted and emerging roles of academic libraries in supporting research 2.0, The Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Lee C.L., Goh G.S., Ali Y.A., Effectiveness of data auditing as a tool to reinforce good Research Data Management (RDM) practice, Abstract Book of the 6th World Conference on Research Integrity, (2019); Semiannual report to Congress, (2019); Semiannual report to Congress; Semiannual report to Congress; Policies of General Applicability – Definitions, C.F.R. Sect, 42, (1989); Read A., Cox A., Underrated or overstated? The need for technological competencies in scholarly communication librarianship, The Journal of Academic Librarianship, 46, 4, (2020); Rice R., Southall J., The Data Librarian’s Handbook, (2016); Schmidt L., Holles J., A Graduate class in research data management, Chemical Engineering Education, 52, 1, pp. 52-59, (2018); Steneck N., ORI introduction to the responsible conduct of research [Report], (2007); Best national university rankings, (2020); Van Loon J.E., Akers K.G., Hudson C., Sarkozy A., Quality evaluation of data management plans at a research university, IFLA Journal, 43, 1, pp. 98-104, (2017); Williams M., Bagwell J., Zozus M.N., Data management plans: The missing perspective, Journal of Biomedical Informatics, 71, pp. 130-142, (2017); Wright D.E., Schneider P.P., Training the research integrity officers (RIO): The federally funded “RIO Boot Camps'' backward design to train for the future, Journal of Research Administration, 41, 3, pp. 99-117, (2010)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85120311737"
"Thieberger N.; Harris A.","Thieberger, Nick (15842713700); Harris, Amanda (55603141200)","15842713700; 55603141200","When Your Data is My Grandparents Singing. Digitisation and Access for Cultural Records, the Pacific and Regional Archive for Digital Sources in Endangered Cultures (PARADISEC)","2022","Data Science Journal","21","1","9","","","","1","10.5334/dsj-2022-009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128662460&doi=10.5334%2fdsj-2022-009&partnerID=40&md5=24fba134d83cbb26b7d5298a10045822","School of Languages and Linguistics, University of Melbourne, Australia; Sydney Conservatorium of Music, The University of Sydney, Australia","Thieberger N., School of Languages and Linguistics, University of Melbourne, Australia; Harris A., Sydney Conservatorium of Music, The University of Sydney, Australia","In this paper we discuss the Pacific and Regional Archive for Digital Sources in Endangered Cultures (PARADISEC), a research repository that explicitly aims to act as a conduit for research outputs to a range of audiences, both within and outside of academia. PARADISEC has been operating for 19 years, and has grown to hold over 390,000 files currently totaling 150 terabytes and representing 1,312 languages, many of them from Papua New Guinea and the Pacific. Our focus is on recordings and transcripts in the many small languages of the world, the songs and stories that are unique cultural expressions. While this research data is created for a particular project, it has huge value beyond academic research as it is typically oral tradition recorded in places where little else has been recorded. There is an increasing focus in academia on reproducible research and research data management, and repositories are the key to successful data management. We discuss the importance for research practice of having discipline-specific repositories. The data in our work is also cultural material that has value to the people recorded and their descendants, it is their grandparents and so we, as outsider researchers, have special responsibilities to treat the materials with respect and to ensure they are accessible to the people we have worked with. © 2022 The Author(s).","language data management; language documentation; linguistics archiving; musicological archiving","Music; Cultural records; Digital sources; Digitisation; Language data management; Language documentation; Linguistic archiving; Musicological archiving; Papua New Guinea; Research data; Research outputs; Information management","","","","","Australian Research Council, ARC, (ARC CE14010004, DP0450342, DP0984419, FT140100214, LE0453247, LE0560711, LE110100142)","Funding text 1: Australian Research Council LIEF grants LE110100142, LE0560711, LE0453247, ARC DP0450342, DP0984419, & FT140100214, ARC CE14010004, Australian Research Data Commons (2019).; Funding text 2: He is interested in digital research methods and their potential to improve research practice and is developing methods for creation of reusable data sets from fieldwork on previously unrecorded languages. He is the editor of the journal Language Documentation & Conservation. He taught in the Department of Linguistics at the University of Hawai’i at Manoa and is nō w an Associate Professor in the School of Languages and Linguistics, University of Melbourne. http://nthieberger.net Amanda Harris’s research combines methods from historical studies, musicology and digital humanities. She is interested in collaborative research that engages speaker communities with archival materials and that mentors emerging Indigenous scholars in developing collaborative methodologies. As Partner Investigator in the Leverhulme funded project True Echoes: reconnecting cultures with recordings from the beginning of sound (2019–21), she collaborates with cultural heritage institutions in PNG, Solomon Islands, New Caledonia and the UK. At the Sydney Conservatorium of Music, she is a Senior Research Fellow and works as part of an interdisciplinary Australian research team on a project funded by the Australian Research Council entitled ‘Hearing the music of early NSW 1788–1860. Her book Representing Australian Aboriginal Music and Dance 1930–70 was published by Bloomsbury Publishing in 2020.","Barwick L., Turning it all upside down… Imagining a distributed digital audiovisual archive, Literary and Linguistic Computing, 19, 3, pp. 253-263, (2004); Barwick L, Thieberger N., Unlocking the archives, Communities in Control: Learning tools and strategies for multilingual endangered language communities. Proceedings of the 2017 XXI FEL conference, pp. 135-139, (2018); Berez-Kroeker AL, Gawne L, Kelly BF, Heston T, Kung S, Holton G, Pulsifer P, Beaver D, Chelliah S, Dubinsky S, Meier R, Thieberger N, Rice K, Woodbury A., Reproducible research in linguistics: A position statement on data citation and attribution in our field, Linguistics, 56, 1, pp. 1-18, (2018); Corti L, Van den Eynden V, Bishop L, Woollard M., Managing and Sharing Research Data: A Guide to Good Practice (Paperback), (2014); Eberhard DM, Simons GF, Fennig CD., Ethnologue: Languages of the World, (2022); Himmelmann N., Documentary and descriptive linguistics, Linguistics, 36, 1, pp. 161-195, (1998); Thieberger N., Technology in support of languages of the Pacific: neo-colonial or post-colonial?, Asian-European Music Research Journal, 5–3, pp. 17-24, (2020)","N. Thieberger; School of Languages and Linguistics, University of Melbourne, Australia; email: thien@unimelb.edu.au","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128662460"
"Zieliński T.; Hay J.; Millar A.J.","Zieliński, Tomasz (56181435000); Hay, Johnny (57211125119); Millar, Andrew J. (7201856684)","56181435000; 57211125119; 7201856684","Period Estimation and Rhythm Detection in Timeseries Data Using BioDare2, the Free, Online, Community Resource","2022","Methods in Molecular Biology","2398","","","15","32","17","0","10.1007/978-1-0716-1912-4_2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118189966&doi=10.1007%2f978-1-0716-1912-4_2&partnerID=40&md5=1050be629d2fe06c09d7379ce7473a20","SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, Edinburgh, United Kingdom; EPCC, University of Edinburgh, Edinburgh, United Kingdom","Zieliński T., SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, Edinburgh, United Kingdom; Hay J., EPCC, University of Edinburgh, Edinburgh, United Kingdom; Millar A.J., SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, Edinburgh, United Kingdom","One of the key objectives of data analysis in circadian research is to quantify the rhythmic properties of the experimental data. BioDare2 is a free, online service which provides fast timeseries analysis, attractive visualizations, and data sharing. This chapter outlines the description of an experiment for BioDare2 and how to upload and analyze the numerical timeseries data. © 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.","Biological clocks; Circadian rhythms; Data repository; Data sharing; Metadata; Period analysis; Research data management; Rhythmicity","Biological Clocks; Circadian Rhythm; Internet; Residence Characteristics; circadian rhythm; data analysis; information service; metadata; quantitative analysis; biological rhythm; demography; Internet","","","","","UK Centre for Mammalian Synthetic Biology, (BB/M018040); Wellcome Trust, WT, (204804/Z/16/Z); Seventh Framework Programme, FP7, (245143); Biotechnology and Biological Sciences Research Council, BBSRC, (BB/D019621); European Commission, EC","Funded by the European Commission through FP7 Integrated Project TiMet (award 245143), by the Wellcome Trust (award 204804/Z/16/Z) and by the Biotechnology and Biological Sciences Research Council (BBSRC) through the Centre for Systems Biology at Edinburgh [BB/D019621] and UK Centre for Mammalian Synthetic Biology [BB/M018040].","Edwards K.D., Akman O.E., Knox K., Lumsden P.J., Thomson A.W., Et al., Quantitative analysis of regulatory flexibility under changing environmental conditions, Mol Syst Biol, 6, (2010); Burg J.P., The relationship between maximum entropy spectra and maximum likelihood spectra, Geophysics, 37, pp. 375-376, (1972); Enright J.T., The search for rhythmicity in biological time-series, J Theoret Biol, 8, pp. 426-268, (1965); Lomb N.R., Least-squares frequency analysis of unequally spaced data, Astrophys Space Sci, 39, pp. 447-462, (1976); Costa M.J., Finkenst€Adt B., Roche V., Levi F., Gould P.D., Et al., Inference on periodicity of circadian time series, Biostatistics, 14, 4, pp. 792-806, (2013); Zielinski T., Moore A.M., Troup E., Halliday K.J., Millar A.J., Strengths and limitations of period estimation methods for circadian data, Plos One, 9, (2014); Hughes M., Hogenesch J., Kornacker K., JTK CYCLE: An efficient non-parametric algorithm for detecting rhythmic components in genome-scale datasets, J Biol Rhythm, 25, pp. 372-380, (2010); Hutchison A.L., Maienschein-Cline M., Chiang A.H., Et al., Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data, Plos Comput Biol, 11, 3, (2015); Yang R., Su Z., Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation, Bioinformatics, 26, 12, pp. i168-i174, (2010)","A.J. Millar; SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, Edinburgh, United Kingdom; email: andrew.millar@ed.ac.uk","","Humana Press Inc.","","","","","","10643745","","","34674164","English","Methods Mol. Biol.","Book chapter","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85118189966"
"Rodrigues J.; Lopes C.T.","Rodrigues, Joana (57203242279); Lopes, Carla Teixeira (57194455159)","57203242279; 57194455159","Solutions for Data Sharing and Storage: A Comparative Analysis of Data Repositories","2022","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13541 LNCS","","","512","517","5","0","10.1007/978-3-031-16802-4_55","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138792962&doi=10.1007%2f978-3-031-16802-4_55&partnerID=40&md5=ad14affaf6c8d6376a7bcf35c684d85f","INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Rodrigues J., INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Lopes C.T., INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Research data management is an essential process in scientific research activities. It includes monitoring data from the moment it is created until it is deposited in a repository so that later it can be accessed and reused by others. Sharing and reuse are the last steps in this process. It is essential to ensure that the data stored in digital repositories is well preserved in the long term and that its adequate interpretation and future reuse is guaranteed. Following this debate, questions arise related to the interoperability of systems and the suitability of platforms. In this study, we study how data management platforms can solve the problems associated with description, preservation, and access in digital media, making their usefulness evident. We identify some of the most relevant repository platforms in the scope of research data management, offering the scientific community an aggregating view of the various solutions and their main characteristics, thus aiming at a better understanding of them for their appropriate choice. © 2022, Springer Nature Switzerland AG.","Data repositories; Research data management; Sharing","Information management; Interoperability; Analysis of data; Comparative analyzes; Data repositories; Data Sharing; Data storage; Management IS; Research data managements; Reuse; Scientific researches; Sharing; Digital storage","","","","","Fundação para a Ciência e a Tecnologia, FCT, (PD/BD/150288/2019)","Acknowledgements. Joana Rodrigues is supported by research grant from FCT - Funda¸cão para a Ciência e Tecnologia: PD/BD/150288/2019.","Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univ. Access Inf. Soc., 16, 4, pp. 851-862, (2016); Armbruster C., Romary L., Comparing repository types: Challenges and barriers for subject-based repositories, research repositories, national repository systems and institutional repositories in serving scholarly communication, SSRN Electron. J., (2010); Guedj D., Ramjoue C., European commission policy on open-access to scientific publications and research data in horizon 2020, Biomed. Data J., 1, pp. 11-14, (2015); Heidorn P., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, pp. 280-299, (2008); Lagoze C., Sompel H., Nelson M., Warner S., The Open Archives Initiative Protocol for Metadata Harvesting, (2002); Lynch C.A., Institutional repositories: Essential infrastructure for scholarship in the digital age, Portal Libr. Acad., 3, 2, pp. 327-336, (2003); Nelson M.L., Sompel H.V.D., Warner S., Advanced overview of version 2.0 of the open archives initiative protocol for metadata harvesting, Proceedings of the 2Nd ACM/IEEE-CS Joint Conference on Digital Libraries, (2002)","J. Rodrigues; INESC TEC, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: joanasousarodrigues.14@gmail.com","Silvello G.; Corcho O.; Manghi P.; Di Nunzio G.M.; Golub K.; Ferro N.; Poggi A.","Springer Science and Business Media Deutschland GmbH","","26th International Conference on Theory and Practice of Digital Libraries, TPDL 2022","20 September 2022 through 23 September 2022","Padua","283349","03029743","978-303116801-7","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85138792962"
"Austin T.; Bei K.; Efthymiadis T.; Koumoulos E.P.","Austin, Timothy (55509991600); Bei, Kyriaki (57259426800); Efthymiadis, Theodoros (57259331500); Koumoulos, Elias P. (36930766000)","55509991600; 57259426800; 57259331500; 36930766000","Lessons learnt from engineering science projects participating in the horizon 2020 open research data pilot","2021","Data","6","9","96","","","","2","10.3390/data6090096","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114943239&doi=10.3390%2fdata6090096&partnerID=40&md5=cee23f7ac33e0a875f2f0c0d586cfb77","Joint Research Centre (JRC), European Commission, Petten, 1755 LE, Netherlands; Data Science Group, Innovation in Research & Engineering Solutions (IRES), Rue Koningin Astridlaan 59B, Wemmel, 1780, Belgium","Austin T., Joint Research Centre (JRC), European Commission, Petten, 1755 LE, Netherlands; Bei K., Data Science Group, Innovation in Research & Engineering Solutions (IRES), Rue Koningin Astridlaan 59B, Wemmel, 1780, Belgium; Efthymiadis T., Data Science Group, Innovation in Research & Engineering Solutions (IRES), Rue Koningin Astridlaan 59B, Wemmel, 1780, Belgium; Koumoulos E.P., Data Science Group, Innovation in Research & Engineering Solutions (IRES), Rue Koningin Astridlaan 59B, Wemmel, 1780, Belgium","Trends in the sciences are indicative of data management becoming established as a feature of the mainstream research process. In this context, the European Commission introduced an Open Research Data pilot at the start of the Horizon 2020 research programme. This initiative followed the success of the Open Access pilot implemented in the prior (FP7) research programme, which thereafter became an integral component of Horizon 2020. While the Open Access phenomenon can reasonably be argued to be one of many instances of web technologies disrupting established business models (namely publication practices and workflows established over several centuries in the case of Open Access), initiatives designed to promote research data management have no established foundation on which to build. For Open Data to become a reality and, more importantly, to contribute to the scientific process, data management best practices and workflows are required. Furthermore, with the scientific community having operated to good effect in the absence of data management, there is a need to demonstrate the merits of data management. This circumstance is complicated by the lack of the necessary ICT infrastructures, especially interoperability standards, required to facilitate the seamless transfer, aggregation and analysis of research data. Any activity aiming to promote Open Data thus needs to overcome a number of cultural and technological challenges. It is in this context that this paper examines the data management activities and outcomes of a number of projects participating in the Horizon 2020 Open Research Data pilot. The result has been to identify a number of commonly encountered benefits and issues; to assess the utilisation of data management plans; and through the close examination of specific cases, to gain insights into obstacles to data management and potential solutions. Although primarily anecdotal and difficult to quantify, the experiences reported in this paper tend to favour developing data management best practices rather than doggedly pursue the Open Data mantra. While Open Data may prove valuable in certain circumstances, there is good reason to claim that managed access to scientific data of high inherent intellectual and financial value will prove more effective in driving knowledge discovery and innovation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Advanced characterisation; Data management plan; Digitisation; Horizon 2020; Interoperability; Materials properties","Interoperability; Materials properties; Open Data; Advanced characterization; Data management plan; Digitisation; Horizon 2020; Management plans; Open datum; OpenAccess; Research data; Research programs; Work-flows; Information management","","","","","Euratom research and training programme 2014–2018; Horizon 2020 Framework Programme, H2020, (760779, 814505); H2020 Euratom, (662320, 755039, 755151, 755269, 945300); European Commission, EC, (760827, 814552, 814588)","Funding text 1: Funding: The paper partially received funding from the European Union under Grant Agreement numbers 760827 (OYSTER), 760779 (SMARTFAN), 814505 (DECOAT), 814588 (REPAIR3D) and 814552 (LIGHTME). Those projects where the JRC has participated have received funding from the Euratom research and training programme 2014–2018 under Grant Agreement numbers 662320 (INCEFA-PLUS), 755039 (M4F), 755151 (MEACTOS), 755269 (GEMMA) and 945300 (INCEFA-SCALE).; Funding text 2: The paper partially received funding from the European Union under Grant Agreement numbers 760827 (OYSTER), 760779 (SMARTFAN), 814505 (DECOAT), 814588 (REPAIR3D) and 814552 (LIGHTME). Those projects where the JRC has participated have received funding from the Euratom research and training programme 2014?2018 under Grant Agreement numbers 662320 (INCEFA-PLUS), 755039 (M4F), 755151 (MEACTOS), 755269 (GEMMA) and 945300 (INCEFA-SCALE).","Digital Agenda and Open Data—From Crisis of Trust to Open Governing, (2012); Communication on Data-Driven Economy | Shaping Europe’s Digital Future, (2014); A European Strategy for Data | Shaping Europe’s Digital Future, (2020); Digitising European Industry | Shaping Europe’s Digital Future, (2020); A New Industrial Strategy for Europe, (2020); A European Strategy for Data, (2020); (2020); Overview of Funders’ Data Policies | Digital Curation Centre; Reporting Standards and Availability of Data, and Protocols | Nature Research; Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016); Collins S., Genova F., Harrower N., Hodson S., Jones S., Laaksonen L., Mietchen D., Petrauskaite R., Wittenburg P., Turning FAIR into Reality. Final Report and Action Plan from the European Commission Expert Group on FAIR Data, (2018); Data Management—H2020 Online Manual; Jacobsen A., Azevedo R.D.M., Juty N., Batista D., Coles S., Cornet R., Courtot M., Crosas M., Dumontier M., Evelo C.T., Et al., FAIR Principles: Interpretations and Implementation Considerations, Data Intell, 2, pp. 10-29, (2020); Groth P., Cousijn H., Clark T., Goble C., FAIR data reuse–the path through data citation, Data Intell, 2, pp. 78-86, (2020); Weigel T., Schwardmann U., Klump J., Bendoukha S., Quick R., Making Data and Workflows Findable for Machines, Data Intell, 2, pp. 40-46, (2020); Implementing FAIR Data Principles The Role of Libraries, (2017); Mons B., Et al., Cloudy, Increasingly FAIR; Revisiting the FAIR Data Guiding Principles for the European Open Science Cloud, Information Services & Use, 37, pp. 49-56, (2017); Wilkinson M.D., Sansone S.-A., Schultes E., Doorn P., Santos L.O.B.D.S., Dumontier M., A design framework and exemplar metrics for FAIRness, Sci. Data, 5, (2018); Wilkinson M.D., Dumontier M., Sansone SA., Et al., Evaluating FAIR maturity through a scalable, automated, community-governed framework, Sci. Data, 6, (2019); Spichtinger D., Blumesberger S., FAIR data and data management requirements in a comparative perspective: Horizon 2020 and FWF policies, Mitt. Ver. Osterr. Bibl. Bibl, 73, pp. 207-216, (2020); Maso Pau J., Serral Montoro I., Results of the Data Management Plan (DMP) and Report on the Participation in the Pilot on Open Research Data in Horizon 2020, (2017); Kuberek M., Guidance for Creating a Data Management Plan in Horizon 2020 Projects, (2018); Procopio I., Cicero S., Mottershead K., Bruchhausen M., Cuvilliez S., INCEFA-PLUS (Increasing safety in NPPs by covering gaps in environmental fatigue assessment), Procedia Struct. Integr, 13, pp. 97-103, (2018); Open Characterisation and Modelling Environment to Drive Innovation in Advanced Nano-Architectured and Bio-Inspired Hard/Soft Interfaces; Smart by Design and Intelligent by Architecture for Turbine Blade Fan and Structural Components Systems; Recycling of Coated and Painted Textile and Plastic Materials, DECOAT Grant Agreement ID: 814505; Recycling and Repurposing of Plastic Waste for Advanced 3D Printing Applications, REPAIR3D Grant Agreement ID: 814588; Ecosystem for Upscaling Production of Lightweight Metal Alloy Composites; Efthymiadis Tau., Paraskevoudis Kappa., Koumoulos E.P., Data Management Ontology, (2019); M4F Data Management Plan; Bloemers M., Montesanti A., The FAIR Funding Model: Providing a Framework for Research Funders to Drive the Transition toward FAIR Data Management and Stewardship Practices, Data Intell, 2, pp. 171-180, (2020); (2020); Vankeerberghen M., Doremus L., Spatig P., Bruchhausen M., Le Roux J.-C., Twite M., Cicero R., Platts N., Mottershead K., Ensuring data quality for environmental fatigue—INCEFA-PLUS testing procedure and data evaluation, Proceedings of the Pressure Vessels and Piping Conference, (2018); Wellington B., How We Found the Worst Place to Park in New York City—Using Big Data, TED Talk; Guedj D., Ramjoue C., European Commission Policy on Open-Access to Scientific Publications and Research Data in Horizon 2020, Biomed. Data J, 1, pp. 11-14, (2015); DataCite Statistics; Cousijn H., Kenall A., Ganley E., Harrison M., Kernohan D., Lemberger T., Murphy F., Polischuk P., Taylor S., Martone M., Et al., A data citation roadmap for scientific publishers, Sci. Data, 5, pp. 1-11, (2018); Jeliazkova N., Chomenidis C., Doganis P., Fadeel B., Grafstrom R., Hardy B., Hastings J., Hegi M., Jeliazkov V., Kochev N., Et al., The eNanoMapper database for nanomaterial safety information, Beilstein J. Nanotechnol, 6, pp. 1609-1634, (2015); Virtual Materials Market Place (VIMMP); Engineering materials—Electronic data interchange—Formats for fatigue test data, (2017); Engineering materials—Electronic data interchange—Instrumented indentation test data; Materials Modelling—Terminology, Classification and Metadata, (2018); (2020); Koumoulos E.P., Sebastiani M., Romanos N., Kalogerini M., Charitidis C., Data Management Plan Template for H2020 Projects (Version v01.100419), (2019); Chada DMP.; Williams M., Bagwell J., Zozus M.N., Data management plans: the missing perspective, J. Biomed. Inform, 71, pp. 130-142, (2017)","T. Austin; Joint Research Centre (JRC), European Commission, Petten, 1755 LE, Netherlands; email: simon.austin@ec.europa.eu; E.P. Koumoulos; Data Science Group, Innovation in Research & Engineering Solutions (IRES), Wemmel, Rue Koningin Astridlaan 59B, 1780, Belgium; email: epk@innovation-res.eu","","MDPI","","","","","","23065729","","","","English","Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85114943239"
"Rodrigues J.; Lopes C.T.","Rodrigues, Joana (57203242279); Lopes, Carla Teixeira (57194455159)","57203242279; 57194455159","Research Data Management in the Image Lifecycle: A Study of Current Behaviors","2022","Lecture Notes in Business Information Processing","446 LNBIP","","","39","54","15","0","10.1007/978-3-031-05760-1_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130890419&doi=10.1007%2f978-3-031-05760-1_3&partnerID=40&md5=4d6e7142c64ae105c377a8fc5fe2eb54","Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Rodrigues J., Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Lopes C.T., Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Research data management (RDM) practices are critical for ensuring research success. Data can assume diverse formats and data in image format have been understudied in RDM. To understand image management habits in research, we have conducted semi-structured interviews with researchers from four research domains. Most researchers do not formally manage their images, nor do they develop RDM plans. They assume that image management is not a topic discussed at project meetings. In turn, they tend to perform some individual practices, depending on the context and their own opinion, such as creating captions to describe the images and organizing and storing the images in specific locations. However, they see these habits as necessary and admit that they will start to do so in a formal and collaborative way with the working group. These results provide valuable information on practical aspects of the use and production of images in research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Image life cycle; Images; Research; Research data management","Information management; Current behaviors; Image; Image format; Image life cycle; Image management; Management plans; Management practises; Research data managements; Research domains; Semi structured interviews; Life cycle","","","","","Fundação para a Ciência e a Tecnologia, FCT, (PD/BD/150288/2019)","Acknowledgement. Joana Rodrigues is supported by research grant from FCT - Funda¸cão para a Ciência e Tecnologia: PD/BD/150288/2019. Thanks to Miguel Fer-nandes who conducted the initial interviews and was involved in the construction of the interview script.","Abell C., The epistemic value of photographs, Philosophical Perspectives on Depiction, pp. 82-103, (2010); Aguiar Castro J., Amorim R., Gattelli R., Karimova Y., Rocha da Silva J., Ribeiro C., Involving data creators in an ontology-based design process for metadata models, Developing Metadata Application Profiles, pp. 181-213, (2017); Ball A., Review of Data Management Lifecycle Models, (2012); Banks M., Using Visual Data in Qualitative Research, (2007); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib J. Inf. Manag., 70, 2, pp. 142-157, (2018); Fernandes M., Rodrigues J., Lopes C.T., Management of research data in image format: An exploratory study on current practices, Proceedings of the International Conference on Theory and Practice of Digital Libraries–Digital Libraries for Open Knowledge, pp. 212-226, (2020); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Huds D., The impact of photography on society, Our Times, (2019); Kallio H., Pietila A.M., Johnson M., Kangasniemi M., Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide, J. Adv. Nurs., 72, pp. 2954-2965, (2016); Lacerda A., A fotografia nos arquivos: Produção e sentido de documentos visuais, História, Ciências, Saúde, 1, (2012); Palmer C., Et al., Site-based data curation based on hot spring geobiology, Plos ONE, 12, 7, (2017); Innovation for Research: Guidelines on Fair Data Management in Horizon, (2016); Sandweiss M.A., Image and artifact: The photograph as evidence in the digital age, J. Am. History, 94, 1, pp. 193-202, (2007); Saunders M., Lewis P., Thornhill A., Research Methods for Business Students, (2012); Structural Reform Group: DDI Version 3.0 Conceptual Model, (2004); Tenopir C., Et al., Data sharing by scientists: Practices and perceptions, Plos ONE, 6, 6, pp. 1-21, (2011)","J. Rodrigues; Faculty of Engineering of the University of Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: joanasousarodrigues.14@gmail.com","Guizzardi R.; Ralyté J.; Franch X.","Springer Science and Business Media Deutschland GmbH","","16th International Conference on Research Challenges in Information Science, RCIS 2022","17 May 2022 through 20 May 2022","Barcelona","277829","18651348","978-303105759-5","","","English","Lect. Notes Bus. Inf. Process.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85130890419"
"Xu Z.","Xu, Zhihong (57204631412)","57204631412","Research Data Management Practice in Academic Libraries","2022","Journal of Librarianship and Scholarly Communication","10","1","eP13700","","","","4","10.31274/jlsc.13700","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138771444&doi=10.31274%2fjlsc.13700&partnerID=40&md5=baf1aadd6ac5c1410133a9243232f013","Texas A&M University Libraries, United States","Xu Z., Texas A&M University Libraries, United States","Purpose: The present scoping review examines research data management (RDM) best practices and empirical studies in academic libraries between 2010 and 2021. Method: The current study developed systematic database searches to locate potential articles for inclusion and designed a detailed and systematic coding scheme to examine the substantive features of RDM and characteristics of RDM practice, with an emphasis on RDM instruction. Results and Discussion: The results from the current study demonstrated that there is great demand for RDM training after 2011. Furthermore, research about RDM training spread across North America, Europe, Asia Pacific, and elsewhere. The findings also proved that RDM training is essential for both STEM and non-STEM subjects but simultaneously indicated that non-STEM subjects such as the social sciences in particular lack RDM training. Results from the current literature also found that a large number of RDM training programs focused on the introduction of RDM or an RDM overview, without in-depth and discipline-based curriculum for researchers across domains. Additionally, this study identified a lack of quantitative research, especially statistical analysis, on the effect of RDM interventions. Conclusion: This study contributes to our comprehensive understanding of some essential elements associated with RDM training, with the primary finding that future practitioners in the RDM field would benefit from stronger collaboration with faculty or researchers to develop more discipline-based curriculums for RDM and more application-based approaches for teaching RDM. © 2022 Xu.","academic libraries; data literacy; research data management; scoping review","","","","","","Texas A and M University, TAMU","This work was supported by the Presidential Transformational Teaching Grant [grant numbers 208, 2020], Texas A&M University Institutional Funding.","Acosta S., Garza T., Hsu H. Y., Goodson P., Assessing quality in systematic literature reviews: A study of novice rater training, SAGE Open, 10, 3, (2020); Adams S., De Castro P., Echenique P., Estrada J., Hanwell M. 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Librariansh. Sch. Commun.","Review","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85138771444"
"Ludwig-Ohm S.; Dirksmeyer W.","Ludwig-Ohm, S. (57212343857); Dirksmeyer, W. (55305747400)","57212343857; 55305747400","HortiCo 4.0: The networking and transfer project for horticulture 4.0 innovation funding in Germany","2021","Acta Horticulturae","1327","","","817","822","5","0","10.17660/ActaHortic.2021.1327.108","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120688661&doi=10.17660%2fActaHortic.2021.1327.108&partnerID=40&md5=0566e56bc7f3d066c0dc2c6405ebd963","Thünen Institute of Farm Economics, Braunschweig, Germany","Ludwig-Ohm S., Thünen Institute of Farm Economics, Braunschweig, Germany; Dirksmeyer W., Thünen Institute of Farm Economics, Braunschweig, Germany","Research and development of innovations should be supported by a professional network management to implement innovations in horticultural value chains successfully. For this reason, the networking and transfer project HortiCo 4.0 was established for the German funding priority Horticulture 4.0. The main objective of this project is to interlink the Horticulture 4.0 research and development (R&D) projects. Technical and economic analyses of innovations under development at the meta level will demonstrate how digital change affects structure, competitiveness and sustainability of the horticultural sector. Technology assessments will address the potential of innovation clusters and their impact on individual enterprises, the horticultural sector and society. The most important results of R&D projects have to be communicated to specialists and stakeholders who then contribute to a wide dissemination of the innovations. Additionally, the public will be informed on socially and politically relevant innovations and challenges. The project started in 2020 to identify and leverage synergies between the different Horticulture 4.0 R&D projects. Four innovation clusters were identified and established for R&D projects with similar areas of work: (1) technology development, (2) digital processes, (3) information systems, and (4) technology assessment and practical application. © 2021 International Society for Horticultural Science. All rights reserved.","Cost-benefit analysis; Implementation; Innovations; Knowledge transfer; Research data management; SWOT analysis; Technology assessment","","","","","","Bundesministerium für Ernährung und Landwirtschaft, BMEL; Bundesanstalt für Landwirtschaft und Ernährung, BLE","The project is supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme.","Bokelmann W., Doernberg A., Schwerdtner W., Kuntosch A., Busse M., Konig B., Siebert R., Koschatzky K., Stahlecker T., Sektorstudie zur Untersuchung des Innovationssystems der deutschen Landwirtschaft, (2012); Dirksmeyer W., Garming H., Homeister H., SWOT-Analyse für den Obst- und Gemüsesektor, Stellungnahme für BMEL, (2017); Griesshammer R., Bergman M., Wissenschaftliche Koordination der BMBF-Fördermaßnahme „Umweltund gesellschaftsverträgliche Transformation der Energiesysteme“, Schlussbericht. Bundesministerium für Bildung und Forschung, (2018); Ludwig-Ohm S., Straeter C., Dirksmeyer W., Geyer M., Homeister H., Lampe I., Rath T., Schmieder M., Ziegler A., BMEL Entscheidungshilfevorhaben „Forschungsstrategie für Innovationen im Gartenbau – HortInnova“: Abschlussbericht, (2017); Shamshiri R., Kalantari F., Ting K., Thorp K., Hameed I., Weltzien C., Ahmad D., Shad Z., Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban farming, Int. J. Agric. Biol. Eng, 11, 1, pp. 1-22, (2018)","S. Ludwig-Ohm; Thünen Institute of Farm Economics, Braunschweig, Germany; email: sabine.ludwig-ohm@thuenen.de","","International Society for Horticultural Science","","","","","","05677572","","","","English","Acta Hortic.","Conference paper","Final","","Scopus","2-s2.0-85120688661"
"Schembera B.","Schembera, Björn (56829559000)","56829559000","Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data","2021","Journal of Supercomputing","77","8","","8946","8966","20","8","10.1007/s11227-020-03602-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100238150&doi=10.1007%2fs11227-020-03602-6&partnerID=40&md5=19afff25a13c224f003f34170200c5c7","High-Performance Computing Center Stuttgart/HLRS, University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany","Schembera B., High-Performance Computing Center Stuttgart/HLRS, University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany","The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation output files scattered through the file system, can be aggregated, parsed and converted to the EngMeta metadata model. Use cases from computational engineering are considered to demonstrate the viability of ExtractIng. The evaluation results show that the metadata extraction is simulation-code independent in the sense that it can handle data outputs from various fields of science, is easy to integrate into simulation workflows and compatible with a multitude of computational environments. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.","Computational engineering; Dark data; High performance computing; Metadata; Metadata extraction; Research data management","Extraction; Information management; Computational engineering; Computational environments; Evaluation results; Management capabilities; Meta-data extractions; Metadata annotations; Metadata information; Simulation outputs; Metadata","","","","","Bundesministerium für Bildung und Forschung, BMBF, (FDM-008)","The author likes to thank the Federal Ministry of Education and Research for funding the Dipling project under Grant No. FDM-008. The author also likes to thank Dr. Martin Thomas Horsch for comments on the script and proofreading. 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Dissertation, (2019); Petersen A.M., Fortunato S., Pan R.K., Kaski K., Penner O., Rungi A., Riccaboni M., Stanley H.E., Pammolli F., Reputation and impact in academic careers, Proc Natl Acad Sci, 111, 43, (2014); Schembera B., Iglezakis D., EngMeta–metadata for computational engineering, Preprint, (2020); Edwards P.N., Mayernik M.S., Batcheller A.L., Bowker G.C., Borgman C.L., Science friction: data, metadata, and collaboration, Soc Stud Sci, 41, 5, (2011); Schembera B., Iglezakis D., The genesis of engmeta: a metadata model for research data in computational engineering, Metadata and semantic research, pp. 127-132, (2019); Caplan P., Understanding PREMIS., (2009); Ammann N., Nielsen L.H., Peters C.S., de Smaele T.M., Datacite metadata schema for the publication and citation of research data., (2011); Riley J., Understanding metadata: What is metadata, And What is It For?: A Primer, (2017); Hess B., van der Spoel D., Lindahl E., Smith J.C., Shirts M.R., Bjelkmar P., Larsson P., Kasson P.M., Schulz R., Apostolov R., Pronk S., Pall S., GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics, 29, 7, (2013); Greenberg J., Metadata extraction and harvesting: a comparison of two automatic metadata generation applications, J Internet Catal, 6, 4, (2004); Giuffrida G., Shek E.C., Yang J., Knowledge-based metadata extraction from PostScript files, Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 77-84, (2000); Spinosa P., Giardiello G., Cherubini M., Marchi S., Venturi G., Montemagni S., NLP-based metadata extraction for legal text consolidation, Proceedings of the 12Th International Conference on Artificial Intelligence and Law, pp. 40-49, (2009); Liu R., Gao L., An D., Jiang Z., Tang Z., Automatic document metadata extraction based on deep networks, National CCF Conference on Natural Language Processing and Chinese Computing (Springer, 2017, pp. 305-317, (2017); Paul A.K., Wang B., Rutman N., Spitz C., Butt A.R., Efficient Metadata Indexing for HPC Storage Systems, 2020 20Th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) (IEEE, 2020), pp. 162-171, (2020); Paul A.K., An Application-Attuned Framework for Optimizing Hpc Storage Systems, (2020); Khan A., Kim T., Byun H., Kim Y., SciSpace: a scientific collaboration workspace for geo-distributed HPC data centers, Fut Gen Comput Syst, 101, (2019); Liang S., Holmes V., Antoniou G., Higgins J., iCurate: a research data management system, pp. 39-47, (2015); Grunzke R., Breuers S., Gesing S., Herres-Pawlis S., Kruse M., Blunk D., de la Garza L., Packschies L., Schafer P., Scharfe C., Schlemmer T., Steinke T., Schuller B., Muller-Pfefferkorn R., Jakel R., Nagel W.E., Atkinson M., Kruger J., Standards-based metadata management for molecular simulations, Concurr Comput Pract Exp, 26, 10, (2014); Grunzke R., Generic Metadata Handling in Scientific Data Life Cycles, (2016); Grunzke R., Hartmann V., Jejkal T., Kollai H., Prabhune A., Herold H., Deicke A., Dressler C., Dolhoff J., Stanek J., Hoffmann A., Muller-Pfefferkorn R., Schrade T., Meinel G., Herres-Pawlis S., Nagel W.E., Future Generation Computer Systems, 94, (2019); Adorf C.S., Dodd P.M., Ramasubramani V., Glotzer S.C., Simple data and workflow management with the signac framework, Comput Mater Sci, 146, (2018); Skluzacek T.J., Dredging a data lake: Decentralized metadata extraction, Proceedings of the 20Th International Middleware Conference Doctoral Symposium, pp. 51-53, (2019); Skluzacek T.J., Chard R., Wong R., Li Z., Babuji Y.N., Ward L., Blaiszik B., Chard K., Foster I., Serverless workflows for indexing large scientific data, Proceedings of the 5Th International Workshop on Serverless Computing, pp. 43-48, (2019); Skluzacek T.J., Kumar R., Chard R., Harrison G., Beckman P., Chard K., Foster I., Skluma: An extensible metadata extraction pipeline for disorganized data, 2018 IEEE 14Th International Conference on E-Science (E-Science) (IEEE, 2018, pp. 256-266, (2018); Padhy S., Jansen G., Alameda J., Black E., Diesendruck L., Dietze M., Kumar P., Kooper R., Lee J., Liu R., Et al., Brown Dog: Leveraging everything towards autocuration, 2015 IEEE International Conference on Big Data (Big Data) (IEEE, 2015), pp. 493-500, (2015); Satheesan S.P., Alameda J., Bradley S., Dietze M., Galewsky B., Jansen G., Kooper R., Kumar P., Lee J., Marciano R., Et al., Brown dog: Making the digital world a better place, a few files at a time, Proceedings of the Practice and Experience on Advanced Research Computing, pp. 1-8, (2018); Rodrigo G.P., Henderson M., Weber G.H., Ophus C., Antypas K., Ramakrishnan L., ScienceSearch: Enabling search through automatic metadata generation, 2018 IEEE 14Th International Conference on E-Science (E-Science) (IEEE, 2018, pp. 93-104, (2018)","B. Schembera; High-Performance Computing Center Stuttgart/HLRS, University of Stuttgart, Stuttgart, Nobelstr. 19, 70569, Germany; email: schembera@hlrs.de","","Springer","","","","","","09208542","","JOSUE","","English","J Supercomput","Article","Final","","Scopus","2-s2.0-85100238150"
"Bourke T.","Bourke, Thomas (35177053800)","35177053800","Bibliographic Control of Research Datasets: reflections from the EUI Library","2022","JLIS.it","13","1","","321","334","13","0","10.4403/jlis.it-12723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128330707&doi=10.4403%2fjlis.it-12723&partnerID=40&md5=6e3da0810126e08e2935ee059d5df6ac","European University Institute, Italy","Bourke T., European University Institute, Italy","The exponential growth in the generation and use of research data has important consequences for scientific culture and library mandates. This paper explores how the bibliographic control function in one academic library has been expanded to embrace research data in the social sciences and humanities. Library bibliographic control (BC) of research datasets has emerged at the same time as library research data management (RDM). These two functions are driven by digital change; the rise of the open science and open data movements; library management of institutional repositories; and the increasing recognition that data sharing serves the advancement of science, the economy and society. Both the research data management function and the bibliographic control function can be enhanced by librarians’ awareness of scholarly projects throughout the research data lifecycle (input, elaboration and output) – and not only when research datasets are submitted for reposit. These library roles require knowledge of data sources and provenance; research project context; database copyright; data protection; data documentation and the FAIR Guiding Principles, to make data findable, accessible, interoperable and reusable. This case study suggests that by creating synergies between the research data management function (during research projects) and the formal bibliographic control function (at the end of research projects) – librarians can make an enhanced contribution to good scientific practice and responsible research. © 2022, The Author(s).","Bibliographic control; Datasets; Research data; Research data management","","","","","","Economic and Social Research Council, ESRC, (RES-000-22-3661); European University Institute, EUI","This dataset, created as part of the research project on ‘The Informal Politics of Codeci-sion’ - funded by the Research Council of the European University Institute (EUI) and the Economic and Social Research Council (ESRC; Grant RES-000-22-3661) - is constituted by all 797 legislative files concluded under codecision between 1999 and 2009. It presents a new variable, ‘early agreement’, indicating whether legislation has been agreed informally, in trilogues, by the Council of Ministers and the European Parliament. It also includes variables with characteristics of the legislative file (legal nature, policy area, complexity, media salience, policy type, duration) and of the legislative negotiators (priorities of the Council Presidency, ideological distance between the Parliament’s rapporteur and the national minister, the Presidency’s workload).","Data Management Expert Guide; Guide to Good Data Protection Practice in Research, (2019); Research Data Guide, (2021); Data, Encyclopaedia of Knowledge Organization; Joudrey Daniel N., Taylor Arlene G., Miller David P., Introduction to Cataloging and mClassification, (2015); Kellam Lynda, Thompson Kristi, Databrarianship: the Academic Data Librarian, Theory and Practice, (2016); Kruse Filip, Thestrup Jesper Boserup, Research Data Management: a European Perspective, (2018); Mons Barend, Data Stewardship for Open Science: implementing FAIR Principles, (2018); Morriello Rossana, Birth and Development of Data Librarianship, JLIS.it, 11, 3, pp. 1-15, (2020); Pradhan Sanghamitra, Cataloguing of Non-Print Resources: a Practical Manual, (2018); Rice Robin, Southall John, The Data Librarian’s Handbook, (2016); Svantesson Lotta, Steletti Monica, DSpace ORCID integration: name authority control solution at the European University Institute, The 14th International Conference on Open Repositories (OR2019), (2019); Wilkinson Mark D., Dumontier Michel, Mons Barend, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016)","T. Bourke; European University Institute, Italy; email: thomas.bourke@eui.eu","","Universita di Firenze, Dipartimento di Storia, Archeologia, Geografia, Arte e Spettacolo","","","","","","20385366","","","","English","JLIS.it","Article","Final","","Scopus","2-s2.0-85128330707"
"Singh S.; Shehab E.; Higgins N.; Fowler K.; Reynolds D.; Erkoyuncu J.A.; Gadd P.","Singh, Sumit (57203111887); Shehab, Essam (6507037138); Higgins, Nigel (57205641944); Fowler, Kevin (57205646090); Reynolds, Dylan (57224050178); Erkoyuncu, John A (36124603700); Gadd, Peter (57222050379)","57203111887; 6507037138; 57205641944; 57205646090; 57224050178; 36124603700; 57222050379","Data management for developing digital twin ontology model","2021","Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","235","14","","2323","2337","14","15","10.1177/0954405420978117","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106807559&doi=10.1177%2f0954405420978117&partnerID=40&md5=d10425f76ae5266b975e86a2c1400cd5","Cranfield University, Cranfield, Bedford, United Kingdom; Nazarbayev University, Nur-Sultan, Kazakhstan; Airbus Operations Ltd, Filton, Bristol, United Kingdom","Singh S., Cranfield University, Cranfield, Bedford, United Kingdom; Shehab E., Cranfield University, Cranfield, Bedford, United Kingdom, Nazarbayev University, Nur-Sultan, Kazakhstan; Higgins N., Airbus Operations Ltd, Filton, Bristol, United Kingdom; Fowler K., Airbus Operations Ltd, Filton, Bristol, United Kingdom; Reynolds D., Airbus Operations Ltd, Filton, Bristol, United Kingdom; Erkoyuncu J.A., Cranfield University, Cranfield, Bedford, United Kingdom; Gadd P., Airbus Operations Ltd, Filton, Bristol, United Kingdom","Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities. © IMechE 2020.","data management; data modelling; Digital twin; ontologies","Data mining; Digital twin; Life cycle; Ontology; Semantics; Conceptual knowledge; Condition-based monitoring; Data management challenges; Domain knowledge; Ideal solutions; Ontology model; Product-life-cycle; Query formulation; Information management","","","","","EPSRC National Productivity Investment Fund; Engineering and Physical Sciences Research Council, EPSRC","The authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC) and AIRBUS for funding this research project. The project is funded via ICASE studentship (2017) which is part of the EPSRC National Productivity Investment Fund. ","Grieves M., Vickers J., Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems, Transdisciplinary perspectives on complex systems, pp. 85-113, (2017); Boschert S., Rosen R., Digital twin—the simulation aspect, Mechatronic futures, pp. 59-74, (2016); Tao F., Zhang M., Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing, IEEE Access, 5, pp. 20418-20427, (2017); Parrott A., Warshaw L., Industry 4.0 and the digital twin, Deloitte Ltd, (2017); Singh S., Shehab E., Higgins N., Et al., Challenges of digital twin in high value manufacturing, (2018); Zhang H., Liu Q., Chen X., Et al., A digital twin-based approach for designing and decoupling of hollow glass production line, IEEE Access, 5, pp. 26091-26911, (2017); Angrish A., Starly B., Lee Y.S., Et al., A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM), J Manuf Syst, 45, pp. 236-247, (2017); Yun S., Park J.H., Kim W.T., Data-centric middleware based digital twin platform for dependable cyber-physical systems, pp. 922-926; Uhlemann T.H.J., Schock C., Lehmann C., Et al., The digital twin: demonstrating the potential of real time data acquisition in production systems, Procedia Manuf, 9, pp. 113-120, (2017); Schroeder G., Steinmetz C., Pereira C.E., Et al., Visualising the digital twin using web services and augmented reality, pp. 522-527; Talkhestani B.A., Jazdi N., Schlogl W., Et al., A concept in synchronization of virtual production system with real factory based on anchor-point method, Procedia CIRP, 67, pp. 13-17, (2018); Rios J., Mas F., Oliva M., Et al., Framework to support the aircraft digital counterpart concept with an industrial design view, Int J Agil Syst Manag, 9, pp. 212-231, (2016); Singh S., Shehab E., Higgins N., Et al., Towards effective data management for digital twin, pp. 167-172; Vazan P., Janikova D., Tanuska P., Et al., Using data mining methods for manufacturing process control, IFAC PapersOnLine, 50, pp. 6178-6183, (2017); Tyagi P., Demirkan H., Data Lakes: the biggest big data challenges, (2018); Tao F., Zhang M., Cheng J., Digital twin workshop: a new paradigm for future workshop, 23, pp. 1-9, (2017); Wagg D.J., Worden K., Barthorpe R.J., Et al., Digital twins: state-of-the-art and future directions for modelling and simulation in engineering dynamics applications, ASCE-ASME J Risk Uncert Eng Sys Part B Mech Eng, 6, (2020); Kennedy M.C., O'Hagan A., Bayesian calibration of computer models, J R Stat Soc Ser B (Statistical Methodol), 63, pp. 1369-7412, (2001); Digital twin market research report, (2020); Azure digital twins, (2020); Digitalization in industry: twins with potential, (2020); Gilabert E., Conde E., Abaunza J., A standard-based data model for configuration management and maintenance support, IFAC Proc Vol, 45, pp. 139-144, (2012); Gruber T.R., Gruber T.R., Translation approach to portable ontology specifications, Knowl Acquis, 5, pp. 199-220, (1993); Martins C.T., Azevedo A., Pinto H.S., Et al., Towards an ontology mapping process for business process composition, Innovation in manufacturing networks. BASYS 2008. IFIP – the international federation for information processing, pp. 169-176, (2008); Munir K., Anjum M.S., The use of ontologies for effective knowledge modelling and information retrieval, Appl Comput Informatics, 14, pp. 116-126, (2018); Astrova I., Korda N., Kalja A., Rule-based transformation of SQL relational databases to OWL ontologies, Proceedings of MTSR. Springer, pp. 1-16, (2007); El-ghalayini H., Odeh M., Mcclatchey R., Et al., Reverse engineering ontology to conceptual data models, pp. 222-227; Vysniauskas E., Nemuraite L., Transforming ontology representation from OWL to relational database, Technol Control, 35, pp. 333-343, (2006); Gali A., Chen C.X., Claypool K.T., Et al., From ontology to relational databases, Conceptual modeling for advanced application domains. ER 2004. Lecture Notes in Computer Science, 3289, pp. 278-289, (2004); Sun C., A natural language interface for querying graph databases, (2018); Vicknair C., Nan X., Chen Y., Et al., A comparison of a graph database and a relational database: a data provenance perspective, pp. 1-6; Miller J.J., Graph database applications and concepts with Neo4j, 2324, 36, pp. 141-147; Erkoyuncu J.A., del Amo I.F., Ariansyah D., Et al., A design framework for adaptive digital twins, CIRP Ann Manuf Technol, 69, pp. 145-148, (2020); Bao Q., Zhao G., Yu Y., Et al., Ontology-based modeling of part digital twin oriented to assembly, Proc IMechE, Part B: J Engineering Manufacture, pp. 1-13, (2020); Mehdi G., Roshchin M., Runkler T., Internet of Turbines: an outlook on smart diagnostics, pp. 1-7, (2017); Banerjee A., Mittal S., Dalal R., Et al., Generating digital twin models using knowledge graphs for industrial production lines; Cachada A., Barbosa J., Leitao P., Et al., Maintenance 4.0: intelligent and predictive maintenance system architecture, pp. 139-146; BS ISO13374-1:2003- Condition monitoring and diagnostics of machines—data processing, communication and presentation—Part 1.1; Mimosa-an operations and Maintenance information open system alliance, ‘MIMOSA OSA-CBM’, (2010); Protégé, (2020); MySQL, (2020)","S. Singh; Cranfield University, Cranfield, United Kingdom; email: sumit.singh@cranfield.ac.uk","","SAGE Publications Ltd","","","","","","09544054","","PIBME","","English","Proc Inst Mech Eng Part B J Eng Manuf","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85106807559"
"Oo C.Z.; Chew A.W.; Wong A.L.H.; Gladding J.; Stenstrom C.","Oo, Cherry Zin (57210290202); Chew, Adrian W. (57208592786); Wong, Adeline L. H. (57222387567); Gladding, Joanne (57222383523); Stenstrom, Cecilia (57222385503)","57210290202; 57208592786; 57222387567; 57222383523; 57222385503","Delineating the successful features of research data management training: a systematic review","2022","International Journal for Academic Development","27","3","","249","264","15","5","10.1080/1360144X.2021.1898399","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102557707&doi=10.1080%2f1360144X.2021.1898399&partnerID=40&md5=bc03e5f25a1798458b737f213184d619","School of Education, University of New South Wales (UNSW), Sydney, Australia; Division of Research, University of New South Wales (UNSW), Sydney, Australia; School of Psychology, University of New South Wales (UNSW), Sydney, Australia","Oo C.Z., School of Education, University of New South Wales (UNSW), Sydney, Australia; Chew A.W., School of Education, University of New South Wales (UNSW), Sydney, Australia; Wong A.L.H., Division of Research, University of New South Wales (UNSW), Sydney, Australia; Gladding J., School of Psychology, University of New South Wales (UNSW), Sydney, Australia; Stenstrom C., Division of Research, University of New South Wales (UNSW), Sydney, Australia","Research Data Management (RDM) is an inherently complex area that presents a challenge for institutions to effectively upskill people in RDM and get them to enact RDM best practices. This systematic review aims to provide a reference point for institutions and academic developers to design and deliver effective training for their students and staff to navigate the RDM landscape. After narrowing down from a pool of 1256 articles across 7 databases to 28 articles for analysis, this systematic review provides an overview of available RDM training and identifies three main themes in relation to key features of this RDM training. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","RDM skills; RDM support; research data management; systematic review; training","","","","","","University of New South Wales, UNSW","This work was supported by the University of New South Wales, Sydney, Australia.","Auckland M., Re-Skilling for Research: An Investigation into the Roles and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Awre C., Baxter J., Clifford B., Colclough J., Cox A., Dods N., Drummond P., Fox Y., Gill M., Gregory K., Gurney A., Harland J., Khokhar M., Lowe D., O'Beirne R., Proudfoot R., Schwamm H., Smith A., Verbaan E., Williamson L., Zawadzki M., Research data management as a “wicked problem”, Library Review, 64, 4-5, pp. 356-371, (2015); Boland A., Cherry M.G., Dickson R., Doing A Systematic Review: A student’s Guide, (2017); Carlson J., Bracke M.S., Planting the seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, 1, pp. 95-110, (2015); Castle C., Getting the central RDM message across: A case study of central versus discipline-specific research data services (RDS) at the University of Cambridge, Libri, 69, 2, pp. 105-116, (2019); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at small liberal arts colleges, IFLA Journal, 43, 1, pp. 105-118, (2017); Cole G., Evans J., University of exeter research data management and open access training for staff, ALISS Quarterly, 10, 1, pp. 22-25, (2014); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Corti L., Van den Eynden V., Learning to manage and share data: Jump-starting the research methods curriculum, International Journal of Social Research Methodology, 18, 5, pp. 545-559, (2015); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library and Information Science Research, 38, 4, pp. 319-326, (2016); 10 questions to help you make sense of qualitative research, (2018); Ekmekcioglu C., Rice R., Research data MANTRA: An online training course, ALISS Quarterly, 7, 2, pp. 28-31, (2012); Helbig K., Research data management training for geographers: First impressions, ISPRS International Journal of Geo-Information, 5, 4, (2016); Johannes C., Fendler J., Seidel T., Teachers’ perceptions of the learning environment and their knowledge base in a training program for novice university teachers, International Journal for Academic Development, 18, 2, pp. 152-165, (2013); Johnson A.M., Bresnahan M.M., DataDay!: Designing and assessing a research data workshop for subject librarians, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Moher D., Liberati A., Tetzlaff J., Altman D.G., Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement, Annals of Internal Medicine, 151, 4, (2009); Morgan A., Duffield N., Hall L.W., Research data management support: Sharing our experiences, Journal of Australian Library and Information Association, 66, 3, pp. 299-305, (2017); OECD principles and guidelines for access to research data from public funding, (2007); Petters J.L., Brooks G.C., Smith J.A., Haas C.A., The impact of targeted data management training for field research projects–A case study, Data Science Journal, 18, 1, (2019); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos One, 9, 12, (2014); Read K., Adapting data management education to support clinical research projects in an academic medical center, Journal of the Medical Library Association, 107, 1, pp. 89-97, (2019); Read K., Koos J., Miller R.S., Miller C.F., Phillips G.A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, Journal of the Medical Library Association: JMLA, 107, 3, (2019); Read K., Larson C., Gillespie C., Oh S.Y., Surkis A., A two-tiered curriculum to improve data management practices for researchers, PLoS One, 14, 5, (2019); Rice R., Macdonald S., Research data management training for librarians - An Edinburgh approach, ALISS Quarterly, 8, 3, pp. 6-9, (2013); Schmidt L., Holles J., A graduate class in research data management, Chemical Engineering Education, 52, 1, pp. 52-59, (2018); Searle S., Using scenarios in introductory research data management workshops for library staff, D-Lib Magazine, 21, 11-12, pp. 1-12, (2015); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program, 49, 4, pp. 440-460, (2015); Sesartic A., Towe M., Research data services at ETH-Bibliothek, IFLA Journal, 42, 4, pp. 284-291, (2016); Shelly M., Jackson M., Research data management compliance: Is there a bigger role for university libraries?, Journal of the Australian Library and Information Association, 67, 4, pp. 394-410, (2018); Southall J., Scutt C., Training for research data management at the Bodleian Libraries: National contexts and local implementation for researchers and librarians, New Review of Academic Librarianship, 23, 2-3, pp. 303-322, (2017); Surkis A., Lapolla F.W., Contaxis N., Read K.B., Data day to day: Building a community of expertise to address data skills gaps in an academic medical center, Journal of the Medical Library Association: JMLA, 105, 2, pp. 185-191, (2017); Sutherland K.A., Holistic academic development: Is it time to think more broadly about the academic development project?, International Journal for Academic Development, 23, 4, pp. 261-273, (2018); Thielen J., Hess A.N., Advancing research data management in the Social Sciences: Implementing instruction for education graduate students into a doctoral curriculum, Behavioral & Social Sciences Librarian, 36, 1, pp. 16-30, (2017); Thomas J., Harden A., Methods for the thematic synthesis of qualitative research in systematic reviews, BMC Medical Research Methodology, 8, 1, (2008); Verbakel E., Grootveld M., ‘Essentials 4 data support’: Five years’ experience with data management training, IFLA Journal, 42, 4, pp. 278-283, (2016); Wang M., Fong B.L., Embedded data librarianship: A case study of providing data management support for a science department, Science & Technology Libraries, 34, 3, pp. 228-240, (2015); Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, And Lessons Learned. Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Wiljes C., Cimiano P., Teaching research data management for students, Data Science Journal, 18, 38, pp. 1-9, (2019); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: Domain-based data management training for liaison librarians, The Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018); Yu F., Deuble R., Morgan H., Designing research data management services based on the research lifecycle–A consultative leadership approach, Journal of the Australian Library and Information Association, 66, 3, pp. 287-298, (2017)","C.Z. Oo; School of Education, University of New South Wales (UNSW), Sydney, Australia; email: cherryzinn@gmail.com","","Routledge","","","","","","1360144X","","","","English","Int. J. Acad. Dev.","Article","Final","","Scopus","2-s2.0-85102557707"
"Boyd C.","Boyd, Ceilyn (57222050200)","57222050200","Data as assemblage","2022","Journal of Documentation","","","","","","","2","10.1108/JD-08-2021-0159","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126043152&doi=10.1108%2fJD-08-2021-0159&partnerID=40&md5=d38c38b074da129bb0c24244be556a05","Simmons University, Boston, MA, United States","Boyd C., Simmons University, Boston, MA, United States","Purpose: A definition of data called data as assemblage is presented. The definition accommodates different forms and meanings of data; emphasizes data subjects and data workers; and reflects the sociotechnical aspects of data throughout its lifecycle of creation and use. A scalable assemblage model describing the anatomy and behavior of data, datasets and data infrastructures is also introduced. Design/methodology/approach: Data as assemblage is compared to common meanings of data. The assemblage model's elements and relationships also are defined, mapped to the anatomy of a US Census dataset and used to describe the structure of research data repositories. Findings: Replacing common data definitions with data as assemblage enriches information science and research data management (RDM) frameworks. Also, the assemblage model is shown to describe datasets and data infrastructures despite their differences in scale, composition and outward appearance. Originality/value: Data as assemblage contributes a definition of data as mutable, portable, sociotechnical arrangements of material and symbolic components that serve as evidence. The definition is useful in information science and research data management contexts. The assemblage model contributes a scale-independent way to describe the structure and behavior of data, datasets and data infrastructures and supports analyses and comparisons involving them. © 2022, Emerald Publishing Limited.","Assemblage theory; Data; Data definitions; Information science; Research data management; Theoretical models","","","","","","","","Achinstein P., Theoretical models, The British Journal for the Philosophy of Science, 16, 62, pp. 102-120, (1965); Participatory Data Stewardship, (2021); Afzal W., An Argument for the Increased Use of Qualitative Research in LIS, pp. 22-25, (2006); Agarwal N.K., Exploring context in information behavior: seeker, situation, surroundings, and shared identities, 9, (2018); Anderson M., The census and the Japanese ‘internment’: apology and policy in statistical practice, Social Research: An International Quarterly, 87, 4, pp. 789-812, (2020); Anderson M., Fienberg S.E., Race and ethnicity and the controversy over the US Census, Current Sociology, 48, 3, pp. 87-110, (2000); Bates M., Information and knowledge: an evolutionary framework for information science, Information Research, 10, 4, (2005); Bates M., Fundamental forms of information, Journal of the American Society for Information Science and Technology, 57, 8, pp. 1033-1045, (2006); Beagrie N., Houghton J., The Value and Impact of Data Sharing and Curation: A Synthesis of Three Recent Studies of UK Research Data Centres, (2014); Borgman C., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Boyd C., Data as assemblage dataset, Harvard Dataverse, 5, (2022); Buchanan I., Assemblage Theory and Method: an Introduction and Guide, (2021); Buckland M.K., Information as thing, Journal of the American Society for Information Science, 42, 5, pp. 351-360, (1991); Capps R., Gelatt J., Van Hook J., Fix M., Commentary on ‘The number of undocumented immigrants in the United States: estimates based on demographic modeling with data from 1990-2016, PLOS ONE, 13, 9, (2018); Capurro R., Hjorland B., The concept of information, Annual Review of Information Science and Technology, 37, pp. 343-411, (2003); Carroll S.R., Garba I., Figueroa-Rodriguez O.L., Holbrook J., Lovett R., Materechera S., Parsons M., Raseroka K., Rodriguez-Lonebear D., Rowe R., Sara R., Walker J.D., Anderson J., Hudson M., The CARE principles for indigenous data governance, Data Science Journal, 19, (2020); Dalrymple P.W., A quarter century of user-centered study: the impact of Zweizig and Dervin on LIS research, Library and Information Science Research, 23, 2, pp. 155-165, (2001); DeLanda M., A New Philosophy of Society: Assemblage Theory and Social Complexity, (2006); DeLanda M., Assemblage Theory, (2016); Deleuze G., Guattari F., A Thousand Plateaus: Capitalism and Schizophrenia, (1987); Devaraju A., Klump J., Tey V., Fraser R., Cox S., Wyborn L., A digital repository for physical samples: concepts, solutions and management, Research and Advanced Technology for Digital Libraries, pp. 74-85, (2017); Drucker J., Humanities approaches to graphical display, Digital Humanities Quarterly, 5, 1, (2011); D'Ignazio C., Klein L.F., Data Feminism, (2020); Faniel I.M., Frank R.D., Yakel E., Context from the data reuser's point of view, Journal of Documentation, 75, 6, pp. 1274-1297, (2019); Floridi L., Information: A Very Short Introduction, (2010); Fricke M., The knowledge pyramid: a critique of the DIKW hierarchy, Journal of Information Science, 35, 2, pp. 131-142, (2008); Furner J., Data: the data, Information Cultures in the Digital Age, pp. 287-306, (2016); Gitelman L., Raw Data Is an Oxymoron, (2013); Gofman A., Leif S.A., Gunderman H., Exner N., Do I have to Be an ‘other’ to Be myself? 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Boyd; Simmons University, Boston, United States; email: ceilyn.boyd@simmons.edu","","Emerald Group Holdings Ltd.","","","","","","00220418","","","","English","J. Doc.","Article","Article in press","","Scopus","2-s2.0-85126043152"
"Laskowski C.","Laskowski, Cas (57202496390)","57202496390","Structuring better services for unstructured data: Academic libraries are key to an ethical research data future with big data","2021","Journal of Academic Librarianship","47","4","102335","","","","3","10.1016/j.acalib.2021.102335","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101983514&doi=10.1016%2fj.acalib.2021.102335&partnerID=40&md5=8bbf234b953cfb8819f325edc171e0b5","Daniel F. Cracchiolo Law Library, University of Arizona School of Law, United States","Laskowski C., Daniel F. Cracchiolo Law Library, University of Arizona School of Law, United States","Academic libraries have been supporting research data management for decades, but are rarely their institutions central research support unit. As more researchers look to leverage big datasets for new insights, research data management is going to get ever more complex. For institutions of higher education to face these new challenges, they need to make libraries institutional leaders in this area. Doing so will reduce waste and risk while providing clearer guidance and fuller support through the entire research cycle. © 2021","Academic libraries; Big data; Data librarianship; Research data management; Research support","","","","","","","","Anderson C., The end of theory: The data deluge makes the scientific method obsolete, Wired Magazine, 16, pp. 1-6, (2008); Biggs M., Sources of Tension and Conflict between Librarians and Faculty, The Journal of Higher Education, 52, 2, (1981); Dekker H., Lackie P., Technical data skills for reproducible research, Databrarianship: The academic data librarian in theory and practice, pp. 93-112, (2016); Edmunds Otter M.L., Wright J.M., King N.V., Developing the librarians’ role in supporting grant applications and reducing waste in research: Outcomes from a literature review and survey in the NIHR Research Design Service, New Review of Academic Librarianship, 23, 2-3, pp. 258-274, (2017); Harford T., Big data: Are we making a big mistake, Significance, 11, 5, pp. 14-19, (2014); Library of Congress, Update on the Twitter archive at the Library of Congress, (2017); Library of Congress, Library of Congress fiscal 2021 budget justification, (2020); Lindsay T., U.S. higher education: Too big not to fail?, (2020); Martin B.E., Potts-kant E., Deceit at Duke: How fraud at a university research lab prompted a $112M fine, Business North Carolina, pp. 1-10, (2019); Smith C., Higher education is drowning in BS, The Chronicle of Higher Education, pp. 1-6, (2018); Tahir T., The irresistible rise of academic bureaucracy, The Guardian, pp. 2-5, (2010); Zhan M., Widen G., Understanding big data in librarianship, Journal of Librarianship and Information Science, 51, 2, pp. 561-576, (2019)","","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85101983514"
"Wieser F.; Stryeck S.; Lang K.; Hahn C.; Thallinger G.G.; Feichtinger J.; Hack P.; Stepponat M.; Merchant N.; Lindstaedt S.; Oberdorfer G.","Wieser, Florian (57267503100); Stryeck, Sarah (57191264224); Lang, Konrad (57267076300); Hahn, Christoph (53063727600); Thallinger, Gerhard G. (6507849206); Feichtinger, Julia (57213168127); Hack, Philipp (57267076400); Stepponat, Manfred (57267932200); Merchant, Nirav (55223734100); Lindstaedt, Stefanie (14022897900); Oberdorfer, Gustav (25229008900)","57267503100; 57191264224; 57267076300; 53063727600; 6507849206; 57213168127; 57267076400; 57267932200; 55223734100; 14022897900; 25229008900","A local platform for user-friendly FAIR data management and reproducible analytics","2021","Journal of Biotechnology","341","","","43","50","7","2","10.1016/j.jbiotec.2021.08.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115436397&doi=10.1016%2fj.jbiotec.2021.08.004&partnerID=40&md5=8b36f9fe2cb1f424b4db9792f55d8469","Institute of Biochemistry, Graz University of Technology, Graz, 8010, Austria; Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria; Know-Center GmbH, Graz, 8010, Austria; Institute of Biology, University of Graz, Graz, 8010, Austria; Institute of Biomedical Informatics, Graz University of Technology, Graz, 8010, Austria; Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, 8010, Austria; Central Information Technology, Graz University of Technology, Graz, 8010, Austria; Data Science Institute, University of Arizona, BSRL 200 A, Tucson, 85721, AZ, United States; BioTechMed-Graz, Austria","Wieser F., Institute of Biochemistry, Graz University of Technology, Graz, 8010, Austria; Stryeck S., Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria, Know-Center GmbH, Graz, 8010, Austria; Lang K., Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria, Know-Center GmbH, Graz, 8010, Austria; Hahn C., Institute of Biology, University of Graz, Graz, 8010, Austria; Thallinger G.G., Institute of Biomedical Informatics, Graz University of Technology, Graz, 8010, Austria, BioTechMed-Graz, Austria; Feichtinger J., Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, 8010, Austria, BioTechMed-Graz, Austria; Hack P., Central Information Technology, Graz University of Technology, Graz, 8010, Austria; Stepponat M., Central Information Technology, Graz University of Technology, Graz, 8010, Austria; Merchant N., Data Science Institute, University of Arizona, BSRL 200 A, Tucson, 85721, AZ, United States; Lindstaedt S., Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria, Know-Center GmbH, Graz, 8010, Austria; Oberdorfer G., Institute of Biochemistry, Graz University of Technology, Graz, 8010, Austria, BioTechMed-Graz, Austria","Collaborative research is common practice in modern life sciences. For most projects several researchers from multiple universities collaborate on a specific topic. Frequently, these research projects produce a wealth of data that requires central and secure storage, which should also allow for easy sharing among project participants. Only under best circumstances, this comes with minimal technical overhead for the researchers. Moreover, the need for data to be analyzed in a reproducible way often poses a challenge for researchers without a data science background and thus represents an overly time-consuming process. Here, we report on the integration of CyVerse Austria (CAT), a new cyberinfrastructure for a local community of life science researchers, and provide two examples how it can be used to facilitate FAIR data management and reproducible analytics for teaching and research. In particular, we describe in detail how CAT can be used (i) as a teaching platform with a defined software environment and data management/sharing possibilities, and (ii) to build a data analysis pipeline using the Docker technology tailored to the needs and interests of the researcher. © 2021 The Author(s)","Bioinformatics; Cyberinfrastructure; CyVerse; FAIR; Research data management; Teaching","Austria; Data Management; Software; Digital storage; Austria; Collaborative research; Cyberinfrastructure; Cyverse; Modern life science; Project participants; Research data managements; Science background; Secure storage; User friendly; article; Austria; bioinformatics; biomedicine; data analysis; data science; human; pipeline; shipyard worker; software; teaching; information processing; software; Information management","","","","","BioTechMed/Graz Hochschulraum-Strukturmittel; BioTechMed/Graz Hochschulraum-Strukturmittel ?; Digitale TU Graz; ERC-StG; Wissenschaft und Forschung Austria; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; Horizon 2020 Framework Programme, H2020, (802217); Austrian Science Fund, FWF, (T923-B26); Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF","Funding text 1: This research was funded by the Austrian infrastructure program 2016/2017 , Bundesministerium für Bildung, Wissenschaft und Forschung Austria , BioTechMed/Graz Hochschulraum-Strukturmittel ‘Integriertes Datenmanagement’ . The project was supported by Digitale TU Graz (Graz University of Technology) . F.W. and G.O were supported by an ERC-StG ( 802217 , HelixMold). J.F. was supported by a grant from the Austrian Science Fund (FWF) : T923-B26 . ; Funding text 2: This research was funded by the Austrian infrastructure program 2016/2017, Bundesministerium f?r Bildung, Wissenschaft und Forschung Austria, BioTechMed/Graz Hochschulraum-Strukturmittel ?Integriertes Datenmanagement?. The project was supported by Digitale TU Graz (Graz University of Technology). F.W. and G.O were supported by an ERC-StG (802217, HelixMold). 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Dev., 52, pp. 7-18, (2008); Ritchie M.E., Et al., Limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43, (2015); Rohl C.A., Strauss C.E.M., Misura K.M.S., Baker D., Meth. Enzymol., 383, pp. 66-93, (2004); RStudio Team, RStudio: Integrated Development for R. RStudio, (2020); Schonbrodt F., Et al., Netzwerk Der Open-Science-Initiativen (NOSI). OSF 22, (2021); Swetnam T.L., cyverse-vice/rstudio-base: First Release (Version v0.1), Zenodo, (2019); Thain D., Tannenbaum T., Livny M., Distributed computing in practice: the Condor experience, Concurr. Comput. Pract. Exp., 17, pp. 323-356, (2005); Wilkinson M.D., Et al., Comment: the FAIR guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Yoo A.B., Jette M.A., Grondona M., Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2862, pp. 44-60, (2003); Directorate-General for Research and Innovation (European Commission), Options for Strengthening Responsible Research and Innovation, (2013); (2021); (2021)","G. Oberdorfer; Institute of Biochemistry, Graz University of Technology, Graz, 8010, Austria; email: gustav.oberdorfer@tugraz.at; S. Lindstaedt; Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria; email: slind@know-center.at","","Elsevier B.V.","","","","","","01681656","","JBITD","34400238","English","J. Biotechnol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85115436397"
"Lipp A.; Sure-Vetter Y.","Lipp, Anne (35221979900); Sure-Vetter, York (57191841983)","35221979900; 57191841983","NFDI - Akteure und Prozesse, Erfolgsfaktoren und Herausforderungen","2022","Zeitschrift fur Bibliothekswesen und Bibliographie","69","1-2","","10","17","7","0","10.3196/1864295020691230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129586997&doi=10.3196%2f1864295020691230&partnerID=40&md5=d567e55a2736e38c927bb65512c6e2d1","Wissenschaftliche Literaturversorgungs-und Informationssysteme, Deutsche Forschungsgemeinschaft (DFG), Kennedyallee 40, Bonn, 53175, Germany; Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany","Lipp A., Wissenschaftliche Literaturversorgungs-und Informationssysteme, Deutsche Forschungsgemeinschaft (DFG), Kennedyallee 40, Bonn, 53175, Germany; Sure-Vetter Y., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany","The National Research Data Infrastructure Germany (NFDI) establishes a culture of data sharing in all academic disciplines, providing the necessary structures, services and processes for innovative and forward-looking research data management. The NFDI has been created as a network of independent consortia. It is being developed in an evolutionary process based on the interaction of many actors. The article traces the formation process, including the review and decision-making processes of the NFDI. It identifies the roles of the different actors and the structural elements of the NFDI, and examines the key challenges, features and success factors involved in setting up the NFDI. © 2022 Vittorio Klostermann. All rights reserved.","","","","","","","","","Nähere Informationen zu den Strukturelementen der NFDI unter; Bund-Länder-Vereinbarung zu Aufbau und Förderung einer Nationalen Forschungsdateninfrastruktur vom 26, (2018); Stellungnahme der Allianz der Wissenschaftsorganisationen zur Konzeption einer Nationalen Forschungsdateninfrastruktur, (2017); (2017); Von 80 Gutachtenden in der ersten und 88 in der zweiten Ausschreibungsrunde kamen 84 % (67 Personen) bzw; Guidance Notes on Funding Criteria NFDI unter; Diskussionsimpulse des Rates für Informationsinfrastrukturen; Der Aufbau einer Nationalen Forschungsdateninfrastruktur. Zweite Stellungnahme des NFDI-Expertengremiums, (2020); Basisdienste in der Nationalen Forschungsdateninfrastruktur. Stellungnahme des NFDI-Expertengremiums zur Vorbereitung und Beantragung von Basisdiensten für die NFDI, (2021); Zu den Rahmenbedingungen für die Antragstellung und Förderung von Basisdienst-Konsortien vgl; Bund-Länder-Vereinbarung zu Aufbau und Förderung einer Nationalen Forschungsdateninfrastruktur vom 26, (2018); Der Aufbau einer Nationalen Forschungsdateninfrastruktur. Zweite Stellungnahme des NFDI-Expertengremiums, (2020); Der Aufbau einer Nationalen Forschungsdateninfrastruktur. Zweite Stellungnahme des NFDI-Expertengremiums, (2020); Der Aufbau einer Nationalen Forschungsdateninfrastruktur. Zweite Stellungnahme des NFDI-Expertengremiums, (2020)","","","Vittorio Klostermann GmbH","","","","","","00442380","","","","German","Z. Bibliothekswes. Bibliogr.","Article","Final","","Scopus","2-s2.0-85129586997"
"Wittenburg P.; Strawn G.","Wittenburg, Peter (6507003409); Strawn, George (6506812070)","6507003409; 6506812070","Revolutions take time","2021","Information (Switzerland)","12","11","472","","","","1","10.3390/info12110472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119585520&doi=10.3390%2finfo12110472&partnerID=40&md5=a2d06ee7d634447705b910cb32ee312b","FDO Forum, Leiden, 2333 CR, Netherlands; National Academy of Sciences, Washington, DC, 20001, United States","Wittenburg P., FDO Forum, Leiden, 2333 CR, Netherlands; Strawn G., National Academy of Sciences, Washington, DC, 20001, United States","The 2018 paper titled “Common Patterns in Revolutionary Infrastructures and Data” has been cited frequently, since we compared the current discussions about research data management with the developments of large infrastructures in the past believing, similar to philosophers such as Luciano Floridi, that the creation of an interoperable data domain will also be a revolutionary step. We identified the FAIR principles and the FAIR Digital Objects as nuclei for achieving the necessary convergence without which such new infrastructures will not take up. In this follow-up paper, we are elaborating on some factors that indicate that it will still take much time until breakthroughs will be achieved which is mainly devoted to sociological and political reasons. Therefore, it is important to describe visions such as FDO as self-standing entities, the easy plug-in concept, and the built-in security more explicitly to give a long-range perspective and convince policymakers and decisionmakers. We also looked at major funding programs which all follow different approaches and do not define a converging core yet. This can be seen as an indication that these funding programs have huge potentials and increase awareness about data management aspects, but that we are far from converging agreements which we finally will need to create a globally integrated data space in the future. Finally, we discuss the roles of some major stakeholders who are all relevant in the process of agreement finding. Most of them are bound by short-term project cycles and funding constraints, not giving them sufficient space to work on long-term convergence concepts and take risks. The great opportunity to get funds for projects improving approaches and technology with the inherent danger of promising too much and the need for continuous reporting and producing visible results after comparably short periods is like a vicious cycle without a possibility to break out. We can recall that coming to the Internet with TCP/IP as a convergence standard was dependent on years of DARPA funding. Building large revolutionary infrastructures seems to be dependent on decision-makers that dare to think strategically and test out promising concepts at a larger scale. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Data infrastructures; Data management; FAIR Digital Objects; FAIR principles","Decision making; Finance; Philosophical aspects; 'current; Data domains; Data infrastructure; Decision makers; Digital Objects; FAIR digital object; FAIR principle; Follow up; Research data managements; Self standings; Information management","","","","","","","Wittenburg P., Strawn G., Common Patterns in Revolutionary Infrastructures and Data, B2SHARE Arch, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); De Smedt K., Koureas D., Wittenburg P., Analysis of Scientific Practice towards FAIR Digital Objects, B2SHARE Arch, (2019); Harari Y.N., Homo Deus, (2016); Floridi L., The Fourth Revolution: How the Infosphere is Reshaping Human Reality, (2014); Strawn G., Open Science, Business Analytics, and FAIR Digital Objects, Proceedings of the 43rd Annual Computer Software and Applications Conference (COMPSAC), (2019); Int. Data Space; Kahn R., Wilensky R., A framework for distributed digital object services, Int. J. Digit. Libr, 6, pp. 115-123, (2006); Hughes T., Networks of Power, (1983); Jeffery K., Wittenburg P., Lannom L., Strawn G., Biniossek C., Betz D., Blanchi C., Not Ready for Convergence in Data Infrastructures, Data Intell, 3, pp. 116-135, (2021)","P. Wittenburg; FDO Forum, Leiden, 2333 CR, Netherlands; email: peter.wittenburg@mpcdf.mpg.de","","MDPI","","","","","","20782489","","","","English","Information","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85119585520"
"Nass A.; Muhlbauer M.; Heinen T.; Bock M.; Munteanu R.; D'Amore M.; Riedlinger T.; Roatsch T.; Strunz G.; Helbert J.","Nass, Andrea (36143326200); Muhlbauer, Martin (57218937452); Heinen, Torsten (6505599043); Bock, Mathias (57566868900); Munteanu, Robert (57568074300); D'Amore, Mario (15069104600); Riedlinger, Torsten (16418065300); Roatsch, Thomas (14822546100); Strunz, Gunter (56038429800); Helbert, Jorn (24597402500)","36143326200; 57218937452; 6505599043; 57566868900; 57568074300; 15069104600; 16418065300; 14822546100; 56038429800; 24597402500","Information System for Planetary Research Data: A Prototype Development for Multi-Mission Application","2022","International Geoscience and Remote Sensing Symposium (IGARSS)","2022-July","","","4038","4041","3","0","10.1109/IGARSS46834.2022.9884300","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140360988&doi=10.1109%2fIGARSS46834.2022.9884300&partnerID=40&md5=5d7ae137425824c5753584b42dedce01","Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany; Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany","Nass A., Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany; Muhlbauer M., Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany; Heinen T., Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany; Bock M., Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany; Munteanu R., Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany; D'Amore M., Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany; Riedlinger T., Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany; Roatsch T., Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany; Strunz G., Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany; Helbert J., Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany","In the planetary sciences, the amount of remote sensing data and derived research products has been continuously increasing over the last few decades. The amount and complexity of the data require growing sophistication in data analysis, data management and data provision targeted at a wider research community. Here we present a prototype for structured storage and visualization of planetary data based on technology originally developed for Earth-based applications. This includes a centralized system for storing scientific findings and data products in order to efficiently manage, cross-link and enhance visibility of data products, including interim findings and source code. The aim here is to facilitate transparent management and re-use of research data and scientific results for long-term access and sustainable research data management. © 2022 IEEE.","Data Management; Information System; Planetary Science; Remote Sensing","Data visualization; Digital storage; Information management; Information systems; Information use; Software prototyping; Centralized systems; Data products; Planetary research; Planetary science; Prototype development; Remote sensing data; Remote-sensing; Research communities; Research data; Scientific data; Remote sensing","","","","","","","Planetary Science Archive (ESA)., (2022); Besse S., Vallat C., Barthelemy M., Et al., ESA's Planetary Science Archive: Preserve and present reliable scientific data sets, Planetary and Space Science, 150, pp. 131-140, (2018); Planetary Data System, (2022); Digital Archives, (2022); National Astronomical Observatories of China, (2022); EMM Science Data Center, (2022); Davies G., Mason N., Green S., Et al., Europlanet Research Infrastructure: Planetary Simulation Facilities, EPSC Abstracts, 4, (2009); TA2 Distributed Planetary Laboratory Facility, (2021); Erard S., Cecconi B., Le Sidaner P., Et al., Virtual European Solar and Planetary Access (VESPA): A Planetary Science Virtual Observatory Cornerstone, Data Science Journal, (2020); Glass G.V., Primary, Secondary, and Meta-Analysis of Research, Educational Researcher, 5, pp. 3-8, (1976); Hox J., Boeije H., Data collection, primary versus secondary, Encyclopedia of Social Measurement, (2005); Laura J.R., Beyer R.A., Knowledge Inventory of Foundational Data Products in Planetary Science, The Planetary Science Journal, (2021); German Remote Sensing Data Center (DFD); Royce W., Managing the development of large software systems: concepts and techniques, ICSE '87, (1987); Systems and Software Engineering-Software Product Quality Requirements and Evaluation (SQuaRE)-Common Industry Format (CIF) for Usability: User Requirements Specification, (2019); Systems and Software Engineering-Systems and Software Quality Requirements and Evaluation (SQuaRE)-Planning and Management, (2014); Nass A., Muhlbauer M., Heinen T., Et al., Multi-Mission Information System for Planetary Science: Prototype for remote sensing data and spatial research products, Remote Sensing, (2022); Roatsch T., Kersten E., Matz K.-D., Et al., High-resolution Ceres High Altitude Mapping Orbit atlas derived from Dawn Framing Camera images, Planetary and Space Science, 129, pp. 103-107, (2016); Preusker F., Scholten F., Matz K.-D., Et al., Dawn at Ceres-Shape model and rotational state, 47th LPSC., (2016); Roatsch T., Kersten E., Matz K.D., High-resolution Ceres Low Altitude Mapping Orbit Atlas derived from Dawn Framing Camera images, Planetary and Space Science, 140, pp. 74-79, (2017); Williams D.A., Buczkowski D.L., Mest S.C., Introduction: The geologic mapping of Ceres, Icarus, 316, pp. 1-13, (2018); Nass A., Review of a Compilation Process: A Map Package based on 15 individual Geological Maps of Ceres, EPSC-DPS Joint Meeting, #1304, (2019); EOC Geoservice, (2022); Muhlbauer M., UKIS-Environmental and Crisis Information Systems, (2022); Bock M., Langbein M., Voinov S., Et al., UKIS Frontend Libraries, (2022); elib-Publikationen des DLR, (2022); Open Geospatial Consortium, (2022)","A. Nass; Deutsches Zentrum für Luft-und Raumfahrt, Institut für Planetenforschung, Berlin, 12489, Germany; email: andrea.nass@dlr.de; M. Muhlbauer; Deutsches Zentrum für Luft-und Raumfahrt, Deutsches Fernerkundungsdatenzentrum, Weßling, Germany; email: martin.muehlbauer@dlr.de","","Institute of Electrical and Electronics Engineers Inc.","The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS)","2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022","17 July 2022 through 22 July 2022","Kuala Lumpur","183276","","978-166542792-0","IGRSE","","English","Dig Int Geosci Remote Sens Symp (IGARSS)","Conference paper","Final","","Scopus","2-s2.0-85140360988"
"Howie J.; Kara H.","Howie, Jess (57214321231); Kara, Hinerangi (57214317009)","57214321231; 57214317009","Research Support in New Zealand University Libraries","2022","New Review of Academic Librarianship","28","1","","7","36","29","3","10.1080/13614533.2019.1700535","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078609582&doi=10.1080%2f13614533.2019.1700535&partnerID=40&md5=11fba63d982a650ea27e7aa8b47f2297","Library, University of Waikato, Hamilton, New Zealand","Howie J., Library, University of Waikato, Hamilton, New Zealand; Kara H., Library, University of Waikato, Hamilton, New Zealand","Researcher-facing librarians in New Zealand are providing guidance across more of the research lifecycle than ever before in response to an increasingly complex scholarly ecosystem. This research explored the development of research support through survey responses from all eight New Zealand University Libraries. From the responses it was possible to ascertain the level of services offered or planned in the four key areas of research impact, Research Data Management, scholarly communication and Kaupapa Māori research. Skills gaps and barriers to service development were also identified. Comparison with an earlier study revealed that research impact and Research Data Management services have developed well over the last 6 years. A good level of maturity was identified in scholarly communication services but support for Kaupapa Māori research was identified as an area for development. Barriers to service development included lack of resourcing and low recognition of library value from the wider institution. ©, Published with license by Taylor & Francis Group, LLC. ©, Jess Howie and Hinerangi Kara.","altmetrics; bibliometrics; collaboration; cultural competence; indigenous library services; job titles; Kaupapa Māori research; librarians; librarianship; library services; postcolonial universities; research data management; research impact; Research support; scholarly communication; workforce planning","","","","","","","","Auckland M., Re‐skilling for research, (2012); Bladek M., Bibilometrics services and the academic library: Meeting the emerging needs of the campus community, College & Undergraduate Libraries, 21, 3-4, pp. 330-344, (2014); Borrego A., Ardanuy J., Urbano C., Librarians as research partners: Their contribution to the scholarly endeavour beyond library and information science, The Journal of Academic Librarianship, 44, 5, pp. 663-670, (2018); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, 64, 3, pp. 224-234, (2015); Cooper D., Ball T., Boyer-Kelly M.N., Carr-Wiggin A., Cornelius C., Cox J.W., Wong D., When research is relational: Supporting the research practices of Indigenous studies scholars, (2019); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A., Gadd E., Petersohn S., Sbaffi L., Competencies for bibliometrics, Journal of Librarianship and Information Science, 51, 3, pp. 1-17, (2017); Faniel I., Connaway L., Librarians' perspectives on the factors influencing research data management programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Gorraiz J., Wieland M., Gumpenberger C., Bibliometric practices and activities at the University of Vienna, Library Management, 33, 3, pp. 174-183, (2012); Green J.A., Nicholls N.H., Sferdean F.C., Akers K.G., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Haddow G., Research support in a research assessment environment: The experience of ‘new’ universities, Library and Information Research, 36, 113, pp. 62-80, (2012); Haddow G., Mamtora J., Research support in Australian academic libraries: Services, resources, and relationships, New Review of Academic Librarianship, 23, 2-3, pp. 89-109, (2017); Hashim A.M., Abdullah A., Embedded librarianship in scholarly communication: Perceived roles of academic librarians in Malaysian research intensive universities, Paper presented at the International Conference on Libraries, (2015); Hua X., Zhuang X., Si L., Zhou L., Xing W., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Ithaka S.R., CONZUL faculty survey: Aggregate report of findings, (2018); Jaguszewski J.M., Williams K., New roles for new times: Transforming liaison roles in research libraries, (2013); Keller A., Research support in Australian university libraries: An outsider view, Australian Academic & Research Libraries, 46, 2, pp. 73-85, (2015); Kennan M.A., Corrall S., Afzal W., Making space in practice and education: Research support services in academic libraries, Library Management, 35, 8-9, pp. 666-683, (2014); Koltay T., Research 2.0 and research data services in academic and research libraries: Priority issues, Library Management, 38, 6-7, pp. 345-353, (2017); Koltay T., Accepted and emerging roles of academic libraries in supporting Research 2.0, The Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Lang L., Wilson T., Wilson K., Kirkpatrick A., Research support at the crossroads: Capability, capacity, and collaboration, New Review of Academic Librarianship, pp. 326-336, (2018); Mamtora J., Transforming library research services: Towards a collaborative partnership, Library Management, 34, 4-5, pp. 352-371, (2013); (2016); McAllister T.G., Kidman J., Rowley O., Theordore R.F., Why isn’t my professor Māori? A snapshot of the academic workforce in New Zealand universities, MAI Journal: A New Zealand Journal of Indigenous Scholarship, 8, 2, (2019); McNab A., Tattersall A., The digital transformation of research support, Paper presented at the Northern Collaboration, (2017); Vision Matauranga, (2007); Parker R., What the library did next: Strengthening our visibility in research support, Paper presented at the VALA - Libraries, Technology and the Future Conference, Melbourne, Australia, (2012); Petersohn S., Bibliometric services in research evaluation: A new task area strengthening the jurisdiction of academic librarians, Paper presented at the International Association of Tertiary and University Libraries Conference, (2014); Petersohn S., Heinze T., Professionalization of bibliometric research assessment. Insights from the history of the Leiden Centre for Science and Technology Studies (CWTS), Science and Public Policy, 45, 4, pp. 565-578, (2018); International survey of research university faculty: Use of library assistance in navigating bibliometrics & altmetrics tools, (2017); Roa T., Beggs J.R., Williams J., Moller H., New Zealand’s Performance Based Research Funding (PBRF) model undermines Māori research, Journal of the Royal Society of New Zealand, 39, 4, pp. 233-238, (2009); Smith L.T., Decolonizing methodologies: Research and indigenous peoples, (2012); (2013); Research and development survey, (2017); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Performance-based research fund, (2019); Wilkinson M., Amos H., Morton L., Flaherty B., Hearne S., Lynch H., Elliot G., Research data management framework report, (2016); Willson M.A., Merrick H., Genoni P., Scholarly communities, e‐research literacy and the academic librarian, The Electronic Library, 24, 6, pp. 734-746, (2006); Wilson J., Martinez-Uribe L., Fraser M., Jeffreys P., An institutional approach to developing research data management infrastructure, 6, 2, pp. 274-287, (2011); Zhao L., Riding the wave of open access: Providing library research support for scholarly publishing literacy, Australian Academic & Research Libraries, 45, 1, pp. 3-18, (2014)","J. Howie; University of Waikato Library, Hamilton, Private Bag 3105, 3240, New Zealand; email: jessiel@waikato.ac.nz","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85078609582"
"Rod A.B.; Isuster M.Y.; Chandler M.","Rod, Alisa B. (55916400900); Isuster, Marcela Y. (57222384637); Chandler, Martin (57532952600)","55916400900; 57222384637; 57532952600","Love Data Week in the time of COVID-19: A content analysis of Love Data Week 2021 events","2021","Journal of Academic Librarianship","47","6","102449","","","","1","10.1016/j.acalib.2021.102449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114506635&doi=10.1016%2fj.acalib.2021.102449&partnerID=40&md5=4233c8cc9936e58c75fa619ab37ab93b","McGill University Library, 550 Sherbrooke Street West, West Tower, 6th floor, Montreal, H3A 1B9, QC, Canada; McGill University Library, 3459 McTavish Street, Montreal, H3A 0C9, QC, Canada; Cape Breton University Library, 1250 Grand Lake Road, Sydney, 1A2, Nova Scotia B1M","Rod A.B., McGill University Library, 550 Sherbrooke Street West, West Tower, 6th floor, Montreal, H3A 1B9, QC, Canada; Isuster M.Y., McGill University Library, 3459 McTavish Street, Montreal, H3A 0C9, QC, Canada; Chandler M., Cape Breton University Library, 1250 Grand Lake Road, Sydney, 1A2, Nova Scotia B1M","A primary role for data-focused librarians is building community through traditional and novel modes of in-person outreach, including consultations, training, and themed events such as Love Data Week. Unfortunately, the COVID-19 pandemic rendered in-person events impossible. However, Love Data Week 2021 persisted in an online format, allowing data-focused librarians a unique chance to initiate outreach to geographically dispersed constituents. In this study, the authors investigate the nature and context of Love Data Week 2021 events to gain insight into current research data services trends, as impacted by the global COVID-19 pandemic. The authors collected qualitative information about 242 Love Data Week 2021 events across 37 organizations and coded the information using manual content analysis. This paper reports on descriptive results from the content analysis, including the dominant topics across events (software or digital tools, research data management, and service or product awareness) and the primary mode of events (workshops). The authors discuss implications for future research on Love Data Week and themed weeks in general as successful modes of outreach, community-building, and as venues for tracking emerging trends in the context of research data services. © 2021 Elsevier Inc.","Data librarianship; Love data week; Research data; Research data services","","","","","","","","Barnett L., Brackett A., Grimshaw A., Nyhan K., The future comes one week at a time: Data outreach at Cushing/Whitney Medical Library, University of Massachusetts and New England Area Librarian e-Science Symposium, (2018); Bernard H.R., Wutich A., Ryan G.W., Analyzing qualitative data: Systematic approaches, (2016); Beauchamp A., Murray C., Teaching foundational data skills in the library, Databrarianship: The academic data librarian in theory and practice, (2016); Chaput J., Walsh R., Opening doors to research success: Data management programming and outreach. [Poster session], (2019); Berkeley Research Data Management, Love Data Week 2019 | Research Data Management, (2019); Bill & Melinda Gates Foundation, Open access policy, (2021); Tri-Agency research data management policy, (2021); Carruthers A., Open data day hackathon 2014 at Edmonton public library, Partnership: The Canadian Journal of Library and Information Practice and Research, 9, 2, (2014); Coates H.L., Atwood T., Bass M., Condon P., Foster E.D., Graebner C., Adamus T., Love Data Week website 2016–2020, (2020); Conway M., The subjective precision of computers: A methodological comparison with human coding in content analysis, Journalism & Mass Communication Quarterly, 83, 1, pp. 186-200, (2006); Cross W.M., Davis H.M., Where do we go from here: Choosing a framework for assessing research data services and training, Proceedings of the Charleston Library conference, (2016); Dai Y., How many ways can we teach data literacy?, IASSIST Quarterly, 43, 4, pp. 1-11, (2020); DeCuir-Gunby J.T., Marshall P.L., McCulloch A.W., Developing and using a codebook for the analysis of interview data: an example from a professional development research project, Field Methods, 23, 2, pp. 136-155, (2011); Downey A., Critical information literacy: Foundations, inspiration, and ideas, (2016); Erlingsson C., Brysiewicz P., A hands-on guide to doing content analysis, African Journal of Emergency Medicine, 7, 3, pp. 93-99, (2017); Library E.P.F.L., Love data week 2019 at EPFL, (2019); Fonteyn M.E., Vettese M., Lancaster D.R., Bauer-Wu S., Developing a codebook to guide content analysis of expressive writing transcripts, Applied Nursing Research, 21, 3, pp. 165-168, (2008); Franklin C., Cody P., Ballan M., Reliability and validity in qualitative research, The handbook of social work research methods, pp. 355-374, (2010); Fritz S., Milligan I., Ruest N., Lin J., Building community at distance: A datathon during COVID-19, (2020); Gao W., Malone A., Simons A., Love Data@ UH: Collaborating with campus partners to promote data services, Collaborative Librarianship, 11, 3, (2019); Harris S.Y., Covid-19 impact on the Caribbean academic library: Jamaica's preliminary response to people, place, product and services, Library Management, 42, 6-7, pp. 340-361, (2021); Love Data Week with ICPSR, (2021); Jaskowska B., Management of Academic Libraries in Poland during the COVID-19 lockdown, Zagadnienia Informacji Naukowej-Studia Informacyjne, 58, 2A (116A), pp. 29-43, (2020); Kim J., Academic library's leadership and stakeholder involvement in research data services, Proceedings of the Association for Information Science and Technology, 57, 1, (2020); Koivisto J., What if you could save the data?: Endangered Data Week & how libraries can protect public data. [Conference presentation], (2019); Krippendorff K., Content analysis: An introduction to its methodology, (2018); Kross S., Guo P.J., Practitioners teaching data science in industry and academia: Expectations, workflows, and challenges, Proceedings of the 2019 CHI conference on human factors in computing systems, pp. 1-14, (2019); Kurasaki K.S., Intercoder reliability for validating conclusions drawn from open-ended interview data, Field Methods, 12, 3, pp. 179-194, (2000); Johnson P.C., International open access week at small to medium US academic libraries: The first five years, The Journal of Academic Librarianship, 40, 6, pp. 626-631, (2014); Jones J., Waller A., McLennan J., Open Access Week; library strategies for advancing change, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 270, pp. 21-26, (2010); Martzoukou K., Academic libraries in COVID-19: A renewed mission for digital literacy, Library Management, 42, 4-5, pp. 266-276, (2020); Love Data Week 2019 | Maynooth University, (2019); Maynooth University, Love Data Week 2020 | Maynooth University, (2020); Milligan I., Casemajor N., Fritz S., Lin J., Ruest N., Weber M., Worby N., Building Community and Tools for Analyzing Web Archives Through Datathons, 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 265-268, (2019); National Institutes of Health, NOT-OD-21-013: Final NIH policy for data management and sharing, (2020); Oliver J.C., Kollen C., Hickson B., Rios F., Data science support at the academic library, Journal of Library Administration, 59, 3, pp. 241-257, (2019); Read K.B., Koos J., Miller R.S., Miller C.F., Phillips G.A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, Journal of the Medical Library Association: JMLA, 107, 3, (2019); Rod A.B., Isuster M.Y., Chandler M., Replication Data for: Love Data Week in the time of COVID-19: A content analysis of Love Data Week 2021 events, Scholars Portal Dataverse, (2021); Ryan G.W., Bernard H.R., Techniques to identify themes, Field Methods, 15, 1, pp. 85-109, (2003); Seton Hall University Libraries, Love Data Week – University Libraries, (2021); Spinellis D., Giannikas V., Organizational adoption of open source software, Journal of Systems and Software, 85, 3, pp. 666-682, (2012); Stemler S., An overview of content analysis, Practical Assessment, Research, and Evaluation, 7, 1, (2000); UC San Diego Library, Love Data Week 2020 Kicks off Next Week, (2020); University of Chicago Library, Celebrate Love Data Week with the Library's data specialists, (2020); University of Notre Dame, Love Data Week 2019 // Notre Dame Events // University of Notre Dame, (2019); University of Notre Dame, Love Data Week 20202 // Notre Dame Events // University of Notre Dame, (2020); Walsh B., Rana H., Continuity of academic library services during the pandemic the University of Toronto Libraries' response, Journal of Scholarly Publishing, 51, 4, pp. 237-245, (2020); Weimer K.H., Olivares M., Bedenbaugh R.A., GIS Day and web promotion: Retrospective analysis of U.S. ARL libraries' involvement, Journal of Map & Geography Libraries, 8, 1, pp. 39-57, (2012); Wissel K., DeLuca L., Learning to love data (week): Creating data services awareness on campus, College & Research Libraries News, 79, 9, (2018); Wong G.K., Chan D.L., Designing library-based research data management services from bottom-up, Future directions in digital information, pp. 55-68, (2021)","A.B. Rod; McGill University Library, Montreal, 550 Sherbrooke Street West, West Tower, 6th floor, H3A 1B9, Canada; email: alisa.rod@mcgill.ca","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85114506635"
"Bodmann L.; Lübke E.; Hartl N.; Jansen L.; Seitz-Moskaliuk H.; Sure-Vetter Y.; Wössner E.","Bodmann, Laura (57678533700); Lübke, Eva (57679761500); Hartl, Nathalie (57234687500); Jansen, Lukas (57679153700); Seitz-Moskaliuk, Hendrik (56835806300); Sure-Vetter, York (57191841983); Wössner, Elena (57211181140)","57678533700; 57679761500; 57234687500; 57679153700; 56835806300; 57191841983; 57211181140","Bibliotheken als Akteure bei NFDI Herausforderungen, Chancen, Zukunftsaussichten","2022","Zeitschrift fur Bibliothekswesen und Bibliographie","69","1-2","","18","25","7","0","10.3196/1864295020691244","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129807525&doi=10.3196%2f1864295020691244&partnerID=40&md5=68ee433135b1950a93924f9265a9ad90","Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Nationale Forschungsdateninfrastruktur (NFDI) e.V., NFDI-Direktorat, Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany","Bodmann L., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Lübke E., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Hartl N., Nationale Forschungsdateninfrastruktur (NFDI) e.V., NFDI-Direktorat, Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Jansen L., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Seitz-Moskaliuk H., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Sure-Vetter Y., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany; Wössner E., Nationale Forschungsdateninfrastruktur (NFDI) e.V., Albert-Nestler-Straße 13, Karlsruhe, 76131, Germany","The aim of the National Research Data Infrastructure Germany association (NFDI) is to create standards for effective research data management (RDM) in the future. Libraries are part of the nationwide network of institutions, which covers all domains. For this article, which focuses on the participation of libraries in the NFDI, the authors interviewed nine experts from the NFDI community with a library background. Digitalisation has not changed the core remit of libraries, i.e. to make information available to users in a structured way. However, if in the future they wish to offer research data management services, they can do so more efficiently in cooperation with scientists and IT service providers. Here the NFDI provides a powerful knowledge- sharing network in which libraries can contribute their perspectives, competences and experiences, allowing all participants to benefit from one another. The NFDI and the libraries can achieve more by jointly establishing a sustainable infrastructure for research data, promoting a cultural shift towards the sharing of research data, and enabling scientists to acquire the skills necessary for this. © 2022 Vittorio Klostermann. All rights reserved.","","","","","","","","","Dierkes Jens; Stille Wolfgang, Et al., Forschungsunterstützung an Bibliotheken. Positionspapier der Kommission für forschungsnahe Dienste des VDB, O-Bib. Das Offene Bibliotheksjournal / Herausgeber VDB, 8, 2, (2021); Depping Ralf, Interview durch Lukas Jansen und Laura Bodmann, Online-Interview per Zoom, (2021); Lindstadt Birte, Interview durch Laura Bodmann, (2021); Brase Jan, (2021); Leistung aus Vielfalt, (2016); Für eine stetig aktualisierte Mitgliederliste des Vereins Nationale Forschungsdateninfrastruktur (NFDI) e.V. vgl; Einen Überblick über die geförderten Konsortien in NFDI und den an ihnen teilnehmenden Institutionen bieten; Zu NFDI4Culture vgl; Rat für Informationsinfrastrukturen: Schritt für Schritt - oder: Was bringt wer mit?, (2017); Einen Überblick über bestehende FDM-Angebote an deutschen Hochschulen und weiteren Forschungseinrichtungen bietet; Lorenz Jorg, Berkemeier Frank, Przibytzin Holger, Interview durch Lukas Jansen, Online-Interview durch die Verfasser*innen, (2021); Depping Ralf, Interview durch Lukas Jansen und Laura Bodmann, Online-Interview per Zoom, (2021); Depping Ralf, (2021); Depping Ralf, Interview durch Lukas Jansen und Laura Bodmann, Online-Interview per Zoom, (2021); Tochtermann Klaus, Eine neue Sicht auf die Bibliotheken der Zukunft. Zehn Thesen zum zukünftigen Profil von wissenschaftlichen Informationsinfrastruktureinrichtungen mit überregionaler Bedeutung, BuB, 65, 11 - 12, (2013); Stille Wolfgang, Et al., Für Beispiele vgl, (2021); Lindstadt Birte, pp. 11-12, (2021); Depping Ralf, (2021); Depping Ralf, (2021); Schumm Irene, Interview durch Lukas Jansen und Nathalie Hartl, Online-Interview per Zoom, (2021); Diepenbroek Michael, Sektionskonzept Common Infrastructures zur Einrichtung einer Sektion im Verein Nationale Forschungsdateninfrastruktur (NFDI) e.V. Zenodo, (2021); Lindstadt Birte, Interview durch Laura Bodmann, (2021); Depping Ralf, (2021); Depping Ralf, Interview durch Lukas Jansen und Laura Bodmann, Online-Interview per Zoom, (2021); Depping Ralf, (2021); Brase Jan, Interview durch Laura Bodmann. Online-Interview per Zoom, 28.10.2021. Zum voraussetzungsreichen Vertrauen in Institutionen vgl. nur Lepsius, M. Rainer, Vertrauen zu Institutionen, pp. 283-293, (2021); Schumm Irene, Interview durch Lukas Jansen und Nathalie Hartl, Online-Interview per Zoom, (2021); Basisdienste in der Nationalen Forschungsdateninfrastruktur (NFDI). Stellungnahme des NFDI-Expertengremiums zur Vorbereitung und Beantragung von Basisdiensten für die NFDI, (2021); Aus dem Koalitionsvertrag 2021 - 2025 zwischen der Sozialdemokratischen Partei Deutschlands (SPD), BÜNDNIS 90/ DIE GRÜNEN und den Freien Demokraten (FDP); Lindstadt Birte, (2021); Strauss Florian, Interview durch Lukas Jansen und Laura Bodmann, Online-Interview per Zoom, (2021); Zu diesem Projekt vgl; Rat für Informationsinfrastrukturen: Nutzung und Verwertung von Daten im wissenschaftlichen Raum. Empfehlungen zur Ausgestaltung von Datendiensten an der Schnittstelle zwischen Wissenschaft und Wirtschaft, (2021)","","","Vittorio Klostermann GmbH","","","","","","00442380","","","","German","Z. Bibliothekswes. Bibliogr.","Article","Final","","Scopus","2-s2.0-85129807525"
"Urs S.R.; Minhaj M.","Urs, Shalini R. (8740475700); Minhaj, Mohamed (57554432700)","8740475700; 57554432700","Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis","2022","Journal of the Association for Information Science and Technology","","","","","","","1","10.1002/asi.24649","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127261265&doi=10.1002%2fasi.24649&partnerID=40&md5=25dcca07c38c1eb1d39b89524c2691b8","International School of Information Management, University of Mysore, Mysore, India; The SDM Institute for Management Development, Mysore, India","Urs S.R., International School of Information Management, University of Mysore, Mysore, India; Minhaj M., The SDM Institute for Management Development, Mysore, India","Data Science (DS) has emerged from the shadows of its parents—statistics and computer science—into an independent field since its origin nearly six decades ago. Its evolution and education have taken many sharp turns. We present an impressionistic study of the evolution of DS anchored to Kuhn's four stages of paradigm shifts. First, we construct the landscape of DS based on curriculum analysis of the 32 iSchools across the world offering graduate-level DS programs. Second, we paint the “field” as it emerges from the word frequency patterns, ranking, and clustering of course titles based on text mining. Third, we map the curriculum to the landscape of DS and project the same onto the Edison Data Science Framework (2017) and ACM Data Science Knowledge Areas (2021). Our study shows that the DS programs of iSchools align well with the field and correspond to the Knowledge Areas and skillsets. iSchool's DS curriculums exhibit a bias toward “data visualization” along with machine learning, data mining, natural language processing, and artificial intelligence; go light on statistics; slanted toward ontologies and health informatics; and surprisingly minimal thrust toward eScience/research data management, which we believe would add a distinctive iSchool flavor to the DS. © 2022 Association for Information Science and Technology.","","Curricula; Data mining; Data visualization; Information management; Learning algorithms; Machine learning; Medical informatics; Natural language processing systems; Clusterings; Frequency patterns; Graduate-level; Knowledge areas; Paradigm shifts; Science curriculum; Science projects; Skill sets; Text-mining; Word frequencies; Data Science","","","","","","","Abbot M., A new path for science?, The fourth paradigm: Data-intensive scientific discovery, pp. 110-111, (2009); Arp R., Smith B., Function, role, and disposition in basic formal ontology, Nature Proceedings, 1, (2008); Balietti S., Mas M., Helbing D., On disciplinary fragmentation and scientific progress, PLoS One, 10, 3, (2015); Bandrowski A., Brinkman R., Brochhausen M., Brush M.H., Bug B., Chibucos M.C., Clancy K., Courtot M., Derom D., Dumontier M., Fan L., Fostel J., Fragoso G., Gibson F., Gonzalez-Beltran A., Haendel M.A., He Y., Heiskanen M., Hernandez-Boussard T., Zheng J., The ontology for biomedical investigations, PLoS One, 11, 4, (2016); Behpour S., Goudarzi A., Hawamdeh S., Employer's perspective on data science; analysis of job requirement & course description, ALISE 2019 Proceedings, pp. 177-182, (2019); Bennett M., Baclawski K., The role of ontologies in linked data, big data and semantic web applications, Applied Ontology, 12, 3-4, pp. 189-194, (2017); Berners-Lee T., Hendler J., Lassila O., The semantic web, Scientific American, 284, 5, pp. 34-43, (2001); Bodenreider O., Biomedical ontologies in action: Role in knowledge management, data integration and decision support, Yearbook of Medical Informatics, 17, 1, pp. 67-79, (2008); Breiman L., Statistical modeling: The two cultures (with comments and a rejoinder by the author), Statistical Science, 16, 3, pp. 199-231, (2001); Cao L., Data science: A comprehensive overview, ACM Computing Surveys (CSUR), 50, 3, pp. 1-42, (2017); Cao L., Data science education, Data science thinking. Data analytics, (2018); Chambers J.M., Greater or lesser statistics: A choice for future research, Statistics and Computing, 3, 4, pp. 182-184, (1993); Chandrasekaran B., Josephson J.R., Benjamins V.R., What are ontologies, and why do we need them?, IEEE Intelligent Systems and their Applications, 14, 1, pp. 20-26, (1999); Cleveland W.S., Data science: An action plan for expanding the technical areas of the field of statistics, International Statistical Review, 69, 1, pp. 21-26, (2001); Cohen E., Lloyd S., Disciplinary evolution and the rise of the transdiscipline, Informing Science, 17, pp. 189-215, (2014); Davenport T.H., Patil D.J., Data scientist, Harvard Business Review, 90, 5, pp. 70-76, (2012); Demchenko Y., Brewer S., Jose C.G.J., Wiktorski T., EDISON Data Science Framework (EDSF): Addressing demand for data science and analytics, (2017); Donoho D., 50 years of data science, Journal of Computational and Graphical Statistics, 26, 4, pp. 745-766, (2017); Fayyad U., Piatetsky-Shapiro G., Smyth P., From data mining to knowledge discovery in databases, AI Magazine, 17, 3, (1996); Fung K.W., Bodenreider O., Knowledge representation and ontologies, Clinical research informatics. Health informatics, (2012); Glanzel W., Thijs B., Using “core documents” for detecting and labelling new emerging topics, Scientometrics, 91, 2, pp. 399-416, (2012); Glushko R.J., The Discipline of Organizing Meets Data Science. Position Paper for “Information Science to Data Science” Workshop at iConference 2017, (2017); Grady D., The golden age of data: Media analytics in study & practice, (2019); Hayashi C., International federation of classification societies & conference of the international federation of classification societies (IFCS-96). (1998), Data science, classification, and related methods: Proceedings of the fifth conference of the international federation of classification societies (IFCS-96), Kobe, Japan, March 27–30, 1996, (1998); Hey T., Tansley S., Tolle K., The fourth paradigm: Data-intensive scientific discovery, (2009); Holmberg K., The conceptual landscape of iSchools: Examining current research interests of faculty members, Information Research: An International Electronic Journal, 18, 3, (2013); White paper – #US44413318, (2018); Jansen J.D., Reddy V., Curriculum analysis, Workshop entitled “Curriculum Development” held at the Peninsula Technikon, Cape Town, South Africa, (1994); Jiang H., Chen C., Data science skills and graduate certificates: A quantitative text analysis, Journal of Computer Information Systems, (2021); Joo S., Choi I., Choi N., Topic analysis of the research domain in knowledge organization: A latent Dirichlet allocation approach, KO Knowledge Organization, 45, 2, pp. 170-183, (2018); Kalantari A., Kamsin A., Kamaruddin H.S., Ebrahim N.A., Gani A., Ebrahimi A., Shamshirband S., A bibliometric approach to tracking big data research trends, Journal of Big Data, 4, 1, pp. 1-18, (2017); Kawintiranon K., Vateekul P., Suchato A., Punyabukkana P., Understanding knowledge areas in curriculum through text mining from course materials, 2016 IEEE international conference on teaching, assessment, and learning for engineering (TALE), pp. 161-168, (2016); Kempa A., Zacny B., The curriculum content analysis for the construction of the teaching process, Studia i Materialy Polskiego Stowarzyszenia Zarzadzania Wiedza/Studies & Proceedings Polish Association for Knowledge Management, (2012); Khan H., Exploring data science learning objectives in LIS education, (2020); Kuhn T.S., The structure of scientific revolutions, (1962); Lesk M., How much information is there in the world?, (1997); Lyman P., Varian H.R.; Matsuda Y., Sekiya T., Yamaguchi K., Curriculum analysis of computer science departments by simplified, supervised LDA, Journal of Information Processing, 26, pp. 497-508, (2018); 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Provost F., Fawcett T., Data science and its relationship to big data and data-driven decision making, Big Data, 1, 1, pp. 51-59, (2013); Raban D.R., Gordon A., The evolution of data science and big data research: A bibliometric analysis, Scientometrics, 122, 3, pp. 1563-1581, (2020); Repko A.F., Szostak R., Interdisciplinary research: Process and theory, (2017); Rudin C., Dunson D., Irizarry R., Ji H., Laber E., Leek J., McCormick T., Rose S., Schafer C., van der Laan M., Wasserman L., Xue L., Discovery with data: Leveraging statistics with computer science to transform science and society, (2014); Shah C., Anderson T., Hagen L., Zhang Y., An iSchool approach to data science: Human-centered, socially responsible, and context-driven—A position paper, Journal of the Association for Information Science and Technology., 72, pp. 793-796, (2021); Si L., Zhuang X., Xing W., Guo W., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, pp. 700-724, (2013); Sir M., Bradac Z., Fiedler P., Ontology versus database, IFAC-PapersOnLine, 48, 4, pp. 220-225, (2015); Tang R., Sae-Lim W., Data science programs in US higher education: An exploratory content analysis of program description, curriculum structure, and course focus, Education for Information, 32, 3, pp. 269-290, (2016); Tukey J.W., The future of data analysis, Annals of Mathematical Statistics, 33, 1, pp. 1-67, (1962); Vicario G., Coleman S., A review of data science in business and industry and a future view, Applied Stochastic Models in Business and Industry, 36, pp. 6-18, (2019); West J., Teaching data science: An objective approach to curriculum validation, Computer Science Education, 28, 2, pp. 136-157, (2018); Wiggins A., Interdisciplinary diversity in the iSchool community, iConference 2009, pp. 9-11, (2009); Wiggins A., Sawyer S., Intellectual diversity and the faculty composition of iSchools, Journal of the American Society for Information Science and Technology, 63, 1, pp. 8-21, (2012); Wu D., He D., Jiang J., Dong W., Vo K.T., The state of iSchools: An analysis of academic research and graduate education, Journal of Information Science, 38, 1, pp. 15-36, (2012); Wu J., Statistics = data science?, (1997); Yadav V., Sadeque F., Heidorn P.B., Cui H., Where are iSchools heading?, 13th international conference on transforming digital worlds, iConference 2018, pp. 665-670, (2018); Zhang P., Yan J.L.S., Hassman K.D., The intellectual characteristics of the information field: Heritage and substance, Journal of the American Society for Information Science and Technology, 64, 12, pp. 2468-2491, (2013); Zuo Z., Zhao K., Eichmann D., The state and evolution of US iSchools: From talent acquisitions to research outcome, Journal of the Association for Information Science and Technology, 68, 5, pp. 1266-1277, (2017)","S.R. Urs; International School of Information Management, University of Mysore, Mysore, India; email: shalini@isim.ac.in","","John Wiley and Sons Inc","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Article in press","","Scopus","2-s2.0-85127261265"
"Musarurwa B.","Musarurwa, Bvumai (57574099500)","57574099500","Research data management practices at Bindura University of science education","2021","Handbook of Research on Information and Records Management in the Fourth Industrial Revolution","","","","56","66","10","0","10.4018/978-1-7998-7740-0.ch004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128041132&doi=10.4018%2f978-1-7998-7740-0.ch004&partnerID=40&md5=81fa699ffbb2de97b28cf8cf24697554","Bindura University of Science Education, Zimbabwe","Musarurwa B., Bindura University of Science Education, Zimbabwe","The study assessed the research data management (RDM) practices at Bindura University of Science Education (BUSE) with the aim of understanding how research data (RD) is managed. The study was prompted by lack of proper RDM policy. UK data archive research data lifecycle model was adopted in the study for benchmarking RDM practices at BUSE in line with international standards. The research used the interpretism approach and is qualitative in nature. Interviews were used to collect qualitative data from the Research and Postgraduate Centre (RPGC), deputy librarian, sub-librarian, and technology librarian. Quantitative data obtained from departmental chairpersons, assistant librarians, and chief library assistant was gathered by using questionnaires. The population was chosen using purposive sampling. The findings revealed that although respondents appreciated RDM practices, some researchers were managing their RD while RPGC was responsible for RD submitted to their office. The concept of RDM was relatively new to most researchers. The study recommended a policy guideline and training of researchers. © 2021, IGI Global.","","","","","","","","","Akullar B., Research data management: Good practice, (2017); Carlsen S., Lost in a sea of science: Librarians are called in to archive huge amounts of information, but cultural and financial barriers stand in the way, The Chronicle of Higher Education, 52, (2006); Chigwada P., Chiparausha B., Kasiroori J., Research Data Management in Research Institutions in Zimbabwe, Data Science Journal, 16, 13, pp. 1-9, (2017); Chiware E., Becker D.A., Research Data Management Services in Southern Africa: A Readiness Survey of Academic and Research Libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Chiware E., Mathe Z., Academic libraries' role in Research Data Management Services: A South African perspective, South African Journal of Library and Information Science, 81, 2, pp. 1-10, (2016); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, (2013); Davidson J., Jones S., Molloy L., Kejser U.B., Emerging good practice in managing research data and research information in UK Universities, Procedia Computer Science, 33, pp. 215-222, (2014); Esayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab Universities, International Federation of Library Association and Institutions, 44, 4, pp. 281-299, (2018); Ihuah P.W., Eaton D., The Pragmatic Research Approach: A Framework for Sustainable Management of Public Housing Estates in Nigeria, Journal of US-China Public Administration, 10, 10, pp. 933-944, (2013); Nhendodzashe N., Pasipamire N., Research data management services: Are academic libraries in Zimbabwe ready?, The case of University of Zimbabwe Library, (2017); Partlo K., Symons D., Carlson J.D., Revolutionary or evolutionary? Making research data management manageable in creating research infrastructures in the 21 century academic library: Conceiving, funding, and building new facilities and staff, (2015); Peters C., Dryden A.R., Assessing the Academic Library's Role in Campus-Wide Research Data Management: A First Step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Stieglitz S., Wilms K., Mirbabaie M., Hofeditz L., Brenger B., Lopez A., Rehwald S., When are researchers willing to share their data? - Impacts of values and uncertainty on open data in academia, PLoS One, 15, 7, (2020); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Currentpractices and plans for the future, (2012); Tenopir C., Talja S., Horstman W., Late E., Hughes D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European Acadmic research libraries, LIBER Quarterly, (2016); Tom K., van der Hoeven J., Insight into Digital Preservation of Research Output in Europe, survey report, PARSE. Insight (American Society of Ophthalmic Registered Nurses), (2009); Responsible conduct in Data management, (2021); Data Management Plans, (2021); Research data management, (2019); Vines T.H., Albert A.Y.K., Andrew R.L., Debarre F., Bock D.G., Franklin M.T., Gilbert K.J., Moore J.-S., Renaut S., Rennison D.J., The Availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2016); Wellington J., Educational research: Contemporary issues and practical approaches (2nd ed.), (2015)","","","IGI Global","","","","","","","978-179987742-4; 978-179987740-0","","","English","Handb. of Res. on Inf. and Rec. Manag. in the Fourth Ind. Revolut.","Book chapter","Final","","Scopus","2-s2.0-85128041132"
"Kusoglu I.M.; Huber F.; Doñate-Buendía C.; Ziefuss A.R.; Gökce B.; Sehrt J.T.; Kwade A.; Schmidt M.; Barcikowski S.","Kusoglu, Ihsan Murat (8731410400); Huber, Florian (7103026431); Doñate-Buendía, Carlos (57190387322); Ziefuss, Anna Rosa (56940913100); Gökce, Bilal (36774684700); Sehrt, Jan T. (54397523800); Kwade, Arno (6603761773); Schmidt, Michael (57205650028); Barcikowski, Stephan (56219696600)","8731410400; 7103026431; 57190387322; 56940913100; 36774684700; 54397523800; 6603761773; 57205650028; 56219696600","Nanoparticle additivation effects on laser powder bed fusion of metals and polymers—a theoretical concept for an inter-laboratory study design all along the process chain, including research data management","2021","Materials","14","17","4892","","","","4","10.3390/ma14174892","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113917780&doi=10.3390%2fma14174892&partnerID=40&md5=8b4c9ea34c248d3f7eb2386771ddf966","Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany; Institute of Photonic Technology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, 91052, Germany; Materials Science and Additive Manufacturing, School of Mechanical Engineering and Safety Engineering, University of Wuppertal, Wuppertal, 42119, Germany; Department of Hybrid Additive Manufacturing, Ruhr University of Bochum, Bochum, 44801, Germany; Institute for Particle Technology, Technical University of Braunschweig, Braunschweig, 38104, Germany","Kusoglu I.M., Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany; Huber F., Institute of Photonic Technology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, 91052, Germany; Doñate-Buendía C., Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany, Materials Science and Additive Manufacturing, School of Mechanical Engineering and Safety Engineering, University of Wuppertal, Wuppertal, 42119, Germany; Ziefuss A.R., Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany; Gökce B., Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany, Materials Science and Additive Manufacturing, School of Mechanical Engineering and Safety Engineering, University of Wuppertal, Wuppertal, 42119, Germany; Sehrt J.T., Department of Hybrid Additive Manufacturing, Ruhr University of Bochum, Bochum, 44801, Germany; Kwade A., Institute for Particle Technology, Technical University of Braunschweig, Braunschweig, 38104, Germany; Schmidt M., Institute of Photonic Technology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, 91052, Germany; Barcikowski S., Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany","In recent years, the application field of laser powder bed fusion of metals and polymers extends through an increasing variability of powder compositions in the market. New powder formulations such as nanoparticle (NP) additivated powder feedstocks are available today. Interestingly, they behave differently along with the entire laser powder bed fusion (PBF-LB) process chain, from flowability over absorbance and microstructure formation to processability and final part properties. Recent studies show that supporting NPs on metal and polymer powder feedstocks enhances processability, avoids crack formation, refines grain size, increases functionality, and improves as-built part properties. Although several inter-laboratory studies (ILSs) on metal and polymer PBF-LB exist, they mainly focus on mechanical properties and primarily ignore nano-additivated feedstocks or standardized assessment of powder feedstock properties. However, those studies must obtain reliable data to validate each property metric’s repeatability and reproducibility limits related to the PBF-LB process chain. We herein propose the design of a large-scale ILS to quantify the effect of nanoparticle additivation on powder characteristics, process behavior, microstructure, and part properties in PBF-LB. Besides the work and sample flow to organize the ILS, the test methods to measure the NP-additivated metal and polymer powder feedstock properties and resulting part properties are defined. A research data management (RDM) plan is designed to extract scientific results from the vast amount of material, process, and part data. The RDM focuses not only on the repeatability and reproducibility of a metric but also on the FAIR principle to include findable, accessible, interoperable, and reusable data/meta-data in additive manufacturing. The proposed ILS design gives access to principal component analysis (PCA) to compute the correlations between the material–process– microstructure–part properties. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","3D printing; AlSi10Mg; Laser melting; Laser sintering; PA12; Round-Robin","3D printers; Computer software reusability; Feedstocks; Information management; Metal nanoparticles; Metals; Microstructure; Polymers; Testing; Application fields; Feedstock properties; Laboratory studies; Microstructure formation; Powder characteristics; Powder formulations; Research data managements; Scientific results; Powder metals","","","","","Deutsche Forschungsgemeinschaft, DFG, (445127149, BA 3580/27-1, BA 3580/28-1, GO 2566/10-1, KW 9/32-1, SCHM 2115/78-1, SE 2935/1-1, SPP 2122)","This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the Priority Program ""Materials for Additive Manufacturing"" (SPP 2122, project BA 3580/28-1 and BA 3580/27-1, SCHM 2115/78-1, KW 9/32-1 and SE 2935/1-1) and under the Heisenberg Programme (GO 2566/10-1, Project-ID 445127149).","Additive Manufacturing–Design–Requirements, Guidelines and Recommendations; Kusoglu I.M., Gokce B., Barcikowski S., Research trends in Laser Powder Bed Fusion of Al alloys within the last decade, Addit. 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Manuf, 2, pp. 179-191, (2017); Wang Y., Shi J., Lu S., Xiao W., Investigation of Porosity and Mechanical Properties of Graphene Nanoplatelets-Reinforced AlSi10Mg by Selective Laser Melting, J. Micro. Nano-Manuf, (2018); Sastri V.R., Plastics in Medical De-vices, pp. 55-72, (2014); Wagner H.D., Vaia R.A., Nanocomposites: Issues at the interface, Mater. Today, 7, pp. 38-42, (2004); Yang Y., Donate-Buendia C., Oyedeji T.D., Gokce B., Xu B.-X., Nanoparticle Tracing during Laser Powder Bed Fusion of Oxide Dispersion Strengthened Steels, Materials, 14, (2021); Gu D., Nanoscale TiC particle-reinforced AlSi10Mg bulk-form nanocomposites by selective laser melting (SLM) additive manufacturing (AM): Tailored microstructures and enhanced properties, Laser Additive Manufacturing of High-Performance Mate-rials, (2015); Metallic Materials—Tensile Testing—Part 1: Method of Test at Room Temperature, DIN Standards; Plastics-Determination of tensile properties—Part 2: Test Conditions for Moulding and Extrusion Plastics; Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Standard Practice for Reporting Data for Test Specimens Prepared by Additive Manufacturing, (2013); New Specification for Additive Manufacturing—Data Registration; Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method, (2019); Luping T., Schouenborg B., Methodology of Inter-comparison Tests and Statistical Analysis of Test Results, SP Report, 35, (2000); Additive manufacturing, Feedstock materials, Methods to characterize metallic powders; Standard Guide for Characterizing Properties of Metal Powders Used for Additive Manufacturing Processes, (2014); Standard Test Method for Tap Density of Metal Powders and Compounds by Fluorescence Spectrometry; Standard Test Method for Particle Size Distribution of Metal Powders and Related Compounds by Light Scat-tering; Particle Size Analysis, Laser Diffraction Methods; Quality Assessment of Metal Powder Feedstock Characterization Data for Additive Manufacturing; The Characterization of Powder Flow Properties for Additive Manufacturing Applications; Additive Manufacturing-Feedstock-Particle Shape Analysis to Identify Agglomerates/Satellites in Feedstock; Standard Guide for Evaluating Mechanical Properties of Metal Materials Made via Additive Manufacturing Processes, (2014); Standard Test Method for Analysis of Stainless and Alloy Steels by Wavelength Dispersive X-Ray Fluorescence Spectrometry, (2013); Standard Test Methods for Tension Testing of Metallic Materials, (2016); Orientation and Location Dependence Mechanical Properties for Metal Additive Manufacturing; Standard Guide for Nondestructive Examination of Metal Additively Manufactured Aerospace Parts after Build; Plastics-Determination of Tensile Properties—Part 1: General Principles; Mechanical Testing of Polymer Additively Manufactured Materials; Standard Practice for Characterization of Particles, (2016); Standard Guide for Measurement of Particle Size Distribution of Nanomaterials in Suspension by Nano-particle Tracking Analysis (NTA), (2018); ASTM E3247-20 Standard Test Method for Measuring the Size of Nano-particles in Aqueous Media Using Dynamic Light Scattering; Nanotechnology, Nanoparticles in Powder Form, Characteristics and Measurements; Surface Chemical Analysis, Characterization of Nanostructured Materials","S. Barcikowski; Technical Chemistry I, Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg Essen, Essen, 45141, Germany; email: stephan.barcikowski@uni-due.de","","MDPI AG","","","","","","19961944","","","","English","Mater.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85113917780"
"Chaerony Siffa I.; Schäfer J.; Becker M.M.","Chaerony Siffa, Ihda (57744565700); Schäfer, Jan (25223462400); Becker, Markus M. (57189635620)","57744565700; 25223462400; 57189635620","Adamant: A JSON schema-based metadata editor for research data management workflows","2022","F1000Research","11","","475","","","","1","10.12688/f1000research.110875.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132080787&doi=10.12688%2ff1000research.110875.1&partnerID=40&md5=5e2dcb18eaa9f7c9641df9ae0aceb3a6","Leibniz Institute for Plasma Science and Technology (INP), Felix-Hausdorff-Straße 2, Greifswald, 17489, Germany","Chaerony Siffa I., Leibniz Institute for Plasma Science and Technology (INP), Felix-Hausdorff-Straße 2, Greifswald, 17489, Germany; Schäfer J., Leibniz Institute for Plasma Science and Technology (INP), Felix-Hausdorff-Straße 2, Greifswald, 17489, Germany; Becker M.M., Leibniz Institute for Plasma Science and Technology (INP), Felix-Hausdorff-Straße 2, Greifswald, 17489, Germany","The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant. © 2022 Chaerony Siffa I et al.","FAIR Principles; JSON Schema; Research Data Management","Data Management; Humans; Metadata; Software; Workflow; accuracy; Article; conception; conceptual framework; editor; information processing; metadata; nonhuman; research; software; technology; validation process; workflow; human","","","","","","","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Israel H., Tobschall E., Tristram F., Dataset for the publication 'Umfrage zum Forschungsdatenmanagement in der Physik, (2021); Shaw F., Etuk A., Minotto A., Et al., COPO: a metadata platform for brokering FAIR data in the life sciences [version 1; peer review: 1 approved, 1 approved with reservations], F1000Res, 9, (2020); Franke S., Paulet L., Schafer J., Et al., Plasma-MDS, a metadata schema for plasma science with examples from plasma technology, Sci. Data, 7, (2020); PDBe-KB: a community-driven resource for structural and functional annotations, Nucleic Acids Res, 48, pp. D344-D353, (2019); (2022); (2022); Pezoa F., Reutter J.L., Suarez F., Et al., Foundations of JSON Schema, Proceedings of the 25th International Conference on World Wide Web, pp. 263-273, (2016); Droettboom M., Et al., Understanding JSON Schema, (2022); (2022); Bray T., The JavaScript Object Notation (JSON) Data Interchange Format, (2014); (2022); (2022); Van Rossum G., Drake F.L., Python 3 Reference Manual, (2009); Merkel D., Docker: lightweight linux containers for consistent development and deployment, Linux journal, 2014, 239, (2014); (2022); (2022); (2022); (2022); (2022); (2022); (2022); Rocha da Silva J., Aguiar Castro J., Ribeiro C., Presutti V., Blomqvist E., Troncy R., Et al., The Semantic Web: ESWC 2014 Satellite Events, pp. 483-487, (2014); (2022); Carpi N., Minges A., Piel M., eLabFTW: An open source laboratory notebook for research labs, J. Open Source Softw, 2, 12, (2017); (2022); (2022); (2022); Chaerony Siffa I., Schafer J., Becker M.M., plasma-mds/adamant: Adamant Initial Release v1.0.0 (v1.0.0), Zenodo, (2022)","I. Chaerony Siffa; Leibniz Institute for Plasma Science and Technology (INP), Greifswald, Felix-Hausdorff-Straße 2, 17489, Germany; email: ihda.chaeronysiffa@inp-greifswald.de","","F1000 Research Ltd","","","","","","20461402","","","35707001","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85132080787"
"Spillner J.; Gkikopoulos P.; Delgado P.; Choirat C.","Spillner, Josef (14042825100); Gkikopoulos, Panagiotis (57215332454); Delgado, Pamela (55843406200); Choirat, Christine (6506331853)","14042825100; 57215332454; 55843406200; 6506331853","Towards reproducible software studies with MAO and Renku","2022","SoftwareX","17","","100947","","","","1","10.1016/j.softx.2021.100947","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121607380&doi=10.1016%2fj.softx.2021.100947&partnerID=40&md5=18027878d83edd3ffd5545d51ab4e969","ZHAW School of Engineering, InIT, Technikumstr. 9, PF, Winterthur, 8401, Switzerland; SDSC, ETH Zurich and EPFL, Switzerland","Spillner J., ZHAW School of Engineering, InIT, Technikumstr. 9, PF, Winterthur, 8401, Switzerland; Gkikopoulos P., ZHAW School of Engineering, InIT, Technikumstr. 9, PF, Winterthur, 8401, Switzerland; Delgado P., SDSC, ETH Zurich and EPFL, Switzerland; Choirat C., SDSC, ETH Zurich and EPFL, Switzerland","In software engineering, the developers’ joy of decomposing and recomposing microservice-based applications has led to an enormous wave of microservice artefact technologies. To understand them better, researchers perform hundreds of experiments and empirical studies on them each year. Improving the reuse and reproducibility of these studies requires two ingredients: A system to automate repetitive experiments, and a research data management system with emphasis on making research reproducible. Both frameworks are now available via the Microservice Artefact Observatory (MAO) and Renku. In this paper, we explain the current capabilities of MAO as a global federated research infrastructure for determining software quality characteristics. Moreover, we emphasise the integration of MAO with Renku to demonstrate how a reproducible end-to-end experiment workflow involving globally distributed research teams looks like. © 2021 The Authors","Data science; Reproducibility; Software technology","Application programs; Computer software selection and evaluation; Current capability; Data management system; Empirical studies; Experiment study; Reproducibilities; Research data managements; Research infrastructure; Reuse; Software quality characteristics; Software technology; Information management","","","","","FAIR, (192-010)","Partially funded by Swissuniversities P-5 Easy FAIR – Supporting the adoption of FAIR and reproducible digital scholarship with Renku (192-010). ","Peng R.D., Reproducible research in computational science, Science, 334, 6060, pp. 1226-1227, (2011); Cleare J., Iacob C., Gemchecker: Reporting on the status of gems in ruby on rails projects, 2018 IEEE international conference on software maintenance and evolution, ICSME 2018, Madrid, Spain, September (2018) 23-29, pp. 700-704, (2018); Chapman P., Xu D., Deng L., Xiong Y., Deviant: A mutation testing tool for solidity smart contracts, IEEE international conference on blockchain, blockchain 2019, Atlanta, GA, USA, July (2019) 14-17, pp. 319-324, (2019); Oumaziz M.A., Falleri J., Blanc X., Bissyande T.F., Klein J., Handling duplicates in dockerfiles families: Learning from experts, 2019 IEEE international conference on software maintenance and evolution, ICSME 2019, Cleveland, OH, USA, September 29 - October 4, 2019, pp. 524-535, (2019); Al-Debagy O., Martinek P., A metrics framework for evaluating microservices architecture designs, J Web Eng, 19, 3-4, pp. 341-370, (2020); Bravetti M., Giallorenzo S., Mauro J., Talevi I., Zavattaro G., A formal approach to microservice architecture deployment, Microservices, science and engineering, pp. 183-208, (2020); Gan Y., Zhang Y., Cheng D., Shetty A., Rathi P., Katarki N., Bruno A., Hu J., Ritchken B., Jackson B., Hu K., Pancholi M., He Y., Clancy B., Colen C., Wen F., Leung C., Wang S., Zaruvinsky L., Espinosa M., Lin R., Liu Z., Padilla J., Delimitrou C., Unveiling the hardware and software implications of microservices in cloud and edge systems, IEEE Micro, 40, 3, pp. 10-19, (2020); Haselbock S., Weinreich R., Buchgeher G., An expert interview study on areas of microservice design, 11th IEEE conference on service-oriented computing and applications, SOCA 2018, Paris, France, November (2018) 20-22, pp. 137-144, (2018); Wist K., Helsem M., Gligoroski D., Vulnerability analysis of 2500 docker hub images, (2020); Shadija D., Rezai M., Hill R., Microservices: Granularity vs. performance, Companion proceedings of the 10th international conference on utility and cloud computing, UCC 2017, Austin, TX, USA, December (2017) 5-8, pp. 215-220, (2017); Malawski M., Gajek A., Zima A., Balis B., Figiela K., Serverless execution of scientific workflows: Experiments with hyperflow, AWS lambda and google cloud functions, Future Gener Comput Syst, 110, pp. 502-514, (2020); Yu D., Jin Y., Zhang Y., Zheng X., A survey on security issues in services communication of microservices-enabled fog applications, Concurr Comput Pract Exp, 31, 22, (2019); Apel S., Hertrampf F., Spathe S., Toward a knowledge model focusing on microservices and cloud computing, Concurr Comput Pract Exp, 32, 13, (2020); Joshi S.L., Deshpande B., Punnekkat S., Experimental analysis of dependency factors of software product reliability using sonarqube, Joint proceedings of the international workshop on software measurement and the international conference on software process and product measurement (IWSM Mensura 2019), Haarlem, the Netherlands, October (2019) 7-9, 2476 of CEUR Workshop Proceedings, pp. 130-137, (2019); Singh P., Airflow, pp. 67-84, (2019); Dagster, (2021); Prefect, (2021); Jablonski E.R., The renku platform, (2020); Gkikopoulos P., Data distribution and exploitation in a global microservice artefact observatory, 15th IEEE world congress on services (SERVICES), Milan, Italy, pp. 319-322, (2019); Gkikopoulos P., Spillner J., Schiavoni V., Monitoring data distribution and exploitation in a global-scale microservice artefact observatory, (2020); Spillner J., Quality assessment and improvement of helm charts for kubernetes-based cloud applications, (2019); Spillner J., Quantitative analysis of cloud function evolution in the AWS serverless application repository, (2019); Qasse I.A., Spillner J., Talib M.A., Nasir Q., A Study on DApps Characteristics, (2020); Muller M., Rudisuli M., DQA: Docker quality analysis, (2020)","J. Spillner; ZHAW School of Engineering, InIT, Technikumstr. 9, PF, Winterthur, 8401, Switzerland; email: josef.spillner@zhaw.ch","","Elsevier B.V.","","","","","","23527110","","","","English","SoftwareX","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85121607380"
"Silva M.L.E.; Cavalcanti M.C.; Campos M.L.M.","Silva, Madalena Lopes E. (57416505100); Cavalcanti, Maria Claudia (7007159537); Campos, Maria Luiza M. (7202803675)","57416505100; 7007159537; 7202803675","Privacy-preservation and the Use of Data for Research: A COVID-19 Use Case in Randomly Generated Healthcare Records","2022","International Conference on Enterprise Information Systems, ICEIS - Proceedings","2","","","317","324","7","0","10.5220/0011057900003179","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140879157&doi=10.5220%2f0011057900003179&partnerID=40&md5=cef523bc9cc8f531bc534adc0958d30c","Instituto Militar de Engenharia, Praça General Tibúrcio 80, RJ, Rio de Janeiro, CEP 22290-270, Brazil; Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 274 - CCMN - Ilha do Fundão, CEP 21941-90, Brazil","Silva M.L.E., Instituto Militar de Engenharia, Praça General Tibúrcio 80, RJ, Rio de Janeiro, CEP 22290-270, Brazil; Cavalcanti M.C., Instituto Militar de Engenharia, Praça General Tibúrcio 80, RJ, Rio de Janeiro, CEP 22290-270, Brazil; Campos M.L.M., Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 274 - CCMN - Ilha do Fundão, CEP 21941-90, Brazil","The provision of clinical data for research purposes has become central to monitoring and understanding the COVID-19 outbreak. In such a pandemic scenario, obtaining new research results is an imperative and urgent requirement. However, nowadays, personal data are protected by different legal regulations, to which all these data must comply, especially those related to the health of individuals. Then, a tough challenge arises in the academic sphere: how to provide a large amount of detailed clinical data for research and, simultaneously, guarantee the privacy of the individuals involved? Thus, this article discusses how the biomedical community may face this challenge and it presents the main ongoing initiatives and available emergent technologies that are useful to meet such urgent demand. Moreover, it also shows, through a use case, how it is possible to deal with this challenge, presenting the applicability of privacy-preserving techniques over a randomly generated typical dataset of COVID-19 health records. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Anonymous Data; COVID-19 Pandemic; Law; Legal Compliance; Research Data Management; Web of Data","Biomedical engineering; Clinical research; Data privacy; Information management; Laws and legislation; Anonymous data; Clinical data; COVID-19 pandemic; Healthcare record; Law; Legal compliance; Privacy preservation; Research data managements; Research purpose; Web of datum; COVID-19","","","","","","","Bondel G., Garrido G., Baumer K., Matthes F., The use of de-identification methods for secure and privacy-enhancing big data analytics in cloud environments, Proc. 22nd Int. Conf. on Enterprise Inf. Syst., (2020); Brito F., Machado J., Preservação de privacidade de dados: Fundamentos, técnicas e aplicações, Jorn. de Atual. em Informática, pp. 91-130, (2017); Carvalho A., Canedo E., Carvalho F., Carvalho P., Anonymisation and compliance to protection data: Impacts and challenges into big data, Proc. 22nd Int. Conf. on Enterprise Inf. Syst., (2020); Recommendations and Guidelines on Data Sharing, final release 30, (2020); Cunha E., Vargens J., Sist. de informação do sistema Único de saúde, Técnico de vigilância em saúde: fundamentos, 2, (2017); de Mendonca R. R., Etl4linkedprov: Managing multigranular linked data provenance, JOURNAL OF INFORMATION AND DATA MANAGEMENT - JIDM, 7, 2, (2016); Delgado J., Llorente S., Security and privacy when applying fair principles to genomic information, Studies in Health Techn. and Inform, 275, pp. 37-41, (2020); Ferreira A., Gdpr: What’s in a year (and a half)?, Proc. 22nd Int. Conf. on Enterprise Inf. Syst., (2020); Fung B. C. M., Wang K., Fu A. W.-C., Yu P. S., Introduction to privacy-preserving data publishing: concepts and techniques, Chapman and Hall/CRC data mining and knowledge discovery series, (2011); Huesch M., Mosher T., Using it or losing it? the case for data scientists inside health care, Nejm Catalyst, 3, 3, (2017); Hutchings E., Loomes M., Butow P., Boyle F., A systematic literature review of researchers’ and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data, Syst Rev, 9, 240, pp. 1-27, (2020); Pandit H. J., O'Sullivan D., Lewis D., Extracting provenance metadata from privacy policies, Provenance and Annotation of Data and Processes, pp. 262-265, (2018); Rautenberg S., Ermilov I., Marx E., Auer S., Ngomo A.-C. N., Lodflow: a workflow management system for linked data processing, Proc. 11th Int. Conf. on Semantic Syst, (2015); Sauermann S., Kanjala C., Templ M., Austin C., Preservation of individuals’ privacy in shared covid-19 related data, COVID-19 Data sharing in epidemiology, (2020); Smaradottir B., Security management in electronic health records: Attitudes and experiences among health care professionals, Int. Conf. on Comp. Science and Comp. Intell. (CSCI), pp. 715-719, (2018); Wilkinson M. D., Dumontier M., Aalbersberg I. J. J., Et al., The fair guiding principles for scientific data management and stewardship, Sci Data, 3, (2016)","","Filipe J.; Smialek M.; Brodsky A.; Hammoudi S.","Science and Technology Publications, Lda","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","24th International Conference on Enterprise Information Systems, ICEIS 2022","25 April 2022 through 27 April 2022","Virtual, Online","183567","21844992","978-989758569-2","","","English","International Conference on Enterprise Information Systems, ICEIS - Proceedings","Conference paper","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85140879157"
"Mayer G.; Müller W.; Schork K.; Uszkoreit J.; Weidemann A.; Wittig U.; Rey M.; Quast C.; Felden J.; Glöckner F.O.; Lange M.; Arend D.; Beier S.; Junker A.; Scholz U.; Schüler D.; Kestler H.A.; Wibberg D.; Pühler A.; Twardziok S.; Eils J.; Eils R.; Hoffmann S.; Eisenacher M.; Turewicz M.","Mayer, Gerhard (57219492558); Müller, Wolfgang (7404302533); Schork, Karin (57191262982); Uszkoreit, Julian (54792355000); Weidemann, Andreas (7004164137); Wittig, Ulrike (6603347010); Rey, Maja (55251083100); Quast, Christian (15060473200); Felden, Janine (36344552700); Glöckner, Frank Oliver (7003727520); Lange, Matthias (36028279400); Arend, Daniel (55531371500); Beier, Sebastian (56123523300); Junker, Astrid (35792080900); Scholz, Uwe (56124842400); Schüler, Danuta (56289878700); Kestler, Hans A (7004325203); Wibberg, Daniel (37006111200); Pühler, Alfred (35508153300); Twardziok, Sven (57193344344); Eils, Jürgen (6507720090); Eils, Roland (57210439970); Hoffmann, Steve (57281294100); Eisenacher, Martin (23466514600); Turewicz, Michael (35756809600)","57219492558; 7404302533; 57191262982; 54792355000; 7004164137; 6603347010; 55251083100; 15060473200; 36344552700; 7003727520; 36028279400; 55531371500; 56123523300; 35792080900; 56124842400; 56289878700; 7004325203; 37006111200; 35508153300; 57193344344; 6507720090; 57210439970; 57281294100; 23466514600; 35756809600","Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases","2021","Briefings in Bioinformatics","22","5","bbab010","","","","9","10.1093/bib/bbab010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105738609&doi=10.1093%2fbib%2fbbab010&partnerID=40&md5=769d8db5f9bb97bd3c76cda99913beca","Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany; Ruhr University Bochum, Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Bochum, Germany; Ulm University, Institute of Medical Systems Biology, Ulm, Germany; Heidelberg Institute for Theoretical Studies (HITS GGmbH), Scientific Databases and Visualization Group, Heidelberg, Germany; Jacobs University Bremen GGmbH, Bremen, Germany; University of Bremen, MARUM-Center for Marine Environmental Sciences, Bremen, Germany; Alfred Wegener Institute-Helmholtz Center for Polar-and Marine Research, Bremerhaven, Germany; Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany","Mayer G., Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany; Müller W., Ruhr University Bochum, Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Bochum, Germany; Schork K., Ulm University, Institute of Medical Systems Biology, Ulm, Germany; Uszkoreit J., Heidelberg Institute for Theoretical Studies (HITS GGmbH), Scientific Databases and Visualization Group, Heidelberg, Germany; Weidemann A., Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany; Wittig U., Ruhr University Bochum, Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Bochum, Germany; Rey M., Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany; Quast C., Ruhr University Bochum, Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Bochum, Germany; Felden J., Heidelberg Institute for Theoretical Studies (HITS GGmbH), Scientific Databases and Visualization Group, Heidelberg, Germany; Glöckner F.O., Heidelberg Institute for Theoretical Studies (HITS GGmbH), Scientific Databases and Visualization Group, Heidelberg, Germany; Lange M., Heidelberg Institute for Theoretical Studies (HITS GGmbH), Scientific Databases and Visualization Group, Heidelberg, Germany; Arend D., Jacobs University Bremen GGmbH, Bremen, Germany; Beier S., Jacobs University Bremen GGmbH, Bremen, Germany; Junker A., University of Bremen, MARUM-Center for Marine Environmental Sciences, Bremen, Germany; Scholz U., Jacobs University Bremen GGmbH, Bremen, Germany; Schüler D., University of Bremen, MARUM-Center for Marine Environmental Sciences, Bremen, Germany; Kestler H.A., Alfred Wegener Institute-Helmholtz Center for Polar-and Marine Research, Bremerhaven, Germany; Wibberg D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Pühler A., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Twardziok S., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Eils J., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Eils R., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Hoffmann S., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Eisenacher M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; Turewicz M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany","This article describes some use case studies and self-Assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.  © 2021 The Author(s) 2021. Published by Oxford University Press.","data management; data maturity; de.NBI; FAIR principles; hourglass model; self-Assessment","Data Management; Genome, Human; High-Throughput Nucleotide Sequencing; Humans; International Cooperation; Metadata; Neural Networks, Computer; Phenotype; Plants; Proteome; Proteomics; Self-Assessment; Software; Workflow; proteome; article; bioinformatics; case report; clinical article; FAIR principles; human; maturity; metadata; self evaluation; software; workflow; genetics; high throughput sequencing; human genome; information processing; international cooperation; metadata; phenotype; plant; procedures; proteomics; self evaluation; software","","Proteome, ","","","","","Meckel H, Stephan C, Bunse C, Et al., The amino acid s backup bone storage solutions for proteomics facilities, Biochim Biophys Acta, 1844, pp. 2-11, (2014); Tauch A, Al-Dilaimi A., Bioinformatics in Germany: Toward a national-level infrastructure, Brief Bioinform, 20, pp. 370-374, (2019); 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In: Mathematical Software-ICMS 2018: 6th International Conference, South Bend, IN, USA, July 24-27, 2018, Proceedings, pp. 289-296; Wibberg D, Batut B, Belmann P, Et al., The de.NBI/ELIXIRDE training platform-bioinformatics training in Germany and across Europe within ELIXIR [version 2; peer review: 2 approved], F1000Research, 8, (2020); Quast C, Pruesse E, Yilmaz P, Et al., The SILVA ribosomal RNA gene database project: improved data processing and webbased tools, Nucleic Acids Res, 41, pp. D590-D596, (2013); Reimer LC, Vetcininova A, Carbasse JS, Et al., BacDive in 2019: bacterial phenotypic data for high-Throughput biodiversity analysis, Nucleic Acids Res, 47, pp. D631-D636, (2019); Jeske L, Placzek S, Schomburg I, Et al., BRENDA in 2019: A European ELIXIR core data resource, Nucleic Acids Res, 47, pp. D542-D549, (2019); Fahrrolfes R, Bietz S, Flachsenberg F, Et al., ProteinsPlus: Aweb portal for structure analysis of macromolecules, Nucleic Acids Res, 45, pp. W337-W343, (2017); Spannagl M, Nussbaumer T, Bader KC, Et al., PGSB PlantsDB: updates to the database framework for comparative plant genome research, Nucleic Acids Res, 44, pp. D1141-D1147, (2016); Bolger AM, Lohse M, Usadel B., Trimmomatic: A flexible trimmer for Illumina sequence data, Bioinformatics, 30, pp. 2114-2120, (2014)","M. Turewicz; Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany; email: michael.turewicz@rub.de","","Oxford University Press","","","","","","14675463","","","33589928","English","Brief. Bioinform.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85105738609"
"Wulf C.; Beller M.; Boenisch T.; Deutschmann O.; Hanf S.; Kockmann N.; Kraehnert R.; Oezaslan M.; Palkovits S.; Schimmler S.; Schunk S.A.; Wagemann K.; Linke D.","Wulf, Christoph (57192166340); Beller, Matthias (7101777373); Boenisch, Thomas (57225269395); Deutschmann, Olaf (7004044947); Hanf, Schirin (57192868277); Kockmann, Norbert (6506448555); Kraehnert, Ralph (27667684800); Oezaslan, Mehtap (36625804900); Palkovits, Stefan (56878858000); Schimmler, Sonja (57217156485); Schunk, Stephan A. (6701692839); Wagemann, Kurt (8407126200); Linke, David (7006760661)","57192166340; 7101777373; 57225269395; 7004044947; 57192868277; 6506448555; 27667684800; 36625804900; 56878858000; 57217156485; 6701692839; 8407126200; 7006760661","A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts","2021","ChemCatChem","13","14","","3223","3236","13","33","10.1002/cctc.202001974","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103172227&doi=10.1002%2fcctc.202001974&partnerID=40&md5=d6acde6cf9fd81779e5dbad3de0a0c71","Leibniz-Institute for Catalysis Rostock, Albert-Einstein-Str. 29a, Rostock, D-18059, Germany; High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Nobelstr. 19, Stuttgart, D-70569, Germany; Karlsruher Institut für Technologie (KIT), Kaiserstraße 12, Karlsruhe, D-76131, Germany; Karlsruher Institut für Technologie (KIT), Engesserstr. 15, Karlsruhe, D-76131, Germany; Biochemical and Chemical Engineering, Equipment Design, TU Dortmund University, Dortmund, D-44221, Germany; BasCat – UniCat BASF JointLab, Technische Universität Berlin, Hardenbergstraße 36, Berlin, D-10623, Germany; Institute of Technical Chemistry, TU Braunschweig, Braunschweig, D-38106, Germany; Institute for Technical and Macromolecular Chemistry, RWTH Aachen University, Worringerweg 2, Aachen, D-52074, Germany; Fraunhofer Institute for Open Communication Systems (FOKUS), Kaiserin-Augusta-Allee 31, Berlin, D-10589, Germany; the high throughput experimentation company, Kurpfalzring 104, Heidelberg, D-69123, Germany; BASF SE, Carl-Bosch Str. 38, Ludwigshafen, D-67056, Germany; DECHEMA e.V., Theodor-Heuss-Allee 25, Frankfurt, D-60486, Germany","Wulf C., Leibniz-Institute for Catalysis Rostock, Albert-Einstein-Str. 29a, Rostock, D-18059, Germany; Beller M., Leibniz-Institute for Catalysis Rostock, Albert-Einstein-Str. 29a, Rostock, D-18059, Germany; Boenisch T., High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Nobelstr. 19, Stuttgart, D-70569, Germany; Deutschmann O., Karlsruher Institut für Technologie (KIT), Kaiserstraße 12, Karlsruhe, D-76131, Germany; Hanf S., Karlsruher Institut für Technologie (KIT), Engesserstr. 15, Karlsruhe, D-76131, Germany; Kockmann N., Biochemical and Chemical Engineering, Equipment Design, TU Dortmund University, Dortmund, D-44221, Germany; Kraehnert R., BasCat – UniCat BASF JointLab, Technische Universität Berlin, Hardenbergstraße 36, Berlin, D-10623, Germany; Oezaslan M., Institute of Technical Chemistry, TU Braunschweig, Braunschweig, D-38106, Germany; Palkovits S., Institute for Technical and Macromolecular Chemistry, RWTH Aachen University, Worringerweg 2, Aachen, D-52074, Germany; Schimmler S., Fraunhofer Institute for Open Communication Systems (FOKUS), Kaiserin-Augusta-Allee 31, Berlin, D-10589, Germany; Schunk S.A., the high throughput experimentation company, Kurpfalzring 104, Heidelberg, D-69123, Germany, BASF SE, Carl-Bosch Str. 38, Ludwigshafen, D-67056, Germany; Wagemann K., DECHEMA e.V., Theodor-Heuss-Allee 25, Frankfurt, D-60486, Germany; Linke D., Leibniz-Institute for Catalysis Rostock, Albert-Einstein-Str. 29a, Rostock, D-18059, Germany","Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high-quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level. © 2021 The Authors. ChemCatChem published by Wiley-VCH GmbH","Catalysis Community; Digitalization; GeCats; NFDI; NFDI4Cat; Research Data Management","Catalysis research; Chemical process; Concept papers; High quality data; Large amounts; Research communities; Research needs; Scientific discipline; Catalysis","","","","","Deutsche Forschungsgemeinschaft, DFG, (670389‐NFDI 2/1)","NFDI4Cat is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) with the project number 670389‐NFDI 2/1. Open access funding enabled and organized by Projekt DEAL. ","GeCatS Whitepaper – The Digitalization of Catalysis-Related Sciences; Norskov J.K., Bligaard T., Angew. 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Soc., 137, pp. 4347-4357, (2015); Geiger S., Kasian O., Mingers A.M., Nicley S.S., Haenen K., Mayrhofer K.J.J., Cherevko S., ChemSusChem, 10, pp. 4140-4143, (2017); (2016); Carson L., Bartneck C., Voges K., Disruptive Sci. Technol., 1, pp. 183-190, (2013); Joly P.-B., J. Innovation Econ. Manage., 22, pp. 79-96, (2017); Benedictus R., Miedema F., Ferguson M.W.J., Nature, 538, (2016); van Den Akker W., Spaapen J., Productive Interactions: Societal Impact of Academic Research in the Knowledge Society; Ayris P., Roman S., Maes K., Labastida I., Open Science and Its Role in Universities: A Roadmap for Cultural Change; Wilsdon J., Allen L., Belfiore E., Campbell P., Curry S., Hill S., Jones R., Kain R., Kerridge S., Thelwall M., Tinkler J., Viney I., Wouters P., Hill B.J.J., The Metric Tide: Independent Review of the Role of Metrics in Research Assessment and Management, (2015); DIGCHEM – a VISION FOR CHEMICAL DATA STANDARDS; Data 8: The Foundations of Data Science","D. Linke; Leibniz-Institute for Catalysis Rostock, Rostock, Albert-Einstein-Str. 29a, D-18059, Germany; email: david.linke@catalyis.de","","John Wiley and Sons Inc","","","","","","18673880","","CHEMK","","English","ChemCatChem","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85103172227"
"Birkbeck G.; Nagle T.; Sammon D.","Birkbeck, Gail (57705460800); Nagle, Tadhg (24832439000); Sammon, David (6602559176)","57705460800; 24832439000; 6602559176","Challenges in research data management practices: a literature analysis","2022","Journal of Decision Systems","31","S1","","153","167","14","1","10.1080/12460125.2022.2074653","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130613691&doi=10.1080%2f12460125.2022.2074653&partnerID=40&md5=924c9bb66f7d5ecb63f9406c633a2a6c","Business Information Systems Department, Cork University Business School, University College Cork, Cork, Ireland","Birkbeck G., Business Information Systems Department, Cork University Business School, University College Cork, Cork, Ireland; Nagle T., Business Information Systems Department, Cork University Business School, University College Cork, Cork, Ireland; Sammon D., Business Information Systems Department, Cork University Business School, University College Cork, Cork, Ireland","Driven by funding and publishing requirements to open and reuse data, Research Data Management (RDM) has become a crucial part of a researcher’s role. However, this key task is often completed by researchers, who sometimes make decisions, without having the necessary support or know-how, resulting in few research datasets being shared. The objective of this study is to identify the challenges in researcher RDM practices that impact the sharing/reusing of research data. Four thematic areas emerge from our coding of the selected literature: (i) alignment of research management and data management, (ii) resourcing, (iii) researcher openness, and (iv) research data governance. Despite the growing field of RDM, there is a limited understanding of RDM practiceshighlighting a requirement for further investigation together with practical tools, decision aids and training to assuage clearly unmet needs. Indeed, this provides an opportunity for the Information Systems (IS) community to better support researchers to implement good RDM practices. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","challenges; data governance; grounded approach; literature review; Research data management","Decision support systems; Technology transfer; Challenge; Data governances; Fundings; Grounded approach; Literature analysis; Literature reviews; Management practises; Research data; Research data managements; Reuse; Information management","","","","","","","Bem D.J., Writing a review article for psychological bulletin, Psychological Bulletin, 118, 2, pp. 172-177, (1995); Borghi J.A., Van Gulick A.E., Data management and sharing: Practices and perceptions of psychology researchers, PLoS ONE, 16, 5, (2021); Borghi J.A., Van Gulick A.E., Pham D., Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers, PLOS ONE, 13, 7, (2018); Chawinga W.A., Zinn S., Global perspectives of research data sharing: A systematic literature review, Library and Information Science Research, 41, 2, pp. 109-122, (2019); Cherry Zin O., Chew A.W., Wong A.L.H., Gladding J., Stenstrom C., Delineating the successful features of research data management training: A systematic review, International Journal for Academic Development, (2021); de Waard A., Research data management at Elsevier: Supporting networks of data and workflows, Information Services & Use, 36, 1-2, (2016); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, International Federation of Library Associations and Institutions, 44, 4, pp. 281-299, (2018); Guidelines on data management in horizon 2020 version 2.0, (2016); Higman R., Bangert D., Jones S., Three camps, one destination: The intersections of research data management, FAIR and Open, Insights, 32, 18, pp. 1-9, (2019); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Koltay T., Data governance, data literacy and the management of data quality, IFLA Journal, 42, 4, pp. 303-312, (2016); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2017); Langley A., Strategies for theorizing from process data, Academy of Management Review, 24, 4, pp. 691-710, (1999); Lefebvre A., Schermerhorn E., Spruit M., How research data management can contribute to efficient and reliable science, Research Papers, (2018); Lefebvre A., Spruit M., A socio-technical perspective on reproducibility in research data management, MCIS 2019 Proceedings. 10, (2019); Link G.J., Lumbard K., Feldman M., Feldman M., Feller J., George J., Germonprez M., Goggins S., Jeske D., Kiely G., Schuster K., Willis M., Contemporary issues of open data in information systems research: Considerations and recommendations, Communications of the Association for Information Systems, 41, 1, pp. 587-610, (2017); Mosley M., Brackett M., Earley S., Henderson D., DAMA guide to the data management body of knowledge, (2010); Nagle T., Sammon D., The data value map: A framework for developing shared understanding on data initiatives, Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, pp. 1439-1452, (2017); Neylon C., Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers, Research Ideas and Outcomes, 3, (2017); Qin J., D'Ignazio J., Lessons learned from a two-year experience in science data literacy education, Proceedings of the 31st annual IATUL conference, (2010); Rowe F., What literature review is not: Diversity, boundaries and recommendations, European Journal of Information Systems, 23, 3, pp. 241-255, (2014); Smale N., Unsworth K., Denyer G., Barr D., The history, advocacy and efficacy of data management plans, bioRxiv, (2018); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); van Panhuis W.G., Paul P., Emerson C., Grefenstette J., Wilder R., Herbst A.J., Heymann D., Burke D.S., A systematic review of barriers to data sharing in public health, BMC Public Health, 14, 1, (2014); Van Wee B., Banister D., How to write a literature review paper, Transport Reviews, 36, 2, pp. 278-288, (2016); Webster J., Watson R.T., Analyzing the past to prepare for the future: Writing a literature review, MIS Quarterly, 26, 2, pp. xiii-xxiii, (2002); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Williams M., Bagwell J., Nahm Zozus M., Data management plans: The missing perspective, Journal of Biomedical Informatics, 71, pp. 130-142, (2017); Wilms K., Brenger B., Lopez A., Rehwald S., Open data in higher education–What prevents researchers from sharing research data?, 39th International Conference on Information System, pp. 1-9, (2018); Wilms K., Stieglitz S., Buchholz A., Vogl R., Rudolph D., Do researchers dream of research data management?, Proceedings of the 51st Hawaii International Conference on System Sciences, (2018); Wolfswinkel J., Furtmueller E., Wilderom C., Using grounded theory as a method for rigorously reviewing literature, European Journal of Information Systems, 22, 1, pp. 45-55, (2011); Zhang L., Eichmann-Kalwara N., Mapping the scholarly literature found in Scopus on research data management: A bibliometric and data visualization approach, Journal of Librarianship and Scholarly Communication, 7, 1, (2019)","G. Birkbeck; Business Information Systems Department, Cork University Business School, University College Cork, Cork, Ireland; email: gailb@outlook.ie","","Taylor and Francis Ltd.","","","","","","12460125","","","","English","J. Decis. Syst.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85130613691"
"Sun C.; Emonet V.; Dumontier M.","Sun, Chang (57201947370); Emonet, Vincent (56497088300); Dumontier, Michel (6701759312)","57201947370; 56497088300; 6701759312","A comprehensive comparison of automated FAIRness Evaluation Tools","2022","CEUR Workshop Proceedings","3127","","","44","53","9","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128897782&partnerID=40&md5=556d8510ae08452d9b18c7c21506e573","Institute of Data Science, Maastricht University, Maastricht, Netherlands","Sun C., Institute of Data Science, Maastricht University, Maastricht, Netherlands; Emonet V., Institute of Data Science, Maastricht University, Maastricht, Netherlands; Dumontier M., Institute of Data Science, Maastricht University, Maastricht, Netherlands","The FAIR Guiding Principles (Findable, Accessible, Interop- erable, and Reusable) have been widely endorsed by the scientific community, funding agencies, and policymakers. However, the FAIR principles leave ample room for different implementations, and several groups have worked towards manual, semi-automatic, and automatic approaches to evaluate the FAIRness of digital objects. This study compares and con- trasts three automated FAIRness evaluation tools namely F-UJI, the FAIR Evaluator, and FAIR Checker. We examine three aspects: 1) tool characteristics, 2) the evaluation metrics, and 3) metrics tests for three public datasets. We find significant differences in the evaluation results for tested resources, along with differences in the design, implementation, and documentation of the evaluation metrics and platforms. While auto- mated tools do test a wide breadth of technical expectations of the FAIR principles, we put forward specific recommendations for their improved utility, transparency, and interpretability.  Copyright © 2022 for this paper by its authors.","Automated Evaluation; FAIR Maturity Indicators; FAIR Principles; Research Data Management","Information management; Automated evaluation; Comprehensive comparisons; Evaluation metrics; Evaluation tool; FAIR maturity indicator; FAIR principle; Fairness evaluation; Guiding principles; Research data managements; Scientific community; Automation","","","","","","","Wilkinson M. D., Dumontier M., Aalbersberg I. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Et al., The fair guiding principles for scientific data management and stewardship, Scientific data, 3, 1, pp. 1-9, (2016); Mons B., Neylon C., Velterop J., Dumontier M., da Silva Santos L. O. B., Wilkinson M. D., Cloudy, increasingly fair; revisiting the fair data guiding principles for the european open science cloud, Information Services & Use, 37, 1, pp. 49-56, (2017); Ammar A., Bonaretti S., Winckers L., Quik J., Bakker M., Maier D., Lynch I., van Rijn J., Willighagen E., A semi-automated workflow for fair maturity indicators in the life sciences, Nanomaterials, 10, 10, (2020); de Miranda Azevedo R., Dumontier M., Considerations for the conduction and interpretation of fairness evaluations, Data Intelligence, 2, 1-2, pp. 285-292, (2020); Fairassist - discover resources to measure and improve fairness, (2021); Wilkinson M. D., Sansone S.-A., Schultes E., Doorn P., da Silva Santos L. O. B., Dumontier M., A design framework and exemplar metrics for fairness, Scientific data, 5, 1, pp. 1-4, (2018); Wilkinson M. D., Dumontier M., Sansone S.-A., da Silva Santos L. O. B., Prieto M., Batista D., McQuilton P., Kuhn T., Rocca-Serra P., Crosas M., Et al., Evaluating fair maturity through a scalable, automated, community-governed framework, Scientific data, 6, 1, pp. 1-12, (2019); Fair-checker, (2021); Devaraju A., Mokrane M., Cepinskas L., Huber R., Herterich P., de Vries J., Akerman V., L'Hours H., Davidson J., Diepenbroek M., From conceptualization to implementation: Fair assessment of research data objects, Data Science Journal, 20, 1, (2021); Devaraju A., Huber R., Mokrane M., Herterich P., Cepinskas L., de Vries J., L'Hours H., Davidson J., White A., Fairsfair data object assessment metrics, Zenodo, 10, (2020); Pangaea - data publisher for earth & environmental science, (2021); Kaggle: Your machine learning and data science community, (2021); Dutch institute for public health and environment data portal, (2021)","","Wolstencroft K.; Splendiani A.; Marshall M.S.; Baker C.; Waagmeester A.; Roos M.; Vos R.; Fijten R.; Castro L.J.","CEUR-WS","Rothamsted Research","13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022","10 January 2022 through 14 January 2022","Virtual, Leiden","178870","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85128897782"
"Donner E.K.","Donner, Eva Katharina (57444066300)","57444066300","Research data management systems and the organization of universities and research institutes: A systematic literature review","2022","Journal of Librarianship and Information Science","","","","","","","2","10.1177/09610006211070282","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124220270&doi=10.1177%2f09610006211070282&partnerID=40&md5=559f1bb2d9427ae3176fc75e0aa6c864","University of Passau, Germany","Donner E.K., University of Passau, Germany","New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research. © The Author(s) 2022.","Data handling; information infrastructure; organization; organizational change; research data management; research data management system","","","","","","","","Adika F.O., Kwanya T., Research data management literacy amongst lecturers at Strathmore University, Kenya, Library Management, 41, 6-7, pp. 447-466, (2020); Ahmadi N.A., Jano Z., Khamis N., Analyzing crucial elements of research data management policy, International Business Management, 10, 17, pp. 3847-3852, (2016); Anderson N.R., Lee E.S., Brockenbrough J.S., Et al., Issues in biomedical research data management and analysis: Needs and barriers, Journal of the American Medical Informatics Association, 14, 4, pp. 478-488, (2007); Ashiq M., Usmani M.H., Naeem M., A systematic literature review on research data management practices and services, Global Knowledge, Memory and Communication, (2020); ARC strategy, research data management, (2021); Avuglah B.K., Underwood P.G., Research Data Management (RDM) Capabilities at the University of Ghana, Legon, (2019); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Bardyn T.P., Patridge E.F., Moore M.T., Et al., Health Sciences libraries advancing collaborative clinical research data management in universities, Journal of eScience Librarianship, 7, 2, (2018); Bellgard M.I., ERDMAS: An exemplar-driven institutional research data management and analysis strategy, International Journal of Information Management, 50, pp. 337-340, (2020); Bishoff C., Johnston L., Approaches to data sharing: An analysis of NSF data management plans from a large research university, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 1231-1327, (2015); Bishop B.W., Borden R.M., Scientists’ research data management questions: Lessons learned at a data help desk, portal Libraries and the Academy, 20, 4, pp. 677-692, (2020); Blask K., Forster A., Designing an information architecture for data management technologies: Introducing the DIAMANT model, Journal of Librarianship and Information Science, 52, pp. 592-600, (2020); Briney K., Goben A., Zilinski L., Do you have an institutional data policy? A review of the current landscape of library data services and institutional data policies, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 1232-1325, (2015); Briney K., Goben A., Zilinski L., Institutional, funder, and journal data policies, Curating Research Data Volume One: Practical Strategies for Your Digital Respository, pp. 1-27, (2017); Bunkar A.R., Bhatt D.D., Perception of researchers & academicians of Parul University towards research data management system & role of library, DESIDOC Journal of Library & Information Technology, 40, 3, pp. 139-146, (2020); Burgi P.Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research output, IFLA Journal, 43, 1, pp. 5-21, (2017); Burke W.W., Litwin G.H., A causal model of organizational performance and change, Journal of Management, 18, 3, pp. 523-545, (1992); Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Castle C., Getting the central RDM message across: A case study of central versus discipline-specific research data services (RDS) at the University of Cambridge, Libri, 69, 2, pp. 105-116, (2019); Chawinga W.D., Zinn S., Global perspectives of research data sharing: A systematic literature review, Library & Information Science Research, 41, 2, pp. 109-122, (2019); Chawinga W.D., Zinn S., Research data management at a public university in Malawi: The role of “three hands, Library Management, 41, 6-7, pp. 467-485, (2020); Chawinga W.D., Zinn S., Research data management at an African medical university: Implications for academic librarianship, The Journal of Academic Librarianship, 46, 4, pp. 102-161, (2020); Chawinga W.D., Zinn S., Research data management in universities: A comparative study from the perspectives of librarians and management, The International Information & Library Review, 53, 2, pp. 97-111, (2021); Chiware E.R.T., Data librarianship in South African academic and research libraries: A survey, Library Management, 41, 6-7, pp. 401-416, (2020); Chiware E.R.T., Becker D.A., Research data management services in southern Africa: A readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Chiware E.R.T., Mathe Z., Academic libraries’ role in research data management services: A South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2016); Clare C., Cruz M., Papadopoulou E., Et al., Engaging Researchers With Data Management – The Cookbook, (2019); Clements A., Research information meets research data management.. in the library?, Insights the UKSG Journal, 26, 3, pp. 298-304, (2013); Coates H., Building data services from the ground up: Strategies and resources, Journal of eScience Librarianship, 3, 1, pp. 52-59, (2014); Cole G., Evans J., University of Exeter Research Data Management and open access training for staff, ALISS Quarterly, 10, 1, pp. 22-25, (2014); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Kennan M.A., Lyon L., Et al., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Kennan M.A., Lyon L., Et al., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library & Information Science Research, 38, 4, pp. 319-326, (2016); Cox A.M., Verbaan E., Exploring Research Data Management, (2018); Cruz M., Dintzner N., Dunning A., Et al., Policy needs to go hand in hand with practice: The learning and listening approach to data management, Data Science Journal, 18, 1, (2019); Dahlberg T., Hokkanen P., Newman M., How business strategy and technology impact the role and the tasks of CIOs: An evolutionary Model, International Journal of IT/Business Alignment and Governance, 7, 1, (2016); Förderung von Informationsinfrastrukturen für die Wissenschaft – Ein Positionspapier der Deutschen Forschungsgemeinschaft. Bonn, (2018); Guidelines for safeguarding good research practice. Code of conduct, (2019); Dierkes J., Wuttke U., The Göttingen eResearch alliance: A case study of developing and establishing institutional support for research data management, ISPRS International Journal of Geo-Information, 5, 8, pp. 133-144, (2016); Draft R.L., Organization Theory and Design, (2013); Eifert T., Muckel S., Schmitz D., Et al., Introducing research data management as a service suite at RWTH Aachen, 9. DFN-Forum Kommunikationstechnologien, pp. 55-64, (2016); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Riding the wave – How Europe can gain from the rising tide of scientific data, (2010); Implementation roadmap for the European Open Science Cloud. Brussels, (2018); Fan Z., Context-based roles and competencies of data curators in supporting research data lifecycle management: Multi-case study in China, Libri, 69, 2, pp. 127-137, (2019); Faniel I., Connaway L., Librarians’ perspectives on the factors influencing research data management programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS One, 10, 2, (2015); Foerster A., Blask K., Lemaire M., Streamlining research data management - the research perspective of the PODMAN project, Bibliothek Forschung und Praxis, 43, 1, pp. 61-67, (2019); Funamori M., Hayashi M., Komiyama Y., Et al., Requirements analysis of system for research data management to prevent scientific misconduct, (2018); Gonzalez A., Peres-Neto P.R., Data curation: Act to staunch loss of research data, Nature, 520, 7548, (2015); Grasse M., Lopez A., Winter N., Landesinitiative NFDI – A central point of contact for RDM for higher education institutions in the German state of north Rhine-Westphalia, Data Science Journal, 17, (2018); Grynoch T., Implementing research data management services in a Canadian context, Dalhousie Journal of Interdisciplinary Management, 12, pp. 1-24, (2016); Gunjal B., Gaitanou P., Research data management: A proposed framework to boost research in higher educational institutes, IASSIST Quarterly, 41, 1-4, pp. 12-13, (2017); Guss S., A studio model for academic data services, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 9-24, (2016); Hamad F., Al-Fadel M., Al-Soub A., Awareness of research data management services at academic libraries in Jordan: Roles, responsibilities and challenges, New Review of Academic Librarianship, 27, pp. 76-96, (2021); Helbig K., Biernacka K., Buchholz P., Et al., Lösungen und Leitfäden für das institutionelle Forschungsdatenmanagement, Das offene Bibliotheksjournal, 6, 3, pp. 21-39, (2019); Henderson M., Raboin R., Shorish Y., Et al., Research data management on a shoestring budget, Bulletin of the Association for Information Science and Technology, 40, 6, pp. 14-17, (2014); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program, 49, 4, pp. 364-381, (2015); Hiom D., Fripp D., Gray S., Et al., Research data management at the University of Bristol: Charting a course from project to service, Program, 49, 4, pp. 475-493, (2015); Hofeditz L., Ross B., Wilms K., Et al., How to design a research data management platform? 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Donner; University of Passau, Germany; email: evakatharina-donner@uni-passau.de","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Review","Article in press","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85124220270"
"Marlina E.; Hidayanto A.N.; Purwandari B.","Marlina, Ekawati (57204890242); Hidayanto, Achmad Nizar (57205093001); Purwandari, Betty (56716397400)","57204890242; 57205093001; 56716397400","Towards a model of research data management readiness in Indonesian context: An investigation of factors and indicators through the fuzzy delphi method","2022","Library and Information Science Research","44","1","101141","","","","1","10.1016/j.lisr.2022.101141","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124716661&doi=10.1016%2fj.lisr.2022.101141&partnerID=40&md5=7a5a8ddc14be6e97d480919bd91644f9","Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, 16424, Indonesia; Indonesian Institute of Sciences, Jl. Gatot Subroto, Jakarta, 12710, Indonesia","Marlina E., Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, 16424, Indonesia, Indonesian Institute of Sciences, Jl. Gatot Subroto, Jakarta, 12710, Indonesia; Hidayanto A.N., Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, 16424, Indonesia; Purwandari B., Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, 16424, Indonesia","The research data management (RDM) readiness model assists research institutions in measuring their readiness level and identifying gaps to develop a strategy for implementing RDM. The technology, organization, people, and environment (TOPE) framework was used as a guideline in selecting the appropriate factors and indicators. The fuzzy delphi method was employed to validate the factors and indicators derived from the literature review. Hardware, policy, management support, organizational structure, situation awareness, training, and funder policy are all factors that reached the expert consensus. Strategy is a factor that included two indicators that did not reach expert consensus. The final result of the analysis indicates that the proposed readiness model should include 13 factors with 32 indicators. This study reveals that the environment is a key dimension of RDM readiness, which previous studies have not discussed. Moreover, research institutions can employ the model to assess their readiness and identify areas for improvement, and to potentially reduce failures in RDM implementation. © 2022 The Authors","Fuzzy delphi Method; Research data management; research data management readiness; TOPE framework","","","","","","PUTI; Universitas Indonesia, UI, (NKB- 4380/UN2, RST/HKP.05.00/2020)","This publication is supported by the PUTI Q3 grant funded by Universitas Indonesia under contract number NKB- 4380/UN2.RST/HKP.05.00/2020 . 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International Conference on Logistics, Informatics and Service Sciences (LISS), pp. 1-7, (2016); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French National Research Center (CNRS), Data Technologies and Applications, 52, pp. 248-265, (2018); Schumacher A., Erol S., Sihn W., A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises, Procedia CIRP, 52, pp. 161-166, (2016); Sensuse D.I., Purwandari B., Rahayu P., Defining e-portofolio factor for competency certification using fuzzy delphi method, Turkish Online Journal of Educational Technology - TOJET, 17, 2, pp. 25-33, (2018); Sucahyo Y.G., Utari D., Budi N.F.A., Hidayanto A.N., Chahyati D., Knowledge management adoption and its impact on organizational learning and non-financial performance, Knowledge Management and E-Learning, 8, pp. 387-413, (2016); Suhr M., Lehmann C., Bauer C.R., Bender T., Knopp C., Freckmann L., Nussbeck S.Y., Menoci: Lightweight extensible web portal enhancing data management for biomedical research projects, BMC Bioinformatics, 21, pp. 1-21, (2020); Sulaiman H.F., Ismail R., Mohd Yusoff H., Anuar N., Mohd Jamil M.R., Daud F., Validation of occupational zoonotic disease questionnaire using fuzzy delphi method, Journal of Agromedicine, 25, pp. 166-172, (2020); Tang R., Hu Z., Providing research data management (rdm) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, pp. 84-101, (2019); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, pp. 84-90, (2014); Triasih H., Devi K.S., Providing research data management services and practices at PDDI-LIPI: Preparedness, roles, challenges, and training, BACA: Jurnal Dokumentasi Dan Informasi, 42, 2, pp. 169-178, (2020); UNESCO, Open access to facilitate research and information on COVID-19, (2020); Wibowo M.P., Perubahan paradigma data penelitian terbuka : Pentingnya platform pengelolaan data penelitian (Research data management (RDM) in Indonesia), OISAA Journal of Indonesia Emas, 2, 1, pp. 1-6, (2019); Yang Z., Sun J., Zhang Y., Wang Y., Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model, Computers in Human Behavior, 45, pp. 254-264, (2015); Yusoff A.F.M., Hashim A., Muhamad N., Hamat W.N.W., Application of fuzzy delphi technique to identify the elements for designing and developing the e-PBM PI-poli module, Asian Journal of University Education, 17, pp. 292-304, (2021)","B. Purwandari; Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, 16424, Indonesia; email: bettyp@cs.ui.ac.id","","Elsevier Ltd","","","","","","07408188","","LISRD","","English","Libr. Inf. Sci. Res.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85124716661"
"Ashiq M.; Warraich N.F.","Ashiq, Murtaza (57221973297); Warraich, Nosheen Fatima (26029582000)","57221973297; 26029582000","A systematized review on data librarianship literature: Current services, challenges, skills, and motivational factors","2022","Journal of Librarianship and Information Science","","","","","","","2","10.1177/09610006221083675","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129023667&doi=10.1177%2f09610006221083675&partnerID=40&md5=9fc2e0940c5f84d94e21edd519b55574","Islamabad Model College for Boys, Pakistan; University of the Punjab, Pakistan","Ashiq M., Islamabad Model College for Boys, Pakistan; Warraich N.F., University of the Punjab, Pakistan","Data librarianship is becoming more common as a means of developing and integrating data-driven library services. Consequently, the academic and research libraries’ traditional role in providing information support and training has been expanded to include support in all aspects of the research lifecycle. Hence, this study systematically reviews the data librarianship literature focusing on current data librarianship services, challenges, skills, and motivational factors. A systematic review was conducted following the PRISMA guidelines. A comprehensive search strategy was formulated to extract maximum relevant results. The bibliographic data were retrieved from the Scopus, Web of Science, Library, Information Science and Technology Abstracts (LISTA), and Library and Information Science Abstracts (LISA). Finally, 27 studies that fulfill the criteria were included in this study. The findings revealed that two main factors that contribute to the success or failure in this emerging data librarianship roles are skills, knowledge, and expertise; and limited support and advocacy from library leadership and higher authorities. One is on the part of library professionals who can develop the required skills, knowledge, and expertise and the other is on the part of library leadership. The library professionals are hesitant to embrace this new role due to non-additional benefits, no relevant job description, and lacking leadership support. Overall, the findings revealed that the data librarianship scope is dynamic and has been expanded, albeit the progress is slow. The theoretical, practical, policy, and social implications described that the data librarianship services tend to be improved, and the relevant skills, knowledge, and expertise should be developed. The policy initiatives need to be taken, improved, and expanded to advance technical services related to data librarianship. © The Author(s) 2022.","Academic and research libraries; data librarianship; research data management; research support services; systematic review","","","","","","","","Allchin O., Collins A., Cox A., Et al., Realising our role in research data management, CILIP Update, 12, 3, pp. 36-38, (2013); Antell K., Foote J.B., Turner J., Et al., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Ashiq M., Rehman S.U., Safdar M., Et al., Academic library leadership in the dawn of the new millennium: A systematic literature review, The Journal of Academic Librarianship, 47, 3, (2021); Ashiq M., Saleem Q.U.A., Asim M., The perception of Library and Information Science (LIS) professionals about research data management services in University Libraries of pakistan, Libri, 71, pp. 239-249, (2021); Ashiq M., Usmani M.H., Naeem M., A systematic literature review on research data management practices and services, Global Knowledge, Memory and Communication, (2020); Bresnahan M.M., Johnson A.M., Assessing scholarly communication and research data training needs, Reference Services Review, 41, pp. 413-433, (2013); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Chawinga W.D., Zinn S., Research data management at an African medical university: Implications for academic librarianship, The Journal of Academic Librarianship, 46, 4, pp. 102-161, (2020); Chiware E.R., Becker D.A., Research data management services in southern Africa: A readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Chiware E.R.T., Data librarianship in South African academic and research libraries: A survey, Library Management, 41, 6-7, pp. 401-416, (2020); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Kennan M.A., Lyon L., Et al., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Kennan M.A., Lyon L., Et al., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Faniel I., Connaway L., Librarians’ perspectives on the factors influencing research data management programs, College & Research Libraries, 79, 1, (2018); Federer L., Defining data librarianship: A survey of competencies, skills, and training, Journal of the Medical Library Association JMLA, 106, 3, pp. 294-303, (2018); Fuhr J., How do I do that? A literature review of research data management skill gaps of Canadian health sciences information professionals, Journal of the Canadian Health Libraries Association, 40, 2, pp. 51-60, (2019); Gowen E., Meier J.J., Research data management services and strategic planning in libraries today: A longitudinal study, Journal of Librarianship and Scholarly Communication, 8, (2020); Grant R., Recordkeeping and research data management: A review of perspectives, Records Management Journal, 27, 2, pp. 159-174, (2017); Hamad F., Al-Fadel M., Al-Soub A., Awareness of research data management services at academic libraries in Jordan: Roles, responsibilities and challenges, New Review of Academic Librarianship, 27, 1, pp. 76-96, (2021); Hayashi C., What is data science? Fundamental concepts and a heuristic example, pp. 40-51, (1996); Hong Q.N., Gonzalez-Reyes A., Pluye P., Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the Mixed Methods Appraisal Tool (MMAT), Journal of Evaluation in Clinical Practice, 24, 3, pp. 459-467, (2018); Huang Y., Cox A.M., Sbaffi L., Research data management policy and practice in Chinese university libraries, Journal of the Association for Information Science and Technology, 72, 4, pp. 493-506, (2021); Jefferson C.O., Business and economics librarians’ insights on data literacy instruction in practice: An exploration of themes, Journal of Business & Finance Librarianship, 25, 3-4, pp. 147-174, (2020); Johnson V.E., The role of information professionals in geoscience data management: A Western Australian Perspective, LIBRES: Library and Information Science Research Electronic Journal, 22, 2, pp. 1-23, (2012); Knight G., Building a research data management service for the London school of hygiene & tropical medicine, Program Electronic Library and Information Systems, 49, 4, pp. 424-439, (2015); McBurney J., Kubas A., Limitations to success in academic data reference support, Journal of Librarianship and Information Science, (2021); Mani N.S., Cawley M., Henley A., Et al., Creating a data science framework: A model for academic research libraries, Journal of Library Administration, 61, 3, pp. 281-300, (2021); Mohammed M.S., Ibrahim R., Challenges and practices of research data management in selected Iraq Universities, DESIDOC Journal of Library & Information Technology, 39, 6, pp. 308-314, (2019); Moher D., Liberati A., Tetzlaff J., Et al., Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement, Journal of Clinical Epidemiology, 62, 10, pp. 1006-1012, (2009); Moher D., Shamseer L., Clarke M., Et al., Preferred reporting items for systematic review and meta-analysis protocols (PRISMAP) 2015 statement, Systematic Reviews, 4, 1, pp. 1-25, (2015); Ohaji I.K., Chawner B., Yoong P., The role of a data librarian in academic and research libraries, IR Information Research, 24, 4, (2019); Oliver J.C., Kollen C., Hickson B., Et al., Data science support at the academic library, Journal of Library Administration, 59, 3, pp. 241-257, (2019); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, 13, 1, (2018); Rice R., Southall S., The Data Librarian’s Handbook, (2016); Semeler A.R., Pinto A.L., Rozados H.B.F., Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2019); Si L., Xing W., Zhuang X., Et al., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Swan A., Brown S., The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs, (2008); Tenopir C., Sandusky R.J., Allard S., Et al., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Talja S., Horstmann W., Et al., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Virkus S., Garoufallou E., Data science from a library and information science perspective, Data Technologies and Applications, 53, 4, pp. 422-441, (2019); Yoon A., Donaldson D.R., Library capacity for data curation services: A US national survey, Library Hi Tech, 37, 4, pp. 811-828, (2019)","M. Ashiq; Islamabad Model College for Boys, Pakistan; email: gmurtazaashiq00@gmail.com","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Review","Article in press","","Scopus","2-s2.0-85129023667"
"Perry A.; Netscher S.","Perry, Anja (56693857200); Netscher, Sebastian (57148407200)","56693857200; 57148407200","Measuring the time spent on data curation","2022","Journal of Documentation","78","7","","282","304","22","1","10.1108/JD-08-2021-0167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124369841&doi=10.1108%2fJD-08-2021-0167&partnerID=40&md5=a77a84e2391c610296bb58128d01fa83","GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany","Perry A., GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany; Netscher S., GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany","Purpose: Budgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management. Design/methodology/approach: We make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work. Findings: We identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics. Originality/value: The different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed. © 2022, Anja Perry and Sebastian Netscher.","Curation tasks; Data curation; Data sharing; Digital curation; Research data management","","","","","","DDP Bildung – Domain Data Protocols for Education Research; Bundesministerium für Bildung und Forschung, BMBF, (16QK01A)","The authors thank the three curators for their participation in the focus group discussion and their very valuable input. This work was funded by the German Federal Ministry of Education and Research as part of the “DDP Bildung – Domain Data Protocols for Education Research” project ( www.ddp-bildung.org ). Grant number: 16QK01A. ","Data management costing tool, (2020); Beagrie C., CESSDA SaW costs factsheet, (2017); Beagrie N., Chruszcz J., Lavoie B., Keeping research data safe - a cost model and guidance for UK universities, (2008); Beagrie N., Lavoie B., Woollard M., Keeping research data safe 2, (2010); Bertelmann R., Gebauer P., Hasler T., Kirchner I., Peters-Kottig W., Razum M., Recker A., Ulbricht D., van Gasselt S., Einstieg ins Forschungsdatenmanagement in den Geowissenschaften”, Potsdam, available at, (2014); Bingert S., Engelhardt C., Kusch H., Handlungsempfehlungen zu Forschungsdatenmanagement und -infrastruktur an Hochschulstandorten, (2019); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Document, discover and interoperate - the website of the DDI alliance, (2021); DDI codebook 2.5, (2021); Donaldson M., Ensberg V., How to ensure that the costs of data management activities are budgeted in grant proposals?, (2018); H2020 programme. AGA - annotated model grant agreement, (2019); General data protection regulation 2016/678, (2018); Open research data and data management plans - information for ERC grantees, (2019); Handling of research data, (2021); Higgins S., Digital curation: the development of a discipline within information science, Journal of Documentation, 74, 6, pp. 1318-1338, (2018); ICPSR curation levels, (2020); Karp P.D., How much does curation cost?, Database, 2016, (2016); Klar J., Enke H., Organisation und Struktur, DFG-Projekt RADIESCHEN - Rahmenbedingungen einer disziplinübergreifenden Forschungsdateninfrastruktur, (2013); Koch U., Akdeniz E., Meichsner J., Hausstein B., Harzenetter K., da|ra Metadata Schema - Documentation for the Publication and Citation of Social and Economic Data, (2017); Koltay T., Data literacy: in search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Lafferty-Hess S., Rudder J., Downey M., Ivey S., Darragh J., Kati R., Conceptualizing data curation activities within two academic libraries, Journal of Librarianship and Scholarly Communication, 8, 1, (2020); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: a qualitative view from repository staff, PLoS ONE, 12, 3, pp. 1-44, (2017); L'Hours H., Kejser U.B., Johansen K.H.E., Thirifays A., Wang D., Strodl S., Ashley K., Davidson J., McCann P., Krupp J., Grindley N., D3.2 cost concept model and gateway specification”, Final report, Colchester, (2014); Mons B., Invest 5% of research funds in ensuring data are reusable, Nature, 578, 7796, (2020); Life Cycle Decisions for Biomedical Data: The Challenge of Forecasting Costs, (2020); Preparing the Workforce for Digital Curation, (2015); Palaiologk A.S., Economides A.A., Tjalsma H.D., Sesink L.B., An activity-based costing model for long-term preservation and dissemination of digital research data: the case of DANS, International Journal on Digital Libraries, 12, 4, pp. 195-214, (2012); Poole A.H., The conceptual landscape of digital curation, Journal of Documentation, 72, 5, pp. 961-986, (2016); Roertgen S., Kusch H., Engelhardt C., Bingert S., Savin V., Wang Y., Rice R., Elsenga C., Stokes P., Donaldson M., Ebert B., (2019); Wie lassen sich die Kosten für das Forschungsdatenmanagement abschätzen?, (2018); Thirifays A., Sisu D., Davidson J., Haage K., Faria L., Grootveld M., Stokes P., Middleton S., D3.3 curation costs Exchange framework, collaboration to clarify the costs of curation”, Final report, (2014); Treloar A., Harboe-Ree C., Data management and the curation continuum: how the Monash experience is informing repository relationships, Presented at the VALA2008 Conference, pp. 1-12, (2017); Treloar A., Klump J., Updating the data curation continuum, International Journal of Digital Curation, 14, 1, pp. 87-101, (2019); UK data service - data management costing tool and checklist, (2015); Guidance on best practice in the management of research data, (2015); Costs of data management - research data management support; Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Bonino da Silva Santos L., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons M., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Zenk-Moltgen W., Dokumentation von Umfragedaten in Länder vergleichender Perspektive mithilfe des ZA Dataset Documentation Managers (DSDM), ZA-Information/Zentralarchiv Für Empirische Sozialforschung, 59, pp. 159-170, (2006)","A. Perry; GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany; email: anja.perry@gesis.org","","Emerald Group Holdings Ltd.","","","","","","00220418","","","","English","J. Doc.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85124369841"
"Masinde J.; Chen J.; Wambiri D.; Mumo A.","Masinde, Johnson (57209141380); Chen, Jing (58024820900); Wambiri, Daniel (56951088800); Mumo, Angela (57221753145)","57209141380; 58024820900; 56951088800; 57221753145","Research Librarians' Experiences of Research Data Management Activities at an Academic Library in a Developing Country","2021","Data and Information Management","5","4","","412","424","12","4","10.2478/dim-2021-0002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131206532&doi=10.2478%2fdim-2021-0002&partnerID=40&md5=94726bd329949b68118c8ea28a9a731d","School of Information Management, Central China Normal University, Wuhan, China; School of Education, Kenyatta University, Nairobi, Kenya; Library and Information Services, University of Nairobi, Nairobi, Kenya","Masinde J., School of Information Management, Central China Normal University, Wuhan, China; Chen J., School of Information Management, Central China Normal University, Wuhan, China; Wambiri D., School of Education, Kenyatta University, Nairobi, Kenya; Mumo A., Library and Information Services, University of Nairobi, Nairobi, Kenya","University libraries have archaeologically augmented scientific research by collecting, organizing, maintaining, and availing research materials for access. Researchers reckon that with the expertise acquired from conventional cataloging, classification, and indexing coupled with that attained in the development, along with the maintenance of institutional repositories, it is only rational that libraries take a dominant and central role in research data management and further their capacity as curators. Accordingly, University libraries are expected to assemble capabilities, to manage and provide research data for sharing and reusing efficiently. This study examined research librarians' experiences of RDM activities at the UON Library to recommend measures to enhance managing, sharing and reusing research data. The study was informed by the DCC Curation lifecycle model and the Community Capability Model Framework (CCMF) that enabled the Investigator to purposively capture qualitative data from a sample of 5 research librarians at the UON Library. The data was analysed thematically to generate themes that enabled the Investigator to address the research problem. Though the UON Library had policies on research data, quality assurance and intellectual property, study findings evidenced no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in skills and training capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited the managing, sharing, and reusing of research data. The study recommends developing an RDM unit within the UON Library to oversee the implementation of RDM activities by assembling all the needed capabilities (policy guidelines, skills and training, technological infrastructure and collaborative partnerships) to support data curation activities and enable efficient managing, sharing and reusing research data. © 2021 Johnson Masinde et al., published by Sciendo","academic libraries; developing countries; research data; research data management activities; research librarians' perceptions","","","","","","CancerCare Manitoba Foundation, CCMF","Technological infrastructure enables RDM activities. It permits ease in accessibility and management of research outputs, creating more appropriate ways of propagating research data, consequently promoting knowledge integration (Schultz, 2017). The variable is supported by the DCC Curation lifecycle model and the CCMF. The DCC Curation lifecycle model builds standards and frameworks, creating room for technological changes and ensuring the transition from each phase while the CCMF ensures all technical capabilities at each stage are executed adeptly. ","Anduvare E.M., eResearch Support: An Exploratory Study of Private University Libraries in Nairobi County, Kenya, (2019); Australian National Data Service, Australian code for responsible research, (2016); Block E., Erskine L., Interviewing by telephone: Specific considerations, opportunities, and challenges, The International Journal of Qualitative Methods, 11, 4, pp. 428-445, (2012); Braun V.A.C.V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2006); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, pp. 1-9, (2017); Chigwada J.P., Hwalima T., Kwangwa N., A proposed framework for research data management services in research institutions in Zimbabwe, Research Data Access and Management in Modern Libraries, pp. 29-53, (2019); Connell J., Carlton J., Grundy A., Taylor Buck E., Keetharuth A.D., Ricketts T., Brazier J., The importance of content and face validity in instrument development: lessons learnt from service users when developing the Recovering Quality of Life measure (ReQoL), Qual Life Res, 27, 7, pp. 1893-1902, (2018); Cox A., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, pp. 299-316, (2014); Crouch M., McKenzie H., The logic of small samples in interview-based qualitative research, Social Science Information, 45, 4, pp. 483-499, (2006); Dalhousie University Libraries, Institutional research data management strategy, (2019); Der A., Exploring the academic libraries' readiness for research data management: cases from Hungary and Estonia, (2015); Dworkin S., Sample size policy for qualitative studies using in-depth interviews, Springer, 41, pp. 1319-1320, (2012); Etikan I., Bala K., Sampling and sampling methods, Biom Biostat Int J, 5, 6, pp. 215-217, (2017); Flores J.R., Brodeur J.J., Daniels M.G., Nichools N., Turnator E., Libraries and the research data management landscape, Council on library and Information resources, pp. 82-102, (2015); Helsinki University Library, Research data managemennt, (2020); Higgins S., Digital curation: The emergence of a new discipline, Int. J. Digit. Curation, 6, pp. 78-88, (2011); Higgins S., The lifecycle of data management, Managing Research Data, pp. 17-46, (2012); Ingram C., How and why you should manage your research data: A guide for researchers: An introduction to engaging with research data management processes, (2019); Janghorban R., Roudsari R.L., Taghipour A., Skype interviewing: The new generation of online synchronous interview in qualitative research, International Journal of Qualitative Studies on Health and Well-being, 9, 1, pp. 1-3, (2014); Josiline Chigwada B.C.A.J.K., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, 31, pp. 1-9, (2017); Kim K., Issues in the Appraisal and Selection of Geospatial Data: A 2013 Report, (2018); Koltay T., Data curation in academic libraries as part of the digital revolution, Zagadnienia Informacji Naukowej - Studia Informacyjne, 57, pp. 28-36, (2019); Kurata K., Matsubayashi M., Mine S., Identifying the complex position of research data and data sharing among researchers in natural science, SAGE Open, 7, 3, pp. 1-13, (2017); Lyon L., Ball A., Duke M., Day M., Developing a Community Capability Model Framework for data-intensive research, iPres 2012: Proceedings of the 9th International Conference on Preservation of Digital Objects, pp. 9-16, (2012); Maguire M., Delahunt B., Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars, All Ireland Journal of Higher Education, 9, 3, pp. 1-14, (2017); Masinde J.M., Wambiri D.M., Chen J., Gender and cognitive factors influencing information seeking of graduate students at Kenyatta University Library, South African Journal of Information Management, 22, 1, pp. 1-10, (2020); Nacosti, National Research Priorities 2018–2022, (2020); Ng'eno E.J., Research data management in Kenya's agricultural research institutes, (2018); Nhendodzashe N., Pasipamire N., Research data management services: Are academic libraries in Zimbabwe ready? The case of University of Zimbabwe library, Proceedings of IFLA WLIC 2017, Wrocław, Poland, (2017); Ohaji I.K., Research data management: An exploration of the data librarian role in New Zealand research organisations, (2016); Pickard A.J., Research methods in information, (2013); Ray J.M., Research data management: Practical strategies for information professionals, (2013); Ricardo A., Joao A., Joao R., Cristina R., A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential, Advances in Intelligent Systems and Computing, 353, pp. 101-111, (2015); Siyao P.O., Whong F.M., Martin-Yeboah E., Namamonde A., Academic libraries in four Sub-Saharan Africa countries and their role in propagating open science, IFLA Journal, 43, pp. 242-255, (2017); Tenopir C., Rice N.M., Allard S., Baird L., Borycz J., Christian L., Sandusky R.J., Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide, PloS one, 15, 3, pp. 1-26, (2020); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Schmidt B., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Tessier S., From field notes, to transcripts, to tape recordings: Evolution or combination, The International Journal of Qualitative Methods, 11, 4, pp. 446-460, (2012); UK Data Service, Managing and sharing research data: A guide to good practice, (2020); UK Research and innovation ovation, RCUK policy and code of conduct on the governance of good research conduct, (2016); University of Nairobi, UoN is now ISO 9001:2015 certified, (2015); University of Nairobi, Student admissions, (2020); 2020 Best global universities in Africa, (2020); Woeber C., Towards best practice in research data management in the humanities, (2017); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College & Research Libraries, 78, 7, pp. 920-933, (2017)","J. Masinde; School of Information Management, Central China Normal University, Wuhan, China; email: masindejohnson@mails.ccnu.edu.cn","","Elsevier Ltd","","","","","","25439251","","","","English","Data. Inf. Manag.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85131206532"
"Munke J.; Hayek M.; Golasowski M.; García-Hernández R.J.; Donnat F.; Koch-Hofer C.; Couvee P.; Hachinger S.; Martinovič J.","Munke, Johannes (57337374600); Hayek, Mohamad (57217292519); Golasowski, Martin (56289893300); García-Hernández, Rubén J. (57204931624); Donnat, Frédéric (57209692397); Koch-Hofer, Cédric (23008653600); Couvee, Philippe (57209748954); Hachinger, Stephan (24080119100); Martinovič, Jan (23392916900)","57337374600; 57217292519; 56289893300; 57204931624; 57209692397; 23008653600; 57209748954; 24080119100; 23392916900","Data System and Data Management in a Federation of HPC/Cloud Centers","2022","HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision","","","","59","80","21","0","10.1201/9781003176664-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138379179&doi=10.1201%2f9781003176664-4&partnerID=40&md5=3bbcb0017ad076864fd42beee9401c41","Leibniz Supercomputing Centre (LRZ), BAdW Garching bei München, Munich, Germany; Leibniz Supercomputing Centre (LRZ), BAdW Garching bei Munchen, Munich, Germany; IT4Innovations VSB, Technical University of Ostrava, Ostrava, Czech Republic; Leibniz Supercomputing Centre (LRZ), BAdW Garching bei München, Munich, Germany; Outpost24, Antibes, France; Atos, Benzons, France; Atos, Benzons, France; Leibniz Supercomputing Centre (LRZ), BAdW Garching bei München, Munich, Germany; IT4Innovations VSB, Technical University of Ostrava, Ostrava, Czech Republic","Munke J., Leibniz Supercomputing Centre (LRZ), BAdW Garching bei München, Munich, Germany; Hayek M., Leibniz Supercomputing Centre (LRZ), BAdW Garching bei Munchen, Munich, Germany; Golasowski M., IT4Innovations VSB, Technical University of Ostrava, Ostrava, Czech Republic; García-Hernández R.J., Leibniz Supercomputing Centre (LRZ), BAdW Garching bei München, Munich, Germany; Donnat F., Outpost24, Antibes, France; Koch-Hofer C., Atos, Benzons, France; Couvee P., Atos, Benzons, France; Hachinger S., Leibniz Supercomputing Centre (LRZ), BAdW Garching bei München, Munich, Germany; Martinovič J., IT4Innovations VSB, Technical University of Ostrava, Ostrava, Czech Republic","This chapter describes the federation of high-performance computing/data centers by a highly reliable distributed data infrastructure (DDI) as part of the LEXIS project (Large-Scale EXecution for Industry and Society, H2020 GA 825532). LEXIS offers user-friendly, cross-site orchestration for simulation and big data workflows, as well as data management, currently implemented at the IT4Innovations National Supercomputing Center (IT4I, CZ) and the Leibniz Supercomputing Centre (LRZ, DE). The chapter outlines requirements and implementation for such an infrastructure in the European context, considering the FAIR principles of modern research data management. Our high-reliability setup of iRODS (Integrated Rule-Oriented Data System) as data middleware is described, as well as the hardware behind the DDI, including burst buffers. A unified user access is ensured using the LEXIS authentication and authorization infrastructure. The LEXIS DDI has been integrated successfully into the LEXIS platform via REST APIs, and into the European data management landscape via EUDAT interfaces and tools. © 2022 selection and editorial matter, Olivier Terzo and Jan Martinovic; individual chapters, the contributors.","","","","","","","Horizon 2020 Framework Programme, H2020, (825532)","","Foster I., Kesselman C., The Grid 2: Blueprint for a New Computing Infrastructure, (2004); Pennington R., Terascale Clusters and the TeraGrid, Proceedings of the 6Th International Conference on High-Performance Computing in Asia-Pacific Region (HPC Asia 2002), (2002); Shiers J., The Worldwide LHC Computing Grid (Worldwide LCG), Computer Physics Communications, 177, 1-2, pp. 219-223, (2007); Kranzlmuller D., De Lucas J.M., Oster P., The European Grid Initiative (EGI), Remote Instrumentation and Virtual Laboratories, pp. 61-66, (2010); Eudat-Collaborative Data Infrastructure, (2020); Solagna P., EGI Position Paper for a European Identity Federation for Researchers, (2014); B2ACCESS-EUDAT, (2020); LEXIS Project-High Performance Computing (HPC) in Europe, (2020); Ystia Suite, (2021); Topology and Orchestration Specification for Cloud Applications Version 1.0-OASIS Standard, (2013); Alien 4 Cloudhttp://Alien4cloud.Github.Io/, (2021); Svaton V., Martinovic J., Krenek J., Esch T., Tomancak P., HPCas-a-Service via HEAppE Platform, Proceedings of the 13Th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), (2019); Xu H., Russell T., Coposky J., Et al., Irods Primer 2: Integrated Rule-Oriented Data System, (2017); B2SAFE-EUDAT, (2020); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2019); Keycloak, (2021); Kawai Y., Hasan A., High-Availability iRODS System (HAIRS), Proceedings of the Irods User Group Meeting 2010: Policy-Based Data Management, Sharing and Preservation, (2010); James J., Configuring Irods for High Availability, (2015); Sakimura N., Bradley J., Jones M.B., De Medeiros B., Mortimore C., Openid Connect Core 1.0 Incorporating Errata Set 1, (2014); Cantor S., Kemp J., Philpott R., Maler E., Assertions and Protocols for the OASIS Security Assertion Markup Language (SAML) V2.0, (2005); B2HANDLE-EUDAT, (2020); Depuydt J., Jensd's I/O Buffer-Setup a Redundant Postgresql Database with Repmgr and Pgpool, (2015); Pgpool Wiki, (2020); Repmgr-Replication Manager for Postgresql Clusters, (2020); Russell T., SC17 Demo: Storage Tiering, (2017); Schwan P., Lustre: Building a File System for 1,000-node Clusters, Proceedings of the Linux Symposium, (2003); Schmuck F., Haskin R., GPFS:A Shared-Disk File System for Large Computing Clusters, Proceedings of the Conference on File and Storage Technologies (FAST’02), (2002); Allcock W., Bresnahan J., Kettimuthu R., Link M., The Globus Striped GridFTP Framework and Server, SC '05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, (2005); Weil S.A., Brandt S.A., Miller E.L., Long D.D.E., Maltzahn C., Ceph: A Scalable, High-Performance Distributed File System, Proceedings of the 7Th USENIX Symposium on Operating Systems Design and Implementation (OSDI '06), (2006); NVM Express over Fabrics 1.0, (2016); Richards R., Representational State Transfer (REST), Pro PHP XML and Web Services, pp. 633-672, (2006); Jones M., Bradley J., Sakimura N., JSON Web Token (Jwt)-Internet Engineering Task Force (IETF), (2015); Garcia-Hernandez R.J., Golasowski M., Supporting Keycloak in Irods Systems with Openid Authentication, (2020); Github-Lexis: Large Scale Execution for Industry & Society, (2021); Zenodo Community-Lexis Project, (2020); Tus-Open Protocol for Resumable File Uploads, (2018); Brase J., DataCite-A Global Registration Agency for Research Data, Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology, (2009); Datacite Metadata Schema 4.4, (2021); (2020); Celery: Distributed Task Queue, (2020); Software P., Messaging that Just Works-Rabbitmq, (2020); B2STAGE-EUDAT, (2020); B2FIND-EUDAT, (2020); Boesch B., Sun S.X., Lannom L., RFC 3650-Handle System-Internet Engineering Task Force (IETF), (2003); B2handle-Handlereverselookupservlet, (2021)","","","CRC Press","","","","","","","978-100048511-0; 978-103200984-1","","","English","HPC, Big Data, and AI Convergence Towards Exascale Chall. and Vis.","Book chapter","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85138379179"
"Akwang N.E.; Chimah J.N.","Akwang, Nse Emmanuel (57221556731); Chimah, Jonathan Ndubuisi (57572957300)","57221556731; 57572957300","Research Data Management (RDM) in the Fourth Industrial Revolution (4IR) era: The case for academic libraries","2021","Handbook of Research on Information and Records Management in the Fourth Industrial Revolution","","","","17","37","20","1","10.4018/978-1-7998-7740-0.ch002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128081502&doi=10.4018%2f978-1-7998-7740-0.ch002&partnerID=40&md5=38d102b463cb8d70646907f4fddb6a1a","Obio Akpa Campus Library of AKSU, Nigeria; Ebonyi State University, Abakaliki, Ebonyi, Nigeria","Akwang N.E., Obio Akpa Campus Library of AKSU, Nigeria; Chimah J.N., Ebonyi State University, Abakaliki, Ebonyi, Nigeria","The emergence of the 4IR has brought new opportunities and possibilities for effective management of research data. Despite the positive impacts and effectiveness of this technological advancement, most academic libraries especially in Africa are not taking advantage of this reality. As a result of this, many libraries in the developing countries are struggling to satisfy the present and future information needs of researchers. Building on the 4IR, sustainable RDM practices in academic libraries become necessary and urgent. The observation led to the decision to address issues related to RDM practices and the 4IR. The authors present the conceptual considerations of RDM, the roles of academic libraries in RDM, and the 4IR technologies as well as strategic actions for academic libraries towards the 4IR. The authors conclude by affirming that the adoption of 4IR will not only connect librarians with machines for ""smart"" performance, but will expand the scope, visibility, and access to research data, among others. © 2021, IGI Global.","","","","","","","","","Ahmat M.A., Hanipah R.A., Preparing the libraries for the Fourth Industrial Revolution (4<sup>th</sup> IR), Journal of Malaysian Librarians, 12, pp. 53-64, (2018); Akwang N.E., Librarians' perception and adoption of Web 2.0 technologies: A survey of academic libraries in Akwa Ibom State, Nigeria,  Biennial Conference of Zimbabwe University Libraries Consortium, (2019); Androulakis S., Buckle A.M., Atkinson I., Groenewegen D., Nicholas N., Treloar A., Beitz A., ARCHER - e-research tools for research data management, International Journal of Digital Curation, 1, 4, pp. 22-33, (2009); Top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher institutions, College & Research Libraries News, 75, 6, pp. 294-302, (2014); Bahga A., Madisetti V., Internet of things: A hands-on approach, (2014); Baro E.E., Godfrey V.Z., Web 2.0, library 2.0, librarian 2.0 and the challenges for librarians in Africa: A review of current literature, International Journal of Information Technology and Library Science, 4, 1, pp. 1-16, (2015); Biziwe T., Mkhathali N., Library 4.0 era: Are academic libraries ready?, Conference Proceedings of the 4'"" Biennial Conference of Zimbabwe University Libraries Consortium, (2019); Chang J.H., Huynh P., ASEAN in transformation: The future of jobs at risk of automation, (2016); Chiparausha B., Chigwada J., Preparedness of Zimbabwean librarians to offer research data management services, Conference Proceedings of the 4'"" Biennial Conference of Zimbabwe University Libraries Consortium, (2019); Chiware E., Mathe Z., Academic libraries' role in research data management services: A South African perspective, Sajlis Journals, (2016); Church J., Butz C., Cassell K., Kamar N., Swindells G., Tallman K., Snellenberg R., Global vision discussion report of the government information and official publications meeting: How a united library field can tackle the challenges of the future. July 5, (2017); Davis H., Working alongside robots at the library, Public Libraries Online, (2019); What is research data?, (2021); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the research data management landscape, (2015); Frederick A., Run Y., The role of academic libraries in Research Data Management: A case in Ghanaian university libraries, Open Access Library (OALib) Journal, 6, 3, pp. 1-16, (2019); Frost M., Goates M., Cheng S., Johnston J., Virtual reality: A survey of use at an academic library, Information Technology and Libraries, 39, 1, (2020); Gabbay L.K., Shoham S., The role of academic libraries in research and teaching, Journal of Librarianship and Information Science, (2019); Gunjal B., Gaitanou P., Research data management: A proposed framework to boost research in Higher Educational Institute, IASSIST Quarterly, 41, 1-4, pp. 1-13, (2017); Hassan A., Industrial revolution 4.0: Implication to libraries and librarians, (2019); Ibredrola S.A., Industry 4.0: Which technologies will mark the Fourth Industrial Revolution?, (2021); Itani O.S., Jaramillo F., Chonko L., Achieving top performance while building collegiality in sales: It all starts with ethics, Journal of Business Ethics, pp. 1-22, (2017); Kamupunga W., Chunting Y., Application of big data in libraries, International Journal of Computers and Applications, 178, 16, pp. 34-38, (2019); Kovacevic A., How does virtual reality technology work? (+ where it's headed), (2019); Kwangwa N., Kusekwa L., Towards research data management services: The journey of the university of Zimbabwe library, Conference Proceedings of the 4th Biennial Conference of Zimbabwe University Libraries Consortium, (2019); Lavoie B., Libraries and RDM: Three decisions, three components, three realities, (2018); Lewis M.J., Libraries and the management of research data, Envisioning future academic library services, (2010); Makori E.O., Promoting innovation and application of internet of things in academic and research information organizations, Library Review, 66, 8-9, pp. 655-678, (2017); Matusiak K.K., Sposito F.A., Types of research data management services: An international perspective, Proceedings of the Association for Information Science and Technology, 54, 1, pp. 754-756, (2017); McHugh A., Innocenti P., Ross S., Ruusalepp R., Risk management foundations for digital libraries: DRAMBORA (Digital Repository Audit Method Based on Risk Assessment), (2015); Middleton S., Curation costs exchange: Supporting smarter investments in digital curation, (2014); Mush G.E., Pienaar H., Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, 1, pp. 1-9, (2020); Nag A., Nikam K., Internet of things applications in academic libraries, International Journal of Information Technology and Library Science, 5, 1, pp. 1-7, (2016); Njung'u N., Signe L., The fourth industrial revolution and digitization will transform Africa into a global powerhouse, (2020); Nwabugwu M.J., Godwin L.S., Research data management (RDM) services in libraries: Lessons for academic libraries in Nigeria, Library Philosophy and Practice (e-journal), (2020); Petrillo A., de Felice F., Cioffi R., Zomparelli F., Fourth industrial revolution: Current practices, challenges, and opportunities, (2018); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationship, activities, drivers, and influences, PLoS One, 9, 12, (2014); Big data: A strategy for improving library discovery, ProQuest Blogs, (2014); Sheeja N.K., Susan M.K., Internet of Things (loT) in academic libraries, (2020); Tenopir C., Allard S., Baird L., Sandusky R., Robert J., Lundeen A., Hughes D., Pollock D., Academic librarians and research data services: Attitudes and practices, School of Information Science - Faculty Publications and other Works, (2019); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Uhegbu A.N., Research and statistical methods in library and information science, (2009); Wang C., Xu S., Chen L., Chen X., Exposing library data with big data technology: A review, (2016); Whyte A., Tedds J., Making the case for research data management, (2011); Xu L.D., He W., Li S., Internet of things in industries: A survey, IEEE Transactions on Industrial Informatics, 10, 4, pp. 2233-2243, (2014); Xu M., David J.M., Kim S.H., The fourth industrial revolution: Opportunities and challenges, International Journal of Financial Research, 9, 2, pp. 90-95, (2018); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries website, College & Research Libraries, 78, 7, pp. 920-933, (2017); Yusuf F., Iwu J., Use of academic library: A case study of Covenant University, Nigeria, Chinese Librarianship: International Electronic Journal, (2010); Zhou Q., Academic libraries in research data management service: Perceptions and practices, Open Access Library Journal, 5, (2018)","","","IGI Global","","","","","","","978-179987742-4; 978-179987740-0","","","English","Handb. of Res. on Inf. and Rec. Manag. in the Fourth Ind. Revolut.","Book chapter","Final","","Scopus","2-s2.0-85128081502"
"Kim S.; Syn S.Y.","Kim, Soojung (15848471000); Syn, Sue Yeon (22836779000)","15848471000; 22836779000","Practical considerations for a library’s research data management services: The case of the national institutes of health library","2021","Journal of the Medical Library Association","109","3","","450","458","8","3","10.5195/jmla.2021.995","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117092509&doi=10.5195%2fjmla.2021.995&partnerID=40&md5=2c3d51d0d8b853e3a5b83ab559141e65","Department of Library and Information Science, Jeonbuk National University, Jeonju, South Korea; Department of Library and Information Science, The Catholic University of America, Washington, DC, United States","Kim S., Department of Library and Information Science, Jeonbuk National University, Jeonju, South Korea; Syn S.Y., Department of Library and Information Science, The Catholic University of America, Washington, DC, United States","Objective: This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services. Methods: We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library’s RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle. Results: The findings show that the two models’ crosstab framework can provide an overview of an institution’s current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers’ needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users. Conclusion: These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services. © 2021, Medical Library Association. All rights reserved.","Case study; Library services; Research data management","Biomedical Research; Data Management; Humans; Librarians; Libraries, Medical; Library Services; National Institutes of Health (U.S.); United States; human; information processing; librarian; library; medical research; national health organization; United States","","","","","LG Yonam Foundation of Korea; National Institutes of Health, NIH","Funding text 1: This work was supported by LG Yonam Foundation of Korea. We would like to express our gratitude to the NIH Library librarians who agreed to participate in the interviews. This paper was proofread by the Writing Center at Jeonbuk National University in July 2020.; Funding text 2: The data creation stage is supported by the library mainly through consultation and technological support with databases, hardware, and software. Supporting the data analysis and sharing stages are training sessions, one-on-one consultations, and technological support for hardware, software, and institutional databases. Among the six stages of the data lifecycle, these three seem well provided for in terms of concrete RDM support through various types of services. On the other hand, the data lifecycle’s remaining stages (description, storage, and preservation stages) are less well supported. Regarding the data preservation stage, librarians introduce a list of public repositories so that researchers can select an appropriate one for their purposes. CIT supports both back-up storage and archival storage for preservation. For the data description stage, although some efforts such as “Electronic Lab Notebooks (ELN) Online Discussion” provide instruction on using an ELN to describe and document research data, further support for this stage could improve RDM practices. Although training classes at the NIH Library mainly assist with the data analysis stage, there are efforts to increase training in data sharing and other stages of the data lifecycle. Among the three categories of RDM services, the library tends to take more responsibility in the education and expertise services categories while relying more heavily on CIT for the curation services category.","Diekema AR, Wesolek A, Walters C., The NSF/NIH effect: surveying the effect of data management requirements on faculty, sponsored programs, and institutional repositories, J Acad Libr, 40, 3–4, pp. 322-331, (2014); Wiley CA, Burnette MH., Assessing data management support needs of bioengineering and biomedical research faculty, J eScience Librariansh, 8, 1, (2019); Latham B., Research data management: defining roles, prioritizing services, and enumerating challenges, J Acad Libr, 43, 3, pp. 263-265, (2017); Flores JR, Brodeur JJ, Daniels MG, Nichools N, Turnator E., Libraries and the research data management landscape, The process of discovery: The CLIR Postdoctoral Fellowship Program and the future of the academy, pp. 82-102, (2015); Creamer AT, Martin ER, Kafel D., Library Publications and Presentations; Tang R, Hu Z., Providing research data management (RDM) services in libraries: preparedness, roles, challenges, and training for RDM practice, Data Inf Mgmt, 3, 2, pp. 84-101, (2019); Johnson LM, Butler JT, Johnston LR., Developing e-science and research services and support at the University of Minnesota Health Sciences Libraries, J Libr Adm, 52, 8, pp. 754-769, (2012); Li M, Chen YB, Clintworth WA., Expanding roles in a library-based bioinformatics service program: a case study, J Med Libr Assoc, 101, 4, pp. 303-309, (2013); Surkis A, LaPolla FWZ, Contaxis N, Read KB., Data day to day: building a community of expertise to address data skills gaps in an academic medical center, J Med Libr Assoc, 105, 2, pp. 185-191, (2017); Read KB., Adapting data management education to support clinical research projects in an academic medical center, J Med Libr Assoc, 107, 1, pp. 89-97, (2019); Gore SA., A librarian by any other name: The role of the informationist on a clinical research team, J eScience Librariansh, 2, 1, pp. 20-24, (2013); Hasman L, Berryman D, Mcintosh S., NLM informationist grant–web assisted tobacco intervention for community college students, J eScience Librariansh, 2, 1, pp. 30-34, (2013); Delserone LM., At the watershed: Preparing for research data management and stewardship at the University of Minnesota Libraries, Lib Trends, 57, 2, pp. 202-210, (2008); Pinfield S, Cox AM, Smith J., Research data management and libraries: relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); Bryant R, Lavoie B, Malpas C., A tour of the research data management (RDM) service space, The realities of research data management, Part 1, (2017); Matusiak KK, Sposito FA., Types of research data management services: an international perspective, Proceedings ASIST, 54, 1, pp. 754-756, (2017); Federer LM, Lu YL, Joubert DJ., Data literacy training needs of biomedical researchers, J Med Libr Assoc, 104, 1, pp. 52-57, (2016); Federer LM, Lu YL, Joubert DJ, Welsh J, Brandys B., Biomedical data sharing and reuse: attitudes and practices of clinical and scientific research staff, PLoS ONE, 10, 6, (2015); Data life cycle; Research data lifecycle; Curation lifecycle model; Data Lifecycle; List of NIH, centers and offices; NIH strategic plan for data science; National Library of Medicine. Glossary; National Library of Medicine. NIH CDE Repository; Cox AM, Kennan MA, Lyon L, Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, J Assoc Inf Sci Tech, 68, 9, pp. 2182-2200, (2017); Thompson H., Managing a biomedical libraries’ instruction program: redefining scope, Med Ref Serv Q, 37, 1, pp. 97-104, (2018); Center for Information Technology (CIT); Federer L., Research data management in the age of big data: roles and opportunities for librarians, Inf Serv Use, 36, 1–2, pp. 35-43, (2016); Lyon L., The information transform: re-engineering libraries for the data decade, Int J Digit Curation, 7, 1, pp. 126-138, (2012); Patel D., Research data management: a conceptual framework, Lib Rev, 65, 4–5, pp. 226-241, (2016); Van Tuyl S, Whitmire AL., Water, water, everywhere: defining and assessing data sharing in academia, PLoS ONE, 11, 2, (2016)","","","Medical Library Association","","","","","","15365050","","JMLAC","34629974","English","J. Med. Libr. Assoc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85117092509"
"Chiarelli A.; Johnson R.; Beagrie N.; May A.W.; Boon L.; Wilson R.; Mallalieu R.","Chiarelli, Andrea (56526564200); Johnson, Rob (57688651900); Beagrie, Neil (57503789700); May, Amy Warner (57484449100); Boon, Lotte (57484280000); Wilson, Rowan (57484111000); Mallalieu, Ruth (57484111100)","56526564200; 57688651900; 57503789700; 57484449100; 57484280000; 57484111000; 57484111100","To protect and to serve: developing a road map for research data management services","2022","Insights: the UKSG Journal","35","","4","","","","0","10.1629/UKSG.566","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126149527&doi=10.1629%2fUKSG.566&partnerID=40&md5=e1b12a2bdfffb0e538949abef0d7d7cc","Charles Beagrie Ltd, United Kingdom; The Bodleian Libraries, University of Oxford, United Kingdom; University of Oxford, United Kingdom","Chiarelli A.; Johnson R.; Beagrie N., Charles Beagrie Ltd, United Kingdom; May A.W., The Bodleian Libraries, University of Oxford, United Kingdom; Boon L., University of Oxford, United Kingdom; Wilson R., University of Oxford, United Kingdom; Mallalieu R., The Bodleian Libraries, University of Oxford, United Kingdom","Research Data Management (RDM) has become a major issue for universities over the last decade. This case study outlines the review of RDM services carried out at the University of Oxford in partnership with external consultants between November 2019 and November 2020. It aims to describe and discuss the processes in undertaking a university-wide review of services supporting RDM and developing a future road map for them, with a strong emphasis on the design processes, methodological approaches and infographics used. The future road map developed is a live document, which the consulting team handed over to the University at the end of the consultation process. It provides a suggested RDM action plan for the University that will continue to evolve and be iterated in the light of additional internal costings, available resources and reprioritization in the budget cycle for each academic year. It is hoped that the contents of this case study will be useful to other research-intensive universities with an interest in developing and planning RDM services to support their researchers. © 2022 Ubiquity Press. All rights reserved.","RDM; Research data; Research data management; Review; Universities","","","","","","","","Research Data Oxford - About RDM; Cox Andrew M., Pinfield Stephen, Smith Jennifer, Moving a Brick Building: UK Libraries Coping with Research Data Management as a 'wicked' Problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2014); Martinsen David, Primary Research Data and Scholarly Communication, Chemistry International, 39, 3, pp. 35-38, (2017); Mourby Miranda, Et al., Governance of Academic Research Data Under the GDPR - lessons from the UK, International Data Privacy Law, 9, 3, pp. 192-206, (2019); Tidy Joe, Blackbaud hack: More UK universities confirm breach, BBC News, (2020); Higman Rosie, Pinfield Stephen, Research Data Management and Openness, Program: Electronic Library and Information Systems, 49, 4, pp. 364-381, (2015); The State of Open Data 2020, (2020); Fosci Mattia, Et al., Emerging from uncertainty: International perspectives on the impact of COVID-19 on university research, (2020); Cooper Alexandra, Et al., Data in the Time of COVID-19: How Data Library Professionals Helped Combat the Pandemic, Partnership: The Canadian Journal of Library and Information Practice and Research, 16, 1, (2021); Wulf Christoph, Et al., A Unified Research Data Infrastructure for Catalysis Research - Challenges and Concepts, ChemCatChem, 13, 14, pp. 3223-3236, (2021); Nitecki Danuta A., Alter Adi, Leading FAIR Adoption Across the Institution: A Collaboration Between an Academic Library and a Technology Provider, Data Science Journal, 20, 1, (2021); University of Oxford Strategic Plan 2018-23; Wilkinson Mark D., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, 1, (2016); Braun Virginia, Clarke Victoria, Using Thematic Analysis in Psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2018)","A. Chiarelli; Research Consulting Limited, United Kingdom; email: andrea.chiarelli@research-consulting.com","","United Kingdom Serials Group","","","","","","20487754","","","","English","Insights UKSG J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85126149527"
"Green A.M.","Green, Ashlea M. (57458316400)","57458316400","Metadata Application Profiles in U. S. Academic Libraries: A Document Analysis","2022","Journal of Library Metadata","21","3-4","","105","143","38","0","10.1080/19386389.2022.2030172","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124910465&doi=10.1080%2f19386389.2022.2030172&partnerID=40&md5=8195498b001483bcf796a7f2ecf5c205","Metadata Librarian, Belk Library, Appalachian State University, Boone, NC, United States","Green A.M., Metadata Librarian, Belk Library, Appalachian State University, Boone, NC, United States","This paper describes a document analysis of 24 metadata application profiles (MAPs) used by academic libraries in the United States. The MAPs under study were collected from (a) the DLF AIG Metadata Application Profile Clearinghouse and (b) a Google search of.edu domains. Data collection and analysis took place between December 2020 and February 2021. While most of the MAPs under review provided metadata guidelines for digital collections, a small number were intended for institutional repositories or research data management. The study’s findings reveal MAP features and content, usage of controlled vocabularies and standards, and other characteristics pertaining to MAP document scope, contents and format in this context. In addition to its discussion of the literature, the paper’s findings should help metadata specialists and others involved in digital collection management gain insights useful in the development or revision of their own metadata documentation. Further, these findings offer a current glimpse of metadata application practices among U.S. academic libraries generally. ©, Ashlea M. Green. Published with license by Taylor & Francis Group, LLC.","academic libraries; data dictionaries; digital collections; Metadata application profiles; metadata standards","","","","","","","","Allinson J., Describing scholarly works with Dublin Core: A functional approach, Part of the Special Issue Institutional Repositories Current State and Future, 57, 2, pp. 221-243, (2008); Andrade M.C., Baptista A.A., The use of application profiles and metadata schemas by digital repositories: Findings from a survey, (2015); Baca M., Metadata (3rd ed.), (2016); Bair S.A., Steuer S.M.B., Developing a premodern manuscript application profile using Dublin Core, Journal of Library Metadata, 13, 1, pp. 1-16, (2013); Bowen G.A., Document analysis as a qualitative research method, Qualitative Research Journal, 9, 2, pp. 27-40, (2009); Carlson S., Lampert C., Washington A., Linked data for the perplexed librarian, (2020); Carson A.L., Ou C., Notes on operations metadata revisited: Updating metadata profiles and practices in a vendor-hosted repository, Library Resources & Technical Services, 63, 4, pp. 196-205, (2019); Clair K., Developing an audiovisual metadata application profile: A case study, Library Collections, Acquisitions, and Technical Services, 32, 1, pp. 53-57, (2008); Costanza J., Knight R.C., Liu-Spencer H., Metadata implementation for building crossinstitutional repositories: Lessons learned from the Liberal Arts Scholarly Repository (LASR), Journal of Library Metadata, 9, 1-2, pp. 153-166, (2009); Cwiok J., The defining element–A discussion of the creator element within metadata schemas, Cataloging & Classification Quarterly, 40, 3-4, pp. 103-133, (2005); Darcovich J., Flynn K., Li M., Born of collaboration: The evolution of metadata standards in an aggregated environment, VRA Bulletin, 45, 1, (2018); Devey M., Cote M.-C., The development and use of metadata application profiles: The Government of Canada experience, The Serials Librarian, 51, 2, pp. 103-115, (2006); Fear K., User understanding of metadata in digital image collections: Or, what exactly do you mean by “coverage”?, The American Archivist, 73, 1, pp. 26-60, (2010); Hebron T.K., Extending and adapting metadata audit tools for Mountain West Digital Library members, Code4Lib Journal, 41, 4, (2018); Hicks E.A., Perkins J., Maurer M.B., Application profile development for consortial digital libraries, Library Resources & Technical Services, 51, 2, pp. 116-131, (2007); Ho J., Stokes C., Core metadata element recommendations for institutional repositories at Texas A&M Libraries, Journal of Library Metadata, 19, 3-4, pp. 187-214, (2019); Jackson A.S., Han M.-J., Groetsch K., Mustafoff M., Cole T.W., Dublin Core metadata harvested through OAI-PMH, Journal of Library Metadata, 8, 1, pp. 5-21, (2008); Kenfield A., Metadata practices at ARL institutional repositories, Portal: Libraries and the Academy, 19, 4, pp. 667-699, (2019); Lourdi I., Papatheodorou C., Nikolaidou M., A multi-layer metadata schema for digital folklore collections, Journal of Information Science, 33, 2, pp. 197-213, (2007); Lynch J.D., Gibson J., Han M.-J., Analyzing and normalizing type metadata for a large aggregated digital library, Code4Lib Journal, 47, (2020); Malta M., Baptista A., A panoramic view on metadata application profiles of the last decade, International Journal of Metadata, Semantics and Ontologies, 9, pp. 58-73, (2014); Manouselis N., Salokhe G., Keizer J., Comparing different metadata application profiles for agricultural learning repositories, Metadata and Semantics, 2009, 6, pp. 469-479, (2008); Manouselis N., Salokhe G., Keizer J., Rudgard S., Towards a harmonization of metadata application profiles for agricultural learning repositories, Agricultural Information Worldwide, 2, 1, pp. 26-30, (2009); Martin K., Marrying local metadata needs with accepted standards: The creation of a data dictionary at the University of Illinois at Chicago, Journal of Library Metadata, 11, 1, pp. 33-50, (2011); Mering M., Transforming the quality of metadata in institutional repositories, Library, Information Science & Technology Abstracts with Full Text, 76, 1-4, pp. 79-82, (2019); Mering M., Wintermute H.E., MAPping metadata, Journal of Digital Media Management, 9, 1, pp. 71-85, (2020); (2020); Monson J., Getting started with digital collections: Scaling to fit your organization, (2017); Potvin S., Thompson S., An analysis of evolving metadata influences, standards, and practices in electronic theses and dissertations, Library Resources & Technical Services, 60, 2, (2016); (2019); Rettig P.J., Liu S., Hunter N., Level A.V., Developing a metadata best practices model: The experience of the Colorado State University Libraries, Journal of Library Metadata, 8, 4, pp. 315-339, (2009); Steele T., Sump-Crethar N., Metadata for electronic theses and dissertations: A survey of institutional repositories, Journal of Library Metadata, 16, 1, pp. 53-68, (2016); Toy-Smith V., UALC best practices metadata guidelines: A consortial approach, Journal of Library Metadata, 10, 1, pp. 1-12, (2010); Williamschen J., Work in progress: The PCC Task Group on metadata application profiles, Cataloging & Classification Quarterly, 58, 3-4, pp. 458-463, (2020); Xie I., Matusiak K.K., Discover digital libraries: Theory and practice, (2016); Zeng M.L., Lee J., Hayes A.F., Metadata decisions for digital libraries: A survey report, Journal of Library Metadata, 9, 3-4, pp. 173-193, (2009)","A.M. Green; Belk Library, Appalachian State University, Boone, United States; email: greenam7@appstate.edu","","Taylor and Francis Ltd.","","","","","","19386389","","","","English","J. Libr. Metadata","Article","Final","","Scopus","2-s2.0-85124910465"
"Neuroth H.; Straka J.; Schneemann C.; Mertzen D.","Neuroth, Heike (6508082130); Straka, Janine (57926604000); Schneemann, Carsten (57424289600); Mertzen, Daniela (57203620137)","6508082130; 57926604000; 57424289600; 57203620137","Cooperative setup and sustainable operation of the state initiative for Research Data Management in Brandenburg (FDM-BB); [Kooperativer Aufbau und nachhaltiger Betrieb der Landesinitiative für Forschungsdatenmanagement in Brandenburg (FDM-BB)]","2022","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-326","","","1329","1340","11","0","10.18420/inf2022_113","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139799237&doi=10.18420%2finf2022_113&partnerID=40&md5=52c690526ae82c007a763b0c8d417119","Fachhochschule Potsdam, Fachbereich Informationswissenschaften, Kiepenheuerallee 5, Potsdam, 14469, Germany; Universität Potsdam, Universitätsbibliothek, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany","Neuroth H., Fachhochschule Potsdam, Fachbereich Informationswissenschaften, Kiepenheuerallee 5, Potsdam, 14469, Germany; Straka J., Universität Potsdam, Universitätsbibliothek, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany; Schneemann C., Fachhochschule Potsdam, Fachbereich Informationswissenschaften, Kiepenheuerallee 5, Potsdam, 14469, Germany; Mertzen D., Universität Potsdam, Universitätsbibliothek, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany","[No abstract available]","Brandenburg; Forschungsdaten; Forschungsdatenmanagement; Landesinitiative","","","","","","","","Gemeinsame Digitalisierungsagenda des Ministeriums für Wissenschaft, Forschung und Kultur des Landes Brandenburg und der brandenburgischen Hochschulen 2021; Neuroth Heike, Straka Janine, Zeunert Miriam, Schneemann Carsten, Hartmann Niklas, Radtke Ina, Handlungs- und Implementierungsempfehlung zum Forschungsdatenmanagement in Brandenburg; Radtke Ina, Hartmann Niklas, Neuroth Heike, Rothfritz Laura, Wuttke Ulrike, Straka Janine, Zeunert Miriam, Schneemann Carsten, Anforderungserhebung bei den brandenburgischen Hochschulen, (2020); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3; Wuttke Ulrike, Neuroth Heike, Rothfritz Laura, Straka Janine, Zeunert Miriam, Schneemann Carsten, Hartmann Niklas, Radtke Ina, Umfeldanalyse zum Aufbau einer neuen Datenkultur in Brandenburg, (2021); Zeunert M., Schneemann C., Handlungs- und Implementierungsempfehlung zum Forschungsdatenmanagement in Brandenburg, (2021); Zeunert M., Schneemann C., Stellenkorpus - Forschungsdaten(management) [Data set], Zenodo","","Demmler D.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg; Krupka D.; Gesellschaft fur Informatik, Anna-Louisa-Karsch-Strasse 2, Berlin; Federrath H.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg","Gesellschaft fur Informatik (GI)","Adesso SE; et al.; Genua GmbH; Google Deutschland GmbH; Hamburger Informatik Technologie Center (HITEC); SAP SE","2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022","26 September 2022 through 30 September 2022","Hamburg","183150","16175468","978-388579720-3","","","German","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85139799237"
"Bossaller J.; Million A.J.","Bossaller, Jenny (21740564300); Million, Anthony J. (56259235100)","21740564300; 56259235100","The research data life cycle, legacy data, and dilemmas in research data management","2022","Journal of the Association for Information Science and Technology","","","","","","","1","10.1002/asi.24645","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126350102&doi=10.1002%2fasi.24645&partnerID=40&md5=3f03b832ff69d6ce8d3eb183a530ce0b","School of Information Science & Learning Technologies, University of Missouri, Columbia, MO, United States; Inter-University Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, United States","Bossaller J., School of Information Science & Learning Technologies, University of Missouri, Columbia, MO, United States; Million A.J., Inter-University Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, United States","This paper presents findings from an interview study of research data managers in academic data archives. Our study examined policies and professional autonomy with a focus on dilemmas encountered in everyday work by data managers. We found that dilemmas arose at every stage of the research data lifecycle, and legacy data presents particularly vexing challenges. The iFields' emphasis on knowledge organization and representation provides insight into how data, used by scientists, are used to create knowledge. The iFields' disciplinary emphasis also encompasses the sociotechnical complexity of dilemmas that we found arise in research data management. Therefore, we posit that iSchools are positioned to contribute to data science education by teaching about ethics and infrastructure used to collect, organize, and disseminate data through problem-based learning. © 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.","","Knowledge organization; Life cycle; Data archives; Data life cycle; Data lifecycle; Interview study; Knowledge organization; Knowledge-representation; Legacy data; Research data; Research data managements; Sociotechnical; article; data science; education; ethics; human; human experiment; information center; interview; life cycle; manager; problem based learning; professional practice; teaching; Managers","","","","","","","Bates M.J., The invisible substrate of information science, Journal of the American Society for Information Science, 50, 12, pp. 1043-1050, (1999); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., Scharnhorst A., Golshan M.S., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, Journal of the Association for Information Science and Technology, 70, 8, pp. 888-904, (2019); Caso R., Ducato R., Intellectual property, open science and research biobanks. Trento Law and Technology Research Group Research Paper, (22), (2014); Hayashi C., What is data science? Fundamental concepts and a heuristic example, Data science, classification, and related methods, pp. 40-51, (1998); The data deluge: An e-science perspective, (2003); Higgins S., The DCC curation lifecycle model, International Journal of Data Curation, 3, 1, pp. 134-140, (2008); Data deposit agreement; Million A.J., Bossaller J., Policies, procedures, and decision-making: Data managers and the research lifecycle, Proceedings of the Meeting of the Association for Information Science & Technology, (2021); Dissemination and sharing of research results—NSF data management plan requirements, (2021); Pasquetto I.V., Borgman C.L., Wofford M.F., Uses and reuses of scientific data: The data creators' advantage, Harvard Data Science Review, 1, 2, (2019); Research Data Management Librarian Academy, (2021); Shipman J.P., Tang R., The collaborative creation of a research data management librarian academy (RDMLA), Information Services & Use, 39, 3, pp. 243-247, (2019); Si L., Zhuang X., Xing W., Guo W., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, pp. 700-724, (2013); Star S.L., Ruhleder K., Steps toward an ecology of infrastructure: Design and access for large information spaces, Information Systems Research, 7, 1, pp. 111-134, (1996); Tang R., Sae-Lim W., Data science programs in US higher education: An exploratory content analysis of program description, curriculum structure, and course focus, Education for Information, 32, 3, pp. 269-290, (2016); Thomas A., Martin E.R., Developing a community of practice: Building the research data management librarian academy, Medical Reference Services Quarterly, 39, 4, pp. 323-333, (2020); Whyte, A., & Wilson, A. (2010). How to appraise and Select research data for Curation (DCC How To Guides). Digital Curation Centre: Edinburgh","A.J. Million; Inter-University Consortium for Political and Social Research, University of Michigan, Ann Arbor, United States; email: millioaj@umich.edu","","John Wiley and Sons Inc","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Article in press","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85126350102"
"Fitriana F.; Sukarni S.; Zulkifli Z.","Fitriana, Fitriana (58168112200); Sukarni, Sukarni (57962842000); Zulkifli, Zulkifli (57963198800)","58168112200; 57962842000; 57963198800","Complexity of Web-based Application for Research and Community Service in Academic","2022","International Journal of Advanced Computer Science and Applications","13","10","","131","135","4","0","10.14569/IJACSA.2022.0131017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141801748&doi=10.14569%2fIJACSA.2022.0131017&partnerID=40&md5=b3f707e2ff74b6386e4dc02dedac9572","Midwifey, Faculty of Biomedical Science, Universitas Aisyah Pringsewu, Lampung, Indonesia; Informatics Engineering, Faculty of Informatics & Technology, Universitas Aisyah Pringsewu, Lampung, Indonesia","Fitriana F., Midwifey, Faculty of Biomedical Science, Universitas Aisyah Pringsewu, Lampung, Indonesia; Sukarni S., Midwifey, Faculty of Biomedical Science, Universitas Aisyah Pringsewu, Lampung, Indonesia; Zulkifli Z., Informatics Engineering, Faculty of Informatics & Technology, Universitas Aisyah Pringsewu, Lampung, Indonesia","Research data and community service in academic environment is a very important asset that must be managed properly. They have to be applied synergically in order to obtain as quality standards of higher education. A centralized web-based application designed for research data management and community service have been applied in terms of supporting the managerial of activities. To make the application suitable for users, it is necessary to estimate the size of the software built. This study aimed at measuring the consistency of the apps based on feature point analysis method which is owned by research and community service in Indonesia. Fourteen Modification Complexity Adjustment Factor (MCAF) were used for calculating a program scale with adequate precision. The main cost is determining the quality of application sequentially, which includes measuring the weighted value of feature point components, namely, Crude Function Points (CFPs), calculating the Relative Complexity Adjustment Factor (RCAF), and estimating the Function Point (FP) by using the formula itself. The results depict that the size of application was estimated about 18381 lines using FPA methods and achieved successful estimation with 2.2 percent of deviation. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.","Application complexity; Function point analysis; Program scale; Software size","Information management; Web services; Websites; Adjustment factors; Application complexity; Community services; Function point; Function point analysis; Program scale; Research communities; Research data; Software size; Web-based applications; Application programs","","","","","","","Sayidah N., Et al., Quality and University Governance in Indonesia, International Journal of Higher Education, 8, 4, pp. 10-17, (2019); Eliot S., Factors Determining the Quality Management of Higher Education: A Case Study at A Business School in Indonesia, Jurnal Cakrawala Pendidikan, 38, pp. 215-227, (2019); Hidayat S., Peningkatan Mutu Penelitian di Indonesia Dalam Mengatasi Masalah Pendidikan, Bioilmi, 4, 2, pp. 34-44, (2018); Knight John, Fundamentals of Dependable Computing for Software Engineers, (2012); Longstreet D., Function Points Analysis Training Course, 2, (2005); Angelica C., Et al., Agile Counting Process of Software Product Maintenance Size: A Statistical Analysis Processo de Contagem de Tamanho Agil de Manutenção de Produto de Software: Uma Análise Estatística, Espacios, 41, 32, (2020); Charles I., Caesar N. I., Quantifying Software Quality in Agile Development Environment, Software Engineering, 9, 2, pp. 36-44, (2021); Irawati M. K., Measuring Software Functionality Using Function Point Method Based on Design Documentation, International Journal of Computer Science Issues, 9, (2012); Sanjaya R., Girsang A. S., Implementation application internal chat messenger using android system, 2017 International Conference on Applied Computer and Communication Technologies (ComCom), pp. 1-4, (2017); Tunali V., Software Size Estimation Using Function Point Analysis-A Case Study for a Mobile Application, Mühendislik ve Teknoloji Sempozyumu, pp. 73-76, (2014); Patwa M. A. K., A Study of Function Point Analysis: Some Suggestive Modifications, Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE) 2019, 2019, pp. 634-637; Banimustafa A., Predicting Software Effort Estimation Using Machine Learning Techniques, 2018 8th International Conference on Computer Science and Information Technology, CSIT 2018, pp. 249-256, (2018); Latif A., Et al., Comparative analysis of software effort estimation using data mining technique and feature selection, JITK, 6, 2, pp. 167-174, (2021); Ripu Ranjan Sinha R. K. G., Software Effort Estimation Using Machine Learning Techniques, Advances in Information Communication Technology and Computing, pp. 1-4, (2021); Eti Kapita R. B. H., Et al., Analisis kualitas perangkat lunak menggunakan metode function point analysis (study kasus: Transaksi Pembelian di eBay), Jurnal Informatika dan Rekayasa Perangkat Lunak, 1, 1, (2019); Balaji B. N., Et al., Software cost estimation using function point with non-algorithmic approach, Global Journal of Computer Science and Technology, 13, 8, pp. 1-5, (2013); Pratiwi D., Implementation of function point analysis in measuring the volume estimation of software system in object oriented and structural model of academic system, International Journal of Computer Applications, 70, 10, pp. 1-4, (2013); Rachmat N., Estimasi ukuran perangkat lunak menggunakan function point analysis-studi kasus aplikasi pengujian dan pembelajaran berbasis web, Annual Research Seminar, 3, 1, pp. 1-5, (2017); Yhurinda A., Putri P., Subriadi A. P., Software Cost Estimation Using Function Point Analysis, The 4th International Seminar on Science and Technology, 1, pp. 79-83, (2018); Madhav H., Kumar V., A Method for Predicting Software Reliability Using Object-oriented Design Metrics, 2019 International Conference on Intelligent Computing and Control Systems (ICCS), pp. 679-682, (2019); Marcheta N., Hermadi I., Nurhadryani Y., Effort Estimation Modeling of E-Government Application Development Using Function Points Based on TOR and SRS Document, Journal of Information Technology and Its Utilization, 3, 1, pp. 5-8, (2020)","","","Science and Information Organization","","","","","","2158107X","","","","English","Intl. J. Adv.  Comput. Sci. Appl.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85141801748"
"Murphy J.","Murphy, Jeannette (7404112102)","7404112102","Global trends health science libraries: Part 1","2021","Health Information and Libraries Journal","38","4","","319","324","5","1","10.1111/hir.12408","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118476086&doi=10.1111%2fhir.12408&partnerID=40&md5=6f46ef3941efc3f5db90cb98074dab32","The Farr Institute of Health Informatics Research, CHIME, University College London, London, United Kingdom","Murphy J., The Farr Institute of Health Informatics Research, CHIME, University College London, London, United Kingdom","This is the first of three articles based on articles published in the Health Information and Libraries Journal's Regular Feature (International Perspectives and Initiatives). Key trends from 12 countries in Europe, North America, Africa and Asia were identified. In this issue, two trends are described: emergence of new roles and challenges for library staff; supporting researchers engaging in research data management and maintaining institutional repositories. Readers are challenged to compare these trends with their own experiences.J.M. © 2021 Health Libraries Group","case studies; continuing professional development; institutional repositories; librarians, health science; library and health information professionals; library services; professional development; research data management; research support","Africa; Humans; Libraries, Medical; Africa; article; Asia; Europe; financial management; human; librarian; library; medical information; North America; professional development; trend study","","","","","","","10 medical and health research data things; Chande-Mallya R., Tanzanian health libraries in the 21st century: Initiatives and challenges, Health Information & Libraries Journal, 36, pp. 283-287, (2019); De Meulemeester A., Schietse B., Vermeeren B., Ghesquiere E., Decleve G., Buysse H., Discart I., Alewaeters K., Durieux N., Peleman R., Pauwels N., Current and future directions in Belgian medical and health sciences librarianship: A user-tailored approach, Health Information & Libraries Journal, 35, pp. 336-340, (2018); Epstein B.A., Health sciences libraries in the United States: New directions, Health Information & Libraries Journal, 34, pp. 307-311, (2017); Ganshorn H., Giustini D., New directions in health sciences libraries in Canada: Research and evidence based practice are key, Health Information & Libraries Journal, 34, pp. 252-257, (2017); Haglund L., Roos A., Wallgren-Bjork P., Health science libraries in Sweden: New directions, expanding roles, Health Information & Libraries Journal, 35, pp. 251-255, (2018); Kinengyere A.A., Ugandan health libraries in the 21st century: Key initiatives and challenges, Health Information & Libraries Journal, 36, pp. 185-189, (2019); Knuttel H., Krause E., Semmler-Schmetz M., Reimann I., Metzendorf M.-I., Health sciences libraries in Germany: New directions, Health Information & Libraries Journal, 37, pp. 83-88, (2020); Lacey Bryant S., Bingham H., Carlyle R., Day A., Ferguson L., Stewart D., Forward view: Advancing health library and knowledge services in England, Health Information & Libraries Journal, 35, pp. 70-77, (2018); Lindberg D.A.B., Humphreys B.L., 2015—The Future of Medical Libraries, New England Journal of Medicine, 352, 11, pp. 1067-1070, (2005); Madge O.-L., Robu I., Medical academic libraries in Romania – Breaking with the past and turning towards the future, Health Information & Libraries Journal, 36, pp. 96-100, (2019); Siemensma G., Ritchie A., Lewis S., Shaping the professional landscape through research, advocacy and education – An Australian perspective, Health Information & Libraries Journal, 34, pp. 171-176, (2017); Xie Z., Zhang J., New directions in health sciences libraries in China, Health Information & Libraries Journal, 35, pp. 165-169, (2018)","J. Murphy; The Farr Institute of Health Informatics Research, CHIME, University College London, London, United Kingdom; email: j_murphy_london@outlook.com","","John Wiley and Sons Inc","","","","","","14711834","","","34730271","English","Health Inf. Libr. J.","Article","Final","","Scopus","2-s2.0-85118476086"
"Griem L.; Zschumme P.; Laqua M.; Brandt N.; Schoof E.; Altschuh P.; Selzer M.","Griem, Lars (57221981907); Zschumme, Philipp (57221981540); Laqua, Matthieu (57904880600); Brandt, Nico (57219502886); Schoof, Ephraim (56422042200); Altschuh, Patrick (57194585570); Selzer, Michael (24503304900)","57221981907; 57221981540; 57904880600; 57219502886; 56422042200; 57194585570; 24503304900","KadiStudio: FAIR Modelling of Scientific Research Processes","2022","Data Science Journal","21","1","16","","","","0","10.5334/dsj-2022-016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138734351&doi=10.5334%2fdsj-2022-016&partnerID=40&md5=ca449d1ff6596bc22a666da9a5a67c25","Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU), Helmholtzstraße 11, Ulm, 89081, Germany; Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Griem L., Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Zschumme P., Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Laqua M., Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Brandt N., Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Schoof E., Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU), Helmholtzstraße 11, Ulm, 89081, Germany; Altschuh P., Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany; Selzer M., Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","FAIR handling of scientific data plays a significant role in current efforts towards a more sustainable research culture and serves as a prerequisite for the fourth scientific paradigm, that is, data-driven research. To enforce the FAIR principles by ensuring the reproducibility of scientific data and tracking their provenance comprehensibly, the FAIR modelling of research processes in form of automatable workflows is necessary. By providing reusable procedures containing expert knowledge, such workflows contribute decisively to the quality and the acceleration of scientific research. In this work, the requirements for a system to be capable of modelling FAIR workflows are defined and a generic concept for modelling research processes as workflows is developed. For this, research processes are iteratively divided into impartible subprocesses at different detail levels using the input-process-output model. The concrete software implementation of the identified, universally applicable concept is finally presented in form of the workflow editor KadiStudio of the Karlsruhe Data Infrastructure for Materials Science (Kadi4Mat). © 2022 The Author(s).","electronic lab notebook; FAIR principles; inputprocess-output model; research data management; workflows","Workflow management; 'current; Electronic lab; Electronic lab notebook; FAIR principle; Inputprocess-output model; Research data managements; Research process; Scientific data; Scientific researches; Work-flows; Data handling","","","","","MWK-BW, (03XP0315B); Deutsche Forschungsgemeinschaft, DFG, (390874152); Bundesministerium für Bildung und Forschung, BMBF, (03XP0435D); Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, MWK, (57); Helmholtz Association, (43.35.01)","This work is partly funded by the German Research Foundation (DFG) under Project ID 390874152 (POLiS Cluster of Excellence), by the German Federal Ministry of Education and Research (BMBF) in the project FB2 TheoDat (project number 03XP0435D), by the Ministry of Science, Research and Art Baden-Württemberg (MWK-BW) in the project MoMaF–Science Data Center, with funds from the state digitization strategy digital@bw (project number 57), by the Helmholtz association in the project INNOPOOL MDMC (program No. 43.35.01) and also funded by the BMBF and MWK-BW as part of the Excellence Strategy of the German Federal and State Governments in the project Kadi4X. We would also like to acknowledge the German Federal Ministry of Education and Research (BMBF) for its financial support within the project AQuaBP, under the grant number 03XP0315B. Some ideas presented in this paper are enhanced by the fruitful discussions in different working groups of the project NFDI4Ing.","Afgan E, Et al., The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update, Nucleic acids research, 46, W1, pp. W537-W544, (2018); Berthold MR, Et al., KNIME-the Konstanz information miner: version 2.0 and beyond, AcM SIGKDD explorations Newsletter, 11, 1, pp. 26-31, (2009); Brandt N, Et al., Kadi4Mat: A Research Data Infrastructure for Materials Science, Data Science Journal, 20, 1, (2021); Crusoe MR, Et al., Methods included: standardizing computational reuse and portability with the common workflow language, (2021); Crusoe MR, Et al., Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language, Commun. ACM, 65, 6, pp. 54-63, (2022); Demsar J, Et al., Orange: Data Mining Toolbox in Python, Journal of Machine Learning Research, 14, pp. 2349-2353, (2013); Di Tommaso P, Et al., Nextflow enables reproducible computational workflows, Nature biotechnology, 35, 4, pp. 316-319, (2017); Dmitry PEA., Qt5 Node Editor, (2017); Draxl C, Scheffler M., Big data-driven materials science and its FAIR data infrastructure, Handbook of Materials Modeling: Methods: Theory and Modeling, pp. 49-73, (2020); Frazer S, Et al., Workflow Description Language – Specification and Implementations, (2012); Goel A., Computer fundamentals, (2010); Hey AJ, Tansley S, Tolle KM, Et al., The fourth paradigm: data-intensive scientific discovery, 1, (2009); Jain A, Et al., FireWorks: A dynamic workflow system designed for high-throughput applications, Concurrency and Computation: Practice and Experience, 27, 17, pp. 5037-5059, (2015); IAM-CMS/kadi-apy: Kadi4Mat API Library; IAM-CMS/kadi: Kadi4Mat; IAM-CMS/workflow-nodes; IAM-CMS/xmlhelpy; Kluyver T, Et al., Jupyter Notebooks? A publishing format for reproducible computational workflows, Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87-90, (2016); Molder F, Et al., Sustainable data analysis with Snakemake, F1000 Research, 10, (2021); Pizzi G, Et al., AiiDA: automated interactive infrastructure and database for computational science, Computational Materials Science, 111, pp. 218-230, (2016); Click – The Pallets Projects, (2014); Wilkinson MD, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, 1, pp. 1-9, (2016); Zelle JM., Python programming: an introduction to computer science, (2004); Zschumme P, Schoof E, Et al., IAM-CMS/process-engine, (2022); Zschumme P, Steinhulb J, Brandt N., IAM-CMS/process-manager, (2022)","L. Griem; Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology (KIT), Karlsruhe, Straße am Forum 7, 76131, Germany; email: lars.griem@kit.edu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138734351"
"Chigwada J.P.","Chigwada, Josiline Phiri (57193754137)","57193754137","Research data management services in tertiary institutions in Zimbabwe","2021","Handbook of Research on Knowledge and Organization Systems in Library and Information Science","","","","419","437","18","1","10.4018/978-1-7998-7258-0.ch022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128441898&doi=10.4018%2f978-1-7998-7258-0.ch022&partnerID=40&md5=d087240f1fd6785b798c5101237b83f6","Chinhoyi University of Technology, Zimbabwe","Chigwada J.P., Chinhoyi University of Technology, Zimbabwe","The chapter seeks to analyze how librarians in Zimbabwe are responding to increasing librarian roles in the provision of research data services. The study sought to ascertain librarians' awareness and preparedness to offer research data management services at their institutions and determine support required by librarians to effectively deliver research data services. Participants were invited to respond to the survey, and survey monkey was used to administer the online questionnaire. The collected data was analyzed using content analysis, and it was thematically presented. Findings revealed that librarians in Zimbabwe are aware of their role in research data management, but the majority are not prepared to offer research data management services due to a lack of the required skills and resources. Challenges that were noted include lack of research data management policy at institutional levels and information technology issues such as obsolescence and security issues. © 2021, IGI Global.","","","","","","","","","Research data management: A brief overview for physical sciences librarians, (2016); Avuglah B.K., Underwood P.G., Research Data Management (RDM) Capabilities at the University of Ghana, Legon, Library Philosophy and Practice (e-journal), (2019); Baker K.S., Yarmey L., Data stewardship: Environmental data curation and a web-of-repos- itories, International Journal of Digital Curation, 4, 2, pp. 12-27, (2009); Barbrow S., Brush D., Goldman J., Research data management and services: Resources for novice data librarians, Internet Resources, 78, 5, (2017); Bezuidenhout L., Chakauya E., Hidden concerns of sharing research data by low/middle income country scientists, Global Bioethics, 29, 1, pp. 39-54, (2018); Burnett P., What is the role of a librarian in Research Data Management?, (2013); Buys C.M., Shaw P.L., Data Management Practices Across an Institution: Survey and Report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Chawinga W.D., Zinn S., Research data management at a public university in Malawi: The role of ""three hands, Library Management, 41, 6-7, pp. 467-485, (2020); Chigwada J., Chiparausha B., Kasiroori J., Research Data Management in Research Institutions in Zimbabwe, Data Science Journal, 16, (2017); Chigwada J.P., Hwalima T., Kwangwa N., A Proposed Framework for Research Data Management Services in Research Institutions in Zimbabwe, Research Data Access and Management in Modern Libraries, pp. 29-53, (2019); Chiparausha B., Chigwada J.P., Accessibility of Research Data at Academic Institutions in Zimbabwe, Research Data Access and Management in Modern Libraries, pp. 81-89, (2019); Chiware E., Mathe Z., Academic libraries' role in Research Data Management Services: A South African perspective, South African Journal of Library and Information Science, 81, 2, (2015); Chiware E.R., Becker D.A., Research data management services in southern Africa: A readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Survey of Research Data Management: Results Kathleen Shearer and Filipe Furtado, Confederation of Open Access Repositories (COAR), (2017); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, The Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Cox A.N., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library & Information Science Research, 38, 4, pp. 319-326, (2016); Davidson J., Developing an organisational profile for research data management services - a guide for HEIs, (2015); Erway R., Starting the Conversation: University-wide Research Data Management Policy, (2013); Fernihough S., E-Research: An implementation framework for South African organisations, (2011); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the Research Data Management Landscape, (2015); Harvey R., Digital curation: A how to do it manual, (2010); Henderson M.E., Knott T.L., Starting a Research Data Management Program Based in a University Library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Ingram C., How and why you should manage your research data: A guide for researchers, (2016); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services - a guide for HEIs'. DCCHow-to Guides, (2013); Kahn M., Higgs R., Davidson J., Jones S., Research Data Management in South Africa: How We Shape Up, Australian Academic and Research Libraries, 45, 4, pp. 296-308, (2014); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Latham B., Research Data Management: Defining Roles, Prioritizing Services, and Enumerating Challenges, Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Lotter L., What can take the dark out of the long tail? Efforts to address the challenges of 'small science', (2011); Lotter L., Data curation implementation at the Human Sciences Research Council: Case study, (2013); Lotter L., Reflections on the RDM Position in South Africa, (2014); Majid S., Foo S., Zhang X., Research data management by academics and researchers: Perceptions, knowledge and practices. Maturity and innovation in digital libraries, 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, pp. 166-178, (2018); Marchionini G., Research Data Stewardship at UNC: Recommendations for Scholarly Practice and Leadership, (2012); Getting to the Bottom Line: 20 Cost Questions for Digital Preservation, (2015); Mosha N.F., Luhanga E.T., Mosha M.V., Marwa J.J., Research Data Management among researchers in higher learning institutions of Sub-Saharan Africa, Handbook of research on connecting research methods for information science research, pp. 479-488, (2019); Climate Data Records from Environmental Satellites: Interim Report, (2004); Ndhlovu P., The state of preparedness for digital curation and preservation: A case study of the National University of Science and Technology Library, (2016); Ndhlovu P., The State of Preparedness for Digital Curation and Preservation: A Case Study of a Developing Country Academic Library, IASSIST Quarterly, 42, 3, pp. 1-22, (2018); Ndhlovu P., Ngwenya S., Research data management services: An investigation of research data management practices at the National University of Science and Technology,  annual international conference on communication and information science, (2017); Nhendodzashe N., Pasipamire N., Research data management services: Are academic libraries in Zimbabwe ready? The case of University of Zimbabwe library, (2017); Patterton L., Research data management at the CSIR: An exploratory survey, (2014); Patterton L., Research data management practices of emerging researchers at a South African research council, (2016); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Pryor G., Managing research data, (2012); Rambo N., Research data management: Roles for libraries, (2015); Rolando L., Doty C., Hagenmaier W., Valk A., Parham S.W., Institutional readiness for data stewardship: Findings and recommendations from the research data assessment, (2013); Shen Y., Varvel V.E., Developing Data Management Services at the Johns Hopkins University, Journal of Academic Librarianship, 39, 6, pp. 552-557, (2013); Stang T., How growth in research data is spurring a shift in the librarian's role, Librarians: The new research data management experts, (2016); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of Escience Librarianship, 1, 2, (2012); Tenopir C., Christian L., Allard S., Borycz J., Research Data sharing: Practices and attitudes of geophysicists, Earth and Space Science (Hoboken, N.J.), 5, 12, pp. 891-902, (2018); Tenopir C., Dalton E.D., Allard S., Frame M., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers' perceptions towards research data in India, International Federation of Library Associations and Institutions, 43, 1, pp. 22-39, (2017); Create and manage data: Research Data Lifecycle, (2018); van Deventer M., Piennar H., Research Data Management in a Developing Country: Personal Journey, International Journal of Digital Curation, 10, 2, pp. 33-47, (2015); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Whyte A., Tedds J., Making the case for research data management, (2011); Yoon A., Schultz T., Research Data Management Services in Academic Libraries in the US: A Content Analysis of Libraries' Websites, College & Research Libraries, 78, 7, (2017); Yu H.H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017); Zhou Q., Academic libraries in research data management service: Perceptions and practices, Open Access Library Journal, 5, 6, (2018)","","","IGI Global","","","","","","","978-179987259-7; 978-179987258-0","","","English","Handb. of Res. on Knowl. and Organ. Syst. in Libr. and Inf. Sci.","Book chapter","Final","","Scopus","2-s2.0-85128441898"
"De Rosa R.; Aragona B.","De Rosa, Rosanna (57202482507); Aragona, Biagio (57191857047)","57202482507; 57191857047","Open Science and the Academic Profession","2021","eJournal of eDemocracy and Open Government","13","2","","184","205","21","0","10.29379/jedem.v13i2.661","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131378981&doi=10.29379%2fjedem.v13i2.661&partnerID=40&md5=929fbc02098d7981b9f6ddf0cd61fe3f","Vico Monte di Pietà 1, Department of Social Sciences, Università degli studi di Napoli Federico II, Italy","De Rosa R., Vico Monte di Pietà 1, Department of Social Sciences, Università degli studi di Napoli Federico II, Italy; Aragona B., Vico Monte di Pietà 1, Department of Social Sciences, Università degli studi di Napoli Federico II, Italy","Open science is considered a new science paradigm to make research accessible, accountable, and effective. Open science is already changing the academic profession starting from micro-practices to professional relations with epistemic communities and stakeholders, with implications that we are not yet able to predict. The article delves first into literature and official documentation to unfold the discursive regimes which sustain the spread of open science. A specific focus is then devoted to the professional transition, highlighting the role of funding organizations in setting the new science environment and the subjective experience of academics. The article is completed by a case study in the field of Research Data Management where the misalignment among incumbent/changing processes can be more apparent. Finally, a research agenda that focuses on how academic micro-practices are affecting organizations and science structures is proposed. This article aims at beginning to plow the ground for new research directions to emerge. © 2021, Department for E-Governance and Administration. All rights reserved.","Academic profession; Citizen science; Open access; Open science; Research data management","","","","","","University of Nice; National Science Foundation, NSF; National Institutes of Health, NIH; European Commission, EC; Erasmus+, (GA 2019-1-FR01-KA203-)","Funding text 1: The role of funding organizations has also been pivotal in dictating the rules for open access publication, also fostering institutional mandates and policies. Figure 6 shows results of open science publications by funding organizations and the model of publishing. By comparing the figures, the role exercised on openness by research sponsors emerged. The compliance to funding rules about open access has been, in fact, one of the key factors enabling a change in research and academic institutions. Organizations such as the US National Science Foundation and the National Institutes of Health have had the strongest role in publishing strategies together with the European Commission and its funding programs. In 2007, the EU Commission adopted a Communication (COM/2007/0056) on scientific information in the digital age for the first time, followed by Council conclusions inviting the Commission to experiment with open access to scientific publications resulting from projects funded by EU research framework program. Later on, in 2012, the EU Commission has recommended (C(2012) 4890) to define clear policies for the dissemination of results and open access to scientific publications resulting from publicly funded research, providing: a) concrete objectives and indicators to measure progress; b) implementation plans, including the allocation of responsibilities; c) associated financial planning.; Funding text 2: https://data.europa.eu/sites/default/files/open_data_maturity_report_2019.pdf In 2020, because of the pandemic crisis and the social pressure, the average open data maturity score of the EU27 countries is increased of 10 percentage points compared to 2019 with a widening of the ‘trend setter’ and ‘fast tracking’ countries clusters. The project is funded by Erasmus+ (GA 2019-1-FR01-KA203-) and led by the University of Nice. University Federico II of Naples is one of the partner organization.","Appadurai A., The future as cultural fact: Essays on the global condition, Rassegna Italiana di Sociologia, 14, 4, pp. 649-650, (2013); Aragona B., De Rosa R., Policy making at the time of big data. Datascape, datasphere, data culture, AIS Journal of Sociology, 11, pp. 173-187, (2018); Ayris P., Et al., Open Science and its role in universities: A roadmap for cultural change, (2018); Bartling S., Friesike S., Opening science: The evolving guide on how the internet is changing research, collaboration and scholarly publishing, (2014); Becher T., The significance of disciplinary differences, Studies in Higher education, 19, 2, pp. 151-161, (1994); JeDEM Issue, 13, 2, pp. 184-205; Borgman C. L., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Bourdieu P., Les catégories de l’entendement professoral, Actes de la recherche en sciences sociales, 24, 2, pp. 2-24, (1978); Bowman N. D., Keene J. R., A layered framework for considering open science practices, Communication Research Reports, 35, 4, pp. 363-372, (2018); Callon M., La science et ses réseaux: Genèse et circulation des faits scientifiques, (1989); Chen X., Dallmeier-Tiessen S., Dasler R., Open is not enough, Nature Phys, 15, pp. 113-119, (2019); Childs S., McLeod J., Lomas E., Cook G., Opening research data: Issues and opportunities, Records Management Journal, 24, 2, pp. 142-162, (2014); De Lourdes Machado-Taylor M., Soare V. M., Teichler U., Challenges and options: The academic profession in Europe, 18, (2017); Decuypere M., Simons M., On the composition of academic work in digital times, European Educational Research Journal, 13, 1, pp. 89-106, (2014); Dewey J., Experience and nature, (1958); Espeland W. N., Sauder M., Engines of Anxiety: Academic Rankings, Reputation and Accountability, (2016); Feyerabend P., How to Defend Society against Science, Introductory Readings in the Philosophy of Science, pp. 54-65, (1975); Foucault M., Discipline and Punish, (1977); Foucault M., Power/knowledge: Selected interviews and other writings, 1972-1977, (1980); Heller L., The R., Bartling S., Dynamic Publication Formats and Collaborative Authoring, Opening Science. The Evolving Guide on How the Internet is Changing Research, Collaboration and Scholarly Publishing, (2014); Irwin A., Citizen science: A study of people, expertise and sustainable development, (1995); Kogan M., Teichler U., Key challenges to the academic profession, UNESCO Forum on Higher Education Research and Knowledge, (2007); Lakomy M., Hlavova R., Machackova H., Open Science and the Science-Society Relationship, Society, 56, 3, pp. 246-255, (2019); Latour B., Science in action, (1987); Latour B., Pasteur: Guerre et paix des microbes, (2001); JeDEM Issue, 13, 2, pp. 184-205; MacCallum C., Research Communication: Open Science & the perverse evaluation cycle, (2018); Merton R. K., Science and technology in a democratic order, Journal of legal and political sociology, 1, 1, pp. 115-126, (1942); Mirowski P., The future (s) of open science, Social studies of science, 48, 2, pp. 171-203, (2018); Nentwich M., Konig R., Cyberscience 2.0: Research in the age of digital social networks, (2012); Normand R., The Changing Epistemic Governance of European Education: The Fabrication of the Homo Academicus Europeanus?, (2016); Novitzky P., Bernstein M.J, Blok V., Braun R., Chan T.T., Lamers W., Loeber A., Meijer I., Linder R., Griessler E., Improve alignment of research policy and societal values, Science, 369, 6499, pp. 39-41, (2020); O'carroll C., Rentier B., Cabello Valdes C., Esposito F., Kaunismaa E., Maas K., Metcalfe J., McAllister D., Vandevelde K., Evaluation of Research Careers fully acknowledging Open Science Practices: Rewards, incentives and/or recognition for researchers practicing Open Science, (2017); Owen R., Macnaghten P., Stilgoe J., Responsible Research and Innovation: From Science in Society to Science for Society, with Society, Science and Public Policy, 39, 6, pp. 751-760, (2012); Owen R., Pansera M., Responsible Innovation and Responsible Research and Innovation, Handbook on Science and Public Policy, (2019); Owen R., von Schomberg R., Macnaghten P., An unfinished journey? Reflections on a decade of responsible research and innovation, Journal of Responsible Innovation, (2021); Patel R., Research data management: A conceptual framework, Library Review, 65, pp. 226-241, (2016); Primeri E., Reale E., How Europe shapes academic research: Insights from participation in European union framework programmes, European Journal of Education, 47, 1, pp. 104-121, (2012); Sidler M., Open Science and the Three Cultures: Expanding Open Science to all Domains of Knowledge Creation, Opening science: The evolving guide on how the internet is changing research, collaboration and scholarly publishing, (2014); Star S. L., The Ethnography of Infrastructure, American Behavioral Scientist, 43, 3, pp. 377-391, (1999); Trela J., The rationalization of academic work, Presentation at the Second ISA forum “Social Justice and Democratization, (2012); Van der Zee T., Reich J., Open education science, AERA Open, 4, 3, (2018); Van Dijck T., Poell T., De Waal M., The Platform Society: Public Values in a Connective World, (2018); Wilkinson M. D., Dumontier M., Aalbersberg I. J., Appleton G., Axton M., Baak A., Bouwman J., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, (2016); Williamson B., The Hidden Architecture of Higher Education: Building a Big Data Infrastructure for the ‘Smarter University’, International Journal of Educational Technology in Higher Education, 15, 1, pp. 1-12, (2018); Zuiderwijk A., Open data infrastructures: The design of an infrastructure to enhance the coordination of open data use, (2015)","R. De Rosa; Vico Monte di Pietà 1, Department of Social Sciences, Università degli studi di Napoli Federico II, Italy; email: rderosa@unina.it","","Department for E-Governance and Administration","","","","","","20759517","","","","English","eJ. eDemocracy Open Gov.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85131378981"
"Alvarez-Romero C.; Martínez-García A.; Sinaci A.A.; Gencturk M.; Méndez E.; Hernández-Pérez T.; Liperoti R.; Angioletti C.; Löbe M.; Ganapathy N.; Deserno T.M.; Almada M.; Costa E.; Chronaki C.; Cangioli G.; Cornet R.; Poblador-Plou B.; Carmona-Pírez J.; Gimeno-Miguel A.; Poncel-Falcó A.; Prados-Torres A.; Kovacevic T.; Zaric B.; Bokan D.; Hromis S.; Djekic Malbasa J.; Rapallo Fernández C.; Velázquez Fernández T.; Rochat J.; Gaudet-Blavignac C.; Lovis C.; Weber P.; Quintero M.; Perez-Perez M.M.; Ashley K.; Horton L.; Parra Calderón C.L.","Alvarez-Romero, Celia (57210788267); Martínez-García, Alicia (55937333600); Sinaci, A. Anil (36905158800); Gencturk, Mert (53063547700); Méndez, Eva (57921665200); Hernández-Pérez, Tony (35213151400); Liperoti, Rosa (9940135200); Angioletti, Carmen (57205739691); Löbe, Matthias (55938448500); Ganapathy, Nagarajan (57202547425); Deserno, Thomas M. (21233724300); Almada, Marta (51160963300); Costa, Elisio (7402527214); Chronaki, Catherine (6701349152); Cangioli, Giorgio (56690472900); Cornet, Ronald (57201825238); Poblador-Plou, Beatriz (35622277100); Carmona-Pírez, Jonás (57202163168); Gimeno-Miguel, Antonio (57201724319); Poncel-Falcó, Antonio (55043233700); Prados-Torres, Alexandra (6505763382); Kovacevic, Tomi (56205406300); Zaric, Bojan (16403676100); Bokan, Darijo (57195593453); Hromis, Sanja (32867618500); Djekic Malbasa, Jelena (57208734534); Rapallo Fernández, Carlos (57966699800); Velázquez Fernández, Teresa (57966863000); Rochat, Jessica (57194129754); Gaudet-Blavignac, Christophe (57090883300); Lovis, Christian (55046580400); Weber, Patrick (57214576183); Quintero, Miriam (57776231400); Perez-Perez, Manuel M. (57217672051); Ashley, Kevin (55761240700); Horton, Laurence (57967213100); Parra Calderón, Carlos Luis (24332533000)","57210788267; 55937333600; 36905158800; 53063547700; 57921665200; 35213151400; 9940135200; 57205739691; 55938448500; 57202547425; 21233724300; 51160963300; 7402527214; 6701349152; 56690472900; 57201825238; 35622277100; 57202163168; 57201724319; 55043233700; 6505763382; 56205406300; 16403676100; 57195593453; 32867618500; 57208734534; 57966699800; 57966863000; 57194129754; 57090883300; 55046580400; 57214576183; 57776231400; 57217672051; 55761240700; 57967213100; 24332533000","FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research","2022","Open Research Europe","2","","34","","","","3","10.12688/openreseurope.14349.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133820721&doi=10.12688%2fopenreseurope.14349.2&partnerID=40&md5=343a1ac3f128dbfd3c01fdebeca500a6","Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain; SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey; Dept. of Library and Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain; Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy; Institute for Medical Informatics (IMISE), University of Leipzig, Leipzig, 04107, Germany; PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany; Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal; HL7 Europe Foundation, Brussels, 1000, Belgium; Amsterdam UMC, University of Amsterdam, Medical Informatics, Amsterdam Public Health, Amsterdam, 1105AZ, Netherlands; EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Aragon Health Service, Zaragoza, 50009, Spain; Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia; Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia; J and A Garrigues, S.L.P., Seville, 41013, Spain; University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland; Nice Computing SA Le Mont-sur-Lausanne, Le Mont-sur-Lausanne, 1052, Switzerland; Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain; Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain; Digital Curation Centre, University of Edinburgh, Argyle House, Edinburgh, EH3 9DR, United Kingdom; Digital Curation Centre, University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Alvarez-Romero C., Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain; Martínez-García A., Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain; Sinaci A.A., SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey; Gencturk M., SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey; Méndez E., Dept. of Library and Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain; Hernández-Pérez T., Dept. of Library and Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain; Liperoti R., Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy; Angioletti C., Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy; Löbe M., Institute for Medical Informatics (IMISE), University of Leipzig, Leipzig, 04107, Germany; Ganapathy N., PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany; Deserno T.M., PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany; Almada M., Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal; Costa E., Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal; Chronaki C., HL7 Europe Foundation, Brussels, 1000, Belgium; Cangioli G., HL7 Europe Foundation, Brussels, 1000, Belgium; Cornet R., Amsterdam UMC, University of Amsterdam, Medical Informatics, Amsterdam Public Health, Amsterdam, 1105AZ, Netherlands; Poblador-Plou B., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; Carmona-Pírez J., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; Gimeno-Miguel A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; Poncel-Falcó A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Aragon Health Service, Zaragoza, 50009, Spain; Prados-Torres A., EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain; Kovacevic T., Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia, Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia; Zaric B., Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia, Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia; Bokan D., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia; Hromis S., Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia, Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia; Djekic Malbasa J., Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia, Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia; Rapallo Fernández C., J and A Garrigues, S.L.P., Seville, 41013, Spain; Velázquez Fernández T., J and A Garrigues, S.L.P., Seville, 41013, Spain; Rochat J., University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland; Gaudet-Blavignac C., University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland; Lovis C., University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland; Weber P., Nice Computing SA Le Mont-sur-Lausanne, Le Mont-sur-Lausanne, 1052, Switzerland; Quintero M., Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain, Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain; Perez-Perez M.M., Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain, Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain; Ashley K., Digital Curation Centre, University of Edinburgh, Argyle House, Edinburgh, EH3 9DR, United Kingdom; Horton L., Digital Curation Centre, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Parra Calderón C.L., Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain","Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance. © 2022 Alvarez-Romero C et al.","Data reuse; Data sharing; FAIR principles; Health data; Health research; Health research data management; HL7 FHIR; Machine learning; Open science; Privacy-preserving computing","","","","","","Horizon 2020 Framework Programme, H2020, (824666)","","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Cost of not having FAIR research data - Cost-Benefit analysis for FAIR research data, (2018); FAIR4Health Guidelines for implementing FAIR open data policy in health research; Sinaci A.A., Nunez-Benjumea F.J., Gencturk M., Et al., From Raw Data to FAIR Data: The FAIRification Workflow for Health Research, Methods Inf Med, 59, 1, pp. e21-e32, (2020); FAIR4Health Data Curation Tool; FAIR4Health Data Privacy Tool; FAIR4Health Common Data Model; FAIR4Health Privacy-Preserving Distributed Data Mining (PPDDM) framework; Han J., Pei J., Yin Y., Mining frequent patterns without candidate generation, ACM sigmod record, 29, 2, pp. 1-12, (2000); Poblador-Plou B., Calderon-Larranaga A., Marta-Moreno J., Et al., Comorbidity of dementia: a cross-sectional study of primary care older patients, BMC Psychiatry, 14, 1, (2014); Prados-Torres A., Calderon-Larranaga A., Hancco-Saavedra J., Et al., Multimorbidity patterns: a systematic review, J Clin Epidemiol, 67, 3, pp. 254-266, (2014); Carmona-Pirez J., Poblador-Plou B., Poncel-Falco A., Et al., Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association With Mortality Through a Frequent Pattern Growth Association Algorithm, Int J Environ Res Public Health, 19, 4, (2022); Alvarez-Romero C., Martinez-Garcia A., Ternero-Vega J.E., Et al., Predicting 30-days Readmission Risk for COPD Patients Care through a Federated Machine Learning Architecture on FAIR Data: Development and Validation Study, JMIR Medical Informatics, 1, (2022); FAIR4Health Report on the demonstrators performance; Observational Health Data Sciences and Informatics (OHDSI); Observational Health Data Sciences and Informatics (OHDSI) suite","C. Alvarez-Romero; Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain; email: celia.alvarez@juntadeandalucia.es","","F1000 Research Ltd","","","","","","27325121","","","","English","Open. Res. Eur.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85133820721"
"Beer A.; Brunet M.; Srivastava V.; Vidal M.-E.","Beer, Anna (57820710400); Brunet, Mauricio (57820710500); Srivastava, Vibhav (57821991300); Vidal, Maria-Esther (7202765018)","57820710400; 57820710500; 57821991300; 7202765018","Leibniz Data Manager – A Research Data Management System","2022","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13384 LNCS","","","73","77","4","0","10.1007/978-3-031-11609-4_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135066653&doi=10.1007%2f978-3-031-11609-4_14&partnerID=40&md5=75f164c1fdd48ae1f46a7d54ec69f204","TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany; Leibniz University Hannover, Hannover, Germany","Beer A., TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany; Brunet M., TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany; Srivastava V., TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany; Vidal M.-E., TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany, Leibniz University Hannover, Hannover, Germany","FAIR principles aim to enhance machine-actionability of research data management, and enable data consumers and providers to scale up to incoming data avalanches. This demo paper describes Leibniz Data Manager (LDM), a research data management repository that resorts to Semantic Web technologies to empower FAIR principles. During the demonstration, the attendees will create various digital objects, and observe the crucial role of metadata in efficient and effective management and analysis of research data management. LDM is publicly available: https://service.tib.eu/ldmservice/. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","FAIR principles; RDF; Research data management","HTTP; Information management; Resource Description Framework (RDF); Actionability; Data management system; Digital Objects; Effective management; Efficient managements; FAIR principle; RDF; Research data managements; Scale-up; Semantic Web technology; Managers","","","","","Deutsche Forschungsgemeinschaft, DFG, (438302423)","Acknowledgements. The project is funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the LIS Funding Programme e-Research Technologies (grant no. 438302423).","Chamanara J., Kraft A., Auer S., Koepler O., Towards semantic integration of federated research data, Datenbank-Spektrum, 19, 2, pp. 87-94, (2019); Mosconi G., Et al., Three gaps in opening science, Comput. Support. Coop. Work, 28, 3-4, pp. 749-789, (2019); Wilkinson M., Et al., The fair guiding principles for scientific data management and stewardship, Sci. Data, 3, 1, pp. 1-9, (2016)","M.-E. Vidal; TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany; email: Maria.Vidal@tib.eu","Groth P.; Rula A.; Schneider J.; Tiddi I.; Simperl E.; Alexopoulos P.; Hoekstra R.; Alam M.; Dimou A.; Tamper M.; Tamper M.","Springer Science and Business Media Deutschland GmbH","","19th European Semantic Web Conference, ESWC 2022","29 May 2022 through 2 June 2022","Hersonissos","280779","03029743","978-303111608-7","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85135066653"
"Rehwald S.; Stegemann J.","Rehwald, Stephanie (57207471272); Stegemann, Jessica (57225890870)","57207471272; 57225890870","Roadmap zur Servicestelle für Forschungsdatenmanagement am Beispiel der Universitätsbibliothek Duisburg-Essen Implementation roadmap to service points for Research Data Management using the example of the University Library of Duisburg-Essen Feuille de route pour le centre de services pour la gestion des données de recherche. L'exemple de la bibliothèque universitaire de Duisburg-Essen","2021","Information-Wissenschaft und Praxis","72","4","","194","203","9","1","10.1515/iwp-2021-2161","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109587997&doi=10.1515%2fiwp-2021-2161&partnerID=40&md5=31734cf6fa18406789167d16174de80c","Universitätsbibliothek Duisburg-Essen, Research Data Services, Universitätsstraße 9-11, Essen, 45141, Germany; Universitätsbibliothek Duisburg-Essen, Research Data Services, Universitätsstraße 9-11, Essen, 45141, Germany","Rehwald S., Universitätsbibliothek Duisburg-Essen, Research Data Services, Universitätsstraße 9-11, Essen, 45141, Germany; Stegemann J., Universitätsbibliothek Duisburg-Essen, Research Data Services, Universitätsstraße 9-11, Essen, 45141, Germany","Der Aufbau einer zentrale Servicestelle für Forschungsdatenmanagement (FDM) an einer Hochschule ist ein mehrjähriger Prozess, der in die Phasen Initiation, Gründung, Aufbau und Roll-out und Verstetigung unterteilt werden kann. Die vorliegende Roadmap führt durch die einzelnen Abschnitte und beleuchtet dabei Herausforderungen und Umsetzungsschritte anhand gesammelter Erfahrungen an der Universitätsbibliothek (UB) Duisburg-Essen. Als zentrale Bausteine des Konzepts für die .,Research Data Services""werden das entwickelte Dienstleistungsportfolio vorgestellt, das die verschiedenen Handlungsfelder des FDM abdeckt, als auch die organisatorische Struktur, die alle relevanten Akteure innerhalb und außerhalb der Hochschule einschließt. Die Verschränkung von Fachwissenschaft und FDM ist wichtige Gelingensbedingung der Etablierung von Services und wird an der UDE insbesondere durch die enge Zusammenarbeit mit Forschungsverbünden erreicht.  © 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.","Aufbauorganisation; Forschungsdaten; ID-Stelle: Datendokumentation; Informationsdienst; Personal; Planung; Universität Duisburg-Essen","","","","","","","","Fournier J., Praxishandbuch Forschungsdatenmanagement, (2021); Wilkinson M.D., The FAIR Guiding Principles for Scientific Data Management and Stewardship, (2016); Leistung Aus Vielfalt; Ludwig J., Harry E., Leitfaden Zum Forschungsdaten-Management; Praxishandbuch Forschungsdatenmanagement; Management von Forschungsdaten - Eine Zentrale Strategische Herausforderung für Hochschulleitungen; Curdt C., Hess V., Lopez A., Magrean B., Rudolph D., Vompras J., Unterstützung der Hochschulen Durch Eine Einrichtungsübergreifende Kooperation in NRW, (2017); Leitlinie Zum Umgang Mit Forschungsdaten An der Universität Duisburg-Essen, (2019); Dierkes J., Curdt C., Von der Idee Zum Konzept, (2018); Brenger B., Online-Survey, (2019); Eifert T., Schilling U., Bauer H.J., Kramer F., Lopez A., Lecture Notes in Computer Science, (2017); Hausen D., Windeck J., Entwicklung Eines Blended Learning Kurses Zum Forschungsdatenmanagement An der RWTH Aachen University, (2018); Strauch A., Information - Wissenschaft & Praxis, (2020); Beisswenger M., Welche Unterstützungsangebote Benötigen Disziplinen für Die Systematische Verankerung von E-Science in der Universitären Forschung und Lehre?, (2020); Engelhardt C., Forschungsdatenmanagement in DFG-Sonderforschungsbereichen, (2013)","S. Rehwald; Universitätsbibliothek Duisburg-Essen, Research Data Services, Essen, Universitätsstraße 9-11, 45141, Germany; email: stephanie.rehwald@uni-due.de","","De Gruyter Saur","","","","","","14344653","","","","German","Inf.-Wiss. Prax.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85109587997"
"Ashiq M.; Saleem Q.U.A.; Asim M.","Ashiq, Murtaza (57221973297); Saleem, Qurat Ul Ain (57202234387); Asim, Muhammad (57209658110)","57221973297; 57202234387; 57209658110","The Perception of Library and Information Science (LIS) Professionals about Research Data Management Services in University Libraries of Pakistan","2021","Libri","71","3","","239","249","10","4","10.1515/libri-2020-0098","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105271352&doi=10.1515%2flibri-2020-0098&partnerID=40&md5=a6f785d84690cdd5d6d008c2b4cba57c","Library and Information Science, Islamabad Model College for Boys, Islamabad, H-9, Pakistan; Institute of Information Management, University of the Punjab, Lahore, Pakistan; Library, Institute for Art and Culture, Punjab, Lahore, Pakistan; Department of Libraries, Pakistan Ministry of Interior, Islamabad, Pakistan","Ashiq M., Library and Information Science, Islamabad Model College for Boys, Islamabad, H-9, Pakistan, Institute of Information Management, University of the Punjab, Lahore, Pakistan; Saleem Q.U.A., Library, Institute for Art and Culture, Punjab, Lahore, Pakistan; Asim M., Department of Libraries, Pakistan Ministry of Interior, Islamabad, Pakistan","Research data management services (RDMS) is considered as an emerging and groundbreaking area for research libraries. A large number of studies focused on researchers' perspectives of how they perform research data management practices. There are some studies that examine this important area of research from library and information science (LIS) professionals' context, especially developing countries like Pakistan. Hence, this study addresses the gap and investigate the RDMS training needs, motivational factors, possible hindrances, and key reasons to support RDMS. A survey method was used and a self-developed questionnaire was prepared using Google Docs survey. The questionnaire link was shared with LIS professionals considering purposive sampling technique. The study highlights the main RDMS supporting reasons, needed training areas, best methods to get training, the motivational factors, and possible hindrances while planning and implementing RDMS. This study fills the gap and addresses research data management literature in developing countries' context, especially Pakistan, and established that RDMS are poorly observed in developing countries and require some drastic steps to be launched and improved. Higher Education Commission/departments, university administrations, and donor agencies take such initiatives that research data should be openly available through repositories and the maximum number of training opportunities should be provided to LIS professionals.  © 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.","data librarianship; research data management; research data management challenges; research data management services; research data management skills; research data skills","","","","","","","","Allchin O., Collins A., Cox A., Lewis J., Scott C., Realising Our Role in Research Data Management, CILIP UPDATE with Gazette, 12, 3, pp. 36-38, (2013); Ameen K., Challenges of Preparing LIS Professionals for Leadership Roles in Pakistan, Journal of Education for Library & Information Science, 47, 3, pp. 200-217, (2006); Ashiq M., Usmani M.H., Naeem M., A Systematic Literature Review on Research Data Management Practices and Services, Global Knowledge, Memory and Communication, (2020); Ashiq M., Ur Rehman S., Batool S.H., Academic Library Leaders' Challenges, Difficulties and Skills: An Analysis of Common Experiences, Libri, 68, 4, pp. 301-313, (2018); Ashiq M., Ur Rehman S., Batool S.H., Academic Library Leaders' Conceptions of Library Leadership in Pakistan, Malaysian Journal of Library & Information Science, 24, 2, pp. 55-71, (2019); Ashiq M., Ur Rehman S., Mujtaba G., Future Challenges and Emerging Role of Academic Libraries in Pakistan: A Phenomenology Approach, Information Development, 37, 1, pp. 158-173, (2021); Borgman C.L., The Conundrum of Sharing Research Data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brochu L., Burns J., Librarians and Research Data Management-A Literature Review: Commentary from a Senior Professional and a New Professional Librarian, New Review of Academic Librarianship, 25, 1, pp. 49-58, (2019); Bryant R., Lavoie B., Malpas C., The Realities of Research Data Management. Part One: A Tour of the Research Data Management (RDM) Service Space, (2017); Carlson J., Stowell-Bracke M., Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, Portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Chadwell F., Sutton S.C., The Future of Open Access and Library Publishing, New Library World, 115, 5-6, pp. 225-236, (2014); Chigwada J., Chiparausha B., Kasiroori J., Research Data Management in Research Institutions in Zimbabwe, Data Science Journal, 16, 31, pp. 1-9, (2017); Chiware E., Mathe Z., Academic Libraries' Role in Research Data Management Services: A South African Perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2015); Chiware E.R.T., Becker D.A., Research Data Management Services in Southern Africa: A Readiness Survey of Academic and Research Libraries, African Journal of Library, Archives and Information Science, 28, 1, pp. 1-16, (2018); Claibourn M., Bigger on the Inside: Building Research Data Services at the University of Virginia, Insights, 28, 2, (2015); Corrall S., Designing Libraries for Research Collaboration in the Network World: An Exploratory Study, LIBER Quarterly, 24, 1, pp. 17-48, (2014); Cox A.M., Verban E., Exploring Research Data Management, (2018); Cox A.M., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Tam W.W.T., A Critical Analysis of Lifecycle Models of the Research Process and Research Data Management, Aslib Journal of Information Management, 70, 2, pp. 142-157, (2018); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing Research Data Services and the Transformation of Academic Libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Elsayed A.M., Saleh E.I., Research Data Management and Sharing among Researchers in Arab Universities: An Exploratory Study, IFLA Journal, 44, 4, pp. 281-299, (2018); Faniel I.M., Connaway L.S., Librarians' Perspectives on the Factors Influencing Research Data Management Programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Horstmann W., Witt M., Libraries Tackle the Challenge of Research Data Management, IFLA Journal, 43, 1, pp. 3-4, (2017); Hswe P., Holt A., Joining in the Enterprise of Response in the Wake of the NSF Data Management Planning Requirement, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 274, pp. 11-17, (2011); Johnson C.A., The Information Diet: A Case for Conscious Consumption, Journal of Evidence-based Complementary and Alternative Medicine, 17, 3, pp. 221-222, (2012); Kennan M.A., Corrall S., Afzal W., making Space"" in Practice and Education: Research Support Services in Academic Libraries, Library Management, 35, 8-9, pp. 666-683, (2014); Koltay T., Data Governance, Data Literacy and the Management of Data Quality, IFLA Journal, 42, 4, pp. 303-312, (2016); Koltay T., Accepted and Emerging Roles of Academic Libraries in Supporting Research 2.0, The Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Latham B., Research Data Management: Defining Roles, Prioritizing Services, and Enumerating Challenges, The Journal of Academic Librarianship, 43, 3, (2017); Mohammed M.S., Ibrahim R., Challenges and Practices of Research Data Management in Selected Iraq Universities, DESIDOC Journal of Library and Information Technology, 39, 6, (2019); Oghenekaro A.P., Academic Library Research Support Services: A Review of Redeemer's University and the Nigeria Natural Medicine Development Agency's Research Activities, Library Philosophy and Practice, (2019); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Piracha H.A., Ameen K., Research Data Management Practices of Faculty Members, Pakistan Journal of Information Management and Libraries, 20, pp. 60-75, (2018); Piracha H.A., Ameen K., Policy and Planning of Research Data Management in University Libraries of Pakistan, Collection and Curation, 38, 2, pp. 39-44, (2019); Saleem Q.U.A., Ashiq M., The Facts of Continuing Professional Development for LIS Professionals in Pakistan: A Literature Review, The Bottom Line, 33, 2, pp. 263-271, (2020); Sanjeeva M., Research Data Management: A New Role for Academic/research Librarians, (2018); Semeler A.R., Pinto A.L., Rozados H.B.F., Data Science in Data Librarianship: Core Competencies of a Data Librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2019); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future: An ACRL White Paper, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic Librarians and Research Data Services: Preparation and Attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A Brief Assessment of Researchers' Perceptions towards Research Data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Van Tuyl S., Michalek G., Assessing Research Data Management Practices of Faculty at Carnegie Mellon University, Journal of Librarianship and Scholarly Communication, 3, 3, pp. 1-30, (2015); Verbakel E., Grootveld M., Essentials 4 Data Support' Five Years' Experience with Data Management Training, IFLA Journal, 42, 4, pp. 278-283, (2016); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Wittenberg J., Elings M., Building a Research Data Management Service at the University of California, Berkeley: A Tale of Collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017)","M. Ashiq; Library and Information Science, Islamabad Model College for Boys, Islamabad, H-9, Pakistan; email: gmurtazaashiq00@gmail.com","","De Gruyter Saur","","","","","","00242667","","","","English","Libri","Article","Final","","Scopus","2-s2.0-85105271352"
"Nezhad A.S.; Droudi F.; Javaran F.J.","Nezhad, Adel Soleimani (55875565800); Droudi, Fariborz (36349658500); Javaran, Farzaneh Jahanshahi (57224409910)","55875565800; 36349658500; 57224409910","Investigating research data management methods and research data requirements in information science researchers in Iran","2021","Iranian Journal of Information Processing and Management","36","2","","329","358","29","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107496602&partnerID=40&md5=2a6c380cb0a226ce6412b87f170abfae","Department of Knowledge and Information Science, Shahid Bahonar University of Kerman, Kerman, Iran; Iranian Research Institute for Information, Science and Technology (IranDoc), Tehran, Iran; Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran","Nezhad A.S., Department of Knowledge and Information Science, Shahid Bahonar University of Kerman, Kerman, Iran; Droudi F., Iranian Research Institute for Information, Science and Technology (IranDoc), Tehran, Iran; Javaran F.J., Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran","Research data management involves all the processes and actions that ensure that research data is well organized, documented, stored, backed up, available, and reusable. The purpose of this study was to evaluate the management and identification of research data problems and needs in the research process among Iranian information and knowledge researchers from five perspectives: production and collection, recording and processing, backup and maintenance, publishing, and sharing. Data were collected through a questionnaire. The statistical population of the study consisted of 96 researchers, professors and postgraduate students in the field of information science and knowledge of Iranian universities. Excel software was used for statistical analysis of each question and the process of data aggregation and distribution were represented in frequency and percentage. The results show that in the process of producing and collecting data, the most common type of data produced is experimental data, most of the data is generated in the form of textual and processed data from software, the frequency of data generation during the research. It has been very high for researchers on a monthly basis and the volume of data generated for each respondent is on average GB level. In terms of data recording and processing, the most common way of recording data is in electronic documents. Different data processing software used are Excel and SPSS and then Word is the most used. Most respondents use personal computers to back up data. It was found that most respondents did not use any appropriate research data management software. In order to disseminate and share data, most respondents do not have sufficient knowledge of the sources, especially of their field magazines. They also have different views on journal data entry, but in fact they consider it a very good thing, while they are not well versed in the repositories and storage conditions and are most familiar with free access. In terms of the need for research data sharing and management services, they all point to problems such as lack of data sharing, data loss, storage, dissemination and data security. Libraries and research centers are also considered to be the most appropriate organization for data management and require services such as policymaking, standardization for data collection, storage, dissemination, and sharing and data security. Finally, referral and accountability services, workshops, social workshops, and seminars are found to enhance research data management and data sharing. Although the survey is conducted in the field of information science, it can be an inspiration for designing a site of library services for other disciplines, particularly in promoting, advising and managing data management, sharing research and research data storage. © 2021 Iranian Research Institute for Scientific Information and Documentation. All rights reserved.","Data Management; Data Services of Library; Data Sharing; Information Science; Research Data Management","","","","","","","","Ahlfeldt J., Johnson M., Research Libraries and Research Data Management within the Humanities and Social Sciences, (2015); Anderson N. R., lee E. S., Brockenbrough J. S., Minie M. E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in biomedical research data management and analysis: Needs and barriers, Journal of the American Medical Informatics Association, 14, 4, pp. 478-488, (2007); Briney K., Data Management for Researchers: Organize, maintain and share your data for research success, (2015); Chard K., Foster I., Tuecke S., Globus: Research data management as service and platform, Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact New York:Association for Computing Machinery, (2017); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chigwada J., Chiparausha B., Kasiroori J., Research Data Management in Research Institutions in Zimbabwe, Data Science Journal, 31, 16, pp. 1-9, (2017); Curdt C., Design and Implementation of a Research Data Management System: TheCRC/TR32 Project Database (TR32DB), (2014); Elsayed A. M., Saleh E. I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Engelhardt C., Enke H., Klar J., Ludwig, Neuroth H., Research Data Management Organiser, Proceedings of the 14th International Conference on Digital Preservation, pp. 25-29, (2017); He L., Nahar V., Reuse of scientific data in academic publications: An investigation of Dryad Digital Repository, Aslib Journal of Information Management, 68, 4, pp. 478-494, (2016); Kim Y., Nah S., Internet researchers’ data sharing behaviors: An integration of data reuse experience, attitudinal beliefs, social norms, and resource factors, Online Information Review, 42, 1, pp. 124-142, (2018); Klump J., Forschungsdaten-Management.InEvolutionderInformationsinfrastruktur. Forschung und Entwicklung als Kooperation von Bibliothek und Fachwissenschaft, pp. 257-276, (2013); Ludwig J., Enke H., Leitfaden zum Forschungsdaten-Management, Ergebnisse aus dem WissGrid-Projekt, (2013); Maron N. L., Smith K. K., Current models of digital scholarly communication: Results of an investigation conducted by Ithaka for the association of research libraries, (2008); Nind T., Galloway J., McAllister G., Scobbie D., Bonney W., Hall C., Doney A., The Research Data Management Platform (RDMP): A novel, process driven, open-source tool for the management of longitudinal cohorts of clinical data, GigaScience, 7, 7, pp. 1-12, (2018); Patel D., Research data management: a conceptual framework, Library Review, 65, (2016); Peters C., Dryden A. R., Assessing the academic library’s role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 403-387, (2011); Pinfield S, Cox AM., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, PLoS ONE, 9, 12, (2014); Pryor G., Why manage research data?, Managing Research Data, (2011); Shen Y., Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape, International Journal of Digital Curation, 10, 2, pp. 157-175, (2016); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: a tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017)","","","Iranian Research Institute for Scientific Information and Documentation","","","","","","22518223","","","","Persian","Iranian J. Info. Pro. Manag.","Article","Final","","Scopus","2-s2.0-85107496602"
"Reichmann S.; Klebel T.; Hasani-Mavriqi I.; Ross-Hellauer T.","Reichmann, Stefan (57210164706); Klebel, Thomas (57215839416); Hasani-Mavriqi, Ilire (36460924100); Ross-Hellauer, Tony (57194287597)","57210164706; 57215839416; 36460924100; 57194287597","Between administration and research: Understanding data management practices in an institutional context","2021","Journal of the Association for Information Science and Technology","72","11","","1415","1431","16","2","10.1002/asi.24492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106316153&doi=10.1002%2fasi.24492&partnerID=40&md5=112e0ba44862ec05dfac87c65c8d7acd","Open and Reproducible Research Group, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria; Know Center GmbH, Graz, 8010, Austria; RDM Team, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria","Reichmann S., Open and Reproducible Research Group, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria; Klebel T., Know Center GmbH, Graz, 8010, Austria; Hasani-Mavriqi I., RDM Team, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria; Ross-Hellauer T., Open and Reproducible Research Group, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria, Know Center GmbH, Graz, 8010, Austria","Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents results of an institutional survey (N = 258) at a medium-sized Austrian university with a STEM focus, supplemented with interviews (N = 18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach. The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda. © 2021 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.","","Binary alloys; Information management; Potassium alloys; Uranium alloys; Administrative tasks; Data-sharing practices; Institutional contexts; Management practices; Research data managements; Research institutions; Research interests; Specific problems; article; interview; major clinical study; Netherlands; Data Sharing","","","","","FAIR Data Austria; Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF","Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie, Grant/Award Number: FAIR Data Austria","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Amorim R.C., Castro J.A., da Silva J.R., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Anagnostou P., Capocasa M., Milia N., Sanna E., Battaggia C., Luzi D., Bisol G.D., When data sharing gets close to 100%: What human paleogenetics can teach the open science movement, PLoS One, 10, 3, (2015); Anderson N.R., Sally Lee E., Scott Brockenbrough J., Minie M.E., Issues in biomedical research data management and analysis: Needs and barriers, Journal of the American Medical Informatics Association, 14, 4, pp. 478-488, (2007); Andreoli-Versbach P., Mueller-Langer F., Open access to data: An ideal professed but not practised, Research Policy, 43, 9, pp. 1621-1633, (2014); Awre C., Baxter J., Clifford B., Colclough J., Research data management as a “wicked problem”, Library Review, 64, 4-5, pp. 356-371, (2015); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Banal-Estanol A., Jofre-Bonet M., Lawson C., The double-edged sword of industry collaboration: Evidence from engineering academics in the UK, Research Policy, 44, 6, pp. 1160-1175, (2015); 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Huang R., RQDA: R-based qualitative data analysis. R package version 0.2-8, (2016); Kalichman M., Sweet M., Plemmons D., Standards of scientific conduct: Disciplinary differences, Science and Engineering Ethics, 21, 5, pp. 1085-1093, (2015); Knight G., Building a research data management service for the London School of Hygiene & Tropical Medicine, Program Electronic Library & Information Systems, 49, 4, pp. 424-439, (2015); Kurata K., Matsubayashi M., Mine S., Identifying the complex position of research data and data sharing among researchers in natural science, SAGE Open, 7, 3, (2017); Leonelli S., Data-centric biology. A philosophical study, (2016); Leonelli S., Learning from data journeys, Data journeys in the sciences, pp. 1-24, (2020); Leonelli S., Spichtinger D., Prainsack B., Sticks and carrots: Encouraging open science at its source, Geo, 2, 1, pp. 12-16, (2015); Mancilla H.A., Teperek M., van Dijck J., den Heijer K., Eggermont R., Plomp E., van der Velden Y.T., Kurapati S., On a quest for cultural change – surveying research data management practices at Delft University of Technology, Liber Quarterly, 29, 1, pp. 1-27, (2019); McKiernan E.C., Bourne P.E., Titus Brown C., Buck S., How open science helps researchers succeed, eLife, 5, (2016); Myneni S., Patel V.L., Steven Bova G., Wang J., Ackerman C.F., Berlinicke C.A., Chen S.H., Lindvall M., Zack D.J., Resolving complex research data management issues in biomedical laboratories: qualitative study of an industry–academia collaboration, Computer Methods and Programs in Biomedicine, 126, pp. 160-170, (2016); Perrier L., Erik B., Patricia Ayala A., Dearborn D., Research data management in academic institutions: A scoping review, PLoS One, 12, 5, (2017); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, 3, (2007); Read K.B., Larson C., Gillespie C., So Young O., A two-tiered curriculum to improve data management practices for researchers, PLoS One, 14, 5, (2019); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the University of Lille (France), Libreas: Library Ideas, 29, pp. 98-112, (2016); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? 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Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices, Program: electronic library and information systems, 49, 4, pp. 382-407, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","S. Reichmann; Open and Reproducible Research Group, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, 8010, Austria; email: stefan.reichmann@tugraz.at","","John Wiley and Sons Inc","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Final","","Scopus","2-s2.0-85106316153"
"Jacob D.; David R.; Aubin S.; Gibon Y.","Jacob, Daniel (15757338400); David, Romain (57189222154); Aubin, Sophie (16052417600); Gibon, Yves (6603662917)","15757338400; 57189222154; 16052417600; 6603662917","Making experimental data tables in the life sciences more FAIR: A pragmatic approach","2021","GigaScience","9","12","giaa144","","","","3","10.1093/gigascience/giaa144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098476122&doi=10.1093%2fgigascience%2fgiaa144&partnerID=40&md5=c239e973088faa383f09e526a7849b49","INRAE, Université de Bordeaux, UMR BFP, 71 av E Bourlaux, Villenave d'Ornon, 33140, France; PMB-Metabolome, INRAE, 2018, Bordeaux Metabolome Facility, MetaboHUB, Villenave d'Ornon, 33140, France; INRAE, Montpellier SupAgro, Université de Montpellier, UMR MISTEA, 2 place Pierre Viala, Montpellier Cedex 2, 34060, France; European Research Infrastructure on Highly Pathogenic Agents (ERINHA-AISBL), 101 rue de Tolbiac, Paris, 75013, France; INRAE, DipSO, 42 rue Georges Morel, Beaucouzé, 49070, France","Jacob D., INRAE, Université de Bordeaux, UMR BFP, 71 av E Bourlaux, Villenave d'Ornon, 33140, France, PMB-Metabolome, INRAE, 2018, Bordeaux Metabolome Facility, MetaboHUB, Villenave d'Ornon, 33140, France; David R., INRAE, Montpellier SupAgro, Université de Montpellier, UMR MISTEA, 2 place Pierre Viala, Montpellier Cedex 2, 34060, France, European Research Infrastructure on Highly Pathogenic Agents (ERINHA-AISBL), 101 rue de Tolbiac, Paris, 75013, France; Aubin S., INRAE, DipSO, 42 rue Georges Morel, Beaucouzé, 49070, France; Gibon Y., INRAE, Université de Bordeaux, UMR BFP, 71 av E Bourlaux, Villenave d'Ornon, 33140, France, PMB-Metabolome, INRAE, 2018, Bordeaux Metabolome Facility, MetaboHUB, Villenave d'Ornon, 33140, France","Making data compliant with the FAIR Data principles (Findable, Accessible, Interoperable, Reusable) is still a challenge for many researchers, who are not sure which criteria should be met first and how. Illustrated with experimental data tables associated with a Design of Experiments, we propose an approach that can serve as a model for research data management that allows researchers to disseminate their data by satisfying the main FAIR criteria without insurmountable efforts. More importantly, this approach aims to facilitate the FAIR compliance process by providing researchers with tools to improve their data management practices.  © 2020 The Author(s).","Experimental data tables; FAIR assessment; FAIR Data principles; Research data management","Biological Science Disciplines; biomedicine; experimental design; FAIR principles; review","","","","","Horizon 2020 Framework Programme, H2020, (731013, 824087)","","Wilkinson MD, Dumontier M, Aalbersberg IJJ, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Turning FAIR into reality, Final Report and Action Plan from the European Commission Expert Group on FAIR Data, (2018); Jacobsen A, de Miranda Azevedo R, Juty N, Et al., FAIR Principles: interpretations and implementation considerations, Data Intell, 2, (2020); Benard C, Biais B, Ballias P, Et al., FRIM - Fruit Integrative Modelling, Portail Data INRAE, (2018); Sansone S, Rocca-Serra P, Field D, Et al., Toward interoperable bioscience data, Nat Genet, 44, pp. 121-126, (2012); Rocca-Serra P, Sansone SA., Experiment design driven FAIRification of omics data matrices, an exemplar, Sci Data, 6, (2019); Cost-Benefit analysis for FAIR research data - Cost of not having FAIR research data, (2018); Wolstencroft K, Owen S, Krebs O, Et al., SEEK: A systems biology data and model management platform, BMC Syst Biol, 9, (2015); David R, Mabile L, Specht A, Et al., FAIRness literacy: the Achilles' heel of applying FAIR principles, Data Science Journal, 19, 1; Leonelli S, Smirnoff N, Moor J., Making open data work for plant scientists, J Exp Bot, 64, pp. 4109-4117, (2013)","D. Jacob; INRAE, Université de Bordeaux, UMR BFP, Villenave d'Ornon, 71 av E Bourlaux, 33140, France; email: daniel.jacob@inrae.fr","","Oxford University Press","","","","","","2047217X","","","33319910","English","GigaScience","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85098476122"
"Rousi A.M.","Rousi, Antti Mikael (57148162700)","57148162700","Using current research information systems to investigate data acquisition and data sharing practices of computer scientists","2022","Journal of Librarianship and Information Science","","","","","","","0","10.1177/09610006221093049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129570258&doi=10.1177%2f09610006221093049&partnerID=40&md5=9b3dd02ab643743fc92c91f96652f104","Aalto University, Finland","Rousi A.M., Aalto University, Finland","Without sufficient information about research data practices occurring in a particular research organisation, there is a risk of mismatching research data service efforts with the needs of its researchers. This study describes how data acquiring and data sharing occurring within a particular research organisation can be investigated by using current research information system publication data. The case study organisation’s current research information system was used to identify the sample of investigated articles. A sample of 193 journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a classification of the main study types was developed to accommodate the multidisciplinary nature of the case department’s research agenda. Furthermore, a coding framework was developed to capture the key elements of data acquiring and data sharing. The articles representing life sciences and computational research relatively frequently reused open data, whereas data acquisition of experimental research, human interaction studies and human intervention studies often relied on collecting original data. Data sharing also differed between the computationally intensive study types of life sciences and computational research and the study types relying on collection of original data. Research data were not available for reuse in only a minority of life science (n = 2; 7%) and computational research (n = 15; 14%) studies. The study types of experimental research, human interaction studies and human intervention studies less frequently made their data available for reuse. The findings suggest that research organisations representing computer sciences may include different subfields that have their own cultures of data sharing. This study demonstrates that analyses of publications listed in current research information systems provide detailed descriptions how the affiliated researchers acquire and share research data. © The Author(s) 2022.","Computer science; current research information systems; data sharing; FAIR data; open science; research data management","","","","","","","","Abduldayan F.J., Abifarin F.P., Oyedum G.U., Et al., Research data management practices of chemistry researchers in federal universities of technology in Nigeria, Digital Library Perspectives, 37, 4, pp. 328-348, (2021); Athukorala K., Hoggan E., Lehtio A., Et al., Information-seeking behaviors of computer scientists: Challenges for electronic literature search tools, Proceedings of the American Society for Information Science and Technology, 50, 1, pp. 1-11, (2013); Azeroual O., Saake G., Abuosba M., Et al., Text data mining and data quality management for research information systems in the context of open data and open science, (2018); Bannier E., Barker G., Borghesani V., Et al., The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data, Human Brain Mapping, 42, 7, pp. 1945-1951, (2021); Biesenbender S., Petersohn S., Thiedig C., Using Current Research Information Systems (CRIS) to showcase national and institutional research (potential): Research information systems in the context of Open Science, Procedia Computer Science, 146, pp. 142-155, (2019); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Chawinga W.D., Zinn S., Global perspectives of research data sharing: A systematic literature review, Library & Information Science Research, 41, 2, pp. 109-122, (2019); Chawinga W.D., Zinn S., Research data management at a public university in Malawi: The role of “three hands., Library Management, 41, 6-7, pp. 467-485, (2020); Colavizza G., Hrynaszkiewicz I., Staden I., Et al., The citation advantage of linking publications to research data, PLoS One, 15, 4, (2020); Cox A.M., Kennan M.A., Lyon L., Et al., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Kennan M.A., Lyon L., Et al., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Durrant A., Markovic M., Matthews D., Et al., How might technology rise to the challenge of data sharing in agrifood?, Global Food Security, 28, (2021); Pure releases: highlights of the 5.19 release, (2020); The EU’s open science policy, (2019); Data protection under GDPR, (2020); Federer L.M., Belter C.W., Joubert D.J., Et al., Data sharing in PLOS ONE: An analysis of data availability statements, PLoS One, 13, 5, (2018); The ethical principles of research with human participants and ethical review in the human sciences in Finland, (2019); Gajbe S.B., Tiwari A., Singh R.K., Evaluation and analysis of Data Management Plan tools: A parametric approach, Information Processing & Management, 58, 3, (2021); Gutierrez R.R., Lefebvre A., Nunez-Gonzalez F., Et al., Towards adopting open and data-driven science practices in bed form dynamics research, and some steps to this end, Earth Surface Processes and Landforms, 46, 1, pp. 47-54, (2021); Huang Y., Cox A.M., Sbaffi L., Research data management policy and practice in Chinese university libraries, Journal of the Association for Information Science and Technology, 72, 4, pp. 493-506, (2021); Imker H.J., Luong H., Mischo W.H., Et al., An examination of data reuse practices within highly cited articles of faculty at a research university, The Journal of Academic Librarianship, 47, 4, (2021); Ivie P., Thain D., Reproducibility in scientific computing, ACM Computing Surveys, 51, 3, pp. 1-36, (2019); Jetten M., Simons E., Rijnders J., The role of CRIS’s in the research life cycle. 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Group privacy and big data analytics in the developing world, Group Privacy: New Challenges to Data Technologies, pp. 13-36, (2017); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: Practices and Perceptions, PLoS One, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Tenopir C., Rice N.M., Allard S., Et al., Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide, PLoS One, 15, 3, (2020); Unal Y., Chowdhury G., Kurbanoglu K., Et al., Research data management and data sharing behaviour of university researchers, Information Research, 24, 1, pp. 1-23, (2019); International compilation of human research standards, (2020); van Panhuis W.G., Paul P., Emerson C., Et al., A systematic review of barriers to data sharing in public health, BMC Public Health, 14, 1, pp. 1144-1149, (2014); Velasquez-Duran A., Ramirez Montoya M.S., Research management systems: systematic mapping of literature (2007-2017), International Journal on Advanced Science Engineering and Information Technology, 8, 1, pp. 44-55, (2018); Wallach J.D., Boyack K.W., Ioannidis J.P.A., Reproducible research practices, transparency, and open access data in the biomedical literature, PLoS Biology, 16, 11, pp. 2015-2017, (2018); Wellings S., Casselden B., An exploration into the information-seeking behaviours of engineers and scientists, Journal of Librarianship and Information Science, 51, 3, pp. 789-800, (2019); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wilms K.L., Stieglitz S., Ross B., Et al., A value-based perspective on supporting and hindering factors for research data management, International Journal of Information Management, 54, (2020)","A.M. Rousi; Aalto University, Finland; email: antti.m.rousi@aalto.fi","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Article","Article in press","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85129570258"
"Kiesler N.; Schiffner D.","Kiesler, Natalie (57205441664); Schiffner, Daniel (50861694400)","57205441664; 50861694400","On the Lack of Recognition of Software Artifacts and IT Infrastructure in Educational Technology Research","2022","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-322","","","201","206","5","1","10.18420/delfi2022-034","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138134013&doi=10.18420%2fdelfi2022-034&partnerID=40&md5=dbcba8b348b06ec2007efb539b57b4ed","DIPF, Leibniz Institute for Research and Information in Education, Information Center Education, Rostocker Straße 6, Frankfurt am Main, 60323, Germany","Kiesler N., DIPF, Leibniz Institute for Research and Information in Education, Information Center Education, Rostocker Straße 6, Frankfurt am Main, 60323, Germany; Schiffner D., DIPF, Leibniz Institute for Research and Information in Education, Information Center Education, Rostocker Straße 6, Frankfurt am Main, 60323, Germany","In the context of educational technology research, it is common practice that computer scientists and IT specialists provide support in terms ofsoftware and infrastructure for data gathering and processing, storage, analysis and many other services. Ever since Big Data, Learning Analytics and machine learning algorithms have become increasingly feasible, the implementation of programs can be considered a cornerstone of today's professional research. Contrary to this trend, software as a method for research is hardly recognized within the community, conferences and publication organs. The same applies to processed research data. Therefore, the authors question the current practices and lack of FAIRness related to the publication of software artifacts by discussing the challenges in terms of acknowledgements, review processes, reproducibility and reuse. The paper concludes with recommendations for future FAIR and Open Science practices. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.","epistemology; FAIR principles; Open Science; research data management; software; technology-based research","Computer software reusability; Data handling; Educational technology; Information management; Learning algorithms; Machine learning; Epistemology; FAIR principle; IT infrastructures; Open science; Research data managements; Software; Software artefacts; Technology research; Technology-based; Technology-based research; Digital storage","","","","","","","Beardsley M., Hernandez-Leo D., Ramirez R., Seeking reproducibility in multimodal learning experiments: Assessing an EEG study of the testing effect, Journal of Computer Assisted Learning, (2018); Center for Open Science; CHI Conference on Human Factors in Computing Systems; Consortium of European Social Science Data Archives; Deutsche Forschungsgemeinschaft; GO Fair; Jay C., Haines R., Katz D.S., Software must be recognised as an important output of scholarly research, International Journal of Digital Curation, 16, 1; Katz D. S., Gruenpeter M., Honeyman T., Taking a fresh look at FAIR for research software, Patterns, 2, 3, (2021); Kinnunen P., Meisalo V., Malmi L., Have we missed something? identifying missing types of research in computing education, Proceedings of the Sixth international workshop on Computing education research, pp. 13-22, (2010); Malmi L., Tools research-what is it?, ACM Inroads, 5, 3, pp. 34-35, (2014); Malmi L., Sheard J. S., Bednarik R., Helminen J., Korhonen A., Myller N., Sorva J., Taherkhani A., Characterizing research in computing education: a preliminary analysis of the literature, Proceedings of the Sixth international workshop on Computing education research, pp. 3-12, (2010); van der Zee T., Reich J., Open Educational Science, SocArXiv Papers; Winfield A. F., Open science-a three level approach, Science, Innovation and Society-Responsible Research and Innovation Conference, (2014)","","Henning P.A.; Hochschule Karlsruhe, Postfach 2440, Karlsruhe; Striewe M.; Univ. Duisburg-Essen, Gerlingstrasse 16, Essen; Wolfel M.; Hochschule Karlsruhe, Postfach 2440, Karlsruhe","Gesellschaft fur Informatik (GI)","","Die 20. Fachtagung Bildungstechnologien der Gesellschaft fur Informatik e.V., DELFI 2022 - 20th Conference on Educational Technologies of the German Informatics Society, DELFI 2022","12 September 2022 through 14 September 2022","Karlsruhe","182372","16175468","978-388579716-6","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85138134013"
"Thoring A.; Rudolph D.; Vogl R.","Thoring, Anne (55758236000); Rudolph, Dominik (56244944800); Vogl, Raimund (6701668973)","55758236000; 56244944800; 6701668973","David against Goliath: How a University Cloud Succeeds in the Niche of Higher Education – a User Survey.","2022","EPiC Series in Computing","86","","","34","43","9","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140433243&partnerID=40&md5=b8bcf08fed85e3bd27c2a9fc2107d5c6","University of Münster, Germany","Thoring A., University of Münster, Germany; Rudolph D., University of Münster, Germany; Vogl R., University of Münster, Germany","In the discussion about digital sovereignty, an important goal of the EU, there is always the fear that no one would stand a chance against the big giants from the U.S. which dominate the cloud market thanks to early starts, huge resources and network effects. However, the state-funded open source project “sciebo” proves the opposite, at least in the higher education sector. High data protection is a central argument for using a private cloud service at universities, but this alone does not make it competitive. University-specific functions and integrations – for example in the area of digital teaching or research data management – can be a unique selling point, but for a majority of students and employees the basic sync and share functions seem to be sufficient. So how can a university cloud service compete with commercial offers and what can make it successful in the long term? In a cloud landscape dominated by big players like Microsoft, Apple or Google, who move their established services to the cloud and encourage customers to also use their cloud storage with the arguments of a central account and seamless linkage with their other products, this question needs to be addressed to those at the front line: the users. To investigate the above question, we conducted a user survey, taking the university cloud service “sciebo” as an example, which has been in use at numerous higher education institutions in the German state of North Rhine-Westphalia since 2015. © 2022, EasyChair. All rights reserved.","","Digital storage; Distributed database systems; Human resource management; Information management; Cloud markets; Cloud services; Digital researches; Digital teachings; Education sectors; High educations; Network effects; Open source projects; Private clouds; User surveys; Surveys","","","","","","","Aharony N., An exploratory study on factors affecting the adoption of cloud computing by information professionals, The Electronic Library, 33, pp. 308-323, (2015); Ali M. B., Wood-Harper T., Mohamad M., Benefits and Challenges of Cloud Computing Adoption and Usage in Higher Education: A Systematic Literature Review, International Journal of Enterprise Information Systems (IJEIS), 14, 4, pp. 64-77, (2018); Al-Samarraie H., Saeed N., A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment, Computers & Education, 124, pp. 77-91, (2018); Arpaci I., A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education, Computers in Human Behavior, 90, pp. 181-187, (2019); Bachleda C., Ouaaziz S., Consumer Acceptance of Cloud Computing, Services Marketing Quarterly, 38, 1, pp. 31-45, (2017); Bhattacherjee A., Park S., European Journal of Information Systems, Why end-users move to the cloud: A migration-theoretic analysis, 23, 3, pp. 357-372, (2014); Burda D., Teuteberg F., Understanding Service Quality and System Quality Success Factors in Cloud Archiving From an End-User Perspective, Information Systems Management, 32, 4, pp. 266-284, (2015); Burda D., Teuteberg F., Exploring consumer preferences in cloud archiving – a student’s perspective, Behaviour & Information Technology, 35, 2, pp. 89-105, (2016); Carcary M., Doherty E., Conway G., The adoption of cloud computing by Irish SMEs-an exploratory study, Electronic Journal of Information Systems Evaluation, 17, 3, (2014); Cegielski C., Allison Jones-Farmer L., Wu Y., Hazen B., Adoption of cloud computing technologies in supply chains: An organizational information processing theory approach, The International Journal of Logistics Management, 23, 2, pp. 184-211, (2012); Changchit C., Chuchuen C., Cloud Computing: An Examination of Factors Impacting Users’ Adoption, Journal of Computer Information Systems, 58, 1, pp. 1-9, (2018); Davis F. D., A Technology Acceptance Model for Empirically Testing New, (1985); Davis F. D., Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13, 3, pp. 319-340, (1989); El Mhouti A., Erradi M., Nasseh A., Using cloud computing services in e-learning process: Benefits and challenges, Education and Information Technologies, 23, pp. 893-909, (2018); Garrison G., Rebman C., Kim S., An identification of factors motivating individuals’ use of cloud-based services, Journal of Computer Information Systems, 58, 1, pp. 19-29, (2018); Gonzalez-Martinez J., Bote-Lorenzo M., Gomez-Sanchez E., Cano-Parra R., Cloud computing and education: A state-of-the-art survey, Computers & Education, 80, pp. 132-151, (2015); Gupta P., Seetharaman A., Raj J., The usage and adoption of cloud computing by small and medium businesses, International Journal of Information Management, 33, 5, pp. 861-874, (2013); Hussein N. H., Khalid A., A survey of cloud computing security challenges and solutions, International Journal of Computer Science and Information Security, 14, 1, pp. 52-56, (2016); Jun C.-J., Lee J.-H., Jeon I.-S., Research about factor affecting the continuous use of cloud storage service: User factor, system factor, psychological switching cost factor, The Journal of Society for e-Business Studies, 19, 1, pp. 15-42, (2014); Khan K., Malluhi Q., Establishing trust in cloud computing, IT Professional, 12, 5, pp. 20-27, (2010); Lian J.-W., Yen D., Wang Y.-T., An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital, International Journal of Information Management, 34, 1, pp. 28-36, (2014); Lin A., Chen N.-C., Cloud computing as an innovation: Percepetion, attitude, and adoption, International Journal of Information Management, 32, pp. 533-540, (2012); Martin M. S., Hugues R. E., Puliatte A., The Use of Cloud-Computing to Promote Collaborative Learning in Higher Education, Preparing the Higher Education Space for Gen Z, pp. 32-45, (2019); Moryson H., Moeser G., Consumer Adoption of Cloud Computing Services in Germany: Investigation of Moderating Effects by Applying an UTAUT Model, International Journal of Marketing Studies, 8, 1, pp. 14-32, (2016); Oliveira T., Thomas M., Espadanal M., Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors, Information & Management, 51, 5, pp. 497-510, (2014); Orehovacki T., Etinger D., Babic S., Perceived security and privacy of cloud computing applications used in educational ecosystem, 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 717-722, (2017); Rogers E., Diffusion of Innovations, (1983); Stieglitz S., Wilms K., Rudolph D., Vogl R., Between Termination and Adoption-The Ex-Users Valley, ICIS 2018 Proceedings, (2018); Venkatesh V., Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model, Information Systems Research, 11, 4, pp. 342-365, (2000); Venkatesh V., Bala H., Technology Acceptance Model 3 and a Research Agenda on Interventions, Decision Sciences, 39, 2, pp. 273-315, (2008); Venkatesh V., Davis F. D., A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46, 2, pp. 186-204, (2000); Venkatesh V., Morris M. G., Davis G. B., Davis F. D., User Acceptance of Information Technology: Toward a Unified View, MIS Quarterly, 27, 3, pp. 425-478, (2003); Vogl R., Angenent H., Rudolph D., Wilmer A., Thoring A., Stieglitz S., Meske C., Predictions on Service Adoption and Utilization Meet Reality, Learning and Collaboration Technologies. LCT 2016. Lecture Notes in Computer Science, 9753, pp. 639-649, (2016); Vogl R., Rudolph D., Thoring A., Angenent H., Stieglietz S., Meske C., How to Build a Cloud Storage Service for Half a Million Users in Higher Education: Challenges Met and Solutions Found, Hawaii International Conference on System Sciences (HICSS), pp. 5328-5337, (2016); Vogl R., Thoring A., Rudolph D., Angenent H., Holters J., Blank-Burian M., The sciebo.RDS Project: Who says research data management has to be complicated?, (2019)","","Desnos J.-F.; Yahyapour R.; Vogl R.","EasyChair","","28th International Congress of European University Information Systems, EUNIS 2022","1 June 2022 through 3 June 2022","Göttingen","283929","23987340","","","","English","EPIC Sre. Comp.","Conference paper","Final","","Scopus","2-s2.0-85140433243"
"Rath M.","Rath, Mamata (56049523700)","56049523700","Intelligent Information System for Academic Institutions: Using Big Data Analytic Approach","2022","Research Anthology on Big Data Analytics, Architectures, and Applications","2","","","788","806","18","1","10.4018/978-1-6684-3662-2.ch036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130112125&doi=10.4018%2f978-1-6684-3662-2.ch036&partnerID=40&md5=315b6c02cf4e7a34e824c2327be488f9","C. V. Raman College of Engineering, India","Rath M., C. V. Raman College of Engineering, India","Research and publication is considered an authenticated certificate of innovative work done by researchers in various fields. In research, new scientific results may be assessed, corrected, and further built up by the scientific neighborhood only if they are available in published form. Guidelines on accountable research and publication are currently set to encourage and promote high ethical standards in the conduct of research and in biomedical publications. They address various aspects of the research and publishing including duties of editors and authorship determination. The chapter presents research and publication system using big data analytics and research data management techniques with a background of information systems and need of information in research data management. © 2022 by IGI Global. All rights reserved.","","","","","","","","","Abadi D., Agrawal R., Ailamaki A., The Beckman reporton database research, SIGMOD Record, 43, 3, pp. 61-70, (2014); Armour F., Introduction to Big Data, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Carlson J., Stowell-Bracke M., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality field station. portal, Libraries and the Academy, 13, 4, pp. 343-361, (2013); Charles W., Bailey J. Research Data Curation Bibliography, (2012); Codd E.F., A relational model of data for large shared data banks, Communications of the ACM, 13, 6, pp. 377-387, (1970); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J Librariansh Inf Sci, (2014); Crosas M., Cloud Dataverse: A Data Repository Platform for the Cloud; Doty J., Survey of Faculty Practices and Perspectives on Research Data Management., (2012); Duracloud Solutions; Farid M., Roatis A., Ilyas I.F., CLAMS: Bringing quality toData Lakes, Proceedings of the 2016 International Conference on Management of Data, (2016); Federer L., Research data management in the age of big data: Roles and opportunities for librarians. Information Services & Use, 36, 35–43. DOI, Doi:10.3233/Isu-160797, (2016); Godse M., Mulik S., An approach for selecting Software-as-a-Service (SaaS) product, 2013 IEEE Sixth International Conference on Cloud Computing, pp. 155-158, (2009); Gordon-Murnane L., Big Data: A Big Opportunity for Librarians, Online (Bergheim), 36, 5, (2012); Hai R., Geisler S., Quix C., Constance: An intelligent Data Lake system, Proceedings of the 2016 International Conference on Management of Data, (2016); Halevy A., Korn F., Noy N.F., Goods: Organizing Google’sdatasets, Proceedings of the 2016 International Conference on Management of Data, (2016); The Four V’s of Big Data, (2016); Laney D., 3-D Data Management: Controlling Data Volume, Velocity and Variety, (2001); Madduri R.K., Dave P., Sulakhe D., Lacinski L., Liu B., Foster I.T., Experiences in building a next-generation sequencing analysis service using Galaxy, Globus Online and Amazon Web Service, Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, pp. 1-34, (2013); Madera L.A., The next information architecture evolution: The data lake wave, Proceedings of the 8Thinternational Conference on Management of Digital Ecosystems, (2016); Parham S.W., Bodnar J., Fuchs S., Supporting tomorrow’s research: Assessing faculty data curation needs at Georgia Tech, College & Research Libraries News, 73, 1, pp. 10-13, (2012); Parker Z., Poe S., Vrbsky S.V., Comparing NoSQL MongoDB to an SQL DB, Proceedings of the 51St ACM Southeast Conference, (2013); Pinfieldsmith C., Research data management and libraries: Relationships, activities, drivers and influences, Plos One, 9, 12, (2014); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Rath, Effective Routing in Mobile Ad-hoc Networks With Power and End-to-End Delay Optimization: Well Matched With Modern Digital IoT Technology Attacks and Control in MANET, Advances in Data Communications and Networking for Digital Business Transformation, (2018); Rath, Patipattanayak, An Overview on Social Networking: Design, Issues, Emerging Trends, and Security, Social Network Analytics: Computational Research Methods and Techniques, (2018); Rath M., Resource provision and QoS support with added security for client side applications in cloud computing, International Journal of Information Technology, 9, 3, pp. 1-8, (2017); Rath M., An Exhaustive Study and Analysis of Assorted Application and Challenges in Fog Computing and Emerging Ubiquitous Computing Technology, International Journal of Applied Evolutionary Computation, 9, 2, pp. 17-32, (2018); Rath M., A Methodical Analysis of Application of Emerging Ubiquitous Computing Technology With Fog Computing and IoT in Diversified Fields and Challenges of Cloud Computing, International Journal of Information Communication Technologies and Human Development, 10, 2, (2018); Rath M., An Analytical Study of Security and Challenging Issues in Social Networking as an Emerging Connected Technology (April 20, 2018), Proceedings of 3Rd International Conference on Internet of Things and Connected Technologies (Iciotct), (2018); Rath M., Panda M.R., MAQ system development in mobile ad-hoc networks using mobile agents, IEEE 2Nd International Conference on Contemporary Computing and Informatics (IC3I), pp. 794-798, (2017); Rath M., Pati B., Load Balanced Routing Scheme for Manets with Power and Delay Optimization, (2017); Rath M., Pati B., Panigrahi C.R., Sarkar J.L., QTM: A QoS Task Monitoring System for Mobile Ad hoc Networks, Recent Findings in Intelligent Computing Techniques. Advances in Intelligent Systems and Computing, 707, (2019); Rath M., Pati B., Panigrahi C.R., Sarkar J.L., QTM: A QoS Task Monitoring System for Mobile Ad hoc Networks, Recent Findings in Intelligent Computing Techniques. Advances in Intelligent Systems and Computing, 707, (2019); Rath M., Pati B., Pattanayak B.K., Inter-Layer Communication Based Qos Platform for Real Time Multimedia Applications in MANET, pp. 613-617, (2016); Rath M., Pati B., Pattanayak B.K., Cross layer based QoS platform for multimedia transmission in MANET, 11Th International Conference on Intelligent Systems and Control (ISCO), pp. 402-407, (2017); Rath M., Pati B., Pattanayak B.K., Relevance of Soft Computing Techniques in the Significant Management of Wireless Sensor Networks, Soft Computing in Wireless Sensor Networks, pp. 86-106, (2019); Rath M., Pattanayak B., MAQ: A Mobile Agent Based QoS Platform for MANETs, International Journal of Business Data Communications and Networking, IGI Global, 13, 1, pp. 1-8, (2017); Rath M., Pattanayak B., Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach, International Journal of Human Rights in Healthcare, (2018); Rath M., Pattanayak B., Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach, International Journal of Human Rights in Healthcare, (2018); Rath M., Pattanayak B.K., A methodical survey on real time applications in MANETS: Focussing On Key Issues, International Conference On, High Performance Computing and Applications (IEEE ICHPCA), pp. 22-24, (2014); Rath M., Pattanayak B.K., Monitoring of QoS in MANET Based Real Time Applications, Smart Innovation, Systems and Technologies, 84, pp. 579-586, (2018); Rath M., Pattanayak B.K., SCICS: A Soft Computing Based Intelligent Communication System in VANET. Smart Secure Systems – IoT and Analytics Perspective, Communications in Computer and Information Science, 808, pp. 255-261, (2018); Rath M., Pattanayak B.K., Security Protocol with IDS Framework Using Mobile Agent in Robotic MANET, International Journal of Information Security and Privacy, 13, 1, pp. 46-58, (2019); Rath M., Pattanayak B.K., Pati B., Energetic Routing Protocol Design for Real-time Transmission in Mobile Ad hoc Network, In Computing and Network Sustainability, Lecture Notes in Networks and Systems, 12, (2017); Riungu L.M., Taipale O., Smolander K., Research issues for software testing in the cloud, 2010 IEEE Second International Conference on Cloud Computing Technology and Science (Cloudcom), pp. 557-564, (2010); Rtah M., Big Data and IoT-Allied Challenges Associated With Healthcare Applications in Smart and Automated Systems, International Journal of Strategic Information Technology and Applications, 9, 2, (2018); Rtah M., Big Data and IoT-Allied Challenges Associated With Healthcare Applications in Smart and Automated Systems, International Journal of Strategic Information Technology and Applications, 9, 2, (2018); Sallans A., Donnelly M., DMP Online and DMPTool: Different Strategies Towards a Shared Goal, International Journal of Digital Curation, 7, 2, pp. 123-129, (2012); Sheng J., Amankwah-Amoah J., Wang X., A multidisciplinary perspective of big data in management research, International Journal of Production Economics, 191, pp. 97-112, (2017); Son N.H., Module on Data Preprocessing Techniques for Data Mining on Data Cleaning and Data Preprocessing, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Terrizzano I., Schwarz P., Roth M., Data wrangling: The challenging journey from the wild to the lake, Proceedings of the 7Th Biennial Conference on Innovative Data Systemsresearch (CIDR’15), pp. 4-7, (2015); Research Data Lifecycle, (2016); Dmptool, (2016); Steps in the Data Life Cycle, (2016); Vaidya M., Handling Critical Issues of Big Data on Cloud, (2016); Vassiliadisskiadopoulos S., Conceptual modeling for ETL processes, Proceedings of the 5Th ACM International Workshop on Data Warehousing and OLAP, (2002); Waddington S., Kindura: Repository services for researchers based on hybrid clouds, Journal of Digital Information, 13, 1, (2012); Wang C., Exposing library data with big data technology: A review, Computer and Information Science (ICIS), 2016 IEEE/ACIS 15Th International Conference On, (2016); Westra R., Selected Internet Resources on Digital Research Data Curation, Issues in Science and Technology Librarianship, 63, (2010); Whyte A., Tedds J., Making the Case for Research Data Management, (2011)","","","IGI Global","","","","","","","978-166843663-9; 978-166843662-2","","","English","Research Anthology on Big Data Analytics, Architectures, and Applications","Book chapter","Final","","Scopus","2-s2.0-85130112125"
"Neumann J.","Neumann, Janna (56054838400)","56054838400","FAIR Data Infrastructure","2022","Advances in Biochemical Engineering/Biotechnology","182","","","195","207","12","1","10.1007/10_2021_193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137745891&doi=10.1007%2f10_2021_193&partnerID=40&md5=9124b2487b5be691f129f90b2f6d0fee","Technische Informationsbibliothek, Hannover, Germany","Neumann J., Technische Informationsbibliothek, Hannover, Germany","In this chapter the concept of research data management is highlighted in the context of the data publication and data infrastructures. One focus of this contribution lies on the topics of metadata and the FAIR data principles associated with data sharing and data infrastructures such as data repositories. The challenges for researchers and research communities towards open science are discussed and the first steps towards FAIR data infrastructures are illustrated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Data infrastructure; Data management; FAIR data; Metadata; Research data","Information Dissemination; Metadata; information dissemination; metadata","","","","","","","Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Reference model for an open archival information system (OAIS). Recommendation for space data system practices. Washington DC (recommended practice, 650.0-M-2), Available Online From, (2012); Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Atici L., Whitcher K.S., Justin L.-T., Kansa Eric C., Other People’s data. A demonstration of the imperative of publishing primary data, J Archaeol Method Theory, 20, 4, pp. 663-681, (2013); Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Piwowar H.A., Carlson J.D., Vision T.J., Beginning to track 1000 datasets from public repositories into the published literature, Proc am Soc Info Sci Tech, 48, 1, pp. 1-4, (2011); Campbell P., Data’s shameful neglect, Nature, 461, 7261, (2009); Ridsdale C., Rothwell J., Smit M., Bliemel M., Irvine D., Kelley D., Et al., Strategies and best practices for data literacy education, Knowledge Synthesis Report, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Gabrielle A., Myles A., Arie B., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci Data, 3, (2016); Haynes D., Metadata for Information Management and Retrieval. Understanding Metadata and Its Use, (2018); Greenberg J., Metadata and the World Wide Web, Encyclopedia of Library and Information Science, pp. 244-261, (2002); Smiraglia R., Introducing metadata, Metadata. a cataloger’s Primer, (2005); Corrado E.M., Moulaison H.L., Digital Preservation for Libraries, Archives, and Museums, (2017); Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Berman F., Wilkinson R., Wood J., Building global infrastructure for data sharing and exchange through the research data alliance, D-Lib Mag, 20, 1-2, (2014); Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Kraft A., The FAIR data principles for research data. Hg. v. TIB Blog. Technische Informationsbibliothek, Available Online From, (2017); GO FAIR. GO FAIR Initiative. Available Online from Https://Www.Go-Fair.Org/Go-Fair-Initiative/. Accessed, (2021); Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Buitrago E., Schneider N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids, PCIM Europe Digital Days 2021; Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Mons B., Cameron N., Jan V., Michel D., Santos D S., Bonino L.O., Wilkinson Mark D., Cloudy, increasingly FAIR; revisiting the FAIR data guiding principles for the European Open Science Cloud, ISU, 37, 1, pp. 49-56, (2017); Thompson M., Burger K., Kaliyaperumal R., Roos M., Santos D S., Bonino L.O., Making FAIR easy with FAIR tools. From creolization to convergence, Data Intell, 2, 1-2, pp. 87-95, (2020); Wilkinson M.D., Verborgh R., da Silva Santos B., Olavo L., Tim C., Swertz Morris A., Kelpin Fleur D.L., Et al., Interoperability and FAIRness through a novel combination of Web technologies, Peerj Comput Sci, 3, (2017); Kitchin R., The Data Revolution. Big Data, Open Data, Data Infrastructures & Their Consequences, (2014); Ribes D., Jackson S.J., Data bite man: The work of sustaining long-term study, Raw Data is an Oxymoron. the MIT Press, pp. 147-166, (2013); Goldstein S., The evolving landscape of federated research data infrastructures. Knowledge Exchange. Zenodo, Available Online From, (2017); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Pampel H., Paul V., Frank S., Roland B., Maxi K., Jens K., Et al., Making research data repositories visible: The re3data.org registry, Plos One, 8, 11, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Nationale Forschungsdateninfrastruktur (NFDI), (2000); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Steinbeck C., Oliver K., Felix B., Sonja H.-P., Nicole J., Johannes L., Et al., NFDI4Chem – towards a national research data infrastructure for chemistry in Germany, RIO, 6, (2020); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Turning FAIR into reality. Final Report and Action Plan on FAIR Data. Hg. v. Directorate-General for Research and Innovation (European Commission), Available Online From, (2018); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013); Schafer B.A., Poetz D., Kramer G.W., Documenting laboratory workflows using the analytical information markup language, J Assoc Lab Autom, 9, 6, pp. 375-381, (2004); Schweizer M., Kolar J.W., Design and implementation of a highly efficient three-level t-type converter for low-voltage applications, IEEE Transactions on Power Electronics, 28, 2, (2013)","J. Neumann; Technische Informationsbibliothek, Hannover, Germany; email: janna.neumann@tib.eu","","Springer Science and Business Media Deutschland GmbH","","","","","","07246145","","","35091812","English","Adv. Biochem. Eng. Biotechnol.","Book chapter","Final","","Scopus","2-s2.0-85137745891"
"Andrikopoulou A.; Rowley J.; Walton G.","Andrikopoulou, Angeliki (58080906700); Rowley, Jennifer (7201756587); Walton, Geoff (37116110400)","58080906700; 7201756587; 37116110400","Research Data Management (RDM) and the Evolving Identity of Academic Libraries and Librarians: A Literature Review","2022","New Review of Academic Librarianship","28","4","","349","365","16","8","10.1080/13614533.2021.1964549","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137642695&doi=10.1080%2f13614533.2021.1964549&partnerID=40&md5=3c8659ad7bb61b4b394ba0b74670688f","Languages, Information and Communications, Manchester Metropolitan University, Manchester, United Kingdom","Andrikopoulou A., Languages, Information and Communications, Manchester Metropolitan University, Manchester, United Kingdom; Rowley J., Languages, Information and Communications, Manchester Metropolitan University, Manchester, United Kingdom; Walton G., Languages, Information and Communications, Manchester Metropolitan University, Manchester, United Kingdom","Academic libraries and their staff are increasingly involved in the Research Data Management (RDM) practices and processes in their universities. This article explores the impact that such initiatives have on the image and identity of academic libraries. This paper proposes that involvement in and leadership of RDM university practices has the potential to re-shape the library’s role, image, and identity within the university, and going forward, to contribute to the library’s continuing relevance to research communities. It also points to the need to develop librarians’ skills and competencies in RDM, and reflects on the dynamics associated with collaboration and competition in RDM. The article concludes with an agenda for future research. © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.","academic librarians; academic libraries; identity; image; Research Data Management","","","","","","National Science Foundation, NSF; National Institutes of Health, NIH; National Endowment for the Humanities, NEH; Canadian Institutes of Health Research, IRSC; Social Sciences and Humanities Research Council of Canada, SSHRC; Engineering and Physical Sciences Research Council, EPSRC","Tenopir, Sandusky, Allard, and Birch () suggest that academic libraries have been driven to develop RDM services as a result of the establishment of the data management and data sharing requirements of research funding organisations. Such organisations include, for the US, the National Science Foundation, the National Endowment for the Humanities, and the National Institutes of Health, and, for Canada the Canadian Institutes of Health Research, the National Sciences and Engineering Research Council, and the Social Sciences and Humanities Research Council. In turn, involvement in RDM has significant consequences for the way that libraries operate within and beyond the university in respect of strategy, space, structures, partnerships and identity (Cox et al., ). Indeed, Verbaan and Cox () demonstrate that university libraries are more important than IT services or Research support offices in the implementation of RDM policy and strategy. Academic libraries’ identity has traditionally also been associated with space and place. Libraries have seen the freeing up of space as a result of the decrease of printed material (Saunders, ). In addition, academic libraries’ increased focus on learning and teaching has led to re-arrangement of physical spaces and the re-purposing of library buildings (Pinfield et al., ). Additionally, the structure and the culture of the library as an organisation has been affected not only by partnerships with other university services and departments but by the employment of other professional groups (Pinfield et al., ). ","Beagrie N., Pink C., Benefits from research data management in universities for industry and not-for-profit research partners, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Briney K., Data management for researchers: Organise, maintain and share your data for research success, (2015); Brochu L., Burns J., A Literature review: Commentary from a senior professional and a new professional librarian, New Review of Academic Librarianship, 25, 1, pp. 49-58, (2019); Brown M.L., White W., Case study 2: University of Southampton–a partnership approach to research data management, Delivering research data management services, pp. 135-162, (2014); Bryant R., Clements A., de Castro P., Cantrell J., Dortmund A., Fransen J., Ennielli M., Practices and patterns in research information management: Findings from a global survey, (2018); Cannon P., A review of professionalism within LIS, Library Management, 38, 2-3, pp. 142-152, (2017); Carter D., Creativity in action–the information professional is poised to exploit the fourth industrial revolution: The business information survey 2017, Business Information Review, 34, 3, pp. 122-137, (2017); Chiware E., Mathe Z., Academic libraries’ role in Research Data Management Services: A South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2016); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing research data, pp. 105-133, (2012); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2019); Cox A., Verbaan E., Exploring research data management, (2018); Cox A., Verbaan E., Sen B., (2012); Cox A., Verbaan E., Sen B., 60, pp. 42-44, (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Pinfield S., Rutter S., The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries, Library Hi Tech, 37, (2018); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Cox J., Positioning the academic library within the institution: A literature review, New Review of Academic Librarianship, 24, 3-4, pp. 217-241, (2018); Davis C., Librarianship in the 21st century–crisis or transformation?, Public Library Quarterly, 27, 1, pp. 57-82, (2008); Delaney G., Bates J., Envisioning the academic library: A reflection on roles, relevancy and relationships, New Review of Academic Librarianship, 21, 1, pp. 30-51, (2015); Dempsey L., Malpas C., Lavoie B., Collection directions: The evolution of library collections and collecting, Portal: Libraries and the Academy, 14, 3, pp. 393-423, (2014); How-to guides & checklists, (2017); Fagan J.C., Ostermiller H., Price E., Sapp L., Librarian, faculty, and student perceptions of academic librarians: Study introduction and literature review, New Review of Academic Librarianship, 27, 1, pp. 38-75, (2021); Gibbons S., Growing competition for libraries, Library Hi Tech, 19, 4, pp. 363-367, (2001); Gioia D.A., Schultz M., Corley K.G., Organisational identity, image, and adaptive instability, The Academy of Management Review, 25, 1, pp. 63-81, (2000); Glusker A., Exner N., Responding to change: Reinventing librarian identities in the age of research mandates, Challenging the “Jacks of all trades but masters of none” librarian syndrome, pp. 91-115, (2018); Gwyer R., Identifying and exploring future trends impacting on academic libraries: A mixed methodology using journal content analysis, focus groups, and trend reports, New Review of Academic Librarianship, 21, 3, pp. 269-285, (2015); Gwyer R., This is an opportunity for librarians to reinvent themselves, but it is about moving out of their areas: New roles for library leaders?, New Review of Academic Librarianship, 24, 3-4, pp. 428-441, (2018); Hariff S., Rowley J., Branding of UK public libraries, Library Management, 32, 4-5, pp. 346-360, (2011); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program: Electronic Library and Information Systems, 49, 4, pp. 364-381, (2015); Hughes C.A., Information services for higher education: A new competitive space, D-Lib Magazine, 12, (2000); Kazi N., (2012); Keller A., Research support in Australian university libraries: An outsider view, Australian Academic & Research Libraries, 46, 2, pp. 73-85, (2015); Koltay T., Facing the challenge of data-intensive research: Research data services and data literacy in academic libraries, Innovation in libraries and information services, pp. 45-61, (2016); Lewis M., Libraries and the management of research data, Envisioning future academic library services: Initiatives, ideas and challenges, pp. 145-168, (2010); Matteson M.L., Anderson L., Boyden C., “Soft skills”: A phrase in search of meaning, Portal: Libraries and the Academy, 16, 1, pp. 71-88, (2016); McGaghie W.C., Varieties of integrative scholarship: Why rules of evidence, criteria, and standards matter, Academic Medicine, 90, 3, pp. 294-302, (2015); McKnight S., Envisioning future academic library services: Initiatives, ideas and challenges, (2010); Principles and Guidelines for Access to Research Data from Public Funding, (2007); Pan J., Hovde K., Professional development for academic librarians: Needs, resources, and administrative support, Chinese Librarianship: An International Electronic Journal, 29, pp. 1-9, (2010); Pinfield S., Cox A.M., Rutter S., (2017); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Pryor G., Jones S., White A., Delivering research data management services: Fundamentals of good practice, (2014); Read A., Cox A., Underrated or overstated? The need for technological competencies in scholarly communication librarianship, The Journal of Academic Librarianship, 46, 4, (2020); Rowley J., Information marketing, (2006); Rowley J., Slack F., Conducting a literature review, Management Research News, 27, 6, pp. 31-39, (2004); Saunders L., Academic libraries’ strategic plans: Top trends and under-recognised areas, The Journal of Academic Librarianship, 41, 3, pp. 285-291, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to Research Data Management, The Journal of Academic Librarianship, 40, 3-4, pp. 211-219, (2014); Whitley E.A., Gal U., Kjaergaard A., Who do you think you are? A review of the complex interplay between information systems, identification and identity, European Journal of Information Systems, 23, 1, pp. 17-35, (2014); Whyte A., Tedds J., (2011); Yu H., The role of academic libraries in research data service (RDS) provision opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017)","J. Rowley; Languages, Information and Communications, Manchester Metropolitan University, Manchester, All Saints Campus, M15 6BH, United Kingdom; email: j.rowley@mmu.ac.uk","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85137642695"
"Maier R.; von der Linden C.","Maier, von Ronald (57384304100); von der Linden, Claudia (57384304200)","57384304100; 57384304200","Digital transformation and innovation for universities – contribution of the representatives of the forum digitalisation of universities Austria; [Digitale transformation und innovation für universitäten – beitrag der vertreter*innen des forums digitalisierung der österreichischen universitätenkonferenz (UNIKO)]","2021","VOEB-Mitteilungen","74","2","","","","","1","10.31263/voebm.v74i2.6380","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121688344&doi=10.31263%2fvoebm.v74i2.6380&partnerID=40&md5=3cbce3bac9a1728de095df195b4eba54","Universität Wien, Vizerektor für Digitalisierung und Wissenstransfer, Austria; TU Graz, Vizerektorin für Digitalisierung und Change Management, Austria","Maier R., Universität Wien, Vizerektor für Digitalisierung und Wissenstransfer, Austria; von der Linden C., TU Graz, Vizerektorin für Digitalisierung und Change Management, Austria","Universities have pursued digital transformations proactively for decades, for example in the early initiatives of university libraries to digitise their collections. Such transformations have been greatly accelerated by COVID-19 and the digitalisation offensive of the Federal Ministry of Education, Science and Research (BMBWF). The latter supports cross-university networking and partnership-based implementation of 34 projects on digital and social transformation in research, teaching, transfer and administration. At the level of university management, the Forum Digitalisation strives for coherent action at universities with ambition and care. University libraries play a key role in bundling centralised and decentralised services, such as fostering open science and research data management. © 2021, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Digital innovation; Digital transformation; Digitalisation; Open science","","","","","","","","Digitale und soziale Transformation – Ausgewählte Digitalisierungsvorhaben an öffentlichen Universitäten 2020 bis 2024, (2020); Cluster Forschungsdaten; Cluster e-Administration; Cluster Informatik und digitale Kompetenzen; Learning Analytics Cluster; Cluster Bilddaten; Forum Digitalisierung; Bauer Bruno, Präsentation der European Open Science Cloud an der Universität Wien (Wien, 23. November 2018), Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare, 71, 3-4, pp. 524-529, (2018); Wien TU, Austrian EOSC Mandated Organisation offiziell gegründet – Ein Meilenstein für Open Science","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85121688344"
"Rodrigues J.; Teixeira Lopes C.","Rodrigues, Joana (57203242279); Teixeira Lopes, Carla (57895077100)","57203242279; 57895077100","Describing Data in Image Format: Proposal of a Metadata Model and Controlled Vocabularies","2022","Journal of Library Metadata","22","3-4","","213","234","21","0","10.1080/19386389.2022.2117511","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138308050&doi=10.1080%2f19386389.2022.2117511&partnerID=40&md5=a8e6fc99a84cc58957a5e51a54dff3a6","Faculty of Engineering, University of Porto and INESC TEC, Porto, Portugal","Rodrigues J., Faculty of Engineering, University of Porto and INESC TEC, Porto, Portugal; Teixeira Lopes C., Faculty of Engineering, University of Porto and INESC TEC, Porto, Portugal","Research data management (RDM) includes people with different needs, specific scientific contexts, and diverse requirements. The description is a big challenge in the domain of RDM. Metadata plays an essential role, allowing the inclusion of essential information for the interpretation of data, enhances the reuse of data and its preservation. The establishment of metadata models can facilitate the process of description and contribute to an improvement in the quality of metadata. When we talk about image data, the task is even more difficult, as there are no explicit recommendations to guide image management. In this work, we present a proposal for a metadata model for image description. To validate the model, we followed an experiment of data description, where eleven participants described images from their research projects, using a metadata model proposed. The experiment shows that participants do not have formal practices for describing their imagery data. Yet, they provided valuable contributions and recommendations to the final definition of a metadata model for image description, to date nonexistent. We also developed controlled vocabularies for some descriptors. These vocabularies aim to improve the image description process, facilitate metadata model interpretation, and reduce the time and effort devoted to data description. © 2022 Joana Rodrigues and Carla Teixeira Lopes Published with license by Taylor & Francis Group, LLC.","controlled vocabulary; image management; image-as-research data; metadata model; research data management","","","","","","Ministry of Education and Science of Portugal; Fundação para a Ciência e a Tecnologia, FCT, (PD/BD/150288/2019)","Funding text 1: For the Research Domain, we followed the guidelines of the Foundation for Science and Technology (FCT), which is an agency of the Ministry of Education and Science of Portugal that evaluates and finances scientific research activities in the country in all scientific areas. This vocabulary contains seven terms. ; Funding text 2: To validate this metadata model, an experiment with researchers from different research domains - Life and Health Sciences (LHS), Exact Sciences and Engineering (ESE), Natural and Environmental Sciences (NES), and Social Sciences and Humanities (SSH) - was designed. Metadata model descriptors were asked to describe two images from two of their research projects. This task was supported by a guiding document. From now on, referred to as description sessions. ","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Borgman C., Advances in Information Science: The Conundrum of Sharing Research Data, Journal of the American Society for Information Science and Technology, 6, 63, pp. 1059-1078, (2012); Castro J.C., Perrotta D., Amorim R., da Silva J.R., Ribeiro C., Ontologies for research data description: A design process applied to vehicle simulation, Metadata and Semantics Research Conference, (2015); Castro J.A., Amorim R.C.A., Gattelli R., Karimova Y., da Silva J.R., Ribeiro C., Involving data creators in an ontology-based design process for metadata models, Developing, pp. 181-214, (2017); Castro J.A., Landeira C., Joao Rocha da S., Ribeiro C., Role of content analysis in improving the curation of experimental data, International Journal of Data Curation, 15, 1, pp. 1-14, (2017); Guidelines on fair data management in horizon 2020, (2016); Fernandes M., Rodrigues J., Lopes C.T., Management of research data in image format: An exploratory study on current practices, pp. 212-226, (2020); Harping P., Introduction to controlled vocabularies: terminology for art, architecture, and other cultural works, (2010); Hedden H., Taxonomies and controlled vocabularies best practices for metadata, Journal of Digital Asset Management, 6, 5, pp. 279-284, (2010); Riley J., Understanding Metadata: What Is Metadata, and What is it for? NISO Primer, (2017); Smith K.P., Seligman L.J., Swarup V., Everybody share: The challenge of data-sharing systems, Computer Magazine, 41, 9, pp. 54-61, (2008); Tenopir C., Allard S., Douglass K., Umur Aydinoglu A., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, pp. e21101-e21121, (2011); Van den Eynden V., Corti L., Woollard M., Bishop L., Laurence H., Managing and sharing data: A guide to good practice, (2011); Willis C., Greenberg J., White H.C., Analysis and synthesis of metadata goals for scientific data, Journal of the American Society for Information Science and Technology, 63, 8, pp. 1505-1520, (2012)","J. Rodrigues; Faculty of Engineering, University of Porto and INESC TEC, Porto, Portugal; email: joanasousarodrigues.14@gmail.com","","Taylor and Francis Ltd.","","","","","","19386389","","","","English","J. Libr. Metadata","Article","Final","","Scopus","2-s2.0-85138308050"
"van Gend T.; Zuiderwijk A.","van Gend, Thijmen (57755779800); Zuiderwijk, Anneke (57188975805)","57755779800; 57188975805","Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse","2022","Journal of Librarianship and Information Science","","","","","","","1","10.1177/09610006221101200","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132362824&doi=10.1177%2f09610006221101200&partnerID=40&md5=5acf2c49fe762ba3a7b033381af7fb8c","Delft University of Technology, Faculty of Technology, Policy and Management, Netherlands","van Gend T., Delft University of Technology, Faculty of Technology, Policy and Management, Netherlands; Zuiderwijk A., Delft University of Technology, Faculty of Technology, Policy and Management, Netherlands","This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands. In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a “carrot” rather than as a “stick.” Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature. Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts. Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university. We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers. © The Author(s) 2022.","Data reuse; data sharing; infrastructural arrangements; institutional arrangements; open data; open science; research data management","","","","","","","","Stats, (2022); 4TU.ResearchData; Data funds; Organisation; Search; Abba S., Birello G., Vallino M., Et al., Shall we share? A repository for open research data in agriculture and environmental sciences, EPPO Bulletin, 45, 2, pp. 311-316, (2015); Ahlers I., Andrews H., Cruz M., Et al., TU Delft research data framework policy (version 2), Zenodo, (2020); Akhmerov A., Bazuine M., Beardsell J., Et al., TU Delft research software policy, Zenodo, (2021); Alasseri R., Rao T.J., Sreekanth K.J., Conceptual framework for introducing incentive-based demand response programs for retail electricity markets, Energy Strategy Reviews, 19, pp. 44-62, (2018); Altayar M.S., Motivations for open data adoption: An institutional theory perspective, Government Information Quarterly, 35, 4, pp. 633-643, (2018); Arlitsch K., Wheeler J., Pham M.T.N., Et al., An analysis of use and performance data aggregated from 35 institutional repositories, Online Information Review, 45, 2, (2020); Austin C.C., Bloom T., Dallmeier-Tiessen S., Et al., Key components of data publishing: Using current best practices to develop a reference model for data publishing, International Journal on Digital Libraries, 18, 2, pp. 77-92, (2017); Open Data; Benbasat I., Goldstein D.K., Mead M., The case research strategy in studies of information systems, MIS Quarterly, 11, 3, pp. 369-386, (1987); Bishop B.W., Orehek A.M., Collier H.R., Job analyses of earth science data librarians and data managers, Bulletin of the American Meteorological Society, 102, 7, pp. E1384-E1393, (2021); Bonoma T.V., Wong K.B., A Case Study in Case Research: Marketing Implementation, (1985); Borgerud C., Borglund E., Open research data, an archival challenge?, Archival Science, 20, pp. 279-302, (2020); Bullini Orlandi L., Ricciardi F., Rossignoli C., Et al., Scholarly work in the Internet age: Co-evolving technologies, institutions and workflows, Journal of Innovation & Knowledge, 4, 1, pp. 55-61, (2019); Campbell E.G., Bendavid E., Data-sharing and data-withholding in genetics and the Life Sciences: Results of a National Survey of technology transfer officers, Journal of Health Care Law & Policy, 6, 2, pp. 241-255, (2003); Charalabidis Y., Loukis E., Alexopoulos C., Evaluating second generation open government data infrastructures using value models, pp. 2114-2126, (2014); Charmaz K., Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis, (2006); Clare C., How to build a community of data champions: Six steps to success, Zenodo, (2019); Clare C., FAIR data fund 2021: Call for applications, (2021); Clare C., Cryan A., Gramsbergen E., Et al., What a year! 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Librariansh. Inf. Sci.","Article","Article in press","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85132362824"
"Mavodza J.","Mavodza, Judith (53264434400)","53264434400","Research Data Management: A review of UAE academic library experience","2022","Open Information Science","6","1","","16","27","11","0","10.1515/opis-2022-0128","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129099849&doi=10.1515%2fopis-2022-0128&partnerID=40&md5=27b89d65bee8eb26b7dfd8b5957b3bc4","Zayed University, United Arab Emirates","Mavodza J., Zayed University, United Arab Emirates","Purpose: This paper is a review of the status of Research Data Management (RDM) efforts in UAE public university libraries. Approach: The investigation is through examining available literature about the topic using region-specific articles when available, librarian comments, and the information provided by UAE university library websites. Existing lessons and policy documents are sought, and plans suggested for local solutions, suggesting avenues for progress. Findings: Though not a new concept, findings indicate that local RDM activities are emerging, but knowledge of their importance of exists. Research limitations: This review is limited to public university libraries though the results and experiences could be generally relevant to more research establishments. Practical implications: Taking advantage of the existing awareness to organize tangible RDM efforts can facilitate retrieval and availability of data relevant to the region. Value: An intricate range of activities involved in the organization of RDM services is revealed.  © 2022 Judith Mavodza, published by De Gruyter.","Big Data; Data Literacy; eFada; Open Access; Research Data Management (RDM)","","","","","","","","ACRL, Keeping Up with ?Research Data Management. Retrieved from, (2019); ACRL, Scholarship As Conversation. Retrieved from, (2015); Association of European Research Libraries, Data Librarian, Expert on Research Data Management, Description, Archiving and Dissemination. Retrieved from, (2018); Becher T., Trowler P.R., Academic Tribes and Territories, (2001); Bezuidenhout L., Chakauya E., Hidden concerns of sharing research data by low/middle-income country scientists, Global Bioethics, 29, 1, pp. 39-54, (2018); Boufarss M., Harviainen J.T., Librarians as gate-openers in open access publishing: A case study in the United Arab Emirates, The Journal of Academic Librarianship, 47, 5, (2021); Boufarss M., Laakso M., Open Sesame? Open access priorities, incentives, and policies among higher education institutions in the United Arab Emirates, Scientometrics, 124, pp. 1553-1577, (2020); Bryant R., Lavoie B., Malpas C., Incentives for Building University RDM Services the Realities of Research Data Management, Part Three, (2018); Chigwada J., Chiparausha B., Kasiroori J., Research Data Management in Research Institutions in Zimbabwe, Data Science Journal, 16, (2017); Chiware E.R.T., Mathe Z., Academic libraries? role in research data management services: A South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2015); Chiware E.R.T., Becker D.A., Research data management services in Southern Africa: A readiness survey of academic and research libraries, African Journal of Library, Archives & Information Science, 28, 1, pp. 1-16, (2018); Cox A.M., Verbaan E., Exploring Research Data Management. London: Facet Publishing, (2018); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Dataverse Project, About the Project. Retrieved from, (2019); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Hammoud J., Vatian A., Dobrenko N., Vedernikov N., Shalyto A., Gusarova N., November New Arabic Medical Dataset for Diseases Classification. in, International Conference on Intelligent Data Engineering and Automated Learning, pp. 196-203, (2021); Hathi Trust, Welcome to HathiTrust. 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Dubai: MBRSG Policy Council Series, Session, 3, (2015); Padilla T., Collections as data: Implications for enclosure, College & Research Libraries News, 79, 6, (2018); Peters J., Shrinking horizons-or pushing boundaries?, Insights the UKSG Journal, 31, 1, pp. 1C-9C, (2018); Petersen M., Pramann B., Toepfer R., Neumann J., Enke H., Hoffmann J., Mauer R., Research Data Management-Current status and future challenges for German non-university research institutions, Research Ideas and Outcomes, 6, (2020); Potterbusch M., What is Open Science, and how can radical collaboration facilitate it?, Research Library Issues, 296, pp. 44-48, (2018); Puri-Mirza A., Number of Higher Education Institutes in the United Arab Emirates (UAE) in the Academic Year 2013-2014 and 2016-2017. Statista, (2020); Roarmap, Data Visualisations. Retrieved from, (2020); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Talib M.A., Abuodeh M., Almansoori A., Alnauimi A., Enhancing social science research in the UAE: An open source software solution. University of Sharjah (UOS) Case Study, JCS, 11, 1, pp. 98-108, (2015); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services. Current Practices and Plans for the Future. 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Retrieved 18 March 2022 from, (2018); Government for the Future We Want: United Nations E-Government Survey 2014, (2014); Yoon A., Schultz T., Research Data Management Services in Academic Libraries in the US: A Content Analysis of Libraries? Websites, College & Research Libraries, 78, 7, (2017); Xu H., Challenges and Opportunities for Research Data Management in the Chinese Library Community. in, Proceedings of the 16th IFLA ILDS Conference: Beyond the Paywall-Resource Sharing in A Disruptive Ecosystem, Held at National Library of Technology in Prague, Czech Republic, October 9-11, 2019, (2019); Zhou Q., Academic Libraries in Research Data Management Service: Perceptions and Practices, Open Access Library Journal, 5, 6, (2018); Zoubi M.R., Mohamed-Nour S., El-Kharraz J., Hassan N., The Arab States. in, UNESCO Science Report: Towards, 2030, pp. 430-469, (2015)","J. Mavodza; Zayed University, United Arab Emirates; email: Judith.Mavodza@zu.ac.ae","","Walter de Gruyter GmbH","","","","","","24511781","","","","English","Open Inf. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85129099849"
"Peters-Von Gehlen K.; Höck H.; Fast A.; Heydebreck D.; Lammert A.; Thiemann H.","Peters-Von Gehlen, Karsten (57610024000); Höck, Heinke (14053755300); Fast, Andrej (57607896700); Heydebreck, Daniel (57218242567); Lammert, Andrea (57608957800); Thiemann, Hannes (56989070600)","57610024000; 14053755300; 57607896700; 57218242567; 57608957800; 56989070600","Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools","2022","Data Science Journal","21","1","7","","","","2","10.5334/dsj-2022-007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127267283&doi=10.5334%2fdsj-2022-007&partnerID=40&md5=172b81988ecd5a7f594f0ea3a88004e0","Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany","Peters-Von Gehlen K., Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany; Höck H., Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany; Fast A., Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany; Heydebreck D., Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany; Lammert A., Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany; Thiemann H., Deutsches Klimarechenzentrum GmbH, Bundesstr. 45a, Hamburg, D-20146, Germany","From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta) data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-for-purpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts. © 2022 The Author(s).","climate science; data curation; FAIR; FAIRness evaluation; long-term archive; reusability; WDCC","Information management; Climate science; Data curation; Datacenter; Evaluation approach; Evaluation tool; Fairness evaluation; Hybrid approach; Hybrid method; Long term archives; World data center for climate; Reusability","","","","","","","FAIR data self-assessment tool, (2021); Austin C, Cousijn H, Diepenbroek M, Petters J, Soares E, Silva M., WDS/RDA Assessment of Data Fitness for Use WG Outputs and Recommendations, (2019); Bahim C, Casorran-Amilburu C, Dekkers M, Herczog E, Loozen N, Repanas K, Russell K, Stall S., The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments, Data Sci. 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Z, 29, pp. 483-491, (2020); Ganske A, Kraft A, Kaiser A, Heydebreck D, Lammert A, Hock H, Thiemann H, Voss V, Grawe D, Leitl B, Schlunzen KH, Kretzschmar J, Quaas J., ATMODAT Standard (v3.0), World Data Center for Climate (WDCC) at DKRZ, (2021); Genova F, Aronsen JM, Beyan O, Harrower N, Holl A, Hooft RW, Principe P, Slavec A, Jones S., Recommendations on FAIR metrics for EOSC, (2021); Giorgi F, Jones C, Asrar GR., Addressing climate information needs at the regional level: The CORDEX framework, World Meteorological Organization (WMO) Bulletin, 58, (2009); Heinzeller D, Dieng D, Smiatek G, Olusegun C, Klein C, Hamann I, Kunstmann H., WASCAL WRF60km with MPI-ESM MR r1i1p1 forcing from the CMIP5 historical experiment, World Data Center for Climate (WDCC) at DKRZ, (2017); Hock H, Toussaint F, Thiemann H., Fitness for Use of Data Objects Described with Quality Maturity Matrix at Different Phases of Data Production, Data Sci. J, 19, (2020); Jacobsen A, de Miranda Azevedo R, Juty NS, Batista D, Coles SJ, Cornet R, Courtot M, Crosas M, Dumontier M, Evelo CTA, Goble CA, Guizzardi G, Hansen KK, Hasnain A, Hettne KM, Heringa J, Hooft RWW, Imming M, Jeffery KG, Kaliyaperumal R, Kersloot MG, Kirkpatrick CR, Kuhn T, Labastida I, Magagna B, McQuilton P, Meyers N, Montesanti A, van Reisen M, Rocca-Serra P, Pergl R, Sansone S-A, da Silva Santos LOB, Schneider J, Strawn GO, Thompson M, Waagmeester A, Weigel T, Wilkinson MD, Willighagen EL, Wittenburg P, Roos M, Mons B, Schultes E., FAIR principles: Interpretations and implementation considerations, Data Intelligence, 2, pp. 10-29, (2020); Jungclaus J, Esch M., mil0021: MPI-M Earth System Modelling Framework: Millennium full forcing experiment using solar forcing of Bard, World Data Center for Climate (WDCC) at DKRZ, (2009); Klepp C, Michel S, Protat A, Burdanowitz J, Albern N, Louf V, Bakan S, Dahl A, Thiele T., Ocean Rainfall And Ice-phase precipitation measurement Network – OceanRAIN-W, World Data Center for Climate (WDCC) at DKRZ, (2017); Kruk J., Good scientific practice and ethical principles in scientific research and higher education, Central European Journal of Sport Sciences and Medicine, 1, pp. 25-29, (2013); L'Hours H, von Stein I, Huigen F, Devaraju A, Mokrane M, Davidson J, de Vries J, Herterich P, Cepinskas L, Huber R., CoreTrustSeal plus FAIR Overview, (2020); Meehl G, Covey C, Delworth T, Latif M, McAvaney B, Mitchell J, Stouffer R, Taylor K., The WCRP CMIP3 multi-model dataset: A new era in climate change research, B. Am. Meteorol. Soc, 88, pp. 1383-1394, (2007); Meyer E, Scholz R, Tinz B., Reconstruction of the 1906 Storm Tide in the German Bright using TRIM-NP, FES2004, and DWD weather data, World Data Center for Climate (WDCC) at DKRZ, (2021); Mokrane M, Recker J., CoreTrustSeal–certified repositories: Enabling Findable, Accessible, Interoperable, and Reusable (FAIR) Data, (2019); Mons B, Neylon C, Velterop J, Dumontier M, da Silva Santos LOB, Wilkinson MD., Cloudy, increasingly FAIR: Revisiting the FAIR Data guiding principles for the European Open Science Cloud, Information Services & Use, 37, pp. 49-56, (2017); Mulmenstadt J, Sourdeval O, Henderson DS, L'Ecuyer TS, Unglaub C, Jungandreas L, Bohm C, Russell LM, Quaas J., Using CALIOP to estimate cloud-field base height and its uncertainty: The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset, World Data Center for Climate (WDCC) at DKRZ, (2018); Murphy F, Bar-Sinai M, Martone ME., A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles, PLOS ONE, 16, pp. 1-22, (2021); Peng G, Privette JL, Kearns EJ, Ritchey NA, Ansari S., A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets, Data Sci. J, 13, pp. 231-253, (2015); Peng G, Wright W, Baddour O, Lief C, The WMO Stewardship Maturity Matrix for Climate Data (SMM-CD), (2020); Pergl R, Hooft RWW, Suchanek M, Knaisl V, Slifka J., “Data StewardshipWizard”: A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning, Data Sci. J, 18, (2019); Peters K, Hock H, Thiemann H., FAIR long-term preservation of climate and Earth System Science data with focus on reusability at the World Data Center for Climate (WDCC), Earth and Space Science Open Archive, 13, (2020); Peters-von Gehlen K., F-UJI evaluation output for the paper “Recommendations for discipline-specific FAIRness evaluation derived from applying an ensemble of evaluation tools”, World Data Center for Climate (WDCC) at DKRZ, (2021); Peters-von Gehlen K, Hock H., Data underlying the publication “Recommendations for discipline-specific FAIRness evaluation derived from applying an ensemble of evaluation tools”, World Data Center for Climate (WDCC) at DKRZ, (2021); Petrie R, Denvil S, Ames S, Levavasseur G, Fiore S, Allen C, Antonio F, Berger K, Bretonnie`re P-A, Cinquini L, Dart E, Dwarakanath P, Druken K, Evans B, Franchisteguy L, Gardoll S, Gerbier E, Greenslade M, Hassell D, Iwi A, Juckes M, Kindermann S, Lacinski L, Mirto M, Nasser AB, Nassisi P, Nienhouse E, Nikonov S, Nuzzo A, Richards C, Ridzwan S, Rixen M, Serradell K, Snow K, Stephens A, Stockhause M, Vahlenkamp H, Wagner R., Coordinating an operational data distribution network for CMIP6 data, Geosci. Model Dev, 14, pp. 629-644, (2021); Pronk TE., The time efficiency gain in sharing and reuse of research data, Data Sci. J, 18, (2019); Schweitzer M, Levett K, Russell K, White A, Unsworth K., auresearch/FAIR-Data-Assessment-Tool: Release v1.0, (2021); Seifert P., HD(CP)2 short-term observation data of Cloudnet products, (2020); Steger C, Schupfner M, Wieners K-H, Wachsmann F, Bittner M, Jungclaus J, Fruh B, Pankatz K, Giorgetta M, Reick C, Legutke S, Esch M, Gayler V, Haak H, de Vrese P, Raddatz T, Mauritsen T, von Storch J-S, Behrens J, Brovkin V, Claussen M, Crueger T, Fast I, Fiedler S, Hagemann S, Hohenegger C, Jahns T, Kloster S, Kinne S, Lasslop G, Kornblueh L, Marotzke J, Matei D, Meraner K, Mikolajewicz U, Modali K, Muller W, Nabel J, Notz D, Peters K, Pincus R, Pohlmann H, Pongratz J, Rast S, Schmidt H, Schnur R, Schulzweida U, Six K, Stevens B, Voigt A, Roeckner E., CMIP6 ScenarioMIP DWD MPI-ESM1-2-HR ssp585 r2i1p1f1 – RCM-forcing data, World Data Center for Climate (WDCC) at DKRZ, (2020); Stendel M, Schmith T, Roeckner E, Cubasch U., ECHAM4 OPYC SRES A2: 110 YEARS COUPLED A2 RUN 6H VALUES, World Data Center for Climate (WDCC) at DKRZ, (2004); Stendel M, Schmith T, Roeckner E, Cubasch U., EH4 OPYC SRES A2 APRS, World Data Center for Climate (WDCC) at DKRZ, (2005); Stockhause M, Hock H, Toussaint F, Lautenschlager M., Quality assessment concept of the World Data Center for Climate and its application to CMIP5 data, Geosci. Model Dev, 5, pp. 1023-1032, (2012); Stockhause M, Lautenschlager M., CMIP6 data citation of evolving data, Data Science Journal, 16, (2017); Taylor KE, Stouffer RJ, Meehl GA., An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc, 93, pp. 485-498, (2012); Tebaldi C, Knutti R., The use of the multi-model ensemble in probabilistic climate projections, Philos. T. Roy. Soc. A, 365, pp. 2053-2075, (2007); MM-Serv_ESIP_2018sum_v2r1_20180709.pdf, (2018); Thomas E., FAIR data assessment tool, (2017); CERA2 Metadata Submission Guide, (2016); Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016); Wilkinson MD, Dumontier M, Sansone S-A, da Silva Santos LOB, Prieto M, Batista D, McQuilton P, Kuhn T, Rocca-Serra P, Crosas M, Schultes E., Evaluating FAIR maturity through a scalable, automated, community-governed framework, Sci. Data, 6, pp. 1-12, (2019); Wilkinson MD, Dumontier M, Sansone S-A, da Silva Santos LOB, Prieto M, McQuilton P, Gautier J, Murphy D, Crosas M, Schultes E., Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework, bioRxiv, (2018); Wilkinson MD, Sansone S-A, Schultes E, Doorn P, Santos LOBDS, Dumontier M., A design framework and exemplar metrics for FAIRness, Sci. Data, 5, (2018); Wimalaratne S, Ulrich R., M4.7 Improved Description of Data Repositories (1.0), Zenodo, (2020); Wu M, Psomopoulos F, Khalsa SJ, de Waard A., Data discovery paradigms: User Requirements and Recommendations for Data Repositories, Data Sci. J, 18, (2019); Yu J, Cox S., 5-Star Data Rating Tool. v5. CSIRO. Software Collection, (2017)","K. Peters-Von Gehlen; Deutsches Klimarechenzentrum GmbH, Hamburg, Bundesstr. 45a, D-20146, Germany; email: peters@dkrz.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127267283"
"Omame I.M.; Alex-Nmecha J.C.","Omame, Isaiah Michael (57210109202); Alex-Nmecha, Juliet C. (57208897864)","57210109202; 57208897864","Application of blockchain in libraries and information centers","2021","Handbook of Research on Knowledge and Organization Systems in Library and Information Science","","","","384","397","13","1","10.4018/978-1-7998-7258-0.ch020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128440586&doi=10.4018%2f978-1-7998-7258-0.ch020&partnerID=40&md5=a48e6a2f3c10cfd3083c58fdce110914","University Library, Federal University of Lafia, Nigeria; Department of library and information science, University of Port Harcourt, Rivers State, Nigeria","Omame I.M., University Library, Federal University of Lafia, Nigeria; Alex-Nmecha J.C., Department of library and information science, University of Port Harcourt, Rivers State, Nigeria","This chapter explored the concept and application of blockchain technology in libraries and information centers. Blockchain is one of the emerging technologies thriving in the fourth industrial revolution. It is the application of cryptography for creating a time-stamped, immutable, and dynamic database, distributed across nodes in a network. Although its emergence began with cryptocurrencies, advancement in this technology has given birth to a fourth generation of blockchain with industrial disruptive capabilities, cutting across various fields including library and information science. Accordingly, the application of blockchain in libraries and information centers was thoroughly examined. Specifically, the chapter underscored the application of blockchain in circulation services, collection development, storage and archiving of records, research data management, cataloging and classification, indexing and abstracting, digital first right (DFR), etc. Lastly, the merits and demerits of blockchain in libraries and information centers were furnished accordingly. © 2021, IGI Global.","","","","","","","","","Atienza-Mendez C., Bayyou D.G., Blockchain technology applications in education, International Journal of Computers and Technology, 6, 11, pp. 68-74, (2019); Berdik D., Otoum S., Schmidt N., Porter D., Jararweh Y., A survey on blockchain for information systems management and security, Information Processing and Managemen, 59, 1, pp. 1-28, (2021); (2018); Casino F.K., Dasaklis T., Patsakis C., A systematic literature review of blockchain-based applications: Current status, classification and open issues, Telematics and Informatics, 36, pp. 55-81, (2019); Chandrakant K., Implementing a simple blockchain in java, (2020); Chingath V., Babu R.H., Advantage blockchain technology for the libraries, International Conference On DigitaI Transformation: A Cognitive Learning towards Artificial Intelligence, (2020); Chingath V., Babu R.H., Advantage blockchain technology for the libraries, International Conference On DigitaI Transformation: A Cognitive Learning towards Artificial Intelligence, pp. 127-134, (2020); Coghill J.G., Blockchain and its implications for libraries, Journal of Electronic Resources Librarianship, 15, 2, pp. 66-70, (2018); Colomo-Palacios R., Sanchez-Gordon M., Arias-Aranda D., A critical review on blockchain assessment initiatives: A technology evolution viewpoint, Wiley Online Library, (2020); Colomo-Palacios R., Sanchez-Gordon M., Arias-Aranda D., A critical review on blockchain assessment initiatives: A technology evolution viewpoint, Journal of Software: Evolution and Process, 32, 2272, (2020); Conway L., BLockchain explained, (2020); Donnelly M., Research data management & the H2020 open data pilot, (2015); Elmasri N., Fundamentals of database systems: Databases and database users, (2011); What is blockchain?, (2020); Herther N.K., Blockchain technology in the library, Information Today, Inc, 42, 5, (2018); Hoy M.B., An introduction to the blockchain and its implications for libraries and medicine, Medi - cal Reference Services Quarterly, 36, 3, pp. 273-279, (2017); Ibryam B., Getting started with blockchain for Java developers, (2019); Meth M., Understanding blockchain: Opportunities for libraries, American Libraries, (2020); O'Byrne R., Applications of blockchain in supply chain, (2020); Rosic A., What is blockchain technology? A step-by-step guide for beginners, (2020); Sanjeeva M., Research data management: A new role for academic/research librarians, Reshaping the Academic Libraries, Trends and Issues, (2014); Sharma T.K., Exciting Ethereum project ideas & topics for beginners, (2021); Thornton B., Blockchain: The new technology and its applications for libraries, Journal of Electronic Resources Librarianship, 31, 4, pp. 278-280, (2019); Blockchain evolution: From 1.0 to 4.0, (2017); van der Vorst J., Supply chain management: Theory and practices, The Emerging World of Chains & Networks, (2004); University of Leicester, (2019); Chingath V., Babu R.H., Advantage blockchain technology for the libraries, International Conference On Digital Transformation: A Cognitive Learning towards Artificial Intelligence, (2020); Coghill J.G., Blockchain and its implications for libraries, Journal of Electronic Resources Librarianship, 15, 2, pp. 66-70, (2018); Colomo-Palacios R., Sanchez-Gordon M., Arias-Aranda D., A critical review on blockchain assessment initiatives: A technology evolution viewpoint, Journal of Software: Evolution and Process, 32, 2272, (2020); Herther N.K., Blockchain technology in the library, Information Today, Inc, 42, 5, (2018); Hoy M.B., An Introduction to the Blockchain and Its Implications for Libraries and Medicine, Medical Reference Services Quarterly, 36, 3, pp. 273-279, (2017); Meth M., Blockchain in Libraries, (2019); Zhang L., Blockchain: The new technology and its applications for libraries, Journal of Electronic Resources Librarianship, 31, 4, pp. 278-280, (2019)","","","IGI Global","","","","","","","978-179987259-7; 978-179987258-0","","","English","Handb. of Res. on Knowl. and Organ. Syst. in Libr. and Inf. Sci.","Book chapter","Final","","Scopus","2-s2.0-85128440586"
"Auge T.; Hanzig M.; Heuer A.","Auge, Tanja (57194833958); Hanzig, Moritz (57221939110); Heuer, Andreas (9533312500)","57194833958; 57221939110; 9533312500","ProSA Pipeline: Provenance Conquers the Chase","2022","Communications in Computer and Information Science","1652 CCIS","","","89","98","9","0","10.1007/978-3-031-15743-1_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137999374&doi=10.1007%2f978-3-031-15743-1_9&partnerID=40&md5=ab9243cbfb6933b850ea761e3b48e15f","University of Rostock, Rostock, Germany","Auge T., University of Rostock, Rostock, Germany; Hanzig M., University of Rostock, Rostock, Germany; Heuer A., University of Rostock, Rostock, Germany","One of the main problems in data minimization is the determination of the relevant data set. Combining the Chase—a universal tool for transforming databases—and data provenance, a (anonymized) minimal sub-database of an original data set can be calculated. To ensure reproducibility, the evaluations performed on the original data set must be feasible on the sub-database, too. For this, we extend the Chase &Backchase with additional why-provenance to handle lost attribute values, null tuples, and duplicates occurring during the query evaluation and its inversion. In this article, we present the ProSA pipeline, which describes the method of data minimization using the Chase &Backchase extended with additional provenance. © 2022, Springer Nature Switzerland AG.","Chase &Backchase; Data minimization; Data provenance; Research data management","Information management; Metadata; Query processing; Attribute values; Chase &backchase; Data minimizations; Data provenance; Data set; Database provenances; Query evaluation; Reproducibilities; Research data managements; Pipelines","","","","","","","Auge T., Heuer A., Tracing the history of the Baltic sea oxygen level, BTW, LNI, Vol. P-311, pp. 337-348, (2021); Auge T., Heuer A., Testing provenance systems, Technical Report CS, 1-22, (2022); Auge T., Scharlau N., Gorres A., Zimmer J., Heuer A., Chateau: A Universal Toolkit for Applying the Chase; Benedikt M., Et al., Benchmarking the chase, PODS, pp. 37-52, (2017); Benczur A., Kiss A., Markus T., On a general class of data dependencies in the relational model and its implication problems, Comput. Math. Appl., 21, 1, pp. 1-11, (1991); Cheney J., Chiticariu L., Tan W.C., Provenance in databases: Why, how, and where, Found. Trends Databases, 1, 4, pp. 379-474, (2009); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema mapping evolution through composition and inversion, Schema Matching and Mapping. Data-Centric Systems and Applications, (2011); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Reverse data exchange: Coping with nulls, ACM Trans. Database Syst., 36, 2, pp. 1-11, (2011); Herschel M., Diestelkamper R., Ben Lahmar H., A survey on provenance-what for? what form? what from?, VLDB J, 26, 6, pp. 881-906, (2017); Han J., Cai Y., Cercone N., Data-driven discovery of quantitative rules in relational databases, IEEE Trans. Knowl. Data Eng., 5, 1, pp. 29-40, (1993); Samarati P., Protecting respondents’ identities in microdata release, IEEE Trans. Knowl. Data Eng., 13, 6, pp. 1010-1027, (2001)","T. Auge; University of Rostock, Rostock, Germany; email: tanja.auge@uni-rostock.de","Chiusano S.; Cerquitelli T.; Wrembel R.; Nørvåg K.; Catania B.; Vargas-Solar G.; Zumpano E.","Springer Science and Business Media Deutschland GmbH","","3rd Workshop on Intelligent Data - From Data to Knowledge, DOING 2022, 1st Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2022, 4th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2022, 2nd Workshop on Advanced Data Systems Management, Engineering, and Analytics, MegaData 2022, 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage, SWODCH 2022 and Doctoral Consortium which accompanied 26th European Conference on Advances in Databases and Information Systems, ADBIS 2022","5 September 2022 through 8 September 2022","Turin","282349","18650929","978-303115742-4","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85137999374"
"Kersloot M.G.; Van Damme P.; Abu-Hanna A.; Arts D.L.; Cornet R.","Kersloot, Martijn G. (57211042043); Van Damme, Philip (57202714542); Abu-Hanna, Ameen (6603738860); Arts, Derk L. (37092517600); Cornet, Ronald (57201825238)","57211042043; 57202714542; 6603738860; 37092517600; 57201825238","FAIRification Eforts of Cinical Researchers: The Current State of Afairs","2021","Studies in Health Technology and Informatics","287","","","35","39","4","1","10.3233/SHTI210807","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120529392&doi=10.3233%2fSHTI210807&partnerID=40&md5=83aaf0e8a3b7dd2b8d36a2ac87b6b783","Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; Castor EDC, Amsterdam, Netherlands","Kersloot M.G., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands, Castor EDC, Amsterdam, Netherlands; Van Damme P., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; Abu-Hanna A., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; Arts D.L., Castor EDC, Amsterdam, Netherlands; Cornet R., Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands","The FAIR Principles are supported by various initiatives in the biomedical community. However, little is known about the knowledge and efforts of individual clinical researchers regarding data FAIRification. We distributed an online questionnaire to researchers from six Dutch University Medical Centers, as well as researchers using an Electronic Data Capture platform, to gain insight into their understanding of and experience with data FAIRification. 164 researchers completed the questionnaire. 64.0% of them had heard of the FAIR Principles. 62.8% of the researchers spent some or a lot of effort to achieve any aspect of FAIR and 11.0% addressed all aspects. Most researchers were unaware of the Principles' emphasis on both human- and machine-readability, as their FAIRification efforts were primarily focused on achieving human-readability (93.9%), rather than machine-readability (31.2%). In order to make machine-readable, FAIR data a reality, researchers require proper training, support, and tools to help them understand the importance of data FAIRification and guide them through the FAIRification process.  © 2021 The European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved.","FAIR data; medical research; Research Data Management","Information management; Medical informatics; 'current; Biomedical community; Clinical researchers; Electronic data; FAIR data; Medical center; Medical research; Medical researchers; Online questionnaire; Research data managements; conference paper; FAIR principles; human; human experiment; medical research; questionnaire; reading; university hospital; article; Surveys","","","","","","","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Et al., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Mons B., Neylon C., Velterop J., Dumontier M., Da Silva Santos L.O.B., Wilkinson M.D., Cloudy, increasingly fair; revisiting the fair data guiding principles for the european open science cloud, Information Services & Use, 37, 1, pp. 49-56, (2017); Jacobsen A., De Miranda Azevedo R., Juty N., Batista D., Coles S., Cornet R., Et al., Fair Principles: Interpretations and Implementation Considerations, (2020); Trifan A., Oliveira J.L., Towards a more reproducible biomedical research environment: Endorsement and adoption of the fair principles, Biomedical Engineering Systems and Technologies, pp. 453-470, (2020); Sinaci A.A., Nunez-Benjumea F.J., Gencturk M., Jauer M.L., Deserno T., Chronaki C., Et al., From raw data to fair data: The fairification workflow for health research, Methods of Information in Medicine, 59, 1, pp. e21-e32, (2020); Reisen M., Oladipo F., Stokmans M., Mpezamihgo M., Folorunso S., Schultes E., Et al., Design of a fair digital data health infrastructure in africa for covid-19 reporting and research, Advanced Genetics, 2, 2, (2021); Jannik S., Dennis K., Jens G., Christian-Alexander B., Marco R., David V.E., Et al., Osse goes fair -implementation of the fair data principles for an open-source registry for rare diseases, Studies in Health Technology and Informatics, 253, pp. 209-213, (2018); Groenen K.H.J., Jacobsen A., Kersloot M.G., Dos Santos Vieira B., Van Enckevort E., Kaliyaperumal R., Et al., The de novo fairification process of a registry for vascular anomalies, Orphanet Journal of Rare Diseases, 16, 1, (2021); Beyan O., Choudhury A., Van Soest J., Kohlbacher O., Zimmermann L., Stenzhorn H., Et al., Distributed analytics on sensitive medical data: The personal health train, Data Intelligence, 2, 1-2, pp. 96-107, (2020); Kersloot M.G., Van Damme P., Abu-Hanna A., Arts D.L., Cornet R., FAIRification Efforts of Clinical Researchers: The Current State of Affairs, (2021); Castor Electronic Data Capture, (2020); R: A Language and Environment for Statistical Computing, (2020); Schultes E., Magagna B., Hettne K.M., Pergl R., Suchanek M., Kuhn T., Reusable fair implementation profiles as accelerators of fair convergence, Lecture Notes in Computer Science, pp. 138-147, (2020); Mons B., Data Stewardship for Open Science, (2018); Van Vlijmen H., Mons A., Waalkens A., Franke W., Baak A., Ruiter G., Et al., The need of industry to go fair, Data Intelligence, 2, 1-2, pp. 276-284, (2020)","M.G. Kersloot; Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands; email: m.g.kersloot@amsterdamumc.nl","Delgado J.; Benis A.; de Toledo P.; Gallos P.; Giacomini M.; Martinez-Garcia A.; Salvi D.","IOS Press BV","","2021 European Federation for Medical Informatics (EFMI) Special Topic Conference, STC 2021","22 November 2021 through 24 November 2021","Virtual, Online","174467","09269630","978-164368236-5","","34795075","English","Stud. Health Technol. Informatics","Conference paper","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85120529392"
"Al-Jaradat O.M.","Al-Jaradat, Omar Mohammad (54584897100)","54584897100","Research data management (RDM) in Jordanian public university libraries: Present status, challenges and future perspectives","2021","Journal of Academic Librarianship","47","5","102378","","","","3","10.1016/j.acalib.2021.102378","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106250153&doi=10.1016%2fj.acalib.2021.102378&partnerID=40&md5=9bf399d5ec12911daa73ffa1f6c77438","Department of Library and Information Science, Irbid University College, Al-Balqa Applied University, Jordan","Al-Jaradat O.M., Department of Library and Information Science, Irbid University College, Al-Balqa Applied University, Jordan","Aim/purpose: The present investigation was undertaken to find the existing position including the policies, practices and the issues and concerns of librarians in collecting, processing and archiving the research data in public university libraries in Jordan. Background: Research is the bastion of scientific growth and development of a society and the data is its chief ingredient which communicates the results and inferences of the research. The research process is directly interrelated with the data life cycle and the two cannot be segregated. Keeping the importance of the curation and management of the research data and the trivial policy interests of the funding agencies and the governments, the current study was undertaken to explore the Research Data Management (RDM) status and practices in Jordan. Methodology: The study used the online questionnaire to get the opinions and the prevailing state of affairs from the librarians/heads of the libraries in the public universities. Contribution: The study is an eye opener for the higher education commission in Jordan to design the policies and suitable guidelines for streamlining the RDM system in the country especially the Public and Private Universities. It can be pursued for the exploration of RDM practices and policies in similar other Middle East countries for determining the issues and challenges in implementing the RDM policies in their research institutes. Findings: It revealed that the Jordan lags RDM polices at all fronts of government, institutional as well as that of the funding agencies which hinders all the progress and growth of RDM in university libraries. However, keeping all the obstacles and the challenges aside, the Jordanian Public University Libraries (JPUL) are providing RDM services, although on a limited scale, at different levels of curating only some valuable research data, preserving and providing access to the preferred users through online platforms substituting the data repositories. There is a lack of national and international RDM policies and ineffective implementation. Coupled with this, is the wider gap between the required skills and the available skills among the library staff which can be filled by conducting suitable training programs and workshops on continuous basis to impart the skills required to manage RDM practices in libraries. Further improvements in providing the effective RDM services is the need among the JPUL and similar may be the case in other middle east countries including the development of infrastructure, policy framing, designing of data repository, specialized courses on data archiving, data cataloguing, data sharing, etc. while protecting the intellectual property rights. Future research: The similar studies can be explored to find the existing policies and the loopholes in implementation of national and international policies of data management in different research institutes. The more important area would be to pursue the role of funding agencies and the framing of suitable policies regarding the data curation and management of their funded projects. Research implications: The distinction of the present research study can be considered by the fact that there is a lack of sufficient studies in Jordanian universities which can present a comprehensive proclamation on the processes and policies adopted by the libraries for research data collection, archiving and its accessibility. The study can help initiating the new policies regarding the data management tools and techniques in Jordan as we witness in some of the well developed countries of the world and even there are many courses running on research data management and its implementation in universities. Relevance of the present study: The results of the present study can be used by other libraries especially in designing and developing the strategies of research data management which can help improving the data re-use and sharing in Jordan universities for secondary analysis. It can also help in refocusing the data and its immense value among research community. Besides, it can also help the research funding agencies and the government of the country to make policy decisions regarding the submission of reports along with the data so as to save lot of human efforts and resources for collecting and collating the necessary data for conducting researchers in future. The study will be helpful in reducing the financial burden of research projects of different funding agencies which otherwise are spending hugely on data collection, surveys, questionnaire designing, pilot studies, interviews, etc. © 2021 Elsevier Inc.","Academic institutes; Jordan; Public university; RDM policies and planning; Research data; Research data management; University libraries","","","","","","Higher Education Accreditation Commission; Scientific Research Support Fund; Ministry of Higher Education, Malaysia, MOHE","There are 10 public and 24 private universities in Jordan which are regulated and monitored by its Ministry of Higher Education and Scientific Research (MHESR). The Ministry works through the Higher Education Council, the Scientific Research Support Fund and the Higher Education Accreditation Commission ( Ministry of Higher Education, 2020 ). Keeping in mind the establishment and supposedly better infra-structure, the present study has undertaken only the public universities of Jordan for getting to know the existing situation of the RDM. Table 1 represents the list of all the 10 universities taken under the study and the list is prepared based on the year of establishment. The table also represents the total employees in the libraries including the Director/Manager, Asst. Director/ Departmental or Section Heads and other supporting staff. Although, the questionnaire was distributed to all the professional staff of the libraries, however, the incomplete responses were not included in the final analysis. The figures in parenthesis represent the no. of employees' responses actually included in the survey. It is worthwhile to mention that the head of the library is the Director or Manager (Public University libraries in Jordan use these names), followed by the Assistant Library Director who are controlling and managing the overall library administration. While as the Section or departmental heads are managing the different sections of the library with other supporting staff in the libraries. ","Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Anilkumar N., Research data management in India: A pilot study, EPJ Web of Conferences, 186, (2018); (2014); (2015); Chawinga W.D., Zinn S., (2020); Chiware E.R.T., Mathe Z., (2016); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Davidson J., Introduction to data management planning, 23, (2016); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); (2017); (2020); Green A., Macdonald S., Rice R., (2009); Hamad F., Al-Fadel M., Al-Soub A., Awareness of research data management services at academic libraries in Jordan: Roles, responsibilities and challenges, New Review of Academic Librarianship, (2019); Hiom D., Fripp D., Gray S., Snow K., Steer D., (2015); (2020); Jones S., Pryor G., Whyte A., (2013); King G., Replication, replication, PS: Political Science and Politics, 28, 3, pp. 443-499, (1995); (2020); Makani J., Knowledge management, research data management, and university scholarship: Towards an integrated institutional research data management support-system framework, VINE, 45, 3, pp. 344-359, (2015); Ministry of Higher Education, (2020); Mohammed M., Ibrahim R., (2019); Mushi G.E., Pienaar H., van Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, 1, pp. 1-9, (2020); Mushi, Et al., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, 1, pp. 1-9, (2020); (2020); (2017); (2020); (2020); (2018); Nhendodzashe N., Pasipamire N., (2017); (2020); (2013); (2010); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Piracha H.A., Ameen K., Policy and planning of research data management in university libraries of Pakistan, Collection and Curation, 38, 2, pp. 39-44, (2019); Registry of Research Data Repositories, (2020); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Tang R., Hu Z., Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tripathi M., Shukla A., Sonkar S., (2017); University of Leeds, (2020); University of Pittsburgh, (2020); Yu H.H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017)","","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85106250153"
"Read K.B.; Ganshorn H.; Rutley S.; Scott D.R.","Read, Kevin B. (57205931894); Ganshorn, Heather (55181733400); Rutley, Sarah (57207620179); Scott, David R. (57217667837)","57205931894; 55181733400; 57207620179; 57217667837","Data-sharing practices in publications funded by the Canadian Institutes of Health Research: a descriptive analysis","2021","CMAJ open","9","4","","E980","E987","7","3","10.9778/cmajo.20200303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121718683&doi=10.9778%2fcmajo.20200303&partnerID=40&md5=5b49dc857a6e9d96d0d8c7a2022f65fa","Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta","Read K.B., Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta; Ganshorn H., Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta; Rutley S., Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta; Scott D.R., Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta","BACKGROUND: As Canada increases requirements for research data management and sharing, there is value in identifying how research data are shared and what has been done to make them findable and reusable. This study aimed to understand Canada's data-sharing landscape by reviewing how data funded by the Canadian Institutes of Health Research (CIHR) are shared and comparing researchers' data-sharing practices to best practices for research data management and sharing. METHODS: We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central published between 1946 and Dec. 31, 2019, that indicated that the research data underlying the results of the publication were shared. We analyzed each publication to identify how and where data were shared, who shared data and what documentation was included to support data reuse. RESULTS: Of 4144 CIHR-funded publications identified, 1876 (45.2%) included accessible data, 935 (22.6%) stated that data were available via request or application, and 300 (7.2%) stated that data sharing was not applicable or possible; we found no evidence of data sharing in 1558 publications (37.6%). Frequent data-sharing methods included via a repository (1549 [37.4%]), within supplementary files (1048 [25.3%]) and via request or application (935 [22.6%]). Overall, 554 publications (13.4%) included documentation that would facilitate data reuse. INTERPRETATION: Publications funded by the CIHR largely lack the metadata, access instructions and documentation to facilitate data discovery and reuse. Without measures to address these concerns and enhanced support for researchers seeking to implement best practices for research data management and sharing, much CIHR-funded research data will remain hidden, inaccessible and unusable. © 2021 CMA Joule Inc. or its licensors.","","Academies and Institutes; Biomedical Research; Canada; Capital Financing; Databases, Factual; Documentation; Humans; Information Dissemination; Publications; Canada; documentation; epidemiology; factual database; financial management; human; information dissemination; medical research; organization; publication","","","","","","","","","","NLM (Medline)","","","","","","22910026","","","34753787","English","CMAJ Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85121718683"
"Zhang Q.; Westra B.","Zhang, Qianjin (57195966583); Westra, Brian (57450905000)","57195966583; 57450905000","Embedded Librarians to Support Data-management Needs of a Multidisciplinary Research Program","2021","ASEE Annual Conference and Exposition, Conference Proceedings","","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124557070&partnerID=40&md5=239204fe77d5bf37a13695532085b6e7","University of Iowa, United States","Zhang Q., University of Iowa, United States; Westra B., University of Iowa, United States","This paper describes the establishment of a partnership between the Libraries and a multidisciplinary research program, and some of the products and outcomes from immersive and embedded roles within that program. Several factors contributed to the development of this partnership: outreach efforts by the Engineering Library and the Data Services Librarian to faculty, staff, students, and research administrators; a research program director who has a history of engagement with the Libraries; and the funder's data management and sharing mandates in the funding opportunity announcement for the research program. The result has been the inclusion of two librarians in the Data Management and Analysis Core of this program, which is expected to continue for the duration of the 5-year funding cycle. This approach yielded the launch of a data management course, and ongoing development of program- and team-specific guidance and strategies to improve data sharing and integration. This paper indicates that these kinds of partnerships can increase the awareness of librarian skills in research data management, support compliance with funder requirements, and enhance the impact and value of research outputs. © American Society for Engineering Education, 2021","","Air navigation; Human resource management; Libraries; Mergers and acquisitions; Data services; Data Sharing; Engineering library; Faculty staff; Funding cycle; Funding opportunities; Immersive; Management course; Multi-disciplinary research; Research programs; Information management","","","","","National Institute of Environmental Health Sciences, NIEHS","The Iowa Superfund Research Program (ISRP) is a multi-project center grant, funded by the National Institute of Environmental Health Sciences (NIEHS) since 2006, to conduct collaborative research on sources, exposures, toxicities, and remediation of polychlorinated biphenyls (PCBs). This multidisciplinary program is currently composed of 22 faculty, 10 staff, and 25 students (also called trainees), from civil and environmental engineering, biomedical engineering, microbiology and immunology, medicinal and natural products chemistry, urban planning, human toxicology, and occupational and environmental health. The program is funded through 5-year grants, and funding for the 2015-2020 fiscal years including supplemental administrative funds for short-term projects was a little over $12 million.","Iowa Superfund Research Program, (2021); Superfund Hazardous Substance Research and Training Program, (2018); Pinfield S., Cox A. M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); Cox A. M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a 'wicked' problem, Journal of Librarianship and Information Science, 48, 1, (2016); Schulte S., Embedded Academic Librarianship: A review of the literature, Evidence Based Library and Information Practice, 7, 4, pp. 122-138, (2012); Olivares O., The sufficiently embedded librarian: Defining and establishing productive librarian-faculty partnerships in academic libraries, Public Services Quarterly, 6, 2, pp. 140-149, (2010); Wang M., Fong B. L., Embedded Data Librarianship: A Case Study of Providing Data Management Support for a Science Department, Science and Technology Libraries, 34, 3, pp. 228-240, (2015); Coates H., Building Data Services From the Ground Up: Strategies and Resources, Journal of eScience Librarianship, 3, 1, pp. 52-59, (2014); Carlson J., Kneale R., Embedded librarianship in the research context, College and Research Libraries News, 72, 3, pp. 167-170, (2011); Bracke M. S., Emerging Data Curation Roles for Librarians: A Case Study of Agricultural Data, Journal of Agricultural & Food Information, 12, 1, pp. 65-74, (2011); Carlson J., Stowell-Bracke M., Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Johnston L., Lafferty M., Petsan B., Training Researchers on Data Management: A Scalable, Cross-Disciplinary Approach, Journal of eScience Librarianship, 1, 2, (2012); Carlson J., Johnston L., Data information literacy: librarians, data, and the education of a new generation of researchers, pp. 271-271, (2015); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to Plan? A Snapshot of Researcher Readiness to Address Data Management Planning Requirements, Journal of eScience Librarianship, 1, 2, (2012); Whitmire A. L., Implementing a Graduate-Level Research Data Management Course: Approach, Outcomes, and Lessons Learned, Journal of Librarianship and Scholarly Communication, 3, 2, pp. eP1246-eP1246, (2015); Jeffryes J. N., Johnston L., An e-learning approach to data information literacy education, pp. 12-12, (2013); Johnston L., Et al., Data Curation Network: How Do We Compare? A Snapshot of Six Academic Library Institutions' Data Repository and Curation Services, Journal of eScience Librarianship, 6, 1, pp. e1102-e1102, (2017); Hettne K. M., Verhaar P., Schultes E., Sesink L., From FAIR leading practices to FAIR implementation and back: An inclusive approach to FAIR at leiden university libraries, Data Science Journal, 19, 1, pp. 1-7, (2020); Rice R., DISC-UK DataShare Project: Final Report, (2009); Newton M. P., Miller C. C., Bracke M. S., Librarian Roles in Institutional Repository Data Set Collecting: Outcomes of a Research Library Task Force, Collection Management, 36, 1, pp. 53-67, (2010); Paul D., Macdonell C., Data Carpentry: Data Cleaning with OpenRefine Ecology lesson, (2017); DataONE Education Module: Data Entry and Manipulation, (2012); The FAIR data principles; Jones S., Grooteveld M., How FAIR are your data?, (2017); Carlson J., Nelson M. S., Data Information Literacy Case Study Directory; Data Curation Network","","","American Society for Engineering Education","","2021 ASEE Virtual Annual Conference, ASEE 2021","26 July 2021 through 29 July 2021","Virtual, Online","176961","21535965","","","","English","ASEE Annu. Conf. Expos. Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85124557070"
"Hachinger S.; Martinovič J.; Terzo O.; Levrier M.; Scionti A.; Magarielli D.; Goubier T.; Parodi A.; Harsh P.; Apopei F.-I.; Munke J.; García-Hernández R.J.; Golasowski M.; Hayek M.; Donnat F.; Ganne L.; Koch-Hofer C.; Vitali G.; Viviani P.; Schorlemmer D.; Danovaro E.; Parodi A.; Murphy S.; Dees A.","Hachinger, Stephan (24080119100); Martinovič, Jan (23392916900); Terzo, Olivier (27868176400); Levrier, Marc (57207257320); Scionti, Alberto (25927553100); Magarielli, Donato (25634104500); Goubier, Thierry (25927084000); Parodi, Antonio (35568218100); Harsh, Piyush (15925345900); Apopei, Florin-Ionut (57337231800); Munke, Johannes (57337374600); García-Hernández, Rubén J. (57204931624); Golasowski, Martin (56289893300); Hayek, Mohamad (57217292519); Donnat, Frédéric (57209692397); Ganne, Laurent (57217297587); Koch-Hofer, Cédric (23008653600); Vitali, Giacomo (57217294605); Viviani, Paolo (57523418700); Schorlemmer, Danijel (6506580187); Danovaro, Emanuele (8389785300); Parodi, Andrea (57550453900); Murphy, Seán (57209691226); Dees, Aaron (57336810800)","24080119100; 23392916900; 27868176400; 57207257320; 25927553100; 25634104500; 25927084000; 35568218100; 15925345900; 57337231800; 57337374600; 57204931624; 56289893300; 57217292519; 57209692397; 57217297587; 23008653600; 57217294605; 57523418700; 6506580187; 8389785300; 57550453900; 57209691226; 57336810800","HPC-Cloud-Big Data convergent architectures and research data management: The LEXIS approach","2021","Proceedings of Science","378","","004","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119017286&partnerID=40&md5=98ef369e0427dc990285a8313482e2a0","Leibniz Supercomputing Centre (LRZ) of the BAdW, Boltzmannstr. 1, Garching b.M., Germany; IT4Innovations National Supercomputing Center, VŠB – Technical University of Ostrava, Studentská 6231/1B, Ostrava, Czech Republic; Advanced Computing and Applications, LINKS Foundation, Via Pier Carlo Boggio, 61, Torino, Italy; Atos, Campus Teratec, 2 rue de la Piquetterie, Bruyères-le-Châtel, France; Avio Aero, Via I. Maggio, 99, Rivalta di Torino, Italy; CEA LIST, Bâtiment 565, Gif-sur-Yvette, France; CIMA Foundation, Via A. Magliotto, 2, Savona, Italy; Cyclops Labs GmbH, Neptunstrasse 63, Zürich, Switzerland; TESEO S.P.A., Corso Alexander Fleming, 25, Druento, Italy; Atos, 1 Rue de Provence, Échirolles, France; Outpost24, 2405 Route des Dolines, Valbonne, France; Helmholtz-Zentrum Potsdam – Deutsches GeoForschungsZentrum (GFZ), Wissenschaftpark ""Albert Einstein"", Telegrafenberg, Potsdam, Germany; European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, Reading, RG2 9AX, United Kingdom; School of Engineering, Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Technikumstrasse 9, Zürich, Switzerland; Irish Centre for High-End Computing (ICHEC), Tower Building, Technology and Enterprise Campus, Grand Canal Quay, Dublin 2, Ireland","Hachinger S., Leibniz Supercomputing Centre (LRZ) of the BAdW, Boltzmannstr. 1, Garching b.M., Germany; Martinovič J., IT4Innovations National Supercomputing Center, VŠB – Technical University of Ostrava, Studentská 6231/1B, Ostrava, Czech Republic; Terzo O., Advanced Computing and Applications, LINKS Foundation, Via Pier Carlo Boggio, 61, Torino, Italy; Levrier M., Atos, Campus Teratec, 2 rue de la Piquetterie, Bruyères-le-Châtel, France; Scionti A., Advanced Computing and Applications, LINKS Foundation, Via Pier Carlo Boggio, 61, Torino, Italy; Magarielli D., Avio Aero, Via I. Maggio, 99, Rivalta di Torino, Italy; Goubier T., CEA LIST, Bâtiment 565, Gif-sur-Yvette, France; Parodi A., CIMA Foundation, Via A. Magliotto, 2, Savona, Italy; Harsh P., Cyclops Labs GmbH, Neptunstrasse 63, Zürich, Switzerland; Apopei F.-I., TESEO S.P.A., Corso Alexander Fleming, 25, Druento, Italy; Munke J., Leibniz Supercomputing Centre (LRZ) of the BAdW, Boltzmannstr. 1, Garching b.M., Germany; García-Hernández R.J., Leibniz Supercomputing Centre (LRZ) of the BAdW, Boltzmannstr. 1, Garching b.M., Germany; Golasowski M., IT4Innovations National Supercomputing Center, VŠB – Technical University of Ostrava, Studentská 6231/1B, Ostrava, Czech Republic; Hayek M., Leibniz Supercomputing Centre (LRZ) of the BAdW, Boltzmannstr. 1, Garching b.M., Germany; Donnat F., Outpost24, 2405 Route des Dolines, Valbonne, France; Ganne L., Atos, 1 Rue de Provence, Échirolles, France; Koch-Hofer C., Atos, 1 Rue de Provence, Échirolles, France; Vitali G., Advanced Computing and Applications, LINKS Foundation, Via Pier Carlo Boggio, 61, Torino, Italy; Viviani P., Advanced Computing and Applications, LINKS Foundation, Via Pier Carlo Boggio, 61, Torino, Italy; Schorlemmer D., Helmholtz-Zentrum Potsdam – Deutsches GeoForschungsZentrum (GFZ), Wissenschaftpark ""Albert Einstein"", Telegrafenberg, Potsdam, Germany; Danovaro E., European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, Reading, RG2 9AX, United Kingdom; Parodi A., School of Engineering, Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Technikumstrasse 9, Zürich, Switzerland; Murphy S.; Dees A., Irish Centre for High-End Computing (ICHEC), Tower Building, Technology and Enterprise Campus, Grand Canal Quay, Dublin 2, Ireland","The LEXIS project (Large-scale EXecution for Industry & Society, H2020 GA825532) provides a platform for optimised execution of Cloud-HPC workflows, reducing computation time and increasing energy efficiency. The system will rely on advanced, distributed orchestration solutions (Atos YSTIA Suite, with Alien4Cloud and Yorc, based on TOSCA), the High-End Application Execution Middleware HEAppE, and new hardware capabilities for maximising efficiency in data processing, analysis and transfer (e.g. Burst Buffers with GPU- and FPGA-based data reprocessing). LEXIS handles computation tasks and data from three Pilots, based on representative and demanding HPC/Cloud-Computing use cases in Industry (SMEs) and Science: i) Simulations of complex turbomachinery and gearbox systems in Aeronautics, ii) Tsunami simulations and earthquake loss assessments which are time-constrained to enable immediate warnings and to support well-informed decisions, and iii) Weather and Climate simulations where massive amounts of in-situ data are assimilated to improve forecasts. A user-friendly LEXIS web portal, as a unique entry point, will provide access to data as well as workflow-handling and remote visualisation functionality. As part of its back-end, LEXIS builds an elaborate system for the handling of input, intermediate and result data. At its core, a Distributed Data Infrastructure (DDI) ensures the availability of LEXIS data at all participating HPC sites, which will be federated with a common LEXIS Authentication and Authorisation Infrastructure (with unified security model, user database and policies). The DDI leverages best of breed data-management solutions from EUDAT, such as B2SAFE (based on iRODS) and B2HANDLE. REST APIs on top of it will ensure a smooth interaction with LEXIS workflows and the orchestration layer. Last, but not least, the DDI will provide functionalities for Research Data Management following the FAIR principles (“Findable, Interoperable, Accessible, Reusable”), e.g. DOI acquisition, which helps to publish and disseminate open data products. © Copyright owned by the author(s) under the terms of the Creative Commons","","Big data; Computer software reusability; Data handling; Energy efficiency; Information management; Open Data; Portals; Application execution; Cloud-computing; Computation tasks; Computation time; Data infrastructure; Distributed data; Gearbox systems; Large-scales; Research data managements; Work-flows; Middleware","","","","","Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT, (CZ LM2018140); Horizon 2020, (825532); Irish Centre for High-End Computing, ICHEC","This work and all contributing authors are funded/co-funded by the EU’s Horizon 2020 research and innovation programme (2014-2020) under Grant Agreement no. 825532 (Project LEXIS – “Large-scale EXecution for Industry and Society”). The work at IT4I is supported by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project “e-Infrastructure CZ LM2018140”. The LEXIS project is making use of various computing and data facilities at LRZ, IT4I and ICHEC, and we thank all colleagues for support.","Lexis Project - High Performance Computing (HPC) in Europe; Ystia Suite; Brogi A., Soldani J., Wang P., TOSCA in a Nutshell: Promises and Perspectives, Service-Oriented and Cloud Computing, pp. 171-186, (2014); FastConnect Bull, Alien 4 Cloud; Svaton V., Martinovic J., Krenek J., Esch T., Tomancak P., HPC-as-a-Service via HEAppE Platform, Advances in Intelligent Systems and Computing, 993, pp. 280-293, (2019); Svaton V., Home · Wiki · ADAS / HEAppE / Middleware · GitLab; Keycloak Community, “Keycloak; Shiers J., The Worldwide LHC Computing Grid (worldwide LCG), Computer Physics Communications, 177, (2007); Imamagic E., Ferrari T., EGI grid middleware and distributed computing infrastructures integration, Proceedings of the International Symposium on Grids and Clouds (ISGC) 2013 – PoS, 179, (2014); Lecarpentier D., Wittenburg P., Elbers W., Michelini A., Kanso R., Coveney P.V., Et al., EUDAT: A New Cross-Disciplinary Data Infrastructure for Science, Int. J. Digit. Curation, 8, (2013); EUDAT - Research Data Services, Expertise & Technology Solutions; Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, (2016); Xu H., Russell T., Coposky J., Rajasekar A., Moore R., de Torcy A., Et al., iRODS primer 2: Integrated Rule-Oriented Data System, (2017); Allcock W., Bresnahan J., Kettimuthu R., Link M., The Globus Striped GridFTP Framework and Server, SC’05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, pp. 54-54, (2005); The Go Programming Language Specification – The Go Programming Language; React – A JavaScript library for building user interfaces; it-accounting-and-billing | Cyclops Labs GmbH; Kirstein P.T., European international academic networking: A 20 year perspective, One step ahead, The 20th Trans European Research and Education Networking Conference, (2004); Schmuck F.B., Haskin R.L., GPFS: A Shared-Disk File System for Large Computing Clusters, Proceedings of the Conference on File and Storage Technologies, FAST’02, (US), pp. 231-244, (2002); Weil S.A., Brandt S.A., Miller E.L., Long D.D.E., Maltzahn C., Ceph: A Scalable, High-Performance Distributed File System, Proceedings of the 7th Symposium on Operating Systems Design and Implementation, OSDI’06, (US), pp. 307-320, (2006); Celery: Distributed Task Queue; Messaging that just works - RabbitMQ; B2HANDLE-EUDAT; Starr J., Gastl A., isCitedBy: A Metadata Scheme for DataCite, D-Lib Magazine, 17, (2011); DataCite Metadata Schema Documentation for the Publication and Citation of Research Data and Other Research Outputs, (2021); B2FIND-EUDAT; Scionti A., Et al., HPC, Cloud and Big-Data Convergent Architectures: The LEXIS Approach, Advances in Intelligent Systems and Computing, 993, pp. 200-212, (2019); Parodi A., Et al., LEXIS Weather and Climate Large-Scale Pilot, Advances in Intelligent Systems and Computing, 1194, pp. 267-277, (2020); Goubier T., Martinovic J., Dubrulle P., Ganne L., Louise S., Martinovic T., Et al., Real-Time Model of Computation over HPC/Cloud Orchestration - The LEXIS Approach, Advances in Intelligent Systems and Computing, 1194, pp. 255-266, (2020); GitHub – LEXIS: Large Scale Execution for Industry & Society; Zenodo Community – LEXIS project; Large-scale EXecution for Industry & Society | LEXIS Project | H2020 | CORDIS | European Commission","S. Hachinger; Leibniz Supercomputing Centre (LRZ) of the BAdW, Garching b.M., Boltzmannstr. 1, Germany; email: hachinger@lrz.de","","Sissa Medialab Srl","","2021 International Symposium on Grids and Cloud, ISGC 2021","22 March 2021 through 26 March 2021","Taipei","173412","18248039","","","","English","Proc. Sci.","Conference paper","Final","","Scopus","2-s2.0-85119017286"
"Martin L.; Henrich A.","Martin, Leon (57219056570); Henrich, Andreas (14034330300)","57219056570; 14034330300","RDFtex: Knowledge Exchange Between -Based Research Publications and Scientific Knowledge Graphs","2022","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13541 LNCS","","","26","38","12","0","10.1007/978-3-031-16802-4_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138809689&doi=10.1007%2f978-3-031-16802-4_3&partnerID=40&md5=4e2aa56be3f609de04fe9f4be21a3ae3","Media Informatics, University of Bamberg, Bamberg, Germany","Martin L., Media Informatics, University of Bamberg, Bamberg, Germany; Henrich A., Media Informatics, University of Bamberg, Bamberg, Germany","Scientific Knowledge Graphs (SciKGs) aim to integrate scientific knowledge in a machine-readable manner. For populating SciKGs, research publications pose a central source of knowledge. The goal is to represent both contextual information, i.e., metadata, and contentual information, i.e., original contributions like definitions and experimental results, of research publications in SciKGs. However, typical forms of research publications like traditional papers do not provide means of integrating contributions into SciKGs. Furthermore, they do not support making direct use of the rich information SciKGs provide. To tackle this, the present paper proposes RDFtex, a framework enabling (1) the import of contributions represented in SciKGs to facilitate the preparation of -based research publications and (2) the export of original contributions to facilitate their integration into SciKGs. As a proof of concept, an RDFtex implementation is provided. We demonstrate the framework’s functionality using the example of the present paper itself since it was prepared using this implementation. © 2022, Springer Nature Switzerland AG.","Data and research infrastructure; Research data management; research publications","Knowledge graph; Publishing; Resource Description Framework (RDF); Central source; Contextual information; Data infrastructure; Direct use; Knowledge exchange; Knowledge graphs; Research data managements; Research infrastructure; Research publication; Scientific knowledge; Knowledge management","","","","","U.S. National Renewable Energy Laboratory Thin Film Partnership Program","This work was supported by the U.S. National Renewable Energy Laboratory Thin Film Partnership Program.","Auer S., Kovtun V., Prinz M., Kasprzik A., Stocker M., Vidal M., Towards a knowledge graph for science, Proceedings of the 8Th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018, Novi Sad, Serbia, 25–27 June 2018, Pp. 1:1–1:6. ACM, (2018); Cyganiak R., Hyland-Wood D., Lanthaler M., RDF 1.1 concepts and abstract, Syntax, (2014); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Kohlhase A., Kohlhase M., Lange C., S<sup>t</sup> e<sup>x</sup> +: A system for flexible formalization of linked data, I-SEMANTICS. ACM, (2010); Kuhn T., Et al., Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data, (2018); Luan Y., He L., Ostendorf M., Hajishirzi H., Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October–4 November 2018, Pp. 3219–3232. Association for Computational Linguistics, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018); Gurpinar E., Ozpineci B., Loss Analysis and Mapping of a SiC MOSFET Based Segmented Two-Level Three-Phase Inverter for EV Traction Systems, IEEE Transportation Electrification Conference and Expo (ITEC), 2018, pp. 1046-1053, (2018)","L. Martin; Media Informatics, University of Bamberg, Bamberg, Germany; email: leon.martin@uni-bamberg.de","Silvello G.; Corcho O.; Manghi P.; Di Nunzio G.M.; Golub K.; Ferro N.; Poggi A.","Springer Science and Business Media Deutschland GmbH","","26th International Conference on Theory and Practice of Digital Libraries, TPDL 2022","20 September 2022 through 23 September 2022","Padua","283349","03029743","978-303116801-7","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85138809689"
"Goedicke M.; Lucke U.","Goedicke, Michael (6603430737); Lucke, Ulrike (24528902500)","6603430737; 24528902500","Research Data Management in Computer Science - the NFDIxCS Approach","2022","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-326","","","1317","1328","11","0","10.18420/inf2022_112","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139795646&doi=10.18420%2finf2022_112&partnerID=40&md5=54191b0add3feb57ceca65125daf5e09","paluno/University of Duisburg-Essen, Faculty of Economics and Business Administration, Gerlingstraße 16, Essen, 45117, Germany; Complex Multimedia Architectures, University of Potsdam, Department of Computer Science, An der Bahn 2, Potsdam, 14476, Germany","Goedicke M., paluno/University of Duisburg-Essen, Faculty of Economics and Business Administration, Gerlingstraße 16, Essen, 45117, Germany; Lucke U., Complex Multimedia Architectures, University of Potsdam, Department of Computer Science, An der Bahn 2, Potsdam, 14476, Germany","This contribution discusses the challenges and architectural considerations for research Data management in computer science and related infrastructure for implementing the so-called FAIR principles. The main challenge is, to cover the research data management requirements of the various sub disciplines of computer science. This diversity must be managed in a uniform way which entails a common structure for this task. We outline these requirements briefly and discuss then the concept of the so-called research data management container (RDMC) which encapsulates a given research data set in conjunction with all accompanying information and support (software, execution environment etc) in order to provide a portable unit for management, distribution and access control. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.","FAIR Principles; Nationale Forschungsdaten Infrastruktur; Research Data Management","Access control; Common structures; Execution environments; FAIR principle; Nationale forschungsdaten infrastruktur; Research data managements; Research data sets; Software execution; Sub-disciplines; Information management","","","","","","","The DIAMANT-Model 2.0 Reference Process; Computer Science Bibliography; Open Research Knowledge Graph; The RISE-DE Reference Model; Software Heritage Foundation; Strickroth S., Bussler D., Lucke U., Container-based Dynamic Infrastructure for Education On-Demand, DELFI 2021, pp. 205-216","","Demmler D.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg; Krupka D.; Gesellschaft fur Informatik, Anna-Louisa-Karsch-Strasse 2, Berlin; Federrath H.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg","Gesellschaft fur Informatik (GI)","Adesso SE; et al.; Genua GmbH; Google Deutschland GmbH; Hamburger Informatik Technologie Center (HITEC); SAP SE","2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022","26 September 2022 through 30 September 2022","Hamburg","183150","16175468","978-388579720-3","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85139795646"
"Feger S.S.; WoÅ°niak P.W.; Niess J.; Schmidt A.","Feger, Sebastian Stefan (56893703100); WoÅ°niak, Paweł W. (55879280600); Niess, Jasmin (57195638917); Schmidt, Albrecht (55596321600)","56893703100; 55879280600; 57195638917; 55596321600","Tailored Science Badges: Enabling New Forms of Research Interaction","2021","DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere","","","","579","588","9","3","10.1145/3461778.3462067","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110076514&doi=10.1145%2f3461778.3462067&partnerID=40&md5=b10fba4350701cef0f243bd492ac7b74","Lmu Munich, Germany; Cern, Geneva, Switzerland; Utrecht University, Netherlands; University of Bremen, Germany","Feger S.S., Lmu Munich, Germany, Cern, Geneva, Switzerland; WoÅ°niak P.W., Utrecht University, Netherlands; Niess J., University of Bremen, Germany; Schmidt A., Lmu Munich, Germany","Science faces a reproducibility crisis. There is a need to establish open science practices within the academic reputation economy. Open Science Badges address this issue by promoting and acknowledging research sharing and documentation. The generic design of currently awarded badges enabled their adoption across the sciences. Yet, their general nature makes it difficult to reflect individual practices and needs of distinct scientific fields. In this paper, we explore uses and effects of highly tailored badges in research data management. We implemented six science badges in a particle physics research preservation service. Our exploration showed that scientists were open to encouraging valuable scientific practices through tailored science badges. They described entirely new opportunities for interaction with research repositories. We present design implications for systems that promote reproducibility, related to meaningful criteria, repository navigation, and content discovery. Finally, we discuss the scope and uses of tailored science badges in modern science. © 2021 ACM.","Discovery; Gamification; Motivation; Navigation; Reproducibility; Tailored Science Badges; Visibility.","Content discoveries; Design implications; General nature; Generic design; Modern science; Reproducibilities; Research data managements; Scientific fields; Information management","","","","","","","Artifact Review and Badging. Website, (2018); Baker M., 1, 500 scientists lift the lid on reproducibility, Nature News, 533, 7604, (2016); Bechhofer S., Buchan I., De Roure D., Missier P., Ainsworth J., Bhagat J., Couch P., Cruickshank D., Delderfield M., Dunlop I., Gamble M., Michaelides D., Owen S., Newman D., Sufi S., Goble C., Why linked data is not enough for scientists, Future Generation Computer Systems, 29, 2, pp. 599-611, (2013); Glenn Begley C., Ellis L.M., Drug development: Raise standards for preclinical cancer research, Nature, 483, 7391, pp. 531-533, (2012); Blandford A., Furniss D., Makri S., Qualitative Hci Research: Going behind the Scenes, pp. 51-60, (2016); Boisvert R.F., Incentivizing reproducibility, Commun. Acm, 59, 10, (2016); Borgman C.L., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Cern Annual Personnel Statistics 2019, (2019); Chen X., Dallmeier-Tiessen S., Dani A., Dasler R., Delgado Fernandez J., Fokianos P., Herterich P., Simko T., Cern analysis preservation: A novel digital library service to enable reusable and reproducible research, International Conference on Theory and Practice of Digital Libraries, pp. 347-356, (2016); Chen X., Dallmeier-Tiessen S., Dasler R., Feger S., Fokianos P., Benito Gonzalez J., Hirvonsalo H., Kousidis D., Lavasa A., Mele S., Et al., Open is not enough, Nature Physics, 1, (2018); Science Collaboration O., An open, large-scale, collaborative effort to estimate the reproducibility of psychological science, Perspectives on Psychological Science, 7, 6, pp. 657-660, (2012); Deterding S., Khaled R., Nacke L., Dixon D., Gamification: Toward a definition, Chi, 2011, pp. 12-15, (2011); Eveleigh A., Jennett C., Lynn S., Cox A.L., I want to be a captain! I want to be a captain!"": Gamification in the old weather citizen science project, Proceedings of the First International Conference on Gameful Design, Research, and Applications, pp. 79-82, (2013); Fecher B., Friesike S., Hebing M., Linek S., A reputation economy: How individual reward considerations trump systemic arguments for open access to data, Palgrave Communications, 3, 1, pp. 1-10, (2017); Feger S., Dallmeier-Tiessen S., Wozniak P., Schmidt A., Just not the usualworkplace: Meaningful gamification in science, Mensch und Computer 2018-Workshopband, (2018); Stefan Feger S., Interactive Tools for Reproducible Science-Understanding, Supporting, and Motivating Reproducible Science Practices, (2020); Feger S.S., Dallmeier-Tiessen S., Schmidt A., Wozniak P.W., Designing for reproducibility: A qualitative study of challenges and opportunities in high energy physics, Proceedings of the Sigchi Conference on Human Factors in Computing Systems-CHI'19, (2019); Feger S.S., Dallmeier-Tiessen S., Wozniak P.W., Schmidt A., Gamification in science: A study of requirements in the context of reproducible research, Proceedings of the Sigchi Conference on Human Factors in Computing Systems-CHI'19, (2019); Feger S.S., Dallmeier-Tiessen S., Wozniak P.W., Schmidt A., The role of hci in reproducible science: Understanding, supporting and motivating core practices, Extended Abstracts of the 2019 Chi Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI Ea '19), pp. 1-6, (2019); Feger S.S., Wozniak P.W., Lischke L., Schmidt A., Yes, I comply!' Motivations and Practices around Research Data Management and Reuse across Scientific Fields, Proceedings of the Acm on Human-Computer Interaction, 4, pp. 1-26, (2020); Open Science Badges. Website, (2021); The Fair Data Principles. Website, (2014); Friesike S., Widenmayer B., Gassmann O., Schildhauer T., Opening science: Towards an agenda of open science in academia and industry, The Journal of Technology Transfer, 40, 4, pp. 581-601, (2015); Hamari J., Koivisto J., Sarsa H., Et al., Does gamification work?-a literature review of empirical studies on gamification, Hicss, 14, pp. 3025-3034, (2014); Ibanez M., Di-Serio A., Delgado-Kloos C., Gamification for engaging computer science students in learning activities: A case study, Ieee Transactions on Learning Technologies, 7, 3, pp. 291-301, (2014); Jianu R., Laidlaw D., An evaluation of how small user interface changes can improve scientists' analytic strategies, Proceedings of the Sigchi Conference on Human Factors in Computing Systems (Austin, Texas, USA) (CHI '12), pp. 2953-2962, (2012); Kidwell M.C., Lazarevic L.B., Baranski E., Hardwicke T.E., Piechowski S., Falkenberg L., Kennett C., Slowik A., Sonnleitner C., Hess-Holden C., Et al., Badges to acknowledge open practices: A simple, low-cost, effective method for increasing transparency, PLoS Biology, 14, 5, (2016); Klein H.J., Wesson M.J., Hollenbeck J.R., Wright P.M., DeShon R.P., The assessment of goal commitment: A measurement model meta-analysis, Organizational Behavior and Human Decision Processes, 85, 1, pp. 32-55, (2001); Knaving K., Wozniak P.W., Niess J., Poguntke R., Fjeld M., Bjork S., Understanding grassroots sports gamification in the wild, Proceedings of the 10th Nordic Conference on Human-Computer Interaction, pp. 102-113, (2018); Mekler E.D., Bruhlmann F., Tuch A.N., Opwis K., Towards understanding the effects of individual gamification elements on intrinsic motivation and performance, Computers in Human Behavior, 71, pp. 525-534, (2017); Munafo M.R., Nosek B.A., Bishop M.D.V., Button K.S., Chambers C.D., Percie Du Sert N., Simonsohn U., Wagenmakers E., Ware J.J., Ioannidis A.J.P., A manifesto for reproducible science, Nature Human Behaviour, 1, 1, (2017); Nacke L.E., Sebastian Deterding C., The maturing of gamification research, Computers in Human Behaviour, pp. 450-454, (2017); Nicholson S., A recipe for meaningful gamification, Gamification in Education and Business, pp. 1-20, (2015); Nust D., Lohoff L., Einfeldt L., Gavish N., Gotza M., Tariq Jaswal S., Khalid S., Meierkort L., Mohr M., Rendel C., Et al., Guerrilla badges for reproducible geospatial data science, Agile, 2019, (2019); Oleksik G., Milic-Frayling N., Jones R., Beyond data sharing: Artifact ecology of a collaborative nanophotonics research centre, Proceedings of the Acm 2012 Conference on Computer Supported Cooperative Work (Seattle, Washington, USA) (CSCW '12). Acm, pp. 1165-1174, (2012); Oprescu F., Jones C., Katsikitis M., I play at work-ten principles for transforming work processes through gamification, Frontiers in Psychology, 5, (2014); Rowhani-Farid A., Allen M., Barnett A.G., What incentives increase data sharing in health and medical research? A systematic review, Research Integrity and Peer Review, 2, 1, (2017); Ryan R.M., Deci E.L., Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being, American Psychologist, 55, 1, (2000); Ryan R.M., Deci E.L., Brick by brick: The origins, development, and future of self-determination theory, Advances in Motivation Science, 6, pp. 111-156, (2019); Seaborn K., Fels D.I., Gamification in theory and action: A survey, International Journal of Human-computer Studies, 74, pp. 14-31, (2015); Thomer A.K., Twidale M.B., Guo J., Yoder M.J., Co-designing scientific software: Hackathons for participatory interface design, Proceedings of the 2016 Chi Conference Extended Abstracts on Human Factors in Computing Systems (San Jose, California, USA) (CHI Ea '16), pp. 3219-3226, (2016); Tondello G.F., Mora A., Nacke L.E., Elements of gameful design emerging from user preferences, Proceedings of the Annual Symposium on Computer-Human Interaction in Play-CHI Play '17, pp. 129-142, (2017); Tyack A., Mekler E.D., Self-determination theory in hci games research: Current uses and open questions, Proceedings of the 2020 Chi Conference on Human Factors in Computing Systems, pp. 1-22, (2020); Wilkinson M.D., Dumontier M., Jan Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Boiten J., Bonino Da Silva Santos L., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray G.A.J., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P.A., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Worden D.J., Emerging technologies for data research: Implications for bias, curation, and reproducible results, Human Capital and Assets in the Networked World, (2017)","","","Association for Computing Machinery, Inc","ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere, DIS 2021","28 June 2021 through 2 July 2021","Virtual, Online","169894","","978-145038476-6","","","English","DIS - Proc. ACM Des. Interact. Syst. Conf.: Nowhere Everywhere","Conference paper","Final","","Scopus","2-s2.0-85110076514"
"Funk C.J.","Funk, Carla J. (7102175044)","7102175044","Promoting new and expanded roles for librarians and information specialists","2022","Information Services and Use","42","2","","205","213","8","0","10.3233/ISU-220152","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132280412&doi=10.3233%2fISU-220152&partnerID=40&md5=ca2c119ed14ccc7035d770f97cb9d746","Medical Library Association (Retired), 345 West Fullerton Parkway, Chicago, 60614, IL, United States","Funk C.J., Medical Library Association (Retired), 345 West Fullerton Parkway, Chicago, 60614, IL, United States","This chapter describes how the U.S. National Library of Medicine (NLM), under the leadership of Donald A.B. Lindberg M.D., promoted new and expanded roles for librarians and information specialists in response to advances in technology and public policy. These advances brought information services directly to all potential users, including health professionals and the public and stimulated NLM to expand its programs, policies, and services to serve all. Dr. Lindberg included librarians and information specialists in all of NLM's new endeavors, helping both to recognize and establish new or expanded roles. The involvement of librarians and information specialists in multidisciplinary healthcare research teams, in underserved communities, and in research data management and compliance has helped to redefine the health sciences information profession for the 21st century.  © 2022 - The authors. Published by IOS Press.","access to information; Donald A.B. Lindberg M.D.; librarians; U.S. National Library of Medicine","Human resource management; Information management; Access to information; Donald AB lindberg MD; Health professionals; Health science; Librarian; National library of medicines; Potential users; Research data managements; Research teams; US national library of medicine; Information services","","","","","National Institutes of Health, NIH; U.S. Food and Drug Administration, FDA; U.S. National Library of Medicine, NLM","Funding text 1: Beginning in 2003, NLM took steps to meet these needs, under the direction of Dr. Valerie Florance, then NLM Associate Director for Extramural Programs. She first established an NLM grant program for budding informationists, the NLM Individual Fellowship for Informationist Training. This fellowship program supported coursework and internships in clinical, biomedical research, public health, and consumer health to prepare Fellows for new career directions. This program concluded in 2008. It was followed in 2010 by Administrative Supplements for Informationist Services, another brainchild of Dr. Florance. Funded by NLM and other NIH institutes, this program provided grants for NIH-funded extramural researchers to immerse informationists in their research teams, often to assist with research data management. These grant programs improved research skills and knowledge about the research community, as well as developing best practices and demonstrating the roles information specialists could play in research data management [20,21].; Funding text 2: Once developed, however, PMC and ClinicalTrials.gov became key enablers of new public policies that emerged from separate series of events involving research advocacy groups, librarians and their professional associations, scientists, patients, journal editors, and the U.S. Congress. For PMC, the precipitating issue was lack of public access to the results of taxpayer-funded research. For ClinicalTrials.gov, it was outrage over deliberate omission of information about serious adverse drug effects in articles reporting the results of clinical trials. By late 2007, in separate actions, the U.S. Congress had mandated: (1) deposit in PMC of papers resulting from research funded by NIH, i.e., the NIH Public Access Policy, and (2) early registration and summary results submission in ClinicalTrials.gov for the majority of trials of drugs and devices subject to regulation by the U.S. Food and Drug Administration (FDA). Many other research papers and trials became subject to similar requirements promulgated by other research funders. Early and complete registration of clinical trials has been required for subsequent publication of results in many influential journals since 2005.","Meyerhoff E., Foundations of medical librarianship, Bull Med Libr Assoc, 65, 4, pp. 409-418, (1977); Cimpl K., Clinical medical librarianship: A review of the literature, Bull Med Libr Assoc, 73, 1, pp. 21-28, (1985); Matheson N., Cooper J.A.D., Academic information in the academic health sciences center: Roles for the library in information management, J Med Educ, 57, 10, pp. 1-93, (1982); Bonham M.D., Nelson L.L., An evaluation of four end-user systems for searching MEDLINE, Bull Med Libr Assoc, 76, 2, pp. 171-180, (1988); Broering N.C., The miniMEDLINE SYSTEMTM : A library-based end-user search system, Bull Med Libr Assoc, 73, 2, pp. 138-145, (1985); NLM long range planning documents; Holst R., Partnering for education and career development of librarians and information specialists, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Dorsch J.L., Faughnan J.G., Humphreys B.L., Grateful Med: Direct access to MEDLINE for health professionals with personal computers, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Board of Regents. Improving health professionals access to information: Challenges and opportunities for the National Library of Medicine, (1989); Humphreys B.L., Adjusting to progress: Interactions between the NLM and health sciences librarians, 1961-2001, J Med Libr Assoc, 90, 1, (2002); Lindberg D.A.B., National Library of Medicine and its role, Bull Med Libr Assoc, 81, 1, pp. 71-73, (1993); White H.S., The Grateful Med program and the medical library profession, Bull Med Libr Assoc, 81, 1, pp. 73-75, (1993); Information STAT: Rx for hospital quality, NLM News, 47, 11-12, pp. 8-10, (1992); Shipman J.P., Burroughs C.M., Rambo N., NLM's library network: A force for outreach, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Davidoff F., Florance V., The informationist: A new health profession?, Ann Intern Med, 132, 12, pp. 996-998, (2000); Shipman J.P., Cunningham D.J., Holst R., Watson L.A., The informationist conference: Report, J Med Libr Assoc, 90, 4, pp. 458-464, (2002); Deardorff A.A., Florance V., VanBiervliet A., Assessing the National Library of Medicine's informationist awards, J Esci Libr, 5, 1, (2016); Gore S.A., A librarian by any other name: The role of the informationist, J E-Sci Libr, 2, 1, pp. 20-24, (2013); Shipman J.P., Kurtz-Rossi S., Funk C.J., The health information literacy research project, J Med Libr Assoc, 97, 4, pp. 273-281, (2009); Doyle J.D., IAIMS and JCAHO: Implications for hospital librarians. Integrated Academic Information Management Systems. Joint Commission on Accreditation of Healthcare Organizations, Bull Med Libr Assoc, 87, 4, pp. 383-386, (1999); Lindberg D.A.B., Humphreys B.L., 2015-the future of medical libraries, New Engl J Med, 352, 11, pp. 1067-1070, (2005); Jones D.A., Shipman J.P., Plaut D.A., Selden C.R., Characteristics of personal health records: Findings of the Medical Library Association/National Library of Medicine Joint Electronic Personal Health Record Task Force, J Med Libr Assoc, 98, 3, pp. 243-249, (2010); Gore S.A., Nordberg J.M., Palmer L.A., Piorun M.E., Trends in health sciences library and information science research: An analysis of research publications in the BMLA and JMLA from 1991-2001, J Med Libr Assoc, 97, 3, pp. 203-216, (2009); Funk M.E., Our words, our story: A textual analysis of articles published in the BMLA/JMLA from 1961-2010, J Med Libr Assoc, 10, 1, pp. 12-20, (2013); Humphreys B.L., Tuttle M.S., Something new and different: The Unified Medical Language System, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Woodsmall R.M., Lyon-Hartmann B., Siegel E.R., Learned Information, (1989); Lindberg D.A., Siegel E.R., Rapp B.A., Wallingford K.T., Wilson S.R., Use of MEDLINE by physicians for clinical problem solving, JAMA, 269, 24, pp. 3124-3129, (1993); Lindberg D.A.B., National Library of Medicine director, MLA News, 55, 5, (2015)","C.J. Funk; Medical Library Association (Retired), Chicago, 345 West Fullerton Parkway, 60614, United States; email: cjfunk46@gmail.com","","IOS Press BV","","","","","","01675265","","ISUSD","","English","Inf Serv Use","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85132280412"
"Riedel C.; Geßner H.; Seegebrecht A.; Ayon S.I.; Chowdhury S.H.; Engbert R.; Lucke U.","Riedel, Christian (57201287665); Geßner, Hendrik (57210977885); Seegebrecht, Anja (57926002000); Ayon, Safial Islam (57208015273); Chowdhury, Shafayet Hossen (57925546400); Engbert, Ralf (6701500744); Lucke, Ulrike (24528902500)","57201287665; 57210977885; 57926002000; 57208015273; 57925546400; 6701500744; 24528902500","Including Data Management in Research Culture Increases the Reproducibility of Scientific Results","2022","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-326","","","1341","1352","11","0","10.18420/inf2022_114","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139744413&doi=10.18420%2finf2022_114&partnerID=40&md5=2890832363c9078bfcacea10893943dc","University of Potsdam, Institute of Computer Science, An der Bahn 2, Potsdam, 14476, Germany; University of Potsdam, Centre for Information Technology and Media Management, Am Neuen Palais 10, Potsdam, 14469, Germany; University of Freiburg, Institute of Physics, Hermann-Herder-Str. 3, Freiburg im Breisgau, 79104, Germany; University of Potsdam, Department of Psychology, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany","Riedel C., University of Potsdam, Institute of Computer Science, An der Bahn 2, Potsdam, 14476, Germany; Geßner H., University of Potsdam, Centre for Information Technology and Media Management, Am Neuen Palais 10, Potsdam, 14469, Germany; Seegebrecht A., University of Potsdam, Institute of Computer Science, An der Bahn 2, Potsdam, 14476, Germany, University of Freiburg, Institute of Physics, Hermann-Herder-Str. 3, Freiburg im Breisgau, 79104, Germany; Ayon S.I., University of Potsdam, Institute of Computer Science, An der Bahn 2, Potsdam, 14476, Germany; Chowdhury S.H., University of Potsdam, Institute of Computer Science, An der Bahn 2, Potsdam, 14476, Germany; Engbert R., University of Potsdam, Department of Psychology, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany; Lucke U., University of Potsdam, Institute of Computer Science, An der Bahn 2, Potsdam, 14476, Germany","Reproducible research results are among the pillars of sustainable science, and considerable progress has been achieved in this direction recently. However, there is much room for improvement across the research communities. Here we analyze the reproducibility of 108 publications from an interdisciplinary Collaborative Research Center on applied mathematics in various scientific fields. Based on a previous reproducibility study in hydrology, we identify the rate of reproducible scientific results and why reproducibility fails. We identify the main problems that hinder reproducible results and relate them to previous interventions targeting the research culture of reproducible scientific findings. Thus, the success of our measures can be estimated, and specific recommendations for future work can be derived. In our study, the number of publications that allow for at least partly reproducible research results increased over time. However, we see an ongoing need for directives and support in research data management among research communities since issues concerning data accessibility and quality limit the reproducibility of scientific results. We argue that our results are representative of other interdisciplinary research areas. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.","open data; reproducibility; research data management","Collaborative research; Open datum; Reproducibilities; Reproducible research; Research center; Research communities; Research culture; Research data managements; Research results; Scientific results; Open Data","","","","","Deutsche Forschungsgemeinschaft, DFG, (318763901 - SFB1294)","This work was funded by Deutsche Forschungsgemeinschaft (DFG) - Project-ID 318763901 - SFB1294. The data supporting the findings of this study are available at https://doi.org/10.5281/zenodo.6543497.","Association for Computing Machinery: Artifact Review and Badging Version 1.1; Baker M., 1, 500 scientists lift the lid on reproducibility, Nature, 533, pp. 452-454, (2016); Chang A. C., Li P., Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say”Usually Not, (2015); Collberg C., Proebsting T.A., Repeatability in computer systems research, Commun. ACM, 59, 3, pp. 62-69, (2016); Guidelines for Safeguarding Good Research Practice, (2022); Guidelines on Implementation of Open Access to Scientific Publications and Research Data in projects supported by the European Research Council under Horizon 2020; EU Directive 2019/1024 on open data and the re-use of public sector information, (2019); Gessner H., Transparently Safeguarding Good Research Data Management with the Lean Process Assessment Model, E-Science-Tage - Share Your Research Data, (2021); Gotzelmann G., Hegel P., Krewet M., Soring S., Tonne D., Aspects of Digital Infrastructures - Challenges and Perspectives of Research Data in the Collaborative Research Centre “Episteme in Motion”, BIBLIOTHEK - Forschung und Praxis, (2019); Kindling M, Schirmbacher P., Die digitale Forschungswelt“als Gegenstand der Forschung: Lehrstuhl Informationsmanagement, Wissenschaft & Praxis, 64, 2-3, pp. 127-136, (2013); Laurinavichyute A, Yadav H., Vasishth S., Share the code, not just the data: A case study of the reproducibility of articles published in the Journal of Memory and Language under the open data policy, PsyArXiv Preprint, (2022); Maassen E., van Assen M.A.L.M., Nuijten M. B, Olsson-Collentine A., Wicherts J. M., Reproducibility of individual effect sizes in meta-analyses in psychology, PLOS ONE, 15, 5, (2020); Nust D., Granell C., Hofer B., Konkol M., Ostermann FO., Sileryte R., Cerutti V., Reproducible research and GIScience: an evaluation using AGILE conference papers, PeerJ, 6, (2018); Obels P., Lakens D., Coles NA., Gottfried J., Green SA., Analysis of open data and computational reproducibility in registered reports in psychology, Adv. Methods Pract. Psychol. Sci, 3, pp. 229-237, (2020); Perrier L., Blondal E., Ayala AP., Dearborn D., Kenny T., Lightfoot D., Et al., Research data management in academic institutions: A scoping review, PLoS ONE, 12, 5, (2017); Raff E., A step toward quantifying independently reproducible machine learning research, Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 5485-5495, (2019); Stockemer D., Koehler S., Lentz T., Data Access, Transparency, and Replication: New Insights from the Political Behavior Literature, PS: Political Science & Politics, 51, 4, pp. 799-803, (2018); Stodden V., Seiler J., Ma Z., An empirical analysis of journal policy effectiveness for computational reproducibility, Proc. Natl. Acad. Sci, 115, pp. 2584-2589, (2018); Stagge J., Rosenberg D., Abdallah A., Et al., Assessing data availability and research reproducibility in hydrology and water resources, Sci Data, 6, (2019); Stagge J., Abdallah A.M., Rosenberg D.E., jstagge/reproduc_hyd: Repository as published in Scientific Data (2.0), Zenodo, (2019); Forschungsdaten-Policy der Universität Potsdam, Auszug aus den Amtlichen Bekanntmachungen Nr. 18 vom 30.9.2019; Wang WM., Gopfert T., Stark R., Data Management in Collaborative Interdisciplinary Research Projects - Conclusions from the Digitalization of Research in Sustainable Manufacturing, ISPRS Int. J. Geo-Inf, 5, (2016); Wiljes C., Cimiano P., Teaching Research Data Management for Students, Data Science Journal, 18, 1, (2019)","","Demmler D.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg; Krupka D.; Gesellschaft fur Informatik, Anna-Louisa-Karsch-Strasse 2, Berlin; Federrath H.; Universitat Hamburg, Vogt-Kolln-Strasse 30, Hamburg","Gesellschaft fur Informatik (GI)","Adesso SE; et al.; Genua GmbH; Google Deutschland GmbH; Hamburger Informatik Technologie Center (HITEC); SAP SE","2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022","26 September 2022 through 30 September 2022","Hamburg","183150","16175468","978-388579720-3","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85139744413"
"Doyle S.; Pavlos R.; Carlson S.J.; Barton K.; Bhuiyan M.; Boeing B.; Borland M.L.; Hoober S.; Blyth C.C.","Doyle, Sarah (36445654400); Pavlos, Rebecca (54966761000); Carlson, Samantha J. (57210697716); Barton, Katherine (57442331400); Bhuiyan, Mejbah (55350288000); Boeing, Bernadett (57442457600); Borland, Meredith L. (7005781555); Hoober, Steven (57442331500); Blyth, Christopher C. (55589360100)","36445654400; 54966761000; 57210697716; 57442331400; 55350288000; 57442457600; 7005781555; 57442331500; 55589360100","Efficacy of Digital Health Tools for a Pediatric Patient Registry: Semistructured Interviews and Interface Usability Testing with Parents and Clinicians","2022","JMIR Formative Research","6","1","e29889","","","","1","10.2196/29889","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124129511&doi=10.2196%2f29889&partnerID=40&md5=536586f102298df96662f59449537b35","Emergency Department, Perth Children's Hospital, Perth, Australia; Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia; University of Western Australia, Perth, Australia; 4ourth Mobile, Mission, KS, United States; Perth Children's Hospital, Perth, Australia","Doyle S., Emergency Department, Perth Children's Hospital, Perth, Australia; Pavlos R., Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia; Carlson S.J., Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia; Barton K., Emergency Department, Perth Children's Hospital, Perth, Australia; Bhuiyan M., Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia; Boeing B., Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia; Borland M.L., Emergency Department, Perth Children's Hospital, Perth, Australia, University of Western Australia, Perth, Australia; Hoober S., 4ourth Mobile, Mission, KS, United States; Blyth C.C., Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia, University of Western Australia, Perth, Australia, Perth Children's Hospital, Perth, Australia","Background: Acute respiratory infection (ARI) in childhood is common, but more knowledge on the burden and natural history of ARI in the community is required. A better understanding of ARI risk factors, treatment, and outcomes will help support parents to manage their sick child at home. Digital health tools are becoming more widely adopted in clinical care and research and may assist in understanding and managing common pediatric diseases, including ARI, in hospitals and in the community. We integrated 2 digital tools—a web-based discharge communication system and the REDCap (Research Electronic Data Capture) platform—into the Pragmatic Adaptive Trial for Acute Respiratory Infection in Children to enhance parent and physician engagement around ARI discharge communication and our patient registry. Objective: The objective of this study is to determine the efficacy and usability of digital tools integrated into a pediatric patient registry for ARI. Methods: Semistructured interviews and software interface usability testing were conducted with 11 parents and 8 emergency department physicians working at a tertiary pediatric hospital and research center in Perth, Western Australia, in 2019. Questions focused on experiences of discharge communication and clinical trial engagement. Responses were analyzed using the qualitative Framework Method. Participants were directly observed using digital interfaces as they attempted predetermined tasks that were then classified as success, failure, software failure, or not observed. Participants rated the interfaces using the System Usability Scale (SUS). Results: Most parents (9/11, 82%) indicated that they usually received verbal discharge advice, with some (5/11, 45%) recalling receiving preprinted resources from their physician. Most (8/11, 73%) would also like to receive discharge advice electronically. Most of the physicians (7/8, 88%) described their usual practice as verbal discharge instructions, with some (3/8, 38%) reporting time pressures associated with providing discharge instructions. The digital technology option was preferred for engaging in research by most parents (8/11, 73%). For the discharge communication digital tool, parents gave a mean SUS score of 94/100 (SD 4.3; A grade) for the mobile interface and physicians gave a mean usability score of 93/100 (SD 4.7; A grade) for the desktop interface. For the research data management tool (REDCap), parents gave a mean usability score of 78/100 (SD 11.0; C grade) for the mobile interface. Conclusions: Semistructured interviews allowed us to better understand parent and physician experiences of discharge communication and clinical research engagement. Software interface usability testing methods and use of the SUS helped us gauge the efficacy of our digital tools with both parent and physician users. This study demonstrates the feasibility of combining qualitative research methods with software industry interface usability testing methods to help determine the efficacy of digital tools in a pediatric clinical research setting. ©Sarah Doyle, Rebecca Pavlos, Samantha J Carlson, Katherine Barton, Mejbah Bhuiyan, Bernadett Boeing, Meredith L Borland, Steven Hoober, Christopher C Blyth.","Acute respiratory infection; Digital health technology; Discharge instructions; Mobile phone; Mobile technology; Pediatric acute respiratory infection; REDCap; Semistructured interview; Usability testing","","","","","","AMP Foundation; National Health and Medical Research Council, NHMRC, (APP1173163); Myer Foundation","SD reports grants from the AMP Foundation and the Myer Foundation, which supported the development of the web and mobile system referred to in this study as the Parent Engagement through Technology Solutions system. The funders had no role in the study design; data collection, analysis, or interpretation; or manuscript creation. CCB is supported by a National Health and Medical Research Council Emerging Leadership Fellowship (APP1173163). The authors acknowledge and thank Mr Kevin O’Brien for his contribution to the usability testing methods.","Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE, Et al., Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis, Lancet, 385, 9966, pp. 430-440, (2015); Sarna M, Ware RS, Sloots TP, Nissen MD, Grimwood K, Lambert SB., The burden of community-managed acute respiratory infections in the first 2-years of life, Pediatr Pulmonol, 51, 12, pp. 1336-1346, (2016); Barnes R, Blyth CC, de Klerk N, Lee WH, Borland ML, Richmond P, Et al., Geographical disparities in emergency department presentations for acute respiratory infections and risk factors for presenting: a population-based cohort study of Western Australian children, BMJ Open, 9, 2, (2019); Sharma A, Harrington RA, McClellan MB, Turakhia MP, Eapen ZJ, Steinhubl S, Et al., Using digital health technology to better generate evidence and deliver evidence-based care, J Am Coll Cardiol, 71, 23, pp. 2680-2690, (2018); Hoober S., Mobile is now everything: mobile matters, UXmatters, (2020); Wosik J, Fudim M, Cameron B, Gellad ZF, Cho A, Phinney D, Et al., Telehealth transformation: COVID-19 and the rise of virtual care, J Am Med Inform Assoc, 20, pp. 957-962, (2020); Tharmalingam S, Hagens S, Zelmer J., The value of connected health information: perceptions of electronic health record users in Canada, BMC Med Inform Decis Mak, 16, (2016); Rosa C, Marsch LA, Winstanley EL, Brunner M, Campbell ANC., Using digital technologies in clinical trials: Current and future applications, Contemp Clin Trials, 100, (2021); Curran JA, Gallant AJ, Zemek R, Newton AS, Jabbour M, Chorney J, Et al., Discharge communication practices in pediatric emergency care: a systematic review and narrative synthesis, Syst Rev, 8, 1, (2019); Taber C, Warren J, Day K., Improving the Quality of Informed Consent in Clinical Research with Information Technology, Stud Health Technol Inform, 231, pp. 135-142, (2016); Wilbanks J., Design issues in e-consent, J Law Med Ethics, 46, 1, pp. 110-118, (2018); Cuttini M., Proxy informed consent in pediatric research: a review, Early Hum Dev, 60, 2, pp. 89-100, (2000); Antal H, Bunnell HT, McCahan SM, Pennington C, Wysocki T, Blake KV., A cognitive approach for design of a multimedia informed consent video and website in pediatric research, J Biomed Inform, 66, pp. 248-258, (2017); Keesara S, Jonas A, Schulman K., Covid-19 and health care's digital revolution, N Engl J Med, (2020); Zhang J., Human-centered computing in health information systems. Part 1: analysis and design, J Biomed Inform, 38, 1, pp. 1-3, (2005); Melnick ER, Dyrbye LN, Sinsky CA, Trockel M, West CP, Nedelec L, Et al., The association between perceived electronic health record usability and professional burnout among US physicians, Mayo Clin Proc, 95, 3, pp. 476-487, (2020); Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG., Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform, 42, 2, pp. 377-381, (2009); Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, The REDCap consortium: Building an international community of software platform partners, J Biomed Inform, 95, (2019); Acworth J, Babl F, Borland M, Ngo P, Krieser D, Schutz J, Et al., Patterns of presentation to the Australian and New Zealand Paediatric Emergency Research Network, Emerg Med Australas, 21, 1, pp. 59-66, (2009); Brooke J., SUS: A 'Quick and Dirty' Usability Scale, Usability Evaluation In Industry, (1996); Sauro J., 5 ways to interpret a SUS Score, Measuring U, (2018); Bangor A, Kortum P, Miller J., Determining what individual SUS scores mean: adding an Adjective Rating Scale, J Usab Stud, 4, 3, pp. 114-123, (2009); Doyle SK, Rippey JC, Jacques A, Rea AM, Kaiser BN, Miller SM, Et al., Effect of personalised, mobile-accessible discharge instructions for patients leaving the emergency department: a randomised controlled trial, Emerg Med Australas, 32, 6, pp. 967-973, (2020); Macefield R., How to specify the participant group size for usability studies: a practitioner's guide, J Usab Stud, 5, 1, pp. 34-45, (2009); Gale NK, Heath G, Cameron E, Rashid S, Redwood S., Using the framework method for the analysis of qualitative data in multi-disciplinary health research, BMC Med Res Methodol, 13, (2013); Jaspers MW., A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence, Int J Med Inform, 78, 5, pp. 340-353, (2009); Glick AF, Farkas JS, Nicholson J, Dreyer BP, Fears M, Bandera C, Et al., Parental management of discharge instructions: a systematic review, Pediatrics, 140, 2, (2017); Hoek AE, Anker SC, van Beeck EF, Burdorf A, Rood PP, Haagsma JA., Patient discharge instructions in the Emergency Department and their effects on comprehension and recall of discharge instructions: a systematic review and meta-analysis, Ann Emerg Med, 75, 3, pp. 435-444, (2020); Johnson A, Sandford J., Written and verbal information versus verbal information only for patients being discharged from acute hospital settings to home: systematic review, Health Educ Res, 20, 4, pp. 423-429, (2005); Sustersic M, Tissot M, Tyrant J, Gauchet A, Foote A, Vermorel C, Et al., Impact of patient information leaflets on doctor-patient communication in the context of acute conditions: a prospective, controlled, before-after study in two French emergency departments, BMJ Open, 9, 2, (2019); Marra C, Chen JL, Coravos A, Stern AD., Quantifying the use of connected digital products in clinical research, NPJ Digit Med, 3, (2020); Saidinejad M, Zorc J., Mobile and web-based education: delivering emergency department discharge and aftercare instructions, Pediatr Emerg Care, 30, 3, pp. 211-216, (2014); Newnham H, Barker A, Ritchie E, Hitchcock K, Gibbs H, Holton S., Discharge communication practices and healthcare provider and patient preferences, satisfaction and comprehension: A systematic review, Int J Qual Health Care, 29, 6, pp. 752-768, (2017); Dean M, Oetzel JG., Physicians' perspectives of managing tensions around dimensions of effective communication in the emergency department, Health Commun, 29, 3, pp. 257-266, (2014); Rhodes KV, Vieth T, He T, Miller A, Howes DS, Bailey O, Et al., Resuscitating the physician-patient relationship: emergency department communication in an academic medical center, Ann Emerg Med, 44, 3, pp. 262-267, (2004); Wallin D, Vezzetti R, Young A, Wilkinson M., Do parents of discharged pediatric Emergency Department patients read discharge instructions?, Pediatr Emerg Care, 37, 8, pp. 468-473, (2021); Mueller SK, Giannelli K, Boxer R, Schnipper JL., Readability of patient discharge instructions with and without the use of electronically available disease-specific templates, J Am Med Inform Assoc, 22, 4, pp. 857-863, (2015); Karliner LS, Auerbach A, Napoles A, Schillinger D, Nickleach D, Perez-Stable EJ., Language Barriers and Understanding of Hospital Discharge Instructions, Med Care, 50, 4, pp. 283-289, (2012); Bargas-Avila JA, Orsini S, Piosczyk H, Urwyler D, Opwis K., Enhancing online forms: use format specifications for fields with format restrictions to help respondents, Interact Comput, 23, 1, pp. 33-39, (2011); Hattink B, Droes R, Sikkes S, Oostra E, Lemstra AW., Evaluation of the digital Alzheimer center: testing usability and usefulness of an online portal for patients with dementia and their carers, JMIR Res Protoc, 5, 3, (2016); Chadwick-Dias A, McNulty M, Tullis T., Web usability and age: how design changes can improve performance, SIGCAPH Comput Phys Handicap, 17, 73-74, pp. 30-37, (2002); Walker K, Dwyer T, Heaton HA., Emergency medicine electronic health record usability: where to from here?, Emerg Med J, 38, 6, pp. 408-409, (2021)","S. Doyle; Emergency Department, Perth Children's Hospital, Perth, 15 Hospital Avenue Nedlands, 6009, Australia; email: sarahkdoyle@me.com","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85124129511"
"Blumesberger S.; Ganguly R.; Gänsdorfer N.; Gergely E.; Gruber A.; Hasani-Mavriqi I.; Kalová T.; Ladurner C.; Macher T.; Miksa T.; Solís B.S.; Schranzhofer H.; Stork C.; Stryeck S.; Töricht H.","Blumesberger, Susanne (27867529800); Ganguly, Raman (56702193000); Gänsdorfer, Nikos (57357305800); Gergely, Eva (57358207400); Gruber, Alexander (57357697100); Hasani-Mavriqi, Ilire (36460924100); Kalová, Tereza (57222005827); Ladurner, Christoph (57357442300); Macher, Therese (57222057509); Miksa, Tomasz (55260160000); Solís, Barbara Sanchéz (56702943200); Schranzhofer, Hermann (55504427100); Stork, Christiane (57357953400); Stryeck, Sarah (57191264224); Töricht, Heike (57357442400)","27867529800; 56702193000; 57357305800; 57358207400; 57357697100; 36460924100; 57222005827; 57357442300; 57222057509; 55260160000; 56702943200; 55504427100; 57357953400; 57191264224; 57357442400","Fair data Austria – aligning the implementation of fair tools and services; [Fair data Austria – abstimmung der implementierung von fair tools und services]","2021","VOEB-Mitteilungen","74","2","","","","","1","10.31263/voebm.v74i2.6379","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120336834&doi=10.31263%2fvoebm.v74i2.6379&partnerID=40&md5=37a26ddbab53a067c69f50240b537075","University of Vienna, Library and Archives, Austria; University of Vienna, Vienna University Computer Center, Austria; Graz University of Technology, Institute of Interactive Systems and Data Science, Austria; Medical University of Graz, Research Documentation and Research Evaluation, Austria; TU Wien, Institute of Software Technology and Interactive Systems, Austria; TU Wien, Center for Research Data Management, Austria; University of Innsbruck, Information Technology Services (IT-Center), Austria","Blumesberger S., University of Vienna, Library and Archives, Austria; Ganguly R., University of Vienna, Vienna University Computer Center, Austria; Gänsdorfer N., University of Vienna, Library and Archives, Austria; Gergely E., University of Vienna, Vienna University Computer Center, Austria; Gruber A., Graz University of Technology, Institute of Interactive Systems and Data Science, Austria; Hasani-Mavriqi I., Graz University of Technology, Institute of Interactive Systems and Data Science, Austria; Kalová T., University of Vienna, Library and Archives, Austria; Ladurner C., University of Vienna, Library and Archives, Austria; Macher T., Medical University of Graz, Research Documentation and Research Evaluation, Austria; Miksa T., TU Wien, Institute of Software Technology and Interactive Systems, Austria; Solís B.S., TU Wien, Center for Research Data Management, Austria; Schranzhofer H., Graz University of Technology, Institute of Interactive Systems and Data Science, Austria; Stork C., TU Wien, Center for Research Data Management, Austria; Stryeck S., Graz University of Technology, Institute of Interactive Systems and Data Science, Austria; Töricht H., University of Innsbruck, Information Technology Services (IT-Center), Austria","This article gives an overview of the FAIR Data Austria project objectives and current results. In collaboration with our project partners, we work on the development and establishment of tools for managing the lifecycle of research data, including machine-actionable Data Management Plans (maDMPs), repositories for longterm archiving of research results, RDM training and support services, models, and profiles for Data Stewards and FAIR Office Austria. © 2021, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Data management plans; Data stewards; FAIR principles; Machine-actionability; Next-generation repositories; RDM support and training; Reproducible research results; Research data management (RDM); Researcher engagement","","","","","","University of Vienna Library; Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF","Funding text 1: The authors are very grateful to Miguel Rey Mazon for proofreading the article and providing valuable comments and suggestions. We would also like to thank Andreas Ferus and Peter Schaffer for their many valuable inputs on this work. This work is supported by the BMBWF funded project FAIR Data Austria (Digital and Social Transformation call, 2020–2024).; Funding text 2: The Training Task Force has also prepared multimedia information websites on RDM core topics, designed for researchers so that they can quickly familiarize themselves with the main aspects of good RDM practices. The curated collection of OER materials includes short videos with transcripts and documentation to ensure accessibility, as well as checklists, guides, interactive quizzes, and links to further information on each topic. The development of the OER materials was supported by interns from the University of Vienna Library, which took part of the new virtual internship program developed as part of the FAIR Data project (Kalová, 2021).","Gansdorfer N., Discussions with data stewards: Requirements, competencies, tasks, (2020); Gruber A., Schranzhofer H., Knopper S., Stryeck S., Hasani-Mavriqi I., Competencies of data stewards at Austrian universities, Announcements of the Association of Austrian Librarians, 74, 1, pp. 12-32, (2021); Kalova T., Gansdorfer N., The role of data stewards: Analyzing current job postings, (2021); Kalova T., Library internship during the COVID-19 pandemic: Experience with online internships at the University Library of Vienna, Announcements from the Association of Austrian Librarians, 73, 3-4, pp. 422-434, (2021); Miksa T., Simms S., Mietchen D., Jones S., Ten principles for machine-actionable data management plans, PLOS Computational Biology, 15, 3, (2019); Miksa T., Walk P., Neish P., RDA DMP Common Standard for Machine-actionable Data Management Plans, (2020); Rauber A., Asmi A., van Uytvanck D., Proll S., Data Citation of Evolving Data: Recommendations of the Working Group on Data Citation (WGDC), (2015); Reichmann S., Data Stewardship Profile - Results from a survey of 6 Austrian research-performing institutions, (2020); Reichmann S., Hasani-Mavriqi I., Development of a concept for data stewards at Austrian universities, (2021)","H. Töricht; University of Innsbruck, Information Technology Services (IT-Center), Austria; email: heike.thoericht@uibk.ac.at","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85120336834"
"Lau H.X.; Lee S.L.C.; Ali Y.","Lau, Hui Xing (57218857855); Lee, Ser Lin Celine (57226382990); Ali, Yusuf (57075104500)","57218857855; 57226382990; 57075104500","Effectiveness of data auditing as a tool to reinforce good research data management (RDM) practice: a Singapore study","2021","BMC Medical Ethics","22","1","103","","","","1","10.1186/s12910-021-00662-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111382580&doi=10.1186%2fs12910-021-00662-y&partnerID=40&md5=1f79d1881de12202001501b88ec55b90","Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore","Lau H.X., Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Lee S.L.C., Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Ali Y., Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore","Background: Institutions, funding agencies and publishers are placing increasing emphasis on good research data management (RDM). RDM lapses in medical science can result in questionable data and cause the public’s confidence in the scientific community to crumble. A fledgling medical school in a young university in Singapore has mandated every funded research project to have a data management plan (DMP). However, researchers’ adherence to their DMPs was unknown until the school embarked on routine data auditing. We hypothesize that research data auditing improves RDM awareness, compliance and reception in the school. Methods: We conducted surveys with research PIs and researchers before and after data auditing to evaluate differences in self-reported RDM awareness, compliance and reception. As it is mandatory to deposit research data in a central data repository system in the school, we tracked data deposition by each laboratory from 2 weeks before to 3 months after data auditing as a marker of actual RDM compliance. Results: Research data auditing had an overall positive effect on self-reported RDM awareness, compliance and reception for both research PIs and researchers. Research PIs agreed more that RDM was important to scientific reproducibility, were more aware of proper RDM, had higher RDM strength in their laboratories and were more compliant with the DMP. Both research PIs and researchers believed data auditing helped them to be more compliant with data deposition in the repository. However, data auditing had no significant impact on laboratories’ data deposition rates over time, which could be due to the short sampling period. Conclusions: Research PIs and researchers generally felt that data auditing was effective in improving RDM practices. It helped to evaluate their RDM practices objectively, propose corrective actions for RDM lapses and spread awareness of the university’s data management policies. Our findings corroborated other studies in medical research, geosciences, engineering and ethics that data auditing promotes good RDM practices. Hence, we recommend research institutions worldwide to adopt data auditing as a tool to reinforce research integrity. © 2021, The Author(s).","Compliance; Data auditing; Data management plan; Research data management; Research integrity","Biomedical Research; Data Management; Humans; Reproducibility of Results; Research Personnel; Singapore; human; information processing; medical research; personnel; reproducibility; Singapore","","","","","Ministry of Education - Singapore, MOE, (RGI03/18); Nanyang Technological University, NTU; Lee Kong Chian School of Medicine, Nanyang Technological University, LKCMedicine","Funding text 1: We would like to thank the NTU Singapore Research Integrity and Ethics Office (RIEO) for proposing a Research Integrity Grant Call. We would also like to thank Professor James Best, Professor Russell Gruen and Professor Fabian Lim and the AVITSS team, in particular Alan Loe Wai Lit and Shim Yan Juen, for their unwavering support of the Good Research Practice Office (GRPO) at the Lee Kong Chian School of Medicine, NTU Singapore. Finally, we would like to thank Goh Su Nee from NTU Library for guidance on DMP.; Funding text 2: This research is supported by the Singapore Ministry of Education under its Singapore Ministry of Education Academic Research Fund Tier 1 (RGI03/18) to Y.A. The funding body did not play any role in the study design, data collection, analysis, and interpretation of data and in writing the manuscript. ","Campos-Varela I., Ruano-Ravina A., Misconduct as the main cause for retraction. A descriptive study of retracted publications and their authors, Gaceta Sanitaria, (2018); (2019); Jones S., Ball A., Ekmekcioglu C., The data audit framework: a first step in the data management challenge, Int J Digit Curation, 3, 2, pp. 112-120, (2008); Noguchi K., Gel Y.R., Brunner E., Konietschke F., NparLD: An R software package for the nonparametric analysis of longitudinal data in factorial experiments, J Stat Softw, 50, 12, pp. 1-23, (2012); Weiss R.B., Systems of protocol review, quality assurance, and data audit, Cancer Chemother Pharmacol, 42, 1, pp. 88-92, (1998); You Y.N., Jacobs L., Martinez E.D., Budinger S.C., Wittlief E.J., Myles S.K., Et al., Improved surgeon performance in clinical trials: an analysis of quality assurance audits from the American College of Surgeons Oncology Group, J Am Coll Surg, 203, 3, pp. 269-276, (2006); Tan A.C., Armstrong E., Close J., Harris I.A., Data quality audit of a clinical quality registry: a generic framework and case study of the Australian and New Zealand Hip Fracture Registry, BMJ Open Qual, 8, 3, (2019); Hoeijmakers F., Beck N., Wouters M.W.J.M., Prins H.A., Steup W.H., National quality registries: how to improve the quality of data?, J Thoracic Dis, 10, (2018); Smelser J., Gardella S., Austin B., Protocol audits for post-approval monitoring of animal use protocols, Lab Anim, 34, pp. 23-27, (2005); Shamoo A.E., Data audit as a way to prevent/contain misconduct, Account Res, 20, 5-6, pp. 369-379, (2013); Estimating the reproducibility of psychological science, Science, 349, 6251, (2015); Glick J.L., On the potential cost effectiveness of scientific audits, Account Res, 1, 1, pp. 77-83, (1989); Wright D.E., Titus S.L., Cornelison J.B., Mentoring and research misconduct: an analysis of research mentoring in closed ORI cases, Sci Eng Ethics, 14, 3, pp. 323-336, (2008); Mancilla H.A., Teperek M., van Dijck J., den Heijer K., Eggermont R., Plomp E., Et al., On a quest for cultural change---surveying research data management practices at Delft University of Technology, Liber Q J Eur Res Libr, 29, 1, pp. 1-27, (2019); Houston L., Probst Y., Martin A., Assessing data quality and the variability of source data verification auditing methods in clinical research settings, J Biomed Inform, 83, pp. 25-32, (2018)","Y. Ali; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; email: Yusuf.ali@ntu.edu.sg","","BioMed Central Ltd","","","","","","14726939","","","34320960","English","BMC Med. Ethics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85111382580"
"Lafia S.; Thomer A.; Bleckley D.; Akmon D.; Hemphill L.","Lafia, Sara (57190124857); Thomer, Andrea (55328529600); Bleckley, David (57224581699); Akmon, Dharma (35848245300); Hemphill, Libby (35772520500)","57190124857; 55328529600; 57224581699; 35848245300; 35772520500","Leveraging machine learning to detect data curation activities","2021","Proceedings - IEEE 17th International Conference on eScience, eScience 2021","","","","149","158","9","5","10.1109/eScience51609.2021.00025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119060035&doi=10.1109%2feScience51609.2021.00025&partnerID=40&md5=aa3dc64799e11ea6e67a3fef7ac81021","University of Michigan, ICPSR, Ann Arbor, MI, United States; University of Michigan, School of Information, Ann Arbor, MI, United States","Lafia S., University of Michigan, ICPSR, Ann Arbor, MI, United States; Thomer A., University of Michigan, School of Information, Ann Arbor, MI, United States; Bleckley D., University of Michigan, ICPSR, Ann Arbor, MI, United States; Akmon D., University of Michigan, ICPSR, Ann Arbor, MI, United States; Hemphill L., University of Michigan, ICPSR, Ann Arbor, MI, United States, University of Michigan, School of Information, Ann Arbor, MI, United States","This paper describes a machine learning approach for annotating and analyzing data curation work logs at ICPSR, a large social sciences data archive. The systems we studied track curation work and coordinate team decision-making at ICPSR. Archive staff use these systems to organize, prioritize, and document curation work done on datasets, making them promising resources for studying curation work and its impact on data reuse, especially in combination with data usage analytics. A key challenge, however, is classifying similar activities so that they can be measured and associated with impact metrics. This paper contributes: 1) a set of data curation activities; 2) a computational model for identifying curation actions in work log descriptions; and 3) an analysis of frequent data curation activities at ICPSR over time. We first propose a set of data curation actions to help us analyze the impact of curation work. We then use this set to annotate a set of data curation logs, which contain records of data transformations and project management decisions completed by archive staff. Finally, we train a text classifier to detect the frequency of curation actions in a large set of work logs. Our approach supports the analysis of curation work documented in work log systems as an important step toward studying the relationship between research data curation and data reuse. © 2021 IEEE.","Data curation; Machine learning; Research infrastructures; Text classification; Workflows","Classification (of information); Decision making; Machine learning; Project management; Text processing; Curation; Data archives; Data curation; Data reuse; Machine learning approaches; Research infrastructure; Social science data; Team decision; Text classification; Work-flows; Human resource management","","","","","National Science Foundation, NSF, (1930645); Institute of Museum and Library Services, IMLS, (LG-37-19-0134-19)","We thank ICPSR curation supervisors including Rujuta Umarji, Julie Eady, Sharvetta Sylvester, Sara Del Norte, Lindsay Blankenship, Katey Pillars, Meghan Jacobs, and Scott Liening who provided feedback on our set of curatorial actions. We also thank Amy Pienta (ICPSR) and Jeremy York (UMSI) for their comments on earlier drafts. This material is based upon work supported by the National Science Foundation under grant 1930645. This project was made possible in part by the Institute of Museum and Library Services LG-37-19-0134-19.","Lord P., Macdonald A., Lyon L., Giaretta D., From data deluge to data curation, Proceedings of the UK E-science All Hands Meeting, 440, pp. 371-375, (2004); Pennock M., Digital curation: A life-cycle approach to managing and preserving usable digital information, Library & Archives, 1, 1, pp. 1-3, (2007); Goble C., Stevens R., Hull D., Wolstencroft K., Lopez R., Data curation + process curation=data integration + science, Brief. Bioinform., 9, 6, pp. 506-517, (2008); Palmer C.L., Weber N.M., Cragin M.H., The analytic potential of scientific data: Understanding re-use value, Proceedings of the American Society for Information Science and Technology, 48, 1, pp. 1-10, (2011); Johnston L., Carlson J., Hswe P., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., Data curation network: How do we compare? A snapshot of six academic library institutions' data repository and curation services, Journal of EScience Librarianship, 6, 1, (2017); Pienta A.M., Alter G.C., Lyle J.A., The enduring value of social science research: The use and reuse of primary research data, the Organisation, Economics and Policy of Scientific Research Workshop, (2010); Daniels M., Faniel I., Fear K., Yakel E., Managing fixity and fluidity in data repositories, Proceedings of the 2012 IConference on - IConference '12, pp. 279-286, (2012); Yakel E., Faniel I., Maiorana Z., Virtuous and vicious circles in the data lifecycle, Information Research, 24, 2, (2019); Chao T.C., Cragin M.H., Palmer C.L., Data practices and curation vocabulary (DPCVocab): An empirically derived framework of scientific data practices and curatorial processes, Journal of the Association for Information Science and Technology, 66, 3, pp. 616-633, (2015); Strauss A., The articulation of project work: An organizational process, Sociological Quarterly, 29, 2, (1988); Hey T., Tansley S., Tolle K.M., Jim Gray on EScience: A Transformed Scientific Method, pp. xvii-xxxi, (2009); Borgman C.L., Scharnhorst A., Golshan M.S., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, Journal of the Association for Information Science and Technology, 70, 8, pp. 888-904, (2019); ICPSR Collection Development Report 2016-2020, (2020); Plantin J.-C., Data cleaners for pristine datasets: Visibility and invisibility of data processors in social science, Science, Technology, & Human Values, 44, 1, pp. 52-73, (2019); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368, 1926, pp. 4023-4038, (2010); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Carlson J., Demystifying the data interview: Developing a foundation for reference librarians to talk with researchers about their data, Reference Services Review, 40, 1, pp. 7-23, (2012); Pham A., Surveying the State of Data Curation: A Review of Policy and Practice in UK HEIs, (2018); Carpenter L., Taxonomy of Digital Curation Users, (2004); Darch P.T., Sands A.E., Borgman C.L., Golshan M.S., Library cultures of data curation: Adventures in astronomy, Journal of the Association for Information Science and Technology, 71, 12, pp. 1470-1483, (2020); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, PLoS One, 12, 3, (2017); Johnston L.R., Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., How important are data curation activities to researchers? Gaps and opportunities for academic libraries, Journal of Librarianship and Scholarly Communication, (2018); Thessen A.E., Woodburn M., Koureas D., Paul D., Conlon M., Shorthouse D.P., Ramdeen S., Proper attribution for curation and maintenance of research collections: Metadata recommendations of the RDA/TDWG working group, Data Science Journal, 18, 1, (2019); Mayernik M.S., Research data and metadata curation as institutional issues, Journal of the Association for Information Science and Technology, 67, 4, pp. 973-993, (2016); Ortu M., Destefanis G., Adams B., Murgia A., Marchesi M., Tonelli R., The JIRA repository dataset: Understanding social aspects of software development, Proceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 1-4, (2015); Parr C., Gries C., O'brien M., Downs R.R., Duerr R., Koskela R., Tarrant P., Maull K.E., Hoelbelheinrich N., Stall S., A discussion of value metrics for data repositories in earth and environmental sciences, Data Science Journal, 18, 1, (2019); Jones K.S., A statistical interpretation of term specificity and its application in retrieval, Journal of Documentation, 28, 5, pp. 111-121, (1972); Stenetorp P., Pyysalo S., Topic G., Ohta T., Ananiadou S., Tsujii J., BRAT: A web-based tool for NLP-Assisted text annotation, Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. Avignon, France: Association for Computational Linguistics, pp. 102-107, (2012); Bird S., Klein E., Loper E., Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, pp. 221-233, (2009); Hemphill L., Schopke-Gonzalez A.M., Two computational models for analyzing political attention in social media, Proceedings of the International AAAI Conference on Web and Social Media, 14, pp. 260-271, (2020); Rennie J.D., Shih L., Teevan J., Karger D.R., Tackling the poor assumptions of Naive Bayes text classifiers, Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 616-623, (2003); Zhang T., Solving large scale linear prediction problems using stochastic gradient descent algorithms, Proceedings of the Twenty-First International Conference on Machine Learning, (2004); Doty J., Herndon J., Lyle J., Stephenson L., Learning to curate, Bulletin of the Association for Information Science and Technology, 40, 6, pp. 31-34, (2014); Higgins S., The DCC Curation Lifecycle Model, pp. 134-140, (2008); (2021); Plantin J.-C., The data archive as factory: Alienation and resistance of data processors, Big Data and Society, 8, 1, (2021); Gal S., Language and the arts of resistance, Cultural Anthropology, 10, 3, (1995); Murgia A., Concas G., Tonelli R., Ortu M., Demeyer S., Marchesi M., On the influence of maintenance activity types on the issue resolution time, Proceedings of the 10th International Conference on Predictive Models in Software Engineering, Ser. PROMISE '14, pp. 12-21, (2014)","","","Institute of Electrical and Electronics Engineers Inc.","","17th IEEE International Conference on eScience, eScience 2021","20 September 2021 through 23 September 2021","Virtual, Online","173214","","978-166540361-0","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85119060035"
"Borkakoti R.; Singh S.K.","Borkakoti, Rituraj (57226432775); Singh, Sanjay Kumar (57208907010)","57226432775; 57208907010","Research Data Management in Central Universities and Institutes of National Importance: a perspective from North East India","2021","Library Philosophy and Practice","2021","","","1","15","14","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111601415&partnerID=40&md5=58aea52246d7a5bf8f432fdca1d2691f","Research Scholar, Department of Library & Information Science, Gauhati University, Assam, India; Professor and Head, Department of Library & Information Science, Gauhati University, Assam, India","Borkakoti R., Research Scholar, Department of Library & Information Science, Gauhati University, Assam, India; Singh S.K., Professor and Head, Department of Library & Information Science, Gauhati University, Assam, India","The current study is an attempt to explore the perception of the library professionals about research data management (RDM). It reports the present scenario of research data management in the libraries and explores the views of professionals on aspects of RDM like stakeholders, awareness on NDSAP, formulation of an institutional research data management policy, requirement of data management plan by research funding agencies, necessary areas of training for library professionals to provide research data management, motivations for library professionals and potential challenges for research data management. It has been found that researchers have approached the library for research data and most of the professionals have intentions to facilitate research data management. Out of the different benefits of research data management, library professionals rated top priority to the opportunity to learn new skills through RDM. Professionals perceived upskilling of library staff to provide RDM to be the most daunting task of all the challenges. © 2021. All Rights Reserved.","challenges; library professionals; North East India; Perception; RDM; Research Data Management; Universities","","","","","","","","Averkamp S., Gu X., Rogers B., Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs, 35, (2014); Bhardwaj R. K., Research data management in higher educational institutions, DESIDOC Journal of Library and Information Technology, 39, 6, pp. 269-270, (2019); Boateng K. A., Owusu-Ansah C. M., Librarian's Perceptions of Research Data Management as a Professional Development Tool: A Norwegian Perspective, All Nations University Journal of Applied Thought (ANUJAT), 6, 2, pp. 122-142, (2019); Carlson J., The Use of Life Cycle Models in Developing and Supporting Data Services, Research Data Management: Practical Strategies for Information Professionals, pp. 69-66, (2014); Faniel I. M., Connaway L. S., Librarians' perspectives on the factors influencing research data management programs, College and Research Libraries, 79, 1, pp. 100-119, (2018); Kahn M., Higgs R., Davidson J., Jones S., Research Data Management in South Africa: How We Shape Up, Australian Academic and Research Libraries, 45, 4, pp. 296-308, (2014); Koltay T., Accepted and Emerging Roles of Academic Libraries in Supporting Research 2.0, Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Latham B., Research Data Management: Defining Roles, Prioritizing Services, and Enumerating Challenges, Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Levine M., Copyright, Open Data, and the Availability-Usability Gap, Research Data Management: Practical Strategies for Information Professionals, pp. 129-134, (2014); Lewis M., Libraries and the management of research data, Envisioning Future Academic Library Services, pp. 145-168, (2010); All India Survey on Higher Education; Naum A., Research data storage and management: Library staff participation in showcasing research data at the University of Adelaide, Australian Library Journal, 63, 1, pp. 35-44, (2014); Pal B., Singh S. K., Indian Academic Research Data Repository (IARDR) With INFLIBNET: 12th International CALIBER-2019, (2019); Patel D., Research data management: a conceptual framework, Library Review, 65, 4-5, pp. 226-241, (2016); Pinfield S., Cox A. M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12, pp. 1-28, (2014); ICSSR Data Service; Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, Electronic Library, 33, 3, (2015); Steiner K., Data management and information literacy. Re:Inventing Information Science in the Networked Society, Proceedings of the 14th International Symposium on Information Science (ISI 2015), pp. 562-568, (2015); National Data Sharing and Accessibility Policy, (2012); Tenopir C., Sandusky R. J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tripathi M., Shukla A., Sonker S. K., Research data management practices in university libraries: A study, DESIDOC Journal of Library and Information Technology, 37, 6, pp. 417-424, (2017)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85111601415"
"Brandt N.; Griem L.; Herrmann C.; Schoof E.; Tosato G.; Zhao Y.; Zschumme P.; Selzer M.","Brandt, Nico (57219502886); Griem, Lars (57221981907); Herrmann, Christoph (57219770733); Schoof, Ephraim (56422042200); Tosato, Giovanna (57221982210); Zhao, Yinghan (57219503490); Zschumme, Philipp (57221981540); Selzer, Michael (24503304900)","57219502886; 57221981907; 57219770733; 56422042200; 57221982210; 57219503490; 57221981540; 24503304900","Kadi4mat: A research data infrastructure for materials science","2021","Data Science Journal","20","1","","1","14","13","22","10.5334/dsj-2021-008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100864593&doi=10.5334%2fdsj-2021-008&partnerID=40&md5=0cb95d63e5f6358be961e9f7af2d58d5","Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU), Helmholtzstraße 11, Ulm, 89081, Germany; Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Brandt N., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Griem L., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Herrmann C., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Schoof E., Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU), Helmholtzstraße 11, Ulm, 89081, Germany; Tosato G., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Zhao Y., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Zschumme P., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany; Selzer M., Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Straße am Forum 7, Karlsruhe, 76131, Germany, Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","The concepts and current developments of a research data infrastructure for materials science are presented, extending and combining the features of an electronic lab notebook and a repository. The objective of this infrastructure is to incorporate the possibility of structured data storage and data exchange with documented and reproducible data analysis and visualization, which finally leads to the publication of the data. This way, researchers can be supported throughout the entire research process. The software is being developed as a web-based and desktop-based system, offering both a graphical user interface and a programmatic interface. The focus of the development is on the integration of technologies and systems based on both established as well as new concepts. Due to the heterogeneous nature of materials science data, the current features are kept mostly generic, and the structuring of the data is largely left to the users. As a result, an extension of the research data infrastructure to other disciplines is possible in the future. The source code of the project is publicly available under a permissive Apache 2.0 license. © 2021 The Author(s).","Electronic lab notebook; Materials science; Open source; Repository; Research data management","Data visualization; Electronic data interchange; Graphical user interfaces; Electronic lab; Research data; Research process; Source codes; Structured data; Technologies and systems; Web based; Digital storage","","","","","Deutsche Forschungsgemeinschaft, DFG, (390874152, 391128822); Bundesministerium für Bildung und Forschung, BMBF, (03XP0174E); Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, MWK, (57)","This work is supported by the Federal Ministry of Education and Research (BMBF) in the projects FestBatt (project number 03XP0174E) and as part of the Excellence Strategy of the German Federal and State Governments, by the German Research Foundation (DFG) in the projects POLiS (project number 390874152) and SuLMaSS (project number 391128822) and by the Ministry of Science, Research and Art Baden-Württemberg in the project MoMaF – Science Data Center, with funds from the state digitization strategy digital@bw (project number 57). The authors are also grateful for the editorial support of Leon Geisen.","Afgan E, Et al., The Galaxy Platform for Accessible, Reproducible and Collaborative Biomedical Analyses: 2018 Update, Nucleic Acids Research, 46, W1, pp. W537-W544, (2018); Amorim RC, Et al., A Comparison of Research Data Management Platforms: Architecture, Flexible Metadata and Interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Bird CL, Willoughby C, Frey JG., Laboratory Notebooks in the Digital Era: The Role of ELNs in Record Keeping for Chemistry and Other Sciences, Chemical Society Reviews, 42, 20, (2013); Brandt N., Kadi4Mat – Karlsruhe Data Infrastructure for Materials Science, (2020); Brandt N, Et al., IAM-CMS/Kadi: Kadi4Mat, (2020); Cantor S, Scavo T., Shibboleth Architecture, Protocols and Profiles, 10, (2005); CARPi N, Minges A, Piel M., eLabFTW: An Open Source Laboratory Notebook for Research Labs, The Journal of Open Source Software, 2, 12, (2017); Carusi A, Reimer T. Jan, Virtual Research Environment Collaborative Landscape Study, (2010); Ckan – The Open Source Data Portal Software, (2014); DataCite Metadata Schema Documentation for the Publication and Citation of Research Data v4.3, (2019); Django – The Web Framework for Perfectionists with Deadlines, (2005); Draxl C, Scheffler M., NOMAD: The FAIR Concept for Big Data-Driven Materials Science, MRS Bulletin, 43, 9, pp. 676-682, (2018); Elastic NV., Elasticsearch – The Official Distributed Search & Analytics Engine, (2010); Elliott MH., Thinking beyond ELN, Scientific computing, 26, 6, pp. 6-10, (2009); European Materials and Modelling Ontology, (2019); Invenio – Open Source Framework for Large-Scale Digital Repositories, (2016); Zenodo, (2013); Fielding RT, Taylor RN., Architectural Styles and the Design of Network-Based Software Architectures, 7, (2000); Ghiringhelli LM, Et al., Towards Efficient Data Exchange and Sharing for Big-Data Driven Materials Science: Metadata and Data Formats, npj Computational Materials, 3, 1, (2017); Greenberg J, Et al., A Metadata Best Practice for a Scientific Data Repository, Journal of Library Metadata, 9, 3–4, pp. 194-212, (2009); Hawker CD., Laboratory Automation: Total and Subtotal, Clinics in Laboratory Medicine, 27, 4, pp. 749-770, (2007); Hey AJG, The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Hey T, Trefethen A., The Data Deluge: An e-Science Perspective, Wiley Series in Communications Networking & Distributed Systems, pp. 809-824, (2003); Hill J, Et al., Materials Science with Large-Scale Data and Informatics: Unlocking New Opportunities, MRS Bulletin, 41, 5, pp. 399-409, (2016); Jain A, Et al., Commentary: The Materials Project: A Materials Genome Approach to Accelerating Materials Innovation, APL Materials, 1, 1, (2013); King G. Nov, An Introduction to the Dataverse Network as an Infrastructure for Data Sharing, Sociological Methods & Research, 36, 2, pp. 173-199, (2007); Kluyver T, Et al., Jupyter Notebooks – a Publishing Format for Reproducible Computational Workflows, Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87-90, (2016); Naughton L, Kernohan D., Making Sense of Journal Research Data Policies, Insights the UKSG journal, 29, 1, pp. 84-89, (2016); Open Archives Initiative Protocol for Metadata Harvesting, (2015); Bryan Heidorn P., Shedding Light on the Dark Data in the Long Tail of Science, Library Trends, 57, 2, pp. 280-299, (2008); Pampel H, Et al., Making Research Data Repositories Visible: The Re3data.Org Registry, PLoS ONE, 8, 11, (2013); PostgreSQL: The World’s Most Advanced Open Source Database, (1996); Potthoff J, Et al., Procedures for Systematic Capture and Management of Analytical Data in Academia, Analytica Chimica Acta: X, 1, (2019); Rubacha M, Rattan AK, Hosselet SC., A Review of Electronic Laboratory Notebooks Available in the Market Today, Journal of Laboratory Automation, 16, 1, pp. 90-98, (2011); Sandfeld S, Et al., Strategiepapier – Digitale Transformation in der Materialwissenschaft und Werkstofftechnik, (2018); Schembera B, Iglezakis D., EngMeta: Metadata for Computational Engineering, International Journal of Metadata, Semantics and Ontologies, 14, 1, (2020); Schindelin J, Et al., The ImageJ Ecosystem: An Open Platform for Biomedical Image Analysis, Molecular Reproduction and Development, 82, 7–8, pp. 518-529, (2015); Schoof E, Brandt N., IAM-CMS/Kadi-Apy: Kadi4Mat API Library, (2020); SciNote – Electronic Lab Notebook & Inventory Management, (2015); Smith M, Et al., DSpace: An Open Source Dynamic Digital Repository, D-Lib Magazine, 9, 1, (2003); Taylor KT., The Status of Electronic Laboratory Notebooks for Chemistry and Biology, Current Opinion in Drug Discovery and Development, 9, 3, (2006); FAIRsharing as a Community Approach to Standards, Repositories and Policies, Nature Biotechnology, 37, 4, pp. 358-367, (2019); MATLAB – MathWorks, (2021); Flask – The Pallets Projects, (2015); Tremouilhac P, Et al., Chemotion ELN: An Open Source Electronic Lab Notebook for Chemists in Academia, Journal of Cheminformatics, 9, 1, (2017); Vue.Js – The Progressive JavaScript Framework, (2014); Wilkinson MD, Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, 1, (2016); Wolstencroft K, Et al., The Taverna Workflow Suite: Designing and Executing Workflows of Web Services on the Desktop, Web or in the Cloud, Nucleic Acids Research, 41, W1, pp. W557-W561, (2013); Zschumme P., IAM-CMS/Process-Engine; Zschumme P., IAM-CMS/Process-Manager; Zschumme P, Et al., IAM-CMS/Workflow-Nodes, (2020)","N. Brandt; Institute for Applied Materials (IAM-CMS), Karlsruhe Institute of Technology (KIT), Karlsruhe, Straße am Forum 7, 76131, Germany; email: nico.brandt@kit.edu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85100864593"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Research Data Management and Data Literacies","2021","Research Data Management and Data Literacies","","","","1","184","183","1","10.1016/C2020-0-02068-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136718318&doi=10.1016%2fC2020-0-02068-2&partnerID=40&md5=fd4d277beee0dcf56b862665116ee9d5","","","Research Data Management and Data Literacies help researchers familiarize themselves with RDM, and with the services increasingly offered by libraries. This new volume looks at data-intensive science, or ‘Science 2.0’ as it is sometimes termed in commentary, from a number of perspectives, including the tasks academic libraries need to fulfil, new services that will come online in the near future, data literacy and its relation to other literacies, research support and the need to connect researchers across the academy, and other key issues, such as ‘data deluge,’ the importance of citations, metadata and data repositories. This book presents a solid resource that contextualizes RDM, including good theory and practice for researchers and professionals who find themselves tasked with managing research data. © 2022 Elsevier Ltd. All rights reserved.","","","","","","","","","Borgman C.L., Scholarship in the digital age: Information, infrastructure, and the Internet, (2007); Borgman C.L., Big data, little data, no data: Scholarship in the networked world, (2015); Cox A.M., Corrall S., Evolving academic library specialties, Journal of the American Society for Information Science and Technology, 64, 8, pp. 1526-1542, (2013); Fotopoulou A., Conceptualising critical data literacies for civil society organizations: Agency, care, and social responsibility, Information, Communication & Society, (2020); Hey T., Hey J., e-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Koltay T., Big data, big literacies?, Čitalište, 24, pp. 3-8, (2014); Koltay T., Spiranec S., Karvalics L.Z., Research 2.0 and the future of information literacy, (2016); LERU roadmap for research data, (2013); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Ackoff R.L., From data to wisdom, Journal of Applied Systems Analysis, 16, 1, pp. 3-9, (1989); Al-Ruithe M., Benkhelifa E., Hameed K., A systematic literature review of data governance and cloud data governance, Personal and Ubiquitous Computing, 23, 5-5, pp. 839-859, (2019); Bawden D., Robinson L., Information and the gaining of understanding, Journal of Information Science, 42, 3, pp. 294-299, (2016); Bawden D., Robinson L., Still minding the gap? 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"Hewera M.; Hänggi D.; Gerlach B.; Kahlert U.D.","Hewera, Michael (57218484701); Hänggi, Daniel (8686914300); Gerlach, Björn (16745015400); Kahlert, Ulf Dietrich (41661514600)","57218484701; 8686914300; 16745015400; 41661514600","eLabFTW as an Open Science tool to improve the quality and translation of preclinical research","2021","F1000Research","10","","","292","","","5","10.12688/f1000research.52157.3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114095526&doi=10.12688%2ff1000research.52157.3&partnerID=40&md5=2569aa1dd672e2c9aa3f9d1c460ab1b0","Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, NRW, Düsseldorf, 40225, Germany; PAASP GmbH, BW, Heidelberg, 69118, Germany; Beijing Neurosurgical Institute, Beijing, China","Hewera M., Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, NRW, Düsseldorf, 40225, Germany; Hänggi D., Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, NRW, Düsseldorf, 40225, Germany; Gerlach B., PAASP GmbH, BW, Heidelberg, 69118, Germany; Kahlert U.D., Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, NRW, Düsseldorf, 40225, Germany, Beijing Neurosurgical Institute, Beijing, China","Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-science labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management. Copyright: © 2021 Hewera M et al.","Electronic Lab Notebook; ELN; Open Science; Quality Management; Reproducibility; Transparency","Biological Science Disciplines; Laboratories; biomedicine; laboratory","","","","","","","","","","NLM (Medline)","","","","","","20461402","","","34381592","English","F1000Res","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85114095526"
"Ringersma J.; Miedema M.","Ringersma, J. (6503879741); Miedema, M. (57297357900)","6503879741; 57297357900","Do I-PASS for FAIR? Measuring the fair-ness of research organizations","2021","Data Science Journal","20","1","30","","","","0","10.5334/dsj-2021-030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117230853&doi=10.5334%2fdsj-2021-030&partnerID=40&md5=0e1770c1abf500f2c4ea6451df4a41fe","Wageningen Data Competence Center, Wageningen University & Research, Netherlands","Ringersma J., Wageningen Data Competence Center, Wageningen University & Research, Netherlands; Miedema M., Wageningen Data Competence Center, Wageningen University & Research, Netherlands","Given the increased use of the FAIR acronym as adjective for other contexts than data or data sets, the Dutch National Coordination Point for Research Data Management initiated a Task Group to work out the concept of a FAIR research organization. The results of this Task Groups are a definition of a FAIR enabling organization and a method to measure the FAIR-ness of a research organization (The Do-I-PASS for FAIR method). The method can also aid in developing FAIR-enabling Road Maps for individual research institutions and at a national level. This practice paper describes the development of the method and provides a couple of use cases for the application of the method in daily research data management practices in research organizations. © 2021 The Author(s).","Data stewarship; FAIR; Research support; Self-assesment","Research and development management; Assesment; Data set; Data stewarship; Research data managements; Research institutions; Research organization; Research support; Roadmap; Self-assesment; Task groups; Information management","","","","","","","RDM@LCRDM_National Coordination Point Research Data Management: Output and products; Miedema M., First NL Survey Do I-pass for FAIR?, (2021); FAIR Data Maturity Model WG; FAIR for Research Software (FAIR4RS) WG; FAIRsharing Registry: Connecting data policies, standards and databases RDA WG; Professionalising Data Stewardship IG; Taco de Bruin SC, de Jong J, Haslinger I, van den Hoogen H, Huigen F, Ringersma J., Do I-PASS for FAIR. A self-assessment tool to measure the FAIR-ness of an organization, (2020); Verheul I, Mordant A, Ringersma J, Sesink L, Smeele T, Boiten J-W, Verheij M., National Coordination Point for Research Data Management (LCRDM) Positioning paper for 2019 and beyond (Version final-online version), (2019); Wilkinson M, Dumontier M, Aalbersberg I., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); KISS principle","J. Ringersma; Wageningen Data Competence Center, Wageningen University & Research, Netherlands; email: jacquelijn.ringersma@wur.nl","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85117230853"
"Päällysaho S.; Latvanen J.; Lehto A.; Riihimaa J.; Lahti P.; Kärki A.; Puhakka-Tarvainen H.","Päällysaho, Seliina (7801638135); Latvanen, Jaana (57313360900); Lehto, Anttoni (57208750395); Riihimaa, Jaakko (57313139000); Lahti, Pekka (57219518772); Kärki, Anne (6603789566); Puhakka-Tarvainen, Helena (52264461700)","7801638135; 57313360900; 57208750395; 57313139000; 57219518772; 6603789566; 52264461700","Key aspects of open data in finnish RDI cooperation between higher education and businesses","2021","Data Intelligence","3","1","","1","188","187","0","10.1162/dint_a_00065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117959893&doi=10.1162%2fdint_a_00065&partnerID=40&md5=f831f82d72c00a619558e066eb837a52","Seinäjoki University of Applied Sciences, Seinäjoki, 60101, Finland; Turku University of Applied Sciences, Turku, 20520, Finland; Haaga-Helia University of Applied Sciences, Helsinki, 00520, Finland; Satakunta University of Applied Sciences, Satakunta, Pori, 28101, Finland; Karelia University of Applied Sciences, Pohjois-Karjala, Joensuu, 80200, Finland","Päällysaho S., Seinäjoki University of Applied Sciences, Seinäjoki, 60101, Finland; Latvanen J., Seinäjoki University of Applied Sciences, Seinäjoki, 60101, Finland; Lehto A., Turku University of Applied Sciences, Turku, 20520, Finland; Riihimaa J., Haaga-Helia University of Applied Sciences, Helsinki, 00520, Finland; Lahti P., Haaga-Helia University of Applied Sciences, Helsinki, 00520, Finland; Kärki A., Satakunta University of Applied Sciences, Satakunta, Pori, 28101, Finland; Puhakka-Tarvainen H., Karelia University of Applied Sciences, Pohjois-Karjala, Joensuu, 80200, Finland","The article highlights aspects that should be considered during an open Research, Development, and Innovation (RDI) process cycle to improve the utilization of research data and foster open cooperation between higher education and businesses. The viewpoint here is in publicly funded joint research projects of the universities of applied sciences (UAS), the concept is, however, applicable in other higher education and research organizations as well. There are various challenges related to research data management in general as well as to the openness and reuse of data and results. The findings of this article are based on the results of a two-day expert workshop, and these results are interlinked with five phases of an open RDI process cycle: Planning, implementation, documentation, sharing, and commercialization. Various drivers and barriers can be identified in different stages of the process. On a general level, special attention must be paid to critical factors such as ownership and sharing of data and results, confidential information and business secrets as well as following the requirements of the Open Science (OS) policies of the participating organizations and funders. This article also highlights several best practices that should be considered in each phase of an open RDI process cycle with businesses. © 2021 Chinese Academy of Sciences.","Business cooperation; Higher education; Open Data; Open RDI; Universities of applied sciences (UAS)","Information management; Unmanned aerial vehicles (UAV); Business cooperation; Development process; High educations; Open datum; Open development; Open innovation; Research development; Research process; University of applied science; Open Data","","","","","","","Aladesanmi A.J., Iwalewa E.O., Adebajo A.C., Akinkunmi E.O., Taiwo B.J., Olorunmola F.O., Lamikanra A., Antimicrobial and antioxidant activities of some Nigerian medicinal plants, Afr. J. Trad. Complem. Altern. Med, 4, pp. 173-184, (2007); Amoo S.O., Aremu A.O., Moyo M., Van Staden J., Antioxidant and acetyl cholinesterase-inhibitory properties of long-term stored medicinal plants, BMC Complem. Altern. Med, 12, (2012); Arbonier M., Trees, Shrubs and Lianas of West African Dry Zones, (2004); Arnold H.J., Gulumian M., Pharmacopoeia of traditional medicine in Venda, J. Ethnopharmacol, 12, pp. 35-74, (1984); Bagla V.P., McGaw L.J., Eloff J.N., The antiviral activity of six South African plants traditionally used against infections in ethno veterinary medicine, Vet. Microbiol, 155, pp. 198-206, (2012); Bevan C.W.L., Ekong D.E.U., Taylor D.A.H., Extractives from West African members of the family Meliaceae, Nature, 206, pp. 1323-1325, (1965); Bruneton J., Pharmacognosie: Phytochimie, Plantes médicinales, pp. 189-609, (1999); Burkill H.M., Ekebergia senegalensis A Juss. [Family Meliaceae], The Useful Plants of West Tropical Africa, 4, (1985); Burrows J.E., Burrows S.M., Lotter M.C., Schmidt E., Trees and shrubs Mozambique, (2018); Chavez D., Chai H.B., Chagwedera T.E., Gao Q., Farnsworth N.R., Cordell G.A., Pezzuto J.M., Kinghorn. 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Res, 18, pp. 379-384, (2004); Ndukwe I.G., Habila J.D., Bello I.A., Adeleye E.O., Phytochemical analysis and antimicrobial screening of crude extracts from the leaves stem bark and root bark of Ekebergia senegalensis A, Juss. Afr. J. Biotechnol, 5, pp. 1792-1794, (2006); Nishiyama Y., Moriyasu M., Ichimarou M., Tachibana Y., Kato A., Mathenge S.G., Juma F.D., Acyclic triterpenoids from Ekebergia capensis, Phytochemistry, 42, pp. 803-807, (1996); Nishiyama Y., Moriyasu M., Ichimaru M., Kato A., Mathenge S.G., Nganga J.N., Juma F.D., Absolute configurations of two acyclic triterpenoids from Ekebergia capensis, Phytochemistry, 52, pp. 1593-1596, (1999); Ochwang'i D.O., Kimwele C.N., Oduma J.A., Gathumbi P.K., Mbaria J.M., Kiama S.G., Medicinal plants used in treatment and management of cancer in Kakamega County, Kenya, J. Ethnopharmacol, 151, pp. 1040-1055, (2014); Opio D.R., Andama E., Kureh G.T., Ethnobotanical survey of antimalarial plants in areas of Abukamola, Angeta, Oculokori and Omarari of Alebtong District in Northern Uganda, Eur. J. Med. Plants, 21, pp. 1-14, (2017); Pujol J., Natur Africa: The Herbalist Handbook: Africa Flora, Medicinal Plants, pp. 1-192, (1990); Rabe T., Van Staden J., Antibacterial activity of South African plants used for medicinal, (1997); J. Ethnopharmacol, 56, pp. 81-87; Schmidt E., Lotter M., McCleland W., Trees and Shrubs of Mpumalanga and Kruger National Park, (2002); Sewram V., Raynor M.W., Mulholland D., Raidoo D.M., The uterotonic activity of compound isolated from the supercritical fluid extract of Ekebergia capensis, J. Pharmaceut. Biomed. Anal, 24, pp. 133-145, (2000); Sofidiya M.D., Odukoya O.A., Familoni O.B., Inya-Agha S.I., Free radical scavenging activity of some Nigerian medicinal plant extracts, Pak. J. Biol. 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Rev, 111, pp. 7437-7522, (2011); Taylor A.R.H., Taylor D.A.H., Limonoids from Ekebergia pterophylla, Phytochemistry, 23, pp. 2676-2677, (1984); Taylor D.A.H., Ekebergin, a limonoid extractive from Ekebergia capensis, Phytochemistry, 20, pp. 2263-2265, (1981); Troupin G., Flore des plantes ligneuses du Rwanda, pp. 398-400, (1982); Tuasha N., Petros B., Asfaw Z., Medicinal plants used by traditional healers to treat malignancies and other human ailments in Dalle District, Sidama zone, Ethiopia, J. Ethnobiol. Ethnomed, 14, (2018); Van Wyk B.E., Van Oudtshoorn B., Gericke N., Medicinal Plants of South Africa, (2013); Williams A., Ngulde S.I., Tijjani M.B., Malgwi B.U., Sandabe U.K., Analgesic activities of the aqueous extract of Ekebergia senegalensis A. Juss stem bark in albino rats, Cont. J. Pharmacol. Toxicol. Res, 6, pp. 17-21, (2013); York T., de Wet H., van Vuuren S.F., Plants used for treating respiratory infections in rural Maputaland, KwaZulu-Natal, South Africa, J. Ethnopharmacol, 135, pp. 696-710, (2011); York T., Van Vuuren S.F., de Wet H., An antimicrobial evaluation of plants used for the treatment of respiratory infections in rural Maputaland, KwaZulu- Natal, South Africa, J. Ethnopharmacol, 144, pp. 118-127, (2012); Zerabruk S., Yirga G., Traditional knowledge of medicinal plants in Gindeberet District, Western Ethiopia, S. Afr. J. Bot, 78, pp. 165-169, (2012)","","","MIT Press Journals","","","","","","20967004","","","","English","Data. Intell.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85117959893"
"Jackson B.","Jackson, Brian (56583406700)","56583406700","Open data policies among library and information science journals","2021","Publications","9","2","25","","","","3","10.3390/publications9020025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108787197&doi=10.3390%2fpublications9020025&partnerID=40&md5=1b08870b37deb9ae5fbbaf038c5a5026","Library, Mount Royal University, 4825 Mount Royal Gate SW, Calgary, T3E 6K6, AB, Canada","Jackson B., Library, Mount Royal University, 4825 Mount Royal Gate SW, Calgary, T3E 6K6, AB, Canada","Journal publishers play an important role in the open research data ecosystem. Through open data policies that include public data archiving mandates and data availability statements, journal publishers help promote transparency in research and wider access to a growing scholarly record. The library and information science (LIS) discipline has a unique relationship with both open data initiatives and academic publishing and may be well-positioned to adopt rigorous open data policies. This study examines the information provided on public-facing websites of LIS journals in order to describe the extent, and nature, of open data guidance provided to prospective authors. Open access journals in the discipline have disproportionately adopted detailed, strict open data policies. Commercial publishers, which account for the largest share of publishing in the discipline, have largely adopted weaker policies. Rigorous policies, adopted by a minority of journals, describe the rationale, application, and expectations for open research data, while most journals that provide guidance on the matter use hesitant and vague language. Recommendations are provided for strengthening journal open data policies. © 2021 by the author. Licensee MDPI, Basel, Switzerland.","Academic publishing; Library and information science; Open data policies; Research data management","","","","","","","","Lin J., Strasser C., Recommendations for the role of publishers in access to data, PLoS Biol, 12, (2014); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, J. Acad. Librariansh, 43, pp. 263-265, (2017); Corrall S., Databrarian ed? Preparing information specialists for participation in an open datafied society, Bold Minds: Library Leadership in a Time of Disruption, pp. 179-210, (2020); Walters W.H., The research contributions of editorial board members in library and information science, J. Sch. Publ, 47, pp. 121-146, (2016); Womack R.P., Research data in core journals in biology, chemistry, mathematics, and physics, PLoS ONE, 10, (2015); Wallach J.D., Boyack K.W., Id J.P.A.I., Reproducible research practices, transparency, and open access data in the biomedical literature, 2015–2017, PLoS Biol, 16, (2018); Johnson J.N., Hanson K.A., Jones C.A., Grandhi R., Guerrero J., Rodriguez J., Data sharing in neurosurgery and neurology journals, Cureus, 10, (2018); Roche D.G., Kruuk L.E.B., Lanfear R., Binning S.A., Public data archiving in ecology and evolution: How well are we doing?, PLoS Biol, 13, (2015); Key E.M., How are we doing? Data access and replication in political science, PS Polit. Sci. Polit, 49, pp. 268-272, (2016); Gorman D.M., Availability of research data in high-impact addiction journals with data sharing policies, Sci. Eng. Ethics, 26, pp. 1625-1632, (2020); Vasilevsky N.A., Minnier J., Haendel M.A., Champieux R.E., Reproducible and reusable research: Are journal data sharing policies meeting the mark?, PeerJ, 5, (2017); Choi H.W., Choi Y.J., Kim S., Compliance of “Principles of Transparency and Best Practice in Scholarly Publishing” in academic society published journals, Sci. Ed, 6, pp. 112-121, (2019); Kim S.Y., Yi H.J., Huh S., Current and planned adoption of data sharing policies by editors of Korean scholarly journals, Sci. Ed, 6, pp. 19-24, (2019); Rousi A.M., Laakso M., Journal research data sharing policies: A study of highly-cited journals in neuroscience, physics, and operations research, Scientometrics, 124, pp. 131-152, (2020); Naughton L., Kernohan D., Making sense of journal research data policies, Insights, 29, pp. 84-89, (2016); Sturges P., Bamkin M., Anders J.H.S., Hubbard B., Hussain A., Heeley M., Research data sharing: Developing a stakeholder-driven model for journal policies, J. Am. Soc. Inf. Sci. Technol, 66, pp. 2445-2455, (2013); Christensen G., Dafoe A., Miguel E., Moore D.A., Rose A.K., A study of the impact of data sharing on article citations using journal policies as a natural experiment, PLoS ONE, 14, (2019); Understanding Our Data Sharing Policies; Research Data Policies; Nuijten M.B., Borghuis J., Veldkamp C.L.S., Dominguez-Alvarez L., Van Assen M.A.L.M., Wicherts J.M., Journal data sharing policies and statistical reporting inconsistencies in psychology, Collabra Psychol, 3, pp. 1-22, (2017); Giofre D., Cumming G., Fresc L., Boedker I., Tressoldi P., The influence of journal submission guidelines on authors’ reporting of statistics and use of open research practices, PLoS ONE, 12, (2017); Vines T.H., Andrew R.L., Bock D.G., Franklin M.T., Gilbert K.J., Kane N.C., Moore J.S., Moyers B.T., Renaut S., Rennison D.J., Et al., Mandated data archiving greatly improves access to research data, FASEB J, 27, pp. 1304-1308, (2013); Piwowar H.A., Chapman W.W., Public sharing of research datasets: A pilot study of associations, J. Informetr, 4, pp. 148-156, (2010); Kidwell M.C., Lazarevic L.B., Baranski E., Hardwicke T.E., Piechowski S., Falkenberg L.S., Kennett C., Slowik A., Sonnleitner C., Hess-Holden C., Et al., Badges to acknowledge open practices: A simple, low-cost, effective method for increasing transparency, PLoS Biol, 14, (2016); Fecher B., Friesike S., Hebing M., Linek S., A reputation economy: How individual reward considerations trump systemic arguments for open access to data, Palgrave Commun, 3, pp. 1-10, (2017); Federer L.M., Belter C.W., Joubert D.J., Livinski A., Lu Y., Snyders L.N., Thompson H., Data sharing in PLoS ONE: An analysis of data availability statements, PLoS ONE, 13, (2018); Hardwicke T.E., Mathur M.B., MacDonald K., Nilsonne G., Banks G.C., Kidwell M.C., Mohr A.H., Clayton E., Yoon E.J., Tessler M.H., Et al., Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal cognition, R. Soc. Open Sci, 5, (2018); Naudet F., Sakarovitch C., Janiaud P., Cristea I., Fanelli D., Moher D., Ioannidis J.P.A., Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: Survey of studies published in the BMJ and PLoS Medicine, BMJ, 360, (2018); Rowhani-Farid A., Barnett A.G., Has open data arrived at the British Medical Journal (BMJ)? An observational study, BMJ Open, 6, pp. 1-8, (2016); Aleixandre-Benavent R., Moreno-Solano L.M., Ferrer Sapena A., Perez E.A.S., Correlation between impact factor and public availability of published research data in information science and library science journals, Scientometrics, 107, pp. 1-13, (2016); Stodden V., Guo P., Ma Z., Toward reproducible computational research: An empirical analysis of data and code policy adoption by journals, PLoS ONE, 8, (2013); Zhu Y., Open-access policy and data-sharing practice in UK academia, J. Inf. Sci, 46, pp. 41-52, (2020); Braun V., Clarke V., Using thematic analysis in psychology, Qual. Res. Psychol, 3, pp. 77-101, (2006); Hrynaszkiewicz I., Birukou A., Astell M., Swaminathan S., Kenall A., Khodiyar V., Standardising and harmonising research data policy in scholary publishing, Int. J. Digit. Curation, 12, pp. 65-71, (2017); Turcios M.E., Agarwal N.K., Watkins L., How much of library and information science literature qualifies as research?, J. Acad. Librariansh, 40, pp. 473-479, (2014); Cox A., Kennan M., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, J. Doc, 75, pp. 1432-1462, (2019); McKiernan E.C., Bourne P.E., Brown C.T., Buck S., Kenall A., Lin J., McDougall D., Nosek B.A., Ram K., Soderberg C.K., Et al., How open science helps researchers succeed, Elife, 5, (2016)","B. Jackson; Library, Mount Royal University, Calgary, 4825 Mount Royal Gate SW, T3E 6K6, Canada; email: bjackson@mtroyal.ca","","MDPI AG","","","","","","23046775","","","","English","Publ.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85108787197"
"Daniel E.; Maksimainen M.M.; Smith N.; Ratas V.; Biterova E.; Murthy S.N.; Tanvir Rahman M.; Kiema T.-R.; Sridhar S.; Cordara G.; Dalwani S.; Venkatesan R.; Prilusky J.; Dym O.; Lehtio L.; Kristian Koski M.; Ashton AlunW.; Sussman JoelL.; Wierenga RikK.","Daniel, Ed (37112049600); Maksimainen, Mirko M. (14325666800); Smith, Neil (57221952367); Ratas, Ville (6504309627); Biterova, Ekaterina (8961914900); Murthy, Sudarshan N. (57194757648); Tanvir Rahman, M. (57211618460); Kiema, Tiila-Riikka (6603249113); Sridhar, Shruthi (57196424211); Cordara, Gabriele (57090617000); Dalwani, Subhadra (57215776954); Venkatesan, Rajaram (36186020700); Prilusky, Jaime (6602502786); Dym, Orly (6602294508); Lehtio, Lari (6507478836); Kristian Koski, M. (6505571372); Ashton, AlunW. (9739405700); Sussman, JoelL. (7102888560); Wierenga, RikK. (7006316503)","37112049600; 14325666800; 57221952367; 6504309627; 8961914900; 57194757648; 57211618460; 6603249113; 57196424211; 57090617000; 57215776954; 36186020700; 6602502786; 6602294508; 6507478836; 6505571372; 9739405700; 7102888560; 7006316503","IceBear: An intuitive and versatile web application for research-data tracking from crystallization experiment to PDB deposition","2021","Acta Crystallographica Section D: Structural Biology","77","","","151","163","12","6","10.1107/S2059798320015223","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097267687&doi=10.1107%2fS2059798320015223&partnerID=40&md5=c7fa993b1b2e010bb643f28500ca73d1","Biocenter Oulu, University of Oulu, Oulu, Finland; Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Diamond Light Source, Harwell Science and Innovation Campus, Didcot, United Kingdom; Bioinformatics and Biological Computing Unit, Life Science Core Facility, Weizmann Institute of Science, Rehovot, 7610001, Israel; Israel Structural Proteomics Center, Life Science Core Facility, Weizmann Institute of Science, Rehovot, 7610001, Israel; Department of Structural Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel; Paul Scherrer Institut, Forschungsstrasse, Villigen, 5232, Switzerland","Daniel E., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Maksimainen M.M., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Smith N., Diamond Light Source, Harwell Science and Innovation Campus, Didcot, United Kingdom; Ratas V., Biocenter Oulu, University of Oulu, Oulu, Finland; Biterova E., Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Murthy S.N., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Tanvir Rahman M., Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Kiema T.-R., Biocenter Oulu, University of Oulu, Oulu, Finland; Sridhar S., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Cordara G., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Dalwani S., Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Venkatesan R., Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Prilusky J., Bioinformatics and Biological Computing Unit, Life Science Core Facility, Weizmann Institute of Science, Rehovot, 7610001, Israel; Dym O., Israel Structural Proteomics Center, Life Science Core Facility, Weizmann Institute of Science, Rehovot, 7610001, Israel; Lehtio L., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Kristian Koski M., Biocenter Oulu, University of Oulu, Oulu, Finland; Ashton AlunW., Diamond Light Source, Harwell Science and Innovation Campus, Didcot, United Kingdom, Paul Scherrer Institut, Forschungsstrasse, Villigen, 5232, Switzerland; Sussman JoelL., Department of Structural Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel; Wierenga RikK., Biocenter Oulu, University of Oulu, Oulu, Finland, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland","The web-based IceBear software is a versatile tool to monitor the results of crystallization experiments and is designed to facilitate supervisor and student communications. It also records and tracks all relevant information from crystallization setup to PDB deposition in protein crystallography projects. Fully automated data collection is now possible at several synchrotrons, which means that the number of samples tested at the synchrotron is currently increasing rapidly. Therefore, the protein crystallography research communities at the University of Oulu, Weizmann Institute of Science and Diamond Light Source have joined forces to automate the uploading of sample metadata to the synchrotron. In IceBear, each crystal selected for data collection is given a unique sample name and a crystal page is generated. Subsequently, the metadata required for data collection are uploaded directly to the ISPyB synchrotron database by a shipment module, and for each sample a link to the relevant ISPyB page is stored. IceBear allows notes to be made for each sample during cryocooling treatment and during data collection, as well as in later steps of the structure determination. Protocols are also available to aid the recycling of pins, pucks and dewars when the dewar returns from the synchrotron. The IceBear database is organized around projects, and project members can easily access the crystallization and diffraction metadata for each sample, as well as any additional information that has been provided via the notes. The crystal page for each sample connects the crystallization, diffraction and structural information by providing links to the IceBear drop-viewer page and to the ISPyB data-collection page, as well as to the structure deposited in the Protein Data Bank. © 2021 International Union of Crystallography. All rights reserved.","crystallization; IceBear; ISPyB; metadata; research-data management; X-ray data collection","Crystallography, X-Ray; Databases, Protein; Internet; Proteins; Software; protein; chemistry; Internet; procedures; protein database; software; X ray crystallography","","protein, 67254-75-5; Proteins, ","","","Biocenter Oulu; MTR; RKW; Horizon 2020 Framework Programme, H2020, (731005); Academy of Finland, (141487, 287063, 289024, 293369, 294085, 297875, 319194, 328117); Sigrid Juséliuksen Säätiö; Biocenter Finland, BF","This work was supported by Instruct-ULTRA, an EU H2020 project to further develop the services of Instruct-ERIC (Grant Agreement No. 731005 to RKW, JLS and AWA). This work was also funded by Diamond Light Source (RKW), by the Academy of Finland (grants No. 328117, 287063, 294085, 297875, 141487, 293369, 289024 and 319194 to LL, RKW and RV), by Biocenter Finland (RKW), by the Sigrid Juselius Foundation (MTR) and by Biocenter Oulu.","Abrahams G. J., Newman J., Acta Cryst, F75, pp. 184-192, (2019); Adams P. D., Afonine P. V., Baskaran K., Berman H. M., Berrisford J., Bricogne G., Brown D. G., Burley S. K., Chen M., Feng Z., Flensburg C., Gutmanas A., Hoch J. C., Ikegawa Y., Kengaku Y., Krissinel E., Kurisu G., Liang Y., Liebschner D., Mak L., Markley J. L., Moriarty N. W., Murshudov G. N., Noble M., Peisach E., Persikova I., Poon B. K., Sobolev O. V., Ulrich E. L., Velankar S., Vonrhein C., Westbrook J., Wojdyr M., Yokochi M., Young J. Y., Acta Cryst, D75, pp. 451-454, (2019); Allen R., Diakun G., Guest M., Keegan R., Nave C., Papiz M., Winter G., Winn M., Henrick K., Cowtan K. D., Young P., Proceedings of the UK e-Science All Hands Meeting, (2003); Berry I. M., Dym O., Esnouf R. M., Harlos K., Meged R., Perrakis A., Sussman J. L., Walter T. S., Wilson J., Messerschmidt A., Acta Cryst, D62, pp. 1137-1149, (2006); Bowler M. W., Nurizzo D., Barrett R., Beteva A., Bodin M., Caserotto H., Delagenie`re S., Dobias F., Flot D., Giraud T., Guichard N., Guijarro M., Lentini M., Leonard G. A., McSweeney S., Oskarsson M., Schmidt W., Snigirev A., von Stetten D., Surr J., Svensson O., Theveneau P., Mueller- Dieckmann C., J. Synchrotron Rad, 22, pp. 1540-1547, (2015); Brodersen D. E., Andersen G. R., Andersen C. B. F., Acta Cryst, F69, pp. 815-820, (2013); Bruno A. E., Charbonneau P., Newman J., Snell E. H., So D. R., Vanhoucke V., Watkins C. J., Williams S., Wilson J., PLoS One, 13, (2018); Daniel E., Lin B., Diprose J. M., Griffiths S. L., Morris C., Berry I. M., Owens R. J., Blake R., Wilson K. S., Stuart D. I., Esnouf R. M., J. Struct. Biol, 175, pp. 230-235, (2011); Dauter Z., Wlodawer A., Protein Pept. Lett, 23, pp. 201-210, (2016); Delagenie` re S., Brenchereau P., Launer L., Ashton A. W., Leal R., Veyrier S., Gabadinho J., Gordon E. J., Jones S. D., Levik K. E., McSweeney S.M., Monaco S., Nanao M., Spruce D., Svensson O., Walsh M. A., Leonard G. A., Bioinformatics, 27, pp. 3186-3192, (2011); De Maria Antolinos A., Pernot P., Brennich M. E., Kieffer J., Bowler M. W., Delageniere S., Ohlsson S., Malbet Monaco S., Ashton A., Franke D., Svergun D., McSweeney S., Gordon E., Round A., Acta Cryst, D71, pp. 76-85, (2015); Dupeux F., Ro? wer M., Seroul G., Blot D., Ma? rquez J. A., Acta Cryst, D67, pp. 915-919, (2011); Fisher S. J., Levik K. E., Williams M. A., Ashton A. W., McAuley K. E., J. Appl. Cryst, 48, pp. 927-932, (2015); Fo? rster A., Schulze-Briese C., Struct. Dyn, 6, (2019); Grimes J. M., Hall D. R., Ashton A. W., Evans G., Owen R. L., Wagner A., McAuley K. E., von Delft F., Orville A.M., Sorensen T., Walsh M. A., Ginn H. M., Stuart D. I., Acta Cryst, D74, pp. 152-166, (2018); Hassell A. M., An G., Bledsoe R. K., Bynum J. M., Carter H. L., Deng S.-J. J., Gampe R. T., Grisard T. E., Madauss K. P., Nolte R. T., Rocque W. J., Wang L., Weaver K. L., Williams S. P., Wisely G. B., Xu R., Shewchuk L. M., Acta Cryst, D63, pp. 72-79, (2007); Helliwell J. R., Biosci. Rep, 37, (2017); Lynch M. L., Dudek M. F., Bowman S. E. J., Patterns, 1, (2020); Malbet-Monaco S., Leonard G. A., Mitchell E. P., Gordon E. J., Acta Cryst, D69, pp. 1289-1296, (2013); Materlik G., Rayment T., Stuart D. I., Philos. Trans. A Math. Phys. Eng. Sci, 373, (2015); Mayo C. J., Diprose J. M., Walter T. S., Berry I. M., Wilson J., Owens R. J., Jones E. Y., Harlos K., Stuart D. I., Esnouf R. M., Structure, 13, pp. 175-182, (2005); Monaco S., Gordon E., Bowler M.W., Delagenie` re S., Guijarro M., Spruce D., Svensson O., McSweeney S. M., McCarthy A. A., Leonard G., Nanao M. H., J. Appl. Cryst, 46, pp. 804-810, (2013); Murthy A. V., Sulu R., Koski M. K., Tu H., Anantharajan J., Sah- Teli S. K., Myllyharju J., Wierenga R. K., Protein Sci, 27, pp. 1692-1703, (2018); Ng J. T., Dekker C., Kroemer M., Osborne M., von Delft F., Acta Cryst, D70, pp. 2702-2718, (2014); Oscarsson M., Beteva A., Flot D., Gordon E., Guijarro M., Leonard G., McSweeney S., Monaco S., Mueller-Dieckmann C., Nanao M., Nurizzo D., Popov A., von Stetten D., Svensson O., Rey-Bakaikoa V., Chado I., Chavas L., Gadea L., Gourhant P., Isabet T., Legrand P., Savko M., Sirigu S., Shepard W., Thompson A., Mueller U., Nan J., Eguiraun M., Bolmsten F., Nardella A., Mila`n-Otero A., Thunnissen M., Hellmig M., Kastner A., Schmuckermaier L., Gerlach M., Feiler C., Weiss M. S., Bowler M. W., Gobbo A., Papp G., Sinoir J., McCarthy A., Karpics I., Nikolova M., Bourenkov G., Schneider T., Andreu J., Cuni? G., Juanhuix J., Boer R., Fogh R., Keller P., Flensburg C., Paciorek W., Vonrhein C., Bricogne G., de Sanctis D., J. Synchrotron Rad, 26, pp. 393-405, (2019); Owen R. L., Juanhuix J., Fuchs M., Arch. Biochem. Biophys, 602, pp. 21-31, (2016); Rao R., Phys. Today, (2020); Rosa N., Ristic M., Thorburn L., Abrahams G. J., Marshall B., Watkins C. J., Kruger A., Khassapov A., Newman J., Crystals, 10, (2020); Thomas S. E., Collins P., James R. H., Mendes V., Charoensutthivarakul S., Radoux C., Abell C., Coyne A. G., Floto R. A., von Delft F., Blundell T. L., Philos. Trans. A Math. Phys. Eng. Sci, 377, (2019); Winter G., Gildea R. J., Paterson N., Beale J., Gerstel M., Axford D., Vollmar M., McAuley K. E., Owen R. L., Flaig R., Ashton A. W., Hall D. R., Acta Cryst, D75, pp. 242-261, (2019)","","","International Union of Crystallography","","","","","","20597983","","","33559605","English","Acta Crystallogr. Sect. D Str. Bio.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85097267687"
"Nie H.; Luo P.; Fu P.","Nie, Hua (57312223000); Luo, Pengcheng (57313329900); Fu, Ping (57312665400)","57312223000; 57313329900; 57312665400","Research data management implementation at Peking university library: Foster and promote open science and open data","2021","Data Intelligence","3","1","","189","204","15","3","10.1162/dint_a_00088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117880526&doi=10.1162%2fdint_a_00088&partnerID=40&md5=8bfc623bd84c18672ba1df25954111a7","Peking University Library, Beijing, 100871, China; Central Washington University Library, 400 E. University Way, Ellensburg, 98926, WA, United States","Nie H., Peking University Library, Beijing, 100871, China; Luo P., Peking University Library, Beijing, 100871, China; Fu P., Central Washington University Library, 400 E. University Way, Ellensburg, 98926, WA, United States","Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL. © 2021 Chinese Academy of Sciences.","Data sharing and reuse; Open Data; Open Science; Research data management implementation; Research life cycle","Information management; Life cycle; Open Data; Data repositories; Data reuse; Data Sharing; Open datum; Open science; Peking University; Research data; Research data management implementation; Research data managements; Research life cycles; Libraries","","","","","China Survey Data Archive; MSDC; University Management Science Data Center; National Natural Science Foundation of China, NSFC; Peking University, PKU","In 2014, the Peking University was awarded a grant by the National Natural Science Foundation of China for the China Survey Data Archive (CSDA) project, which aimed to develop a data repository administrated by the University Management Science Data Center (MSDC), a department within the ISSS. This grant provided an opportunity for the PKUL to build a more collaborative relationship with the ISSS. With the assistance of the research administrative units such as the Office of Science Research and the Office of Social Science Research, the PKUL and the ISSS decided to work together on this project. Initially, the responsibilities were split as follows: The MSDC supervised by the ISSS was responsible for research data collection and cleaning-up, standardization and analysis, data repository platform testing, and feedback. The PKUL was responsible for requirements analysis, functional design, software selection, as well as the development and maintenance of data repository, data storage, classification and metadata, systems administration, and associated technical and technological services.","Burgelman J.-C., Et al., Open science, open data, and open scholarship: European policies to make science fit for the twenty-first century, Frontiers in Big Data, 2, (2019); Giglia E., Open Access to research data as a driver for Open Science, JLIS.it: Italian Journal of Library and Information Science, 6, 2, pp. 225-247, (2015); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS ONE, 10, 8, (2015); Shen Y., Research data sharing and reuse practices of academic faculty researchers: A study of the Virginia Tech data landscape, International Journal of Digital Curation, 10, 2, pp. 157-175, (2016); Schober D., Et al., NmrML: A community supported open data standard for the description, storage, and exchange of NMR data, Analytical Chemistry, 90, 1, pp. 649-656, (2018); Tenopir C., Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Cox A. M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Kruse F., Thestrup J. B., Research libraries’ new role in research data management, current trends and visions in Denmark, LIBER Quarterly, 23, 4, pp. 310-335, (2014); Chiware E., Mathe Z., Academic libraries’ role in research data management services: A South African perspective, South African Journal of Library and Information Science, 81, 2, pp. 1-10, (2016); Cox A.M., Et al., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Ayris P., Ignat T., Defining the role of libraries in the Open Science landscape: A reflection on current European practice, Open Information Science, 2, 1, pp. 1-22, (2018); Hamad F., Al-Fadel M., Al-Soub A., Awareness of research data management services at academic libraries in Jordan: Roles, responsibilities and challenges, The New Review of Academic Librarianship, pp. 1-21, (2019); Pampel H., Et al., Making research data repositories visible: The re3data.org registry, PLoS ONE, 8, 11, (2013); Moon J., Developing a research data management service - a case study, Partnership, 9, 1, pp. 1-14, (2014); Curdt C., Supporting the interdisciplinary, long-term research project ‘patterns in soil-vegetationatmosphere-systems’ by data management services, Data Science Journal, 18, 5, pp. 1-9, (2019); Tripathi M., Shukla A., Sonkar S. K., Research data management practices in university libraries: A study, DESIDOC Journal of Library & Information Technology, 37, 6, (2017); Johnston L., Et al., Data curation network: How do we compare? A snapshot of six academic library institutions’ data repository and curation services, Journal of Escience Librarianship, 6, 1, (2017); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, PLoS ONE, 12, 3, (2017); Curdt C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project, Program: Electronic Library and Information Systems, 49, 4, pp. 494-512, (2015); McKinney B., Et al., Extension of research data repository system to support direct compute access to biomedical datasets: Enhancing Dataverse to support large datasets, Annals of the New York Academy of Sciences, 1387, 1, pp. 95-104, (2017); Mannheimer S., Et al., Qualitative data sharing: Data repositories and academic libraries as key partners in addressing challenges, The American Behavioral Scientist (Beverly Hills), 63, 5, pp. 643-664, (2018); Dovidonyte R., Implementation of Open Science in Lithuania, Nordic perspectives on Open Science, pp. 1-12, (2019); Pontika N., Roles and jobs in the open research scholarly communications environment: Analysing job descriptions to predict future trends, LIBER Quarterly, 29, 1, pp. 1-20, (2019); Clare C., Et al., Engaging researchers with data management: The cookbook, (2019); Alonso-Arevalo J., Research data management (RDM) on the horizon of academics and research libraries, Cuadernos De Documentación Multimedia, 30, pp. 75-88, (2019); Soderholm M., Sunikka A., Collaboration in RDM activities - Practices and development at Aalto University, The 12th Munin Conference on Scholarly Publishing, pp. 1-10, (2017); Tang R., Hu Z., Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Zhu L., Et al., The construction of Peking University open research data platform: Exploration and practice, Library and Information Service, 60, 4, pp. 44-51, (2016)","H. Nie; Peking University Library, Beijing, 100871, China; email: hnie@lib.pku.edu.cn","","MIT Press Journals","","","","","","20967004","","","","English","Data. Intell.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85117880526"
"Bezuidenhout L.; Drummond-Curtis S.; Walker B.; Shanahan H.; Alfaro-Córdoba M.","Bezuidenhout, Louise (16401022900); Drummond-Curtis, Sara (57222471807); Walker, Bridget (57222464261); Shanahan, Hugh (7004258684); Alfaro-Córdoba, Marcela (56416522000)","16401022900; 57222471807; 57222464261; 7004258684; 56416522000","A school and a network: Codata-rda data science summer schools alumni survey","2021","Data Science Journal","20","1","10","1","12","11","1","10.5334/DSJ-2021-010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102770129&doi=10.5334%2fDSJ-2021-010&partnerID=40&md5=e5ca0e1fbe07f5b58a29988a39b4b9e5","University of Oxford, United Kingdom; CODATA-RDA Schools for Research Data Science, United Kingdom; Research Data Alliance, United Kingdom; Royal Holloway University of London, United Kingdom; University of Costa Rica, Costa Rica","Bezuidenhout L., University of Oxford, United Kingdom; Drummond-Curtis S., CODATA-RDA Schools for Research Data Science, United Kingdom; Walker B., Research Data Alliance, United Kingdom; Shanahan H., Royal Holloway University of London, United Kingdom; Alfaro-Córdoba M., University of Costa Rica, Costa Rica","The CODATA-RDA Schools for Research Data Science (SRDS) is a network of schools originating in the RDA in 2016. In 2019 it was recognized as an RDA output. To date, over 400 students from 40 countries have been trained in 10 schools. The majority of these students were postgraduates from low/middle-income countries (LMICs). In contrast to many other data science training approaches, the SRDS schools are designed to be 2-week, disciplinarily-agnostic, residential events where students are introduced to a broad range of tools requisite for efficient and responsible data-centric research. This paper presents the results of a survey carried out on alumni from schools held between 2016 and 2019 (45% response). The results of the survey strongly support the SRDS’s long-term goals of facilitating data science training/capacity building within LMICs, and to foster communities of early career researchers (ECRs) conducting responsible and open data science research. The survey results demonstrated that 90% of respondent alumni continued to conduct research and make use of the skills acquired at the SRDS. Modules on open and responsible research and research data management were rated as important for future research. 79% of respondents confirmed that they maintained contact with peers, and 31% had set up academic collaborations with peers and/or instructors. Many had gone on to present content from the schools in their home institutions. The survey results clearly demonstrate the impact of the SRDS, and the value of an expanding network of schools supported by the RDA and CODATA. © 2021 The Author(s).","Alumni; CODATA; Data science; RDA; School; Survey","Data Science; Information management; Network coding; Open Data; Students; Alumni survey; Data centric; Long-term goals; Research data; Research data managements; Summer school; Surveys","","","","","","","Bezuidenhout L, Et al., Beyond the Digital Divide: Towards a Situated Approach to Open Data, Science and Public Policy, 44, 4, pp. 464-475, (2017); Bezuidenhout L, Quick R, Shanahan H., Ethics When You Least Expect It”: A Modular Approach to Short Course Data Ethics Instruction, Science and Engineering Ethics, pp. 1-25, (2020); AuthorCarpentry homepage, (2017); Demchenko Y, Comminiello L, Reali G., Designing Customisable Data Science Curriculum Using Ontology for Data Science Competences and Body of Knowledge, (2019); Feldon DF, Et al., Null effects of boot camps and short-format training for PhD students in life sciences, Proceedings of the National Academy of Sciences of the United States of America, 114, 37, pp. 9854-9858, (2017); Fosci M., Et al., Research Capacity Strengthening in LMICs: Rapid Evidence Assessment Prepared for DFID, (2019); Song I-Y, Zhu Y., Big data and data science: what should we teach? Expert Systems, Blackwell Publishing Ltd, 33, 4, pp. 364-373, (2016); Teal TK, Et al., Data Carpentry: Workshops to Increase Data Literacy for Researchers, International Journal of Digital Curation, 10, 1, pp. 135-143, (2015); Vermeir K, Et al., Global Access to Research Software: The Forgotten Pillar of Open Science Implementation, (2018); Wilson G., Software carpentry: Getting scientists to write better code by making them more productive, Computing in Science and Engineering, pp. 66-69, (2006)","L. Bezuidenhout; University of Oxford, United Kingdom; email: louise.bezuidenhout@insis.ox.ac.uk","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85102770129"
"Yazdi M.A.; Schimmel D.; Nellesen M.; Politze M.; Müller M.","Yazdi, M. Amin (57190273762); Schimmel, David (7004845293); Nellesen, Marcel (58062843900); Politze, Marius (57195741179); Müller, Matthias (35249260000)","57190273762; 7004845293; 58062843900; 57195741179; 35249260000","DA4RDM: Data Analysis for Research Data Management Systems","2021","International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings","3","","","177","183","6","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146199358&partnerID=40&md5=83fb372f58fe0d6bd68c753235f3e26c","IT Center, RWTH Aachen University, Aachen, Germany","Yazdi M.A., IT Center, RWTH Aachen University, Aachen, Germany; Schimmel D., IT Center, RWTH Aachen University, Aachen, Germany; Nellesen M., IT Center, RWTH Aachen University, Aachen, Germany; Politze M., IT Center, RWTH Aachen University, Aachen, Germany; Müller M., IT Center, RWTH Aachen University, Aachen, Germany","Research Data Management (RDM) systems are becoming an essential part of every researcher's academic career. Often, researchers use various resources and web applications to handle their research data, causing complications for maintaining data and assessing research projects against FAIR principles. Consequently, RDM platforms help researchers with data administration tasks while providing the necessary tools for managing research projects. Furthermore, user engagement with such RDM platforms leaves traces of user interaction with research data; thus, studying user behaviors over research data becomes an exciting territory. However, running periodic data analysis studies proves to be a time-consuming and challenging task and requires the help of scientific staff to run pre-and post-processing pipelines per use case in order to be able to produce results that are usable by domain experts. This paper introduces Data Analysis for Research Data Management systems (DA4RDM) as a scalable web application that supports reusing pre-defined pre-and post-processing pipelines to enable domain experts to utilize the system without the need for scientific expertise. We use real data acquired from an RDM system, explain the tool's applicability, and present the preliminary findings, demonstrating its use cases and capabilities. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda.All rights reserved.","Data Analysis; Pre-processing Pipeline; Process Mining; Requirement Engineering; Research Data Management; Web Application","Behavioral research; Data handling; Information analysis; Information management; Pipeline processing systems; Data management system; Management platforms; Pre-processing; Pre-processing pipeline; Process mining; Requirement engineering; Research data; Research data managements; WEB application; Web applications; Pipelines","","","","","","","Berti A., van Zelst S. J., van der Aalst W., Process mining for python (pm4py): bridging the gap between process- and data science, (2019); Celik U., Akcetin E., Process mining tools comparison, Online Academic Journal of Information Technology, 9, pp. 97-104, (2018); Gargiulo P., Galimberti P., Tammaro A. M., Zane A., Fair rdm (research data management): Italian initiatives towards eosc implementation, IRCDL, pp. 42-52, (2021); Kebede M., Dumas M., Comparative evaluation of process mining tools, (2015); Kindler E., Rubin V., Schafer W., Process mining and petri net synthesis, International Conference on Business Process Management, pp. 105-116, (2006); Malkawi R., Saifan A. A., Alhendawi N., Bani-Ismaeel A., Data mining tools evaluation based on their quality attributes, International Journal of Advanced Science and Technology, 29, 3, pp. 13867-13890, (2020); Politze M., Claus F., Brenger B., Yazdi M. A., Heinrichs B., Schwarz A., How to manage it resources in research projects? towards a collaborative scientific integration environment, European Journal of Higher Education IT, 2, (2020); Rafiei M., von Waldthausen L., van der Aalst W. M., Ensuring confidentiality in process mining, Proceedings of the 8th International Symposium on Data-driven Process Discovery and Analysis-SIMPDA, 18, pp. 3-17, (2018); van der Aalst W., Process mining: data science in action, (2016); Wilkinson M. D., Dumontier M., Aalbersberg I. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Et al., The fair guiding principles for scientific data management and stewardship, Scientific data, 3, 1, pp. 1-9, (2016); Yazdi M. A., Enabling operational support in the research data life cycle, Proceedings of the First International Conference on Process Mining, pp. 1-10, (2019); Yazdi M. A., Farhadi P., Heinrichs B., Event log abstraction in client-server applications, IC3K 2021: Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: KDIR, (2021); Yazdi M. A., Politze M., Reverse engineering: The university distributed services, Proceedings of the Future Technologies Conference, pp. 223-238, (2020)","","Bernardino J.; Masciari E.; Rolland C.; Filipe J.","Science and Technology Publications, Lda","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","13th International Conference on Knowledge Management and Information Systems, KMIS 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021","25 October 2022 through 27 October 2022","Virtual, Online","181965","21843228","978-989758533-3","","","English","International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings","Conference paper","Final","","Scopus","2-s2.0-85146199358"
"Liu Y.-H.; Chen H.-L.; Kato M.P.; Wu M.; Huvila I.","Liu, Ying-Hsang (26662786100); Chen, Hsin-Liang (57204847223); Kato, Makoto P. (57929292200); Wu, Mingfang (55729392400); Huvila, Isto (17434414800)","26662786100; 57204847223; 57929292200; 55729392400; 17434414800","Supporting open research data practice through data curation and discovery: A global perspective","2020","Proceedings of the Association for Information Science and Technology","57","1","e291","","","","0","10.1002/pra2.291","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139964073&doi=10.1002%2fpra2.291&partnerID=40&md5=ba82d38e4d36605679769bf88639855c","Department of Design and Communication, University of Southern Denmark, Kolding, Denmark; Missouri University of Science and Technology, Rolla, MO, United States; University of Tsukuba, Tsukuba, Japan; Faculty of Library, Information and Media Science, Australian Research Data Commons, Caulfield East, VIC, Australia; Department of ALM (Archival Studies, Library and Information Studies and Museums and Cultural Heritage Studies), Uppsala University, Uppsala, Sweden","Liu Y.-H., Department of Design and Communication, University of Southern Denmark, Kolding, Denmark; Chen H.-L., Missouri University of Science and Technology, Rolla, MO, United States; Kato M.P., University of Tsukuba, Tsukuba, Japan; Wu M., Faculty of Library, Information and Media Science, Australian Research Data Commons, Caulfield East, VIC, Australia; Huvila I., Department of ALM (Archival Studies, Library and Information Studies and Museums and Cultural Heritage Studies), Uppsala University, Uppsala, Sweden","This panel will address the issues associated with the practice and service of open research data curation and discovery from a global perspective. The sub-fields of information science such as information retrieval, information curation, information practices and human-centered data science have approached the open research data initiatives from multiple lenses. The issues of data creation, capturing, curation, sharing, discovery and reuse of cut across the sub-fields. We will identify and discuss the emerging themes in open data curation and discovery drawing on active research projects, repository practices and research data capturing and reuse in a selection of disciplines from health domain to archaeology and cultural heritage. 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.","data curation; data discovery; open data; research data; research data management","Information management; Curation; Data capturing; Data curation; Data discovery; Data reuse; Global perspective; Open datum; Research data; Research data managements; Sub fields; Open Data","","","","","","","Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, (2019); Faniel I.M., Frank R.D., Yakel E., Context from the data reuser's point of view, Journal of Documentation, 75, 6, pp. 1274-1297, (2019); Kacprzak E., Koesten L., Ibanez L.-D., Blount T., Tennison J., Simperl E., Characterising dataset search: An analysis of search logs and data requests, Journal of Web Semantics, 55, pp. 37-55, (2019); Khalsa S., Cotroneo P., Wu M., A survey of current practices in data search services, Mendeley, (2018); Polona V., Research data management and research data literacy in Slovenian science, Journal of Documentation, 75, 1, pp. 24-43, (2019); Walsh D., Clough P., Hall M.M., Hopfgartner F., Foster J., Kontonatsios G., Analysis of transaction logs from National Museums Liverpool, Digital libraries for open knowledge. TPDL 2019, pp. 84-98, (2019); Wu M., Psomopoulos F., Khalsa S.J., de Waard A., Data discovery paradigms: User requirements and recommendations for data repositories, Data Science Journal, 18, 1, (2019)","Y.-H. Liu; Department of Design and Communication, University of Southern Denmark, Kolding, Denmark; email: yingliu@sdu.dk","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85139964073"
"Jamwal V.; Kaur S.","Jamwal, Vineet (57265868100); Kaur, Simran (57224856301)","57265868100; 57224856301","Global presence of open-source research data management platform for libraries: the Dataverse project","2021","Library Hi Tech News","38","9","","8","12","4","0","10.1108/LHTN-10-2021-0066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118120902&doi=10.1108%2fLHTN-10-2021-0066&partnerID=40&md5=5bc94bc44d6bcf1b021452ad17fd81ab","Central Library, Indian Institute of Technology Ropar, Rupnagar, India; Department of Library and Information Science, Panjab University, Chandigarh, India","Jamwal V., Central Library, Indian Institute of Technology Ropar, Rupnagar, India; Kaur S., Department of Library and Information Science, Panjab University, Chandigarh, India","Purpose: This paper aims to provide statistical information on the worldwide spread of the open-source research data management application, the Dataverse Project, to librarians, data managers and information managers who are considering using the application at their own institution. Design/methodology/approach: To produce a list of dataverse repositories, the official Dataverse website was evaluated, and JSON data were downloaded and parsed. Data standardisation was performed to assess the state of installations in various nations and continents across the world. Findings: Globally, the Dataverse repositories have seen a rise in overall installations. The year 2020 alone saw a 23.21% rise. In a country-by-country comparison, the USA (13) has the most dataverse installations, while Europe (25) has the highest number of installations worldwide. Originality/value: This research will be useful to librarians, data managers and information managers, among others, who want to learn more about Dataverse repositories throughout the world before deploying at their local level. © 2021, Emerald Publishing Limited.","Data set; Dataverse; JSON data; Open-source software; Research data management; The Dataverse project","","","","","","","","Crosas M., The dataverse network: an open-source application for sharing, discovering and preserving data, D-Lib Magazine, 17, (2011); King G., An introduction to the dataverse network as an infrastructure for data sharing, Sociological Methods and Research, 36, pp. 173-199, (2007); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French national research center (CNRS), Data Technologies and Applications, 52, 2, pp. 248-265, (2018); The dataverse project official webpage, (2021); Whyte A., Tedds J., Making the case for research data management, DCC Briefing Papers, (2011)","V. Jamwal; Central Library, Indian Institute of Technology Ropar, Rupnagar, India; email: vineetjmwl@gmail.com","","Emerald Group Holdings Ltd.","","","","","","07419058","","","","English","Libr. Hi Tech News","Article","Final","","Scopus","2-s2.0-85118120902"
"Ignat T.; Ayris P.","Ignat, Tiberius (57204049125); Ayris, Paul (22233574500)","57204049125; 22233574500","Built to last! Embedding open science principles and practice into European universities","2021","Insights: the UKSG Journal","33","","","","","","10","10.1629/UKSG.501","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099068943&doi=10.1629%2fUKSG.501&partnerID=40&md5=84c40c3d7ab3e812ed262a4ea00b4228","Scientific Knowledge Services, Switzerland; UCL Library Services, United Kingdom","Ignat T., Scientific Knowledge Services, Switzerland; Ayris P., UCL Library Services, United Kingdom","The purpose of this article is to examine the cultural change needed by universities, as identified by LERU in its report Open Science and its role in universities: a roadmap for cultural change.1 It begins by illustrating the nature of that cultural change. Linked to that transformation is a necessary management change to the way in which organizations perform research. Competition is not the only, or necessarily the best, way to conduct this transformation. Open science brings to the fore the values of collaboration and sharing. Building on a number of Focus on Open Science Workshops held over five years across Europe, the article identifies best practice in changing current research practices, which will then contribute to the culture change necessary to deliver open science. Four case studies, delivered at Focus on Open Science Workshops or other conferences in Europe, illustrate the advances that are being made: the findings of a Workshop on Collaboration and Competition at the OAI 11 meeting in Geneva in June 2019; alternative publishing platforms, exemplified by UCL Press; open data, FAIR data and reproducibility; and a Citizen Science Workshop held at the LIBER Conference in Dublin in June 2019.  © 2020 Tiberius Ignat and Paul Ayris.","Change management; Citizen science; Open access publishing; Open science; Research data management; Research metrics","","","","","","Horizon 2020 Framework Programme, H2020, (654139)","Funding text 1: Research data is the new currency in research activity. A useful set of tools and insights on the role of research data was established by the EC-funded LEARN project,24which received funding from the European Union’s Horizon 2020 research and innovation programme.; Funding text 2: • project competitions have the potential for producing great ideas that simply do not fit into calls for projects. Continue to launch project competitions, but find complementary routes for funding research ambitions","Open Science and its role in universities: a roadmap for cultural change, (2018); Be Open to Open Science: Stakeholders Should Prepare for the Future, not Cling to the Past, LIBER, (2016); Focus on Open Science; Other events were in Korør (Denmark); Open Science roadmap; Open Science roadmap; FAIR; OAI 11, CERN-UNIGE Workshop on Innovations in Scholarly Communication, (2019); 2019 UCL Research Strategy; UCL 2034: a new 20-yr strategy for UCL; UCL 2034, Delivering global impact; Taylor Mike, Do monographs have a future? Publishers, funders and research evaluators must decide, LSE Impact Blog, (2019); The Academic Book of the Future; Deegan Marilyn, What does the future hold for academic books?, LSE Impact Blog, (2017); UCL Discovery; Open Access Monographs, (2018); How the World Changed Social Media; Principles and Implementation; Fellous-Sigrist M., UCL researchers and their research data: practices, challenges & recommendations: report on the 2016 RDM survey); UCL Research Data Repository; The H2020 Online Manual provided an earlier version of this diagram; We are extremely grateful to Dr James Wilson of the UCL Information Systems Division for sharing this information; Hackert Marvin L., Open Data in a Big Data World: A position paper for crystallography; Boulton Geoffrey, Why Open Data?, the LEARN Toolkit of Best Practice for Research Data Management, (2016); Guidelines on FAIR Data Management in Horizon 2020; FAIR Data Principles, FORCE11; Disciplinary Metadata; Creating & analysing data; Collins Sandra, Et al., Turning FAIR into reality: Final Report and Action Plan from the European Commission Expert Group on FAIR Data, (2018); Collins, Et al., Turning FAIR into reality; Collins, Et al., Turning FAIR into reality; Collins, Et al., Turning FAIR into reality, pp. 70-71; The Equator Network provides numerous reporting guidelines for health research: NC3Rs' ARRIVE guidelines; Munafo Marcus, Et al., A manifesto for reproducible science, Nature Human Behaviour, 1, (2017); UCL Academic Careers Framework; Open Science roadmap; Research Transparency; Sanz Fermin Serrano, Et al., White Paper on Citizen Science for Europe, produced for the Socientize consortium, (2014); Heigl Florian, Et al., Opinion: Toward an international definition of citizen science, Proceedings of the National Academy of Sciences of the United States of America, 116, 17, pp. 8089-8092, (2019); Auerbach Jeremy, Et al., Letter: The problem with delineating narrow criteria for citizen science, Proceedings of the National Academy of Sciences of the United States of America, 116, 31, pp. 15336-15337, (2019); LIBER Launches Open Science Roadmap; About the project | Capturing our Coast: An innovation in marine citizen science, (2019); Curieuzeneuzen Vlaanderen"" |citizen science project; Curieuzeneuzen Vlaanderen - De Standaard; Theobald Elinore, Et al., Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research, Biological Conservation, 181, pp. 236-244, (2015); Awards - Transcribe Bentham; Citations - Transcribe Bentham, UCL; LIBER Launches Open Science Roadmap; Citizen science at universities: Trends, guidelines and recommendations; Science Europe Briefing Paper on Citizen Science, (2018); Ten Principles of Citizen Science, European Citizen Science Association, (2019); Cavalier Darlene, Et al., Scistarter | Science we can do together, Librarian's Guide to Citizen Science, (2019); UCL Library Services Strategy 2019-22, (2019)","T. Ignat; Scientific Knowledge Services, Germany; email: tiberius@scientificknowledgeservices.com","","United Kingdom Serials Group","","","","","","20487754","","","","English","Insights UKSG J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85099068943"
"Altun O.; Sheveleva T.; Castro A.; Oladazimi P.; Koepler O.; Mozgova I.; Lachmayer R.; Auer S.","Altun, Osman (57221950364); Sheveleva, Tatyana (57221965627); Castro, André (57672591600); Oladazimi, Pooya (57385463000); Koepler, Oliver (6507094492); Mozgova, Iryna (27067881500); Lachmayer, Roland (6602616454); Auer, Sören (23391879500)","57221950364; 57221965627; 57672591600; 57385463000; 6507094492; 27067881500; 6602616454; 23391879500","Integration of a digital machine park into a research data management system; [Integration eines digitalen Maschinenparks in ein Forschungsdatenmanagementsystem]","2021","Proceedings of the 32nd Symposium Design for X, DFX 2021","","","","","","","4","10.35199/dfx2021.23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121958291&doi=10.35199%2fdfx2021.23&partnerID=40&md5=1c563952da2298b626cca83fb8febea5","Institut für Produktentwicklung und Gerätebau, Leibniz Universität Hannover, Germany; TIB - Leibniz-Informationszentrum Technik, Naturwissenschaften und Universitätsbibliothek, Germany","Altun O., Institut für Produktentwicklung und Gerätebau, Leibniz Universität Hannover, Germany; Sheveleva T., TIB - Leibniz-Informationszentrum Technik, Naturwissenschaften und Universitätsbibliothek, Germany; Castro A., TIB - Leibniz-Informationszentrum Technik, Naturwissenschaften und Universitätsbibliothek, Germany; Oladazimi P., TIB - Leibniz-Informationszentrum Technik, Naturwissenschaften und Universitätsbibliothek, Germany; Koepler O., TIB - Leibniz-Informationszentrum Technik, Naturwissenschaften und Universitätsbibliothek, Germany; Mozgova I., Institut für Produktentwicklung und Gerätebau, Leibniz Universität Hannover, Germany; Lachmayer R., Institut für Produktentwicklung und Gerätebau, Leibniz Universität Hannover, Germany; Auer S., TIB - Leibniz-Informationszentrum Technik, Naturwissenschaften und Universitätsbibliothek, Germany","The global trend towards comprehensive digitization of technologies in product manufacturing leadings to radical changes in engineering processes and requires a new, expanded understanding in the handling of data. Especially in large interdisciplinary projects with several subprojects, the use of a research data management (RDM) system is necessary. This paper describes the concept to realise a FDM system according to FAIR (Findable, Accessible, Interoperable, Reusable) data principles and using open source systems. The approach is explained on the example of a digital machine park within the Collaborative Research Centre Oxygen-free production (CRC 1368).  © 2021 die Autoren.","Digitization of scientific data; FAIR data principles; Knowledge management; Research data management; Semantic information linking","Data handling; Interoperability; Parks; Semantics; Data management system; Digital machines; Digitisation; Digitization of scientific data; Findable, accessible, interoperable, reusable data principle; Global trends; Research data managements; Scientific data; Semantic information linking; Semantics Information; Knowledge management","","","","","","","Kapogiannis Georgios, Sherratt Fred, Impact of integrated collaborative technologies to form a collaborative culture in construction projects, Built Environment Project and Asset Management, 8, 1, pp. 24-38, (2018); Sandfeld Stefan, Et al., Strategiepapier Digitale Transformation in der Materialwissenschaft und Werkstofftechnik, (2018); Amorim Ricardo, Et al., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Wilkinson Mark D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Mozgova Iryna, Et al., Research Data Management System for a large Collaborative Project, DS101: Proceedings of NordDesign 2020, (2020); Koppe R., Et al., O2A: A Generic Framework for Enabling the Flow of Sensor Observations to Archives and Publication, OCEANS 2015, pp. 1-6, (2015); Sachmerkmal-Listen-Begriffe und Grundsätze, (2019); Pellegrini Tassilo, Sack Harald, Auer Soren, Linked Enterprise Data-Management und Bewirtschaftung vernetzter Unternehmensdaten mit Semantic Web Technologien, (2014); Vandenbussche Pierre-Yves, Et al., Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web, Semantic Web, 8, 3, pp. 437-452, (2014); Maier Hans Jurgen, Et al., Towards Dry Machining of Titanium-Based Alloys: A New Approach Using an Oxygen-Free Environment, Metals, 10, 9, (2020); Szafarska Maik, Gustus Rene, Maus-Friedrichs Wolfgang, Sauerstofffreier Transport, Präparation und Transfer von Materialproben für die Oberflächenanalytik, Tagungsband 4 . Symposium Materialtechnik, pp. 829-839, (2021); Eva Effertz, The Funder's Perspective: Data Management in Coordinated Programmes of the German Research Foundation (DFG), Proceedings of the Data Management Workshop 29.-30.10.2009 Cologne, pp. 35-38, (2010); Sheveleva Tatyana, Et al., Development of a Domain-Specific Ontology to Support Research Data Management for the Tailored Forming Technology, Procedia Manufacturing, 52, 1, pp. 107-112, (2020); Haller Armin, Et al., Semantic Sensor Network Ontology, (2021); Arp Robert, Smith Barry, Spear Andrew, Building Ontologies With Basic Formal Ontology, (2015); Daniele Laura, SAREF4INMA: an extension of SAREF for the industry and manufacturing domain, (2019); Ameri Farhad, Dutta Debasish, An Upper Ontology for Manufacturing Service Description, Proceedings of the ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 3, pp. 651-661, (2006); Lemaignan Severin, Et al., MASON: A proposal for an ontology of manufacturing domain, EEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06), pp. 195-200, (2006); Mazzola Luca, Et al., CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance, IC3K 2016: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 136-143, (2016); Über Maschinen und zur Änderung der Richtlinie 95/16/EG, (2006); Elektromagnetische Verträglichkeit (EMV)-Produktfamiliennorm für Werkzeugmaschinen-Störfestigkeit, (2003)","O. Altun; Institut für Produktentwicklung Gerätebau, Garbsen, An der Universität 1 (8143), 30823, Germany; email: altun@ipeg.uni-hannover.de","Krause D.; Paetzold K.; Wartzack S.","The Design Society","","32nd Symposium Design for X, DFX 2021","27 September 2021 through 28 September 2021","Virtual, Online","173730","","","","","German","Proc. Symp. Des. X, DFX","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85121958291"
"Inau E.T.; Sack J.; Waltemath D.; Zeleke A.A.","Inau, Esther Thea (57218617966); Sack, Jean (57221867306); Waltemath, Dagmar (36471561200); Zeleke, Atinkut Alamirrew (56502027700)","57218617966; 57221867306; 36471561200; 56502027700","Initiatives, concepts, and implementation practices of FAIR (Findable, accessible, interoperable, and reusable) data principles in health data stewardship practice: Protocol for a scoping review","2021","JMIR Research Protocols","10","2","e22505","","","","14","10.2196/22505","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100459804&doi=10.2196%2f22505&partnerID=40&md5=4a21d3d0b928fdf2477771317a49a895","Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States","Inau E.T., Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; Sack J., International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Waltemath D., Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; Zeleke A.A., Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany","Background: Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic. Objective: This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain. Methods: The Arksey and O’Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers. Results: The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021. Conclusions: We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing. © Esther Thea Inau, Jean Sack, Dagmar Waltemath, Atinkut Alamirrew Zeleke.","Data stewardship; FAIR data principles; Health research; PRISMA; Scoping review","","","","","","Deutsche Forschungsgemeinschaft, DFG, (393148499); Universität Greifswald","We acknowledge support for the article processing charge from the DFG (German Research Foundation; 393148499) and the Open Access Publication Fund of the University of Greifswald.","Khan MA, Uddin MF, Gupta N., Seven V's of Big Data: Understanding Big Data to Extract Value, 2014 Presented at: 2014 Zone 1 Conference of the American Society for Engineering Education; Vayena E, Dzenowagis J, Langfeld M., Evolving health data ecosystem; McCue ME, McCoy AM., The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges, Front Vet Sci, 4, (2017); Dash S, Shakyawar SK, Sharma M, Kaushik S., Big data in healthcare: management, analysis and future prospects, J Big Data, 6, 1, (2019); McKee M, van Schalkwyk MCI, Stuckler D., The second information revolution: digitalization brings opportunities and concerns for public health, Eur J Public Health, 29, pp. 3-6, (2019); Staunton C, Slokenberga S, Mascalzoni D., The GDPR and the research exemption: considerations on the necessary safeguards for research biobanks, Eur J Hum Genet, 27, 8, pp. 1159-1167, (2019); Goldsteen A, Ezov G, Shmelkin R, Moffie M, Farkash A., Data minimization for GDPR Compliance in machine learning models; Sousa M, Ferreira D, Santos-Pereira C, Bacelar G, Frade S, Pestana O, Et al., openEHR Based Systems and the General Data Protection Regulation (GDPR), Stud Health Technol Inform, 247, pp. 91-95, (2018); Holub P, Kohlmayer F, Prasser F, Mayrhofer MT, Schlunder I, Martin GM, Et al., Enhancing Reuse of Data and Biological Material in Medical Research: From FAIR to FAIR-Health, Biopreserv Biobank, 16, 2, pp. 97-105, (2018); Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46, Off J Eur Union, 59, 294, pp. 1-88, (2016); Boeckhout M, Zielhuis GA, Bredenoord AL., The FAIR guiding principles for data stewardship: fair enough?, Eur J Hum Genet, 26, 7, pp. 931-936, (2018); Beyan O, Choudhury A, van Soest J, Kohlbacher O, Zimmermann L, Stenzhorn H, Et al., Distributed Analytics on Sensitive Medical Data: The Personal Health Train, Data Intelligence, 2, 1-2, pp. 96-107, (2020); European Open Science Cloud, Nat Genet, 48, 8, pp. 821-821, (2016); Semler S, Wissing F, Heyder R., German Medical Informatics Initiative, Methods Inf Med, 57, pp. e50-e56, (2018); Cuggia M, Combes S., The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare, Yearb Med Inform, 28, 1, pp. 195-202, (2019); Ferrari T, Scardaci D, Andreozzi S., The Open Science Commons for the European Research Area, Earth Observation Open Science and Innovation, pp. 43-67, (2018); Wittenburg P, Sustkova H, Montesanti A., The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data; Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Martone M., FORCE11: Building the Future for Research Communications and e-Scholarship, BioScience, 65, 7, pp. 635-635, (2015); Hagstrom S., The FAIR data principles; Mons B, Neylon C, Velterop J, Dumontier M, da Silva Santos LOB, Wilkinson MD., Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud, ISU, 37, 1, pp. 49-56, (2017); Michener WK., Ten Simple Rules for Creating a Good Data Management Plan, PLoS Comput Biol, 11, 10, (2015); Boeckhout M, Zielhuis GA, Bredenoord AL., The FAIR guiding principles for data stewardship: fair enough?, Eur J Hum Genet, 26, 7, pp. 931-936, (2018); Jacobsen A, de Miranda Azevedo R, Juty N, Batista D, Coles S, Cornet R, Et al., FAIR Principles: Interpretations and Implementation Considerations, Data Intelligence, 2, 1-2, pp. 10-29, (2020); Wise J, de Barron AG, Splendiani A, Balali-Mood B, Vasant D, Little E, Et al., Drug Discov Today, 24, 4, pp. 933-938, (2019); Vesteghem C, Brondum RF, Sonderkaer M, Sommer M, Schmitz A, Bodker JS, Et al., Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives, Brief Bioinform, 21, 3, pp. 936-945, (2020); Kamel PI, Nagy PG., Patient-Centered Radiology with FHIR: an Introduction to the Use of FHIR to Offer Radiology a Clinically Integrated Platform, J Digit Imaging, 31, 3, pp. 327-333, (2018); Jakob R., ICD-11-Adapting ICD to the 21st century, Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz, 61, 7, pp. 771-777, (2018); Matthews P., FAIR data needed to liberate hepatitis B virus (HBV) from the catch-22 of neglect, J Glob Health, 9, 1, (2019); Goble C, Cohen-Boulakia S, Soiland-Reyes S, Garijo D, Gil Y, Crusoe MR, Et al., FAIR Computational Workflows, Data Intelligence, 2, 1-2, pp. 108-121, (2020); Rychlik M, Zappa G, Anorga L, Belc N, Castanheira I, Donard OFX, Et al., Ensuring Food Integrity by Metrology and FAIR Data Principles, Front Chem, 6, (2018); Draxl C, Scheffler M., Big Data-Driven Materials Science and Its FAIR Data Infrastructure, Handbook of Materials Modeling, pp. 1-25, (2019); Tanhua T, Pouliquen S, Hausman J, O'Brien K, Bricher P, de Bruin T, Et al., Ocean FAIR Data Services, Front Mar Sci, 6, (2019); Arksey H, O'Malley L., Scoping studies: towards a methodological framework, Int J Soc Res Methodol, 8, 1, pp. 19-32, (2005); Tang C, Plasek JM, Bates DW., Rethinking Data Sharing at the Dawn of a Health Data Economy: A Viewpoint, J Med Internet Res, 20, 11, (2018); Fecher B, Friesike S, Hebing M., What Drives Academic Data Sharing?, PLoS ONE, 10, 2, (2015); Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Et al., PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation, Ann Intern Med, 169, 7, pp. 467-473, (2018); Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A., Rayyan-a web and mobile app for systematic reviews, Syst Rev, 5, 1, (2016); RDA COVID19 Case Statement; Mons B., The VODAN IN: support of a FAIR-based infrastructure for COVID-19, Eur J Hum Genet, 28, 6, pp. 724-727, (2020)","E.T. Inau; Medical Informatics Institute for Community Medicine, University Medicine Greifswald, Greifswald, Ellernholzstraße 1-2, 17487, Germany; email: inaue@uni-greifswald.de","","JMIR Publications Inc.","","","","","","19290748","","","","English","JMIR Res. Prot.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85100459804"
"Hewera M.; Kahlert U.D.; Hänggi D.; Gerlach B.","Hewera, Michael (57218484701); Kahlert, Ulf Dietrich (41661514600); Hänggi, Daniel (8686914300); Gerlach, Björn (16745015400)","57218484701; 41661514600; 8686914300; 16745015400","eLabFTW as an Open Science tool to improve the quality and translation of preclinical research","2021","F1000Research","10","","292","","","","1","10.12688/f1000research.52157.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112010376&doi=10.12688%2ff1000research.52157.2&partnerID=40&md5=b77a06f22e9cff2d29096d845144041a","Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, NRW, 40225, Germany; PAASP GmbH, Heidelberg, BW, 69118, Germany; Beijing Neurosurgical Institute, Beijing, China","Hewera M., Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, NRW, 40225, Germany; Kahlert U.D., Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, NRW, 40225, Germany, Beijing Neurosurgical Institute, Beijing, China; Hänggi D., Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, NRW, 40225, Germany; Gerlach B., PAASP GmbH, Heidelberg, BW, 69118, Germany","Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-sceience labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management. © 2021 Hewera M et al.","Electronic Lab Notebook; ELN; Open Science; Quality Management; Reproducibility; Transparency","article; data quality; preclinical study; reproducibility; total quality management","","","","","","","Baker M., Is there a reproducibility crisis?, Nature, 533, 7604, pp. 452-454, (2016); Diggle P.J., Zeger S.L., Editorial, Biostatistics, 11, 3, (2010); Nielsen M., An informal definition of open science, The OpenScience Project, (2011); Adam B., Lindstadt B., ELN-Wegweiser, (2020); Kihlen M., Electronic lab notebooks - do they work in reality?, DDT, 10, (2005); Gerlach B., Untucht C., Stefan A., Electronic Lab Notebooks and Experimental Design Assistants, Handb Exp Pharmacol, 257, pp. 257-275, (2020); Carpi N., Minges A., Piel M., eLabFTW: An open source laboratory notebook for research labs, JOSS, 2, 12, (2017); Walsh E., Cho I., Using Evernote as an Electronic Lab Notebook in a Translational Science Laboratory, J Lab Autom, 18, 3, pp. 229-234, (2012); Hewera M., Et al., An inexpensive and easy-to-implement approach to a Quality Management System for an academic research lab, F1000Res, 30, 9, (2020); Wetzel C., Pohlenz P., Schirmer D., Wissenschaft zwischen Konkurrenz und Kooperation - Zum Potenzial kooperationsfördernder Managementinstrumente, DUZ OPEN; Bespalov A., Et al., Introduction to the EQIPD Quality System, OSF, (2007); Loveluck J., Finding the Right Electronic Lab Notebook with the Corey Lab; Dirnagl U., Przesdzing I., A pocket guide to electronic laboratory notebooks in the academic life sciences, F1000Res, 5, (2016); Lippi G., Da R.G., Advantages and limitations of total laboratory automation: a personal overview, CCLM, 75, 6, (2019); Vargas-Toscano A., Et al., Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma, CNS Onkol, 9, 2, (2020)","M. Hewera; Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, NRW, 40225, Germany; email: michael.hewera@med.uni-duesseldorf.de; U.D. Kahlert; Clinic for Neurosurgery, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, NRW, 40225, Germany; email: Ulf.Kahlert@med.uni-duesseldorf.de","","F1000 Research Ltd","","","","","","20461402","","","","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85112010376"
"Funk C.J.","Funk, Carla J. (7102175044)","7102175044","Promoting New and Expanded Roles for Librarians and Information Specialists","2021","Studies in Health Technology and Informatics","288","","","213","222","9","0","10.3233/SHTI210996","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124056032&doi=10.3233%2fSHTI210996&partnerID=40&md5=e96f0f958fb35268f003dc9f75fb76b9","Medical Library Association, United States","Funk C.J., Medical Library Association, United States","This chapter describes how the U.S. National Library of Medicine (NLM), under the leadership of Donald A. B. Lindberg M.D., promoted new and expanded roles for librarians and information specialists in response to advances in technology and public policy. These advances brought information services directly to all potential users, including health professionals and the public and stimulated NLM to expand its programs, policies, and services to serve all. Dr. Lindberg included librarians and information specialists in all of NLM's new endeavors, helping both to recognize and establish new or expanded roles. The involvement of librarians and information specialists in multidisciplinary healthcare research teams, in underserved communities, and in research data management and compliance has helped to redefine the health sciences information profession for the 21st century.  © 2021 The authors and IOS Press.","Access to Information; Donald A.B. Lindberg M.D.; Librarians; U.S. National Library of Medicine","Humans; Information Services; Librarians; National Library of Medicine (U.S.); United States; Health; Human resource management; Information management; Access to information; Donald AB lindberg MD; Health professionals; Health science; Librarian; National library of medicines; Potential users; Research data managements; Research teams; US national library of medicine; access to information; article; human; informatician; information service; leadership; librarian; occupation; public policy; Information services","","","","","National Institutes of Health, NIH; U.S. Food and Drug Administration, FDA; U.S. National Library of Medicine, NLM","Funding text 1: Beginning in 2003, NLM took steps to meet these needs, under the direction of Dr. Valerie Florance, then NLM Associate Director for Extramural Programs. She first established an NLM grant program for budding informationists, the NLM Individual Fellowship for Informationist Training. This fellowship program supported coursework and internships in clinical, biomedical research, public health, and consumer health to prepare Fellows for new career directions. This program concluded in 2008. It was followed in 2010 by Administrative Supplements for Informationist Services, another brainchild of Dr. Florance. Funded by NLM and other NIH institutes, this program provided grants for NIH-funded extramural researchers to immerse informationists in their research teams, often to assist with research data management. These grant programs improved research skills and knowledge about the research community, as well as developing best practices and demonstrating the roles information specialists could play in research data management [20-21].; Funding text 2: Once developed, however, PMC and ClinicalTrials.gov became key enablers of new public policies that emerged from separate series of events involving research advocacy groups, librarians and their professional associations, scientists, patients, journal editors, and the Congress. For PMC, the precipitating issue was lack of public access to the results of taxpayer-funded research. For ClinicalTrials.gov, it was outrage over deliberate omission of information about serious adverse drug effects in articles reporting the results of clinical trials. By late 2007, in separate actions, the U.S. Congress had mandated: (1) deposit in PMC of papers resulting from research funded by NIH, i.e., the NIH Public Access Policy, and (2) early registration and summary results submission in ClinicalTrials.gov for the majority of trials of drugs and devices subject to regulation by the U.S. Food and Drug Administration (FDA). Many other research papers and trials became subject to similar requirements promulgated by other research funders. Early and complete registration of clinical trials has been required for subsequent publication of results in many influential journals since 2005.","Meyerhoff E., Foundations of medical librarianship, Bull Med Libr Assoc., 65, 4, pp. 409-418, (1977); Cimpl K., Clinical medical librarianship: A review of the literature, Bull Med Libr Assoc., 73, 1, pp. 21-28, (1985); Matheson N., Cooper J.A.D., Academic information in the academic health sciences center: Roles for the library in information management, J Med Educ., 57, 10, pp. 1-93, (1982); Bonham M.D., Nelson L.L., An evaluation of four end-user systems for searching MEDLINE, Bull Med Libr Assoc, 2, 76, pp. 171-180, (1988); Broering N.C., The miniMEDLINE SYSTEM™: A library-based end-user search system, Bull Med Libr Assoc, 2, 73, pp. 138-145, (1985); Network of the National Library of Medicine; Donald A.B., Lindberg M.D., (2014); NLM Long Range Planning Documents; Holst R., Partnering for education and career development of librarians and information specialists, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Dorsch J.L., Faughnan J.G., Humphreys B.L., Grateful med: Direct access to MEDLINE for health professionals with personal computers, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); 395 - A Joint Resolution Making Further Continuing Appropriations for the Fiscal Year 1988, and for Other Purposes, Pub. L. 100-202, (1987); Board of Regents. Improving Health Professionals Access to Information: Challenges and Opportunities for the National Library of Medicine, (1989); Humphreys B.L., Adjusting to progress: Interactions between the NLM and health sciences librarians, 1961-2001, J Med Libr Assoc., 1, 90, (2002); Lindberg D.A.B., National library of medicine and its role, Bull Med Libr Assoc., 1, 81, pp. 71-73, (1993); White H.S., The grateful med program and the medical library profession, Bull Med Libr Assoc., 1, 81, pp. 73-75, (1993); Information STAT: Rx for hospital quality, NLM News, 47, 11-12, pp. 8-10, (1992); Shipman J.P., Burroughs C.M., Rambo N., NLM's library network: A force for outreach, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Davidoff F., Florance V., The informationist: A new health profession?, Ann Intern Med., 132, 12, pp. 996-998, (2000); Shipman J.P., Cunningham D.J., Holst R., Watson L.A., The informationist conference: Report, J Med Libr Assoc., 90, 4, pp. 458-464, (2002); Deardorff A.A., Florance V., Van Biervliet A., Assessing the national library of Medicine's informationist awards, J Esci Libr., 5, 1, (2016); Gore S.A., A librarian by any other name: The role of the informationist, J E-sci Libr., 2, 1, pp. 20-24, (2013); Shipman J.P., Kurtz-Rossi S., Funk C.J., The health information literacy research project, J Med Libr Assoc., 4, 97, pp. 273-281, (2009); Doyle J.D., IAIMS and JCAHO: Implications for hospital librarians. Integrated academic information management systems. Joint commission on accreditation of healthcare organizations, Bull Med Libr Assoc., 87, 4, pp. 383-386, (1999); Lindberg D.A.B., Humphreys B.L., 2015 - The future of medical libraries, New Engl J Med., 352, 11, pp. 1067-1070, (2005); Jones D.A., Shipman J.P., Plaut D.A., Selden C.R., Characteristics of personal health records: Findings of the medical library Association/National library of medicine joint electronic personal health record task force, J Med Libr Assoc., 98, 3, pp. 243-249, (2010); Gore S.A., Nordberg J.M., Palmer L.A., Piorun M.E., Trends in health sciences library and information science research: An analysis of research publications in the BMLA and JMLA from 1991-2001, J Med Libr Assoc., 97, 3, pp. 203-216, (2009); Funk M.E., Our words, our story: A textual analysis of articles published in the BMLA/JMLA from 1961-2010, J Med Libr Assoc., 10, 1, pp. 12-20, (2013); Shaping the Future: MLA's Strategic Plan, (1987); Humphreys B.L., Tuttle M.S., Something new and different: The unified medical language system, Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, (2021); Woodsmall R.M., Lyon-Hartmann B., Siegel E.R., MEDLINE on CD-ROM: National Library of Medicine Evaluation Forum, Bethesda, Maryland, September 23, 1988, (1989); Lindberg D.A., Siegel E.R., Rapp B.A., Wallingford K.T., Wilson S.R., Use of MEDLINE by physicians for clinical problem solving, JAMA, 269, 24, pp. 3124-3129, (1993); National library of medicine director donald A. B. Lindberg retires, MLA News, 55, 5, (2015)","C.J. Funk; Chicago, 345 West Fullerton Parkway, Apt. 2701, 60614, United States; email: cjfunk46@gmail.com","Humphreys B.L.; Logan R.A.; Miller R.A.; Siegel E.R.","IOS Press BV","","","","","","09269630","978-164368238-9","","35102842","English","Stud. Health Technol. Informatics","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85124056032"
"Feger S.S.; Wozniak P.W.; Lischke L.; Schmidt A.","Feger, Sebastian S. (56893703100); Wozniak, Paweł W. (55879280600); Lischke, Lars (56159613600); Schmidt, Albrecht (55596321600)","56893703100; 55879280600; 56159613600; 55596321600","'Yes, i comply!': Motivations and Practices around Research Data Management and Reuse across Scientific Fields","2020","Proceedings of the ACM on Human-Computer Interaction","4","CSCW2","141","","","","13","10.1145/3415212","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094185024&doi=10.1145%2f3415212&partnerID=40&md5=51431af4c6fc504d2ef44bcc0d0dc0b0","LMU Munich, Munich, Germany; CERN, Geneva, Switzerland; Utrecht University, Utrecht, Netherlands; Vrije Universiteit Amsterdam, Amsterdam, Netherlands","Feger S.S., LMU Munich, Munich, Germany, CERN, Geneva, Switzerland; Wozniak P.W., Utrecht University, Utrecht, Netherlands; Lischke L., Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Schmidt A., LMU Munich, Munich, Germany","As science becomes increasingly data-intensive, the requirements for comprehensive Research Data Management (RDM) grow. This often overwhelms scientists, requiring more workload and training. The failure to conduct effective RDM leads to producing research artefacts that cannot be reproduced or reused. Past research placed high value on supporting data science workers, but focused mainly on data production, collection, processing, and sensemaking. In order to understand practices and needs of data science workers in relation to documentation, preservation, sharing, and reuse, we conducted a cross-domain study with 15 scientists and data managers from diverse scientific domains. We identified five core concepts which describe requirements, drivers, and boundaries in the development of commitment for RDM, essential for generating reproducible research artefacts: Practice, Adoption, Barriers, Education, and Impact. Based on those concepts, we introduce a stage-based model of personal RDM commitment evolution. The model can be used to drive the design of future systems that support a transition to open science. We discuss infrastructure, policies, and motivations involved at the stages and transitions in the model. Our work supports designers in understanding the constraints and challenges involved in designing for reproducibility in an age of data-driven science.  © 2020 ACM.","data-processing science; human data interventions; motivation; reproducibility; research data management; reuse","Data Science; Driver training; Human resource management; Information management; Motivation; Comprehensive research; Data intensive; Data production; Reproducibilities; Reproducible research; Research artefacts; Research data managements; Scientific fields; Digital storage","","","","","Bundesministerium für Bildung und Forschung, BMBF, (05E15CHA)","This work has been sponsored by the Wolfgang Gentner Programme of the German Federal Ministry of Education and Research (grant no. 05E15CHA).","Artifact Review and Badging, (2018); Akers K.G., Doty J., Disciplinary Differences in Faculty Research Data Management Practices and Perspectives., 2013, (2013); Baker M., 1, 500 scientists lift the lid on reproducibility, Nature, 533, 7604, pp. 452-454, (2016); Bechhofer S., Buchan I., De Roure D., Missier P., Ainsworth J., Bhagat J., Couch P., Cruickshank D., Delderfield M., Dunlop I., Gamble M., Michaelides D., Owen S., Newman D., Sufi S., Goble C., Why linked data is not enough for scientists, Future Generation Computer Systems, 29, 2, pp. 599-611, (2013); Belhajjame K., Zhao J., Garijo D., Hettne K., Palma R., Corcho O., Gomez-Perez J.-M., Bechhofer S., Klyne G., Goble C., The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web, (2014); Bell G., Hey T., Szalay A., Beyond the data deluge, Science, 323, 5919, pp. 1297-1298, (2009); Berners-Lee T., Cailliau R., Groff J.-F., Pollermann B., World-wide Web: The Information Universe., pp. 52-58, (1992); Birnholtz J.P., Bietz M.J., Data at work: Supporting sharing in science and engineering, Proceedings of the 2003 International ACM SIGGROUP Conference on Supporting Group Work. 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IEEE, pp. 525-532, (2012); Kidwell M.C., Lazarevic L.B., Baranski E., Hardwicke T.E., Piechowski S., Falkenberg L.-S., Kennett C., Slowik A., Sonnleitner C., Hess-Holden C., Et al., Badges to acknowledge open practices: A simple, low-cost, effective method for increasing transparency, PLoS Biology, 14, 5, (2016); Mackay W.E., Appert C., Beaudouin-Lafon M., Chapuis O., Du Y., Fekete J.-D., Guiard Y., Touchstone: Exploratory design of experiments, CHI '07 Proceedings of the SIGCHI Conference on Human Factors in Computing System, pp. 1425-1434, (2007); Mayernik M.S., Wallis J.C., Pepe A., Borgman C.L., Whose Data Do You Trust? Integrity Issues in the Preservation of Scientific Data., (2008); Muller M., Curiosity, creativity, and surprise as analytic tools: Grounded theory method, Ways of Knowing in HCI, pp. 25-48, (2014); Muller M., Lange I., Wang D., Piorkowski D., Tsay J., Vera Liao Q., Dugan C., Erickson T., How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation (CHI '19), (2019); Nosek B.A., Spies J.R., Motyl M., Scientific utopia: II. Restructuring incentives and practices to promote truth over publishability, Perspectives on Psychological Science, 7, 6, pp. 615-631, (2012); Paine D., Sy E., Piell R., Lee C.P., Examining data processing work as part of the scientific data lifecycle: Comparing practices across four scientific research groups, IConference 2015 Proceedings, (2015); Pasquetto I.V., Sands A.E., Darch P.T., Borgman C.L., Open Data in Scientific Settings: From Policy to Practice (CHI '16), pp. 1585-1596, (2016); Qin J., Metadata and reproducibility: A case study of gravitational wave data management, International Journal of Digital Curation, 11, 1, pp. 218-231, (2016); Rolland B., Lee C.P., Beyond trust and reliability: Reusing data in collaborative cancer epidemiology research, Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 435-444, (2013); Rosenblatt M., An incentive-based approach for improving data reproducibility, Science Translational Medicine, 8, 336, (2016); Rowhani-Farid A., Allen M., Barnett A.G., What incentives increase data sharing in health and medical research? A systematic review, Research Integrity and Peer Review, 2, 1, (2017); Russell J.F., If a job is worth doing, it is worth doing twice: Researchers and funding agencies need to put a premium on ensuring that results are reproducible, Nature, 496, 7443, pp. 7-8, (2013); Stodden V., Miguez S., Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research., 2013, (2013); Stodden V., Miguez S., Best practices for computational science: Software infrastructure and environments for reproducible and extensible research, Journal of Open Research Software, 2, 1, (2014); Tang R., Hu Z., Providing Research Data Management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 1, (2019); Vertesi J., Dourish P., The value of data: Considering the context of production in data economies, Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work. ACM, pp. 533-542, (2011); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, (2013); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J., Dasilva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Vander Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Worden D.J., Emerging technologies for data research: Implications for bias, curation, and reproducible results, Human Capital and Assets in the Networked World, (2017); Zimmerman A., Not by metadata alone: The use of diverse forms of knowledge to locate data for reuse, International Journal on Digital Libraries, 7, 1-2, pp. 5-16, (2007)","","","Association for Computing Machinery","","","","","","25730142","","","","English","Proc. ACM Hum. Comput. Interact.","Article","Final","","Scopus","2-s2.0-85094185024"
"Karimova Y.; Ribeiro C.; David G.","Karimova, Yulia (57195369729); Ribeiro, Cristina (7201734594); David, Gabriel (16635163900)","57195369729; 7201734594; 16635163900","Institutional support for data management plans: case studies for a systematic approach","2021","International Journal of Metadata, Semantics and Ontologies","15","3","","178","191","13","0","10.1504/IJMSO.2021.123041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131181129&doi=10.1504%2fIJMSO.2021.123041&partnerID=40&md5=5b6fc234ba564ff87559f3871b1e08bb","INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal","Karimova Y., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; Ribeiro C., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; David G., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal","Researchers have to ensure that their projects comply with Research Data Management (RDM) requirements. Consequently, the main funding agencies require Data Management Plans (DMPs) for grant applications. So, institutions are investing in RDM tools and implementing RDM workflows in order to support their researchers. In this context, we propose a collaborative DMP-building method that involves researchers, data stewards and other parties if required. This method was applied as part of an RDM workflow in research groups across several scientific domains. We describe it as a systematic approach and illustrate it through a set of case studies. We also address the DMP monitoring process during the life cycle of projects. The feedback from the researchers highlighted the advantages of creating DMPs and their growing need. So, there is motivation to improve the DMP support process according to the machine-actionable DMPs concept and to the best practices in each scientific community. © 2021 Inderscience Enterprises Ltd.. All rights reserved.","data management plan; open data; open science.; research data management; research workflow","Information management; Life cycle; Case-studies; Data management plan; Data management workflow; Management plans; Open datum; Open science; Open science.; Research data managements; Research workflow; Work-flows; Open Data","","","","","DMPs; Instituto de Financiamento de Agricultura e Pescas; Integrated Administrative and Control System; Land Parcel Information Systems; National Science Foundation, NSF; Horizon 2020 Framework Programme, H2020; European Commission, EC; national institute for funding agriculture and fisheries","Funding text 1: The importance of RDM, including the creation of DMPs, is growing. Some funding agencies require the inclusion of DMPs in grant applications, while others require DMPs within the first months of the approval of project grants (e.g., projects under Horizon2020 (European Commission, 2016), National Science Foundation (National Science Foundation, 2011)).3 So, in order to comply with these requirements, institutions have been developing different ways to support researchers with creating DMPs and other RDM activities during the life cycle of projects. They focus on both the engagement of researchers and the establishment of RDM infrastructures according to their needs.; Funding text 2: The goal of the ‘FARSYD’ project (Lomba et al., 2020), conducted by the Research Centre in Biodiversity and Genetic Resources (CIBIO), part of the Research Network in Biodiversity and Evolutionary Biology (InBIO) at the University of Porto, was to examine the relationship between farming systems, biodiversity and ecosystem services in high nature value farmlands. The PI had experience in RDM but not in DMP creation. However, for this project, she created an Excel file with a detailed description of each experience in the project. This file and the description of the project helped the data steward to understand the project’s context and identify the existence of private and sensitive data that cannot be publicly disclosed. Following the collaborative method, the data steward analysed all the obtained information, collected examples of DMPs in the biodiversity domain, and experimented with the GFBio DMP Tool15 and the Best Practice Guide (Cadman et al., 2012) promoted by the German Federation for Biological Data. This analysis helped to prepare a list with specific questions that were validated with the PI. Two existing checklists were verified: the one prepared for the Environmental radioactivity project and the one specific to the Biodiversity domain. Although the first list of questions was not immediately applicable to this plan, some of the points were adapted and used. This led to the inclusion of the description of the specific tools used during the project, the software used, the training areas for habitat mapping and the several approaches used to obtain data depending on the specific target and location. This type of verification is not part of the proposed methodology; however, it is very important for our future work as it helps to identify differences between scientific domains during the DMP creation. The analysed GFBio DMP Tool helped the researcher to add specific information such as Project type, with possible values Field Work, Observational, Simulation, Assimilation, Experimental, Laboratory and Modelling, where Field Work and Observational values were considered for FARSYD more suitable. This tool also demonstrated to the researcher the existence of different metadata standards and legal requirements specific to the biodiversity domain. Examples of the latter include the IUCN Red List of Threatened Species and the Nagoya Protocol. Moreover, this project contained private data provided by the Instituto de Financiamento de Agricultura e Pescas (the national institute for funding agriculture and fisheries), the Integrated Administrative and Control System and the Land Parcel Information Systems.","Ahokas M., Kuusniemi M.E., Friman J., The tuuli project: accelerating data management planning in finnish research organisations, International Journal of Digital Curation, 12, 2, pp. 107-115, (2017); Anne K.M., Managing research data, Research Methods, (2018); Barbosa S., Karimova Y., SAIL Data Management Plan (Version 1.0.0), Zenodo.Project SAIL Community, (2020); Bote J., Termens M., Reusing data: technical and ethical challenges, DESIDOC Journal of Library Information Technology, pp. 329-337, (2019); Cadman M.J., Et al., GBIF-ICLEI best practice guide for publishing biodiversity data by local governments, (2012); CESSDA Data Management Expert Guide, (2020); Clare C., Et al., Engaging Researchers with Data Management: The Cookbook, (2019); Cox A., Verbaan E., Data management planning, Exploring Research Data Management, (2018); Dillo I., FAIR data in trustworthy data repositories, Proceedings of the 14th International Conference on Digital Preservation, (2017); Dressler V.A., Yeager K., Richardson E., Developing a data management consultation service for faculty researchers: a case study from a large Midwestern public university, International Journal of Digital Curation, 14, 1, (2019); Directorate-General for Research Innovation, H2020 Programme, Guidelines on FAIR Data Management in Horizon 2020, (2016); Annex L. Conditions Related to Open Access to Research Data, (2017); Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance), (2016); Fearon D., Et al., SPEC Kit 334: Research Data Management Services, (2013); Garoufallou E., Ovalle-Perandones M.A., Metadata and semantic research conference, Proceedings of the 14th International Conference Metadata and Semantic Research, Madrid, Spain, Proceedings of the Communications in Computer and Information Science (CCIS), (2020); Hodson S., Et al., The Beijing Declaration on Research Data, CODATA, Committee on Data of the International Science Council, (2019); Karimova Y., Ribeiro C., The collaborative method between curators and researchers in the preparation of a Data Management Plan and Privacy Impact Assessment, 5 Forum Gestão de Dados de Investigação, (2019); Karimova Y., Et al., Promoting semantic annotation of research data by their creators: a use case with B2NOTE at the end of the RDM workflow, Metadata and Semantic Research, pp. 112-122, (2017); Karimova Y., Et al., Research Data Management Workflows and maDMPs, Zenodo, (2020); Karimova Y., Castro J.A., Ribeiro C., Data deposit in a CKAN repository: a Dublin core-based simplified workflow, Proceedings of the 5th Italian Research Conference on Digital Libraries, (2019); Karimova Y., Pedrosa D., Cravino J., Morgado L., SCReLProg (Self and Co-regulation in e-Learning of Computer Programming) (Version 1), Zenodo, (2021); Karimova Y., Ribeiro C., David G., Institutional support for data management plans: five case studies, Metadata and Semantic Research, (2021); Lomba A., Et al., Back to the future: rethinking socioecological systems underlying high nature value farmlands, Frontiers in Ecology and the Environment, 18, 1, pp. 36-42, (2020); Managing Data@ Melbourne: an online research data management training program, (2017); McAlister D.T., The project management plan: improving team process and performance, Marketing Education Review, 16, 1, pp. 97-103, (2006); Michener W.K., Ten simple rules for creating a good data management plan, PLOS Computational Biology, 11, 10, pp. 1-9, (2015); Molloy L., Jisc Research Data MANTRA Project at EDINA, Information Services, University of Edinburgh: Evaluation, Information Processing and Management, Research Report, (2012); Grants.Gov Application Guide A Guide for Preparation and Submission of NSF Applications via Grants.gov, National Science Foundation, (2011); Proposal Award Policies Procedures Guide, Chapter II - Proposal Preparation Instructions, (2019); Pasquetto I.V., Randles B.M., Borgman C.L., On the reuse of scientific data, Data Science Journal, 16, 8, (2017); Graham Pryor, Managing Research Data, (2012); Ray J.M., Research Data Management: Practical Strategies for Information Professionals, (2013); Rb-Silva R., Karimova Y., aMILE: Application of Text Mining to Clinical Reports of Patients with Acute Myeloid Leukemia, Zenodo, (2021); RDA for the Sustainable Development Goals. Introduction: Fit with the overall RDA vision and mission, (2019); Ribeiro C., Et al., Research data management tools and workflows: experimental work at the university of Porto, IASSIST Quarterly, 42, 2, (2018); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: understanding the motivation to publish research data, Government Information Quarterly, 30, pp. 19-31, (2013); Schopfel J., Et al., Research data management in the French national research center (CNRS), Data Technologies and Applications, (2018); Simms S., Jones S., Next-generation data management plans: global, machine-actionable, FAIR, International Journal of Digital Curation, 12, 1, (2017); Simms S., Et al., The future of data management planning: tools, policies, and players, International Journal of Curation, 11, pp. 208-217, (2016); What is a Project Management Plan and How to Create One, (2021); Tenopir C., Et al., Research data services in European academic research libraries, Liber Quarterly, 27, 1, (2017); Tomas T., Janouskova S., Moldan B., Sustainable development goals: a need for relevant indicators, Ecological Indicators, 60, (2016); Vitale C.H., Sandy H.E.M., Data management plans: a review, DESIDOC Journal of Library Information Technology, 39, 6, (2019); Wilkinson M.D., Et al., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wittenberg J., Elings M., Building a research data management service at the university of California, Berkeley: a tale of collaboration, IFLA Journal, 43, 1, (2017)","Y. Karimova; INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; email: ylaleo@gmail.com","","Inderscience Publishers","","","","","","17442621","","","","English","Int. J. Metadata Semant. Ontol.","Conference paper","Final","","Scopus","2-s2.0-85131181129"
"Nyakurerwa A.T.","Nyakurerwa, Austin Tonderai (57574099900)","57574099900","Quality Assurance and Marketing of Library Services and Products: The Case of Midlands State University","2021","Examining the Impact of Industry 4.0 on Academic Libraries","","","","165","188","23","0","10.1108/978-1-80043-656-520201021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131481723&doi=10.1108%2f978-1-80043-656-520201021&partnerID=40&md5=1af9fea7be8a9d7c34b8d5ee5dc80427","Midlands State University Library, Midlands State University, Zimbabwe","Nyakurerwa A.T., Midlands State University Library, Midlands State University, Zimbabwe","The chapter focused on quality assurance and marketing of library services and products at the Midlands State University (MSU). The chapter’s main objective was to identify the quality assurance mechanisms at the MSU Library. The major findings of the research were; the MSU library was practising quality assurance, staff was trained on the latest trends in the profession, the collection was multidisciplinary and in different forms, and that there were Information Communication Technologies (ICTs) used in enhancing service provision. The researcher recommended that the library needed to continuously train librarians on issues to do with quality, improve the infrastructure, introduce Research Data Management to enhance the Research Support Services and improve on the Information Literacy Skills training programmes. The author identified some areas for further research and the major one was that there is need for clarification on the concept of the 4th Industrial Revolution. © 2021 by Emerald Publishing Limited.","Information Communication Technologies; Libraries; Library Services and Products; Marketing; Midlands State University; Policies; Quality Assurance; Standards","","","","","","","","Adebayo E.L., Quality assurance and the implication for the management of University Libraries in Nigeria, Library Philosophy and Practice, (2009); Adedibu L.O., Akinboro E.O., Abdussalam T.A.B., Cataloguing and classifi-cation of library resources in the 21st century, Library and information science in developing countries: Contemporary issues, (2012); Balog K.P., Jelusic T.A., Matosic M., Quality assurance practices in Croatian academic libraries: Two case-studies, 34th international conference on organiza-tional science development: Internationalization and cooperation, (2015); Dube L., Quality Assurance practices in university libraries in South Africa, SA Journal in Library and Information Science, 77, 1, pp. 26-36, (2011); Egberongbe H., Sen B., Willett P., Quality management approaches in academic libraries: A pilot study of a Nigerian University Library, Qualitative and Quantitative Methods in Libraries (QQML), 4, pp. 399-412, (2015); Gupta D.K., Marketing of library and information services: Building a new dis-cipline for Library and Information Science Education in Asia, Malaysian Journal of Library & Information Science, 2, 8, pp. 95-108, (2003); Islam S., Islam N., Marketing of library and information products and ser-vices: A theoretical analysis, Business Information Review, 26, 2, pp. 123-132, (2009); Jain A.K., Jambhekar A., Rao T.P.R., Rao S.S., Marketing of library ser-vices and products: A primer for librarians and information professionals, (2009); Jestin J., Parameswari B., Marketing of information products and services for libraries in India, Library Philosophy and Practice, (2005); Jose A., Bhat I., Marketing of library and information services: A strategic perspective, Vision: The Journal of Business Perspective, (2007); Khalid M.S.J.M., Marketing of library and information services in university libraries: A case study of University of Malaya Central Library, Kuala Lumpur, Malaysia, The Eurasia Proceedings of Educational & Social Sciences (EPESS), 13, pp. 50-59, (2019); Kumar A., Marketing of Information Products & Services in Kurukshetra University Library in the disciplines of Social Science: A study, IOSR Journal of Humanities and Social Science, 19, 2, pp. 72-85, (2014); Madhusudhan M., Marketing of library and information services and prod-ucts in university libraries: A case study of Goa University Library, Library Philosophy and Practice, 175, pp. 1-6, (2008); Makanga D.F., Jorosi B.N., Information Literacy Skills among the under-graduate students at the University of Livingstonia, Malawi, International Journal of Library and Information Services, 7, 2, pp. 43-56, (2018); Manghani K., Quality assurance: Importance of systems and standard operating procedures, Perspectives in Clinical Research, 2, 1, pp. 34-37, (2011); Quality assurance policy framework, (2018); Vision 2030 complaint strategic plan: 2019-2023, (2019); Library policies, (2019); Standard operating proce-dures, (2019); Ogunlana K., Amusa O.I., Quality assurance in Subject Librarian services and library management, (2008); Osinulu L.F., Amusa O.I., Information technology, quality assurance, and aca-demic library management, Library Philosophy and Practice, 324, pp. 1-12, (2010); Patange J.T., Marketing of library and information products and services, Global Journal of Human Social Science Linguistics & Education, 13, 1, pp. 33-36, (2013); Patil S.K., Pradhan P., Library promotion practices and marketing of Library services: A role of Library professionals, Procedia - Social and Behavioral Sciences, 133, 2014, pp. 249-254, (2014); Sali M.I.B., Abubakar T., Application of quality assurance for effective library services in academic libraries in Nigeria, The Information Manager, 12, 1, pp. 38-44, (2012); Quality assurance standards for Higher Education","","","Emerald Group Publishing Ltd.","","","","","","","978-180043656-5; 978-180043657-2","","","English","Examining the Impact of Industry 4.0 on Academic Libraries","Book chapter","Final","","Scopus","2-s2.0-85131481723"
"Das A.; Banerjee S.","Das, Aditi (57224901276); Banerjee, Swapna (55728291600)","57224901276; 55728291600","OPTIMISING RESEARCH SUPPORT SERVICES THROUGH LIBRARIES: A REVIEW OF PRACTICES","2021","Library Philosophy and Practice","2021","","","1","43","42","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108561000&partnerID=40&md5=531ce25f4224d0d29cbb4982b9b4110b","University of Calcutta, Kolkata, West Bengal, India","Das A., University of Calcutta, Kolkata, West Bengal, India; Banerjee S., University of Calcutta, Kolkata, West Bengal, India","In the changing landscape of libraries, the area of Research Support Service is emerging, with new opportunities, and challenges. The study is based on the role of the libraries in supporting research in the Institutes and Universities. This literature review identifies the current level of publications that deal with the relationship of the libraries and their role in the research process, and an examination of science researcher's information seeking needs, role of university libraries in supporting research, changing electronic environment and changing librarian's role, and challenges faced by the libraries with these changes. It aims to determine whether notable changes in the research support services for the doctoral students and scientists have emerged in recent years. The study shows that the support services of doctoral students and scientists provided by libraries follow a steady trend, with many subtle changes, particularly on the technological basis. The selected literature review spreads from 2000 to 2019. The coverage is balanced in that here on one hand the support service of the researchers, the researchers' information seeking need, the environment for the research is discussed, and on the other hand, the challenges for the libraries in print and electronic environment is discussed. © 2021. All Rights Reserved.","Artificial Intelligence; Data citation; Data curation; Data repository; Electronic journals; Institutional Repositories; Library websites; Metadata librarian; Research Data Management; Research Data Service; Research life cycle; Scholarly publishing; Social media tools; Virtual Research Environment","","","","","","","","Akeriwa Miriam, Penzhorn Cecilia, Holmner Marlene, Using mobile technologies for social media based library services at the University of Development Studies Library, Ghana, Information Development, 31, 3, pp. 284-293, (2015); Asemi A., Information searching habits of Internet users: A case study on the Medical Sciences University of Isfahan, Iran, Webology, 2, 1, (2005); Asemi A., Riyahiniya N., Awareness and use of digital resources in the libraries of Isfahan University of Medical Sciences, Iran, The Electronic Library, 25, 3, pp. 316-327, (2007); Asemi Asefeh, Asemi Adeleh, Artificial Intelligence (AI) application in Library Systems in Iran: A taxonomy study, Library Philosophy and Practice (e-journal), (2018); Atilgan D., Bayram O., An evaluation of faculty use of the digital library at Ankara University, Turkey, Journal of Academic Librarianship, 32, pp. 86-93, (2006); Bar-Ilan Judith, Peritz Bluma C., Wolman Yecheskel, A survey on the use of electronic databases and electronic journals accessed through the web by the academic staff of Israeli Universities, Journal of Academic Librarianship, 29, 6, pp. 346-361, (2003); Biradar B.S., Kumar B.T. Sampath, Periodicals use pattern by teachers and research scholars of Kuvempu University: A case study, SRELS Journal of Information and Management, 37, 4, pp. 301-314, (2000); Bourg Chris, Ross Coleman, Ricky Erway, Support for the Research Process: An Academic Library Manifesto, (2009); Bracke Paul J., Public presentations of professional change in academic research library strategic plans, (2012); Brantley Steve, Bruns Todd A., Duffin Kirstin I., Librarians in transition: Scholarly communication support as a developing core competency, Journal of Electronic Resources Librarianship, 29, 3, pp. 137-150, (2017); Brewerton Antony, Re-Skilling for Research: Investigating the Needs of Researchers and How Library Staff Can Best Support Them, New Review of Academic Librarianship, 18, 1, (2012); Bussell Hilary, Hagman Jessica, Christopher S., Research Needs and Learning Format Preferences of Graduate Students at a Large Public University: An Exploratory Study, College and Research Libraries, 78, 7, (2017); Cawthorne Jon Edward, Viewing the Future of University Research Libraries through the Perspectives of Scenarios, (2013); Chapman John W., The roles of the metadata librarian in a research library, Library Resources & Technical Services, 51, 4, pp. 279-285, (2007); Bourg Chris, Et al., Support for the Research Process: An Academic Library Manifesto, (2009); Coombs Jenny, Thomas Mandy, Rush Nathan, Martin Elizabeth, A Community of Practice Approach to Delivering Research Support Services in a Post-92 Higher Education Institution: A Reflective Case Study, New Review of Academic Librarianship, 23, 2-3, pp. 159-170, (2017); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Pinfield S., Rutter S., The intelligent library: Thought leaders' views on the likely impact of artificial intelligence on academic libraries, Library Hi Tech, pp. 1-19, (2018); Cox Andrew M., Pinfield Stephen, Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Cox Andrew M., Kennan Mary Anne, Lyon Liz, Pinfield Stephen, Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Dempsey Paula R., Resource Delivery and Teaching in Live Chat Reference: Comparing Two Libraries, College and Research Libraries, 78, 7, (2017); Dilek-Kayaoglu H., Use of electronic journals by faculty at Istanbul University, Turkey: The results of a survey, The Journal of Academic Librarianship, 34, 3, pp. 239-275, (2008); Divya L.R., Pillai Sudhier K.G., Use of Internet Tools and Services by Research Scholars of the University of Kerala, SRELS Journal of Information Management, pp. 471-478, (2015); Faniel Ixchel M., Connaway Lynn Silipigni, Librarians' Perspectives on the Factors Influencing Research Data Management Programs, College and Research Libraries, 79, 1, (2018); Farrell Shannon L., Neeser Amy E., Bishoff Carolyn, Academic Uses of Video Games: A Qualitative Assessment of Research and Teaching Needs at a Large Research University, College and Research Libraries, 78, 5, (2017); Fenner John T., Fenner Audrey, Retrospective Collection Development, Selecting a Core Collection for Research in “New Thought, The Acquisitions Librarian, 16, 31-32, pp. 177-215, (2004); Gaby Haddow, Mamtora Jayshree, Research Support in Australian Academic Libraries: Services, Resources, and Relationships, New Review of Academic Librarianship, 23, 23, pp. 89-109, (2017); Gaddagimath R.B., Jange Suresh, Gadagin B.R., Transformation of role of librarian in the web environment, 4th international convention caliber-2006, (2006); Gowda Vasappa, Shivalingaiah D., Information Seeking Patterns of Researchers in the University Libraries in Karnataka State, SRELS Journal of Information Management, (2014); Kumar Satish, Et al., Approaches to Information Seeking by Life Scientists of Defence Research and Development Organization, India, Annals of Library and Information Studies, 5, 8, (2011); Healy Annette M., Increasing the Visibility of the Library within the Academic Research Enterprise, Issues in Science and Technology Librarianship, (2010); Hurych J., After Bath: Scientists, social scientists and humanities in the context of online searching, Journal of Academic Librarianship, 12, pp. 158-165, (1986); Johnson Andrew, Kuglitsch Rebecca, Megan Bresnahan, Using Participatory and Service Design to Identify Emerging Needs and Perceptions of Library Services among Science and Engineering Researchers Based at a Satellite Campus, Issues in Science and Technology Librarianship, (2015); Kaur Kulvir, Role of University Libraries in Support of Social Science Research: A Comparative Study of Panjab, Punjabi and Guru Nanak Dev Universities, (2012); Kawatra P.S., Attitude of Research Scholars towards the Resources and Services of University Libraries in Rajasthan: A Study, Annals of Library science and Documentation, 35, 4, pp. 17-25, (1988); Kennan Mary Anne, Corrall Sheila, Afzal Waseem, “Making space” in practice and education: research support services in academic libraries, Library Management, 35, 8, (2014); Khademizadeh Shahnaz, Venkatesha Y., A Study on User's Opinion About Library Staff and Use of Library Services in Scientific and Research Institute Libraries in Iran, SRELS Journal of Information Management, pp. 403-409, (2014); Khan Amjid, Ahmed Shamshad, Masrek Mohamad Noorman, Scholars' Satisfaction With Digital Library Collection and Gaps in the Provision of Effective Information Resources and Services: A Pakistani Perspective, Journal of Electronic Resources Librarianship, 26, 4, pp. 250-267, (2014); Khot Namita B., Patil Sandhyarani, Library and information services in Shivaji University's Barr. 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Philos. Pract.","Article","Final","","Scopus","2-s2.0-85108561000"
"Jagesar R.R.; Vorstman J.A.; Kas M.J.","Jagesar, Raj R. (57203413381); Vorstman, Jacob A. (12344759400); Kas, Martien J. (6602425807)","57203413381; 12344759400; 6602425807","Requirements and operational guidelines for secure and sustainable digital phenotyping: Design and development study","2021","Journal of Medical Internet Research","23","4","e20996","","","","5","10.2196/20996","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103994693&doi=10.2196%2f20996&partnerID=40&md5=962a32a8e1b0228f7f859a33cf368ad0","Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands; Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada","Jagesar R.R., Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands; Vorstman J.A., Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada, Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Kas M.J., Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands","Background: Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way. Objective: This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform. Methods: We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform. Results: We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design. Conclusions: Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping-driven research.  © 2021 Raj R Jagesar, Jacob A Vorstman, Martien J Kas.","Digital phenotyping; Mobile behavioral monitoring; Mobile phone; Passive behavioral monitoring; Psychoinformatics; Research data management; Smartphone-based behavioral monitoring","Communication; Data Collection; Humans; Privacy; Reproducibility of Results; access to information; Article; behavior change; clinical outcome; digital technology; human; information processing; information security; internet access; law; mobile application; multicenter study (topic); practice guideline; privacy; program sustainability; software; sustainable development; sustainable digital phenotyping; interpersonal communication; privacy; reproducibility","","","","","","","Torous J, Onnela J, Keshavan M., New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices, Transl Psychiatry, 7, 3, (2017); Ebner-Priemer U, Santangelo P., Digital phenotyping: hype or hope?, The Lancet Psychiatry, 7, 4, pp. 297-299, (2020); Insel TR., Digital Phenotyping: Technology for a New Science of Behavior, JAMA, 318, 13, pp. 1215-1216, (2017); Kargl F, van DHR, Erb B, Bosch C., Privacy in Mobile Sensing, Studies in Neuroscience, Psychology and Behavioral Economics, (2019); Venters C, Lau L, Griffiths M, Holmes V, Ward R, Jay C, Et al., The Blind Men and the Elephant: Towards an Empirical Evaluation Framework for Software Sustainability, Journal of Open Research Software, pp. 1-6, (2014); Roberts SJ., The Necessity of Information Security in the Vulnerable Pharmaceutical Industry, JIS, pp. 147-153, (2014); Insel TR., Digital Phenotyping: Technology for a New Science of Behavior, JAMA, 318, 13, pp. 1215-1216, (2017); Aledavood T, Triana HAM, Alakorkko T, Kaski K, Saramaki J, Isometsa E, Et al., Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype, JMIR Res Protoc, 6, 6, (2017); de Grood C, Raissi A, Kwon Y, Santana MJ., Adoption of e-health technology by physicians: a scoping review, J Multidiscip Healthc, 9, pp. 335-344, (2016); Torous J, Kiang MV, Lorme J, Onnela J., New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research, JMIR Ment Health, 3, 2, (2016); Torous J, Wisniewski H, Bird B, Carpenter E, David G, Elejalde E, Et al., Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach, J. technol. behav. sci, 4, 2, pp. 73-85, (2019); Berrouiguet S, Ramirez D, Barrigon ML, Moreno-Munoz P, Carmona Camacho R, Baca-Garcia E, Et al., Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study, JMIR Mhealth Uhealth, 6, 12, (2018); Kubiak T, Smyth J., Connecting Domains?Ecological Momentary Assessment in a Mobile Sensing Framework, Studies in Neuroscience, Psychology and Behavioral Economics, (2019); Torous J, Kiang MV, Lorme J, Onnela J., New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research, JMIR Ment Health, 3, 2, (2016); Aharony N, Pan W, Ip C, Khayal I, Pentland A., Social fMRI: Investigating and shaping social mechanisms in the real world, Pervasive and Mobile Computing, 7, 6, pp. 643-659, (2011); Ferreira D, Kostakos V, Dey AK., AWARE: Mobile Context Instrumentation Framework, Front. ICT, 2, pp. 2-6, (2015); Jongs N, Jagesar R, van Haren NEM, Penninx BWJH, Reus L, Visser PJ, Et al., A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data, Transl Psychiatry, 10, 1, (2020); Bilderbeck AC, Penninx BWJH, Arango C, van der Wee N, Kahn R, Winter-van Rossum I, Et al., Overview of the clinical implementation of a study exploring social withdrawal in patients with schizophrenia and Alzheimer's disease, Neurosci Biobehav Rev, 97, pp. 87-93, (2019); European Medical Device Regulation Internet, (2017); Hasselbring W, Reussner R., Toward Trustworthy Software Systems, Computer, 39, 4, pp. 91-92, (2006); Pool K., Akhlaghpour S., Causes and Impacts of Personal Health Information (PHI) Breaches: A Scoping Review and Thematic Analysis, SSRN Journal, (2019); Li H, Yu L, He W., The Impact of GDPR on Global Technology Development, Journal of Global Information Technology Management, 22, 1, pp. 1-6, (2019); Mulder T, Jagesar RR, Klingenberg AM, P Mifsud Bonnici J, Kas MJ., New European privacy regulation: Assessing the impact for digital medicine innovations, Eur Psychiatry, 54, pp. 57-58, (2018); Stytz M., Considering defense in depth for software applications, IEEE Secur. Privacy Mag, 2, 1, pp. 72-75, (2004); Bilge L, Dumitras T., Before we knew it: an empiral study of zero-day attacks in the real world, 2012 Presented at: Proceedings of the ACM conference on Computer and communications security, (2012); Wagner N, Sahin C, Winterrose M, Riordan J, Pena J, Hanson D, Et al., Towards automated cyber decision support: A case study on network segmentation for security, 2016 Presented at: IEEE Symposium Series on Computational Intelligence (SSCI), (2016); Schneider F., Least privilege and more [computer security], IEEE Secur. Privacy, 1, 5, pp. 55-59, (2003); Smid M, Branstad D., Data Encryption Standard: past and future, Proc. IEEE, 76, 5, pp. 550-559, (1988); Simmons GJ., Symmetric and Asymmetric Encryption, ACM Comput. Surv, 11, 4, pp. 305-330, (1979); Fumy W, Landrock P., Principles of key management, IEEE J. Select. Areas Commun, 11, 5, pp. 785-793, (1993); Safa NS, Solms RV, Futcher L., Human aspects of information security in organisations, Computer Fraud & Security, 2016, 2, pp. 15-18, (2016); Johnson ME, Goetz E., Embedding Information Security into the Organization, IEEE Secur. Privacy Mag, 5, 3, pp. 16-24, (2007); Pfleeger S, Angela SM, Furnham A., From Weakest Link to Security Hero: Transforming Staff Security Behavior, Journal of Homeland Security and Emergency Management, pp. 489-510, (2014); Hone K, Eloff J., What Makes an Effective Information Security Policy?, Network Security, 2002, 6, pp. 14-16, (2002); Grassi P, Garcia M, Fenton J., Digital identity guidelines: revision 3, NIST Digital Identity Guidelines, (2017); Kirlappos I, Parkin S, Angela SM., Learning from Shadow Security: Why Understanding Non-Compliant Behaviors Provides the Basis for Effective Security, 2014 Presented at: Proceedings Workshop on Usable Security, (2014); Approaches to software sustainability; CISQ - Consortium for Information & Software Quality; Lientz BP, Swanson EB, Tompkins GE., Characteristics of application software maintenance, Commun. ACM, 21, 6, pp. 466-471, (1978); Aljawarneh SA, Alawneh A, Jaradat R., Cloud security engineering: Early stages of SDLC, Future Generation Computer Systems, 74, pp. 385-392, (2017); Bankhead P., Reminder SMS/Call Log Policy Changes, Android Developers Blog; Iannino A, Musa J., Software Reliability, Advances in Computers, (1990); Fayad M, Hamza H, Sanchez H., Towards scalable and adaptable software architectures, 2005 Presented at: IRI - IEEE International Conference on Information Reuse and Integration, Conf, (2005); Baldini I, Castro P, Chang K, Cheng P, Fink S, Ishakian V, Et al., Serverless computing: Current trends and open problems, Research Advances in Cloud Computing, pp. 1-20, (2017); Zang H, Bolot J., Anonymization of location data does not work: A large-scale measurement study, 2011 Presented at: Proceedings of the 17th annual international conference on Mobile computing and networking, pp. 145-156, (2011); Wachter S, Mittelstadt B., A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI, Colum. Bus. L, (2019)","M.J. Kas; Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, 9747 AG, Netherlands; email: m.j.h.kas@rug.nl","","JMIR Publications Inc.","","","","","","14388871","","","33825695","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85103994693"
"Langer A.; Göpfert C.; Gaedke M.","Langer, André (57193121679); Göpfert, Christoph (57205020723); Gaedke, Martin (8905803700)","57193121679; 57205020723; 8905803700","CARDINAL: Contextualized Adaptive Research Data Description INterface Applying LinkedData","2021","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12706 LNCS","","","11","27","16","0","10.1007/978-3-030-74296-6_2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111120265&doi=10.1007%2f978-3-030-74296-6_2&partnerID=40&md5=2c53f31c473801d6f14121be0bb79e62","Chemnitz University of Technology, Chemnitz, Germany","Langer A., Chemnitz University of Technology, Chemnitz, Germany; Göpfert C., Chemnitz University of Technology, Chemnitz, Germany; Gaedke M., Chemnitz University of Technology, Chemnitz, Germany","In the publishing process for research data, common user interfaces for gathering descriptive structured metadata traditionally rely on static free-text input elements. This constitutes an obstacle for interdisciplinary, unambiguous, fine-grained data descriptions. Reusing already existing domain-specific metadata models based on semantic ontologies are a more promising approach, but the careful selection and presentation of relevant properties is not trivial. In this paper, we present the CARDINAL approach, which takes the current research context into consideration to request additional but only meaningful domain-specific characteristics. It generates and presents an adaptive user input interface to the user that allows the structured input of knowledge-domain specific descriptive metadata based on existing ontologies. We show in a proof-of-concept the feasibility of such a contextualized web form for research metadata and discuss challenges in the selection process for relevant ontologies and properties. A web-based survey experiment with 83 participants of varying research domain and expertise shows, that the CARDINAL approach allows to collect additional relevant metadata in a structured way without overstraining the user. © 2021, Springer Nature Switzerland AG.","Adaptive user interface; Contextualization; Data publishing; Linked data; Ontologies; Research data management","Data description; Metadata; Ontology; Semantics; Surveys; Descriptive metadata; Existing domains; Knowledge domains; Publishing process; Research domains; Semantic ontology; Structured metadatas; Web-based surveys; User interfaces","","","","","Deutsche Forschungsgemeinschaft, DFG, (416228727 – SFB 1410)","Acknowledgment. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 416228727 – SFB 1410.","Baclawski K., Schneider T., The open ontology repository initiative: Requirements and research challenges, Proceedings of Workshop on Collaborative Construction, Management and Linking of Structured Knowledge, ISWC, (2009); Brooke J., Sus: A quick and dirty usability scale, Usability Evaluation in Industry, 189, (1995); Cimino J., Ayres E., The clinical research data repository of the us national institutes of health, Stud. Health Technol. Inform., 160, pp. 1299-1303, (2010); Sharing Research Data, (2021); Goncalves R., Tu S., Nyulas C., Tierney M., Musen M., An ontology-driven tool for structured data acquisition using web forms, J. Biomed. Semant., 8, 1, pp. 1-14, (2017); Gopfert C., Langer A., Gaedke M., Ontoform: Deriving web input forms from ontologies, Web Engineering-21Th International Conference, ICWE 2021, Biarritz, France, May 18–21, (2021); Hemel Z., Kats L.C., Groenewegen D.M., Visser E., Code generation by model transformation: A case study in transformation modularity, Softw. Syst. Model., 9, 3, pp. 375-402, (2010); Langer A., PIROL: Cross-domain research data publishing with linked data technologies, Proceedings of the Doctoral Consortium Papers Presented at the 31St Caise 2019, pp. 43-51, (2019); Langer A., Bilz E., Gaedke M., Analysis of current RDM applications for the interdisciplinary publication of research data, CEUR Workshop Proceedings, 2447, (2019); Langer A., Gopfert C., Gaedke M., URI-aware user input interfaces for the unobtrusive reference to Linked Data, IADIS International Journal on Computer Science and Information Systems, 13, 2, (2018); Pampel H., Vierkant P., Scholze F., Et al., Making research data repositories visible: The re3data.org registry, Plos One, 8, 11, pp. 1-10, (2013); Paulheim H., Probst F., Ontology-enhanced user interfaces: A survey, Int. J. Seman. Web Inf. Syst., 6, pp. 36-59, (2010); Preim B., Dachselt R., Interaktive Systeme, (2010); Sauro J., Lewis J.R., Quantifying the User Experience, Second Edition: Practical Statistics for User Research, 38, (2016); Schilit B., Adams N., Want R., Context-aware computing applications, 1994 First Workshop on Mobile Computing Systems and Applications, pp. 85-90, (1994); Schmidt A., Beigl M., Gellersen H.W., There is more to context than location, Comput. Graph., 23, 6, pp. 893-901, (1999); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The fair guiding principles for scientific data management and stewardship, Sci. Data, 3, 1, (2016); Wolstencroft K., Owen S., Horridge M., Et al., RightField: Embedding ontology annotation in spreadsheets, Bioinformatics, 27, 14, pp. 2021-2022, (2011)","A. Langer; Chemnitz University of Technology, Chemnitz, Germany; email: andre.langer@informatik.tu-chemnitz.de","Brambilla M.; Chbeir R.; Frasincar F.; Manolescu I.","Springer Science and Business Media Deutschland GmbH","","21st International Conference on Web Engineering, ICWE 2021","18 May 2021 through 21 May 2021","Virtual, Online","260409","03029743","978-303074295-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85111120265"
"Wildemuth B.M.","Wildemuth, Barbara M. (6701769850)","6701769850","Libraries’ Contributions to the Quality of UK University Research Environments Were Not Acknowledged in REF 2014, but Could Be Made More Visible in REF 2021","2021","Evidence Based Library and Information Practice","16","1","","112","114","2","0","10.18438/eblip29889","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103736561&doi=10.18438%2feblip29889&partnerID=40&md5=7b7deaf8fe1cc048d5ac105b6f939589","Emeritus School of Information & Library Science University of North Carolina at Chapel Hill Chapel Hill, North Carolina, United States","Wildemuth B.M., Emeritus School of Information & Library Science University of North Carolina at Chapel Hill Chapel Hill, North Carolina, United States","Objective – To measure the extent to which libraries’ contributions to United Kingdom (UK) university research excellence were referenced in the Research Excellence Framework (REF) 2014 unit-level research environment statements, and to make recommendations to libraries for increasing their visibility in the research setting. Design – Content analysis of an existing corpus. Setting – Evaluation of research environments conducted as part of the UK REF 2014 assessment. Subjects – 1,891 unit-level research environment statements submitted for REF 2014. Methods – Each unit-level research environment statement was categorized in terms of how extensively it referenced library or librarian contributions: no mention, brief mention, or substantive mention. The frequency and percentage of each level of mention are reported overall and by disciplinary panel. Main Results – Across all panels, only 25.8% of the statements included substantive references to the library or librarians; most of these were lists of electronic and physical collections, but they also included discussions of the research support services offered by librarians. There were disciplinary differences in the extent of the references to libraries, from 7.2% containing substantive references in a panel examining science, technology, engineering, and mathematics (STEM) units to 44.0% containing substantive references in the panel examining arts and humanities units. Conclusion – In REF 2014, libraries and librarians were rarely discussed in unit-level research environment statements. While this lack of representation may be due to shortcomings of the library’s relationship with the university’s research office, librarians could use a number of approaches to becoming more visible in the REF 2021 research environment statements. Specifically, they could highlight their roles in: ensuring discoverability and accessibility of information resources to researchers; improving research practices through teaching informational and organizational skills, providing direct support to research students and staff, and providing research data management services; managing the research information systems that capture and make discoverable the university’s non-article research outputs; providing support in relation to the responsible use of bibliometrics and other measures of article quality and impact; further developing article impact by training researchers to use social media to their advantage; developing open research initiatives; and assisting with the REF submission process. © 2021","","","","","","","","","Boyce G., Greenwood A., Haworth A., Hodgson J., Jones C., Marsh G., Mawson M., Sadler R., Visions of value: Leading the development of a view of the University Library in the 21<sup>st</sup> century, The Journal of Academic Librarianship, 45, 5, (2019); Cox J., Positioning the academic library within the institution: A literature review, New Review of Academic Librarianship, 24, 3-4, pp. 217-241, (2018); Glynn L., A critical appraisal tool for library and information research, Library Hi Tech, 24, 3, pp. 387-399, (2006); Hicks D., Performance-based university research funding systems, Research Policy, 41, 2, pp. 251-261, (2012)","B.M. Wildemuth; Emeritus School of Information & Library Science University of North Carolina at Chapel Hill Chapel Hill, North Carolina, United States; email: wildemuth@unc.edu","","University of Alberta","","","","","","1715720X","","","","English","Evid. Based Libr. Inf. Pract.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85103736561"
"Suhr M.; Lehmann C.; Bauer C.R.; Bender T.; Knopp C.; Freckmann L.; Öst Hansen B.; Henke C.; Aschenbrandt G.; Kühlborn L.K.; Rheinländer S.; Weber L.; Marzec B.; Hellkamp M.; Wieder P.; Sax U.; Kusch H.; Nussbeck S.Y.","Suhr, M. (57210809413); Lehmann, C. (57219813561); Bauer, C.R. (57200871605); Bender, T. (57204583786); Knopp, C. (57219822129); Freckmann, L. (57219813563); Öst Hansen, B. (57219818056); Henke, C. (57219812098); Aschenbrandt, G. (57219813458); Kühlborn, L.K. (57219817001); Rheinländer, S. (57219816437); Weber, L. (57219819086); Marzec, B. (56192473700); Hellkamp, M. (16303925600); Wieder, P. (14034873100); Sax, U. (8956991900); Kusch, H. (6601998997); Nussbeck, S.Y. (55815970400)","57210809413; 57219813561; 57200871605; 57204583786; 57219822129; 57219813563; 57219818056; 57219812098; 57219813458; 57219817001; 57219816437; 57219819086; 56192473700; 16303925600; 14034873100; 8956991900; 6601998997; 55815970400","Menoci: lightweight extensible web portal enhancing data management for biomedical research projects","2020","BMC Bioinformatics","21","1","582","","","","5","10.1186/s12859-020-03928-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097648035&doi=10.1186%2fs12859-020-03928-1&partnerID=40&md5=f495a65b404df57ebe2e10c3f269d6fa","Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; GWDG, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen, Am Faßberg 11, Göttingen, 37077, Germany; Department of Molecular Biology, University Medical Center Göttingen, Humboldtallee 23, Göttingen, 37075, Germany; University Medical Center Göttingen, UMG Biobank, Robert-Koch-Str. 40, Göttingen, 37075, Germany","Suhr M., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Lehmann C., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Bauer C.R., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Bender T., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Knopp C., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Freckmann L., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Öst Hansen B., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Henke C., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Aschenbrandt G., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Kühlborn L.K., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Rheinländer S., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Weber L., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Marzec B., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Hellkamp M., GWDG, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen, Am Faßberg 11, Göttingen, 37077, Germany; Wieder P., GWDG, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen, Am Faßberg 11, Göttingen, 37077, Germany; Sax U., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany; Kusch H., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany, Department of Molecular Biology, University Medical Center Göttingen, Humboldtallee 23, Göttingen, 37075, Germany; Nussbeck S.Y., Department of Medical Informatics, University Medical Center Göttingen, von-Siebold-Str. 3, Göttingen, 37075, Germany, University Medical Center Göttingen, UMG Biobank, Robert-Koch-Str. 40, Göttingen, 37075, Germany","Background: Biomedical research projects deal with data management requirements from multiple sources like funding agencies’ guidelines, publisher policies, discipline best practices, and their own users’ needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability. Results: We introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform. Conclusions: Management of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects. © 2020, The Author(s).","Data management; Drupal; FAIR; Linked data; Metadata; Open source; Persistent identifiers; Research data management; Software","Biomedical Research; Data Management; Databases, Factual; Information Storage and Retrieval; Software; Computer software reusability; Digital storage; Ecosystems; Electronic document exchange; Metadata; Open source software; Open systems; Portals; Reusability; Security of data; Biomedical research; Collaboration platforms; Content management system; Data management software; Quality requirements; Research data managements; Service infrastructure; Structural transformation; article; artifact; documentation; ecosystem; FAIR principles; information storage; medical research; metadata; software; factual database; information processing; information retrieval; procedures; Information management","","","","","Aleksandr Statciuk; FPDI; German Research Fonundation; Ploty Inc.; Deutsche Forschungsgemeinschaft, DFG","Funding text 1: Open Access funding enabled and organized by Projekt DEAL. The human resources for conceptualisation, development, and continuous improvement of the menoci platform were mainly funded by the German Research Fonundation (DFG) since July 2012 through project funding of the infrastructure (INF) project within the Collaborative Research Center (CRC) 1002 and the Z project of CRC 1190. ; Funding text 2: We thank all (former) colleagues and collaborators who have contributed to the conceptualisation and development of the menoci system. The following third-party libraries are used in menoci source code: PHPExcel, Wikidata PHP library by Aleksandr Statciuk, FPDI by Setasign, TCPDF by Nicola Asuni, Select2 by Kevin Brown and Igor Vaynberg, Ploty by Ploty Inc.","Munafo M.R., Nosek B.A., Bishop D.V.M., Button K.S., Chambers C.D., du Sert N.P., Et al., A manifesto for reproducible science, Nat Hum Behav, 1, (2017); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: a guide to good practice, (2014); Spichtinger D., Siren J., The development of research data management policies in Horizon 2020, Research data management—a European perspective, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci Data, 3, (2016); McMurry J.A., Juty N., Blomberg N., Burdett T., Conlin T., Conte N., Et al., Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data, PLoS Biol, 15, (2017); Sansone S.-A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hofmann O., Et al., Toward interoperable bioscience data, Nat Genet, 44, pp. 121-126, (2012); Li F., Hu J., Xie K., He T.-C., Authentication of experimental materials: a remedy for the reproducibility crisis?, Genes Dis, 2, (2015); Baker M., Reproducibility crisis: blame it on the antibodies, Nature, 521, pp. 274-276, (2015); Freedman L.P., Gibson M.C., Ethier S.P., Soule H.R., Neve R.M., Reid Y.A., Reproducibility: changing the policies and culture of cell line authentication, Nat Methods, 12, pp. 493-497, (2015); Pamies D., Advanced good cell culture practice for human primary, stem cell-derived and organoid models as well as microphysiological systems, Altex, 35, pp. 353-378, (2018); Kilkenny C., Parsons N., Kadyszewski E., Festing M.F.W., Cuthill I.C., Fry D., Et al., Survey of the quality of experimental design, statistical analysis and reporting of research using animals, PLoS ONE, 4, (2009); Guidelines for Safeguarding Good Research Practice. In: Guidelines for Safeguarding Good Research Practice. Weinheim, pp. 1-109, (2013); Re3data.Org Project Consortium. 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Drupal.Org; CVE—common Vulnerabilities and Exposures (CVE; Martinez-Caro J.-M., Aledo-Hernandez A.-J., Guillen-Perez A., Sanchez-Iborra R., Cano M.-D., A comparative study of web content management systems, Information, 9, (2018); Venters C.C., Lau L., Griffiths M.K., Holmes V., Ward R.R., Jay C., Et al., The blind men and the elephant: towards an empirical evaluation framework for software sustainability, J Open Res Softw, (2014); Jimenez R.C., Kuzak M., Alhamdoosh M., Barker M., Batut B., Borg M., Et al., Four simple recommendations to encourage best practices in research software, Research., 6, (2017); Smith A.M., Katz D.S., Niemeyer K.E., FORCE11 software citation working group. Software citation principles, PeerJ Comput Sci, 2, (2016); Mooney S.D., Baenziger P.H., Extensible open source content management systems and frameworks: a solution for many needs of a bioinformatics group, Brief Bioinform, 9, pp. 69-74, (2008); Drupal 7 Will Reach End-Of-Life in November of 2021—PSA-2019-02-25; Drupal.Org: Migrating from Drupal 7 to 8 to 9; Patel S.K., Rathod V.R., Prajapati J.B., Performance analysis of content management systems Joomla, Drupal and WordPress, Int J Comput Appl, 21, pp. 39-43, (2011); The Dataverse Project; Bauer C.R., Umbach N., Baum B., Buckow K., Franke T., Grutz R., Et al., Architecture of a biomedical informatics research data management pipeline, Stud Health Technol Inf, 228, pp. 262-266, (2016); Enhancing Research Data Management: Performance through Diversit, Recommendations regarding Structures, Processes, and Financing for Research Data Management in Germany, (2016); Wittenburg P., Strawn G., Common Patterns in Revolutionary Infrastructures and Data, (2018); Bauch A., Adamczyk I., Buczek P., Elmer F.-J., Enimanev K., Glyzewski P., Et al., openBIS: a flexible framework for managing and analyzing complex data in biology research, BMC Bioinform, 12, (2011); Barillari C., Ottoz D.S.M., Fuentes-Serna J.M., Ramakrishnan C., Rinn B., Rudolf F., openBIS ELN-LIMS: an open-source database for academic laboratories, Bioinformatics, 32, pp. 638-640, (2016); Smith M., Barton M., Bass M., Branschofsky M., McClellan G., Stuve D., Et al., DSpace: an open source dynamic digital repository, (2003); Dspace; Payette S., Lagoze C., Flexible and extensible digital object and repository architecture (FEDORA), Research and advanced technology for digital libraries, pp. 41-59, (1998); Fedora—The Flexible, Modular, Open-Source Repository Platform; Arend D., Lange M., Chen J., Colmsee C., Flemming S., Hecht D., Et al., e!DAL—a framework to store, share and publish research data, BMC Bioinform, 15, (2014); Invenio Software; Wolstencroft K., Owen S., du Preez F., Krebs O., Mueller W., Goble C., Et al., The SEEK: a platform for sharing data and models in systems biology, Methods Enzymol, 500, pp. 629-655, (2011)","M. Suhr; Department of Medical Informatics, University Medical Center Göttingen, Göttingen, von-Siebold-Str. 3, 37075, Germany; email: markus.suhr@med.uni-goettingen.de","","BioMed Central Ltd","","","","","","14712105","","BBMIC","33334310","English","BMC Bioinform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85097648035"
"Dogan G.; Taskin Z.; Aydinoglu A.U.","Dogan, Guleda (56289638800); Taskin, Zehra (55865568297); Aydinoglu, Arsev Umur (36701092900)","56289638800; 55865568297; 36701092900","Research data management in Turkey: A survey to build an effective national data repository","2021","IFLA Journal","47","1","","51","64","13","0","10.1177/0340035220917985","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084554536&doi=10.1177%2f0340035220917985&partnerID=40&md5=89ecbb8cb6226845b72a7e5a7c2696a4","Department of Information Manegement, Hacettepe University, Ankara, Turkey; Science and Technology Policy Studies, Middle East Technical University, Ankara, Turkey; Scholarly Communication Research Group, Adam Mickiewicz University, Poznan, Poland","Dogan G., Department of Information Manegement, Hacettepe University, Ankara, Turkey; Taskin Z., Department of Information Manegement, Hacettepe University, Ankara, Turkey, Scholarly Communication Research Group, Adam Mickiewicz University, Poznan, Poland; Aydinoglu A.U., Science and Technology Policy Studies, Middle East Technical University, Ankara, Turkey","Research data management is an important topic for funding agencies, universities and researchers. In this context, the main aim of this study is to collect preliminary information for Aperta, which is being developed by the Scientific and Technological Research Council of Turkey, to fulfil the following goals: determine the research data management awareness levels of researchers in Turkey; understand current research data management practices in their research environments; and find out their experiences of policy issues. For this, a questionnaire was distributed to 37,223 researchers, with 1577 researchers completing it. The results indicated that researchers who spend more time with data have more concerns about data management issues. The levels of experience of creating a data management plan were quite low. The importance of this study lies in how it is able to show the current research data management practices of Turkish scholars during the new repository’s foundational development stage. © The Author(s) 2020.","data practices; data repository; Data services; open data; research data management; services to user populations; Turkey","","","","","","","","Allard S., Aydinoglu A.U., Et al., Environmental researchers’ data practices: An exploratory study in Turkey, E-Science and Information Management, pp. 13-24, (2012); Aperta T.U.B.I.T.A.K., Aperta – TÜBİTAK Açık Arşivi [Aperta – TÜBİTAK Open Archive], (2019); (2019); (2019); Aydinoglu A.U., Suomela T., Malone J., Data management in astrobiology: challenges and opportunities for an interdisciplinary community, Astrobiology, 14, 6, pp. 451-461, (2014); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Practices and attitudes, Library Hi Tech, 35, 2, pp. 271-289, (2017); Bertzky B., Stoll-Kleemann S., Multi-level discrepancies with sharing data on protected areas: What we have and what we need for the global village, Journal of Environmental Management, 90, 1, pp. 8-24, (2009); Bishop B., Gunderman H., Davis R., Et al., Data curation profiling to assess data management training needs and practices to inform a toolkit, Data Science Journal, 19, 1, pp. 1-8, (2020); Cochran W.G., Sampling techniques, (1963); YÖK açık erişim - açık bilim çalışmaları [Open access and open science works of the Council of Higher Education], (2019); Cragin M.H., Palmer C.L., Carlson J.R., Et al., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society A, 368, 1926, pp. 4023-4038, (2010); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Open access and data management, (2016); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLOS ONE, 10, 2, (2015); Ferguson A.R., Nielson J.L., Cragin M.H., Et al., Big data from small data: Data-sharing in the ‘long tail’ of neuroscience, Nature Neuroscience, 17, pp. 1442-1447, (2014); Grootveld M., Leenarts E., Jones S., Et al., OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans (version 1.0.0), (2018); Hardisty A., Roberts D., Addink W., Et al., A decadal view of biodiversity informatics: Challenges and priorities, BMC Ecology, 13, (2013); Herold P., Data sharing among ecology, evolution, and natural resources scientists: An analysis of selected publications, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program, 49, 4, pp. 364-381, (2015); Ioannidis J.P.A., Allison D.B., Ball C.A., Et al., Repeatability of published microarray gene expression analyses, Nature Genetics, 41, pp. 149-155, (2009); Ioannidis J.P.A., Munafo M.R., Fusar-Poli P., Et al., Publication and other reporting biases in cognitive sciences: Detection, prevalence, and prevention, Trends in Cognitive Sciences, 18, 5, pp. 235-241, (2014); John L.K., Loewenstein G., Prelec D., Measuring the prevalence of questionable research practices with incentives for truth telling, Psychological Science, 23, 5, pp. 524-532, (2012); Koltay T., Accepted and emerging roles of academic libraries in supporting Research 2.0, Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Koole S.L., Lakens D., Rewarding replications: A sure and simple way to improve psychological science, Perspectives in Psychological Sciences, 7, 6, pp. 608-614, (2012); Kratz J.E., Strasser C., Researcher perspectives on publication and peer review of data, PLOS ONE, 10, 2, (2015); LEARN toolkit of best practice for research data management, (2017); McAfee A., Brynjolfsson E., Big data: The management revolution, Harvard Business Review, 90, pp. 60-68, (2012); MacMillan D., Data sharing and discovery: What librarians need to know, Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); Mennes M., Biswal B.B., Castellanos F.X., Et al., Making data sharing work: The FCP/INDI experience, NeuroImage, 82, pp. 683-691, (2013); Michener W.K., Ecological data sharing, Ecological Informatics, 29, pp. 33-44, (2015); Data and Information Policy, (2011); Revised policy on enhancing public access to archived publications resulting from NIH-funded research, (2008); Scientists seeking NSF funding will soon be required to submit data management plans, Press Release 10-777, (2010); Nosek B.A., Spies J.R., Motyl M., Scientific utopia: II, Restructuring incentives and practices to promote truth over publishability. Perspectives of Psychological Science, 7, 6, pp. 615-631, (2012); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLOS ONE, 9, 12, (2014); Piwowar H., Value all research products. Nature 493(159), (2013); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLOS ONE, (2007); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Poldrack R.A., Baker C.I., Durnez J., Et al., Scanning the horizon: Towards transparent and reproducible neuroimaging research, Nature Reviews Neuroscience, 18, pp. 115-126, (2017); Pollock D., Understanding scientific data sharing outside of the academy, 79th Association for Information Science and Technology annual meeting, (2016); Roettger T.B., Winter B., Baayen H., Emergent data analysis in phonetic sciences: Towards pluralism and reproducibility, Journal of Phonetics, 73, pp. 1-7, (2019); Science as an open enterprise: Final report, (2012); Sesartic A., Dieude A., Research data management training and support services at both ETH Zurich and EPF Lausanne, (2017); Spallek H., Weinberg S.M., Manz M., Perceptions and attitudes toward data sharing among dental researchers, JDR Clinical and Translational Research, 4, 1, pp. 68-75, (2019); Spires-Jones T.L., Poirazi P., Grubb M.S., Opening up: Open access publishing, data sharing, and how they can influence your neuroscience career, European Journal of Neuroscience, 43, 11, pp. 1413-1419, (2016); Steeleworthy M., Research data management and the Canadian academic library: An organizational consideration of data management and data stewardship, Partnership, 9, 1, (2014); Strasser C.A., Hampton S.E., The fractured lab notebook: Undergraduates and ecological data management training in the United States, Ecosphere, 3, 12, (2012); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: Practices and perceptions, PLOS ONE, 6, 6, (2011); Tenopir C., Christian L., Allard S., Et al., Research data sharing: Practices and attitudes of geophysicists, Earth and Space Science, 5, 12, pp. 891-902, (2018); Tenopir C., Dalton E.D., Allard S., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLOS ONE, 10, 8, (2015); Tenopir C., Hughes D., Allard S., Et al., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tenopir C., Pollock D., Allard S., Et al., Research data services in European and North American libraries: Current offerings and plans for the future, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Tenopir C., Talja S., Horstmann W., Et al., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Tonta Y., Açık erişim, kurumsal arşivler ve MedOANet Projesi [Open access, institutional repositories and the MedOANet Project], Ulusal açık erişim çalıştayı, (2012); Tonta Y., Açık erişimin geleceği ve araştırma verilerine açık erişim [The future of open access and open access for research data], Bilkent’te kütüphanecilik seminerleri [Librarianship seminars at Bilkent], (2013); TUBITAK open science policy, (2019); Unal Y., Chowdhury G., Kurbanoglu S., Et al., Research data management and data sharing behaviour of university researchers, Information Research, 24, 1, (2019); Unal Y., Kurbanoglu S., Araştırma verilerinin yönetimi: Türk araştırmacılar verilerini açmaya hazır mı? [Research data management: Are Turkish researchers prepared to open their data?], Türk Kütüphaneciliği, 32, 4, pp. 287-311, (2018); Research data management @ Pitt, (2020); Verbakel E., Noordegraaf M., De Smaele M., Data-intelligence training for library staff, (2013); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them?, Data sharing and reuse in the long tail of science and technology. PLOS ONE, 8, 7, (2013); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program, 49, 4, pp. 382-407, (2015); Whyte A., Tedds J., Making the case for research data management, (2011); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: Domain-based data management training for liaison librarians, Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018); Xia F., Wang W., Bekele T., Et al., Big scholarly data: A survey, IEEE Transactions on Big Data, 3, 1, pp. 18-35, (2017); Yegros-Yegros A., Van Leeuwen T.N., Production and uptake of open access publications involving the private sector: The case of big pharma, (2019)","Z. Taskin; Department of Information Manegement, Hacettepe University, Ankara, Turkey; email: zehrayanar@gmail.com; Z. Taskin; Scholarly Communication Research Group, Adam Mickiewicz University, Poznan, Poland; email: zehrayanar@gmail.com","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85084554536"
"Øvrelid E.; Bygstad B.; Thomassen G.","Øvrelid, Egil (57190580470); Bygstad, Bendik (22233399900); Thomassen, Gard (57216428952)","57190580470; 22233399900; 57216428952","TSD: A research platform for sensitive data","2021","Procedia Computer Science","181","","","127","134","7","0","10.1016/j.procs.2021.01.112","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105617788&doi=10.1016%2fj.procs.2021.01.112&partnerID=40&md5=4faa9d8a3c21885ab40e8030f3348127","Dept. Of Informatics, University of Oslo, N-0176, Norway; University Center of Information Technology, University of Oslo, N-0176, Norway","Øvrelid E., Dept. Of Informatics, University of Oslo, N-0176, Norway; Bygstad B., Dept. Of Informatics, University of Oslo, N-0176, Norway; Thomassen G., University Center of Information Technology, University of Oslo, N-0176, Norway","Digitalisation has led to a strong increase of research data, but most of these data are managed in unsatisfactory ways, and research data management has been characterized as a “wicked problem”. Several research data platforms have been launched, but security and privacy issues remain. Our research question is how can a research platform for sensitive data be built and used? Based on platform research, we propose a framework to analyze requirements. Our empirical evidence is a research platform called TSD, i.e. a platform for sensitive data. We analyze the development of TSD and offer two contributions; first, we discuss a framework to understand the architectural requirements for a research data platform, and second, we show how a research platform can be developed through a process of platformization. © 2021 The Authors. Published by Elsevier B.V.","Boundary resources; Data management; Platformization; Research platform","Boundary resource; Data platform; Elsevier; Platformization; Research data; Research data managements; Research platforms; Security and privacy issues; Sensitive datas; Wicked problems; Information management","","","","","","","Vicente-Saez R., Martinez-Fuentes C., Open science now: A systematic literature review for an integrated definition, Journal of Business Research, 88, pp. 428-436, (2018); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French National Research Center (CNRS), Data Technologies and Applications, (2018); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ''wicked'' problem, J Librariansh Inf Sci, (2014); Treloar A., The research data alliance: Globally co-ordinated action against barriers to data publishing and sharing, Learned Publishing, 27, 5, pp. 9-13, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., DaSilva L.B., Santos, Bourne P.E., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Cox A.M., Kennan M.A., Pinfield S., Developments in research data management in academic libraries: Towards and understanding of research data service maturity, Journal of the Association for Information Science and Technology, (2017); Huh T., Park G., Ahn S., Hwang S., Jung H., Design Criteria of Korean LTER Data Platform Model for Full Life-cycle Data Management, International Journal of Applied Engineering Research, 12, 3, pp. 336-342, (2017); Widmann H., Thiemann H., A cross-discipline metadata service and discovery portal, EGU General Assembly Conference Abstracts, 18, (2016); Michener W., Vieglais D., Vision T., Dataone: Data Observation Network for Earth - Preserving data and enabling innovation in the biological and environmental sciences, D- Lib Magazine, 17, pp. 1-2, (2011); Suhr M., Lehmann C., Bauer C.R., Bender T., Knopp C., Freckmann L.K., Ost Hansen B., Henke C., Aschenbrandt G., Kuhlborn L.K., Rheinlander S., Weber L., Marzec B., Hellkamp M., Wieder P., Kusch H., Sax U., Nussbeck S.Y., Menoci: Lightweight Extensible Web Portal Enabling FAIR Data Management for Biomedical Research Projects, (2020); Final Report and Action Plan from the European Commission Expert Group on FAIR Data, (2018); Lefebvre A., Schermerhorn E., Spruit M., How research data management can contribute to efficient and reliable science, The 25th European Conference of Information Systems, (2018); Parker G., van Alstyne M., Choudary S.P., Platform Revolution: How Networked Markets Are Transforming the Economy-and How to Make Them Work for You, (2016); Baldwin C.Y., Woodard J.C., The architecture of platforms: A unified view” platforms, markets and innovation., Research Collection School of Information Systems, 19, (2009); Ipsos Digital, (2020); Askeland C., Solberg O.V., Bakeng J.B.L., CuStUSX: An open-source research platform for image-guided therapy, Int J CARS, 11, pp. 505-519, (2016); McGrath M., Dishongh T., A common personal health research platform - Shimmer and biomodus, Intel Technology Journal, 13, 3, pp. 122-147, (2009); (2020); (2020); (2020); (2017); (2020); Kanngieser A., Neilson B., Rossiter N., What is a research platform? Mapping methods, mobilities and subjectivities, Media, Culture & Society, 36, 3, pp. 302-318, (2014); Ghazawneh A., Henfridsson O., Balancing platform control and external contribution in third-party development: The boundary resources model, Information Systems Journal, 23, 2, pp. 173-192, (2013); Tiwana A., Platform Ecosystems, Aligning Architecture,Governance, and Strategy, (2014); (2017); (2020); UIO (2020A; (2020); Parnas D.L., On the criteria to be used in decomposing systems into modules, Communications of the ACM, 15, 12, pp. 1053-1058, (1972); Yourdon E., Constantine L.L., Structured Design: Fundamentals of a Discipline of Computer Program and System Design Prentice Hall, (1986); Yoo Y., Henfridsson O., Lyytinen K., Research commentary -The new organizing logic of Digital Innovation: An agenda for Information Systems research, Information System Research, 21, 4, pp. 724-735, (2010); Gamma E., Helm R., Johnson R., Vlissides J., Design Patterns: Elements of Reusable Object-Oriented Software, (1995); George A.L., Bennet A., Case Studies and Theory Development in the Social Sciences, (2005); Sigma, (2020); Bygstad B., Munkvold B.E., Volkoff O., Identifying generative mechanisms through affordances: A framework for critical realist data analysis, Journal of Information Technology, 31, pp. 83-96, (2016)","E. Øvrelid; Dept. Of Informatics, University of Oslo, N-0176, Norway; email: egilov@ifi.uio.no","Cruz-Cunha M.M.; Martinho R.; Rijo R.; Mateus-Coelho N.; Domingos D.; Peres E.","Elsevier B.V.","","2020 International Conference on ENTERprise Information Systems - International Conference on Project MANagement and International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2020","21 October 2020 through 23 October 2020","Vilamoura","168736","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85105617788"
"","","","IRCDL 2021 - Proceedings of the 17th Italian Research Conference on Digital Libraries","2021","CEUR Workshop Proceedings","2816","","","","","196","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101781875&partnerID=40&md5=c6f6cae60dcae895655f591af2c9d4a6","","","The proceedings contain 18 papers. The topics discussed include: efficient keyphrase generation with GANs; toward automatic floor plan interpretation; FullBrain: a social e-learning platform; FAIR RDM (research data management): Italian initiatives towards EOSC implementation; a quantitative/qualitative approach to OCR error detection and correction in old newspapers for corpus-assisted discourse studies; background linking: joining entity linking with learning to rank models; serious games for information literacy: assessing learning in the NAVIGATE project; and s-AWARE: using crowd judgments in supervised measure-based methods for IR evaluation.","","","","","","","","","","","Dosso D.; Ferilli S.; Manghi P.; Poggi A.; Serra G.; Silvello G.","CEUR-WS","","17th Italian Research Conference on Digital Libraries, IRCDL 2021","18 February 2021 through 19 February 2021","Virtual, Padua","167286","16130073","","","","English","CEUR Workshop Proc.","Conference review","Final","","Scopus","2-s2.0-85101781875"
"Masenya T.M.","Masenya, Tlou Maggie (57223083222)","57223083222","Research data management practices and services in South African academic libraries","2021","Library Philosophy and Practice","2021","","","1","22","21","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120311124&partnerID=40&md5=d7073107103ffb6c13821c9444139571","Durban University of Technology, South Africa","Masenya T.M., Durban University of Technology, South Africa","Research data is being produced at a rapid rate in a wide variety of digital forms in academic and research institutions, however, this data is most prone to loss due to mismanagement. Proper management and preservation of this research data is essential for productivity, securing grant funding, enabling collaboration, increases data sharing, ensuring accessibility and the future use of data. Although academic libraries have recognised a need for effective management of research data, however, the management of their fast-growing number of research data poses major challenge to academic librarianship. The purpose of this study was to investigate the research data management practices and services within academic libraries in South Africa,in order to suggest solutions for effective research data management. Review of literature revealed that academic libraries are experiencing difficulties in managing their research data because of the absence of established policies and standards, inadequate standardised storage infrastructure, time constraint to organise data, limited funding, inadequate resources, lack of skills and training in managing research data and lack of incentives for researchers to share their data. All these challenges have created the dire need for best practices and solutions in ensuring proper management and long-term preservation of research data of enduring value in the academic libraries. Effective research data management strategies are thus needed to protect the enormous financial and time investments that have been made by mitigating data loss and avoiding the need for duplication of efforts to recreate lost data. The study suggests the need for implementation of research data management policies and strategies, provision of adequate resources, sufficient funding, collaborative approach and capacitating research data managers and administrators. © 2021. All Rights Reserved.","Academic libraries; Institutional repositories; Research data; Research data management; Research data sharing","","","","","","University of Cape Coast; University of Michigan, U-M; Council for Scientific and Industrial Research, South Africa, CSIR; Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी","Funding text 1: Multi-institutional collaboration is another strategy to strengthen the provision of research data management infrastructure and services (Joanna, et al, 2012). In South Africa, the Department of Science and Technology also established DIRISA as one of the three pillars of the National Integrated Cyber Infrastructure System managed by the Council for Scientific and Industrial Research (CSIR). DIRISA is primarily responsible for the creation, management, storage and sharing of research data. This initiative is supported by the South African National Research Network for the transmission of data and by the Centre for High Performance Computing for the processing of data. DIRISA developed South African Data Management Planning tool (SA-DMP), coupled with sound data management practices that supports the findable, accessible, interoperable and reusable principles of open data as well as the processes, policies, ethics and legal compliance regulations that include:; Funding text 2: Research data is being produced at a rapid rate in a wide variety of digital forms in academic and research institutions, however, this data is most prone to loss due to mismanagement. Proper management and preservation of this research data is essential for productivity, securing grant funding, enabling collaboration, increases data sharing, ensuring accessibility and the future use of data. Although academic libraries have recognised a need for effective management of research data, however, the management of their fast-growing number of research data poses major challenge to academic librarianship. The purpose of this study was to investigate the research data management practices and services within academic libraries in South Africa,in order to suggest solutions for effective research data management. Review of literature revealed that academic libraries are experiencing difficulties in managing their research data because of the absence of established policies and standards, inadequate standardised storage infrastructure, time constraint to organise data, limited funding, inadequate resources, lack of skills and training in managing research data and lack of incentives for researchers to share their data. All these challenges have created the dire need for best practices and solutions in ensuring proper management and long-term preservation of research data of enduring value in the academic libraries. Effective research data management strategies are thus needed to protect the enormous financial and time investments that have been made by mitigating data loss and avoiding the need for duplication of efforts to recreate lost data. The study suggests the need for implementation of research data management policies and strategies, provision of adequate resources, sufficient funding, collaborative approach and capacitating research data managers and administrators.","Adam D., Exploring the academic libraries' readiness for research data management: cases from Hungary and Estonia, (2015); Allard R. S., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, pp. 84-90, (2014); Allard S, Birch B, Sandusky R.J, Tenopir C., Academic libraries and Research data services: Preparations and attitudes, International Federation of Library Associations and Institutions, pp. 70-78, (2012); Alter G.C, Vardigan M., Addressing global data sharing challenges, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 317-323, (2015); Ashiq M, Rehman S.U, Mujtaba G., Future challenges and emerging role of academic libraries in Pakistan: a phenomenology approach, Information Development, pp. 1-16, (2020); Avuglah B.K, Underwood P.G., Research Data Management capabilities at the University of Ghana, Legon, Library Philosophy and Practice (e-journal), (2019); Aydinoglu A. U, Dogan G, Taskin Z., Research Data Management in Turkey: Perceptions and Practices, Libr Hi Tech, 35, pp. 271-289, (2017); Bella B., eScience Curation Report, (2019); Boateng K. A., Academic Library and Research Data management Roles, (2015); Borglund E, Engvall T., Open Data: Data, Information, Document or Record?, Records Management Journal, 24, 2, pp. 163-180, (2014); Briney K, Goben A, Zilinski L., Do You Have an Institutional Data Policy? A Review of the Current Landscape of Library Data Services and Institutional Data Policies, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Briney K., Data Management for Researchers: Organize, Maintain and Share your Data for Research Success, (2015); Bryce A, Et al., Globus: A Case Study in Software as a Service for Scientists, Proc. 8th Working Science Cloud Computing, pp. 25-32, (2017); Carlson J.R., Garritano J.R., Cyberinfrastructure and the E-science, Cyberinfrastructure and the Changing Face of Scholarship Organizing for New Models of Research Support at the Purdue University Libraries, (2010); Chengyu F, Et al., Managing scientific data with named data networking, Proc. 15th International Working Network-aware. Data management, pp. 1-7, (2015); Chigwada J, Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, 31, pp. 1-9, (2017); Chiware E.R., Becker D. A., Research data management services in southern Africa: a readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Collins K. M, Onwuegbuzie A. J, Jiao Q. G., A Framework for Assessing Legitimation in Mixed Research: Implications for the Field of Stress and Coping, (2010); Corti L., Managing and sharing data: best practice for researchers, (2011); Corti L, Van den Eynden V, Bishop L, Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2011); Corti M. W., Managing and Sharing of Data, Best practice for researchers, pp. 2-40, (2011); Cox A. M, Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib Journal of Information Management, 70, 2, pp. 42-157, (2018); Cox A.M, Kennan M.A, Lyon L, Pinfield S, Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); De Waard A., Research Data Management at Elsevier: Supporting Networks of Data and Workflows, Inf Serv Use, 36, pp. 49-55, (2016); Doucette L, Fyfe B., Drowning in Research Data: Addressing Data Management Literacy of Graduate Students, Proc ACRL 2013 Conf, pp. 165-171, (2013); Elsayed A.M, Saleh E.I., Research data management and sharing among researchers in Arab universities: an exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Erway R, Rinehart A., If you build it, will they fund? Making Research Data Management sustainable, (2016); Gunjal B, Gaitanou P., Research Data Management: A Practical Approach to Overcome Challenges to Boost Research, IASSIST Conference, (2016); Henty M, Weaver B, Bradbury S.J, Porter S., Investigating Data Management Practices in Australian Universities, (2008); Hey A, Trefethen A., Grid Computing -Making the Global Infrastructure a Reality, (2003); Berman G. Fox, Hey A., The Data Deluge: An e-Science Perspective; Hey T, Tansley T, Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Higman R, Pinfield S., Research Data Management and Openness: The Role of Data Sharing in Developing Institutional Policies and Practices, Program, 49, pp. 364-381, (2015); Hyogi S, Et al., Tag IT: An Integrated Indexing and Search Service for File Systems, Proc. SC, 17, (2017); Jao I, Et al., Involving research stakeholders in developing policy on sharing public health research data in Kenya: Views on fair process for informed consent, access oversight and community engagement, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 264-277, (2015); Li Y.F, Kennedy G., Ngoran F, Wu P, Hunter J., An Ontology-Centric Architecture for Extensible Scientific Data Management Systems, Futur Gener Comput Sys, 29, pp. 641-653, (2013); Naum A., Research data storage and management: Library staff participation in showcasing research data at the University of Adelaide, The Australian Library Journal, 63, pp. 35-44, (2014); Ndemo B., Daily nation, (2015); Ndlovu P., The state of preparedness for digital curation and preservation: a case study of a developing country, (2018); Manu T.R, Gala B., Research Data Management lifecycle: An overview, book: The trends, challenges and opportunities for LIS education and Practice, (2019); Marcus C, Ball S, Delserone L, Hribar A, Loftus W., Understanding research behaviors, information resources and service needs of scientist and graduate students: A study for the University of Minnesota Libraries, (2007); Mark M, Bracket M, Earley S, Henderson D., The DAMA guide to the data management body of knowledge, (2009); Markauskaite L., Kennan M.A., Research Data Management Practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Marlina E., Purwandari B., Strategy for Research Data Management services in Indonesia, The fifth Information Systems International Conference Procedia Computer Science, 161, pp. 788-796, (2019); Masenya T.M., Ngulube P., Digital preservation systems and technologies in South African academic libraries, South African Journal of Information Management, 23, 1, pp. 12-49, (2021); Matusiak K.K., Sposito F.A., Types of Research Data Management services. An international perspective, Proceedings of the Association of Information Science and Technology, 54, 1, pp. 754-756, (2017); Mobley A, Linder S.K, Braeuer R, Ellis L.M, Zwelling L., A survey on data reproducibility in cancer research provides insights into our limited ability to translate findings from the laboratory to the clinic, Plos One, 8, 5, (2003); Merson L, Et al., Trust, respect and reciprocity: Developing culturally appropriate data sharing practices in Viet Nam, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 251-263, (2015); Palumbo L., Preparing to Accept Research Data: Creating Guidelines for Librarian, Journal of eScience Librarianship, 4, 2, (2015); Patel D., Research Data Management: A Conceptual Framework, Libr Rev, 65, pp. 226-241, (2016); Pinfield A. M., Research data management and libraries: Current activities and future priorities, Journal of Library and Information Science, (2013); Piorun M. E, Et al., Teaching Research Data Management: An Undergraduate/Graduate Curriculum, Journal of eScience Librarianship, 1, (2012); Penev L., From Open Access to Open Science from the Viewpoint of a Scholarly Publisher, Research Ideas and Outcomes, 3, (2017); Poole A. H., How has your science data grown? Digital curation and the human factor: a critical literature review, Archival Science, (2015); Pryor G., Managing research data, (2012); Read A., Research Data Management, Journal of the Medical Library Association (JMLA), 103, 3, pp. 154-156, (2015); Open Access to Research Data; Policy for The Research Council of Norway, (2015); Richard G, Hartmann V, Jejkal T, Kollai H., The MASi Repository Service: Comprehensive, Metadata-Driven and Multi-Community Research Data Management, Futur Gener Comput Syst, (2018); Richardson J, Nolan-Brown T, Loria P, Bradbury S., Library Research Support in Queensland: A Survey, Aust Acad Res Libr, 43, pp. 258-277, (2012); Sanjeeva M., Research data management: a new role for academic/research librarians, (2018); Serrano-Vicente R., Melero R., Abadal E., Open Access Awareness and Perceptions in an Institutional Landscape, The Journal of Academic Librarianship, 42, pp. 595-603, (2016); Sonker S. K., Shukla A, Tripathi M., Research Data Management Practices in University Libraries: A Study, DESIDOC Journal of Library & Information Technology, pp. 417-4, (2017); Stillerman J, Greenwald m, Wright J., Scientific data management with navigational metadata, Fusion Eng Des, 128, pp. 113-116, (2018); Surkis A., Research Data Management, Journal of the Medical Library, 103, pp. 154-156, (2015); Takeda K., Et al., Data management for all: the institutional data management blueprint project, Presentation at the 6th International Digital Curation Conference, (2010); Tenopir C, Birch B, Allard S., Academic libraries and research data services: Current practices and plans for the future, (2012); Tenopir C, Sandusky R.J, Allard S, Birch B., Academic librarians and research data services: Preparation and attitudes, International Federation of Library Associations and Institutions, 39, 1, pp. 70-78, (2013); Tenopir C, Sandusky R.J, Allard S, Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Trifan A, Oliveira J.L., A FAIR Marketplace for Biomedical Data Custodians and Clinical Researchers, 2018 IEEE. 31st Int. Symp. Comput. Med. Syst, pp. 188-19, (2018); Tripathi A, Shukla M, Sonkar S.K., Research data management practices in University libraries: A study, Journal of Library & Information Technology, 37, 6, pp. 417-424, (2017); Van den Eynden V., Data lifecycle and data management planning, (2013); Wambani H. N., Improving scientific research at Kenya agricultural: research institute (kari)-kakamega research centre, (2011); Ward C, Freiman L., Molloy L., Jones S., Snow K., Making Sense: Talking Data Management with Researchers, International Journal of Digital Curation, 6, 2, (2011); Weber N. M., Palmer C. L, Chao T. C., Current Trends and Future Directions in Data Curation Research and Education, Journal of Web Librarianship, pp. 305-320, (2012); Whyte A. T. J., Making the case for research data management, (2011); Van de Venter M., Pienaar H., Research data management in a developing country: a personal journey, International Journal of Digital Curation, 10, 2, pp. 33-47, (2015); Yoon A, Schultz T., Research Data Management Services in Academic Libraries in the US: A Content Analysis of Libraries, College & Research Libraries, 78, 7, (2017)","","","University of Nebraska-Lincoln","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85120311124"
"Hamad F.; Al-Fadel M.; Al-Soub A.","Hamad, Faten (57191645647); Al-Fadel, Maha (57191646029); Al-Soub, Aman (57212026201)","57191645647; 57191646029; 57212026201","Awareness of Research Data Management Services at Academic Libraries in Jordan: Roles, Responsibilities and Challenges","2021","New Review of Academic Librarianship","27","1","","76","96","20","16","10.1080/13614533.2019.1691027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075720395&doi=10.1080%2f13614533.2019.1691027&partnerID=40&md5=498d5405eccdc76a9d4abd7fc9bf9411","Library and Information Science Department, The University of Jordan, Amman, Jordan","Hamad F., Library and Information Science Department, The University of Jordan, Amman, Jordan; Al-Fadel M.; Al-Soub A.","Research data management services require new skills and collaboration among library staff to work with both researchers and end-users as they manage data going forward. However, there is an urgent need to increase awareness of the new trends about the partnership between research community and academic libraries, there is also a need for new research data management (RDM)-related skills among librarians. And therefore, this research aims at investigating requirements, roles and responsibilities as well as challenges of RDM services in academic libraries in Jordan. A questionnaire was developed and used to collect the required data from 21 academic libraries in Jordan. The results indicate high perception and awareness of libraries roles and responsibilities of RDM as well as requirements and challenges in academic libraries in Jordan to provide RDM services. It was noted that all factors such as job title, experience and library type had no effect on the results. ©, Published with license by Taylor & Francis Group, LLC. ©, Faten Hamad, Maha Al-Fadel and Aman Al-Soub.","academic libraries; data curation; library services; research data management","","","","","","","","pp. 311-320, (2012); ACRL research planning and review committee. Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 75, 6, pp. 294-302, (2014); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Auckland M., (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brewerton A., Re-skilling for research: Investigating the needs of researchers and how library staff can best support them, New Review of Academic Librarianship, 18, 1, pp. 96-110, (2012); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Brochu L., Burns J., Librarians and research data management–a literature review: Commentary from a senior professional and a new professional librarian, New Review of Academic Librarianship, 25, 1, pp. 49-58, (2019); Carlson J., Kneale R., Embedded librarianship in the research context: Navigating new waters, College & Research Libraries News, 72, 3, pp. 167-170, (2011); Chen H.L., Doty P., Mollman C., Niu X., Yu J.C., Zhang T., Library assessment and data analytics in the big data era: Practice and policies, Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community, (2015); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, Liber Quarterly, 24, 1, pp. 17-48, (2014); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Covert-Vail L., Collard S., New roles for new times: Research library services for graduate students, (2012); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library & Information Science Research, 38, 4, pp. 319-326, (2016); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services & Use, 36, 1-2, pp. 35-43, (2016); Fonseca A.J., Viator V.P., Escaping the island of lost faculty: Collaboration as a means of visibility, Collaborative Librarianship, 1, 3, (2009); Garritano J.R., Carlson J.R., A subject librarian’s guide to collaborating on e-science projects, Issues in Science and Technology Librarianship, No 57 (Spring 2009)., (2009); Giarlo M.J., Academic Libraries as Data Quality Hubs, Journal of Librarianship & Scholarly Communication, 1, 3, pp. 1-10, (2013); Gore S.A., E-science and data management resources on the web, Medical Reference Services Quarterly, 30, 2, pp. 167-177, (2011); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Jaguszewski J., Williams K., (2013); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2017); Larsen S., Riis C., Aarhus University Library: Restructuring a university’s library organisation, Bibliothek Forschung und Praxis, 36, 2, pp. 235-240, (2012); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); MacColl J., Library roles in university research assessment, Liber Quarterly, 20, 2, pp. 152-168, (2010); MacColl J., Jubb M., Supporting Research: Environments, Administration and Libraries, (2011); NIH Data Sharing Policy and Implementation Guidance, (2003); Newton M.P., Miller C.C., Bracke M.S., Librarian roles in institutional repository data set collecting: Outcomes of a research library task force, Collection Management, 36, 1, pp. 53-67, (2010); O'Brien L., Richardson J., Supporting research through partnership, Partnerships and new roles in the 21st-century academic library: Collaborating, embedding, and cross training for the future, pp. 191-212, (2015); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Posner M., No half measures: Overcoming common challenges to doing digital humanities in the library, Journal of Library Administration, 53, 1, pp. 43-52, (2013); Reznik-Zellen R.C., Adamick J., McGinty S., Tiers of research data support services, Journal of eScience Librarianship, 1, 1, pp. 27-35, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Tenopir C., Birch B., Allard S., Academic libraries and research data services, Current practices and plans for the future, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Vandegrift M., Varner S., Evolving in common: Creating mutually supportive relationships between libraries and the digital humanities, Journal of Library Administration, 53, 1, pp. 67-78, (2013); Vaughan K.T.L., Hayes B.E., Lerner R.C., McElfresh K.R., Pavlech L., Romito D., Morris E.N., Development of the research lifecycle model for library services, Journal of the Medical Library Association, 101, 4, (2013); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices, Program, 49, 4, pp. 382-407, (2015)","","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85075720395"
"Heinrichs B.; Preuß N.; Politze M.; Muller M.S.; Pelz P.F.","Heinrichs, Benedikt (57211110293); Preuß, Nils (58062822800); Politze, Marius (57195741179); Muller, Matthias S. (35249260000); Pelz, Peter F. (56005365800)","57211110293; 58062822800; 57195741179; 35249260000; 56005365800","Automatic General Metadata Extraction and Mapping in an HDF5 Use-case","2021","International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings","1","","","172","179","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146198087&partnerID=40&md5=c0248af7f4d41ea7194c5ef5b5163315","IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany; Chair of Fluid Systems, Technical University of Darmstadt, Darmstadt, Germany","Heinrichs B., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany; Preuß N., Chair of Fluid Systems, Technical University of Darmstadt, Darmstadt, Germany; Politze M., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany; Muller M.S., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany; Pelz P.F., Chair of Fluid Systems, Technical University of Darmstadt, Darmstadt, Germany","Extracting interoperable metadata from data entities is not an easy task. A method for this would need to extract non-interoperable metadata values first and then map the extracted metadata to some sensible representation. In the case of HDF5 files, metadata annotation is already an option, making it an easy target for extracting these non-interoperable metadata values. This paper describes a use-case, that utilizes this property to automatically annotate their data. However, the issue arises, that these metadata values are not reusable, due to their missing interoperability, and validatable since they do not follow any defined metadata schema. Therefore, this paper provides a solution for mapping the defined metadata values to interoperable metadata by extracting them first using a general metadata extraction pipeline and then proposing a method for mapping them. This method can receive a number of application profiles and creates interoperable metadata based on the best fit. The method is validated against the introduced use-case and shows promising results for other research domains as well. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Linked Data; Metadata Extraction; Metadata Generation; Metadata Mapping; Research Data Management","Computer software reusability; Extraction; Information management; Information systems; Information use; Linked data; Metadata; Data entities; Interoperable metadata; Linked datum; Meta-data extractions; Metadata annotations; Metadata generation; Metadata mappings; Metadata schema; Property; Research data managements; Mapping","","","","","","","JSON2RDF, (2019); De Meester Ben, Heyvaert Pieter, Delva Thomas, RDF Mapping Language (RML), (2021); Das S., Sundara S., Cyganiak R., R2RML: RDB to RDF mapping language, (2012); Gronewald M., Mund P., Bodenbrenner M., Fuhrmans M., Heinrichs B., Muller M. S., Pelz P. F., Marius P., Preuss N., Schmitt R. H., Stacker T., Mit AIMS zu einem Metadatenmanagement 4.0: FAIRe Forschungsdaten benötigen interoperable Metadaten, (2021); Harrow I., Balakrishnan R., Jimenez-Ruiz E., Jupp S., Lomax J., Reed J., Romacker M., Senger C., Splendiani A., Wilson J., Woollard P., Ontology mapping for semantically enabled applications, Drug Discovery Today, 24, 10, pp. 2068-2075, (2019); Heinrichs B., Politze M., Moving Towards a General Metadata Extraction Solution for Research Data with State-of-the-Art Methods, 12th International Conference on Knowledge Discovery and Information Retrieval, (2020); Iglesias E., Jozashoori S., Chaves-Fraga D., Collarana D., Vidal M.-E., SDM-RDFizer, Proceedings of the 29th ACM International Conference on Information & Knowledge Management, (2020); Kontokostas D., Knublauch H., Shapes Constraint Language (SHACL), (2017); Labra Gayo J., Prud'hommeaux E., Boneva I., Kontokostas D., Validating rdf data, Synthesis Lectures on the Semantic Web: Theory and Technology, 7, pp. 1-328, (2017); Ledvinka M., Kremen P., A comparison of object-triple mapping libraries, Semantic Web, 11, pp. 483-524, (2020); Mattmann C., Zitting J., Tika in Action, (2011); Perego A., Beltran A. G., Albertoni R., Cox S., Browning D., Winstanley P., Data Catalog Vocabulary (DCAT) - Version 2, (2020); Politze M., Claus F., Brenger B. D., Yazdi M. A., Heinrichs B., Schwarz A., How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment, European journal of higher education IT, 1, (2020); Prabhune A., Stotzka R., Sakharkar V., Hesser J., Gertz M., MetaStore: an adaptive metadata management framework for heterogeneous metadata models, Distributed and Parallel Databases, 36, 1, pp. 153-194, (2018); Souza D., Freire F., Freire C., Enhancing JSON to RDF Data Conversion with Entity Type Recognition, (2017); Importing Data using Active Data Shapes, (2021); Verborgh R., Taelman R., Ldflex: A read/write linked data abstraction for front-end web developers, The Semantic Web - ISWC 2020, pp. 193-211, (2020); OWL 2 Web Ontology Language Document Overview (Second Edition), (2012); Wilkinson M. D., Dumontier M., Aalbersberg I. J. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Bouwman J., Brookes A. J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo Chris T., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, (2016); Wood D., Lanthaler M., Cyganiak R., RDF 1.1 Concepts and Abstract Syntax, (2014)","","Cucchiara R.; Fred A.; Filipe J.","Science and Technology Publications, Lda","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","13th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021","25 October 2022 through 27 October 2022","Virtual, Online","181965","21843228","978-989758533-3","","","English","International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings","Conference paper","Final","","Scopus","2-s2.0-85146198087"
"Virkus S.; Garoufallou E.","Virkus, Sirje (6507680734); Garoufallou, Emmanouel (23666959600)","6507680734; 23666959600","Data science and its relationship to library and information science: a content analysis","2020","Data Technologies and Applications","54","5","","643","663","20","10","10.1108/DTA-07-2020-0167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092270099&doi=10.1108%2fDTA-07-2020-0167&partnerID=40&md5=a59d20219b76b5eb48655e5ba16e12d6","School of Digital Technologies, Tallinn University, Tallinn, Estonia; Department of Library Science, Archives and Information Systems, School of Social Sciences, International Hellenic University, Thessaloniki, Greece; Deltos Group, Thessaloniki, Greece","Virkus S., School of Digital Technologies, Tallinn University, Tallinn, Estonia; Garoufallou E., Department of Library Science, Archives and Information Systems, School of Social Sciences, International Hellenic University, Thessaloniki, Greece, Deltos Group, Thessaloniki, Greece","Purpose: The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective. Design/methodology/approach: Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective. Findings: A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related. Research limitations/implications: Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database. Originality/value: The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS. © 2020, Emerald Publishing Limited.","Content analysis; Data science; Education; Information science; Library science; Literature review","Data Science; Database systems; Knowledge management; Publishing; Content analysis; Design/methodology/approach; Education and training; Health science; Library and information science; Library science; Literature reviews; Science education; Science-data; Web of Science; Libraries","","","","","","","Agarwal R., Dhar V., Big data, data science, and analytics: the opportunity and challenge for IS research, Information Systems Research, 25, 3, pp. 443-448, (2014); Alluqmani A., Shamir L., Writing styles in different scientific disciplines: a data science approach, Scientometrics, 115, 2, pp. 1071-1085, (2018); Almugbel R., Hung L.H., Hu J., Almutairy A., Ortogero N., Tamta Y., Yeung K.Y., Reproducible Bioconductor workflows using browser-based interactive notebooks and containers, Journal of the American Medical Informatics Association, 25, 1, pp. 4-12, (2018); Amirian P., van Loggerenberg F., Lang T., Data science and analytics, Big Data in Healthcare, SpringerBriefs in Pharmaceutical Science and Drug Development, pp. 15-37, (2017); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: science librarians’ participation in data management at association of research libraries institutions, College and Research Libraries, 75, 4, pp. 557-574, (2014); Aristodemou L., Tietze F., The state-of-the-art on intellectual property analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data, World Patent Information, 55, pp. 37-51, (2018); Barbuti N., Caldarola T., Ferilli S., A graphic matching process for searching and retrieving information in digital libraries of manuscripts, Digital Libraries and Multimedia Archives. 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Park H.W., Leydesdorff L., Decomposing social and semantic networks in emerging “big data” research, Journal of Informetrics, 7, 3, pp. 756-765, (2013); Poulova P., Mikulecka J., Kozel T., Klimova B., Data science study program, 12th International Scientific Conference on Distance Learning in Applied Informatics (DIVAI), pp. 337-347, (2018); Qasim M.A., Ul Hassan S., Aljohani N.R., Lytras M.D., Human behavior analysis in the production and consumption of scientific knowledge across regions: a case study on publications in Scopus, Library Hi Tech, 35, 4, pp. 577-587, (2017); Rempel E.S., Barnett J., Durrant H., Public engagement with UK government data science: propositions from a literature review of public engagement on new technologies, Government Information Quarterly, 35, 4, pp. 569-578, (2018); Rentier B., Open science: a revolution in sight?, Interlending and Document Supply, 44, 4, pp. 155-160, (2016); Saar-Tsechansky M., Editor’s comments: the business of business data science in IS journals, MIS Quarterly, 39, 4, (2015); Saltz J., Shamshurin I., Connors C., Predicting data science sociotechnical execution challenges by categorizing data science projects, Journal of the Association for Information Science and Technology, 68, 12, pp. 2720-2728, (2017); Schweighofer E., The role of AI & law in legal data science, Legal Knowledge and Information Systems, JURIX 2015: The Twenty-Eight Annual Conference, pp. 191-192, (2015); Sexton A., Shepherd E., Duke-Williams O., Eveleigh A., A balance of trust in the use of government administrative data, Archival Science, 17, 4, pp. 305-330, (2017); Sheble L., Research synthesis methods and library and information science: shared problems, limited diffusion, Journal of the Association for Information Science and Technology, 67, 8, pp. 1990-2008, (2016); Shera J.H., Documentation: its scope and limitations, The Library Quarterly, 21, 1, pp. 13-26, (1951); Si L., Zhuang X., Xing W., Guo W., The cultivation of scientific data specialists: development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Song I.Y., Zhu Y., Big data and data science: opportunities and challenges of iSchools, Journal of Data and Information Science, 2, 3, pp. 1-18, (2017); Song P., Zheng C., Zhang C., Yu X., Data analytics and firm performance: an empirical study in an online B2C platform, Information and Management, 55, 5, pp. 633-642, (2018); Spruit M., Lytras M., Applied data science in patient-centric healthcare: adaptive analytic systems for empowering physicians and patients, Telematics and Informatics, 35, 4, pp. 643-653, (2018); Stanton J.M., Palmer C.L., Blake C., Allard S., Interdisciplinary data science education, Special Issues in Data Management (ACS Symposium Series, Vol. 1110), (2012); Sundararajan A., Provost F., Oestreicher-Singer G., Aral S., Information in digital, economic, and social networks, Information Systems Research, 24, 4, pp. 883-905, (2013); Tang R., Sae-Lim W., Data science programs in US higher education: an exploratory content analysis of program description, curriculum structure, and course focus, Education for Information, 32, 3, pp. 269-290, (2016); Thirathon U., Wieder B., Matolcsy Z., Ossimitz M.L., Big data, analytic culture and analytic-based decision making evidence from Australia, Procedia Computer Science, 121, pp. 775-783, (2017); Ting I., Developing analytic talent: becoming a data scientist, Online Information Review, 39, 2, (2015); Umachandran K., Ferdinand-James D.S., Affordances of data science in agriculture, manufacturing, and education, Privacy and Security Policies in Big Data, pp. 14-40, (2017); Virkus S., Garoufallou E., Data science from a library and information science perspective, Data Technologies and Applications, 53, 4, pp. 422-441, (2019); Virkus S., Garoufallou E., Data science from a perspective of computer science, Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, 1057, pp. 209-219, (2019); Waltl B., Zec M., Matthes F., Rotolo A., A data science environment for legal texts, Legal Knowledge and Information Systems. JURIX 2015: The Twenty-Eight Annual Conference, pp. 193-194, (2015); Wang K., Twinning data science with information science in schools of library and information science, Journal of Documentation, 74, 6, pp. 1243-1257, (2018); Wilson T.D., Review of: Kelleher, John D. and Tierney, Brendan. Data science. Cambridge, MA: MIT Press, 2018, Information Research, 23, 2, (2018); Xia W., Wan Z., Yin Z., Gaupp J., Liu Y., Clayton E.W., Malin B.A., It’s all in the timing: calibrating temporal penalties for biomedical data sharing, Journal of the American Medical Informatics Association, 25, 1, pp. 25-31, (2017); Yousafzai A., Chang V., Gani A., Noor R.M., Directory-based incentive management services for ad-hoc mobile clouds, International Journal of Information Management, 36, 6, pp. 900-906, (2016); Zhou X., Li W., Arundel S.T., A spatio-contextual probabilistic model for extracting linear features in hilly terrains from high-resolution DEM data, International Journal of Geographical Information Science, 33, 4, pp. 666-686, (2018); Zoltan G., Big data, science, causality, Informacios Tarsadalom, 16, 2, (2016)","S. Virkus; School of Digital Technologies, Tallinn University, Tallinn, Estonia; email: sirje.virkus@tlu.ee","","Emerald Publishing","","","","","","25149288","","","","English","Data Technol. Appl.","Article","Final","","Scopus","2-s2.0-85092270099"
"Schöpfel J.; Azeroual O.","Schöpfel, Joachim (14619562900); Azeroual, Otmane (57201378256)","14619562900; 57201378256","Rewarding Research Data Management","2021","The Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021","","","","446","450","4","2","10.1145/3442442.3451367","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107646060&doi=10.1145%2f3442442.3451367&partnerID=40&md5=de3e67d164141ec9f201f12316183680","GERiiCO Laboratory, University of Lille, Villeneuve-d Ascq, France; German Center for Higher Education Research and, Science Studies (DZHW), Berlin, Germany","Schöpfel J., GERiiCO Laboratory, University of Lille, Villeneuve-d Ascq, France; Azeroual O., German Center for Higher Education Research and, Science Studies (DZHW), Berlin, Germany","In the context of open science, good research data management (RDM), including data sharing and data reuse, has become a major goal of research policy. However, studies and monitors reveal that open science practices are not yet widely mainstream. Rewards and incentives have been suggested as a solution, to facilitate and accelerate the development of open and transparent RDM. Based on relevant literature, our paper provides a critical analysis of three main issues: what should be rewarded and incentivized, who should be rewarded, and what kind of rewards and incentives should be used? Concluding the analysis, we ask if it is really necessary and appropriate to consider RDM as an individual (behavioral) issue, as the main challenges are elsewhere, not personal, but technological, institutional and financial. © 2021 ACM.","incentives; open science; research data management; rewards","Information management; World Wide Web; Critical analysis; Data reuse; Good research; Open science; Research data managements; Research policies; Data Sharing","","","","","National Research Foundation, NRF; Deutsche Forschungsgemeinschaft, DFG; Bundesministerium für Bildung und Forschung, BMBF","The verifiability of research results through re-analyzes is one of the formalized criteria of good scientific practice that were developed by the research community. In Germany, for instance, enabling reuse of research data by transferring it to suitable data repositories are part of the funding guidelines of the National Research Foundation (DFG) and the Federal Ministry of Education and Research (BMBF). The consistent implementation of this obligation depends on the scientific discipline. Data sharing enables scientifically valuable feedback processes, so that the data producers can increase the quality of their data and the effectiveness of their data collection and analysis. Moreover, the researchers’ results become better known through external data evaluation and thus also their reputation.","Allen C., Mehler D.M.A., Open Science Challenges, Benefits and Tips in Early Career and Beyond. PLOS Biology, 17, 5, (2019); Azeroual O., Herbig N., Mapping and Semantic Interoperability of the German RCD Data Model with the Europe-wide Accepted CERIF. Information Services and Use, 40, 1-2, pp. 87-113, (2020); Besancon L., Peiffer-Smadja N., Segalas C., Jiang H., Masuzzo P., Smout C., Billy E., Maxime Deforet V., Leyrat C., 2020 Open Science Saves Lives: Lessons from the COVID-19 Pandemic. BioRxiv, 8, 13, (2020); Borgman C.L., 2012. The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., 2015. Big Data, Little Data, No Data : Scholarship in the Networked World; COAR, (2013); Colledge L., Snowball Metrics Recipe Book: Standardised research metrics-by the sector, for the sector, Snowball Metrics, (2017); Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2017); San Francisco Declaration on Research Assessment, (2012); Wilkinson M.D., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3, 1, (2016); Hrynaszkiewicz I., Publishers? Responsibilities in Promoting Data Quality and Reproducibility, Good Research Practice in Non-Clinical Pharmacology and Biomedicine. Handbook of Experimental Pharmacology, 257, pp. 319-348, (2019); Joseph H., 2021 Building Momentum to Realign Incentives to Support Open Science, Data Intelligence 3, 1, 2021, pp. 71-78; Malingre M., Mignon M., Pierre C., Serres A., 2019 Construction(s) et Contradictions des Données de Recherche en SHS. Recherche D?information, Document et Web Sémantique, 2, 1, pp. 1-21, (2019); Man Chang C., Aernoudts R.H.R.M., Towards Scholarly Communication 2.0: Peer-To-Peer Review & Ranking in Open Access Preprint Repositories. Social Science Research Network, (2010); MESRI 2018. Plan National Pour la Science Ouverte. Paris: Ministère de L?Enseignement Supérieur, de la Recherche et de L?Innovation; Ocarroll C., Rentier B., Cabello Valdes C., Esposito F., Kaunismaa E., Maas K., Metcalfe J., McAllister D., Vandevelde K., Evaluation of Research Careers Fully Acknowledging Open Science Practices: Rewards, Incentives And/or Recognition for Researchers Practicing Open Science, (2017); OECD, (2007); Pagliaro M., 2021. Purposeful Evaluation of Scholarship in the Open Science Era; Rowhani-Farid A., Allen M., Barnett A.G., 2017 What Incentives Increase Data Sharing in Health and Medical Research? A Systematic Review. Research Integrity and Peer Review, 2, 1, (2017); Schopfel J., Prost H., Rebouillat V., 2017 Research Data in Current Research Information Systems, Procedia Computer Science, 106, pp. 305-320, (2017); Spitschan M., Schmidt M.H., Blume C., 2020 Transparency and open science principles in reporting guidelines in sleep research and chronobiology journals, Wellcome Open Research, 5, 172, (2020); Stieglitz S., Wilms K., Mirbabaie M., Hofeditz L., Brenger B., Lopez A., Rehwald S., When are researchers willing to share their data?-Impacts of values and uncertainty on open data in academia, PLOS. ONE, 15, 7, (2020); Suhr B., Dungl J., Stocker A., 2020Search, reuse and sharing of research data in materials science and engineering-A qualitative interview study, PLOS. ONE, 15, 9, (2020); Tatum C., What is the evaluative object of Open Science?, 22nd Nordic Workshop on Bibliometrics and Research Policy, Helsinki, 09 November 2017, (2017); Tatum C., De Rijcke S., The (mis)alignment of Open Science and research evaluation: Addressing complexity with existing resources and contextsensitive evaluation, EuroCRIS Strategic Membership Meeting Autumn 2018 (Warsaw University of Technology, Warsaw, Poland, Nov 26-28, 2018, (2018); Vicente-Saez R., Gustafsson R., Van Den Brande L., 2020. The Dawn of An Open Exploration Era: Emergent Principles and Practices of Open Science and Innovation of University Research Teams in A Digital World. Technological Forecasting and Social Change 156, (2020)","J. Schöpfel; GERiiCO Laboratory, University of Lille, Villeneuve-d Ascq, France; email: joachim.schopfel@univ-lille.fr","","Association for Computing Machinery, Inc","Amazon; et al.; Facebook; FINVOLUTION; Microsoft Research; Pinterest","30th World Wide Web Conference, WWW 2021","19 April 2021 through 23 April 2021","Ljubljana","169373","","978-145038313-4","","","English","Web Conf. - Companion World Wide Web Conf., WWW","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85107646060"
"Biernacka K.; Helbig K.; Buchholz P.","Biernacka, Katarzyna (57222904641); Helbig, Kerstin (57188698727); Buchholz, Petra (57222902650)","57222904641; 57188698727; 57222902650","Adaptable methods for training in research data management","2021","Data Science Journal","20","1","14","","","","1","10.5334/dsj-2021-014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104175624&doi=10.5334%2fdsj-2021-014&partnerID=40&md5=74283ef09218a0a454432c7fd6279e40","Humboldt-Universität zu Berlin, Germany; Freie Universität Berlin, Germany","Biernacka K., Humboldt-Universität zu Berlin, Germany; Helbig K., Humboldt-Universität zu Berlin, Germany; Buchholz P., Freie Universität Berlin, Germany","The management of research data has become an essential aspect of good scientific practice. Education in research data management is, however, scarce. The low number of trainers can be attributed on the one hand to a lack of educational paths. On the other hand, qualification opportunities for academics who have already completed their studies and are in employment are missing. Within the research project FDMentor a Train-the-Trainer programme was therefore developed to teach potential multipliers of research data management, and at the same time impart basic didactic knowledge. The resulting concept was created, in addition to freely re-usable materials, to support researchers and research support staff in passing on this knowledge. In addition, the generic development and free licensing of the concept enables transferability to other thematic contexts, such as Open Access or Open Science. © 2021 The Author(s).","Curriculum development; Methods; Research data management; Train-the-Trainer; Training","Computer applications; Computer science; A-train; Good scientific practices; Open science; Research data; Research data managements; Research support; Information management","","","","","","","Arnold R., Wie man lehrt, ohne zu belehren: 29 Regeln für eine kluge Lehre, (2018); Biernacka K, Bierwirth M, Buchholz P, Dolzycka D, Helbig K, Neumann J, Odebrecht C, Wiljes C, Wuttke U., Train-the-Trainer Concept on Research Data Management, (2020); Biernacka K, Buchholz P, Dolzycka D, Helbig K, Neumann J, Odebrecht C, Wiljes C, Wuttke U., Train-the-Trainer Konzept zum Thema Forschungsdatenmanagement, (2020); Biernacka K, Dolzycka D, Helbig K, Buchholz P., Train-the-Trainer Konzept zum Thema Forschungsdatenmanagement, (2018); Branson RK, Rayner GT, Cox JL, Furman JP, King FJ, Hannum WH., Interservice Procedures for Instructional Systems Development: Executive Summary and Model, (1975); DINI/nestor-AG Forschungsdaten; Dolzycka D., Ergebnisse und Erkenntnisse aus Pilotschulungen eines Train-the-Trainer-Programms zum Thema Forschungsdatenmanagement, o-bib, 7, 2, pp. 1-19, (2020); Dolzycka D, Biernacka K, Helbig K, Buchholz P., Train-the-Trainer Konzept zum Thema Forschungsdatenmanagement, (2019); Doring KW., Handbuch Lehren und Trainieren in der Weiterbildung, (2008); Weiterbildung. Kompetenzaufbau im Kontext FDM, (2020); Materialkatalog zum Forschungsdatenmanagement, (2018); FDNext, (2020); Gold A., Guter Unterricht: Was wir wirklich darüber wissen, (2015); Gross H., Munterbrechungen. 22 aktivierende Auflockerungen für Seminare und Sitzungen, (2010); Gross H., Munterrichtsmethoden. 22 weitere aktivierende Methoden für die Seminarpraxis, (2014); Gross H., Kleiner Hebel – Große Wirkung. Teil 1: 22 didaktische Kniffe, (2017); Gross H., Arbeitsheft Nr. 1: Das Lernen auslösen; Gross H, Boden B, Boden N., Munterbrechungen. 22 aktivierende Auflockerungen für Seminare und Sitzungen, (2012); Gross H, Boden B, Boden N., Munterrichtsmethoden. 22 aktivierende Lehrmethoden für die Seminarpraxis, (2012); Helbig K, Biernacka K, Buchholz P, Dolzycka D, Hartmann N, Hartmann T, Hiemenz B, Jacob B, Kuberek M, Weiss N, Dreyer M., Lösungen und Leitfäden für das institutionelle Forschungsdatenmanagement, o-bib, 6, 3, pp. 21-39, (2019); Mons B, Neylon C, Velterop J, Dumontier M, da Silva Santos L, Wilkinson M., Cloudy, Increasingly FAIR; Revisiting the FAIR Data Guiding Principles for the European Open Science Cloud, Information Services & Use, 37, 1, pp. 49-56, (2017); Munterrichtsmethode 46: Stichwortsalat, (2016); Siebert H., Didaktisches Handeln in der Erwachsenenbildung: Didaktik aus konstruktivistischer Sicht, (2012); Szepansky W-P., Souverän Seminare leiten, (2017); 2020 UAG Schulungen/Fortbildungen","K. Biernacka; Humboldt-Universität zu Berlin, Germany; email: katarzyna.biernacka@huberlin.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85104175624"
"Uiterwaal F.; Niccolucci F.; Krauwer S.; Hollander H.; Admiraal F.; Romary L.; Bruseker G.; Meghini C.; Edmond J.; Hedges M.","Uiterwaal, Frank (57412316600); Niccolucci, Franco (37662032900); Krauwer, Steven (57514894000); Hollander, Hella (57194034405); Admiraal, Femmy (57411776100); Romary, Laurent (22942401100); Bruseker, George (57189099266); Meghini, Carlo (57191963474); Edmond, Jennifer (56406953500); Hedges, Mark (24343293100)","57412316600; 37662032900; 57514894000; 57194034405; 57411776100; 22942401100; 57189099266; 57191963474; 56406953500; 24343293100","FROM DISPARATE DISCIPLINES TO UNITY IN DIVERSITY: HOW THE PARTHENOS PROJECT HAS BROUGHT EUROPEAN HUMANITIES RESEARCH INFRASTRUCTURES TOGETHER","2021","International Journal of Humanities and Arts Computing","15","1-2","A9","101","116","15","0","10.3366/IJHAC.2021.0264","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122836930&doi=10.3366%2fIJHAC.2021.0264&partnerID=40&md5=3ba0313bc8ba227fa089312c3fb6436f","","","Since the first ESFRI roadmap in 2006, multiple humanities Research Infrastructures (RIs) have been set up all over the European continent, supporting archaeologists (ARIADNE), linguists (CLARIN-ERIC), Holocaust researchers (EHRI), cultural heritage specialists (IPERION-CH) and others. These examples only scratch the surface of the breadth of research communities that have benefited from close cooperation in the European Research Area. While each field developed discipline-specific services over the years, common themes can also be distinguished. All humanities RIs address, in varying degrees, questions around research data management, the use of standards and the desired interoperability of data across disciplinary boundaries. This article sheds light on how cluster project PARTHENOS developed pooled services and shared solutions for its audience of humanities researchers, RI managers and policymakers. In a time where the convergence of existing infrastructure is becoming ever more important - with the construction of a European Open Science Cloud as an audacious, ultimate goal - we hope that our experiences inform future work and provide inspiration on how to exploit synergies in interdisciplinary, transnational, scientific cooperation. © Edinburgh University Press 2021","Best practices; Computational methods; Digital humanities; Dissemination; European projects; Horizon 2020; Interdisciplinarity; International cooperation; Interoperability; Research data management; Research infrastructure; Standards; Training","","","","","","Horizon 2020 Framework Programme, H2020, (654119)","","Sula C.A., Hill H., The early history of digital humanities, Proceedings of Digital Humanities 2017, pp. 349-35, (2017); Mahey M., Et al., Open a GLAMLab. Digital cultural heritage innovation labs, (2019); Foka A, Et al., Beyond humanities qua digital: Spatial and material development for digital research infrastructures in HumlabX, Digital Scholarship in the Humanities, 33, pp. 264-278, (2018); Unsworth J., Et al., Our cultural commonwealth: the report of the American Council of Learned Societies Commission on cyberinfrastructure for the humanities & social sciences, (2006); European Research Area (ERA); European roadmap for Research Infrastructures-Report, (2006); Website DARIAH; Website CLARIN-ERIC; Website CENDARI; Website EHRI; Website ARIADNE; Website IPERION-CH; Leathem C., Survey and analysis of humanities and social science research at the science academies and related research institutes of Europe, Facing the future: European Research Infrastructures for the humanities and social sciences, pp. 39-43, (2014); Lauer G., Challenges for the humanities: digital infrastructures, Facing the future, pp. 35-38; Cockburn A., Writing effective use cases, (1999); Pietrzak M., Paliszkiewicz J., Framework of strategic learning: the PDCA cycle, Management, 2, pp. 149-161, (2015); Drude S., Et al., D2.1 Report on user requirements, pp. 117-118, (2016); Heidorn P. B., Shedding light on the dark data in the long tail of science, Library Trends, 2, pp. 280-299, (2015); Wilkinson M. D., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Hollander H., Et al., PARTHENOS guidelines to FAIRify data management and make data reusable, (2019); Website ISO standards catalogue on language resource management; EAD project website; Bruseker G., Et al., Cultural heritage data management: the role of formal ontology and CIDOC CRM, Heritage and archaeology in the digital age: Acquisition, curation, and dissemination of spatial cultural heritage data, pp. 93-131, (2017); Illmayer K., Puren M., How to work together successfully with e-Humanities and eHeritage Research Infrastructures, PARTHENOS eHumanities and eHeritage webinar series; Romary L., Et al., D4.1 Standardization survival kit, (2016); Romary L., Et al., D4.2 Report on standardization, (2017); Tanackovic S. F., Et al., The meaning of interoperability and its implications for archival institutions: challenges and opportunities in Croatia, Finland and Sweden, Information Research, 1, (2017); Holmes M., Whatever happened to interchange?, Digital Scholarship in the Humanities, 1, pp. 63-68, (2017); The vocabulary of a technical infrastructure and a human network borrows heavily from EHRI's mission, European Holocaust Research Infrastructure-mission statement; Nowiskie B., #Alt-Academy: 01. Alternative academic careers for humanities scholars, (2011); Rockwell G., Sinclair S., Challenges for the humanities: digital infrastructures, Digital humanities pedagogy: practices, principles and politics, pp. 177-212, (2012); Website PARTHENOS on-line training suite; Platform DARIAH teach; CLARIN VideoLectures portal; Antonjevic S., Amongst digital humanists: an ethnographic study of digital knowledge production, (2015); Schroeder R., Fry J., deBeer J. A., E-research infrastructures and scientific communication, Proceedings of the IATUL conferences, (2007); Speck R., Et al., D8.2 Initial communication plan, (2015); Blanke T., Kristel C., Romary L., Crowds for clouds: Recent trends in humanities research infrastructures, Cultural heritage infrastructures in digital humanities, pp. 48-62, (2017); Edmond J., Morselli F., CENDARI D2.4 Sustainability plan; Guidelines for Web-based data publication in archaeology; Sustainability of digital formats: planning for Library of Congress collections; Bannour I., Et al., CRMCR - a CIDOC-CRM extension for supporting semantic interoperability in the conservation and restoration domain, 3rd Digital Heritage International Congress (DigitalHERITAGE), (2018); Ronzino P., Felicetti A., CRMBA and CRMarchaeo models harmonization, 34th Joint Meeting of CIDOC CRM SIG and ISO/TC46/SC4/WG9, (2015)","","","Edinburgh University Press","","","","","","17538548","","","","English","Int. J. Humanit. Arts Comput.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85122836930"
"Abduldayan F.J.; Abifarin F.P.; Oyedum G.U.; Alhassan J.A.","Abduldayan, Fatimah Jibril (57194235098); Abifarin, Fasola Petunola (56969184600); Oyedum, Georgina Uchey (37662134600); Alhassan, Jibril Attahiru (55557695700)","57194235098; 56969184600; 37662134600; 55557695700","Research data management practices of chemistry researchers in federal universities of technology in Nigeria","2021","Digital Library Perspectives","37","1","","70","90","20","5","10.1108/DLP-06-2020-0051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099714667&doi=10.1108%2fDLP-06-2020-0051&partnerID=40&md5=6f187fa2a42202a5224bd78836897862","Department of Library Information Technology, Federal University of Technology, Minna, Niger State, Nigeria","Abduldayan F.J., Department of Library Information Technology, Federal University of Technology, Minna, Niger State, Nigeria; Abifarin F.P., Department of Library Information Technology, Federal University of Technology, Minna, Niger State, Nigeria; Oyedum G.U., Department of Library Information Technology, Federal University of Technology, Minna, Niger State, Nigeria; Alhassan J.A., Department of Library Information Technology, Federal University of Technology, Minna, Niger State, Nigeria","Purpose: The purpose of this study was to understand the research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Appropriate research data management practice ensures that research data are available for reuse by secondary users, and research findings can be verified and replicated within the scientific community. A poor research data management practice can lead to irrecoverable data loss, unavailability of data to support research findings and lack of trust in the research process. Design/methodology/approach: An exploratory research technique involving semi-structured, oral and face-to-face interview is used to gather data on research data management practices of chemistry researchers in Nigeria. Interview questions were divided into four major sections covering chemistry researchers’ understanding of research data, experience with data loss, data storage method and backup techniques, data protection, data preservation and availability of data management plan. Braun and Clarke thematic analysis approach was adapted, and the Provalis Qualitative Data Miner (version 5) software was used for generating themes and subthemes from the coding framework and for presenting the findings. Findings: Findings revealed that chemistry researchers in Nigeria have a good understanding of the concept of research data and its importance to research findings. Chemistry researchers have had several experiences of irrecoverable loss of data because of poor choice of storage devices, back-up methods and weak data protection systems. Even though the library was agreed as the most preferred place for long-term data preservation, there is the issue of trust and fear of loss of ownership of data to unauthorized persons or party. No formal data management plan is used while conducting their scientific research. Research limitations/implications: The research focused on research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Although the findings of the study are similar to perceptions and practices of researchers around the world, it cannot be used as a basis for generalization across other scientific disciplines. Practical implications: This study concluded that chemistry researchers need further orientation and continuous education on the importance and benefits of appropriate research data management practice. The library should also roll out research data management programs to guide researchers and improve their confidence throughout the research process. Social implications: Appropriate research data management practice not only ensures that the underlying research data are true and available for reuse and re-validation, but it also encourages data sharing among researchers. Data sharing will help to ensure better collaboration among researchers and increased visibility of the datasets and data owners through the use of standard data citations and acknowledgements. Originality/value: This is a qualitative and in-depth study of research data management practices and perceptions among researchers in a particular scientific field of study. © 2020, Emerald Publishing Limited.","Abubakar Tafawa Balewa University Bauchi; Chemistry researcher (chemists); Federal universities of technology; Federal University of Technology Akure; Federal University of Technology Minna; Federal University of Technology Owerri; Modibbo Adama University of Technology Yola; Nigeria; Research; Research data; Research data management","Data Sharing; Privacy by design; Virtual storage; Continuous educations; Design/methodology/approach; Exploratory research; Face-to-face interview; Research data managements; Scientific community; Scientific discipline; Scientific researches; Information management","","","","","Central University of Technology, CUT","This study was funded by thesis grant offered by the Federal University of Technology, Minna, Nigeria.","Abduldayan F.J., Dang T.L., Karemani A., Obadia S.B., The role of academic libraries in enhancing workflow in African universities, CEUR Workshop Proceedings, 1830, pp. 158-163, (2016); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Borghi J.A., Van Gulick A.E., Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers, PLoS One, 13, 7, (2018); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2006); Buys C.M., Shaw P.L., Data management practices across an institution: survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chiware E., Mathe Z., Academic libraries’ role in research data management services: a South African perspective, South Africa Journal of Libraries and Information Science, 81, 2, (2015); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Dudovskiy J., Research methodology, (2005); FloresJasonMorganNatsuko J.R., Ece T., Libraries and the research data management landscape, the process of discovery, pp. 82-102, (2015); Gonzalez A., Peres-Neto P.R., Act to staunch loss of research data, (2015); Helmenstine A.M., Why study chemistry?, (2016); JonesGuy S., Pickton M., Research data management for libraries, (2013); Kuula A., Borg S., Open access to and reuse of research data: the state of the art in Finland, (2008); McLure M., Allison V.L., Catherine L.C., Beth O., Data curation: a study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Piracha H.A., Ameen K., Research data management practices of faculty members, Pakistan Journal of Information Management and Libraries (PJIM and L), 20, pp. 60-75, (2018); Raju R., Schombee L., Research support through the lens of transformation inacademic libraries with reference to the case of Stellenbosch university libraries, South African Journal of Library and Information Science, (2013); Sewerin C., Dearborn D., Henshilwood A., Spence M., Zahradnik T., Research data management faculty practices: a Canadian perspective, Proceedings of the IATUL Conferences, (2014); Smith P.L., Exploring the data management and curation practices of scientists in research labs within a research university, (2014); Tammaro A.M., Matusiak K.K., Sposito F.A., Casarosa V., Data curator’s roles and responsibilities: an international perspective, Libri, 69, 2, pp. 89-104, (2018); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, pp. 1-18, (2017); Van Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon University, Journal of Librarianship and Scholarly Communication, 3, 3, (2015); Abduldayan F.J., Research data management: the Nigerian perspectives, (2020); Qualitative data analysis software (version 5), (2018)","F.J. Abduldayan; Department of Library Information Technology, Federal University of Technology, Minna, Nigeria; email: fj.dayan@futminna.edu.ng","","Emerald Group Holdings Ltd.","","","","","","20595816","","","","English","Digit. Library Perspect.","Article","Final","","Scopus","2-s2.0-85099714667"
"Denker M.; Grün S.; Wachtler T.; Scherberger H.","Denker, Michael (8362049300); Grün, Sonja (57207523382); Wachtler, Thomas (6602841638); Scherberger, Hansjörg (6507913380)","8362049300; 57207523382; 6602841638; 6507913380","Reproducibility and efficiency in handling complex neurophysiological data","2021","Neuroforum","27","1","","27","34","7","2","10.1515/nf-2020-0041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100183550&doi=10.1515%2fnf-2020-0041&partnerID=40&md5=d7c1f36bbf2790e06b7a48f0815edeb3","Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, Kellnerweg 4, Göttingen, 37077, Germany; Department of Biology and Psychology, University of Goettingen, Goettingen, Germany; Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany; Department Biologie II, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany","Denker M., Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Grün S., Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany, Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany; Wachtler T., Department Biologie II, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; Scherberger H., Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, Kellnerweg 4, Göttingen, 37077, Germany, Department of Biology and Psychology, University of Goettingen, Goettingen, Germany","Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity. © 2021 De Gruyter. All rights reserved.","FAIR; NFDI; Open data; Research data management; Systems neuroscience","data integrity; electrophysiology; neuroscience; practice guideline; publishing; reproducibility; review; workflow","","","","","Helmholtz School; Horizon 2020 Framework Programme, H2020, (945539); Deutsche Forschungsgemeinschaft, DFG, (CRC889, FOR1847); Bundesministerium für Bildung und Forschung, BMBF, (01GQ1903); Helmholtz Association","Research funding: This study is supported by LMUexcellent, Helmholtz Association, European Union’s Horizon 2020 Framework Programme under grant no. 945539 (Human Brain Project SGA3), Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), the German Federal Ministry of Education and Research (BMBF 01GQ1903), and the German Research Foundation (CRC889, FOR1847). ","Bower M.R., Stead M., Brinkmann B.H., Dufendach K., Worrell G.A., Metadata and annotations for multi-scale electrophysiological data, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2811-2814, (2009); Brochier T., Zehl L., Hao Y., Duret M., Sprenger J., Denker M., Grun S., Riehle A., Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task, Sci. Data, 5, (2018); Buccino A.P., Hurwitz C.L., Garcia S., Magland J., Siegle J.H., Hurwitz R., Hennig M.H., SpikeInterface, a unified framework for spike sorting, eLife, 9, (2020); Denker M., Grun S., Designing workflows for the reproducible analysis of electrophysiological data, Brain-Inspired Computing, pp. 58-72, (2016); Garcia S., Guarino D., Jaillet F., Jennings T., Propper R., Rautenberg P.L., Rodgers C.C., Sobolev A., Wachtler T., Yger P., Et al., Neo: An object model for handling electrophysiology data in multiple formats, Front. Neuroinf., 8, (2014); Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Flandin G., Ghosh S.S., Glatard T., Halchenko Y.O., Et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci. Data, 3, (2016); Grewe J., Wachtler T., Benda J., A bottom-up approach to data annotation in neurophysiology, Front. Neuroinf., 5, (2011); Hines M.L., Morse T., Migliore M., Carnevale N.T., Hines M.L., ModelDB: A database to support computational neuroscience, J. Comput. Neurosci., 17, pp. 7-11, (2004); Imam F., Larson S., Grethe J., Gupta A., Bandrowski A., Martone M., Development and use of ontologies inside the neuroscience information framework: A practical approach, Front. Genet., 3, (2012); Papez V., Moucek R., Data and metadata models in electrophysiology domain: Separation of data models into semantic hierarchy and its integration into EEGBase, 2013 IEEE International Conference on Bioinformatics and Biomedicine, pp. 539-543, (2013); Pernet C.R., Appelhoff S., Gorgolewski K.J., Flandin G., Phillips C., Delorme A., Oostenveld R., EEG-BIDS, an extension to the brain imaging data structure for electroencephalography, Sci. Data, 6, (2019); Plesser H.E., Reproducibility vs. Replicability: A brief history of a confused terminology, Front. Neuroinf., 11, (2018); Reimer M.L., Bangalore L., Waxman S.G., Tan A.M., Core principles for the implementation of the neurodata without borders data standard, J. Neurosci. Methods, (2020); Stoewer A., Kellner C.J., Benda J., Wachtler T., Grewe J., File format and library for neuroscience data and metadata, Front. Neuroinform. Conference Abstract: Neuroinformatics 2014, (2014); Teeters J.L., Godfrey K., Young R., Dang C., Friedsam C., Wark B., Asari H., Peron S., Li N., Peyrache A., Et al., Neurodata without borders: Creating a common data format for neurophysiology, Neuron, 88, pp. 629-634, (2015); Teeters J.L., Harris K.D., Millman K.J., Olshausen B.A., Sommer F.T., Data sharing for computational neuroscience, Neuroinformatics, 6, pp. 47-55, (2008); Tripathy S.J., Savitskaya J., Burton S.D., Urban N.N., Gerkin R.C., Neuroelectro: A window to the world's neuron electrophysiology data, Front. Neuroinf., 8, (2014); Wachtler T., Bauer P., Denker M., Grun S., Hanke M., Klein J., Oeltze-Jafra S., Ritter P., Rotter S., Scherberger H., Et al., NFDI-Neuro: Building a community for neuroscience research data management in Germany, Neuroforum, (2021); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Zehl L., Jaillet F., Stoewer A., Grewe J., Sobolev A., Wachtler T., Brochier T.G., Riehle A., Denker M., Grun S., Handling metadata in a neurophysiology laboratory, Front. Neuroinf., 10, (2016)","H. Scherberger; Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, Göttingen, Kellnerweg 4, 37077, Germany; email: hscherb@gwdg.de","","De Gruyter Open Ltd","","","","","","09470875","","","","English","Neuroforum","Review","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85100183550"
"Motoki M.; Sone H.; Suganuma T.; Oriuchi S.","Motoki, Masakazu (56223034100); Sone, Hideaki (7102062675); Suganuma, Takuo (7102673640); Oriuchi, Shinji (57773452200)","56223034100; 7102062675; 7102673640; 57773452200","Results of Research Data Management Survey at Tohoku University","2021","Proceedings - 2021 10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021","","","","277","282","5","0","10.1109/IIAI-AAI53430.2021.00048","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133178956&doi=10.1109%2fIIAI-AAI53430.2021.00048&partnerID=40&md5=a03159d49062e1d079d6a99f213ba070","Information Synergy Organization, Tohoku University, Sendai, Japan","Motoki M., Information Synergy Organization, Tohoku University, Sendai, Japan; Sone H., Information Synergy Organization, Tohoku University, Sendai, Japan; Suganuma T., Information Synergy Organization, Tohoku University, Sendai, Japan; Oriuchi S., Information Synergy Organization, Tohoku University, Sendai, Japan","Appropriate design of organizational RDM (research data management) environment is required for each institution in accordance with social demands such as promotion of open science and research fairness. We conducted a survey on research data management using the questionnaire template published on AXIES (Academic eXchange for Information Environment and Strategy) in order to collect data that provide a starting point to understand Tohoku university researcher's needs and concerns regarding research data management. This paper will report the result of the survey at Tohoku university. © 2021 IEEE.","AXIES; organizational RDM environment; questionnaire; survey","Environmental management; Information management; Academic exchange for information environment and strategy; Appropriate designs; Information environment; Information strategy; Management environments; Organizational research; Organizational research data management environment; Questionnaire; Research data managements; Tohoku University; Surveys","","","","","","","Aoki T., Funamori M., Yuuki K., Miyamoto T., Nishimura K., Design and implementation of questionaire to researchers on research data management, IPSJ SIG Technical Report","M. Motoki; Information Synergy Organization, Tohoku University, Sendai, Japan; email: masakazu.motoki.e2@tohoku.ac.jp","","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics","10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021","11 July 2021 through 16 July 2021","Virtual, Online","180066","","978-166542420-2","","","English","Proc. - Int. Congr. Adv. Appl. Informatics, IIAI-AAI","Conference paper","Final","","Scopus","2-s2.0-85133178956"
"Lu Y.-C.; Ke H.-R.","Lu, Yi-Ching (57221470340); Ke, Hao-Ren (7102102213)","57221470340; 7102102213","A study on scholars’ perceptions and practices of research data management","2020","Journal of Library and Information Studies","18","2","","103","137","34","3","10.6182/jlis.202012_18(2).103","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099259619&doi=10.6182%2fjlis.202012_18%282%29.103&partnerID=40&md5=23dc0fb584105546e71e2a88a3ddd736","Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan; National Taiwan Normal University Library, Taipei, Taiwan","Lu Y.-C., Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan; Ke H.-R., Graduate Institute of Library & Information Studies, National Taiwan Normal University, Taipei, Taiwan, National Taiwan Normal University Library, Taipei, Taiwan","The trends of digital scholarship, the fourth-paradigm, and data sharing attract attention to issues on research data management (RDM). RDM involves a series of activities throughout the whole research life cycle, including the production, description, storing, backing-up, processing, analyzing, preserving, sharing and reusing of research data. RDM assures scientific research affordable, accessible, fair, re-producible, verifiable, and sustainable. This study explores Taiwanese scholars’ perceptions and practices of RDM via a questionnaire survey. It investigated issues including sources and types of research data, data storage and search, metadata of research data, data management plan (DMP) mandates or requirements, training and support, and data sharing and reuse. Furthermore, it attempts to understand if scholars’ perceptions and practices reveal significant differences regarding subject disciplines, years of research experiences, and profession titles. © 2020, National Taiwan University, Department of Library and Information Science. All rights reserved.","Data management plan (DMP); Digital scholarship; Metadata; Research data management (RDM)","","","","","","Joint Information Systems Committee, JISC","Chowdhury, G., Boustany, J., Kurbanoğlu, S., Ünal, Y., & Walton, G. (2017). Preparedness for research data sharing: A study of university researchers in three European countries. In S. Choemprayong, F. Crestani, & S. Cunningham (Eds.), Digital libraries: Data, information, and knowledge for digital live (pp. 104-116). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-70232-2_9 Cox, A. M., Kennan, M. A., Lyon, L., & Pinfield, S. (2017). Developments in research data management in academic libraries: Towards an understanding of research data service maturity. Journal of the Association for Information Science & Technology, 68(9), 2182-2200. doi: 10.1002/asi.23781 DataONE. (n.d.). Data life cycle. Retrieved from https://www.dataone.org/data-life-cycle Data Curation Centre. (n.d.). Data management plans. Retrieved from http://www.dcc. ac.uk/resources/data-management-plans Eindhoven University of Technology Library. (n.d.). What is research data management? Retrieved from https://www.tue.nl/en/ our-university/library/education-research-support/scientific-publishing/data-coach/ general-terms-and-background/what-is-research-data-management/ Hey, T., Tansley, S., & Tolle, K. (Eds.). (2009). The fourth paradigm: Data-intensive scientific discovery. Retrieved from http://research.microsoft.com/en-us/ collaboration/fourthparadigm/ Joint Information Systems Committee. (2011). Developing digital literacies: Briefing paper in support of JISC grant funding 4/11. Retrieved from http://www.jisc. ac.uk/media/documents/funding/2011/04/ Briefingpaper.pdf Krosnick, J. A. (1999). Survey research. Annual Review of Psychology, 50(1), 537-567. doi: 10.1146/annurev.psych.50.1.537 Massey University Library. (2020). Introduction to research data management (RDM). Retrieved from http://www.massey.ac.nz/ massey/research/library/library-services/ research-services/manage-data/manage-data_home.cfm National Science Board. (2005). Long-lived digital data collections: Enabling research and education in the 21st century. Retrieved from https://www.nsf.gov/ pubs/2005/nsb0540/ National Science Foundation. (2017). Proposal & award policies & procedures guide: Chapter II - Proposal preparation instructions. Retrieved from https://www.nsf.gov/pubs/policydocs/ pappg17_1/pappg_2.jsp Organization for Economic Co-operation and Development. (2007). OECD principles and guidelines for access to research data from public funding. Retrieved from http:// www.oecd.org/dataoecd/9/61/38500813.pdf Perrier, L., & Barnes, L. (2018). Developing research data management services and support for researchers: A mixed methods study. Partnership: The Canadian Journal","Li Dandan, Wu Zhenxin, A review of research data management services, Research on Library Science, 2012, 9, pp. 54-59, (2012); Lin Chi-Shiou, Lai Ching-Yi, Data reuse behavior among Taiwan social scientists, Journal of Library and Information Science Research, 11, 2, pp. 95-138, (2017); Lin Chi-Shiou, Lai Ching-Yi, The reuse of quantitative data in social sciences in Taiwan: 2001-2015, Journal of Educational Media & Library Sciences, 55, 1, pp. 39-69, (2018); Ke Ji Bu bu zhu zhuan ti yan jiu ji hua zuo ye yao dian], (2019); Zhuan ti yan jiu ji hua zhi xing tong yi shu]; Chin Yun-Han, A research on how to develop data curation service in academic library from professors’ viewpoint, (2012); Lu Yi-Ching, A study on research data use, management and sharing cognition and behavior of universities and research institutes scholars, (2019); Chen Hsueh-Hua, Chen Kuang-Hua, [e-Research: Xue shu tu shu guan chuang xin fu wu], (2012); 2016 top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 77, 6, pp. 274-281, (2016); Akers K. G., Doty J., Differences among faculty ranks in views on research data management, IASSIST Quarterly, 36, 2, pp. 16-20, (2013); Beagrie N., Houghton J., The value and impact of data sharing and curation: A synthesis of recent studies of UK research data centres, (2014); Borgman C. L., The conundrum of sharing research data, Journal of the American Society for Information Science & Technology, 63, 6, pp. 1059-1078, (2012); Borgman C. L., Big data, little data, no data: Scholarship in the networked world, (2015); Briney K., Data management for researchers: Organize, maintain and share your data for research success, (2015); Buys C. M., Shaw P. L., Data management practices across an institution: Survey and report, Journal of Librarianship & Scholarly Communication, 3, 2, (2015); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chowdhury G., Boustany J., Kurbanoglu S., Unal Y., Walton G., Preparedness for research data sharing: A study of university researchers in three European countries, Digital libraries: Data, information, and knowledge for digital live, pp. 104-116, (2017); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science & Technology, 68, 9, pp. 2182-2200, (2017); Data life cycle; Data management plans; What is research data management?; Hey T., Tansley S., Tolle K., The fourth paradigm: Data-intensive scientific discovery, (2009); Developing digital literacies: Briefing paper in support of JISC grant funding 4/11, (2011); Krosnick J. A., Survey research, Annual Review of Psychology, 50, 1, pp. 537-567, (1999); Introduction to research data management (RDM), (2020); Long-lived digital data collections: Enabling research and education in the 21st century, (2005); Proposal & award policies & procedures guide: Chapter II -Proposal preparation instructions, (2017); OECD principles and guidelines for access to research data from public funding, (2007); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, 13, 1, (2018); Piwowar H. A., Who shares? Who doesn’t? Factors associated with openly archiving raw research data, PloS ONE, 6, 7, (2011); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the University of Lille (France), Libreas: Library Ideas, 29, pp. 98-112, (2016); Steeleworthy M., Research data management and the Canadian academic library: An organizational consideration of data management and data stewardship, Partnership: The Canadian Journal of Library and Information Practice and Research, 9, 1, (2014); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of eScience Librarianship, 1, 2, pp. 63-78, (2012); Surkis A., Read K., Research data management, Journal of the Medical Library Association, 103, 3, pp. 154-156, (2015); Tenopir C., Allard S., Douglass K., Aydinoglu A. U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PloS ONE, 6, 6, (2011); Tenopir C., Dalton E. D., Allard S., Frame M., Pjesivac I., Birch B., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PloS ONE, 10, 8, (2015); Benefits of managing and sharing your data, (2014); Data management planning for ESRC researchers; Unal Y., Chowdhury G., Kurbanoglu S., Boustany J., Walton G., Research data management and data sharing behaviour of university researchers, Information Research: An International Electronic Journal, 24, 1, (2019); University of Oxford policy on the management of data supporting research outputs, (2018); Van den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and sharing data: Best practice for researchers, (2011); Weller M., The digital scholar: How technology is transforming scholarly practice, (2011); Whitmire A. L., Boock M., Sutton S. C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library & Information Systems, 49, 4, pp. 382-407, (2015)","H.-R. Ke; National Taiwan Normal University Library, Taipei, Taiwan; email: clavenke@ntnu.edu.tw","","National Taiwan University, Department of Library and Information Science","","","","","","16067509","","","","Chinese","J. Lib. Inf. Stud.","Article","Final","","Scopus","2-s2.0-85099259619"
"Moher D.; Cobey K.D.","Moher, David (56350378600); Cobey, Kelly D. (36542132200)","56350378600; 36542132200","Ensuring the success of data sharing in Canada","2021","Facets","6","","","1534","1538","4","1","10.1139/FACETS-2021-0031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115983385&doi=10.1139%2fFACETS-2021-0031&partnerID=40&md5=df44fc776b4ef9bcc1e739db8ab95cb2","Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, K1H 8L6, ON, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, K1G 5Z3, ON, Canada","Moher D., Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, K1H 8L6, ON, Canada, School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, K1G 5Z3, ON, Canada; Cobey K.D., Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, K1H 8L6, ON, Canada, School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, K1G 5Z3, ON, Canada","The Canadian federal Tri-Agency Research Data Management Policy has recently been released. This will require Canadian universities and other research institutes to create and share strategic plans regarding data management and to equip their researchers with skills to complete data deposits. To help maximize the success of data sharing we outline five domains for research institutions to consider during implementation: training and education, paying for data sharing, audit and feedback, meta-science, and career advancement. Copyright: © 2021 Moher and Cobey.","","","","","","","","","Baden LR, El Sahly HM, Essink B, Kotloff K, Frey S, Novak R, Et al., Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine, The New England Journal of Medicine, 384, 5, pp. 403-416, (2021); Tri-Agency Statement of Principles on Digital Data Management, (2021); Charité Metrics Dashboard, (2021); Declaration on Research Assessment, (2021); New funding instrument to stimulate Open Science, (2020); Goldacre B, DeVito NJ, Heneghan C, Irving F, Bacon S, Fleminger J, Et al., Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource, BMJ, 362, (2018); Mello MM, Lieou V, Goodman SN., Clinical trial participants’ views of the risks and benefits of data sharing, The New England Journal of Medicine, 378, pp. 2202-2211, (2018); Naudet F, Sakarovitch C, Janiaud P, Cristea I, Fanelli D, Moher D, Et al., Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in The BMJ and PLOS Medicine, BMJ, 360, (2018); Launch of the Tri-Agency Research Data Management Policy and a new requirement for postsecondary institutions and research hospitals, (2021); Rice DB, Raffoul H, Ioannidis JPA, Moher D., Academic criteria for promotion and tenure in faculties of medicine: a cross-sectional study of the Canadian U15 universities, FACETS, 6, pp. 58-70, (2021); Sumner JQ, Haynes L, Nathan S, Hudson-Vitale C, McIntosh LD., Reproducibility and reporting practices in COVID-19 preprint manuscripts, medRxiv, (2020); Taylor F, Moher D, Cobey K., Creating an audit and feedback loop to monitor and enhance data sharing in Canada, NDRIO: call for white papers on Canada’s future DRI ecosystem, (2020); Open Dialogue with the UN Deputy Secretary-General on Science for Development in the Context of COVID-19, (2021); New fund to support groundbreaking open research, (2018); Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak, (2020)","D. Moher; Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, K1H 8L6, Canada; email: dmoher@ohri.ca","","Canadian Science Publishing","","","","","","23711671","","","","English","Facet.","Review","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85115983385"
"Heinrichs B.; Politze M.","Heinrichs, Benedikt (57211110293); Politze, Marius (57195741179)","57211110293; 57195741179","Asynchronous Data Provenance for Research Data in a Distributed System","2021","International Conference on Enterprise Information Systems, ICEIS - Proceedings","2","","","361","367","6","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137952444&partnerID=40&md5=85698c4cec8126bfc69b0e0a0d801b4b","IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany","Heinrichs B., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany; Politze M., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany","Many provenance systems assume that the data flow is being directly orchestrated by them or logs are present which describe it. This works well until these assumptions do not hold anymore. The Coscine platform is a way for researchers to connect to different storage providers and annotate their stored data with discipline-specific metadata. These storage providers, however, do not inform the platform of externally induced changes for example by the user. Therefore, this paper focuses on the need of data provenance that is not directly produced and has to be deduced after the fact. An approach is proposed for dealing with and creating such asynchronous data provenance which makes use of change indicators that deduce if a data entity has been modified. A representation on how to describe such an asynchronous data provenance in the Resource Description Framework (RDF) is discussed. Finally, a prototypical implementation of the approach in the Coscine use-case is described and the future steps for the approach and prototype are detailed. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda.","Data Provenance; Distributed Systems; Research Data Management","Distributed computer systems; Information management; Resource Description Framework (RDF); Semantics; After-the-fact; Asynchronous data; Change indicators; Data entities; Data provenance; Dataflow; Distributed systems; Research data; Research data managements; Resources description frameworks; Digital storage","","","","","","","Ametepe W., Wang C., Ocansey S., Li X., Hussain F., Data provenance collection and security in a distributed environment: a survey, International Journal of Computers and Applications, pp. 1-15, (2018); Belhajjame K., Cheney J., Corsar D., Garijo D., Soiland-Reyes S., Zednik S., Zhao J., Prov-o: The prov ontology, (2012); Bensberg S., An efficient semantic search engine for research data in an RDF-based knowledge graph, (2020); Brickley D., Miller L., Foaf vocabulary specification, (2014); Cruz S., Campos M., Mattoso M., Towards a taxonomy of provenance in scientific workflow management systems, SERVICES 2009 - 5th 2009 World Congress on Services, (2009); Cyganiak R., Lanthaler M., Wood D., RDF 1.1 concepts and abstract syntax, (2014); Davidson S., Cohen-Boulakia S., Eyal A., Ludascher B., McPhillips T., Bowers S., Anand M., Freire J., Provenance in scientific workflow systems, IEEE Data Eng. Bull, 30, pp. 44-50, (2007); Davidson S., Freire J., Provenance and scientific workflows: Challenges and opportunities, pp. 1345-1350, (2008); Stephan E., Raju B., Elsethagen T., Pouchard L., Gamboa C., A scientific data provenance harvester for distributed applications, 2017 New York Scientific Data Summit (NYSDS), pp. 1-9, (2017); Zenodo, (2013); Foster E. D., Deardorff A., Open science framework (osf), Journal of the Medical Library Association: JMLA, 105, 2, pp. 203-206, (2017); Guha R. V., Brickley D., Macbeth S., Schema.org: Evolution of structured data on the web, Commun. ACM, 59, 2, pp. 44-51, (2016); Heinrichs B., Politze M., Moving towards a general metadata extraction solution for research data with state-of-the-art methods, (2020); Herschel M., Diestelkamper R., Ben Lahmar H., A survey on provenance: What for? what form? what from?, The VLDB Journal, 26, (2017); Hu R., Yan Z., Ding W., Yang L. T., A survey on data provenance in iot, World Wide Web, 23, 2, pp. 1441-1463, (2020); Interlandi M., Ekmekji A., Shah K., Gulzar M. A., Tetali S. D., Kim M., Millstein T., Condie T., Adding data provenance support to apache spark, The VLDB Journal, 27, 5, pp. 595-615, (2018); Mufti Z., Elkhodr M., Data Provenance in the Internet of Things: Views and Challenges, (2018); Perez B., Rubio J., Saenz-Adan C., A systematic review of provenance systems, Knowledge and Information Systems, 57, 3, pp. 495-543, (2018); Politze M., Claus F., Brenger B. D., Yazdi M. A., Heinrichs B., Schwarz A., How to manage it resources in research projects? towards a collaborative scientific integration environment, European journal of higher education IT, 1, (2020); Schmitz D., Politze M., Forschungsdaten managen - bausteine für eine dezentrale, forschungsnahe unterstützung. o-bib, Das offene Bibliotheksjournal/Herausgeber VDB, 5, 3, pp. 76-91, (2018); Schwardmann U., epic persistent identifiers for ere-search, Presentation at the joint DataCite-ePIC workshop Persistent Identifiers: Enabling Services for Data Intensive Research, 21, (2015); Smith W., Moyer T., Munson C., Curator: Provenance management for modern distributed systems, Proceedings of the 10th USENIX Conference on Theory and Practice of Provenance, TaPP'18, (2018); Talia D., Thramboulidis K., Lai B. C., Cao J., Workflow systems for science: Concepts and tools, ISRN Software Engineering, 2013, (2013); Wilkinson M. D., Dumontier M., Aalbersberg I. J. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Bouwman J., Brookes A. J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo Chris T., Mons B., The fair guiding principles for scientific data management and stewardship, Scientific data, 3, (2016)","","Filipe J.; Smialek M.; Brodsky A.; Hammoudi S.","Science and Technology Publications, Lda","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","23rd International Conference on Enterprise Information Systems, ICEIS 2021","26 April 2021 through 28 April 2021","Virtual, Online","180136","21844992","978-989758509-8","","","English","International Conference on Enterprise Information Systems, ICEIS - Proceedings","Conference paper","Final","","Scopus","2-s2.0-85137952444"
"Auge T.; Heuer A.","Auge, Tanja (57194833958); Heuer, Andreas (9533312500)","57194833958; 9533312500","Tracing the History of the Baltic Sea Oxygen Level Evolution and Provenance for Research Data Management","2021","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-311","","","337","348","11","1","10.18420/btw2021-18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136281210&doi=10.18420%2fbtw2021-18&partnerID=40&md5=14d4315c93c238aacfcb7abcd06a45e6","University of Rostock, Dbis, Germany","Auge T., University of Rostock, Dbis, Germany; Heuer A., University of Rostock, Dbis, Germany","In order to guarantee the reproducibility of research results, large research communities, conferences and journals increasingly demand the provision of original research data. Since this is often not possible or desired, a certain tact and sensitivity is needed. With our method, combining provenance and evolution, we can identify the source tuples necessary for the reconstruction of a query result also in temporal databases. To avoid dirty data caused by the inverse evolution, we introduced the what-provenance, which remembers the data types of the source relation. © 2021 Gesellschaft fur Informatik (GI). All rights reserved.","CHASE; Long-term Data; Provenance; Research Data Management; Schema Evolution","Oxygen; Query processing; Baltic sea; CHASE; Long-term data; Oxygen levels; Provenance; Reproducibilities; Research communities; Research data managements; Research results; Schema evolution; Information management","","","","","","","Amsterdamer Yael, Deutch Daniel, Tannen Val, Provenance for Aggregate Queries, PODS. ACM, pp. 153-164, (2011); Auge Tanja, Heuer Andreas, Combining Provenance Management and Schema Evolution, IPAW. volume 11017 of LNCS, pp. 222-225, (2018); Auge Tanja, Heuer Andreas, The Theory behind Minimizing Research Data-Result equivalent CHASE-inverse Mappings, LWDA. volume 2191 of CEUR Workshop Proceedings, pp. 1-12, (2018); Athinaiou Christos, Kondylakis Haridimos, VESEL: VisuaL Exploration of Schema Evolution using Provenance Queries, EDBT/ICDT Workshops. volume 2322 of CEUR Workshop Proceedings, (2019); Auge Tanja, Scharlau Nic, Heuer Andreas, Privacy Aspects of Provenance Queries, (2020); Auge Tanja, Extended Provenance Management for Data Science Applications, PhD@VLDB. volume 2652 of CEUR Workshop Proceedings, (2020); Auge Tanja, Manthey Erik, Jugensmann Susanne, Feistel Susanne, Heuer Andreas, Schema Evolution and Reproducibility of Long-term Hydrographic Data Sets at the IOW, LWDA. volume 2738 of CEUR Workshop Proceedings. CEUR-WS.org, pp. 258-269, (2020); Benedikt Michael, Konstantinidis George, Mecca Giansalvatore, Motik Boris, Papotti Paolo, Santoro Donatello, Tsamoura Efthymia, Benchmarking the Chase, PODS. ACM, pp. 37-52, (2017); Buneman Peter, Khanna Sanjeev, Tan Wang Chiew, Why and Where: A Characterization of Data Provenance, ICDT, 1973, pp. 316-330, (2001); Carstensena Jacob, Andersena Jesper H., Gustafssonb Bo G., Conley Daniel J., Deoxygenation of the Baltic Sea during the last century, J. Artif. Soc. Soc. Simul, 111, 15, (2014); Curino Carlo A., Moon Hyun J., Zaniolo Carlo, Graceful Database Schema Evolution: the PRISM Workbench, Proc. VLDB Endow, 1, 1, pp. 761-772, (2008); Curino Carlo, Moon Hyun Jin, Deutsch Alin, Zaniolo Carlo, Automating the database schema evolution process, VLDB J, 22, 1, pp. 73-98, (2013); Deutsch Alin, Hull Richard, Provenance-Directed Chase&Backchase, Search of Elegance in the Theory and Practice of Computation. volume 8000 of Lecture Notes in Computer Science, pp. 227-236, (2013); Fagin Ronald, Kolaitis Phokion G., Popa Lucian, Tan Wang Chiew, Schema Mapping Evolution Through Composition and Inversion, Schema Matching and Mapping, Data-Centric Systems and Applications, pp. 191-222, (2011); Green Todd J., Tannen Val, The Semiring Framework for Database Provenance, PODS. ACM, pp. 93-99, (2017); Herrmann Kai, Voigt Hannes, Behrend Andreas, Rausch Jonas, Lehner Wolfgang, Living in Parallel Realities: Co-Existing Schema Versions with a Bidirectional Database Evolution Language, SIGMOD Conference, pp. 1101-1116, (2017); Manthey Erik, Beschreibung der Veränderungen von Schemata und Daten am IOW mit Schema-Evolutions-Operatoren, (2020); Meier Michael, The backchase revisited, VLDB J, 23, 3, pp. 495-516, (2014); Herschel Melanie, Diestelkamper Ralf, Lahmar Houssem Ben, A survey on provenance: What for? What form? What from?, VLDB J, 26, 6, pp. 881-906, (2017); Wilkinson Mark D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016)","","Sattler K.-U.; Herschel M.; Lehner W.","Gesellschaft fur Informatik (GI)","","2021 Datenbanksysteme fur Business, Technologie und Web, BTW 2021 - 2021 Database Systems for Business, Technology and Web, BTW 2021","13 September 2021 through 17 September 2021","Dresden","181603","16175468","978-388579705-0","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85136281210"
"Wachtler T.; Bauer P.; Denker M.; Grün S.; Hanke M.; Klein J.; Oeltze-Jafra S.; Ritter P.; Rotter S.; Scherberger H.; Stein A.; Witte O.W.","Wachtler, Thomas (6602841638); Bauer, Pavol (56460819100); Denker, Michael (8362049300); Grün, Sonja (57207523382); Hanke, Michael (35859076500); Klein, Jan (56473168700); Oeltze-Jafra, Steffen (56414900700); Ritter, Petra (9234339700); Rotter, Stefan (7004581948); Scherberger, Hansjörg (6507913380); Stein, Alexandra (57216483995); Witte, Otto W. (35592935100)","6602841638; 56460819100; 8362049300; 57207523382; 35859076500; 56473168700; 56414900700; 9234339700; 7004581948; 6507913380; 57216483995; 35592935100","NFDI-Neuro: Building a community for neuroscience research data management in Germany","2021","Neuroforum","27","1","","3","15","12","3","10.1515/nf-2020-0036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100147892&doi=10.1515%2fnf-2020-0036&partnerID=40&md5=6db3894046fa5a2413989b8eef5e15f1","Department Biologie II, Ludwig-Maximilians-Universität München, Grosshaderner Str. 2, Planegg-Martinsried, 82152, Germany; Department of Cellular Neuroscience, Leibniz Institute for Neurobiology, Brenneckestrasse 6, Magdeburg, 39118, Germany; Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Research Center Jülich, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine Brain and Behaviour (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, 40225, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, Bremen, 28359, Germany; Department of Neurology, Otto von Guericke University Magdeburg, Leipziger Str. 44, Magdeburg, 39120, Germany; Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany; Department of Neurology, Brain Simulation Section, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, Hansastraße 9a, Freiburg, 79104, Germany; Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, Kellnerweg 4, Göttingen, 37077, Germany; Department of Biology and Psychology, University of Göttingen, Göttingen, Germany; Institute of Neuroscience and Medicine Computational and Systems Neuroscience (INM-6, Research Center Jülich, Jülich, Germany; Bernstein Coordination Site, Bernstein Network Computational Neuroscience, Albert-Ludwigs Universität Freiburg, Hansastraße 9a, Freiburg, 79104, Germany; Hans Berger Department of Neurology, Jena University Hospital, Am Klinikum 1, Jena, 07747, Germany; Deutsche Gesellschaft für Klinische Neurophysiologie und Funktionelle Bildgebung (DGKN), Salvador-Allende-Platz 29, Jena, 07747, Germany","Wachtler T., Department Biologie II, Ludwig-Maximilians-Universität München, Grosshaderner Str. 2, Planegg-Martinsried, 82152, Germany; Bauer P., Department of Cellular Neuroscience, Leibniz Institute for Neurobiology, Brenneckestrasse 6, Magdeburg, 39118, Germany; Denker M., Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Research Center Jülich, Jülich, Germany; Grün S., Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Research Center Jülich, Jülich, Germany, Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany; Hanke M., Institute of Neuroscience and Medicine Brain and Behaviour (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, Germany, Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, 40225, Germany; Klein J., Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, Bremen, 28359, Germany; Oeltze-Jafra S., Department of Neurology, Otto von Guericke University Magdeburg, Leipziger Str. 44, Magdeburg, 39120, Germany; Ritter P., Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany, Department of Neurology, Brain Simulation Section, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany; Rotter S., Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, Hansastraße 9a, Freiburg, 79104, Germany; Scherberger H., Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, Kellnerweg 4, Göttingen, 37077, Germany, Department of Biology and Psychology, University of Göttingen, Göttingen, Germany; Stein A., Institute of Neuroscience and Medicine Computational and Systems Neuroscience (INM-6, Research Center Jülich, Jülich, Germany, Bernstein Coordination Site, Bernstein Network Computational Neuroscience, Albert-Ludwigs Universität Freiburg, Hansastraße 9a, Freiburg, 79104, Germany; Witte O.W., Hans Berger Department of Neurology, Jena University Hospital, Am Klinikum 1, Jena, 07747, Germany, Deutsche Gesellschaft für Klinische Neurophysiologie und Funktionelle Bildgebung (DGKN), Salvador-Allende-Platz 29, Jena, 07747, Germany","Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions. © 2021 De Gruyter. All rights reserved.","Collaboration; Data management; FAIR; Research data infrastructure","Germany; neuroscience; review","","","","","Berlin Institute of Health & Foundation Charité; Deutsche Gesellschaft für klinische Neurophysiologie; Johanna Quandt Excellence Initiative; US BRAIN; University of Excellence; Horizon 2020 Framework Programme, H2020, (683049, 826421, 945539); Neurowissenschaftliche Gesellschaft; European Research Council, ERC; Deutsche Forschungsgemeinschaft, DFG, (CRC 1315, CRC 936, CRC-TRR 295, RI 2073/6-1); Bundesministerium für Bildung und Forschung, BMBF, (01GQ1905); Ludwig-Maximilians-Universität München, LMU","Funding text 1: Research funding: This research was funded by LMU excellent as part of LMU Munich’s funding as University of Excellence in the German Excellence Strategy, Helmholtz Metadata Collaboration (HMC), European Union’s Horizon 2020 research and innovation programme under grant agreement no. 826421 (VirtualBrainCloud) and 945539 (HBP SGA3), Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), German Federal Ministry of Education and Research (BMBF 01GQ1905), ERC 683049; German Research Foundation CRC 1315, CRC 936, CRC-TRR 295 and RI 2073/6-1; Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative. ; Funding text 2: DANDI is a platform for publishing, sharing and processing cellular neurophysiology data, funded by the US BRAIN Initiative. It aims to enable reproducible practices, publications and reuse of data, reduce the need to contact data producers by enriching the data with comprehensive metadata, with the goal to build a living repository that enables collaboration within and across labs, and for others, the entry point for research. ; Funding text 3: The consortium initiative NFDI Neuroscience (NFDI-Neuro, https://nfdi-neuro.de ) formed as an open community network, with the aim of acting as a platform that brings together existing solutions for RDM and assists researchers in establishing RDM as part of everyday research practice. The initiative is supported by three major neuroscience associations: Neurowissenschaftliche Gesellschaft (NWG), Bernstein Network Computational Neuroscience and Deutsche Gesellschaft für klinische Neurophysiologie (DGKN). ","Denker M., Grun S., Wachtler T., Scherberger H., Reproducibility and efficiency in handling complex neurophysiological data, Neuroforum, 27, (2021); Garcia S., Guarino D., Jaillet F., Jennings T.R., Propper R., Rautenberg P.L., Rodgers C., Sobolev A., Wachtler T., Yger P., Davison A.P., Neo: An object model for handling electrophysiology data in multiple formats, Front. Neuroinf., 8, (2014); Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci. Data, 3, pp. 1-9, (2016); Grewe J., Wachtler T., Benda J., A bottom-up approach to data annotation in neurophysiology, Front. Neuroinf., 5, (2011); Hanke M., Pestilli F., Wagner A.S., Markiewicz C.J., Poline J.-B., Halchenko Y.O., In defense of decentralized research data management, Neuroforum, 27, (2021); Hines M.L., Morse T., Migliore M., Carnevale N.T., Hines M.L., ModelDB: A database to support computational neuroscience, J. Comput. Neurosci., 17, pp. 7-11, (2004); Klingner C.M., Ritter P., Brodoehl S., Gaser C., Scherag A., Gullmar D., Rosenow F., Ziemann U., Witte O.W., Research data management in clinical neuroscience - The NFDI initiative, Neuroforum, (2021); Pernet C.R., Appelhoff S., Gorgolewski K.J., Flandin G., Phillips C., Delorme A., Oostenveld R., EEG-BIDS, an extension to the brain imaging data structure for electroencephalography, Sci. Data, 6, (2019); Ramaswamy S., Courcol J.D., Abdellah M., Adaszewski S.R., Antille N., Arsever S., Ateneken G., Bilgili A., Brukau Y., Chalimourda A., Et al., The neocortical microcircuit collaboration portal: A resource for rat somatosensory cortex, Front. Neural Circ., 9, (2015); Ritzau-Jost A., Seidenbecher S., NFDI neuroscience: Advocating cross-community data management in neuroscience, Neuroforum, 25, pp. 279-280, (2019); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Bouwman J., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016)","","","De Gruyter Open Ltd","","","","","","09470875","","","","English","Neuroforum","Review","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85100147892"
"Muellenbach J.M.","Muellenbach, Joanne M. (56174231900)","56174231900","A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities","2021","Evidence Based Library and Information Practice","16","1","","104","106","2","2","10.18438/eblip29879","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103698352&doi=10.18438%2feblip29879&partnerID=40&md5=871781d9b311864c4b46a41971570543","California Health Sciences University, Clovis, California, United States","Muellenbach J.M., California Health Sciences University, Clovis, California, United States","Objectives – To initiate or expand research data management (RDM) services within the participating libraries serving health sciences populations. Design – Case report. Setting – Six institutions consisting of three academic health sciences and three university libraries within the National Network of Libraries of Medicine Middle Atlantic Region in the United States of America. Subjects – Between two and eight librarians participated from each institution, for a total of twenty-six librarian participants. Methods – Pre-pilot phone interviews were conducted and included open-ended questions about RDM services, the library’s motivation for participating, and their degree of institutional commitment. To deepen their understanding of RDM, the participants were required to complete eight educational modules that included text, videos, and quizzes. The participating institutions received data interview questions to connect with their research community to be better informed about their attitudes, language, and practices. The participants also received a Teaching Toolkit, complete with slides, a script, and an attendee evaluation form. The participants were provided with a data series, consisting of branded classes for teaching over a designated period with instructors from within and outside of the library. Collaboration with library partners was encouraged as was the use of a focused marketing plan. In fact, a major component of the pilot was the expert support, provided through biweekly meetings that included marketing tips and presentations on such topics as clinical research data management and data visualization. Finally, post-pilot program interviews were conducted, and the open-ended questions covered the pilot program as a whole and its individual components. Main Results – Of the six participating institutions, five institutions rated the RDM educational modules very positively. Conducting data interviews was valuable for all six institutions because it allowed the librarians to meet with researchers, build relationships, and use what they learned to develop RDM services for the future. The Teaching Toolkit was rated positively by the six institutions, especially for its adaptability, the time saved over developing the content from scratch, and its usability. Finally, the two institutions that held the data series courses stated that the series succeeded in further marketing the RDM services developed by the library. Conclusion – The pilot project met its objectives: the librarians at the participating institutions completed the educational modules, administered the data interviews, and taught an RDM foundations class based on the Teaching Toolkit. In addition, a data series was hosted at two institutions. The components of the pilot project had the intended results at each institution, and the classes were reviewed favorably. Based on the pilot participants’ positive outcomes, the authors are certain that the freely available program materials would achieve success elsewhere. © 2021. Muellenbach. This is an Open Access article distributed under the terms of the Creative Commons- Attribution-Noncommercial-Share Alike License 4.0 International (http://creativecornmons.org/licenses/by-nc-sa/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one.","","","","","","","","","Federer L., Foster E. D., Glusker A., Henderson M., Read K., Zhao S., The Medical Library Association data services competency: A framework for data science and open science skills development, Journal of the Medical Library Association, 108, 2, pp. 304-309, (2020); Martin E., Goldman J., Best practices for biomedical research data management, Canvas Network, (2019); Martin E., Goldman J., New England collaborative data management curriculum, (2017); Perryman C., Rathbun-Grubb S., The CAT: A generic critical appraisal tool, (2014)","J.M. Muellenbach; Library Director and Associate Professor, California Health Sciences University, Clovis, United States; email: jmuellenbach@chsu.edu","","University of Alberta","","","","","","1715720X","","","","English","Evid. Based Libr. Inf. Pract.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85103698352"
"Flores M.M.S.; Ramos R.C.; Sánchez M.V.G.; Toledo-Roy J.C.; Calderón Y.G.G.","Flores, María Magdalena Sierra (57223926372); Ramos, Romel Calero (57201391159); Sánchez, María Victoria Guzmán (55334732100); Toledo-Roy, Juan Claudio (23487034200); Calderón, Yolsy Gabriela Gamboa (57875251400)","57223926372; 57201391159; 55334732100; 23487034200; 57875251400","SIGI, an integrated scientometric and curricular information system for teaching-research institutions; [SIGI, un sistema integral de información cienciomé-trica y curricular para instituciones de investigación-enseñanza]","2021","Investigacion Bibliotecologica","35","89","","111","131","20","1","10.22201/IIBI.24488321XE.2021.89.58431","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137290838&doi=10.22201%2fIIBI.24488321XE.2021.89.58431&partnerID=40&md5=e7e646c9a9fcbd4015955f55944f4e35","Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico; Instituto Finlay de Vacunas, Cuba","Flores M.M.S., Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico; Ramos R.C., Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico; Sánchez M.V.G., Instituto Finlay de Vacunas, Cuba; Toledo-Roy J.C., Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico; Calderón Y.G.G., Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico","We hereby present SIGI, an integral automated, web-based system for Research Data Management and generation of metric indicators. SIGI is an information platform and database system created to manage all data related to the academic activity of a teaching-re-search institution, and to generate annual reports and metric indicators at the individual and institutional levels. It integrates data related to traditional academic bibliographic products with other aspects of the academic activity (e.g., those related to teaching or outreach). From this rich database, SIGI automatically generates metric indicators that are used in the creation of individual researcher profiles and in institutional evaluations and reports. We also show two exam-ples of scientometrics analyses that can be carried out using the system: the internal collaboration network and the international collaboration map of a particu-lar research institute. We conclude by discussing the example institute´s overall experience with the system and how it could be adopted by other institutions, bo-th nationally and internationally. © 2021, Universidad Nacional Autonoma de Mexico. All rights reserved.","Bibliometric Indicators; Integrated Information System; Research Data Management; Web Applications","","","","","","","","Arciniegas Tinjaca Eliana, Marcela Gomez Gutierrez Yury, Grego-rio-Chaviano Orlando, La biblioteca universitaria y su rol en los procesos de inves-tigación: una mirada desde los servicios de información con enfoque bibliométri-co en Colombia, Biblios. Journal of Librarianship and Information Science, 72, pp. 113-129, (2018); Cox Andrew M., Kennan Mary Anne, Lyon Liz, Pinfield Stephen, Deve-lopments in research data management in academic libraries: Towards an un-derstanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Guillot Jimenez Javier, SIGUA: Sistema Informático de Gestión Universita-ria, the 14th International Congress on Informatics for Education, (2011); Gonzalez Fernandez-Villavicencio Nieves, Unidades de Bibliometría y biblio-tecas universitarias: hacia la transparencia, ThinkEPI, 11, 1, pp. 86-94, (2017); Humphries Mark, Gurney Kevin, Network ‘small-world-ness’: a quanti-tative method for determining canonical network equivalence, PLoS One, 3, 4, (2008); Manual de Frascati 2015: Guía para la recopilación y presentación de información sobre la inves-tigación y el desarrollo experimental, (2015); Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation, (2018); Gonzalez Maria Josefa, Guzman Maylin Frias, Gregorio-Cha-viano Orlando, Criterios, clasificaciones y tendencias de los indicadores bibliomé-tricos en la evaluación de la ciencia, Revista Cubana de Información en Ciencias de la Salud, 26, 3, pp. 290-309, (2015); Guidance on best practice in the management of research data, (2015); CRIStin (Current Research Information System in Norway), (2004); Schopfel Joachim, Prost Helene, Rebouillat Violane, Research data in current research information systems, Procedia Computer Science, 106, pp. 305-320, (2017); Flores Sierra, Magdalena Maria, Guzman Maria Victoria, Raga Alejandro, Perez Igna-cio, The productivity of Mexican astronomers in the field of out-flows from Young stars, Scientometrics, 81, (2009); Tarrats Pons Elisenda, Sitkis: una herramienta bibliométrica para el desarro-llo del estado de la cuestión, Textos Universitaris de Biblioteconomia i Documen-tació, 28, pp. 1-16, (2012); Toft Martin, Sparkar i gang Frida, Nettavis for Universitetet i Oslo UNI-FORUM Okt, (2003); Torres-Salinas Daniel, Jimenez-Contreras Evaristo, Hacia las unidades de bibliometría en las universidades: modelo y funciones, Revista Española de Documentación Científica, 35, 3, pp. 469-480, (2012); Acuerdo por el que se establecen los Lineamientos Generales de la Política de Acceso Abierto de la Universidad Nacional Autónoma de México, Gaceta UNAM, pp. 29-30, (2015); Flores Maria Magdalena, Ramos Romel Calero, Victoria Guz-man Sanchez Maria, Toledo-Roy Juan Claudio, Calde-ron Yolsy Gabriela Gamboa, SIGI, an integrated scientometric and curricular information system for teaching-research institutions, Investigación Bibliotecológica: archivonomía, bibliotecología e información, 35, 89, pp. 111-131, (2021)","","","Universidad Nacional Autonoma de Mexico","","","","","","0187358X","","","","English","Invest. Bibl.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85137290838"
"de Carvalho É.R.S.; Leite F.C.L.; Bertin P.R.B.","de Carvalho, Érika Rayanne Silva (57221869093); Leite, Fernando César Lima (24069071500); Bertin, Patrícia Rocha Bello (55481293200)","57221869093; 24069071500; 55481293200","Academic libraries and research data management: A bibliographic review; [Bibliotecas acadêmicas e gestão de dados de pesquisa: Uma revisão bibliográfica]; [Bibliotecas académicas y gestión de datos de investigación: Una revisión bibliográfica]","2021","Investigacion Bibliotecologica","35","86","eib0865826605","99","121","22","1","10.22201/iibi.24488321xe.2021.86.58266","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100484806&doi=10.22201%2fiibi.24488321xe.2021.86.58266&partnerID=40&md5=2b4864475660360f8e8dd8127081482b","Procuradoria Regional da República da 1ª Região, Ministério Público Federal, Brazil; Universidade de Brasília, Faculdade de Ciência da Informação, Brazil; Secretaria de Desenvolvimento Institucional, Empresa Brasileira de Pesquisa Agropecuária, Brazil","de Carvalho É.R.S., Procuradoria Regional da República da 1ª Região, Ministério Público Federal, Brazil; Leite F.C.L., Universidade de Brasília, Faculdade de Ciência da Informação, Brazil; Bertin P.R.B., Secretaria de Desenvolvimento Institucional, Empresa Brasileira de Pesquisa Agropecuária, Brazil","This article aimed to analyze the challenges faced by academic libraries in the research data management, as it is presented in the scientific literature. To this end, a bibliographic investigation was carried out using the Library and Information Science Abstracts (LISA) database. After careful evaluation, sixteen articles were selected, which constituted the basis for the elaboration of a descriptive model for research data management in academic libraries. The results of this showed that the elaboration of the policy and of the research data management plan, the development of technological infrastructure, the processing and analysis of data, the sharing and preservation of data, and the training of researchers and librarians, are the main actions developed by libraries. However, the libraries face challenges such as dealing with disciplinary differences, the lack of financial resources, the awareness of researchers for developing the research data management plan, the creation of data repositories, the definition of standards for data sharing and archiving, and the lack of training for librarians in research data management services. To overcome all of this, it is concluded that the professional updating of the librarian, is essential. © 2021, Universidad Nacional Autonoma de Mexico. All rights reserved.","ScientiData; Scientific Communication; University Libraries","","","","","","National Science Foundation, NSF; National Institutes of Health, NIH","Peters e Dryden (2011) relataram estudo conduzido por bibliotecários do Department of Liaison Services sobre as necessidades de gestão de dados dos principais pesquisadores que trabalham em projetos na National Science Foundation (NSF) e no National Institutes of Health (NIH). A pesquisa in-cluiu entrevistas com estudantes de graduação, pós-doutorado e técnicos de laboratório. Entre os resultados obtidos, destacam-se dificuldades dos pes-quisadores relacionadas ao cumprimento de requisitos de gestão de dados estabelecidos por agências financiadoras, à obtenção de suporte para visua-lização de dados e de sistema apropriado para o compartilhamento de dados.","Awre Chris, Baxter Jim, Clifford Brian, Colclough Janette, Cox Andrew, Dods Nick, Drummond Paul, Et al., Research data management as a “wicked problem, Library Review, 64, 4, pp. 356-371, (2015); Bosc Helene, Harnad Stevan, In a paperless world a new role for academic libraries: providing open access, Learned Publishing, 18, 2, pp. 95-99, (2005); Leite Erika Rayanne Silva de e Fernando Cesar Lima, Diferenças na produção, no compartilhamento e no (re) uso de dados de pesquisa: a percepção de pesquisadores de Química, Antropologia e Educação, Em Questão, 25, 3, pp. 321-347, (2019); Cherryholmes Cleo H., Notes on pragmatism and scientific realism, Educational Researcher, 21, 6, pp. 13-17, (1992); Chiware Elisha, Mathe Zanele, Academic libraries’ role in research data management services: a South African perspective, South African Journal of Library and Information Science, 81, 2, pp. 1-10, (2015); Corrall Sheila, Kennan Mary Anne, Afzal Waseem, Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox Andrew M., Kennan Mary Anne, Lyon Liz, Pinfield Stephen, Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox Andrew M., Pinfield Stephen, Smith Jennifer, Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Cox Andrew M., Verbaan Eddy, Sen Barbara, Upskilling liaison librarians for research data management, Ariadne: Web Magazine for Information Professionals, 70, (2012); Cox Andrew M., Verbaan Eddy, Sen Barbara, A new role for academic librarians? Research Data Management, Multimedia Information & Technology, 38, 4, pp. 29-30, (2012); Creswell John W., Projeto de pesquisa: métodos qualitativo, quantitativo e misto, (2010); Delserone Leslie M., At the Watershed: preparing for research data management and stewardship at the University of Minnesota Libraries, Library Trends, 57, 2, pp. 202-210, (2008); Faqueti Marouva Fallgatter, Alves Joao Bosco da Mota, Steil Andrea Valeria, Aprendizagem organizacional em bibliotecas acadêmicas: uma revisão sistemática, Perspectivas em Ciência da Informação, 21, 4, pp. 156-179, (2016); Gray Jim, Jim Gray on eScience: a transformed scientific method, The fourth paradigm: data-intensive scientific discovery, pp. xvii-xxxi, (2011); Henderson Margaret, Knott Teresa, Starting a research data management program based in a University Library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Marcum Deanna B., George Gerald, Introduction”, em The data deluge: can libraries cope with e-science?, (2010); Morgan David L., Paradigms lost and pragmatism regained: methodological implications of combining qualitative and quantitative methods, Journal of Mixed Methods Research, 1, 1, pp. 48-76, (2007); Orera-Orera Luisa, La biblioteca universitaria ante el nuevo modelo social y educativo, El Profesional de la Información, 16, 4, pp. 329-337, (2007); Peters Christie, Dryden Anita Riley, Assessing the academic library’s role in campus-wide research data management: a first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Piracha Haseeb Ahmad e Kanwal Ameen, Policy and planning of research data management in university libraries of Pakistan, Collection and Curation, 38, 2, pp. 39-44, (2019); Piwowar Heather A., Becich Michael J., Bilofsky Howard, Crowley Rebecca S., Towards a data sharing culture: recommendations for leadership from Academic Health Centers, PLoS Med, 5, 9, pp. 1315-1319, (2008); Read Kevin B., Koos Jessica, Miller Rebekah S., Miller Cathryn F., Phillips Gesina A., Scheinfeld Laurel, Surkis Alisa, A model for initiating research data management services at academic libraries, Journal of the Medical Library Association, 107, 3, pp. 432-441, (2019); Sayao Luis Fernando, Sales Luana Farias, Guia de gestão de dados de pesquisa para bibliotecários e pesquisadores, (2015); Shipman Jean P., Tang Rong, The collaborative creation of a Research Data Management Librarian Academy (RDMLA), Information Services & Use, 39, 3, pp. 243-247, (2019); Steeleworthy Michael, Research data management and the canadian academic library: an organizational consideration of data management and data stewardship, Partnership: The Canadian Journal of Library and Information Practice and Research, 9, 1, pp. 1-11, (2014); Surkis Alisa, Read Kevin, Research data management, Journal of the Medical Library Association, 103, 3, pp. 154-156, (2015); Tenopir Carol, Sandusky Robert J., Allard Suzie, Birch Ben, Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tripathi Manorama, Shukla Archana, Sonkar Sharad Kumar, Research data management practices in University libraries: a study, Desidoc: Journal of Library & Information Technology, 37, 6, pp. 417-424, (2017); Yu Holly H., The role of academic libraries in research data services (RDS) provision: opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017); Yu Siu Hong, Research Data Management: a library practitioner’s perspective, Public Services Quarterly, 13, 1, pp. 48-54, (2017); Carvalho Erika Rayanne Silva de, Bertin Fernando Cesar Lima Leite e Patricia Rocha Bello, Bibliotecas acadêmicas e gestão de dados de pesquisa: uma revisão bibliográfica, Investigación Bibliotecológica: archivonomía, bibliotecología e información, 35, 86, pp. 99-121, (2021)","","","Universidad Nacional Autonoma de Mexico","","","","","","0187358X","","","","Portuguese","Invest. Bibl.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85100484806"
"Doniparthi G.; Mühlhaus T.; Deßloch S.","Doniparthi, Gajendra (57218700467); Mühlhaus, Timo (16402484700); Deßloch, Stefan (7801627054)","57218700467; 16402484700; 7801627054","A Hybrid Data Model and Flexible Indexing for Interactive Exploration of Large-Scale Bio-science Data","2021","Communications in Computer and Information Science","1450 CCIS","","","27","37","10","2","10.1007/978-3-030-85082-1_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115186504&doi=10.1007%2f978-3-030-85082-1_3&partnerID=40&md5=b773c11480a7b6c9b5f7e9448ac92092","Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany; Computational Systems Biology, University of Kaiserslautern, Kaiserslautern, Germany","Doniparthi G., Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany; Mühlhaus T., Computational Systems Biology, University of Kaiserslautern, Kaiserslautern, Germany; Deßloch S., Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany","The advancement of high-throughput technologies has considerably increased the amount of research data generated from bio-science experiments. The integrated analysis of these large datasets provides opportunities to understand complex biological systems better. We present a novel research data management framework that uses a hybrid relational and NoSQL data model for interactively querying and exploring large-scale bio-science research data. Our framework uses a fast, scalable, space-efficient, and flexible indexing scheme leveraging bitmaps purpose-built for exploratory data analysis and supports containment, point, and range query types. © 2021, Springer Nature Switzerland AG.","Cross-omics; Interactive data exploration; Interval trees; Relational JSON; Research data management","Indexing (of information); Information management; Information systems; Information use; Query processing; Complex biological systems; Exploratory data analysis; High throughput technology; Indexing scheme; Integrated analysis; Interactive exploration; Research data managements; Space efficient; Large dataset","","","","","","","Copeland G.P., Khoshafian S., A Decomposition Storage Model; Gadepally V., Et al., BigDAWG version 0.1, IEEE High Performance Extreme Computing Conference (HPEC), pp. 1-7, (2017); Kaur K., Rani R., Managing data in healthcare information systems: Many models, one solution, Computer, 48, 3, pp. 52-59, (2015); Mishra C., Koudas N., Interactive Query Refinement; Sansone S.A., Rocca-Serra P., Field D., Maguire E., Taylor C., Et al., Toward interoperable bioscience data, Nat. Genet., 44, 2, pp. 121-126, (2012); Stonebraker M., Brown P., Zhang D., Becla J., SciDB: A database management system for applications with complex analytics, Comput. Sci. Eng., 15, 3, pp. 54-62, (2013)","G. Doniparthi; Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany; email: doniparthi@informatik.uni-kl.de","Bellatreche L.; Dumas M.; Karras P.; Matulevičius R.","Springer Science and Business Media Deutschland GmbH","","25th East-European Conference on Advances in Databases and Information Systems, ADBIS 2021 co-allocated with Workshops on DOING, SIMPDA, MADEISD, MegaData, CAoNS 2021","24 August 2021 through 26 August 2021","Tartu","264009","18650929","978-303085081-4","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85115186504"
"Zendulková D.; Rysula B.; Putalová A.","Zendulková, Danica (56278062400); Rysula, Boris (57222555989); Putalová, Andrea (57222551666)","56278062400; 57222555989; 57222551666","Representation of Slovak research information (a case study)","2021","Information (Switzerland)","12","3","137","","","","1","10.3390/info12030137","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103200704&doi=10.3390%2finfo12030137&partnerID=40&md5=be4fa542394ec4802bae9b2122346847","Slovak Centre of Scientific and Technical Information, Bratislava, 81104, Slovakia","Zendulková D., Slovak Centre of Scientific and Technical Information, Bratislava, 81104, Slovakia; Rysula B., Slovak Centre of Scientific and Technical Information, Bratislava, 81104, Slovakia; Putalová A., Slovak Centre of Scientific and Technical Information, Bratislava, 81104, Slovakia","In the light of the increasing importance of the societal impact of research, this article attempts to address the question as to how social sciences and humanities (SSH) research outputs from 2019 are represented in Slovak research portfolios in comparison with those of the EU-28 and the world. The data used for the analysis originate from the R&D SK CRIS and bibliographic Central Register of Publication Activities (CREPC) national databases, and WoS Core Collection/InCites. The research data were appropriate for the analysis at the time they were structured, on the national level; of high quality and consistency; and covering as many components as possible and in mutual relations. The data resources should enable the research outputs to be assigned to research categories. The analysis prompts the conclusion that social sciences and humanities research outputs in Slovakia in 2019 are appropriately represented and in general show an increasing trend. This can be documented by the proportion represented by the SSH research projects and other entities involved in the overall Slovak research outputs, and even the higher ratio of SSH research publications in comparison with the EU-28 and the world. Recommendations of a technical character include research data management, data quality, and the integration of individual systems and available analytical tools. © 2021 by the authors.","CREPČ central register of publication activity; Research areas classification; Research organisations; Research projects; Research results; Researchers; social sciences and humanities; current research information system; Web of science core collection","Behavioral research; Humanities computing; Quality control; Humanities research; Individual systems; Mutual relations; National database; Publication activities; Research data managements; Research outputs; Societal impacts; Information management","","","","","European Commission, EC; European Regional Development Fund, ERDF, (313011I407)","Funding text 1: A comprehensive picture of all aspects resulting from R&D activities is usually provided by the current research information system (CRIS). The scope and structure of the data to be registered in CRIS is determined by the methodology and standards for research information. This is primarily the CERIF format [14], supported by the European Commission, which is being developed by the international research information organisation euroCRIS. The basic entities (objects) of the CRIS system are:; Funding text 2: From the WoS database, it is also possible to obtain information on how many publications in the social sciences and humanities were the result of an R&D project. Out of 1214 publications in social sciences, 66% were published as a result of funding, with proceedings papers once again predominating. In arts and humanities, 19% out of 223 publications resulted from projects funded by various grant agencies. It is important to note that several publications may be an output of one project and, conversely, that one publication may be funded by several grant agencies, which renders analysis more complicated.","Správa o Stave Výskumu a Vývoja v Slovenskej Republike a jeho Porovnanie so Zahraničím za rok, (2017); Správa o Stave Výskumu a Vývoja v Slovenskej Republike a jeho Porovnanie so Zahraničím za rok, (2018); Správa o Stave Výskumu a Vývoja v Slovenskej Republike a jeho Porovnanie so Zahraničím za rok, (2019); Stratégia Výskumu a Inovácií pre Inteligentnú Špecializáciu Slovenskej Republiky, (2013); Fina F., Proven J., Using a CRIS to support communication of research: Mapping the publication cycle to deposit workflows for data and publications, Procedia Comput. Sci, 106, pp. 232-238, (2017); Biesenbender S., Petersohn S., Thiedig C., Using Current Research Information Systems (CRIS) to showcase national and institutional research (potential): Research information systems in the context of Open Science, Procedia Comput. Sci, 146, pp. 142-155, (2019); Pinto Sousa C., Simoes C., Amaral L., CERIF-Is the Standard Helping to Improve CRIS?, Procedia Comput. Sci, 33, pp. 80-85, (2014); Lee D.J., Stvilia B., Wu S., Toward a metadata model for research information management systems, Library Hi Tech, 38, pp. 577-592, (2018); Shearman A., Zendulkova D., Use of National and International Databases for Evaluation of International Project Award Potential of Slovak Research Organisations, Procedia Comput. Sci, 146, pp. 102-111, (2019); Gartner R., Cox M., Jeffery K., A CERIF-based schema for recording research impact, Electron. Library, 31, pp. 465-482, (2013); Markovets O., Pazderska R., Horpyniuk O., Syerov Y., Informational Support of Effective Work of the Community Manager with Web Communities, Proceedings of the CEUR Workshop Proceedings, Vol-2654: Proceedings of the International Workshop on Cyber Hygiene (CybHyg-2019), pp. 710-722, (2019); Molodetska K., Brodskiy Y., Fedushko S., Model of Assessment of Information-Psychological Influence in Social Networking Services Based on Information Insurance, Proceedings of the CEUR Workshop Proceedings, Vol 2616: Proceedings of the 2nd International Workshop on Control, Optimisation and Analytical Processing of Social Networks (COAPSN-2020), pp. 187-198; Azeroual O., Saake G., Abuosba M., Data Quality Measures and Data Cleansing for Research Information Systems, J. Digit. Inf. Manag, 16, pp. 12-21, (2018); Main Features of CERIF; Slovak Current Research Information System (SK CRIS); Centrálny Register Evidencie Publikačnej Činnosti CREPČ; Centrálny Informačný Portal pre výskum, vývoj a Inovácie. Štatistické Ukazovatele; Turna J., Noge J., Zendulkova D., The system SK CRIS, scientific publications and theses-mirror of Slovak science, Proceedings of the 11th International Conference on Current Research Information Systems, (2012); Sile L., Polonen J., Sivertsen G., Guns R., Engels T. C. E., Arefiev P., Teitelbaum R., Comprehensiveness of national bibliographic databases for social sciences and humanities: Findings from a European survey, Res. Eval, 27, pp. 310-322, (2018); (2017); Kulczycki E., Engels T. C. E., Polonen J., Bruun K., Duskova M., Guns R., Zuccala A., Publication patterns in the social sciences and humanities: Evidence from eight European countries, Scientometrics, 116, pp. 463-486, (2018); Guidelines for Collecting and Reporting Data on Research and Experimental Development, (2015); Číselník Odborov Vedy a Techniky; Duskova M., Central Registry of Publication Activity (CREPČ)-Slovakia, (2018)","D. Zendulková; Slovak Centre of Scientific and Technical Information, Bratislava, 81104, Slovakia; email: danica.zendulkova@cvtisr.sk","","MDPI AG","","","","","","20782489","","","","English","Information","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85103200704"
"Wade Bishop B.; Nobles R.; Collier H.","Wade Bishop, Bradley (57273741600); Nobles, Robert (57194258214); Collier, Hannah (57226268725)","57273741600; 57194258214; 57226268725","Research integrity officers’ responsibilities and perspectives on data management plan compliance and evaluation","2021","Journal of Research Administration","52","1","","76","101","25","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115738914&partnerID=40&md5=56b599cdb775530c1e5a2b232ae614f8","School of Information Sciences, University of Tennessee, 1345 Circle Park Dr. Room 454 Communications Bldg., Knoxville, 37996, TN, United States; Vice President for Research Administration Emory University, 1599 Clifton Road NE, Atlanta, 30322, GA, United States; Graduate Research Assistant School of Information Sciences, University of Tennessee, 1345 Circle Park Dr. Suite 451 Communications Bldg., Knoxville, 37996, TN, United States","Wade Bishop B., School of Information Sciences, University of Tennessee, 1345 Circle Park Dr. Room 454 Communications Bldg., Knoxville, 37996, TN, United States; Nobles R., Vice President for Research Administration Emory University, 1599 Clifton Road NE, Atlanta, 30322, GA, United States; Collier H., Graduate Research Assistant School of Information Sciences, University of Tennessee, 1345 Circle Park Dr. Suite 451 Communications Bldg., Knoxville, 37996, TN, United States","This paper presents findings from interviews with US Research Integrity Officers (RIOs) on their overall responsibilities as well as perspectives on Data Management Plans (DMPs). DMPs are formal documents describing the roles and activities for managing data during and after research. DMPs are now a required research criterion by many funding agencies globally. A purposive sample of Research Integrity Officers (RIOs) from the top ten US private and public universities were recruited for interviews using an open-ended questionnaire related to their job duties and perspectives on data management plan implementation and evaluation. Responses from 12 participants were transcribed, anonymized, and coded in NVivo. RIO backgrounds, duties, and perspectives varied. The mode number of staff/ faculty people dedicated to the RIO role at these institutions was a halftime appointment. All RIOs had some responsibilities related to Authorship, Publication, and Inventorship and Integrity and Information with 11 participants also responsible for offering some Responsible Conduct of Research (RCR) training. Most RIOs assumed that Principle Investigators are responsible for DMP compliance during sponsored projects as well as the long-term data management after a project ends. None of the twelve participants has received any Research Data Management training. Given the sea change in research practices, RIOs should have more training as data-intensive research emerges and DMPs become commonplace. © 2021, Society of Research Administrators International. All rights reserved.","Data Management Plan; Research Data Management; Research Integrity Officer; Responsible Conduct of Research","","","","","","National Science Foundation, NSF","overall job responsibilities and perspectives on data management plans (DMPs). This study addresses literature gaps for both RIOs’ responsibilities in general and their perspectives on DMPs. In 2011, the National Science Foundation (NSF) began requiring DMPs, and by 2016 all federal funding agencies began requiring similar documentation for any data generated from federally funded research activities (Holdren, 2013). DMPs are formal documents describing the roles and activities for managing data during and after research. Several US science funding agencies require researchers to submit a two-page document concerning data curation with data, including a variety of digital objects to enable reproducibility (e.g., notes, code, software, and so forth).","Ali Y., Effectiveness of data auditing as a tool to reinforce good Research Data Management (RDM) practice, 6th World Conference on Research Integrity, (2019); Bishop B. W., Hank C., Curation, digital, International Encyclopedia of Human Geography, (2020); Bonito A., Titus S., Wright D., Assessing the preparedness of Research Integrity Officers (RIOs) to appropriately handle possible research misconduct cases, Science and Engineering Ethics, 18, (2011); Campos-Varela I., Ruano-Ravina A., Misconduct as the main cause for retraction. A descriptive study of retracted publications and their authors, Gaceta Sanitaria, 33, 4, pp. 356-360, (2019); Frewer L., Hunt S., Brennan M., Kuznesof S., Ness M., Ritson C., The views of scientific experts on how the public conceptualize uncertainty, Journal of Risk Research, 6, 1, pp. 75-85, (2003); Public Health Service Policies on Research Misconduct, (2005); Holdren J. P., Memorandum for the Heads of Access to the results of federally funded scientific research, (2013); Jaguszewski J., Williams K., New roles for new times: Transforming liaison roles in research libraries, (2013); Koumoulos E. P., Sebastiani M., Romanos N., Kalogerini M., Charitidis C., Data Management Plan template for H2020 projects (Version v01.100419), (2019); Mayer T., Steneck N. H., Promoting research integrity in a global environment, (2011); Science literacy: Concepts, contexts, and consequences, (2016); Reproducibility and replicability in science, (2019); Rankings by total R&D expenditures, (2017); RIO Bootcamp; C.F.R, 42, (1989); Data availability; Resnik D. B., Shamoo A. E., The Singapore statement on research integrity, Accountability in Research, 18, 2, pp. 71-75, (2011); Data sharing and mining, (2017); Titus S. L., Wells J. A., Rhoades L. J., Repairing research integrity, Nature, 453, 7198, pp. 980-982, (2008); Smale N., Unsworth K., Denyer G., Barr D., The history, advocacy and efficacy of data management plans, (2018); Steneck N., ORI introduction to the responsible conduct of research, (2007); Van Loon J. E., Akers K. G., Hudson C., Sarkozy A., Quality evaluation of data management plans at a research university, IFLA Journal, 43, 1, pp. 98-104, (2017); Wilkinson M. D., Dumontier M., Aalbersberg I. J., Appleton G., Axton M., Baak A., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Witt M., Carlson J., Brandt D. S., Cragin M. H., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009); Wright D. E., Schneider P. P., Training the Research Integrity Officers (RIO): The federally funded, Journal of Research Administration, 41, 3, pp. 99-117, (2010)","B. Wade Bishop; School of Information Sciences, University of Tennessee, Knoxville, 1345 Circle Park Dr. Room 454 Communications Bldg., 37996, United States; email: wade.bishop@utk.edu","","Society of Research Administrators International","","","","","","15391590","","","","English","J. Res. Adm.","Article","Final","","Scopus","2-s2.0-85115738914"
"De Sarkar T.","De Sarkar, Tanmay (55213896400)","55213896400","Integrating research tools with library websites","2021","Library Hi Tech News","38","8","","16","21","5","1","10.1108/LHTN-09-2021-0059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117706369&doi=10.1108%2fLHTN-09-2021-0059&partnerID=40&md5=c55a4bc33f597c1ceb95dcc49ab38318","Central Library, University of Calcutta, Kolkata, India","De Sarkar T., Central Library, University of Calcutta, Kolkata, India","Purpose: The present investigation aims to measure the extent of the adoption of research tools among libraries of higher education (HE) institutes across the world based on the types of research tools and their diverse purposes of use. This study also intends to identify the current practices followed by the HE libraries to implement research tools. Design/methodology/approach: First, a stratified sampling method was used to select 130 HE libraries from four continents – Asia, Oceania, Europe and North America. Second, a two-step Web content analysis was followed to collect data from the selected libraries along the parameters chosen for the survey. Findings: This study gives an overview of the recent advances in the use of research tools by the libraries with numerous examples. Highlighting the differential rate of adoption of research tools across the regions, this study illustrates the degree of acceptance of research tools among the libraries. Research limitations/implications: This study limits itself to a handful of libraries with English websites in four regions only to avoid the language limitation of the researcher. Inaccessible websites of libraries of HE institutes were also excluded from the survey. Originality/value: Future researchers may use the evaluation instruments as basic tools to develop advanced research instruments to carry out Web content analysis in diverse spheres. This study guides librarians to develop an improved understanding of the requirements of an immersive online environment with enhanced accessibility to a multiplicity of research tools and facilities to provide improved research support throughout the entire research life cycle. © 2021, Emerald Publishing Limited.","Content analysis; Library; Research collaboration; Research data management; Research instrument; Research support; Research tool; Social media; Website","","","","","","","","Akwang N.E., A study of librarians' perceptions and adoption of web 2.0 technologies in academic libraries in Akwa Ibom state, Nigeria, The Journal of Academic Librarianship, 47, 2, (2021); Astuti S., Sayekti I., Krishna B., Google form in engineering mathematics: innovation in assignment method, Journal of Physics: Conference Series, 1613, 1, (2020); Bailey A., Back G., LibX - a Firefox extension for enhanced library access, Library Hi Tech, 24, 2, pp. 290-304, (2006); Balaji B.P., Vinay M.S., Shalini B.G., Js M.R., Web 2.0 use in academic libraries of top ranked Asian universities, The Electronic Library, 37, 3, pp. 528-549, (2019); Behera S.K., Using libguides/research guide to enhance personalized service in academic library, (2019); Burke J.J., Tumbleson B.E., Communicating, collaboration, and citing, Library Technology Reports, 52, 2, pp. 28-33, (2016); Chan A.K., Nickson C.P., Rudolph J.W., Lee A., Joynt G.M., Social media for rapid knowledge dissemination: early experience from the COVID‐19 pandemic, Anaesthesia, 75, 12, pp. 1579-1582, (2020); Chua A.Y.K., Goh D.H., A study of Web 2.0 applications in library websites, Library & Information Science Research, 32, 3, pp. 203-211, (2010); De Sarkar T., The prevalence of web browser extensions use in library services: an exploratory study, The Electronic Library, 33, 3, pp. 334-354, (2015); Fernandez P., Zotero: information management software 2.0, Library Hi Tech News, 28, 4, pp. 5-7, (2011); Garoufallou E., Charitopoulou V., Web 2.0 in library and information science education: the Greek case, New Library World, 113, 3-4, pp. 202-217, (2012); Greenberg R., Bar-Ilan J., ‘Ask a librarian’: comparing virtual reference services in an Israeli academic library, Library & Information Science Research, 37, 2, pp. 139-146, (2015); Gruzd A., Staves K., Wilk A., Connected scholars: examining the role of social media in research practices of faculty using the UTAUT model, Computers in Human Behavior, 28, 6, pp. 2340-2350, (2012); Guraya S.Y., The usage of social networking sites by medical students for educational purposes: a meta–analysis and systematic review, North American Journal of Medical Sciences, 8, 7, pp. 268-278, (2016); Hanrath S., Kottman M., Use and usability of a discovery tool in an academic library, Journal of Web Librarianship, 9, 1, pp. 1-21, (2015); Hupe M., EndNote X9, Journal of Electronic Resources in Medical Libraries, 16, 3-4, pp. 117-119, (2019); Jones E., Developing a library toolbar, Library Hi Tech News, 25, 9, pp. 7-9, (2008); Kapoor K.K., Tamilmani K., Rana N.P., Patil P., Dwivedi Y.K., Nerur S., Advances in social media research: past, present and future, Information Systems Frontiers, 20, 3, pp. 531-558, (2018); Kusnadi F.N., Rachmawati T.K., Syaf A.H., Sugilar H., 3D grapher application: mathematical spatial ability, Journal of Physics: Conference Series, 1869, 1, (2021); Li B., Wang W., Sun Y., Zhang L., Ali M.A., Wang Y., GraphER: token-centric entity resolution with graph convolutional neural networks, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 5, pp. 8172-8179, (2020); Linh N.C., A survey of the application of Web 2.0 in Australasian university libraries, Library Hi Tech, 26, 4, pp. 630-653, (2008); Mahmood K., Richardson J.V., Impact of web 2.0 technologies on academic libraries: a survey of ARL libraries, The Electronic Library, 31, 4, pp. 508-520, (2013); Makori E.O., Osebe N.M., Koha enterprise resource planning system and its potential impact on information management organizations, Library Hi Tech News, 33, 4, pp. 17-23, (2016); Mm R.Y., Asniati M., Anwar C., Development of google form based on scientific literacy principles for junior high school students in heat material, In Journal of Physics: Conference Series, 1467, 1, (2020); Rowlands I., Nicholas D., Russell B., Canty N., Watkinson A., Social media use in the research workflow, Learned Publishing, 24, 3, pp. 183-195, (2011); Santosh S., Adoption of web 2.0 applications in academic libraries in India, DESIDOC Journal of Library & Information Technology, 37, 3, pp. 192-198, (2017); Shabdar A., Integrating CRM with Zoho ecosystem, Mastering Zoho CRM, pp. 159-176, (2017); Sotiriadou P., Brouwers J., Le T.A., Choosing a qualitative data analysis tool: a comparison of NVivo and Leximancer, Annals of Leisure Research, 17, 2, pp. 218-234, (2014); Sugimoto C.R., Work S., Lariviere V., Haustein S., Scholarly use of social media and altmetrics: a review of the literature, Journal of the Association for Information Science and Technology, 68, 9, pp. 2037-2062, (2017); Tella A., Oladapo O.J., A comparative analysis of available features and web 2.0 tools on selected Nigerian and South African university library websites, The Electronic Library, 34, 3, pp. 504-521, (2016)","T. De Sarkar; Central Library, University of Calcutta, Kolkata, India; email: tdesarkar@caluniv.ac.in","","Emerald Group Holdings Ltd.","","","","","","07419058","","","","English","Libr. Hi Tech News","Article","Final","","Scopus","2-s2.0-85117706369"
"Gajbe S.B.; Tiwari A.; Gopalji; Singh R.K.","Gajbe, Sagar Bhimrao (57221079066); Tiwari, Amit (57221313896); Gopalji (57221854076); Singh, Ranjeet Kumar (57221852042)","57221079066; 57221313896; 57221854076; 57221852042","Evaluation and analysis of Data Management Plan tools: A parametric approach","2021","Information Processing and Management","58","3","102480","","","","8","10.1016/j.ipm.2020.102480","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100419019&doi=10.1016%2fj.ipm.2020.102480&partnerID=40&md5=064d1039209c1048a9998740ec70e0c0","Research Scholar at DRTC, Indian Statistical Institute, 8th Mile Mysore Road, Bangalore, 560059, India","Gajbe S.B., Research Scholar at DRTC, Indian Statistical Institute, 8th Mile Mysore Road, Bangalore, 560059, India; Tiwari A., Research Scholar at DRTC, Indian Statistical Institute, 8th Mile Mysore Road, Bangalore, 560059, India; Gopalji, Research Scholar at DRTC, Indian Statistical Institute, 8th Mile Mysore Road, Bangalore, 560059, India; Singh R.K., Research Scholar at DRTC, Indian Statistical Institute, 8th Mile Mysore Road, Bangalore, 560059, India","This paper explores the openly available DMP tools and forms a comparative analysis aimed at assisting researchers and data managers to formulate effective data management plans. Based on a literature review 14 DMP tools were selected and were evaluated using 45 selected parameters. The study enlists and enunciates the features of DMP tools, spots several gaps in DMP practices, and provides a few recommendations that can improve the existing tools and DMP practices. Compared to other related works, present work sheds extra light on percentage coverage of parameters by each tool and percentage coverage of tools by each parameter. It is identified that selected tools cover 50%–84% parameters, whereas 78% parameters are covered by half the selected DMP tools. Moreover, 28% of the tools cover 60% of the DMP assisting parameters. Additionally, co-occurrence of parameters and correlation among the tools are illustrated using matrices. It was found that co-occurrence of data description/summary/collection, documentation and metadata, findability, and accessibility parameters are relatively higher and all the selected tools are positively correlated to each other. The study is impactful for the researchers, librarians, data managers, and funding agencies for selecting an appropriate DMP tool as per their requirement. © 2021 Elsevier Ltd","Data Life Cycle; Data Management Plan; Data Management Plan tools; Research Data Management","Managers; Co-occurrence; Comparative analysis; Evaluation and analysis; Funding agencies; Literature reviews; Management plans; Parametric approach; Related works; Information management","","","","","","","Aleixandre-Benavent R., Vidal-Infer A., Alonso-Arroyo A., Peset F., Sapena A., Research data sharing in spain: Exploring determinants, practices, and perceptions, Data, 5, 2, (2020); Antonio M., Schick-Makaroff K., Doiron J., Sheilds L., White L., Molzahn A., Qualitative data management and analysis within a data repository, Western Journal of Nursing Research, (2019); Bakos A., Miksa T., Rauber A., Research data preservation using process engines and machine-actionable data management plans, Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNCS, 11057, pp. 69-80, (2018); Ball A., Review of data management lifecycle models, (2012); Baykoucheva S., Coping with “big data”: escience, Managing scientific information and research data, pp. 71-84, (2015); Bishop B., Gunderman H., Davis R., Lee T., Howard R., Samors R., Murphy F., Ungvari J., Data curation profiling to assess data management training needs and practices to inform a toolkit, Data Science Journal, 19, 1, (2020); Black E., Dmp assistant, Journal of Librarianship and Scholarly Communication, 6, 1, (2018); Bowman M., Maxwell R., A beginner's guide to avoiding protected health information (phi) issues in clinical research – with how-to's in redcap data management software, Journal of Biomedical Informatics, 85, pp. 49-55, (2018); Brand S., Bartlett D., Farley M., Fogelson M., Hak J.B., Hu G., Montana O.D., Pierre J.H., Proeve J., Qureshi S., Shen A., Stockman P., Chamberlain R., Neff K., A model data management plan standard operating procedure., Therapeutic Innovation & Regulatory Science, 49, 5, pp. 720-729, (2015); Bunkar A., Bhatt D., Perception of researchers & academicians of parul university towards research data management system & role of library: A study, DESIDOC Journal of Library and Information Technology, 40, 3, pp. 139-146, (2020); Cardoso J., Proenca D., Borbinha J., Machine-actionable data management plans: A knowledge retrieval approach to automate the assessment of funders’ requirements, Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNCS, 12036, pp. 118-125, (2020); Cauchick-Miguel P., Moro S., Rivera R., Amorim M., Data management plan in research: characteristics and development, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 319, pp. 3-14, (2020); Claibourn M.P., Bigger on the inside: building research data services at the university of virginia, Insights, 28, 2, pp. 100-106, (2015); Cope J., Using DMPonline with postgraduate research students, (2013); Welcome to datawiz knowledge base, (2017); Delserone L.M., At the watershed: Preparing for research data management and stewardship at the university of minnesota libraries, Library Trends, 57, 2, pp. 202-210, (2008); Welcome, (2010); Welcome to the dmptool: Create data management plans that meet institutional and funder requirements., (2011); Wizard for data management plan creation (experimental), (2015); Dogan G., Taskin Z., Aydinoglu A., Research data management in Turkey: A survey to build an effective national data repository, IFLA Journal, (2020); Donnelly M., Jones S., Checklist for a data management plan, Digital Curation Centre, 3, pp. 03-17, (2011); Donnelly M., Jones S., Pattenden-Fail J.W., DMP Online: the digital curation centre's web-based tool for creating, maintaining and exporting data management plans, International Journal of Digital Curation, 5, 1, pp. 187-193, (2010); Data stewardship wizard: Create smart data management plans for fair open science, (2015); Easy.dmp: Data management plan generator, (2015); Exner N., Data management support for faculty facing new funding mandates: The case of the u. s. Department of agriculture's national institute of food and agriculture, New Review of Academic Librarianship, 24, 1, pp. 90-104, (2018); Ezdmp: Data management plans made easy, (2018); Giorgio S., Ronzino P., Parthenos data management plan template for open research in archaeology, 2018 3rd digital heritage international congress (DigitalHERITAGE) held jointly with 2018 24th international conference on virtual systems multimedia (VSMM 2018), pp. 1-4, (2018); Gupta S., Muller-Birn C., A study of e-research and its relation with research data life cycle: A literature perspective, Benchmarking: An International Journal, 25, 6, pp. 1656-1680, (2018); Han J., Haihong E., Le G., Du J., Survey on nosql database, 2011 6th international conference on pervasive computing and applications, pp. 363-366, (2011); Holles J., Schmidt L., Graduate research data management course content: Teaching the data management plan (dmp), ASEE annual conference and exposition, conference proceedings, Vol. 2018-June, (2018); Houston L., Probst Y., Yu P., Martin A., Exploring data quality management within clinical trials, Applied Clinical Informatics, 9, 1, pp. 72-81, (2018); Data management plan (dmp) tool, (2011); Jones S., Pergl R., Hooft R., Miksa T., Samors R., Ungvari J., Davis R.I., Lee T., Data management planning: How requirements and solutions are beginning to converge, Data Intelligence, pp. 208-219, (2019); Kaari J., Researchers at arab universities hold positive views on research data management and data sharing, Evidence Based Library and Information Practice, 15, 2, pp. 168-170, (2020); Kamocki P., Mapelli V., Choukri K., Data Management Plan (dmp) for language data under the new General data protection Regulation (gdpr), pp. 135-139, (2019); Kennan M.A., Managing research data, Research methods: Information, systems, and contexts, pp. 505-515, (2018); Kim Y., Fostering scientists’ data sharing behaviors via data repositories, journal supplements, and personal communication methods, Information Processing & Management, 53, 4, pp. 871-885, (2017); Kuberek M., Guidance for creating a data management plan in horizon 2020 projects, (2018); Lefebvre A., Bakhtiari B., Spruit M., Exploring research data management planning challenges in practice, IT - Information Technology, 62, 1, pp. 29-37, (2020); de Leon M., de Ferrer L., From open access to open data: collaborative work in the university libraries of catalonia, LIBER Quarterly, 28, 1, (2018); Levitin A., Redman T., Quality dimensions of a conceptual view, Information Processing & Management, 31, 1, pp. 81-88, (1995); Mallery M., Dmptool: Guidance and resources for your data management plan, Technical Services Quarterly, 31, 2, pp. 197-199, (2014); Melero R., Navarro-Molina C., Researchers’ attitudes and perceptions towards data sharing and data reuse in the field of food science and technology, Learned Publishing, 33, 2, pp. 163-179, (2020); Miksa T., Cardoso J., Borbinha J., Framing the scope of the common data model for machine-actionable data management plans, pp. 2733-2742, (2019); Miksa T., Simms S., Mietchen D., Jones S., Ten principles for machine-actionable data management plans, PLoS Computational Biology, 15, 3, (2019); Nightingale A., Data management plans: Time wasting or time saving?, Biochemist, 42, 3, pp. 38-39, (2020); Welcome to argos: Create, link, share data management plans, (2017); Parham S., Carlson J., Hswe P., Westra B., Whitmire A., Using data management plans to explore variability in research data management practices across domains, International Journal of Digital Curation, 11, pp. 53-67, (2016); PARTHENOS D.M.P.; Pergl R., Hooft R., Suchanek M., Knaisl V., Slifka J., ”data stewardship wizard”: A tool bringing together researchers, data stewards, and data experts around data management planning, Data Science Journal, 18, 1, (2019); Rdmo: Research data management organiser, (2017); Redkina N., Current trends in research data management, Scientific and Technical Information Processing, 46, 2, pp. 53-58, (2019); Reilly M., Dryden A., Building an online data management plan tool, Journal of Librarianship & Scholarly Communication, 1, 3, pp. 1-11, (2013); Romanos N., Kalogerini M., Koumoulos E., Morozinis A., Sebastiani M., Charitidis C., Innovative data management in advanced characterization: Implications for materials design, Materials Today Communications, 20, (2019); Sallans A., Donnelly M., Dmp online and dmptool: Different strategies towards a shared goal, IJDC, 7, 2, pp. 123-129, (2012); Smale N., Unsworth K.J., Denyer G., Barr D.P., The history, advocacy and efficacy of data management plans, bioRxiv, (2018); Stodden V., Ferrini V., Gabanyi M., Lehnert K., Morton J., Berman H., Open access to research artifacts: Implementing the next generation data management plan, Proceedings of the Association for Information Science and Technology, 56, 1, pp. 481-485, (2019); Sutter R.D., Wainscott S.B., Boetsch J.R., Palmer C.J., Rugg D.J., Practical guidance for integrating data management into long-term ecological monitoring projects, Wildlife Society Bulletin (2011-), 39, 3, pp. 451-463, (2015); Swauger S., Dmptool, Charleston Advisor, 16, 3, pp. 12-15, (2015); Tub-dmp, (2015); UWA D.M.P.; Vitale C., Moulaison Sandy H., Data management plans: A review, DESIDOC Journal of Library and Information Technology, 39, 6, pp. 322-328, (2019); Wiley C., Data sharing and engineering faculty: An analysis of selected publications, Science and Technology Libraries, 37, 4, pp. 409-419, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Williams M., Bagwell J., Nahm Zozus M., Data management plans: the missing perspective, Journal of Biomedical Informatics, 71, pp. 130-142, (2017); Wittman J., Aukema B., A guide and toolbox to replicability and open science in entomology, Journal of Insect Science, 20, 3, (2020)","Gopalji; email: gopalji@drtc.isibang.ac.in","","Elsevier Ltd","","","","","","03064573","","IPMAD","","English","Inf. Process. Manage.","Article","Final","","Scopus","2-s2.0-85100419019"
"Gargiulo P.; Galimberti P.; Tammaro A.M.; Zane A.","Gargiulo, Paola (57221373224); Galimberti, Paola (35789595000); Tammaro, Anna Maria (8554921900); Zane, Antonella (57204030261)","57221373224; 35789595000; 8554921900; 57204030261","FAIR RDM (Research data Management): Italian initiatives towards EOSC implementation","2021","CEUR Workshop Proceedings","2816","","","42","52","10","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101773479&partnerID=40&md5=0b409b7ddcb2f0b9d7cbdd148e0b18bc","IOSSG; Università di Milano, Italy; Università di Parma, Italy; Università di Padova, Italy","Gargiulo P., IOSSG; Galimberti P., Università di Milano, Italy; Tammaro A.M., Università di Parma, Italy; Zane A., Università di Padova, Italy","EOSC's vision of opening the research cycle and facilitating the collaboration of researchers will certainly lead to better quality science and in theory it is easy to share. However, its realization must be made possible through policies at national and institutional level. There is a need of creating a socio-technical infrastructure in order to improve FAIR RDM in Italy. FAIR RDM initiatives in Italy are still based on communities of practice that voluntarily carry out national awareness and training activities. The authors have investigated the perception of some leaders of FAIR RDM initiatives in Italy with respect to good practices, challenges and the strategic vision that should be sought. Findings evidence that to implement EOSC in Italy, investments and policies are needed to support the initiatives and services launched by the forerunner institutions. There is also the need of trained data stewards as competent professionals. Copyright © 2021 for this paper by its authors.","EOSC; FAIR data; FAIR RDM initiatives Italy; Research data management RDM","Information management; Investments; Communities of Practice; Good practices; Research cycle; Research data managements; Sociotechnical; Strategic vision; Training activities; Digital libraries","","","","","","","EOSC Pillar National Initiatives GARR [Report], (2020); Fava I., Gargiulo P., What do Italian Researchers think about Open Research Data?, (2013); Galimberti P., Open science and evaluation SCIRES-IT - SCIentific RESearch and Information Technology, 10, pp. 65-70, (2020); Galimberti P., La valutazione della ricerca a livello istituzionale: problemi, sfide e possibili soluzioni. Il caso dell'Italia, Rassegna italiana di valutazione, XV, 52, pp. 66-80, (2012); Gargiulo P., Open Science, open research data and the role of IOSSG SCIRES-IT - SCIentific RESearch and Information Technology, 10, pp. 53-58, (2020); Borgman C.L., Et al., Knowledge infrastructures in science: data, diversity, and digital libraries, Int. J. Digit. Libr, 16, 3-4, pp. 207-227, (2015); Beamer J.E., Digital Libraries for Open Science: Using a Socio-Technical Interaction Network Approach, Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, 988, (2019); Research assessment in the transition to Open Science, (2019)","","Dosso D.; Ferilli S.; Manghi P.; Poggi A.; Serra G.; Silvello G.","CEUR-WS","","17th Italian Research Conference on Digital Libraries, IRCDL 2021","18 February 2021 through 19 February 2021","Virtual, Padua","167286","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85101773479"
"Daniel S.; Grieb D.; Schwartz A.L.; Weiß P.","Daniel, Silvia (57202894119); Grieb, Dorothée (57402429500); Schwartz, Anna Lisa (57402572000); Weiß, Philipp (57402288600)","57202894119; 57402429500; 57402572000; 57402288600","Orientiert am Bedarf der Wissenschaft Zwei Umfragen der Fachinformationsdienste Geschichtswissenschaft und Altertumswissenschaften","2021","Zeitschrift fur Bibliothekswesen und Bibliographie","68","5","","272","281","9","0","10.3196/186429502068519","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122364063&doi=10.3196%2f186429502068519&partnerID=40&md5=aa5322367457dca604b7a01429167830","Zentrums für elektronisches Publizieren/Fachinformation Geschichte, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany; Fachinformationsdienst Altertumswissenschaften, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany; Fachinformationsdienst Geschichtswissenschaft, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany","Daniel S., Zentrums für elektronisches Publizieren/Fachinformation Geschichte, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany; Grieb D., Fachinformationsdienst Altertumswissenschaften, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany; Schwartz A.L., Fachinformationsdienst Geschichtswissenschaft, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany; Weiß P., Fachinformationsdienst Geschichtswissenschaft, Bayerische Staatsbibliothek, Ludwigstraße 16, München, 80539, Germany","After five years of funding, the Specialised Information Services for Classical Studies and for Historical Studies conducted two online surveys among scholars at the end of 2020. The current services were to be evaluated on the basis of a representative survey; the participants were also asked to assess their own individual research practices. The article provides an overview of the results in the areas of research, specialist bibliographies, collection building, interlibrary loans, Specialised Information Service (SIS) licences, open access, research data management and information behaviour. Based on this, the authors formulate propositions for future SIS work and evaluate surveys as a feedback measure between the specialised academic communities and the Specialised Information Services. © 2021 Vittorio Klostermann. All rights reserved.","","","","","","","","","SOMMeR Dorothea, Fachinformationsdienste an der Bayerischen Staatsbibliothek: ergebnisse des Transformationsprozesses und Ausblick, ABI Technik, 39, 4, pp. 271-281, (2019); DFG-Vordruck 12.10 – 11/20: Merkblatt und ergänzender Leitfaden Fachinformationsdienste für die Wissenschaft, (2020); FORSCHUNGSGeMeiNSCHAFT DeUTSCHe, Weiterentwicklung des Förderprogramms»Fachinformationsdienste für die Wissenschaft«: Stellungnahme der Kommission zur Evaluierung des Förderprogramms»Fachinformationsdienste für die Wissenschaft«, (2019); PFeiFeNBeRGeR Regina, ich habe mich noch nicht mit Pollux beschäftigt« – eine Zufriedenheits- und Bedarfsanalyse des Fachinformationsdienstes Politikwissenschaft, 8, 1, (2021); KULLiK Andrea, KReUSCH Julia, Bitte keine neuen Repositorien, bitte keine neuen Portale«. ergebnisse einer Online-Befragung des Fachinformationsdienstes erziehungswissenschaft und Bildungsforschung, O-Bib. Das Offene Bibliotheksjournal, 4, 2, pp. 56-71, (2017); DANieL Silvia, HORSTKeMPeR Gregor, Fachinformationsdienst für eine große Geisteswissenschaft. Der FiD Geschichtswissenschaft im Spannungsverhältnis von Förderpolitik, Nutzerwünschen und bibliothekarischem Handlungsrahmen, Zeitschrift für Bibliothekswesen und Bibliographie, 65, 2 – 3, pp. 80-84, (2018); Für die DHB wurde auch die technisch aufwendige einspielung der Daten der Jahresberichte für deutsche Geschichte von den Wissenschaftler*innen weiterhin gewünscht; Vor allem Promovierte und Habilitierte stellen diese Anforderungen; Da G vier und A drei Antwortoptionen bei dieser Frage angeboten haben, sind die ergebnisse allerdings nicht komplett vergleichbar; Anschaffungswünsche und Digitalisierungsaufträge an die lokale Bibliothek (A: 69%; G: 65%), private Beschaffung z.B. durch Kauf oder Vermittlung über private Kontakte (A: 67%; G: 62%), Suche nach Titeln bei Online-Anbietern wie z.B. Academia oder Google Books (A: 82%; G: 53%), Aufsuchen anderer Bibliotheken; Zu aufwendig wäre die Aufbereitung für 11% der Altertumswissenschaftler*innen und für 15% der Historiker*innen, nicht veröffentlichen können oder wollen 15% der Altertumswissenschaftler*innen und 13% der Historiker*innen; Fachbezogene Mailingliste (A: 86%; G: 73%), Fachportale wie z.B. historicum.net, Clio-online, H-Soz-Kult bzw. Propylaeum, KiRKe (A: 58%; G: 86%), Social Media; Dies entspricht auch dem ergebnis der Umfrage des FiD Germanistik: MiCHeL, LARRAT, (2021)","","","Vittorio Klostermann GmbH","","","","","","00442380","","","","German","Z. Bibliothekswes. Bibliogr.","Article","Final","","Scopus","2-s2.0-85122364063"
"Mabweazara R.M.; Zinn S.","Mabweazara, Rangarirai Moira (57222292258); Zinn, Sandy (56289796700)","57222292258; 56289796700","Expectations of academics from the 21st century academic library: Experiences from zimbabwe","2020","African Journal of Library Archives and Information Science","30","2","","99","111","12","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102180301&partnerID=40&md5=b92ea1272aa3b0ad58f86fa2fd2f658e","Department of Library and Information Science, University of the Western Cape, Cape Town, South Africa","Mabweazara R.M., Department of Library and Information Science, University of the Western Cape, Cape Town, South Africa; Zinn S., Department of Library and Information Science, University of the Western Cape, Cape Town, South Africa","The study investigated the needs and expectations of academics from two academic libraries. To explore this broad question, the study sought to determine the scholarly communication, research data management, collaboration, teaching and learning, and new pedagogical needs and expectations of academics. The study also identified the social web tools used by academics for maintaining their research. The literature review is anchored in the study’s objectives. A quantitative approach that deployed a web-based questionnaire is adopted. Data was collected from a combined sample of 227 academics and the response rate was 60%. The findings reinforce established studies by highlighting that academics expect their libraries to provide scholarly communication support, online information literacy sessions, teaching and learning support, and co-hosting workshops, co-teaching information literacy, co-deploying new technologies and co-publishing. In spite of these expectations, the Zimbabwean academic library is yet to fulfil its expected role of providing the necessary services and resources to academics. It is recommended that academic libraries in Zimbabwe find the means to address the demands made by academics. Further research should compare the views of both academics and the academic library. © 2020, Archlib and Information Services Ltd. All rights reserved.","Academic Library; Higher Education; Zimbabwe","","","","","","","","Abduldayan F.J., Dang T.L., Karemani A., Obadia S., The Role of Academic Libraries in Enhancing Workflow in African Universities, In: International Conference on Information and Communication Technology and Its Applications (ICTA), (2016); ACRL Scholarly Communication Toolkit, (2017); Afebende G.B., Ma L.F.H., Mubarak M., Torrens A.F., Forreira S., Beasley G., Chu C.M., Ford B.J., A Pulse on the World of Academic Libraries: Six Regions and Six Insights, College and Research Libraries News, 77, 8, pp. 389-395, (2016); Al-Majed A., Al-Kathiri F., Al-Ajmi S., Al-Hamam S., 21<sup>st</sup> Century Professional Skill Training Programs for Faculty Members: A Comparative Study between Virginia Tec University, American University and King Saud University, Higher Education Studies, 7, 3, pp. 122-131, (2017); Atkinson J., Collaboration and Academic Libraries: An Overview and Literature Review, Collaboration and the Academic Library: Internal and External, Local and Regional, National and International, pp. 11-33, (2018); Avdeeva T.I., Kulik A.D., Koseva L.A., Zhilkina T.A., Belogurov A.Y., Problems and Prospects of Higher Education System Development in Modern Society, European Research Studies Journal, 20, 4B, pp. 112-124, (2017); Babbie E.R., The Practice of Social Research, (2007); Bell S.J., New Information Marketplace Competitors: Issues and Strategies for Academic Libraries, Portal: Libraries and the Academy, 2, 2, pp. 277-303, (2002); Bell S., Dempsey L., Fister B.,  Anniversary, (2015); Borrego A., Anglada L., Faculty Information Behaviour in the Electronic Environment: Attitudes Towards Searching, Publishing and Libraries, New Library World, 117, 3-4, pp. 173-185, (2015); Braddlee D., Vanscoy A., Bridging the Chasm: Faculty Support Roles for Academic Librarians in the Adoption of Open Educational Resources, College and Research Libraries, 80, 4, pp. 426-464, (2019); Brochu L., Burns J., Librarians and Research Data Management: A Literature Review Commentary from a Senior Professional and a New Professional Librarian, New Review of Academic Librarianship, 25, 1, pp. 49-58, (2019); Buwule R.S., Mutula S.M., Research Support Services to Small and Medium Enterprises by University Libraries in Uganda: An Entrepreneurial and Innovation Strategy, South African Journal of Information Management, 19, 1, pp. 1-8, (2017); Chawinga W.D., Zinn S., Global Perspectives of Research Data Sharing: A Systematic Literature Review, Library and Information Science Research, 41, 2, pp. 109-122, (2019); The Challenges of Revenue Generation in State Universities: The Case of Zimbabwe., 6, 1, pp. 1-10, (2020); Chisita C., Fombad M., The Conundrum of Resource Sharing in Zimbabwe: Case of Academic Libraries,  International Federal Library Associations and Institutions Interlending and Document Supply Conference (ILDS) Conference., (2019); Chiware E.R.T., Becker D.A., Research Data Management Services in Southern Africa: A Readiness Survey of Academic and Research Libraries, African Journal of Library, Archives and Information Science, 28, 1, pp. 1-16, (2018); Cloete N., Bailey T., Pillay P., Bunting I., Maassen P., Universities and Economic Development in Africa, (2011); Cowan S., Eva N., Changing Our Aim: Infiltrating Faculty with Information Literacy, Communications in Information Literacy, 10, 2, pp. 163-177, (2016); Daniel D., Journal Articles are the most widely used Information Resource for Research and Teaching in All Academic Disciplines, Evidence Based Library and Information Practice, 11, 3, pp. 99-101, (2016); Echezona R.I., Chigbu E.N., User Expectations and Innovative Strategies for Improved Patronage in University Libraries in Nigeria, African Journal of Library, Archives and Information Science, 28, 1, pp. 93-105, (2018); Fatkullina F., Morozkina E.A., Suleimanova A., Modern Higher Education: Problems and Perspectives, Procedia: Social and Behavioural Sciences, 214, pp. 571-577, (2015); Fullan M.A., Langworthy M., A Rich Seam: How New Pedagogies Find Deep Learning, (2014); Fullard A., Using the ACRL Framework for Information Literacy to Foster Teaching And, (2016); Learning Partnerships, 82, 2, pp. 46-56; Defining the 21st Century Technological Research Library, Georgia Institute of Technology Library, (2013); Hallam G., Thomas A., Beach B., Creating a Connected Future through Information and Digital Literacy: Strategic Directions at the University of Queensland Library, Journal of the Australian Library and Information Association, 67, 1, pp. 42-54, (2018); Hills C., Meeting Community Needs: A Practical Guide for Librarians (Practical Guides for Librarians, No. 21), The Australian Library Journal, 65, 4, (2016); The Australian Library Journal; Jaguszewski J.M., Williams K., New Roles for New Times: Transforming Liaison Roles in Research Libraries, (2013); Jain P., Akakandelwa A., Challenges of Twenty-First Century Academic Libraries in Africa, African Journal of Library, Archives and Information Science, 26, 2, pp. 145-153, (2016); Jaring P., Back A., How Researchers Use Social Media to Promote their Research and Network with Industry, Technology Innovation Management Review, 7, 8, pp. 32-39, (2017); Joubert M., Costas R., Getting to Know Science Tweeters: A Pilot Analysis of South African Twitter Uses, Tweeting About Research Articles, Journal of Altmetrics, 2, 1, pp. 2-15, (2019); Enhancing Access to Electronic Resources Through Collaborations and E-Document Delivery: Experiences of University Libraries in Kenya,  General Conference International Federal Library Associations and Institutions, World Library Information Congress, Cape Town., pp. 15-21, (2015); Kiran K., Service Quality and Customer Satisfaction in Academic Libraries: Perspectives From a Malaysian University, Library Review, 59, 4, (2010); Scholarly Communication and the Academic Library: Perceptions and Recent Developments, (2019); Klain-Gabbay L., Shoham S., The Role of Academic Libraries in Research and Teaching, Journal of Librarianship and Information Science, 51, 3, pp. 721-731, (2019); Krejcie R.V., Morgan D.W., Determining Sample Size for Research Activities, Educational and Psychological Measurement, 30, 3, pp. 607-610, (1970); The Role Libraries. [Online].https://libereurope. eu/wp-content/uploads/2017/12/liber-fair-data, Implementing FAIR Data Principles, (2017); Who We Are, Where we are from and where we are going, Lupane State University, (2020); Mabweazara R.M., Zinn S., Assessing the Appropriation of Social Media by Academic Librarians in South Africa and Zimbabwe, South African Journal of Libraries and Information Science, 82, 1, pp. 1-12, (2016); Majoni C., Challenges Facing Universities in Zimbabwe, Greener Journal of Education and Training Studies, 2, 1, pp. 20-24, (2014); Mathuews K., Harper D., One Size Does Not Fit All: Maintaining Relevancy in the Modern Makerspace Movement, College and Research Libraries News, 79, 7, pp. 358-359, (2018); Matthews D., If You Love Research, Academia may not be for you, Times Higher Education, (2018); Mawere T., Sai K., An Investigation on E-Resource Utilisation among University Students Developing Country: A Case of Great Zimbabwe University, South African Journal of in a Information Management, 20, 1, pp. 1-7, (2018); Historical Note, (2020); Mohamed S., A Critical Praxis in the Information Literacy Education Classroom Using The ACRL Framework for Information Literacy for Higher Education, Communications in Computer and Information Science, pp. 506-521, (2019); Mugwisi T., Role of Librarians in Teaching Information Literacy in Zimbabwe and South African Universities: A Comparative Study, Mousaion, 33, 1, pp. 23-42, (2015); Mushemeza E.D., Opportunities and Challenges of Academic Staff in Higher Education in Africa, International Journal of Higher Education, 5, 3, pp. 236-246, (2016);  Century Innovation Environment: Proceedings of a Workshop – in Brief, (2019); Ngibe M., Lekhaya L.M., Perceptions of Research Structures and Service Quality within Various Faculties at DUT: Staff and Students Perspectives, Problems and Perspectives in Management, 14, 1, pp. 63-71, (2016); Ocholla L., Mutsvunguma G., Hadebe Z., The Impact of New Information Services on Teaching, Learning and Research at University of Zululand Library. South African Journal of Libraries and Information, Science, 82, 2, pp. 11-19, (2016); Onyancha O.B., Social Media and Research: An Assessment of the Coverage of South African Universities in Researchgate, Web of Science and Webometrics Ranking of World Universities, South African Journal of Libraries and Information Science, 81, 1, pp. 1-13, (2015); Onyancha O.B., Navigating the Rising Metrics Tide in the 21<sup>st</sup> Century: Which Way for Academic Librarians in Support of Researchers in Sub-Saharan Africa?, South African Journal of Libraries and Information Science, 84, 2, pp. 1-13, (2018); Pham H.T., Tanner K., Collaboration between Academics and Library Staff: A Structurationist Perspective, Australian Academic and Research Libraries, 46, 1, pp. 2-18, (2015); Mapping the Future of Academic Libraries: A Report for Society of College, National and University Libraries (SCONUL), (2017); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influencers, Plos ONE, 9, 12, pp. 1-28, (2014); Pontika N., Roles and Jobs in the Open Research Scholarly Communications Environment: Analysing Job Descriptions to Predict Future Trends, LIBER Quarterly, 29, pp. 1-20, (2019); Raju R., From Life Support to Collaborative Partnership: A Local/Global View for Academic Libraries in South Africa, College and Research Libraries News, 79, 1, pp. 30-33, (2018); Renwick S., Winter M., Gill M., Managing Research Data at an Academic Library in a Developing Country, International Federation of Library Associations and Institutions Journal, 43, 1, pp. 51-64, (2017); The Value of Libraries for Research and Researchers, UK: Research Information Network and Research Libraries UK, (2011); Rolloff E.K., We’re Engaged: ACommunity-University Library Collaboration, Metropolitan Universities, 24, 3, pp. 20-35, (2013); Tang R., Hu Z., Providing Research Data Management Services in Libraries: Preparedness, Roles, Challenges and Training for RDM Practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Thomas D., Saib M., Collaboration between Academics and Librarians: The Case of the Durban University of Technology, Proceedings of the International Association of Science and Technological University Libraries Conference, (2013); Tshuma T., Chigada J., Analysing Information Literacy Practices at Selected Academic Libraries in Zimbabwe, South African Journal of Information Management, 20, 1, pp. 1-7, (2018); Unal Y., Chowdhury G., Kurbanoglu S., Boustany J., Walton G., Research Data Management and Data Sharing Behaviour of University Researchers, Information Research, 24, 1, (2019); Wang K., Zhu C., MOOC-Based Flipped Learning in Higher Education: Students’ Participation, Experience and Learning Performance, International Journal of Educational Technology in Higher Education, 16, 1, pp. 33-50, (2019); Wilkinson F., Lubas R., Practical Strategies for Academic Library Managers: Leading with Vision through All Levels, (2016); Young S.W.H., Choo Z., Chandler A., User Experience Methods and Maturity in Academic Libraries, Information Technology and Libraries, 39, 1, pp. 1-31, (2020)","","","Archlib and Information Services Ltd","","","","","","07954778","","","","English","Afr. J. Libr. Arch. Inf. Sci.","Article","Final","","Scopus","2-s2.0-85102180301"
"Gupta N.; Arora S.; Chakravarty P.R.","Gupta, Nidhi (57223388876); Arora, Surbhi (57223093530); Chakravarty, Prof. Rupak (36674495400)","57223388876; 57223093530; 36674495400","Science Mapping and Visualization of Research Data Management (RDM): Bibliometric and Scientometric Study","2021","Library Philosophy and Practice","2021","","","1","23","22","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115385237&partnerID=40&md5=331c60faf50de408b586e77219134ed5","Department of Library & Information Science, Panjab University Chandigarh; Department of Library & Information Science, Panjab University, Chandigarh; Department of Library & Information Science, Panjab University, Chandigarh, 160014","Gupta N., Department of Library & Information Science, Panjab University Chandigarh; Arora S., Department of Library & Information Science, Panjab University, Chandigarh; Chakravarty P.R., Department of Library & Information Science, Panjab University, Chandigarh, 160014","Research Data Management (RDM) is an ever-evolving phenomenon focused to augment and manage the research capital of an organization, especially in context with developing countries. This study aims at to understand and highlight the state of art of RDM literature between 1989 and 2021 using Web of Science Core Collection (WoSCC) database and VOSviewer software with a bibliometric or scientometric approach thereby highlighting the most influential authors, countries, journals, institutions, and to their co-authorship pattern, co- citation pattern, bibliographic coupling pattern, the co-occurrence of keywords pattern in the field of RDM.The data comprises of a total of 797 documents, further analysed by using VOSviewer software. Visualization analysis reveales that the number of publications related to RDM are increasing year by year, reaching a peak in 2020. The most productive author in this field is Rafael Alexandrine-benavent and USA is the most productive country published on RDM. The Zeitschrift fur Bibliothekswesen und Bibliographie Journal ismost influential journal. The most frequently used keywords are data sharing, research data management, research data, science and metadata. The analysis shows collaboration relation between authors, countries and sources. The visualizations conducted on this topic offer exploratory information on current status and to identify the main trends in RDM research and its future research initiatives. © 2021. All Rights Reserved.","Research data management; Science mapping. Co-citation. Bibliographic coupling. Cooccurrence","","","","","","","","Baier-Fuentes H, Cascon-Katchadourian J, Sanchez AM, Herrera-Viedma E, Merigo J., A Bibliometric Overview of the International Journal of Interactive Multimedia and Artificial Intelligence, International Journal of Interactive Multimedia and Artificial Intelligence, 5, 3, (2018); Cabrera LC, Talamini E, Dewes H., What about scientific collaboration in agriculture? A bibliometric study of publications about wheat and potato (1996-2016), Pesquisa Brasileira em Ciência da Informação e Biblioteconomia, 13, 1, (2018); Corrall S, Kennan MA, Afzal W., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research, Library Trends, 61, 3, pp. 636-674, (2013); Data Lifecycle | NNLM, (2015); Elango B., A Bibliometric Analysis of Franchising Research (1988-2017), The Journal of Entrepreneurship, 28, 2, pp. 223-249, (2019); Gajdacs M., A bibliometric analysis of Acta Pharmaceutica Hungarica (1965-2018), Acta Pharmaceutica Hungarica, 89, 1, pp. 23-29, (2019); Gil-Domenech D, Berbegal-Mirabent J, Merigo JM., STEM Education: A Bibliometric Overview, Modelling and Simulation in Management Sciences, pp. 193-205, (2019); Heersmink R, van den Hoven J, van Eck NJ, van den Berg J., Bibliometric mapping of computer and information ethics, Ethics and Information Technology, 13, 3, pp. 241-249, (2011); Koltay T., Data governance, data literacy and the management of data quality, IFLA Journal, 42, 4, pp. 303-312, (2016); Onyancha OB., Open Research Data in Sub-Saharan Africa: A Bibliometric Study Using the Data Citation Index, Publishing Research Quarterly, 32, 3, pp. 227-246, (2016); do Prado JW, de Castro Alcantara V, de Melo Carvalho F, Vieira KC, Machado LKC, Tonelli DF., Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968-2014), Scientometrics, 106, 3, pp. 1007-1029, (2016); Stobierski T., (2021); Surkis A, Read K., Research data management, Journal of the Medical Library Association: JMLA, 103, 3, pp. 154-156, (2015); What are Scientometrics and Bibliometrics?; Research data management explained; Zhang L, Eichmann-Kalwara N., Mapping the Scholarly Literature Found in Scopus on “Research Data Management”: A Bibliometric and Data Visualization Approach, Journal of Librarianship and Scholarly Communication, 7, 1, (2019); Zhong B, Wu H, Li H, Sepasgozar S, Luo H, He L., A scientometric analysis and critical review of construction related ontology research, Automation in Construction, 101, pp. 17-31, (2019)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85115385237"
"Pradhan P.; Zala L.N.","Pradhan, Pallab (57222044714); Zala, Lavji N (57222541689)","57222044714; 57222541689","Bibliometrics Analysis and Comparison of Global Research Literatures on Research Data Management extracted from Scopus and Web of Science during 2000 - 2019","2021","Library Philosophy and Practice","2021","","","","","","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108781974&partnerID=40&md5=51c00c9f929e367414c0fbf0d329e80f","Information and Library Network (INFLIBNET) Centre, Gandhinagar, 382007, Gujarat, India; Department of Library & Information Science, Sardar Patel University, Vallabh Vidyanagar, 388120, Gujarat, India","Pradhan P., Information and Library Network (INFLIBNET) Centre, Gandhinagar, 382007, Gujarat, India; Zala L.N., Department of Library & Information Science, Sardar Patel University, Vallabh Vidyanagar, 388120, Gujarat, India","Researchers, to conduct any bibliometric analysis prefer to retrieve publications data mostly from Elsevier’s Scopus or/and Clarivate Analytics’ Web of Science (WoS) databases, though many other platforms/databases, i.e. Google Scholar, Dimensions, Crossref, PubMed, etc. are now available those are providing bibliographic data of publications. This study is based on the globally published literatures on research data management during 2000 – 2019 (20 years of duration) data extracted from the Scopus & Web of Science (WoS) databases and their Merged file. The analysis and results compares the similarity and differences in between Scopus & WoS, and further each one of them with the Merged file. The study reveals that around 32% of globally published literatures on research data management were indexed in both the Scopus and WoS databases. It compares both the sources in terms of parameters like annual literatures growth & trends, top authors production, authorship & collaboration pattern, most relevant sources & affiliations, country scientific production and international collaboration, etc. along with the merged file of both the datasets as well wherever possible. © 2021. All Rights Reserved.","Bibliometric Analysis; Bibliometrix; BiblioShiny; Research Data Management; Scopus; Web of Science","","","","","","","","Archambault E., Campbell D., Lariviere Y. G., Comparing Bibliometric Statistics Obtained From the Web of Science and Scopus, Journal of the American Society for Information Science and Technology, 64, July, pp. 1852-1863, (2013); Bakkalbasi N., Bauer K., Glover J., Wang L., Three options for citation tracking: Google Scholar, Scopus and Web of Science, Biomedical Digital Libraries, 3, 1, (2006); Chirici G., Assessing the scientific productivity of Italian forest researchers using the Web of Science, SCOPUS and SCIMAGO databases, IForest - Biogeosciences and Forestry, 5, 1, pp. 101-107, (2012); Echchakoui S., Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019, Journal of Marketing Analytics, 8, 3, pp. 165-184, (2020); Fernandez Maria Isabel Escalona, Barbosa Pilar Lagar, Guerrero A. P., Web of Science Vs. Scopus: Un Estudio Cuantitativo En Ingeniería Química, Anales de Documentación, 13, pp. 159-175, (2010); Gavel Y., Iselid L., Web of Science and Scopus: a journal title overlap study, Online Information Review, 32, 1, pp. 8-21, (2008); Mongeon P., Paul-Hus A., The journal coverage of Web of Science and Scopus: a comparative analysis, Scientometrics, 106, 1, pp. 213-228, (2016); Pritchard A., Statistical Bibliography or Bibliometrics?, Journal of Documentation, 254, pp. 348-349, (1969); Sanchez A. D., de la Cruz Del Rio Rama M., Garcia J. A., Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS, European Research on Management and Business Economics, 23, 1, pp. 8-15, (2017); Wagner A. Ben, A Practical Comparison of Scopus and Web of Science Core Collection, (2015)","P. Pradhan; Information and Library Network (INFLIBNET) Centre, Gandhinagar, 382007, India; email: ppradhan86@gmail.com; L.N. Zala; Department of Library & Information Science, Sardar Patel University, Vallabh Vidyanagar, 388120, India; email: lavji_zala@rediffmail.com","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85108781974"
"Fischer C.; Sester M.; Schön S.","Fischer, Colin (57188565613); Sester, Monika (21735637400); Schön, Steffen (55944738800)","57188565613; 21735637400; 55944738800","Spatio-temporal research data infrastructure in the context of autonomous driving","2020","ISPRS International Journal of Geo-Information","9","11","626","","","","0","10.3390/ijgi9110626","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094951038&doi=10.3390%2fijgi9110626&partnerID=40&md5=030c0f3980d29b18b0a21c709b3d4392","DFG Research Training Group i.c.sens (GRK 2159), Leibniz University Hannover, Institut für Erdmessung, Schneiderberg 50, Hannover, 30167, Germany; Institut für Kartographie und Geoinformatik, Leibniz University Hannover, Appelstraße 9a, Hannover, 30167, Germany; Institut für Erdmessung, Leibniz University Hannover, Schneiderberg 50, Hannover, 30167, Germany","Fischer C., DFG Research Training Group i.c.sens (GRK 2159), Leibniz University Hannover, Institut für Erdmessung, Schneiderberg 50, Hannover, 30167, Germany; Sester M., Institut für Kartographie und Geoinformatik, Leibniz University Hannover, Appelstraße 9a, Hannover, 30167, Germany; Schön S., Institut für Erdmessung, Leibniz University Hannover, Schneiderberg 50, Hannover, 30167, Germany","In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks.  © 2020 by the authors.","Data management; Internet GIS; Metadata; Spatial database; Spatio-temporal data infrastructure","","","","","","German Science Foundation, (GRK2159); Leibniz University's Department; Deutsche Forschungsgemeinschaft, DFG, (RTG2159)","Funding text 1: An example for such a complex research project is a research training group (RTG) funded by the German Science Foundation, entitled “Integrity and collaboration in dynamic sensor networks” (GRK2159). This RTG investigates concepts for ensuring the integrity of collaborative systems in dynamic sensor networks in the context of autonomous driving and environmental perception [2]. The exploitation of different—collaborating—sensors, in conjunction with new and advanced concepts of describing the integrity of measurements is considered an important key to ultimately allow a safe interplay of autonomous systems and human beings. The project relies on the assumption that the collaboration of diverse sensors and sensor systems leads to an improvement of the navigation and the sensing of the environment by an autonomous system. The project relies on large-scale; Funding text 2: This work was supported by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens (RTG2159). Acknowledgments: We thank the Leibniz University's Department for IT services (LUIS) for their ongoing support of our IT infrastructure.; Funding text 3: The RTG hosts nine PhD candidates at a time in 3-year periods over a maximum funding period of nine years, leading to nearly 30 PhD researchers funded by the program. One of the pillars of the RTG is the continuous collection of experimental data, leading to a large pool of spatio-temporal datasets that can be integrated in arbitrary ways, this way supporting a rich variety of different research","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., de Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016); Schon S., Brenner C., Alkhatib H., Coenen M., Dbouk H., Garcia-Fernandez N., Kuntzsch C., Heipke C., Lohmann K., Neumann I., Et al., Integrity and Collaboration in Dynamic Sensor Networks, Sensors, 18, (2018); Principles for the Handling of Research Data; Kaplan E.D., Hegarty C.J., Understanding GPS/GNSS: Principles and Applications, (2017); Reid T.G., Houts S.E., Cammarata R., Mills G., Agarwal S., Vora A., Pandey G., Localization requirements for autonomous vehicles, SAE Int. J. Connect. Autom. Veh, 2, pp. 173-190, (2019); Schon S., Integrity-A Topic for Photogrammetry?, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020, Proceedings of the XXIV ISPRS Congress, Virtual Event, pp. 565-571; Voges R., Wieghardt C.S., Wagner B., Finding Timestamp Offsets for a Multi-Sensor System Using Sensor Observations, Photogramm. Eng. Remote Sens, 84, pp. 357-366, (2018); Dbouk H., Schon S., Reliability and Integrity Measures of GPS Positioning via Geometrical Constraints, Proceedings of the 2019 International Technical Meeting of The Institute of Navigation, pp. 730-743, (2019); Garcia Fernandez N., Schon S., Optimizing Sensor Combinations and Processing Parameters in Dynamic Sensor Networks, Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), pp. 2048-2062, (2019); Schachtschneider J., Schlichting A., Brenner C., Assessing Temporal Behavior in LiDAR Point Clouds of Urban Environments, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, XLII-1/W1, pp. 543-550, (2017); Peters T., Brenner C., Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering, PFG, 88, pp. 257-269, (2020); Coenen M., Rottensteiner F., Heipke C., Precise vehicle reconstruction for autonomous driving applications, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci, IV-2/W5, pp. 21-28, (2019); Nguyen U., Rottensteiner F., Heipke C., Confidence-aware pedestrian tracking using a stereo camera, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci, IV-2/W5, pp. 53-60, (2019); Gray J., Chaudhuri S., Bosworth A., Layman A., Reichart D., Venkatrao M., Pellow F., Pirahesh H., Datacube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals, Proceedings of the 12th International Conference on Data Engineering, pp. 152-159, (1996); Kraak M.-J., The space-time cube revisited from a geovisualization perspective, Proceedings of the 21st International Cartographic Conference, pp. 1988-1995, (2003); Konkol M., Kray C., In-depth examination of spatiotemporal figures in open reproducible research, Cartogr. Geogr. Inf. Sci, 46, pp. 412-427, (2018); Miller E., An introduction to the resource description framework, Bull. Am. Soc. Inf. Sci, 25, pp. 15-19, (1998); Weibel S., The Dublin Core: A simple content description model for electronic resources, Bull. Am. Soc. Inf. Sci. Technol, 24, pp. 9-11, (1997); Dublin Core Metadata for Resource Discovery; Geographic Information-Metadata-Part1: Fundamentals; Geospatial Metadata Standards and Guidelines; Metadata Directory; List of Metadata Standards; Coetzee S., Ivanova I., Mitasova H., Brovelli M.A., Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future, ISPRS Int. J. Geo-Inf, 9, (2020); Breunig M., Bradley P.E., Jahn M., Kuper P., Mazroob N., Rosch N., Al-Doori M., Stefanakis E., Jadidi M., Geospatial Data Management Research: Progress and Future Directions, ISPRS Int. J. Geo-Inf, 9, (2020); Bernard L., Brauner J., Mas S., Wiemann S., Geodateninfrastrukturen, Geoinformatik, pp. 91-122, (2019); Sensor Observation Service; Sensor Model Language (SensorML); Registry of Research Data Repositories; Harvard Geospatial Library; The Open Archives Initiative Protocol for Metadata Harvesting; Apache Hadoop; Heinzle F., Anders K.H., Sester M., Pattern recognition in road networks on the example of circular road detection, Proceedings of the 4th International Conference on Geographic Information Science, pp. 153-167, (2006); PostGIS","C. Fischer; DFG Research Training Group i.c.sens (GRK 2159), Leibniz University Hannover, Institut für Erdmessung, Hannover, Schneiderberg 50, 30167, Germany; email: colin.fischer@ikg.uni-hannover.de","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85094951038"
"Hanke M.; Pestilli F.; Wagner A.S.; Markiewicz C.J.; Poline J.-B.; Halchenko Y.O.","Hanke, Michael (35859076500); Pestilli, Franco (6506525986); Wagner, Adina S. (57194276814); Markiewicz, Christopher J. (57190371018); Poline, Jean-Baptiste (7003479971); Halchenko, Yaroslav O. (6503870081)","35859076500; 6506525986; 57194276814; 57190371018; 7003479971; 6503870081","In defense of decentralized research data management","2021","Neuroforum","27","1","","17","25","8","11","10.1515/nf-2020-0037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100192814&doi=10.1515%2fnf-2020-0037&partnerID=40&md5=10b066f165f886976e38bc39e92add32","Institute of Neuroscience and Medicine Brain and Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, 40225, Germany; Department of Psychology, University of Texas atAustin, 108 E Dean Keeton St, Austin, 78712, TX, United States; Department of Psychology, Stanford University, 450 Jane Stanford Way, Building 420, Stanford, 94305, CA, United States; McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal, H3A 2B4, QC, Canada; Department of Psychological and Brain Sciences, Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, 03755, NH, United States","Hanke M., Institute of Neuroscience and Medicine Brain and Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, Germany, Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, 40225, Germany; Pestilli F., Department of Psychology, University of Texas atAustin, 108 E Dean Keeton St, Austin, 78712, TX, United States; Wagner A.S., Institute of Neuroscience and Medicine Brain and Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Markiewicz C.J., Department of Psychology, Stanford University, 450 Jane Stanford Way, Building 420, Stanford, 94305, CA, United States, Department of Psychological and Brain Sciences, Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, 03755, NH, United States; Poline J.-B., McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal, H3A 2B4, QC, Canada; Halchenko Y.O., Department of Psychological and Brain Sciences, Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, 03755, NH, United States","Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation. © 2021 De Gruyter. All rights reserved.","BrainLife; Canadian open neuroscience platform; DataLad; Interoperability; OpenNeuro","cloud computing; information center; neuroscience; review","","","","","Microsoft Faculty Fellowship; NIDM; NIH-NIBIB, (P41 EB019936); NIH-NIMH, (R01 MH083320, RF1 MH120021); US National Science Foundation; US-German; National Science Foundation, NSF, (1912266); National Institutes of Health, NIH; National Institute of Mental Health, NIMH, (R01MH096906); National Institute of Biomedical Imaging and Bioengineering, NIBIB; Microsoft, (1P41EB019936-01A1, R24-MH117179); McGill University; Fondation Brain Canada; Horizon 2020 Framework Programme, H2020, (826421, 945539, BCS-1734853, IIS-1636893, IIS-1912270, OAC-1916518); Health Canada; Bundesministerium für Bildung und Forschung, BMBF, (01GQ1905, NSF 1912266); Horizon 2020; Canada First Research Excellence Fund, CFREF","Funding text 1: A.S.W., F.P., M.H., and Y.O.H. were, in part, supported by a US-German CRCNS project, co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1905; NSF 1912266). A.S.W. and M.H. were supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 826421 (VirtualBrainCloud) and 945539 (HBP SGA3). A.S.W. is supported by a ReproNim/INCF Training Fellowship. NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship supported F.P. C.J.M. is supported by NIH R24-MH117179. Y.O.H. was also, in part, supported by NIH 1P41EB019936-01A1. J-.B.P. was partially funded by the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 (ReproNim), NIH-NIMH R01 MH083320 (CANDIShare), and NIH RF1 MH120021 (NIDM), the National Institute Of Mental Health under Award Number R01MH096906 (Neurosynth), as well as the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative and the Brain Canada Foundation with support from Health Canada. ; Funding text 2: Research funding: This article was funded by National Science Foundation (BMBF 01GQ1905; NSF 1912266), European Union’s Horizon 2020 under grant agreement no. 826421 (VirtualBrainCloud), and 945539 (HBP SGA3), Microsoft Faculty Fellowship, National Institutes of Health, National Institute of Mental Health under Award Number R01MH096906, Canada First Research Excellence Fund, Health Canada, Bundesministerium für Bildung und Forschung, and National Institute of Biomedical Imaging and Bioengineering. ","Avesani P., McPherson B., Hayashi S., Caiafa C.F., Henschel R., Garyfallidis E., Kitchell L., Bullock D., Patterson A., Olivetti E., Et al., The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services, Sci. Data, 6, pp. 1-13, (2019); Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Flandin G., Ghosh S.S., Glatard T., Halchenko Y.O., Et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci. Data, 3, pp. 1-9, (2016); Gorgolewski K., Esteban O., Schaefer G., Wandell B., Poldrack R., OpenNeuro-A free online platform for sharing and analysis of neuroimaging data, Organ. Hum. Brain Mapp., (2017); Guthrie S., Lichten C., Harte E., Parks S., Wooding S., International Mobility of Researchers, (2017); Hanke M., Halchenko Y.O., Poldrack B., Meyer K., Solanky D.S., Alteva G., Gors J., MacFarlane D., Hausler C.O., Olson T., Et al., Datalad/Datalad: 0.13.5 (October 30, 2020, version 0.13.5), Zenodo, (2020); Hess J., Git-Annex, (2020); Kunze J., Littman J., Madden E., Scancella J., Adams C., The bagIt File Packaging Format (V1.0), (2018); McKelvey K., Buus M., Dat Protocol Foundation, (2020); Miller K.L., Alfaro-Almagro F., Bangerter N.K., Thomas D.L., Yacoub E., Xu J., Bartsch A.J., Jbabdi S., Sotiropoulos S.N., Andersson J.L., Et al., Multimodal population brain imaging in the UK Biobank prospective epidemiological study, Nat. Neurosci., 19, pp. 1523-1536, (2016); Sansone S.A., Gonzalez-Beltran A., Rocca-Serra P., Alter G., Grethe J.S., Xu H., Fore I.M., Lyle J., Gururaj A.E., Chen X., Et al., DATS, the data tag suite to enable discoverability of datasets, Sci. Data, 4, (2017); van Essen D.C., Smith S.M., Barch D.M., Behrens T.E., Yacoub E., Ugurbil K., The Wu-Minn human connectome project: An overview, Neuroimage, 80, pp. 62-79, (2013); Wagner A.S., Waite L.K., Meyer K., Heckner M.K., Kadelka T., Reuter N., Waite A.Q., Poldrack B., Markiewicz C.J., Halchenko Y.O., Et al., The datalad handbook (version v0.13), Zenodo, (2020); Walsh P., Pollock R., Frictionless Data Package, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016)","M. Hanke; Institute of Neuroscience and Medicine Brain and Behavior (INM-7), Research Center Jülich, Jülich, Wilhelm-Johnen-Straße, 52425, Germany; email: michael.hanke@gmail.com","","De Gruyter Open Ltd","","","","","","09470875","","","","English","Neuroforum","Review","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85100192814"
"Ashiq M.; Rehman S.U.; Mujtaba G.","Ashiq, Murtaza (57221973297); Rehman, Shafiq Ur (57209497056); Mujtaba, Ghulam (6602765061)","57221973297; 57209497056; 6602765061","Future challenges and emerging role of academic libraries in Pakistan: A phenomenology approach","2021","Information Development","37","1","","158","173","15","20","10.1177/0266666919897410","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078220491&doi=10.1177%2f0266666919897410&partnerID=40&md5=03c613d31f260a19c5efd9ceae964fbd","Islamabad Model College for Boys, Pakistan; Imam Abdulrehman Bin Faisal University, Saudi Arabia; University of the Punjab, Pakistan","Ashiq M., Islamabad Model College for Boys, Pakistan; Rehman S.U., Imam Abdulrehman Bin Faisal University, Saudi Arabia; Mujtaba G., University of the Punjab, Pakistan","This study was carried out to investigate the current and prospective challenges faced by academic libraries in Pakistan and to present possible solutions addressing these challenges. The research design was qualitative, adopting the phenomenology approach. In-depth interviews of 14 senior academic library leaders from public and private sector universities of Pakistan were conducted. Leadership crisis was identified as the top challenge followed by those related to changing user behavior, human resources, financial, technological issues, and changes in higher education. Prospective challenges encompassed issues related to technological modalities, human resources, research data management and library space. While the study participants indicated their readiness to cope with these challenges, they agreed that there was a need of collective effort for human capacity building, initiation of compatible smart services, effective policy making and creation of societal awareness. The support from key players such as the library professionals, library associations, top management, LIS schools, HEC and other funding agencies was deemed to be vital for this purpose. © The Author(s) 2020.","academic library challenges; academic library trends; future library challenges; library administration; library leadership; Pakistan","","","","","","","","Ameen K., Challenges of preparing LIS professionals for leadership roles in Pakistan, Journal of Education for Library and Information Science, 47, 3, pp. 200-217, (2006); Ashiq M., Rehman S.U., Batool S.R., Academic library leaders’ challenges, difficulties and skills: An analysis of common experiences, Libri: International Journal of Libraries and Information Studies, 68, 4, pp. 301-313, (2018); Ashiq M., Rehman S.U., Batool S.R., Academic library leaders’ conception of library leadership in Pakistan, Malaysian Journal of Library & Information Science, 24, 2, pp. 55-71, (2019); Aslam M., Current trends and issues affecting academic libraries and leadership skills, Library Management, 39, 1-2, pp. 78-92, (2018); Bowen-Chang P., Hosein Y., Continuing professional development of academic librarians in Trinidad and Tobago, Global Knowledge, Memory and Communication, 68, 1-2, pp. 93-111, (2018); Chadwell F., Sutton S.C., The future of open access and library publishing, New Library World, 115, 5-6, pp. 225-236, (2014); Christensen L.B., Johnson R.B., Turner L.A., Research methods, design, and analysis, (2015); Chuang F.-H., Weng H.-C., Hsieh P.-N., A qualitative study of barriers to innovation in academic libraries in Taiwan, Library Management, (2019); Fought R.L., Misawa M., Accepting the challenge: what academic health sciences library directors do to become effective leaders, Journal of the Medical Library Association: JMLA, 106, 2, pp. 219-226, (2018); Gwyer R., Identifying and exploring future trends impacting on academic libraries: a mixed methodology using journal content analysis, focus groups, and trend reports, New review of academic librarianship, 21, 3, pp. 269-285, (2015); Haider S.J., Perspectives on…coping with change: issues facing university libraries in Pakistan, The Journal of Academic Librarianship, 30, 3, pp. 229-236, (2004); Harland F., Stewart G., Bruce C., Ensuring the academic library’s relevance to stakeholders: the role of the Library Director, The Journal of Academic Librarianship, 43, 5, pp. 397-408, (2017); Hendrix J.C., Checking out the future: Perspectives from the library community on information technology and 21st-century libraries, (2010); Hernon P., Powell R.R., Young A.P., The next library leadership: Attributes of academic and public library directors, (2003); Hicks D., Given L.M., Principled, transformational leadership: Analyzing the discourse of leadership in the development of librarianship’s core competences, The Library Quarterly: Information, Community, Policy, 83, 1, pp. 7-25, (2013); Hisle W.L., Top issues facing academic libraries: A report of the focus on the future task force, C&RL News, 63, 10, pp. 714-715, (2002); Kennedy M.L., The Opportunity for Research Libraries in 2018 and Beyond, portal: Libraries and the Academy, 18, 4, pp. 629-637, (2018); Knight J.A., Academic librarians as change champions: a framework for managing change, Library Management, 38, 6-7, pp. 294-301, (2017); Koltay T., Accepted and emerging roles of academic libraries in supporting research, The Journal of Academic Librarianship, 45, pp. 75-80, (2019); Le B.P., Academic library leadership in the digital age, Library Management, 36, 4-5, pp. 300-314, (2015); Levine-Clark M., Imagining the future academic library collection, Collection Development, 44, 2-4, pp. 87-94, (2019); Lewis D.W., Reimagining the academic library: what to do next. Review article, El profesional de la información (EPI), 28, 1, pp. 1-29, (2019); Maciel M.L., Kaspar W.A., vanDuinkerken W., (Desperately) Seeking service leadership in academic libraries: An analysis of dean and director position advertisements, Journal of Library Administration, 58, 1, pp. 18-53, (2018); Madge O.L., The current role of librarians and future challenges for academic libraries in Romania, Studii de Biblioteconomie şi Ştiinţa Informării, 20, pp. 61-68, (2016); Madge O.L., Robu I., Medical academic libraries in Romania–breaking with the past and turning towards the future, Health Information & Libraries Journal, 36, 1, pp. 96-100, (2019); McGillis L., The lights are on but nobody’s home: the future of academic libraries?, Partnership: The Canadian Journal of Library and Information Practice and Research, 11, 1, pp. 1-5, (2016); Muthanna A., Sang G., State of university library: Challenges and solutions for Yemen, The Journal of Academic Librarianship, 45, 2, pp. 119-125, (2019); Mwaniki P.W., Envisioning the future role of librarians: skills, services and information resources, Library Management, 39, 1-2, pp. 2-11, (2018); Perrier L., Barnes L., Developing research data management services and support for researchers: a mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, 13, 1, pp. 1-23, (2018); Salisbury F., Peasley J., Measuring the academic library: Translating today’s inputs and outputs into future impact and value, Information and Learning Science, 119, 1-2, pp. 109-120, (2018); Schopfel J., Six futures of academic libraries, The End of Wisdom? The Future of Libraries in a Digital Age, (2017); Shea G., Derry S., Academic libraries and autism spectrum disorder: what do we know?, The Journal of Academic Librarianship, 45, pp. 326-331, (2019); Tait E., Martzoukou K., Reid P., Libraries for the future: the role of IT utilities in the transformation of academic libraries, Palgrave Communications, 2, 1, pp. 1-9, (2016); Wong G.K.W., Leadership and leadership development in academic libraries: a review, Library Management, 38, 2-3, pp. 153-166, (2017); Wong G.K.W., Chan D.L.H., Adaptive leadership in academic libraries, Library Management, 39, 1-2, pp. 106-115, (2018)","M. Ashiq; Islamabad Model College for Boys, Pakistan; email: gmurtazaashiq00@gmail.com","","SAGE Publications Ltd","","","","","","02666669","","","","English","Inf. Dev.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85078220491"
"Zhanel G.G.; Kosar J.; Baxter M.; Dhami R.; Borgia S.; Irfan N.; MacDonald K.S.; Dow G.; Lagacé-Wiens P.; Dube M.; Bergevin M.; Tascini C.; Keynan Y.; Walkty A.; Karlowsky J.","Zhanel, George G. (7102836009); Kosar, Justin (57215140036); Baxter, Melanie (55002247300); Dhami, Rita (57215127612); Borgia, Sergio (56789936700); Irfan, Neal (56830362400); MacDonald, Kelly S. (35454011900); Dow, Gordon (7006042066); Lagacé-Wiens, Philippe (12791684900); Dube, Maxime (57221947914); Bergevin, Marco (57194076386); Tascini, Carlo (6701507404); Keynan, Yoav (19640018800); Walkty, Andrew (6506656194); Karlowsky, James (7005105215)","7102836009; 57215140036; 55002247300; 57215127612; 56789936700; 56830362400; 35454011900; 7006042066; 12791684900; 57221947914; 57194076386; 6701507404; 19640018800; 6506656194; 7005105215","Real-life experience with ceftobiprole in Canada: Results from the CLEAR (CanadianLEadership onAntimicrobialReal-life usage) registry","2021","Journal of Global Antimicrobial Resistance","24","","","335","339","4","7","10.1016/j.jgar.2021.01.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100716065&doi=10.1016%2fj.jgar.2021.01.014&partnerID=40&md5=ca45a8a7a0f0b20462e0bf69bb0e1f53","Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Department of Pharmacy, Royal University Hospital, Saskatoon, SK, Canada; Department of Pharmacy, London Health Sciences Centre, London, ON, Canada; Section of Infectious Diseases, William Osler Health System, Brampton, ON, Canada; Department of Pharmacy, Hamilton Health Sciences Centre, Hamilton, ON, Canada; Section of Infectious Diseases, Department of Medicine, The Moncton Hospital, NB, Canada; Department of Pharmacy, Sainte-Croix Hospital, Drummondville, Québec, Canada; Section of Infectious Diseases, Department of Medicine, Cité de la Santé, Montreal, Québec, Canada; First Division of Infectious Diseases, Cotugno Hospital, Naples, Italy","Zhanel G.G., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Kosar J., Department of Pharmacy, Royal University Hospital, Saskatoon, SK, Canada; Baxter M., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Dhami R., Department of Pharmacy, London Health Sciences Centre, London, ON, Canada; Borgia S., Section of Infectious Diseases, William Osler Health System, Brampton, ON, Canada; Irfan N., Department of Pharmacy, Hamilton Health Sciences Centre, Hamilton, ON, Canada; MacDonald K.S., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Dow G., Section of Infectious Diseases, Department of Medicine, The Moncton Hospital, NB, Canada; Lagacé-Wiens P., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Dube M., Department of Pharmacy, Sainte-Croix Hospital, Drummondville, Québec, Canada; Bergevin M., Section of Infectious Diseases, Department of Medicine, Cité de la Santé, Montreal, Québec, Canada; Tascini C., First Division of Infectious Diseases, Cotugno Hospital, Naples, Italy; Keynan Y., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Walkty A., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Karlowsky J., Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada","Objectives: Ceftobiprole is an advanced-generation cephalosporin with a favourable safety profile. Published data on the clinical use of ceftobiprole are limited. We report use of ceftobiprole in Canadian patients using data captured by the CLEAR registry. Methods: The CLEAR registry uses the web-based research data management program REDCap™ (online survey) to facilitate clinicians entering details associated with their clinical experiences using ceftobiprole. Results: Data were available for 38 patients treated with ceftobiprole. The most common infections treated were endocarditis (42.1% of patients), bone and joint infection (23.7%) and hospital-associated bacterial pneumonia (15.8%). 92.1% of patients had bacteraemia and 21.1% were in intensive care. Ceftobiprole was used because of failure of (71.1%), resistance to (18.4%) or adverse effects from (10.5%) previously prescribed antimicrobial agents. Ceftobiprole was primarily used as directed therapy for methicillin-resistant Staphylococcus aureus (MRSA) infections (94.7% of patients). Ceftobiprole susceptibility testing was performed on isolates from 47.4% of patients. It was used concomitantly with daptomycin in 55.3% of patients and with vancomycin in 18.4% of patients. Treatment duration was primarily >10 days (65.8% of patients) with microbiological success in 97.0% and clinical success in 84.8% of patients. 2.6% of patients had gastrointestinal adverse effects. Conclusion: In Canada to date, ceftobiprole is used as directed therapy to treat a variety of severe infections caused by MRSA. It is primarily used in patients failing previous antimicrobials, is frequently added to, and thus used in combination with daptomycin or vancomycin with high microbiological and clinical cure rates and an excellent safety profile. © 2021 The Author(s)","Adverse effects; Ceftobiprole; CLEAR registry; Efficacy; Endocarditis; Pneumonia","Anti-Bacterial Agents; Canada; Cephalosporins; Humans; Methicillin-Resistant Staphylococcus aureus; Registries; ceftobiprole; creatinine; daptomycin; vancomycin; antiinfective agent; ceftobiprole; cephalosporin derivative; adult; adverse drug reaction; aged; antibiotic sensitivity; Article; bacteremia; bacterial pneumonia; bacterium isolate; bone infection; Canada; central nervous system infection; clinical article; clinical outcome; clinical practice; creatinine clearance; demography; device infection; drug use; endocarditis; gastrointestinal symptom; hospital infection; human; infectious arthritis; intensive care unit; methicillin resistant Staphylococcus aureus infection; multicenter study; online monitoring; pathogenesis; priority journal; register; skin infection; skin structure infection; tertiary health care; treatment duration; web browser; methicillin resistant Staphylococcus aureus","","ceftobiprole, 209467-52-7; creatinine, 19230-81-0, 60-27-5; daptomycin, 103060-53-3; vancomycin, 1404-90-6, 1404-93-9; Anti-Bacterial Agents, ; ceftobiprole, ; Cephalosporins, ","","","AVIR Pharma; AVIR Pharma and Basilea; HIKMA; Health Sciences Centre; University of Manitoba, UM; Basilea Pharmaceutica; Merck Canada","Funding text 1: The CLEAR registry is supported in part by the Health Sciences Centre (Winnipeg, Manitoba, Canada), the University of Manitoba (Winnipeg, Manitoba, Canada), AVIR Pharma (Montreal, Québec, Canada), Merck Canada (Montreal, Québec, Canada) and Verity (Toronto, Ontario, Canada). ; Funding text 2: GGZ has received research funding from AVIR Pharma and Basilea; NI is a member of the AVIR Pharma speakers bureau; CT has received research funding from AVIR Pharma, Basilea and HIKMA. All other authors declare no competing interests. ","Zhanel G.G., Adam H.J., Baxter M.R., Fuller J., Nichol K.A., Denisuik A.J., Et al., 42936 pathogens from Canadian hospitals: 10 years of results (2007–16) from the CANWARD surveillance study, J Antimicrob Chemother, 74, pp. iv5-iv21, (2019); Zhanel G.G., Lam A., Schweizer F., Thompson K., Walkty A., Rubinstein E., Et al., Ceftobiprole: a review of a broad-spectrum and anti-MRSA cephalosporin, Am J Clin Dermatol, 9, pp. 245-254, (2008); Nicholson S.C., Weite T., File T.M., Strauss R.S., Michaels B., Kaul P., Et al., A randomised, double-blind trial comparing ceftobiprole medocaril with ceftriaxone with and without linezolid for the treatment of patients with community-acquired pneumonia requiring hospitalisation, Int J Antimicrob Agents, 39, pp. 240-246, (2012); Awad S., Rodriguez A.H., Chuang Y.C., Marjanek Z., Pareigis A.J., Reis G., Et al., A phase 3 randomized double-blind comparison of ceftobiprole medocaril versus ceftazidime plus linezolid for the treatment of hospital-acquired pneumonia, Clin Infect Dis, 59, pp. 51-61, (2014); Overcash J.C., Kim C., Keech R., Gumenchuk I., Ninov B., Gonzalez-Rojas Y., Et al., Ceftobiprole compared with vancomycin plus aztreonam in the treatment of acute bacterial skin and skin structure infections: results of a phase 3, randomized, double-blind trial (TARGET), Clin Infect Dis, September, (2020); Nichol K.A., Adam H.J., Golding G.R., Lagace-Wiens P.R.S., Karlowsky J.A., Hoban D.J., Et al., Characterization of MRSA in Canada from 2007 to 2016, J Antimicrob Chemother, 74, pp. iv55-iv63, (2019); Zhanel G.G., Voth D., Nichol K., Karlowsky J.A., Noreddin A.M., Hoban D.J., Pharmacodynamic activity of ceftobiprole compared with vancomycin versus methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-intermediate Staphylococcus aureus (VISA) and vancomycin-resistant Staphylococcus aureus (VRSA) using an in vitro model, J Antimicrob Chemother, 64, pp. 364-369, (2009); Salem A.H., Zhanel G.G., Ibrahim S.A., Noreddin A.M., Monte Carlo simulation analysis of ceftobiprole, dalbavancin, daptomycin, tigecycline, linezolid and vancomycin pharmacodynamics against intensive care unit-isolated methicillin-resistant Staphylococcus aureus, Clin Exp Pharmacol Physiol, 41, pp. 437-443, (2014); Barnea Y., Ceftobiprole medocaril is an effective treatment against methicillin-resistant Staphylococcus aureus (MRSA) mediastinitis in a rat model, Eur J Clin Microbiol Infect Dis, 33, pp. 325-329, (2014); MacDonald A., Dow G., Ceftobiprole: first reported experience in osteomyelitis, Can J Infect Dis Med Microbiol, 21, pp. 138-140, (2010); Tascini C., Attanasio V., Ripa M., Carozza A., Palotto C., Bernardo M., Et al., Ceftobiprole for the treatment of infective endocarditis: a case series, J Glob Antimicrob Resist, 20, pp. 56-59, (2020); Durante-Mangoni E., Andini R., Mazza M.C., Sangiovanni F., Bertolini L., Ursi P., Et al., Real-life experience with ceftobiprole in a tertiary-care hospital, J Glob Antimicrob Resist, 22, pp. 386-390, (2020); Mahmoud E., Al Mansour S., Bosaeed M., Alharbi A., Alsaedy A., Aljohani S., Et al., Ceftobiprole for treatment of MRSA blood stream infection: a case series, Infect Drug Resist, 13, pp. 2667-2672, (2020); Soriano A., Morata L., Ceftobiprole: experience in staphylococcal bacteremia, Rev Esp Quimioter, 32, pp. 24-28, (2019); Barber K.E., Werth B.J., Ireland C.E., Stone N.E., Nonejuie P., Sakoulas G., Et al., Potent synergy of ceftobiprole plus daptomycin against multiple strains of Staphylococcus aureus with various resistance phenotypes, J Antimicrob Chemother, 69, pp. 3006-3010, (2014); Fernandez J., Abbanat D., Shang W., He W., Amsler K., Hastings J., Et al., Synergistic activity of ceftobiprole and vancomycin in a rat model of infective endocarditis caused by methicillin-resistant and glycopeptide-intermediate Staphylococcus aureus, Antimicrob Agents Chemother, 56, pp. 1476-1484, (2012); Hamed K., Engelhardt M., Jones M.E., Saulay M., Holland T.L., Seifert H., Et al., Ceftobiprole versus daptomycin in Staphylococcus aureus bacteremia; a novel protocol for a double-blind phase III trial, Future Microbiol, 15, pp. 35-48, (2020); Crapis M., Venturini S., Siega P.D., Tonizzo M., Garlatti E., De Rosa R., Et al., Ceftobiprole and pneumonia in adults admitted to emergency department: is it time to assess the new therapeutic algorithm?, J Chemother, September, (2020); Scheeren T.W.L., Weite T., Saulay M., Engelhart M., Santerre-Henriksen A., Hamed K., Early improvement in severely ill patients with pneumonia treated with ceftobiprole: a retrospective analysis of two major trials, BMC Infect Dis, 19, pp. 195-201, (2019)","G.G. Zhanel; MS673-Microbiology, Health Sciences Centre, Winnipeg, 820 Sherbrook Street, R3A 1R9, Canada; email: ggzhanel@pcsinternet.ca","","Elsevier Ltd","","","","","","22137165","","","33540083","English","J. Global Antimicrob. Resist.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85100716065"
"Znamirowski B.","Znamirowski, Barbara (24469446600)","24469446600","ASSOCIATION OF CANADIAN MAP LIBRARIES AND ARCHIVES BULLETIN GIS Trends","2021","Association of Canadian Map Libraries and Archives Bulletin","","169","","19","22","3","0","10.15353/ACMLA.N169.4739","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122366569&doi=10.15353%2fACMLA.N169.4739&partnerID=40&md5=e9057aec020e00151e0f11f690560c7f","","","In March 2021 the Tri-Agency released its Research Data Management Policy, including its three pillar requirements. This article reviews some key points from the Alliance RDM (Portage Network) workshop ""Putting the Tri-Agency Policy into Practice: Workshopping Your Institutional Research Data Management Strategy."". © 2021 Association of Canadian Map Libraries and Archives. All rights reserved.","","Canada; data management; GIS; map; trend analysis","","","","","","","Tri-Agency Research Data Management Policy, (2021); Tri-Agency Research Data Management Policy, (2021); NDRIO and Portage now rebranded to the Digital Research Alliance of Canada and Alliance RDM; Institutional RDM Strategy Template Revision Working Group, Institutional Research Data Management Strategy Development Template v. 3.0, (2021); Frequently Asked Questions: Tri-Agency Research Data Management Policy, (2021); Lucas Matthew, SSHRCC, Tri-Agency Research Data Management Policy; Putting the Tri-Agency Policy Into Practice: Workshopping Your Institutional Research Data Management Strategy, Pillar 1: Institutional RDM Strategies; Institutional RDM Strategy Template Revision Working Group, RDM Maturity Assessment Model in Canada (MAMIC), (2021); Institutional RDM Strategy Template Revision Working Group, Institutional Research Data Management Strategy Development Template v. 3.0, (2021); Fair Principles; CARE Principles for Indigenous Data Governance","","","Association of Canadian Map Libraries and Archives","","","","","","08409331","","","","English","Assoc. Can. Map Libr. Arch. Bull.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85122366569"
"Kinder-Kurlanda K.E.; Weller K.","Kinder-Kurlanda, Katharina E. (55836531600); Weller, Katrin (23986745200)","55836531600; 23986745200","Perspective: Acknowledging Data Work in the Social Media Research Lifecycle","2020","Frontiers in Big Data","3","","509954","","","","0","10.3389/fdata.2020.509954","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118770048&doi=10.3389%2ffdata.2020.509954&partnerID=40&md5=1421805867799b17718a4a4f4f389a77","CAIS – Center for Advanced Internet Studies, Bochum, Germany; Computational Social Science Department, GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany","Kinder-Kurlanda K.E., CAIS – Center for Advanced Internet Studies, Bochum, Germany; Weller K., Computational Social Science Department, GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany","This perspective article suggests considering the everyday research data management work required to accomplish social media research along different phases in a data lifecycle to inform the ongoing discussion of social media research data’s quality and validity. Our perspective is informed by practical experience of archiving social media data, by results from a series of qualitative interviews with social media researchers, as well as by recent literature in the field. We emphasize how social media researchers are entangled in complexities between social media platform providers, social media users, other actors, as well as legal and ethical frameworks, that all affect their everyday research practices. Research design decisions are made iteratively at different stages, involving many decisions that may potentially impact the quality of research. We show that these decisions are often hidden, but that making them visible allows us to better understand what drives social media research into specific directions. Consequently, we argue that untangling and documenting choices during the research lifecycle, especially when researchers pursue specific approaches and may have actively decided against others (often due to external factors) is necessary and will help to spot and address structural challenges in the social media research ecosystem that go beyond critiques of individual opportunistic approaches to easily accessible data. © Copyright © 2020 Kinder-Kurlanda and Weller.","data archiving; data lifecycle; digital trace data; epistemology; methodology; research data management; social media research","","","","","","","","Acker A.,., Kreisberg A., Social media data archives in an API-driven world, Arch. Sci, 20, (2019); Boellstorff T., Making Big Data, in theory, Fire Mater, 18, (2013); Borgman C.L., The lives and after lives of data, Harvard Data Sci. Rev, 1, 1, pp. 1-10, (2019); boyd D., Crawford K., Critical questions for Big Data: provocations for a cultural, technological, and scholarly phenomenon, Inf. Commun. Soc, 15, 5, pp. 662-679, (2012); Bruns A., Faster than the speed of print: reconciling ‘Big Data’ social media analysis and academic scholarship, Clin. Hemorheol. Microcirc, 18, (2013); Bruns A., After the ‘APIcalypse’: social media platforms and their fight against critical scholarly research, Inf. Commun. Soc, 22, 11, pp. 1544-1566, (2019); Bruns A.,., Weller K., Twitter as a first draft of the present: and the challenges of preserving it for the future, pp. 183-189, (2016); Carlson J., The use of life cycle models in developing and supporting data services, Research data management: practical strategies for information professionals, pp. 63-86, (2014); Crawford K.,., Joler V., Anatomy of an AI System-The Amazon Echo as an anatomical map of human labor, data and planetary resources, (2018); Ekbia H., Mattioli M., Kouper I., Arave G., Ghazinejad A., Bowman T., Et al., Big Data, bigger dilemmas: a critical review, J. Assn. Inf. Sci. Tech, 66, 8, pp. 1523-1545, (2015); Fiesler C., Proferes N., “Participant” perceptions of twitter research ethics, Social. Media Soc, 4, (2018); franzke A.S., Bechmann A., Zimmer M., Ess C.M., Internet research: ethical guidelines 3.0, (2020); Gebru T., Morgenstern J., Vecchione B., Vaughan J.W., Wallach H., Daume'e H., Et al., Datasheets for datasets, (2018); Halford S., Weal M., Tinati R., Carr L., Pope C., Understanding the production and circulation of social media data: towards methodological principles and praxis, New Media Soc, 20, 9, pp. 3341-3358, (2018); Hemphill L., Hedstrom M., Leonard S., How can we save social media data?, (2019); Kaczmirek L., Mayr P., Vatrapu R., Bleier A., Blumenberg M.S., Gummer T., Et al., Social media monitoring of the campaigns for the 2013 German bundestag elections on Facebook and twitter, (2014); Karpf D., Social science research methods in internet time, Inf. Commun. Soc, 15, 5, pp. 639-661, (2012); Kinder-Kurlanda K.E., Weller K., ‘I always feel it must be great to be a hacker!’: the role of interdisciplinary work in social media research, pp. 91-98, (2014); Kinder-Kurlanda K.E., Weller K., Zenk-Moltgen W., Pfeffer J., Morstatter F., Archiving information from geotagged tweets to promote reproducibility and comparability in social media research, Big Data Soc, 4, 2, pp. 1-14, (2017); Kitchin R., Big Data, new epistemologies and paradigm shifts, Big Data Soc, 1, 1, pp. 1-12, (2014); Koch G.,., Kinder-Kurlanda K., Source criticism of data platform logics on the internet, Histor. Soc. Res, 45, 3, pp. 270-287, (2020); Langlois G., Redden J., Elmer G., Chapter 1. introduction: compromised data — from social media to Big Data, Compromised. Data: from social media to big data, pp. 1-13, (2015); Latour B., On actor-network theory: a few clarifications, Soziale Welt, 47, pp. 369-381, (1996); Mayr P.,., Weller K., Think before you collect: setting up a data collection approach for social media studies, The SAGE handbook of social media research methods, pp. 107-124, (2017); Mol A., Moser I., Pols J., Care in practice: on tinkering in clinics, homes and farms, 8, (2015); Olteanu A., Castillo C., Diaz F., Kiciman E., Social data: biases, methodological pitfalls, and Ethical boundaries, Front. Big Data, 2, (2019); Orlikowsi W., Sociomaterial practices: exploring technology at work, Organ. Stud, 28, 9, pp. 1435-1448, (2007); Pouchard L., Revisiting the data lifecycle with Big Data curation, Int. J. Dig. Curat, 10, (2015); Rogers R., Digital methods for web research, Emerging trends in the social and behavioral sciences, (2015); Schroeder R., Big Data and the brave new world of social media research, Big Data Soc, 1, (2014); Sen I., Flock F., Wagner C., On the reliability and validity of detecting approval of political actors in tweets, (2020); Sen I., Flock F., Weller K., Weiss B., Wagner C., A total Error framework for digital traces of humans, (2019); Suchman L., Plans and situated actions. The problem of human-machine communication, (1987); Thomson S.D., Preserving social media, (2016); Weller K.,., Kinder-Kurlanda K.E., Uncovering the challenges in collection, sharing and documentation: the hidden data of social media research? in standards and practices in large-scale social media research: papers from the 2015 ICWSM workshop, pp. 28-37, (2015); Weller K.,., Kinder-Kurlanda K.E., A manifesto for data sharing in social media research, pp. 166-172, (2016); Weller K.,., Kinder-Kurlanda K.E., Internet research ethics for the social age, pp. 115-129, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M.,  and Baak A., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, 1, pp. 1-9, (2016); Wu A.X., Taneja H., Platform enclosure of human behavior and its measurement: using behavioral trace data against platform episteme, New Media Soc, 2020, pp. 1-18, (2020); Zimmer M.,., Proferes N.J., A topology of Twitter research: disciplines, methods, and ethics, Aslib J. Inf. Manag, 66, 3, pp. 250-261, (2014)","K.E. Kinder-Kurlanda; CAIS – Center for Advanced Internet Studies, Bochum, Germany; email: katharina.kinder-kurlanda@cais.nrw","","Frontiers Media S.A.","","","","","","2624909X","","","","English","Frontiers. Big. Data.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85118770048"
"Nitecki D.A.; Alter A.","Nitecki, Danuta A. (7004392762); Alter, Adi (57221980546)","7004392762; 57221980546","Leading fair adoption across the institution: A collaboration between an academic library and a technology provider","2021","Data Science Journal","20","1","","1","8","7","4","10.5334/dsj-2021-006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100851910&doi=10.5334%2fdsj-2021-006&partnerID=40&md5=e72ffc511e6b0ef40ce58b87ff00f33c","Drexel University, United States; Ex Libris, IL, United States","Nitecki D.A., Drexel University, United States; Alter A., Ex Libris, IL, United States","Universities strive to foster knowledge sharing and greater research productivity. Some recognize that this requires research output to be findable, accessible, interoperable and reusable. But current tools do not yet allow a comprehensive adoption of these FAIR principles for making research openly and globally accessible to generate new knowledge. To address this gap, diverse stakeholders are collaborating to build effective research data management [RDM] solutions for institutional research output [publications and data] that benefit researchers, institutions, and developers. This paper illustrates a university-industry collaboration between a private U.S. university (Drexel University) and a global commercial vendor (Ex Libris, a ProQuest company). The authors examine how an emerging technology infrastructure for Research Data Management will enable librarians to help institutions adopt the FAIR principles at scale. They highlight an approach for collaborative product development that aims not to change researcher habits or add to librarians’ workloads. Their first year working together confirms factors recognized as contributing to successful collaborations, such as aligning goals, building understanding of each other’s organizations, and sustaining honest engagement. Though FAIR offers a simple articulation to help build campus infrastructure and change culture, its implementation will vary between different groups of researchers. Libraries and technology providers have a mutual interest in collaborating to address RDM challenges, but must recognize that collaboration takes time, perseverance, and flexibility to effect change. Librarians, researchers, and administrators from such campus offices as Research, Compliance, IT, Legal, and Graduate Studies will benefit from key lessons raised by this case study. © 2021 The Author(s).","Drexel University; Esploro; Ex Libris; FAIR principles; Research Data Management; University-Industry Collaboration","Information management; Academic libraries; Collaborative product development; Emerging technologies; Knowledge-sharing; Research data managements; Research productivity; Technology providers; University-industry collaboration; Libraries","","","","","","","Bjursell C, Engstrom A., A Lewinian approach to managing barriers to university-industry collaboration, Higher Education Policy, 32, pp. 129-148, (2019); Borgman C., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Chiware ER, Lockhart J, Becker D, Omar Y, Majal S., CPUT Libraries Digital Platforms: Adding Value to Teaching, Learning and Research, (2018); Cope AP, Barnes MR, Belson A, Binks M, Brockbank S, Bonachela-Capdevila F, Carini C, Fisher BA, Goodyear CS, Emery P, Ehrenstein MR., The RA-MAP Consortium: A working model for academia–industry collaboration, Nature Reviews Rheumatology, 14, 1, (2018); Cousijn H, Feeney P, Lowenberg D, Presani E, Simons N., Bringing citations and usage metrics together to make data count, Data Science Journal, 18, 9, pp. 1-7, (2019); Cox A, Kennan MA, Lyon E, Pinfield S, Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, (2019); FAIR Principles; Levine-Clark M., Library-vendor collaboration: Sleeping with the enemy?, Collaborative Librarianship, 8, 2, pp. 55-57, (2016); Ogungbeni JI, Obiamalu AR, Ssemambo S, Bazibu CM., The roles of academic libraries in propagating open science: A qualitative literature review, Information Development, 34, 2, pp. 113-121, (2018); Pertuze JA, Calder ES, Greitzer EM, Lucas WA., Best practices for industry-university collaboration, MIT Sloan Management Review, 51, 4, pp. 83-90, (2010); Russell JC, Wise A, Dinsmore CS, Spears LI, Phillips RV, Taylor L., Academic library and publisher collaboration: Utilizing an institutional repository to maximize the visibility and impact of articles by university authors, Collaborative Librarianship, 8, 2, (2016); Rybnicek R, Konigsgruber R., What makes industry-university collaboration succeed? A systematic review of the literature, Journal of Business Economics, 89, 2, pp. 221-250, (2019); Sandberg A, Pareto L, Arts T., Agile collaborative research: Action principles for industry-academia collaboration, IEEE Software, 28, 4, pp. 74-83, (2011); Savage JL, Cadwallader L., Establishing, developing, and sustaining a community of data champions, Data Science Journal, 18, 23, pp. 1-8, (2019); Schirrwagen J, Cimiano P, Ayer V, Pietsch C, Wiljes C, Vompras J, Pieper D., Expanding the research data management service portfolio at Bielefeld University according to the three-pillar principle towards data FAIRness, Data Science Journal, 18, 6, pp. 1-10, (2019); Sherman S, Hadar I, Luria G., Leveraging organizational climate theory for understanding industry-academia collaboration, Information and Software Technology, 98, pp. 148-160, (2018); Researcher’s guide to working with industry; Yamaguchi Y, Fujimoto J, Yamazaki A, Koshiyama T., A study of the factors influencing industry-academia collaboration activities in private universities, Proceedings of PICMET’18: Technology Management for Interconnected World, (2018); Zhang L, Eichmann-Kalwara N., Mapping the scholarly literature found in Scopus on “research data management”: A bibliometric and data visualization approach, Journal of Librarianship and Scholarly Communication, 7, 1, (2019)","D.A. Nitecki; Drexel University, United States; email: dan44@drexel.edu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85100851910"
"Franke S.; Paulet L.; Schäfer J.; O’Connell D.; Becker M.M.","Franke, Steffen (57205584512); Paulet, Lucian (57219526048); Schäfer, Jan (25223462400); O’Connell, Deborah (57219748844); Becker, Markus M. (57189635620)","57205584512; 57219526048; 25223462400; 57219748844; 57189635620","Plasma-MDS, a metadata schema for plasma science with examples from plasma technology","2020","Scientific Data","7","1","439","","","","8","10.1038/s41597-020-00771-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097682708&doi=10.1038%2fs41597-020-00771-0&partnerID=40&md5=f4adca90fc5f4bc6808d927f8b3a8347","Leibniz Institute for Plasma Science and Technology (INP), Greifswald, 17489, Germany; York Plasma Institute, Department of Physics, University of York, Heslington, York, YO10 5DD, United Kingdom","Franke S., Leibniz Institute for Plasma Science and Technology (INP), Greifswald, 17489, Germany; Paulet L., Leibniz Institute for Plasma Science and Technology (INP), Greifswald, 17489, Germany; Schäfer J., Leibniz Institute for Plasma Science and Technology (INP), Greifswald, 17489, Germany; O’Connell D., York Plasma Institute, Department of Physics, University of York, Heslington, York, YO10 5DD, United Kingdom; Becker M.M., Leibniz Institute for Plasma Science and Technology (INP), Greifswald, 17489, Germany","A metadata schema, named Plasma-MDS, is introduced to support research data management in plasma science. Plasma-MDS is suitable to facilitate the publication of research data following the FAIR principles in domain-specific repositories and with this the reuse of research data for data driven plasma science. In accordance with common features in plasma science and technology, the metadata schema bases on the concept to separately describe the source generating the plasma, the medium in which the plasma is operated in, the target the plasma is acting on, and the diagnostics used for investigation of the process under consideration. These four basic schema elements are supplemented by a schema element with various attributes for description of the resources, i.e. the digital data obtained by the applied diagnostic procedures. The metadata schema is first applied for the annotation of datasets published in INPTDAT—the interdisciplinary data platform for plasma technology. © 2020, The Author(s).","","article; diagnostic procedure; FAIR principles; human tissue; metadata","","","","","Bundesministerium für Bildung und Forschung, BMBF, (16FDM005, 16QK03A)","M.M.B. thanks D. Loffhagen and D. Uhrlandt for supportive discussions and the scientists involved at INP for helpful suggestions for the design of the metadata schema. The work was funded by the Federal Ministry of Education and Research (BMBF) under the grant marks 16FDM005 and 16QK03A. The responsibility for the content of this publication lies with the authors.","Kates-Harbeck J., Svyatkovskiy A., Tang W., Predicting disruptive instabilities in controlled fusion plasmas through deep learning, Nat., 568, pp. 526-531, (2019); Albertsson K., Et al., Machine learning in high energy physics community white paper, J. Phys.: Conf. Ser., 1085, (2018); Spears B.K., Et al., Deep learning: A guide for practitioners in the physical sciences, Phys. Plasmas, 25, (2018); Mesbah A., Graves D.B., Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas, J. Phys. D: Appl. Phys., 52, (2019); Kruger F., Gergs T., Trieschmann J., Machine learning plasma-surface interface for coupling sputtering and gas-phase transport simulations, Plasma Sources Sci. Technol., 28, (2019); Brandenburg R., Et al., White paper on the future of plasma science in environment, for gas conversion and agriculture, Plasma Process. Polym., 16, (2018); Cvelbar U., Et al., White paper on the future of plasma science and technology in plastics and textiles, Plasma Process. Polym., 16, (2018); Simek M., Et al., White paper on the future of plasma science for optics and glass, Plasma Process. Polym., 16, (2019); von Woedtke T., Reuter S., Masur K., Weltmann K.-D., Plasmas for medicine, Phys. Rep., 530, pp. 291-320, (2013); Weltmann K.-D., von Woedtke T., Plasma medicine—current state of research and medical application, Plasma Phys. Control. Fusion, 59, (2016); Bekeschus S., Favia P., Robert E., von Woedtke T., White paper on plasma for medicine and hygiene: Future in plasma health sciences, Plasma Process. Polym., 16, (2018); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Chen X., Et al., Open is not enough, Nat. Phys., 15, pp. 113-119, (2019); Sansone S.-A., Et al., DATS, the data tag suite to enable discoverability of datasets, Sci. Data, 4, (2017); Martens L., Et al., Pride: The proteomics identifications database, PROTEOMICS, 5, pp. 3537-3545, (2005); Ghiringhelli L.M., Et al., Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats. npj Comput, Mat., 3, (2017); Maguire E., Heinrich L., Watt G., HEPData: a repository for high energy physics data, J. Phys.: Conf. Ser., 898, (2017); Pitchford L.C., Et al., LXCat: an open-access, web-based platform for data needed for modeling low temperature plasmas, Plasma Process. Polym., 14, (2017); Tennyson J., Et al., QDB: a new database of plasma chemistries and reactions, Plasma Sources Sci. Technol., 26, (2017); Imbeaux F., Et al., Design and first applications of the ITER integrated modelling & analysis suite, Nucl. Fusion, 55, (2015); Fredian T., Et al., MDSplus yesterday, today and tomorrow, Fusion Eng. Des., 127, pp. 106-110, (2018); . Enhancing Research Data Management: Performance through Diversity., (2016); Datacite Metadata Schema for the Publication and Citation of Research Data, (2019); Schafer J., Sigeneger F., Sperka J., Rodenburg C., Foest R., Searching for order in atmospheric pressure plasma jets, Plasma Phys. Control. Fusion, 60, (2018); Schafer J., Correlation of Helicality and Rotation Frequency of Filaments in the Ntappj, (2019); Gorbanev Y., Chechik V., O'Connell D., Non-Thermal Plasma in Contact with Water: The Origin of Species, (2015); Gorbanev Y., O'Connell D., Chechik V., Non-thermal plasma in contact with water: The origin of species, Chem. Eur. J., 22, pp. 3496-3505, (2016); Explanation of the FAIR Data Principles, (2018); Fenner M., Using Schema.Org for DOI Registration, (2017); Ball A., How to License Research Data. DCC How-To Guides, (2014); Becker M.M., Plasma-MDS and DCAT metadata records for INPTDAT nodes 43 and 98, Zenodo, (2020)","M.M. Becker; Leibniz Institute for Plasma Science and Technology (INP), Greifswald, 17489, Germany; email: markus.becker@inp-greifswald.de","","Nature Research","","","","","","20524463","","","33335096","English","Sci. Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85097682708"
"Roche D.G.; Granados M.; Austin C.C.; Wilson S.; Mitchell G.M.; Smith P.A.; Cooke S.J.; Bennett J.R.","Roche, Dominique G. (25623797000); Granados, Monica (55114362000); Austin, Claire C. (7201679656); Wilson, Scott (57145644800); Mitchell, Gregory M. (57221309536); Smith, Paul A. (55588809739); Cooke, Steven J. (24320083600); Bennett, Joseph R. (57206712889)","25623797000; 55114362000; 7201679656; 57145644800; 57221309536; 55588809739; 24320083600; 57206712889","Open government data and environmental science: a federal Canadian perspective","2020","Facets","5","1","","942","962","20","8","10.1139/FACETS-2020-0008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098763601&doi=10.1139%2fFACETS-2020-0008&partnerID=40&md5=ca870728dbfcd3d2c6178955c5979ba9","Canadian Centre for Evidence-Based Conservation, Department of Biology, Institute of Environmental and Interdisciplinary Sciences, Carleton University, Ottawa, K1S 5B6, ON, Canada; Science and Technology Strategies Directorate, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada; Canadian Wildlife Service, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada","Roche D.G., Canadian Centre for Evidence-Based Conservation, Department of Biology, Institute of Environmental and Interdisciplinary Sciences, Carleton University, Ottawa, K1S 5B6, ON, Canada; Granados M., Science and Technology Strategies Directorate, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada; Austin C.C., Science and Technology Strategies Directorate, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada; Wilson S., Canadian Wildlife Service, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada; Mitchell G.M., Canadian Wildlife Service, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada; Smith P.A., Canadian Wildlife Service, Environment and Climate Change Canada, Gatineau, K1A 0H3, QC, Canada; Cooke S.J., Canadian Centre for Evidence-Based Conservation, Department of Biology, Institute of Environmental and Interdisciplinary Sciences, Carleton University, Ottawa, K1S 5B6, ON, Canada; Bennett J.R., Canadian Centre for Evidence-Based Conservation, Department of Biology, Institute of Environmental and Interdisciplinary Sciences, Carleton University, Ottawa, K1S 5B6, ON, Canada","Governments worldwide are releasing data into the public domain via open government data initiatives. Many such data sets are directly relevant to environmental science and complement data collected by academic researchers to address complex and challenging environmental problems. The Government of Canada is a leader in open data among Organisation for Economic Co-operation and Development countries, generating and releasing troves of valuable research data. However, achieving comprehensive and FAIR (findable, accessible, interoperable, reusable) open government data is not without its challenges. For example, identifying and understanding Canada’s international commitments, policies, and guidelines on open data can be daunting. Similarly, open data sets within the Government of Canada are spread across a diversity of repositories and portals, which may hinder their discoverability. We describe Canada’s federal initiatives promoting open government data, and outline where data sets of relevance to environmental science can be found. We summarize research data management challenges identified by the Government of Canada, plans to modernize the approach to open data for environmental science and best practices for data discoverability, access, and reuse. Copyright: © 2020 Roche et al.","Conservation; Data sharing; Ecology; FAIR data; Government of Canada; Public data archiving; Science policy","","","","","","Anil Arora; Statistics Canada; Treasury Board of Canada Secretariat; Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA, (838237); Natural Sciences and Engineering Research Council of Canada, NSERC; Environment and Climate Change Canada, ECCC, (GCXE19S058)","Funding text 1: We acknowledge funding from Environment and Climate Change Canada (DGR, JRB, SJC; GCXE19S058), the Natural Sciences and Engineering Research Council of Canada (JRB, SJC), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 838237-OPTIMISE (DGR).; Funding text 2: We thank Patrick Little from the Treasury Board of Canada Secretariat, Anil Arora and Andr? Loranger from Statistics Canada, and Jennifer Vincent from Environment and Climate Change Canada?s Science and Technology Strategies Directorate for sharing valuable information. We acknowledge funding from Environment and Climate Change Canada (DGR, JRB, SJC; GCXE19S058), the Natural Sciences and Engineering Research Council of Canada (JRB, SJC), and the European Union?s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No. 838237-OPTIMISE (DGR).","Altayar MS., Motivations for open data adoption: an institutional theory perspective, Government Information Quarterly, 35, 4, pp. 633-643, (2018); Austin CC., The open science ecosystem: a systematic framework anchored in values, ethics, and FAIRER data, (2020); Austin CC, Baker D, Best M, Born A, Brown S, Cui X, Et al., Guidelines for the deposit and preservation of research data in Canada, (2015); Barone L, Williams J, Micklos D., Unmet needs for analyzing biological big data: a survey of 704 NSF principal investigators, PLoS Computational Biology, 13, 10, (2017); 2020 biodiversity goals and targets for Canada, (2015); Borer ET, Seabloom EW, Jones MB, Schildhauer M., Some simple guidelines for effective data management, Bulletin of the Ecological Society of America, 90, 2, pp. 205-214, (2009); Borgman CL., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Canino A., Deconstructing Google dataset search, Public Services Quarterly, 15, 3, pp. 248-255, (2019); Chapman AD, Grafton O., Guide to best practices for generalising primary species occurrence-data, (2008); Cheruvelil KS, Soranno PA., Data-intensive ecological research is catalyzed by open science and team science, BioScience, 68, 10, pp. 813-822, (2018); The Beijing declaration on research data, (2019); Culina A, Baglioni M, Crowther TW, Visser ME, Woutersen-Windhouwer S, Manghi P., Navigating the unfolding open data landscape in ecology and evolution, Nature Ecology & Evolution, 2, 3, pp. 420-426, (2018); Dick M, Rous AM, Nguyen VM, Cooke SJ., Necessary but challenging: multiple disciplinary approaches to solving conservation problems, FACETS, 1, 1, pp. 67-82, (2016); Dodds L., Publisher’s guide to open data licensing, (2013); Dyke SOM, Linden M, Lappalainen I, De Argila JR, Carey K, Lloyd D, Et al., Registered access: authorizing data access, European Journal of Human Genetics, 26, 12, pp. 1721-1731, (2018); 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Big data interoperability framework: volume 1, definitions, (2019); Digital government and open government data, Government at a Glance 2019, (2019); Main science and technology indicators: GERD as a percentage of GDP, (2019); Model policy on scientific integrity, (2018); Roadmap for open science, (2020); Mandate letters, (2019); Monitoring open science implementation in federal science-based departments and agencies: metrics and indicators, (2018); Poisot T, Bruneau A, Gonzalez A, Gravel D, Peres-Neto P., Ecological data should not be so hard to find and reuse, Trends in Ecology & Evolution, 34, 6, pp. 494-496, (2019); Popkin G., Data sharing and how it can benefit your scientific career, Nature, 569, 7756, pp. 445-447, (2019); A Data Strategy Roadmap for the federal public service, (2018); Data management plan (DMP) common standard, (2020); Reid AJ, Brooks JL, Dolgova L, Laurich B, Sullivan BG, Szekeres P, Et al., Post-2015 Sustainable Development Goals still neglecting their environmental roots in the Anthropocene, Environmental Science & Policy, 77, pp. 179-184, (2017); Reid WV, Chen D, Goldfarb L, Hackmann H, Lee YT, Mokhele K, Et al., Earth system science for global sustainability: grand challenges, Science, 330, 6006, pp. 916-917, (2010); Renaut S, Budden AE, Gravel D, Poisot T, Peres-Neto P., Management, archiving, and sharing for biologists and the role of research institutions in the technology-oriented age, BioScience, 68, pp. 400-411, (2018); Roche DG., Open data: policies need policing, Nature, 538, 7623, (2016); Roche DG, Lanfear R, Binning SA, Haff TM, Schwanz LE, Cain KE, Et al., Troubleshooting public data archiving: suggestions to increase participation, PLoS Biology, 12, 1, (2014); Roche DG, Kruuk LE, Lanfear R, Binning SA., Public data archiving in ecology and evolution: how well are we doing?, PLoS Biology, 13, 11, (2015); Ruijer EH, Martinius E., Researching the democratic impact of open government data: a systematic literature review, Information Polity, 22, 4, pp. 233-250, (2017); The State of Open Data Report 2019, (2019); Canada year book. Science and technology, (2012); Strasser C, Cook R, Michener W, Budden A., Primer on data management: what you always wanted to know, (2012); Foundation framework for treasury board policies, (2008); Directive on Open Government, (2014); DRAFT open government data and information quality standards, (2018); Open government guidebook: government of Canada’s guide for releasing open government resources on open.canada.ca, (2018); Open government registry guide, (2018); Scientific integrity policies, (2018); Open data portal catalogue, (2019); Policy on service and digital, (2020); Vines TH, Andrew RL, Bock DG, Franklin MT, Gilbert KJ, Kane NC, Et al., Mandated data archiving greatly improves access to research data, The FASEB Journal, 27, 4, pp. 1304-1308, (2013); White EP, Baldridge E, Brym ZT, Locey KJ, McGlinn DJ, Supp SR., Nine simple ways to make it easier to (re)use your data, Ideas in Ecology and Evolution, 6, 2, pp. 1-10, (2013); Wickham H., Tidy data, Journal of Statistical Software, 59, 10, pp. 1-23, (2014); Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wilkinson MD, Sansone S-A, Schultes E, Doorn P, Da Silva Santos LOB, Dumontier M., A design framework and exemplar metrics for FAIRness, Scientific Data, 5, (2018); Wolkovich EM, Regetz J, O'Connor MI., Advances in global change research require open science by individual researchers, Global Change Biology, 18, 7, pp. 2102-2110, (2012); Zeleti FA, Ojo A, Curry E., Exploring the economic value of open government data, Government Information Quarterly, 33, 3, pp. 535-551, (2016); Zipper SC, Stack Whitney K, Deines JM, Befus KM, Bhatia U, Albers SJ, Et al., Balancing open science and data privacy in the water sciences, Water Resources Research, 55, pp. 5202-5211, (2019); Zuiderwijk A, Shinde R, Janssen M., Investigating the attainment of open government data objectives: is there a mismatch between objectives and results?, International Review of Administrative Sciences, 85, 4, pp. 645-672, (2019)","D.G. Roche; Canadian Centre for Evidence-Based Conservation, Department of Biology, Institute of Environmental and Interdisciplinary Sciences, Carleton University, Ottawa, K1S 5B6, Canada; email: dominique.roche@mail.mcgill.ca","","Canadian Science Publishing","","","","","","23711671","","","","English","Facet.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85098763601"
"Chamanara J.; Gaikwad J.; Gerlach R.; Algergawy A.; Ostrowski A.; König-Ries B.","Chamanara, Javad (55508841900); Gaikwad, Jitendra (6603452902); Gerlach, Roman (57369762600); Algergawy, Alsayed (26029721900); Ostrowski, Andreas (57211405961); König-Ries, Birgitta (55864942100)","55508841900; 6603452902; 57369762600; 26029721900; 57211405961; 55864942100","BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data","2021","Biodiversity Data Journal","9","","e72901","","","","1","10.3897/BDJ.9.E72901","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120955578&doi=10.3897%2fBDJ.9.E72901&partnerID=40&md5=1ef39b5f7ce72d4ca0b2b655c794f1ac","Friedrich-Schiller-Universität Jena, Jena, Germany; TIB, Leibniz Information Centre for Science and Technology and University Library, Hannover, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Jena, Germany","Chamanara J., Friedrich-Schiller-Universität Jena, Jena, Germany, TIB, Leibniz Information Centre for Science and Technology and University Library, Hannover, Germany; Gaikwad J., Friedrich-Schiller-Universität Jena, Jena, Germany; Gerlach R., TIB, Leibniz Information Centre for Science and Technology and University Library, Hannover, Germany; Algergawy A., Friedrich-Schiller-Universität Jena, Jena, Germany; Ostrowski A., TIB, Leibniz Information Centre for Science and Technology and University Library, Hannover, Germany; König-Ries B., Friedrich-Schiller-Universität Jena, Jena, Germany, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Jena, Germany","Background Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research. New information We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel. During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved. This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system. © 2021. Chamanara J et al.","biodiversity; data lifecycle; ecology; environmental science; FAIR data maturity model; FAIR data principles; open-source; research data management","biodiversity; concrete; data management; environmental stress; life cycle","","","","","AquaDiva, (193925721, 202548816, 218627073, 229241684); BMBF-funded, (01DH16009); Cornelia Fürstenau; Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen; Helmholtz Umweltforschungszentrum Halle/Leipzig; Thüringer Universitäts; Thüringer Universitäts-und Landesbibliothek Jena; Deutsche Forschungsgemeinschaft, DFG, (189571761); Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig, iDiv","Funding text 1: We acknowledge the support provided under the Open Access Publication Fund by the Thüringer Universitäts-und Landesbibliothek Jena.; Funding text 2: Funding: The development of BEXIS2 has been funded by the German Science Foundation (DFG) as part of the BExIS++ project (189571761). Funding for specific extensions and modules is further provided by the DFG through the projects: Biodiversity Exploratories, AquaDiva, iDiv and GFBio (193925721, 218627073, 202548816, 229241684) and BMBF-funded Managing Multimedia Data for Science (MAMUDS) project (01DH16009).; Funding text 3: We want to thank the developers who contributed towards the development of the BEXIS2 over the years, making no claim to completeness; these include (in alphabetical order): Arefeh Bahrami (Bauhaus University Weimar, Friedrich-Schiller-University Jena), David Schöne (Max-Planck-Institut for Biogeochemistry Jena, Friedrich-Schiller-University Jena), Eleonora Petzold (Friedrich-Schiller-University Jena), Franziska Zander (Friedrich-Schiller-University Jena), Hamdi Hamed (Friedrich-Schiller-University Jena), Markus Steinberg (Friedrich-Schiller-University Jena), Martin Hohmuth (Friedrich-Schiller-University Jena), Masoud Allahyari (Bauhaus University Weimar, Friedrich-Schiller-University Jena), Michael Owonibi (Friedrich-Schiller-University Jena), Nafiseh Navabpour (Friedrich-Schiller-University Jena, Helmholtz Umweltforschungszentrum Halle/Leipzig), Payam Adineh (Bauhaus University Weimar, Friedrich-Schiller-University Jena), Sirko Schindler (Friedrich-Schiller-University Jena), Sven Thiel (TU Munich, Friedrich-Schiller-University Jena) and Thorsten Hindermann (Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen). In addition, we want to thank former colleagues Jens Krumpe and Ken Prager; the current and previous data managers and curators: Sebastian Meyer, Markus Guderle, Yuan Yuan, Anahita Kazem (iDiv) and Cornelia Fürstenau (Friedrich-Schiller-University Jena) for providing insights about the user perspective and feedback to improve the usability of the BEXIS2 system. All authors are grateful to our colleagues Felicitas Löffler, Franziska Zander and Sven Thiel for being willing to act as internal reviewers and their valuable comments. Further, we also want to thank the reviewers for their generous time and greatly appreciate their insightful comments that improved the manuscript. We acknowledge the support provided under the Open Access Publication Fund by the Thüringer Universitäts- und Landesbibliothek Jena.","Access to biological collection data (ABCD), version 2.06. 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— Intercomparison of two major German project databases for interdisciplinary collaborative functional biodiversity research, Ecological Informatics, 8, pp. 10-19, (2012); Marcial LH, Hemminger B, Scientific data repositories on the web: an initial survey, Journal of the American Society for Information Science and Technology, 61, 10, pp. 2029-2048, (2010); Michener W, Ecological data sharing, Ecological Informatics, 29, pp. 33-44, (2015); Miyakawa T, No raw data, no science: another possible source of the reproducibility crisis, Molecular Brain, 13, 1, (2020); Mons B, Invest 5% of research funds in ensuring data are reusable, Nature, 578, 7796, pp. 491-491, (2020); Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, O Tuama E, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S, Strategies and guidelines for scholarly publishing of biodiversity data, Research Ideas and Outcomes, 3, (2017); Recknagel F, Michener W, Ecological informatics, (2018); Roberts D, Moritz T, A framework for publishing primary biodiversity data, BMC Bioinformatics, 12, 15, (2011); Roche D, Evaluating science’s open-data policy, Science, 357, 6352, pp. 1-654, (2017); Sales L, Henning P, Veiga V, Costa MM, Sayao LF, da Silva Santos LOB, Pires LF, Go FAIR Brazil: A challenge for Brazilian data science, Data Intelligence, 2, pp. 238-245, (2020); Digital Science, Hahnel M, Fane B, Treadway J, Baynes G, Wilkinson R, Mons B, Schultes E, Silva Santos LOBD, Arefiev P, Osipov I, The state of open data report 2018, Digital Science, (2018); Smits DAB, Teperek M, Research data management for master’s students: from awareness to action, Data Science Journal, 19, (2020); Research data policies, (2021); Stuart D, Baynes G, Hrynaszkiewicz I, Allin K, Penny D, Lucraft M, Astell M, Whitepaper: practical challenges for researchers in data sharing, Figshare, (2018); Wieczorek J, Bloom D, Guralnick R, Blum S, Doring M, Giovanni R, Robertson T, Vieglais D, Darwin core: an evolving community-developed biodiversity data standard, PLOS One, 7, 1, (2012); Wilkinson M, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J, da Silva Santos LB, Bourne P, Bouwman J, Brookes A, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo C, Finkers R, Gonzalez-Beltran A, Gray AG, Groth P, Goble C, Grethe J, Heringa J, 't Hoen PC, Hooft R, Kuhn T, Kok R, Kok J, Lusher S, Martone M, Mons A, Packer A, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S, Schultes E, Sengstag T, Slater T, Strawn G, Swertz M, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B, The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Wilkinson M, Dumontier M, Sansone S, Bonino da Silva Santos LO, Prieto M, Batista D, McQuilton P, Kuhn T, Rocca-Serra P, Crosas M, Schultes E, Evaluating FAIR maturity through a scalable, automated, community-governed framework, Scientific Data, 6, 1, (2019); Wittenburg P, Lautenschlager M, Thiemann H, Baldauf C, Trilsbeek P, FAIR practices in Europe, Data Intelligence, 2, pp. 257-263, (2020); FAIR data maturity model: specification and guidelines, Research Data Alliance, (2020)","J. Gaikwad; Friedrich-Schiller-Universität Jena, Jena, Germany; email: jitendra.gaikwad@uni-jena.de","","Pensoft Publishers","","","","","","13142828","","","","English","Biodivers. Data J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85120955578"
"da Cunha M.B.; Costa M.M.","da Cunha, Murilo Bastos (55667284400); Costa, Maira Murrieta (55196004000)","55667284400; 55196004000","Information sources on research data management; [Fontes de informação sobre gestão de dados de pesquisa]","2020","Informacao e Sociedade","30","4","57183","","","","1","10.22478/UFPB.1809-4783.2020V30N4.57183","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100751902&doi=10.22478%2fUFPB.1809-4783.2020V30N4.57183&partnerID=40&md5=5540e1d10d5f66e76d96c5a98dda8118","Programa de Pós-Graduação em Ciência da Informação, Universidade de Brasília, Brazil; Ministério de Ciência, Tecnologia, Inovações, Brazil","da Cunha M.B., Programa de Pós-Graduação em Ciência da Informação, Universidade de Brasília, Brazil; Costa M.M., Ministério de Ciência, Tecnologia, Inovações, Brazil","The article presents a selective and annotated bibliography on the major sources of information necessary to the study of research data management. It is divided into two parts; the first enrolls books and manuals and general documents. In the second part are included the documents related to the flow of research data: management policies, plans for data management, requirements of funding agencies, norms and standards, metadata, workflows, legal aspects, economic aspects, sharing and reuse of data, data. © 2020 Universidade Federal de Campina Grande. All rights reserved.","Bibliography; Digital preservation; Research data management","","","","","","","","BAILEY JUNIOR C. W., Research data curation bibliography, (2019); CUNHA M. B., Bibliografia sobre o fluxo do documento na biblioteca digital, DataGramaZero Revista de Ciência da Informação, (2009); Resources for digital curators; GASPAR A. C., Documento técnico contendo revisão da literatura e análise de publicações sobre Ciência Aberta (Open Science) e temas correlatos para elaboração de bibliografia especializada com resumos traduzidos e comentados, (2014); GOBEN A., RASZEWSKI R., Research Data Management Self-Education for Librarians: A Webliography, Issues in Science and Technology Librarianship, 82, (2015); State University Libraries; WESTRA B., Et al., Science and Technology Resources on the Internet: Selected Internet Resources on Digital Research Data Curation, Issues in Science and Technology Librarianship, 63, (2010); ATKINS D. E., Et al., Revolutionizing science and engineering through cyberinfrastructure: report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure, (2003); BAYKOUCHEVA S., Managing Scientific Information and Research Data, (2015); BORGMAN C. L., Big data, little data, no data: scholarship in the networked world, (2015); BRINEY K., Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success, (2015); CORTI L., EYNDEN V. Van den, BISHOP L., WOOLLARD M., Managing and Sharing Research Data: A Guide to Good Practice, (2019); Research Data Management: Principles, Practices, and Prospects, (2013); DIAS G. A., OLIVEIRA B. M. J. F., Dados científicos: perspectivas e desafios, (2019); Os capítulos seguintes abordam questões relacionadas com a curadoria dos dados científicos, repositórios eletrônicos de dados e o Ciclo de Vida dos Dados); ERWAY R., HORTON L., NURNBERGER A., OTSUJI R., RUSHING A., Building Blocks: Laying the Foundation for a Research Data Management Program, (2016); GONZALEZ MORENO L. M., PESET MANCEBO F., Ciencia abierta y gestión de datos de investigación (RDM), (2017); GREEN A., MACDONALD S., RICE R., Policy-making for research data in Repositories: a guide, (2009); HARVEY R., Digital Curation: A How-To-Do-It Manual, (2016); Guide to social science data preparation and archiving, (2020); JOHNSTON L. R., Curating Research Data, (2017); KELLAM L., THOMPSON K., Databrarianship: The Academic Data Librarian in Theory and Practice, (2016); KRIER L., STRASSER C. A., Data management for libraries: a LITA guide, (2014); LAKE S, SALLANS A., PRALLE B., FEARON D., GUNIA B., Research data management services, (2013); Guia de dados abertos; RAY J. M., Research data management: practical strategies for information professionals, (2014); PRYOR G., JONES S., WHYTE A., Delivering research data management services: fundamentals of good practice, (2014); PRYOR G., Managing Research Data, (2013); RICE R. C., SOUTHALL J., The Data Librarian's Handbook, (2016); SALES L. F., SAYAO L. F., Dados de pesquisa: quem ama cuida, (2019); SAYAO L. F., SALES L. F., Guia de gestão de dados de pesquisa para bibliotecários e pesquisadores, (2015); SILVA F. C. C., Gestão de dados científicos, (2019); STRASSER C., Research Data Management, (2015); TENOPIR C., BIRCH B., ALLARD S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2012); VAN DEN EYNDE V., CORTI L., WOOLLARD M., BISHOP L., HORTON L., Managing and Sharing Data: Best Practice for Researchers, (2011); AKERS K. G., SFERDEAN F. C., NICHOLLS N. H., GREEN J. A., Building Support for Research Data Management: Biographies of Eight Research Universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); ALBAGLI S., APPEL A. L., MACIEL M. L., E-Science, ciência aberta e o regime de informação em ciência e tecnologia, Tendências da Pesquisa Brasileira em Ciência da Informação, 7, 1, (2014); Joint Task Force on Library Support for E-Science. Agenda for Developing E-Science in Research Libraries, (2007); To Stand the Test of Time: Long-term Stewardship of Digital Data Sets in Science and Engineering; A Report to the National Science Foundation from the ARL Workshop on New Collaborative Relationships: The Role of Academic Libraries in the Digital Data Universe, (2006); BRASE J., FARQUHAR A., Introduction. Access to Research Data, D-Lib Magazine, 17, 1-2, (2011); COATES H. L., Building data services from the ground up: strategies and resources, Journal of eScience Librarianship, 3, 1, (2014); No Brief Candle: Reconceiving Research Libraries for the 21st Century, (2008); COX A. M., PINFIELD S., SMITH J., Moving a Brick Building: UK Libraries Coping with Research Data Management as a 'Wicked' Problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); DONATE-CIFUENTES A., PESET F., FERRER-SAPENA A. G., Research data services in academic libraries: the essential list, LIBER ANNUAL CONFERENCE, 2016 Libraries opening paths to knowledge, (2016); ERWAY R., HORTON L., NURNBERGER A., OTSUJI R., RUSHING A., Building Blocks: Laying the Foundation for a Research Data Management Program, (2016); Executive Office of the President. National Science and Technology Council. Interagency Working Group on Digital Data to the Committee on Science. Harnessing the Power of Digital Data for Science & Society, (2009); GASPAR A. C., ALVARES L., PEREIRA M., de N. F., Gestão dos dados de pesquisa: oportunidades e desafios, SEMINARIO HISPANO BRASILERO DE INVESTIGACIÓN EN INFORMACIÓN, (2014); GOLD A., Cyberinfrastructure, Data, and Libraries, Part 1. A Cyberinfrastructure Primer for Librarians, D-Lib Magazine, 13, 9, (2007); GOLD A., Cyberinfrastructure, Data, and Libraries, Part 2. Libraries and the Data Challenge: Roles and Actions for Libraries, D-Lib Magazine, 13, 9, (2007); GOODMAN A., Et al., Ten Simple Rules for the Care and Feeding of Scientific Data, PLoS Computational Biology, 10, 4, (2014); JAHNKE L., ASHER A., KERALIS S. D. C., The Problem of Data, (2012); JONES S., PRYOR G., WHYTE A., How to Develop Research Data Management Services-A Guide for HEIs, (2013); PARSON T., Creating a research data management service, International Journal of Digital Curation, 8, 2, pp. 146-156, (2013); PINFIELD S., COX A. M., SMITH J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); RABOIN R., REZNIK-ZELLEN R. C., SALO D., Forging New Service Paths: Institutional Approaches to Providing Research Data Management Services, Journal of eScience Librarianship, 1, 3, (2012); RAMBO N., Research Data Management Roles for Libraries, (2015); The Data Harvest, how sharing research data can yield knowledge, jobs and growth, (2014); SALES L. F., SAYAO L. F., O impacto da curadoria digital dos dados de pesquisa na comunicação científica, Encontros Bibli: revista eletrônica de biblioteconomia e ciência da informação, 17, 2, pp. 118-135, (2012); SI L., Et al., Investigation and analysis of research support services in academic libraries, Electronic Library, 37, 2, pp. 281-301, (2019); SILVA F. C. C., Infraestructuras y políticas internacionales de desarrollo para gestión de los datos de investigación, 63, (2016); Guidelines on open access to scientific publications and research data in horizon 2020, (2016); WITT M., Co-designing, Co-developing, and Co-implementing an Institutional Data Repository Service, Journal of Library Administration, 52, 2, pp. 172-188, (2012); BERTIN P. R. B., Et al., A Política de Governança de Dados, Informação e Conhecimento da Embrapa como mecanismo para a gestão de dados de pesquisa agropecuários, Liinc em revista, 15, 2, pp. 194-204, (2019); BRINEY K., GOBEN A., ZILINSKI L., Do You Have an Institutional Data Policy? A Review of the Current Landscape of Library Data Services and Institutional Data Policies, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 12-32, (2015); CALLAGHAN S., Et al., Guidelines on Recommending Data Repositories as Partners in Publishing Research Data, International Journal of Digital Curation, 9, 1, pp. 152-163, (2014); CORREA P. L. P., Modelo organizacional para gestão integrada de dados da biodiversidade brasileira, Dados científicos: perspectivas e desafios, pp. 153-175, (2019); UK Institutional Data Policies, (2016); DILLO I., DOORN P., The Front Office-Back Office Model: Supporting Research Data Management in the Netherlands, International Journal of Digital Curation, 9, 2, pp. 39-46, (2014); ERWAY R., Starting the Conversation: University-wide Research Data Management Policy, (2013); HALL N., COREY B., MANN W., WILSON T., ASERL/SURA Research Data Coordinating Committee, (2013); JONES S., Developments in Research Funder Data Policy, International Journal of Digital Curation, 7, 1, pp. 114-125, (2012); KROES N., Open Science for the 21st century: A declaration of all European Academies, (2012); RODRIGUES E., SARAIVA R., Os Repositórios de dados científicos: estado da arte, (2010); SHEARER K., Comprehensive Brief on Research Data Management Policies, (2015); WILSON J. A. J., MARTINEZ-URIBE L., FRASER M. A., JEFFREYS P., An Institutional Approach to Developing Research Data Management Infrastructure, International Journal of Digital Curation, 6, 2, pp. 274-287, (2011); BALL J., Research Data Management for Libraries: Getting Started, Insights: The UKSG Journal, 26, 3, pp. 256-260, (2013); Plano de Ação para a implantação da infraestrutura de dados espaciais, (2010); CLAIBOURN M. P., Bigger on the Inside: building Research Data Services at the University of Virginia, Insights: The UKSG Journal, 28, 2, pp. 100-106, (2015); CLEMENTS A., Research Information Meets Research Data Management in the Library?, Insights: The UKSG Journal, 26, 3, pp. 298-304, (2013); Checklist for a Data Management Plan, (2014); DMPonline; ERWIN T., SWEETKIND-SINGER J., LARSGAARD M. L., The National Geospatial Digital Archives-Collection Development: Lessons Learned, Library Trends, 57, 3, pp. 490-515, (2009); FARY M., OWEN K., Developing an Institutional Research Data Management Plan Service, (2013); HENDERSON M. E., KNOTT T. L., Starting a Research Data Management Program Based in a University Library, Medical Reference Services Quarterly, 34, 1, pp. 387-403, (2015); JOHNSON A. M., KNUTH S., Data Management Plan Requirements for Campus Grant Competitions: Opportunities for Research Data Services Assessment and Outreach, Journal of eScience Librarianship, 5, 1, (2016); JONES S., PRYOR G., WHYTE A., How to Develop Research Data Management Services - a guide for HEIs, (2015); MAYERNIK M. S., Et al., The Data Conservancy Instance: Infrastructure and Organizational Services for Research Data Curation, D-Lib Magazine, 18, 9, (2012); PARSONS T., Creating a Research Data Management Service, International Journal of Digital Curation, 8, 2, pp. 146-156, (2013); PEER L., GREEN A., Building an Open Data Repository for a Specialized Research Community: Process, Challenges and Lessons, International Journal of Digital Curation, 7, 1, pp. 151-162, (2012); SILVA JUNIOR L. P. da, SANTOS T. H., repositórios de dados científicos: um panorama teórico-prático, Dados científicos: perspectivas e desafios, pp. 89-111, (2019); Guidelines on data management in Horizon 2020, (2017); California Digital Library; VEIGA V. 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M., Institutional and individual factors affecting scientists' data-sharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2016); KOWALCZYK S., SHANKAR K., Data Sharing in the Sciences, Annual Review of Information Science and Technology, 45, 1, pp. 247-294, (2011); MACMILLAN D., Data Sharing and Discovery: What Librarians Need to Know, Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); MEDEIROS J., da S., CAREGNATO S. E., Compartilhamento de dados e e-Science: explorando um novo conceito para a comunicação científica, Liinc em Revista, 8, 2, (2012); NINA-ALCOCER V., BLASCO-GIL Y., PESET F., Datasharing: guía práctica para compartir datos de investigación, El profesional de la información, 22, 6, pp. 562-568, (2013); PASQUETTO I. V., RANDLES B. M., BORGMAN C. L., On the reuse of scientific data, Data Science Journal, 16, 8, pp. 1-9, (2017); PIWOWAR H. A, DAY R. S, FRIDSMA D. B., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, 3, (2007); SHEN Yi, Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape, International Journal of Digital Curation, 10, 2, (2015); TENOPIR C., Et al., Data Sharing by Scientists: Practices and Perceptions, PLoS ONE, 6, 6, (2011); VAN DE SANDT T., Et al., The definition of reuse, Data Science Journal, 18, 1, (2019); WALLIS J., ROLANDO E., TENOPIR C. L., If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology, PLoS ONE, 8, 7, (2013); WALPORT M., BREST P., Sharing research data to improve public health, Lancet, 377, 9765, pp. 537-539, (2011); WILKINSON M. D., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific data, 3, (2016); COATES H. L., Building Data Services From the Ground Up: Strategies and Resources, Journal of eScience Librarianship, 3, 1, (2014); COX A., KNIGHT G., Building a Research Data Management Service for the London School of Hygiene & Tropical Medicine, Program: Electronic Library and Information Systems, 49, 4, pp. 424-439, (2015); COX A., KENNAN M. A., LYON E. J., PINFEED S., SBAFF L., Progress in Research Data Services: An International Survey of University Libraries, International Journal of Digital Curation, 14, 1, pp. 126-135, (2019); FEARON D., GUNIA B., PRALLE B., LAKE S., SALLANS A. L., Research data management services, (2013); SI L., XING W., ZHUANG X., HUA X., XHOU L., Investigation and Analysis of Research Data Services in University Libraries, Electronic Library, 33, 3, pp. 417-449, (2015); YOON A., SCHULTZ T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College & Research Libraries, 78, 7, pp. 920-933, (2017); AKMON D., ZIMMERMAN A., DANIELS M., HEDSTROM M., The Application of Archival Concepts to a Data-Intensive Environment: Working with Scientists to Understand Data Management and Preservation Needs, Archival Science, 11, 3-4, pp. 329-348, (2011); BARATEIRO J., ANTUNES G., CABRAL M., BORBINHA J., RODRIGUES R., Digital Preservation of Scientific Data, Lecture Notes in Computer Science, 5173, pp. 388-391, (2008); BEAGRIE N., BEAGRIE R., ROWLANDS I., Research Data Preservation and Access: The Views of Researchers, Ariadne, 60, (2009); BEAGRIE N., LAVOIE B., WOOLLARD M., Keeping Research Data Safe 2, (2010); BERMAN F., Got Data? A Guide to data preservation in the information age, Communications of the ACM, 51, 12, pp. 50-56, (2008); Buenas prácticas sobre el almacenamiento y seguridad de los datos de investigación [Video], (2017); CONWAY E., GIARETTA D., LAMBERT S., MATTHEWS B., Curating Scientific Research Data for the Long Term: A Preservation Analysis Method in Context, International Journal of Digital Curation, 6, 2, pp. 38-52, (2011); CONWAY E., Et al., Managing Risks in the Preservation of Research Data with Preservation Networks, International Journal of Digital Curation, 7, 1, pp. 3-15, (2012); DALLMEIER-TIESSEN S., Et al., Exemplar Good Governance Structures and Data Policies; DURANTI L., The Long-Term Preservation of Accurate and Authentic Digital Data: The INTERPARES Project, Data Science Journalv, 4, pp. 106-118, (2006); MARDERO ARELLANO M., Cariniana: uma rede nacional de preservação digital, Ciência da Informação, 41, 1, (2012); Trustworthy Repositories Audit & Certification (TRAC): Criteria and checklist, (2007); PINTO F., de M. A. G., AMARAL J. C., SANTOS M., Curadoria de dados de pesquisa em repositórios de ensaios clínicos: uma revisão de escopo, Liinc em Revista, Rio de Janeiro, 15, 2, pp. 84-100, (2019); SAYAO L. F., SALES L. F., Curadoria digital: um novo patamar para preservação de dados digitais de pesquisa, Informação & Sociedade: Estudos, 22, 3, pp. 179-191; SCHUMACHER J., VANDECREEK D., Intellectual Capital at Risk: Data Management Practices and Data Loss by Faculty Members at Five American Universities, International Journal of Digital Curation, 10, 2, pp. 96-109, (2015); HELBIG K., HAUSSTEIN B., TOEPFER R., Supporting Data Citation: Experiences and Best Practices of a DOI Allocation Agency for Social Sciences, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); INTERNATIONAL STANDARD NAME IDENTIFIER (ISNI); ISO 26324:2012 Information and documentation, Digital object identifier system; ALTMAN M., BORGMAN C., CROSAS M., MATONE M., An Introduction to the Joint Principles for Data Citation, Bulletin of the Association for Information Science and Technology, 41, 3, pp. 43-45, (2015); BALL A., DUKE M., DCC How-to Guides, (2015); BRASE J., SENS I., LAUTENSCHLAGER M., The tenth anniversary of assigning DOI names to scientific data and a five-year history of DataCite, D-Lib Magazine, 21, 1-2, (2015); CROSAS M., The evolution of data citation: from principles to implementation, IASSIST Quarterly, 37, 1-4, (2014); Joint Declaration of Data Citation Principles Final, (2014); HERTERICH P., DALLMEIER-TIESSEN S., Data Citation Services in the High-Energy Physics Community, D-Lib Magazine, 22, 1-2, (2016); Data Citation: What You Need to Know, (2016); BALL A., Et al., Visualizing Research Data Records for Their Better Management, Journal of Digital Information, 13, 1, (2012); CHEN C., Information Visualization: Beyond the Horizon, (2010); KIRK A., Data visualization: a handbook for data driven design, (2019); SILVA F. C. C., Visualização de dados: passado, presente e futuro, Liinc em revista, 15, 2, pp. 205-223, (2019); TELEA A. C., Data visualization: principles and practice, (2014); UNWIN A., CHEN C., HARDLE W., Handbook of data visualization, (2008); YAU N., Data points: visualization that means something, (2013); AVERKAMP S., GU X., ROGERS B., Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs, (2014); AKERS K. G., Disciplinary Differences in Faculty Research Data Management Practices and Perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); BARDYN T. P., RESNICK T., CAMINA S. K., Translational Researchers' Perceptions of Data Management Practices and Data Curation Needs: Findings from a Focus Group in an Academic Health Sciences Library, Journal of Web Librarianship, 6, 4, pp. 274-287, (2012); BORGMAN C. L, WALLIS J. C., ENYEDY N., Building digital libraries for scientific data: An exploratory study of data practices in habitat ecology, Lecture Notes in Computer Science: Research and Advanced Technology for Digital Libraries, 4172, pp. 170-183, (2006); BUYS C. M., SHAW P. L., Data Management Practices Across an Institution: Survey and Report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); CAREGNATO S. E., Et al., Práticas e percepções dos pesquisadores brasileiros sobre serviços de acesso aberto a dados de pesquisa, Liinc em Revista, 15, 2, pp. 121-141, (2019); Data dimensions: disciplinary differences in research data sharing, reuse and long term viability, (2010); FEIJEN M., What Researchers Want, (2011); GRIFFITHS A., The Publication of Research Data: Researcher Attitudes and Behaviour, International Journal of Digital Curation, 4, 1, pp. 46-56, (2009); MARTINEZ-URIBE L., MACDONALD S., User engagement in research data curation, Lecture Notes in Computer Science, 5714, pp. 309-314, (2009); MCLURE M., Et al., Data Curation: A Study of Researcher Practices and Needs portal, 14, 2, (2014); NORTON H. F., Et al., Assessment of and Response to Data Needs of Clinical and Translational Science Researchers and Beyond, Journal of eScience Librarianship, 5, 1, (2016); RIBEIRO C., FERNANDES M. E. M., Data Curation at U. Porto: Identifying Current Practices across Disciplinary Domains, IASSIST Quarterly, 35, 4, pp. 14-17, (2011); RIBEIRO C. J. S., Modelo de Maturidade para Repositórios Digitais: um caminho para sua adoção na gestão de dados de pesquisa, Liinc em Revista, 15, 2, pp. 224-243, (2019); SCARAMOZZINO J. M., RAMIREZ M. L., MCGAUGHY K. J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, pp. 349-365, (2012); SCHOPF J. M., NEWHOUSE S., User Priorities for Data: Results from SUPER, International Journal of Digital Curation, 2, 1, (2007); WELLER T., Understanding Methodological and Disciplinary Differences in the Data Practices of Academic Researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); WHITMIRE A. L., BOOCK M., SUTTON S. C., Variability in Academic Research Data Management Practices: Implications for Data Services Development from a Faculty Survey, Program, 49, 4, pp. 382-407, (2015)","","","Universidade Federal de Campina Grande","","","","","","01040146","","","","Portuguese","Inf. Soc.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85100751902"
"Arora S.; Chakravarty R.","Arora, Surbhi (57223093530); Chakravarty, Rupak (36674495400)","57223093530; 36674495400","Preserving Global Research Data: Role and Status of Re3data in RDM","2021","Library Philosophy and Practice","2021","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108183204&partnerID=40&md5=f872e46f7e1827a9a12ab72de5fde996","","","Purpose: Considering that scientific data is being increasingly renowned as an important raw material for current and future technological advances, many research collaborators have joined together to create mechanisms to secure and preserve it. However, irrespective of the generation of rich analysis results, this study was undertaken to examine the RDM activities on the global Registry of Research Data Repositories platform (Re3data) to increase its level of visualization. Design/methodology/approach: The study approached the Re3 website, a global registry of research data repositories to collect the data. The researcher specifically assessed the 9 alternative search strategies that are available in the Re3 database; namely subject, content, keyword, metadata standards, quality management, repository languages, software, repository types and country. Findings: It is observed that behaviors related to structured study results are more evident in developed countries as opposed to developing countries, although the U.S. is placed first. Results also indicated that research data is more structured in the case of scientific and statistical formats and disciplinary databases, particularly the life sciences. Overall, the software is mainly used for processing data and the English language is strongly supported. Dublin core metadata is often used to increase the quality of data from analysis. Originality/value: This study presented an overall picture of the research data practices throughout the investigation on the Re3data platform. The research proposed best practices focused on RDM operations to improve the amount of Research Data activities. © 2021. All rights reserved","Re3data; Repository; Research Data; Research Data Management; Scientific data","","","","","","","","Re3data.Org Registry Of Research Data Repository, (2012); Research Data Management Explained. Foster Facilitate Open Science Training For European Research; Angus W., Jonathan T., Making the Case for Research Data Management, pp. 1-8, (2011); Research Data Management, (2018); Pennock M., Digital Curation: A Life-Cycle Approach to Managing and Preserving Usable Digital Information, Library & Archives Journal, pp. 1-3, (2007); Eynden Veerle Van Den, Data Life Cycle & Data Management Planning, pp. 24-25, (2013); Chakravarty R., Research Data Management (RDM): A Systematic Approach to Big Data Challenge in R&D and Higher Education, Transforming Dimension of IPR: Challenges for New Age Libraries, pp. 481-491, (2015); Jonathan R., Angus W., Using RISE the Research Infrastructure Self-Evaluation Framework, pp. 1-19, (2017); Piracha H. A., Ameen K., Policy and planning of research data management in university libraries of Pakistan, Collection and Curation, 38, 2, pp. 39-44, (2019); Thielen J., Hess A. N., Advancing Research Data Management in the Social Sciences: Implementing Instruction for Education Graduate Students Into a Doctoral Curriculum, Behavioral & Social Sciences Librarian, 36, 1, pp. 16-30, (2017); Lang L., Wilson T., Wilson K., Kirkpatrick A., Research Support at the Crossroads: Capability, Capacity, and Collaboration, New Review of Academic Librarianship, 24, 3–4, pp. 326-336, (2018); Shelly M., Jackson M., Research data management compliance: is there a bigger role for university libraries?, Journal of the Australian Library and Information Association, 67, 4, pp. 394-410, (2018); Arora S., Chakravarty R., Making research data discoverable: an outreach activity of Datacite, Library Philosophy and Practice (E-Journal), (2021); Zhou Q., Academic Libraries in Research Data Management Service: Perceptions and Practices, OALib, pp. 1-4, (2018)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85108183204"
"Huang Y.-H.","Huang, Yuan-Ho (23492551600)","23492551600","An Overview of Scholarly Communication, Research Data Management and Digital Scholarship Services in American Academic Libraries: An Empirical Study from Five University Libraries in the States of Massachusetts and Missouriψ; [綜論美 學術圖書館之學術傳播、 研究資料管理與數位學術研究服務: 麻州與密蘇里州五所大學圖書館 實證研究ψ]","2021","Journal of Educational Media and Library Sciences","58","3","","339","376","37","0","10.6120/JoEMLS.202111_58(3).0016.OR.AM","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124532635&doi=10.6120%2fJoEMLS.202111_58%283%29.0016.OR.AM&partnerID=40&md5=4b185f979ae6cd45deec5e166526c7eb","Department of Library and Information Science, Fu Jen Catholic University, New Taipei, Taiwan","Huang Y.-H., Department of Library and Information Science, Fu Jen Catholic University, New Taipei, Taiwan","The core task of academic librarians are to support academic research. The recruitment for the following librarians’ positions including scholarly communication librarians, research data management librarians, and digital scholarship librarians is popular in American academic libraries. Few university libraries provide in-depth digital scholarship services in Taiwan. In order to learn experiential knowledge about digital scholarship services of American academic libraries, qualitative interviews were applied in this research. The interviewees include four scholarly communication librarians, three research data management librarians, two digital scholarship librarians. The research results were stated as the following items: core job descriptions, multiple skills including communication and challenges, the personal traits of good librarians and their passion, the roles and tasks in digital age. Several suggestions were provided in this study, including prioritizing tasks for future work, proposing plans for most challenging works, flexible organizational structure, and developing librarians’ competencies and skills. The research results are helpful to propose a plan for the practical work required of digital scholarship professionals and services in university libraries in Taiwan. © 2021. Journal of Educational Media and Library Sciences. All Rights Reserved.","Academic libraries; Core competence; Digital scholarship librarian; Research data management librarian; Scholarly communication librarians; University libraries","","","","","","Foundation for Scholarly Exchange; Harvard University","This research is supported by the Foundation for Scholarly Exchange (Fulbright Taiwan). The author would like to extend appreciation to the anonymous American librarians who accepted and participated in the interviews for this study. I would also like to extend appreciation to Paul Scott Lapinski, Associate Director of Publishing and Data Services in Harvard University, for proofreading the manuscript.","11, pp. 95-138; 55, 1, pp. 39-69; 18, 2, pp. 103-137; Scholarly communication toolkit: Scholarly communication overview, (2020); Bonn M., Tooling up: Scholarly communication education and training, College & Research Libraries News, 75, 3, pp. 132-135, (2014); Brantley S., Bruns T. A., Duffin K. I., Librarians in transition: Scholarly communication support as a developing core competency, Journal of Electronic Resources Librarianship, 29, 3, pp. 137-150, (2017); Bueno-de-la-Fuente G., What is open science? Introduction, (2016); Calarco P., Shearer K., Schmidt B., Tate D., Librarians' competencies profile for scholarly communication and open access, Joint Task Force on Librarians' Competencies in Support of E-Research and Scholarly Communication, (2016); Cross W. M., The state of the scholarly communication librarian: A content analysis of position descriptions from association of research libraries member institutions, (2011); Eclevia M. R., Fredeluces J. C. L. T., Maestro R. S., Eclevia C. L., What makes a data librarian? An analysis of job descriptions and specifications for data librarian, Qualitative and Quantitative Methods in Libraries, 8, 3, pp. 273-290, (2019); Faniel I. M., Connaway L. S., Librarians' perspectives on the factors influencing research data management programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Genoni P., Merrick H., Willson M. A., Scholarly communities, e-research literacy and the academic librarian, The Electronic Library, 24, 6, pp. 4-746, (2006); Developing digital literacies: Briefing paper in support of Grant Funding 4/11, (2011); King G., An introduction to the Dataverse Network as an infrastructure for data sharing, Sociological Methods & Research, (2007); King M., Digital scholarship librarian: What skills and competences are needed to be a collaborative librarian, International Information & Library Review, 50, 1, pp. 40-46, (2018); Mackenzie A., Digital scholarship: Scanning library services and spaces, (2016); Mackenzie, Martin L., Developing digital scholarship: Emerging practices in academic libraries, pp. 23-40; Mackenzie A., Martin L., Developing digital scholarship: Emerging practices in academic libraries, (2016); NASIG core competencies for scholarly communication librarians, (2017); Pontika N., Roles and jobs in the open research scholarly communications environment: Analysing job descriptions to predict future trends, LIBER Quarterly, 29, 1, (2019); Schmidt K., Shearer B. S., Librarians' competencies profile for research data management, Joint Task Force on Librarians' Competencies in Support of E-Research and Scholarly Communication, (2016); Semeler A. R., Pinto A. L., Rozados H. B. F., Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2017); Tenopir C., Sandusky R. J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2012); Tenopir C., Sandusky R. J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Wong S. H. R., Digital humanities: What can libraries offer?, portal: Libraries and the Academy, 16, 4, pp. 669-690, (2016)","Y.-H. Huang; Department of Library and Information Science, Fu Jen Catholic University, New Taipei, Taiwan; email: yuanho@lins.fju.edu.tw","","Tamkang University","","","","","","1013090X","","CYTHD","","Chinese","J. Educ. Media Libr. Sci.","Article","Final","","Scopus","2-s2.0-85124532635"
"Cummings R.; Ozburn L.; Payant A.; Rozum B.; Shelton M.; Bushman R.","Cummings, Rebekah (57202307016); Ozburn, Lindsay (57216156473); Payant, Andrea (57195509894); Rozum, Betty (8871760100); Shelton, Michael (57218535757); Bushman, Ryan (57218535301)","57202307016; 57216156473; 57195509894; 8871760100; 57218535757; 57218535301","Assessing Research Compliance for Federally Funded Projects: The Good, the Bad, and the Publicly Accessible","2020","Journal of Library Administration","60","7","","726","751","25","0","10.1080/01930826.2020.1786985","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089461511&doi=10.1080%2f01930826.2020.1786985&partnerID=40&md5=0ff1ba3e922443319bdceb7d37a55d40","Digital Matters Librarian, J. Willard Marriott Library, University of Utah, Salt Lake City, UT, United States; Assessment Coordinator, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Metadata Librarian, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Data Librarian, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Research Data Library Assistant, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Data Analytics Assistant, Merrill-Cazier Library, Utah State University, Logan, UT, United States","Cummings R., Digital Matters Librarian, J. Willard Marriott Library, University of Utah, Salt Lake City, UT, United States; Ozburn L., Assessment Coordinator, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Payant A., Metadata Librarian, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Rozum B., Data Librarian, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Shelton M., Research Data Library Assistant, Merrill-Cazier Library, Utah State University, Logan, UT, United States; Bushman R., Data Analytics Assistant, Merrill-Cazier Library, Utah State University, Logan, UT, United States","In 2016, Utah State University launched a program to ensure their campus’ federal grant recipients were in compliance with funder mandates to share any data or publications produced as a result of the award. This article discusses how a cross-institutional team of librarians and administrators evaluated the success of this program using online asynchronous focus groups (OAFG) in conjunction with a traditional survey. The challenges and successes of using OAFGs to assess library services are also examined. An OAFG gave participants greater convenience, flexibility, participation, and time to craft answers, eliminating some of the hurdles to traditional focus group participation. © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.","assessment; grant compliance; online asynchronous focus groups (OAFG); Research data management","","","","","","","","(2020); Broadbent J.R., Payant A., Peterson K., Rozum B., Woolcott L., A campus partnership to foster compliance with funder mandates, The Journal of Academic Librarianship, 44, 1, pp. 96-104, (2018); Gaiser T.J., Conducting on-line focus groups: A methodological discussion, Social Science Computer Review, 15, 2, pp. 135-144, (1997); Greenbaum T., (2008); Hamilton R.J., Bowers B.J., Internet recruitment and E-mail interviews in qualitative studies, Qualitative Health Research, 16, 6, pp. 821-835, (2006); Han J., Torok M., Gale N., Wong Q.J., Werner-Seidler A., Hetrick S.E., Christensen H., Use of web conferencing technology for conducting online focus groups among young people with lived experience of suicidal thoughts: Mixed methods research, JMIR Mental Health, 6, 10, (2019); Hoffman D.L., Novak T.P., Stein R., The digital consumer, The Routledge companion to digital consumption, pp. 28-38, (2013); Holdren J., (2013); Kenny A.J., Interaction in cyberspace: An online focus group, Journal of Advanced Nursing, 49, 4, pp. 414-422, (2005); Krueger R.A., The future of focus groups, Qualitative Health Research, 5, 4, pp. 524-530, (1995); Matthews J., Cramer E., Using technology to enhance qualitative research with hidden populations, The Qualitative Report, 13, 2, pp. 301-315, (2008); O'Connor H., Madge C., Focus groups in cyberspace: Using the internet for qualitative research, Qualitative Market Research: An International Journal, 6, 2, pp. 133-143, (2003); Popper K., (2005); Ramo D.E., Meacham M., Thrul J., Belohlavek A., Sarkar U., Humfleet G., Exploring identities and preferences for intervention among LGBTQ + young adult smokers through online focus groups, Journal of Adolescent Health, 64, 3, pp. 390-397, (2019); Redd K., Steen K., Nusser S., Smith T., Walters T., Chasen J., Luther J., Reecy J., (2019); Robinson N., The use of focus group methodology—With selected examples from sexual health research, Journal of Advanced Nursing, 29, 4, pp. 905-913, (1999); Sim J., Collecting and analysing qualitative data: Issues raised by the focus group, Journal of Advanced Nursing, 28, 2, pp. 345-352, (1998); Stewart D.W., Shamdasani P., Online focus groups, Journal of Advertising, 46, 1, pp. 48-60, (2017); Stewart K., Williams M., Researching online populations: The use of online focus groups for social research, Qualitative Research, 5, 4, pp. 395-416, (2005); Stover C.M., The use of online synchronous focus groups in a sample of lesbian, gay, and bisexual college students, CIN: Computers, Informatics, Nursing, 30, 8, pp. 395-399, (2012); Tuttas C.A., Lessons learned using web conference technology for online focus group interviews, Qualitative Health Research, 25, 1, pp. 122-133, (2015); Twinn D.S., An analysis of the effectiveness of focus groups as a method of qualitative data collection with Chinese populations in nursing research, Journal of Advanced Nursing, 28, 3, pp. 654-661, (1998); Vitale C.R.H., Is research reproducibility the new data management for libraries?, Bulletin of the Association for Information Science and Technology, 42, 3, pp. 38-41, (2016); Watson M., Peacock S., Jones D., The analysis of interaction in online focus groups, International Journal of Therapy and Rehabilitation, 13, 12, pp. 551-557, (2006); Williams S., Clausen M.G., Robertson A., Peacock S., McPherson K., Methodological reflections on the use of asynchronous online focus groups in health research, International Journal of Qualitative Methods, 11, 4, pp. 368-383, (2012); Zwaanswijk M., van Dulmen S., Advantages of asynchronous online focus groups and face-to-face focus groups as perceived by child, adolescent and adult participants: A survey study, BMC Research Notes, 7, 1, (2014)","","","Routledge","","","","","","01930826","","","","English","J. Libr. Adm.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85089461511"
"König A.; Frey J.-C.; Stemle E.W.","König, Alexander (57210123466); Frey, Jennifer-Carmen (55917572200); Stemle, Egon W. (23010728400)","57210123466; 55917572200; 23010728400","Exploring reusability and reproducibility for a research infrastructure for l1 and l2 learner corpora","2021","Information (Switzerland)","12","5","199","","","","0","10.3390/info12050199","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106637277&doi=10.3390%2finfo12050199&partnerID=40&md5=58e9cdc06d981ababd6c5704484b111b","CLARIN ERIC, Utrecht, 3512 BS, Netherlands; Institute for Applied Linguistics, Eurac Research, Bolzano, 39100, Italy","König A., CLARIN ERIC, Utrecht, 3512 BS, Netherlands; Frey J.-C., Institute for Applied Linguistics, Eurac Research, Bolzano, 39100, Italy; Stemle E.W., Institute for Applied Linguistics, Eurac Research, Bolzano, 39100, Italy","Up until today research in various educational and linguistic domains such as learner corpus research, writing research, or second language acquisition has produced a substantial amount of research data in the form of L1 and L2 learner corpora. However, the multitude of individual solutions combined with domain-inherent obstacles in data sharing have so far hampered comparability, reusability and reproducibility of data and research results. In this article, we present work in creating a digital infrastructure for L1 and L2 learner corpora and populating it with data collected in the past. We embed our infrastructure efforts in the broader field of infrastructures for scientific research, drawing from technical solutions and frameworks from research data management, among which the FAIR guiding principles for data stewardship. We share our experiences from integrating some L1 and L2 learner corpora from concluded projects into the infrastructure while trying to ensure compliance with the FAIR principles and the standards we established for reproducibility, discussing how far research data that has been collected in the past can be made comparable, reusable and reproducible. Our results show that some basic needs for providing comparable and reusable data are covered by existing general infrastructure solutions and can be exploited for domain-specific infrastructures such as the one presented in this article. Other aspects need genuinely domain-driven approaches. The solutions found for the corpora in the presented infrastructure can only be a preliminary attempt, and further community involvement would be needed to provide templates and models acknowledged and promoted by the community. Furthermore, forward-looking data management would be needed starting from the beginning of new corpus creation projects to ensure that all requirements for FAIR data can be met. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Learner corpus research; Research infrastructures","Information management; Regulatory compliance; Reusability; Community involvement; Digital infrastructures; Domain specific infrastructure; Research data managements; Research infrastructure; Scientific researches; Second language acquisition; Technical solutions; Data Sharing","","","","","","","Granger S., Learner corpora in foreign language education, Language and Technology. 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Bibl, 72, pp. 383-391, (2019); Social Sciences and Humanities Pathway Towards the European Open Science Cloud, (2020); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, pp. 1-9, (2016); Barbot L., Biller T., Broeder D., Dekker R., Durco M., Vipavc I., Willems M., Agile development of the SSH open marketplace: User workshop, ITM Web of Conferences, 33, (2020); de Jong F.M.G., Maegaard B., De Smedt K., Fiser D., Van Uytvanck D., CLARIN: Towards FAIR and responsible data science using language resources, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), (2018); Abel A., Vettori C., Wisniewski K., KOLIPSI: Gli Studenti Altoatesini e la Seconda Lingua; Indagine Linguistica e Psicosociale= KOLIPSI: die Südtiroler SchülerInnen und die Zweitsprache; Eine Linguistische und Sozialpsychologische Untersuchung, (2012); Vettori C., Abel A., KOLIPSI II Gli studenti altoatesini e la seconda lingua: indagine linguistica e psicosociale, Die Südtiroler SchülerInnen und die Zweitsprache: Eine Linguistische und Sozialpsychologische Untersuchung, (2017); Abel A., Glaznieks A., Nicolas L., Stemle E., An extended version of the KoKo German L1 Learner corpus, Proceedings of the Third Italian Conference on Computational Linguistics, (2016); Boyd A., Hana J., Nicolas L., Meurers D., Wisniewski K., Abel A., Schone K., Stindlova B., Vettori C., The MERLIN corpus: Learner language and the CEFR, Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 14), pp. 1281-1288; Zanasi L., Stopfner M., Rilevare, osservare, consultare. 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Serv. & Use, 37, pp. 49-56, (2017); Lindstrom T., Lenardic J., Fiser D., L2 learner corpus survey–Towards improved verifiability, reproducibility and inspiration in learner corpus research, Proceedings of the CLARIN Annual Conference 2018, pp. 146-150; Van Uytvanck D., Stehouwer H., Lampen L., Semantic metadata mapping in practice: the Virtual Language Observatory, LREC 2012: 8th International Conference on Language Resources and Evaluation, pp. 1029-1034, (2012); Megyesi B., Granstedt L., Johansson S., Prentice J., Rosen D., Schenstrom C.J., Sundberg G., Wiren M., Volodina E., Learner corpus anonymization in the age of gdpr: Insights from the creation of a learner corpus of swedish, Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018) at SLTC, pp. 47-56, (2018); Volodina E., Janssen M., Tiedemann T.L., Preradovic N.M., Ragnhildstveit S., Tenfjord K., de Smedt K., Interoperability of Second Language Resources and Tools, Proceedings of the CLARIN Annual Conference 2018, pp. 90-94; Chiarcos C., Nordhoff S., Hellmann S., Linked Data in Linguistics, (2012); Granger S., Paquot M., Towards standardization of metadata for L2 corpora, Proceedings of the workshop on Interoperability of Second Language Resources and Tools, (2017); Wittenburg P., Van Uytvanck D., Zastrow T., Strak P., Broeder D., Schiel F., Boehlke V., Reichel U., Offersgaard L., CLARIN B Centre Checklist, (2018); Eskevich M., de Jong F., Konig A., Fiser D., Van Uytvanck D., Aalto T., Borin L., Gerassimenko O., Hajic J., van den Heuvel H., Et al., CLARIN: Distributed language resources and technology in a European infrastructure, Proceedings of the 1st International Workshop on Language Technology Platforms, pp. 28-34, (2020); Nicolas L., Stemle E., Glaznieks A., Abel A., A Generic Data Workflow for Building Annotated Text Corpora, Stud. 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König; CLARIN ERIC, Utrecht, 3512 BS, Netherlands; email: alex@clarin.eu","","MDPI AG","","","","","","20782489","","","","English","Information","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85106637277"
"Azeroual O.","Azeroual, Otmane (57201378256)","57201378256","Treatment of bad big data in research data management (RDM) systems","2020","Big Data and Cognitive Computing","4","4","29","1","11","10","2","10.3390/bdcc4040029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094667338&doi=10.3390%2fbdcc4040029&partnerID=40&md5=55cb3825513b3b92b0b49ff9408b18a6","German Center for Higher Education Research and Science Studies (DZHW), Schützenstraße 6a, Berlin, 10117, Germany","Azeroual O., German Center for Higher Education Research and Science Studies (DZHW), Schützenstraße 6a, Berlin, 10117, Germany","Databases such as research data management systems (RDMS) provide the research data in which information is to be searched for. They provide techniques with which even large amounts of data can be evaluated efficiently. This includes the management of research data and the optimization of access to this data, especially if it cannot be fully loaded into the main memory. They also provide methods for grouping and sorting and optimize requests that are made to them so that they can be processed efficiently even when accessing large amounts of data. Research data offer one thing above all: the opportunity to generate valuable knowledge. The quality of research data is of primary importance for this. Only flawless research data can deliver reliable, beneficial results and enable sound decision-making. Correct, complete and up-to-date research data are therefore essential for successful operational processes. Wrong decisions and inefficiencies in day-to-day operations are only the tip of the iceberg, since the problems with poor data quality span various areas and weaken entire university processes. Therefore, this paper addresses the problems of data quality in the context of RDMS and tries to shed light on the solution for ensuring data quality and to show a way to fix the dirty research data that arise during its integration before it has a negative impact on business success. © 2020 by the author. Licensee MDPI, Basel, Switzerland.","Big data; Data integrity and quality; Institutional decision making; Poor quality of information; Research data life cycle; Research data management systems (RDMS)","","","","","","","","Surkis A., Research data management, J. Med. Libr. Assoc, 103, pp. 154-156, (2015); Heuer A., Research Data Management, It-Inf. Technol, 62, pp. 1-5, (2020); Tammaro A.M., Casarosa V., Research Data Management in the curriculum: An interdisciplinary Approach, Procedia Comput. Sci, 38, pp. 138-142, (2014); Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univers. Access Inf. Soc, 16, pp. 851-862, (2017); Azeroual O., Data Wrangling in Database Systems: Purging of Dirty Data, Data, 5, (2020); Batini C., Barone D., Mastrella M., Maurino A., Ruffini C., A Framework and {A} Methodology for Data Quality Assessment and Monitoring, Proceedings of the 12th International Conference on Information Quality, pp. 333-346; Aljumaili M., Karim R., Tretten P., Metadata-based data quality assessment, VINE J. Inf. Knowl. Manag. Syst, 46, pp. 232-250, (2016); Haegemans T., Snoeck M., Lemahieu W., A theoretical framework to improve the quality of manually acquired data, Inf. Manag, 56, pp. 1-14, (2019); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, PLoS ONE, 9, (2014); Kindling M., Schirmbacher P., Die digitale Forschungswelt als Gegenstand der Forschung, Inf.-Wiss. Prax, 64, pp. 137-148, (2013); OECD Principles and Guidelines for Access to Research Data for Public Funding; Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, J. Assoc. Inf. Sci. Technol, 68, pp. 2182-2200, (2017); McDonald J.P., Toward more effective data use in teaching, Phi Delta Kappan, 100, pp. 50-54, (2019); Tang R., Hu Z., Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice, Data Inf. Manag, 3, pp. 84-101, (2019); Azeroual O., Lewoniewski W., How to Inspect and Measure Data Quality about Scientific Publications: Use Case of Wikipedia and CRIS Databases, Algorithms, 13, (2020); Wang R.Y., Strong D.M., Beyond Accuracy: What Data Quality means to Data Consumers, J. Manag. Inf. Syst, 12, pp. 5-33, (1996); Tayi G.K., Ballou D., Examining Data Quality, Commun. ACM, 41, pp. 54-57, (1998); Lee Y.W., Strong D.M., Wang R.Y., 10 Potholes in the Road to Information Quality, IEEE Comput, 30, pp. 38-46, (1997); Azeroual O., Saake G., Abuosba M., Data Quality Measures and Data Cleansing for Research Information Systems, J. Digit. Inf. Manag, 16, pp. 12-21, (2018); Azeroual O., Saake G., Schallehn E., Analyzing data quality issues in research Information systems via data profiling, Int. J. Inf. Manag, 41, pp. 50-56, (2018)","O. Azeroual; German Center for Higher Education Research and Science Studies (DZHW), Berlin, Schützenstraße 6a, 10117, Germany; email: azeroual@dzhw.eu","","MDPI AG","","","","","","25042289","","","","English","Big Data Cogn. Computing","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85094667338"
"Gruber V.A.; Schranzhofer H.; Knopper S.; Stryeck S.; Hasani-Mavriqi I.","Gruber, von Alexander (57247694700); Schranzhofer, Hermann (55504427100); Knopper, Sabrina (57247694800); Stryeck, Sarah (57191264224); Hasani-Mavriqi, Ilire (36460924100)","57247694700; 55504427100; 57247694800; 57191264224; 36460924100","Competencies of data stewards at austrian universities; [Kompetenzen von data stewards an österreichischen universitäten]","2021","VOEB-Mitteilungen","74","1","","","","","1","10.31263/voebm.v74i1.6255","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114321541&doi=10.31263%2fvoebm.v74i1.6255&partnerID=40&md5=7e0830a09d812ff6091ffd0d794fdad0","Technische Universität Graz, Institute of Interactive Systems and Data Science Handlungsfeld Forschung – Programm „Digitale TU Graz“, Austria","Gruber V.A., Technische Universität Graz, Institute of Interactive Systems and Data Science Handlungsfeld Forschung – Programm „Digitale TU Graz“, Austria; Schranzhofer H., Technische Universität Graz, Institute of Interactive Systems and Data Science Handlungsfeld Forschung – Programm „Digitale TU Graz“, Austria; Knopper S., Technische Universität Graz, Institute of Interactive Systems and Data Science Handlungsfeld Forschung – Programm „Digitale TU Graz“, Austria; Stryeck S., Technische Universität Graz, Institute of Interactive Systems and Data Science Handlungsfeld Forschung – Programm „Digitale TU Graz“, Austria; Hasani-Mavriqi I., Technische Universität Graz, Institute of Interactive Systems and Data Science Handlungsfeld Forschung – Programm „Digitale TU Graz“, Austria","In April 2021, the project members of „FAIR Data Austria“ met for a joint workshop to identify the competencies of data stewards and map them to predefined data steward models. These models and corresponding data steward profiles and tasks were developed in a previous workshop in October 2020 for the Austrian context. The workshop participants expanded competencies identified in advance to include new competencies, and/or new aspects were added or specified. In breakout sessions, the individual models were filled with the appropriate competencies from the competence pool and discussed in the plenary. Data stewards of TU Graz gave insights into their current work and explained which competencies, in their opinion, are needed. In the next step, suitable training modules will be established from the collected information tailored to the individual models. At the end of the project, a self-assessment toolkit will be developed that considers all aspects of data stewardship and provides guidance to research institutions on which model is most suitable for them. © 2021 Universitatea de Vest Vasile Goldis din Arad. All rights reserved.","Competencies; Data stewards; Research data management","","","","","","","","Reichmann S., Hasani-Mavriqi I., Entwicklung eines Konzepts für Data Stewards an österreichischen Universitäten, (2021); Gnahs D., Kompetenzen – Erwerb, Erfassung, Instrumente, (2010); Rychen D. S., Sagalnik L. H., Highlights from the OECD Project Definition and Selection Competencies: Theoretical and Conceptual Foundations (DeSeCo), (2003); Zendler A., Bausteine eines Kompetenzmodells: Ein Literaturüberblick zur Kompetenzorientierung in der Informatikdidaktik, Notes on Educational Informatics, Section A: Concepts and Techniques, 9, pp. 1-21, (2013); The Definition and Selection of Key Competencies: Executive Summary, (2005); Gnahs D., Kompetenzen – Erwerb, Erfassung, Instrumente, (2010); Gansdorfer N., Gespräche mit Data Stewards: Anforderungen, Kompetenzen, Aufgaben, (2020); TU Delft Data Stewardship","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85114321541"
"Ran C.; Yang L.; Hu L.","Ran, Congjing (23398706500); Yang, Le (56340057900); Hu, Linxiao (57222242442)","23398706500; 56340057900; 57222242442","Revisit the implementation status of research data management in Chinese academia","2021","Journal of Academic Librarianship","47","3","102350","","","","3","10.1016/j.acalib.2021.102350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101966831&doi=10.1016%2fj.acalib.2021.102350&partnerID=40&md5=86427685e3368764d96ace6abeff770f","Wuhan University, China; Wenzhou-Kean University, China","Ran C., Wuhan University, China; Yang L., Wuhan University, China, Wenzhou-Kean University, China; Hu L., Wenzhou-Kean University, China","The paper examines the current literature of RDM implementation status, then follows a three-level model to revisit the implementation status of RDM in Chinese academia by reviewing the governmental documents, funding agencies' policies, and institutional efforts. The findings suggest that the macro-environment of RDM in China is interrupted at the agency level caused by the inactions of strategies and lack of policies. The results also suggest that national universities varied in RDM services and repositories, and the macro-environment of RDM in China is problematic. The paper then identifies the problems, discusses the fundamental issues, and proposes solutions from three principles. © 2021 Elsevier Inc.","Data curation; Data policy; Data repository; Data stewardship; Research data management; University libraries","","","","","","Ministry of Science and Technology of the People's Republic of China, MOST; Department of Education of Zhejiang Province, (Y201943066)","The paper is supported by the 2019 General Program of Education Department of Zhejiang Province. [Grant Number Y201943066. Principal Investigator Le Yang]The study finds that as early as 2004, the Ministry of Science and Technology (MOST) of China had joined several other countries, along with the members of the Organization for Economic Co-operation and Development (OECD), to adopt a declaration about ensuring open access to research data that are supported by public funding (OECD, 2004). The OECD is an intergovernmental economic organization with 37 member countries committed to seeking answers to common problems and coordinating domestic and international policies. In order to recognize the importance of open access to research data, the OECD followed up in 2007 to publish principles and guidelines that intend to set out standards and objectives for the agreed governments to follow (OECD, 2007).","ACRL, Top ten trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 73, 6, pp. 311-320, (2012); ACRL, Academic library trends and statistics for Carnegie classifications: Associates of Arts Colleges Baccalaureate Colleges Master's college and institutions doctorate granting institutions, (2016); Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Akpan N., Jaggard V., Fauci: No scientific evidence the coronavirus was made in a Chinese lab, (2020); Allison D., Managing the life-cycle of data, (2015); Atkins D.E., Droegemeier K.K., Feldman S.I., Garcia-Molina H., Klein M.L., Messerschmitt D.G., Messina P., Ostriker J.P., Wright M.H., Revolutionizing science and engineering through cyberinfrastructure: Report of the National Science Foundation blue-ribbon advisory panel on cyberinfrastructure, (2003); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Aydinoglu A.U., Suomela T., Malone J., Data management in astrobiology: Challenges and opportunities for an interdisciplinary community, Astrobiology, 14, 6, pp. 451-461, (2014); Brwester J., A timeline of the COVID-19 Wuhan lab origin theory, (2020); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chiu A., Trump has no qualms about calling coronavirus the ‘Chinese virus.’ That's a dangerous attitude, experts say, (2020); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2019); Donnelly M., Update to analysis of open science policies finds new activity in multiple countries, (2018); GOSC, 国务院办公厅关于印发科学数据管理办法的通知, (2018); Holdren J.P., Report to congress, (2017); Huang L., Li L., The fort Detrick horror: A closer look at the US' largest biochemical weapons research center, (2020); Hudson-Vitale C., Imker H., Johnston L.R., Carlson J., Kozlowski W., Olendorf R., Stewart C., SPEC kit 354: Data curation, (2017); Liu X., Ding N., Research data management in universities of central China, The Electronic Library, 34, 5, pp. 808-822, (2016); Liu Z., Zeng L., Investigation and comparative analysis of scientific research data management and sharing platform of universities in China, Information and Document Services, 2017, 6, pp. 90-95, (2017); McNutt M., Editorial retraction, Science, 348, 6239, (2015); Mervis J., NSF to ask every grant applicant for data management plan, (2010); Mons B., Data stewardship for Open Science: Implementing FAIR principles, (2018); MOST, 十一五国家科技基础条件平台建设实施意见, (2005); NIH, NIH data sharing policy, (2003); NSF, Dissemination and sharing of research results, (2011); OECD, Declaration on access to research data from public funding, (2004); OECD, OECD principles and guidelines for access to research data from public funding, (2007); OSTP, Increasing access to the results of federally funded scientific research, (2013); OSTP, OSTP public access policy forum, (2017); Ou S., Zhou Y., Current status of scientific data curation research and practices in mainland China, LIBRES: Library & Information Science Research Electronic Journal, 26, 1, pp. 73-88, (2016); Powell K., Young, talented and fed-up: Scientists tell their stories, Nature News, 538, 7626, pp. 446-449, (2016); Pralle B., Lake S., Gunia B., Sallans A., Fearon D., SPEC kit 334: Research data management services, (2013); Read K.B., Koos J., Miller R.S., Miller C.F., Phillips G.A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, Journal of the Medical Library Association, 107, 3, pp. 432-441, (2019); Redkina N.S., Current trends in research data management, Scientific and Technical Information Processing, 46, 2, pp. 53-58, (2019); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French national research center (CNRS), Data Technologies and Applications, 52, 2, pp. 248-265, (2018); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program: electronic library and information systems, 49, 4, pp. 440-460, (2015); Sheehan J., Increasing access to the results of federally funded science, (2016); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Stuart D., Baynes G., Hrynaszkiewicz K.A., Penny D., Lucraft M., Astell M., Whitepaper: Practical challenges for researchers in data sharing, (2018); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Teperek M., Views on data stewardship – Report of preliminary findings at the Faculty of Technology, policy and management (TPM) at TU Delft, (2018); Vazquez M., Calling COVID-19 the “Wuhan virus” or “China virus” is inaccurate and xenophobic, (2020); Ward C., Freiman L., Molloy L., Jones S., Snow K., Making sense: Talking data management with researchers, International Journal of Digital Curation, 6, 2, pp. 265-273, (2011); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Bouwman J., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, pp. 1-9, (2016); Winter L., Chinese officials blame US Army for coronavirus, (2020); Yozwiak N.L., Schaffner S.F., Sabeti P.C., Data sharing: Make outbreak research open access, Nature, 518, 7540, pp. 477-479, (2015)","L. Yang; Wuhan University, China; email: yanglegd@gmail.com","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85101966831"
"Kovacs M.; Hoekstra R.; Aczel B.","Kovacs, Marton (57221101702); Hoekstra, Rink (8638732500); Aczel, Balazs (36482485300)","57221101702; 8638732500; 36482485300","The Role of Human Fallibility in Psychological Research: A Survey of Mistakes in Data Management","2021","Advances in Methods and Practices in Psychological Science","4","4","","","","","2","10.1177/25152459211045930","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117455903&doi=10.1177%2f25152459211045930&partnerID=40&md5=a95fb282a97b4c10a7d1da097e7aad42","Doctoral School of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary; Institute of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary; GION Education/Research, University of Groningen, Groningen, Netherlands","Kovacs M., Doctoral School of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary, Institute of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary; Hoekstra R., GION Education/Research, University of Groningen, Groningen, Netherlands; Aczel B., Institute of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary","Errors are an inevitable consequence of human fallibility, and researchers are no exception. Most researchers can recall major frustrations or serious time delays due to human errors while collecting, analyzing, or reporting data. The present study is an exploration of mistakes made during the data-management process in psychological research. We surveyed 488 researchers regarding the type, frequency, seriousness, and outcome of mistakes that have occurred in their research team during the last 5 years. The majority of respondents suggested that mistakes occurred with very low or low frequency. Most respondents reported that the most frequent mistakes led to insignificant or minor consequences, such as time loss or frustration. The most serious mistakes caused insignificant or minor consequences for about a third of respondents, moderate consequences for almost half of respondents, and major or extreme consequences for about one fifth of respondents. The most frequently reported types of mistakes were ambiguous naming/defining of data, version control error, and wrong data processing/analysis. Most mistakes were reportedly due to poor project preparation or management and/or personal difficulties (physical or cognitive constraints). With these initial exploratory findings, we do not aim to provide a description representative for psychological scientists but, rather, to lay the groundwork for a systematic investigation of human fallibility in research data management and the development of solutions to reduce errors and mitigate their impact. © The Author(s) 2021.","data-management mistakes; human error; life cycle of the data; open data; open materials; preregistered; research workflow","","","","","","","","Aczel B., Szaszi B., Sarafoglou A., Kekecs Z., Kucharsky S., Benjamin D., Chambers C.D., Fisher A., Gelman A., Gernsbacher M.A., Ioannidis J.P., Johnson E., Jonas K., Kousta S., Lilienfeld S.O., Lindsay D.S., Morey C.C., Munafo M., Newell B.R., Wagenmakers E.-J., A consensus-based transparency checklist, Nature Human Behaviour, 4, 1, pp. 4-6, (2020); Arslan R.C., How to automatically document data with the codebook package to facilitate data reuse, Advances in Methods and Practices in Psychological Science, 2, 2, pp. 169-187, (2019); Bareille R., Baudouin-Massot B., Carreno M.P., Fournier S., Lebret N., Remy-Jouet I., Giesen E., Preventive actions to avoid questionable research practices. Use of EERM (Ethical and Efficient Research Management) during Arrival and Departure of a co-worker, International Journal of Metrology and Quality Engineering, 8, (2017); Barone L., Williams J., Micklos D., Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators, PLOS Computational Biology, 13, 10, (2017); Baumer B., Udwin D., R markdown, Wiley Interdisciplinary Reviews: Computational Statistics, 7, 3, pp. 167-177, (2015); Blischak J.D., Davenport E.R., Wilson G., A quick introduction to version control with Git and GitHub, PLOS Computational Biology, 12, 1, (2016); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2006); Buchanan E.M., Crain S.E., Cunningham A.L., Johnson H.R., Stash H., Papadatou-Pastou M., Isager P.M., Carlsson R., Aczel B., Getting started creating data dictionaries: How to create a shareable data set, Advances in Methods and Practices in Psychological Science, 4, 1, (2021); Chambers C.D., Registered reports: A new publishing initiative at Cortex, Cortex, 49, 3, pp. 609-610, (2013); Giesen E., Ethical and efficient research management: A new challenge for an old problem, International Journal of Metrology and Quality Engineering, 6, 4, (2015); Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Flandin G., Ghosh S.S., Glatard T., Halchenko Y.O., Handwerker D.A., Hanke M., Keator D., Li X., Michael Z., Maumet C., Nichols B.N., Nichols T.E., Pellman J., Poldrack R.A., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Scientific Data, 3, 1, pp. 1-9, (2016); Hardwicke T.E., Jameel L., Jones M., Walczak E.J., Weinberg L.M., Only human: Scientists, systems, and suspect statistics, Opticon1826, 16, 25, pp. 1-12, (2014); Hardwicke T.E., Mathur M.B., MacDonald K., Nilsonne G., Banks G.C., Kidwell M.C., Hofelich Mohr A., Clayton E., Yoon E.J., Henry Tessler M., Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal Cognition, Royal Society Open Science, 5, 8, (2018); Hardwicke T.E., Serghiou S., Janiaud P., Danchev V., Cruwell S., Goodman S.N., Ioannidis J.P.A., Calibrating the scientific ecosystem through meta-research, Annual Review of Statistics and Its Application, 7, pp. 11-37, (2020); John L.K., Loewenstein G., Prelec D., Measuring the prevalence of questionable research practices with incentives for truth telling, Psychological Science, 23, 5, pp. 524-532, (2012); Johnson H.R., Stash H., Papadatou-Pastou M., Isager P.M., Carlsson R., Aczel B., Getting started creating data dictionaries: How to create a shareable dataset Erin M. Buchanan 12abc Sarah E. Crain 1abc Arielle Cunningham 1abc; Klein O., Hardwicke T.E., Aust F., Breuer J., Danielsson H., Mohr A.H., Ijzerman H., Nilsonne G., Vanpaemel W., Frank M.C., A practical guide for transparency in psychological science, Collabra: Psychology, 4, 1, (2018); Michener W.K., Ten simple rules for creating a good data management plan, PLOS Computational Biology, 11, 10, (2015); Nelson L.D., Simmons J., Simonsohn U., Psychology’s renaissance, Annual Review of Psychology, 69, 1, pp. 511-534, (2018); Nosek B.A., Beck E.D., Campbell L., Flake J.K., Hardwicke T.E., Mellor D.T., Veer A., Vazire S., Preregistration is hard, and worthwhile, Trends in Cognitive Sciences, 23, 10, pp. 815-818, (2019); Nuijten M.B., Hartgerink C.H., Van Assen M.A., Epskamp S., Wicherts J.M., The prevalence of statistical reporting errors in psychology (1985–2013), Behavior Research Methods, 48, 4, pp. 1205-1226, (2016); Rosenthal R., How often are our numbers wrong?, American Psychologist, 33, 11, pp. 1005-1008, (1978); Rouder J.N., Haaf J.M., Snyder H.K., Minimizing mistakes in psychological science, Advances in Methods and Practices in Psychological Science, 2, 1, pp. 3-11, (2019); Rybicki J., Best practices in structuring data science projects, Information systems architecture and technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018, pp. 348-357, (2019); Semeler A.R., Pinto A.L., Rozados H.B.F., Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2019); Simmons J.P., Nelson L.D., Simonsohn U., False-positive psychology undisclosed flexibility in data collection and analysis allows presenting anything as significant, Psychological Science, 22, 11, pp. 1359-1366, (2011); Tenopir C., Allard S., Sinha P., Pollock D., Newman J., Dalton E., Frame M., Baird L., Data management education from the perspective of science educators, International Journal of Digital Curation, 11, 1, (2016); Vazire S., Implications of the credibility revolution for productivity, creativity, and progress, Perspectives on Psychological Science, 13, 4, pp. 411-417, (2018); Veldkamp C.L., Nuijten M.B., Dominguez-Alvarez L., van Assen M.A., Wicherts J.M., Statistical reporting errors and collaboration on statistical analyses in psychological science, PLOS ONE, 9, 12, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","B. Aczel; Institute of Psychology, ELTE Eotvos Lorand University, Budapest, Hungary; email: aczel.balazs@ppk.elte.hu","","SAGE Publications Inc.","","","","","","25152459","","","","English","Adv. Method. Pract. Psychol. Sci.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85117455903"
"Herres-Pawlis S.; Liermann J.C.; Koepler O.","Herres-Pawlis, Sonja (9277407800); Liermann, Johannes C. (24376894100); Koepler, Oliver (6507094492)","9277407800; 24376894100; 6507094492","Research Data in Chemistry – Results of the first NFDI4Chem Community Survey","2020","Zeitschrift fur Anorganische und Allgemeine Chemie","646","21","","1748","1757","9","4","10.1002/zaac.202000339","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096120188&doi=10.1002%2fzaac.202000339&partnerID=40&md5=0d6e3b6035db583be189cb9a3cfb3e60","Institute of Inorganic Chemistry, RWTH Aachen University, Aachen, Germany; Department of Chemistry, Johannes Gutenberg University Mainz, Mainz, Germany; Dr. Oliver Koepler, Hannover, Germany","Herres-Pawlis S., Institute of Inorganic Chemistry, RWTH Aachen University, Aachen, Germany; Liermann J.C., Department of Chemistry, Johannes Gutenberg University Mainz, Mainz, Germany; Koepler O., Dr. Oliver Koepler, Hannover, Germany","Good research data management (RDM) requires a constant endeavour of the researchers but – first and foremost – user-friendly infrastructure and well accepted standards need to be developed for the researchers. The national research data infrastructure initiative for chemistry in Germany NFDI4Chem sets out to embrace the challenge of creating a general research data management portal and hereby connecting already existing infrastructure as well as to foster the cultural change in chemistry towards digitalization by developing general minimum information standards for all methods used and teaching RDM principles to the community. In order to serve the needs of the chemical community at its best, NFDI4Chem accomplishes regular community surveys. The first survey has been performed in 2019 and the results were condensed into the NFDI4Chem proposal. This article summarizes the design of the study and its results. With regard to the project development, this first national survey serves as zero-point of all upcoming efforts in research data management. © 2020 The Authors. Zeitschrift für anorganische und allgemeine Chemie published by Wiley-VCH GmbH","FAIR Data; NFDI; Research data management","Information management; Surveys; Cultural changes; Good research; Minimum information; National surveys; Project development; Research data; Research data managements; User friendly; Research and development management","","","","","Deutsche Forschungsgemeinschaft, DFG","We thank all participants of the survey providing valuable insights in their daily work with data. The presented work was conducted as part of the NFDI4Chem project (DFG project) (no. 441958208). The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for funding and support. Open access funding enabled and organized by Projekt DEAL.","PDB Content, Can Be Found Under; CSD Entries, Can Be Found Under; Benson D.A., Karsch-Mizrachi I., Lipman D.J., Ostell J., Sayers E.W., Nucleic Acids Res, 39, pp. D32-D37, (2011); Knowledge without Borders: Report of the GÉANT Expert Group, (2011); CERN, “Storage at Cern”; Frequently Asked Questions about the SKA; CAS Registry Content; Open Research Data and Data Management Plans Information for ERC Grantees; Code of Conduct Guidelines for Safeguarding Good Research Practice, (2019); DFG – Deutsche Forschungsgemeinschaft – Umgang Mit Forschungsdaten; Informationsbibliothek T., Chemie F.I.Z., Paderborn U., Vernetzte Primärdaten-Infrastruktur Für Den Wissenschaftler-Arbeitsplatz in Der Chemie : Konzeptstudie, (2010); Tristram F., Bamberger P., Cayoglu U., Hertzer J., Knopp J., Kratzke J., Rex J., Schwabe F., Shcherbakov D., Svoboda D.-F., Wehrle D., Öffentlicher Abschlussbericht von bwFDM-Communities – Wissenschaftliches Datenmanagement an Den Universitäten Baden-Württembergs, (2015); Chen X., Wu M., J. Academic Librarianship, 43, pp. 346-353, (2017); Hausen D.A., (2019); Sosci Survey - Professionelle Onlinebefragung Made in Germany; Herres-Pawlis S., Koepler O., Steinbeck C., Angew. Chem. Int. Ed, 58, pp. 10766-10768, (2019); Fachrepositorium Lebenswissenschaften: Elektronische Laborbücher Im Kontext von Forschungsdatenmanagement und guter wissenschaftlicher Praxis – Ein Wegweiser für die Lebenswissenschaften, (2019); Steinbeck C., Koepler O., Bach F., Herres-Pawlis S., Jung N., Liermann J., Neumann S., Razum M., Baldauf C., Biedermann F., Bocklitz T., Boehm F., Broda F., Czodrowski P., Engel T., Hicks M., Kast S., Kettner C., Koch W., Lanza G., Link A., Mata R., Nagel W., Porzel A., Schlorer N., Schulze T., Weinig H.-G., Wenzel W., Wessjohann L., Wulle S., 6, (2020)","S. Herres-Pawlis; Institute of Inorganic Chemistry, RWTH Aachen University, Aachen, Germany; email: sonja.herres-pawlis@ac.rwth-aachen.de; O. Koepler; Dr. Oliver Koepler, Hannover, Germany; email: Oliver.Koepler@tib.eu","","Wiley-VCH Verlag","","","","","","00442313","","ZAACA","","English","Z. Anorg. Allg. Chem.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85096120188"
"Wang W.; Zhang X.; Shi Y.; Gao F.; Xu W.","Wang, Wei (57225120547); Zhang, Xinhua (57223234697); Shi, Yabin (57223211827); Gao, Feng (57223232810); Xu, Wenpeng (57223250130)","57225120547; 57223234697; 57223211827; 57223232810; 57223250130","Exploration and Analysis of University Scientific Research Data Management Strategies under Big Data Environment","2021","Journal of Physics: Conference Series","1881","3","032059","","","","1","10.1088/1742-6596/1881/3/032059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105264591&doi=10.1088%2f1742-6596%2f1881%2f3%2f032059&partnerID=40&md5=a6588cd60feda865c0c15edadea9ea73","Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China","Wang W., Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China; Zhang X., Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China; Shi Y., Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China; Gao F., Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China; Xu W., Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China","From the perspective of the current big data environment, briefly describe the urgency of university scientific research data management, and use this as a basis to discuss the university scientific research data management strategy under the big data environment, and propose to conduct sufficient research on scientific research data management and pay attention to scientific research data Strategies such as dynamic management, strengthening the storage and security management of scientific research data, and enhancing the value of scientific research data results through publication and citation.  © Published under licence by IOP Publishing Ltd.","Big Data; Scientific Research Data; Scientific Research Data Management","Big data; Data Science; Digital storage; Environmental management; Data environment; Dynamic management; Scientific research datum; Security management; Information management","","","","","","","Ding Pei, Research on the Management Policy of Scientific Research Data in Foreign Universities Library Forum, pp. 103-110, (2014); Sun Jianjun, Ke Qing, On the construction of the national digital information resource strategic system, Journal of the Library Science in China, pp. 81-86, (2007); Fan Junhao, Research on the role of libraries in scientific data management Library and Information Service, pp. 38-42, (2014); Juanle Wang, Kun Bu, Yanjie Wang, Yating Shao, Progress in Activities of WDS-China Data Centers Data, Science Journal, 19, (2020); Ren Yazhong, The Feasibility Study of University Library Participating in Data Governance, Journal of Library Science, 42, pp. 14-17, (2020); Yang Yang, Yang Ye, Research on Knowledge Management and Collaborative Innovation Platform Construction in Scientific Research Institutions Information, Science, 38, pp. 101-106, (2020); Wei Liu, Bing Liang, Qu Baoqiang, Research on service model based on technology management data [J/OL], Information theory and practice, pp. 1-10; Yin Huaiqiong, Xiong Yongjun, Liu Haixia, Analysis and Enlightenment of Scientific Research Data Management Service of University Libraries at Home and Abroad Research in Library Science, 2020, pp. 33-41; Liu Guifeng, Qian Jinlin, Zhang Jiyong, Research on the Model Construction of Scientific Research Data Governance in Chinese Universities Information, Science, 38, pp. 28-36, (2020)","W. Wang; Education and Research Support Center, Dalian Naval Academy of the Navy, Dalian, China; email: xiyang@dlust.edu.cn","","IOP Publishing Ltd","","2nd International Conference on Computing and Data Science, CONF-CDS 2021","28 January 2021 through 30 January 2021","Stanford, Virtual","168592","17426588","","","","English","J. Phys. Conf. Ser.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85105264591"
"Huang Y.; Cox A.M.; Sbaffi L.","Huang, Yingshen (57219114443); Cox, Andrew M. (7402563906); Sbaffi, Laura (6506992618)","57219114443; 7402563906; 6506992618","Research data management policy and practice in Chinese university libraries","2021","Journal of the Association for Information Science and Technology","72","4","","493","506","13","16","10.1002/asi.24413","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091320110&doi=10.1002%2fasi.24413&partnerID=40&md5=091cd520ef8a6e0dc8c87e82e8eacd09","Health Science Library, Peking University, Beijing, China; The Information School, University of Sheffield, Regent Court, Sheffield, United Kingdom","Huang Y., Health Science Library, Peking University, Beijing, China; Cox A.M., The Information School, University of Sheffield, Regent Court, Sheffield, United Kingdom; Sbaffi L., The Information School, University of Sheffield, Regent Court, Sheffield, United Kingdom","On April 2, 2018, the State Council of China formally released a national Research Data Management (RDM) policy “Measures for Managing Scientific Data”. In this context and given that university libraries have played an important role in supporting RDM at an institutional level in North America, Europe, and Australasia, the aim of this article is to explore the current status of RDM in Chinese universities, in particular how university libraries have been involved in taking the agenda forward. This article uses a mixed-methods data collection approach and draws on a website analysis of university policies and services; a questionnaire for university librarians; and semi-structured interviews. Findings indicate that Research Data Service at a local level in Chinese Universities are in their infancy. There is more evidence of activity in developing data repositories than support services. There is little development of local policy. Among the explanations of this may be the existence of a national-level infrastructure for some subject disciplines, the lack of professionalization of librarianship, and the relatively weak resonance of openness as an idea in the Chinese context. © 2020 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.","","Information management; Surveys; Chinese context; Chinese universities; Data repositories; Research data managements; Scientific data; Semi structured interviews; Support services; University libraries; article; Australia and New Zealand; Europe; human; human experiment; infancy; librarian; library; North America; questionnaire; semi structured interview; Libraries","","","","","Ministry of Education - Singapore, MOE; National Natural Science Foundation of China, NSFC; Ministry of Education, MOE; Ministry of Science and Technology, Taiwan, MOST","Reflecting on the findings, it is apparent that RDM in Chinese universities remains in its infancy. As evidence of this, firstly, only one university has a publically accessible RDM policy, which has been adapted from the Oxford University policy, and not even revised since the Chinese national policy was issued (The University of Hong Kong, 2015 ); three other universities have policies in place but they are not openly online. In comparison, as early as 2016, 80 universities in the United Kingdom already had an institutional RDM policy (Horton, 2016 ), and a significant number of libraries and their institutions in Australia and the Netherlands have research data policy in place or to be implemented within a year (Cox et al., 2017 ). Secondly, compared to the previous study by Cox et al. ( 2019 ), the level of services offered by Chinese institutions was lower in every instance. The low response rate to the survey was also suggestive of a lack of awareness of RDM, as confirmed by comments in the interviews, where library directors often showed little engagement with the topic. This is despite the fact that our target participants were in the best‐funded institutions in China, the Double First‐Class Universities. One can infer that less well‐funded institutions among the 3,000 higher education institutions in China (MOE, Ministry of Education, 2017a , 2017b ) would have yielded a picture of even less awareness and activity. Some of the key drivers and barriers are rather familiar from other contexts (Cox et al., 2019 ), for example, library role, researchers' needs and funder's requirement as drivers, lack of skills as barrier, but the picture in China seems to be still in a posture of “wait and see.” We suggest that there may be three main reasons for the low development of RDM services in academic libraries in China. The first reason is that some data‐intensive disciplines already have their own data management infrastructure. Disciplines such as meteorology, geography, population health, and earth science already have a place to deposit and share their data through the National Science & Technology Infrastructure (NSTI, National Science and Technology Infrastruture, 2019 ). This is a national project hosted by the Ministry of Science and Technology (MOST, Ministry of Science and Technology, 2003 ) launched in 2002 and which passed its final evaluation in 2013. From 2010 to 2015, Higher Education Institutions (HEI) in China have led more than 80% of National Natural Science Foundation of China (NSFC) projects (MOE, Ministry of Education, 2016 ), which is the main funding body in China and is administrated by MOST (NSFC, National Natural Science Foundation of China, 2020 ). Research teams applying for funding from NSFC, both from university and non‐university research institutions, have to follow the requirement to deposit and share data. The need for this in such disciplines reflects recognition of the big scale of data collected via large and expensive instrumentation like telescopes that are financed by the state. There is also a national‐level priority for the creation of a data service network with various discipline data centers, which is intended to conduct the integration, sorting, classification, mining, and curation of data submitted from national projects and to promote open data sharing (Yuan, 2018 ). Currently, 20 national data centers and 30 national biological germplasm and experimental material resource banks have been approved to strengthen the construction and implementation of a scientific resources sharing system in order to promote the sharing and opened these resources to the public (MOST, Ministry of Science and Technology, 2019 ). ","Bezuidenhout L.M., Leonelli S., Kelly A.H., Rappert B., Beyond the digital divide: Towards a situated approach to open data, Science and Public Policy, 44, 4, pp. 464-475, (2017); (2008); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A., Kennan M., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Erway R., (2013); Horton L., (2016); Huang R., Wang B., Zhou Z., Cujin woguo kexue shuju gongxiang de duice [a study on countermeasures for promoting scientific data sharing in China], Tushuguan, 2014, 3, pp. 7-13, (2014); Huang R., Wen F., Woguo zhengfu shuju kaifang gongxiang de zhengce kuangjia yu neirong: Guojia cengmian zhengce wenben de neirong fenxi [Policy framework and content of openning and sharing government data in China: A content analysis of policy documents at the national level], Tushu Qingbao Gongzuo, 61, 20, pp. 12-25, (2017); Li J., Yu L., Zhang B., Liu F., Wu Z., Zhongkeyuan kexue shuju yun jiagou tanxi [Analysis of the Chinese Academy of Sciences scientific data cloud architecture], Zhongguo Jiaoyu Wangluo, 10, pp. 33-34, (2015); Liu X., Rao Y., Gaoxiao tushuguan kexue shuju guanli yu fuwu chutan—Wuhan daxue tushuguan anli fenxi [Scientific Data Management and Service in University Library—A case study of Wuhan University Library], Tushu Qingbao Gongzuo, 57, 6, pp. 33-38, (2013); Luo P., Zhu L., Cui H., Nie H., Jiyu Dataverse de beijing daxue kaifang yanjiu shuju pingtai jianshe [The construction of Peking University Open Research Data Platform based on Dataverse], Tushu Qingbao Gongzuo, 60, 3, pp. 52-58, (2016); (2007); (2016); (2017); (2017); (2003); (2004); (2019); (2020); (2019); OECD principles and guidelines for access to research data from public funding, (2007); (2016); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice, (2014); (2017); Tenopir C., Pollock D., Allard S., Hughes D., Research data services in European and North American libraries: Current offerings and plans for the future, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); (2018); (2018); (2015); (2016); Ward C., Freiman L., Molloy L., Jones S., Snow K., Making sense: Talking data management with researchers, International Journal of Digital Curation, 6, 2, pp. 8-17, (2010); (2012); Yin S., Wang Y., “Zhongguo gaoxiao tushuguan yanjiu shuju guanli tuijin gongzuozu” zheng shi cheng li [""China Academic Library Research Data Management Implementation Group"" was formally established], Shanghai Gaoxiao Tushu Qingbao Gongzuo Yanjiu, 2014, 4, (2014); Yuan Y., Rang kexue shuju kaifang gongxiang chengwei changtai [Make the open and sharing of scientific data become the norm], Guanming Daily, (2018); Zhang J., Yin S., Zhang Y., Guo Y., Zhang Y., Shehui kexue shuju de gongxiang yu fuwu—yi fudan daxue shehui kexue shuju gongxiang pingtai wei li [Social scientific data sharing and serving—An example of Fudan University Social Scientific Data Platform], Daxue Tushuguan Xuebao, 33, 1, pp. 74-79, (2015); Zhou L., Duan X., Song Y., Woguo gaoxiao tushuguan keyan shuju guanli fuwu diaocha yu fenxi [Investigation and analysis of research data management services in Chinese University Libraries], Tushu Qingbao Gongzuo, 61, 20, pp. 77-86, (2017)","A.M. Cox; The Information School, University of Sheffield, Regent Court, Sheffield, United Kingdom; email: a.m.cox@sheffield.ac.uk","","John Wiley and Sons Inc","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85091320110"
"Zhang X.-F.","Zhang, Xiao-Feng (57217292819)","57217292819","Application of blockchain technology in data management of university scientific research","2021","Advances in Intelligent Systems and Computing","1195 AISC","","","606","613","7","0","10.1007/978-3-030-50399-4_60","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087028888&doi=10.1007%2f978-3-030-50399-4_60&partnerID=40&md5=08924ab8406ae422081d30de41d829cc","Engineering College of Information, Engineering University of PAP, Xi’an, 710086, China","Zhang X.-F., Engineering College of Information, Engineering University of PAP, Xi’an, 710086, China","In the daily scientific research activities, the university will form a large number of files. The goal of data management of university scientific research activities is to ensure the availability, authenticity and validity of scientific research data, but the electronic data is more easily modified. There are problems in data security, storage security and utilization security of scientific research data. The blockchain technology is applied to the data management of scientific research activities, which can realize the management of the whole life cycle of scientific research data and ensure the effective use and security of scientific research data. SWOT analysis is helpful for university research managers to understand the real situation of research management. Therefore, this paper makes a detailed SWOT analysis of university scientific research data management, and proposes a data management system of university scientific research activities based on blockchain technology. © Springer Nature Switzerland AG 2021.","","Blockchain; Digital storage; Life cycle; Network security; Ubiquitous computing; Web services; Data management system; Electronic data; Research management; Scientific research datum; Scientific researches; Storage security; University research; Whole life cycles; Information management","","","","","","","Baldonado M., Chang C.-C.K., Gravano L., Paepcke A., The stanford digital library metadata architecture, Int. J. Digit. Libr, 1, pp. 108-121, (1997); Bruce K.B., Cardelli L., Pierce B.C., Comparing object encodings, Theoretical Aspects of Computer Software. Lecture Notes in Computer Science, 1281, pp. 415-438, (1997); van Leeuwen J., Computer Science Today. Recent Trends and Developments. Lecture Notes in Computer Science, 1000, (1995); Michalewicz Z., Genetic Algorithms + Data Structures = Evolution Programs, (1996); Hu Q., He J., Dong Q., Research on emergency materials supply information management of medical epidemic prevention under blockchain architecture targeted donation of COVID-19 prevention materials as an example, Health Econ. Res, 37, 4, pp. 10-14, (2020); Chen Z., Jin B., Yang X., Research on data security transmission scheme based on blockchain technology, Electron. Test, 1, 433, (2020); Bo Z., Liu Y., Li X., Li J., Zou J., TrustBlock: an adaptive trust evaluation of SDN network nodes based on double-layer blockchain, PloS one, 15, 3, (2020); Li D., Hu Y., Lan M., IoT device location information storage system based on blockchain, Future Gener. Comput. Syst, 109, (2020); Islam A., Shin S.Y., A blockchain-based secure healthcare scheme with the assistance of unmanned aerial vehicle in Internet of Things, Comput. Electr. Eng, 84, (2020); Narbayeva S., Bakibayev T., Abeshev K., Makarova I., Shubenkova K., Pashkevich A., Blockchain technology on the way of autonomous vehicles development, Transp. Res. Procedia, 44, (2020); Kim S., Park H., Lee J., Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: a study on blockchain technology trend analysis, Expert Syst. Appl, (2020); Balaji S., Application research of blockchain in higher education system-intelligent education system, J. Educ. Res. Policies, 2, 3, (2020); Cao H., Li R., Tian W., Xu Z., Xiao W., Blockchain-based accountability for multi-party oblivious RAM, J. Parallel Distrib. Comput, 137, C, (2020)","X.-F. Zhang; Engineering College of Information, Engineering University of PAP, Xi’an, 710086, China; email: zxf801031@126.com","Barolli L.; Poniszewska-Maranda A.; Park H.","Springer","","14th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2020","1 July 2020 through 3 July 2020","Lodz","240979","21945357","978-303050398-7","","","English","Adv. Intell. Sys. Comput.","Conference paper","Final","","Scopus","2-s2.0-85087028888"
"Klingner C.M.; Ritter P.; Brodoehl S.; Gaser C.; Scherag A.; Güllmar D.; Rosenow F.; Ziemann U.; Witte O.W.","Klingner, Carsten M. (36051767100); Ritter, Petra (9234339700); Brodoehl, Stefan (8235320900); Gaser, Christian (6701527848); Scherag, André (6506273019); Güllmar, Daniel (8388894800); Rosenow, Felix (7004179118); Ziemann, Ulf (57209482550); Witte, Otto W. (35592935100)","36051767100; 9234339700; 8235320900; 6701527848; 6506273019; 8388894800; 7004179118; 57209482550; 35592935100","Research data management in clinical neuroscience: The national research data infrastructure initiative","2021","Neuroforum","27","1","","35","43","8","1","10.1515/nf-2020-0039","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100014185&doi=10.1515%2fnf-2020-0039&partnerID=40&md5=2e22d91efffec21b974457393fcbfda6","Hans Berger Department of Neurology, University Hospital Jena, Erlanger Allee 101, Jena, 07747, Germany; Biomagnetic Center, Jena University Hospital, Jena, Germany; Brain Simulation Section, Department of Neurology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany; Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany; Department of Neurology, Epilepsy Center Frankfurt Rhine-Main, University Hospital Frankfurt, Frankfurt am Main, Germany; Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt, Frankfurt am Main, Germany; Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany","Klingner C.M., Hans Berger Department of Neurology, University Hospital Jena, Erlanger Allee 101, Jena, 07747, Germany, Biomagnetic Center, Jena University Hospital, Jena, Germany; Ritter P., Brain Simulation Section, Department of Neurology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany, Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany; Brodoehl S., Hans Berger Department of Neurology, University Hospital Jena, Erlanger Allee 101, Jena, 07747, Germany, Biomagnetic Center, Jena University Hospital, Jena, Germany; Gaser C., Hans Berger Department of Neurology, University Hospital Jena, Erlanger Allee 101, Jena, 07747, Germany, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Scherag A., Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany; Güllmar D., Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany; Rosenow F., Department of Neurology, Epilepsy Center Frankfurt Rhine-Main, University Hospital Frankfurt, Frankfurt am Main, Germany, Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt, Frankfurt am Main, Germany; Ziemann U., Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Witte O.W., Hans Berger Department of Neurology, University Hospital Jena, Erlanger Allee 101, Jena, 07747, Germany","In clinical neuroscience, there are considerable difficulties in translating basic research into clinical applications such as diagnostic tools or therapeutic interventions. This gap, known as the “valley of death,” was mainly attributed to the problem of “small numbers” in clinical neuroscience research, i.e. sample sizes that are too small (Hutson et al., 2017). As a possible solution, it has been repeatedly suggested to systematically manage research data to provide long-term storage, accessibility, and federate data. This goal is supported by a current call of the DFG for a national research data infrastructure (NFDI). This article will review current challenges and possible solutions specific to clinical neuroscience and discuss them in the context of other national and international health data initiatives. A successful NFDI consortium will help to overcome not only the “valley of death” but also promises a path to individualized medicine by enabling big data to produce generalizable results based on artificial intelligence and other methods. © 2021 De Gruyter. All rights reserved.","FAIR principles; NFDI; Research data management","artificial intelligence; basic research; big data; FAIR principles; language; neuroscience; personalized medicine; public health; review; sample size","","","","","","","Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Flandin G., Ghosh S.S., Glatard T., Halchenko Y.O., Handwerker D.A., Et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci Data, 3, (2016); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, pp. 280-299, (2008); Hutson P.H., Clark J.A., Cross A.J., CNS target identification and validation: Avoiding the valley of death or naive optimism?, Annu. Rev. Pharmacol. Toxicol., 57, pp. 171-187, (2017); Khvastova M., Witt M., Krefting D., Towards interoperability in clinical research: Enabling FHIR on the open source research Platform XNAT, Stud. Health Technol. Inf., 258, pp. 3-5, (2019); Klingner C.M., Brodoehl S., Huonker R., Witte O.W., The processing of somatosensory information shifts from an early parallel into a serial processing mode: A combined fMRI/MEG study, Front. Syst. Neurosci., 10, (2016); Longo D.L., Drazen J.M., Data sharing, N. Engl. J. Med., 374, pp. 276-277, (2016); Administrative simplification: Change to the compliance date for the international classification of diseases, 10th Revision (ICD-10-CM and ICD-10-PCS) medical data code sets, Final Rule. Fed. Regist., 79, pp. 45128-45134, (2014); Packer M., Data sharing in medical research, BMJ, 360, (2018); Pernet C.R., Heunis S., Herholz P., Halchenko Y.O., The Open Brain Consent: Informing Research Participants and Obtaining Consent to Share Brain Imaging Data, (2020); Pisani E., AbouZahr C., Sharing health data: Good intentions are not enough, Bull. World Health Organ., 88, pp. 462-466, (2010); Rauch G., Rohmel J., Gerss J., Scherag A., Hofner B., Current challenges in the assessment of ethical proposals-aspects of digitalization and personalization in the healthcare system, Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz, 62, pp. 758-764, (2019); Ritter P., Schirner M., McIntosh A.R., Jirsa V.K., The virtual brain Integrates computational modeling and multimodal neuroimaging, Brain Connect, 3, pp. 121-145, (2013); Schirner M., McIntosh A.R., Jirsa V., Deco G., Ritter P., Inferring multi-scale neural mechanisms with brain network modelling, Elife, 7, (2018); Sonntag H., Haueisen J., Maess B., Quality assessment of MEG-to-MRI coregistrations, Phys. Med. Biol., 63, (2018); Terry R.F., Littler K., Olliaro P.L., Sharing health research data - The role of funders in improving the impact, F1000Res, 7, (2018); ICD-11, Lancet, 393, (2019); van Panhuis W.G., Paul P., Emerson C., Grefenstette J., Wilder R., Herbst A.J., Heymann D., Burke D.S., A systematic review of barriers to data sharing in public health, BMC Public Health, 14, (2014); Vines T.H., Albert A.Y., Andrew R.L., Debarre F., Bock D.G., Franklin M.T., Gilbert K.J., Moore J.S., Renaut S., Rennison D.J., The availability of research data declines rapidly with article age, Curr. Biol., 24, pp. 94-97, (2014); Wartenberg D., Thompson W.D., Privacy versus public health: The impact of current confidentiality rules, Am. J. Public Health, 100, pp. 407-412, (2010); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Winter A., Staubert S., Ammon D., Aiche S., Beyan O., Bischoff V., Daumke P., Decker S., Funkat G., Gewehr J.E., Et al., Smart medical information technology for healthcare (SMITH): Data integration based on interoperability standards, Methods Inf. Med., 57, pp. e92-e105, (2018)","","","De Gruyter Open Ltd","","","","","","09470875","","","","English","Neuroforum","Review","Final","","Scopus","2-s2.0-85100014185"
"Shajitha C.","Shajitha, C. (57222104208)","57222104208","Digital curation practices in institutional repositories in South India: a study","2020","Global Knowledge, Memory and Communication","69","8-9","","557","578","21","4","10.1108/GKMC-10-2019-0125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084471586&doi=10.1108%2fGKMC-10-2019-0125&partnerID=40&md5=66ce29565678c23b67833fed2306a281","Department of Computer Applications, Cochin University of Science and Technology, Kochi, India","Shajitha C., Department of Computer Applications, Cochin University of Science and Technology, Kochi, India","Purpose: The purpose of this study was to identify the digital curation practices in institutional repositories (IRs) in South India. Design/methodology/approach: A voluntary survey was conducted among the IR managers of 23 South Indian IRs, and the response rate was 87%. Findings: This study found that the active participation of South Indian IRs was only seen in a few digital curation activities. However, of the 33 digital curation activities analyzed, the active participation of repositories was only seen in ten digital curation activities. The performance of preservation activities was extremely low, and disagreements were recorded by the survey participants toward several digital curation activities. The most disagreed digital curation activities were emulation and cease data curation. All the participants had assigned metadata and allowed file downloads in their repositories. Raman Research Institute had provided a good number of digital curation services in their IR. Originality/value: This is an in-depth study investigating the digital curation practice currently underway in South Indian IRs, and the researcher could not find similar studies in this niche. © 2020, Emerald Publishing Limited.","Data curation; Digital curation; Institutional repository; Preservation; Research data management; South India","","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Allen M., The SAGE Encyclopedia of Communication Research Methods, (2017); Anilkumar N., Research data management in India: a pilot study, European Physical Journal Web of Conferences, 186, (2018); Anyaoku E., Echedom A., Baro E., Digital preservation practices in university libraries: an investigation of institutional repositories in Africa, Digital Library Perspectives, 35, 1, pp. 41-64, (2019); Callicott B.B., Scherer D., Wesolek A., Making Institutional Repositories Work, (2016); Choudhury G.S., Case study in data curation at Johns Hopkins university, Library Trends, 57, 2, pp. 211-220, (2008); Constantopoulos P., Dallas C., Androutsopoulos I., Angelis S., Deligiannakis A., Gavrilis D., Papatheodorou C., DCC&U: an extended digital curation lifecycle model, International Journal of Digital Curation, 4, 1, pp. 34-45, (2009); What is digital curation?, (2019); DeRidder J.L., Helms A.M., Intake of digital content: survey results from the field, D-Lib Magazine, 22, 11-12, (2016); Digital preservation handbook, (2019); Fritz A., So many options, so little time: how to evaluate a digital preservation system that is right for your institution, Digital Preservation in Libraries: preparing for a Sustainable Future, pp. 77-92, (2019); Ganesan P., Sridhar K., Kirana Kumar D., Tutu P., Preservation of digital content in indian libraries: Issues and strategies, paper presented at 16th General Conference on Southeast Asian Librarians (CONSAL XVI), 10-13 June, (2015); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Hudson-Vitale C., Imker H., Johnston L.R., Carlson J., Kozlowski W., Olendorf R., Stewart C., SPEC Kit# 354: Data Curation, (2017); Johnston L.R., Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., Data curation network: a cross-institutional staffing model for curating research data, International Journal of Digital Curation, 13, 1, (2018); Johnston L.R., Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., How important is data curation? Gaps and opportunities for academic libraries, Journal of Librarianship and Scholarly Communication, 6, 1, pp. 1-24, (2018); Karasti H., Baker K.S., Halkola E., Enriching the notion of data curation in e-science: Data managing and information infrastructuring in the long term ecological research (LTER) network, Computer Supported Cooperative Work (CSCW), 15, 4, pp. 321-358, (2006); Kari K.H., Baro E.E., Digital preservation practices in university libraries: a survey of institutional repositories in Nigeria, Preservation, Digital Technology and Culture, 45, 3, pp. 134-144, (2016); Katre D., Digital preservation: converging and diverging factors of libraries, archives and museums: an indian perspective, IFLA Journal, 37, 3, pp. 195-203, (2011); Kaushik A., Perceptions of LIS professionals about data curation, World Digital Libraries, 10, 2, pp. 89-98, (2017); Kim J., Moen W., Warger E., Competencies required for digital curation: an analysis of job advertisements, International Journal of Digital Curation, 8, 1, (2013); Kumar K., Analytical survey on digital preservation and techniques among engineering education institutional libraries in Rayalaseema region of Andhra Pradesh, (2014); Lage K., Losoff B., Maness J., Receptivity to library involvement in scientific data curation: a case study at the university of Colorado boulder, Portal: Libraries and the Academy, 11, 4, pp. 915-937, (2011); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: a qualitative view from repository staff, PloS One, 12, 3, (2017); Li Y., Banach M., Institutional repositories and digital preservation: assessing current practices at research libraries, D-Lib Magazine, 17, 5-6, (2011); Lord P., Macdonald A., e-science curation report, prepared for the JISC committee for the support of research, (2003); National data sharing and accessibility policy, (2012); Morgana A., Baptista A.A., The use of application profiles and metadata schemas by digital repositories: findings from a survey, (2015); Moulaison H.L., Dykas F., Gallant K., OpenDOAR repositories and metadata practices, D-Lib Magazine, 21, 3-4, (2015); Niu J., Appraisal and selection for digital curation, International Journal of Digital Curation, 9, 2, pp. 65-82, (2014); Palmer C.L., Cragin M.H., Heidorn P.B., Smith L.C., Data curation for the long tail of science: the case of environmental sciences, paper presented at the 3rd International Digital Curation Conference, (2007); Park J., Tosaka Y., Metadata creation practices in digital repositories and collections: schemata, selection criteria, and interoperability, Information Technology and Libraries, 29, 3, pp. 104-116, (2010); Pham A., Surveying the state of data curation: a review of policy and practice in UK HEIs, (2018); Punzalan R.L., Kriesberg A., Library-mediated collaborations: data curation at the national agricultural library, Library Trends, 65, 3, pp. 429-447, (2017); RoyMukhopadhyay B.K., Biswas S.C., An analytical study of institutional digital repositories in India, (2012); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College and Research Libraries, 73, 4, pp. 349-365, (2012); Serrano-Vicente R., Melero R., Abadal E., Evaluation of spanish institutional repositories based on criteria related to technology, procedures, content, marketing and personnel, Data Technologies and Applications, 52, 3, pp. 384-404, (2018); Shajitha C., Institutional Repositories in South India: An Exploratory Study, (2020); Shajitha C., Abdul Majeed K.C., Content growth of institutional repositories in South India: a status report, Global Knowledge, Memory and Communication, 67, 8-9, pp. 547-565, (2018); Shen Y., Varvel V.E., Developing data management services at the Johns Hopkins university, The Journal of Academic Librarianship, 39, 6, pp. 552-557, (2013); Shivarama J., Sheela V., Deepak A.B., Agadi K.B., Digital curation strategies for information management in higher education institutions, 10th Convention PLANNERNEHU, North-Eastern Hill University Shillong, Meghalaya, November 09-11, 2016, pp. 117-126, (2016); Shrivastava P., Gupta D.K., Research data preservation in India: an analysis based on research data registry, World Digital Libraries, 11, 2, pp. 107-121, (2018); Shrivastava P., Gupta D.K., Emergence of research data literacy with special reference to India, SRELS Journal of Information Management, 56, 2, pp. 112-118, (2019); Steele T., Sump-Crethar N., Metadata for electronic theses and dissertations: a survey of institutional repositories, Journal of Library Metadata, 16, 1, pp. 53-68, (2016); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Tripathi M., Shukla A., Sonkar S., Research data management practices in university libraries: a study, DESIDOC Journal of Library and Information Technology, 37, 6, pp. 417-424, (2017); Weber N.M., Palmer C.L., Chao T.C., Current trends and future directions in data curation research and education, Journal of Web Librarianship, 6, 4, pp. 305-320, (2012); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2009); Yoon A., Schultz T., Research data management services in academic libraries in the US: a content analysis of libraries, College and Research Libraries, 78, 7, pp. 920-933, (2017); Yoon A., Tibbo H., Examination of data deposit practices in repositories with the OAIS model, IASSIST Quarterly, 35, 4, pp. 6-13, (2011)","C. Shajitha; Department of Computer Applications, Cochin University of Science and Technology, India; email: shajitha.c@gmail.com","","Emerald Group Holdings Ltd.","","","","","","25149342","","","","English","Glob. Knowl.,  Mem. Commun.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85084471586"
"Auge T.; Scharlau N.; Heuer A.","Auge, Tanja (57194833958); Scharlau, Nic (57222062871); Heuer, Andreas (9533312500)","57194833958; 57222062871; 9533312500","Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance","2021","Communications in Computer and Information Science","1479 CCIS","","","52","62","10","0","10.1007/978-3-030-87101-7_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115884593&doi=10.1007%2f978-3-030-87101-7_6&partnerID=40&md5=cb80b2158e20934a130505c0acd4033c","University of Rostock, Rostock, Germany","Auge T., University of Rostock, Rostock, Germany; Scharlau N., University of Rostock, Rostock, Germany; Heuer A., University of Rostock, Rostock, Germany","Consciously collecting (research) data and respecting privacy aspects are two contradictions, which seem to be mutually exclusive at first moment. However, this does not have to be the case. But before we can address this conflict and its resolution, we want to understand what the terms privacy, provenance, and research data management actually mean. We are not interested in the formal definitions but in the community’s understanding of these terms. We have the intention to explore how far the theoretical definitions are known in science and economy. Hence, we interviewed 20 people – scientists and non-scientists – and evaluated their answers for discussing the relevance of combining provenance and privacy in the field of research data management. We discovered that provenance is generally understood as the origin of data or physical objects, and privacy often refers to the protection of personal data. We found that all participants have a very good understanding of their own research data, which in most cases is based on a well-developed research data management. Nevertheless, there is still some uncertainty, especially in the area of provenance and privacy. © 2021, Springer Nature Switzerland AG.","Guided interview; Privacy; Provenance; Research data management","Information management; First moments; Formal definition; Guided interview; Intention to explore; Privacy; Privacy aspects; Privacy aware; Provenance; Research data; Research data managements; Data privacy","","","","","","","Herschel M., Diestelkamper R., Ben Lahmar H., A survey on provenance: What for? What form? What from?, VLDB J, 26, 6, pp. 881-906, (2017); Samarati P., Protecting respondents’ identities in microdata release, IEEE Trans. Knowl. Data Eng., 13, 6, pp. 1010-1027, (2001); Sweeney L., Simple Demographics Often Identify People Uniquely. Carnegie Mellon University, School of Computer Science, Data Privacy Lab White Paper Series LIDAP-WP4. Pittsburgh, (2000)","T. Auge; University of Rostock, Rostock, Germany; email: tanja.auge@uni-rostock.de","Kotsis G.; Tjoa A.M.; Khalil I.; Moser B.; Mashkoor A.; Sametinger J.; Fensel A.; Martinez-Gil J.; Fischer L.; Czech G.; Sobieczky F.; Khan S.","Springer Science and Business Media Deutschland GmbH","","12th International Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd International Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st International Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st International Workshop on Time Ordered Data, ProTime2021 and 1st International Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd International Conference on Database and Expert Systems Applications, DEXA 2021","27 September 2021 through 30 September 2021","Virtual, Online","265709","18650929","978-303087100-0","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85115884593"
"Canali S.","Canali, Stefano (57205766489)","57205766489","Towards a contextual approach to data quality","2020","Data","5","4","90","1","10","9","7","10.3390/data5040090","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091593828&doi=10.3390%2fdata5040090&partnerID=40&md5=64acefdecc3dabcbd99b527d64253e2a","Institute of Philosophy, Leibniz University Hannover, Im Moore 21, Hannover, 30167, Germany","Canali S., Institute of Philosophy, Leibniz University Hannover, Im Moore 21, Hannover, 30167, Germany","In this commentary, I propose a framework for thinking about data quality in the context of scientific research. I start by analyzing conceptualizations of quality as a property of information, evidence and data and reviewing research in the philosophy of information, the philosophy of science and the philosophy of biomedicine. I identify a push for purpose dependency as one of the main results of this review. On this basis, I present a contextual approach to data quality in scientific research, whereby the quality of a dataset is dependent on the context of use of the dataset as much as the dataset itself. I exemplify the approach by discussing current critiques and debates of scientific quality, thus showcasing how data quality can be approached contextually. © 2020 by the author. Licensee MDPI, Basel, Switzerland.","Data quality; FAIR; Reproducibility crisis; Research data management; Scientific epistemology","Data reduction; Data quality; FAIR; Philosophy of information; Philosophy of science; Property; Reproducibilities; Reproducibility crisis; Research data managements; Scientific epistemology; Scientific researches; Information management","","","","","Institute of Philosophy of Leibniz University Hannover; Deutsche Forschungsgemeinschaft, DFG, (254954344/GRK2073)","Funding: This research was partly funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project 254954344/GRK2073, as part of the graduate research group “Integrating Ethics and Epistemology of Scientific Research” and by the Institute of Philosophy of Leibniz University Hannover.","Leonelli S., Scientific Research and Big Data, The Stanford Encyclopedia of Philosophy (Summer 2020 Edition); Canali S., Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS, Big Data Soc, (2016); Leonelli S., What Difference Does Quantity Make? On the Epistemology of Big Data in Biology, Big Data Soc, (2014); Cai L., Yangyong Z., The challenges of data quality and data quality assessment in the Big Data era, Data Sci. J, 14, (2015); Wilkinson M., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Floridi L., Illari P., Information Quality, Data and Philosophy, The Philosophy of Information Quality, pp. 5-24, (2014); Boumans M., Leonelli S., Introduction: On the Philosophy of Science in Practice, J. Gen. Philos. Sci, 44, pp. 259-261, (2013); Wang R.Y., Reddy M.P., Kon H.B., Toward quality data: An attribute-based approach, Decis. Support Syst, 13, pp. 349-372, (1995); Wang R.Y., A product perspective on total data quality management, Commun. ACM, 41, pp. 58-65, (1998); Batini C., Scannapieco M., Data Quality: Concepts, Methodologies and Techniques, (2006); Wand Y., Wang R.Y., Anchoring data quality dimensions in ontological foundations, Commun. ACM, 39, pp. 86-95, (1996); Primiero G., Algorithmic Check of Standards for Information Quality Dimensions, The Philosophy of Information Quality, pp. 107-143, (2014); Illari P., IQ: Purpose and Dimensions, The Philosophy of Information Quality, pp. 281-302, (2014); Leonelli S., Tempini N., Data Journeys in the Sciences, (2020); Leonelli S., Data-Centric Biology: A Philosophical Study, (2016); Stegenga J., Down with the Hierarchies, Topoi, 33, pp. 313-322, (2014); Leonelli S., Global Data Quality Assessment and the Situated Nature of “Best” Research Practices in Biology, Data Sci. J, 16, pp. 1-11, (2017); Hacking I., Representing and Intervening, (1983); Rheinberger H.J., An Epistemology of the Concrete, (2010); Chang H., Cartwright N., Measurement, The Routledge Companion to Philosophy of Science, pp. 367-375, (2008); Van Fraassen B.C., Scientific Representation: Paradoxes of Perspective, (2008); Mari L., Epistemology of Measurement, Measurement, 34, pp. 17-30, (2003); Boumans M., Invariance and Calibration, Measurement in Economics: A Handbook, pp. 231-248, (2007); Tal E., Old and New Problems in Philosophy of Measurement, Philos. Compass, 8, pp. 1159-1173, (2013); Sackett D.L., Rosenberg W.M.C., Muir Gray J.A., Haynes R.B., Richardson W.S., Evidence based medicine: What it is and what it isn’t, BMJ, 312, (1996); Bluhm R., From hierarchy to network: A richer view of evidence for evidence-based medicine, Perspect. Biol. Med, 48, pp. 535-547, (2005); Worrall J., What evidence in evidence-based medicine?, Philos. Sci, 69, pp. S316-S330, (2002); Clarke B., Gillies D., Illari P., Russo F., Williamson J., Mechanisms and the evidence hierarchy, Topoi, (2014); Campaner R., Galavotti M.C., Evidence and the Assessment of Causal Relations in the Health Sciences, Int. Stud. Philos. Sci, 26, pp. 27-45, (2012); Kerry R., Eriksen T.E., Lie S.A.N., Mumford S.D., Anjum R.L., Causation and evidence-based practice: An ontological review, J. Eval. Clin. Pract, 18, pp. 1006-1012, (2012); Stegenga J., Is meta-analysis the platinum standard of evidence?, Stud. Hist. Philos. Biol. Biomed. Sci, 42, pp. 497-507, (2011); Jukola S., On the evidentiary standards for nutrition advice, Stud. Hist. Philos. Biol. Biomed. Sci, 73, pp. 1-9, (2019); Floridi L., Philosophy of Information, (2011); Canali S., Making Evidential Claims in Epidemiology: Three Strategies for the Study of the Exposome, Stud. Hist. Philos. Biol. Biomed. Sci, (2020); Leonelli S., On the Locality of Data and Claims about Phenomena, Philos. Sci, 76, pp. 737-749, (2009); Popper K., The Logic of Scientific Discovery, (1959); Romero F., Philosophy of Science and the Replicability Crisis, Philos. Compass, 14, (2019); Ioannidis J.P., Implausible results in human nutrition research, BMJ, 347, (2013); Romero F., Novelty versus Replicability: Virtues and Vices in the Reward System of Science, Philos. Sci, 84, pp. 1031-1043, (2017); Feest U., Why Replication Is Overrated, Philos. Sci, 86, pp. 895-905, (2019); Leonelli S., Re-Thinking Reproducibility as a Criterion for Research Quality, Res. Hist. Econ. Thought Methodol, 36, pp. 129-146, (2018); Guttinger S., The limits of replicability, Eur. J. Philos. Sci, 10, (2020); Canali S., Evaluating evidential pluralism in epidemiology: Mechanistic evidence in exposome research, Hist. Philos. Life Sci, 41, (2019); Jukola S., Casuistic Reasoning, Standards of Evidence, and Expertise on Elite Athletes’ Nutrition, Philosophies, 4, (2019)","S. Canali; Institute of Philosophy, Leibniz University Hannover, Hannover, Im Moore 21, 30167, Germany; email: stefano.canali@philos.uni-hannover.de","","MDPI","","","","","","23065729","","","","English","Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85091593828"
"Demchenko Y.; Stoy L.","Demchenko, Yuri (8904483500); Stoy, Lennart (57226720236)","8904483500; 57226720236","Research data management and data stewardship competences in university curriculum","2021","IEEE Global Engineering Education Conference, EDUCON","2021-April","","9453956","1717","1726","9","1","10.1109/EDUCON46332.2021.9453956","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112433907&doi=10.1109%2fEDUCON46332.2021.9453956&partnerID=40&md5=2fc809fa03048d23d29c9a82355268a7","University of Amsterdam, Netherlands; EUA, Belgium","Demchenko Y., University of Amsterdam, Netherlands; Stoy L., EUA, Belgium","Skills for data governance and management are critical for the wide adoption of Open Science practices and effective use of the data in research, industry, business and other economic sectors. The FAIR (Findable - Accessible - Interoperable - Reusable) data management principles and data stewardship provide a foundation for effective research data management. The 2018 'Turning FAIR into Reality' report and other documents recommend that data skills should be more widely included in university curricula and that a concerted effort should be made to coordinate and accelerate the pedagogy for professional data roles. Throughout Europe and beyond, many organisations, projects and initiatives work on providing training on FAIR data competences. However, wider adoption of the FAIR data culture can be achieved by including FAIR competences into university curricula. This paper presents the ongoing work of the FAIRsFAIR project to develop a Data Stewardship competence framework and to provide recommendations for implementing this framework in university curricula by means of defining the Data Stewardship Body of Knowledge Model Curricula. The proposed approach and identified competences and knowledge topics are supported by a job market analysis. The presented work is actively using the EDISON Data Science Framework as a basis for Data Stewardship competences definition and methodology for linking competences, skills, knowledge, and intended learning outcomes when designing curricula. © 2021 IEEE.","Big Data; Data Management and Governance; Data Steward Professional; Data Stewardship Competence Framework (CF-DSP); EDISON Data Science Framework (EDSF); FAIR data principles; Research Data Management","Data Science; Engineering education; Information management; Body of knowledge; Data governances; Data stewardship; Economic sectors; Intended learning outcomes; Management principles; Research data managements; University curricula; Curricula","","","","","Horizon 2020 Framework Programme, H2020, (831558)","","Cuadrado-Gallego J.J., Demchenko Y., The data science framework, a view from the edison project, Springer Nature Switzerland AG, (2020); EDISON Data Science Framework (EDSF); Mons B., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship; European Open Science Cloud (EOSC); Research Data Alliance; Final Report and Action Plan from the European Commission Expert Group on FAIR Data, (2018); FAIRdata Forum; FAIRsFAIR Project: Fostering FAIR Data Practices in Europe; Briefing on FAIR Competences and Synergies, (2021); Demchenko Y., Belloum A., De Laat C., Loomis C., Wiktorski T., Spekschoor E., Customisable data science educational environment: From competences management and curriculum design to virtual labs on-demand, Proc. 4th IEEE STC CC Workshop on Curricula and Teaching Methods in Cloud Computing, Big Data, and Data Science (DTW2017) Part of the 9th IEEE International Conference and Workshops on Cloud Computing Technology and Science (CloudCom2017), (2017); Demchenko Y., Belloum A., Los W., Wiktorski T., Manieri A., Brewer S., Brocks H., Becker J., Heutelbeck D., Hemmje M., Edison data science framework: A foundation for building data science profession for research and industry, 3rd ieee stc cc and rda workshop on curricula and teaching methods in cloud computing, big data, and data science (dtw2016), Proc. The 8th IEEE International Conference and Workshops on Cloud Computing Technology and Science (CloudCom2016), (2016); Demchenko Y., Comminiello L., Reali G., Designing customisable data science curriculum using ontology for science and body of knowledge, 2019 International Conference on Big Data and Education (ICBDE2019), (2019); Data Management, Extension of the Open Research Data Pilot in Horizon, (2020); Data Management and Data Management Plan Template; Data Management, European Commission Portal; European Cloud Initiative-Building A Competitive Data and Knowledge Economy in Europe; Ferrari T., Scardaci D., Andreozzi S., The Open Science Commons for the European Research Area, Part of the ISSI Scientific Report Series Book Series (ISSI), 15; SRIA Solutions for A Sustainable EOSC. A Tinman Report from the EOSC Sustainability Working Group, (2019); FAIRsFAIR Project Deliverable D7. 3 Data Stewardship Professional Competence Framework, (2021); EOSCpilot D7. 5 Strategy for Sustainable Development of Skills and Capabilities; Towards FAIR Data Steward As Profession for the Life Sciences, Final Report ZonMw & ELIXIR-NL Projects, (2019); The Danish E-Infrastructure Cooperation (DeIC) and Danish National Forum for Research Data Management (DM Forum) Report on National Coordination of Data Steward Education in Demark; GO FAIR Initiative; Bahim C., Casorran-Amilburu C., Dekkers M., Herczog E., Loozen N., Repanas K., Russell K., Stall S., The fair data maturity model: An approach to harmonise fair assessments, Data Science Journal, 19, 1, (2020); RDA Data Maturity Model Working Group; DAMA Data Management Body of Knowledge (DMBOK2), (2017); CCS, 2012 the 2012 ACM Computing Classification System; European Skills, Competences, Qualifications and Occupations (ESCO) Framework; Persistent Identifiers, Groups of European Data Experts, (2017); Demchenko Y., Wiktorski T., Brewer S., Jose Cuadrado Gallego J., Edison data science framework (edsf) extension to address transversal skills required by emerging industry 4. 0 transformation, Proc. 5th IEEE STC CC Workshop on Curricula and Teaching Methods in Cloud Computing, Big Data, and Data Science (DTW2019) Part of the EScience 2019 Conference, (2019); Demchenko Y., Wiktorski T., Cuadrado-Gallego J.J., Chertov O., Big data platforms and tools for data analytics in the big data and data science curricula, The Data Science Framework, A View from the EDISON Project, (2020); Three Courses Announced on Big Data and Data Management for Maritime and Offshore Sectors, Project MATES","","Klinger T.; Kollmitzer C.; Pester A.","IEEE Computer Society","Axians; Boston Micro Fabrication (BMF); Infineon","2021 IEEE Global Engineering Education Conference, EDUCON 2021","21 April 2021 through 23 April 2021","Vienna","170895","21659559","978-172818478-4","","","English","IEEE Global Eng. Edu. Conf., EDUCON","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85112433907"
"Llebot C.; Rempel H.G.","Llebot, Clara (23492943800); Rempel, Hannah Gascho (7004559776)","23492943800; 7004559776","Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption","2021","Journal of Librarianship and Scholarly Communication","9","1","eP2321","","","","1","10.7710/2162-3309.2321","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136701138&doi=10.7710%2f2162-3309.2321&partnerID=40&md5=ca27ecb0bdbc515114a4628ca927ed42","Oregon State University, United States","Llebot C., Oregon State University, United States; Rempel H.G., Oregon State University, United States","Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective. © 2021 Llebot & Rempel.","","","","","","","","","Azoulay P., Zivin J. S. G., Wang J., Superstar extinction, The Quarterly Journal of Economics, 125, 2, pp. 549-589, (2010); Baker M., Is there a reproducibility crisis?, Nature, 533, 7604, pp. 452-454, (2016); Choudhury P., Haas M. R., Scope versus speed: Team diversity, leader experience, and patenting outcomes for firms, Strategic Management Journal, 39, 4, pp. 977-1002, (2018); Cooper A., The inmates are running the asylum, (2004); Cooper D., Springer R., When enough isn’t enough: Rethinking research support in the sciences, Association of College & Research Libraries, pp. 132-138, (2019); Corrall S., Kennan M. A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A. M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cummings J. N., Work groups, structural diversity, and knowledge sharing in a global organization, Management Science, 50, 3, pp. 352-364, (2004); Davis F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13, 3, pp. 319-340, (1989); De Dreu C. K. W., Weingart L. R., Task versus relationship conflict, team performance, and team member satisfaction: A meta-analysis, Journal of Applied Psychology, 88, 4, pp. 741-749, (2003); De Hoogh A. H. B., Greer L. L., Den Hartog D. N., Diabolical dictators or capable commanders? An investigation of the differential effects of autocratic leadership on team performance, The Leadership Quarterly, 26, 5, pp. 687-701, (2015); De Wit F. R., Greer L. L., Jehn K. A., The paradox of intragroup conflict: A meta-analysis, Journal of Applied Psychology, 97, 2, (2012); Fane B., Ayris P., Hahnel M., Hrynaszkiewicz I., Baynes G., Farrell E., The State of Open Data Report 2019, (2019); Hahnel M., Treadway J., Fane B., Kiley R., Peters D., Baynes G., The State of Open Data Report 2017, (2017); H2020 programme guidelines on fair data management in Horizon 2020, (2016); Ductor L., Does co-authorship lead to higher academic productivity?, Oxford Bulletin of Economics and Statistics, 77, 3, pp. 385-407, (2015); Evans J. A., Industry collaboration, scientific sharing, and the dissemination of knowledge, Social Studies of Science, 40, 5, pp. 757-791, (2010); Gladstein D. L., Groups in context: A model of task group effectiveness, Administrative Science Quarterly, 29, 4, pp. 499-517, (1984); Goldman J., Kafel D., Martin E., Martin E., Assessment of data management services at New England region resource libraries, Journal of EScience Librarianship, 4, 1, (2015); Goodman A., Pepe A., Blocker A. W., Borgman C. L., Cranmer K., Crosas M., Di Stefano R., Gil Y., Groth P., Hedstrom M., Hogg D. W., Kashyap V., Mahabal A., Siemiginowska A., Slavkovic A., Ten simple rules for the care and feeding of scientific data, PLoS Computational Biology, 10, 4, (2014); DRAFT Tri-Agency Research Data Management Policy For Consultation. Innovation, Science and Economic Development Canada, (2018); Griffith T. L., Mannix E. A., Neale M. A., Conflict and virtual teams, Virtual teams that work: Creating conditions for virtual team effectiveness, pp. 335-352, (2003); Hackett E. J., Essential tensions: Identity, control, and risk in research, Social Studies of Science, 35, 5, pp. 787-826, (2005); Hampton S. E., Strasser C. A., Tewksbury J. J., Gram W. K., Budden A. E., Batcheller A. L., Duke C. S., Porter J. H., Big data and the future of ecology, Frontiers in Ecology and the Environment, 11, 3, pp. 156-162, (2013); Healey M. P., Vuori T., Hodgkinson G. P., When teams agree while disagreeing: Reflexion and reflection in shared cognition, Academy of Management Review, 40, 3, pp. 399-422, (2015); Hoffman D. M., Blasi B., Culum B., Dragsic Z., Ewen A., Horta H., Nokkala T., Rios-Aguilar C., The methodological illumination of a blind spot: Information and communication technology and international research team dynamics in a higher education research program, Higher Education, 67, 4, pp. 473-495, (2014); Holdren J., Memorandum for the heads of executive departments and agencies: Increasing access to the results of federally funded scientific research, (2013); Im I., Hong S., Kang M. S., An international comparison of technology adoption: Testing the UTAUT model, Information & Management, 48, 1, pp. 1-8, (2011); Kim D. 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E., Johnston A. P., Hasazi S. B., Schattman R. A., Teaming in qualitative research: Lessons learned, International Journal of Qualitative Studies in Education, 7, 1, pp. 77-88, (1994); Llebot C., Rempel H. G., Instruments to explore the adoption of good data management practices through a variation of the UTAUT model, (2020); Lopez-Yanez J., Altopiedi M., Evolution and social dynamics of acknowledged research groups, Higher Education (00181560), 70, 4, pp. 629-647, (2015); Ensuring Access to Research Data, Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, (2009); Dear Colleague Letter: Effective Practices for Data (nsf19069) | NSF–National Science Foundation, (2019); Neylon C., Compliance culture or culture change? The role of funders in improving data management and sharing practice amongst researchers, Research Ideas and Outcomes, 3, (2017); Nosek B. A., Alter G., Banks G. C., Borsboom D., Bowman S. D., Breckler S. J., Buck S., Chambers C. D., Chin G., Christensen G., Contestabile M., Dafoe A., Eich E., Freese J., Glennerster R., Goroff D., Green D. P., Hesse B., Humphreys M., Yarkoni T., Promoting an open research culture, Science, 348, 6242, pp. 1422-1425, (2015); Perkel J. M., 11 ways to avert a data-storage disaster, Nature, 568, 7750, pp. 131-132, (2019); Piwowar H. A., Who shares? Who doesn’t? Factors associated with openly archiving raw research data, PLoS ONE, 6, 7, (2011); Rans J., White A., Using RISE, the research infrastructure self evaluation framework, (2017); Rempel H. G., Markland M., Bridging the relationship gap: Using social network theories to inform library services for graduate students, the Library with the Lead Pipe, (2018); Rempel H. G., Robertshaw M. B., Supporting the research practices of agricultural scientists at Oregon State University, Journal of Agricultural & Food Information, 18, 3–4, pp. 276-292, (2017); Reznik-Zellen R., Adamick J., McGinty S., Tiers of research data support services, Journal of EScience Librarianship, pp. 27-35, (2012); Salazar M. R., Lant T. K., Facilitating innovation in interdisciplinary teams: The role of leaders and integrative communication, Informing Science: The International Journal of an Emerging Transdiscipline, 21, (2018); Stuart D., Baynes G., Hrynaszkiewicz I., Allin K., Penny D., Lucraft M., Astell M., Whitepaper: Practical challenges for researchers in data sharing, (2018); Tenopir C., Allard S., Douglass K., Aydinoglu A. U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLOS ONE, 6, 6, (2011); Tenopir C., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Hughes D., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of EScience Librarianship, 4, 2, (2015); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future [ACRL white paper], (2012); Tenopir C., Dalton E. D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLOS ONE, 10, 8, (2015); Tenopir C., Sandusky R. J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Velden T., Explaining field differences in openness and sharing in scientific communities, Proceedings of the 2013 Conference on Computer Supported Cooperative Work, (2013); Venkatesh V., Morris M. G., Davis G. B., Davis F. D., User acceptance of information technology: Toward a unified view, MIS Quarterly, 27, 3, pp. 425-478, (2003); Whitmire A. L., Boock M., Sutton S. C., Variability in academic research data management practices: Implications for data services development from a faculty survey, (2015); Whyte A., Final results from the DCC RDM 2014 survey, DCC Because Good Research Needs Good Data, (2014); Wilkinson M. D., Dumontier M., Aalbersberg Ij. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Bouwman J., Brookes A. J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C. T., Finkers R., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wilson S. R., Barley W. C., Ruge-Jones L., Poole M. S., Tacking amid tensions: Using oscillation to enable creativity in diverse teams, The Journal of Applied Behavioral Science, (2020); Wuchty S., Jones B. F., Uzzi B., The increasing dominance of teams in production of knowledge, Science, 316, 5827, pp. 1036-1039, (2007); Zuiderwijk A., Janssen M., Dwivedi Y. K., Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology, Government Information Quarterly, 32, 4, pp. 429-440, (2015)","C. Llebot; Corvallis, 121 The Valley Library, 97331–4501, United States; email: clara.llebot@oregonstate.edu","","Iowa State University Digital Press","","","","","","21623309","","","","English","J. Librariansh. Sch. Commun.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85136701138"
"Karimova Y.; Ribeiro C.; David G.","Karimova, Yulia (57195369729); Ribeiro, Cristina (7201734594); David, Gabriel (16635163900)","57195369729; 7201734594; 16635163900","Institutional Support for Data Management Plans: Five Case Studies","2021","Communications in Computer and Information Science","1355 CCIS","","","308","319","11","1","10.1007/978-3-030-71903-6_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104898465&doi=10.1007%2f978-3-030-71903-6_29&partnerID=40&md5=108f7dcd6cbdefddf0594c93561b9417","INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Karimova Y., INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; David G., INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Researchers are being prompted by funders and institutions to expose the variety of results of their projects and to submit a Data Management Plan as part of their funding requests. In this context, institutions are looking for solutions to provide support to research data management activities in general, including DMP creation. We propose a collaborative approach where a researcher and a data steward create a DMP, involving other parties as required. We describe this collaborative method and its implementation, by means of a set of case studies that show the importance of the data steward in the institution. Feedback from researchers shows that the DMP are simple enough to lead people to engage in data management, but present enough challenges to constitute an entry point to the next level, the machine-actionable DMP. © 2021, Springer Nature Switzerland AG.","Data management plan; Research data management; Research workflow","Metadata; Semantics; Case-studies; Collaborative approach; Entry point; Funding requests; Institutional support; Management plans; Research data managements; Information management","","","","","","","Ahokas M., Kuusniemi M.E., Friman J., The Tuuli project: Accelerating data management planning in Finnish research organisations, Int. J. Digit. Curation, 12, 2, pp. 107-115, (2017); Annex L. Conditions Related to Open Access to Research Data, (2017); Cox A., Verbaan E., Data management planning, Exploring Research Data Management, (2018); Dressler V.A., Yeager K., Richardson E., Developing a data management consultation service for faculty researchers: A case study from a large Midwestern public university, Int. J. Digit. Curation, 14, 1, (2019); Fearon D., Gunia B., Lake S., Pralle B.E., Sallans A.L., SPEC Kit 334: Research Data Management Services, (2013); Graham P., Managing Research Data, (2012); Managing Data@ Melbourne: An Online Research Data Management Training Program, (2017); Hodson S., Mons B., Uhlir P., Zhang L., The Beijing Declaration on Research Data, (2019); Karimova Y., Ribeiro C., The Collaborative Method between Curators and Researchers in the Preparation of a Data Management Plan and Privacy Impact Assessment. 5 Forum Gestão De Dados De Investigação, (2019); Ray J.M., Research Data Management: Practical Strategies for Information Professionals, (2013); Anne K.M., Managing research data, Research Methods, (2018); Molloy L., JISC research data MANTRA project at EDINA, Information Services, University of Edinburgh: Evaluation, Information Processing and Management, (2012); Pasquetto I.V., Randles B.M., Borgman C.L., On the reuse of scientific data, Data Sci. J., 16, 8, (2017); RDA: RDA for the Sustainable Development Goals. Introduction: Fit with the Overall RDA Vision and Mission, (2019); Ribeiro C., da Silva J.R., Castro J.A., Amorim R.C., Lopes J.C., Research data management tools and workflows: Experimental work at the University of Porto, IASSIST Q, 42, 2, pp. 1-16, (2018); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Gov. Inf. Q., 30, pp. 19-31, (2013); Simms S., Jones S., Next-generation data management plans: Global, machine-actionable, FAIR, Int. J. Digit. Curation, 12, 1, (2017); Tenopir C., Et al., Research data services in European academic research libraries, Liber Q, 27, 1, (2017); Tomas H., Janouskova S., Moldan B., Sustainable development goals: A need for relevant indicators, Ecol. Indicators, 60, (2016); Vitale C.H., Sandy H.L.M., Data management plans: A review, DESIDOC J. Libr. Inf. Technol., 39, 6, (2019); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA J, 43, 1, (2017)","Y. Karimova; INESC TEC, Faculty of Engineering, University of Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: ylaleo@gmail.com","Garoufallou E.; Ovalle-Perandones M.","Springer Science and Business Media Deutschland GmbH","","14th International Conference on Metadata and Semantics Research, MTSR 2020","2 December 2020 through 4 December 2020","Madrid","256669","18650929","978-303071902-9","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85104898465"
"Trippel T.; Zinn C.","Trippel, Thorsten (55237014700); Zinn, Claus (57196765933)","55237014700; 57196765933","Lessons learned: on the challenges of migrating a research data repository from a research institution to a university library","2021","Language Resources and Evaluation","55","1","","191","207","16","2","10.1007/s10579-019-09474-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074033954&doi=10.1007%2fs10579-019-09474-4&partnerID=40&md5=cb6c7ca5498086b55dc350c433157f42","University of Tübingen, Wilhelmstrasse 19, Tübingen, 72074, Germany","Trippel T., University of Tübingen, Wilhelmstrasse 19, Tübingen, 72074, Germany; Zinn C., University of Tübingen, Wilhelmstrasse 19, Tübingen, 72074, Germany","The transfer of research data management from one institution to another infrastructural partner is all but trivial, but can be required, for instance, when an institution faces reorganization or closure. In a case study, we describe the migration of all research data, identify the challenges we encountered, and discuss how we addressed them. It shows that the moving of research data management to another institution is a feasible, but potentially costly enterprise. Being able to demonstrate the feasibility of research data migration supports the stance of data archives that users can expect high levels of trust and reliability when it comes to data safety and sustainability. © 2019, Springer Nature B.V.","Data migration; Data repositories; Research data management","","","","","","Deutsche Forschungsgemeinschaft, DFG, (75650358, 88614379, SFB 833); Bundesministerium für Bildung und Forschung, BMBF","This work has been supported by the German Research Foundation (DFG reference no. 88614379), and the SFB 833 data management project INF (DFG reference no. 75650358). The data centre cooperates closely with the CLARIN-D centre in Tübingen which is funded by the German Federal Ministry of Education and Research (BMBF). ","Dima E., Henrich V., Hinrichs E., Hinrichs M., Hoppermann C., Trippel T., Zastrow T., Zinn C., A Repository for the sustainable management of research data, Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), (2012); Dima E., Hoppermann C., Hinrichs E., Trippel T., Zinn C., A metadata editor to support the description of linguistic resources, Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), ELRA., (2012); Language resource management—Persistent identification and sustainable access (PISA), International Standard, (2011); Language resource management—Component Metadata infrastructure (CMDI)—Part 1: The component metadata model, International Standard, (2015); Kamocki P., Ketzan E., Creative Commons and Language Resources: General Issues and what’s New in CC 4.0. Tech. Rep., (2014); Lyse G.I., Meurer P., Smedt K.D., Comedi: A component metadata editor, Selected Papers from the CLARIN 2014 Conference, 116, 8, pp. 82-98, (2015); Trippel T., Zinn C., Enhancing the quality of metadata by using authority control, 5Th Workshop on Linked Data in Linguistic (LDL-2016) at LREC-2016., (2016); Wilkinson M.D., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, (2016); Zinn C., Trippel T., Kaminski S., Dima E., Crosswalking from CMDI to Dublin Core and MARC 21, Proceedings of the Tenth International Conference on Language Resources and Evaluation, (2016); [U1] the Dublin Core Metadata Initiative; [U2] the MARC 21 Standard; [U3] the EAD Standard; U5] the Handle System; U6] the Fedora Repository Platform; U7] Proai; [U8] the OAI-PMH Protocol; [U10] Docuteam Packer; [U13] Example of a Deposit Agreement (University of Reading, UK); [U16] the International Standard Name Identifier; [U18] the Schema.Org Vocabulary","C. Zinn; University of Tübingen, Tübingen, Wilhelmstrasse 19, 72074, Germany; email: claus.zinn@uni-tuebingen.de","","Springer Science and Business Media B.V.","","","","","","1574020X","","","","English","Lang. Resour. Eval.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85074033954"
"Darch P.T.; Sands A.E.; Borgman C.L.; Golshan M.S.","Darch, Peter T. (35766212500); Sands, Ashley E. (55206994400); Borgman, Christine L. (7006568281); Golshan, Milena S. (56735959200)","35766212500; 55206994400; 7006568281; 56735959200","Do the stars align?: Stakeholders and strategies in libraries' curation of an astronomy dataset","2021","Journal of the Association for Information Science and Technology","72","2","","239","252","13","3","10.1002/asi.24392","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088150227&doi=10.1002%2fasi.24392&partnerID=40&md5=0c3890cd2965dfba5be2e35c04375376","School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States; Department of Information Studies, University of California Los Angeles, Los Angeles, CA, United States","Darch P.T., School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States; Sands A.E., Department of Information Studies, University of California Los Angeles, Los Angeles, CA, United States; Borgman C.L., Department of Information Studies, University of California Los Angeles, Los Angeles, CA, United States; Golshan M.S., Department of Information Studies, University of California Los Angeles, Los Angeles, CA, United States","When developing university-based research data curation services, libraries face critical decisions around organization and sustainability that can affect dataset producers' satisfaction with these services. We present a study, involving interviews (n = 43) and ethnographic observation, of how two libraries partnered with the Sloan Digital Sky Survey (SDSS) to curate a significant astronomy dataset. Each library took different decisions: one library assigned activities to a unit specializing in digital curation, while the other distributed activities across its existing units. Neither approach proved a silver bullet. While library staff members felt the outcomes largely met their expectations, SDSS leaders expressed mixed opinions. We identify three factors that contributed to these differences in perspective: differing strategic motivations for undertaking this Data Transfer Process, SDSS leaders' misperceptions about libraries, and organizational mismatches. These factors contributed to four differences in perspective between SDSS leaders and library staff: provenance as technical information or as information about social context, dataset as a live research object or as a static object to be preserved, systems and services tailored to the dataset or easily adaptable to other datasets, and obstacles as setbacks or as opportunities. Only those differences that emerged when SDSS collaboration members and library staff communicated frequently were resolved. © 2020 Association for Information Science and Technology","","Data curation; Data transfer; Libraries; Digital curation; Ethnographic observations; Research object; Sloan Digital Sky Survey; Social context; Static objects; Technical information; Transfer process; article; astronomy; clinical article; expectation; human; interview; leadership; library; motivation; social environment; Digital libraries","","","","","Bernadette Boscoe; National Science Foundation, NSF; Directorate for Computer and Information Science and Engineering, CISE, (1145888); Alfred P. Sloan Foundation, (20113194, 201514001)","Funding text 1: Funding from the National Science Foundation (award # 1145888) and the Alfred P. Sloan Foundation (awards #20113194, #201514001) supported this research. The UCLA Institutional Review Board, Study Protocol ID# 10-000909, approved this study. We thank L Wynholds and David Fearon for conducting early interviews. We thank Bernadette Boscoe, Irene Pasquetto, Michael Scroggins, and Sharon Traweek for comments on drafts. We also thank Sharon Traweek for mentorship during the fieldwork. Finally, we are grateful to those people interviewed and observed.; Funding text 2: Many studies have addressed tensions that arise when building digital tools and services to support academic research in a variety of ways (Jirotka, Lee, & Olson, 2013 ). Early studies focused on projects funded by fixed‐term grants that aspire to become sustainable infrastructure (Spencer, Zimmerman, & Abramson, 2011 ). In these projects, software developers partner with domain researchers to build computational tools and services. e‐Science projects frequently face challenges similar to those of libraries, such as ensuring long‐term sustainability and facilitating collaboration between builders and prospective users of tools and services. Tensions that emerge between domain researchers and developers may compromise satisfaction with project outcomes (Ribes & Finholt, 2009 ). e‐Science ; Funding text 3: Alfred P. Sloan Foundation, Grant/Award Numbers: 20113194, 201514001; National Science Foundation, Grant/Award Number: 1145888 Funding information ","Aanestad M., Grisot M., Hanseth O., Vassilakopoulou P., Information infrastructures and the challenge of the installed base, Information infrastructures within European health care, pp. 25-33, (2017); Benson E., One infrastructure, many global visions: The commercialization and diversification of Argos, a satellite-based environmental surveillance system, Social Studies of Science, 42, 6, pp. 843-868, (2012); Bicarregui J., Gray N., Henderson R., Jones R., Lambert S., Matthews B., Data management and preservation planning for big science, International Journal of Digital Curation, 8, 1, pp. 29-41, (2013); Bietz M.J., Paine D., Lee C.P., (2013); Borgman C.L., Big data, little data, no data: Scholarship in the networked world, (2015); Borgman C.L., Sands A.E., Darch P.T., Golshan M.S., The durability and fragility of knowledge infrastructures: Lessons learned from astronomy, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-10, (2016); Borgman C.L., Scharnhorst A., Golshan M.S., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, Journal of the Association for Information Science and Technology, 70, 8, pp. 888-904, (2019); Borgman C.L., Wallis J.C., Mayernik M.S., Who's got the data? Interdependencies in science and technology collaborations, Computer Supported Cooperative Work (CSCW), 21, 6, pp. 485-523, (2012); Borne K., Virtual observatories, data mining, and astroinformatics, Planets, Stars and Stellar Systems, pp. 403-443, (2013); Britto M., Kinsley K., Academic libraries and the academy: Strategies and approaches to demonstrate your value, impact, and return on investment, (2018); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Darch P.T., Sands A.E., Borgman C.L., Golshan M.S., Library cultures of data curation: Adventures in astronomy, Journal of the Association for Information Science and Technology., (2020); Edwards P.N., A vast machine: Computer models, climate data, and the politics of global warming, (2013); Eschenfelder K.R., Shankar K., Williams R.D., Salo D., Zhang M., Langham A., A nine dimensional framework for digital cultural heritage organizational sustainability: A content analysis of the LIS literature (2000–2015), Online Information Review, 43, 2, pp. 182-196, (2019); Universities Push for Greater Global Open Access to Research Data, (2020); Gregory K., Groth P., Cousijn H., Scharnhorst A., Wyatt S., Searching data: A review of observational data retrieval practices in selected disciplines, Journal of the Association for Information Science and Technology, 70, 5, pp. 419-432, (2019); Grisot M., Vassilakopoulou P., Aanestad M., Dealing with tensions in technology enabled healthcare innovation: Two cases from the Norwegian healthcare sector, Controversies in healthcare innovation, pp. 109-132, (2018); Hammersley M., Atkinson P., Ethnography: Principles in practice, (2007); Higgins S., The DCC Curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Jirotka M., Lee C.P., Olson G.M., Supporting scientific collaboration: Methods, tools and concepts, Computer Supported Cooperative Work (CSCW), 22, 4-6, pp. 667-715, (2013); Jolak R., Wortmann A., Chaudron M., Rumpe B., Does distance still matter? Revisiting collaborative distributed software design, IEEE Software, 35, 6, pp. 40-47, (2018); Kee K.F., Browning L.D., The dialectical tensions in the funding infrastructure of Cyberinfrastructure, Computer Supported Cooperative Work (CSCW), 19, 3-4, pp. 283-308, (2010); Kitchin R., Collins S., Frost D., Funding models for open access digital data repositories, Online Information Review, 39, 5, pp. 664-681, (2015); Mills M., Plan S—What is its meaning for open access journals and for the JACMP ?, Journal of Applied Clinical Medical Physics, 20, 3, pp. 4-6, (2019); O'Donoghue T.A., Punch K., Qualitative educational research in action: Doing and reflecting, (2003); Olson G.M., Olson J.S., Distance Matters, Human–Computer Interaction, 15, 2-3, pp. 139-178, (2000); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Ribes D., Finholt T., The long now of technology infrastructure: Articulating tensions in development, Journal of the Association for Information Systems, 10, 5, pp. 375-398, (2009); Saad-Sulonen J., Eriksson E., Halskov K., Karasti H., Vines J., Unfolding participation over time: Temporal lenses in participatory design, CoDesign, 14, 1, pp. 4-16, (2018); Sands A.E., (2017); Segal J., Software development cultures and cooperation problems: A field study of the early stages of development of software for a scientific community, Computer Supported Cooperative Work (CSCW), 18, 5-6, pp. 581-606, (2009); Spencer D., Zimmerman A., Abramson D., Special theme: Project Management in E-science: Challenges and opportunities, Computer Supported Cooperative Work (CSCW), 20, 3, pp. 155-163, (2011); Star S.L., Ruhleder K., Steps toward an ecology of infrastructure: Design and access for large information spaces, Information Systems Research, 7, 1, pp. 111-134, (1996); Tenopir C., Allard S., Baird L., Sandusky R.J., Lundeen A., Hughes D., Pollock D., Academic librarians and research data services: Attitudes and practices, IT Lib: Information Technology and Libraries Journal, 1, pp. 24-37, (2019); Vinopal J., McCormick M., Supporting digital scholarship in research libraries: Scalability and sustainability, Journal of Library Administration, 53, 1, pp. 27-42, (2013)","P.T. Darch; School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, United States; email: ptdarch@illinois.edu","","John Wiley and Sons Inc","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Final","","Scopus","2-s2.0-85088150227"
"Tayler F.; Jafary M.","Tayler, Felicity (57220336465); Jafary, Maziar (57222669085)","57220336465; 57222669085","Shifting Horizons: A Literature Review of Research Data Management Train-the-Trainer Models for Library and Campus-Wide Research Support Staff in Canadian Institutions","2021","Evidence Based Library and Information Practice","16","1","","78","90","12","1","10.18438/eblip29814","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103699887&doi=10.18438%2feblip29814&partnerID=40&md5=6544ed1445fe5b300f5e9388339bc941","University of Ottawa, Ottawa, Ontario, Canada; School of Sociological and Anthropological Studies, University of Ottawa, Ottawa, Ontario, Canada","Tayler F., University of Ottawa, Ottawa, Ontario, Canada; Jafary M., School of Sociological and Anthropological Studies, University of Ottawa, Ottawa, Ontario, Canada","Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community? 2) What international training models and course offerings are available and appropriate for a local context? 3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice. © 2021. Taylor and Jafary. This is an Open Access article distributed under the terms of the Creative Commons- Attribution-Noncommercial-Share Alike License 4.0 International (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one.","","","","","","","","","(2020); Baker J., Moore C., Priego E., Alegre R., Cope J., Price L., Stephens O., Strien D. van, Wilson G., Library Carpentry: Software skills training for library professionals, LIBER Quarterly, 26, 3, pp. 141-162, (2016); Bryant R., Lavoie B., Malpas C., Sourcing and Scaling University RDM Services (The Realities of Research Data Management, Part 4), (2018); Clare C., Cruz M., Papadopoulou E., Savage J., Teperek M., Wang Y., Witkowska I., Yeomans J., Engaging researchers with data management: The cookbook, (2019); Cooper D., Springer R., Data communities: A new model for supporting STEM data sharing (Issue Brief), (2019); Fry J., Doiron J., Letourneau D., Perrier L., Perry C., Watkins W., Research data management training landscape in Canada: A white paper, Prepared by the Portage Training Expert Group on behalf of the Canadian Association of Research Libraries (CARL), (2017); Grootveld M. J., Verbakel E., Essentials for data support: Training the front office, International Journal of Digital Curation, 10, 1, pp. 240-248, (2015); Haddow L., Training subject librarians in RDM, (2014); Helbig K., Research data management training for geographers: First impressions, ISPRS International Journal of Geo-Information, 5, 4, pp. 1-9, (2016); Higman R., Bangert D., Jones S., Three camps, one destination: The intersections of research data management, FAIR and Open, Insights, 32, 1, pp. 1-9, (2019); Humphrey C., The CARL Portage partnership story, Partnership: The Canadian Journal of Library and Information Practice and Research, 15, 1, pp. 1-7, (2020); Papadopoulou E., Miller K., Dealing with Data’ Conference at University of Edinburgh, Engaging researchers with data management: The cookbook, pp. 70-72, (2019); Papadopoulou E., Grabauskiene R., DuoDi: The ‘Days of Data’ at Vilnius University, Engaging researchers with data management: The cookbook, pp. 74-76, (2019); Read K. B., Larson C., Gillespie C., Oh S. Y., Surkis A., A two-tiered curriculum to improve data management practices for researchers, PLoS ONE, 14, 5, pp. 1-14, (2019); Shipman J. P., Tang R., The collaborative creation of a Research Data Management Librarian Academy (RDMLA), Information Services & Use, 39, 3, pp. 243-247, (2019); Southall J., Scutt C., Training for research data management at the Bodleian Libraries: National contexts and local implementation for researchers and librarians, New Review of Academic Librarianship, 23, 2–3, pp. 303-322, (2017); Surkis A., Read K., Research data management, Journal of the Medical Library Association, 103, 3, pp. 154-156, (2015); Tang R., Hu Z., Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Vision for Research Data Management Training at TU Delft, (2019); Wenger-Trayner E., Wenger-Trayner B., Communities of practice: A brief introduction, (2015); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: Domain- based data management training for liaison librarians, The Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018)","F. Tayler; Research Data Management Librarian, University of Ottawa, Ottawa, Canada; email: ftayler@uottawa.ca","","University of Alberta","","","","","","1715720X","","","","English","Evid. Based Libr. Inf. Pract.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85103699887"
"Moller J.; Jankowski D.; Hahn A.","Moller, Julius (57221906757); Jankowski, Dennis (57367062200); Hahn, Axel (8648542100)","57221906757; 57367062200; 8648542100","Towards an Architecture to Support Data Access in Research Data Spaces","2021","Proceedings - 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science, IRI 2021","","","","310","317","7","1","10.1109/IRI51335.2021.00049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123457412&doi=10.1109%2fIRI51335.2021.00049&partnerID=40&md5=2b856909c2d8cf5b13893508dec816b7","Carl von Ossietzky University Oldenburg, Department of Computer Science, Oldenburg, Germany; Institute for Computer Science, RD Division Transportation OFFIS, Oldenburg, Germany","Moller J., Carl von Ossietzky University Oldenburg, Department of Computer Science, Oldenburg, Germany; Jankowski D., Institute for Computer Science, RD Division Transportation OFFIS, Oldenburg, Germany; Hahn A., Carl von Ossietzky University Oldenburg, Department of Computer Science, Oldenburg, Germany","Using data from different data sources is a common procedure in data-driven research. As required data is often not available from centrally managed sources, the concept of data spaces has more and more frequently been utilized to integrate decentral data sources and supporting the access to these. However, decentrally organized data management leads to differences in data models, formats and technologies and the problem of matching data from different sources most often still requires significant manual work, either from the provider or the consumer of the data. Especially in research data spaces, properties of data sources are often dynamically modified or extended, and complex queries are formulated to access the data. In this paper, we present an architecture to approach this problem by automatically exploring the different data sources and searching for similar attributes. Matching attributes are then assembled to so-called vocabularies, that represent common concepts in the data space independently from their actual representations. This dynamic knowledge base is then used to efficiently query and access data in the data space. Finally, an application of the concept is presented to demonstrate the applicability of our architectural framework.  © 2021 IEEE.","data architecture; data space access; data vocabulary; research data management","Architecture; Knowledge based systems; Data access; Data architectures; Data space; Data space access; Data vocabulary; Data-source; Matchings; Research data; Research data managements; Space access; Information management","","","","","","","Franklin M., Halevy A., Maier D., From databases to dataspaces: A new abstraction for information management, ACM Sigmod Record, 34, 4, pp. 27-33, (2005); Jayaraman P.P., Perera C., Georgakopoulos D., Dustdar S., Thakker D., Ranjan R., Analyticsasaservice in a multicloud environment through semanticallyenabled hierarchical data processing, Software: Practice and Experience, 47, 8, pp. 1139-1156, (2017); Miloslavskaya N., Tolstoy A., Big data, fast data and data lake concepts, Procedia Computer Science, 88, 300-305, (2016); Ning H., Wang T., Constructing a dataspace based on metadata and ontology for complicated scientific data management, 2007 2nd International Conference on Pervasive Computing and Applications, pp. 512-514, (2007); Curry E., Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems, (2020); Bader S., Al E., The International Data Spaces Information Model-An Ontology for Sovereign Exchange of Digital Content, The Semantic Web-ISWC 2020, 12507, pp. 176-192, (2020); Xiao G., Al E., Ontology-based data access: A survey, IJCAI Organization, (2018); Pullmann J., Petersen N., Mader C., Lohmann S., Kemeny Z., Ontology-based Information Modelling in the Industrial Data Space, pp. 1-8, (2017); Nadal S., Rabbani K., Romero O., Tadesse S., ODIN: A Dataspace Management System, (2019); Dimou A., Vander Sande M., Colpaert P., Verborgh R., Mannens E., Vande Walle R., RML: A Generic Language for Integrated RDF Mappings of Heterogeneous Data, 1184, (2014); Curry E., Fundamentals of Real-time Linked Dataspaces, Real-time Linked Dataspaces, Enabling Data Ecosystems for Intelligent Systems, pp. 63-80, (2019); Elsayed I., Brezany P., Towards Large-Scale Scientific Dataspaces for e-Science Applications, Database Systems for Advanced Applications, Berlin, Heidelberg, pp. 69-80, (2010); Freitas A., O'Riain S., Curry E., Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces, Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems, pp. 105-124, (2020); Rahm E., Bernstein P.A., A survey of approaches to automatic schema matching, The VLDB Journal, 10, 4, pp. 334-350, (2001); Ning H., Wang T., Constructing a Dataspace Based on Metadata and Ontology for Complicated Scientific Data Management, 2007 2nd International Conference on Pervasive Computing and Applications, pp. 512-514, (2007); Partescano E., Brosich A., Lipizer M., Cardin V., Giorgetti A., From heterogeneous marine sensors to sensor web: (Near) real-time open data access adopting OGC sensor web enablement standards, Open Geospatial Data, Software and Standards, 2, (2017); Fayyad U., Piatetsky-Shapiro G., Smyth P., From Data Mining to Knowledge Discovery in Databases, 17, pp. 37-54, (1996); Dong X., Halevy A., Indexing dataspaces, Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data-SIGMOD '07, Beijing, China, (2007); Werbrouck J., Senthilvel M., Beetz J., Pauwels P., Querying heterogeneous linked building data with context-expanded GraphQL queries, The Proc. of the 7th Linked Data in Arch Constr. Workshop, Portugal, (2019); Pradhan S., Towards an integrated framework for querying collection of heterogeneous data, Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication-ICUIMC '09, Suwon, Korea, (2009); Kontchakov R., Lutz C., Toman D., Wolter F., Zakharyaschev M., The Combined Approach to Ontology-based Data Access, (2011); Spanos D.-E., Stavrou P., Mitrou N., Bringing Relational Databases into the Semantic Web: A Survey, Semantic Web, 3, pp. 169-209, (2012); Bizer C., Cyganiak R., D2R Server-Publishing Relational Databases on the Semantic Web, International Semantic Web Conference ISWC, (2006); Curino C., Orsi G., Panigati E., Tanca L., Accessing and Documenting Relational Databases Through OWL Ontologies, (2009); Jimenez-Ruiz E., Al E., BootOX: Practical Mapping of RDBs to OWL 2, The Semantic Web-ISWC 2015, Cham, pp. 113-132, (2015); Corcho O., Priyatna F., Chaves-Fraga D., Towards a new generation of ontology based data access, SW, 11, 1, pp. 153-160, (2020); Janowicz K., Hitzler P., Adams B., Kolas D., Vardeman I.I.C., Five stars of Linked Data vocabulary use, Semantic Web, 5, 3, pp. 173-176, (2014); Agrawal S., Chaudhuri S., Das G., DBXplorer: A system for keyword-based search over relational databases, Proceedings 18th International Conference on Data Engineering, San Jose, CA, USA, pp. 5-16, (2002); Mami M.N., Graux D., Scerri S., Jabeen H., Auer S., Lehmann J., Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources, The Semantic Web-ISWC 2019, Cham, pp. 229-245, (2019); Bogatu A., Fernandes A., Paton N., Konstantinou N., Dataset Discovery in Data Lakes, 2020 IEEE 36th International Conference on Data Engineering (ICDE), (2020); Castro Fernandez R., Min J., Nava D., Madden S., Lazo: A Cardinality-Based Method for Coupled Estimation of Jaccard Similarity and Containment, 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1190-1201, (2019); Yang Y., Zhang Y., Zhang W., Huang Z., GB-KMV: An Augmented KMV Sketch for Approximate Containment Similarity Search, 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 458-469, (2019); Zhu E., Deng D., Nargesian F., Miller R.J., JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes, Proceedings of the 2019 International Conference on Management of Data, New York, NY, USA, pp. 847-864, (2019); Flores Herrera J.D.J., Nadal Francesch S., Romero Moral O., Towards scalable data discovery, Advances in Database Technology: EDBT 2021, 24th International Conference on Extending Database Technology: Nicosia, Cyprus, pp. 433-438, (2021); Curry E., Dataspaces: Fundamentals, Principles, and Techniques, Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems, pp. 45-62, (2020); Hassan U., Ojo A., Curry E., Catalog and Entity Management Service for Internet of Things-Based Smart Environments, Realtime Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems, pp. 89-103, (2020); Shvaiko P., Euzenat J., A survey of schema-based matching approaches, Journal on Data Semantics, 4, pp. 146-171, (2005); Sutanta E., Wardoyo R., Mustofa K., Winarko E., Survey: Models and Prototypes of Schema Matching, International Journal of Electrical & Computer Engineering (2088-8708), 6, 3, (2016); Cohen W., Ravikumar P., Fienberg S., A Comparison of String Metrics for Matching Names and Records, Proc of the KDD Workshop on Data Cleaning and Object Consolidation, (2003); Vazquez-Ingelmo A., Cruz-Benito J., Garcia-Penalvo F.J., Improving the OEEU's Data-driven Technological Ecosystem's Interoperability with GraphQL, (2017); Larsson M., Angstrom D., A Performance Comparison of Auto-Generated GraphQL Server Implementations, (2020); Sahay T., Mehta A., Jadon S., Schema Matching using Machine Learning, 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 359-366, (2020)","","","Institute of Electrical and Electronics Engineers Inc.","Society for Information Reuse and Integration (SIRI)","22nd IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2021","10 August 2021 through 12 August 2021","Virtual, Online","174291","","978-166543875-9","","","English","Proc. - IEEE Int. Conf. Inf. Reuse Integr. Data Sci., IRI","Conference paper","Final","","Scopus","2-s2.0-85123457412"
"Börjesson L.","Börjesson, Lisa (56585553600)","56585553600","Legacy in the Making - A Knowledge Infrastructural Perspective on Systems for Archeological Information Sharing","2021","Open Archaeology","7","1","","1636","1647","11","1","10.1515/opar-2020-0213","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121821527&doi=10.1515%2fopar-2020-0213&partnerID=40&md5=1ee5fabd1dd1bde5a68be236addfcc69","Department of ALM, Uppsala University, Box 625, Uppsala, SE-751 26, Sweden","Börjesson L., Department of ALM, Uppsala University, Box 625, Uppsala, SE-751 26, Sweden","Archeological research depends on a complex infrastructure of information systems and services built on different funding models. The information systems enable innovative approaches and progress in information making, but each system also organizes information by means of the system design, the structures, and relations established and the terminologies promoted. This article adopts a knowledge infrastructural perspective on systems used for information sharing in archeology. The purpose is first to expand the perspective on the systems for research information sharing in archeology and second to discuss the potential impact of the knowledge infrastructure on disciplinary knowledge-making, the shaping of archeological information, and legacy data. Based on an analysis of qualitative interviews (N = 31) with archeologists from Europe and the United States, the results show that the interviewees use sharing solutions developed within the archeology discipline as well as general information sharing systems. One important task for further research is to better understand how archeologists choose information sharing systems and how their choices impact what information they share. Also, information sharing for specific topics or with specific coverage appears to be developed with project funding outside of the more established sharing institutions. A key question for the infrastructural sustainability is how to support the inclusion of innovative sharing solutions in institutionalized sharing environments. The results emphasize the need for further studies of how information systems shape archeological legacy in the making, which in turn will support data literacy awareness and training.  © 2021 Lisa Börjesson, published by De Gruyter.","archeological data; archeological management; information sharing; information systems; knowledge infrastructures; research data management","","","","","","Horizon 2020 Framework Programme, H2020, (818210); European Research Council, ERC; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO","Funding text 1: The UK Portable Antiquities Scheme and the Dutch Portable Antiquities of the Netherlands are examples of data sharing solutions specifically for objects found by the public and in private collections that cover the same types of finds in different national contexts but that build on different funding solutions. While the UK scheme is hosted by a national museum, the Dutch version is primarily funded by the Dutch organization for Scientific Research (NWO). ; Funding text 2: In addition to the archeology and nondisciplinary-specific but national sharing systems mentioned earlier, the interview data reveal the usage of a range of different nondisciplinary-specific general sharing solutions used not only for sharing code and software but also for data sharing and sharing of presentations, papers, and video recordings from fieldwork. While these systems vary from being commercial solutions like GitHub, Figshare, and YouTube, other systems like Arxiv, Zenodo, and Dataverse are in different ways hosted by university institutions and funded by research infrastructures or research grants. The Open Science Foundation’s funding is a hybrid solution of public and commercial funding. Common to all these solutions is that use is free of charge at the entry level. ","Allison P., Dealing with legacy data - An introduction, Internet Archaeology, 24, (2008); Aloia N., Binding C., Cuy S., Doerr M., Fanini B., Felicetti A., Wright H., Enabling European archaeological research: The ARIADNE E-infrastructure, Internet Archaeology, 43, (2017); Bibby D., Free and open source software development in archaeology. Two interrelated case studies: GvSIG CE and survey2gis, Internet Archaeology, 43, (2017); Bolen M.C., Calisir H., Ozen U., Flow theory in the information systems life cycle: The state of the art and future research agenda, International Journal of Consumer Studies, 45, 4, pp. 546-580, (2021); Borgman C.L., Darch P.T., Sands A.S., Golshan M.S., The durability and fragility of knowledge infrastructures: Lessons learned from astronomy, Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology, 57, pp. 1-57, (2016); Borgman C.L., Scharnhorst A., Golshan M.S., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, Journal of the Association for Information Science and Technology, 70, 8, pp. 888-904, (2019); Dell'Unto N., Landeschi G., Apel J., Poggi G., 4D recording at the Trowel's edge: Using three-dimensional simulation platforms to support field interpretation, Journal of Archaeological Science: Reports, 12, pp. 632-645, (2017); Geels F.W., Schot J., Typology of sociotechnical transition pathways, Research Policy, 36, 3, pp. 399-417, (2007); Geser G., D2.1 Initial Report on Community Needs, (2019); Gorichanaz T., Information creation and models of information behavior: Grounding synthesis and further research, Journal of Librarianship and Information Science, 51, 4, pp. 998-1006, (2019); Gupta N., Blair S., Nicholas R., What we see, what we don't see: Data Governance, archaeological spatial databases and the rights of indigenous peoples in an age of big data, Journal of Field Archaeology, 45, pp. S39-S50, (2020); Huvila I., The Ecology of Information Work: A Case Study of Bridging Archaeological Work and Virtual Reality Based Knowledge Organisation, (2006); Kansa E., Kansa S.W., Digital data and data literacy in archaeology now and in the new decade, Advances in Archaeological Practice, 9, 1, pp. 81-85, (2021); Lucas G., Understanding the Archaeological Record, (2012); Petrides A.K., Tanasijevic M.J., Goonan E.M., Landman A.B., Kantartjis M., Bates D.W., Melanson S.E.F., Top ten challenges when interfacing a laboratory information system to an electronic health record: Experience at a large academic medical center, International Journal of Medical Informatics, 106, pp. 9-16, (2017); Pilerot O., LIS research on information sharing activities - People, places, or information, Journal of Documentation, 68, 4, pp. 559-581, (2012); Robinson O.C., Sampling in interview-based qualitative research: A theoretical and practical guide, Qualitative Research in Psychology, 11, 1, pp. 25-41, (2014); Smiraglia R.P., Smiraglia R.P., Classification: Bringing order with concepts, The Elements of Knowledge Organization, pp. 57-64, (2014); Swanson E.B., Information systems, Encyclopedia of Library and Information Science, (2018); Wright H., Richards J.D., Reflections on collaborative archaeology and large-scale online research infrastructures, Journal of Field Archaeology, 43, pp. S60-S67, (2018)","L. Börjesson; Department of ALM, Uppsala University, Uppsala, Box 625, SE-751 26, Sweden; email: Lisa.borjesson@abm.uu.se","","De Gruyter Open Ltd","","","","","","23006560","","","","English","Open Archaeol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85121821527"
"Kim J.","Kim, Jeonghyun (37106972000)","37106972000","Determining research data services maturity: The role of library leadership and stakeholder involvement","2021","Library and Information Science Research","43","2","101092","","","","5","10.1016/j.lisr.2021.101092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107092007&doi=10.1016%2fj.lisr.2021.101092&partnerID=40&md5=e65a95c987eae7a7f0efca619797721f","Department of Information Science, University of North Texas, 1155 Union Circle #311068, Denton, 76203-5017, TX, United States","Kim J., Department of Information Science, University of North Texas, 1155 Union Circle #311068, Denton, 76203-5017, TX, United States","As technology and the stewardship of research data continue to improve, academic libraries have made progress in establishing themselves as hubs and leaders for research data services on campus. They have been called on to assure cross-campus collaboration and support to develop a united service to meet their community's needs. However, evidence as to how libraries play a crucial role in leadership, whether other stakeholders' involvement makes a difference, and if so, how that involvement makes a difference is not well-documented. The findings of this study add empirical evidence in supporting the value of library's leadership and stakeholder engagement in developing research data policy and services. The secondary analysis of the survey data found that libraries tend to offer more extensive services when they take primary responsibility for developing the policy and service. It also found that the more internal stakeholders involved in developing the policy and service, the higher level of service maturity the libraries offer. Partnership with external stakeholders leads to more comprehensive and deeper services. © 2021","Academic library; Research data; Research data management; Research data services; Stakeholder","","","","","","","","ACRL Research Planning and Review Committee, 2020 top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 81, (2020); Ayuso S., Angel Rodriguez M., Garcia-Castro R., Arino M.A., Does stakeholder engagement promote sustainable innovation orientation?, Industrial Management & Data Systems, 111, pp. 1399-1417, (2011); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, LIBER Quarterly, 24, pp. 17-48, (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, pp. 2182-2200, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, pp. 1432-1462, (2019); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Progress in research data services: An international survey of university libraries, International Journal of Digital Curation, 14, 1, (2019); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., International survey of research data management in libraries [Data file], (2019); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, pp. 3-17, (2014); Cox J., Positioning the academic library within the institution: A literature review, New Review of Academic Librarianship, 24, pp. 217-241, (2018); Erway R., Starting the conversation: University-wide research data management policy, (2013); Faniel I.M., Connaway L.S., Librarians’ perspectives on the factors influencing research data management programs, College & Research Libraries, 79, pp. 100-119, (2018); Fearon D., Gunia B., Lake S., Pralle B.E., Sallans A.L., Research data management services: SPEC kit 334, (2013); Flores J.R., Brodeur J.R., Daniels M.G., Nichools N., Turnator E., Libraries and the research data management landscape, The process of discovery: The CLIR Postdoctoral Fellowship Program and the future of the academy, 2015, pp. 82-102, (2015); Freeman E., Strategic management: A stakeholder approach, (1984); Harland F., Steward G., Bruce C., Aligning library and university strategic directions: A constructivist grounded theory study of academic library leadership in Australia and the USA, New Review of Academic Librarianship, 24, pp. 263-285, (2018); Hofelich Mohr A., Johnston L.R., Lindsay T.A., The data management village: Collaboration among research support providers in the large academic environment, Databrarianship: The academic data librarian in theory and practice, (2016); Ippoliti C., Koshoffer A., Julian R., Vandegrift M., Soper D., Meridien S., Scaling research data management services along the maturity spectrum: Three institutional perspectives, (2018); Jones T.M., Instrumental stakeholder theory: A synthesis of ethics and economics, Academy of Management Review, 20, pp. 404-437, (1995); Kouper I., Fear K., Ishida M., Kollen C., Williams S.C., Research data services maturity in academic libraries, Curating research data, volume one: Practical strategies for your digital repository, pp. 153-170, (2017); Lang L., Wilson T., Wilson K., Kirkpatrick A., Research support at the crossroads: Capability, capacity, and collaboration, New Review of Academic Librarianship, 24, pp. 326-336, (2018); Luesebrink M., Huang H., Bogdan K., Salo D., Thomas C., West J., Wilson L., Curation and policy issues in collaborative research data management communities: Perspectives from key stakeholders, Proceedings of the American Society for Information Science and Technology, 51, pp. 1-4, (2014); Mush G.E., Pienaar H., van Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, 4, (2020); Perrier L., Blondal E., MacDonald H., Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies, Library and Information Science Research, 40, pp. 173-183, (2018); Pillai M., Locating learning development in a university library: Promoting effective academic help seeking, New Review of Academic Librarianship, 16, pp. 121-144, (2010); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influence, PLoSOne, 9, (2014); Qin J., Crowston K., Flynn C., Kirkland A., Pursuing best performance in research data management by using the capability maturity model and rubrics, Journal of eScience Librarianship, 6, 2, (2017); Tang R., Hu Z., Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, (2019); Tenopir C., Kaufman J., Sandusky R., Pollock D., Research data services in academic libraries: Where are we today?, (2019); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, pp. 23-44, (2017); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, The Journal of Academic Librarianship, 40, pp. 211-219, (2014); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, pp. 89-97, (2017); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College & Research Libraries, 78, pp. 920-933, (2017); Yu F., Deuble R., Morgan H., Designing research data management services based on the research lifecycle – A consultative leadership approach, Journal of the Australian Library and Information Association, 66, pp. 287-298, (2017); Yu H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, The Electronic Library, 35, pp. 783-797, (2017)","J. Kim; Department of Information Science, University of North Texas, Denton, 1155 Union Circle #311068, 76203-5017, United States; email: Jeonghyun.Kim@unt.edu","","Elsevier Ltd","","","","","","07408188","","LISRD","","English","Libr. Inf. Sci. Res.","Article","Final","","Scopus","2-s2.0-85107092007"
"Hansen J.S.; Gadegaard S.; Hansen K.K.; Larsen A.V.; Møller S.; Thomsen G.S.; Holmstrand K.F.","Hansen, Jitka Stilund (7404334819); Gadegaard, Signe (57233643500); Hansen, Karsten Kryger (57233395200); Larsen, Asger Væring (57191164653); Møller, Søren (56253617700); Thomsen, Gertrud Stougård (57234629000); Holmstrand, Katrine Flindt (57234130400)","7404334819; 57233643500; 57233395200; 57191164653; 56253617700; 57234629000; 57234130400","Research data management challenges in citizen science projects and recommendations for library support services. A scoping review and case study","2021","Data Science Journal","20","1","","1","29","28","3","10.5334/dsj-2021-025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113678502&doi=10.5334%2fdsj-2021-025&partnerID=40&md5=ab57072d08345104dce2df00ce69ae20","DTU Library, Technical University of Denmark, Denmark; Aalborg University, Denmark; University Library of Southern Denmark, Denmark; Roskilde University Library, Denmark; Aarhus University Library, Royal Danish Library, Denmark","Hansen J.S., DTU Library, Technical University of Denmark, Denmark; Gadegaard S., DTU Library, Technical University of Denmark, Denmark; Hansen K.K., Aalborg University, Denmark; Larsen A.V., University Library of Southern Denmark, Denmark; Møller S., Roskilde University Library, Denmark; Thomsen G.S., Aarhus University Library, Royal Danish Library, Denmark; Holmstrand K.F., DTU Library, Technical University of Denmark, Denmark","Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries. However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges. A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects. Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects. Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises. Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists. © 2021 The Author(s).","Citizen science; FAIR principles; Research data management; University library","Data privacy; Libraries; Managers; Population statistics; Academic projects; Contextualisation; Data aggregation; National practices; Project managers; Research data managements; Systematic research; University libraries; Information management","","","","","Danmarks Elektroniske Fag-og Forskningsbibliotek; Danmarks Elektroniske Fagog Forskningsbibliotek; Rural Development Administration, RDA; Horizon 2020, (777388)","Funding text 1: Research integrity could be compromised in CS projects, where data collectors or project initiators are aiming to address a community-issue of particular concern. Projects may also be funded by organisations or corporate funds with e.g. lobbying, legal or political interests. Both financial and non-financial conflicts of interest should be addressed in the project, both in the beginning and when publishing data and results. Disclosure of conflict of interest could be performed individually or as a group.; Funding text 2: This article is part of a project funded by Danmarks Elektroniske Fagog Forskningsbibliotek. The Danish RDA Node supported this article through a grant from RDA Europe 4.0 to establish national nodes and promote the work of RDA. The EU Horizon 2020 research and innovation programme funded RDA Europe 4.0 (Grant Agreement no. 777388).","Adriaens T, Sutton-Croft M, Owen K, Brosens D, Van Valkenburg J, Kilbey D, Groom Q, Ehmig C, Thurkow F, Van Hende P, Schneider K., Trying to engage the crowd in recording invasive alien species in Europe: experiences from two smartphone applications in northwest Europe, Management of Biological Invasions, 6, 2, pp. 215-225, (2015); Anhalt-Depies C, Stenglein JL, Zuckerberg B, Townsend PA, Rissman AR., Tradeoffs and tools for data quality, privacy, transparency, and trust in citizen science, Biological Conservation, 238, (2019); August T, Harvey M, Lightfoot P, Kilbey D, Papadopoulos T, Jepson P., Emerging technologies for biological recording, Biological Journal of the Linnean Society, 115, 3, pp. 731-749, (2015); Bastin L, Schade S, Schill C., Data and Metadata Management for Better VGI Reusability, Mapping and the Citizen Sensor, pp. 249-272, (2017); What is BASE?; Bonn A, Richter A, Vohland K, Pettibone L, Brandt M, Feldmann R, Goebel C, Grefe C, Hecker S, Hennen L, Hofer H, Kiefer S, Klotz S, Kluttig T, Krause J, Kusel K, Liedtke C, Mahla A, Neumeier V, Premke-Kraus M, Rillig MC, Roller O, Schaffer L, Schmalzbauer B, Schneidewind U, Schumann A, Settele J, Tochtermann K, Tockner K, Vogel J, Volkmann W, von Unger H, Walter D, Weisskopf M, Wirth C, Witt TDW, Ziegler D., Green Paper Citizen Science Strategy 2020 for Germany, (2016); 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A Specialization Approach, International journal of spatial data infrastructures research, 13, pp. 38-47, (2018); Skov C., Database from citizen science project “Fangstjournalen, (2021); Sturm U, Schade S, Ceccaroni L, Gold M, Kyba C, Claramunt B, Haklay M, Kasperowski D, Albert A, Piera J, Brier J, Kullenberg C, Luna S., Defining principles for mobile apps and platforms development in citizen science, Research Ideas and Outcomes, 4, (2018); Syberg K., Data for Mass Experiment [Data set], (2020); Tauginiene L., Ethical concerns in citizen science projects and public engagement related research projects, Ethical Perspectives, 26, 1, pp. 119-134, (2019); Tweddle JC, Robinson LD, Pocock MJ, Roy HE, Guide to citizen science: developing, implementing and evaluating citizen science to study biodiversity and the environment in the UK, (2012); The principles of planning, collecting and using citizen science data, (2013); GitHub, (2019); Handbook for Citizen Science Quality Assurance and Documention-Version 1, (2019); Manage Your Data; Venturelli PA, Hyder K, Skov C., Angler apps as a source of recreational fisheries data: opportunities, challenges and proposed standards, Fish and Fisheries, 18, 3, pp. 578-595, (2017); Wahlberg M., Harbour porpoises sightings 2019 [Dataset], (2020); Wang Y, Kaplan N, Newman G, Scarpino R., CitSci.org: A New Model for Managing, Documenting, and Sharing Citizen Science Data, PLOS Biology, 13, 10, (2015); Ward-Fear G, Pauly GB, Vendetti JE, Shine R., Authorship Protocols Must Change to Credit Citizen Scientists, Trends in Ecology & Evolution, 35, 3, pp. 187-190, (2020); Wiggins A, Bonney R, Graham E, Henderson S, Kelling S, Littauer R, Lebuhn G, Lotts G, Michener W, Newman G, Russel E, Stevenson R, Weltzin J., DataOne, (2013); Wiggins A, Wilbanks J., The Rise of Citizen Science in Health and Biomedical Research, The American Journal of Bioethics, 19, 8, pp. 3-14, (2019); Wilkinson MD, Dumontier M, Aalbersberg Ij J, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, 't Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Williams J, Chapman C, Leibovici DG, Lois G, Matheus A, Oggioni A, Schade S, See L, van Genuchten PPL., Maximising the impact and reuse of citizen science data, Citizen Science – Innovation in Open Science, Society and Policy, pp. 321-336, (2018); Wolf M, Trejos G, Hoeberechts M, Flagg R, Jenkyns R, Morley M, Biffard B, Kot M, Hogman N, Tomlin M., Best Practices in Data Management at Ocean Networks Canada: a Citizen Scientist case study, OCEANS 2019 MTS/IEEE SEATTLE, pp. 1-6, (2019)","J.S. Hansen; DTU Library, Technical University of Denmark, Denmark; email: jstha@dtu.dk","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85113678502"
"Barfi F.K.; Sackey E.K.-A.","Barfi, Faustina Kyerewaa (57225189574); Sackey, Emmanuel Kofi-Agyir (57779400500)","57225189574; 57779400500","TOPIC: The Role of the Technical Universities' Librarians in the Generation and Management of Technical Research Data (TRD) to Advance Inventions, Innovation and Commercialization in Ghana.","2021","Library Philosophy and Practice","2021","","","1","20","19","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109442138&partnerID=40&md5=9ecd06493e6b6ab79a522fd8954933a2","Ghana Institute of Journalism-Accra, Ghana","Barfi F.K., Ghana Institute of Journalism-Accra, Ghana; Sackey E.K.-A.","The act of the Technical Universities (TU) in Ghana mandates publications which promote invention and innovations. The study examined the role of librarians in the generation and management of technical research data to promote invention, innovation and commercialisation. This is an exploration study which adopted quantitative approach to present its findings. All the librarians from the Ten (10) Technical Universities became the resultant population. Questionnaire was used to collect the data. Emails and mobile app on monkey survey were used to reach respondents. Majority of the Librarians attested that management of technical research data play a key role in advancing invention and innovations. The study discovered some of the sources and varying formats of such data to include ‘'workshop report, laboratory recording and discoveries, prototypes from engineering practical centres, speeches, patterns, amongst others. Data formats encompassed manuscript, photography, interviews, videos, audios and artefacts. The study revealed minimal integration of Technical Research Data (TRD) management in the research strategic objectives of the selected Technical Universities in Ghana. Majority of the respondents (70%) indicated inadequate infrastructure and resources needed to generate and store such data. Inadequate expertise recorded (60%). Lack of policies on research data recorded (40%), poor collaboration (30%), and inadequate funding for training and logistics (20%). ' ' In addressing the identified challenges, provision of infrastructure and resources represented (38%). Funding (31%), capacity building on the part of the librarians represented (12%). Deepening collaboration on research data with stakeholders recorded (13%). Further consideration were the establishment of a centralised repository on technical research data among the Technical Universities instead of working in silos. Findings from the study also revealed the need to revamp the curriculum of the Library and Information science schools on emerging fields. Again the librarians are to allocate resources, services and infrastructure which distinctively support research, teaching, and training. © 2021. All Rights Reserved.","Invention and Innovation; Librarians role in research; Research Data Management; Technical Research Data; Technical Universities","","","","","","","","Abduldayan F.J., Abifarin F.P., Oyedum G.U., Alhassan J.A., Research data management practices of chemistry researchers in Federal Universities of Technology in Nigeria, Digital Library Perspectives, (2021); Adei S., Enhancing the Development of Ghana through Technical and Vocational Education Training (TVET): The Role of Technical Universities, (2018); The importance of TVET and its contribution to sustainable development Conference Proceedings, (2017); What Is Community Engagement, (2021); Avuglah B. K., Underwood P., Research Data Management (RDM) Capabilities at the University of Ghana, Legon, Library Philosophy and Practice (e-journal), (2019); Barfi F.K., Alemna A.A., Adjei E., Mends-Brew E., Contemporary roles of librarians in navigating and addressing academic research ethical dilemmas: Technical Universities in perspective, (2018); Botolf M., Roger S., The Importance of Tacit Knowledge: Dynamic Inventor Activity in the Commercialization Phase, Research Policy, 49, 1, (2020); Chawinga W, Zinn S., Research data management at a Public University in Malawi: the role of “three hands, Library Management, 41, 6, pp. 467-485, (2020); Chiware E.T., Data librarianship in South African academic and research libraries: a survey, Library Management, 41, 6, pp. 401-416, (2020); Calarcob P., Kuchmac I, Shearerd K., Time to Adopt: Librarians' New Skills and Competency, (2016); Bayh-Dole Act, (2021); Dwomoh G., Delivering the mandate of technical universities, (2016); Ei FL, Digital Research Literacy Training Programme Outline for Librarians, (2020); Fosci M., Loffreda L., Chamberlain A., Naidoo N., Assessing the needs of the research system in Ghana, (2019); Flores J. R., Brodeur J. J., Daniels M. G., Nicholls N., Turnator E., Libraries and the research data management landscape, The process of discovery: The CLIR postdoctoral fellowship program and the future of the academy, pp. 82-102, (2015); This Ghanaian makes amazing artworks using eggshells, (2020); Glusker A., Exner N., Responding to Change: Reinventing Librarian Identities in the Age of Research Mandates, Challenging the “Jacks of All Trades but Masters of None” Librarian Syndrome (Advances in Library Administration and Organization, 39, pp. 91-115, (2018); Koforidua Poly designs fufu pounding machine, (2011); Honyenuga B., (2020); Min Jae-Woong, Vonortas Nicholas S., Kime YoungJun, Commercialization of transferred public technologies, Technological Forecasting & Social Change, 138, 1, pp. 10-20, (2019); Jae-Woong M., Kimb Y., Vonortas N.S, Public technology transfer, commercialization and business growth, Technological Forecasting & Social Change, 138, (2019); Jayaram N., Whither Innovations in Higher Education in India: In ‘'The Forefront of International Higher Education, (2013); Johnsson M.,  Plenary Meeting, (2019); Kenney, Patton, Reconsidering the Bayh-Dole Act and the Current University Invention Ownership Model, Research Policy, 38, pp. 1407-1422, (2009); Menna A, Walsh P., Assessing environments of commercialization of innovation for SMEs in the global wine industry: A market dynamics approach Wine Economics and Policy, 8, (2019); Definition of Research, (1828); National Policy Public -Private Partnership, (2020); National Science, Technology and Innovation Policy, (2017); Process for conversion of Polytechnics to Technical Universities, (2016); Research Process, (2021); Obeng E.A., Ghana Beyond Aid to crash without Technical University funding, (2019); Pries F, Guild P., Commercializing inventions resulting from university research: Analyzing the impact of technology characteristics on subsequent business models, Technovation, 31, pp. 151-160, (2011); Opoku E., Takoradi Technical University Student Invents Water Bicycle, (2018); Qin J., Infrastructure, Standards, and Policies for Research Data Management, In: Sharing of Scientific and Technical Resources in the Era of Big Data: The Proceedings of COINFO 2013, pp. 214-219, (2013); Quaye W., Akon-Yamga G., Daniels C., Ting B., Adelaide Asante A., Mapping of Science, Technology and Innovation Policy Development in Ghana Using the Transformative Change Lens, (2019); Sampat B., The Bayh-Dole Model in Developing Countries: Reflections on the Indian Bill on Publicly Funded Intellectual Property: UNCTAD - ICTSD Project on IPRs and Sustainable Development, 5, (2009); Samantha S., Wolski M., Simons N., Richardson J, Librarians as partners in research data service development at Griffith University, Research data management information systems, 49, 4, pp. 440-460, (2015); Shadik S., Promises and Pitfalls of Public Private Partnerships (PPPs) Projects in Bangladesh: Whether this Dimension is to flourish or to be relinquished?, (2019); Smyth N., Strategic Engagement and Librarians, 'The Academic Book of the Future“, (2016); Joo S., Schmidt G.M., Research data services from the perspective of academic librarians, Digital Library Perspectives, (2021); Technology Research Explained See discussions, (2007); Eshun F., Takoradi Technical University Convocation: Ghana's Future and Economic Growth: The Role of the Technical Universities, (2019); Research Data Management (RDM) life Cycle, (2020); Ghana's Science, Technology and Innovation Policy - Ghana united nations conference on trade and development, (2011); Wang M., Supporting the research process through expanded library data services, Program: electronic library and information systems, 47, 3, pp. 282-303, (2013); Wang M., Supporting the research process through expanded library data services electronic library and information systems, 47, 3, pp. 282-303, (2013); Zhang L., Huang Y., Yan Q, Wang X., Research on commercial potential evaluation of newly and emerging technology: a case study of Grapheme, 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) IEEE, (2016)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85109442138"
"Borycz J.","Borycz, Joshua (57207788260)","57207788260","Implementing data management workflows in research groups through integrated library consultancy","2021","Data Science Journal","20","1","9","1","9","8","2","10.5334/DSJ-2021-009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102773704&doi=10.5334%2fDSJ-2021-009&partnerID=40&md5=9cfff916dd45594d85315ad98862a45a","Vanderbilt University, United States","Borycz J., Vanderbilt University, United States","Comprehensive research data management is fundamental to ensuring reproducible, open scientific research. However, sufficient research data assistance is often not offered at universities, and when offered, typically only provides basic services that are viewed as optional. Integrating information specialists into research groups provides a potentially promising means of improving data management by providing personalized data management workflows. Workflows are comprehensive, executable guides that require planning, implementation, feedback, and adaptation. Comprehensive data management workflows should include a file organization scheme, the creation of data management roles for members, a data storage/sharing guide, and training and evaluation. Librarians, who regularly interact with faculty and students and are familiar with data management tools, are uniquely situated to assist with the creation and assessment of these workflows. © 2021 The Author(s).","Coding; Consultant; Data management; Git; Global challenges; Incentives; Library; Open data; Science; Training","Digital storage; Personnel training; Comprehensive research; Data management tools; Data storage; Integrating information; Research data; Research groups; Scientific researches; Work-flows; Information management","","","","","","","Akers KG, Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Borer ET, Seabloom EW, Jones MB, Schildhauer M., Some Simple Guidelines for Effective Data Management, Bulletin of the Ecological Society of America, 90, 2, pp. 205-214, (2009); Borycz J, Carroll B., Managing Digital Research Objects in an Expanding Science Ecosystem: 2017 Conference Summary Digital Objects – The Core of Our Complex Data Market, Data Science Journal, (2018); Borycz J, Carroll B., Implementing FAIR data for people and machines: Impacts and implications – results of a research data community workshop, Information Services & Use, 1, pp. 1-15, (2020); Brocke J, Lippe S., Managing collaborative research projects: A synthesis of project management literature and directives for future research, International Journal of Project Management, 33, 5, pp. 1022-1039, (2015); Burnette M, Williams S, Imker H., From Plan to Action: Successful Data Management Plan Implementation in a Multidisciplinary Project, Journal of EScience Librarianship, 5, 1, (2016); Carroll AJ, Eskridge HN, Chang BP., Lab-integrated librarians: A model for research engagement, College and Research Libraries, 81, 1, pp. 8-26, (2020); The Open Science Framework, (2020); Cox AM, Kennan MA, Lyon L, Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox AM, Kennan MA, Lyon L, Pinfield S, Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Curdt C., Supporting the Interdisciplinary, Long-Term Research Project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services, Data Science Journal, 18, 1, pp. 1-9, (2019); Curdt C, Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DB project database, Program, 49, 4, pp. 494-512, (2015); Curty RG, Crowston K, Specht A, Grant BW, Dalton ED., Attitudes and norms affecting scientists’ data reuse, PLoS ONE, 12, 12, pp. 1-22, (2017); Divine P., Case Styles: Camel, Pascal, Snake, and Kebab Case, (2018); Dorch SBF, Drachen TM, Ellegaard O., The data sharing advantage in astrophysics, Proceedings of the International Astronomical Union, 11, A29A, pp. 172-175, (2015); GitHub Desktop, (2020); Henneken EA, Accomazzi A., Linking to Data – Effect on Citation Rates in Astronomy, (2011); Hothorn T, Leisch F., Case studies in reproducibility, Briefings in Bioinformatics, 12, 3, pp. 288-300, (2011); Jones S., The range and components of RDM infrastructure and services, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 89-114, (2013); Kim J, Schuler ER, Pechenina A., Predictors of Data Sharing and Reuse Behavior in Academic Communities, Knowledge Discovery and Data Design Innovation, 14, pp. 1-25, (2018); Kim Y, Yoon A., Scientists’ Data Reuse Behaviors: A Multilevel Analysis, Journal of the Association for Information Science and Technology, 68, 12, pp. 2709-2719, (2017); Kollen C, Kouper I, Ishida M, Williams S, Fear K, Kouper I, Williams SC, Et al., Research Data Services Maturity in Academic Libraries, Curating Your Reserch Data: Practical Strategies for Your Digital Repository, pp. 153-170, (2017); Lage K, Losoff B, Maness J., Receptivity to library involvement in scientific data curation: A case study at the University of Colorado Boulder, Portal, 11, 4, pp. 915-937, (2011); Li X, Prasad C., Effectively teaching coding standards in programming, Proceedings of the 6th Conference on Information Technology Education, SIGITE, 2005, pp. 239-244, (2005); Organize your files, (2018); Mons B., Invest 5% of research funds in ensuring data are reusable, Nature, 578, (2020); Murray M, O'Donnell M, Laufersweiler M, Novak J, Rozum B, Thompson S., A survey of the state of research data services in 35 U.S. academic libraries, or “Wow, what a sweeping question, Research Ideas and Outcomes, 5, (2019); NOAA National Weather Service NWS/OHD General Software Coding Standards and Guidelines General Software Development Standards and Guidelines Version 3.5, (2007); Noble WS., A Quick Guide to Organizing Computational Biology Projects, PLoS Computational Biology, 5, 7, (2009); Ogier A, Brown A, Petters J, Hilal A, Porter N., Enhancing collaboration across the research ecosystem: Using libraries as hubs for discipline-specific data experts, ACM International Conference Proceeding Series, pp. 1-6, (2018); OpenRefine, (2020); Petters JL, Brooks GC, Smith JA, Haas CA., The Impact of Targeted Data Management Training for Field Research Projects – A Case Study, Data Science Journal, 18, 1, pp. 1-7, (2019); Pisani E, Aaby P, Breugelmans JG, Carr D, Groves T, Helinski M, Guerin PJ, Et al., Beyond open data: realising the health benefits of sharing data, BMJ (Clinical Research Ed.), 355, (2016); Piwowar HA, Day RS, Fridsma DB., Sharing detailed research data is associated with increased citation rate, PloS One, 2, 3, pp. e308-e308, (2007); Popic S, Velikic G, Jaroslav H, Spasic Z, Vulic M., The Benefits of the Coding Standards Enforcement and it’s Influence on the Developers’ Coding Behaviour: A Case Study on Two Small Projects, 2018 26th Telecommunications Forum, TELFOR 2018 – Proceedings, (2018); Popkin G., Data sharing and how it can benefit your scientific career, Nature, 569, pp. 445-447, (2019); Reed M., Workshops Don’t Work, (2013); Rethlefsen ML, Farrell AM, Osterhaus Trzasko LC, Brigham TJ., Librarian co-authors correlated with higher quality reported search strategies in general internal medicine systematic reviews, Journal of Clinical Epidemiology, 68, 6, pp. 617-626, (2015); Roesch EB, Scrivener CL, Ras I, Malik A, Lindner M, Dubuc TT, Figueiredo N, Et al., EBRLab, (2016); Ruder D, Ge J., All-In-One Code Framework Coding Standards, (2014); Si L, Zeng Y, Guo S, Zhuang X., Investigation and analysis of research support services in academic libraries, Electronic Library, 37, 2, pp. 281-301, (2019); Soehner C, Steeves C, Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Our Lessons, (2020); Strasser C, Cook R, Michener W, Budden A., Primer on Data Management: What you always wanted to know, (2011); Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, Frame M, Et al., Data Sharing by Scientists: Practices and Perceptions, PLoS ONE, 6, 6, (2011); Tenopir C, Christian L, Allard S, Borycz J., Research Data Sharing: Practices and Attitudes of Geophysicists, Earth and Space Science, 5, 12, pp. 891-902, (2018); Tenopir C, Dalton ED, Allard S, Frame M, Pjesivac I, Birch B, Dorsett K, Et al., Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide, PLOS ONE, 10, 8, (2015); Tenopir C, Rice NM, Allard S, Baird L, Borycz J, Christian L, Sandusky RJ, Et al., Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide, PLOS ONE, 15, 3, (2020); Tenopir C, Sandusky RJ, Allard S, Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Data Stewardship, (2020); Wilson G, Aruliah DA, Brown CT, Chue Hong NP, Davis M, Guy RT, Wilson P, Et al., Best Practices for Scientific Computing, PLoS Biology, 12, 1, (2014); Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK., Good Enough Practices in Scientific Computing, (2016); Wyble B, Glenn A, Hess M, Maceyko A, Callahan-Flintoft C, Swan G, O'Donnell RE, Et al., Wyble Lab, (2017); Yu F, Deuble R, Morgan H., Designing research data management services based on the research lifecycle – a consultative leadership approach, Journal of the Australian Library and Information Association, 66, 3, pp. 287-298, (2017)","J. Borycz; Vanderbilt University, United States; email: joshua.d.borycz@vanderbilt.edu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85102773704"
"Palsdottir A.","Palsdottir, Agusta (24381890700)","24381890700","Data literacy and management of research data – a prerequisite for the sharing of research data","2021","Aslib Journal of Information Management","73","2","","322","341","19","7","10.1108/AJIM-04-2020-0110","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099694849&doi=10.1108%2fAJIM-04-2020-0110&partnerID=40&md5=1253c752dd85c22e6fe1c90e0b81d743","Information Science, School of Social Sciences, University of Iceland, Reykjavík, Iceland","Palsdottir A., Information Science, School of Social Sciences, University of Iceland, Reykjavík, Iceland","Purpose: The purpose of this paper is to investigate the knowledge and attitude about research data management, the use of data management methods and the perceived need for support, in relation to participants’ field of research. Design/methodology/approach: This is a quantitative study. Data were collected by an email survey and sent to 792 academic researchers and doctoral students. Total response rate was 18% (N = 139). The measurement instrument consisted of six sets of questions: about data management plans, the assignment of additional information to research data, about metadata, standard file naming systems, training at data management methods and the storing of research data. Findings: The main finding is that knowledge about the procedures of data management is limited, and data management is not a normal practice in the researcher's work. They were, however, in general, of the opinion that the university should take the lead by recommending and offering access to the necessary tools of data management. Taken together, the results indicate that there is an urgent need to increase the researcher's understanding of the importance of data management that is based on professional knowledge and to provide them with resources and training that enables them to make effective and productive use of data management methods. Research limitations/implications: The survey was sent to all members of the population but not a sample of it. Because of the response rate, the results cannot be generalized to all researchers at the university. Nevertheless, the findings may provide an important understanding about their research data procedures, in particular what characterizes their knowledge about data management and attitude towards it. Practical implications: Awareness of these issues is essential for information specialists at academic libraries, together with other units within the universities, to be able to design infrastructures and develop services that suit the needs of the research community. The findings can be used, to develop data policies and services, based on professional knowledge of best practices and recognized standards that assist the research community at data management. Originality/value: The study contributes to the existing literature about research data management by examining the results by participants’ field of research. Recognition of the issues is critical in order for information specialists in collaboration with universities to design relevant infrastructures and services for academics and doctoral students that can promote their research data management. © 2020, Emerald Publishing Limited.","Data management; Field of research; Knowledge of data management; Research data","Data Sharing; Libraries; Surveys; Academic libraries; Design infrastructures; Design/methodology/approach; Measurement instruments; Professional knowledge; Quantitative study; Research communities; Research data managements; Information management","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, The International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Alter G., Gonzalez R., Responsible practices for data sharing, American Psychologist, 73, 2, pp. 146-156, (2018); Andreoli-Versbach P., Mueller-Langerac F., Open access to data: an ideal professed but not practised, Research Policy, 43, 9, pp. 1621-1633, (2014); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers' data management practices at UVM: findings from the quantitative phase, Journal of eScience Librarianship, 6, 1, (2017); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Bryant R., Lavoie B., Malpas C., Scoping the University RDM Service Bundle: The Realities of Research Data Management, (2017); Coate H.L., Building data services from the ground up: strategies and resources, Journal of eScience Librarianship, 3, 1, pp. 52-59, (2014); Version 10, 17th July 2015, (2015); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A., Williamson L., The 2014 DAF survey at the university of Sheffield, International Journal of Digital Curation, 10, 1, pp. 210-229, (2015); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society, 368, pp. 4023-4038, (2010); H2020: guidelines on fair data management in horizon 2020, (2016); Haendel M.A., Vasilevsky N.A., Wirz J.A., Dealing with data: a case study on information and data management literacy, PLoS Biology, 10, 5, (2012); Hartter J., Ryan S.J., MacKenzie C.A., Parker J.N., Strasser C.A., Spatially explicit data: stewardship and ethical challenges in science, PLoS Biology, 11, 9, (2013); Vísindasiðareglur [Háskóli Íslands], (2014); Johnson R., Parsons T., Chiarelli A., Kaye J., Findings of the 2016 data assessment framework (DAF) surveys, (2016); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2017); Martin E.R., What is data literacy?, Journal of eScience Librarianship, 3, 1, pp. 1-2, (2014); McMillan D., Data sharing and discovery: what librarians need to know, The Journal of Academic Librarianship, 40, pp. 541-549, (2014); Opin vísindi, (2020); Palsdottir A., Data literacy, collaboration and sharing of research data among academics at the University of Iceland, Communications in Computer and Information Science, 810, pp. 178-185, (2018); Insight: insight into digital preservation of research output in Europe: survey report, (2009); If you build it, will they come? How researchers perceive and use web 2.0: research information network report, (2010); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College and Research Libraries, 73, 4, pp. 349-365, (2012); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of eScience Librarianship, 1, 2, pp. 63-78, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS ONE, 10, 8, (2015); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research data services in academic libraries: data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Thessen A.E., Patterson D.J., Data issues in the life sciences, ZooKeys, 150, pp. 15-51, (2011); Unal Y., Chowdhury G., Kurbanoglu K., Boustany J., Walton G., Research data management and data sharing behaviour of university researchers, Information Research, 24, 1, (2019); Van Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon university, Journal of Librarianship and Scholarly Communication, 3, 3, (2015); Whyte A., Tedds J., Making the Case for Research Data Management: DCC Briefing Papers, (2011); Wiley C.A., Kerby E.E., Managing research data: graduate student and postdoctoral researcher perspectives, Science and Technology Librarianship, 89, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Zuiderwijk A., Spiers H., Sharing and re-using open data: a case study of motivations in astrophysics, International Journal of Information Management, 49, pp. 228-241, (2019); (2014); Open access policy, (2014)","A. Palsdottir; Information Science, School of Social Sciences, University of Iceland, Reykjavík, Iceland; email: agustap@hi.is","","Emerald Group Holdings Ltd.","","","","","","20503806","","","","English","Aslib J. Inf. Manage.","Article","Final","","Scopus","2-s2.0-85099694849"
"Tang R.; Hu Z.; Henry N.; Thomas A.","Tang, Rong (7202299626); Hu, Zhan (57224576415); Henry, Nicole (57224574899); Thomas, Ashley (57219565732)","7202299626; 57224576415; 57224574899; 57219565732","A Usability Evaluation of Research Data Management Librarian Academy (RDMLA): Examining the Impact of Learner Differences in Pedagogical Usability","2021","Journal of Web Librarianship","15","3","","154","193","39","1","10.1080/19322909.2021.1937442","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107943324&doi=10.1080%2f19322909.2021.1937442&partnerID=40&md5=a128bdb1d33ce3c888d53c6b0c7816e8","School of Library and Information Science, Simmons University, Boston, MA, United States; Countway Library of Medicine, Harvard Medical School, Harvard University, Boston, MA, United States","Tang R., School of Library and Information Science, Simmons University, Boston, MA, United States; Hu Z., School of Library and Information Science, Simmons University, Boston, MA, United States; Henry N., School of Library and Information Science, Simmons University, Boston, MA, United States; Thomas A., Countway Library of Medicine, Harvard Medical School, Harvard University, Boston, MA, United States","This paper reports the results of a usability and user experience (UX) investigation into the Research Data Management Librarian Academy (RDMLA) course site, a free online learning platform developed through community effort. Remote synchronous usability test sessions were run with 42 participants. Participants completed a pre-session interview, performed tasks related to the course orientation unit and two content units, and filled out a post-session questionnaire. Significant differences were found among people with different demographic attributes, educational backgrounds, occupations, and levels of research data management (RDM) training, in areas related to self-rating, task performance, and post-task and post-session evaluations of RDMLA. Significant correlations occurred among participants’ pre-session, post-task, and post-session ratings, as well as performance measures. Qualitative descriptions of the participants’ perceptions of the future of RDM in LIS were also reported. The study results indicate a need to add a new criterion - diverse learner population - into the framework of pedagogical usability. © 2021 Rong Tang, Zhan Hu, Nicole Henry, Ashley Thomas.","learner differences; pedagogical usability; research data management; Research Data Management Librarian Academy; task performance; Usability; user experience; UX","","","","","","","","Albion P., Heuristic evaluation of educational multimedia: from theory to practice [Paper presentation]. The 16th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, (1999); Alghamdi A.S., Al-Badi A., Alroobaea R., Mayhew P.J., A comparative study of synchronous and asynchronous remote usability testing methods, International Review of Basic and Applied Sciences, 1, 3, pp. 61-97, (2013); Alvarez I.B., Silva N.S., Correia L.S., Cyber education: Towards a pedagogical and heuristic learning, ACM SIGCAS Computers and Society, 45, 3, pp. 185-192, (2016); Andreasen M.S., Nielsen H.V., Schroder S.O., Stage J., What happened to remote usability testing? An empirical study of three methods [Paper presentation], ACM SIGCHI Conference on Human Factors in Computing Systems, (2007); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Arroyo Y., Navarro C.X., Redondo M., Molina A.I., Applying CIAM mobile methodology: A case study for smartphones [Paper presentation], The Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, Salamanca, (2016); Black T.R., Kruskal Wallis h test, The SAGE encyclopedia of social science research methods, (2004); Brush A., Ames M., Davis J., A comparison of synchronous remote and local usability studies for an expert interface, (2004); (1998); Castillo J.C., Hartson H.R., Critical incident data and their importance in remote usability evaluation [Paper presentation], The 44th Human Factors and Ergonomics Society Annual Meeting, (2000); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Kennan M., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Fearon D., Gunia B., Lake S., Pralle B.E., Sallans A.L., (2013); Getler M., Sisu D., Jones S., Miller K., DMPOnline version 4.0: User-led innovation, International Journal of Digital Curation, 9, 1, pp. 193-219, (2014); Hanson K., Bakker T., Svirsky M., Neuman A., Rambo N., Informationist role: Clinical data management in auditory research, Journal of eScience Librarianship, 2, 1, pp. 25-29, (2013); Horila M., Nokelainen P., Syvanen A., Overlund J., Criteria for the pedagogical usability, version 1.0, (2002); (2012); Madathil K., Greenstein J., Synchronous remote usability testing: a new approach facilitated by virtual worlds [Paper presentation], The SIGCHI Conference on Human Factors in Computing Systems, (2011); Montague-Hellen B., (2019); Revised policy on enhancing public access to archived publications resulting from NIH-funded research, (2008); Scientists seeking NSF funding will soon be required to submit data management plans, (2010); Nielsen J., (2000); Nielsen J., (2012); Nokelainen P., An empirical assessment of pedagogical usability criteria for digital learning material with elementary school students, Educational Technology & Society, 9, 2, pp. 178-197, (2006); Palmisano J.M., World of sociology, (2001); Quinn C., Pragmatic evaluation: Lessons from usability [Paper presentation], Australasian Society for Computers in Learning in Tertiary Education Conference 1996, (1996); (2018); Reeves T.C., Evaluating what really matters in computer-based education, Computer education: New perspectives, pp. 219-246, (1994); Sauer J., Sonderegger A., Heyden K., Biller J., Klotz J., Uebelbacher A., Extra-laboratorial usability tests: An empirical comparison of remote and classical field testing with lab testing, Applied Ergonomics, 74, pp. 85-96, (2019); Schneider R., Training trainers for research data literacy: A content- and method-oriented approach, Communications in Computer and Information Science, 810, pp. 139-147, (2018); Squires D., Preece J., Usability and learning: Evaluating the potential of educational software, Computers & Education, 27, 1, pp. 15-22, (1996); Squires D., Preece J., Predicting quality in educational software: Evaluating for learning, usability and the synergy between them, Interacting with Computers, 11, 5, pp. 467-483, (1999); Tang R., Hu Z., Providing research data management (RDM) services in libraries preparedness roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); (2002); Volentine R., Owens A., Tenopir C., Frame M., Usability testing to improve research data services, Qualitative and Quantitative Methods in Libraries, 4, 1, pp. 59-68, (2017); Vuorio J., Okkonen J., Viteli J., Enhancing user value of educational technology by three layer assessment [Paper presentation], The 21st International Academic Mindtrek Conference, (2017); Zurita G., Baloian N., Penafiel S., Jerez O., Applying pedagogical usability for designing a mobile learning application that supports reading comprehension [Paper presentation], 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, (2019)","","","Routledge","","","","","","19322909","","","","English","J. Web Librariansh.","Article","Final","","Scopus","2-s2.0-85107943324"
"Hasani S.; Stefanova E.; Georgiev A.; Stefanov K.","Hasani, Silvester (57221763799); Stefanova, Eliza (7004567025); Georgiev, Atanas (35092436400); Stefanov, Krassen (7007134516)","57221763799; 7004567025; 35092436400; 7007134516","First steps towards Open Science in Albania","2021","International Conference Automatics and Informatics, ICAI 2021 Proceedings","","","","235","238","3","1","10.1109/ICAI52893.2021.9639622","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123850562&doi=10.1109%2fICAI52893.2021.9639622&partnerID=40&md5=4c503f8f4ef9b3b9d9b78a5158ae3006","Sofia University 'St. Kliment Ohridski', Faculty of Mathematics and Informatics, Sofia, Bulgaria","Hasani S., Sofia University 'St. Kliment Ohridski', Faculty of Mathematics and Informatics, Sofia, Bulgaria; Stefanova E., Sofia University 'St. Kliment Ohridski', Faculty of Mathematics and Informatics, Sofia, Bulgaria; Georgiev A., Sofia University 'St. Kliment Ohridski', Faculty of Mathematics and Informatics, Sofia, Bulgaria; Stefanov K., Sofia University 'St. Kliment Ohridski', Faculty of Mathematics and Informatics, Sofia, Bulgaria","Open Science is directly linked to the present trend of Research Data Management. To have fully open research data, systems that allow free access to the data need to be present. As a result, research institutions have tried to adapt to the changes required to be deemed as Open Science supporters. Several institutions implement a Current Research Information System (CRIS) and an Open Science Policy for all their published research data.Interoperable data between different systems implemented is a key feature of Open Science. In Europe, the European Open Science Cloud provides a set of standards and guidelines that must be followed so the implemented systems offer an interoperable service to their researchers.Previous research revealed that Albania is ready to take its first steps toward Open Science, and future ideas for how to contribute to the field were proposed. There were two significant findings: first, there is no CRIS implementation in Albanian institutions, and second, there is no strategy or policy to support Open Science. The research also proposed an Albanian Open Science Digital Repository.In this paper, we show how the implementation of the system is done and how it relates to the newly proposed Open Science Policy. The goal is to demonstrate how Albanian institutions are implementing advances towards Open Science.In conclusion, this paper demonstrates Albanian researchers' support for Open Science and the European Open Science Cloud.  © 2021 IEEE.","Open Access; Open Science; Open Science Policy; Research Data Management","Interoperability; Open Data; Sounding apparatus; 'current; Albania; Albanians; Open science; Open science policy; OpenAccess; Research data; Research data managements; Science policies; Information management","","","","","National Scientific Program “Information and Communication Technologies in Science, Education and Security; Ministry of Education and Science, MES","ACKNOWLEDGMENT The research is partially funded by the National Scientific Program “Information and Communication Technologies in Science, Education and Security” (ICTinSES) financed by the Bulgarian Ministry of Education and Science.","Woelfle M., Olliaro P., Todd M.H., Open science is a research accelerator, Nat. Chem., 3, 10, (2011); Open Science Policies | FOSTER; What is open science?, ORION Open Science, (2017); What Is Open Access?, (2020); The Open Definition - Open Definition - Defining Open in Open Data, Open Content and Open Knowledge, (2020); Iatropoulou K., OpenAIRE, OpenAIRE; European Open Science Cloud, Shaping Europe’S Digital Future - European Commission, (2016); Fair principles, GO FAIR; Digital Repository - An Overview | ScienceDirect Topics, (2020); DSpace-CRIS Home - DSpace-CRIS - LYRASIS Wiki; Hasani S., Stefanova E., Georgiev A., Stefanov K., Current state of open science in Balkan universities, 2020 International Conference Automatics and Informatics (ICAI), pp. 1-6, (2020); Hasani S., Stefanova E., Stefanov K., Georgiev A., Are we ready for open science – The answer of the balkan universities, ICERI2020 Proceedings, Online Conference, pp. 1947-1953; Home | Albanian DSpace-CRIS","","","Institute of Electrical and Electronics Engineers Inc.","","2021 International Conference Automatics and Informatics, ICAI 2021","30 September 2021 through 2 October 2021","Varna","175600","","978-166542661-9","","","English","Int. Conf. Autom. Informatics, ICAI Proc.","Conference paper","Final","","Scopus","2-s2.0-85123850562"
"Kinde A.A.; Addis A.C.; Abebe G.G.","Kinde, Alehegn Adane (57217949982); Addis, Assefa Chekole (57424437700); Abebe, Getachew Gedamu (57424437800)","57217949982; 57424437700; 57424437800","Research data management practice in higher education institutions in Ethiopia","2021","Public Services Quarterly","17","4","","213","230","17","1","10.1080/15228959.2021.1879707","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123371973&doi=10.1080%2f15228959.2021.1879707&partnerID=40&md5=1dfe30cbc5b8f3f51d94e0ff34d85746","Department of Information Science, Faculty of Informatics, University of Gondar, Gondar, Ethiopia","Kinde A.A., Department of Information Science, Faculty of Informatics, University of Gondar, Gondar, Ethiopia; Addis A.C., Department of Information Science, Faculty of Informatics, University of Gondar, Gondar, Ethiopia; Abebe G.G., Department of Information Science, Faculty of Informatics, University of Gondar, Gondar, Ethiopia","There is increasing pressure from funders, publishers, the public and other research organizations on researchers to improve their research data management. The main objective of the study was to examine the practices of research data management at higher education institutions in Ethiopia. The study was employed multi-stage sampling technique to select the actual sample unit of respondents. Eight first generation Universities and one Science and Technology University were selected based on purposive sampling technique. 390 faculty member and 119 stakeholder respondents were selected based on simple random and purposive sampling techniques. The findings revealed that majority of faculty members were used and produced spreadsheet and structured scientific and statistical research data formats. Majority of faculty members were used their personal device to hold their research data. The findings indicated that majority of faculty members were identified their needs of research data management services such as, data management template, storage facilities, research data management policy, guidelines and providing training. The level of awareness on research data management among faculty members was very low. The study concludes that proper research data management is not being practiced. Further research can be done on research data management framework at higher education institutions. © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.","Data management plan; data management services; research data; research data management","","","","","","University of Gondar, UoG","We would like acknowledge all respondents who filled out our questionnaires properly. We would like to also acknowledge the University of Gondar that supports this research by providing necessary expenses.","Academy E., (2020); Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Anilkumar N., RDMin India: A pilot study, EPJ Web of Conferences, 186, pp. 03002-03010, (2018); Arias-Coello A., Simon-Blas C., Arranz-Val P., Simon-Martin J., Research data management in three Spanish universities, Communications in Computer and Information Science, 810, pp. 195-204, (2018); Avuglah B.K., Underwood P.G., Research data management (RDM) capabilities at the University of Ghana, Legon, (2019); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, pp. 31-39, (2017); Erway R., (2013); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Huang F., Human interface and the management of information: Applications and services, 9735, pp. 531-541, (2016); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: How we shape up, Australian Academic & Research Libraries, 45, 4, pp. 296-308, (2014); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Kiran N.S.K.R., Program: Electronic library and information systems article information, Electronic Library and Information Systems, 49, 3, pp. 266-288, (2015); Krahe M.A., Toohey J., Wolski M., Scuffham P.A., Reilly S., Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia, Health Information Management: Journal of the Health Information Management Association of Australia, 49, 2-3, pp. 108-116, (2020); Kurbanoglu S., Spiranec S., (2013); Liu X., Zhang J., Guo C., Full-text citation analysis: A new method to enhance, Journal of the American Society for Information Science and Technology, 64, 9, pp. 1852-1863, (2013); Morgan A., Duffield N., Hall L.W., Research data management support: Sharing our experiences, Journal of the Australian Library and Information Association, 66, 3, pp. 299-305, (2017); Netscher S., Katsanidou A., Understanding and Implementing Research Data Management, Handbuch Methoden Der Politikwissenschaft, pp. 1-18, (2018); Patel D., Research data management: A conceptual framework, Library Review, 65, 4-5, pp. 226-241, (2016); Redkina N.S., Current trends in research data management, Scientific and Technical Information Processing, 46, 2, pp. 53-58, (2019); Sanches T., Information literacy in everyday life, Ecil 2018, 989, (2019); Sewerin C., Dearborn D., Henshilwood A., Spence M., (2015); Singh N.K., Monu H., Dhingra N., (2018); Tripathi M., Shukla A., Sonker S.K., Research data management practices in university libraries: A study, 37, 6, pp. 417-424, (2017); Unal Y., Chowdhury G., Kurbanoglu S., Boustany J., Walton G., Research data management and data sharing behaviour of University researchers, Information Research: An International Electronic Journal, 24, 1, (2019); Wiorogorska Z., Lesniewski J., Rozkosz E., Data literacy and research data management in two top universities in Poland. Raising awareness, Communications in Computer and Information Science, 810, pp. 205-214, (2018); Wolski M., Richardson J., pp. 1-9, (2011)","G.G. Abebe; Department of Information Science, Faculty of Informatics, University of Gondar, Gondar, Ethiopia; email: gedamugetachew96@gmail.com","","Routledge","","","","","","15228959","","","","English","Public Serv. Q.","Article","Final","","Scopus","2-s2.0-85123371973"
"Khvastova M.; Witt M.; Essenwanger A.; Sass J.; Thun S.; Krefting D.","Khvastova, Maryna (57208244135); Witt, Michael (36722574100); Essenwanger, Andrea (57193266107); Sass, Julian (57201945716); Thun, Sylvia (26535208000); Krefting, Dagmar (23004694400)","57208244135; 36722574100; 57193266107; 57201945716; 26535208000; 23004694400","Towards Interoperability in Clinical Research - Enabling FHIR on the Open-Source Research Platform XNAT","2020","Journal of Medical Systems","44","8","137","","","","6","10.1007/s10916-020-01600-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087682590&doi=10.1007%2fs10916-020-01600-y&partnerID=40&md5=92e70b831f8b0b23eed7241b83d61da4","University of Applied Sciences Berlin, Center of Biomedical Image and Information Processing, Ostendstraße 25, Berlin, 12459, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, Berlin, 10178, Germany; Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, Göttingen, 37075, Germany","Khvastova M., University of Applied Sciences Berlin, Center of Biomedical Image and Information Processing, Ostendstraße 25, Berlin, 12459, Germany; Witt M., University of Applied Sciences Berlin, Center of Biomedical Image and Information Processing, Ostendstraße 25, Berlin, 12459, Germany; Essenwanger A., Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, Berlin, 10178, Germany; Sass J., Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, Berlin, 10178, Germany; Thun S., Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, Berlin, 10178, Germany; Krefting D., University of Applied Sciences Berlin, Center of Biomedical Image and Information Processing, Ostendstraße 25, Berlin, 12459, Germany, Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, Göttingen, 37075, Germany","This paper presents an approach to enable interoperability of the research data management system XNAT by the implementation of the HL7 standards framework Fast Healthcare Interoperability Resources (FHIR). The FHIR implementation is realized as an XNAT plugin (Source code: https://github.com/somnonetz/xnat-fhir-plugin), that allows easy adoption in arbitrary XNAT instances. The approach is demonstrated on patient data exchange between a FHIR reference implementation and XNAT. © 2020, The Author(s).","FHIR standards framework; Medical data management; Patient resource; REST architecture style; System interoperability; XNAT","Data Management; Electronic Health Records; Health Level Seven; Humans; Medical Records Systems, Computerized; Neuroimaging; Systems Integration; adoption; adult; article; clinical research; data interoperability; Fast Healthcare Interoperability Resources; human; patient coding; electronic health record; electronic medical record system; health level 7; information processing; neuroimaging; organization and management; procedures; system analysis","","","","","Project Innovation Hub Digital Health of European Regional Development Fund; Bundesministerium für Bildung und Forschung, BMBF, (03FH0061X5); Allianz Industrie Forschung, AiF, (ZF4507601827); European Regional Development Fund, FEDER;ERDF;EFRE, (2016011187)","Open Access funding provided by Projekt DEAL. The work has been supported by Project Innovation Hub Digital Health of European Regional Development Fund under grant 2016011187, Project HAND of German Federation of Industrial Research Associations (AiF) under grant ZF4507601827, Project BB-IT-Boost of the German Ministry of Education and Research under grant 03FH0061X5. ","Beier M., Jansen C., Mayer G., Penzel T., Rodenbeck A., Siewert R., Witt M., Wu J., Krefting D., Multicenter data sharing for collaboration in sleep medicine, Future Generation Computer Systems, 67, pp. 466-480, (2017); Haarbrandt B., Schreiweis B., Rey S., Sax U., Scheithauer S., Rienhoff O., Knaup-Gregori P., Bavendiek U., Dieterich C., Brors B., Et al., Highmed–an open platform approach to enhance care and research across institutional boundaries, Methods of information in medicine, 57, S 01, pp. e66-e81, (2018); Khvastova M., Witt M., Mollenhauer S., Herrmann T., Rivadeneira I.K., Xnat-Fhir-Plugin, (2018); Marcus D.S., Olsen T.R., Ramaratnam M., Buckner R.L., The Extensible Neuroimaging Archive Toolkit: An informatics platform for managing, exploring, and sharing neuroimaging data, Neuroinformatics, 5, 1, pp. 11-34, (2007); McDonald C.J., Hammond W.E., Standard formats for electronic transfer of clinical data, Annals of Internal Medicine, 110, 5, pp. 333-335, (1989); Montjoie A.J., Introducing the CDISC Standards: New Efficiencies for Medical Research, (2009); Ulriksen G.H., Pedersen R., Ellingsen G., Infrastructuring in healthcare through the openehr architecture, Computer Supported Cooperative Work (CSCW), 26, 1-2, pp. 33-69, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","M. Khvastova; University of Applied Sciences Berlin, Center of Biomedical Image and Information Processing, Berlin, Ostendstraße 25, 12459, Germany; email: Maryna.Khvastova@HTW-Berlin.de","","Springer","","","","","","01485598","","JMSYD","32642856","English","J. Med. Syst.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85087682590"
"Cesevičiute I.; Tautkevičiene G.","Cesevičiute, Ieva (57869743800); Tautkevičiene, Gintare (57208389774)","57869743800; 57208389774","Research data management support at Kaunas University of technology","2020","Cases on Research Support Services in Academic Libraries","","","","52","71","19","1","10.4018/978-1-7998-4546-1.ch003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136694386&doi=10.4018%2f978-1-7998-4546-1.ch003&partnerID=40&md5=24fdf1fcff11ca60f071258503e161bd","Kaunas University of Technology, Lithuania","Cesevičiute I., Kaunas University of Technology, Lithuania; Tautkevičiene G., Kaunas University of Technology, Lithuania","Kaunas University of Technology is one of the largest technical universities in the Baltic region. The university staff has been involved in different Open Access- and Open Science-related activities for more than a decade. Different initiatives have been implemented: stand-alone and series of training and awareness-raising events, promotion of Open Access and Open Science ideas so that institutions develop their Open Access policies and make their repositories compliant with larger research infrastructures. Within the institution, the initiatives of Open Science are implemented as a result of joint effort of the library, the departments of research, studies, and doctoral school. The current tasks involve revising the institutional Open Access guidelines and facilitating the implementation of data management plans in doctoral studies. In this chapter, the aim is to provide an overview of the efforts highlighting the successes and failures on the way to best practice in research data management support both institutionally and on the national level. © 2021, IGI Global. All rights reserved.","","","","","","","","","Ceseviciute I., The Open Access Escape Room: A game as a way of reaching out?, (2019); Ceseviciute I., FOSTER training events revisiting Lithuania, (2019); Ceseviciute I., Towards research data management support in Lithuania, (2019); Clare C., Cruz M., Papadopoulou E., Savage J., Teperek M., Wang Y., Yeomans J., Engaging Researchers with Data Management: The Cookbook, Open Reports Series, (2019); [How to promote Open Access] [Video file], (2011); [What is Open Access] [Video file], (2011); Apie KTU vienu žvilgsniu. [A single glance at KTU], (2019); Kennan M., Corrall S., Afzal W., Making space"" in practice and education: Research support services in academic libraries, Library Management, 35, 8-9, pp. 666-683, (2014); [Why is Open Access important] [Video file], (2011); (2006); (2015); Naudojimosi elektroniniais mokslo informacijos šaltiniais mokymo moduliai.[Training modules for independent studies by scientists and other researchers], (2010); Open Access Escape Room - Script. Figshare, (2018); Promoting Open Access to young researchers, EIFL, (2012); Guidelines on Open Access to Scientific Publications and Data, (2016); Revez J., Opening the Heart of Science: A Review of the Changing Roles of Research Libraries, Publications, 6, 1, (2018); Sundsbo K., Open Access Escape Room, (2018); Tautkeviciene G., Lietuvos mokslo ir studiju instituciju mokslinès veiklos rezultatu viešinimo atvirosios prieigos žurnaluose ir institucinèse talpyklose studija: Tyrimo ataskaita [Study on publishing the research results of Lithuanian research and study institutions in Open Access journals and institutional repositories: Research report], (2011); Tautkeviciene G., Banionyte E., Information competence development for Lithuanian academic community, Library Connect, 10, 3, (2012); Tautkeviciene G., Duobiniene G., Banionyte E., Vaskeviciene A., Demand for and practice of developing information competencies among researchers, Libraries for an open environment: Strategies, technologies and partnerships: IATUL 32nd Conference, (2011); Tautkeviciene G., Duobiniene G., Kretaviciene M., Kriviene I., Petrauskiene Z., Mokslininka ir kitu tyreju naudojimosi elektroniniais mokslo informacijos šaltiniais ugdymo poreikio apimties ir sudèties mokslinis tyrimas: Mokslo studija [Research on researchers' needs for training in using electronic research databases: A study], (2010); Tautkeviciene G., Kuchma I., Ceseviciute I., The role of Open Access in the scholarly communication process, Changes in Social and Business Environment: Proceedings of the 3rd international conference: Selected papers, pp. 438-444, (2009); Tautkeviciene G., Manzuch Z., Testinis mokslininku ir kitu tyreju naudojimosi elektroniniais mokslo duomenu ištekliais ugdymo poreikio apimties ir sudeties mokslinis tyrimas. [Research on researchers' needs for training in using electronic research databases], (2015); Tautkeviciene G., Petrikaite V., Eidukeviciute M., Open Access from the perspectives of young researchers, Sciecominfo: Nordic-Baltic Forum for Scientific Communication, 9, 1, (2013); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research Data Services in European Academic Research Libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017)","","","IGI Global","","","","","","","978-179984547-8; 978-179984546-1","","","English","Cases on res. Support Services in Academic Libraries","Book chapter","Final","","Scopus","2-s2.0-85136694386"
"Lafferty-Hess S.; Rudder J.; Downey M.; Ivey S.; Darragh J.; Kati R.","Lafferty-Hess, Sophia (57200621530); Rudder, Julie (58157355200); Downey, Moira (57208014754); Ivey, Susan (57190681449); Darragh, Jennifer (58157355300); Kati, Rebekah (57221830624)","57200621530; 58157355200; 57208014754; 57190681449; 58157355300; 57221830624","Conceptualizing Data Curation Activities Within Two Academic Libraries","2020","Journal of Librarianship and Scholarly Communication","8","1","eP2347","","","","4","10.7710/2162-3309.2347","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124333465&doi=10.7710%2f2162-3309.2347&partnerID=40&md5=70f27bab807c36be2eac1d3005f9efca","Duke University, United States; Repository Services Department, University of North Carolina at Chapel Hill, United States; Repository Services Analyst, Duke University, United States; North Carolina State University, United States; University of North Carolina at Chapel Hill, United States","Lafferty-Hess S., Duke University, United States; Rudder J., Repository Services Department, University of North Carolina at Chapel Hill, United States; Downey M., Repository Services Analyst, Duke University, United States; Ivey S., North Carolina State University, United States; Darragh J., Duke University, United States; Kati R., University of North Carolina at Chapel Hill, United States","INTRODUCTION As funders and journals increasingly create policies that require effective data management and data sharing, many institutions have developed research data management (RDM) programs to help researchers meet these mandates. While there is not a standard set of services for these RDM programs, some institutions, particularly those with repositories that accept data deposits, provide data curation services as a way to add value to research data and help make data more accessible and reusable. Stakeholder communities within the field, such as the Data Curation Network (DCN), are also developing guidelines, procedures, and best practices to support and expand data curation practices. DESCRIPTION OF PROJECT This paper examines the data curation activities defined by the DCN, and describes an activity undertaken by library staff at Duke University and the University of North Carolina at Chapel Hill to create a structured model of these tasks to more easily conceptualize and communicate data curation within these two institutional settings. The purpose of this paper is to describe how this model provided a basis for the implementation and expansion of data curation services at each institution and concludes with overall lessons learned. NEXT STEPS As we develop our services, libraries have an opportunity to make the often-invisible work of curation more transparent. This paper aims to provide a point of reference for other libraries as they consider how to scale up their data curation programs as well as contribute to discussions around prioritization of services, program assessment, and communication with stakeholders. © 2020 Lafferty-Hess et al.","","","","","","","","","Reference model for an open archival information system (OAIS) (Magenta Book No. 650.0-M-2), (2012); Fearon D., Gunia B., Pralle B., Lake S., Sallans A., SPEC Kit 334: Research Data Management Services (July 2013), (2013); Hudson-Vitale C., Imker H., Johnston L., Carlson J., Kozlowski W., Olendorf R., Stewart C., SPEC Kit 354: Data Curation, (2017); Johnston L. R., Curating research data: A handbook of current practice, 2, (2017); Johnston L. R, Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., Definitions of data curation activities used by the Data Curation Network, (2016); Johnston L. R, Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., Blake M., Herndon J., McGeary T., Hull E., Data curation network: A cross-institutional staffing model for curating research data, International Journal of Digital Curation, 13, 1, pp. 125-140, (2018); Johnston L. R., Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., How important is data curation? Gaps and opportunities for academic libraries, Journal of Librarianship and Scholarly Communication, 6, 1, (2018); Lafferty-Hess S., Rudder J., Downey M., Ivey S., Darragh J., Conceptualizing data curation activities within two academic libraries, LIS Scholarship Archive, (2018); Lee D. J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, PLOS ONE, 12, 3, (2017); Lin D., Crabtree J., Dillo I., Downs R. R., Edmunds R., Hugo W., Mokrane M., The TRUST principles white paper (version 0.01), (2019); Llebot C., Van Tuyl S., Peer review of research data submissions study, Presentation at the annual meeting of the Research Data Access and Preservation Association, (2019); Mitchell C., Does the UC open access policy miss the mark? Depends on which mark [Blog post], (2016); Munafo M. R., Nosek B. A., Bishop D. V. M., Button K. S., Chambers C. D., Percie du Sert N., Simonsohn U., Wagenmakers E.-J., Ware J. J., Ioannidis J. P. A., A manifesto for reproducible science, Nature Human Behaviour, 1, 1, (2017); Dissemination and sharing of research results, (2011); Nosek B. A., Alter G., Banks G. C., Borsboom D., Bowman S. D., Breckler S. J., Buck S., Chambers C.D., Chin G., Christensen G., Contestable M., Dafoe A., Eich E., Freese J., Glennerster R., Goroff D., Green D. P., Hesse B., Humphreys M., Yarkoni T., Promoting an open research culture, Science, 348, 6242, pp. 1422-1425, (2015); Peer L., Green A., Stephenson E., Committing to data quality review, International Journal of Digital Curation, 9, 1, pp. 263-291, (2014); Data availability policy, (2014); Raboin R., Reznik-Zellen R., Salo D., Forging new service paths: Institutional approaches to providing research data management services, Journal of eScience Librarianship, 1, 3, (2012); Wilkinson M.D., Dumontier M., Aalbersberg Ij.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Bonino da Silva Santos L., Bourne P.E., Bouwman J., Brookes A. J, Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C. T., Finkers F., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","S. Lafferty-Hess; Durham, Box 104732, 27708, United States; email: sophia.lafferty.hess@duke.edu","","Iowa State University Digital Press","","","","","","21623309","","","","English","J. Librariansh. Sch. Commun.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124333465"
"Anna N.E.V.; Mannan E.F.","Anna, Nove E. Variant (56875386100); Mannan, Endang Fitriyah (57202090309)","56875386100; 57202090309","Big data adoption in academic libraries: a literature review","2020","Library Hi Tech News","37","4","","1","5","4","8","10.1108/LHTN-11-2019-0079","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079714290&doi=10.1108%2fLHTN-11-2019-0079&partnerID=40&md5=26b991228f71e5e52f2c5a169b2ba884","Universitas Airlangga, Surabaya, Indonesia","Anna N.E.V., Universitas Airlangga, Surabaya, Indonesia; Mannan E.F., Universitas Airlangga, Surabaya, Indonesia","Purpose: The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation. Design/methodology/approach: The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019). Findings: It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well. Originality/value: This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library. © 2020, Emerald Publishing Limited.","Bibliometrics; Big data; Libraries; Library technology; Literature review; Voxviewer","","","","","","","","Alagu A., Thanuskodi S., Bibliometric Analysis of Digital Literacy Research Output: A Global Perspective, (2019); Ali I., Big data: Apa dan pengaruhnya pada perpustakaan?, Jurnal Media Pustakawan, 22, 4, pp. 19-23, (2015); Barik N., Jena P., Bibliometric portrait of select open access journals in the field of library and information science: a scopus based analysis, Library Hi Tech News, pp. 1-18, (2019); Bhat W.A., Long-term preservation of big data: prospects of current storage technologies in digital libraries, Library Hi Tech, 36, 3, pp. 539-555, (2018); Cervone H.S., Evaluating social media presence: a practical application of big data analytics in information organization, Digital Library Perspectives, 33, 1, pp. 2-7, (2017); Farida U., Pengelolaan big data pada perpustakaan: tantangan bagi pustakawan di era perpustakaan digital, Journal Net. Library and Information, 1, 1, pp. 19-29, (2018); Golub K., Hansson J., (Big) data in library and information science: a brief overview of some important problem areas, Journal of Universal Computer Science, 23, 11, pp. 1098-1108, (2017); Jantti M., Libraries and big data, Quality and the Academic Library, pp. 267-273, (2016); Jin X., Wah B.W., Cheng X., Wang Y., Significance and challenges of big data research, Big Data Research, 2, 2, pp. 59-64, (2015); Johnson C., Cassady S., How librarians make decisions: the interplay of subjective and quantitative factors in the cancellation of big deals, Collection and Curation, (2018); Johnson V., Leveraging technical library expertise for big data management, Journal of the Australian Library and Information Association, 66, 3, pp. 271-286, (2017); Kim Y.S., Cooke L., Big data analysis of public library operations and services by using the chernoff face method, Journal of Documentation, 73, 3, pp. 466-480, (2017); Linlin Z., Analysis of the information processing technology of university libraries in the big data era, Agro Food Industry Hi-Tech, 28, 1, pp. 2036-2040, (2017); Sirait E.R.E., Implementasi teknologi big data di lembaga pemerintahan Indonesia, Jurnal Penelitian Pos Dan Informatika, 6, 2, pp. 113-136, (2016); Sun N.N., Ma L.H., Research on information security and privacy of libraries in big data era, Advanced Material Research, (2014); Teets M., Goldner M., Libraries’ role in curating and exposing big data, Future Internet, 5, 3, pp. 429-438, (2013); Weihong F., Chenhui L., Xingwang Z., Xiaozhu Q., Zikuan G., How do libraries need ‘big data’?, Library Journal, 11, (2012); Ye C., Research on the key technology of big data service in university library, 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 2573-2578, (2017); Yingying W., Research on the construction of digital library under the background of the era of big data, Journal of Advanced Oxidation Technologies, 21, 2, (2018); Zhan M., Widen G., Public libraries: roles in big data, The Electronic Library, 36, 1, pp. 133-145, (2018); Zhao J., Cai W., Zhu X., Research on smart library big service application in big data environment, International Symposium on Computational Science and Computing, pp. 238-245, (2018); Ahmad K., JianMing Z., Rafi M., An analysis of academic librarians competencies and skills for implementation of big data analytics in libraries: a correlational study, Data Technologies and Applications, 53, 2, (2019); Ahmed W., Ameen K., Defining big data and measuring its associated trends in the field of information and library management, Library Hi Tech News, 34, 9, pp. 21-24, (2017); AnnaMannanSri Rahayu N.E.V., Mutiai F., Library and information (LIS) research topics in Indonesia from 2006 to 2017, Library Philosophy and Practice, pp. 1-7, (2018); BhaktaKar J., Bhui T., Bibliometric mapping of LIS research in India: a study seen through the mirror of Indian citation index, Library Philosophy and Practice, (2019); Darmandeh M., Noruzi A., Givi M.E., Opportunities of big data management in libraries and information centers: structural-interpretive analysis and finding a solution, Iranian Journal of Information Processing Management, 34, 2, pp. 841-870, (2019); Li J., Lu M., Dou G., Wang S., Big data application framework and its feasibility analysis in library, Information Discovery and Delivery, 45, 4, pp. 161-168, (2017); Li S., Jiao F., Zhang Y., Xu X., Problems and changes in digital libraries in the age of big data from the perspective of user services, The Journal of Academic Librarianship, 45, 1, pp. 22-30, (2019); Quan Y., Lei C., Use of the internet technology in the intelligent library services of colleges and universities library in the big data era, Advances in Intelligent Systems and Computing, (2019); Rufaidah V.W., Kolaborasi dan graf komunikasi artikel ilmiah peneliti bidang pertanian: Studi kasus pada jurnal penelitian dan pengembangan pertanian, Jurnal Perpustakaan Pertanian, 17, 1, pp. 10-21, (2008); Xiaodan D., Wei W., Discussion on university library service pattern in big data era, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 597-600, (2017); Xu S., Du W., Wang C., Liu D., The library big data research: status and directions, International Journal of Software Innovation, 5, 3, (2017); Zhang J., Design of university library and information management system based on big data fusion, MATEC Web of Conferences, 232, (2018)","N.E.V. Anna; Universitas Airlangga, Surabaya, Indonesia; email: nove.anna@vokasi.unair.ac.id","","Emerald Group Holdings Ltd.","","","","","","07419058","","","","English","Libr. Hi Tech News","Review","Final","","Scopus","2-s2.0-85079714290"
"Von Spichtinger D.; Blumesberger S.","Von Spichtinger, Daniel (18042824400); Blumesberger, Susanne (27867529800)","18042824400; 27867529800","Fair data and data management requirements in a comparative perspective: Horizon 2020 and FWF policies; [Faire Daten Und Anforderungen An Das Datenmanagement In Vergleichender Perspektive: Horizon 2020 Und Fwf Policies]","2020","VOEB-Mitteilungen","73","2","","207","216","9","1","10.31263/voebm.v73i2.3504","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095700887&doi=10.31263%2fvoebm.v73i2.3504&partnerID=40&md5=e623e6e85b8f4c2471eb76bdf1aa7cb2","Ludwig Boltzmann Gesellschaft, Grant Service and Policy, Germany; University of Vienna, Library and Archives, Austria","Von Spichtinger D., Ludwig Boltzmann Gesellschaft, Grant Service and Policy, Germany; Blumesberger S., University of Vienna, Library and Archives, Austria","In this paper we provide a comparative perspective on the open data and data management requirements in the European Union’s Horizon 2020 programme and those of a national funder, the Austrian FWF. We consider that such a compa­rative analysis of the requirements pertaining to research data management can help avoiding duplication and assist researchers when drawing up data management plans for their respective funders. We conclude that, although there are some differences in terminology and specific requirements, both the FWF and Horizon 2020 DMPs essentially cover the same ground. © 2020, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Citizen Science; Data Management; Data Management Plan; FWF; Horizon 2020; Open Data; Open Science; Research Data Management","","","","","","","","Austrian Citizen Science Conference, (2019); Blumesberger Susanne, Wie mache ich meine Daten FAIR?, (2019); The Economic Impact of Open Data Executive Summary, (2020); The world’s most valuable resource is no longer oil, but data, (2017); Annotated Model Grant Agreement, (2019); Template Horizon 2020 Data Management Plan, (2016); ERC Data Management Plan Template, (2017); Data Management Plan Template (DMP) Guide, (2019); Research Data Management; Leonelli Sabina, Why the Current Insistence on Open Access to Scientific Data? Big Data, Knowledge Production, and the Political Economy of Contemporary Biology, Bulletin of Science, Technology & Society, 33, 1-2, pp. 6-11, (2013); Danish Business Authority, Enhanced Access to Data: Reconciling risks and benefits of data re-use OECD Expert Workshop, (2017); Science Europe Practical Guide to the International Alignment of Research Data Management, (2018); Spichtinger Daniel, Siren Jarkko, The Development of Re­search Data Management Policies in Horizon 2020, Research Data Management - A Euro­pean Perspective, pp. 11-23, (2017)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85095700887"
"Blask K.; Förster A.","Blask, Katarina (55311613900); Förster, André (57191476680)","55311613900; 57191476680","Designing an information architecture for data management technologies: Introducing the DIAMANT model","2020","Journal of Librarianship and Information Science","52","2","","592","600","8","3","10.1177/0961000619841419","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064621125&doi=10.1177%2f0961000619841419&partnerID=40&md5=3e6b5c6f210cbee047092e4a42619e76","Service Centre eSciences, Trier University, Germany; GESIS – Leibniz Institute for the Social Sciences, Germany","Blask K., Service Centre eSciences, Trier University, Germany; Förster A., GESIS – Leibniz Institute for the Social Sciences, Germany","Although research institutions take on increased responsibility for providing infrastructures and services around the proper handling of research data, there is no comprehensive framework addressing the ideal conditions of this implementation process. To overcome this gap, we present the DIAMANT model, a reference model aimed at providing an orientation framework for the implementation of research data management guided by the research process itself. It builds upon a central research data management information unit controlling the information flow between all other organizational units involved in research data management. Due to the possibility of outsourcing organizational units, the implementation process is maximally flexible and efficient. © The Author(s) 2019.","Information architecture; reference model; research data management; research process service and infrastructure landscape","","","","","","Bundesministerium für Bildung und Forschung, BMBF, (16FDM023)","We would like to thank Marina Lemaire, Gisela Minn, Georg Müller-Fürstenberger and Erich Weichselgartner for their support in writing this paper within the PODMAN project. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is a result of the PODMAN project (www.fdm.uni-trier.de/en) that received a specific grant from the German Federal Ministry of Education and Research directed at the investigation of research data management and its life cycle at universities and research institutions (Grant Number 16FDM023).","The European Code of Conduct for Research Integrity, (2017); Blask K., Forster A., Problembereiche der FDM-Integration (Daten der 1. Interviewwelle des BMBF-Projekts PODMAN; PODMAN W1): Version 1.0.0 [Data set], (2018); Dfg, Vorschläge zur Sicherung guter wissenschaftlicher Praxis: Denkschrift Empfehlungen der Kommission ‘Selbstkontrolle in der Wissenschaft’ = Proposals for Safeguarding Good Scientific Practice Memorandum: Recommendations of the Commission on Professional Self Regulation in Science, (2013); Higgins S., The DCC Curation Lifecycle Model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Hinrichs-Krapels S., Grant J., Exploring the effectiveness, efficiency and equity (3e’s) of research and research impact assessment, Palgrave Communications, (2016); Guide to Social Science Data Preparation and Archiving, (2012); King P.J.H., Decision Tables, Computer Journal, 10, 2, pp. 135-142, (1967); Klump J., Ludwig J., Et al., Forschungsdaten-Management, Evolution der Informationsinfrastruktur: Kooperation zwischen Bibliothek und Wissenschaft, pp. 257-275, (2013); Porter M.E., Competitive Advantage: Creating and Sustaining Superior Performance, (1985); Scheer A.-W., ARIS – Business Process Modeling, (2000); Schwickert A.C., Muller L., Bodenbender N., Et al., Geschäftsprozessmodellierung mit ARIS, (2011); Surkis A., Read K., Research data management, Journal of the Medical Library Association JMLA, 103, 3, pp. 154-156, (2015); Managing and sharing data: Best practice for researchers, (2011); Vardigan M., Heus P., Thomas W., Data Documentation Initiative: Toward a standard for the social sciences, International Journal of Digital Curation, 1, 3, pp. 107-113, (2008); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","A. Förster; GESIS – Leibniz Institute for the Social Sciences, Germany; email: Andre.Foerster@gesis.org","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85064621125"
"Black K.","Black, Kylie (57218991572)","57218991572","Mutual benefit from library collaboration with computational biologists: the cropPAL project at the University of Western Australia","2020","Technology, Change and the Academic Library: Case Studies, Trends and Reflections","","","","105","114","9","0","10.1016/B978-0-12-822807-4.00010-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142075061&doi=10.1016%2fB978-0-12-822807-4.00010-5&partnerID=40&md5=28e9ff8a846a3de67c2c4dbe95606f3a","University Library, University of Western Australia, Perth, WA, Australia","Black K., University Library, University of Western Australia, Perth, WA, Australia","In 2016-17, the University of Western Australia (UWA) Library partnered with researchers in the Australian Research Council’s Centre of Excellence in Plant Energy Biology to produce cropPAL2, a database providing the subcellular locations for proteins in crops significant for food production. The project team consisted of computational biologists, software engineers and a librarian, in which the Library contributed expertise in developing search strategies, research data management and enhancing discoverability of cropPAL2 and its dataset. The Library continues to be a key player in this collaboration, a first for UWA, both in the innovative process and as a key driver in directing the development of commercial software for the wider benefit of researchers at UWA and beyond. © 2021 Jeremy Atkinson, Published by Elsevier Ltd. All rights reserved.","collaboration; commercialisation; cropPAL; DeweyFish; market research; ON Prime; partnerships","","","","","","","","Black K., Hooper C., Castleden I., Aryamanesh N., Millar H., Going to market with DeweyFish: The journey from partnership to commercialisation, Shifting sands and rising tides: Leading libraries through innovation; The 40th International Association of University Libraries Conference, Perth, Western Australia, (2019); Hooper C.M., Final report: High Value Collections program., The University of Western Australia ARC Centre of Excellence in Plant Energy Biology: The compendium of, (2017); Hooper C.M., Castleden I.R., Aryamanesh N., Jacoby R.P., Millar A.H., Finding the subcellular location of barley, wheat, rice and maize proteins: The compendium of crop proteins with annotated locations (cropPAL), Plant and Cell Physiology, 57, 1, (2016); Kafkas S., Pi X., Marinos N., Talo F., Morrison A., McEntyre J.R., Section level search functionality in Europe PMC, Journal of Biomedical Semantics, 6, 7, (2015); University Library strategic directions, (2014); UWA 2020 vision: Strategic plan 2014-2020, (2013)","","","Elsevier Science Ltd.","","","","","","","978-012822807-4; 978-012823228-6","","","English","Technology, Change and the Academic Library: Case Studies, Trends and Reflections","Book chapter","Final","","Scopus","2-s2.0-85142075061"
"Wiley C.","Wiley, Chris (57192433861)","57192433861","The case for data sharing policies and FAIR sharing principles: Analyzing journals and articles of engineering and medical faculty","2020","ASEE Annual Conference and Exposition, Conference Proceedings","2020-June","","1337","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095731936&partnerID=40&md5=d25fbee3d9e9e964e9c661fc25475d01","University of Illinois, Urbana - Champaign, United States","Wiley C., University of Illinois, Urbana - Champaign, United States","Numerous government and private funding agencies require data management plans and encourage data sharing. These mandates and levels of encouragement have extended to journal publications and publishers. Yet current literature indicates data sharing is infrequent despite recommendations and mandates. This article examines one hundred and one research data policies and publisher statements to understand data sharing policies, trends and patterns within scholarly journals. More specifically, it addresses the following research questions: (1) What are the data sharing policies of these research journals, (2) Have these policies improved since the Joint Information Systems Committee (JISC) study, and (3) What are current journal articles applicability to Findability, Accessibility, Interoperability and Reusability (FAIR) Sharing Principles. Examining these journals' research data sharing policies and articles applicability to FAIR Sharing principles provides an opportunity for librarians to review and enhance research data management services provided to researchers and research groups. © American Society for Engineering Education 2020.","","Information management; Interoperability; Reusability; Joint information systems committees; Journal articles; Journal publication; Management plans; Research data managements; Research journals; Research questions; Scholarly journals; Data Sharing","","","","","","","Khoury M.J., Et al., From public health genomics to precision public health: a 20-year journey,, Genetics in Medicine, 20, 6, pp. 574-582, (2018); Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease, (2011); Kim Y., Stanton J., Institutional and individual factors affecting scientists' data sharing behaviors: A multilevel analysis. , Journal of the Association for Information and Technology, 67, 4, pp. 776-799, (2015); Taichman D.B., Et al., Data Sharing Statements for Clinical Trials - A Requirement of the International Committee of Medical Journal Editors,, New England Journal of Medicine, 376, pp. 2277-2279, (2017); Melo M.M., Lieou V., Goodman S.N., Clinical trials participants' views on the risks and benefits of data sharing., New England Journal of Medicine, 378, pp. 2202-2211, (2018); Budin-Ljosne I., Et al., Dynamic consent: a potential solution to some of the challenges of modern biomedical research,, BMC Medical Ethics, 18, 4, (2017); Ploug T., Holm S., Meta consent: a flexible and autonomous way of obtaining informed consent for secondary research,, BMJ, 350, (2015); Sheehan M., Can broad consent be informed consent?, Public Health Ethics, 4, pp. 226-235, (2011); Steinsbekk K.S., Kare-Myskja B., Solberg B., Broad consent versus dynamic consent in biobank research: is passive participation an ethical problem?, European Journal of Human Genetics, 21, pp. 897-902, (2013); Tenopir C., Et al., Data sharing by Scientists: Practices and Perceptions., PLOS One, 6, 6, (2011); Rowhani-Farid A., Barnett A.G., A.G. 'Has open data arrived at the British Medical Journal (BMJ)? An observational study, BMJ Open, 6, 10, (2016); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide,, PLOS One, 10, 8, (2015); Mellor D., The Landscape of Open Data Policies, (2018); Gorgolewski K.J., Margulies D.S., Milham M.P., Making data sharing count: A publication-based solution,, Frontiers in Neuroscience, 7, 9, pp. 1-7, (2013); Haendel M.A., Wasilevsky N.A., Wirz J.A., Dealing with data: A case study on information and data management literacy,, PLOS Biology, 10, 5, (2012); Volk C., Lucero Y., Barnas K., Why is data sharing in collaborative natural resource efforts so hard and what can we do to improve it, , Environmental Management, 53, 5, pp. 883-893, (2014); Tenopir C., Et al., Research data sharing: Practices and attitudes of geophysicists.,, Earth and Space Science, 5, 12, pp. 891-902, (2018); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, , Scientific Data, 3, (2016); Stodden V., Guo G. P., Ma Z., Toward reproducible computational research: An empirical analysis of data and code policy adoption by journals, , PLOS One, 8, 6, (2016); Vasilevsky N.A., Et al., Reproducible and reusable research: are journal data sharing policies meeting the mark, , PeerJ, 5, (2017); 'Fair Principles'; Eder C., Jedinger A., A. 'FAIR national election studies: How well are we doing,, European Consortium for Political Research, 18, pp. 651-668, (2018); Calamai S., Frontini F., FAIR data principles and their application to speech and oral archives, , Journal of New Music Research, 47, 4, pp. 339-354, (2018); Naughton L., Kernohaugn D., Making sense of journal research data policies, Insights, 29, 1, pp. 84-89, (2016); Wiley C., Data Sharing and Engineering Faculty: An Analysis of Selected Publications,, Science and Technology Libraries, 37, 4, pp. 409-419, (2018); McCain K.W., Mandating sharing: Journal policies in the natural sciences,, Science Communication, 16, 4, pp. 403-431, (1995); Piwowar H.A., Chapman W. W., A review of journal policies for sharing research data., International Conference on Electronic Publishing, (2008); Dallmeier-Tiessen S. R, Et al., Enabling sharing and reuse of scientific data,, New Review of Information Networking, 19, 1, pp. 16-43, (2014); Lin J., Strasser C., Recommendations for the role of publishers in access to data, , PLOS Biology, 12, 1, (2014); Sturges P., Bamkin M., Anders J., Hussain A., Journals and their policies on research data sharing, Joint Information Systems Committee; Curty R., Et al., Attitudes and norms affecting scientists' data reuse,, PLOS ONE, 12, 2, (2017)","","","American Society for Engineering Education","Abet; Engineering Unleashed; et al.; Gradescope; IEEE Xplore; Keysight Technologies","2020 ASEE Virtual Annual Conference, ASEE 2020","22 June 2020 through 26 June 2020","Virtual, Online","164392","21535965","","","","English","ASEE Annu. Conf. Expos. Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85095731936"
"Chawinga W.D.; Zinn S.","Chawinga, Winner Dominic (57191247774); Zinn, Sandy (56289796700)","57191247774; 56289796700","Research data management at an African medical university: Implications for academic librarianship","2020","Journal of Academic Librarianship","46","4","102161","","","","7","10.1016/j.acalib.2020.102161","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083651637&doi=10.1016%2fj.acalib.2020.102161&partnerID=40&md5=a452b55138d8a5422bfcf63ed3979e6b","Department of Information Sciences, Mzuzu University, Malawi; Department of Library & Information Science, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa","Chawinga W.D., Department of Information Sciences, Mzuzu University, Malawi; Zinn S., Department of Library & Information Science, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa","The study investigates and provides results on the current status of research data management at a medical school in a developing African country. The study focusses on four aspects of research data management: generation; preservation and backup; competencies; and challenges. Quantitative data collected using a questionnaire from 84 health researchers and 16 librarians was complemented by qualitative data gathered through conducting structured interviews with the Director of Research of the university. The study results provide the research community with a better understanding of research data management perspectives amongst health researchers in a developing country. This could help research data management stakeholders to adopt better strategies for championing research data management activities in this particular country and in other developing countries. The study notes that the current research data management status creates an opportunity for an academic library to consolidate and fortify its widely viewed natural role in research data management. In view of the findings, the study proposes that the adoption of research data management policies is essential in inspiring the conceptualisation, popularisation, adoption, and operationalisation of various research data management activities within the African university environment. © 2020 Elsevier Inc.","Academic librarians; Africa; Developing country; Research data management, university.; Researchers","","","","","","","","Anane-Sarpong E., Wangmo T., Ward C.L., Sankoh O., Tanner M., Elger B.S., You cannot collect data using your own resources and put it on open access: Perspectives from Africa about public health data-sharing, Developing World Bioethics, pp. 1-12, (2017); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: Findings from a focus group in an academic health sciences library, Journal of Web Librarianship, 6, 4, pp. 274-287, (2012); Bond-Lamberty B., Data sharing and scientific impact in eddy covariance research, Journal of Geophysical Research: Biogeosciences, 123, 4, pp. 1440-1443, (2018); Brambilla P., Digital Curation in the Italian context: New roles and professions for digital librarians, (2015); Brown S., Bruce R., Kernohan D., Directions for research data management in UK universities, (2015); Bryant R., Brian L., Malpas C., A tour of the research data management (RDM) service space: The realities of research data management, part 1, (2017); Burgi P.Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs, IFLA Journal, 43, 1, pp. 5-21, (2017); Charbonneau D.H., Strategies for data management engagement, Medical Reference Services Quarterly, 32, 3, pp. 365-374, (2013); Chawinga W.D., Zinn S., Global perspective of research data sharing: A systematic literature review, Library & Information Science Research, 41, 2, pp. 109-122, (2019); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at small liberal arts colleges, IFLA Journal, 43, 1, pp. 105-118, (2017); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2016); Creswell J.W., Creswell J.D., Research design: Qualitative, quantitative and mixed methods approaches, (2018); Creswell J.W., Plano Clark V.L., Designing and conducting mixed methods research, (2011); Curty R.G., Crowston K., Specht A., Grant B.W., Dalton E.D., Attitudes and norms affecting scientists’ data reuse, PLoS One, 12, 12, (2017); Dai S.Q., Li H., Xiong J., Ma J., Guo H.Q., Xiao X., Zhao B., Assessing the extent and impact of online data sharing in eddy covariance flux research, Journal of Geophysical Research: Biogeosciences, 123, 1, pp. 129-137, (2018); Davenport T.H., Patil D.J., Data scientist, Harvard Business Review, 90, 5, pp. 70-76, (2012); Denny S.G., Silaigwana B., Wassenaar D., Bull S., Parker M., Developing ethical practices for public health research data sharing in South Africa: The views and experiences from a diverse sample of research stakeholders, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 290-301, (2015); Doorn P., Dillo I., Van Horik R., Lies, damned lies and research data: Can data sharing prevent data fraud?, International Journal of Digital Curation, 8, pp. 229-243, (2013); Edmonds W.A., Kennedy T.D., An applied reference guide to research designs: Quantitative, qualitative and mixed methods, (2013); Enke N., Thessen A., Bach K., Bendix J., Seeger B., Gemeinholzer B., The user's view on biodiversity data sharing - Investigating facts of acceptance and requirements to realize a sustainable use of research data, Ecological Informatics, 11, pp. 25-33, (2012); European Commission, Scientific data: Open access to research results will boost Europe's innovation capacity, (2012); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS One, 10, 2, (2015); Fienberg S.E., Martin M.E., Straf M.L., Sharing research data, (1985); Glaeser P.S., Scientific and technical data in a new era, (1990); Heidorn P.B., The emerging role of libraries in data curation and E-science, Journal of Library Administration, 51, pp. 662-672, (2011); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Houtkoop B.L., Chambers C., Macleod M., Bishop D.V., Nichols T.E., Wagenmakers E.J., Data sharing in psychology: A survey on barriers and preconditions, Advances in Methods and Practices in Psychological Science, 1, 1, pp. 70-85, (2018); Israel G.D., Determining sample size, (2013); Jeng W., He D., Chi Y., Social science data repositories in data deluge: A case study of ICPSR's workflow and practices, The Electronic Library, 35, 4, pp. 626-649, (2017); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: How we shape up, Australian Academic & Research Libraries, 45, 4, pp. 296-308, (2014); Kaye J., Terry S.F., Juengst E., Coy S., Harris J.R., Chalmers D., Bezuidenhout L., Including all voices in international data-sharing governance, Human Genomics, 12, 1, (2018); Kim J., Warga E., Moen W., Competencies required for digital curation: An analysis of job advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2017); Koopman M.M., Data archiving, management initiatives and expertise in the Biological Sciences Department, (2015); Lapan S.D., Quartaroli M.T., Riemer F.J., Qualitative research: An introduction to methods and designs, (2012); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Newton M.P., Miller C.C., Bracke M.S., Librarian roles in institutional repository data set collecting: Outcomes of a research library task force, Collection Management, 36, 1, pp. 53-67, (2011); Patterton L., Bothma T.J., van Deventer M.J., From planning to practice: An action plan for the implementation of research data management services in resource- constrained institutions, South African Journal of Libraries and Information Science, 84, 2, pp. 14-26, (2018); Peng C., Song X., Jiang H., Zhu Q., Chen H., Chen J.M., Zhou X., Towards a paradigm for open and free sharing of scientific data on global change science in China, Ecosystem Health and Sustainability, 2, 5, (2016); Pisani E., AbouZahr C., Sharing health data: Good intentions are not enough, Bulletin of the World Health Organization, 88, 6, pp. 462-466, (2010); Pitt M.A., Tang Y., What should be the data sharing policy of cognitive science?, Topics in Cognitive Science, 5, 1, pp. 214-221, (2013); Piwowar H.A., Who shares? Who doesn't? Factors associated with openly archiving raw research data, PLoS One, 6, 7, (2011); Ray J., The rise of digital curation and cyberinfrastucture, Library Hi Tech, 30, 4, pp. 604-622, (2012); Ross J.S., Clinical research data sharing: What an open science world means for researchers involved in evidence synthesis, Systematic Reviews, 5, 1, (2016); Saunders M., Lewis P., Thornhill A., Research methods for business students, (2012); Schumacher J., VandeCreek D., Intellectual capital at risk: Data management practices and data loss by faculty members at five American universities, International Journal of Digital Curation, 10, 2, pp. 96-109, (2015); Scott M., Research data management, (2014); Shakeri S., Data curation perspectives and practices of researchers at Kent State University's Liquid Crystal Institute: A case study, (2013); Silverman D., Interpreting qualitative data: A guide to the principles of qualitative research, (2011); Soehner C., Steeves C., Ward J., E-science and data support services: A study of ARL member institutions, (2010); Steen R.G., Retractions in the scientific literature: Is the incidence of research fraud increasing?, Journal of Medical Ethics, 37, 4, pp. 249-253, (2011); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Van Horn J.D., Gazzaniga M.S., Why share data? Lessons learned from the fMRIDC, Neuro-Image, 82, pp. 677-682, (2013); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); Walters T., Skinner K., New roles for new times: Digital curation for preservation, (2011); Watson M., When will “open science” become simply “science”?, Genome Biology, 16, 1, (2015); Whyte A., Tedds J., Making the case for research data management. DCC briefing papers, (2011); Williams S.C., Using a bibliographic study to identify faculty candidates for data services, Science & Technology Libraries, 32, 2, pp. 202-209, (2013); Woolfrey L., Archiving social survey data in Africa: An overview of African microdata curation and the role of survey data archives in data management in Africa, (2009); Yoon A., Data reuse and users' trust judgments: Toward trusted data curation, (2015); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College & Research Libraries, 78, 7, pp. 920-933, (2017); Zvyagintseva L., Articulating a vision for community-engaged data curation in the digital humanities, (2015)","W.D. Chawinga; Department of Information Sciences, Mzuzu University, Malawi; email: winnchawinga@gmail.com","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85083651637"
"Saleem Q.U.A.; Ashiq M.","Saleem, Qurat Ul Ain (57202234387); Ashiq, Murtaza (57221973297)","57202234387; 57221973297","The facts of continuing professional development for LIS professionals in Pakistan: a literature review","2020","Bottom Line","33","3","","263","271","8","9","10.1108/BL-02-2020-0013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087168384&doi=10.1108%2fBL-02-2020-0013&partnerID=40&md5=1b69f5a9378b03a14441c63b58891445","Institute for Art and Culture, Lahore, Pakistan; Islamabad Model College for Boys, Islamabad, Pakistan","Saleem Q.U.A., Institute for Art and Culture, Lahore, Pakistan; Ashiq M., Islamabad Model College for Boys, Islamabad, Pakistan","Purpose: Continuous professional development is an essential in-service and on-going learning process that provides an opportunity to young and mid-career professionals to update and align their skills in connection with the contemporary needs of library users. This study aims to identify and synthesize the literature on continuing professional development (CPD) opportunities for Pakistani librarians and information professionals. Design/methodology/approach: This study is based on descriptive literature review published by Pakistani researchers with specific reference to Pakistani librarians and information professionals. Findings: The findings revealed that albeit many initiatives have been taken by library and information science (LIS) schools and professional associations for developing the capabilities of librarians and information professionals, however, these initiatives remained spasmodic and limited to developing surface-level skills. Some emerging areas need to be addressed including information/digital literacy, research data management, data analysis and visualization and the skills to establish institutional repositories. Research limitations/implications: The study was limited to CPD literature contributed by Pakistani researchers and the efforts taken by LIS schools and professional associations. Originality/value: There is an immediate need to initiate a cohesive approach involving key stakeholders and to establish a platform purely working for CPD of librarians and information professionals focusing on current and future needs. The finding will be helpful for drawing foundation guidelines by library associations, LIS schools and librarian’s parent’s organizations regarding CPD opportunities. © 2020, Emerald Publishing Limited.","Continuing education; Continuing professional development; On job training; Pakistan; Professional development","","","","","","","","Ahmad P., Library organizations in the Punjab province of Pakistan: an appraisal, Malaysian Journal of Library and Information Science, 12, 2, pp. 77-88, (2017); Ahmed S., Rehman A.U., Perceptions and level of ICT competencies: a survey of librarians at public sector universities in khyber pakhtunkhwa, Pakistan, Pakistan Journal of Information Management and Libraries, 18, 1, pp. 1-11, (2016); Ameen K., Challenges of preparing LIS professionals for leadership roles in Pakistan, Journal of Education for Library and Information Science, 47, 3, pp. 200-217, (2006); Ameen K., Needed competencies for collection managers and their development: perceptions of university librarians, Library Management, 30, 4-5, pp. 266-275, (2009); Ameen K., Changing scenario of librarianship in Pakistan: managing with thechallenges and opportunities, Library Management, 32, 3, pp. 171-182, (2011); Anwar U., Warraich F.N., Status of digital novice academic librarians’ continuing professional development: a case of university of the Punjab, Pakistan Journal of Information Management and Libraries, 14, 1, pp. 33-37, (2013); Ashiq M., Rehman S.U., Batool S.H., Academic library leaders’ challenges, difficulties and skills: an analysis of common experience, Libri, 68, 4, pp. 301-313, (2018); Ashiq M., Rehman S.U., Batool S.R., Academic library leaders’ conception of library leadership in Pakistan, Malaysian Journal of Library and Information Science, 24, 2, pp. 55-71, (2019); Ashiq M., Rehman S.U., Mujtaba G., Future challenges and emerging role of academic libraries in Pakistan: a phenomenology approach, Information Development, 1, pp. 1-16, (2020); Awan M.R., Mahmood K., Relationship among leadership style, organizational culture and employee commitment in university libraries, Library Management, 31, 4-5, pp. 253-266, (2010); Batool S.H., Ameen K., Status of technological competencies: a case study of university librarians, Library Philosophy and Practice, 1, (2010); Bhatti R., Information literacy: furthering the cause of higher education in Pakistan, Pakistan Library and Information Science Journal, 43, 1, pp. 3-11, (2012); Bhatti R., Chohan T.M., Assessing the role of library associations in promoting research culture in LIS, Library Philosophy and Practice (e-journal), (2012); Bhatti R., Nadeem M., Assessing training needs of LIS professionals: aprerequisite for developing training programs in university libraries of Pakistan, Chinese Librarianship: An International Electronic Journal, 37, pp. 47-62, (2014); Haider S.J., Not financial issues alone: moving towards better resource sharing in Pakistan, The Bottom Line, 16, 2, pp. 55-64, (2003); Haider S.J., Teaching of cataloging and classification in Pakistan, Cataloging and Classification Quarterly, 43, 1, pp. 53-65, (2006); Hamid A., Impact of continuing education programs in LIS profession: a survey of library professionals of Pakistan, (2014); Hamid A., Soroya S., Current trends of continuing education programs in LIS profession, Pakistan Library and Information Science Journal, 46, 3, pp. 4-12, (2015); Hamid A., Soroya M.S., Continuing education for LIS professionals: Why, Library Review, 66, 1-2, pp. 83-89, (2017); Haq I., Human resources at medical libraries of Islamabad and rawalpindi, Pakistan Library and Information Science Journal, 40, 3, (2009); Jabeen H.M., Continuing education for development of information technology in Pakistani libraries, Pakistan Library and Information Science Journal, 41, 3, pp. 16-26, (2010); Khalil I.K., Career developement and progression of university librarians in khyber pakhtunkhwa, (2014); Khan S.A., Bhatti R., Digital competencies for developing and managing digital libraries: an investigation from university librarians in Pakistan, The Electronic Library, 35, 3, pp. 573-597, (2017); Khan A., Du J.T., Professional development through social media applications: a study of female librarians in Pakistan, Information and Learning Science, 118, 7-8, pp. 342-353, (2017); Khan A., Rafiq M., Designing effective in-service training for librarians in Pakistan, Library Philosophy and Practices (e-journal), (2013); Mahmood K., Competencies needed for future academic librarians in Pakistan, Education for Information, 20, 1, pp. 27-43, (2002); Mahmood K., A comparison between needed competencies of academic librariansand LIS curricula in Pakistan, The Electronic Library, 21, 2, pp. 99-109, (2003); Mahmood K., Ajmal Khan M., ICT training for LIS professionals in Pakistan: a needs assessment, Program, 41, 4, pp. 418-427, (2007); Majid S., Continuing professional development (CPD) activities organized by library and information study programs in Southeast Asia, Journal of Education for Library and Information Science, 45, 1, pp. 58-70, (2004); Rafiq M., Jabeen M., Arif M., Continuing education (CE) of LIS professionals: need analysis and role of LIS schools, The Journal of Academic Librarianship, 43, 1, pp. 25-33, (2017); Robinson L., Glosiene A., Continuing professional development for library and information science: case study of a network of training centres, Aslib Proceedings, 59, 4-5, pp. 462-474, (2007); Saka K.A., Oyedum G.U., Song I.S., Influence of continuing professional development and skills acquisition on librarians’ performance in two state capitals in Northern Nigeria, Journal of Balkan Libraries Union, 4, 1, pp. 1-7, (2016); Sharif A., Mahmood K., Impact of computer training on professional library activities in Pakistan, Information Development, 17, 3, pp. 173-178, (2001); Ukachi N.B., Onuoha U.D., Continuing professional development and innovative information service delivery in nigerian libraries: inhibitors and the wayout, Annals of Library and Information Studies (ALIS), 60, 4, pp. 269-275, (2014); Ullah M., Anwar M.A., Developing competencies for medical librarians in Pakistan, Health Information and Libraries Journal, 30, 1, pp. 59-71, (2013); Ullah M., Mahmood K., Pakistan library and information council: a proposal, (2012); Ullah M., Ameen K., Bakhtar S., Professional activities, needed competencies and training needs of medical librarians in Pakistan, Education for Information, 28, 2-4, pp. 115-123, (2011); Warraich N.F., Ameen K., Employment and learning outcomes of LIS graduates: a case of Pakistan, Education for Information, 28, 2-4, pp. 315-324, (2011); Warraich N.F., Ameen K., Human resource management in pakistani university libraries: managers’ viewpoint, Paper presented at the International Conference on Studies in Humanities and Social Sciences (ICSHSS’15), (2015); Ameen K., Ullah M., Information literacy instruction: an overview ofresearch and professional development in Pakistan, European Conference on Information Literacy, pp. 555-562, (2016); Chaudhary M.Y., Continuing professional education of librarians working in theuniversity libraries of Pakistan and azad Jammu And Kashmir, Inspel, 35, 1, pp. 67-73, (2001); Haider S.J., Perspectives on… coping with change: issues facing university libraries in Pakistan, The Journal of Academic Librarianship, 30, 3, pp. 229-236, (2004)","M. Ashiq; Islamabad Model College for Boys, Islamabad, Pakistan; email: gmurtazaashiq00@gmail.com","","Emerald Group Holdings Ltd.","","","","","","0888045X","","","","English","Bottom line","Review","Final","","Scopus","2-s2.0-85087168384"
"Heuer A.","Heuer, Andreas (9533312500)","9533312500","Research Data Management","2020","IT - Information Technology","62","1","","","","","1","10.1515/itit-2020-0002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079626046&doi=10.1515%2fitit-2020-0002&partnerID=40&md5=31b0ff0c2eeb44bd67e900bf491472a5","University of Rostock, Computer Science Institute, Rostock, 18059, Germany","Heuer A., University of Rostock, Computer Science Institute, Rostock, 18059, Germany","[No abstract available]","","Research data managements; Information management","","","","","","","Carvalho Amorim R., Castro J.A., Rocha Da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Auge T., Heuer A., LWDA 2018, CEUR Workshop Proceedings, 2191, pp. 1-12, (2018); Auge T., Heuer A., Combining Provenance Management and Schema Evolution, pp. 222-225, (2018); Boisvert R.F., Incentivizing reproducibility, Commun. ACM, 59, 10, (2016); Dittrich J., Bender P., Janiform intra-document analytics for reproducible research, PVLDB, 8, 12, pp. 1972-1975, (2015); Feitelson D., From Repeatability to Reproducibility and Corroboration, Operating Systems Review, 49, 1, pp. 3-11, (2015); Herschel M., Diestelkamper R., Ben Lahmar H., A survey on provenance: What for? What form? What from?, VLDB J., 26, 6, pp. 881-906, (2017); Max-Planck-Gesellschaft. Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, 2003..; Moreau L., Groth P.T., Provenance: An Introduction to PROV, (2013); Ruscheinski A., Gjorgevikj D., Dombrowsky M., Budde K., Uhrmacher A.M., Towards A PROV Ontology for Simulation Models, pp. 192-195, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","A. Heuer; University of Rostock, Computer Science Institute, Rostock, 18059, Germany; email: andreas.heuer@uni-rostock.de","","De Gruyter Oldenbourg","","","","","","16112776","","","","English","IT Info. Tech.","Editorial","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85079626046"
"Millar A.; Hay J.; Troup E.; Clark I.; Pietsch J.; Zieliński T.","Millar, Andrew (7201856684); Hay, Johnny (57211125119); Troup, Eilidh (56181565100); Clark, Ivan (13205796700); Pietsch, Julian (57202358064); Zieliński, Tomasz (56181435000)","7201856684; 57211125119; 56181565100; 13205796700; 57202358064; 56181435000","PyOmeroUpload: A Python toolkit for uploading images and metadata to OMERO","2020","Wellcome Open Research","5","","96","","","","3","10.12688/wellcomeopenres.15853.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089778395&doi=10.12688%2fwellcomeopenres.15853.1&partnerID=40&md5=7e12bf01594af4b88c95e611b7897a17","EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom","Millar A., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Hay J., EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom, SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Troup E., EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom, SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Clark I., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Pietsch J., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Zieliński T., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom","Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in portable, platform-independent Docker images that enable users to deploy and run the utilities easily, regardless of Operating System constraints. A selection of use cases is provided, illustrating the primary capabilities and flexibility offered with the toolkit, along with a discussion of limitations and potential future extensions. PyOmeroUpload is available from: https://github.com/SynthSys/pyOmeroUpload. © 2020 Hay J et al.","Data sharing; Docker; Metadata; Microscopy; OMERO; Research data management","","","","","","Conda channel; UK Centre for Mammalian Synthetic Biology, (BB/M018040); Wellcome Trust, WT, (204804); Biotechnology and Biological Sciences Research Council, BBSRC","Funding text 1: Grant information: This work was funded by the Wellcome Trust [204804; Institutional Strategic Support Fund]. This work was also supported by the Biotechnology and Biological Sciences Research Council (BBSRC) through the UK Centre for Mammalian Synthetic Biology [BB/M018040].; Funding text 2: The authors thank The Open Microscopy Environment team for providing such comprehensive software platforms, packages, tools and support, and for assistance in making effective use of the OMERO client library API. Finally, the authors thank the Bioconda Development Team for assistance with creating, merging and re-building packages for the Conda channel.","Molloy J.C., The Open Knowledge Foundation: Open Data Means Better Science, PLoS Biol, 9, 12, (2011); Concordat on Open Research Data, (2016); Realising the Potential: Final Report of the Open Research Data Task Force, (2018); Open Research: How data sharing can advance scientific impact in Scotland, (2019); Zielinski T., Hay J., Millar A.J., The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation, Wellcome Open Res, 4, (2019); The Open Microscopy Environment - OMERO, openmicroscopy.org, (2020); Swedlow J.R., Goldberg I.G., Eliceiri K.W., Et al., Bioimage Informatics for Experimental Biology, Annu Rev Biophys, 38, pp. 327-346, (2009); Allan C., Burel J.M., Moore J., Et al., OMERO: flexible, model-driven data management for experimental biology, Nat Methods, 9, 3, pp. 245-253, (2012); Granados A.A., Pietsch J.M.J., Cepeda-Humerez S.A., Et al., Distributed and dynamic intracellular organization of extracellular information, Proc Natl Acad Sci U S A, 115, 23, pp. 6088-6093, (2018); Importing Data with OMERO.insight Version 5, Open Microscopy Environment (OME) | Help; Import images - OMERO 5.4.10 documentation, Open Microscopy Environment (OME) | Docs, (2019); The Open Microscopy Environment - Bio-Formats, openmicroscopy.org, (2020); OMERO clients overview - OMERO 5.4.10 documentation, Open Microscopy Environment (OME) | Docs, (2019); omero-py: Python bindings to the OMERO.blitz server, (2020); PyPI. The Python Package Index, PyPI, (2020); Conda | Conda documentation, Conda, (2017); Gruning B., Dale R., Sjodin A., Et al., Bioconda: sustainable and comprehensive software distribution for the life sciences, Nat Methods, 15, 7, pp. 475-476, (2018); Empowering App Development for Developers | Docker, Docker, (2020); Project Jupyter, Project Jupyter, (2020); Blitz Gateway documentation - OMERO 5.5.1 documentation, Open Microscopy Environment (OME) | Docs, (2019); Docker Hub | OpenJDK, Docker Hub, (2020); Docker Hub, Docker Hub, (2020); OMERO Demo Server, Open Microscopy Environment (OME) | Help, (2018); pandas: Powerful data structures for data analysis, time series, and statistics, (2020); numpy: NumPy is the fundamental package for array computing with Python, (2020); Hunter J.D., Droettboom M., matplotlib: Python plotting package, (2020); Waskom M., seaborn: seaborn: statistical data visualization, (2020); JSON API - OMERO 5.4.10 documentation, Open Microscopy Environment (OME) | Docs, (2019); Reitz K., requests: Python HTTP for Humans, (2020); pandas.DataFrame - pandas 1.0.3 documentation, Pandas | API Reference, (2014); PyCharm: the Python IDE for Professional Developers by JetBrains, JetBrains, (2020); O'Brien T., VSCodium - Open Source Binaries of VSCode, VSCodium.com, (2020); Visual Studio Code - Code Editing. Redefined, Visual Studio Code, (2020); OpenSSH, OpenSSH, (2020); X.Org, X.Org, (2019); MobaXterm free Xserver and tabbed SSH client for Windows, (2020); Harrison C., Xming X Server for Windows - Official Website, straightrunning.com, (2020); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Representational state transfer, Wikipedia, (2020); Hay J., Zielinski T., pyOmeroUpload (Version v5.6.2_2.0.0), Zenodo, (2020); Hay J., Zielinski T., OMEROConnect (Version v5.6.2_2.0.0), Zenodo, (2020); Hay J., Zielinski T., omero_connect_demo (Version v1.0.0), Zenodo, (2020)","A. Millar; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; email: Andrew.Millar@ed.ac.uk","","F1000 Research Ltd","","","","","","2398502X","","","","English","Wellcome Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85089778395"
"Gowen E.; Meier J.J.","Gowen, Elise (57209567776); Meier, John J. (24073571600)","57209567776; 24073571600","Research Data Management Services and Strategic Planning in Libraries Today: A Longitudinal Study","2020","Journal of Librarianship and Scholarly Communication","8","1","eP2336","","","","3","10.7710/2162-3309.2336","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129227468&doi=10.7710%2f2162-3309.2336&partnerID=40&md5=c9ed0155fb0a7eb67f08653a04d7b11b","Earth and Mineral Sciences Librarian, PA, United States; Head of STEM for Engagement and Outreach, PA, United States","Gowen E., Earth and Mineral Sciences Librarian, PA, United States; Meier J.J., Head of STEM for Engagement and Outreach, PA, United States","INTRODUCTION Research data services have been adopted by many academic libraries. This study tracked the changes in research data management services and staffing among Association of American Universities (AAU) libraries over the past 5 years and compared them to the libraries’ goals for research data management (RDM) in their strategic plan. METHODS This quantitative study examined libraries at the 60 U.S. AAU institutions. In order to examine longitudinal changes, portions of Briney et.al. (2015a) were used as a basis for measuring data librarian staffing and services. These trends were compared to the contemporary strategic priorities of libraries interviewed by Meier (2016), as well as against strategic plans of 2014 and 2019 available online. RESULTS & DISCUSSION While there have been modest increases in libraries in the sample population offering data services, most of those gains have been among the libraries that did not consider RDM a priority in 2014. Interestingly, some of the libraries that mentioned RDM as a priority in 2014 have lost data librarian positions. Over half of the libraries in this study now provide or support a data repository. Many library strategic plans that mentioned RDM as an explicit goal 5 years ago now no longer mention it. CONCLUSION Data librarian positions, data services, and data repositories have now become common features of large research university libraries. However, research data services are no longer as prominent in many library strategic plans at institutions where such services are more established, and libraries instead seem to be moving on to the work of rethinking the nature of the services or expanding them. © 2020 Gowen & Meier.","","","","","","","National Science Foundation, NSF","Research data management was given a major boost of importance in the sciences with the announcement that the National Science Foundation would begin requiring the inclusion of a data management plan for funding. A majority of responding libraries starting RDM services in 2011 said the NSF requirement was the main reason they introduced the services (Fearon, Jr., Gunia, Pralle, Lake, & Sallans, 2013). The ARL SPEC Kit provides a useful and broad-ranging overview of the state of RDM services in 2013, the year before Briney et al. conducted their research. The Kit includes surveys of the kinds of RDM services on offer, snapshots of the strategic plans and data repositories of the surveyed institutions, and titles of data librarians.","Briney K., Strategic planning for research data services, Bulletin of the Association for Information Science and Technology, 42, 4, pp. 39-41, (2016); Briney K., Goben A., Zilinski L., Data from: Do you have an institutional data policy? A review of the current landscape of library data services and institutional data policies, Harvard Dataverse, (2015); Briney K., Goben A., Zilinski L., Do you have an institutional data policy? A review of the current landscape of library data services and institutional data policies, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Bryant R., Lavoie B. F., Malpas C., The realities of research data management. Part three, Incentives for building university RDM services, (2018); Delserone L. M., At the Watershed: Preparing for Research Data Management and Stewardship at the University of Minnesota Libraries, (2008); Fallaw C., Et al., Overly honest data repository development, (2016); Fearon D., Gunia B., Pralle B., Lake S., Sallans A., SPEC Kit 334: Research Data Management Services, (2013); Federer L., Defining data librarianship: a survey of competencies, skills, and training, Journal of the Medical Library Association: JMLA, 106, 3, pp. 294-303, (2018); Gold A., Cyberinfrastructure, data, and libraries, part 2, D-Lib Magazine, 13, (2007); Maxwell D., Norton H., Wu J., The data science opportunity: Crafting a holistic strategy, Journal of Library Administration, 58, 2, pp. 111-127, (2018); Meier J. J., The future of academic libraries: Conversations with today’s leaders about tomorrow, Portal: Libraries and the Academy, 16, 2, pp. 263-288, (2016); Perrier L., Blondal E., MacDonald H., Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies, Library & Information Science Research, 40, 3–4, pp. 173-183, (2018); Saunders L., Academic libraries’ strategic plans: top trends and under-recognized areas, The Journal of Academic Librarianship, 41, 3, pp. 285-291, (2015); Shen Y., Shen Y., Strategic planning for a data-driven, shared-access research enterprise: virginia tech research data assessment and landscape study, College & Research Libraries, 77, 4, pp. 500-519, (2016); Shreeves S. L., Cragin M. H., Introduction: institutional repositories: current state and future, Library Trends, 57, 2, pp. 89-97, (2008); (2017); Walters T., The Future Role of Publishing Services in University Libraries, 12, 4, pp. 425-454, (2012); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008); Yoon A., Schultz T., Research data management services in academic libraries in the u.s.: A content analysis of libraries’ websites, College & Research Libraries, 78, 7, (2017)","E. Gowen; 105 Deike Building, University Park, 16802-2710, United States; email: edg16@psu.edu","","Iowa State University Digital Press","","","","","","21623309","","","","English","J. Librariansh. Sch. Commun.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85129227468"
"Wong G.K.W.; Chan D.L.H.","Wong, Gabrielle K. W. (26634515800); Chan, Diana L. H. (20336656300)","26634515800; 20336656300","Designing library-based research data management services from bottom-up","2020","Future Directions in Digital Information: Predictions, Practice, Participation","","","","55","68","13","1","10.1016/B978-0-12-822144-0.00004-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114492291&doi=10.1016%2fB978-0-12-822144-0.00004-5&partnerID=40&md5=4458baf9f11d0fbfbc9e5bf6ade964f1","Hong Kong University of Science and Technology, Kowloon, Hong Kong","Wong G.K.W., Hong Kong University of Science and Technology, Kowloon, Hong Kong; Chan D.L.H., Hong Kong University of Science and Technology, Kowloon, Hong Kong","Research data management (RDM) services in a research institution require the broad collaboration of multiple administration and service entities, including research office, IT office, and library. Cross-unit RDM support coordinated at the institutional level (top-down) is a common model in many pioneering universities, especially those in Europe and the United Kingdom. For institutions in which such top-down RDM initiatives are not yet present, libraries can play a pivotal role in bringing changes from bottom-up. © 2021 David Baker & Lucy Ellis Published by Elsevier Ltd All rights reserved.","Bottom-up strategies; Data management plans; Library-based research support; Research data management; Service design; Strategic thinking","","","","","","","","Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, Aust. Libr. J., 64, 3, pp. 224-234, (2015); Clare C., Cruz M., Papadopoulou E., Savage J., Teperek M., Wang Y., Witkowska I., Yeomans J., Engaging Researchers with Data Management: The Cookbook, (2019); Cooper D., Springer R., Data Communities: A New Model for Supporting STEM Data Sharing, (2019); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, J. Assoc. Inf. Sci. Technol., 68, 9, pp. 2182-2200, (2017); Erway R., Horton L., Nurnberger A., Otsuji R., Rushing A., Building Blocks: Laying the Foundation for a Research Data Management Program, (2016); Joo S., Peters C., User needs assessment for research data services in a research university, J. Librariansh. Inf. Sci., (2019); Majid S., Foo S., Zhang X., Research data management by academics and researchers: Perceptions, knowledge and practices, Maturity and Innovation in Digital Libraries, 11279, pp. 166-178, (2018); Parham S.W., Doty C., NSF DMP content analysis: What are researchers saying?, Bull. Am. Soc. Inf. Sci. Technol., 39, 1, (2012); Pasek J.E., Mayer J., Education needs in research data management for science-based disciplines, Issues Sci. Technol. Librariansh., 92, (2019); Petters J.L., Brooks G.C., Smith J.A., Haas C.A., The impact of targeted data management training for field research projects-a case study, Data Sci. J., 18, 1, (2019); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Ragon B., Alignment of library services with the research lifecycle, J. Med. Libr. Assoc., 107, 3, (2019); Rambo N., Research Data Management: Roles for Libraries, (2015); Read K.B., Koos J., Miller R.S., Miller C.F., Phillips G.A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, J. Med. Libr. Assoc., 107, 3, (2019); Schmidt B., Shearer K., Librarians’ Competencies Profile for Research Data Management. Joint Task Force on Librarians’ Competencies in Support of e-Research and Scholarly Communication, (2016); Sewell C., Kingsley D., Developing the 21st century academic librarian: The Research Support Ambassador Programme, New Rev. Acad. Librariansh., 23, 2-3, pp. 148-158, (2017); Southall J., Scutt C., Training for Research Data Management at the Bodleian Libraries: National contexts and local implementation for researchers and librarians, New Rev. Acad. Librariansh., 23, 2-3, pp. 303-322, (2017); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Libr. Inf. Sci. Res., 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research data services in European academic research libraries, LIBER Q., 27, 1, pp. 23-44, (2017); Van Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon University, J. Librariansh. Sch. Commun., 3, 3, (2015); Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, J. Librariansh. Sch. Commun., 3, 2, (2015); Wiley C., Mischo W.H., Data management practices and perspectives of atmospheric scientists and engineering faculty, Issues Sci. Technol. Librariansh., 85, (2016); Yoon A., Donaldson D.R., Library capacity for data curation services: A US national survey, Library Hi Tech, (2019)","","","Elsevier","","","","","","","978-012822144-0","","","English","Future Directions in Digital Information: Predictions, Practice, Participation","Book chapter","Final","","Scopus","2-s2.0-85114492291"
"Abdolahzadeh S.; Braun P.G.; Elsenga C.; Folgering-van der Vliet M.; Knauer B.; van der Leij A.W.; Sheedfar F.; Trentacosti G.; Weber-Boer K.O.","Abdolahzadeh, Shaghayegh (57870184500); Braun, Peter G. (57869743900); Elsenga, Christina (57870340000); Folgering-van der Vliet, Marijke (57869744000); Knauer, Babette (57870036900); van der Leij, Ane W. (57870184600); Sheedfar, Fareeba (57869288900); Trentacosti, Giulia (57222548695); Weber-Boer, Kathryn O. (57869588400)","57870184500; 57869743900; 57870340000; 57869744000; 57870036900; 57870184600; 57869288900; 57222548695; 57869588400","From project to customized service: Research support at the university of groningen library","2020","Cases on Research Support Services in Academic Libraries","","","","1","24","23","0","10.4018/978-1-7998-4546-1.ch001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137102414&doi=10.4018%2f978-1-7998-4546-1.ch001&partnerID=40&md5=3d085d9377b4e1fbb731d363089f5f09","University of Groningen, Netherlands; University Medical Center Groningen, University of Groningen, Netherlands","Abdolahzadeh S., University of Groningen, Netherlands; Braun P.G., University Medical Center Groningen, University of Groningen, Netherlands; Elsenga C., University of Groningen, Netherlands; Folgering-van der Vliet M., University of Groningen, Netherlands; Knauer B., University of Groningen, Netherlands; van der Leij A.W., University of Groningen, Netherlands; Sheedfar F., University of Groningen, Netherlands; Trentacosti G., University of Groningen, Netherlands; Weber-Boer K.O., University of Groningen, Netherlands","The academic landscape of the Netherlands has been influenced in recent years by new governmental policies regarding open access and open science, national and European legal guidelines, developments in ICT, and changes in how researchers are assessed. The University of Groningen Library (UB) has seized the opportunity in these developments, providing research support in the domains of registration and archiving of research output, open access publishing, research data management, and research analytics. Increased efficiency in traditional library procedures and the introduction of project-based funding have provided staff capacity for these developments. Full-service customization, to meet the needs of researchers and alleviate their time and work pressure, lies at the heart of the UB's research support. © 2021, IGI Global. All rights reserved.","","","","","","","","","(2019); De helft van alle peer-reviewed artikelen van de Nederlandse universiteiten is open access beschikbaar [Half of all peer-reviewed articles from Dutch universities are open access], (2018); The Dutch Approach, (2020); Standard Evaluation Protocol 2015-2021: Protocol for Research Assessments in the Netherlands, (2014); Bieker F., Friedewald M., Hansen M., Obersteller H., Rost M., A Process for Data Protection Impact Assessment under the European General Data Protection Regulation, Privacy Technologies and Policy, Fourth Annual Privacy Forum, APF 2016 Frankfurt, pp. 21-37, (2016); Coalition S., Principles and Implementation, (2019); List of Members, (2019); Dahl M., Inside-out Library Services, Challenging the ""Jacks of All Trades but Masters of None"" Librarian Syndrome, pp. 14-34, (2018); Dekker S., Brief van de Staatssecretaris van Onderwijs, Cultuur en Wetenschap [Letter from the State Secretary of Education, Culture, and Science], Kamerstuk, 354, (2013); Dempsey L., Outside-in and inside-out, (2010); San Francisco Declaration on Research Assessment, (2012); NWO's Incentive Fund for Open Access to end on 1 January 2018, (2017); Hicks D., Wouters P., Waltman L., de Rijcke S., Rafols I., Bibliometrics: The Leiden Manifesto for research metrics, Nature, 520, 7548, pp. 429-431, (2015); Hoorn E., Montagner C., Starting with a DPIA methodology for human subject research, (2018); Glossary Landelijk Coördinatiepunt Research Data Management [Glossary of the National Coordination Point for Research Data Management], (2020); Nieboer M., Bibliotheek in beweging: Visiedocument Bibliotheek RUG 2020 [Library in Movement: Vision Document for the University of Groningen Library on the Way to 2020; Memorandum], (2011); Signatories, (2020); You share, we take care!, (2020); Responsible Research Data Management and the Prevention of Scientific Misconduct, (2013); Schalken A., Evaluatierapport pilot You Share, We Take Care (publieke versie) [Evaluation report on the pilot 'You Share, We Take Care' (public version)], (2019); Our members, (2020); Setting the default to open. OACA list, (2020); Practical Guide to the International Alignment of Research Data Management, (2018); (2020); Think Bold: University of Groningen Strategic Plan 2015-2020, (2015); University of Groningen Research Data Policy, (2015); Data Federation Hub, (2019); History of the University, (2019); RDMP Web tool, (2019); Facts and Figures, (2020); Global Focus, (2020); Quality Assurance and Benchmarking of Research Impact: Guidelines for the prudent use of SciVal and Incites, (2017); UB 2025: The future is open. Strategic Plan University of Groningen Library 2020-2025, (2019); University of Groningen Press, (2020); (2020); van Laarhoven P., Faculty of 1000: Een verrassende dienst [a surprising service], Pictogram, 6, 1, pp. 9-11, (2014); van Wezenbeek W., Touwen H., Versteeg A., van Wesenbeeck A., Nationaal plan open science [National Plan for Open Science], (2017); Visser D., The Open Access provision in Dutch copyright contract law, Journal of Intellectual Property Law & Practice, 10, 11, pp. 872-878, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","IGI Global","","","","","","","978-179984547-8; 978-179984546-1","","","English","Cases on res. Support Services in Academic Libraries","Book chapter","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85137102414"
"","","","A model for initiating research data management services at academic libraries. (J Med Libr Assoc.), 2019, 107(3), 432–41, 10.5195/jmla.2019.545)","2020","Journal of the Medical Library Association","108","2","","352","","","0","10.5195/jmla.2020.947","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083398163&doi=10.5195%2fjmla.2020.947&partnerID=40&md5=ad86947e066907d1d2d2834e465e7e25","","","Pages 439–40: Acknowledgments of pilot program participants Deborah Chiarella, Pamela M. Rose, Aletia Morgan, and Gretchen Sneff are missing. The “Acknowledgments” should be: This program was supported by the National Library of Medicine (NLM), National Institutes of Health (NIH), under cooperative agreement number UG4LM012342 with the University of Pittsburgh, Health Sciences Library System, and NIH R25LM012283. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We acknowledge pilot participants of this program, specifically those from University at Buffalo: Nell Aronoff, Donna R. Berryman, AHIP, Deborah Chiarella, Amy Gische Lyons, AHIP, FMLA, Pamela M. Rose, Michelle L. Zafron, Elizabeth Stellrecht, and Linda Lohr; University of Delaware: Sarah E. Katz, Tom Melvin, Natalia Lopez, Sandra Millard, and Aletia Morgan; Drexel University: Abby L. Adamczyk, AHIP, Elizabeth Ten Have, Kathleen Turner, Janice Masud-Paul, and Deborah Morley; and Temple University: Jenny Pierce, Natalie Tagge, Gretchen Sneff, and Nancy Turner. The authors also thank Richard McGowan for discussion and comment on the manuscript. © 2020, Medical Library Association. All rights reserved.","","","","","","","National Institutes of Health, NIH, (R25LM012283, UG4LM012342); U.S. National Library of Medicine, NLM","Pages 439–40: Acknowledgments of pilot program participants Deborah Chiarella, Pamela M. Rose, Aletia Morgan, and Gretchen Sneff are missing. The “Acknowledgments” should be: This program was supported by the National Library of Medicine (NLM), National Institutes of Health (NIH), under cooperative agreement number UG4LM012342 with the University of Pittsburgh, Health Sciences Library System, and NIH R25LM012283. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We acknowledge pilot participants of this program, specifically those from University at Buffalo: Nell Aronoff, Donna R. Berryman, AHIP, Deborah Chiarella, Amy Gische Lyons, AHIP, FMLA, Pamela M. Rose, Michelle L. Zafron, Elizabeth Stellrecht, and Linda Lohr; University of Delaware: Sarah E. Katz, Tom Melvin, Natalia Lopez, Sandra Millard, and Aletia Morgan; Drexel University: Abby L. Adamczyk, AHIP, Elizabeth Ten Have, Kathleen Turner, Janice Masud-Paul, and Deborah Morley; and Temple University: Jenny Pierce, Natalie Tagge, Gretchen Sneff, and Nancy Turner. The authors also thank Richard McGowan for discussion and comment on the manuscript.","","","","Medical Library Association","","","","","","15365050","","JMLAC","","English","J. Med. Libr. Assoc.","Erratum","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85083398163"
"Albrecht H.; Bauer B.; Blumesberger S.; Erasimus E.; Formanek D.; Reitbrecht C.","Albrecht, Harald (54881274700); Bauer, Bruno (57200436821); Blumesberger, Susanne (27867529800); Erasimus, Elisabeth (57221597878); Formanek, Daniel (54881288700); Reitbrecht, Caroline (57221596982)","54881274700; 57200436821; 27867529800; 57221597878; 54881288700; 57221596982","Cooperative report of #vbib20 – the virtual conference of bib and tib about library topics (May 26–28, 2020); [Kooperativer bericht Über #vbib20 – die virtuelle konferenz von bib und tib rund um bibliothekarische themen (26.–28. mai 2020)]","2020","VOEB-Mitteilungen","73","2","","326","341","15","0","10.31263/voebm.v73i2.3989","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099573403&doi=10.31263%2fvoebm.v73i2.3989&partnerID=40&md5=03ea755c874d6151a1947d3af54ac63f","Medizinische Universität Wien, Universitätsbibliothek, Austria; Universität Wien, Bibliotheks-und Archivwesen, Austria","Albrecht H., Medizinische Universität Wien, Universitätsbibliothek, Austria; Bauer B., Medizinische Universität Wien, Universitätsbibliothek, Austria; Blumesberger S., Universität Wien, Bibliotheks-und Archivwesen, Austria; Erasimus E., Medizinische Universität Wien, Universitätsbibliothek, Austria; Formanek D., Medizinische Universität Wien, Universitätsbibliothek, Austria; Reitbrecht C., Medizinische Universität Wien, Universitätsbibliothek, Austria","After the cancellation of the 109. Deutscher Bibiothekartag (109th German Librarian Day), which should have taken place in Hannover from Mai 26th to 29th 2020, the Berufsverband Information Bibliothek (BIB) and the TIB – Leibniz Information Centre for Science and Technology University Library – together with #vBIB20 organised at short notice a virtual conference about library topics from May 26th to 28th 2020. In this cooperative report lectures are presented about publication system, Open Access, research data management, library statistics and provenance research. © Harald Albrecht, Bruno Bauer, Susanne Blumesberger, Elisabeth Erasimus, Daniel Formanek, Caroline Reitbrecht.","#vBIB20; 109<sup>th</sup> German Librarian Day; Cancellation; Cooperative report; Hannover 2020; Virtual conference","","","","","","","","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85099573403"
"Morriello R.","Morriello, Rossana (57218379694)","57218379694","Birth and development of data librarianship","2020","JLIS.it","11","3","","1","15","14","5","10.4403/jlis.it-12653","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090907724&doi=10.4403%2fjlis.it-12653&partnerID=40&md5=23a826d32e5389703b7fccc694dafdc6","Politecnico di Torino, Servizio Programmazione Sviluppo e Qualità, Italy","Morriello R., Politecnico di Torino, Servizio Programmazione Sviluppo e Qualità, Italy","Data librarianship and the role of the data librarian are an established reality in many countries, even though at different levels. Particularly, academic librarians have been involved in research data management for a long time and this role is acquiring precise features. In Italy, the data librarian is a figure still to be built and defined. The aim of the article is to offer a first systematic exploration in the fields of data librarianship and the role of the data librarian, both in their practical (what activities) and methodological (how activities are performed) features. The hope is to encourage the beginning of a necessary reflection on these topics. © 2020, The Author(s).","Academic libraries; Big data; Data librarian; Data librarianship; Data science; Library and information science; Open data; Repositories; Research data management","","","","","","","","Tinjaca Arciniegas, Camila Eliana, Gutierrez Yury Marcela Gomez, Gregorio-Chaviano Orlando, La biblioteca universitaria y su rol en los procesos de investigación: una mirada desde los servicios de información con enfoque bibliométrico en Colombia, Biblios: Journal of Librarianship and Information Science, 72, pp. 113-129, (2018); Bell Steven J., Shank John, The blended librarian: A blueprint for redefining the teaching and learning role of academic librarians, College & Research Libraries News, 65, 7, pp. 372-375, (2004); Carlson Jake, Nelson Megan Sapp, Bracke Marianne, Wright Sarah, The Data Information Literacy Toolkit, (2015); Cassella Maria, Dal digital curator al data librarian, Biblioteche oggi, 34, pp. 13-21, (2016); Chou Chiu-chuang Lu, 50 Years of Social Science Data Services: A Case Study from the University of Wisconsin-Madison, International Journal of Librarianship, 2, 1, pp. 42-52, (2017); Cox Andrew M., Kennan Mary Anne, Lyon Liz, Pinfield Stephen, Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Dempsey Lorcan, La facilitated collection: una riflessione sulle collezioni come servizio, Biblioteche oggi, 38, pp. 3-8, (2020); Federer Lisa, Defining data librarianship: a survey of competencies, skills, and training, Journal of the Medical Library Association, 106, 3, pp. 294-303, (2018); Guerrini Mauro, Possemato Tiziana, Linked data: un nuovo alfabeto del web semantico, Biblioteche oggi, 30, 3, pp. 7-15, (2012); Guy Marieke, RDM Training for Librarians: DCC RDM Services case studies, (2004); Khan Hammad Rauf, Du Yunfei, What is a Data Librarian?: A Content Analysis of Job Advertisements for Data Librarians in the United States Academic Libraries, IFLA World Library and Information Congress, (2018); Kruse Fillip, Thestrup Jesper Boserup, Research Data Management-A European Perspective, (2018); Latham Bethany, Research Data Management: Defining Roles, Prioritizing Services, and Enumerating Challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Luperi Patrizia, Ponzani Vittorio, Formare i bibliotecari per (in)formare gli utenti, La biblioteca (in)forma. Digital Reference, Information Literacy, e-learning, Atti del Convegno Stelline, pp. 143-152, (2018); Manoni Paola, Merola Giovanna Mazzola, Cancedda Flavia, Michetti Giovanni, Bibliotecario e archivista nelle norme UNI 11535:2014 e UNI 11536:2014, AIB studi, 55, 1, pp. 105-134, (2015); Report of the 2011 Canadian Research Data Summit, (2011); Martin Elaine R., Highlighting the Informationist As a Data Librarian Embedded in a Research Team, Journal of eScience Librarianship, 2, 1, pp. 1-2, (2013); Padilla Thomas G., Collections as data: Implications for enclosure, College & Research Libraries News, 79, 6, pp. 296-300, (2018); Perrier Laure, Blondal Erik, MacDonald Heather, Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies, Library & Information Science Research, 40, 3–4, pp. 173-183, (2018); Pinfield Stephen, Cox Andrew, Rutter Sophie A., Mapping the future of academic libraries: a report for SCONUL, (2017); 23 Things: Libraries for Research Data, (2016); Ribas Semeler Alexandre, Pinto Adilson Luiz, Rozados Helen Beatriz Frota, Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2019); Rice Robin, Southall John, The data librarian’s handbook, (2016); Ridi Riccardo, La piramide dell’informazione: una introduzione, AIB studi, 59, 1–2, pp. 69-96, (2019); Robinson Lyn, Bawden David, The story of data’: A socio-technical approach to education for the data librarian role in the CityLIS library school at City, University of London, Library Management, 38, 6, pp. 312-322, (2017); Schulte Jurgen, Tiffen Belinda, Edwards Jackie, Abbott Scott, Luca Edward, Shaping the Future of Academic Libraries: Authentic Learning for the Next Generation, College & Research Libraries, 79, 5, pp. 685-696, (2018); Shearer Birgit Schmidt Kathleen, Librarians’ Competencies Profile for Research Data Management, Oint Task Force on Librarians’ Competencies in Support of E­Research and Scholarly Communication, (2016); Tammaro Anna Maria, Biblioteche accademiche e data literacy: un primo (parziale) rapporto dall’Italia, (2017); Tammaro Anna Maria, Data literacy: formare docenti e studenti alla gestione dei dati di ricerca, Biblioteche oggi, 35, 8, pp. 19-25, (2017); The Santa Barbara Statement on Collections as Data, Always Already Computational-Collections as Data, (2019); Thomas Camille, Urban Richard, What Do Data Librarians Think of the MLIS? Professionals’ Perceptions of Knowledge Transfer, Trends, and Challenges, College & Research Libraries, 79, 3, pp. 401-423, (2018); Thompson Kristi, Editorial: Introducing this Special Issue on Data Librarianship, International Journal of Librarianship, 2, 1, pp. 1-2, (2017); Policy sulla gestione dei dati della ricerca RDM, (2017); Research Data Unipd, (2018); Den Eynden Veerle, Knight Gareth, Vlad Anca, Radler Barry, Tenopir Carol, Leon David, Manista Frank, Whitworth Jimmy, Corti Louise, Towards Open Research: Practices, Experiences, Barriers and Opportunities, Wellcome Trust, (2016); Vivarelli Maurizio, La lettura: storie, teorie, luoghi, (2018); Vivarelli Maurizio, Dai frattali alle reti: un punto di vista olistico per la lettura, La biblioteca che cresce: contenuti e servizi tra frammentazione e integrazione, Atti del convegno Stelline, (2019); Volume of Data/Information Created Worldwide from 2010 to 2025, (2020)","R. Morriello; Politecnico di Torino, Servizio Programmazione Sviluppo e Qualità, Italy; email: rossana.morriello@polito.it","","Universita di Firenze, Dipartimento di Storia, Archeologia, Geografia, Arte e Spettacolo","","","","","","20385366","","","","Italian","JLIS.it","Article","Final","","Scopus","2-s2.0-85090907724"
"Kenyon J.; Attebury R.; Doney J.; Godfrey B.; Martinez J.; Seiferle-Valencia M.","Kenyon, Jeremy (55531964300); Attebury, Ramirose (26639070600); Doney, Jylisa (55206340900); Godfrey, Bruce (55532337700); Martinez, Jessica (57210733961); Seiferle-Valencia, Marco (57221670928)","55531964300; 26639070600; 55206340900; 55532337700; 57210733961; 57221670928","Help-seeking behaviors in research data management","2020","Issues in Science and Technology Librarianship","2020","96","","1","17","16","3","10.29173/istl2568","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100889019&doi=10.29173%2fistl2568&partnerID=40&md5=2c3274e3ba1d05fef72dc80092357a8e","University of Idaho Library, Moscow, ID, United States; Technical Services University of Idaho Library, Moscow, ID, United States; Social Sciences Librarian University of Idaho Library, Moscow, ID, United States; GIS Librarian University of Idaho Library, United States; Science Librarian University of Idaho Library, Moscow, ID, United States; Open Education Librarian University of Idaho Library, Moscow, ID, United States","Kenyon J., University of Idaho Library, Moscow, ID, United States; Attebury R., Technical Services University of Idaho Library, Moscow, ID, United States; Doney J., Social Sciences Librarian University of Idaho Library, Moscow, ID, United States; Godfrey B., GIS Librarian University of Idaho Library, United States; Martinez J., Science Librarian University of Idaho Library, Moscow, ID, United States; Seiferle-Valencia M., Open Education Librarian University of Idaho Library, Moscow, ID, United States","Investigations on the help-seeking behavior of academic library patrons have, to date, primarily focused on the undergraduate experience, most often in the context of reference interactions. This study seeks to explore the help-seeking behaviors of a different audience-faculty in the natural and physical sciences at an R2 land-grant university. Eighteen faculty in the natural and physical sciences at the University of Idaho were interviewed, and it was found that faculty seek help from colleagues; peers outside the university, via connections formed in graduate school or professional circles; and through do-it-yourself solutions like ""just googling it,"" but less often through university resources and programs. These results are a starting point to explore how libraries might better understand the help-seeking behavior of research faculty, with an eye towards developing services and sources that better meet faculty research needs. © 2020, Association of College and Research Libraries. All rights reserved.","","","","","","","","","Peer comparison [Data set], (2018); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: Integrated findings to develop research data services, Journal of eScience Librarianship, 6, 1, (2017); Black S., Psychosocial reasons why patrons avoid seeking help from librarians: A literature review, The Reference Librarian, 57, 1, pp. 35-56, (2016); Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Cooper D., Bankston S., Bracke M.S., Callahan B., Change H., Delserone L.M., Diekmann F., Farmer D., Farrell S., Supporting the changing research practices of agriculture scholars, Ithaka S+R, (2017); Cooper D., Springer R., Benner J.G., Bloom D., Carrillo E., Carroll A., Chang B., Chen X., Daix E., Dommermuth E., Et al., Supporting the changing research practices of civil and environmental engineering scholars, (2019); Dawson D., The scholarly communications needs of faculty: An evidence-based foundation for the development of library services, Evidence Based Library and Information Practice, 9, 4, pp. 4-28, (2014); Federer L.M., Lu Y.-L., Joubert D.J., Welsh J., Brandys B., Biomedical data sharing and reuse: Attitudes and practices of clinical and scientific research staff, PLoS ONE, 10, 6, (2015); Goben A., Griffin T., In aggregate: Trends, needs, and opportunities from research data management surveys, College & Research Libraries, 80, 7, pp. 903-924, (2019); Hendrix B.R., Agricultural research practices through a local lens: Adapting the Ithaka S + R study for your campus, Journal of Agricultural & Food Information, 20, 1, pp. 12-24, (2019); Keefer J.A., Karabenick S.A., Help seeking in the information age, Strategic Help Seeking: Implications for Learning and Teaching, pp. 219-250, (1998); Mohr A.H., Bishoff J., Bishoff C., Braun S., Storino C., Johnston L.R., When data is a dirty word: A survey to understand data management needs across diverse research disciplines, Bulletin of the Association for Information Science and Technology, 42, 1, pp. 51-53, (2015); Monroe-Gulick A., Valentine G., Brooks-Kieffer J., You need to have a street beat"": A qualitative study of faculty research needs and challenges, portal: Libraries and the Academy, 17, 4, pp. 777-802, (2017); Mullen C.A., Murthy U., Teague G., Listening to those we serve: Assessing the research needs of university faculty, Journal of Research Administration, 39, 1, pp. 10-31, (2008); Nickels C., Davis H., Understanding researcher needs and raising the profile of library research support, Insights, 33, 1, pp. 1-13, (2020); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Stodden V., The data science life cycle: A disciplined approach to advancing data science as a science, Communications of the ACM, 63, 7, pp. 58-66, (2020); Taylor R.S., Question negotiation and information seeking in libraries, College & Research Libraries, 29, 3, pp. 178-194, (1968); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS ONE, 10, 8, (2015); Thompson K.A., Kellam L., Introduction to databrarianship: The academic data librarian in theory and practice, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 1-6, (2016); Westra B., Data services for the sciences: A needs assessment, Ariadne, 64, (2010); Wiley C.A., Burnette M.H., Assessing data management support needs of bioengineering and biomedical research faculty, Journal of eScience Librarianship, 8, 1, (2019)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85100889019"
"Heinrichs B.; Politze M.","Heinrichs, Benedikt (57211110293); Politze, Marius (57195741179)","57211110293; 57195741179","Moving towards a general metadata extraction solution for research data with state-of-the-art methods","2020","IC3K 2020 - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","1","","","227","234","7","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107123241&partnerID=40&md5=dc2b1d5d46fecd92d58e8759c4785de1","IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany","Heinrichs B., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany; Politze M., IT Center, RWTH Aachen University, Seffenter Weg 23, Aachen, Germany","Many research data management processes, especially those defined by the FAIR Guiding Principles, rely on metadata for making it findable and re-usable. Most Metadata workflows however require the researcher to describe their data manually, a tedious process which is one of the reasons it is sometimes not done. Therefore, automatic solutions have to be used in order to ensure the findability and re-usability. Current solutions only focus and are effective on extracting metadata in single disciplines using domain knowledge. This paper aims, therefore, at identifying the gaps in current metadata extraction processes and defining a model for a general extraction pipeline for research data. The results of implementing such a model are discussed and a proof-ofconcept is shown in the case of video-based data. This model is basis for future research as a testbed to build and evaluate discipline-specific automatic metadata extraction workflows.  Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.","Linked data; Metadata extraction; Metadata generation; Research data management","Extraction; Knowledge management; Metadata; Search engines; Automatic metadata extractions; Domain knowledge; Guiding principles; Meta-data extractions; Research data; Research data managements; State-of-the-art methods; Video-based data; Data mining","","","","","","","Burgess A. B., Mattmann C. A., Automatically classifying and interpreting polar datasets with apache tika, 2014 IEEE 15th International Conference on Information Reuse and Integration (IRI), pp. 863-867, (2014); Corcho O., Eriksson M., Kurowski K., Ojstersek M., Choirat C., van de Sanden M., Coppens F., Eosc interoperability framework (v1.0): Draft for community consultation, (2020); Corcoglioniti F., Rospocher M., Aprosio A. P., Frame-based ontology population with pikes, IEEE Transactions on Knowledge and Data Engineering, 28, 12, pp. 3261-3275, (2016); Cyganiak R., Lanthaler M., Wood D., RDF 1.1 concepts and abstract syntax, (2014); Dcmi metadata terms, (2002); Grunzke R., Hartmann V., Jejkal T., Kollai H., Prabhune A., Herold H., Deicke A., Dressler C., Dolhoff J., Stanek J., Hoffmann A., Muller-Pfefferkorn R., Schrade T., Meinel G., Herres-Pawlis S., Nagel W. E., The masi repository service - comprehensive, metadata-driven and multi-community research data management, Future Generation Computer Systems, (2018); Heilmann J., Tucci A., Plante E., Miller J. F., Assessing functional language in school-aged children using language sample analysis, Perspectives of the ASHA Special Interest Groups, 5, 3, pp. 622-636, (2020); Iglezakis D., Schembera B., EngMeta - a Metadata Scheme for the Engineering Sciences, (2019); Greenberg Jane, Metadata extraction and harvesting, Journal of Internet Cataloging, 6, 4, pp. 59-82, (2004); Knublauch H., Kontokostas D., Shapes constraint language (SHACL), (2017); Kumar Y., Singh N., A comprehensive view of automatic speech recognition system - a systematic literature review, 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pp. 168-173, (2019); Lehmann J., Isele R., Jakob M., Jentzsch A., Kontokostas D., Mendes P. N., Hellmann S., Morsey M., van Kleef P., Auer S., Bizer C., Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia, Semantic Web, 6, 2, pp. 167-195, (2015); Lubas R. L., Jackson A. S., Schneider I., Introduction to metadata, The Metadata Manual, Chandos Information Professional Series, pp. 1-15, (2013); Mattmann C., Zitting J., Tika in action, (2011); Politze M., Bensberg S., Muller M., Managing discipline-specific metadata within an integrated research data management system, Proceedings of the 21st International Conference on Enterprise Information Systems ICEIS 2019, pp. 253-260, (2019); Politze M., Schwarz A., Kirchmeyer S., Claus F., Muller M. S., Kollaborative Forschungsunterst ützung : Ein Integriertes Probenmanagement, E-Science-Tage 2019 : data to knowledge / herausgegeben von Vincent Heuveline, Fabian Gebhart und Nina Mohammadianbisheh, pp. 58-67, (2019); Smith R., An overview of the tesseract ocr engine, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 629-633, (2007); Rodrigo G. P., Henderson M., Weber G. H., Ophus C., Antypas K., Ramakrishnan L., Sciencesearch: Enabling search through automatic metadata generation, 2018 IEEE 14th International Conference on e-Science (e-Science), pp. 93-104, (2018); Schmitz D., Politze M., Forschungsdaten managen - bausteine für eine dezentrale, forschungsnahe unterstützung. o-bib, Das offene Bibliotheksjournal / Herausgeber VDB, 5, 3, pp. 76-91, (2018); Wilkinson M. D., Dumontier M., Aalbersberg I. J. J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L. B., Bourne P. E., Bouwman J., Brookes A. J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C. T., Finkers R., Gonzalez-Beltran A., Gray A. J. G., Groth P., Goble C., Grethe J. S., Heringa J., 't Hoen P. A. C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S. J., Martone M. E., Mons A., Packer A. L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M. A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The fair guiding principles for scientific data management and stewardship, Scientific data, 3, (2016)","","Fred A.; Filipe J.","SciTePress","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","12th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2020 - Part of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2020","2 November 2020 through 4 November 2020","Virtual, Online","165136","","978-989758474-9","","","English","IC3K - Proc. Int. Jt. Conf. Knowl. Discov., Knowl. Eng. Knowl. Manag.","Conference paper","Final","","Scopus","2-s2.0-85107123241"
"Asamer E.-M.; Stryeck S.; Kalová T.","Asamer, Eva-Maria (57144629000); Stryeck, Sarah (57191264224); Kalová, Tereza (57222005827)","57144629000; 57191264224; 57222005827","Report on the “fair data austria” workshop at the Vienna university of technology (Vienna, 8 September 2020); [Bericht zum “fair data Austria” workshop an der tu Wien (Wien, 8. September 2020)]","2020","VOEB-Mitteilungen","73","3-4","","610","619","9","0","10.31263/voebm.v73i3-4.5082","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100934164&doi=10.31263%2fvoebm.v73i3-4.5082&partnerID=40&md5=c1ff5cedcfaa9158051ad1b515075cda","TU Wien, Zentrum für Forschungsdatenmanagement, Austria; TU Graz, Institute for Interactive Systems and Data Science, Austria; Universität Wien, Bibliotheks- und Archivwesen, Austria","Asamer E.-M., TU Wien, Zentrum für Forschungsdatenmanagement, Austria; Stryeck S., TU Graz, Institute for Interactive Systems and Data Science, Austria; Kalová T., Universität Wien, Bibliotheks- und Archivwesen, Austria","This report presents the results of a full-day workshop held with partners of the project FAIR Data Austria2 and a representative of the Horizon 2020 project EOSC-Pillar.3 The aim of the workshop was to determine the objective of a FAIR Office Austria. The participants explored whom a FAIR Office Austria should serve, who the partners and the clients are, which services it should offer as well as what is in scope and what is out of scope in relation to the overall project. The workshop was designed to find a clear mission statement and help the participants develop a common understanding and expectations. The results will be the basis for a concrete action plan. The participants agreed that a FAIR Office Austria should serve predominantly as an information hub and a mediator between (international) organizations (e.g. GO FAIR,4 EOSC,5 RDA6) and local reference points located within the Austrian research institutions and other stakeholders. A FAIR Office Austria must furthermore be organized and run by several (research) institutions to reflect a truly trans-disciplinary, trans-institutional and national dimension. The local FAIR reference points shall be installed at research-performing institutions. Other types of institutions will be encouraged to appoint local reference points as well. © Eva-Maria Asamer, Sarah Stryeck, Tereza Kalová.","European open science cloud; FAIR data; FAIR office austria; FAIR services; Research data management","","","","","","","","Dieser Bericht ist eine Übersetzung der englischen Originalfassung; Open Consultation for the Strategic Research and Innovation Agenda (SRIA) of the European Open Science Cloud (EOSC); Project proposal FAIR Data Austria; Assessment report on ‘FAIRness of services; Osterwalder A., Pigneur Y., Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, (2010); Asamer Eva-Maria","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85100934164"
"Late E.; Kekalainen J.","Late, Elina (57193320263); Kekalainen, Jaana (6508128789)","57193320263; 6508128789","Use and users of a social science research data archive","2020","PLoS ONE","15","8 August","e0233455","","","","3","10.1371/journal.pone.0233455","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089170468&doi=10.1371%2fjournal.pone.0233455&partnerID=40&md5=1370f7c899796c7dfd99ebd61a81c096","Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland","Late E., Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland; Kekalainen J., Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland","This study focuses on the use and users of Finnish social science research data archive. Study is based on enriched user data of the archive from years 2015 2018. Study investigates the number and type of downloaded datasets, the number of citations for data, the demographics of data downloaders and the purposes data are downloaded for. Datasets were downloaded from the archive 10346 times. Majority of the downloaded datasets are quantitative. Quantitative datasets are also more often cited, but the number of citations vary and does not always correlate with the number of downloads. Use of the archive varies by user's country, organization, and discipline. Datasets from the archive were downloaded most often for study work, bachelor's and master's theses, and research purposes. It is likely that reusing research data will increase in the near future as more data will become available, scholars are more informed about research data management, and data citation practices are established. © 2020 Late, Kekäläinen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Archives; Databases, Factual; Finland; Humans; Information Dissemination; Research; Social Sciences; article; demography; human; information center; sociology; factual database; Finland; information center; information dissemination; research","","","","","","","FORCE11 Manifesto | FORCE11; Nosek BA, Alter G, Banks GC, Borsboom D, Bowman SD, Breckler SJ, Et al., Promoting an open research culture, Science, 348, 6242, pp. 1422-1425, (2015); Bartling S, Friesike S., Towards Another Scientific Revolution, Opening Science, pp. 3-15, (2014); Open Science—Research and Innovation—European Commission; Notes on the Federal Research Public Access Act—Harvard Open Access Project, (2019); Wilkinson MD, Dumontier M, Aalbersberg Ij J, Appleton G, Axton M, Baak A, Et al., Comment: The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Notes on the Fair Access to Science and Technology Research Act—Harvard Open Access Project, (2017); Dunning A, De Smaele M, Bohmer J., Are the FAIR Data Principles fair?, Int J Digit Curation, 12, 2, pp. 177-195, (2017); (2018); Open access—H2020 Online Manual; Open Science | ANR; DARIAH-DE Impactomatrix; Open Science—Research and Innovation—European Commission; Borgman CL., Big data, little data, no data: scholarship in the networked world, (2015); Borgman CL, Scharnhorst A, Golshan MS., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, J Assoc Inf Sci Technol, 70, 8, pp. 888-904, (2019); Nature; NCAR's Research Data Archive; About—CESSDA; About ICPSR—Finding & Using Data from ICPSR—Research Guides at Vanderbilt University; About us: The UK Data Archive; Language Bank | Kielipankki; Vardigan M, Whiteman AC., ICPSR meets OAIS: applying the OAIS reference model to the social science archive context, Arch Sci, 7, 1, pp. 73-87, (2007); Corti L., The European landscape of qualitative social research archives: Methodological and practical issues, Forum Qual Sozialforsch, 12, 3, (2011); Clement T, Hagenmaier W, Knies JL., Toward a Notion of the Archive of the Future: Impressions of Practice by Librarians, Archivists, and Digital Humanities Scholars, (2013); Thakar AR, Szalay A, Fekete G, Gray J., The Catalog Archive Server Database Management System, ComputSci Eng, 10, 1, pp. 30-37, (2008); Wynholds L, Fearon DS, Borgman CL, Traweek S., When use cases are not useful: Data practices, astronomy, and digital libraries, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 383-386, (2011); Bicarregui J, Gray N, Henderson R, Jones R, Lambert S, Matthews B., Data Management and Preservation Planning for Big Science, IntJ Digit Curation, 8, 1, pp. 29-41, (2013); Borgman CL, Van de Sompel H, Scharnhorst A, van den Berg H, Treloar A., Who uses the digital data archive? An exploratory study of DANS, Proc Assoc Inf SciTechnol, 52, 1, pp. 1-4, (2015); Pampel H, Dallmeier-Tiessen S., Open Research Data: From Vision to Practice, Opening Science, pp. 213-224, (2014); Floca R., Challenges of Open Data in Medical Research, Opening Science, pp. 297-307, (2014); Darch PT, Knox EJM., Ethical perspectives on data and software sharing in the sciences: A research agenda, Libr Inform Sci Res, 39, 4, pp. 295-302, (2017); Corti L, Fielding N., Opportunities From the Digital Revolution: Implications for Researching, Publishing, and Consuming Qualitative Research, SAGE Open, 6, 4, (2016); Jones K, Alexander SM, Bennett N, Bishop L, Budden A, Cox M, Et al., Qualitative data sharing and reuse for socio-environmental systems research: A synthesis of opportunities, challenges, resources and approaches, SESYNC, (2018); Shrout PE, Rodgers JL., Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis, Annu Rev Psychol, 69, 1, pp. 487-510, (2018); Ferguson L., How and why researchers share data (and why they don't), (2014); Tenopir C, Dalton ED, Allard S, Frame M, Pjesivac I, Birch B, Et al., Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide, PLoSOne, 10, 8, (2015); Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, Et al., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, pp. 1-21, (2011); Kim Y, Adler M., Social scientists' data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories, IntJ Inf Manage, 35, 4, pp. 408-418, (2015); Chawinga WD, Zinn S., Global perspectives of research data sharing: A systematic literature review, Libr Inform Sci Res, 41, 2, pp. 109-122, (2019); Pasquetto IV, Randles BM, Borgman CL., On the Reuse of Scientific Data, Data Sci J, 16, (2017); Hitzler P., Keynote Talk 1: Knowledge Modeling for Data Sharing, Integration, and Reuse, MAICS Mod Artif Intell Cogn Sci Conf, (2016); Wallis JC, Rolando E, Borgman CL., If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology, PLoS One, 8, 7, (2013); Roos A, Kumpulainen S, Jarvelin K, Hedlund T., The information environment of researchers in molecular medicine, Inform Res, 13, 3, (2008); Faniel IM, Kriesberg A, Yakel E., Social scientists' satisfaction with data reuse, J Assoc Inf Sci Technol, 67, 6, pp. 1404-1416, (2015); Faniel IM, Kriesberg A, Yakel E., Data reuse and sensemaking among novice social scientists, Proc ASIST Annu Meet, 49, 1, pp. 1-10, (2012); Curty RG, Qin J., Towards a model for research data reuse behaviour, Proc ASIST Annu Meet, 51, 1, pp. 1-4, (2015); Curty RG, Crowston K, Specht A, Grant BW, Dalton ED., Attitudes and norms affecting scientists' data reuse, PLoSOne, 12, 12, (2017); Yoon A., Red flags in data: Learning from failed data reuse experiences, Proc ASIST Annu Meet, 53, 1, pp. 1-6, (2016); Yoon A, Kim Y., Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories, Libr Inf Sci Res, 39, 3, pp. 224-233, (2017); Curty RG., Beyond ""Data Thrifting"": Factors Influencing Research Data Reuse in the Social Sciences: An Exploratory Study, (2015); Bishop L, Kuula-Luumi A., Revisiting qualitative data reuse: A decade on, SAGE Open, 7, 1, (2017); CoreTrustSeal-Core Trustworthy Data Repositories; Frontpage—Finnish Social Science Data Archive (FSD); Finnish Social Science Data Archive: FSD User Data 2015-2018; Berghmans S, Cousijn H, Deakin G, Meijer I, Mulligan A, Plume A, Et al., Open Data: The Researcher Perspective, (2017)","E. Late; Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland; email: Elina.Late@tuni.fi","","Public Library of Science","","","","","","19326203","","POLNC","32760066","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85089170468"
"Doniparthi G.; Mühlhaus T.; Deßloch S.","Doniparthi, Gajendra (57218700467); Mühlhaus, Timo (16402484700); Deßloch, Stefan (7801627054)","57218700467; 16402484700; 7801627054","A Bloom Filter-Based Framework for Interactive Exploration of Large Scale Research Data","2020","Communications in Computer and Information Science","1259 CCIS","","","166","176","10","1","10.1007/978-3-030-54623-6_15","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090094796&doi=10.1007%2f978-3-030-54623-6_15&partnerID=40&md5=a590e1609e57340aed1b7a73ceecf8b2","Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany; Computational Systems Biology, University of Kaiserslautern, Kaiserslautern, Germany","Doniparthi G., Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany; Mühlhaus T., Computational Systems Biology, University of Kaiserslautern, Kaiserslautern, Germany; Deßloch S., Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany","We present a novel RDBMS-based framework for interactively querying and exploring large-scale bio-science research data. We focus on the interactive exploration model and its evaluation support using Bloom filter indexing techniques for Boolean containment expressions. In particular, our framework helps explore structured research data augmented with schema-less contextual information. Our experiments show significant improvements over traditional indexing techniques, enabling scientists to move from batch-oriented to interactive exploration of research data. © 2020, Springer Nature Switzerland AG.","Bloom filter indices; Cross-omics; Interactive data exploration; Relational JSON; Research data management","Indexing (of information); Information systems; Information use; Relational database systems; Bio-science; Bloom filters; Contextual information; Indexing techniques; Interactive exploration; ITS evaluation; Large-scale research; Research data; Data structures","","","","","","","Bloom B.H., Space/time trade-offs in hash coding with allowable errors, Commun. ACM, 13, 7, pp. 422-426, (1970); Copeland G.P., Khoshafian S., A decomposition storage model, Proceedings of the 1985 ACM SIGMOD International Conference on Management of Data, pp. 268-279, (1985); Corwin J., Silberschatz A., Miller P.L., Marenco L.N., Application of information technology: dynamic tables: an architecture for managing evolving, heterogeneous biomedical data in relational database management systems, JAMIA, 14, 1, pp. 86-93, (2007); Guo D., Wu J., Chen H., Yuan Y., Luo X., The dynamic bloom filters, IEEE Trans. Knowl. Data Eng, 22, 1, pp. 120-133, (2010); Liu Z.H., Hammerschmidt B.C., McMahon D., JSON data management: supporting schema-less development in RDBMS, International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, pp. 1247-1258; Sansone S.A., Rocca-Serra P., Field D., Maguire E., Taylor C., Et al., Toward interoperable bioscience data, Nat. Genet, 44, 2, pp. 121-126, (2012); Wang X., Williams C., Liu Z.H., Croghan J., Big data management challenges in health research-a literature review, Briefings Bioinform, 20, 1, pp. 156-167, (2019)","G. Doniparthi; Heterogeneous Information Systems Group, University of Kaiserslautern, Kaiserslautern, Germany; email: doniparthi@informatik.uni-kl.de","Darmont J.; Novikov B.; Wrembel R.","Springer","","24th European Conference on Advances in Databases and Information Systems, ADBIS 2020","25 August 2020 through 27 August 2020","Lyon","243829","18650929","978-303054622-9","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85090094796"
"Joo S.; Schmidt G.M.","Joo, Soohyung (36621159100); Schmidt, Gisela M. (57222295231)","36621159100; 57222295231","Research data services from the perspective of academic librarians","2020","Digital Library Perspectives","37","3","","242","256","14","5","10.1108/DLP-10-2020-0106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102190518&doi=10.1108%2fDLP-10-2020-0106&partnerID=40&md5=53a43d7dbfda884674e8ca960d514af1","School of Information Science, University of Kentucky, Lexington, KY, United States","Joo S., School of Information Science, University of Kentucky, Lexington, KY, United States; Schmidt G.M., School of Information Science, University of Kentucky, Lexington, KY, United States","Purpose: This study aims to investigate the perceptions of academic librarians regarding research data services (RDS) in academic library environments. This study also examines a range of challenges in RDS from the perspectives of academic librarians. Design/methodology/approach: A nationwide online survey was administered to academic librarians engaged in data services at research universities around the USA. The collected survey responses were analyzed quantitatively using descriptive statistics, hierarchical clustering and multidimensional scaling. Findings: Academic librarians perceived that consultation services would be more valuable to users than technical services in offering RDS. Accordingly, skills associated with consultation services such as instructional skills and data management planning were perceived by participants to be more important. The results revealed that academic libraries would need to seek collaboration opportunities with other units on campus to develop and offer RDS, especially technical services. Originality/value: This study contributes to the existing body of research on the topic of data services in research universities. The study investigated various types of specific professional competencies and used clustering analysis to identify closely associated groups of service types. In addition, this study comprehensively examined both relevant resources for and barriers to RDS. © 2020, Emerald Publishing Limited.","Academic libraries; Data librarianship; Data repositories; Research data management; Research data services; Research libraries","Hierarchical clustering; Information management; Surveys; Clustering analysis; Consultation services; Descriptive statistics; Design/methodology/approach; Management planning; Multi-dimensional scaling; Professional competencies; Research universities; Libraries","","","","","Institute of Museum and Library Services, IMLS, (re-32-16-0410-16)","The authors would like to acknowledge Christie Peters for reviewing the initial survey items and providing feedback. This work was supported by the Institute of Museum and Library Services [re-32-16-0410-16].","Antell K., Foote J.B., Turner J., Shults B., Dealing with data: science librarians’ participation in data management at Association of Research Libraries institutions, College and Research Libraries, 75, 4, pp. 557-574, (2014); Bedi S., Walde C., Transforming roles: Canadian academic librarians embedded in faculty research projects, College and Research Libraries, 78, 3, pp. 314-327, (2017); Burgi P.Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: national efforts to guarantee the sustainability of research outputs, IFLA Journal, 43, 1, pp. 5-21, (2017); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Cox A., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Faniel I., Connaway L., Librarians’ perspectives on the factors influencing research data management programs, College and Research Libraries, 79, 1, pp. 100-119, (2018); Gordon A.S., Millman D.S., Steiger L., Adolph K.E., Gilmore R.O., Researcher- library collaborations: data repositories as a service for researchers, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Harp M.R., Ogborn M., Collaborating externally and training internally to support research data services, Journal of eScience Librarianship, 8, 2, (2019); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Johnson R., Parsons T., Chiarelli A., Kaye A., JISC Research Data Assessment Support – Findings of the 2016 Data Assessment Framework (DAF) Surveys, (2016); Keil D.E., Research data needs from academic libraries: the perspective of a faculty researcher, Journal of Library Administration, 54, 3, pp. 233-240, (2014); Kim J., Warga E., Moen W.E., Competencies required for digital curation: an analysis of job advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Koltay T., Accepted and emerging roles of academic libraries in research 2.0, The Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Lacy J., Survey report: research data management services in Oberlin Group Libraries, Library Faculty Research, 86, (2017); Mons B., Data Stewardship for Open Science, (2018); Peters C., Dryden A.R., Assessing the academic library’s role in campus-wide research data management: a first step at the University of Houston, Science and Technology Libraries, 30, 4, pp. 387-403, (2011); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA Journal, 43, 1, pp. 51-64, (2017); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College and Research Libraries, 73, 4, pp. 349-365, (2012); Tammaro A.M., Matusiak K.K., Sposito F.A., Casarosa V., Data curator’s roles and responsibilities: an international perspective, Libri, 69, 2, pp. 89-104, (2019); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, White Paper, (2012); Tenopir C., Kaufman J., Sandusky R.J., Pollock D., The time has come … to talk about why research data management isn’t easy, Proceedings of the Charleston Library Conference, (2019); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Wiley C., Mischo W.H., Data management practices and perspectives of atmospheric scientists and engineering faculty, Issues in Science and Technology Librarianship, 1, 85, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, 1, (2016); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: a tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: domain-based data management training for liaison librarians, The Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018); Yoon A., Schultz T., Research data management services in academic libraries in the US: a content analysis of libraries’ websites, College and Research Libraries, 78, 7, pp. 920-933, (2017); Jeng W., He D., Chi Y., Social science data repositories in data deluge: a case study of ICPSR’s workflow and practices, The Electronic Library, 35, 4, pp. 626-649, (2017)","S. Joo; School of Information Science, University of Kentucky, Lexington, United States; email: soohyung.joo@uky.edu","","Emerald Group Holdings Ltd.","","","","","","20595816","","","","English","Digit. Library Perspect.","Article","Final","","Scopus","2-s2.0-85102190518"
"Niu J.","Niu, Jinfang (55324047400)","55324047400","Diffusion and adoption of research data management services","2020","Global Knowledge, Memory and Communication","69","3","","117","133","16","1","10.1108/GKMC-05-2019-0057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073997202&doi=10.1108%2fGKMC-05-2019-0057&partnerID=40&md5=5c43f260cc4675f1431d10343e4beb7d","School of Information, University of South Florida, Tampa, FL, United States","Niu J., School of Information, University of South Florida, Tampa, FL, United States","Purpose: This paper aims to identify the diffusion patterns, especially the communication channels, in the diffusion and adoption of research data management services (RDMS) among libraries. Design/methodology/approach: Literature about the RDMS in individual libraries was gathered and analyzed. Data relevant to the research questions were extracted and analyzed. Findings: Early adopters conduct much original research to create RDMS and they often serve as change agents in diffusing their RDMS and related innovations to other libraries. In contrast, late adopters usually learn from early adopters and use their innovations for establishing their own RDMS. Communication channels used in diffusing RDMS deviate slightly from those reported in general diffusion of innovations (DOI) theories. Research limitations/implications: Gathered literature provides incomplete and uneven information for RDMS adopters. This makes it difficult to identify adopter categories and test many generalizations in DOI theories. To overcome these limitations, surveys and interviews will be conducted in the future. Originality/value: Findings from this project contribute to general DOI theories because RDMS is unique compared with many other innovations. The diffusion of RDMS is a decentralized process that involves a high-degree of reinvention and it involves the generation and diffusion of many relevant innovations. The project also identified scholarly communication and inter-organization networks as new types of communication channels that are not well accounted for in existing DOI theories. © 2019, Emerald Publishing Limited.","Communication channels; Diffusion of innovations; Innovation; Libraries; Research data management","","","","","","George C. Gordon Library; Worcester Polytechnic Institute; U.S. National Library of Medicine, NLM; Institute of Museum and Library Services, IMLS; University of Massachusetts Medical School, UMMS"," The RDMS development of early adopters was primarily based on the needs of supporting e-research and the anticipation of government funders’ requirements. As mentioned earlier, although funders require DMPs, they do not specify how RDMS should be provided. Because there is no established model to follow, early adopters also have to be innovators. They conducted original research to determine how to provide such services. For example, Cornell University Libraries decided the types of RDMS for researchers based on the 2009 report issued by the Interagency Working Group on Digital Data (2009) . Purdue, Johns Hopkins and the University of Minnesota did not mention the RDMS of any other libraries in reporting their experiences of creating RDMS. Cornell University Library did refer to the RDMS activities at a few other institutions before deciding how to provide its own RDMS – but only very briefly – because those RDMS activities are still on-going and not established. Many research activities conducted by early adopters were supported by government or private funders. For example, the University of Minnesota Libraries participated in the NSF DataNet grant: Terra Populus: A Global Population/Environment Data Network. They were also awarded an Institute of Museum and Library Services (IMLS) grant for data management and data literacy in 2011 in collaboration with Purdue University, the University of Oregon and Cornell University. Purdue University Libraries and the Graduate School of Library and Information Science at the University of Illinois Urbana-Champaign (UIUC) received an IMLS grant to develop the Data Curation Profiles in 2009. The Lamar Soutter Library (University of Massachusetts Medical School) and the George C. Gordon Library (Worcester Polytechnic Institute) received funding from IMLS and National Library of Medicine in 2010 to develop frameworks for a data management curriculum. RDMS knowledge and tools generated by early adopters are often disseminated widely among libraries. ","To stand the test of time: long-term stewardship of digital data sets in science and engineering, (2006); Atkins D.E., Droegemeier K.K., Feldman S.I., Garcia-Molina H., Klein M.L., Messerschmitt D.G., Messina P., Ostriker J.P., Wright M.H., Revolutionizing science and engineering through cyberinfrastructure: report of the national science foundation blue-ribbon advisory panel on cyberinfrastructure, (2003); Barbrow S., Brush D., Goldman J., Research data management and services: resources for novice data librarians, College and Research Libraries News, 78, 5, (2017); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: integrated findings to develop research data services, Journal of eScience Librarianship, 6, 1, (2017); Booth C., Hope, hype, and VoIP: riding the library technology cycle, Library Technology Reports, 46, 5, pp. 1-3, (2010); Briney K., Goben A., Zilinski L., Do you have an institutional data policy? A review of the current landscape of library data services and institutional data policies, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Callahan D.R., The librarian as change agent in the diffusion of technological innovation, The Electronic Library, 9, 1, pp. 13-15, (1991); Cervone H.F., The effect of professional advice networks on receptivity to innovation in academic librarians, (2007); Chiware E., Mathe Z., Academic libraries' role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2015); Choudhury G.S., Et al., Johns Hopkins university data management services, Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Clayton P., Implementation of Organizational Innovation: Studies of Academic and Research Libraries, (1997); Coleman J.S., Katz E., Menzel H., Medical Innovation: A Diffusion Study, (1966); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Damanpour F., Childers T., The adoption of innovation in public libraries, Library and Information Science Research, 7, 3, pp. 231-246, (1985); Davis F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13, 3, pp. 319-340, (1989); Deiss K.J., Innovation and strategy: risk and choice in shaping user-centered libraries, (2004); Drake M.A., Olsen H.A., The economics of library innovation, (1979); Fearon D., Gunia B., Pralle B.E., Lake S., Sallans A.L., ARL Spec Kit 334: Research Data Management Services, (2013); Fong B.L., Wang M., Required data management training for graduate students in an earth and environmental sciences department, Journal of eScience Librarianship, 4, 1, (2015); Fowler R.K., The university library as learning organization for innovation: an exploratory study, College and Research Libraries, 59, 3, pp. 220-231, (1998); Goben A., Nelson M.S., Teaching librarians about data: the ACRL research data management RoadShow, College and Research Libraries News, 79, 7, (2018); Goulding A., Walton J.G., Distributed leadership and library service innovation, Advances in Librarianship, pp. 37-81, (2014); Hampson C., The adoption of open access funds among Canadian academic research libraries, 2008-2012, Partnership: The Canadian Journal of Library and Information Practice and Research, 9, 2, (2014); Holland M., Diffusion of innovation theories and their relevance to understanding the role of librarians when introducing users to networked information, The Electronic Library, 15, 5, pp. 389-394, (1997); Howard H.A., Organizational structure and innovation in academic libraries, (1981); Harnessing the power of digital data for science and society. Report to the committee on science of the national science and technology council, (2009); Jantz R.C., Innovation in academic libraries: an analysis of university librarians' perspectives, Library and Information Science Research, 34, 1, pp. 3-12, (2012); Jantz R.C., A framework for studying organizational innovation in research libraries, College and Research Libraries, 73, 6, pp. 525-541, (2012); Jantz R.C., Incremental and radical innovations in research libraries: an exploratory examination regarding the effects of ambidexterity, organizational structure, leadership, and contextual factors, (2013); Jantz R.C., The determinants of organizational innovation: an interpretation and implications for research libraries, College and Research Libraries, 76, 4, pp. 512-536, (2015); JonesPryor S., Whyte A., How to develop research data management services-a guide for HEIs, (2013); Leong J., Anderson C., Fostering innovation through cultural change, Library Management, 33, 8-9, pp. 490-497, (2012); Luquire W., Attitudes toward automation/innovation in academic libraries, Journal of Academic Librarianship, 8, 6, pp. 344-351, (1983); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data curation: a study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data management pilot course at an academic library, Journal of eScience Librarianship, 3, 1, (2014); Long-lived digital data collections: enabling research and education in the 21st century, (2005); Olaisen J., Lovhoiden H., Djupvik O.A., The innovative library: innovation theory applied to library services, Libri, 45, 2, pp. 79-90, (1995); Peters C., Dryden A.R., Assessing the academic library's role in campus-wide research data management: a first step at the University of Houston, Science and Technology Libraries, 30, 4, pp. 387-403, (2011); Pinfield S., Cox A.M., Smith J., Research data management and libraries: relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Reznik-Zellen R.C., Adamick J., McGinty S., Tiers of research data support services, Journal of eScience Librarianship, 1, 1, (2012); Rogers E.M., Diffusions of Innovation, (2003); Rolando L., Doty C., Hagenmaier W., Valk A., Parham S.W., Institutional Readiness for Data Stewardship: Findings and Recommendations from the Research Data Assessment, (2013); Rowley J., Innovation for survival: from cooperation to collaboration, Librarianship in Times of Crisis, pp. 207-224, (2011); Rowley J., Should your library have an innovation strategy?, Library Management, 32, 4-5, pp. 251-265, (2011); Ryan B., Gross N.C., The diffusion of hybrid seed corn in two Iowa communities, Rural Sociology, 8, 1, (1943); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); TenopirBirch C., Allard S., Academic libraries and research data services. Current practices and plans for the future; an ACRL white paper, (2012); Vaughan J., Technological Innovation: Perceptions and Definitions, (2013); Venkatesh V., Morris M.G., Davis G.B., Davis F.D., User acceptance of information technology: toward a unified view, MIS Quarterly, pp. 425-478, (2003); Ward D.M., Innovation in academic libraries during a time of crisis, (2013); White M.D., Diffusion of an innovation: digital reference service in Carnegie foundation master’s (comprehensive) academic institution libraries, The Journal of Academic Librarianship, 27, 3, pp. 173-187, (2001); Williamson K., Schauder D., Wright S., Stockfeld L., Handley N., Electronic databases in public libraries: issues of organisational adoption, Australasian Public Libraries and Information Services, 15, 3, (2002); WirthChauAvery A.A., Vondracek R., OSU libraries and research dataset curation: a beginning, (2010); Xia J., Diffusionism and open access, Journal of Documentation, 68, 1, pp. 72-99, (2012); Yakel E., Kim J., Adoption and diffusion of encoded archival description, Journal of the American Society for Information Science and Technology, 56, 13, pp. 1427-1437, (2005); Yoon A., Schultz T., Research data management services in academic libraries in the US: a content analysis of libraries’ websites, College and Research Libraries, 78, 7, (2017)","J. Niu; School of Information, University of South Florida, Tampa, United States; email: jinfang@usf.edu","","Emerald Group Holdings Ltd.","","","","","","25149342","","","","English","Glob. Knowl.,  Mem. Commun.","Article","Final","","Scopus","2-s2.0-85073997202"
"Hedeland H.","Hedeland, Hanna (57198810767)","57198810767","Providing digital infrastructure for audio-visual linguistic research data with diverse usage scenarios: Lessons learnt","2020","Publications","8","2","33","","","","0","10.3390/PUBLICATIONS8020033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089037902&doi=10.3390%2fPUBLICATIONS8020033&partnerID=40&md5=c4ce97b5e2411783cbfa647e71f612bd","Hamburg Centre for Language Corpora/CLARIN-D, Universität Hamburg, Hamburg, 22765, Germany","Hedeland H., Hamburg Centre for Language Corpora/CLARIN-D, Universität Hamburg, Hamburg, 22765, Germany","This article describes the development of the digital infrastructure at a research data centre for audio-visual linguistic research data, the Hamburg Centre for Language Corpora (HZSK) at the University of Hamburg in Germany, over the past ten years. The typical resource hosted in the HZSK Repository, the core component of the infrastructure, is a collection of recordings with time-aligned transcripts and additional contextual data, a spoken language corpus. Since the centre has a thematic focus on multilingualism and linguistic diversity and provides its service to researchers within linguistics and other disciplines, the development of the infrastructure was driven by diverse usage scenarios and user needs on the one hand, and by the common technical requirements for certified service centres of the CLARIN infrastructure on the other. Beyond the technical details, the article also aims to be a contribution to the discussion on responsibilities and services within emerging digital research data infrastructures and the fundamental issues in sustainability of research software engineering, concluding that in order to truly cater to user needs across the research data lifecycle, we still need to bridge the gap between discipline-specific research methods in the process of digitalisation and generic digital research data management approaches. © 2020 by the authors.","Audio-visual data; Data quality; Domain-specific solutions; Linguistic data; Research data","","","","","","Deutsche Forschungsgemeinschaft, DFG; Bundesministerium für Bildung und Forschung, BMBF, (01UG1620G); Universität Hamburg, UH, (01UG1120G, 01UG1420G, SCHM 2585/1-1, WO 1886/1-2)","Funding text 1: Funding: This research was funded by the BMBF under grant number 01UG1620G (CLARIN-D).; Funding text 2: This research was funded by the BMBF under grant number 01UG1620G (CLARIN-D). The article describes collaborative work, carried out within several projects at the University of Hamburg; in CLARIN-D 01UG1120G, 01UG1420G and 01UG1620G (2011-2020) and, in particular, in the projects SCHM 2585/1-1 and WO 1886/1-2 within the DFG LIS program (2011-2017).","Schmidt T., Worner K., EXMARaLDA, Handbook on Corpus Phonology;, pp. 402-419, (2014); Hedeland H., Lehmberg T., Schmidt T., Worner K., Multilingual Corpora at the Hamburg Centre for Language Corpora, Multilingual Resources and Multilingual Applications, Proceedings of the Conference of the German Society for Computational Linguistics and Language Technology (GSCL) 2011;, pp. 227-232, (2011); Wittenburg P., van Uytvanck D., Zastrow T., Stranak P., Broeder D., Schiel F., Boehlke V., Reichel U., Offersgaard L., CLARIN B Centre Checklist (CE-2013-0095);, (2019); Reichel U., Schiel F., Kisler T., Draxler C., Porner N., The BAS Speech Data Repository, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016);, pp. 786-791, (2016); Drude S., Broeder D., Trilsbeek P., Wittenburg P., The Language Archive-A new hub for language resources, Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012);, pp. 3264-3267, (2012); Windhouwer M., Kemps-Snijders M., Trilsbeek P., Moreira A., van der Veen B., Silva G., von Reihn D., FLAT: Constructing a CLARIN Compatible Home for Language Resources, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016);, pp. 2478-2483, (2016); Schmidt T., The database for spoken German-DGD2, Proceedings of the Ninth Conference on International Language Resources and Evaluation (LREC 2014);, pp. 1451-1457, (2014); Lehmberg T., Wissenstransfer und Wissensressourcen: Support und Helpdesk in den Digital Humanities, Forschungsdaten in den Geisteswissenschaften (FORGE 2015). Programm und Abstracts;, pp. 25-27, (2015); Sambale H., Hedeland H., Pirinen T., User Support for the Digital Humanities, Selected Papers from the CLARIN Annual Conference 2019;; Hedeland H., Lehmberg T., Rau F., Salffner S., Seyfeddinipur M., Witt A., Introducing the CLARIN knowledge centre for linguistic diversity and language documentation, Proceedings ofthe Eleventh International Conference on Language Resources and Evaluation (LREC 2018);, pp. 2340-2343, (2018); Hedeland H., Ferger A., Towards Continuous Quality Control for Spoken Language Corpora, International Journal for Digital Curation;; Ochs E., Transcription as theory, Developmental Pragmatics;, pp. 43-72, (1979); Edwards J., The Transcription of Discourse, The Handbook of Discourse Analysis;, pp. 321-348, (2001); Fandrych C., Frick E., Hedeland H., Iliash A., Jettka D., Meissner C., Schmidt T., Wallner F., Weigert K., Westpfahl S., User, who art thou? User profiling for oral corpus platforms, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016);, pp. 280-287, (2016); Meyer B., Buhrig K., Kliche O., Pawlack B., Nurses as interpreters. Aspects of interpreter training for bilingual medical employees, Multilingualism at Work. From Policies to Practices in Public, Medical, and Business Settings;, pp. 163-184, (2010); Jettka D., Stein D., The HZSK Repository: Implementation, Features, and Use Cases of a Repository for Spoken Language Corpora, D-Lib Mag, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Sci. Data, 3, (2016); Pirinen T., Jettka D., Hedeland H., Developing a CLARIN compatible AAI solution for academic and restricted resources, CLARIN Annual Conference 2017 Book of Abstracts;, (2017); Sloetjes H., ELAN: Multimedia annotation application, Handbook on Corpus Phonology;, pp. 305-320, (2014); Barras C., Geoffrois E., Wu Z., Liberman M., Transcriber: Development and use of a tool for assisting speech corpora production, Speech Commun, 33, pp. 5-22, (2000); Boersma P., Praat, a system for doing phonetics by computer, Glot Int, 5, pp. 341-345, (2001); MacWhinney B., The CHILDES project: Tools for Analyzing Talk, Volume I, (2000); Language Resource Management-Transcription of Spoken Language;, (2016); TEIP5: Guidelines for Electronic Text Encoding and Interchange;, (2016); Krause T., Zeldes A., ANNIS3: A new architecture for generic corpus query and visualization, Digit. Scholarsh. Humanit, 31, pp. 118-139, (2016); Yimam S.M., Gurevych I., de Eckart Castilho R., Biemann C., WebAnno: A Flexible, Web-based and Visually Supported System for Distributed Annotations, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations;, pp. 1-6, (2013); Hinrichs E., Hinrichs M., Zastrow T., WebLicht: Web-Based LRT Services for German, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics: System Demonstrations;, pp. 25-29, (2010); Schmidt T., Hedeland H., Jettka D., Conversion and Annotation Web Services for Spoken Language Data in CLARIN, Selected Papers from the CLARIN Annual Conference 2016;, pp. 113-130, (2017); Remus S., Hedeland H., Ferger A., Buhrig K., Biemann C., WebAnno-MM: EXMARaLDA meets WebAnno, Selected Papers from the CLARIN Annual Conference 2018;, pp. 166-172, (2019); Arkhangelskiy T., Ferger A., Hedeland H., Uralic multimedia corpora: ISO/TEI corpus data in the project INEL, Proceedings ofthe Fifth International Workshop on Computational Linguistics for Uralic Languages;, pp. 115-124, (2019)","H. Hedeland; Hamburg Centre for Language Corpora/CLARIN-D, Universität Hamburg, Hamburg, 22765, Germany; email: hanna.hedeland@uni-hamburg.de","","MDPI AG","","","","","","23046775","","","","English","Publ.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85089037902"
"Bertagnolli M.M.; Anderson B.; Quina A.; Piantadosi S.","Bertagnolli, Monica M (7004709364); Anderson, Brian (57216337070); Quina, Andre (57201078676); Piantadosi, Steven (7006788081)","7004709364; 57216337070; 57201078676; 7006788081","The electronic health record as a clinical trials tool: Opportunities and challenges","2020","Clinical Trials","17","3","","237","242","5","14","10.1177/1740774520913819","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083239827&doi=10.1177%2f1740774520913819&partnerID=40&md5=e8324188de7ac6bbd2509880fed2f6d3","Brigham and Women’s Hospital, Alliance for Clinical Trials in Oncology Foundation, Boston, MA, United States; MITRE Corporation, Bedford, MA, United States","Bertagnolli M.M., Brigham and Women’s Hospital, Alliance for Clinical Trials in Oncology Foundation, Boston, MA, United States; Anderson B., MITRE Corporation, Bedford, MA, United States; Quina A., MITRE Corporation, Bedford, MA, United States; Piantadosi S., Brigham and Women’s Hospital, Alliance for Clinical Trials in Oncology Foundation, Boston, MA, United States","Clinical trials provide evidence essential for progress in health care, and as the complexity of medical care has increased, the demand for such data has dramatically expanded. Conducting clinical trials has also become more complicated, evolving to meet increasing challenges in delivering clinical care and meeting regulatory requirements. Despite this, the general approach to data collection remains the same, requiring that researchers submit clinical data in response to study treatment protocols, using precisely defined data structures made available in study-specific case report forms. Currently, research data management is not integrated within the patient’s clinical care record, creating added burden for clinical staff and opportunities for error. During the past decade, the electronic health record has become standard across the US healthcare system and is increasingly used to collect and analyze data reporting quality metrics for clinical care delivery. Recently, electronic health record data have also been used to address clinical research questions; however, this approach has significant drawbacks due to the unstructured and incomplete nature of current electronic health record data. This report describes steps necessary to use the electronic health record as a tool for conducting high-quality clinical research. © The Author(s) 2020.","clinical trials design; Data standards; electronic health records","Biomedical Research; Data Collection; Delivery of Health Care; Electronic Health Records; Humans; Randomized Controlled Trials as Topic; Research Design; adult; article; case report; clinical article; clinical protocol; electronic health record; female; health care system; human; male; medical care; health care delivery; information processing; medical research; methodology; procedures; randomized controlled trial (topic)","","","","","","","Framework for FDA’s Real-World Evidence Program, (2018); (2019); Clinical Information Interoperability Council, (2017); CDISC Standards in the Clinical Research Process, (2011); Patient-reported outcomes version of the common terminology criteria for adverse events; Program Requirements Medicare, (2019); Sync for Science Project, (2019)","M.M. Bertagnolli; Brigham and Women’s Hospital, Alliance for Clinical Trials in Oncology Foundation, Boston, United States; email: mbertagnolli@bwh.harvard.edu","","SAGE Publications Ltd","","","","","","17407745","","","32266833","English","Clin. Trials","Article","Final","","Scopus","2-s2.0-85083239827"
"Schuster K.; Reyes V.","Schuster, Kristen (57188814468); Reyes, Vanessa (55941174600)","57188814468; 55941174600","Manage your data: Information management strategies for DH practitioners","2020","Routledge International Handbook of Research Methods in Digital Humanities","","","","125","136","11","0","10.4324/9780429777028-10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117985710&doi=10.4324%2f9780429777028-10&partnerID=40&md5=0c48c7286fc2accb9ec1ae40fe2980ad","King’s College, London, United Kingdom","Schuster K., King’s College, London, United Kingdom; Reyes V.","Digital humanities (DH) research benefits from the integration of social and scientific research methods and tools. Multidisciplinary methodologies regularly facilitate the production and use of increasingly large datasets and complex technical infrastructures. The growing potential for multidisciplinary research and innovation has brought more attention to a broader interest in traditional humanities and humanities data. (ECAR;2017). The problem: DH research relies heavily on data collected and analysed for increasingly diverse reasons. While multidisciplinary methods and practices offer immense opportunities for innovation, there is, simultaneously, a higher likelihood that researchers will struggle to communicate strategies for curating and preserving their data. What can be done? Research funding agencies are acknowledging the value and importance of research data management strategies, but have done little to reconcile the multi or interdisciplinary nature of DH research. Acknowledging and tackling the challenge of data management and digital curation is not, thankfully, an unknown issue. For at least as long as DH has existed as an area of research and work, information professionals (e.g. librarians, archivists and research data managers) have investigated and developed strategies for data management. The Academic Libraries and Research Data Services outlines that information professionals can aid DH researchers in the areas of data discovery, mining, analysis, metadata creation, selection, curation, and research partnerships. These partnerships can result in the creation of research data services departments and DH labs in academic libraries and facilitate data management services to researchers who need support with the creation and curation of their research data. However, despite emerging best practices in the information professions, researchers are not always aware of them. To address this potential roadblock, our chapter presents methods for negotiating interests, needs and understandings of research data management. © 2021 selection and editorial matter, Kristen Schuster and Stuart Dunn individual chapters, the contributors.","","","","","","","","","Bashkar M., Curation: The Power of Selection in a World of Excess, (2017); Borgman C., From Gutenberg to the global information structure: Access to information in the networked world, (2003); Borgman C., Big data, little data, no data: Scholarship in the networked world, (2016); Bowker G., Starr S., Sorting things out: Classification and its consequences (inside technology), (2000); Bush V., As we may think, The Atlantic, 176, 1, pp. 101-108, (1945); Constantopoulos P., Dallas C., Androutsopoulos I., Angelis S., Deligiannakis A., Gavrilis D., Papatheodorou C., DCC & U: An extended digital curation lifecycle model, The International Journal of Digital Curation, 4, 1, pp. 34-45, (2009); Conway P., Digital transformations and the archival nature of surrogates, Archival Science, 15, 1, pp. 51-69, (2015); Corrado E.M., Jaffe R., Access’s unsung hero: The [impending] rise of embedded Metadata, International Information and Library Review, 49, 2, pp. 124-130, (2017); Dalbello M., A genealogy of digital humanities, Journal of Documentation, 67, 3, pp. 480-506, (2011); Dunn S., Hedges M., How the Crowd Can Surprise Us: Humanities Crowdsourcing and the Creation of Knowledge, Crowdsourcing our Cultural Heritage, pp. 231-246, (2014); Building capacity for digital humanities: A framework for institutional planning, (2017); Etzel B., Thomas P., Personal information management, Personal Information Management, (1996); Feinberg M., Reading databases: Slow information interactions beyond the retrieval paradigm, Journal of Documentation, 73, 2, pp. 336-356, (2017); Gilliland-Swetland A., Enduring Paradigm, New Opportunities: The Value of the Archival Perspective in the Digital Environment, Library Hi Tech, 18, 4, pp. 383-386, (2000); Given L.M., McTavish L., What’s old is new again: The reconvergence of libraries, archives and museums in the digital age, Library Quarterly, 80, 1, pp. 7-32, (2010); Goggins S., Million A.J., Link G., Germonprez M., Schuster K., The Open Community Data Exchange: Advancing Data Sharing and Discovery in Open Online Community Science, Big Data Factories: Collaborative Approaches, pp. 23-36, (2017); Green H.E., Courtney A., Beyond the scanned image: A needs assessment of scholarly users of digital collections, College & Research Libraries, 76, 5, pp. 690-707, (2015); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Humphrey C., e-Science and the Life Cycle of Research, (2006); Huvila I., The unbearable lightness of participating? Revisiting the discourses of “participation” in archival lit-erature, Journal of Documentation, 71, 2, pp. 358-386, (2015); Lansdale M., The psychology of personal information management, Applied Ergonomics, 19, 1, pp. 55-66, (1988); Liew C.L., Cheetham F., Participatory culture in memory institutions: Of diversity, ethics and trust?, D-Lib, 22, 7-8, pp. 12-17, (2016); Liu A., N+1: A Plea for Cross-Domain Data in the Digital Humanities, Debates in the Digital Humanities 2016, pp. 559-568, (2016); Moore G., Cramming more components onto integrated circuits, Electronics, 38, 8, (1965); Oliver G., Harvey R., Digital Curation, (2016); Owens T., Making Crowdsourcing Compatible with the Missions and Values of Cultural Heritage Organizations, Crowdsourcing Our Cultural Heritage, pp. 269-280, (2014); Poeter D., How Moore’s Law changed history (and your smart phone), PC Magazine, (2015); Ribes D., Bowker G., Between meaning and machine: Learning to represent the knowledge or communities, Information ad Organization, 19, 4, pp. 199-217, (2009); Ridge M., Crowdsourcing Our Cultural Heritage: Introduction, Crowdsourcing Our Cultural Heritage, pp. 1-16, (2014); Sagner Buurma R., Tione L.A., The Sympathetic Research Imagination: Digital Humanities and the Liberal Arts, Debates in the Digital Humanities 2016, pp. 274-279, (2016); Senchyne J., Between Knowledge and Metaknowledge: Shifting Disciplinary Borders in Digital Humanities and Library and Information Studies, Debates in the Digital Humanities 2016s, pp. 368-376, (2016); St Jean B., Rieh S., Yakel E., Market K., Unheard voices; Institutional repository end-users, College & Research Libraries, 77, 1, pp. 21-42, (2011); Sula C.A., Digital humanities and libraries: A conceptual model, Journal of Library Administration, 53, 1, pp. 10-26, (2013); Sundt C.L., Research resources at our fingertips, Visual Resources, 29, 4, pp. 269-272, (2013); Taylor A., Joudrey D., Wisser K., The Organization of Information, (2017); Terras M., Opening access to collections: The making and using of open digitised cultural content, Online Information Review, 39, 5, pp. 733-752, (2015)","","","Taylor and Francis","","","","","","","978-042967174-6","","","English","Routledge International Handb. of Research Methods in Digital Humanities","Book chapter","Final","","Scopus","2-s2.0-85117985710"
"Liu G.; Zotoo I.K.; Su W.","Liu, Guifeng (57211488088); Zotoo, Isidore Komla (57219296679); Su, Wencheng (57192394793)","57211488088; 57219296679; 57192394793","Research data management policies in USA, UK and Australia universities: An online survey","2020","Malaysian Journal of Library and Information Science","25","2","","21","42","21","5","10.22452/mjlis.vol25no2.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092104199&doi=10.22452%2fmjlis.vol25no2.2&partnerID=40&md5=f863a3d73e88c03ab3be83ab4c3478e6","Institute of Science and Technology Information, Jiangsu University, Zhenjiang, 212013, China; School of Management, Jiangsu University, Zhenjiang, China","Liu G., Institute of Science and Technology Information, Jiangsu University, Zhenjiang, 212013, China; Zotoo I.K., School of Management, Jiangsu University, Zhenjiang, China; Su W., Institute of Science and Technology Information, Jiangsu University, Zhenjiang, 212013, China","There has been an increase in demand for research data management (RDM) policies to improve the quality of research data however, there is no clear-cut policy content to guide the process. The purpose of this study is to identify the existing RDM policies. Specifically, the study compares and differentiates the RDM policies in three developed nations (USA, UK and Australia) to ascertain how the policies vary. The RDM policies of 100 universities from the three countries, that are present online were retrieved and content analysis approach using NVivo and SPSS were performed. The results from the analysis revealed that the common underlying facts of the policies were found in the areas of access, retention, sharing, storage and ownership. All the universities share the same core values in the management of their research. They exhibit that Data Management Plan (DMP) is essential. The study concluded that the differences in the data management are mostly issues of focus areas. More so, there is no fixed retention period for research data. To resolve the few differences identified, common criteria for data management is proposed for policy considerations to ensure compliance. Finding from the study is significant to developing countries as it adds to the discourse on data management policies. The study will also enable policy makers in developing countries to draw empirical evidence from the developed world on RDM and this will form a basis for policy direction. © Faculty of Computer Science and Information Technology.","Data management plan; Data services; Data sharing; Open data; Research data management policy","","","","","","National Social Science Foundation of China; National Office for Philosophy and Social Sciences, NPOPSS, (17BTQ025)","Funding text 1: The results of this study demonstrate that the top universities in USA, UK and Australia emphasized RDM policies, which is of significant importance. At most universities, funding agencies’ policies take precedence over university policies, except at the University of Liverpool, which is quite emphatic that their policies take centre stage. All of the UK universities provide RDM training. While some provide central storage, others do not. In the UK, where research is supported by contract or grants to the university that include specific provisions regarding ownership, retention, and data access, the provisions of the agreement take precedence. The university provides access to services and facilities for the storage, backup, deposit, and retention of research data and records to enable researchers to meet their requirements under this policy and those of their research funders.; Funding text 2: This work was supported by the National Social Science Foundation of China under the project entitled ""Study of Research Data Governance under the Concept of Open Science"" (No: 17BTQ025).","Democratizing data-The IASSIST strategic plan for 2010-2014, International Association for Social Science Information Service and Technology (¡ASSIST), (2010); Australian code for the responsible conduct of research, 201, (2018); Education ranking & advice, US News and World, (2019); Akers K.G., Sferdean F.C., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Asher A., Deards K., Esteva M., Halbert M., Jahnke L., Jordan C., Keralis S.D.C., Kulasekaran S.S., Moen W.E., Stark S., Research data management: Principles, practices, and prospects, (2013); Blahous B., Gorraiz J., Gumpenberger C., Lehner O., Ulrych U., Data policies in journals under scrutiny: Their strength, scope and impact, Bibliometrie-Praxis und Forschung, Band, 5, pp. 1-16, (2016); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Corti L., van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2014); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib Journal of Information Management, 70, 2, pp. 142-157, (2018); Daraio C., Lenzerini M., Leporelli C., Moed H.F., Naggar P., Bonaccorsi A., Bartolucci A., Data integration for research and innovation policy: An ontology-based data management approach, Scientometrics, 106, 2, pp. 857-871, (2016); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program: Electronic Library and Information Systems, 49, 4, pp. 364-381, (2015); Hsieh H.F., Shannon S.E., Three approaches to qualitative content analysis, Qualitative Health Research, 15, 9, pp. 1277-1288, (2005); Keralis S.D., Stark S., Halbert M., Moen W.E., Research Data Management in policy and practice: The DataRes Project, In Research Data Management: Principles, practice, and prospects. Council on Library and Information Resources Publication No, 160, pp. 16-38, (2013); Matusiak K.K., Sposito F.A., Types of research data management services: An international perspective, Proceedings of the Association for Information Science and Technology, 54, 1, pp. 754-756, (2017); Moen W.E., Halbert M., Support for research data management among US academic institutions: Results from a national survey report, (2012); Moles N., Data-PE: A framework for evaluating data publication policies at scholarly journals, Data Science Journal, 13, pp. 192-202, (2015); Mushi G.E., Pienaar H., van Deventer M., Identifying and implementing relevant research data management services for the library at the University of Dodoma, Tanzania, Data Science Journal, 19, 1, (2020); Peng R.D., Reproducible research and biostatistics, Biostatistics, 10, 3, (2009); Ray J.M., Research data management: Practical strategies for information professionals, (2014); Ribeiro C., da Silva J.R., Castro J.A., Amorim R.C., Lopes J.C., David G., Research data management tools and workflows: Experimental work at the University of Porto, IASSIST Quarterly, 42, 2, pp. 16-16, (2018); Spichtinger D., Siren J., The development of Research Data Management policies in Horizon 2020, Research Data Management-A European Perspective, (2017); Steeleworthy M., Research data management and the Canadian academic library: An organizational consideration of data management and data stewardship, The Canadian Journal of Library Information Practice and Research, 9, (2014); Stoltenberg J., Parrish P., Geographic Information Systems and libraries, Library Trends, Vol, 55, (2006); Sturges P., Bamkin M., Anders J.H., Hubbard B., Hussain A., Heeley M., Research data sharing: Developing a stakeholder-driven model for journal policies, Journal of the Association for Information Science and Technology, 66, 12, (2015); Tammaro A.M., Matusiak K., Casarosa V., Sposito F.A., Practice of Research Data Management: Findings from the IFLA LTR Project, (2018); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European academic research libraries, Library Quarterly Journal, 27, 1, pp. 23-44, (2017); Tripathi M., Shukla A., Sonkar S.K., Research Data Management practices in university libraries: A study, DESIDOC Journal of Library & Information Technology, 37, 6, pp. 417-424, (2017); van den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and sharing data: Best practice for researchers, (2011); Vardakosta I., Kapidakis S., Geospatial data collection policies, technology and open source in websites of academic libraries worldwide, The Journal of Academic Librarianship, 42, 4, pp. 319-328, (2016); Viscusi G., Spahiu B., Maurino A., Batini C., Compliance with open government data policies: An empirical assessment of Italian local public administrations, Information Policy, 19, 3-4, pp. 263-275, (2014); Whyte A., Tedds J., Making the case for research data management, DCC Briefing Papers, (2011); Williams M., Bagwell J., Zozus M.N., Data management plans: The missing perspective, Journal of Biomedical Informatics, 71, pp. 130-142, (2017); Yoon A., Teresa Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College of Research & Libraries, 78, 7, pp. 920-933, (2017); Zuiderwijk A., Helbig N., Gil-Garcia J.R., Janssen M., Special issue on innovation through open data-A review of the state-of-the-art and an emerging research agenda: Guest editors' introduction, Journal of Theoretical and applied Electronic Commerce Research, 9, 2, pp. 1-13, (2014); Zuiderwijk A., Janssen M., Open data policies, their implementation and impact: A framework for comparison, Government Information Quarterly, 31, 1, pp. 17-29, (2014)","G. Liu; Institute of Science and Technology Information, Jiangsu University, Zhenjiang, 212013, China; email: 100003805@ujs.edu.cn","","Faculty of Computer Science and Information Technology","","","","","","13946234","","","","English","Malays. J. Libr. Inf. Sci.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85092104199"
"Adams M.T.; Inger K.K.; Meckfessel M.D.","Adams, Mollie T. (57197837353); Inger, Kerry K. (56118166300); Meckfessel, Michele D. (56586005400)","57197837353; 56118166300; 56586005400","MEETING THE DEMANDS OF THE ACCOUNTING CURRICULUM: AN INTEGRATED APPROACH USING A TAX RESEARCH CASE ASSIGNMENT","2020","Advances in Accounting Education: Teaching and Curriculum Innovations","24","","","49","66","17","0","10.1108/S1085-462220200000024009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135181925&doi=10.1108%2fS1085-462220200000024009&partnerID=40&md5=ea48b204aeac6ed706505d055eb887aa","","","This paper discusses a pedagogical approach that incorporates multiple critical topics in the accounting curriculum using an integrated tax research case. Our approach is designed to develop students research, data management and analysis, critical thinking, decision-making, and professional communication skills. These goals are achieved through the use of an integrated assignment requiring students to conduct research, decide how to use an assortment of information sources, conduct analysis of data and business documents, and arrive at and communicate a conclusion. The key issue is reasonable compensation, a highly litigated tax issue which requires students to identify relevant authority found across many court cases. The use of a closely held business with multiple family members with different fact patterns exposes students to different outcomes with a varying degree of complexity. Students must analyze business documents and firm- and industry-level data to determine the appropriate tax treatment. Further, the case scenario exploits the fact that reasonable compensation is a tax issue in which circuit courts have ruled differently on the same issue, requiring in-depth research and interpretation of primary authority. Students are also exposed to differing outcomes based on entity type. We provide discussion of our multiple implementations and student questionnaire results to support the efficacy of our approach. We have prepared resources to help instructors implement this pedagogical approach, including a completed data analysis, supporting summary tables, and an in-depth discussion of the primary authority related to reasonable compensation. © 2020 by Emerald Publishing Limited All rights of reproduction in any form reserved","Information analysis; Integrated assignment; Pedagogical approach; Reasonable compensation; Tax education; Tax research","","","","","","","","Adams M., Inger K., Meckfessel M.D., The not so Pokey Hokies: A tax research case., Issues in Accounting Education, 32, 4, pp. 81-99, (2017); Amadio W., Heywood M.E., Data analytics and the case collection process: An adaptable case employing excel and tableau, Advances in Accounting Education: Teaching and Curriculum Innovations, 22, pp. 45-70, (2019); The pathways commission: Charting a national strategy for the next generation of accountants, (2012); CPA Horizons 2025 Report, (2010); CPA Evolution, (2020); Anderson S.E., Stallworth H.L., Sweetness and spice: Tax issues for foodies, Issues in Accounting Education, 37, 1, pp. 111-117, (2016); Eligibility procedures and accreditation standards for accounting accreditation, (2018); Borthick A., Smeal L.N., Data analytics in tax research: Analyzing worker agreements and compensation data to distinguish between independent contractors and employees using IRS factors, Issues in Accounting Education, (2020); Brink W., Stoel D., Analytics knowledge, skills, and abilities for accounting graduates, Advances in Accounting Education: Teaching and Curriculum Innovations, 22, pp. 23-43, (2019); Cheng C., Eagan J., Yurko A.J.N., Chicagoland Popcorn - Online Retailer Nexus Following Wayfair Using Robotics Process Automation and Data, (2020); Cheng C., Sapkota P., Duquesne A., A Case Study of Effective Tax Rates Using Data Analytics, (2020); Richardson V., Shan Y., Data analytics in the accounting curriculum, Advances in Accounting Education: Teaching and Curriculum Innovations, 23, pp. 67-79, (2019); Report to the committee on finance, U.S. Senate, (2017)","","","Emerald Group Holdings Ltd.","","","","","","10854622","","","","English","Adv. Account. Educ. Teach. Curric. Innov.","Article","Final","","Scopus","2-s2.0-85135181925"
"Ayris P.","Ayris, Paul (22233574500)","22233574500","""The future depends on what you do today"": The library as a leader in open science","2020","Cases on Research Support Services in Academic Libraries","","","","25","51","26","1","10.4018/978-1-7998-4546-1.ch002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136717851&doi=10.4018%2f978-1-7998-4546-1.ch002&partnerID=40&md5=aa71246d3f1b1c08ce5bb7ec4dad566e","University College London, United Kingdom","Ayris P., University College London, United Kingdom","UCL (University College London) strongly supports the implementation of Open Science policies and practices. The library has taken the lead in the university across all eight areas of Open Science: the Future of Scholarly Communication, the EOSC, FAIR data, Skills, Research Integrity, Rewards, Altmetrics, and Citizen Science. UCL has modified these themes slightly to better fit its academic requirements, developing ambitious programmes and services to support the change of culture which is required. From the future of scholarly publishing, with the formation of UCL Press as the UK's first fully open access university press, to research data management, rewards, research integrity and next-generation metrics, UCL has become a leader in Open Science. This chapter analyses the success of UCL to date, describes the challenges, shows the benefits, and indicates what future steps are being planned to deliver a culture where Open Science is the default, thus delivering on the prophecy of Mahatma Ghandi, one of UCL's most illustrious alumni, 'The future depends on what you do today'. © 2021, IGI Global. All rights reserved.","","","","","","","","","(2016); Ayris P., The risks of not sharing data are greater than the costs, (2020); Chan G., The Research University in Today's Society, (2017); Coalition S., Plan S, (2018); Data Summit in Paris, (2020); Denning S., How Do You Change An Organizational Culture?, Forbes Magazine, (2012); Evaluation of Research Careers fully acknowledging Open Science Practices: Rewards, incentives and/or recognition for researchers practicing Open Science, (2017); Implementation Roadmap for the European Open Science Cloud, (2018); Open Science Policy Platform; Realising the European open science cloud, (2016); EUA Statement on Open Science to EU Institutions and National Governments, (2017); Furlong G., Treasures from UCL, (2015); Harte N., North J., Brewis G., World of UCL, (2018); Hicks D., Wouters P., Waltman L., Rijcke S., Rafols I., Bibliometrics: The Leiden Manifesto for research metrics, Nature, (2015); Journal of Bentham Studies: Statistics; Open Science and its role in universities: A roadmap for cultural change, (2018); Miller D., Costa E., Haynes N., McDonald T., Nicolescu R., Sinanan J., Wang X., How the World Changed Social Media, (2016); Munafo M.R., Nosek B.A., Bishop D.V.M., Button K.S., Chambers C.D., Percie du Sert N., Simonsohn U., Wagenmakers E.-J., Ware J.J., Ioannidis J.P.A., A manifesto for reproducible science, Nature Human Behaviour, 1, 1, (2017); Cost-benefit analysis for FAIR research data, (2018); Rentier B., Open Science will never prevail without a thorough revisiting of the way evaluations of researchers are conducted, (2019); Open access research, (2020); Royal Historical Society Publishes Guidance Paper on ""Plan S and History Journals"", (2019); (2012); The 97% consensus on global warming, (2016); Sorbonne Declaration on Research Data Rights, (2020); Welcome to Library Research Support, (2020); Welcome to Transcribe Bentham!, (2017); UCL Code of Conduct for Research, (2013); UCL Statement on Research Integrity, (2015); UCL Academic Promotions Framework, (2018); UCL Library Services Strategy 2019-22, (2018); UCL Libraries, (2019); UCL Research Strategy, (2019); UCL Statement on Transparency in Research, (2019); Financial information, (2020); Key statistics, (2020); Strategy and policy, (2020); UCL 2034, (2020); UCL Press, (2020); Extreme Citizen Science: Analysis and Visualisation (ECSAnVis); UK Reproducibility Network","","","IGI Global","","","","","","","978-179984547-8; 978-179984546-1","","","English","Cases on res. Support Services in Academic Libraries","Book chapter","Final","","Scopus","2-s2.0-85136717851"
"Li X.; Sha R.; Yao C.; Jin F.; Wang X.; Yan X.; Zhu S.; Shang M.","Li, Xueying (36612105400); Sha, Ruoqi (57219727724); Yao, Chen (56449979100); Jin, Feifei (57216340143); Wang, Xicheng (57219726817); Yan, Xiaoyan (35328871400); Zhu, Sainan (36083804300); Shang, Meixia (57188630655)","36612105400; 57219727724; 56449979100; 57216340143; 57219726817; 35328871400; 36083804300; 57188630655","Choice of clinical research data governance model for real-world data","2020","Chinese Journal of Evidence-Based Medicine","20","10","","1150","1156","6","1","10.7507/1672-2531.202003122","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094980980&doi=10.7507%2f1672-2531.202003122&partnerID=40&md5=dbd40f0dc3f79b6055ce5255cf5ae22a","Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China; Data Governance Center, Baoshang Bank, Beijing, 100101, China; Peking University, Clinical Research Institute, Beijing, 100191, China; Beijing Dublin International Collage, Beijing University of Technology, Beijing, 100124, China","Li X., Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China; Sha R., Data Governance Center, Baoshang Bank, Beijing, 100101, China; Yao C., Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China, Peking University, Clinical Research Institute, Beijing, 100191, China; Jin F., Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China; Wang X., Beijing Dublin International Collage, Beijing University of Technology, Beijing, 100124, China; Yan X., Peking University, Clinical Research Institute, Beijing, 100191, China; Zhu S., Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China; Shang M., Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China","Objectives: To establish an appropriate data governance mode in according with the database status of clinical study. Methods: Forty-six doctors of different seniority with clinical research experience from six hospitals in Beijing were selected by stratified purposeful sampling and semi-structured interview and were used to understand the status and shortcomings of data acquisition and storage in clinical research. The data resource of current clinical studies were summarized and the main target of data governance and the characteristics of clinical study data were explored to establish the domains of clinical study data governance to construct the framework of clinical research data governance. Results: Currently, the data sources of clinical studies were diverse, including real-world data from various medical and health records, data collected independently for clinical studies and numerous other sources. However, since collecting the data from electronic medical records was difficult for numerous reasons, a large number of researchers still collected research data by hand writing and stored it insecurely. In addition, the combination of electronic information from multiple sources was difficult. Building ALCOA+CCEA standard clinical research data management system based on clinical research data governance was urgent. Data governance includes data architecture, data model, data standards, data quality, master data, timeliness management, metadata and data security, while life cycle management and data insight were not essential parts. Conclusions: Based on the real-world data resources, domains of data governance in clinical study should include data architecture, data model, data standards, data quality, master data, timeliness management, metadata and data security. © 2020 West China University of Medical Science. All rights reserved.","Clinical study; Data governance; Real-world data; Source data","article; China; clinical research; computer security; electronic medical record; human; life cycle; metadata; multicenter study; semi structured interview; timeliness (data); writing","","","","","","","Guidance for industry computerized systems used in clinical investigations; Electronic source data in clinical investigations; Integrated Addendum to ICH E6 (R1): Guideline for Good Clinical Practice (R2); Abraham R, Schneider J, Brocke J., Data governance: a conceptual framework, structured review, and research agenda, Int J Inf Manage, 49, pp. 424-438, (2019); Information technology-governance of IT-governance of data-part 1: application of ISO/IEC 38500 to the governance of data ISO/IEC 38505-1: 2017; Guide to the data management body of knowledge (DAMA-DMBOK) 2009 functional framework, 3; Wang CS, Lin SL, Chou TH, An integrated data analytics process to optimize data governance of nonprofit organization, Comput Hum Behav, 101, 12, pp. 495-505, (2019); Sherman RE, Anderson SA, Dal Pan GJ, Et al., Real-world evidence - what is it and what can it tell us?, N Engl J Med, 375, 23, pp. 2293-2297, (2016)","X. Li; Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China; email: xyinglee@163.com; C. Yao; Department of Biostatistics, Peking University First Hospital, Beijing, 100034, China; email: yaochen@hsc.pku.edu.cn","","West China University of Medical Science","","","","","","16722531","","","","Chinese","Chin. J. Evid.-Based Med.","Article","Final","","Scopus","2-s2.0-85094980980"
"Töwe M.; Barillari C.","Töwe, Matthias (57192257570); Barillari, Caterina (57606036600)","57192257570; 57606036600","Who does what? - Research data management at ETH Zurich","2020","Data Science Journal","19","1","36","1","6","5","2","10.5334/DSJ-2020-036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099758724&doi=10.5334%2fDSJ-2020-036&partnerID=40&md5=14538c5e1e09d1e47336f1075e461f42","ETH Library, ETH Zurich, Zurich, Switzerland; Scientific IT Services, ETH Zurich, Zurich, Switzerland","Töwe M., ETH Library, ETH Zurich, Zurich, Switzerland; Barillari C., Scientific IT Services, ETH Zurich, Zurich, Switzerland","We present the approach to Research Data Management (RDM) support for researchers taken at ETH Zurich. Overall requirements are governed by institutional guidelines for Research Integrity, funders’ regulations, and legal obligations. The ETH approach is based on the distinction of three phases along the research data life-cycle: 1. Data Management Planning; 2. Active RDM; 3. Data Publication and Preservation. Two ETH units, namely the Scientific IT Services and the ETH Library, provide support for different aspects of these phases, building on their respective competencies. They jointly offer trainings, consulting, information, and materials for the first phase. The second phase deals with data which is in current use in active research projects. Scientific IT Services provide their own platform, openBIS, for keeping track of raw, processed and analysed data, in addition to organising samples, materials, and scientific procedures. ETH Library operates solutions for the third phase within the infrastructure of ETH Zurich’s central IT Services. The Research Collection is the institutional repository for research output including Research Data, Open Access publications, and ETH Zurich’s bibliography. © 2020 The Author(s).","Data management tools; Data sharing; Research data management; University services","Information services; Laws and legislation; Life cycle; Data publications; Institutional repositories; Legal obligations; Management planning; Research data; Research data managements; Research integrities; Research outputs; Information management","","","","","","","Barillari C, Et al., openBIS ELN-LIMS: an open-source database for academic laboratories, Bioinformatics, 32, 4, pp. 638-640, (2016); The DLCM Project, (2017); Software - Our tools for digital archives, (2018); About DSpace, (2018); Richtlinien für Integrität in der Forschung - Guidelines for Research Integrity, (2011); openBIS ELN-LIMS - Features, (2017); IT in Research - Your IT support for research, (2018); Research Data at ETH Zurich, (2018); Research Collection, (2017); Rosetta - Preserve your digital assets for the future, (2018); Kluyver T, Et al., Jupyter Notebooks - a publishing format for reproducible computational workflows, Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87-90, (2016); Open Research Data, (2017); IT in Research - Your IT support for research, (2018); Research Data at ETH Zurich, (2018); Research Collection, (2017); Rosetta - Preserve your digital assets for the future, (2018); Kluyver T, Et al., Jupyter Notebooks - a publishing format for reproducible computational workflows, Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87-90, (2016); Open Research Data, (2017)","M. Töwe; ETH Library, ETH Zurich, Zurich, Switzerland; email: matthias.toewe@library.ethz.ch","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85099758724"
"Ganzinger M.; Glaab E.; Kerssemakers J.; Nahnsen S.; Sax U.; Schaadt N.S.; Schapranow M.-P.; Tiede T.","Ganzinger, Matthias (18433968800); Glaab, Enrico (35190313500); Kerssemakers, Jules (35345348500); Nahnsen, Sven (36446537500); Sax, Ulrich (8956991900); Schaadt, Nadine Sarah (57189311054); Schapranow, Matthieu-P. (26221907500); Tiede, Thorsten (35238696600)","18433968800; 35190313500; 35345348500; 36446537500; 8956991900; 57189311054; 26221907500; 35238696600","Biomedical and Clinical Research Data Management","2020","Systems Medicine: Integrative, Qualitative and Computational Approaches: Volume 1-3","1-3","","","532","543","11","0","10.1016/B978-0-12-801238-3.11621-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151214298&doi=10.1016%2fB978-0-12-801238-3.11621-6&partnerID=40&md5=2eeac69381cc9f723e10b2476dbe53af","Institute of Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg; German Cancer Research Center (DKFZ), Heidelberg, Germany; Quantitative Biology Center, Eberhard Karls Universität Tübingen, Tübingen, Germany; Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany; Hannover Medical School, Institute for Pathology, Hannover, Germany; Hasso Plattner Insitute for Digital Engineering (HPI), University of Potsdam, Potsdam, Germany; Department of Computer Science, Eberhard Karls Universität Tübingen, Tübingen, Germany","Ganzinger M., Institute of Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany; Glaab E., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg; Kerssemakers J., German Cancer Research Center (DKFZ), Heidelberg, Germany; Nahnsen S., Quantitative Biology Center, Eberhard Karls Universität Tübingen, Tübingen, Germany; Sax U., Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany; Schaadt N.S., Hannover Medical School, Institute for Pathology, Hannover, Germany; Schapranow M.-P., Hasso Plattner Insitute for Digital Engineering (HPI), University of Potsdam, Potsdam, Germany; Tiede T., Department of Computer Science, Eberhard Karls Universität Tübingen, Tübingen, Germany","Systems medicine is an interdisciplinary approach in medicine that relies on computational models based on data from a variety of sources. Typically, such sources include clinical and biomedical data with heterogeneous data definitions that are sometimes not even structured in a useful way. Consequently, the systematic management of data is an important element for the successful implementation of systems medicine in both research and clinical application. In this article, we provide an overview over the following selected aspects of data management: • Integration of multiple data sources • IT infrastructures • Data protection regulations • Data history and data quality • Data sharing/FAIR principles • Use and access policies  The presented best practices and experiences result from several systems medicine projects in which the authors have participated. They can be considered as recommendations for future projects in order to quickly set up data management infrastructures for systems medicine. © 2021 Elsevier Inc. 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Proc, 2014, pp. 96-101, (2014); Schuh G., Brakling A., Valdez A.C., Schaar A.-K., Ziefle M., Using Liferay as an Interdisciplinary Scientific Collaboration Portal, Social Computing and Social Media, pp. 405-414, (2016); Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Research, 13, pp. 2498-2504, (2003); Shortliffe E.H., Barnett G.O., Biomedical data: Their acquisition, storage, and use, Biomedical Informatics: Computer Applications in Health Care and Biomedicine, pp. 39-66, (2014); Tauch A., Al-Dilaimi A., Bioinformatics in Germany: Toward a national-level infrastructure, Briefings in Bioinformatics, 20, pp. 370-374, (2019); Terry S.F., The global alliance for genomics & health, Genetic Testing and Molecular Biomarkers, 18, pp. 375-376, (2014); Truta T.M., Vinay B., Privacy protection: P-sensitive k-anonymity property, (2006); PROV-DM: The PROV Data Model, (2013); Wang X., Williams C., Liu Z.H., Croghan J., Big data management challenges in health research-a literature review, Briefings in Bioinformatics, 20, pp. 156-167, (2019); Weber G.M., Mandl K.D., Kohane I.S., Finding the missing link for big biomedical data, JAMA, 311, pp. 2479-2480, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., ׳T Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wittig U., Rey M., Weidemann A., Muller W., Data management and data enrichment for systems biology projects, Journal of Biotechnology, 261, pp. 229-237, (2017); Zhang J., Lu X., Chakraborty S., Panda D.K., Slurm-V: Extending Slurm for building efficient HPC cloud with SR-IOV and IVShmem, Euro-Par 2016: Parallel Processing, pp. 349-362, (2016)","","","Elsevier","","","","","","","978-012816077-0; 978-012816078-7","","","English","Systems Medicine: Integr., Qualitative and Computational Approaches: Volume 1-3","Book chapter","Final","","Scopus","2-s2.0-85151214298"
"Gargiulo P.","Gargiulo, Paola (57221373224)","57221373224","OPEN SCIENCE, OPEN RESEARCH DATA AND THE ROLE OF IOSSG","2020","SCIRES-IT","10","","","53","58","5","2","10.2423/i22394303v10Sp53","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098960661&doi=10.2423%2fi22394303v10Sp53&partnerID=40&md5=d5b88a7d3fdd11490b26f1cc32e65867","IOSSG, Italy","Gargiulo P., IOSSG, Italy","The paper illustrates the objectives and the on-going activities of the Italian Open Science Support Group (IOSSG) whose main goal is to promote the development and the dissemination of Open Science in Italy. It provides support and practical tools to researchers in the different steps of research process with reference to data collection, management, legal protection, access, archiving, preservation within the frame of the European Open Science Cloud (EOSC) initiative. © 2020. SCIentific RESearch and Information Technology Ricerca Scientifica e Tecnologie dell'Informazione. All Rights Reserved.","European Open Science Cloud; Open Research Data; Open Science; Open Science Support; Research Data Management","","","","","","","","Modello di policy di ateneo relativa ai dati della ricerca, (2017); European Open Science Cloud (EOSC); Digital Science in Horizon 2020. Concept paper of a vision of Digital Science and its integration with the funding programme Horizon 2020, (2013); Open Access, Horizon 2020 online Manual; The FAIR data principles, (2016); Il Manuale per i Formatori di Scienza Aperta, (2018); ICDI- Italian Computing and Data Infrastructure; IOSSG- Italian Open Science Suppot Group; Griglia per l’elaborazione del piano di gestione della ricerca, (2017); Modello di policy sulla gestione dei dati della ricerca, (2017); FAQ in materia di banche dati e diritti di proprietà intellettuale, (2018); Single Point of Entry. Uno studio per i servizi alla ricerca di Ateneo nel contesto di EOSC. Attività, competenze e ruoli, (2019); LERU Roadmap to Research Data, (2013); Open Science and its role in universities: a roadmap for cultural change, (2018); EU funded project to create resources to help Research Performing Institutions to manage their research data); EU funded project collecting and making access to OA publications and data resulting from EU and nationally funded research projects); Research Data Alliance- RDA- Italy; Policy sulla gestione dei dati della ricerca, (2017); Policy sulla gestione dei dati della ricerca, (2018)","","","Caspur -Ciber Publishing","","","","","","22394303","","","","English","SCIRES-IT","Article","Final","","Scopus","2-s2.0-85098960661"
"Arend D.; König P.; Junker A.; Scholz U.; Lange M.","Arend, Daniel (55531371500); König, Patrick (57204672703); Junker, Astrid (35792080900); Scholz, Uwe (56124842400); Lange, Matthias (36028279400)","55531371500; 57204672703; 35792080900; 56124842400; 36028279400","The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition","2020","GigaScience","9","10","","","","","4","10.1093/GIGASCIENCE/GIAA107","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094164483&doi=10.1093%2fGIGASCIENCE%2fGIAA107&partnerID=40&md5=69bd04fe8cbe08e6b9336ca95d3e8a1f","Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, Seeland, D-06466, Germany","Arend D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, Seeland, D-06466, Germany; König P., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, Seeland, D-06466, Germany; Junker A., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, Seeland, D-06466, Germany; Scholz U., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, Seeland, D-06466, Germany; Lange M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, Seeland, D-06466, Germany","Background: The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results: To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion: The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process. © The Author(s) 2020. Published by Oxford University Press GigaScience. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.","Digital object identifier; FAIR principles; Phenomics; Plant genomics; Research data management","Databases, Factual; Genomics; Information Dissemination; Plants; Software; article; elixir; FAIR principles; genomics; Germany; information storage; phenomics; software; spectroscopy; time series analysis; visibility; factual database; genomics; information dissemination; plant; software","","","","","Bundesministerium für Bildung und Forschung, BMBF, (FKZ 031A053, FKZ 031A536A, FKZ 031B0770A)","This work was supported by the German Federal Ministry of Education and Research (BMBF) in the frame of the projects German-Plant-Phenotyping Network - DPPN (FKZ 031A053), Modernste Virtualitäts-und erweiterte Realitäts-Verfahren für den Zyklus von Samen zu Samen - AVATARS (FKZ 031B0770A), and German Network for Bioinformatics Infrastructure - de.NBI (FKZ 031A536A).","Martone ME., FORCE11: Building the future for research communications and e-scholarship, Bioscience, 65, 7, (2015); Wilkinson MD, Dumontier M, Aalbersberg IJ, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Guidelines on FAIR Data Management in Horizon 2020; Mons B, Neylon C, Velterop J, Et al., Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud, Inf Serv Use, 37, pp. 49-56, (2017); Burgelman JC, Pascu C, Szkuta K, Et al., Open science, open data, and open scholarship: European policies to make science fit for the twenty-first century, Front Big Data, 2, (2019); Wolstencroft K, Krebs O, Snoep JL, Et al., FAIRDOMHub: A repository and collaboration environment for sharing systems biology research, Nucleic Acids Res, 45, D1, pp. D404-D407, (2016); Durinx C, McEntyre J, Appel R, Et al., Identifying ELIXIR Core Data Resources, (2017); Database resources of the National Center for Biotechnology Information, Nucleic Acids Res, 44, D1, pp. D7-19, (2015); Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database Collaboration. The Sequence Read Archive, Nucleic Acids Res, 39, pp. D19-D21, (2011); Submission System of the European Nucleotide Archive; Papoutsoglou EA, Faria D, Arend D, Et al., Enabling reusability of plant phenomic datasets with MIAPPE 1.1, New Phytol, 227, 1, pp. 260-273, (2020); ISA-Tab-for-plant-phenotyping; Arend D, Lange M, Chen J, Et al., e!DAL - A framework to store, share and publish research data, BMC Bioinformatics, 15, (2014); Brase J., DataCite-A global registration agency for research data, 2009 Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology IEEE, pp. 257-261, (2009); Arend D, Junker A, Scholz U, Et al., PGP repository: A plant phenomics and genomics data publication infrastructure, Database, (2016); Halewood M, Chiurugwi T, Sackville Hamilton R, Et al., Plant genetic resources for food and agriculture: opportunities and challenges emerging from the science and information technology revolution, New Phytol, 217, 4, pp. 1407-1419, (2018); Ferranti P., Preservation of food raw materials, Reference Module in Food Science, (2016); Milner SG, Jost M, Taketa S, Et al., Genebank genomics highlights the diversity of a global barley collection, Nat Genet, 51, pp. 319-326, (2019); Rosenqvist E, Grosskinsky DK, Ottosen CO, Et al., The phenotyping dilemma—The challenges of a diversified phenotyping community, Front Plant Sci, (2019); Oppermann M, Weise S, Dittmann C, Et al., GBIS: The information system of the German Genebank, Database, 2015, (2015); Schmutzer T, Bolger ME, Rudd S, Et al., Bioinformatics in the plant genomic and phenomic domain: The German contribution to resources, services and perspectives, J Biotechnol, 261, pp. 37-45, (2017); Leonelli S, Davey RP, Arnaud E, Et al., Data management and best practice for plant science, Nat Plants, 3, (2017); Leinonen R, Akhtar R, Birney E, Et al., The European Nucleotide Archive, Nucleic Acids Res, 39, pp. D28-D31, (2011); Apweiler R, Bairoch A, Wu CH, Et al., UniProt: the Universal Protein knowledgebase, Nucleic Acids Res, 32, pp. D115-D119, (2004); Perez-Riverol Y, Csordas A, Bai J, Et al., The PRIDE database and related tools and resources in 2019: Improving support for quantification data, Nucleic Acids Res, 47, D1, pp. D442-D450, (2019); Le Novere N, Bornstein B, Broicher A, Et al., BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems, Nucleic Acids Res, 34, pp. D689-D691, (2006); Crosswell LC, Thornton JM., ELIXIR: a distributed infrastructure for European biological data, Trends Biotechnol, 30, 5, pp. 241-242, (2012); Schomburg I, Jeske L, Ulbrich M, Et al., The BRENDA enzyme information system - From a database to an expert system, J Biotechnol, 261, pp. 194-206, (2017); Quast C, Pruesse E, Yilmaz P, Et al., The SILVA ribosomal RNA gene database project: improved data processing and web-based tools, Nucleic Acids Res, 41, D1, pp. D590-D596, (2013); Chavan V, Penev L., The data paper: A mechanism to incentivize data publishing in biodiversity science, BMC Bioinformatics, 12, (2011); Fenner M, Crosas M, Grethe JS, Et al., A data citation roadmap for scholarly data repositories, Sci Data, 6, (2019); Haak LL, Fenner M, Paglione L, Et al., ORCID: a system to uniquely identify researchers, Learn Publ, 25, 4, pp. 259-264, (2012); Dreyer B, Hagemann-Wilholt S, Vierkant P, Et al., Die Rolle der ORCID iD in der Wissenschaftskommunikation: Der Beitrag des ORCID-Deutschland-Konsortiums und das ORCID-DE-Projekt, ABI Tech, 39, 2, (2019); JSON-LD 1.0: a JSON-based serialization for linked data, (2014); Guha RV, Brickley D, Macbeth S., Schema.org: Evolution of structured data on the web, Commun ACM, 59, 2, (2016); Weibel S., The Dublin Core: a simple content description model for electronic resources, Bull Am Soc Information Sci Technol, 24, 1, pp. 9-11, (1997); Linden M, Prochazka M, Lappalainen I, Et al., Common ELIXIR Service for Researcher Authentication and Authorisation [version 1; peer review: 3 approved, 1 approved with reservations, (2018); Hardt D., The OAuth 2.0 Authorization Framework (RFC 6749), (2012); Schembera B, Duran JM., Dark data as the new challenge for big data science and the introduction of the scientific data officer, Philos Technol, 33, 1, pp. 93-115, (2019); Tauch A, Al-Dilaimi A., Bioinformatics in Germany: Toward a national-level infrastructure, Brief Bioinform, 20, 2, pp. 370-374, (2019); Cousijn H, Feeney P, Lowenberg D, Et al., Bringing citations and usage metrics together to make data count, Data Sci J, 18, 1, (2019); Konkiel S., Tracking citations and altmetrics for research data: challenges and opportunities, Bull Am Soc Inf Sci Technol, 39, 6, pp. 27-32, (2013); Tenopir C, Dalton ED, Allard S, Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, (2015); Parsons MA, Duerr RE, Jones MB., The history and future of data citation in practice, Data Sci J, 18, 1, (2019); Data citation needed, Sci Data, 6, (2019); Hahnel M, Valen D., How to (easily) extend the FAIRness of existing repositories, Data Intell, 2, 1-2, pp. 192-198, (2020); Arend D, Konig P, Junker A, Et al., Supporting software for “The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition, GigaScience Database, (2020)","D. Arend; Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Corrensstrasse 3, D-06466, Germany; email: arendd@ipk-gatersleben.de","","Oxford University Press","","","","","","2047217X","","","33090199","English","GigaScience","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85094164483"
"Lefebvre A.; Bakhtiari B.; Spruit M.","Lefebvre, Armel (57169815200); Bakhtiari, Baharak (57214990376); Spruit, Marco (16178767900)","57169815200; 57214990376; 16178767900","Exploring research data management planning challenges in practice","2020","IT - Information Technology","62","1","","29","37","8","3","10.1515/itit-2019-0029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079650037&doi=10.1515%2fitit-2019-0029&partnerID=40&md5=a823e74e2686217b794c6ed09b31a7c3","Utrecht University, Department of Information and Computing Sciences, Utrecht, 3584 CC, Netherlands","Lefebvre A., Utrecht University, Department of Information and Computing Sciences, Utrecht, 3584 CC, Netherlands; Bakhtiari B., Utrecht University, Department of Information and Computing Sciences, Utrecht, 3584 CC, Netherlands; Spruit M., Utrecht University, Department of Information and Computing Sciences, Utrecht, 3584 CC, Netherlands","Research data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.","Data management plan; Data reuse; FAIR principles; Open science; Research data management","Finance; Data reuse; FAIR principles; Governmental policies; Management plans; Open science; Research data managements; Scientific information; Technological challenges; Information management","","","","","European Commission, EC; Horizon 2020","Public funding agencies and research institutions are facing novel challenges related to the management of research outputs produced in Academia. In recent years, governing bodies across the world have started to promote new open science policies and practices for managing scientific information. Unlike open access policies that exclusively promote the public availability of scientific articles [], open science (OS) policies expand open access to a more extensive set of research outputs into consideration. Accordingly, OS includes scientific publications and their corresponding artifacts, such as software, data, and sample material, into the scope of scientific information [], []. As a result, research data management (RDM) is introduced by funding agencies as a critical capability of research institutions that benefit from training programs, dedicated IT services, and new roles in research organizations []. Nevertheless, previous research on research data management planning (RDMP) practices has reported challenges related to a lack of knowledge about the usefulness and best practices of RDMP. So far, other studies have shown that (1) funder policies for RDMP are quite general [], (2) researchers are often reluctant to disseminate curated data [], and (3) there is a lack of knowledge and detailed guidelines to support RDMP in research institutions []. Moreover, in the United States (US), [] showed that requirements from US funding agencies are inconsistent and emphasize post-publication data management rather than foster an up-stream data strategy, which could guarantee more robust data management from the start of a research project. Likewise, Science Europe, a European association of national public funding agencies, acknowledged the complexity of current policies and recently proposed standardized RDMP guidelines []. In this work, we investigate research data management planning (RDMP) as a function of research data management (RDM). RDMP is an essential part of the grant application process. In Europe, for instance, the European Commission has (partially) incorporated data management and planning in its grant application procedures for the Horizon 2020 (H2020) funding program []. In Europe, for instance, the European Commission has (partially) incorporated data management and planning in its grant application procedures for the Horizon 2020 (H2020) funding program []. As part of the application procedure of H2020, applicants submit data management plans (DMP). A DMP is an additional document in which grant applicants outline how data is acquired for their research project, which technology and standards they intend to use (e. g., storage, back-up, software), how data will be preserved and, possibly, shared and, the costs induced by additional resources and services needed to manage data []. ","L. Chan et Al., ""budapest Open Access Initiative | Budapest Open Access Initiative,"" 2002. [Online]. Available:. [Accessed: 18-Apr-2016].; Ardestani S.B., 2015 IEEE 11th Int. Conf. E-Science, pp. 448-453, (2015); European Commission, EU Open Innovation, Open Science, Open to the World, 2016; Lefebvre A., Schermerhorn E., Spruit M., The 25th European Conference of Information Systems, (2018); Dietrich D., Adamus T., Miner A., Steinhart G., De-mystifying the data management requirements of research funders, Issues Sci. Technol. Librariansh., 70, (2012); Wilms K., Stieglitz S., Buchholz A., Vogl R., Rudolph D., Proc. 51st Hawaii Int. Conf. Syst. Sci., pp. 4411-4420, (2018); Williams M., Bagwell J., Nahm Zozus M., Data management plans, the missing perspective, Journal of Biomedical Informatics, 71, pp. 130-142, (2017); Science Europe, ""practical Guide to the International Alignment of Research Data Management,"" 2018; Michener W.K., Ten simple rules for creating a good data management plan, PLOS Comput. Biol., (2015); Goodhue D.L., Quillard J.A., Rockart J.F., Managing the data resource: A contingency perspective, MIS Q., 12, 3, pp. 373-392, (1988); Shanks G., The challenges of strategic data planning in practice: An interpretive case study, J. Strateg. Inf. Syst., (1997); L. Corti, V. Van Den Eynden, L. Bishop, and M. Woollard, Managing and Sharing Research Data: A Guide to Good Practice, 2014; Hartter J., Ryan S.J., Mackenzie C.A., Parker J.N., Strasser C.A., Spatially Explicit Data: Stewardship and Ethical Challenges in Science, PLoS Biol., (2013); Wilkinson M.D., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Hislop D., Bosua R., Helms R., Knowledge Management in Organizations: A Critical Introduction, (2018); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Akers K.G., Going beyond data management planning: Comprehensive research data services, Coll. Res. Libr. News, (2017); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, Int. J. Digit. Curation, (2013)","A. Lefebvre; Utrecht University, Department of Information and Computing Sciences, Utrecht, 3584 CC, Netherlands; email: a.e.j.lefebvre@uu.nl","","De Gruyter Oldenbourg","","","","","","16112776","","","","English","IT Info. Tech.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85079650037"
"Schröder W.; Nickel S.","Schröder, Winfried (7202192117); Nickel, Stefan (56377131200)","7202192117; 56377131200","Research data management as an integral part of the research process of empirical disciplines using landscape ecology as an example","2020","Data Science Journal","19","1","","1","14","13","4","10.5334/dsj-2020-026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090024657&doi=10.5334%2fdsj-2020-026&partnerID=40&md5=4b86030e57e6494265c6f883381e9618","Landscape Ecology, University of Vechta, Vechta, Germany","Schröder W., Landscape Ecology, University of Vechta, Vechta, Germany; Nickel S., Landscape Ecology, University of Vechta, Vechta, Germany","Research Data Management (RDM) is regarded as an elementary component of empirical disciplines. Taking Landscape Ecology in Germany as an example the article demonstrates how to integrate RDM into the research design as a complement of the classic quality control and assurance in empirical research that has, so far, generally been limited to data production. Sharing and reuse of empirical data by scientists as well as thorough peer reviews of knowledge produced by empirical research requires that the problem of the research in question, the operationalized definitions of the objects of investigation and their representative selection are documented and archived as well as the methods of data production including indicators for data quality and all data collected and produced. On this basis, the extent to which this complemented design of research processes has already been realized is demonstrated by research projects of the Chair of Landscape Ecology at the University of Vechta, Germany. This study is part of a joined research project on Research Data Management funded by the German Federal Ministry of Education and Research. © 2020 The Author(s).","Data life cycle; Data repository; Empirical science; Subject database; University","Information management; Data production; Empirical research; German Federal Ministry of Education and Research; Landscape ecology; Representative selection; Research data managements; Research designs; Research process; Ecology","","","","","Centre for Marine Environmental Sciences; German Federal Ministry of Education and Research; MARUM; European Commission, EC; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, AWI; Universität Bremen","Funding text 1: Two publicly accessible research data repositories are4 currently used by the Chair of Landscape Ecology: PANGAEA – Data Publisher for Earth & Environmental Science® (www.pangaea.org), operated by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) and the Centre for Marine Environmental Sciences (MARUM) at the University of Bremen and ZENODO® (www.zenodo.org), a data service funded by the European Commission. Both guarantee a long-term storage of the data (at least 10 years) and support the allocation of Digital Object Identifiers (DOI), whereby the provided information objects can be clearly referenced and quoted. They offer the possibility of describing and indexing digital resources through publicly visible bibliographic metadata. They have a user-friendly access to the resources and offer the possibility to select licenses for their use. While PANGAEA® includes a quality check of the data (important for publicly accessible data), ZENODO® accepts data publications in open access as well as in restricted access. ZENODO® also supports the archiving of software products that are important for ensuring the replicability of scientific calculations.; Funding text 2: Research Data Management (RDM) is regarded as an elementary component of empirical disciplines. Taking Landscape Ecology in Germany as an example the article demonstrates how to integrate RDM into the research design as a complement of the classic quality control and assurance in empirical research that has, so far, generally been limited to data production. Sharing and reuse of empirical data by scientists as well as thorough peer reviews of knowledge produced by empirical research requires that the problem of the research in question, the operationalized definitions of the objects of investigation and their representative selection are documented and archived as well as the methods of data production including indicators for data quality and all data collected and produced. On this basis, the extent to which this complemented design of research processes has already been realized is demonstrated by research projects of the Chair of Landscape Ecology at the University of Vechta, Germany. This study is part of a joined research project on Research Data Management funded by the German Federal Ministry of Education and Research.","Aden C, Kleppin L, Schmidt G, Schroder W, WaldIS – a web-based reference data system for the forests in Germany, Geospatial Crossroads @ GI_Forum ‘08: Proceedings of the Geoinformatics Forum Salzburg, pp. 1-10, (2008); Aden C, Kleppin L, Schmidt G, Schroder W., Consolidation, visualisation and analysis of forest condition relevant data in the WebGIS WaldIS, Angewandte Geoinformatik 2009, pp. 506-515, (2009); Aden C, Schmidt G, Schonrock S, Schroder W., Data analyses with the WebGIS WaldIS, European Journal of Forest Research, 129, pp. 489-497, (2010); Aden C, Schmidt G, Schroder W., A web-based geographical information system for the monitoring of genetically modified organisms, GMO-Monitoring vor der Umsetzung, pp. 97-112, (2007); Aden C, Schmidt G, Schroder W., A web-based geo-information system for the monitoring of genetically modified organisms, E-Journal of Informatics in Agriculture, 2, (2007); Aden C, Schmidt G, Schroder W., WebGIS GMO Monitoring, Journal of Consumer Protection and Food Safety, 1, pp. 62-64, (2007); Aden C, Schmidt G, Schroder W., A web-based GIS for the monitoring of genetically modified organisms, GI-Days-Young researchers forum. 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Workshop of the Working Group ‘Environmental Databases’ of the Section ‘Informatics in Environmental Protection’, (2007); Kleppin L, Aden C, Schmidt G, Schroder W., Monitoring of genetically modified maize cultivation by means of WebGIS and Google Maps, Geospatial Crossroads @ GI_Forum ‘08: Proceedings of the Geoinformatics Forum Salzburg, pp. 170-179, (2008); Kleppin L, Pesch R, Schmidt G, Schroder W., The WebGIS MossMet, Handbuch der Umweltmedizin, pp. 39-41, (2007); Kleppin L, Schroder W, Pesch R, Schmidt G., Development and application of the WebGIS ‘MossMet, GI-Days-Young researchers forum. Proceedings of the 5th Geographic Information Days, pp. 217-227, (2007); Kleppin L, Schroder W, Pesch R, Schmidt G., Development and testing of a metadata and WebGIS application for exposure monitoring with mosses in Germany. A contribution to the LTER network, Environmental Sciences and Pollution Research, 20, pp. 38-48, (2008); Kresse W, Fadaie K., ISO Standards for Geographic Information, (2004); Kuhn I., Incorporating spatial autocorrelation may invert observed patterns, Diversity and Distribution, 13, pp. 66-69, (2007); Legendre P., Spatial autocorrelation: Trouble or new paradigm?, Ecology, 74, pp. 1659-1673, (1993); Legendre P, Dale MT, Fortin MJ, Gurevitch J, Hohn M, Myers D., The consequences of spatial structure for the design and analysis of ecological field surveys, Ecography, 25, pp. 601-615, (2002); Legendre P, Date MTR, Fortin MJ, Casgrain P, Gurevitch J., Effects of spatial structures on the results of field experiments, Ecology, 85, pp. 3202-3214, (2004); Legendre P, Fortin MJ., Spatial pattern and ecological analysis, Vegetation, 80, pp. 107-138, (1989); Liebhold AM, Sharov AA., Testing for correlation in the presence of spatial autocorrelation in insect count data, Population and community ecology for insect management and conservation, pp. 11-18, (1998); Lunsmann I, Pesch R, Schmidt G, Schroder W., Integration of environmental monitoring data with metadata, multivariate statistical natural space classification, geostatistics and GIS. ‘In environmental databases. Environmental Information Act and Environmental Databases, pp. 83-96, (2004); MacNeil MA., Making empirical progress in observational ecology, Environmental Conservation, 35, pp. 193-196, (2008); Matejka H, Busch W, Gorczyk J, Mauersberger F, Nickel S, Riekeberg T, Vosen P., Metadata concepts to support GIS processing in monitoring mining environmental impacts, 25th Scientific-Technical Annual Conference of the DGPF, pp. 65-74, (2005); Meyer M., Site-specifically differentiated recording of atmospheric nitrogen and heavy metal inputs by means of mosses with consideration of the eaves effect and supplementary investigations on the relationship between nitrogen inputs and accompanying vegetation, pp. 1-262, (2017); Neuschafer M., Handbook Digital Humanities, (2015); Nickel S, Schroder W., Restructuring of the German moss monitoring network 2015, link to research data and scientific software (version v1) [Data set], (2017); Nickel S, Schroder W., Heavy metal inputs into forest areas of Germany determined with modelling and moss monitoring, link to research data and scientific software (version v1) [Data set], (2017); Nickel S, Schroder W., GIS-integrated fuzzy-rule based model for calculating ecological soil moisture according to Hofmann, (2018); Nickel S, Schroder W, Jenssen M., Changes in German forests due to climate change and nitrogen deposition, Swiss Forest Review, 166, 5, pp. 325-334, (2015); Nickel S, Schroder W, Jenssen M., Semi-natural forest ecosystem types of Germany and the Kellerwald National Park (Hesse, Germany), link to shape files, PANGAEA, (2019); Nickel S, Schroder W, Jenssen M, Riediger J., Integrity of Forest Ecosystems under the Influence of Climate Change and Atmospheric Substance Inputs, Handbuch der Umweltwissenschaften, 1–26. Fundamentals and applications of ecosystem research, (2017); Peres-Neto PR, Legendre P., Estimating and controlling for spatial structure in the study of ecological communities, Global Ecology and Biogeography, 19, pp. 174-184, (2010); Perry JN, Liebhold AM, Rosenberg MS, Dungan J, Miriti M, Jakomulska A, Citron-Pousty S., Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data, Ecography, 25, pp. 578-600, (2002); Pesch R, Schmidt G, Schroder W, Aden C, Kleppin L, Holy M., Development, implementation and application of the WebGIS MossMet, The Geospatial Web. How geo-browsers, social software and the Web 2.0 are shaping the network society, pp. 191-200, (2007); Leistung aus Vielfalt – Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland, (2016); Reuter H, Middelhoff U, Verhoeven R, Breckling B, Filser J, Batz T, Bonn G, Schmidt G, Schroder W, Weis M, Pappenberger R, Kreft I, Rosing J, Winter G., ISMO – Information System for the Monitoring of Genetically Modified Organisms, pp. 1-304, (2007); Schmidt G, Aden C, Kleppin L, Pesch R, Schroder W., Integration of long-term environmental data by the example of the Unece Heavy Metals in Mosses Survey in Germany: Application of a WebGISbased metadata system, Long-Term Ecological Research – Between Theory and Application (Part 5), pp. 299-313, (2010); Schmidt G, Loesewitz L., A WebGIS data retrieval system for use in environmental monitoring, Proceedings of the 19th international conference ‘Informatics for Environmental Protection Networking environmental information, pp. 161-166, (2005); Schroder B, Reineking B., Modelling the species-habitat relationship – an overview of habitat modelling techniques, Habitatmodelle – Methodology, Application, Benefits, 5–25. Proceedings of the workshop from October 8–10. 2003 at the UFZ Leipzig, (2004); Schroder W., Knowledge Acquisition, Hypothesis Formation and Statistics, Neuere Statistische Verfahren und Modellbildung in der Geoökologie S, pp. 1-16, (1994); Schroder W, Garbe-Schonberg CD, Franzle O., The validity of environmental data – criteria for their reliability: representativeness, quality assurance and control, Environmental sciences and pollution research, 3, pp. 237-241, (1991); Schroder W, Nickel S, Jenssen M, Riediger J., Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: a pilot study in Germany, Science of the Total Environment, 521, pp. 108-122, (2015); Schroder W, Pesch R., Hypothesis testing in landscape ecology, Handbuch der Umweltwissenschaften. Fundamentals and applications of ecosystem research, pp. 1-10, (2013); Schroder W, Pesch R, Harmens H, Fagerli H, Ilyin I., Does spatial auto-correlation call for a revision of latest heavy metal and nitrogen deposition maps?, Environmental Sciences Europe, 20, pp. 1-15, (2012); Schroder W, Pesch R, Schmidt G., Soil monitoring in Germany. Spatial representativity and methodical comparability, Journal of Soils and Sediments, 4, 1, pp. 49-58, (2004); Schroder W, Pesch R, Schmidt G., Identifying and closing gaps in environmental monitoring by means of metadata, ecoregionalisation and geostatistics. The UNESCO biosphere reserve Rhön (Germany) as an example, Environmental Monitoring and Assessment, 114, pp. 461-488, (2006); Schroder W, Schmidt G., Cross-media environmental monitoring in Baden-Württemberg. Results of a model project, Cross-media environmental monitoring, pp. 39-60, (2003); Schroder W, Schmidt G., Metadatabases and GIS as technical support for material exposure and impact analysis in environmental monitoring, Lehrbuch der Technischen Umweltchemie, 11, pp. 211-227, (2005); Schroder W, Schmidt G, Pesch R., Representation and comparability of data and areas of long-term soil monitoring, Journal of Plant Nutrition and Soil Science, 166, pp. 649-659, (2003); Schroder W, Schmidt G, Pesch R., Harmonization of environmental monitoring. Tools for examination of methodical comparability and spatial representativity, Gate to Environmental and Health Sciences, pp. 1-13, (2003); Schroder W, Schmidt G, Pesch R., Gaps in environmental monitoring? Analysis of measurement programmes and transfer of data on immission, deposition and metal accumulation in mosses from measurement networks in Bavaria, Hesse and Thuringia to the Rhön biosphere reserve, Journal for Applied Environmental Research, 15, 1, pp. 53-77, (2005); Schroder W, Schmidt G, Pesch R, Eckstein T., Harmonisation of environmental monitoring. Instruments for testing methodological comparability and spatial representation, Manual on environmental sciences. Fundamentals and applications of ecosystem research, pp. 1-22, (2002); Schroder W, Weis M, Schmidt G., FKZ 804 67 030 in the Environmental Research Plan of the Federal Minister for the Environment, Nature Conservation and Nuclear Safety, (2006); Stachowiak H., General model theory, (1973); Stevens SS., On the theory of scales of measurement, Science, 103, pp. 677-680, (1946); Troll C., Aerial photo plan and ecological soil research, Journal of the Gesellschaft für Erdkunde zu Berlin, 7, 8, pp. 241-298, (1939); Guidance on choosing a sampling design for environmental data collection for use in developing a quality assurance project plan EPA (QA/G-5S), pp. 1-166, (2002); Uzhinskiy A, Ososkov G, Frontasyeva M., Data management of the Envorinment, Open System Publications, 4, pp. 42-43, (2017); Valcu M, Kempenaers B., Spatial autocorrelation: An overlooked concept in behavioral ecology, Behavioral Ecology, (2010); Vetter L, Schroder W, Franzle O., Scientific theoretical aspects of hypothesis acquisition and operationalization in geography, Geoecological environmental assessment. Scientific theoretical and methodological contributions to analysis and planning, 64, pp. 1-17, (1986); Wagner HH, Fortin M-J., Spatial analysis of landscapes: Concepts and statistics, Ecology, 86, pp. 1975-1987, (2005); Wheeler J, Servilla M, Vanderbilt K., Beyond discovery: Cross-platform application of ecological metadata language in support of quality assurance and control, Curating Research Data, pp. 184-187, (2017); Recommendations on research infrastructures in the humanities and social sciences, (2011)","S. Nickel; Landscape Ecology, University of Vechta, Vechta, Germany; email: stefan.nickel@uni-vechta.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85090024657"
"Zondergeld J.J.; Scholten R.H.H.; Vreede B.M.I.; Hessels R.S.; Pijl A.G.; Buizer-Voskamp J.E.; Rasch M.; Lange O.A.; Veldkamp C.L.S.","Zondergeld, Jelmer J. (57208124129); Scholten, Ron H.H. (57200517780); Vreede, Barbara M.I. (55934383800); Hessels, Roy S. (55039504100); Pijl, A.G. (57218767851); Buizer-Voskamp, Jacobine E. (24780588900); Rasch, Menno (57218766219); Lange, Otto A. (57218771458); Veldkamp, Coosje L.S. (56449021800)","57208124129; 57200517780; 55934383800; 55039504100; 57218767851; 24780588900; 57218766219; 57218771458; 56449021800","FAIR, safe and high-quality data: The data infrastructure and accessibility of the YOUth cohort study","2020","Developmental Cognitive Neuroscience","45","","100834","","","","2","10.1016/j.dcn.2020.100834","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090240878&doi=10.1016%2fj.dcn.2020.100834&partnerID=40&md5=1595e42c6e83c19f494eac0efdb16cf3","Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands; Utrecht University Library, Utrecht University, Netherlands; Developmental Psychology, Utrecht University, Netherlands; University Medical Center Utrecht, Netherlands; Faculty of Social Sciences, Utrecht University, Netherlands; Information and Technology Services, Utrecht University, Netherlands","Zondergeld J.J., Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands; Scholten R.H.H., Utrecht University Library, Utrecht University, Netherlands; Vreede B.M.I., Utrecht University Library, Utrecht University, Netherlands; Hessels R.S., Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands, Developmental Psychology, Utrecht University, Netherlands; Pijl A.G., University Medical Center Utrecht, Netherlands; Buizer-Voskamp J.E., Faculty of Social Sciences, Utrecht University, Netherlands; Rasch M., Information and Technology Services, Utrecht University, Netherlands; Lange O.A., Utrecht University Library, Utrecht University, Netherlands; Veldkamp C.L.S., Faculty of Social Sciences, Utrecht University, Netherlands","The YOUth cohort study aims to be a trailblazer for open science. Being a large-scale, longitudinal cohort following children in their development from gestation until early adulthood, YOUth collects a vast amount of data through a variety of research techniques. Data are collected through multiple platforms, including facilities managed by Utrecht University and the University Medical Center Utrecht. In order to facilitate appropriate use of its data by research organizations and researchers, YOUth aims to produce high-quality, FAIR data while safeguarding the privacy of participants. This requires an extensive data infrastructure, set up by collaborative efforts of researchers, data managers, IT departments, and the Utrecht University Library. In the spirit of open science, YOUth will share its experience and expertise in setting up a high-quality research data infrastructure for sensitive cohort data. This paper describes the technical aspects of our data and data infrastructure, and the steps taken throughout the study to produce and safely store FAIR and high-quality data. Finally, we will reflect on the organizational aspects that are conducive to the success of setting up such an enterprise, and we consider the financial challenges posed by individual studies investing in sustainable science. © 2020 The Authors","Cohort study; Data infrastructure; FAIR data; Information technology; Open science; Research data management","Adolescent; Child; Child, Preschool; Cohort Studies; Data Management; Female; Humans; Infant; Infant, Newborn; Longitudinal Studies; Male; Research Design; adult; article; cohort analysis; female; human; information technology; juvenile; library; male; manager; organization; privacy; adolescent; child; cohort analysis; infant; information processing; longitudinal study; methodology; newborn; preschool child; procedures","","","","","Dutch Ministry of Education, Culture, and Science; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO, (024.001.003)","The YOUth cohort is part of the Consortium on Individual Development (CID), which is funded through the Gravitation programme of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.001.003 ). ","(2019); Buimer E.E.L., Pas P., Brouwer R.M., Froeling M., Hoogduin H., Leemans A., Luijten P., van Nierop B.J., Raemaekers M., Schnack H.G., Teeuw J., Vink M., Visser F., Hulshoff Poll H.E., Mandl R.C.W., The YOUth cohort study: MRI protocol and test-retest reliability in adults, Dev. Cogn. Neurosci., 45, (2020); Cavoukian A., Privacy by Design in Law, Policy and Practice: A White Paper for Regulators, Decision-makers and Policy-makers, (2011); (2018); Hessels R.S., Hooge I.T.C., Eye tracking in developmental cognitive neuroscience – the good, the bad and the ugly, Dev. Cogn. Neurosci., 40, (2019); International Organization for Standardization, Information Technology — Security Techniques — Code of Practice for Information Security Controls (ISO Standard no. 27001:2013), (2013); Marcus D.S., Olsen T.R., Ramaratnam M., Buckner R.L., The extensible neuroimaging archive toolkit, Neuroinformatics, 5, 1, pp. 11-33, (2007); Mons B., Data Stewardship for Open Science: Implementing the FAIR Principles, (2018); Onland-Moret N.C., Buizer-Voskamp J.E., Albers M.E.W.A., Brouwer R.M., Buimer E.E.L., Hessels R.S., de Heus R., Huijding J., Junge C.M.M., Mandl R.C.W., Pas P., Vink M., van der Wal J.J.M., Hulshoff Poll H.E., Kemner C., The YOUth study: rationale, design, and study procedures, Dev. Cogn. Neurosci., (2020); Smeele T., Westerhof L.R., Using iRODS to manage, share and publish research data: Yoda, iRODS User Group Meeting 2018 Proceedings, iRODS Consortium, (2018); Utrecht University, Informatiebeveiliging binnen Universiteit Utrecht: Deel 1, (2015); (2018); van Deursen S., Kummeling H., The New Silk Road: a bumpy ride for Sino-European collaborative research under the GDPR?, High. Educ., 78, 5, pp. 911-930, (2019); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, 1, pp. 1-9, (2016)","J.J. Zondergeld; Heidelberglaan 1, Utrecht, 3584 CS, Netherlands; email: j.j.zondergeld@uu.nl","","Elsevier Ltd","","","","","","18789293","","","32906086","English","Dev. Cognitive Neurosci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85090240878"
"Bloemers M.; Montesanti A.","Bloemers, Margreet (57310848800); Montesanti, Annalisa (57310318000)","57310848800; 57310318000","The fair funding model: Providing a framework for research funders to drive the transition toward fair data management and stewardship practices","2020","Data Intelligence","2","1-2","","171","180","9","18","10.1162/dint_a_00039","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117752690&doi=10.1162%2fdint_a_00039&partnerID=40&md5=f31d69f010319e132af529870bc60ea0","The Netherlands Organization for Health Research and Development (ZonMw), The Hague, 2509 AE, Netherlands; Health Research Board (HRB), Dublin 2, DO2 H638, Ireland","Bloemers M., The Netherlands Organization for Health Research and Development (ZonMw), The Hague, 2509 AE, Netherlands; Montesanti A., Health Research Board (HRB), Dublin 2, DO2 H638, Ireland","A growing number of research funding organizations (RFOs) are taking responsibility to increase the scientific and social impact of research output. Also reusable research data are recognized as relevant output for gaining impact. RFOs are therefore promoting FAIR research data management and stewardship (RDM) in their research funding cycle. However, the implementation of FAIR RDM still faces important obstacles and challenges. To solve these, stakeholders work together to develop innovative tools and practices. Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle, integrated with research community specific guidance, criteria and metadata, and enabling automatic assessments of progress and output from RDM. The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community. To fully benefit from the model, RFOs, research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures. As many stakeholders still need to get familiar with “human actionable” FAIR data practices, the introduction of the model will be stepwise, with an active role of the RFOs in driving FAIR RDM processes as effectively as possible. © 2019 Chinese Academy of Sciences Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.","Data management plan (DMP); Data stewardship; FAIR funder; Policy; Tools","Digital storage; Finance; Data management plan; Data stewardship; FAIR funder; Funding cycle; Management plans; Research communities; Research data; Research data managements; Research funding; Social impact; Information management","","","","","","","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); GLOPID-R: Roadmap for data sharing in public health emergencies, (2019); ZonMw: Strengthening Impact in The Netherlands. The case of ZonMw, (2018); Ayris P., de San Roman A.L., Maes K., Labastida I., Open science and its role in universities: A roadmap for cultural change, League of European Research Universitie (LERU), (2018); Ball M., Bloemers M., Carr D., Cavalli V., Haglund M., Kalaitzi V., Vandevelde K., A vision for open science, The workshop “Research Institutions and Libraries and the role of Funders in the European Open Science Cloud” held at the LIBER 2018 Conference in Lille, (2018); Turning FAIR into reality, Final report and action plan from the European Commission expert group on FAIR data, (2018); Landi A., Thompson M., Giannuzzi V., Bonifazi F., Labastida I., Bonino da Silva Santos L.O., Roos M., The “A” of FAIR – as open as possible, as closed as necessary, Data Intelligence, 2, pp. 47-55, (2020); Jacobsen A., de Miranda Azevedo R., Juty N., Batista D., Coles S., Cornet R., Schultes E., FAIR principles: Interpretations and implementation considerations, Data Intelligence, 2, pp. 10-29, (2020); Scholtens S., Anbeek P., Bohmer J., Brullemans M., van der Geest M., Jetten M., van Gelder C., Towards a community-endorsed data steward profession description for life science research, (2019); Jacobsen A., Kaliyaperumal R., Bonino da Silva Santos L.O., Mons B., Schultes E., Roos M., Thompson M., A generic workflow for the data FAIRification process, Data Intelligence, 2, pp. 56-65, (2020); (2018); Jones S., Pergl R., Hooft R., Miksa T., Samors R., Ungvari J., Davis R.I., Lee T., Data management planning: How requirements and solutions are beginning to converge, Data Intelligence, 2, pp. 208-219, (2020); Thompson M., Burger K., Kaliyaperumal R., Roos M., Bonino da Silva Santos L.O., Making FAIR easy with FAIR tools: From creolization to convergence, Data Intelligence, 2, pp. 87-95, (2020); Wittenburg P., Sustkova H.P., Montesanti A., Bloemers S.M., de Waard S.H., Musen M.A., Schultes E.A., The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR data, (2019); van Vlijmen H., Mons A., Waalkens A., Franke W., Baak A., Ruiter G., Neefs J.-M., The need of Industry to go FAIR, Data Intelligence, 2, pp. 276-284, (2020)","M. Bloemers; The Netherlands Organization for Health Research and Development (ZonMw), The Hague, 2509 AE, Netherlands; email: Bloemers@zonmw.nl","","MIT Press Journals","","","","","","20967004","","","","English","Data. Intell.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85117752690"
"Posavec K.; Celjak D.; Musap L.J.","Posavec, Kristina (57211213982); Celjak, Draženko (57224097116); Musap, Ljiljana Jertec (57224071343)","57211213982; 57224097116; 57224071343","Role of a croatian national repository infrastructure in promotion and support of research data management","2020","Data Science Journal","19","1","48","","","","0","10.5334/dsj-2020-048","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106921096&doi=10.5334%2fdsj-2020-048&partnerID=40&md5=45beaa5246c0f06256b881b3ba68a7dc","Middleware and Data Services Department, University of Zagreb, University Computing Centre (SRCE), Zagreb, Croatia","Posavec K., Middleware and Data Services Department, University of Zagreb, University Computing Centre (SRCE), Zagreb, Croatia; Celjak D., Middleware and Data Services Department, University of Zagreb, University Computing Centre (SRCE), Zagreb, Croatia; Musap L.J., Middleware and Data Services Department, University of Zagreb, University Computing Centre (SRCE), Zagreb, Croatia","The paper will give an overview of national infrastructure for digital repositories, Digital Academic Archives and Repositories (DABAR), and its role as technology steward in raising awareness about research data management (RDM) and promoting good practices in the Croatian A&R community. The University of Zagreb, University Computing Centre (SRCE) is providing national infrastructure DABAR suitable for storing and dissemination of different types of digital objects. Through DABAR, all Croatian higher education and research institutions can establish their digital repository. A strong collaboration between SRCEs DABAR team and institutions repository managers has proven to be important in the process of disseminating knowledge about research data management among researchers and the scientific community at large. The paper will provide information about this collaboration during the project RDA Europe 4.0 – The European plug-in to the global Research Data Alliance (RDA). The main goal of this collaboration is to raise awareness about the importance of managing and sharing research data. © 2020 The Author(s).","Data steward; Datasets; RDM; Repository; Research data","Information management; Croatian higher educations; Digital Objects; Digital repository; Good practices; National infrastructure; Research data; Research data managements; Scientific community; Human resource management","","","","","Horizon 2020 Framework Programme, H2020, (777388)","“This paper was supported by the RDA Europe 4.0 project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777388.”","Celjak D, Bekic Z, Cundekovic M, Jertec LJ, Milinovic M, Zubic A., DABAR – the national infrastructure for digital repositories, EUNIS 23rd Annual Congress: Shaping the Digital Future of Universities: Book of Proceedings, pp. 16-24, (2017); Clare C, Cruz M, Papadopoulou E, Savage J, Teperek M, Wang Y, Yeomans J, Et al., Engaging Researchers with Data Management: The Cookbook (epub), (2019); What Is DABAR? | Digital Academic Archives And Repositories, (2020); Hockx-Yu H., Digital preservation in the context of institutional repositories, Program: electronic library and information systems, 40, 3, pp. 232-243, (2006); Katayoon K, Abrizah A., Librarians’ role as Change Agents for Institutional Repositories: A Case Of Malaysian Academic Libraries, Malaysian Journal of Library & Information Science, 15, 3, pp. 121-133, (2017); Peng G, Ritchey NA, Casey KS, Kearns EJ, Privette JL, Saunders D, Ansari S, Et al., Scientific Stewardship in the Open Data and Big Data Era-Roles and Responsibilities of Stewards and Other Major Product Stakeholders, D-Lib Magazine, 22, pp. 1-24, (2016); Libguides: Research Data Management @ Pitt: Understanding Research Data Management, (2020); Whyte A, Tedds J., Making the Case for Research Data Management. DCC Briefing Papers, (2011)","K. Posavec; Middleware and Data Services Department, University of Zagreb, University Computing Centre (SRCE), Zagreb, Croatia; email: kristina.posavec@srce.hr","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85106921096"
"Boutard G.","Boutard, Guillaume (25122182100)","25122182100","Alter-value in data reuse: Non-designated communities and creative processes","2020","Data Science Journal","19","1","23","1","12","11","2","10.5334/dsj-2020-023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086799099&doi=10.5334%2fdsj-2020-023&partnerID=40&md5=42b724d6412c146025614e9a66b8f608","Université de Montréal, École de bibliothéconomie et des sciences de l’information, Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT), Canada","Boutard G., Université de Montréal, École de bibliothéconomie et des sciences de l’information, Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT), Canada","This paper builds on the investigation of data reuse in creative processes to discuss ‘epistemic pluralism’ and data ‘alter-value’ in research data management. Focussing on a specific non-des-ignated community, we conducted semi-structured interviews with five artists in relation to five works. Data reuse is a critical component of all these works. The qualitative content analysis brings to light agonistic-antagonistic practices in data reuse and shows multiple deconstruc-tions of the notion of data value as it is portrayed in the data reuse literature. Finally, the paper brings to light the benefits of including such practices in the conceptualization of data curation. © 2020 The Author(s).","Alter-value; Creative process; Data reuse; Data value; Epistemic pluralism; New media arts; Non-designated communities; Sound art","Information management; Content analysis; Creative process; Critical component; Data reuse; Data values; Research data managements; Semi structured interviews; Data curation","","","","","","","Abella A, Ortiz-de-Urbina-Criado M, De-Pablos-Heredero C., The process of open data publi-cation and reuse, Journal of the Association for Information Science and Technology, 70, 3, pp. 296-300, (2019); Akmon D, Zimmerman A, Daniels M, Hedstrom M., The application of archival concepts to a data-intensive environment: working with scientists to understand data management and preservation needs, Archival Science, 11, 3–4, pp. 329-348, (2011); Arzberger P, Schroeder P, Beaulieu A, Bowker G, Casey K, Laaksonen L, Wouters P, Et al., Promoting Access to Public Research Data for Scientific, Economic, and Social Development, Data Science Journal, 3, pp. 135-152, (2004); Baker KS, Duerr RE, Parsons MA., Scientific Knowledge Mobilization: Coevolution of Data Products and Designated Communities, International Journal of Digital Curation, 10, 2, pp. 110-135, (2015); Barry A, Born G, Weszkalnys G., Logics of interdisciplinarity, Economy and Society, 37, 1, pp. 20-49, (2008); Bettivia RS., The Power of Imaginary Users: Designated Communities in the OAIS Reference Model, Proceedings of the 79th ASIS&T Annual Meeting, 38, pp. 1-38, (2016); Birnbaum D, Wallenstein S-O., From Immaterials to Resistance: The Other Side of Les Imma-tériaux, 30 Years After Les Immatériaux: Art, Science and Theory, pp. 245-267, (2015); Bishop L, Kuula-Luumi A., Revisiting Qualitative Data Reuse: A Decade On, SAGE Open, 7, 1, (2017); Borgman CL, Scharnhorst A, Golshan MS., Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, Journal of the Association for Information Science and Technology, 70, 8, pp. 888-904, (2019); Born G., For a Relational Musicology: Music and Interdisciplinarity, Beyond the Practice Turn, Journal of the Royal Musical Association, 135, 2, pp. 205-243, (2010); Brunner C., Digital) anarchival platforming, The Go-To How-To Book of Anarchiving, pp. 75-79, (2016); Cai L, Zhu Y., The Challenges of Data Quality and Data Quality Assessment in the Big Data Era, Data Science Journal, 14, (2015); Chow-White PA, Sandy Green J., Data Mining Difference in the Age of Big Data: Communication and the Social Shaping of Genome Technologies from 1998 to 2007, International Journal of Communication, 7, (2013); Research Data Management Glossary, (2020); Reference Model OAIS, (2012); Cook S., Information, (2016); Cragin MH, Palmer CL, Carlson JR, Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368, 1926, pp. 4023-4038, (2010); Curty RG, Crowston K, Specht A, Grant BW, Dalton ED., Attitudes and norms affecting scientists’ data reuse, PLOS ONE, 12, 12, (2017); Davenport JH, Grant J, Jones CM., Data Without Software Are Just Numbers, Data Science Journal, 19, 1, (2020); Drisko JW, Maschi T., Content analysis, (2016); Duguet A-M., The “anarchive” Series as a Challenge between Art and Information. A singular approach of media art history, Digital art through the looking glass: new strategies for archiving, collecting and preserving in digital humanities, pp. 73-77, (2019); Edwards D., DYAD, (2009); Faniel IM, Frank RD, Yakel E., Context from the data reuser’s point of view, Journal of Documentation, 75, 6, pp. 1274-1297, (2019); Feldman S, Shaw L., The Epistemological and Ethical Challenges of Archiving and Sharing Qualitative Data, American Behavioral Scientist, 63, 6, pp. 699-721, (2019); Fricke M., The knowledge pyramid: a critique of the DIKW hierarchy, Journal of Information Science, 35, 2, pp. 131-142, (2008); Gallo F., Contemporary Art as “Immatériaux”: Yesterday and Today, 30 Years After Les Immatériaux: Art, Science and Theory, pp. 119-135, (2015); Gottschalk J., Experimental Music Since 1970, (2016); Grisey G., Écrits ou l’invention de la musique spectrale, (2019); Guerlot-Kourouklis A, Royo-Letelier J, Wilhelm A., Murs Invisibles, (2019); Guffond J., Anywhere, All the Time, A Permanent Soundtrack to Your Life, (2015); Guffond J, Gonzalez Fuster G., Sounds Of Surveillance – The Wire, (2020); Harvey R., Appraisal and Selection, DCC Digital Curation Manual, (2007); Hui Y, Broeckmann A., Introduction, 30 Years After Les Immatériaux: Art, Science and Theory, pp. 9-24, (2015); Johnston LR., Introduction to data curation, Curating Research Data Volume One: Practical Strategies for Your Digital Repository, 1, pp. 1-30, (2017); Johnston LR., How a network of data curators can unlock the tremendous reuse value of research data, (2020); Johnston LR, Carlson J, Hudson-Vitale C, Imker H, Kozlowski W, Olendorf R, Stewart C., How Important is Data Curation? Gaps and Opportunities for Academic Libraries, Journal of Librarian-ship and Scholarly Communication, 6, 1, (2018); Kaase M., Databases, core: political science and political behavior, International encyclopedia of the social & behavioral sciences, 5, (2001); Kahn D., Earth Sound Earth Signal: Energies and Earth Magnitude in the Arts, (2013); Kethers S, Treloar A, Wu M., Building Tools to Facilitate Data Reuse, International Journal of Digital Curation, 11, 2, pp. 1-12, (2017); Kirilova D, Karcher S., Rethinking Data Sharing and Human Participant Protection in Social Science Research: Applications from the Qualitative Realm, Data Science Journal, 16, (2017); Klein A., Archive(s), mémoire, art: éléments pour une archivistique critique, (2019); Lemay Y, Klein A., Les archives et l’émotion: un atelier d’exploration et d’échanges, Archives, 44, 2, pp. 91-109, (2012); Ma L., Meanings of information: The assumptions and research consequences of three foun-dational LIS theories, Journal of the American Society for Information Science and Technology, 63, 4, pp. 716-723, (2012); Manovich L., Database as a genre of new media, AI & SOCIETY, 14, 2, pp. 176-183, (2000); Mason J., Re-Using’ Qualitative Data: On the Merits of an Investigative Epistemology, Sociological Research Online, 12, 3, pp. 39-42, (2007); Massumi B., 99 theses on the revaluation of value: a postcapitalist manifesto, (2018); Mauthner NS, Parry O., Qualitative data preservation and sharing in the social sciences: On whose philosophical terms?, Australian Journal of Social Issues, 44, 3, pp. 291-307, (2009); McAllister JW., Scientists’ Reuse of Old Empirical Data: Epistemological Aspects, Philosophy of Science, 85, 5, pp. 755-766, (2018); Miranda E, Brouse A., Toward direct brain-computer musical interfaces, Proceedings of the 2005 conference on New interfaces for musical expression, pp. 216-219, (2005); Morris R., Intimate mapping – In conversation with Jonathon Reus & Sissel Marie Tonn, (2020); Morton T., Hyperobjects: philosophy and ecology after the end of the world, (2013); Mumma G., Live-Electronic Music, The development and practice of electronic music, pp. 286-335, (1974); Nielsen HJ, Hjorland B., Curating research data: the potential roles of libraries and information professionals, Journal of Documentation, 70, 2, pp. 221-240, (2014); Nyman M., Experimental music: Cage and beyond, (1974); Oliveros P., Deep listening: a composers’s sound practice, (2005); Ortiz M., A Brief History of Biosignal-Driven Art: From biofeedback to biophysical performance, eContact!, 14, 2, (2012); Pasquetto IV, Randles BM, Borgman CL., On the Reuse of Scientific Data, Data Science Journal, 16, pp. 1-9, (2017); Pate A, Boschi L, Dubois D, Le Carrou J-L, Holtzman B., Auditory display of seismic data: On the use of experts’ categorizations and verbal descriptions as heuristics for geoscience, The Journal of the Acoustical Society of America, 141, 3, pp. 2143-2162, (2017); Paul C., Challenges for a ubiquitous museum: From the white cube to the black box and beyond, New Media in the White Cube and Beyond: Curatorial Models for Digital Art, pp. 53-75, (2008); Ramapriyan H, Moses J, Duerr R., Preservation of data for Earth system science – Towards a content standard, Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium, pp. 5304-5307, (2012); Rhee J., The Materialities of Big Data, Energy Accounts: Architectural Representations of Energy, Climate, and the Future, pp. 68-77, (2017); Slavnic Z., Towards Qualitative Data Preservation and Re-Use—Policy Trends and Academic Controver-sies in UK and Sweden, Forum: Qualitative Social Research, 14, 2, (2013); Smith JA, Flowers P, Larkin M., Interpretative phenomenological analysis: theory, method and research, (2009); Straebel V, Thoben W., Alvin Lucier’s Music for Solo Performer: Experimental music beyond sonification, Organised Sound, 19, 1, pp. 17-29, (2014); Strauss AL, Corbin JM., Basics of qualitative research: techniques and procedures for developing grounded theory, (1998); Thieberger EM, Dodge C., An Interview with Charles Dodge, Computer Music Journal, 19, 1, pp. 11-24, (1995); van de Sandt S, Dallmeier-Tiessen S, Lavasa A, Petras V., The Definition of Reuse, Data Science Journal, 18, 1, pp. 1-19, (2019); Vesna V., Database Aesthetics: Art in the Age of Information Overflow, (2007); Weil B., Notes on the immersive datascapes of Ryoji Ikeda, Ryoji Ikeda: datamatics, pp. 121-129, (2012); Yoon A., Role of Communication in Data Reuse, Proceedings of the 80th Annual Meeting of the Association for Information Science and Technology, (2017); Yoon A, Lee YY., Factors of trust in data reuse, Online Information Review, 43, 7, pp. 1245-1262, (2019)","G. Boutard; Université de Montréal, École de bibliothéconomie et des sciences de l’information, Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT), Canada; email: guillaume.boutard@umontreal.ca","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85086799099"
"Schröder M.; Leblanc H.; Spors S.; Krüger F.","Schröder, Max (57221587005); Leblanc, Hayley (57526960800); Spors, Sascha (18038954600); Krüger, Frank (57198054205)","57221587005; 57526960800; 18038954600; 57198054205","Intra-consortia data sharing platforms for interdisciplinary collaborative research projects","2020","IT - Information Technology","62","1","","19","28","9","1","10.1515/itit-2019-0039","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079598543&doi=10.1515%2fitit-2019-0039&partnerID=40&md5=005bd2383c110b0349e78db6056cccac","University of Rostock, Institute of Communications Engineering, R.-Wagner-Str. 31 (Haus 8), Rostock, D-18119, Germany; Denison University, 7554 Slayter Union, Granville, 43023, OH, United States","Schröder M., University of Rostock, Institute of Communications Engineering, R.-Wagner-Str. 31 (Haus 8), Rostock, D-18119, Germany; Leblanc H., Denison University, 7554 Slayter Union, Granville, 43023, OH, United States; Spors S., University of Rostock, Institute of Communications Engineering, R.-Wagner-Str. 31 (Haus 8), Rostock, D-18119, Germany; Krüger F., University of Rostock, Institute of Communications Engineering, R.-Wagner-Str. 31 (Haus 8), Rostock, D-18119, Germany","As the importance of data in today's research increases, the effective management of research data is of central interest for reproducibility. Research is often conducted in large interdisciplinary consortia that collaboratively collect and analyse such data. This raises the need of intra-consortia data sharing. In this article, we propose the use of data management platforms to facilitate this exchange among research partners. Based on the experiences of a large research project, we customized the CKAN software to satisfy these needs for intra-consortia data sharing. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.","Data Exchange; Data Sharing; Research Data Management","Electronic data interchange; Information management; Collaborative research projects; Data-sharing platforms; Effective management; Management platforms; Reproducibilities; Research data; Research data managements; Data Sharing","","","","","","","Dataverse Website. URL. Last Visited: Dec. 5th, 2019.; CKAN Open Source Data Portal Website. URL. Last Visited: Dec. 5th, 2019.; DCAT-extension Website. URL. 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Technical report, PeerJ Preprints, (2016); F. Krüger and S. Spors. A Questionnaire to Estimate the Needs for Research Data Management, 2018. Doi:.; Merkel D., Docker: Lightweight linux containers for consistent development and deployment, Linux Journal, 2014, 239, (2014); Moreau L., Groth P., Cheney J., The rationale of PROV, Journal of Web Semantics, 35, pp. 235-257, (2015); Mountantonakis M., Tzitzikas Y., Large-scale semantic integration of linked data, ACM Computing Surveys, 52, 5, pp. 1-40, (2019); Raben H., Kammerer P.W., Bader R., Van Rienen U., Establishment of a numerical model to design an electro-stimulating system for a porcine mandibular critical size defect, Applied Sciences, 9, 10, (2019); Schroder M., Kruger F., Zepf R., Van Rienen U., Spors S., WissKom2019, (2019); Tenopir C., Allard S., Douglass K., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Wilkinson M.D., Dumontier M., Aalbersberg I.J., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Winn J., Open Data and the Academy: An Evaluation of Ckan for Research Data Management, (2013)","M. Schröder; University of Rostock, Institute of Communications Engineering, Rostock, R.-Wagner-Str. 31 (Haus 8), D-18119, Germany; email: max.schroeder@uni-rostock.de","","De Gruyter Oldenbourg","","","","","","16112776","","","","English","IT Info. Tech.","Article","Final","","Scopus","2-s2.0-85079598543"
"Wang Z.; Wang X.","Wang, Zheng (55821566300); Wang, Xuemao (57212587069)","55821566300; 57212587069","From information, to data, to knowledge – Digital Scholarship Centers: An emerging transdisciplinary digital knowledge and research methods integrator in academic and research libraries","2020","IFLA Journal","46","1","","5","14","9","0","10.1177/0340035219885145","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077151220&doi=10.1177%2f0340035219885145&partnerID=40&md5=2c84fd9a80ca929ff03ffddd64465470","University of Notre Dame, IN, United States; University of Cincinnati, OH, United States","Wang Z., University of Notre Dame, IN, United States; Wang X., University of Cincinnati, OH, United States","In this essay, the authors will discuss the similarities and differences of knowledge management and librarianship. They will propose and articulate the emerging role of academic and research libraries as the integrators of digital knowledge and research methods among academic enterprises, a role which they believe will transform librarians to knowledge professionals. The authors will try to answer or stimulate further discussion of multi-dimensional and provocative questions such as: What are the critical differences between knowledge management and library and information science? Will emerging functions or services, such as digital scholarship centers and research data management practices, allow academic and research libraries to more fully perform the functions of knowledge management? Will libraries’ emerging role in the knowledge creation ecosystem help define their new value proposition, from a collection-centric to knowledge-centric service model? How should libraries position library-based digital scholarship centers to be digital integrators for enterprise-wide digital learning, research, and knowledge creation? © The Author(s) 2019.","Academic and research libraries; digital scholarship center; knowledge management; services to user populations","","","","","","","","Drucker P., Managing in a Time of Great Change, (1995); Farrell M., Leadership reflections: Leadership skills for knowledge management, Journal of Library Administration, 57, 6, pp. 674-682, (2017); Fraser-Arnott M., Moving from librarian to knowledge manager, Partnership: The Canadian Journal of Library and Information Practice and Research, 9, 2, (2014); Daland H., Managing knowledge in academic libraries. Are we? Should we?, Liber Quarterly: The Journal of European Research Libraries, 26, 1, pp. 28-41, (2016); Gao T., Chai Y., Liu Y., A review of knowledge management and future research trend, Proceedings of the 2nd international conference on crowd science and engineering, pp. 82-92, (2017); Hajric E., Knowledge management tools; Koenig M.E., What is KM? Knowledge management explained, (2018); Koloniari M., Fassoulis K., Knowledge management perceptions in academic libraries, Journal of Academic Librarianship, 43, 2, pp. 135-142, (2017); Marouf L., Are academic libraries ready for knowledge management?, The Electronic Library, 35, 1, pp. 137-151, (2017); Wenger E.C., Communities of Practice: Learning, Meaning and Identity, (1998); Wenger E.C., Snyder W.M., Communities of practice: The organizational frontier, Harvard Business Review, 78, 1, pp. 139-145, (1999); Zlatos C., How are they faring? Knowledge management practices in support of senior librarians in Association of Research Libraries (ARL) member libraries, IFLA WLIC 2017 – Libraries. Solidarity. Society, (2017); Bergstrom T., Brower D., Meyers N., Utilizing digital humanities methods for quantifying Howell’s state trials, Proceedings of the 14th ACM/IEEE-CS joint conference on digital libraries, pp. 441-442, (2014)","X. Wang; University of Cincinnati, United States; email: xuemao.wang@gmail.com","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85077151220"
"Tzanova S.","Tzanova, Stefka (57219351456)","57219351456","Changes in academic libraries in the era of Open Science","2020","Education for Information","36","3","","281","299","18","5","10.3233/EFI-190259","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092394523&doi=10.3233%2fEFI-190259&partnerID=40&md5=073a14166f1654c63459d409ab79050c","York College, City University of New York, New York, United States","Tzanova S., York College, City University of New York, New York, United States","In this paper we study the changes in academic library services inspired by the Open Science movement and especially the changes prompted from Open Data as a founding part of Open Science. We argue that academic libraries face the even bigger challenges for accommodating and providing support for Open Big Data composed from existing raw data sets and new massive sets generated from data driven research. Ensuring the veracity of Open Big Data is a complex problem dominated by data science. For academic libraries, that challenge triggers not only the expansion of traditional library services, but also leads to adoption of a set of new roles and responsibilities. That includes, but is not limited to development of the supporting models for Research Data Management, providing Data Management Plan assistance, expanding the qualifications of library personnel toward data science literacy, integration of the library services into research and educational process by taking part in research grants and many others. We outline several approaches taken by some academic libraries and by libraries at the City University of New York (CUNY) to meet necessities imposed by doing research and education with Open Big Data - from changes in libraries' administrative structure, changes in personnel qualifications and duties, leading the interdisciplinary advisory groups, to active collaboration in principal projects. © 2020 - IOS Press and the authors. All rights reserved.","academic libraries; data science; data science literacy; institutional repositories; open big data; open data; Open science; research data management; science literacy","Big data; Data Science; Human resource management; Information management; Libraries; Research and development management; Academic libraries; Administrative structures; Complex problems; Educational process; Library services; Personnel qualification; Research data managements; Science literacy; Open Data","","","","","FASTR; U.S. National Science Foundation; National Science Foundation, NSF; National Institutes of Health, NIH","Funding text 1: As mentioned above, Open Science has several constructing components that aim to support high quality reproducible science. Open Science is often described as a multifaceted notion encompassing open access to publications, open research data, open source software, open collaboration, open peer review, open notebooks, open educational resources, open monographs, citizen science, or research crowdfunding (FOSTER, 2017) in order to remove barriers in the sharing of scientific research output and raw data. In this section we will focus on OA and OD because these two components inspire the largest changes in academic libraries’ services and operations under Open Science model. Historically, the driving force behind OD and OBD originated from scientific communities and not-for-profit organizations but are now the result of governments’ efforts (and consequently are requirements from institutions) to set up mediated data repositories and formulate the rules and policies for sharing the research data coming from all publicly funded projects. For example, numerous pilot initiatives such “H2020 Programme” (European Commission, 2011) in Europe requires any research funded by public sources to be published in Open-Access journals and data to be stacked in Open-Access data collections. In the US, the two main research funding agencies – National Science Foundation (NSF) and National Institutes of Health (NIH) – have similar mandatory requirements in accordance with FASTR (Fair Access to Scientific and Technology Research Act), approved by the Congress in 2013, instructing all U.S. science funding agencies to provide public access to federally supported research outputs.; Funding text 2: Scientific research can be defined as planned, organized, and systematic collection, interpretation and analysis of data done with the purpose of contributing to global scientific knowledge. In other words, scientific research has intrinsic public nature – as the French physiologist Claude Bernard once said, “Art is I, science is we” (Bernard, 1957). Ensuring open and reproducible research has become a main goal across scientific communities and is supported by political circles and funding organizations (Boulton, 2016). The understanding is that open and reproducible research practices enable scientific re-use, accelerating future projects and discoveries in any discipline (Chen et al., 2019).; Funding text 3: Indeed Open Science propagation is facilitated by the development of digital technologies and the exponential growth of data produced by the global scientific community. Due to the advancement of information technologies and computers, scientific experiments generate unprecedented enormous amounts of data which can be made accessible at any place/country by any researcher via the World Wide Web. Open Science is also a direct result from changes in the research process and the increasing need of collaborative and interdisciplinary research. However it is important to mention that Open Science as global phenomenon requires as well significant socio-cultural changes at all levels along with harmonizing legislation systems and political support. In Europe the European Science Cloud (EOSC) is an umbrella for academic and research libraries, universities and research centers with the goal to provide solutions for the scientific community in the context of Open Science (Mons et al., 2017). In the US the Open Science Chain (OSC), a project in progress funded by National Science Foundation (NSF), aims to develop a cyberinfrastructure platform that would allow researchers to make available metadata and verification information about their scientific datasets and update this information as the datasets change over the time.","Adams J., Collaborations: The rise of research networks, Nature, 490, pp. 335-336, (2012); Affelt A., The Accidental Data Scientist: Big Data Applications and Opportunities for Librarians and Information Professionals, (2015); Akers K.G., Going beyond data management planning: Comprehensive research data services, College & Research Libraries News, 75, 8, pp. 435-436, (2014); Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Akers K.G., Looking out for the little guy: Small data curation, Bulletin of the American Society for Information Science and Technology, 39, 3, pp. 58-59, (2013); Bernard C., An Introduction to the Study of Experimental Medicine, (1957); Boulton G., Reproducibility: International accord on open data, Nature, 530, (2016); Chen X., Dallmeier-Tiessen S., Dasler R., Feger S., Fokianos P., Gonzalez J.B., Rodriguez D.R., Open is not enough, Nature Physics, 15, pp. 113-119, (2019); Corrado E.M., The importance of open access, open source, and open standards for libraries, Issues in Science and Technology Librarianship, 42, (2005); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Craig R.T., Communication theory as a field, Communication Theory, 9, 2, pp. 119-161, (1999); Open Educational Resources: New York State Open Educational Resources Funds Cuny Year One Report, (2018); Czarnitzki D., Grimpe C., Pellens M., Access to research inputs: Open science versus the entrepreneurial university, Journal of Technology Transfer, 40, 6, pp. 1050-1063, (2015); Dain P., Scholarship, Higher Education, and Libraries in the United States: Historical Questions and Quests, (1990); David A., The historical origins of ""Open Science"": An essay on patronage, reputation and common agency contracting in the scientific revolution, Capitalism and Society, 3, 2, pp. 1-103, (2008); What Is Digital Curation?, (2014); Horizon 2020-The Framework Programme for Research and Innovation, (2011); Fearon D., Gunia B., Sherry L., Pralle B.E., Sallans A.L., Arl Spec Kit 334: Research Data Management Services, (2013); Fecher B., Friesike S., Open Science: One term, five schools of thought, Openning Science, (2014); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services & Use, 36, 1-2, pp. 35-43, (2016); What Is Open Science? Introduction, (2017); Gewin V., Data sharing: An open mind on open data, Nature, 529, 7584, pp. 117-119, (2016); Gitelman L., Raw Data Is An Oxymoron, (2013); Grand A., Wilkinson C., Bultitude K., Winfield A.F.T., Open Science: A new ""trust technology""?, Science Communication, 34, 5, pp. 679-689, (2012); Grigas V., Fedosejevaite R., Mierzecka A., Librarians as educators: Affective dimensions experienced in teaching, European Conference on Information Literacy, pp. 619-633, (2016); Quentin G., Weatherdon L., Geijzendorffer I.R., Is citizen science an open science in the case of biodiversity observations?, Journal of Applied Ecology, 54, 2, pp. 612-617, (2017); Hart E.M., Barmby P., LeBauer D., Michonneau F., Mount S., Mulrooney P., Hollister J.W., Ten simple rules for digital data storage, PLoS Computational Biology, 12, 10, (2016); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Hesse B.W., Can psychology walk the walk of open science?, American Psychologist, 73, 2, pp. 126-137, (2018); Howse D.K., Bracke P.J., Keim S.M., Technology mediator: A new role for the reference librarian?, Biomedical Digital Libraries, 3, 1, (2006); Hoy M.B., Big data: An introduction for librarians, Medical Reference Services Quarterly, 33, 3, pp. 320-326, (2014); Hunter L., Leahey E., Collaborative research in sociology: Trends and contributing factors, The American Sociologist, 39, 4, pp. 290-306, (2008); Jensen K., West Q., Open educational resources and the higher education environment: A leadership opportunity for libraries, College & Research Libraries News, 76, 4, pp. 215-218, (2015); Kindling M., Van De Sandt S., Rucknagel J., Schirmbacher P., The landscape of research data repositories in 2015: A re3data analysis, D-Lib Magazine, 23, 3-4, (2017); Knapp A.K., Collins S.L., Turkington R., Long R., White S., Cahill J.F., Lind E., Coordinated distributed experiments: An emerging tool for testing global hypotheses in ecology and environmental science, Frontiers in Ecology and the Environment, 11, 3, pp. 147-155, (2013); Kraker P., Leony D., Reinhardt W., Beham G., The case for an open science in technology enhanced learning, International Journal of Technology Enhanced Learning, 3, 6, pp. 643-654, (2011); Kouper I., Akers K.G., Nicholls N.H., Sferdean F.C., A roadmap for data services, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 375-376, (2013); Kulczycki E., Rethinking Open Science: The role of communication, Analele Universitatii Din Craiova. Seria Filosofie, 37, 1, pp. 82-97, (2016); Lukoianova T., Rubin V.L., Veracity roadmap: Is Big Data objective, truthful and credible?, Advances in Classification Research Online, 24, 1, pp. 4-15, (2014); Marcial L.H., Hemminger B.M., Scientific data repositories on the Web: An initial survey, Journal of the American Society for Information Science and Technology, 61, 10, pp. 2029-2048, (2010); Berlin Declaration to Open Access to Knowledge in the Sciences and Humanities, (2003); McNutt M., Lehnert K., Hanson B., Nosek B.A., Ellison A.M., King J.L., Liberating field science samples and data, Science, 351, 6277, pp. 1024-1026, (2016); Medaille A., Librarians view instruction as integral to their professional identities, Evidence Based Library and Information Practice, 6, 4, pp. 120-123, (2011); Molloy J.C., The open knowledge foundation: Open data means better science, PLoS Biol, 9, 12, (2011); Mons B., Neylon C., Velterop J., Dumontier M., Da Silva Santos L.O.B., Wilkinson M.D., Cloudy, increasingly FAIR; Revisiting the FAIR data guiding principles for the european open science cloud, Information Services & Use, 37, 1, pp. 49-56, (2017); Recommended Data Repositories, (2016); Memorandum for the Heads of Executive Departments and Agencies, (2013); Okamoto K., Making higher education more affordable, one course reading at a time: Academic libraries as key advocates for open access textbooks and educational resources, Public Services Quarterly, 9, 4, pp. 267-283, (2013); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Dierolf U., Making research data repositories visible: The re3data org registry, PloS One, 8, 11, (2013); Potter W.G., Cook C., Kyrillidou M., Arl Profiles: Qualitative Descriptions of Research Libraries in the Early 21st Century, pp. 25-32, (2010); Pountain D., The Penguin Dictionary of Computing, (2003); Roe D.M., Moody D., The Librarian As Mediator: A Significant Change in the Educational Role of Librarians, (1999); Saez R.C.M.-F., Open Science now: A systematic literature review for an integrated definition, Journal of Business Research, 88, pp. 428-436, (2018); Schwartz H.A., Eichstaedt J.C., Kern M.L., Dziurzynski L., Ramones S.M., Agrawal M., Ungar L.H., Personality, gender, and age in the language of social media: The open-vocabulary approach, PLoS One, 8, 9, (2013); Shen Y., Varvel V.E., Developing data management services at the Johns Hopkins University, The Journal of Academic Librarianship, 39, 6, pp. 552-557, (2013); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of Arl Member Institutions, (2010); Stodden V., McNutt M., Bailey D.H., Deelman E., Gil Y., Hanson B., Taufer M., Enhancing reproducibility for computational methods, Science, 354, 6317, pp. 1240-1241, (2016); Stuart D., Baynes G., Hrynaszkiewicz I., Allin K., Penny D., Lucraft M., Astell M., Practical challenges for researchers in data sharing, Springer Nature, 59750, 11, (2018); Sumbal M.S., Tsui E., See-To E.W., Interrelationship between big data and knowledge management: An exploratory study in the oil and gas sector, Journal of Knowledge Management, 21, 1, pp. 180-196, (2017); Tammaro A.M., Casarosa V., Research data management in the curriculum: An interdisciplinary approach, Procedia Computer Science, 38, pp. 138-142, (2014); Tennant J.P., Waldner F., Jacques D.C., Masuzzo P., Collister L.B., Hartgerink C.H., The academic, economic and societal impacts of Open Access: An evidence-based review, F1000Res, 5, (2016); Van Tuyl S., Whitmire A.L., Water, water, everywhere: Defining and assessing data sharing in academia, PLoS One, 11, 2, (2016); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PloS One, 8, 7, (2013); White E.P., Baldridge E., Brym Z.T., Locey K.J., McGlinn D.J., Supp S.R., Nine simple ways to make it easier to (re) use your data, Ideas in Ecology and Evolution, 6, 2, pp. 1-10, (2013); Zuccala A., Open access and civic scientific information literacy, Information Research: An International Electronic Journal, 15, 1, pp. 1-27, (2010)","S. Tzanova; York College, City University of New York, New York, United States; email: stzanova@york.cuny.edu","","IOS Press BV","","","","","","01678329","","EDINE","","English","Educ Inf","Article","Final","","Scopus","2-s2.0-85092394523"
"Finkel M.; Baur A.; Weber T.K.D.; Osenbrück K.; Rügner H.; Leven C.; Schwientek M.; Schlögl J.; Hahn U.; Streck T.; Cirpka O.A.; Walter T.; Grathwohl P.","Finkel, M. (7103289378); Baur, A. (57205604843); Weber, T.K.D. (56033320200); Osenbrück, K. (6507361555); Rügner, H. (6602322522); Leven, C. (12143264100); Schwientek, M. (24475418600); Schlögl, J. (57203285260); Hahn, U. (57214318230); Streck, T. (6701768093); Cirpka, O.A. (7003413532); Walter, T. (57220701968); Grathwohl, P. (7004147643)","7103289378; 57205604843; 56033320200; 6507361555; 6602322522; 12143264100; 24475418600; 57203285260; 57214318230; 6701768093; 7003413532; 57220701968; 7004147643","Managing collaborative research data for integrated, interdisciplinary environmental research","2020","Earth Science Informatics","13","3","","641","654","13","1","10.1007/s12145-020-00441-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078603379&doi=10.1007%2fs12145-020-00441-0&partnerID=40&md5=7fa89aaade64e0f6788f53d7816f9229","Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Central Data Administration, Department of Informatics, University of Tübingen, Wächterstraße 76, Tübingen, 72074, Germany; Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, 70593, Germany","Finkel M., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Baur A., Central Data Administration, Department of Informatics, University of Tübingen, Wächterstraße 76, Tübingen, 72074, Germany; Weber T.K.D., Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, 70593, Germany; Osenbrück K., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Rügner H., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Leven C., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Schwientek M., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Schlögl J., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Hahn U., Central Data Administration, Department of Informatics, University of Tübingen, Wächterstraße 76, Tübingen, 72074, Germany; Streck T., Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, 70593, Germany; Cirpka O.A., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany; Walter T., Central Data Administration, Department of Informatics, University of Tübingen, Wächterstraße 76, Tübingen, 72074, Germany; Grathwohl P., Center for Applied Geoscience, University of Tübingen, Hölderlinstrasse 12, Tübingen, 72074, Germany","The consistent management of research data is crucial for the success of long-term and large-scale collaborative research. Research data management is the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility. Both funding agencies and publishers increasingly require this long term and open access to research data. Joint environmental studies typically take place in a fragmented research landscape of diverse disciplines; researchers involved typically show a variety of attitudes towards and previous experiences with common data policies, and the extensive variety of data types in interdisciplinary research poses particular challenges for collaborative data management. In this paper, we present organizational measures, data and metadata management concepts, and technical solutions to form a flexible research data management framework that allows for efficiently sharing the full range of data and metadata among all researchers of the project, and smooth publishing of selected data and data streams to publicly accessible sites. The concept is built upon data type-specific and hierarchical metadata using a common taxonomy agreed upon by all researchers of the project. The framework’s concept has been developed along the needs and demands of the scientists involved, and aims to minimize their effort in data management, which we illustrate from the researchers’ perspective describing their typical workflow from the generation and preparation of data and metadata to the long-term preservation of data including their metadata. © 2020, The Author(s).","Interdisciplinary environmental research; Metadata; Research data management; Taxonomy","data assimilation; environmental research; hierarchical system; integrated approach; interdisciplinary approach; metadata; participatory approach; research work","","","","","Deutsche Forschungsgemeinschaft, DFG, (SFB 1253/1 2017); German-Israeli Foundation for Scientific Research and Development, GIF"," Open Access funding provided by Projekt DEAL. This work was supported by the Collaborative Research Center 1253 CAMPOS, funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017).  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Communications in Computer and Information Science, (2016); Curdt C., Supporting the interdisciplinary, long-term research project ‘patterns in soil-vegetation-atmosphere-systems’ by data management services, Data Science Journal, 18, 1, pp. 1-9, (2019); Dehnhard I., Weichselgartner E., Krampen G., Researcher’s willingness to submit data for data sharing: A case study on a data archive for psychology, Data Science Journal, Data Science Journal, 12, pp. 172-180, (2013); Deutsche Forschungsgemeinschaft (German Research Foundation): Information für Die Wissenschaft Nr. 68 (Information for Researchers No 68) Appell Zur Nutzung Offener Lizenzen in Der Wissenschaft (Appeal for the Use of Open Licenses in Science), (2014); H2020 Programme Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020, (2017); Prepare Data for Analysis and Visualisations, (2012); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS One, 10, 2, (2015); Geosling E., Pollak J., Hooper R., Advancing water science through community collaboration, Environ Earth Sci, 73, pp. 1919-1924, (2015); Glatard T., Rousseau M.-E., Camarasu-Pop S., Adalat R., Beck N., Das S., da Silva R.F., Khalili-Mahani N., Korkhov V., Quirion P.-O., Rioux P., Olabarriaga S.D., Bellec P., Evans A.C., Software architectures to integrate workflow engines in science gateways, Future Generation Computer Systems, Future Generation Computer Systems, 75, pp. 239-255, (2017); Grunzke R., Hartmann V., Jejkal T., Kollai H., Prabhune A., Herold H., Deicke A., Dressler C., Dolhoff J., Stanek J., Hoffmann A., Muller-Pfefferkorn R., Schrade T., Meinel G., Herres-Pawlis S., Nagel W.E., The MASi repository service - comprehensive, metadata-driven and multi-community research data management, Futur Gener Comput Syst, 94, pp. 879-894, (2019); Harvey M.J., McLean A., Rzepa H.S., A metadata-driven approach to data repository design, Journal of Cheminformatics, 9, (2017); Hedden H.J., Taxonomies and controlled vocabularies best practices for metadata, Digit Asset Manag, 6, pp. 279-284, (2010); Horsburgh J.S., Tarboton D.G., Maidment D.R., Zaslavsky I., A relational model for environmental and water resources data, Water Resour Res, 44, (2008); Horsburgh J.S., Tarboton D.G., Hooper R.P., Zaslavsky I., Managing a community shared vocabulary for hydrologic observations, Environ Model Softw, 52, pp. 62-73, (2014); Horsburgh J.S., Reeder S.L., Jones A.S., Meline J., Open source software for visualization and quality control of continuous hydrologic and water quality sensor data, Environ Model Softw, 70, pp. 32-44, (2015); Hsu L., Martin R.L., McElroy B., Litwin-Miller K., Kim W., Data management, sharing, and reuse in experimental geomorphology: challenges, strategies, and scientific opportunities, Geomorphology, Geomorphology, 244, pp. 180-189, (2015); Kaminski S., Brandt O., Das institutionelle Forschungsdatenrepositorium FDAT der Universität Tübingen, O-Bib. Das Offene Bibliotheksjournal, 5, 3, pp. 61-75, (2018); Klimpel P., Freies Wissen Dank Creative-Commons-Lizenzen: Folgen, Risiken Und Nebenwirkungen Der Bedingung „nicht-kommerziell“– NC (Free Knowledge with Creative Commons Licenses: Consequences, Risks and Side Effects of the Condition ""Non-Commercial"" – NC)., (2012); Kratz J.E., Strasser C., Researcher perspectives on publication and peer review of data, PLoS One, 10, 2, (2015); Latham B., Research data management: defining roles, prioritizing services, and enumerating challenges, J Acad Librariansh, 43, pp. 263-265, (2017); Martone M., Data Citation Synthesis Group: Joint Declaration of Data Citation Principles, FORCE11, (2014); Nosek B.A., Alter G., Banks G.C., Borsboom D., Bowman S.D., Breckler S.J., Buck S., Chambers C.D., Chin G., Christensen G., Contestabile M., Dafoe A., Eich E., Freese J., Glennerster R., Goroff D., Green D.P., Hesse B., Humphreys M., Ishiyama J., Karlan D., Kraut A., Lupia A., Mabry P., Madon T., Malhotra N., Mayo-Wilson E., McNutt M., Miguel E., Paluck E.L., Simonsohn U., Soderberg C., Spellman B.A., Turitto J., VandenBos G., Vazire S., Wagenmakers E.J., Wilson R., Yarkoni T., Promoting an open research culture, Science, 348, pp. 1422-1425, (2015); Piasecki M., Beran B., A semantic annotation tool for hydrologic sciences, Earth Sci Inform, 2, pp. 157-168, (2009); Pinfield S., Cox A., Smith J.R., Research data management and Libraries: Relationships, Activities, Drivers and Influences, PLOS ONE, (2014); Razum M., Schwichtenberg F., Fridman R., Versioning of Digital Objects in a Fedora-Based Repository, (2007); Research Data Portal FDAT, (2018); Rodrigues J., Castro J.A., da Silva J.R., Ribeiro C., Hands-On Data Publishing with Researchers: Five Experiments with Metadata in Multiple Domains, Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, (2019); Sadler J.M., Ames D.P., Khattar R., A recipe for standards-based data sharing using open source software and low-cost electronics, J Hydroinf, 18, pp. 185-197, (2016); Smith A.M., Katz D.S., Niemeyer K.E., 2016. Software citation principles, Peerj Computer Science, (2016); Specht A., Guru S., Houghton L., Keniger L., Driver P., Ritchie E.G., Lai K., Treloar A., Data management challenges in analysis and synthesis in the ecosystem sciences, Sci Total Environ, 534, pp. 144-158, (2015); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); van Den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data – Best Practice for Researchers. UK Data Archive, (2011); Wang W.M., Gopfert T., Stark R., Data management in collaborative interdisciplinary research projects - conclusions from the digitalization of research in sustainable manufacturing, ISPRS International Journal of Geo-Information, ISPRS International Journal of Geo-Information, (2016); White H.C., Descriptive metadata for scientific data repositories: A comparison of information scientist and scientist organizing behaviors, J Libr Metadata, 14, 1, pp. 24-51, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J.'., Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van Der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data 3, (2016); Wilkinson M.D., Sansone S.-A., Schultes E., Doorn P., da Silva Santos L.B., Dumontier M., A design framework and exemplar metrics for FAIRness, Scientific Data, 5, pp. 1-4, (2018)","M. Finkel; Center for Applied Geoscience, University of Tübingen, Tübingen, Hölderlinstrasse 12, 72074, Germany; email: michael.finkel@uni-tuebingen.de","","Springer","","","","","","18650473","","","","English","Earth Sci. Informatics","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85078603379"
"Koch V.; Brôse S.","Koch, Vanessa (57192592711); Brôse, Stefan (57215026585)","57192592711; 57215026585","Modern research data management - A natural product project as a case study; [Modernes Forschungsdatenmanagement – ein Naturstoffprojekt als Fallstudie]","2020","BioSpektrum","26","1","","107","109","2","0","10.1007/s12268-020-1316-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079705117&doi=10.1007%2fs12268-020-1316-3&partnerID=40&md5=1f157da41640e8c2a3b9425ebfb47c5f","Institut für Biologische und Chemische Systeme — IBCS-FMS, Karlsruher Institut für Technologie (KIT), Campus Nord, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany","Koch V., Institut für Biologische und Chemische Systeme — IBCS-FMS, Karlsruher Institut für Technologie (KIT), Campus Nord, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany; Brôse S., Institut für Biologische und Chemische Systeme — IBCS-FMS, Karlsruher Institut für Technologie (KIT), Campus Nord, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany","[No abstract available]","","","","","","","","","Agrawal A.A., Petschenka G., Bingham R.A., Et al., Toxic cardenolides: chemical ecology and coevolution of specialized plant-herbivore interactions, New Phytol, 194, pp. 28-45, (2012); Bernays E.A., Chapman R.F., Host-Plant Selection by Phytophagous Insects, (2007); Petschenka G., Fei C.S., Araya J.J., Et al., Relative selectivity of plant cardenolides for Na+/K+-ATPases from the monarch butterfy and non-resistant insects, Front Plant Sci, 9, (2018); You H., Lei M., Song W., Et al., Cytotoxic cardenolides from the root bark of Calotropis gigantea, Steroids, 78, pp. 1029-1034, (2013); Kupchan S.M., Knox J.R., Kelsey J.E., Et al., Calotropin, a cytotoxic principle isolated from Asclepias curassavica L, Science, 146, pp. 1685-1686, (1994); Seiber J.N., Nelson C.J., Lee S.M., Cardenolides in the latex and leaves of seven Asclepias species and Calotropis pro-cera, Phytochemistry, 21, pp. 2343-2348, (1982); Jung N., Deckers A., Brase S., Ein Molekülarchiv als akademisch integrierte Service-Einrichtung, BIOspektrum, 23, pp. 212-214, (2017); Tremouilhac P., Nguyen A., Huang Y.-C., Et al., Chemotion ELN: an Open Source electronic lab notebook for chemists in academia, J Cheminformatics, 9, (2017); Chemotion - Repository for Molecules, Reactions and Research Data; Cheung H.T.A., Chiu F.C.K., Watson T.R., Et al., Cardenolide glycosides of the Asclepiadaceae. New glycosides from Asclepias fruticosa and the stereochemistry of uscharin, voruscharin and calotoxin, J Chem Soc, Perkin Trans, 1, pp. 2827-2835, (1983); Cheung H.T.A., Nelson C.J., Cardenolide glycosides with 5,6-unsaturation from Asclepias vestita, J Chem Soc, Perkin Trans, 1, pp. 1563-1570, (1989); Cheung H.T.A., Nelson C.J., Watson T.R., Newgluco-side conjugates and other cardenolide glycosides from the monarch butterfy reared on Asclepias fruticosa L, J Chem Soc, Perkin Trans, 1, pp. 1851-1857, (1988); Koch V., Brase S., Pd-mediated cross-coupling of C-17 lithiated androst-16-en-3-ol - access to functionalized arylated steroid derivatives, Org Biomol Chem, 15, pp. 92-95, (2017); Koch V., Nieger M., Brase S., Stille and Suzuki cross-coupling reactions as versatile tools for modifcations at C-17 of steroidal skeletons - a comprehensive study, Adv Synth Catal, 359, pp. 832-840, (2017); Koch V., Meschkov A., Feuerstein W., Et al., Synthesis, characterization, and biological properties of steroidal ruthenium(II) and iridium(III) complexes based on the androst-16-en-3-ol framework, Inorg Chem, 58, pp. 15917-15926, (2019); Ishibashi M., Screening for natural products that affect Wnt signaling activity, J Nat Med, 73, pp. 697-705, (2019); Park H.Y., Toume K., Arai M.A., Et al., Calotropin: a cardenolide from Calotropis gigantea that inhibits Wnt signaling by increasing casein kinase 1a in colon cancer cells, ChemBioChem, 15, pp. 872-878, (2014)","S. Brôse; Institut für Biologische und Chemische Systeme — IBCS-FMS, Karlsruher Institut für Technologie (KIT), Campus Nord, Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, D-76344, Germany; email: braese@kit.edu","","Springer Spektrum","","","","","","09470867","","","","German","BioSpektrum","Review","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85079705117"
"Anggawira D.; Mayesti N.","Anggawira, Deka (57220761367); Mayesti, Nina (57216859691)","57220761367; 57216859691","The Indonesian national scientific repository: A case study of research data sharing","2020","Preservation, Digital Technology and Culture","49","1","","14","25","11","4","10.1515/pdtc-2019-0015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087055636&doi=10.1515%2fpdtc-2019-0015&partnerID=40&md5=e026efdebec3b2f9b38a1cad626062c8","Department of Library and Information Science, Faculty of Humanities, University of Indonesia, Depok, Indonesia","Anggawira D., Department of Library and Information Science, Faculty of Humanities, University of Indonesia, Depok, Indonesia; Mayesti N., Department of Library and Information Science, Faculty of Humanities, University of Indonesia, Depok, Indonesia","This study discusses the sharing of research data through the Repositori Ilmiah Nasional, the Indonesian national scientific repository, which is managed by the Center for Scientific Data and Documentation, Indonesian Institute of Sciences (Pusat Dokumentasi dan Informasi Ilmiah, Lembaga Ilmu Pengetahuan Indonesia, known by the abbreviation PDDI-LIPI). The purpose of this study is to describe the process of research data sharing and identify supporting factors and obstacles faced in that process. This study uses a qualitative approach, with a case study method. Data collection techniques included field observations and observations on the repository system; semi-structured interviews with several informants, including researchers as well as development and librarian teams; and, analysis of policy documents and guidelines. Through these investigations, we discovered that while the Center has developed a new DataVerse repository system to enable research data sharing, there are still several issues that impede the repository from meeting institutional goals for increased data access. There is a need for additional training and socialization of researchers, to encourage and motivate them to share their research data through this service. Additionally, staff members need to gain competence in the management and curation of data. Researchers and librarians involved in research data sharing activities still face various obstacles in the areas of policy, service visibility, and promotion. This research is expected to increase the awareness of researchers, librarians, and repository development teams about each other's needs and to aid them in collaborating with each other to optimize the sharing of research of data through the repository. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.","National scientific repository; Research data management; Research data repository; Research data sharing","","","","","","DRPM Universitas Indonesia, (NKB-0987/UN2.R3.1/HKP.05.00/2019)","This work is supported by Hibah PITMA B 2019 funded by DRPM Universitas Indonesia No. NKB-0987/UN2.R3.1/HKP.05.00/2019.","Borgman C.L., Research Data: Who Will Share What, with Whom, When, and Why?, (2010); Corti L., Van Den Eynden V., Bishop L., Woolard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Jones C., Institutional Repositories: Content and Culture in An Open Access Environment, (2007); King G., An introduction to the dataverse network as an infrastructure for data sharing, Sociological Methods & Research, 36, 2, pp. 173-199, (2007); Kroes N., Opening science through e-infrastructures, Transcript of Speech Given at European Federation of Academies of Sciences and Humanities, (2012); Marlina E., Riyanto S., Asih Y., Peran Pusat Dokumentasi Dan Informasi Dalam Pengelolaan Data Penelitian (""the Roles of Documentation and Information Center in the Management of Research Data""). Konferensi Internasional Peran Science Mapping Dalam Pengembangan Ilmu Pengetahuan di Indonesia (International Conference on Science Mapping and the Development of Science), (2016); Pampel H., Dallmeier-Tiessen S., Bartling S., Friesike S., Open research data: From vision to practice, Opening Science: The Evolving Guide on How the Internet Is Changing Research, Collaboration, and Scholarly Publishing, pp. 213-224, (2014); Peng R.D., Reproducible research in computational science, Science, 334, 6060, pp. 1226-1227, (2011); Pennock M., Lewis S., Institutional repositories: The new university challenge, Aliss Quarterly, 2, 3, pp. 19-22, (2007); Petunjuk Teknis Pengguna: Pengelolaan Data Ilmiah Melalui Sistem Repositori Ilmiah Nasional, (2020); Uzwyshyn R., Research data repositories: The what, when, why, and how, Infotoday.com, pp. 18-21, (2016); Uzwyshyn R., Developing and implementing an online research data repository for your university or college campus, Presentation Given at University of San Diego Digital Initiatives Symposium, (2017); Van Den Eynden V., Bishop L., Incentives and Motivations for Sharing Research Data: A Researcher's Perspective, (2014); Van Den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data: Best Practice for Researchers, (2011); Wissik T., Urco M., Research data workflows: From research data lifecycle models to institutional solutions, Selected Papers from the Clarin Annual Conference 2015, Wrocław, Poland, October 14-16, 2015, pp. 94-107, (2015)","D. Anggawira; Department of Library and Information Science, Faculty of Humanities, University of Indonesia, Depok, Indonesia; email: anggawira.deka@gmail.com; N. Mayesti; Department of Library and Information Science, Faculty of Humanities, University of Indonesia, Depok, Indonesia; email: nina.mayesti@ui.ac.id","","De Gruyter Open Ltd","","","","","","21952965","","","","English","Preser. Digital Tech. Cult.","Article","Final","","Scopus","2-s2.0-85087055636"
"Bishop B.W.; Borden R.M.","Bishop, Bradley Wade (24469564000); Borden, Rose M. (57219721451)","24469564000; 57219721451","Scientists’ research data management questions: Lessons learned at a data help desk","2020","Portal","20","4","","677","692","15","7","10.1353/pla.2020.0032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093969588&doi=10.1353%2fpla.2020.0032&partnerID=40&md5=6f006a55625b3d753e25780096f2a7b1","School of Information Sciences, University of Tennessee, Knoxville, United States; Sandia National Laboratories in Albuquerque, NM, United States","Bishop B.W., School of Information Sciences, University of Tennessee, Knoxville, United States; Borden R.M., Sandia National Laboratories in Albuquerque, NM, United States","This study investigates scientists’ data needs to provide a basis for professionals in research data management (RDM) to tailor services to meet those needs. Eighty-one participants completed a survey after they had asked a question at a Data Help Desk staffed by data management professionals at one of two science conferences. The qualitative responses were coded for common themes, and the quantitative questions were tabulated to compare results between the two conferences. The combined results provide an overview of scientists’ questions and training experiences on RDM. The study found that 70 percent of scientists in the survey had no prior RDM training, and the most common RDM need for both ecologists and geologists was storage. This study provides evidence that scientists need additional information about RDM. Traditional service desk models offer one way of assisting them with data needs, but the results indicate a need for more training to meet the expectations of data sharing. Academic and other libraries should consider expanding hours and services for RDM to provide help where and when scientists need it. © 2020 by Johns Hopkins University Press, Baltimore, MD 21218.","","adult; article; ecologist; expectation; female; human; human experiment; library; major clinical study; male","","","","","Data Help Desk; Earth Science Information Partners; University of Tennessee, UT","The authors would like to acknowledge support from the Earth Science Information Partners (ESIP), administered by the University of Tennessee, Knoxville; and those volunteers sta 耀ng the Data Help Desk, as well as the study participants and graduate teaching assistant Hannah Armendarez.","Higgins Sarah, The Lifecycle of Data Management, pp. 57-61, (2016); Smale Nicholas, Unsworth Kathryn, Denyer Gareth, Barr Daniel, The History, Advocacy and Efficacy of Data Management Plans, bioRxiv, (2018); Bishop Bradley Wade, Hank Carolyn F., Curation, Digital, International Encyclopedia of Human Geography, (2020); Bishop Bradley Wade, Hank Carolyn, Earth Science Data Management: Mapping Actual Tasks to Conceptual Actions in the Curation Lifecycle Model, Transforming Digital Worlds. iConference 2018. Lecture Notes in Computer Science, 10766, pp. 598-608, (2018); Unal Yurdagul, Chowdhury Gobinda, Kurbanoglu Serap, Boustany Joumana, Walton Geoff, Research Data Management and Data Sharing Behaviour of University Researchers, Information Research, 24, (2019); Michener William K., Brunt James W., Helly John J., Kirchner Thomas B., Stafford Susan G., Nongeospatial Metadata for the Ecological Sciences, Ecological Applications, 7, 1, pp. 330-342, (1997); Carlson Jake, Stowell-Bracke Marianne, Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Buys Cunera M., Shaw Pamela L., Data Management Practices across an Institution: Survey and Report, Journal of Librarianship and Scholarly Communication, 3, (2015); Diekema Anne R., Wesolek Andrew, Walters Cheryl D., The NSF/NIH [National Science Foundation/National Institutes of Health] Effect: Surveying the Effect of Data Management Requirements on Faculty, Sponsored Programs, and Institutional Repositories, Journal of Academic Librarianship, 40, 3–4, pp. 322-331, (2014); Wiley, Kerby, Managing Research Data; Carlson, Stowell-Bracke, Data Management and Sharing from the Perspective of Graduate Students; Sharma, Qin, Data Management; Diekema Wesolek, Walters, The NSF/NIH Effect; Bishoff, Johnston, Approaches to Data Sharing; Halbert, The Problematic Future of Research Data Management; Diekema Wesolek, Walters, The NSF/NIH Effect; Gordon Ian D., Meindl Patricia, White Michael, Szigeti Kathy, Information Seeking Behaviors, Attitudes, and Choices of Academic Chemists, Science & Technology Libraries, 37, 2, pp. 130-151, (2018); Niu, Hemminger, A Study of Factors That Affect the Information-Seeking Behavior of Academic Scientists; Sahu, Singh, Information Seeking Behaviour of Astronomy/Astrophysics Scientists; Gordon Meindl, White, Szigeti, Information Seeking Behaviors, Attitudes, and Choices of Academic Chemists; Alstad, Hertzum, Information Seeking by Geoscientists; Adamick Jessica, Reznik-Zellen Rebecca C., Sheridan Matt, Data Management Training for Graduate Students at a Large Research University, Journal of eScience Librarianship, 1, 3, pp. 180-188, (2012); Schmidt, Implementing a Graduate Class in Research Data Management for Science/Engineering Students, A Graduate Class in Research Data Management; Helbig Kerstin, Research Data Management Training for Geographers: First Impressions, ISPRS [International Society for Photogrammetry and Remote Sensing] International Journal of Geo-Information, 5, 4, (2016); Hou Chung-Yi, Meeting the Needs of Data Management Training: The Federation of Earth Science Information Partners (ESIP) Data Management for Scientists Short Course, Issues in Science and Technology Librarianship, 80, (2015); Tyckoson David A., Issues and Trends in the Management of Reference Services: A Historical Perspective, Journal of Library Administration, 52, 6–7, pp. 581-600, (2012)","","","Johns Hopkins University Press","","","","","","15312542","","","","English","Portal","Article","Final","","Scopus","2-s2.0-85093969588"
"Fernandes M.; Rodrigues J.; Lopes C.T.","Fernandes, Miguel (57221036415); Rodrigues, Joana (57203242279); Lopes, Carla Teixeira (57194455159)","57221036415; 57203242279; 57194455159","Management of Research Data in Image Format: An Exploratory Study on Current Practices","2020","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12246 LNCS","","","212","226","14","2","10.1007/978-3-030-54956-5_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090097769&doi=10.1007%2f978-3-030-54956-5_16&partnerID=40&md5=c151f9b68adfdb4104911d699a0f301c","Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Fernandes M., Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Rodrigues J., Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Lopes C.T., Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Research data management is the basis for making data more Findable, Accessible, Interoperable and Reusable. In this context, little attention is given to research data in image format. This article presents the preliminary results of a study on the habits related to the management of images in research. We collected 107 answers from researchers using a questionnaire. These researchers were PhD students, fellows and university professors from Life and Health Sciences, Exact Sciences and Engineering, Natural and Environmental Sciences and Social Sciences and Humanities. This study shows that 83.2% of researcher use images as research data, however, its use is generally not accompanied by a guidance document such as a research data management plan. These results provide valuable insights into the processes and habits regarding the production and use of images in the research context. © 2020, Springer Nature Switzerland AG.","Image as research data; Image management; Research data management","Information management; Current practices; Environmental science; Exploratory studies; Guidance document; Health science; Image format; Research data; Research data managements; Digital libraries","","","","","Fundação para a Ciência e a Tecnologia, FCT, (PD/BD/150288/2019)","Joana Rodrigues is supported by a research grant from FCT-Fundação para a Ciência e Tecnologia: PD/BD/150288/2019. Special thanks to Professor Luis Teixeira for his help in the validation of the questionnaire. Thanks also to the Master in Information Science at FEUP for supporting the registration in this conference.","Catharine A., The epistemic value of photographs, Philosophical Perspectives on Depiction, pp. 82-103, (2010); Alex B., Review of Data Management Lifecycle Models, (2012); Marcus B., Using visual data in qualitative research, (2007); Gordon B., Foreword. In: The Fourth Paradigm: Data-Intensive Scientific Discovery, pp. xi-xvii, (2009); Christine L.B., Wallis J.C., Enyedy N., Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries, Int. J. Dig. Libraries, 7, 1-2, pp. 17-30, (2007); Kristin B., Data management for researchers: organize, maintain and share your data for research success, (2015); Andrew M.C., Winnie W.T.T., A critical analysis of lifecycle models of the research process and research data management, Aslib J. Inf. Manage, 70, 2, pp. 142-157, (2018); Amany M.E., Emad I.S., Research data management and sharing among researchers in Arab universities: an exploratory study, IFLA Journal, 44, 4, pp. 281-299, (2018); Enser P., Visual image retrieval: seeking the alliance of concept-based and content-based paradigms, J. Inf. Sci, 26, 4, pp. 199-210, (2000); Filipe F., Pedro P., Jose C., Kit sobre dados de investigação, Tech. rep. RCAAP, pp. 1-34, (2017); Rosie H., Daniel B., Sarah J., Three camps, one destination: the intersections of research data management, FAIR and Open, Insights the UKSG Journal, 32, (2019); Layne Sara Shatford, Some issues in the indexing of images, J. Am. Soc. Inf Sci, 45, 8, pp. 583-588, (1994); Making Open Science a Reality, (2015); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Cristina R., Et al., Os repositórios de dados científicos: estado da arte, Tech. rep, (2010); Sandweiss M.A., Image and artifact: the photograph as evidence in the digital age, J. Am. History, 94, 1, pp. 193-202, (2007); James R.S., Fotograa e ciência: a utopia da imagem objetiva e seus usos nas ciências e na medicina, Boletim do Museu Paraense Emilio Goeldi: Ciencias Humanas, 9, 2, pp. 343-360, (2014); DDI Version 3.0 Conceptual Model, (2004); Carol T., Et al., Data sharing by scientists: practices and perceptions, PLoS ONE, 6, 6, pp. 1-21, (2011); Research data lifecycle, (2019); Berenica V.K., Jane B., Paula L., Image Management as a Data Service, IASSIST Quarterly, 40, pp. 27-34, (2016)","M. Fernandes; Faculty of Engineering of the University of Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: miguelfernandes197@gmail.com","Hall M.; Mercun T.; Risse T.; Duchateau F.","Springer","","24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020","25 August 2020 through 28 August 2020","Lyon","243869","03029743","978-303054955-8","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85090097769"
"Niu J.","Niu, Jinfang (55324047400)","55324047400","Comparing the diffusion and adoption of linked data and research data management services among libraries","2020","Information Research","25","2","855","1","","","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096556563&partnerID=40&md5=eb4b1a262e0bfca5d5529c6410145d7c","School of Information, University of South Florida, United States","Niu J., School of Information, University of South Florida, United States","Introduction. Libraries face innovations periodically. It is important to identify consistent patterns in the diffusion and adoption of innovations so that libraries and relevant stakeholders will be informed and well-prepared for future innovations. Method. This paper compares findings from two previous projects, each of which was conducted to investigate the diffusion and adoption of two recent innovations, research data management service and linked data, respectively. The two projects were conducted using similar methods: collecting and analysing literature about the adoption of these innovations in libraries in the United States. Literature was collected through Google Scholar search, citation chasing, and target search for people or libraries that are involved in their adoption. Analysis. The gathered articles were then coded and analysed based on diffusion of innovation theories. Results. Similarities and disparities between the diffusion and adoption of the two innovations were identified. Conclusions. Findings from this study are informative for the decision-making of libraries, librarians, funders, and professional associations facing future innovations. They also contribute to diffusion of innovation theories through revealing new communication channels and alternative adoption processes, as well as redefining existing concepts. © the author, 2020.","","","","","","","","","Barbrow S., Brush D., Goldman J., Research data management and services: resources for novice data librarians, College and Research Libraries News, 78, 5, pp. 274-278, (2017); Berman E.A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: integrated findings to develop research data services, Journal of eScience Librarianship, 6, 1, (2017); Fallgren N., Experimentation with BIBFRAME at the National Library of Medicine, (2015); Futornick M., Stanford tracer bullets, (2018); Henderson M.E., Knott T. L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Jett J., Cole T. W., Han M. J. K., Szylowicz C., Linked opendData (LOD) for library special collections, Proceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries, pp. 309-310, (2017); Kovari J., Folsom S., Younes R., Towards a BIBFRAME implementation: the bibliotic-o framework, Proceedings of the 2017 DCMI International conference on Dublin Core and Metadata Applications, (2017); Lampert C. K., Southwick S. B., Leading to linking: introducing linked data to academic library digital collections, Journal of Library Metadata, 13, 2-3, pp. 230-253, (2013); BIBFRAME extension ontologies for modeling bibliographic metadata in the art and rare materials domains; BIBFRAME Pilot, (2016); McCallum S.H., BIBFRAME and linked data for libraries, Linked data for cultural heritage, pp. 105-123, (2016); Mitchell E. T., Library linked data: early activity and development, Library Technology Reports, 52, 1, (2016); Myntti J., Neatrour A., Use existing data first: Reconcile metadata before creating new controlled vocabularies, Journal of Library Metadata, 15, 3-4, pp. 191-207, (2015); Niu J, Diffusion and adoption of research data management services, Global Knowledge, Memory and Communication, 69, 3, pp. 117-133, (2019); Niu J, Diffusion and adoption of linked data among libraries, (2020); Peters C., Dryden A.R., Assessing the academic library’s role in campus-wide research data management: a first step at the University of Houston, Science and Technology Libraries, 30, 4, pp. 387-403, (2011); Rogers E.M., Diffusions of innovation, (2003); Schreur P. E., Linked Data for Production (LD4P): a multi-institutional approach to technical services transformation, WWW’18: Companion Proceedings of the Web Conference 2018, pp. 429-430, (2018); Shieh J., A transformative opportunity: BIBFRAME at the George Washington University, an early experimenter, (2013); Shieh J., Reese T., The importance of identifiers in the new web environment and using the uniform resource identifier (URI) in subfield zero ($0): a small step that is actually a big step, Journal of Library Metadata, 15, 3-4, pp. 208-226, (2015); Simic J., Seymore S., From silos to opaquenamespace: Oregon Digital’s migration to linked open data in Hydra, Art Documentation, 35, 2, pp. 305-320, (2016); Smith-Yoshimura K., Analysis of 2018 international linked data survey for implementers, Code4Lib Journal, 42, (2018); Steele T., What comes next: understanding BIBFRAME, Library Hi Tech, 37, 3, pp. 513-524, (2019); Wang Y., Yang S. Q., Linked data technologies and what libraries have accomplished so far, International Journal of Librarianship, 3, 1, pp. 3-20, (2018); Witt M., Carlson J., Brandt D. S., Cragin M. H., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009)","","","University of Boras","","","","","","13681613","","","","English","Inf. Res.","Article","Final","","Scopus","2-s2.0-85096556563"
"Rajabali Beglou R.; Akhshik S.S.","Rajabali Beglou, Reza (56512164200); Akhshik, Somaye Sadat (57192238087)","56512164200; 57192238087","Improving and extending activities of university and research libraries in Iran: the role of Iranian Research Institute for Information Science and Technology (Irandoc)","2020","Library Management","41","4-5","","135","151","16","1","10.1108/LM-01-2019-0001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085883590&doi=10.1108%2fLM-01-2019-0001&partnerID=40&md5=d1cfe84b399b3e1e5c35e682935519a7","Iranian Research Institute for Information Science and Technology, Tehran, Iran; Department of Library and Information Studies, Kharazmi University, Tehran, Iran","Rajabali Beglou R., Iranian Research Institute for Information Science and Technology, Tehran, Iran; Akhshik S.S., Department of Library and Information Studies, Kharazmi University, Tehran, Iran","Purpose: The purpose of this paper is to define the need to a center for improving and extending university and research libraries in Iran, and identifying capabilities and position of Iranian Research Institute for Science and Technology (Irandoc) for accepting possible roles. Design/methodology/approach: This research was documentary with scoping review in which the scope of research defined with internal and external organizational documents, related research studies in National Information System (NATIS) and international successful organizations in this field. Findings: Findings show that Irandoc can play roles in developing a standard, establishing experts and professional network, developing information and knowledge sharing process, facilitating access to the scholarly contents, leadership and change management, developing infrastructures for research data management, gathering information and statistics of these libraries and developing interlibrary collaborations in these libraries. Originality/value: This research is one of the seldom research studies related to clarification of an organization' role and position in NATIS in universities and improving and also extending activities of university and research libraries in Iran. © 2020, Emerald Publishing Limited.","Improving libraries; Iran; Iranian Research Institute for Information Science and Technology (Irandoc); National Information System; Research libraries; University libraries","","","","","","","","Abidi S., Huttemann L., Development of national information and documentation network for Uganda, (1990); Akhshik S.S., Analysis of Factors Affecting on the Planned Change in Information Center & Central Library of Ferdowsi University of Mashhad Based on Lewin's Field Theory, Knowledge Stickiness and Unlearning, (2015); Alqaheri H.A., Daftedar M., Architectural approach: a conceptual framework for the national information system of the state of Kuwait, Arab Journal of Administrative Sciences, 7, 1, pp. 153-176, (2000); Atramont A., Bonnet-Zamponi D., Bourdel-Marchasson I., Tuppin P., Health status and drug use 1 year before and 1 year after skilled nursing home admission during the first quarter of 2013 in France: a study based on the French National Health Insurance Information System, European Journal of Clinical Pharmacology, 74, 1, pp. 109-118, (2018); Azzena G., Busonera R., Nurra F., Petruzzi E., From the archaeological map of Italy to the national geographical archaeological information system, The Sardinian Experience Archeologia e Calcolatori, 26, pp. 115-129, (2015); Boon J.A., Pienaar H., National information system for current research in South Africa, South African Journal of Library and Information Science, 60, 1, pp. 8-16, (1992); Das S., Mondal P., Kumar Das R., Assessing the effectiveness of information services: a case study of National Agricultural Research System (NARS), Bangladesh, Asian Journal of Information Science and Technology (AJIST), 7, 2, pp. 21-27, (2017); Dijkers M., What is a scoping review?, (2015); Dodds L., Robinson K.M., Daking L., Dunstan L., The concept of ‘intent’ within australian coronial data: factors affecting the national coronial information system’s classification of mortality attributable to intentional self-harm, Health Information Management, 43, 3, pp. 13-22, (2014); Dunstan L., The national coronial information system: saving lives through the power of data, Australian Economic Review, 52, 2, pp. 247-254, (2019); Dusan L., National information system a tool for internationalization of higher education in Slovenia, Issues in Information Systems, 18, 2, pp. 9-19, (2017); Emrani E., Academic libraries in Iran: an overview of the status of libraries in Tehran university after the Islamic republic of Iran, Book Review Quarterly, 134, pp. 32-35, (2008); Fattahi R., The Challenges of expanding the national information system for science and technology in Iran, Iranian Journal of Information Processing and Management, 26, 2, pp. 195-197, (2010); Felipe Pinto L., Soares de Freitas M.P., Sant'Anna de Figueiredo A.W., National information and population survey systems: selected contributions from the Ministry of Health and the IBGE for analysis of Brazilian state capitals over the past 30 years, 23, 6, pp. 1859-1870, (2018); Gelfand M.A., University libraries for development, UNESCO, (1971); Grant M.J., Booth A., A typology of reviews: an analysis of 14 review types and associated methodologies, Health Information and Libraries Journal, 26, 2, pp. 91-108, (2009); Hafezian Razavi K., A few points about specialized libraries in Iran after the Islamic Republic of Iran, Book Review Quarterly, 134, pp. 26-27, (2008); Harvard-Williams P., National information system (NATIS), Encyclopedia of Library and Information Science, (2003); Hasanzadeh M., National information system requires national will and serious action, Book Review Quarterly, 123, pp. 14-19, (2007); Hill M.W., National Information Policies and Straegies, (1994); Horri A., NATIS Plan, (1998); (2018); Issolah R., Giovannetti J., Building on the findings of agricultural research through the establishment of a national information system: the case of the Algerian agricultural documentation network (RADA), IAALD Quarterly Bulletin, 1-2, pp. 10-15, (2005); Khan I., Sher M., Aslam S., Saqlain S.M., Ashraf M.U., Khan J.I., Rauf M., Ghani A., Medical drop box (MDB): a national health information exchange and management system for medical inndustry, Professional Medical Journal, 23, 4, pp. 489-497, (2016); Kim Y., Jeong H., A cloud computing-based analysis system for the national R&D information concerning with the data security, Wireless Personal Communications, 89, 3, pp. 977-992, (2016); Lind K., Kaariainen J., Kuoppamaki S., From problem gambling to crime? Findings from the Finnish national police information system, Journal of Gambling Issues, 30, pp. 98-123, (2015); Line L.W., Kari D., A qualitative study of the implementation and use of anational information system, Norwegian Centre for E-health Research, (2017); Lundu M.C., The National Information System (NATIS) concept and the development of libraries in Zambia, 16, 4, pp. 373-385, (1984); Mannan S.M., Bose M., Resource sharing and information networking of libraries in Bangladesh: a study on user satisfaction, Malaysian Journal of Library and Information Science, 3, 2, pp. 67-86, (1998); Muhamed S.Q., Mohammed M.Q., Nayl T., Chyrkova K., The concept of building a model of the national blood information system, Iraqi Journal for Computers and Informatics (IJCI), 43, 1, pp. 17-21, (2017); Nahid Tabatabaee F., National information system (NATIS), Encyclopedia of Library and Information Science, (2006); Nusseir Y., Establishing a national information system for Jordan; Omidvar M., Proposing the Design of the National Scientific and Technical Information System: Final Report of the First Phase, (2001); Parker J.S., Aspects of Library Development Planning, (2003); Pham M.T., A scoping review of scoping reviews: advancing the approach and enhancing the consistency, Research Synthesis Methods, 5, 4, pp. 371-385, (2014); Potter I., Potter I., Petersen T., D'Agostino M., Doane D., Ruiz P., Marti M., Fitzgerald J., Del Riego A., De Cosio F.G., Espinal M., The Virgin Islands National Information Systems for Health: vision, actions, and lessons learned for advancing the national public health agenda, Pan American Journal of Public Health, (2018); Rajabali Beglou R., Designing a Conceptual Model of the Position and Pole of Irandoc in Improving and Expanding the Libraries of the MSRT, Scoping Review, (2016); Rajagopalan T.S., Towards evolution of a national information system for science and technology in India, Annals of Library Science and Documentation, 23, 2, pp. 169-172, (1976); Scanff P., Crescini D., Roy H., Billarand Y., Rannou A., National Dose Register in France within the National Information System Siseri, Radiation Protection Dosimetry, 170, 1-4, pp. 429-432, (2017); Singh S., Sharma S., National information system in humanities (NISH): a proposal, Journal of Library and Information Science –Delhi, 30, 1-2, (2005); Stone M.B., (1996); Subba Rao S., Networking of libraries and information centres: challenges in India, Library Hi Tech, 19, 2, pp. 167-179, (2001); Wijasuriya D.E.K., The development of national information systems, Journal of Information Science, 1, 1, pp. 27-34, (1979); Zandian F., National Academic Information System, (2008); Zandian F., Horri A., Reviewing the operational strategies of the national information system in the universities, Journal of Academic Librarianship and Information Research, 38, 41, pp. 91-124, (2004); Kalle L., Juha K., Sanna-Mari K., From problem gambling to crime? Findings from the Finnish national police information system, Journal of Gambling, 30, (2015); Saleh M., Kabangu Y., Mpeli F., Salumu S., Kabeya P., Okitolonda E., Evolution du système national d'information sanitaire de la république démocratique du Congo entre 2009 et 2015, Pan African Medical Journal, (2017)","R. Rajabali Beglou; Iranian Research Institute for Information Science and Technology, Tehran, Iran; email: beglou@irandoc.ac.ir","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","","Scopus","2-s2.0-85085883590"
"Nwabugwu M.J.; Godwin L.S.","Nwabugwu, Mgbodichima Jummai (57221588948); Godwin, Lucky Stephen (57208900473)","57221588948; 57208900473","Research Data Management (Rdm) Services In Libraries: Lessons For Academic Libraries In Nigeria","2020","Library Philosophy and Practice","2020","","","1","18","17","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099508913&partnerID=40&md5=6f14e944bad89d62e15cae78a0fae60c","Taslim Elias Library, Nigerian Institute of Advanced Legal Studies, University of Lagos Campus, Lagos, Nigeria; Tekena Tamuno Library, Redeemer’s University, Ede, Osun, Nigeria","Nwabugwu M.J., Taslim Elias Library, Nigerian Institute of Advanced Legal Studies, University of Lagos Campus, Lagos, Nigeria; Godwin L.S., Tekena Tamuno Library, Redeemer’s University, Ede, Osun, Nigeria","Research funding organizations understand the importance of infrastructure and services to organize and preserve research data. Academic research libraries have been identified as locations in which to base these research data management services. Research data management services include data management planning, digital curation (selection, preservation, maintenance, and archiving), and metadata creation and conversion. However, some libraries are beginning to provide structure for research data management services. These services are starting to record some degree of success as local data policies are being formulated. The aim of this paper, therefore, is to discuss the importance of research data management in the academic libraries in Nigeria. The article summarized the research data management life cycle to include: data creation; data collection and description, data storage; data archiving and preservation; data access; data discovery and analysis, and data reuse and transformation. The paper further identified research data management tools and applications, which include DMPonline, Data Asset framework, Collaborative Assessment of Research Data Infrastructure and Objectives (CARDIO), and Curation cost exchange. Specifically, the paper examines some skills requirements for research data management in academic libraries. Some of the challenges facing effective research data management services identified by this paper include technology obsolescence, technology fragility; Lack of guidelines on good practice; Inadequate financial and human resources to manage data well, and Lack of evidence about best infrastructures. © 2020, Library Philosophy and Practice. All Rights Reserved.","Data Management; Data Planning Tools Academic libraries; Research Data Management Service","","","","","","","","E-Science and data support services: A study of ARL member institutions, (2010); Bach K., Schafer D., Enke N., Seeger B., Gemeinholzer B., Bendix J., A comparative evaluation of technical solutions for long-term data repositories in integrative biodiversity research, Ecological Informatics, 11, (2012); Chiware E., Mathe Z., Academic libraries’ role in research data management services: A South African perspective, Sajlis.journals, (2016); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A Guide to Good Practice, (2011); Cox A. M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, (2014); Cox A. M., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Understanding and comparing digital curation costs to support smarter investments, (2020); Davidson J., Emerging good practice in managing research data and research information in UK universities, Procedia Computer Science, 33, pp. 215-222, (2014); Data management in perspective: The career profile of data managers, (2011); Research Data Management for Librarians, (2019); DCC Lifecycle Model, (2015); Digital Repository Audit Method Based on Risk Assessment, (2020); Fary M., Owen K., Developing an institutional research data management plan service, (2013); Flores J. R., Brodeur J. J., Daniels M. G., Nicholls N., Turnator E., Libraries and the Research Data Management Landscape, (2015); Galleto M., What is data management?, (2016); Higgins S., The DCC Curation Lifecycle Model, The International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Holden J. P., Office of Science and Technology Policy, (2013); Jones S., Pryor G., Whyte A., How to develop RDM services: A guide for HEIs. DCC How-to Guides, (2013); Jones S., The range and components of RDM infrastructure and services, Delivering research data management services, pp. 89-114, (2014); Kennan M. A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Lewis M. J., Libraries and the management of research data, Envisioning Future Academic Library Services, pp. 145-168, (2010); Peters C., Dryden A. R., Assessing the academic library's role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, (2011); Pinfield S., Cox A. M., Smit J., Research data management and libraries: relationships, activities, drivers and influences, (2014); Rouse M., What is data management and why is it important, (2019); Tenopir C., Hughes D., Allard S., Fram M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tenopir C., Sandusky R. J., Allard S., Birch B., Academic librarians and research data services: preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Shearer B. S. K., Librarians’ Competencies Profile for Research Data Management, (2016); Surkis A., Read K., Research data management, Journal of the Medical Library Association: JMLA, 103, 3, pp. 154-156, (2015); Whyte A., Tedds J., Making the case for research data management, DCC Briefing Papers, (2011)","M.J. Nwabugwu; Taslim Elias Library, Nigerian Institute of Advanced Legal Studies, University of Lagos Campus, Nigeria; email: ujprecious@yahoo.com","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85099508913"
"Joo S.; Peters C.","Joo, Soohyung (36621159100); Peters, Christie (55014387000)","36621159100; 55014387000","User needs assessment for research data services in a research university","2020","Journal of Librarianship and Information Science","52","3","","633","646","13","17","10.1177/0961000619856073","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068613180&doi=10.1177%2f0961000619856073&partnerID=40&md5=235388198386cce2b8be5e34ee2e64a1","University of Kentucky, United States","Joo S., University of Kentucky, United States; Peters C., University of Kentucky, United States","This study assesses the needs of researchers for data-related assistance and investigates their research data management behavior. A survey was conducted, and 186 valid responses were collected from faculty, researchers, and graduate students across different disciplines at a research university. The services for which researchers perceive the greatest need include assistance with quantitative analysis and data visualization. Overall, the need for data-related assistance is relatively higher among health scientists, while humanities researchers demonstrate the lowest need. This study also investigated the data formats used, data documentation and storage practices, and data-sharing behavior of researchers. We found that researchers rarely use metadata standards, but rely more on a standard file-naming scheme. As to data sharing, respondents are likely to share their data personally upon request or as supplementary materials to journal publications. The findings of this study will be useful for planning user-centered research data services in academic libraries. © The Author(s) 2019.","Academic libraries; data-related assistance; research data management; research data services; user needs assessment","","","","","","Institute of Museum and Library Services, IMLS, (RE-32-16-0140-16)","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Institute of Museum and Library Services (RE-32-16-0140-16).","2016 Top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, C&RL News, 77, 6, pp. 274-281, (2016); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Alexogiannopoulos E., McKenney S., Pickton M., Research data management project: A DAF investigation of research data management practices at the University of Northampton, (2010); Cox A., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A., Williamson L., The 2014 DAF survey at the University of Sheffield, International Journal of Digital Curation, 10, 1, pp. 210-229, (2015); Cox A.M., Kennan M.A., Lyon L., Et al., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Gray J., Escience: A transformed scientific method, NRC-CSTB, (2007); Data Asset Framework implementation guide, (2009); Jones S., Ball A., Ekmekcioglu C., The Data Audit Framework: A first step in the data management challenge, International Journal of Digital Curation, 3, 2, pp. 112-120, (2008); Jones S., Ross S., Ruusalepp R., Data Audit Framework methodology, (2009); Johnson R., Parsons T., Chiarelli A., Et al., Jisc Research Data Assessment support: Findings of the 2016 Data Assessment Framework (DAF) surveys, (2016); Johnston L., Lafferty M., Petsan B., Training researchers on data management: A scalable, cross-disciplinary approach, Journal of eScience Librarianship, 1, 2, pp. 79-87, (2012); Joo S., Kim S., Kim Y., An exploratory study of health scientists’ data reuse behaviors: Examining attitudinal, social, and resource factors, Aslib Journal of Information Management, 69, 4, pp. 389-407, (2017); Kaye J., Finding out what researchers really, really want from a research data shared service, (2016); Keil D.E., Research data needs from academic libraries: The perspective of a faculty researcher, Journal of Library Administration, 54, 3, pp. 233-240, (2014); Knight G., Research data management at London School of Hygiene and Tropical Medicine: Web survey report, (2013); Kim J., A study on research data management services of research university libraries in the US, Journal of the Korean BIBLIA Society for Library and Information Science, 25, 3, pp. 165-189, (2014); Mutz R., Bornmann L., Daniel H.D., Cross-disciplinary research: What configurations of fields of science are found in grant proposals today?, Research Evaluation, 24, 1, pp. 30-36, (2014); Nassiri S., Worthington B., Digital Asset Framework (DAF) survey analysis, (2012); Parham S.W., Bodnar J., Fuchs S., Supporting tomorrow’s research: Assessing faculty data curation needs at Georgia Tech, C&RL News, 73, 1, pp. 10-13, (2012); Parsons T., Grimshaw S., Williamson L., Research data management survey,the University of Nottingham, (2013); Peters C., Dryden A.R., Assessing the academic library’s role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA Journal, 43, 1, pp. 51-64, (2017); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future, Association of College & Research Libraries, (2012); Tenopir C., Hughes D., Allard S., Et al., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tenopir C., Sandusky R.J., Allard S., Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Tripathi M., Chand M., Sonkar, Et al., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Wiley C., Mischo W.H., Data management practices and perspectives of atmospheric scientists and engineering faculty, Issues in Science and Technology Librarianship, 1, 95, (2016); Williams S.C., Gathering feedback from early-career faculty: Speaking with and surveying Agricultural faculty members about research data, Journal of eScience Librarianship, 2, 2, pp. 43-52, (2013); Wilson J., University of Oxford research data management survey 2012: The results, (2013); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); Yoon A., Kim Y., Social scientists’ data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories, Library & Information Science Research, 39, 3, pp. 224-233, (2017); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College & Research Libraries, 78, 7, (2017)","S. Joo; University of Kentucky, United States; email: soohyung.joo@uky.edu","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Article","Final","","Scopus","2-s2.0-85068613180"
"Wilms K.L.; Stieglitz S.; Ross B.; Meske C.","Wilms, Konstantin L. (57190278061); Stieglitz, Stefan (8982225800); Ross, Björn (57195061365); Meske, Christian (55806873000)","57190278061; 8982225800; 57195061365; 55806873000","A value-based perspective on supporting and hindering factors for research data management","2020","International Journal of Information Management","54","","102174","","","","6","10.1016/j.ijinfomgt.2020.102174","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086800812&doi=10.1016%2fj.ijinfomgt.2020.102174&partnerID=40&md5=941037f8464de6a98ed42ab17154dacf","Department of Computer Science and Applied Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, Duisburg, Germany; Digital Transformation & Strategic Information Management, School of Business & Economics, Freie Universität Berlin, Germany","Wilms K.L., Department of Computer Science and Applied Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, Duisburg, Germany; Stieglitz S., Department of Computer Science and Applied Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, Duisburg, Germany; Ross B., Department of Computer Science and Applied Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, Duisburg, Germany; Meske C., Digital Transformation & Strategic Information Management, School of Business & Economics, Freie Universität Berlin, Germany","Research data management (RDM) is an important prerequisite for a substantial and sustainable contribution to knowledge. There is a pressing need to examine why researchers hesitate to store, annotate, share and manage their research data. To model underlying psychological factors influencing researchers’ refusal to conduct RDM, the social exchange theory is extended with elements from prospect theory. Thus, it allows psychological insights into researchers’ decision-making, and illustrates the role of cost and benefit evaluations under uncertainty. Data management policies of a major funding agency were presented to a homogeneous group of researchers from the Information Systems community in Germany. The findings show that many researchers see a high value in RDM but are still held back by uncertainty. While the benefits seem to outweigh the costs, we ascertain the uncertainty factors which hinder researchers’ intention from conducting RDM in the future. The perceived fear of losing control over one's data is identified as a major hindering factor, while the fear of losing one's unique value did not prevail. The study provides novel insights for executives, administrators, and developers in higher education institutions, which are especially important for furthering RDM implementation strategies, as well as for system development. © 2020 The Authors","Data sharing; Higher education; Knowledge management; Knowledge sharing; Research management","Decision making; Higher education institutions; Implementation strategies; Psychological factors; Psychological insights; Research data managements; Social exchange theory; System development; Uncertainty factors; Information management","","","","","National Science Foundation, NSF; Deutsche Forschungsgemeinschaft, DFG","In this regard, several international research funding institutions, such as the National Science Foundation (NSF) or the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) have set up mandatory RDM policies, compliance with which is a basic prerequisite for future funding ( Wilms, Stieglitz, Buchholz, Vogl, & Rudolph, 2018 ). Comprehensive RDM encompasses, for example, the long-term storage and annotation of research data, but also making the data accessible and usable by anyone, a concept also known as open data ( Link et al., 2017 ; Wilms, Stieglitz, Buchholz et al., 2018 ). While there is an increasing pressure on higher education institutions to promote RDM, there is still a great deal of mistrust across several academic fields when it comes to recording, preserving, and sharing research data ( Borgman, 2012 ; Perrier et al., 2017 ; Piwowar, 2011 ; Sayogo & Pardo, 2013 ). 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"Wang R.; Qiu J.; Zheng X.; Shi C.; Zhang C.; Wang Z.; Wang J.; Liu Y.; Jia N.; Feng W.","Wang, Rui (57199208345); Qiu, Jiqing (55957829400); Zheng, Xinzhang (56587137500); Shi, Changsheng (57216883982); Zhang, Chen (57190015375); Wang, Zhibo (56176408700); Wang, Jinbang (57203244627); Liu, Yali (55958054200); Jia, Nan (56767660200); Feng, Weihua (57216879035)","57199208345; 55957829400; 56587137500; 57216883982; 57190015375; 56176408700; 57203244627; 55958054200; 56767660200; 57216879035","Survey on status of scientific research data in tobacco industry; [烟草科研数据现状调查与分析]","2020","Tobacco Science and Technology","53","2","","107","112","5","4","10.16135/j.issn1002-0861.2019.0220","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085083251&doi=10.16135%2fj.issn1002-0861.2019.0220&partnerID=40&md5=cce5feb4fd0f93925ecc40df47b76718","Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Science and Technology Department, State Tobacco Monopoly Administration, Beijing, 100045, China; Beijing Cigarette Factory Co., Ltd., Shanghai Tobacco Group Co., Ltd., Beijing, 100024, China","Wang R., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Qiu J., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Zheng X., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Shi C., Science and Technology Department, State Tobacco Monopoly Administration, Beijing, 100045, China; Zhang C., Beijing Cigarette Factory Co., Ltd., Shanghai Tobacco Group Co., Ltd., Beijing, 100024, China; Wang Z., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Wang J., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Liu Y., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Jia N., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; Feng W., Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China","In order to grasp the status of scientific research data in the tobacco industry, a survey was conducted by questionnaire combined with site visit. The results indicated that the industrial enterprises, commercial enterprises and scientific research institutes in the tobacco industry had all owned considerable scientific research data, because the differences between the data owners in the business works and data management, their research data were quite different in terms of data type, volume and increasing rate. The problems in the scientific research data management, such as lack of unified standards and irregular data storage, had affected data sharing within the industry. In view of that, some suggestions were put forward in the aspects of institutional guarantee, idea popularization, data standardization, scope of data sharing, data service, data publication, etc. The results of this study provide supports for the promotion of scientific data sharing in the tobacco industry. © 2020, Editorial Office of Tobacco Science and Technology. All right reserved.","Data sharing; Industrial/commercial enterprise; Scientific research data; Status investigation; Tobacco industry","","","","","","","","Wang Q., Zhong Y., Jiang H., Bibliometric analysis on scientific data sharing in China, Journal of Information, 27, 7, (2008); Li J., Liu D., Jiang H., Research on international data sharing principles and policies, Library and Information Service, 52, 12, pp. 77-80, (2008); Wen F., Research on policy system construction of open government data in China, (2019); Li J., Liu D., Jiang H., Research on international scientific data sharing, Library Development, 2, (2009); Wen F., A research of the opening and sharing policies of scientific data, Research on Library Science, 9, pp. 91-101, (2017); You X., Sheng X., Comparison of 8 international organizations' scientific data open sharing policies and their characteristics analysis, Information Studies: Theory & Application, 40, 12, pp. 44-45, (2017); Chu J., Wang M., The strategy of open sharing of scientific data in the United States and its enlightenment to China, Information Studies: Theory & Application, 42, 8, pp. 153-158, (2019); Zhang J., Lu J., Tian Y., Review of factors influencing the data sharing behaviors at abroad, Library and Information Service, 58, 4, pp. 136-142, (2014); Liu G., Pu J., Qian J., Analysis of several influencing factors in research data sharing and interpretation of their roles, Library Tribune, 38, 11, (2018); Song Z., Liu H., Li X., Et al., Current status of marine scientific data sharing platforms domestic and overseas, Science & Technology Information, 36, pp. 20-23, (2013); Wei J., Zhang C., A comparative study of research data management platform domestic and abroad, Document, Information & Knowledge, 5, pp. 97-107, (2017); Li Z., Study on the current situation and development strategy of domestic scientific data sharing platform, Library Theory and Practice, 8, pp. 108-112, (2018); Li X., Study on the influential factors of sustainable use of Agricultural Science Data Sharing Platform, (2018); Wang G., Li J., Deng L., Et al., China meteorological data sharing service system: design and development, Journal of Applied Meteorological Science, 15, pp. 10-16, (2004); Li Z., Hu Z., Sun H., Et al., Research on evaluation index for resources in the national scientific data sharing platform for population and health, China Digital Medicine, 13, 3, pp. 2-4, (2018); Zhou Y., Guo W., Li X., Et al., Correlation between free amino acids and sensory quality for flue-cured tobacco of fresh flavor type tobacco-planting areas, Tobacco Science & Technology, 51, 11, pp. 28-35, (2018); Luo Z., Li Z., Zhang J., Et al., Cloning and expression analysis of NtDSX2 from Nicotiana tabacum, Tobacco Science & Technology, 51, 11, pp. 1-7, (2018); Fu Q., Yang B., Ge J., Et al., Studies on near infrared model for predicting major physical indices of flue-cured tobacco, Tobacco Science & Technology, 5, pp. 5-8, (2014); Zuo J., Chen Y., The analysis on sharing mode of scientific data in the environment of big data, Journal of Intelligence, 12, pp. 151-154, (2013); Huang R., Wang B., Zhou Z., A study on countermeasures for promoting scientific data sharing in China, Library, 3, pp. 7-13, (2014)","W. Feng; Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China; email: fwhtt@ztri.com.cn","","Editorial Office of Tobacco Science and Technology","","","","","","10020861","","","","Chinese","Tob. Sci. Technol.","Article","Final","","Scopus","2-s2.0-85085083251"
"Williams A.; O’Connor M.","Williams, Andrew (57967772200); O’Connor, Mary (57968075700)","57967772200; 57968075700","Making researchers’ lives easier and managing risk at the University of Adelaide: The research data project","2020","Technology, Change and the Academic Library: Case Studies, Trends and Reflections","","","","151","160","9","0","10.1016/B978-0-12-822807-4.00015-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142077434&doi=10.1016%2fB978-0-12-822807-4.00015-4&partnerID=40&md5=1c2da6c065548919b333f6f7b0ddc494","University Library, University of Adelaide, Adelaide, SA, Australia; Formerly University Library, University of Adelaide, Adelaide, SA, Australia","Williams A., University Library, University of Adelaide, Adelaide, SA, Australia; O’Connor M., Formerly University Library, University of Adelaide, Adelaide, SA, Australia","The case study describes the research data (ReDa) project that was executed at the University of Adelaide between 2017 and 2019 to provide systems and services to support improved management of research data. The ReDa project is considered to have been very successful, and the case study presents the key reasons for that success; namely, good strategic planning, highly effective change management and engagement with researchers, skilled project management including good management of staff turnover, skilled and expert staff and significant effort to transition to business as usual throughout the project. © 2021 Jeremy Atkinson, Published by Elsevier Ltd. All rights reserved.","academic libraries; change management; electronic research notebook; Figshare; LabArchives; Research data management; research data management planning; research data repository; training","","","","","","","","","","","Elsevier Science Ltd.","","","","","","","978-012822807-4; 978-012823228-6","","","English","Technology, Change and the Academic Library: Case Studies, Trends and Reflections","Book chapter","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85142077434"
"Abouelenein S.; Williams T.; Baldner J.; Zozus M.N.","Abouelenein, Saly (57208001616); Williams, Tremaine (57195233283); Baldner, Jaime (57217245023); Zozus, Meredith Nahm (56744770800)","57208001616; 57195233283; 57217245023; 56744770800","Analysis of professional competencies for the clinical research data management profession","2020","Studies in Health Technology and Informatics","270","","","1199","1200","1","3","10.3233/SHTI200361","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086886283&doi=10.3233%2fSHTI200361&partnerID=40&md5=0dad8c51aff2a927d4f7484f477f92b0","University of Arkansas for Medical Sciences, Little Rock, AR, United States; University of Texas of Health, San Antonio, TX, United States","Abouelenein S., University of Arkansas for Medical Sciences, Little Rock, AR, United States; Williams T., University of Arkansas for Medical Sciences, Little Rock, AR, United States; Baldner J., University of Arkansas for Medical Sciences, Little Rock, AR, United States; Zozus M.N., University of Arkansas for Medical Sciences, Little Rock, AR, United States, University of Texas of Health, San Antonio, TX, United States","Objective: This job analysis was conducted to compare, assess and refine the competencies of the clinical research data management profession. Materials and Methods: Two questionnaires were administered in 2015 and 2018 to collect information from data managers on professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge. Results: In 2018 survey, 67 professional competencies were identified. Job tasks differed between early- to mid-career and mid- to late-career practitioners. A large variation in the types of studies conducted and variation in the data managed by the participants was observed. Discussion: Clinical research data managers managed different types of data with variety of research settings, which indicated a need for training in methods and concepts that could be applied across therapeutic areas and types of data. Conclusion: The competency survey reported here serves as the foundation for the upcoming revision of the Certified Clinical Data Manager (CCDMTM) exam. © 2020 European Federation for Medical Informatics (EFMI) and IOS Press.","Clinical data management; Professional competencies; SCDM","Certification; Data Management; Humans; Professional Competence; Surveys and Questionnaires; Information management; Job analysis; Managers; Medical informatics; Personnel training; Professional aspects; Surveys; Clinical data; Professional competencies; Research data managements; adult; career; clinical research; conference paper; female; human; human experiment; job analysis; male; manager; physician; questionnaire; certification; information processing; professional competence; Clinical research","","","","","","","Good Clinical Data Management Practices (GCDMP), (2013); Zozus M.N., Lazarov A., Smith L.R., Et al., Analysis of professional competencies for the clinical research data management profession: Implications for training and professional certification, J Am Med Inform Assoc, 24, pp. 737-745, (2017); Bornstein S., Clinical data management task list, Data Basics, 5, pp. 8-10, (1999)","S. Abouelenein; University of Arkansas for Medical Sciences, Little Rock, United States; email: sabouelenein@uams.edu","Pape-Haugaard L.B.; Lovis C.; Madsen I.C.; Weber P.; Nielsen P.H.; Scott P.","IOS Press","","30th Medical Informatics Europe Conference, MIE 2020","28 April 2020 through 1 May 2020","Geneva","161256","09269630","978-164368082-8","","32570578","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-85086886283"
"Vaidya M.A.; Sanjeeva M.","Vaidya, Madhavi Arun (57697413200); Sanjeeva, Meghana (57486134200)","57697413200; 57486134200","A study of big data analytical frameworks in research data management using data mining techniques","2020","Handbook of Research on Modern Educational Technologies, Applications, and Management (2 Vol.)","","","","48","67","19","1","10.4018/978-1-7998-3476-2.ch004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130287624&doi=10.4018%2f978-1-7998-3476-2.ch004&partnerID=40&md5=daa24062d864055229dfb8fcc54a5a0f","Vivekanand Education Society's College of Arts, Science and Commerce, India","Vaidya M.A., Vivekanand Education Society's College of Arts, Science and Commerce, India; Sanjeeva M., Vivekanand Education Society's College of Arts, Science and Commerce, India","Research, which is an integral part of higher education, is undergoing a metamorphosis. Researchers across disciplines are increasingly utilizing electronic tools to collect, analyze, and organize data. This ""data deluge"" creates a need to develop policies, infrastructures, and services in organisations, with the objective of assisting researchers in creating, collecting, manipulating, analysing, transporting, storing, and preserving datasets. Research is now conducted in the digital realm, with researchers generating and exchanging data among themselves. Research data management in context with library data could also be treated as big data without doubt due its properties of large volume, high velocity, and obvious variety. To sum up, it can be said that big datasets need to be more useful, visible, and accessible. With new and powerful analytics of big data, such as information visualization tools, researchers can look at data in new ways and mine it for information they intend to have. © 2021, IGI Global.","","","","","","","","","Abadi D., Agarwal R., Ailamaki A., The Beckman report on database research, SIGMOD Record, 43, 3, pp. 61-70, (2014); Bailey C., Research Data Curation Bibliography, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Carlson J., Stowell-Bracke M., Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, Portal. Portal (Baltimore, Md.), 13, 4, pp. 343-361, (2013); Codd E.F., A relational model of data for large shared data banks, Commun ACM, 13, pp. 377-387, (1970); Crosas M.C., Cloud Dataverse: A Data Repository Platform for the Cloud, (2017); Dean J., Ghemawat S., MapReduce: Simplified data processing on large clusters, OSDI'04 Proceedings of the 6th Conference on Symposium on Opearting Systems Design and Implementation, pp. 1-13, (2004); (2018); FAIR Principles - GO FAIR, (2016); Farid M., Roatis A., Ilyas I., CLAMS: Bringing Quality to Data Lakes, International Conference on Management of Data, pp. 2089-2092, (2016); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services & Use, 36, 1-2, pp. 35-43, (2016); Godse M., Mullik S., An approach for selecting Software-as-a-Service (SaaS) product, IEEE Sixth International Conference on Cloud Computing, pp. 155-158, (2013); Halevy A., Korn F., Noy N.F., Olston C., Polyzotis N., Roy S., Whang S.E., Goods, Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16, (2016); The Four V's of Big Data, (2016); King W.R., A research agenda for the relationships between culture and knowledge management, Knowledge and Process Management, 14, 3, pp. 226-236, (2007); Laney D., 3-D data management: Controlling data volume, velocity and variety, (2001); Madduri R.K., Dave P., Sulakhe D., Lacinski L., Liu B., Foster I.T., Experiences in building a next-generation sequencing analysis service using Galaxy, Globus Online and Amazon Web Service, Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, pp. 341-343, (2013); Madera L., The next information architecture evolution:The data lake wave, 8thInternational Conference on Management of Digital EcoSystems, pp. 174-190, (2016); Data Citation Synthesis Group: Joint Declaration of Data Citation Principles, (2014); Parham S.W., Bodnar J., Fuchs S., Supporting tomorrow's research: Assessing faculty data curation needs at Georgia Tech, College & Research Libraries News, 73, 1, pp. 10-13, (2012); Parker Z., Poe S., Vrbsky, Comparing NoSQL MongoDB to an SQL DB, ACMSE'13: 51st ACM Southeast Conference, pp. 1-6, (2013); Pearson S., Benameur A., Privacy, security and trust issues arising from cloud computing, Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, pp. 693-702, (2010); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, PLoS One, 9, 12, (2014); Pryor G., Managing Research Data, (2012); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Riungu L.M., Taipale O.O., Smolander K., Resarch issues for software testing in the cloud. es, IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), (2010); Russom P., Best Practices for Big Data Analytics, Big Data Analytics, pp. 93-109, (2015); Sallans A., Donnelly M., DMP Online and DMPTool: Different Strategies Towards a Shared Goal, International Journal of Digital Curation, 7, 2, pp. 123-129, (2012); Scott S.L., Blocker A.W., Bonassi F.V., Chipman H.A., George E.I., McCulloch R.E., Bayes and big data: The consensus Monte Carlo algorithm, International Journal of Management Science and Engineering Management, 11, 2, pp. 78-88, (2016); Sheng J., Amankwah-Amoah J., Wang X., A multidisciplinary perspective of big data in management research, International Journal of Production Economics, 191, pp. 97-112, (2017); Son S., Module on Data Preprocessing Techniques for Data Mining on Data Cleaning and Data Preprocessing, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Terrizzano I., Schwarz P., Roth M., Proceedings of the 7th Biennial Conference on Innovative Data SystemsResearch (CIDR '15), (2015); DMPTool, (2018); Vaidya M., Managing Big Data in cloud computing environments, Handling critical issues of Big Data on cloud, pp. 101-131, (2016); Vassiliadis P., Simitsis, Skiadopoulos, Conceptual modellingfor ETLprocesses, Proceedings of the 5th ACM International Workshop on Data Warehousing and OLAP, (2002); Waddington J., Knight G., Zhang J., Hedges M., Downing R., Kindura: Repository services for researchers based on hybrid clouds, Journal of Digital Information, 13, 1, (2012); Wang C., Exposing library data with big data technology: A review, 2016IEEE/ACIS 15 th International Conference on Computer and Information Science (ICIS): Proceedings, (2016); Westra R., Parham, Selected internet resources on digital research data curation, Issues in Science and Technology Librarianship, 63, (2010); Witt M., Gairlo M.J., Databib: IMLS LG-46-11-0091-11 Final Report, (2012); Cambridge U.O., University of Cambridge, Research Data, (2018); Ibrahim S., Jin H., Lu L., Qi L., Wu S., Shi X., Evaluating mapreduce on virtual machines: The hadoop case, IEEE International Conference on Cloud Computing, pp. 519-528, (2009); Leicester U.O., University of Leicester, (2018); Xu Z., Jiang H., Hass: Highly available, scalable and secure distributed data storage systems, Computational Science and Engineering, 2009. CSE'09. International Conference on, 2, pp. 772-780, (2009)","","","IGI Global","","","","","","","978-179983477-9; 978-179983476-2","","","English","Handb. of Res. on Mod. Educ. Technol., Appl., and Manag. (2 Vol.)","Book chapter","Final","","Scopus","2-s2.0-85130287624"
"Yuxia W.; Fengjiao W.; Jiaoteng G.","Yuxia, Wang (36931847100); Fengjiao, Wang (58148729500); Jiaoteng, Gong (58148442200)","36931847100; 58148729500; 58148442200","Innovation and Enlightenment of University of Sussex Library Supporting Think-Tank Construction; [英国萨塞克斯大学图书馆支撑智库建设的创新与启示]","2020","Journal of Library and Information Science in Agriculture","32","8","","68","78","10","0","10.13998/j.cnki.issn1002-1248.2020.08.20-0358","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150418472&doi=10.13998%2fj.cnki.issn1002-1248.2020.08.20-0358&partnerID=40&md5=401db6144c76985482d02a96c6b2d2eb","School of Public Administration, Xiangtan University, Xiangtan, 411105, China","Yuxia W., School of Public Administration, Xiangtan University, Xiangtan, 411105, China; Fengjiao W., School of Public Administration, Xiangtan University, Xiangtan, 411105, China; Jiaoteng G., School of Public Administration, Xiangtan University, Xiangtan, 411105, China","[Purpose/Significance] This article aims to introduce the innovative practice of University of Sussex Library to support think-Tank construction for Institute of Development Studies (IDS) and Science Policy Research Unit (SPRU), and to provide suggestions for Chinese university libraries to serve the construction of university-Affiliated think tanks, based on the analysis of the inadequacy of the supporting role of Chinese university libraries. [Method/Process] By using website survey method and literature review method, this study summarizes the charac teristics of think-Tanks of the two university libraries in terms of information resource supply, scientific research data management, human resource construction, and innovation in library space to carry out activities. [Results/Conclusions] The successful experience should be used for reference for university libraries in China, such as establishing the dedicated libraries for think tanks and enhancing interaction between think tank digital libraries and other network platforms; attaching importance to knowledge resource management of think tanks and jointly building think tank repositories; supporting think tanks in educational functions and helping develop first-class disciplines; understanding the space requirements of think-Tank users and expanding library space services. © Lithologic Reservoirs 2020.","Enlightenment; Think tank; University library; University of Sussex","","","","","","","","MCGANN J G., Global go to think tank index report, (2019); BUTCHER K, GEBHART T., Global projects at the british library for development studies, Focus on international library and infor mation work, 46, 3, pp. 108-113, (2015); British library for development studies; News & opinion; News & opinion; Library search [EB/OL]; Library subject guides; Subject guides and support: Development studies; Keith pavitt library and resource centre [EB/OL]; Special collections; The keep; Sussex research online; Elements [EB/OL]; Digital skills; Workshops and events; Masters study; Writing support with the RLF; Critical thinking and reading [EB/OL]; Group study room bookings; Research hive; BALL J., The sussex research hive: Providing peer-led support for doctoral researchers, Sconul focus, 56, pp. 10-13, (2012)","","","Agricultural Information Institute, Chinese Academy of Agricultural Sciences","","","","","","10021248","","","","Chinese","J. Libr. Inf. Sci. Agric.","Article","Final","","Scopus","2-s2.0-85150418472"
"Choi M.-S.; Lee S.","Choi, Myung-Seok (55461985900); Lee, Sanghwan (57254817300)","55461985900; 57254817300","Research data management status of science and technology research institutes in Korea","2020","Data Science Journal","19","1","29","1","11","10","1","10.5334/DSJ-2020-029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090098467&doi=10.5334%2fDSJ-2020-029&partnerID=40&md5=3a263163ebba507761a31833ae723ce4","Research Data Sharing Center, Korea Institute of Science and Technology Information, South Korea","Choi M.-S., Research Data Sharing Center, Korea Institute of Science and Technology Information, South Korea; Lee S., Research Data Sharing Center, Korea Institute of Science and Technology Information, South Korea","Recent advances in digital technology and the data-driven science paradigm has led to a prolif-eration of research data, which are becoming more important in scholarly communications. The sharing and reuse of research data can play a key role in enhancing the reusability and repro-ducibility of research, and data from publicly funded projects are assumed to be public goods. This is seen as a movement of open science and, more specifically, open research data. Many countries, such as the USA, UK, and Australia, are pushing ahead with implementing policies and infrastructure for open research data. In this paper, we present survey results pertaining to the creation, management, and utilization of data for researchers from government-funded research institutes of science and technology in Korea. We then introduce recent regulations stipulating a mandated data management plan for national R&D projects and on-going efforts to realize open research data in Korea. © 2020 The Author(s).","Data management plan; Data repository; Open research data; Open science; Research data platform","Binary alloys; Information management; Potassium alloys; Reusability; Surveys; Uranium alloys; Digital technologies; Open science; Public goods; Research data; Research data managements; Research institutes; Scholarly communication; Science and Technology; Open Data","","","","","Institute for Information & Communication Technology Planning & Evaluation; Korea Institute of Science and Technology Information, KISTI; National Research Foundation of Korea, NRF; Institute for Information and Communications Technology Promotion, IITP","Funding text 1: Following an amendment of the Regulations, the R&D project management regulations of individual governmental ministries are under revision. In addition, internal R&D project management regulations and guidelines were amended in R&D funding institutions and NST under MSIT. Mandatory DMP stipulations were applied to certain research projects from the National Research Foundation of Korea (NRF) and the Institute for Information & Communication Technology Planning & Evaluation (IITP) in 2019. Government-funded research institutes under NST have also been revising their own R&D project management regulations, and most of them will begin to mandate a DMP later in 2020. In the long term, a separate legislation for open research data on a national level would be more desirable.; Funding text 2: This research was supported by Korea Institute of Science and Technology Information (KISTI).","Baker M., Is There a Reproducibility Crisis?, Nature, 533, 7604, pp. 452-454, (2016); Barsky E., Research Data Management Survey: Science and Engineering, (2015); Beagrie N, Houghton J., The Value and Impact of the European Bioinformatics Institute, (2016); Begley C, Ellis L., Raise standards for preclinical cancer research, Nature, 483, 7391, pp. 531-533, (2012); Excel errors and science papers, (2016); Freedman L, Cockburn I, Simcoe T., The Economics of Reproducibility in Preclinical Research, PLOS Biology, 13, 6, (2015); Hey T, TanSley S, Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Holdren J., Increasing Access to the Results of Federally Funded Scientific Research, (2013); Kim Y, Yoon A., Scientists’ data reuse behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 68, 12, pp. 2709-2719, (2017); Making Open Science a Reality, (2015); Concordat on Open Research Data, (2016); Salmi J., Study on Open Science: Impact, Implications and Policy Options, (2015); Shearer K, Furtado F., COAR Survey of Research Data Management, COAR Report, (2017); Tenopir C, Et al., Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide, PLOS ONE, 10, 8, (2015); Science as an open enterprise, (2012); Wilkinson M, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","S. Lee; Research Data Sharing Center, Korea Institute of Science and Technology Information, South Korea; email: sanglee@kisti.re.kr","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85090098467"
"Schembera B.; Durán J.M.","Schembera, Björn (56829559000); Durán, Juan M. (56182301000)","56829559000; 56182301000","Dark Data as the New Challenge for Big Data Science and the Introduction of the Scientific Data Officer","2020","Philosophy and Technology","33","1","","93","115","22","22","10.1007/s13347-019-00346-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077128681&doi=10.1007%2fs13347-019-00346-x&partnerID=40&md5=61e764f4e76346fb5d3b75e234da311b","High-Performance Computing Center Stuttgart, University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany; Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, Delft, 2628 BX, Netherlands","Schembera B., High-Performance Computing Center Stuttgart, University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany; Durán J.M., Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, Delft, 2628 BX, Netherlands","Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing (HPC) facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart (HLRS). We also propose a new position tailor-made for coping with dark data and general data management. We call it the scientific data officer (SDO) and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns—and proposes solutions—to the control and authority that the SDO would acquire. © 2019, The Author(s).","Big data; Computer simulations; Dark data; Data curation; High-performance computing; Research data management; Scientific data officer","","","","","","High-Performance Computing Center Stuttgart; Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, MWK","We thank the High-Performance Computing Center Stuttgart for their support during this research. Juan M. Durán also thanks the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (MWK) for their support. Finally, both authors thank Johannes Lenhard for valuable comments on an earlier version.","Austin B., Dark data: What is it and why should I care?, (2014); Babcock C., IBM Cognitive colloquium spotlights uncovering dark data, Information Week., (2015); Barba L.A., The hard road to reproducibility, Science, 354, 6308, (2016); Barba L.A., Thiruvathukal G.K., Reproducible research for computing in science & engineering, Computing in Science & Engineering, 19, 6, pp. 85-87, (2017); Barberousse A., Marion V., Computer simulations and empirical data, Computer Simulations and the Changing Face of Scientific Experimentation, pp. 29-45, (2013); Bergstra J.A., Burgess M., Handbook of network and system administration, (2011); Brantley B., The API Briefing: The Challenge of government’s Dark Data, (2015); Choudhury S., Fishman J.R., McGowan M.L., Juengst E.T., Big data, open science and the brain: lessons learned from genomics, Frontiers in Human Neuroscience, 8, 239, (2014); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 1-18, (2013); Darch P.T., Sands A.E., Beyond Big Or Little Science: Understanding Data Lifecycles in Astronomy and the Deep Subseafloor Biosphere, (2015); Dennies P., Factories of the future: The value of dark data, Forbes Brandvoice, (2015); Proposals for safeguarding good scientific practice recommendations of the Commission on Professional Self-Regulation in Science, Tech. Rep., Deutsche Forschungsgemeinschaft, (2013); Duran J.M., Computer simulations in science and engineering. Concepts - practices - perspectives, (2018); Duran J.M., Formanek N., Grounds for trust: Essential epistemic opacity and computational reliabilism, (2018); Edwards K., Gaber M.M., Astronomy and big data. A data clustering approach to identifying uncertain galaxy morphology, (2014); (2016); Glass R., Callahan S., The Big Data-driven business: how to use big data to win customers, beat competitors, and boost profits, (2014); Goetz T., Freeing the dark data of failed scientific experiment, Wired Magazine, 15, 10, pp. 7-12, (2007); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Heidorn P.B., Stahlman G.R., Steffen J., The astrolabe project: identifying and curating astronomical ‘dark data’ through development of cyberinfrastructure resources, Astrophysical Journal Supplement Series, 236, 1, (2018); Hick J., HPSS in the Extreme Scale Era: Report to DOE Office of Science on HPSS in 2018-2022, (2010); Humphreys P.W., What are data about?, Computer Simulations and the Changing Face of Scientific Experimentation, (2013); Iglezakis D., Schembera B., Anforderungen der Ingenieurwissenschaften an das Forschungsdatenmanagement der Universität Stuttgart-Ergebnisse der Bedarfsanalyse des Projektes DIPL-ING, o-bib Das offene Bibliotheksjournal/Herausgeber VDB, 5, 3, pp. 46-60, (2018); Lawry R., Waddell D., Singh M., Roles, Responsibilities and futures of chief information officers (CIOs) in the public sector, Proceedings of European and Mediterranean Conference on Information Systems, (2007); Lee Y., Madnick S.E., Wang R.Y., Wang F., Zhang H., A Cubic Framework for the Chief Data Officer: Succeeding in a World of Big Data, (2014); Leonelli S., Why the current insistence on open access to scientific data? Big data, knowledge production, and the political economy of contemporary biology, Bulletin of Science Technology & Society, 33, 1-2, pp. 6-11, (2013); Leonelli S., What difference does quantity make?, On the epistemology of Big Data in Biology, Big data & society, 1, 1, pp. 1-11, (2014); Leonelli S., What counts as scientific data? A relational framework, Philosophy of Science, 82, 5, pp. 810-821, (2015); Mann M., Sachs W., Aschemann G., Krabbe G., Austermuhle S., Jellinghaus A., Gedanken Zum Berufsbild Des Systemadministrators - Diskussionsgrundlage für Sage@Guug, (2003); Mattmann C.A., Computing: A Vision for Data Science, 493, (2013); Mayer-Schonberger V., Cukier K., Big Data: A Revolution that Will Transform How We Live, Work, and Think, (2013); Nemeth E., Whaley G.S.T.R.H.B., UNIX and Linux system administration handbook, 7, (2015); Reference Model for an Open Archival Information System (OAIS), Recommended Practice, (2012); Oren T.I., Rationale for a code of professional ethics for simulationists, Summer Computer Simulation Conference, pp. 428-433, (2002); Oren T.I., Elzas M.S., Smit I., Birta L.G., Code of professional ethics for simulationists, Summer Computer Simulation Conference, pp. 434-435, (1998); Oren T.I., Birta L.G., Elzas M.S., Fairchild B., Smit I., I Erols M.A.P., Code of professional ethics for simulationist, Society for Modeling and Simulation International, (2002); Peppard J., Unlocking the performance of the chief information officer (CIO), California Management Review, 52, 4, pp. 73-99, (2010); Quantum White Paper. LTO: The New “Enterprise Tape Drive”., (2018); Reilly S., Schallier W., Schrimpf S., Smit E., Wilkinson M., Report on integration of data and publications, (2011); Schembera B., Bonisch T., Challenges of research data management for high performance computing, Proceedings of the International Conference on Theory and Practice of Digital Libraries, pp. 140-151, (2017); Shahzad M.A., The big data challenge of transformation for the manufacturing industry, IBM Big Data & Analytics Hub, (2017); Smith R.D., The chief technology officer: strategic responsibilities and relationships, Research-Technology Management, 46, 4, pp. 28-36, (2003); Suber P., Open Access, (2012); Whitten D., The chief information security officer: an analysis of the skills required for success, Journal of Computer Information Systems, 48, 3, pp. 15-19, (2008); Wienke S., Iliev H., An Mey D., Muller M.S., Modeling the productivity of HPC systems on a Computing Center Scale, International Conference on High Performance Computing, pp. 358-375, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 'T Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van Der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","J.M. Durán; Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Jaffalaan 5, 2628 BX, Netherlands; email: j.m.duran@tudelft.nl","","Springer","","","","","","22105433","","","","English","Philos. Technol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85077128681"
"Khan N.; Pink C.J.; Thelwall M.","Khan, Nushrat (57200319120); Pink, Catherine J. (6507009460); Thelwall, Mike (57527841900)","57200319120; 6507009460; 57527841900","Identifying Data Sharing and Reuse with Scholix: Potentials and Limitations","2020","Patterns","1","1","100007","","","","6","10.1016/j.patter.2020.100007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100694103&doi=10.1016%2fj.patter.2020.100007&partnerID=40&md5=58950a2896544540547898c367f352d3","Library Research Data Service, University of Bath, Bath, BA2 7AY, Somerset, United Kingdom; Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, WV1 1LY, United Kingdom","Khan N., Library Research Data Service, University of Bath, Bath, BA2 7AY, Somerset, United Kingdom, Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, WV1 1LY, United Kingdom; Pink C.J., Library Research Data Service, University of Bath, Bath, BA2 7AY, Somerset, United Kingdom; Thelwall M., Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, WV1 1LY, United Kingdom","The Scholexplorer API, based on the Scholix (Scholarly Link eXchange) framework, aims to identify links between articles and supporting data. This quantitative case study demonstrates that the API vastly expanded the number of datasets previously known to be affiliated with University of Bath outputs, allowing improved monitoring of compliance with funder mandates by identifying peer-reviewed articles linked to at least one unique dataset. Availability of author names for research outputs increased from 2.4% to 89.2%, which enabled identification of ten articles reusing non-Bath-affiliated datasets published in external repositories in the first phase, giving valuable evidence of data reuse and impact for data producers. Of these, only three were formally cited in the references. Further enhancement of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies would be beneficial. The adoption of standardized data citations by journals will be critical to creating links in a more systematic manner. The number of research data repositories has substantially increased in response to growing requirements for publication of data supporting research findings. However, the lack of a common language between repositories and journals makes it difficult to find connections between datasets and articles and to identify secondary data-reuse cases. This study explores how the Scholix (Scholarly Link eXchange) framework can be used to create these links in order to validate research findings, to demonstrate compliance with funder mandates, and to understand the value and impact of research data. This is the first quantitative analysis of data gathered from the Scholexplorer API and demonstrates its potential for identifying data reuse. A content analysis of citing articles reusing data also shows that few of these links resulted from standard data citation practice. The findings of this study provide the basis for further comparative analyses to develop standard community practices. Identifying links between articles and supporting data is vital for demonstrating reuse and impact of published data. Scholix creates these links, and we find that the Scholexplorer API can locate more article-dataset links than was previously possible in practice. Our study finds evidence of data reuse, but we suggest that further enhancement of the Scholix schema and enrichment of Scholexplorer metadata through controlled vocabulary and inclusion of persistent identifiers would recover more cases of secondary data use. © 2020 The Authors","data publication; data repository; data reuse; data sharing; DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems; research data; research data management; research impact; scholarly communication; Scholexplorer; Scholix","Metadata; Vocabulary control; Common languages; Comparative analysis; Content analysis; Requirements for publication; Research outputs; Secondary datum; Standard communities; University of Bath; Data Sharing","","","","","University of Bath Library","We wish to acknowledge the University of Bath Library for funding this study. ","Holdren J.P., Increasing Access to the Results of Federally Funded Scientific Research, (2013); Kratz J.E., Strasser C., Comment: Making data count, Sci. Data, 2, (2015); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, (2013); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, (2007); Henneken E.A., Accomazzi A., Linking to data—effect on citation rates in astronomy, Astronomical Data Analysis and Software Systems XXI. ASP Conference Series, 461, (2011); Drachen T., Ellegaard O., Larsen A., Dorch S., Sharing data increases citations, Liber Quarterly, 26, (2016); Burton A., Koers H., Manghi P., Stocker M., Fenner M., Aryani A., La Bruzzo S., Diepenbroek M., Schindler U., Authr C., The Scholix framework for interoperability in data-literature information exchange, D-Lib Magazine, 23, (2017); Mayo C., Vision T.J., Hull E.A., The location of the citation: changing practices in how publications cite original data in the Dryad Digital Repository, Int. J. Digit. Curation, 11, pp. 150-155, (2016); Khan N., Thelwall M., Kousha K., Data citation and reuse practice in biodiversity—challenges of adopting a standard citation model, Proceedings of the 17th International Conference on Scientometrics & Infometrics, pp. 1220-1225, (2019); Colavizza G., Hrynaszkiewicz I., Staden I., Whitaker K., McGillivray B., The citation advantage of linking publications to research data, arXiv, (2019); Robinson-Garcia N., Jimenez-Contreras E., Torres-Salinas D., Analyzing data citation practices using the data citation index, J. Assoc. Inf. Sci. Technol., 67, pp. 2964-2975, (2016); Mathiak B., Boland K., Challenges in matching dataset citation strings to datasets in social science, D-Lib Magazine, 21, pp. 23-28, (2015); Ghavimi B., Mayr P., Vahdati S., Lange C., Identifying and improving dataset references in social sciences full texts, Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 105-114, (2016); Silvello G., Theory and practice of data citation, J. Assoc. Inf. Sci. Technol., 69, pp. 6-12, (2018); Scholix, Scholix: A Framework for Scholarly Link Exchange, (2019); Burton A., Koers H., Manghi P., La Bruzzo S., Aryani A., Diepenbroek M., Schindler U., The data-literature interlinking service: towards a common infrastructure for sharing data-article links, Program Electron. Libr. Inf. Syst., 51, pp. 75-100, (2017); The OpenAIRE Scholexplorer: the data literature interlinking service, (2020); Cousijn H., Feeney P., Lowenberg D., Presani E., Simons N., Bringing citations and usage metrics together to make data count, Data Sci. J., 18, (2019); Hersh G., Making open access/open data/open science a reality, Against the Grain, 29, (2019); Limani F., Latif A., Tochtermann K., Linked publications and research data: use cases for digital libraries, International Conference on Theory and Practice of Digital Libraries, pp. 363-367, (2018); Gibson C., From Couch to Almost 5K: Raising Research Data Visibility at the University of Manchester, (2019); Syrotiuk N., scholix, (2019); Tay A., How does Scopus find and link to related research data? Or an attempt to understand how to link datasets to articles via Scholix, (2018); Gazra K., Fenner M., Glad You Asked: A Snapshot of the Current State of Data Citation, (2018); Khan N., Dataset for “Linking Datasets and Articles—Potentials and Challenges of Scholix Framework”, (2020); Fear K.M., Measuring and Anticipating the Impact of Data Reuse, (2013); ORCID, ORCID: Connecting Research and Researchers; Hrynaszkiewicz I., Publishers’ responsibilities in promoting data quality and reproducibility, Good Research Practice in Non-Clinical Pharmacology and Biomedicine Handbook of Experimental Pharmacology, 257, pp. 319-348, (2019); R: A language and environment for statisitical computing, (2019)","N. Khan; Library Research Data Service, University of Bath, Bath, BA2 7AY, United Kingdom; email: n.j.khan@bath.ac.uk","","Cell Press","","","","","","26663899","","","","English","Patterns","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85100694103"
"Sheveleva T.; Koepler O.; Mozgova I.; Lachmayer R.; Auer S.","Sheveleva, Tatyana (57221965627); Koepler, Oliver (6507094492); Mozgova, Iryna (27067881500); Lachmayer, Roland (6602616454); Auer, Sören (23391879500)","57221965627; 6507094492; 27067881500; 6602616454; 23391879500","Development of a domain-specific ontology to support research data management for the tailored forming technology","2020","Procedia Manufacturing","52","","","107","112","5","4","10.1016/j.promfg.2020.11.020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100824785&doi=10.1016%2fj.promfg.2020.11.020&partnerID=40&md5=fc11eefcc9d8a39ffeb7a05194ecd5e4","Leibniz Information Centre for Science and Technology (TIB), Welfengarten 1B, Hanover, 30167, Germany; Institute of Product Development, An der Universität 1, Garbsen, 30823, Germany","Sheveleva T., Leibniz Information Centre for Science and Technology (TIB), Welfengarten 1B, Hanover, 30167, Germany; Koepler O., Leibniz Information Centre for Science and Technology (TIB), Welfengarten 1B, Hanover, 30167, Germany; Mozgova I., Institute of Product Development, An der Universität 1, Garbsen, 30823, Germany; Lachmayer R., Institute of Product Development, An der Universität 1, Garbsen, 30823, Germany; Auer S., Leibniz Information Centre for Science and Technology (TIB), Welfengarten 1B, Hanover, 30167, Germany","The global trend towards the comprehensive digitisation of technologies in product manufacturing is leading to radical changes in engineering processes and requires a new extended understanding of data handling. The amounts of data to be considered are becoming larger and more complex. Data can originate from process simulations, machines used or subsequent analyses, which together with the resulting components serve as a complete and reproducible description of the process. Within the Collaborative Research Centre”Process Chain for Manufacturing of Hybrid High Performance Components by Tailored Forming”, interdisciplinary work is being carried out on the development of process chains for the production of hybrid components. The management of the generated data and descriptive metadata, the support of the process steps and preliminary and subsequent data analysis are fundamental challenges. The objective is a continuous, standardised data management according to the FAIR Data Principles so that process-specific data and parameters can be transferred together with the components or samples to subsequent processes, individual process designs can take place and processes of machine learning can be accelerated. A central element is the collaborative development of a domain-specific ontology for a semantic description of data and processes of the entire process chain. © 2020 The Authors. Published by Elsevier B.V.","Digitisation of Scientific Data; FAIR Data Principles; Manufacturing Process Chains; Ontology Development; Research Data Management","","","","","","Collaborative Research Centre, (252662854); Deutsche Forschungsgemeinschaft, DFG","The authors gratefully acknowledge the support from the Collaborative Research Centre (CRC) 1153 Process Chain for Manufacturing of Hybrid High Performance Components by Tailored Forming, Project number 252662854 (INF), funded by the German Research Foundation (DFG).","Sandfeld S., Dahmen T., Strategiepapier Digitale Transformation in Der Materialwissenschaft Und Werkstofftechnik, (2018); Kapogiannis G., Sherratt F., Impact of integrated collaborative technologies to form a collaborative culture in construction projects, Built Environment Project and Asset Management, 8, 1, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Ax-Ton M., Baak A., Blomberg N., Boiten J.-W., Bonino da Silva S., Luiz O., Bourne P., Bouwman J., Brookes A., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C., Finkers R., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Amorim R.C., Et al., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univ Access Inf Soc, 16, (2017); Krotzsch M., Vrandecic D., Semantic Wikipedia, Social Semantic Web, (2009); Frischmuth P., Martin M., Tramp S., Riechert T., Auer S., Ontowiki - An authoring, publication and visualization interface for the data web, Semantic Web, 6, (2015); Behrens B.-A., Breidenstein B., Duran D., Herbst S., Lachmayer R., Lohnert S., Matthias T., Mozgova I., Nurnberger F., Prasanthan, Et al., Simulation-aided process chain design for the manufacturing of hybrid shafts, HTM Journal of Heat Treatment and Materials, 74, 2, pp. 115-135, (2019); Parsia B., Patel-Schneider P.F., Rudolf S., Web Ontology Language, (2012); Wellner K., B 6 Ontologien, Grundlagen Der Praktischen Information Und Dokumentation: Handbuch Zur Einführung in Die Informationswissenschaft Und -Praxis, 6, pp. 207-218, (2013); Struktur Der Verwaltungsschale: Fortentwicklung Des Referenzmodells Für Die Industrie 4.0 - Komponente, (2016); El Kadiri S., Kiritsis D., Ontologies in the context of product lifecycle management: State of the art literature review, International Journal of Production Research, 53, 18, pp. 5657-5668, (2015); Negri E., Fumagalli L., Garetti M., Tanca L., Requirements and languages for the semantic representation of manufacturing systems, Computers in Industry, 81, pp. 55-66, (2015); Becker P., Papa F., Olsina L., Process ontology specification for enhancing the process compliance of a measurement and evaluation strategy, CLEI ELECTRONIC JOURNAL, 18, 1, (2015); Chungoora N., A model driven ontology approach for manufacturing system inseparability and knowledge sharing, Computers Industry, 64, pp. 392-401, (2013); Lemaignan S., Siadat A., Dantan J., Semenenko A., Mason: A proposal for an ontology of manufacturing domain, IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06), (2006); Dietrich C., Hadlich T., Thron M., Semantik durch Merkmale für Industrie 4.0, Handbuch Industrie 4.0: Produktion, Automatisierung Und Logistik, pp. 1-16, (2017); Epple U., Merkmale als Grundlage der Interoperabilität technischer Systeme, Automatisierungstechnik - At, 59, 7, pp. 440-450, (2011); Heeg M., Ein Beitrag Zur Modellierung Von Merkmalen Im Umfeld Der Prozessleittechnik, Ausgabe 1060 Von Fortschritt-Berichte VDI: Reihe 8, Mess-, Steuerungs- Und Regelungstechnik, (2005); Fertigungsverfahren: Begriffe, Einteilung, (2003); Begriffsbestimmungen Für Stahlerzeugnisse, (2007); Fertigungsverfahren Umformen: Einordnung, Unterteilung, Begriffe,, (2003); Fertigungsverfahren Fügen, Teil 0: Allgemeines, Einordnung, Unterteilung, (2003)","T. Sheveleva; Leibniz Information Centre for Science and Technology (TIB), Hanover, Welfengarten 1B, 30167, Germany; email: Tatyana.Sheveleva@tib.eu","","Elsevier B.V.","","Proceedings of the 5th International Conference on System-Integrated Intelligence, SysInt 2020","11 November 2020 through 13 November 2020","Bremen","144848","23519789","","","","English","Procedia Manuf.","Conference paper","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85100824785"
"Kim J.","Kim, Jeonghyun (37106972000)","37106972000","Academic library's leadership and stakeholder involvement in research data services","2020","Proceedings of the Association for Information Science and Technology","57","1","e304","","","","2","10.1002/pra2.304","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114499605&doi=10.1002%2fpra2.304&partnerID=40&md5=3eeeab55ab66167c956b2edb88496f14","Department of Information Science, University of North Texas, Denton, TX, United States","Kim J., Department of Information Science, University of North Texas, Denton, TX, United States","In the last decade, academic libraries have made progress in establishing themselves as hubs and leaders for research data services on campus. The importance of collaborating with a range of institutional stakeholders, such as the research or information technology office, as well as external partners in developing and delivering research data policy, services, and infrastructure, has been well-documented. However, there is less evidence as to how libraries play a crucial role in leadership, whether other stakeholders’ involvement actually makes a difference and if so, how they make a difference. As such, the goal of this study is to explore the academic library's leadership role in research data services and how collaboration and partnership with stakeholders and interested parties might impact the maturity of the research data services the library provides. The secondary analysis of the existing survey data found that libraries offer more mature services when they take a primary responsibility in developing the services. It also found internal stakeholders’ and external part-ners’ involvement leads to more mature services in the selected activities. © 2020, John Wiley and Sons Inc. All rights reserved.","academic library; research data; research data management; research data services","","","","","","CEMUP team; QREN, (IF/00514/2014, IF/01501/2013, NORTE-07-0124-FEDER-000013, NORTE-07-0124-FEDER-000015, NORTE-07-0162-FEDER-000050, ON2, PD/BD/52623/2014); Fundação para a Ciência e a Tecnologia, FCT; European Social Fund, ESF","This work was financially supported by: Project POCI-01-0145-FEDER-006984 – Associate Laboratory LSRE-LCM funded by FEDER funds through COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT – Fundação para a Ciência e a Tecnologia, and by QREN, ON2 and FEDER (NORTE-07-0124-FEDER-000015, NORTE-07-0124-FEDER-000013, NORTE-07-0162-FEDER-000050). FCT is acknowledged for the research grant PD/BD/52623/2014 (MJL). AMTS and CGS acknowledge the FCT Investigator Programme (IF/01501/2013 and IF/00514/2014, respectively) with financing from the European Social Fund and the Human Potential Operational Programme. The authors acknowledge Dr. V.J.P. Vilar for providing the lab-scale CPC. We are indebted to Dr. Carlos Sá and the CEMUP team (Portugal) for technical assistance and advice with XPS and SEM/EDXS measurements. We also thank Prof. Pedro B. Tavares (UTAD, Portugal) for the assistance with XRD analysis.","Ayuso S., Angel Rodriguez M., GarciaCastro R., Arino M. A., Does stakeholder engagement promote sustainable innovation orientation?, Industrial Management & Data Systems, 111, 9, pp. 1399-1417, (2011); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Fearon D., Gunia B., Lake S., Pralle B. E., Sallans A. L., Research data management services: SPEC Kit 334, (2013); Koltay T., Accepted and emerging roles of academic libraries in supporting research 2.0, The Journal of Academic Librarianship, 45, 2, pp. 75-80, (2019); Lang L., Wilson T., Wilson K., Kirkpatrick A., Research support at the crossroads: Capability, capacity, and collaboration, New Review of Academic Librarianship, 24, pp. 326-336, (2018); Tang R., Hu Z., Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, (2019); Tenopir C., Kaufman J., Sandusky R., Pollock D., Research data services in academic libraries: Where are we today? [White paper], (2019); Verbaan E., Cox A. M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, The Journal of Academic Librarianship, 40, 3–4, pp. 211-219, (2014); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College & Research Libraries, 78, 7, pp. 920-933, (2017); Yu H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017)","J. Kim; Department of Information Science, University of North Texas, Denton, 76203, United States; email: jeonghyun.kim@unt.edu","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85114499605"
"Broude Geva S.; Brunson D.; Cheatham T., III; Deaton J.; Griffioen J.; Hillegas C.W.; Jennewein D.M.; Krovitz G.; Magle T.; Schmitz P.; Tomko K.; Wilgenbusch J.C.","Broude Geva, Sharon (57218477683); Brunson, Dana (55826135100); Cheatham, Thomas (7003445061); Deaton, James (55827249800); Griffioen, James (7004035805); Hillegas, Curtis W. (56821992900); Jennewein, Douglas M. (35490460900); Krovitz, Gail (6506541494); Magle, Tobin (57210577738); Schmitz, Patrick (57220485710); Tomko, Karen (6602573768); Wilgenbusch, James C. (6603275323)","57218477683; 55826135100; 7003445061; 55827249800; 7004035805; 56821992900; 35490460900; 6506541494; 57210577738; 57220485710; 6602573768; 6603275323","Fostering Collaboration among Organizations in the Research Computing and Data Ecosystem","2020","ACM International Conference Proceeding Series","","","","393","401","8","0","10.1145/3311790.3396645","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089277507&doi=10.1145%2f3311790.3396645&partnerID=40&md5=69851d6fbe5ac8d1dbf0021c805e7d1a","University of Michigan, Office of Research, Ann Arbor, MI, United States; Internet2, Stillwater, OK, United States; University of Utah, Dept. of Medicinal Chemistry, Salt Lake City, UT, United States; Great Plains Network, Columbia, MO, United States; University of Kentucy, Center for Computational Sciences, Lexington, KY, United States; Research Computing, Princeton University, Princeton, NJ, United States; Research Technology, Arizona State University, Tempe, AZ, United States; Internet2, Denver, CO, United States; University of Wisconsin-Madison, Research Data Services, Madison, MI, United States; Semper Cogito Consulting, Oakland, CA, United States; Ohio Supercomputing Center, Columbus, OH, United States; Research Computing, University of Minnesota, Minneapolis, MN, United States","Broude Geva S., University of Michigan, Office of Research, Ann Arbor, MI, United States; Brunson D., Internet2, Stillwater, OK, United States; Cheatham T., III, University of Utah, Dept. of Medicinal Chemistry, Salt Lake City, UT, United States; Deaton J., Great Plains Network, Columbia, MO, United States; Griffioen J., University of Kentucy, Center for Computational Sciences, Lexington, KY, United States; Hillegas C.W., Research Computing, Princeton University, Princeton, NJ, United States; Jennewein D.M., Research Technology, Arizona State University, Tempe, AZ, United States; Krovitz G., Internet2, Denver, CO, United States; Magle T., University of Wisconsin-Madison, Research Data Services, Madison, MI, United States; Schmitz P., Semper Cogito Consulting, Oakland, CA, United States; Tomko K., Ohio Supercomputing Center, Columbus, OH, United States; Wilgenbusch J.C., Research Computing, University of Minnesota, Minneapolis, MN, United States","The widespread application and success of computational and data intensive research approaches in every discipline represented on our campuses has resulted in a rapid proliferation of organizations, technologies, and professions affiliated in different ways with the support and advancement of activities related to research computing and data (RCD). While most agree that this growth is helping to advance numerous disciplines, the proliferation of organizations seeking to support, promote, and advance RCD has led to some challenges. Specifically, a lack of understanding and consensus concerning which organizations should be considered a part of RCD support hampers our ability to encourage collaborations among its complementary constituents, leads to unneeded and redundant activities, and makes it difficult to identify strategic priorities and address gaps where specific needs are not being met to advance various disciplinary activities. In this paper we introduce the ecosystem metaphor to help characterize the rapidly changing relationships among the growing set of organizations that in some way support and enable activities related to RCD. The ecosystem concept lends itself well to describing the many entities related to RCD because it emphasizes the larger system over its individual component parts and highlights their interdependence, while explicitly expecting their change over time. Our work to characterize the current RCD ecosystem, while imperfect, will serve as a foundation and framework for the development of a more complete view of the ever-changing RCD ecosystem. A more complete view of the RCD ecosystem will in turn help to advance the broad goals of its members by helping to foster and accelerate new and meaningful collaborations among them. © 2020 ACM.","community; cyberinfrastructure; ecosystem; people; research computing; research data management; research IT","Computer applications; Computer programming; Data intensive; Individual components; Research computing; Ecosystems","","","","","National Science Foundation, NSF, (1620695)","This work was partially supported by NSF grant 1620695 (“RCN: Advancing Research and Education Through a National Network of Campus Research Computing Infrastructures – The CaRC Consortium”). This work was enabled by the contributions of time and expertise from the April 2019 workshop participants and the CaRCC Ecosystem working group members: Amy Neeser, Barr von Oehsen, Bob Freeman, Cliff Lynch, Curt Hillegas, Damian Clarke, Dana Brun-son, Douglas Jennewein, Erica Johns, Gail Krovitz, James Deaton, Jim Griffioen, James Wilgenbusch, Jennifer Schopf, Jim Bottum, Joel Cuther-Gershenfeld, John Goodhue, John Towns, Karen Tomko, Karen Wetzel, Kate Cahill, Lauren Michael, Lawrence Landweber, Marisa Brazil, Mary Lee Kennedy, Neil Bright, Patrick Schmitz, Robert McDonald, Sharon Broude Geva, Stephen Harrell, Susan Mehringer, Thomas Cheatham, and Toni Collis; and the Coalition for Networked Information who hosted the workshop.","About ACM SIGHPC Education, (2020); ACM SIGHPC Systems Professionals Virtual Chapter Website, (2020); Ask. CI, the Q&A Platform for People Who Do Research Computing, (2020); Association of Research Libraries Homepage, (2020); Baldin I., Nikolich A., Griffioen J., Monga I., Wang K., Lehman T., Ruth P., Fabric: A national-scale programmable experimental network infrastructure, IEEE Internet Computing, 23, 6, pp. 38-47, (2019); Barbosa O., Dos Santos R., Alves C., Werner C., Jansen S., A Systematic Mapping Study on Software Ecosystems from A Threedimensional Perspective, pp. 59-81, (2013); XSEDE Campus Champions Program, (2020); XSEDE Campus Champions Program Mailing List, (2020); Campus Research Computing Consortium, (2020); Campus Research Computing Consortium Slack Channel, (2020); Coalition for Academic Scientific Computation, (2020); Campus Champions Slack Channel, (2020); Chameleon Cloud: A Configurable Experimental Environment for Large-scale Cloud Research, (2020); Coalition for Networked Information, (2020); Cross R., Prusak L., The people who make organizations go-or stop, Harvard Business Review, 80, 106, pp. 104-112, (2002); Duplyakin D., Ricci R., Maricq A., Wong G., Duerig J., Eide E., Stoller L., Hibler M., Johnson D., Webb K., Akella A., Wang K., Ricart G., Landweber L., Elliott C., Zink M., Cecchet E., Kar S., Mishra P., The design and operation of cloudlab, Proceedings of the USENIX Annual Technical Conference (ATC), pp. 1-14, (2019); EDUCAUSE Homepage, (2020); The Engagement and Performance Operations Center, (2020); Eastern Regional Network, (2020); Falkner K., Vivian R., Williams S., An ecosystem approach to teacher professional development within computer science, Computer Science Education, 28, 4, pp. 303-344, (2018); Fleming L., Waguespack D.M., Brokerage, boundary spanning, and leadership in open innovation communities, Organization Science, 18, 2, pp. 165-180, (2007); Global Environment for Network Innovations, (2020); Grimshaw A., Prodhan M.A., Thomas A., Stewart C., Knepper R., Campus compute co-operative (ccc): A service oriented cloud federation, 2016 IEEE 12th International Conference on E-Science (E-Science), pp. 1-10, (2016); Harrison T., Pardo T., Cook M., Creating open government ecosystems: A research and development agenda, Future Internet, 4, pp. 900-928, (2012); HPC University Homepage, (2020); (2020); Keen A., Punch W., Mason G., Lessons Learned When Building A Greenfield High Performance Computing Ecosystem, pp. 237-246, (2012); Lederer H., Deisa2: Supporting and developing a european highperformance computing ecosystem, Journal of Physics: Conference Series, 125, (2008); Long J.C., Cunningham F.C., Braithwaite J., Bridges, brokers and boundary spanners in collaborative networks: A systematic review, BMC Health Services Research, 13, 1, (2013); Midscale Experimental Research Infrastructure Forum, (2020); Minority Serving-Cyberinfrastructure Consortium, (2020); NSF Big Data Innovation Hubs Homepage, (2020); Open Science Grid Homepage, (2020); Open Storage Network, (2020); Practices and Experiences in Advanced Research Computing, (2020); PlanetLab: An Open Platform for Developing, Deploying, and Accessing Planetary-scale Services, (2020); The Quilt Homepage, (2020); Research Data Access&Preservation Association Homepage, (2020); Rimal B., Choi E., Lumb I., A Taxonomy, Survey, and Issues of Cloud Computing Ecosystems, pp. 21-46, (2010); The SC Conference Series, (2020); The Carpentries Homepage, (2020); Women in High Performance Computing Homepage, (2020); The Extreme Science and Engineering Discovery Environment Homepage, (2020)","","","Association for Computing Machinery","Association for Computing Machinery (ACM)","2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020","27 July 2020 through 31 July 2020","Virtual, Online","161955","","978-145036689-2","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85089277507"
"Krahe M.A.; Toohey J.; Wolski M.; Scuffham P.A.; Reilly S.","Krahe, Michelle A (57216794508); Toohey, Julie (57207760002); Wolski, Malcolm (25961220900); Scuffham, Paul A (57221971995); Reilly, Sheena (57200939394)","57216794508; 57207760002; 25961220900; 57221971995; 57200939394","Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia","2020","Health Information Management Journal","49","2-3","","108","116","8","8","10.1177/1833358319831318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062860005&doi=10.1177%2f1833358319831318&partnerID=40&md5=f49506a34f955b4b55aabeec47c5d846","Health Group, Griffith University, Gold Coast, QLD, Australia; Library and Learning Services, Griffith University, Gold Coast, QLD, Australia; eResearch Services, Griffith University, Nathan, QLD, Australia; Centre for Applied Health Economics, Griffith University, Nathan, QLD, Australia; Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia","Krahe M.A., Health Group, Griffith University, Gold Coast, QLD, Australia; Toohey J., Library and Learning Services, Griffith University, Gold Coast, QLD, Australia; Wolski M., eResearch Services, Griffith University, Nathan, QLD, Australia; Scuffham P.A., Centre for Applied Health Economics, Griffith University, Nathan, QLD, Australia, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia; Reilly S., Health Group, Griffith University, Gold Coast, QLD, Australia","Background: Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. Objective: This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. Method: A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Results: Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Conclusion: Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. Implications: This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality. © The Author(s) 2019.","academies and institutes; best practices; data collection; health information management; medical informatics; research","Australia; Biomedical Research; Cross-Sectional Studies; Information Management; Medical Informatics; Research Personnel; Surveys and Questionnaires; Universities; article; Australia; clinical article; convenience sample; human; human experiment; life cycle; medical informatics; medical information system; organization; postgraduate student; scientist; staff; Australia; cross-sectional study; information system; medical informatics; medical research; personnel; psychology; questionnaire; university","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, pp. 5-26, (2013); Akers K.G., Sferdean F.C., Nicholls N.H., Et al., Building support for research data management: biographies of eight research universities, International Journal of Digital Curation, 9, pp. 171-191, (2014); Anderson N.R., Lee E.S., Brockenbrough J.S., Et al., Issues in biomedical research data management and analysis: needs and barriers, Journal of the American Medical Informatics Association, 14, pp. 478-488, (2007); Data Availability and Use, (2017); Review of research policy and funding arrangements: Case studies on university business collaboration, (2015); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: findings from a focus group in an academic health sciences library, Journal of Web Librarianship, 6, pp. 274-287, (2012); Bell G., Hey T., Szalay A., Computer science, Beyond the data deluge. Science, 323, 5919, pp. 1297-1298, (2009); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, pp. 1059-1078, (2012); Borschmann R., Young J.T., Moran P., Et al., Accuracy and predictive value of incarcerated adults’ accounts of their self-harm histories: findings from an Australian prospective data linkage study, CMAJ Open, 5, 3, pp. E694-E701, (2017); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, Australian Library Journal, 64, pp. 224-234, (2015); Buys C.M., Shaw P.L., Data management practices across an institution: survey and report, Journal of Librarianship and Scholarly Communication, 3, (2015); Chung T.K., Kukafka R., Johnson S.B., Reengineering clinical research with informatics, Journal of Investigative Medicine, 54, pp. 327-333, (2006); Cox A.M., Kennan M.A., Lyon L., Et al., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, pp. 2182-2200, (2017); Digital curation glossary, (2018); Guidelines to the rules on open access to scientific publications and open access to research data in Horizon 2020. H2020 Programme version 3.2. March, (2017); Fear K., You made it, you take care of it’: data management as personal information management, International Journal of Digital Curation, 6, pp. 53-77, (2011); Fischbacher C.M., Bhopal R., Povey C., Et al., Record linked retrospective cohort study of 4.6 million people exploring ethnic variations in disease: myocardial infarction in South Asians, BMC Public Health, 7, (2007); Gardner D., Toga A.W., Ascoli G.A., Et al., Towards effective and rewarding data sharing, Neuroinformatics, 1, 3, pp. 289-295, (2003); Gonzalez A., Peres-Neto P.R., Data curation: act to staunch loss of research data, Nature, 520, (2015); Henty M., Weaver B., Bradbury S., Et al., Investigating Data Management Practices in Australian Universities, (2008); Hickson S., Poulton K.A., Connor M., Et al., Modifying researchers’ data management practices: a behavioural framework for library practitioners, International Federation of Library Associations and Institutions, 42, pp. 253-265, (2016); Holman C.D., Bass A.J., Rosman D.L., Et al., A decade of data linkage in Western Australia: strategic design, applications and benefits of the WA data linkage system, Australian Health Review, 32, pp. 766-777, (2008); Houghton J., Gruen N., Open Research Data, (2014); Australia 2030: Prosperity Through Innovation, (2017); Johnson L.M., Butler J.T., Johnston L.R., Developing e-science and research services and support at the university of Minnesota health sciences libraries, Journal of Library Administration, 52, pp. 754-769, (2012); Jutte D.P., Roos L.L., Brownell M.D., Administrative record linkage as a tool for public health research, Annual Review of Public Health, 32, pp. 91-108, (2011); Kelman C.W., Bass A.J., Research use of linked health data – a best practice protocol, Australian and New Zealand Journal of Public Health, 26, pp. 251-255, (2002); Kennan M.A., Markauskaite L., Research data management practices: a snapshot in time, International Journal of Digital Curation, 10, pp. 69-95, (2015); Kowalczyk S., Modelling the digital research data lifecycle, International Journal of Digital Curation, 12, 2, pp. 331-361, (2017); Knottnerus J.A., Research data as a global public good, Journal of Clinical Epidemiology, 70, pp. 270-271, (2016); Margolis R., Derr L., Dunn M., Et al., The national institutes of health’s big data to knowledge (BD2K) initiative: capitalizing on biomedical big data, Journal of the Medical Library Association, 21, pp. 957-958, (2014); McEwen E.C., Guthridge S.L., He V.Y., Et al., What birthweight percentile is associated with optimal perinatal mortality and childhood education outcomes?, American Journal of Obstetrics and Gynecology, 218, 2S, pp. S712-S724, (2018); McFadden E.T., LoPresti F., Bailey L.R., Et al., Approaches to data management, Controlled Clinical Trials, 16, pp. 30S-65S, (1995); Nagy R., Boutin T.S., Marten J., Et al., Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants, Genome Medicine, 9, 1, (2017); Australian code for the responsible conduct of research, (2018); NIH data sharing policy and implementation guidance, (2003); Neylon C., Compliance culture or culture change? The role of funders in improving data management and sharing practice amongst researchers, Research Ideas and Outcomes, 3, (2017); O'Keefe C.M., Connolly C.J., Privacy and the use of health data for research, Medical Journal of Australia, 193, 9, pp. 537-541, (2010); OECD Principles and Guidelines for Access to Research data from Public Funding, (2007); Perrier L., Blondal E., Ayala A.P., Et al., Research data management in academic institutions: a scoping review, PLoS One, 12, (2017); Ray B., Henaff M., Ma S., Et al., Information content and analysis methods for multi-modal high-throughput biomedical data, Scientific Reports, 4, (2014); Read K.B., Sheehan J.R., Huerta M.F., Et al., Sizing the problem of improving discovery and access to NIH-funded data: a preliminary study, PLoS One, 10, (2015); Read K.B., Surkis A., Larson C., Et al., Starting the data conversation: informing data services at an academic health sciences library, Journal of the Medical Library Association, 103, pp. 131-135, (2015); Research Data Alliance Outputs, (2015); Schumacher J., VandeCreek D., Intellectual capital at risk: data management practices and data loss by faculty members at five American universities, International Journal of Digital Curation, 10, pp. 96-109, (2015); Si L., WXing W., Zhuang X., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, pp. 417-449, (2015); Surkis A., LaPolla F.W.Z., Contaxis N., Et al., Data day to day: building a community of expertise to address data skills gaps in an academic medical center, Journal of the Medical Library Association, 105, pp. 185-191, (2017); Surkis A., Read K., Research data management, Journal of the Medical Library Association, 103, pp. 154-156, (2015); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: practices and perceptions, PLoS One, 6, (2011); Create and manage data: research data lifecycle, (2012); Policy statement on F.A.I.R access to Australia’s research outputs, (2016); Vaughn K.T.L., Hayes B.E., Lerner R.C., Et al., Development of the research lifecycle model for library services, Journal of the Medical Libraries Association, 101, pp. 310-314, (2013); Vines T.H., Albert A.Y., Andrew R.L., Et al., The availability of research data declines rapidly with article age, Current Biology, 24, pp. 94-97, (2014); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, pp. 467-482, (2014); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: implications for data services development from a faculty survey, Program: Electric Library and Information Systems, 49, 4, pp. 382-407, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Willoughby C., Bird C.L., Coles S.J., Et al., Creating context for the experiment record. User-defined metadata: investigations into metadata usage in the LabTrove ELN, Journal of Chemical Information and Modeling, 54, 12, pp. 3268-3283, (2014); Wolski M., Howard L., Richardson J., The importance of tools in the data lifecycle, Digital Library Perspectives, 33, pp. 235-252, (2017); Yu F., Deuble R., Morgan H., Designing research data management services based on the research lifecycle: a consultative leadership approach, Journal of the Australian Library and Information Association, 66, pp. 287-298, (2017)","M.A. Krahe; Health Group, Griffith University, Gold Coast, Australia; email: m.krahe@griffith.edu.au","","SAGE Publications Inc.","","","","","","18333583","","","30857424","English","Health Inf. Manage. J.","Article","Final","","Scopus","2-s2.0-85062860005"
"Chawinga W.D.; Zinn S.","Chawinga, Winner Dominic (57191247774); Zinn, Sandy (56289796700)","57191247774; 56289796700","Research Data Management in Universities: A Comparative Study from the Perspectives of Librarians and Management","2020","International Information and Library Review","","","","1","15","14","7","10.1080/10572317.2020.1793448","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088322548&doi=10.1080%2f10572317.2020.1793448&partnerID=40&md5=7675429d3b04e77f6266d98d47d69be3","Department of Information Sciences, Mzuzu University, Mzuzu, Malawi; Department of Library & Information Science, University of the Western Cape, Cape Town, South Africa","Chawinga W.D., Department of Information Sciences, Mzuzu University, Mzuzu, Malawi; Zinn S., Department of Library & Information Science, University of the Western Cape, Cape Town, South Africa","With the research landscape tilting toward a more intensive research, propelled by the power of information and communication technologies, the research community is overwhelmed with tons of research data generated on a daily basis. Research data is hailed as the cornerstone for current and future science discoveries. Hence, key research stakeholders especially research funders and publishers demand proper research data management (RDM) practices. The burden to manage research data has been placed on academic libraries. In light of this, the current study employs questionnaires and interviews to explore the status of RDM at two public university libraries in Malawi. The study unveils that RDM remains a new and emerging concept in the country. However, with librarians involved in some very basic RDM activities, the future is promising. Key challenges interrupting RDM initiatives are exposed and their remedies suggested. Generally, university affiliation has no effect on the key factors affecting RDM initiatives. © 2020, © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.","Academic libraries; Malawi; research data management; researchers; university","","","","","","","","Ahmad K., JianMing Z., Rafi M., An analysis of academic librarians competencies and skills for implementation of big data analytics in libraries, Data Technologies and Applications, 53, 2, pp. 201-216, (2019); Anyaoku E.N., Echedom A.U.N., Baro E.E., Digital preservation practices in university libraries, Digital Library Perspectives, 35, 1, pp. 41-64, (2019); Atiso K., Kammer J., Bossaller J., Predatory publishing and the Ghana experience: A call to action for information professionals, IFLA Journal, 45, 4, pp. 277-288, (2019); Ball R., Big data and their impact on libraries, American Journal of Information Science and Technology, 3, 1, pp. 1-9, (2019); Brochu L., Burns J., Librarians and research data management–a literature review: Commentary from a senior professional and a new professional librarian, New Review of Academic Librarianship, 25, 1, pp. 49-58, (2019); Chawinga W.D., Research data management in public universities in Malawi, (2019); Chawinga W.D., Zinn S., Global perspective of research data sharing: A systematic literature review, Library & Information Science Research, 41, 2, pp. 109-122, (2019); Chawinga W.D., Zinn S., Research data management at an African medical university: Implications for academic librarianship, The Journal of Academic Librarianship, 46, 4, (2020); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Sbaffi L., Maturing research data services and the transformation of academic libraries, Journal of Documentation, 75, 6, pp. 1432-1462, (2019); Federer L., Foster E.D., Glusker A., Henderson M., Read K., Zhao S., The Medical Library Association Data Services Competency: A framework for data science and open science skills development, Journal of the Medical Library Association, 108, 2, pp. 303-309, (2020); Hamad F., Fakhuri H., Jabbar S., Big data opportunities and challenges for analytics strategies in Jordanian Academic Libraries, New Review of Academic Librarianship, pp. 1-19, (2020); Heidorn P.B., The emerging role of libraries in data curation and E-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Howie J., Kara H., Research support in New Zealand University libraries, New Review of Academic Librarianship, pp. 1-29, (2020); Hrynaszkiewicz I., Simons N., Hussain A., Grant R., Goudie S., Developing a research data policy framework for all journals and publishers, Data Science Journal, 19, 5, pp. 1-15, (2020); Johnston L.R., Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., How important are data curation activities to researchers? Gaps and opportunities for academic libraries, Journal of Librarianship and Scholarly Communication, 6, 1, pp. 2198-2124, (2018); Jones L., Grant R., Hrynaszkiewicz I., Implementing publisher policies that inform, support and encourage authors to share data: Two case studies, Insights, 32, 1, pp. 1-11, (2019); Joo S., Peters C., User needs assessment for research data services in a research university, Journal of Librarianship and Information Science, pp. 1-14, (2019); Kalusopa T., Zibani P., Kanguti R., Leonard A., (2020); Koltay T., Identifying new roles for academic libraries in supporting data-intensive research, 4, pp. 97-102, (2019); Li S., Jiao F., Zhang Y., Xu X., Problems and changes in digital libraries in the age of big data from the perspective of user services, The Journal of Academic Librarianship, 45, 1, pp. 22-30, (2019); Mosha N.F., Luhanga E.T., Mosha M.V., Marwa J.J., (2020); Pearson G.S., Increasing awareness about predatory publishers, Journal of the American Psychiatric, 25, 5, pp. 343-345, (2019); Pinfield S., Wakeling S., Bawden D., Robinson L., Open access in theory and practice: The theory-practice relationship and openness, (2020); Rahman M.H., Changing roles of university libraries of Bangladesh: An exploratory study, Library Hi Tech News, 2, pp. 5-9, (2020); Read K.B., Koos J., Miller R.S., Miller C.F., Phillips G.A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, Journal of the Medical Library Association, 107, 3, pp. 432-441, (2019); Sayre F., Riegelman A., Replicable services for reproducible research: A model for academic libraries, College & Research Libraries, 80, 2, (2019); Semeler A.R., Pinto A.L., Rozados H.B.F., Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2019); Tremouilhaca P., Lina C.L., Huanga P.C., Huanga Y.C., Nguyena A., Jung N., Bach F., Ulrich R., Neumair B., Striet A., Brase S., pp. 1-23, (2020); Wilson K., Kiuna A., Bruce Lamptey R., Veldsman S., Montgomery L., Neylon C., Hosking R., Huang K., Ozaygen A., (2020)","","","Taylor and Francis Ltd.","","","","","","10572317","","","","English","Int. Inf. Libr. Rev.","Article","Final","","Scopus","2-s2.0-85088322548"
"Bauer B.; Budroni P.","Bauer, Bruno (57200436821); Budroni, Paolo (56624471600)","57200436821; 56624471600","Open science: Paolo budroni answers ten questions from bruno bauer on the importance of research data management and the development of the european open science cloud; [Open science: Paolo budroni beantwortet 10 fragen von bruno bauer zur bedeutung von forschungsdatenmanagement sowie zur entwicklung der european open science cloud]","2020","VOEB-Mitteilungen","73","2","","217","237","20","0","10.31263/voebm.v73i2.4013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092936764&doi=10.31263%2fvoebm.v73i2.4013&partnerID=40&md5=55b309f0bb6a2f8681c5193f585ed6bd","Medizinische Universität Wien, Universitätsbibliothek, Austria; TU Wien, Bibliothek, Austria","Bauer B., Medizinische Universität Wien, Universitätsbibliothek, Austria; Budroni P., TU Wien, Bibliothek, Austria","Paolo Budroni has been working in the university sector since 1991. At the beginning he answers questions about his professional career in the field of research documentation and research data management. The interview addresses national and international projects and initiatives for research data management with a focus on the European Open Science Cloud. Finally, Budroni encourages libraries to actively implement the concept of Open Science. © Bruno Bauer, Paolo Budroni.","EOSC; European Open Science Cloud; Interview; Open Science; Research data; Research data management; Research information system","","","","","","","","Budroni Paolo, Über Forschung und Entwicklung (F&E) – Technologietransfer und F&E Politik als Strategische Instrumente der Wirtschaftspolitik, (1997); Amt für Öffentliche Veröffentlichungen der Europäischen Gemeinschaften, (1995); (2013); Budroni Paolo, Burgelman Jean-Claude, Schouppe Michel, The European Open Science Cloud, ABI Technik, 39, 2, pp. 130-141, (2019); Budroni Paolo, Hanslik Stefan, Solis Barbara Sanchez, Developing EOSC – A view on an ongoing process; Dondi Cristina, Printing revolution 1450–1500. I cinquant'anni che hanno cambiato l'Europa. Catalogo della mostra, (2018)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85092936764"
"","","","24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020","2020","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12246 LNCS","","","","","248","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090083095&partnerID=40&md5=d2278378f47c6ccfa20c4a5ed6154227","","","The proceedings contain 18 papers. The special focus in this conference is on Theory and Practice of Digital Libraries. The topics include: Knowledge-Based Categorization of Scientific Articles for Similarity Predictions; participatory Indexing in the Eyes of Its Potential Users: An Example of a Co-design of Participatory Services in an Academic Digital Library; Understanding User Behavior in Digital Libraries Using the MAGUS Session Visualization Tool; characteristics of Dataset Retrieval Sessions: Experiences from a Real-Life Digital Library; context-Driven Discoverability of Research Data; management of Research Data in Image Format: An Exploratory Study on Current Practices; layout Detection and Table Recognition – Recent Challenges in Digitizing Historical Documents and Handwritten Tabular Data; online News Monitoring for Enhanced Reuse of Audiovisual Archives; question Answering on Scholarly Knowledge Graphs; context-Compatible Information Fusion for Scientific Knowledge Graphs; veTo: Expert Set Expansion in Academia; an Observational Study of Equivalence Links in Cultural Heritage Linked Data for agents; correspondence as the Primary Measure of Quality for Web Archives: A Grounded Theory Study; Assessing and Minimizing the Impact of OCR Quality on Named Entity Recognition; on the Persistence of Persistent Identifiers of the Scholarly Web; ontology Design for Pharmaceutical Research Outcomes.","","","","","","","","","","","Hall M.; Mercun T.; Risse T.; Duchateau F.","Springer","","24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020","25 August 2020 through 28 August 2020","Lyon","243869","03029743","978-303054955-8","","","English","Lect. Notes Comput. Sci.","Conference review","Final","","Scopus","2-s2.0-85090083095"
"Schembera B.; Iglezakis D.","Schembera, Björn (56829559000); Iglezakis, Dorothea (22334176800)","56829559000; 22334176800","Engmeta: Metadata for computational engineering","2020","International Journal of Metadata, Semantics and Ontologies","14","1","","26","38","12","10","10.1504/IJMSO.2020.107792","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087970632&doi=10.1504%2fIJMSO.2020.107792&partnerID=40&md5=1ccfacc9ef542646ba26e3947b9ff741","High-Performance Computing Center Stuttgart, University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany; University Library, University of Stuttgart, Holzgartenstr. 16, Stuttgart, 70174, Germany","Schembera B., High-Performance Computing Center Stuttgart, University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany; Iglezakis D., University Library, University of Stuttgart, Holzgartenstr. 16, Stuttgart, 70174, Germany","The huge amounts of data produced in computational engineering make the handling and documentation of the resulting data a challenge. EngMeta is a metadata model based on existing standards and developed to enable a structured documentation of the research process and the simulation environment all together with discipline specific information about the simulated system. A qualitative analysis shows that EngMeta fulfils the criteria of a good metadata model. According to a quantitative survey, EngMeta meets the needs of engineering scientists. The metadata model is defined as an XSD scheme and in practical use in an institutional data repository. Supported by automated metadata extraction and a repository, EngMeta enables specific research data management in computational engineering. Copyright © 2020 Inderscience Enterprises Ltd.","Automation; Big data; Computational engineering; Engineering; High-performance computing; Metadata; Metadata extraction; Repository; Research data management; Simulation; Survey","Information management; Computational engineering; Data repositories; Meta-data extractions; Qualitative analysis; Research data managements; Research process; Simulation environment; Specific information; Metadata","","","","","Institute of Aerodynamics and Gas Dynamics and Hamzeh Kraus; Institute of Thermodynamics of the University of Stuttgart; Technical Information and Communication Services; University Library of Stuttgart; Bundesministerium für Bildung und Forschung, BMBF, (16FDM008)","The DiplIng project is funded by the Federal Ministry of Education and Research under Grant No. 16FDM008. The metadata model was developed together with Gernot Bauer from the Institute of Thermodynamics of the University of Stuttgart. The example in subsection 3.2 is based on a use case from him. The conversion to the metadata model of Dataverse was done by Anett Seeland from the Technical Information and Communication Services. Other contributing project members were Björn Selent from the Institute of Aerodynamics and Gas Dynamics and Hamzeh Kraus from the Institute of Thermodynamics. Many thanks to Angela Wesser from the University Library of Stuttgart for proofreading the paper on language correctness.","Apache Spark - Lightning-Fast Unified Analytics Engine, (2019); Belhajjame K., Deus H., Garijo D., Klyne G., Missier P., Soiland-Reyes S., Zednik S., PROV Model Primer, Technical Report, (2013); Belhajjame K., Reza B., Cheney J., Coppens S., Cresswell S., Gil Y., Groth P., Klyne G., Lebo T., McCusker J., Miles S., Myers J., Sahoo S., Tilmes C., PROV-DM: The PROV Data Model, W3C Recommendation, (2013); Bruce T.R., Hillmann D.I., The continuum of metadata quality: Defining, expressing, exploiting, Metadata in Practice, pp. 238-256, (2004); Chemical Markup Language - CML, (2012); DataCite Metadata Schema for the Publication and Citation of Research Data, (2017); About the Project, (2019); Erickson J., Maali F., Data Catalog Vocabulary (DCAT), W3C Recommendation, (2014); Gavrilis D., Makri D.-N., Papachristopoulos L., Angelis S., Kravvaritis K., Papatheodorou C., Constantopoulos P., Measuring quality in metadata repositories, Research and Advanced Technology for Digital Libraries, pp. 56-67, (2015); Green A., Humphrey C., Building the DDI, IASSIST Quarterly, 37, pp. 36-44, (2013); Grunzke R., Breuers S., Gesing S., Herres-Pawlis S., Kruse M., Blunk D., de la Garza L., Packschies L., Schafer P., Scharfe C., Schlemmer T., Steinke T., Schuller B., Muller-Pfefferkorn R., Jakel R., Nagel W.E., Atkinson M., Kruger J., Standards-based metadata management for molecular simulations, Concurrency and Computation: Practice and Experience, 26, 10, pp. 1744-1759, (2014); Hillmann D.I., Metadata quality: From evaluation to augmentation, Cataloging and Classification Quarterly, 46, 1, pp. 65-80, (2008); Iglezakis D., Relevance of Different Metadata Fields for the Description of Research Data from the Engineering Sciences, (2019); Iglezakis D., Schembera B., Anforderungen der Ingenieurwissenschaften an das Forschungsdatenmanagement der Universität Stuttgart - Ergebnisse der Bedarfsanalyse des Projektes DIPL-ING, Das Offene Bibliotheksjournal, 3, (2018); Iglezakis D., Schembera B., EngMeta - A Metadata Scheme for the Engineering Sciences, (2019); Lautenschlager M., Toussaint F., Thiemann H., Reinke M., The CERA-2 Data Model, (1998); Murray-Rust P., Rzepa H.S., CML: Evolution and design, Journal of Cheminformatics, 3, (2011); Metadata4Ing, (2019); A Framework of Guidance for Building Good Digital Collections, (2007); Park J.-R., Metadata quality in digital repositories: A survey of the current state of the art, Cataloging and Classification Quarterly, 47, 3-4, pp. 213-228, (2009); Park J.-R., Tosaka Y., Metadata quality control in digital repositories and collections: Criteria, semantics, and mechanisms, Cataloging and Classification Quarterly, 48, 8, pp. 696-715, (2010); Research Metadata Schemas WG, (2019); Research Data Management in Engineering IG, (2019); Metadata Standards Catalogue, (2019); Rousidis D., Garoufallou E., Balatsoukas P., Sicilia M.-N., Metadata for big data: A preliminary investigation of metadata quality issues in research data repositories, Information Services and Use, 34, 3-4, pp. 279-286, (2014); Rousidis D., Sicilia M.-N., Garoufallou E., Balatsoukas P., Data quality issues and content analysis for research data repositories: The case of dryad, ELPUB, pp. 49-58, (2014); Dataset, (2019); Schembera B., Bonisch T., Challenges of research data management for high performance computing, Proceedings of the International Conference on Theory and Practice of Digital Libraries, pp. 140-151, (2017); Schembera B., Iglezakis D., The genesis of EngMeta - A metadata model for research data in computational engineering, Metadata and Semantic Research, pp. 127-132, (2019); Vardigan M., Heus P., Thomas W., Data documentation initiative: Toward a standard for the social sciences, IJDC, 3, 1, pp. 107-113, (2008); Walker A., CMLComp - eMinerals and Materials Grid Resources, (2012); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Et al., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, pp. 1-10, (2016)","D. Iglezakis; University Library, University of Stuttgart, Stuttgart, Holzgartenstr. 16, 70174, Germany; email: dorothea.iglezakis@ub.uni-stuttgart.de","","Inderscience Publishers","","","","","","17442621","","","","English","Int. J. Metadata Semant. Ontol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85087970632"
"Faucett K.; Kennedy H.P.","Faucett, Kendra (57216737832); Kennedy, Holly Powell (55666726300)","57216737832; 55666726300","Accuracy in Reporting of Kentucky Certified Nurse-Midwives as Attendants in Birth Registration Data","2020","Journal of Midwifery and Women's Health","65","5","","688","693","5","2","10.1111/jmwh.13111","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084442003&doi=10.1111%2fjmwh.13111&partnerID=40&md5=0ac59ccb5d2d960c05df625194e5aec9","Department of Midwifery and Women's Health, Frontier Nursing University, Hyden, KY, United States; Yale University School of Nursing, West Haven, CT, United States","Faucett K., Department of Midwifery and Women's Health, Frontier Nursing University, Hyden, KY, United States; Kennedy H.P., Yale University School of Nursing, West Haven, CT, United States","Introduction: Birth certificate data are used nationally to determine health care policy and health care reimbursement and to demonstrate the legitimacy and value of certified nurse-midwives (CNMs) and certified midwives (CMs) in perinatal and neonatal outcomes. However, the validity of birth certificate data is questionable, in part because of the data collection process. These data are particularly crucial for midwife-attended births because the correct birth attendant is not always accurately identified on the birth certificate. The purpose of this project was to examine the actual number of CNM-reported births compared with those recorded by the Kentucky Office of Vital Statistics and to examine the process used by birth registrars to complete the birth certificate. Methods: CNMs attending births in hospitals in Kentucky in 2017 logged their birth statistics. These numbers were compared with the 2017 Kentucky Vital Statistics Birth Certificate data of CNM-attended births. Kentucky birth registrars (50%) who work in facilities where CNMs attend births completed a 32-question survey to describe their process of collecting birth certificate data. Results: The comparison data revealed that CNM-attended births in Kentucky are underrepresented in the state vital statistics by as much as 19.2%. Birth registrars identified barriers to collecting accurate data including lack of training, multiple sources of data, incomplete prenatal records, and absence of systems to help ensure accuracy. Discussion: CNMs/CMs should keep personal and practice birth logs and routinely compare these with hospital data kept by the birth registrar. The state office of vital statistics and hospitals should target training to specific facilities that have the most inaccurate data. The Improving Midwifery Birth Numbers Initiative through the American College of Nurse-Midwives Division of Research Data Management Section should continue to encourage midwifery students to complete this research in all 50 states. © 2020 by the American College of Nurse-Midwives","birth certificates; certified nurse-midwives; data accuracy; data collection; vital statistics","Birth Certificates; Data Accuracy; Female; Humans; Kentucky; Nurse Midwives; Parturition; Pregnancy; Registries; Vital Statistics; article; birth certificate; data accuracy; human; Kentucky; midwifery student; nurse midwife; vital statistics; birth; female; Kentucky; measurement accuracy; nurse midwife; pregnancy; register; vital statistics","","","","","","","Essential Facts About Midwives, (2019); Accuracy of Attending Provider Data in Birth Certificates - Is Research on This Topic Justified?, (2016); Biscone E.S., Cranmer J., Lewitt M., Martyn K.K., Are CNM-attended births in texas hospitals underreported?, J Midwifery Womens Health, 62, 5, pp. 614-619, (2017); Walker D.S., Schmunk S.B., Summers L., Do birth certificate data accurately reflect the number of CNM-attended births? an exploratory study, J Midwifery Womens Health, 49, 5, pp. 443-448, (2004); Paine L.L., Greener D.L., Strobino D.M., Birth registration: nurse-midwifery roles and responsibilities, J Nurse Midwifery, 33, 3, pp. 107-114, (1988); Diers D., Finding midwifery in administrative data systems, J Midwifery Womens Health, 52, 2, pp. 98-105, (2007); Brumberg H.L., Dozor D., Golombek S.G., History of the birth certificate: from inception to the future of electronic data, J Perinatol, 32, 6, pp. 407-411, (2012); Melnik T.A., Guldal C.G., Schoen L.D., Alicandro J., Henfield P., Barriers in accurate and complete birth registration in New York state, Matern Child Health J, 19, 9, pp. 1943-1948, (2015); Northam S., Polancich S., Restrepo E., Birth certificate methods in five hospitals, Public Health Nurs, 20, 4, pp. 318-327, (2003); Committee opinion summary no. 639: the importance of vital records and statistics for the obstetrician-gynecologist, Obstet Gynecol, 126, 3, (2015); Rothwell C.J., Reengineering vital registration and statistics systems for the United States, Prev Chronic Dis, 1, 4, (2004); DiGiuseppe D.L., Aron D.C., Ranbom L., Harper D.L., Rosenthal G.E., Reliability of birth certificate data: a multi-hospital comparison to medical records information, Matern Child Health J, 6, 3, pp. 169-179, (2002); Walker D.S., Visger J.M., Levi A., Midwifery data collection: options and opportunities, J Midwifery Womens Health, 53, 5, pp. 421-429, (2008); Guide to Completing the Facility Worksheets for the Certificate of Live Birth and Report of Fetal Death, (2017); Applying best practices for reporting medical and health information on birth certificates; Kentucky Registrar Guidelines, (2019); Sonenberg A., Medicaid and state regulation of nurse-midwives: the challenge of data retrieval, Policy Polit Nurs Pract, 11, 4, pp. 253-259, (2010); Equitable Medicare reimbursement: victory! victory! victory!; Zollinger T.W., Przybylski M.J., Gamache R.E., Reliability of Indiana birth certificate data compared to medical records, Ann Epidemiol, 16, 1, pp. 1-10, (2006); Howland R.E., Madsen A.M., Toprani A., Gambatese M., Mulready-Ward C., Begier E., How well do birth records serve maternal and child health programs? Birth registration system evaluation, New York City, 2008-2011, Maternal Child Health J, 19, 7, pp. 1559-1566, (2015)","K. Faucett; Department of Midwifery and Women's Health, Frontier Nursing University, Hyden, United States; email: kendrafaucett302@gmail.com","","John Wiley and Sons Inc","","","","","","15269523","","JMWHA","32391962","English","J. Midwifery Women's Health","Article","Final","","Scopus","2-s2.0-85084442003"
"Chiware E.R.T.","Chiware, Elisha R.T. (23491297400)","23491297400","Open research data in African academic and research libraries: a literature analysis","2020","Library Management","41","6-7","","383","399","16","4","10.1108/LM-02-2020-0027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085892220&doi=10.1108%2fLM-02-2020-0027&partnerID=40&md5=b17548c4c3d4901c74882863de48e136","Library, Cape Peninsula University of Technology, Bellville, South Africa","Chiware E.R.T., Library, Cape Peninsula University of Technology, Bellville, South Africa","Purpose: The paper presents a literature review on research data management services in African academic and research libraries on the backdrop of the advancing open science and open research data infrastructures. It provides areas of focus for library to support open research data. Design/methodology/approach: The literature analysis and future role of African libraries in research data management services were based on three areas as follows:open science, research infrastructures and open data infrastructures. Focussed literature searches were conducted across several electronic databases and discovery platforms, and a qualitative content analysis approach was used to explore the themes based on a coded list. Findings: The review reports of an environment where open science in Africa is still at developmental stages. Research infrastructures face funding and technical challenges. Data management services are in formative stages with progress reported in a few countries where open science and research data management policies have emerged, cyber and data infrastructures are being developed and limited data librarianship courses are being taught. Originality/value: The role of the academic and research libraries in Africa remains important in higher education and the national systems of research and innovation. Libraries should continue to align with institutional and national trends in response to the provision of data management services and as partners in the development of research infrastructures. © 2020, Emerald Publishing Limited.","Academic libraries; Africa; Data; Open access; Open data; Open science; Research infrastructures; Research libraries","","","","","","","","Abankwa F., Yuan R., The role of academic libraries in research data management: a case in ghanaian university libraries, Open Access Library, 6, 3, pp. 1-16, (2019); African open science platform – landscape, (2019); Albornoz D., Huang M., Martin I.M., Mateus M., Toure A.Y., Chan L., Framing power: tracing key discourses in open science policies, Open Edition, (2018); Arpaci I., Antecedents and consequences of cloud computing adoption in education to achieve knowledge management, Computers in Human Behavior, 70, pp. 382-390, (2017); Aviamu Y.A., Popoola B.O., Atuase D., Adoption of cloud computing by academic libraries for research data protection, Library Philosophy and Practice, (2019); Ayris P., Ignat T., Defining the role of libraries in the open science landscape: a reflection on current European practice, Open Information Science, 2, 1, pp. 1-22, (2018); Bangani, Moyo, Data sharing practices among researchers at South African universities, Data Science Journal, 18, 28, pp. 1-14, (2019); Bezuidenhout L., Rappert B., The Open Access Movement and the Future of the Africa's Knowledge Economy, (2016); Bezuidenhout L., Kelly A.H., Leonelli S., Rappert B., ‘$100 is not much to you': open science and neglected accessibilities for scientific research in Africa, Critical Public Health, 27, 1, pp. 39-49, (2017); Bezuidenhout L., Chakauya E., Hidden concerns of sharing research data by low/middle-income country scientists, Global Bioethics, 29, 1, pp. 39-54, (2018); Borgman C.L., Darch P.T., Sands A.E., Pasquetto I.V., Golshan M.S., Wallis J.C., Traweek S., Knowledge infrastructures in science: data, diversity, and digital libraries, International Journal of Digital Libraries, 16, pp. 207-227, (2015); Role of Research Infrastructures in Africa-EU Cooperation: Conclusions of the 2012 CAAST-Net -PAERIP stakeholder conference (3-4 December 2012), (2012); Caliari T., Rapini M.S., Chiarini T., Research infrastructures in less developed countries: the Brazilian case, Scientometrics, 122, pp. 451-475, (2020); Castelli D., Manghi P., Thanos C., A vision towards scientific communication infrastructures: on bridging the realms of research digital libraries and scientific data Centers, International Journal of Digital Libraries, 13, pp. 155-169, (2013); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, (2017); Chiware E.R., Becker D.A., Research data management services in southern Africa: a readiness survey of academic and research libraries, African Journal of Library, Archives and Information Science, 28, 1, pp. 1-16, (2018); Chiware E.R.T., Mathe Z., Academic libraries' role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, (2015); Delva W., Data science institutions focused on Africa are being built across the continent, Quartz Daily Brief, (2019); Guest Post: Overview of the African Open Access Landscape, with a Focus on Scholarly Publishing, (2019); Finnemann N.O., Research libraries and the internet: on transformative dynamic between institutions and digital media, Journal of Documentation, 70, 2, pp. 202-220, (2012); Foley M., The role and status of national research and education networks (NRENs) in Africa, World Bank Education, Technology and Innovation SABER-ICT Technical Paper Series, (2016); Frederick A., Run Y., The role of academic libraries in research data management: a case in Ghanaian university libraries, Open Access Library Journal, 6, (2019); Friesike S., Widenmayer B., Gassmann O., Schildhauer T., Opening science: towards an agenda of open science in academia and industry, Journal of Technology Transfer, 40, pp. 581-601, (2015); Gillwald A., Moyo M., The Cloud over Africa, Research ICT Africa; Glushko B., Shoyama R., Unpacking open access: a theoretical framework for understanding open access initiatives, Feliciter, 61, 1, pp. 8-11, (2015); Goben A., Sandusky R.J., Open data repositories, College and research Libraries News, 81, 2, pp. 1-4, (2020); Kaniki A., Information needs for basic research: an African perspective, Open Access and the Public Domain in Digital Data and Information for Science: Proceedings of an International Symposium, (2004); Kennedy M.L., Research Libraries as Catalytic Leaders in a Society in Constant Flux: A Report of the ARL-CNI Fall Forum 2019, (2019); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: how we shape up, Australian Academic and Research Libraries, 45, 4, pp. 296-308, (2014); Koopman M.M., De Jager K., Archiving South African digital research data: how ready are we?, South African Journal of Science, 112, 7-8, pp. 1-7, (2016); Leonelli S., Rappert B., Bezuidenhout L., Introduction: open data and Africa, Data Science Journal, 17, 5, pp. 1-3, (2018); Lopez-Ballesteros A., Beck J., Bombelli A., Grieco E., Lorencova E.K., Merbold L., Saunders M., Towards a feasible and representative Pan-African research infrastructure network for GHG observations, Environmental Research Letters, 13, 8, pp. 1-15, (2018); Lossau N., An overview of research infrastructures in Europe — and recommendations to LIBER, Liber Quarterly, 21, 3-4, pp. 313-329, (2012); Lwasa S., Buyinza A., Nabaasa B., Weather forecasts for pastoralism in a changing climate: navigating the data space in north eastern Uganda, Data Science Journal, 16, (2017); Koltay T., Identifying new roles for academic libraries in supporting data-intensive research, Bibliospehere, 4, pp. 97-102, (2019); Ma X., Fu Z., Jiang Y., Yang M., Stephen H., Cyberinfrastructure as a service to empower multidisciplinary, data-driven scientific research, International Journal of Computer Science and Information Technology, 9, 3, pp. 31-41, (2017); Makoni M., Universities welcome new national open access policy, (2019); Matlatse R., An Evaluation of a Structured Training Event Aimed At Enhancing The Research Data Management Knowledge and Skills of Library And Information Science Professionals In South African Higher Education Institutions, (2016); Mutuku C.M., Engaging a data revolution: open science data hubs and the new role for universities in Africa, Open Information Science, 3, 1, pp. 98-114, (2019); Ng'eno E.J., Research Data Management in Kenya's Agricultural Research Institutes, (2018); Nhendodzashe N., Pasipamire N., Research data management services: are academic libraries in Zimbabwe ready? The case of University of Zimbabwe, (2017); Onyancha O.B., Open research data in sub-saharan Africa: a bibliometric study using the data citation Index, Publishing Research Quarterly, 32, 3, pp. 227-246, (2016); Open Research Data Task Force, Realising the potential: Final report of the Open Research Data Task Force, (2018); Making Open Science a Reality, (2015); Parsons M.A., Godoy O., Ledrew E., de Bruin T.F., Danis B., Tomlinson S., Carlson D., A conceptual framework for managing very diverse data for complex, interdisciplinary science, Journal of Information Science, 36, 6, pp. 555-569, (2011); Patterton L., Bothma T.J.D., van Deventer M.J., From planning to practice: an action plan for the implementation of research data management services in resource-constrained institutions, South African Journal of Library and Information Sciences, 84, 2, pp. 14-26, (2018); Patel D., Research data management: a conceptual framework, Library Review, 65, 4-5, pp. 226-241, (2016); Perrier L., Blondal E., MacDonald H., Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies, Library and Information Science Research, 40, 3-4, pp. 173-183, (2018); Raju J., Knowledge and skills for the digital era academic library, The Journal of Academic Librarianship, 40, pp. 163-170, (2014); Ramoutar-Prieschl R., Hachigonta S., Management of Research Infrastructures: A South African Funding Perspective, (2020); Shaffer J.G., Tangara C.O., Kassogue Y., Srivastav S.K., Thiero O., Diakite M., Sangare M., Dabitao D., Toure M., Djimde A.A., Traore S., Diakite B., Coulibaly M.B., Liu Y., Lacey M., Lefante J.J., Koita O., Schieffelin J.S., Krogstad D.J., Doumbia S.O., Development of a data collection and management system in West Africa: challenges and sustainability, Infectious Diseases of Poverty, 7, 125, (2018); Simmonds R., Taylor R., Horrell J., Fanaroff B., Sithole H., Janse van Rensburg S., Pretorius B., IST-Africa Week Conference, pp. 1-8, (2016); South Africa, South African Research Infrastructure Roadmap, (2016); Spyridonis F., Taylor S.J.E., Abbott P., Barbera R., Nungu A., Gustafsson L.L., Pehrson B., Oaiya O., Banda T., A study on the state-of-the-art of e-Infrastructures uptake in Africa, Palgrave Communications, 1, (2015); Tang, Hu, Providing research data management (RDM) services in libraries: preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Teng-Zeng F.K., Research infrastructure and innovation systems in Africa: enhancing higher education sector research, (2005); Tijssen R., Kraemer-Mbula E., Research excellence in Africa: policies, perceptions, and performance, Science and Public Policy, 45, 3, pp. 392-403, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., Comment: the FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Africa data consensus: addis ababa, (2015); Zotoo I.K., Liu G., Research data management (RDM) strategy for academic libraries in Ghana: setting a national development agenda, Open Access Library, 6, 4, pp. 1-24, (2019); Hetu M., Koutouki K., Joly Y., Genomics for all: international open science genomics projects and capacity building in the developing world, Frontiers in Genetics, 10, (2019); Penev L., From open access to open science from the viewpoint of a scholarly publisher, Research Ideas and Outcomes, 3, (2017); Science Europe, Practical guide to international alignment of research data management, (2018)","E.R.T. Chiware; Library, Cape Peninsula University of Technology, Bellville, South Africa; email: chiwaree@cput.ac.za","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Review","Final","","Scopus","2-s2.0-85085892220"
"Kingsley D.","Kingsley, Danny (27267610200)","27267610200","The ‘Impact Opportunity’ for Academic Libraries through Grey Literature","2020","Serials Librarian","79","3-4","","281","289","8","5","10.1080/0361526X.2020.1847744","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097625250&doi=10.1080%2f0361526X.2020.1847744&partnerID=40&md5=cc01020472cba21b1349a68fcd413381","Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, Australia","Kingsley D., Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, Australia","This paper proposes a new role for academic libraries as part of a wider ‘research practice’ activity for research institutions, incorporating support, training and expertise in relation to scholarly communication and research impact. The role libraries hold within research institutions is changing as the world shifts towards a digital and increasingly open future. This requires a rethink of the types of services and skill sets that are appropriate for an academic library to encompass. The increased focus of institutions and funders on the societal impact of research offers an opportunity for academic libraries to further integrate their work into the open research agenda. Libraries can draw on what is now over a decade of experience introducing open access, institutional repositories and research data management service to their academic communities to inform the development of impact services. An immediate service that libraries can offer is assisting with the identification of, and sometimes deposit into the institutional repository of, works that are sitting outside the peer reviewed literature–grey literature. This material needs to be collected for the purposes of demonstrating outcomes and pathways to impact. This paper describes the need to consider item classifications within digital repositories. If this new service is considered an option into the future, libraries themselves and potentially research offices will need to look not just at workflows but also item classifications within systems to ensure they encompass this broader collection of works. © 2020, Published with license by Taylor & Francis. Group, LLC. © 2020 Danny Kingsley.","academic libraries; gray literature; grey literature; Impact; open research; open scholarship; research data management; research funding; research libraries; skill sets","article; funding; grey literature; human; library; scholarly communication; sitting; skill; workflow","","","","","Australian Research Council, ARC; National Health and Medical Research Council, NHMRC","Funding text 1: There is now an increased emphasis in Australia on research impact in research grant applications. As of 2020, both Australian funding bodies, the NHMRC and the ARC, require statements about research impact in their grant application processes. For example, NHMRC 2020 Investigator Grants Assessment Criteria allocate 20% to ‘Research impact’ as part of the ‘Track Record’, which is “the value of an individual’s past research achievements, relative to opportunity, not prospective achievements, using evidence”. ARC Linkage Projects assessment criteria allocates 30% to ‘Benefit’ which includes “the economic, commercial, environmental, social and/or cultural benefits for relevant Australian research end-users “ and “benefits of the research for Partner Organisation(s) and other relevant end-users” and the ARC Research Opportunity and Performance Evidence statement includes a category for “identifiable benefits outside academia”. ; Funding text 2: One potential reason for this focus, in Australia at least, is the declining proportion of research funding from government grants. Figures from 2018, the most recent available, show Australian competitive grants only constitute 14.6% of expenditure on research. Funding for research is increasingly looking to industry – fuelling a focus on how the research might have real world implications. The recent collapse of the international student demand because of COVID-19 has forced a rethink of research funding in Australia, with the announcement of a 900 million AUD National Priorities and Industry Linkage Fund which will allocate block grants to universities to support their engagement with industry. ","","D. Kingsley; Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, Australia; email: danny.kingsley@anu.edu.au","","Routledge","","","","","","0361526X","","","","English","Ser. Libr.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85097625250"
"Auge T.; Heuer A.","Auge, Tanja (57194833958); Heuer, Andreas (9533312500)","57194833958; 9533312500","Extended provenance management for data science applications","2020","CEUR Workshop Proceedings","2652","","","","","","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090505895&partnerID=40&md5=bf9527f79670e3634e34cb9f0fd1b281","University of Rostock, Germany","Auge T., University of Rostock, Germany; Heuer A., University of Rostock, Germany","Research data management deals with tracking and archiving of data collected during scientific projects, experiments or observations. The path from data collection to publication should thus be kept comprehensible, reconstructable and plausible. The continuous growth of data, frequent schema changes as well as the varied evaluation of the data makes the storage of every possible database state a very complicated and lengthy task. With the help of data provenance, however, we can determine which part of the primary research data must be stored long-term in order to ensure the reproducibility of the evaluations. It should also be possible to recalculate changes to data and schemata so that old data records do not have to be archived completely. In addition, the stored data must not conflict with existing privacy guidelines. Copyright © 2020 for this paper by its authors. Copying permitted for private and academic purposes.","","Data Science; Database systems; Information management; Petroleum reservoir evaluation; Data collection; Data provenance; Reconstructable; Reproducibilities; Research data managements; Schema changes; Science applications; Scientific projects; Digital storage","","","","","Universität Rostock; Leibniz-Institut für Ostseeforschung Warnemünde, IOW","Special thanks go to my PhD supervisor Andreas Heuer and my mentor Goetz Graefe as well as to my colleagues from the database chair of the University of Rostock for their support during my PhD studies so far. Thanks to the Leibniz Institute for Baltic Sea Research Warnemünde for providing their research data and thanks also to my students for the many interesting discussions about privacy, provenance, evolution and the CHASE algorithm.","Auge T., Heuer A., Combining Provenance Management and Schema Evolution, IPAW, volume 11017 of Lecture Notes in Computer Science, pp. 222-225, (2018); Auge T., Heuer A., The Theory behind Minimizing Research Data - Result equivalent CHASE-inverse Mappings, LWDA, volume 2191 of CEUR Workshop Proceedings, pp. 1-12, (2018); Auge T., Heuer A., ProSA - Using the CHASE for Provenance Management, ADBIS, volume 11695 of Lecture Notes in Computer Science, pp. 357-372, (2019); Auge T., Scharlau N., Heuer A., Privacy Aspects of Provenance Queries, (2020); Benedikt M., Konstantinidis G., Mecca G., Motik B., Papotti P., Santoro D., Tsamoura E., Benchmarking the Chase, PODS, pp. 37-52, (2017); Buneman P., Khanna S., Tan W. C., Why and Where: A Characterization of Data Provenance, ICDT, pp. 316-330, (1973); Cheney J., Chiticariu L., Tan W. C., Provenance in Databases: Why, How, and Where, Foundations and Trends in Databases, 1, 4, pp. 379-474, (2009); Curino C., Moon H. J., Deutsch A., Zaniolo C., Update rewriting and integrity constraint maintenance in a schema evolution support system: PRISM++, Proc. VLDB Endow, 4, 2, pp. 117-128, (2010); Fagin R., Kolaitis P. G., Miller R. J., Popa L., Data Exchange: Semantics and Query Answering, Theor. Comput. Sci, 336, 1, pp. 89-124, (2005); Fagin R., Kolaitis P. G., Popa L., Tan W. C., Schema Mapping Evolution Through Composition and Inversion, Schema Matching and Mapping, pp. 191-222, (2011); Gao S., Zaniolo C., Provenance Management in Databases Under Schema Evolution, (2012); Glavic B., Alonso G., Miller R. J., Haas L. M., TRAMP: Understanding the Behavior of Schema Mappings through Provenance, Proc. VLDB Endow, 3, 1, pp. 1314-1325, (2010); Greco S., Molinaro C., Spezzano F., Incomplete Data and Data Dependencies in Relational Databases, Synthesis Lectures on Data Management, (2012); Green T. J., Karvounarakis G., Tannen V., Provenance semirings, PODS, pp. 31-40, (2007); Samarati P., Protecting Respondents' Identities in Microdata Release, IEEE Trans. Knowl. Data Eng, 13, 6, pp. 1010-1027, (2001)","T. Auge; University of Rostock, Germany; email: tanja.auge@uni-rostock.de","Abedj Z.; Hose K.","CEUR-WS","","2020 International Conference on Very Large Databases PhD Workshop, VLDB-PhD 2020","31 August 2020 through 4 September 2020","Virtual, Online","162322","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85090505895"
"McLeod J.; O’Connor K.","McLeod, Julie (25422552000); O’Connor, Kate (56781589100)","25422552000; 56781589100","Ethics, archives and data sharing in qualitative research","2020","Educational Philosophy and Theory","53","5","","523","535","12","4","10.1080/00131857.2020.1805310","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089692785&doi=10.1080%2f00131857.2020.1805310&partnerID=40&md5=b5c8659d5cb7452ae854c4d2bfa1d624","Melbourne Graduate School of Education, University of Melbourne, Melbourne, Australia; School of Education, La Trobe University, Bundoora, Australia","McLeod J., Melbourne Graduate School of Education, University of Melbourne, Melbourne, Australia; O’Connor K., School of Education, La Trobe University, Bundoora, Australia","This article investigates dilemmas in the archiving and sharing of qualitative data in educational research, critically engaging with practices and debates from across the social sciences. Ethical, epistemological and methodological challenges are examined in reference to open access agendas, the politics of knowledge production, and transformations in research practices in the era of data management. We first consider practical and interpretive decisions in archiving qualitative data, then map current policy and regulatory frameworks governing research data management, taking Australia as a case-study. We argue that governance and protocols for data sharing have not attended sufficiently to the distinctive ethical and methodological dimensions and knowledge claims of qualitative research. Instead, approaches associated with quantitative data are extrapolated in ways which construct an imaginary of decontextualised data, abstracted from the conditions of its production. We further argue for more critical attention to the double-edged affordances and ambivalent effects of data sharing and openness and to how data archives are imagined, constructed and curated. This includes greater acknowledgement of the affective and temporal dynamics involved in data archiving, understanding them as practices of (re)invention that also curate ‘archives for the future’ and help to foster an historicising imaginary in educational research. © 2020 Philosophy of Education Society of Australasia.","archives; Data sharing; methodology; open research; qualitative research","","","","","","Australian Research Council, ARC, (FT110100646)","Research for this article was supported by funding from the Australian Research Council, Future Fellowship, ‘Youth Identity and Educational Change in Australia since 1950: Digital Archiving, Re-using Qualitative Data and Histories of the Present’ [grant number FT110100646, Julie McLeod].","(2017); Research data management strategy, (2018); Ball S., Education Policy and Social Class, (2006); (2015); Bishop L., Ethical sharing and re-use of qualitative data, Australian Journal of Social Issues, 44, 3, pp. 255-272, (2009); Broom A., Cheshire L., Emmison M., Qualitative researchers’ understandings of their practice and the implications for data archiving and sharing, Sociology, 43, 6, pp. 1163-1180, (2009); Burton A.M., Archive stories: Facts, fictions, and the writing of history, (2005); Corti L., Progress and problems of preserving and providing access to qualitative data for social research - the international picture of an emerging culture, Forum: Qualitative Sozialforschung/Forum: Qualitative Social Research, 1, 3, pp. 1-22, (2000); Corti L., Thompson P., Secondary analysis of archive data, Qualitative research practice, pp. 327-343, (2004); Denzin N., Lincoln Y., The Sage handbook of qualitative research, (2005); Derrida J., Archive fever: A Freudian impression, (1996); Guidelines on FAIR Data Management in Horizon 2020, v. 3, (2016); Fielding N., Getting the most from archived qualitative data, International Journal of Social Research Methodology, 7, 1, pp. 97-104, (2004); Fraser J.W., The future of the study of our educational past–Whither the history of education?, History of Education Quarterly, 55, 1, pp. 1-31, (2015); Goodman J., Grosvenor I., Educational research–history of education a curious case?, Oxford Review of Education, 35, 5, pp. 601-616, (2009); Heaton J., Reworking qualitative data, (2004); (2016); Hughes G., Sharing research data, Emergency Medicine Australasia: EMA, 29, 1, pp. 4-5, (2017); Jackson A.Y., Mazzei L.A., Thinking with theory in qualitative research: Viewing data across multiple perspectives, (2012); Kuntz A.M., The responsible methodologist: Inquiry, truth-telling and social justice, (2015); Lewis A., Neish P., Pathways, parallels and pitfalls: The scholarly web, the ESRC and linked open data, The Australian Library Journal, 65, 3, pp. 224-231, (2016); Lingard B., Sellar S., Savage G.C., Re-articulating social justice as equity in schooling policy: The effects of testing and data infrastructures, British Journal of Sociology of Education, 35, 5, pp. 710-730, (2014); Massumi B., Working principles, The go-to how to book of anarchiving, (2016); Mauthner N., The past was never simply there to begin with the future is not simply what will unfold”: A posthumanist performative approach to qualitative longitudinal research, International Journal of Social Research Methodology, 18, 3, pp. 321-336, (2015); Mauthner N., Parry O., Open access digital data sharing: Principles, policies and practices, Social Epistemology, 27, 1, pp. 47-67, (2013); Mauthner N., Parry O., Backett-Milburn K., The data are out there, or are they? Implications for archiving and revisiting qualitative data, Sociology, 32, 4, pp. 733-745, (1998); McLeod J., Marking time, making methods: Temporality and untimely dilemmas in the sociology of youth and educational change, British Journal of Sociology of Education, 38, 1, pp. 13-25, (2017); Manoff M., (2004); McLeod J., Thomson R., Researching social change: Qualitative approaches, (2009); Mouromtsev D., d' Aquin M., Open data for education: Linked, shared, and reusable data for teaching and learning, (2016); Moore N., (2007); Moore N., Salter A., Stanley L., Tamboukou M., The archive project: Archival research in the social sciences, (2017); Murphy A.E., The Go-To How To Book of Anarchiving, (2016); About museums victoria collections; OECD principles and guidelines for access to research data from public funding, (2007); The Australian Research Council and Universities Australia, (2018); The Australian Research Council and Universities Australia, (2018); Neale B., Henwood K., Holland J., Researching lives through time: An introduction to the timescapes approach, Qualitative Research, 12, 1, pp. 4-15, (2012); Peters M.A., The virtues of openness in higher education, Global creationl space, mobility and synchrony in the age of the knowledge economy, pp. 249-265, (2010); Peters M.A., Education, science and knowledge capitalism: Creativity and the promise of openness, (2013); Peters M.A., Inside the global teaching machine: MOOCs, academic labour and the future of the university, Learning and Teaching, 9, 2, pp. 66-88, (2016); Peters M.A., Roberts P., The virtues of openness: Education, science and scholarship in the digital age, (2012); Ricoeur P., Memory, history, forgetting, (2004); Savage M., Identities and social change in Britain since 1940, (2010); Seddon T., McLeod J., Sobe N., Reclaiming comparative historical sociologies of education, World Yearbook of Education 2018: Uneven space-times of education: Historical sociologies of concepts, methods and practices, (2018); Sellar S., Data infrastructure: A review of expanding accountability systems and large-scale assessments in education, Discourse: Studies in the Cultural Politics of Education, 36, 5, pp. 765-777, (2015); Stoler A., Along the archival grain: Epistemic anxieties and colonial commonsense, (2009); Thomson R., Webb S., McGeeney E., Moore N., Ronan A., Reanimating data: Experiments with people, places and archives; Thomson R., Berriman L., Bragg S., Researching everyday childhoods: Time, technology and documentation in a digital age, (2018); Thomson R., McLeod J., New frontiers in qualitative longitudinal research: an agenda for research, International Journal of Social Research Methodology, 18, 3, pp. 243-250, (2015); Policy on data, software and materials management and sharing, (2017); Yates L., Interpretive claims and methodological warrant in small-number qualitative, longitudinal research, International Journal of Social Research Methodology, 6, 3, pp. 223-332, (2003)","; ","","Routledge","","","","","","00131857","","","","English","Educ.Philos. Theor.","Article","Final","","Scopus","2-s2.0-85089692785"
"Zhu H.; Wei R.","Zhu, Hui (58178895000); Wei, Ruibin (58179032400)","58178895000; 58179032400","Hot Spots and Trend Analysis of Chinese Studies on Research Policy; [国内科研政策研究热点分析]","2020","Journal of Library and Information Science in Agriculture","32","3","","20","28","8","0","10.13998/j.cnki.issn1002-1248.2019.12.10-1078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152203824&doi=10.13998%2fj.cnki.issn1002-1248.2019.12.10-1078&partnerID=40&md5=d4e6bc7e220ef81117495a952cfc31d7","School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu, 233030, China","Zhu H., School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu, 233030, China; Wei R., School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu, 233030, China","[Purpose / Significance]This paper's objective is to understand hot spots and trends of Chinese researchers' studies on research policies. [Methods / Process]A scientometric method was used to analyze the literature of scien tific research policy, We revealled the research hot spots and trends of Chinese research policy studies by using word frequency statistics and keywords cluster analysis.[Results / Conclusions]The results show seven hot spots: preferential policies for scientific research taxation, incentive policy for scientific research, research results transformation policy, research funding management policy, research data management policy, research reform policy and research talent policy aspect. Four topics will draw more Chinese researchers' attention: Chinese research data privacy protection policy, evaluation of policies on transforming scientific achivements, policies on scientific research innovation in colleges and universities, and the policy for the integrity of scientific research. © 2020 The Author(s).","keywords co-occurrence; research hot spots; research policy","","","","","","","","3; pp. 65-69; pp. 3-8; (2013); (2019); pp. 60-62; (2009); 9, pp. 46-51, (2013); 5, pp. 29-35, (2012); 34, 4, (2017); 11, pp. 9-17, (2018); 7, pp. 95-98; 2; 27, 3, (2017); 3; (2017); 7, (2015); 2, (2016); pp. 3-11; 6, pp. 60-65; 3, pp. 20-26; 35, 5, pp. 36-46, (2018); pp. 10-13; pp. 4-10; 35, 4, pp. 126-131, (2018); 7, pp. 91-99, (2012); 4, pp. 23-24; 1, pp. 61-66; 34, 8, pp. 147-151, (2015); 33, 1, pp. 79-87, (2019); pp. 32-40; (2019); 6, (2015); 8; pp. 73-76; 2, pp. 153-156; 29, pp. 85-88, (2008); 36, 5, pp. 97-100, (2018); 8, pp. 77-80; pp. 1-7; pp. 59-64; 6, pp. 77-78; 9, pp. 92-100; 11, pp. 81-92, (2009); 10, pp. 41-45; 6, pp. 130-137, (2007); 4, pp. 125-128, (2009); 17, 3, (2019); (2015); 3, pp. 180-184","","","Agricultural Information Institute, Chinese Academy of Agricultural Sciences","","","","","","10021248","","","","Chinese","J. Libr. Inf. Sci. Agric.","Article","Final","","Scopus","2-s2.0-85152203824"
"Auge T.; Manthey E.; Jürgensmann S.; Feistel S.; Heuer A.","Auge, Tanja (57194833958); Manthey, Erik (57219974061); Jürgensmann, Susanne (54083166200); Feistel, Susanne (55499177600); Heuer, Andreas (9533312500)","57194833958; 57219974061; 54083166200; 55499177600; 9533312500","Schema evolution and reproducibility of long-term hydrographic data sets at the IOW","2020","CEUR Workshop Proceedings","2738","","","258","269","11","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096361442&partnerID=40&md5=b5f7f31fdb8a6a4375065f0c0f3bff2c","University of Rostock, Germany; Leibniz Institute for Baltic Sea Research Warnemünde, Germany","Auge T., University of Rostock, Germany; Manthey E., University of Rostock, Germany; Jürgensmann S., Leibniz Institute for Baltic Sea Research Warnemünde, Germany; Feistel S., Leibniz Institute for Baltic Sea Research Warnemünde, Germany; Heuer A., University of Rostock, Germany","National and international exploration of the Baltic Sea ecosystem can be traced back to the 19th century. In its quite long history, the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) is the only research institution in Germany that has made interdisciplinary research of the Baltic Sea its central mission. The IOW hosts data from more than 130 years of research work. Using the example of hydrographic datasets that have been created over a period of about 50 years, this paper examines changes in the data and the associated schemes that have resulted from the continuous development and refinement of measurement methods over time. The paper focuses on the schema development operators: What kind of schema development has taken places over the years, and what are the important basic schema development operators that can be identified? It classifies well-known schema evolution operators which can be expressed as schema mappings, and defines two new operators for merging and splitting attributes, up to now not considered in other research works. These operators have proven to be essential for development of a new universal schema for the central oceanographic database on the IOW - the IOWDB. © 2020 by the paper's authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).","Baltic Sea; Long-term Data; Research Data Management; Schema Evolution","Continuous development; Interdisciplinary research; Measurement methods; Merging and splitting; Oceanographic database; Reproducibilities; Research institutions; Schema development; Hydrographic surveys","","","","","","","Auge T, Extended Provenance Management for Data Science Applications, PhD@VLDB, CEUR Workshop Proceedings, (2020); Auge T., Heuer A., Combining Provenance Management and Schema Evolution, IPAW, pp. 222-225, (2018); Bock S., Feistel F., Jurgensmann S., Data Management at IOW. Poster, (2014); Bruder I., Klettke M., Moller M. L., Meyer F., Jurgensmann S., Feistel S., Daten wie Sand am Meer - Datenerhebung, -strukturierung, -management und Data Provenance für die Ostseeforschung, Datenbank-Spektrum, 17, 2, pp. 183-196, (2017); Curino C., Moon H. J., Deutsch A., Zaniolo D., Update Rewriting and Integrity Constraint Maintenance in a Schema Evolution Support System: PRISM++, PVLDB, 2, 4, pp. 117-128, (2010); Curino C. A., Moon H. J., Zaniolo C., Graceful Database Schema Evolution: the PRISM Workbench, PVLDB, 1, 1, pp. 761-772, (2008); Curino C. A., Tanca L., Moon H. J., Zaniolo C., Schema Evolution in Wikipedia: Toward a Web Information System Benchmark, ICEIS, 1, pp. 323-332, (2008); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema Mapping Evolution Through Composition and Inversion, Schema Matching and Mapping, pp. 191-222, (2011); Manthey E., Beschreibung der Veränderungen von Schemata und Daten am IOW mit Schema-Evolutions-Operatoren, (2020); Moon H. J., Curino C., Deutsch A., Hou C.-Y., Zaniolo C., Managing and Querying Transaction-time Databases under Schema Evolution, PVLDB, 1, 1, pp. 882-895, (2008); Qiu D., Li B., Su Z., An Empirical Analysis of the Co-evolution of Schema and Code in Database Applications, ESEC/SIGSOFT FSE, ACM, pp. 125-135, (2013); Wilkinson M., Dumontier M., Aalbersberg I., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Wu S., Neamtiu I., Schema evolution analysis for embedded databases, ICDE Workshops, IEEE Computer Society, pp. 151-156, (2011)","","Trabold D.; Welke P.; Piatkowski N.","CEUR-WS","","2020 Conference ""Learning, Knowledge, Data, Analytics"", LWDA 2020","9 September 2020 through 11 September 2020","Virtual, Online","164862","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85096361442"
"Zencir M.B.; Oğuz T.","Zencir, Mithat Baver (55799612100); Oğuz, Tülay (57218950059)","55799612100; 57218950059","The Attitudes of Ankara University Researchers towards Research Data Management and Barriers to Data Sharing; [Ankara Üniversitesi Araştırmacılarının Araştırma Verilerinin Yönetimine Yönelik Tutumları ve Veri Paylaşımı Önündeki Engeller]","2020","Bilgi Dunyasi","21","1","","89","123","34","0","10.15612/BD.2020.806","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090897199&doi=10.15612%2fBD.2020.806&partnerID=40&md5=45843f3a28f1a565d4c91d3e847cb040","Faculty of Humanities and Social Sciences, Department of Information and Records Management, United States; Department of Information and Records Management, United States","Zencir M.B., Faculty of Humanities and Social Sciences, Department of Information and Records Management, United States; Oğuz T., Department of Information and Records Management, United States","Research data has a key importance for the science world, for their verification use, economic and social values. Therefore, the data must be made open and the barriers to sharing must be removed. This study aims to reveal the attitudes of researchers -who managed Scientific Research Projects within Ankara University- towards research data management and describe barriers to data sharing. Descriptive methodwas usedin the study andsurvey technique was preferredfor data collection. Data collected from 194 researchers revealed that researchers were not able to manage data effectively. Most of the researchers do not prepare a written data management plan, or they do not plan applications related to data after the completion of the research, and they retain large amounts of data outside the institutional storage areas as well as not using accepted standards in creating metadata. In this study, the effect of scientific fields on data management process has also been revealed, and findings demonstrated that there are significant differences among scientific fields in terms of types of data created, types of data files, amount of data produced, familiarity with metadata and reasons affecting data sharing. One of the most important results revealed in this study is that participants find it sufficient to share data only through publication. Therefore, researchers prefer to share their results/findings, not research data. This contradicts the idea of open research data. Barriers to researchers’ data sharing are closely related to the current academic system and the anxiety of stealing and research ideas/methods. In addition, issues such as problems in data management processes, legal regulations and service deficiencies are other barriers to data sharing. © 2020 University and Research Librarians Association (UNAK). All rights reserved.","Data sharing; Open data; Open research data; Open science; Research data management","","","","","","","","Akers K. G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Allard S., Aydinoglu A.U., Environmental researchers’ data practices: an exploratory study in Turkey, E-Science and Information Management, IMCW 2012, Communications in Computer and Information Science, Practice içinde, pp. 13-24, (2012); Ankara Üniversitesi Açık Bilim Politikası, (2019); Ankara Üniversitesi Bilimsel Araştırma Projeleri Yönergesi, (2017); Ankara Üniversitesi Dosya Depolama Servisi, (2015); Averkamp S., Gu X., Rogers B., Data management at the University of Iowa: A university libraries report on campus research data needs, (2014); Aydinoglu A. U., Dogan G., Taskin Z., Research data management in Turkey: perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); BAP Tamamlanan Projeler; Berman E. A., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: Findings from the quantitative phase, Journal of eScience Librarianship, 6, 1, (2017); T. C. T. C., Resmi Gazete, (2019); Bishoff C., Johnston L., Approaches to data sharing: An analysis of NSF data management plans from a large research university, Journal of Librarianship & Scholarly Communication, 3, 2, (2015); Borgman C. L., Scholarship in the digital age: Information, infrastructure, and the internet, (2007); Borgman C. L., Big data, little data, no data: Scholarship in the networked World, (2015); Borgman C. L., Wallis J. C., Mayernik M. S., Who’s got the data? Interdependencies in science and technology collaborations, Computer Supported Cooperative Work, 21, 6, pp. 485-523, (2012); Briney K., Data management for researchers: Organize, maintain and share your data for research success, (2015); Buys C. M., Shaw P. L., Data management practices across an institution: Survey and report, Journal of Librarianship & Scholarly Communication, 3, 2, (2015); Carlson J., The use of life cycle models in developing and supporting data services, Research Data Management: Practical Strategies for Information Professionals içinde, pp. 63-86, (2014); Cochran W. G., Some methods for strengthening the common X2 tests, Biometrics, 10, 4, pp. 417-451, (1954); Cochran W. G., Sampling techniques, (1977); H2020 programme: Guidelines on FAIR data management in Horizon 2020, (2017); Commission staff working document: Implementation roadmap for the European Open Science Cloud, (2018); Freeman G., Halton J. H., Note on an exact treatment of contingency, goodness of fit and other problems of significance, Biometrika, 38, 1, pp. 141-149, (1951); Gezelter D., What, exactly, is Open Science?, (2009); Gibbs H., Southampton data survey: Our experience and lessons learned, (2009); Goben A., Griffin T., In aggregate: Trends, needs, and oportunities from research data management surveys, College and Research Libraries, 80, 7, pp. 903-924, (2019); Gurbuz S., Sahin F., Sosyal bilimlerde araştırma yöntemleri, (2015); Hamalainen W., New upper bounds for tight and fast approximation of Fisher’s exact test in dependency rule mining, Computational Statistics & Data Analysis, 93, pp. 469-482, (2016); Hey T., Tansley S., Tolle K. M., The fourth paradigm: data-intensive scientific discovery, (2009); Hickson S., Poulton K. A., Connor M., Richardson J., Wolski M., Modifying researchers’ data management practices: A behavioural framework for library practitioners, IFLA journal, 42, 4, pp. 253-265, (2016); Higman R., Bangert D., Jones S., Three camps, one destination: the intersections of research data management, FAIR and Open, Insights, 32, 1, (2019); Fisher Exact Test shows a value for a test statistic in R x C tables, (2020); Jahnke L. M., Asher A., The problem of data: Data management and curation practices among university researchers, pp. 3-28, (2012); Kalayci S., SPSS uygulamalı çok değişkenli istatistik teknikleri, (2006); Kansa E. C., Kansa S. W., We all know that a 14 is a sheep: Data publication and professionalism in archaeological communication, Journal of Eastern Mediterranean Archaeology and Heritage Studies, 1, 1, pp. 88-97, (2013); Kim Y., Stanton J. M., Institutional and individual factors affecting scientists’ datasharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2016); Krahe M. A., Toohey J., Wolski M., Scuffham P. A., Reilly S., Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia, Health Information Management Journal, pp. 1-9, (2019); Kroll S., Forsman R., A slice of research life: information support for research in the United States, (2010); Lydersen S., Pradhan V., Senchaudhuri P., Laake P., Choice of test for association in small sample unordered r× c tables, Statistics in medicine, 26, 23, pp. 4328-4343, (2007); Mancilla H. A., Teperek M., van Dijck J., den Heijer K., Eggermont R., Plomp E., Kurapati S., On a quest for cultural change-surveying research data management practices at Delft University of Technology, LIBER Quarterly, 29, 1, pp. 1-27, (2019); Mehta C. R., Patel N. R., A network algorithm for performing Fisher’s exact test in r× c contingency tables, Journal of the American Statistical Association, 78, 382, pp. 427-434, (1983); Mehta C. R., Patel N. R., IBM SPSS Exact Tests, (2011); Mosconi G., Li Q., Randall D., Karasti H., Tolmie P., Barutzky J., Pipek V., Three Gaps in Opening Science, Computer Supported Cooperative Work (CSCW), 28, 3-4, pp. 749-789, (2019); Proposal preparation instructions, (2017); Parsons T., Grimshaw S., Williamson L., Research data management survey, (2013); Paton N. W., Managing and sharing experimental data: standards, tools and pitfalls, Biochemical Society Transactions, 36, 1, pp. 33-36, (2008); Data management best practices: Storage and backups, (2019); Peters C., Dryden A. R., Assessing the academic library’s role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Piwowar H. A., Vision T. J., Whitlock M. C., Data archiving is a good investment, Nature, (2011); Ray J., Introduction to research data management, Research Data Management: Practical Strategies for Information Professionals içinde, pp. 1-23, (2014); To share or not to share: Publication and quality assurance of research data outputs, (2008); Rusbridge C., Create, curate, re-use: The expanding life course of digital research data, (2007); Scaramozzino J. M., Ramirez M. L., McGaughey K. J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Sprent P., Fisher Exact Test, International Encyclopedia of Statistical Science içinde, pp. 524-525, (2011); Stamatoplos A., Neville T., Henry D., Analyzing the Data Management Environment in a Master’s-level Institution, The Journal of Academic Librarianship, 42, 2, pp. 154-160, (2016); Tenopir C., Allard S., Douglass K., Aydinoglu A. U., Wu L., Read E., Frame M., Data sharing by scientists: practices and perceptions, PLoS ONE, 6, 6, (2011); Tonta Y., Açıkbilimveaçıkerişim, pp. 235-250, (2015); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu [TÜBİTAK] Açık Bilim Politikası, (2019); Unal Y., Kurbanoglu S., Araştırma verilerinin yönetimi: Türk araştırmacılar verilerini açmaya hazır mı?, Türk Kütüphaneciliği, 32, 4, pp. 287-311, (2018); Guidelines for a data management plan, (2016); Van Loon J. E., Akers K. G., Hudson C., Sarkozy A., Quality evaluation of data management plans at a research university, IFLA Journal, 43, 1, pp. 98-104, (2017); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Whyte A., Tedds J., Making the case for research data management, (2011); YÖK, akademide “açık erişim ve açık bilim” çalışmalarını başlattı, (2018); Yükseköğretim Kurumları Bilimsel Araştırma Projeleri Hakkında Yönetmelik, (2016); Zencir M. B., Ankara Üniversitesi akademisyenlerinin araştırma verilerinin yönetimi ile ilgili tutumları ve bir model önerisi (Yayımlanmamış doktora tezi), (2019)","M.B. Zencir; Faculty of Humanities and Social Sciences, Department of Information and Records Management, United States; email: mithatb.zencir@ikcu.edu.tr","","University and Research Librarians Association (UNAK)","","","","","","13023217","","","","Turkish","Bilgi Dunyasi","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85090897199"
"Mozgova I.; Koepler O.; Kraft A.; Lachmayer R.; Auer S.","Mozgova, Iryna (27067881500); Koepler, Oliver (6507094492); Kraft, Angelina (57219665463); Lachmayer, Roland (6602616454); Auer, Sören (23391879500)","27067881500; 6507094492; 57219665463; 6602616454; 23391879500","Research data management system for a large collaborative project","2020","Proceedings of the NordDesign 2020 Conference, NordDesign 2020","","","","","","","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094680714&partnerID=40&md5=82daae749a91945dfd71264c2bec8037","Leibniz University Hannover, Germany; TIB-Leibniz Information Centre for Science and Technology University Library, Germany","Mozgova I., Leibniz University Hannover, Germany; Koepler O., TIB-Leibniz Information Centre for Science and Technology University Library, Germany; Kraft A., TIB-Leibniz Information Centre for Science and Technology University Library, Germany; Lachmayer R., Leibniz University Hannover, Germany; Auer S., TIB-Leibniz Information Centre for Science and Technology University Library, Germany","Data processing is an essential element of scientific work in engineering. The ongoing digitization in the engineering sciences generates more and more data, having a tremendous impact on the development and realisation of technological processes. In large collaborative projects Research Data Management Systems are major assets to implement a research data management according to the FAIR data principles and to support scientist in their interdisciplinary research across multiple teams. Based on the example of the Collaborative Research Centre 1153 ""Process chain for the production of hybrid high-performance components through tailored forming"", where an interdisciplinary team researches novel process chains for the manufacturing of hybrid components, an approach to organize the research data management process using a Research Data and Knowledge Management System and a domain specific vocabulary is being developed. © Proceedings of the NordDesign 2020 Conference, NordDesign 2020. All rights reserved.","Design support system; Knowledge management; Research data management; Vocabulary","Human resource management; Industrial research; Information retrieval; Knowledge based systems; Knowledge management; Manufacture; Collaborative projects; Collaborative research; High-performance components; Interdisciplinary research; Interdisciplinary teams; Knowledge management system; Research data managements; Technological process; Data handling","","","","","Collaborative Research Centre, (252662854); Deutsche Forschungsgemeinschaft, DFG","The authors gratefully acknowledge the support from the Collaborative Research Centre (CRC) 1153 “Process Chain for Manufacturing Hybrid High Performance Components by Tailored Forming”, Project number 252662854 (INF), funded by the German Research Foundation (DFG).","Ackoff R. L., From data to wisdom, J. Appl. Syst. Anal, 16, 1, pp. 3-9, (1989); Amorim R.C., Castro J.A., Rocha da Silva J., Et al., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univ Access Inf Soc, 16, (2017); Behrens B.-A., Bouguecha A., Frischkorn C., Huskic A., Stakhieva A., Duran D., Tailored Forming Technology for Three Dimensional Components: Approaches to Heating and Forming, Proceedings of the 5th Conference on Thermomechanical Processing, (2016); Behrens B.-A., Breidenstein B., Duran D., Herbst S., Lachmayer R., Lohnert S., Matthias T., Mozgova I., Nornberger F., Prasanthan V., Siqueira R., Toller F., Wriggers P., Simulation-Aided Process Chain Design for the Manufacturing of Hybrid Shafts, HTM Journal of Heat Treatment and Materials, 74, 2, pp. 115-135, (2019); Effertz E., The Funder's Perspective: Data Management in Coordinated Programmes of the German Research Foundation (DFG), Geographisches Institut der Universität zu Köln-Kölner Geographische Arbeiten, (2010); Feldhusen J., Gebhardt B., Product Lifecycle Management for practice, (2008); Feldhusen J., Grote K.-H., Pahl/Beitz Konstruktionslehre, (2013); Frischmuth P., Martin M., Tramp S., Riechert T., Auer S., Ontowiki-An authoring, publication and visualization interface for the data web, Semantic Web, 6, pp. 215-240, (2015); Halilaj L., Petersen N., Grangel-Gonzalez I., Lange C., Auer S., Coskun G., Lohmann S., VoCol: An Integrated Environment to Support Version-Controlled Vocabulary Development, Knowledge Engineering and Knowledge Management, (2016); Kapogiannis G., Sherratt F., Impact of integrated collaborative technologies to form a collaborative culture in construction projects, Built Environment Project and Asset Management, 8, 1, pp. 24-38, (2018); Krotzsch M., Vrandecic D., Semantic Wikipedia, Social Semantic Web, (2009); Mons B., Neylon C., Velterop J., Dumontier M., da Silva Santos L. O. B., Wilkinson M. D., Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud, Information Services & Use, 37, (2017); Redohl B., The DFG Perspective: Research Data Management with a Focus on Collaborative Research Centres (SFB), Geographisches Institut der Universität zu Köln-Kölner Geographische Arbeiten, (2016); Leistung aus Vielfalt. Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland, (2016); Sandfeld S., Dahmen T., Fischer F. O.R., Eberl C., Klein S., Selzer M., Nestler B., Moller J., Mocklich F., Engstler M., Diebels S., Tschuncky R., Prakash A., Steinberger D., Kobel C., Herrmann H.-G., Schubotz R., Strategiepapier Digitale Transformation in der Materialwissenschaft und Werkstofftechnik, (2018); Savolainen J., Saari A., Mannisto A., Kahkonen K., Indicators of collaborative design management in construction projects, Journal of Engineering, Design and Technology, 16, 4, pp. 674-691, (2018); Scheidel W., Mozgova I., Lachmayer R., Structuring Information in Technical Inheritance by PDM Systems, Proceedings of the 21st International Conference on Engineering Design (ICED17), 6, pp. 217-226, (2017); Schmitt R., Et al., Positionspapier zur geplanten Nationalen Forschungsdaten-infrastruktur (NFDI) för die Ingenieurwissenschaften, (2018); Siqueira R., Bibani M., Duran D., Mozgova I., Lachmayer R., Behrens B.-A., An Adapted Case-based Resoaning for Design and Manufacturing of Tailored Forming Multi-material Components, Int. J. Interact. Des. Manuf, 13, pp. 1175-1184, (2019); Ulrich T., Datamanagement for production companies, PLM-Jahrbuch 2016-Der Leitfaden för den PLM Markt, pp. 60-73, (2016); VDI-Richtlinie 2219 Informationsverarbeitung in der Produktentwicklung-Einföhrung und Betrieb von PDM-Systemen, (2016); VDI-Richtlinie 5610 ? Blatt 1: Wissensmanagement im Ingenieurwesen-Grundlagen, (2009); Entscheidungshilfe zur Einföhrung von PDM Systemen, (2005); Wang W.M., Gopfert T., Stark R., Data management in collaborative interdisciplinary research projects-conclusions from the digitalization of research in sustainable manufacturing, ISPRS International Journal of Geo-Information, 5, 4, (2016); Wilkinson M. D, Dumontier M., Aalbersberg I. J. J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, (2016)","","Mortensen N.H.; Hansen C.T.; Deininger M.","The Design Society","","13th Biennial NordDesign Conference, NordDesign 2020","11 August 2020 through 14 August 2020","Lyngby","163725","","978-191225408-8","","","English","Proc. NordDesign Conf., NordDesign","Conference paper","Final","","Scopus","2-s2.0-85094680714"
"Chiware E.R.T.","Chiware, Elisha R.T. (23491297400)","23491297400","Data librarianship in South African academic and research libraries: a survey","2020","Library Management","41","6-7","","401","416","15","9","10.1108/LM-03-2020-0045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085744319&doi=10.1108%2fLM-03-2020-0045&partnerID=40&md5=6e5330b64f7f07e4e39e83b6e8d23488","CPUT Libraries, Cape Peninsula University of Technology, Cape Town, South Africa","Chiware E.R.T., CPUT Libraries, Cape Peninsula University of Technology, Cape Town, South Africa","Purpose: The purpose of this study was to establish the current skills base of librarians working in research data management services in academic and research libraries in South Africa. The purpose was also to determine the relevance of courses and programmes that are currently being offered by library and information studies programmes in response to the needs of research data management services and make recommendations on curriculum improvement. Design/methodology/approach: About 13 institutions which were considered early adopters of research data management services were identified as participants in an online survey. In addition, a review of Web pages of existing library and information studies schools was carried to establish courses that would support research data management services. Data collected through the two approaches were analysed and presented quantitatively and qualitatively. Findings: The findings reveal an environment in a developmental stage, with limited skilled personnel to run research data management services. The findings also show an absence of specific data librarianship courses within existing library and information studies programmes and a very limited scope for the full range of data management courses within professional development programmes. Originality/value: The paper provides information on approaches to further develop existing curriculum and contribute to the data management needs and support governments, funders and publishers' requirements for the discoverability and re-use of research data across research domains. © 2020, Emerald Publishing Limited.","Data librarianship; Data literacy; Library and information studies; Research data management; South Africa","","","","","","","","Ball D., The emergence of open data, ELucidate, 15, 1-2, pp. 11-18, (2018); Bryant R., Lavoie B., Malpas C., A tour of the research data management (RDM) service space. The realities of research data management, Part 1. Dublin, OH: OCLC Research, (2017); Chant I., Academic: drexel rolls iSchools into new college, Library Journal, 138, 17, pp. 20-23, (2013); Chiware E.R., Becker D.A., Research data management services in Southern Africa: a readiness survey of academic and research libraries, African Journal of Library, Archives & Information Science, 28, 1, pp. 1-16, (2018); Corral S., Roles and responsibilities: libraries, librarians and data, Managing Research Data, pp. 105-134, (2012); Cox A.M., Academic librarianship as a data profession: the familiar and unfamiliar in the data role spectrum, ELucidate, 15, 1-2, pp. 7-10, (2018); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library and Information Science Research, 38, pp. 319-326, (2016); Davenport T.H., Patil D.J., Data scientist: the sexiest job of the 21st century, Harvard Business Review, 90, 10, pp. 70-76, (2012); DuBois J., Is there a data scientist shortage in 2019?, (2019); Federer L., Defining data librarianship: a survey of competencies, skills, and training, Journal of the Medical Library Association, 106, 3, pp. 294-303, (2018); Goben A., Sandusky R.J., Open data repositories, College and Research Libraries News, 81, 2, pp. 1-4, (2020); Heidorn P.B., The emerging role of libraries in data curation and e-Science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Karikari T.K., Quansah E., Mohamed W.M., Developing expertise in bioinformatics for biomedical research in Africa, Applied and Translational Genomics, 6, pp. 31-34, (2015); Khan H.R., Du Y., What is a data librarian? a content analysis of job advertisements for data librarians in the United States academic libraries, The IFLA World Library and Information Congress, (2018); Koltay T., Data literacy: in search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Accepted and emerging roles of academic libraries in supporting research 2.0, The Journal of Academic Librarianship, 45, pp. 75-80, (2019); Kumuthini J., Zass L., Panji S., Salifu S.P., Kayondo J.K., Nembaware V., Mbiyavanga M., Olabode M., Kishk A., Wells G., Mulder N.J., The H3ABioNet helpdesk: an online bioinformatics resource, enhancing Africa's capacity for genomics research, BMC Bioinformatics, 20, 741, pp. 1-7, (2019); Lyon L., Mattern E., Education for real-world data science roles (part 2): a transitional approach to curriculum development, The International Journal of Digital Curation, 11, 2, pp. 13-26, (2016); Maxwell D., Norton H., Wu J., The data science opportunity: crafting a holistic strategy, The Journal of Library Administration, 58, 2, pp. 111-127, (2017); Nrf, Statement on open access to research publications from the National Research Foundation (NRF)-Funded Research, (2015); Ohaji I.K., Chawner B., Yoong P., The role of data librarian in academic and research libraries, IR Information Research, 24, 4, pp. 1-14, (2019); Robinson L., Bawden D., The story of data: a socio-technical approach to education for the data librarian role in the city-lis library school at city, Library Management, 38, 6-7, pp. 312-322, (2017); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviours and attitudes at a teaching-cantered university, College and Research Libraries, 73, 4, pp. 349-365, (2012); Semeler A.R., Pinto A.L., Rozados H.B.F., Data science in data librarianship: core competencies of a data librarian, Journal of Librarianship and Information Science, 51, 3, pp. 771-780, (2017); Shaffer J.G., Mather F.J., Wele M., Li J., Tangara C.O., Kassogue Y., Srivastav S.K., Thiero O., Diakite M., Sangare M., Dabitao D., Toure M., Djimde A.A., Traore S., Diakite B., Coulibaly M.B., Liu Y., Lacey M., Lefante J.J., Koita O., Schieffelin J.S., Krogstad D.J., Doumbia S.O., Expanding research capacity in sub-saharan Africa through informatics, bioinformatics, and data science training programs in Mali, Frontiers in Genetics, 10, pp. 1-13, (2019); Snipes G., Everyone's a data librarian now, Journal of New Librarianship, 3, 1, pp. 28-31, (2018); Tang, Hu, Providing research data management (RDM) services in libraries: preparedness, roles, challenges, and training for RDM practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2012); Thomas C.V.L., Urban R.J., What do data librarians think of the MLIS? Professionals' perceptions of knowledge transfer, trends, and challenges, College and Research Libraries, 79, 3, pp. 401-423, (2018); Thompson K., Editorial: introducing this special issue on data librarianship, International Journal of Librarianship, 2, 1, pp. 1-2, (2017); Matlatse R.L., An evaluation of a structured training event aimed at enhancing the Research Data Management (RDM) knowledge and skills of library and information science (lis) professionals in South African Higher Education Institutions (HEIs), (2016); Raju J., Knowledge and skills for the digital era academic library, Journal of Academic Librarianship, 40, 3, pp. 163-170, (2014)","E.R.T. Chiware; CPUT Libraries, Cape Peninsula University of Technology, Cape Town, South Africa; email: chiwaree@cput.ac.za","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","","Scopus","2-s2.0-85085744319"
"Spreckelsen F.; Rüchardt B.; Lebert J.; Luther S.; Parlitz U.; Schlemmer A.","Spreckelsen, Florian (56951343100); Rüchardt, Baltasar (57216924047); Lebert, Jan (57191838347); Luther, Stefan (7005503111); Parlitz, Ulrich (56211906500); Schlemmer, Alexander (47861369300)","56951343100; 57216924047; 57191838347; 7005503111; 56211906500; 47861369300","Guidelines for a standardized filesystem layout for scientific data","2020","Data","5","2","43","","","","1","10.3390/data5020043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085334374&doi=10.3390%2fdata5020043&partnerID=40&md5=83855b475f55fd415cff43af011ecda8","Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany; Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany; German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany; Department of Cardiology and Pneumology, University Medical Center Göttingen, Göttingen, 37075, Germany; Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, 37075, Germany","Spreckelsen F., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany; Rüchardt B., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany; Lebert J., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany, Department of Cardiology and Pneumology, University Medical Center Göttingen, Göttingen, 37075, Germany; Luther S., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany, Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, 37075, Germany; Parlitz U., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany; Schlemmer A., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, 37075, Germany","Storing scientific data on the filesystem in a meaningful and transparent way is no trivial task. In particular, when the data have to be accessed after their originator has left the lab, the importance of a standardized filesystem layout cannot be underestimated. It is desirable to have a structure that allows for the unique categorization of all kinds of data from experimental results to publications. They have to be accessible to a broad variety of workflows, e.g., via graphical user interface as well as via command line, in order to find widespread acceptance. Furthermore, the inclusion of already existing data has to be as simple as possible. We propose a three-level layout to organize and store scientific data that incorporates the full chain of scientific data management from data acquisition to analysis to publications. Metadata are saved in a standardized way and connect original data to analyses and publications as well as to their originators. A simple software tool to check a file structure for compliance with the proposed structure is presented. © 2020 by the authors.","FAIR; File structure; Filesystem layout; Research data management","Compliance control; Data acquisition; File organization; Information management; Command line; FAIR; File structure; Filesystem; Filesystem layout; Research data managements; Scientific data; Simple++; Three-level; Work-flows; Graphical user interfaces","","","","","Deutsches Zentrum für Herz-Kreislaufforschung, DZHK; Deutsche Forschungsgemeinschaft, DFG, (SFB 1002); Bundesministerium für Bildung und Forschung, BMBF, (FKZ 031A147)","Funding: We acknowledge support from the German Federal Ministry of Education and Research (BMBF) (project FKZ 031A147, GO-Bio), the German Research Foundation (DFG) (Collaborative Research Centers SFB 1002 Project C03) and the German Center for Cardiovascular Research (DZHK e.V.).","Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Flandin G., Ghosh S.S., Glatard T., Halchenko Y.O., Et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci. Data, 3, pp. 1-9, (2016); Sansone S.A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hofmann O., Fang H., Neumann S., Tong W., Amaral-Zettler L., Et al., Toward interoperable bioscience data, Nat. Genet, 44, (2012); Ma X., Fox P., Tilmes C., Jacobs K., Waple A., Capturing provenance of global change information, Nat. Clima. Chang., 4, pp. 409-413, (2014); Diepenbroek M., Grobe H., Reinke M., Schindler U., Schlitzer R., Sieger R., Wefer G., PANGAEA—An information system for environmental sciences, Comput. Geosci., 28, pp. 1201-1210, (2002); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Dominici M., An overview of Pandoc, Tugboat, 35, pp. 44-50, (2014); Ben-Kiki O., Evans C., Dot Net I., ) Version, 2, (2009); Fitschen T., Schlemmer A., Hornung D., Tom Worden H., Parlitz U., Luther S., CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows, Data, 4, (2019)","A. Schlemmer; Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany; email: alexander.schlemmer@ds.mpg.de","","MDPI","","","","","","23065729","","","","English","Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85085334374"
"Muñoz A.; Harris F.C.; Dascalu S.","Muñoz, Andrew (57213819426); Harris, Frederick C. (57213805178); Dascalu, Sergiu (6602297823)","57213819426; 57213805178; 6602297823","NRDC data visualization web suite: Tool for data visualization, comparison, and prediction analysis","2020","EPiC Series in Computing","69","","","32","39","7","0","10.29007/rkqh","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090838783&doi=10.29007%2frkqh&partnerID=40&md5=fb6ab63d33948ab7955b99692f5d7d2b","University of Nevada, Reno, United States","Muñoz A., University of Nevada, Reno, United States; Harris F.C., University of Nevada, Reno, United States; Dascalu S., University of Nevada, Reno, United States","The Nevada Research Data Center (NRDC) is a research data management center that collects sensor-based data from various locations throughout the state of Nevada. The measurements collected are specifically environmental data, which are used in cross-disciplinary research across different facilities. Since data is being collected at a high rate, it is necessary to be able to visualize the data quickly and efficiently. This paper discusses in detail a web application that can be used by researchers to make visualizations that can help in data comparisons. While there exist other web applications that allows researchers to visualize the data, this project expands on that idea by allowing researchers the ability to not only visualize the data but also make comparisons and predictions. © 2020, EasyChair. All rights reserved.","","Information management; Visualization; Cross-disciplinary research; Data comparisons; Environmental data; High rate; Research data; Research data managements; Sensor based data; WEB application; Data visualization","","","","","","","What it is and Why it Matters?, (2019); Data Visualization Beginner's Guide: A Definition, Examples, and Learning Resources, (2019); Jirasessakul P., Waller Z., Marquis P., Le V., Scully-Allison C., Strachan S., Harris F. C., Dascalu S.M., Generalized Software Interface for CHORDS, ISCA 27th International Conference on Software Engineering and Data Engineering (SEDE 2018), (2018); Nevada Research Data Center: Streaming Data Management for Sensor Networks, (2019); Scully-Allison C., Munoz H., Le V., Strachan S., Fritzinger E., Harris F. C., Dascalu S.M., Advancing Quality Assurance Through Metadata Management: Design and Development of a Mobile Application for the NRDC, IJCA International Journal of Computers and Their Applications, 25, 1, pp. 20-29, (2018); Martinez-Fernandez S., Vollmer A. M., Jedlitschka A., Franch X., Lopez L., Ram P., Rodriguez P., Aarama S., Bagnato S., Choras M., Partanen J., Continuously Assessing and Improving Software Quality With Software Analytics Tools: A Case Study, IEEE Access, 7, (2019); Hummel O., Eichelberger H., Giloj A., Werle D., Schmid K., A Collection of Software Engineering Challenges for Big Data System Development, 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 362-369, (2018); Lin Y., Huang S., The Design of a Software Engineering Lifecycle Process for Big Data Projects, IT Professional, 20, 1, pp. 45-52, (2018); Visual Studio 2019, (2019); What is NET Framework, (2019); A Tour of the C# Language, (2019); Introduction to HTML, (2019)","","Lee G.; Jin Y.","EasyChair","","35th International Conference on Computers and Their Applications, CATA 2020","23 March 2020 through 25 March 2020","San Francisco","159890","23987340","","","","English","EPIC Sre. Comp.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85090838783"
"M'kulama A.C.; Akakandelwa A.","M'kulama, Abel Christopher (57878819300); Akakandelwa, Akakandelwa (36650436100)","57878819300; 36650436100","Research data sharing and reuse through open data: Assessing researcher awareness and perceptions at the Zambia Agricultural Research Institute (ZARI)","2020","Open Access Implications for Sustainable Social, Political, and Economic Development","","","","264","306","42","0","10.4018/978-1-7998-5018-2.ch015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137428404&doi=10.4018%2f978-1-7998-5018-2.ch015&partnerID=40&md5=0ca9d3dfa52adc31f3938bc4470f5408","The University of Zambia, Zambia","M'kulama A.C., The University of Zambia, Zambia; Akakandelwa A., The University of Zambia, Zambia","Research data management is considered a critical step in the research process among researchers. Researchers are required to submit RDM plans with details about data storage, data sharing, and reuse procedures when submitting research proposals for grants. This chapter presents findings of an investigation into the perceptions and practices of ZARI researchers towards research data management. Mixed methods research using a self-administered questionnaire was adopted for data collection. Fifty-one researchers were sampled and recruited for participation into the study. The study established that the majority of the researchers were not depositing their research data in central repositories; data was kept on individual's devices and was therefore not readily available for sharing. The major challenges being faced by researchers included lack of a policy, lack of a repository, and inadequate knowledge in RDM. The study concludes that research data at ZARI was not being professionally managed. The study recommends for formulation of policies, establishment of repository and staff training. © 2021, IGI Global.","","","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Aydinoglu A., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Bangani S., Moyo M., Data Sharing Practices among Researchers at South African Universities, Data Science Journal, 18, 28, pp. 1-14, (2019); Borgman C.L., Big data, little data, no data: Scholarship in the networked world, (2015); Chigwada J., Chiparausha B., Kasiroori J., Research Data Management in Research Institutions in Zimbabwe, Data Science Journal, 16, 31, pp. 1-9, (2017); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2014); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with RDM as a ""wicked"" problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2014); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences, 368, 1926, pp. 4023-4038, (2010); Curty R.G., Crowston K., Specht A., Grant B.W., Dalton E.D., Attitudes and norms affecting scientists' data reuse, PLoS ONE, 12, 12, (2017); Doty J., Survey of faculty practices and perspectives on research data management, (2012); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS One, 10, 2, (2015); Federer L.M., Lu Y., Jourbert D.J., Welsh J., Brandys B., Biomedical data sharing and reuse: Attitudes and practices of clinical and scientific research staff, PLoS One, 10, 6, (2015); Fry J., Lockyer S., Oppenheim C., Identifying Benefits Arising from the Curation and Open Sharing of Research Data Produced by UK Higher Education and Research Institutes, (2009); Gurin J., Manley L., Ariss A., Sustainable Development Goals and Open Data, (2015); Johnston L., Jeffryes J., Civil Engineering/ Graduate Students/Johnston & Jeffryes/University of Minnesota/ 2012, Data Information Literacy Case Study Directory, 3, 1, (2015); Jones S., Research Data Policies: Principles, Requirements and Trends, Managing Research Data, pp. 47-66, (2012); Kahn M., Higgs R., Davidson J., Jones S., Research Data Management in South Africa: How We Shape Up, Australian Academic and Research Libraries, 45, 4, pp. 296-308, (2014); Kim Y., Adler M., Social scientists' data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories, International Journal of Information Management, 35, 4, pp. 408-418, (2015); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists' data-sharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2016); Linde P., Norling E., Pettersson A., Petersson L., Pettersson K., Stockmann A., Swartz S., Researchers and Open Data - Attitudes and Culture at Blekinge Institute of Technology, (2015); Parham S.W., Bodnar J., Fuchs S., Supporting tomorrow's research: Assessing faculty data curation needs at Georgia Tech, College & Research Libraries News, 73, 1, pp. 10-13, (2012); Patel D., Research data management: a conceptual framework, (2016); Patterton L.H., Research data management practices of emerging researchers at a South African research council, (2016); Pham-Kanter G., Zinner D.E., Campbell E.G., Codifying Collegiality: Recent Developments in Data Sharing Policy in the Life Sciences, PLoS One, 9, 9, (2014); Punch K., Introduction to social research: Quantitative and Qualitative Approaches, (2005); Ray M.J., Research data Management: Practical Strategies for Informational Professionals, (2014); Sharma S., Quin J., Data management: Graduate student's Awareness of practices and Policy, (2015); Shen Y., Research data sharing and reuse practices of academic faculty researchers: A study of the Virginia Tech data landscape, International Journal of Digital Curation, 10, 2, pp. 157-175, (2015); Suber P., Open Access, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data Sharing by Scientists: Practices and Perceptions, PLoS One, 6, 6, (2011); Towards Open Research: practices, experiences, barriers and opportunities, (2016); Van Tuyl S., Michalek G., Assessing Research Data Management Practices of Faculty at Carnegie Mellon University, Journal of Librarianship and Scholarly Communication, 3, 3, (2015); Whitmire A., Boock M., Sutton S., Variability in academic research data management practices: Implications for data services development from a faculty survey, Electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009)","","","IGI Global","","","","","","","978-179985019-9; 978-179985018-2","","","English","Open Access Implic. for Sustain. Soc., Polit., and Econ. Dev.","Book chapter","Final","","Scopus","2-s2.0-85137428404"
"Sales L.; Henning P.; Veiga V.; Costa M.M.; Sayão L.F.; Santos L.O.B.D.S.; Pires L.F.","Sales, Luana (25646168600); Henning, Patrícia (57310688100); Veiga, Viviane (57216042998); Costa, Maira Murrieta (55196004000); Sayão, Luís Fernando (7801523487); Santos, Luiz Olavo Bonino da Silva (57310671600); Pires, Luís Ferreira (7006556572)","25646168600; 57310688100; 57216042998; 55196004000; 7801523487; 57310671600; 7006556572","Go fair brazil: A challenge for brazilian data science","2020","Data Intelligence","2","1-2","","238","245","7","6","10.1162/dint_a_00046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098657854&doi=10.1162%2fdint_a_00046&partnerID=40&md5=febb4e224e75cd9d3426994ad1c70574","Instituto Brasileiro em Informação em Ciência e Tecnologia, Rio de Janeiro-RJ, 22290-160, Brazil; Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro-RJ, 22290-240, Brazil; Fundação Oswaldo Cruz, Rio de Janeiro-RJ, 21040-900, Brazil; Ministério da Ciência, Tecnologia, Inovações e Comunicações, Esplanada dos Ministérios, Bloco R-Brasília, DF, CEP 70067-900, Brazil; Comissão Nacional de Energia Nuclear, Rua Gal. Severiano, nº 90; Bairro: Botafogo, Rio de Janeiro, CEP 22290-901, Brazil; GO FAIR International Support & Coordination Office (GFISCO), Leiden, 2333 AA, Netherlands; University of Twente, Enschede, 7522 NH, Netherlands","Sales L., Instituto Brasileiro em Informação em Ciência e Tecnologia, Rio de Janeiro-RJ, 22290-160, Brazil; Henning P., Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro-RJ, 22290-240, Brazil; Veiga V., Fundação Oswaldo Cruz, Rio de Janeiro-RJ, 21040-900, Brazil; Costa M.M., Ministério da Ciência, Tecnologia, Inovações e Comunicações, Esplanada dos Ministérios, Bloco R-Brasília, DF, CEP 70067-900, Brazil; Sayão L.F., Comissão Nacional de Energia Nuclear, Rua Gal. Severiano, nº 90; Bairro: Botafogo, Rio de Janeiro, CEP 22290-901, Brazil; Santos L.O.B.D.S., GO FAIR International Support & Coordination Office (GFISCO), Leiden, 2333 AA, Netherlands; Pires L.F., University of Twente, Enschede, 7522 NH, Netherlands","The FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, are recognised worldwide as key elements for good practice in all data management processes. To understand how the Brazilian scientific community is adhering to these principles, this article reports Brazilian adherence to the GO FAIR initiative through the creation of the GO FAIR Brazil Office and the manner in which they create their implementation networks. To contextualise this understanding, we provide a brief presentation of open data policies in Brazilian research and government, and finally, we describe a model that has been adopted for the GO FAIR Brazil implementation networks. The Brazilian Institute of Information in Science and Technology is responsible for the GO FAIR Brazil Office, which operates in all fields of knowledge and supports thematic implementation networks. Today, GO FAIR Brazil-Health is the first active implementation network in operation, which works in all health domains, serving as a model for other fields like agriculture, nuclear energy, and digital humanities, which are in the process of adherence negotiation. This report demonstrates the strong interest and effort from the Brazilian scientific communities in implementing the FAIR principles in their research data management practices. © 2019 Chinese Academy of Sciences Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.","FAIR principles; GO FAIR; GO FAIR Brazil; Open Science; Research data","Information management; Community IS; FAIR principle; GO FAIR; GO FAIR brazil; Good practices; Key elements; Management process; Open science; Research data; Scientific community; Open Data","","","","","","","Schultes E., Strawn G., Mons B., Ready, set, GO FAIR: Accelerating convergence to an Internet of FAIR Data and Services, Proceedings of the XX International Conference “Data Analytics and Management in Data Intensive Domains” (DAMDID/ RCDL’2018), (2018); SayAo L.F., O papel dos repositórios digitais na curadoria de dados, Repositórios digitais: teoria e prática, 1, pp. 143-166, (2017); Wilkinson M.D., Dumontier M., Jan Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Mons B., The FAIR guiding principles for scientific data management and stewardship, Nature, 3, (2016); Hening P., Ribeuro C.J.S., Sales L., Moreira J., Bonino da Silva Santos L.O., Desmistificando os Princípios FAIR: Conceitos, métricas, tecnologias e aplicações inseridas no ecossistema dos dados FAIR, XIX Eencontro Nacional De Pesquisa Em CiÊncia Da InformacÃo – Enancib Nancib, (2018); Internet of FAIR Data & Services (IFDS); Brazilian Law on Access to Information (LAI); Open Government Partnership (OGP); National Open Data Infrastructure (INDA); Brazilian National Action Plan 2018-2020; The Open Data Policy of the Federal Executive Branch; Ministry of Science, Technology, Innovation and Communications (MSTIC)-Working Group; GO FAIR Brazil Declaration; 20 Years of SCIELO; Dunning A., Smaele M., BOhmer J., Are the FAIR data principles fair?,  International Digital Curation Conference, (2017); FAIR data advanced use cases: From principles to practice in the Netherlands, (2018); A pilot programme to make it easy for funders to require and for grantees to produce FAIR Data","P. Henning; Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro-RJ, 22290-240, Brazil; email: henningpatricia@gmail.com","","MIT Press Journals","","","","","","20967004","","","","English","Data. Intell.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85098657854"
"Chawinga W.D.; Zinn S.","Chawinga, Winner Dominic (57191247774); Zinn, Sandy (56289796700)","57191247774; 56289796700","Research data management at a public university in Malawi: the role of “three hands”","2020","Library Management","41","6-7","","467","485","18","7","10.1108/LM-03-2020-0042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085056827&doi=10.1108%2fLM-03-2020-0042&partnerID=40&md5=004f76f184b42092cf9ebd39a0c3e001","Department of Information Sciences, Mzuzu University, Mzuzu, Malawi; Department of Library and Information Science, University of the Western Cape, Cape Town, South Africa","Chawinga W.D., Department of Information Sciences, Mzuzu University, Mzuzu, Malawi; Zinn S., Department of Library and Information Science, University of the Western Cape, Cape Town, South Africa","Purpose: Considering that research data is increasingly hailed as an important raw material for current and future science discoveries, many research stakeholders have joined forces to create mechanisms for preserving it. However, regardless of generating rich research data, Africa lags behind in research data management thereby potentially losing most of this valuable data. Therefore, this study was undertaken to investigate the research data management practices at a Malawian public university with the aim to recommend appropriate data management strategies. Design/methodology/approach: The study is inspired by the pragmatic school of thought thereby adopting quantitative and qualitative research approaches. Quantitative data was collected using a questionnaire from 150 researchers and 25 librarians while qualitative data was collected by conducting an interview with the Director of Research. Findings: Researchers are actively involved in research activities thereby generating large quantities of research data. Although researchers are willing to share their data, only a handful follow through. Data preservation is poor because the university uses high risk data storage facilities, namely personal computers, flash disks, emails and external hard drives. Researchers and librarians lacked core research data-management competencies because of the lack of formal and information training opportunities. Challenges that frustrate research data-management efforts are many but the key ones include absence of research data management policies, lack of incentives, lack of skills and unavailability of data infrastructure. Research limitations/implications: The study's findings are based on one out of four public universities in the country; hence, the findings may not adequately address the status of research data management practices in the other universities. Practical implications: Considering that the university under study and its counterparts in Malawi and Africa in general operate somewhat in a similar economic and technological environment, these findings could be used as a reference point for other universities intending to introduce research data management initiatives. Originality/value: With seemingly limited studies about research data management in Africa and particularly in Malawi, the study sets the tone for research data management debates and initiatives in the country and other African countries. © 2020, Emerald Publishing Limited.","Librarians; Malawi; Research data; Research data management; Researchers; University","","","","","","","","Alvaro E., Brooks H., Ham M., Poegel S., Rosencrans S., E-science librarianship: field undefined, Issues in Science and Technology Librarianship, 66, (2011); Anane-Sarpong E., Wangmo T., Ward C.L., Sankoh O., Tanner M., Elger B.S., You cannot collect data using your own resources and put it on open access: perspectives from Africa about public health data-sharing, Developing World Bioethics, 18, 4, pp. 394-405, (2017); Bishoff C., Johnston L., Approaches to data sharing: an analysis of NSF Data Management plans from a large research university, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Bond-Lamberty B., Data sharing and scientific impact in eddy covariance research, Journal of Geophysical Research: Biogeosciences, 123, 4, pp. 1440-1443, (2018); Borgman C.L., The conundrum of sharing research data, Journal of the Association for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brambilla P., Digital Curation in the Italian Context: New Roles and Professions for Digital Librarians, (2015); Brown S., Bruce R., Kernohan D., Directions for research data management in UK universities, (2015); Cantwel P.J., Census, Encyclopedia of Survey Research Methods, pp. 90-93, (2008); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: a study of students and research faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Chawinga W.D., Zinn S., Global perspective of research data sharing: a systematic literature review, Library and Information Science Research, 41, 2, pp. 109-122, (2019); Chawinga W.D., Zozie P.A., Information needs and barriers to information sources by open and distance learners: a case of Mzuzu University, Malawi, South African Journal of Information Management, 18, 1, (2016); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chipeta G.T., Chawinga W.D., Chaura M.G., Dube G.A., Malemia L., Information-seeking behaviour of first-year undergraduate students at Mzuzu University, Malawi, Mousaion: South African Journal of Information Studies, 36, 1, pp. 2-18, (2019); Chiware E., Mathe Z., Academic libraries' role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2016); Corrall S., Roles and responsibilities: libraries, librarians and data, Managing Research Data, pp. 105-133, (2012); Costello M.J., Motivating online Publication of data, BioScience, 59, pp. 418-427, (2009); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2016); Davenport T.H., Patil D.J., Data scientist, Harvard Business Review, 90, 5, pp. 70-76, (2012); Denny S.G., Silaigwana B., Wassenaar D., Bull S., Parker M., Developing ethical practices for public health research data sharing in South Africa: the views and experiences from a diverse sample of research stakeholders, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 290-301, (2015); Doorn P., Dillo I., Van Horik R., Lies, damned lies and research data: can data sharing prevent data fraud?, International Journal of Digital Curation, 8, pp. 229-243, (2013); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: an exploratory study, IFLA Journal, pp. 1-9, (2018); Enke N., Thessen A., Bach K., Bendix J., Seeger B., Gemeinholzer B., The user's view on biodiversity data sharing - investigating facts of acceptance and requirements to realize a sustainable use of research data, Ecological Informatics, 11, pp. 25-33, (2012); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PloS One, 10, 2, (2015); Fry J., Lockers S., Oppenheim C., Houghton J., Rasmussen B., Identifying Benefits Arising from the Curation and Open Sharing of Research Data Produced by UK Higher Education and Research Institutes, (2008); Gabridge T., The last mile: liaison roles in curating science and engineering research data, Research Library Issues, 265, pp. 15-21, (2009); Heidorn P.B., The emerging role of libraries in data curation and E-science, Journal of Library Administration, 51, pp. 662-672, (2011); Hey T., Hey J., E-Science and its implications for the library community, Library Hi Tech, 21, 4, pp. 515-528, (2006); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 31, pp. 134-140, (2008); Higgins S., Digital curation: the emergence of a new discipline, The International Journal of Digital Curation, 2, 6, pp. 78-88, (2011); Housewright R., Schonfeld R.C., Wulfson K., Ithaka S+R | JISC | RLUK UK Survey of Academics 2012, (2013); Houtkoop B.L., Chambers C., Macleod M., Bishop D.V., Nichols T.E., Wagenmakers E.J., Data sharing in psychology: a survey on barriers and preconditions, Advances in Methods and Practices in Psychological Science, 1, 1, pp. 70-85, (2018); Huang X., Hawkins B.A., Lei F., Miller G.L., Favret C., Zhang R., Qiao G., Willing or unwilling to share primary biodiversity data: results and implications of an international survey, Conservation Letters, 5, pp. 399-406, (2012); Israel G.D., Determining Sample Size, (2013); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: how we shape up, Australian Academic and Research Libraries, 45, 4, pp. 296-308, (2014); Kaye J., Terry S.F., Juengst E., Coy S., Harris J.R., Chalmers D., Bezuidenhout L., Including all voices in international data-sharing governance, Human Genomics, 12, 1, (2018); Kim J., Warga E., Moen W., Competencies required for digital curation: an analysis of job advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Koopman M.M., Data Archiving, Management Initiatives and Expertise in the Biological Sciences Department, (2015); Lewis M., Libraries and the management of research data, Envisioning Future Academic Library Services Initiatives, Ideas and Challenges, pp. 145-168, (2010); Lyon L., The inf ormatics transform: Re-engineering libraries of the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Lyon L., Ball A., Duke M., Day M., Community capability model framework, (2011); Matlatse R.L., An Evaluation of a Structured Training Event Aimed at Enhancing the Research Data Management (RDM) Knowledge and Skills of Library and Information Science (LIS) Professionals in South African Higher Education Institutions (HEIs) Dissertation, (2016); Monastersky R., Publishing frontiers: the library reboot, Nature, 495, 7442, pp. 430-432, (2013); Newton M.P., Miller C.C., Bracke M.S., Librarian roles in institutional repository data set collecting: outcomes of a research library task force, Collection Management, 36, 1, pp. 53-67, (2011); Ng'eno E.J., Research Data Management in Kenya's Agricultural Research Institutes Dissertation, (2018); Peng C., Song X., Jiang H., Zhu Q., Chen H., Chen J.M., Gong P., Jie C., Xiang W., Yu G., Zhou X., Towards a paradigm for open and free sharing of scientific data on global change science in China, Ecosystem Health and Sustainability, 2, 5, (2016); Pisani E., AbouZahr C., Sharing health data: good intentions are not enough, Bulletin of the World Health Organization, 88, 6, pp. 462-466, (2010); Pitt M.A., Tang Y., What should be the data sharing policy of cognitive science?, Topics in Cognitive Science, 5, 1, pp. 214-221, (2013); Ray J., The rise of digital curation and cyberinfrastucture, Library Hi Tech, 30, 4, pp. 604-622, (2012); Schmidt B., Gemeinholzer B., Treloar A., Open data in global environmental research: the Belmont Forum's open data survey, PloS One, 11, 1, (2016); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French national research center (CNRS), Data Technologies and Applications, 52, 2, pp. 248-265, (2018); Schumacher J., VandeCreek D., Intellectual capital at risk: data management practices and data loss by faculty members at five American universities, International Journal of Digital Curation, 10, 2, pp. 96-109, (2015); Scott M., Research Data Management, (2014); Shakeri S., Data Curation Perspectives and Practices of Researchers at Kent State University's Liquid Crystal Institute: A Case Study, (2013); Soehner C., Steeves C., Ward J., E-science and Data Support Services: A Study of ARL Member Institutions, (2010); Swan A., Brown S., The skills, role and career structure of data scientists and curators: an assessment of current practice and future needs, (2008); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: practices and perceptions, PloS One, 6, 6, (2011); van Horn J.D., Gazzaniga M.S., Why share data? Lessons learned from the fMRIDC, NeuroImage, 82, pp. 677-682, (2013); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them?” Data sharing and reuse in the long tail of science and technology, PloS One, 8, 7, (2013); Walters T., Skinner K., New Roles for New Times: Digital Curation for Preservation, (2011); Watson M., When will ‘open science’become simply ‘science’?, Genome biology, 16, 1, (2015); Whitlock M.C., Data archiving in ecology and evolution: best practices, Trends in Ecology and Evolution, 26, 2, pp. 61-65, (2011); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Wiley C., Data sharing and engineering faculty: an analysis of selected publications, Science and Technology Libraries, 37, 4, pp. 409-419, (2018); Woolfrey L., Archiving Social Survey Data in Africa: An Overview of African Microdata Curation and the Role of Survey Data Archives in Data Management in Africa, (2009); Yoon A., Data Reuse and Users' Trust Judgments: Toward Trusted Data Curation, (2015); Yoon A., Schultz T., Research data management services in academic libraries in the US: a Content analysis of libraries' websites, College and Research Libraries, 78, 7, pp. 920-933, (2017); Zvyagintseva L., Articulating a Vision for Community-Engaged Data Curation in the Digital Humanities, (2015)","W.D. Chawinga; Department of Information Sciences, Mzuzu University, Mzuzu, Malawi; email: winnchawinga@gmail.com","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","","Scopus","2-s2.0-85085056827"
"Van Wyk J.; Bothma T.; Holmner M.","Van Wyk, Johann (57225420199); Bothma, Theo (14618870600); Holmner, Marlene (38361271800)","57225420199; 14618870600; 38361271800","A conceptual virtual research environment model for the management of research data, a South African perspective","2020","Library Management","41","6-7","","417","446","29","0","10.1108/LM-02-2020-0037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087562548&doi=10.1108%2fLM-02-2020-0037&partnerID=40&md5=cfa0358cc99806279f7cb701643e25bc","Department of Information Science, University of Pretoria, Pretoria, South Africa","Van Wyk J., Department of Information Science, University of Pretoria, Pretoria, South Africa; Bothma T., Department of Information Science, University of Pretoria, Pretoria, South Africa; Holmner M., Department of Information Science, University of Pretoria, Pretoria, South Africa","Purpose: The purpose of this article is to give an overview of the development of a Virtual Research Environment (VRE) conceptual model for the management of research data at a South African university. Design/methodology/approach: The research design of this article consists of empirical and non-empirical research. The non-empirical part consists of a critical literature review to synthesise the strengths, weaknesses (limitations) and omissions of identified VRE models as found in literature to develop a conceptual VRE model. As part of the critical literature review concepts were clarified and possible applications of VREs in research lifecycles and research data lifecycles were explored. The empirical part focused on the practical application of this model. This part of the article follows an interpretivist paradigm, and a qualitative research approach, using case studies as inquiry method. Case studies with a positivist perspective were selected through purposive sampling, and inferences were drawn from the sample to design and test a conceptual VRE model, and to investigate the management of research data through a VRE. Investigation was done through a process of participatory action research (PAR) and included semi-structured interviews and participant observation data collection techniques. Evaluation of findings was done through formative and summative evaluation. Findings: The article presents a VRE conceptual model, with identified generic component layers and components that could potentially be applied and used in different research settings/disciplines. The article also reveals the role that VREs play in the successful management of research data throughout the research lifecycle. Guidelines for setting up a conceptual VRE model are offered. Practical implications: This article assisted in clarifying and validating the various components of a conceptual VRE model that could be used in different research settings and disciplines for research data management. Originality/value: This article confirms/validates generic layers and components that would be needed in a VRE by synthesising these in a conceptual model in the context of a research lifecycle and presents guidelines for setting up a conceptual VRE model. © 2020, Emerald Publishing Limited.","Components; Conceptual model; Research data management; South Africa; Virtual research environments; VRE","","","","","","National Research Foundation, NRF","The authors would like to thank the National Research Foundation for partial funding that led to the completion of this study.","Alber A., Nabrzyski J., Wright T., The HUBzero Platform: Extensions and Impressions, (2011); Allan R., Virtual Research Environments: From Portals to Science Gateways, (2009); Allan R.J., Crouchley R., Ingram C., JISC information environment portal activity: e-research, portals and digital repositories workshop: CSI Consultancy technical report, Ariadne, 30, Issue 49, (2006); Almaturi A.F., Gardner G.E., McCarthy A., Practical guidance for the use of pattern-matching technique in case-study research: a case presentation, Nursing and Health Sciences, 16, 2, pp. 239-244, (2014); 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Candela L., Castelli D., Pagano P., On-demand Virtual Research Environments and the changing roles of librarians, 27, 2, pp. 239-251, (2009); Candela L., Castelli D., Pagano P., Virtual Research Environments: an overview and research agenda, Data Science Journal, 12, pp. GRDI75-GRDI81; Carusi A., Reimer T., Virtual Research Environment Collaborative Landscape Study: A JISC Funded Project, (2010); Chambers S., Supporting teaching and research in an online environment: developing the University of London Library model, LIBER Quarterly, 12, 4, pp. 381-392, (2002); Creswell J.W., Qualitative Enquiry and Research Design: Choosing Among Five Approaches, (2007); DCC Curation Lifecycle Model, Digital Curation Centre, (2018); De Roure D., Goble C., Aleksejevs S., Bechhofer S., Bhagat J., Cruikshank D., Michaelides D., Newman D., The myExperiment Open Repository for Scientific Workflows, (2009); Di Muro D., Saunders E., Virtual research environments: what do libraries have to do with it?, Paper Presented at ALIA Web2.0: Beyond the Hype Symposium, (2008); Dunn S., Dealing with the complexity deluge: VREs in the arts and humanities, Library Hi Tech, 27, 2, pp. 205-216, (2009); Edirisingha P., Writing a critical literature review, Research Paradigms and Approaches, (2016); (2010); Fernihough S., E-research: An Implementation Framework for South African Organisations, (2011); Filetti M., Gnauck A., A concept of a Virtual Research Environment for long-term ecological projects with free and open source software, Environmental Software Systems: Frameworks of eEnvironment: Proceedings of the 9th IFIP WG 5.11 International Symposium, 359, pp. 235-244, (2011); Fraser M., Virtual Research Environments: Overview and Activity, (2005); Hey T., Hey J., E-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Jesson J., Lacey F., How to do (or not to do) a critical literature review, Pharmacy Education, 6, 2, pp. 139-148, (2006); JISC Virtual Research Environments Programme: Phase 2 Roadmap, (2006); Virtual research environments programme, Joint Information Systems Committee (JISC), (2012); Karlsen J.I., Action research as method: reflections from a program for developing methods and competence, Participatory Action Research, Sage Focus Edition, 123, pp. 143-158, (1991); Kashyap V., What are APIs, and how are open APIs changing the Internet?, MakeUseOf, (2010); Keraminiyage K., Amaratunga D., Haigh R., Achieving success in collaborative research: the role of Virtual Research Environments, ITcon, 14, Special Issue, pp. 59-69, (2009); Keraminiyage K., Amaratunga D., Haigh R., A human-computer interaction principles-based framework to assess the user perception of web based virtual research environments, International Journal of Strategic Property Management, 13, pp. 129-142, (2009); Kirkham R., Pybus J., Bowman A.K., Crowther C., Fraser M., Jirotka M., Building a Virtual Research Environment for the Humanities: Report of the User Requirements Survey, (2006); Klein H.K., Myers, A set of principles for conducting and evaluating interpretive field studies in information systems, MIS Quarterly, 23, 1, pp. 67-94, (1999); Klyne G., Sakai VRE Demonstrator project user requirements, OSS Watch Wiki, (2006); Kumar S., Interoperability Protocols and Standards in LIS, (2009); Leonardo C., Castelli D., Pagano P., On-demand virtual research environments and the changing roles of librarians, 27, 2, pp. 239-225, (2009); Maastricht University Library, Virtual Research Environment – VRE: Research Support, (2018); MacDonald C., Understanding Participatory Action Research: a qualitative research methodology option, Canadian Journal of Action Research, 13, 2, pp. 34-50, (2012); Martinez-Uribe L., MacDonald S., User engagement in research data citation, Research and Advanced Technology for Digital Libraries: 13th European Conference, ECDL 2009, Corfu, Greece, 27 September-2 October 2009: Proceedings, pp. 309-314, (2009); Masson A., VRE library services: learning from supporting VLE users, Library Hi Tech, 27, 2, pp. 217-227, (2009); McLennan M., Kennell R., HUBzero: a platform for dissemination and collaboration in computational science and engineering, Computing in Science and Engineering, 12, 2, pp. 48-53, (2010); Merriam S.B., Qualitative Research: A Guide to Design and Implementation, (2009); Myhill M., Shoebridge M., Snook L., Virtual research environments: a Web 2.0 cookbook?, Library Hi Tech, 27, 2, pp. 228-238, (2009); Scientific Workflow, (2013); Ever-est, (2018); National Policy on Standards for the United States and a Recommended Implementation Plan, (1978); Neuroth H., Lohmeyer F., Smith K.M., TextGrid-virtual research environment for the humanities, International Journal of Digital Curation, 6, 2, pp. 223-331, (2011); Paulovich B., Design to improve the health education experience: using participatory design methods in hospitals with clinicians and patients, Visible Language Journal, 49, 1-2, (2015); What is data management?; Pickard A.J., Research Methods in Information, (2007); Pienaar H., Van Deventer M., To VRE or not to VRE: do South African Malaria researchers need a Virtual Research Environment?, Ariadne, 30, 59, (2009); From data collection: primary research methods tutorial, KnowThis.com, (2020); VREs (Virtual Research Environments); Runeson P., Host M., Guidelines for conducting and reporting case study research in software engineering, 14, 2, pp. 131-164, (2009); Schurink E.M., The methodology of unstructured face-to-face interviewing, Research at Grass Roots: A Primer for the Caring Professions, pp. 297-300, (1998); Scriven M., Evaluation Thesaurus, (1991); Sergeant D.M., Andrews S., Farquhar A., Embedding a VRE in an Institutional Environment EVIE: Workpackage 2: User Requirements Analysis, (2006); Simeoni F., Pagano P., Simi M., Connor R., Application-level research e-infrastructures: the gCube approach, Paper Presented at the UK e-Science All Hands Meeting 2008, (2008); Singh M.P., Vouk M.A., Scientific Workflows: Scientific Computing Meets Transactional Workflows, (1996); Smith J., Evaluating Training – what You Need to Know: Definitions, Best Practices, Benefits and Practical Solutions, (2012); Swanborn P.G., Case Study Research: What, Why and How?, (2010); Stern P., Porr C., Essentials of Accessible Grounded Theory, (2011); Streubert H.J., Carpenter D.R., Qualitative Research in Nursing: Advancing the Humanistic Imperative, (1995); Research data management (Research Guides); Thanos C., A vision for global research data infrastructures, Data Science Journal, 13, 12, pp. 71-90, (2013); UK Data Archive, Research Data Lifecycle, UK Data Archive, (2014); Data management (Research Guides); Virtual Research Environment; Research Data Management: Guide, (2014); Unsworth J., Our Cultural Commonwealth: The Report of the American Council of Learned Societies Commission on Cyberinfrastructure for the Humanities and Social Sciences, (2006); Van der Vaart L., Collaboratories: Connecting Researchers: How to Facilitate Choice, Design and Uptake of Online Research Collaborations, (2010); Van Deventer M., Research data management introduction, Paper Presented at the Carnegie CPD 3 Workshop, (2015); Van Deventer M., Pienaar H., Morris J., Ngcete Z., Virtual research environments: learning gained from a situation and needs analysis for malaria researchers, Paper Presented at the African Digital Scholarship and Curation Conference, (2009); Van Deventer M., Becker B., Pienaar H., Morris J., Nyakuna E., African Researchers Using Natural Products to Improve Lives, (2011); Van Wyk J., Research Data Management Report, (2014); Van Wyk B.J., The Relationship between Research Data Management and Virtual Research Environments, (2018); Van Wyk J., Kleyn L., Butler-Adam, Research Data Management Policy, (2017); Vollman A.R., Anderson E.T., McFarlane J., Canadian Community as Partner, (2004); Voss A., Procter R., Virtual research environments in scholarly work and communications, Library Hi Tech, 27, 9, pp. 174-190, (2009); Whyte W.F., Greenwood D.J., Lazes P., Participatory action research: through practice to science in social research, Participatory Action Research, 123, pp. 19-55, (1991); Wiggins A., Bonney R., Graham E., Henderson S., Kelling S., Lebuhn G., Littauer R., Lotts K., Michener W., Newman G., Russell E., Stevenson R., Weltzin J., Data Management Guide for Public Participation in Scientific Research, (2013); Wilson A., Rimpilainen S., Skinner D., Cassidy C., Christie D., Coutts N., Sinclair C., Using a Virtual Research Environment to support new models of collaborative and participative research in Scottish education, Technology, Pedagogy and Education, 16, 3, pp. 289-304, (2007); Wing J.M., A specifier's introduction to formal methods, High-integrity System Specification and Design, pp. 167-200, (1999); Winter R., Learning from Experience: Principles and Practices in Action Research, (1989); Woolfrey L., UCT Research Data Management Policy Project: Report, (2014); Wusteman J., Editorial: virtual Research Environments: what is the librarian's role?, Journal of Librarianship and Information Science, 40, 2, pp. 67-70, (2008); Wusteman J., Editorial: virtual research environments: issues and opportunities for librarians, Library Hi Tech, 27, 2, pp. 169-173, (2009); Yang X., Allan R., Sakai VRE demonstrator project: realise e-research through virtual research environments, WSEAS Transactions on Computers, 6, 3, pp. 539-545, (2007); Yang X., Allan R.J., Web-based virtual research environments, Web-based Support Systems, pp. 65-79, (2010); Yin R.K., Case study research: design and methods, 5, (2009); Yin R., Applications of Case Study Research, (2012)","J. Van Wyk; Department of Information Science, University of Pretoria, Pretoria, South Africa; email: johann.vanwyk@up.ac.za","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","","Scopus","2-s2.0-85087562548"
"Bellgard M.I.","Bellgard, Matthew I. (6701705865)","6701705865","ERDMAS: An exemplar-driven institutional research data management and analysis strategy","2020","International Journal of Information Management","50","","","337","340","3","6","10.1016/j.ijinfomgt.2019.08.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072337313&doi=10.1016%2fj.ijinfomgt.2019.08.009&partnerID=40&md5=5a298727a3ac05078b4435afb8750a94","eResearch Office, Queensland University of Technology, Brisbane, 4001, Australia","Bellgard M.I., eResearch Office, Queensland University of Technology, Brisbane, 4001, Australia","Devising fit-for-purpose research data management strategies within a university is challenging. This is because the five ‘Vs’ for generated research data; its Volume, Variety, Velocity, Veracity and its Value must be constantly considered. Invariably, a combination of data V's for any given research endeavour determine how best to manage it appropriately addressing archiving, compliance, security, privacy, sharing, reuse and so forth. As such, institutions are faced with defining, shaping and refining strategies and practicies to ensure there are consistent and adequate research data management polices and guidelines in place for their researchers. FAIR data principles are very important for embracing open data opportunities, but more broadly, research data management practices need to be established in a comprehensive way. Additionally, new ICT options have rapidly become available where institutions can make considered choices on whether to continue to use ‘on prem’, private Cloud or public Cloud infrastructure. If a hybrid approach is adopted, then the potential impact on existing institutional research data management strategies must be continually assessed and revised accordingly. Getting the balance right between developing a relevant institutional policy on the one hand yet also dynamically catering for the eclectic research data management and analytics needs of researchers and their evolving interactions with external collaborators on the other, must be continually navigated. In this manuscript, an exemplar-driven research data management and analytics conceptual framework is introduced. A key feature of this framework is that it is couched in two dimensions. On one axis is the ‘standard’ linear approach of developing the research data management policy, guidelines, procedures, audit and risk assessment and an options matrix. Importantly, a second axis comprising a researcher-driven focus is introduced where exemplar research activities are used to define ‘classes’ of research data management and analysis requirements. This exemplar-driven dimension enables an ongoing system-wide comparative review to occur in parallel that can continually inform policy and guidelines refinement. © 2019 The Author","Analytics; Exemplars; Hybrid Cloud; Policy and guidelines; Research data management; Researcher-driven","Information management; Risk assessment; Analytics; Exemplars; Hybrid clouds; Research data managements; Researcher-driven; Open Data","","","","","","","Bellgard M.I., Chartres N., Watts G.F., Wilton S., Fletcher S., Hunter A., Et al., Comprehending the health informatics Spectrum: Grappling with system entropy and advancing quality clinical research, Frontiers in Public Health, 5, (2017); Bellgard M.I., Napier K.R., Bittles A.H., Szer J., Fletcher S., Zeps N., Et al., Design of a framework for the deployment of collaborative independent rare disease-centric registries: Gaucher disease registry model, Blood Cells, Molecules & Diseases, 68, pp. 232-238, (2018); Bruns A., After the “APIcalypse”: Social media platforms and their fight against critical scholarly research, Information, Communication & Society, (2019); Burgess J., Bruns A., Easy data, hard data: The politics and pragmatics of twitter research after the computational turn, Compromised data: From social media to big data, pp. 93-111, (2015); Burton P.R., Banner N., Elliot M.J., Knoppers B.M., Banks J., Policies and strategies to facilitate secondary use of research data in the health sciences, International Journal of Epidemiology, 46, 6, pp. 1729-1733, (2017); Gandomi A., Haider M., Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35, pp. 137-144, (2015); Hunter A., Bowland G., Chang S., Szabo T., Napier K., Lum M., Et al., The bioplatforms australia data portal, eResearch Australasia Conference 2018, August, (2019); Jones S., eGovernment Document Management System: A case analysis of risk and reward, International Journal of Information Management, 32, pp. 396-400, (2012); Laney D., 3D data management: Controlling data volume, velocity and variety, (2001); Lim C., Kim K., Kim M., Heo J., Kim K., Maglio P.P., From data to value: A nine-factor framework for data-based value creation in information-intensive services, International Journal of Information Management, 39, pp. 121-135, (2018); Maican C., Lixandroiu R., A system architecture based on open source enterprise content management systems for supporting educational institutions, International Journal of Information Management, 36, pp. 207-214, (2016); Ozmen-Ertekina D., Ozbayb K., Dynamic data maintenance for quality data, quality research, International Journal of Information Management, 32, pp. 282-293, (2012); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PloS One, 9, 12, (2014); Vilminko-Heikkinen R., Pekkola S., Changes in roles, responsibilities and ownership in organizing master data T management, International Journal of Information Management, 47, pp. 76-87, (2019); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","Elsevier Ltd","","","","","","02684012","","IJMAE","","English","Int J Inf Manage","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85072337313"
"Thomas A.; Martin E.R.","Thomas, Ashley (57219565732); Martin, Elaine R. (57675049000)","57219565732; 57675049000","Developing a Community of Practice: Building the Research Data Management Librarian Academy","2020","Medical Reference Services Quarterly","39","4","","323","333","10","2","10.1080/02763869.2020.1826185","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093960449&doi=10.1080%2f02763869.2020.1826185&partnerID=40&md5=e881a9f5d7bd97cc85442f93c710ae41","Countway Library of Medicine, Harvard Medical School, Boston, MA, United States","Thomas A., Countway Library of Medicine, Harvard Medical School, Boston, MA, United States; Martin E.R., Countway Library of Medicine, Harvard Medical School, Boston, MA, United States","The Research Data Management Librarian Academy (RDMLA) is a free, online global professional development program designed by librarians for librarians working in research-intensive environments. Developed through a unique partnership that includes a Library and Information Sciences academic program, research and health sciences libraries, and industry, the RDMLA’s inception, development, and launch provide helpful insights into the creation of online professional development courses. The RDMLA team’s experience building the course’s curriculum with an instructional designer (ID) and evaluating the operation and usefulness of the course’s content through usability testing provides valuable lessons learned for librarians constructing an online continuing education (CE) course. © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.","Continuing education; data librarians; online curriculum; research data management","Adult; Curriculum; Data Management; Education, Distance; Education, Professional; Female; Humans; Librarians; Libraries, Medical; Male; Middle Aged; Research Personnel; United States; article; continuing education; curriculum; human; human experiment; information science; librarian; library; professional development; adult; education; female; information processing; librarian; male; middle aged; organization and management; personnel; United States; vocational education","","","","","Research Data Management Librarian Academy; Sanda Erdelez","The Research Data Management Librarian Academy (RDMLA) is financially supported by Elsevier. The authors wish to acknowledge and thank Jean Shipman, Rong Tang, Zhan Hu, Roger Vargas, Amber Budden, Adam Kriesberg, Scott Lapinski, Julie Goldman, Danielle Adams, Ceilyn Boyd, Rachel Lewellen, Alison Thornton, Andrew Creamer, Danielle Pollock, Mary Blanchard, Philip Espinola Coombs, Richard Kaplan, Eric Albright, Rebecca Morin, Berika Williams, Jen Ferguson, Nicole Henry, and Sanda Erdelez.","(2018); Martin E.R., The Role of Librarians in Data Science: A Call to Action, Journal of eScience Librarianship, 4, 2, (2016); Federer L., Defining Data Librarianship: A Survey of Competencies, Skills, and Training, Journal of the Medical Library Association: JMLA, 106, 3, pp. 294-303, (2018); Tang R., Hu Z., Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice, Data and Information Management, 3, 2, pp. 84-101, (2019); Dataone.Org; Summey T.P., Valenti S., But We Don’t Have an Instructional Designer: Designing Online Library Instruction Using ISD Techniques, Journal of Library & Information Services in Distance Learning, 7, 1-2, pp. 169-182, (2013)","A. Thomas; Countway Library of Medicine, Harvard Medical School, Boston, 10 Shattuck Street, 02115, United States; email: ashley_thomas@hms.harvard.edu","","Routledge","","","","","","02763869","","MRSQD","33085951","English","Med. Ref. Serv. Q.","Article","Final","","Scopus","2-s2.0-85093960449"
"Kim S.","Kim, Suntae (57218108774)","57218108774","Machine-actionable Data Management Plans Model Analysis and Improvement Direction","2020","Journal of Information Science Theory and Practice","8","4","","20","28","8","0","10.1633/JISTaP.2020.8.4.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101516792&doi=10.1633%2fJISTaP.2020.8.4.2&partnerID=40&md5=1ca4008296bec34c78ab6011953ec260","Department of Library and Information Science, Jeonbuk National University, Jeonju, South Korea","Kim S., Department of Library and Information Science, Jeonbuk National University, Jeonju, South Korea","In this study, the RDA DMP Common Standard (RDCS), a data model for implementing a machine actionable Data Management Plan (maDMP), was analyzed in four aspects. First, the twelve class models proposed by RDCS were analyzed. Second, whether the DMP attribute was included in the class attribute was analyzed. Third, we analyzed the namespace used for RDCS properties. Fourth, the values and identifiers used in RDCS properties were analyzed. As a result of the analysis, four directions for improvement were derived. First, it is necessary to add an academic record class to describe information such as papers and reports, which are representative academic documents. Second, the primary research institution, responsibility, resources, option attribute, and additional attributes are needed to describe the researcher’s affiliation information. Third, it is necessary to additionally use a namespace such as Friend of a Friend that can be used universally. Fourth, the use of digital object identifier should be considered to identify academic literature. © 2020. Suntae Kim. All rights reserved.","data management plans; DMP; machine-actionable DMP; research data; research data management","","","","","","Korea Institute of Science and Technology Information, KISTI; National Research Foundation of Korea, NRF; Jeonbuk National University, JBNU","Funding text 1: or creators as a domestic ISNI registrar by issuing individual author authority IDs (Byeon & Oh, 2018). The National Research Foundation of Korea (NRF) issues Researcher Registration Numbers to researchers, and the NTIS system operated by the Korea Institute of Science and Technology Information (KISTI) issues Researcher Numbers. The Researcher Registration Number and Researcher Number are identical identifiers that differ only in their names. In addition, KISTI is currently conducting research to identify researchers, articles, reports, and research institutions belonging to articles and reports written by Korean researchers.; Funding text 2: In addition, various effects can be expected when maDMPs are common. If maDMPs are shared in public and under an open license, anyone can aggregate them, re-slice the corpora, use, and re-share the resulting information. Such front-ends to maDMP collections could be generic—which would help with the standardization and spread of good data management practices across domains—or be tailored for specific audiences, e.g., to facilitate discovery in a given area or education about research in the domain, including associated data management practices (Miksa, Simms, Mietchen, & Jones, 2019). maDMPs can also provide a variety of benefits to funding institutions. According to Noh, Kwon, and Moon (2018), it is important for funding institutions to understand the impact of research and investment support programs in funding strategies, program design, and mission coordination. Funding institutions continue to challenge the performance of R&D investment programs and measure their impact.","Burnette M. H., Williams S. C., Imker H. J., From plan to action: Successful data management plan implementation in a multidisciplinary project, Journal of eScience Librarianship, 5, 1, (2016); Byeon H. -K., Oh B., A study on integrated management system of researcher identifiers based on the ISNI (International Standard Name Identifier), Journal of the Korean BIBLIA Society for Library and Information Science, 29, 3, pp. 139-155, (2018); Colavizza G., Hrynaszkiewicz I., Staden I., Whitaker K., McGillivray B., The citation advantage of linking publications to research data, PLoS ONE, 15, 4, (2020); Persistent identifiers, (2020); Hodson S., Molloy L., Current best practice for research data management policies, (2014); DOI® handbook, (2019); Kim S. -T., Functional requirements of data repository for DMP support and CoreTrustSeal authentication, International Journal of Knowledge Content Development & Technology, 10, 1, pp. 7-20, (2020); Koo C. M., Kim S. -H., Research records management in scientific research institutes by applying DMP, Journal of Korean Society of Archives and Records Management, 19, 1, pp. 1-21, (2019); Miksa T., Simms S., Mietchen D., Jones S., Ten principles for machine-actionable data management plans, PLOS Computational Biology, 15, 3, (2019); Miksa T., Walk P., Neish P., RDA DMP Common standard for machine-actionable data management plans, (2019); Information strategy planning for advancement of national resources union catalog: Final report, (2016); About ISNI, (2020); Noh K. -R., Kwon O. -J., Moon Y. -H., Utilization of ORCID data to track the performance impact of national R&D investment, Proceedings of the Korea Contents Association, 2018, pp. 405-406, (2018); ORCID and ISNI issue joint statement on interoperation, (2013); ex3-datasetfinished.json, (2020); ex1-headerfundedProject.json","S. Kim; Assistant Professor, Department of Library and Information Science, Jeonbuk National University, 567 Baekje-daero, Jeonju, Deokjin-gu, 54896, South Korea; email: kim.suntae@jbnu.ac.kr","","Korea Institute of Science and Technology Information","","","","","","22879099","","","","English","J. Inf. Sci. Theory Pract.","Article","Final","","Scopus","2-s2.0-85101516792"
"Iyemori T.; Aoki T.; Kajita S.; Motoki T.; Kawaguchi T.; Amano E.","Iyemori, Toshihiko (6701604107); Aoki, Takaaki (57224195316); Kajita, Shoji (7006312319); Motoki, Tamaki (11438913600); Kawaguchi, Tomoko (57215419417); Amano, Eriko (45661121600)","6701604107; 57224195316; 7006312319; 11438913600; 57215419417; 45661121600","A Campus-wide Survey of Consciousness on Research Datasets","2020","Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020","","","9430361","312","315","3","0","10.1109/IIAI-AAI50415.2020.00070","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107192348&doi=10.1109%2fIIAI-AAI50415.2020.00070&partnerID=40&md5=c4c95a83f5242a691056497599f97783","Kyoto University, Unit of Academic Data Innovation, Academic Center for Computing and Media Studies, Kyoto, Japan; Kyoto University, Kyoto University Unit of Academic Data Innovation, Kyoto University Archives, Kyoto, Japan; Kyoto University, Kyoto University Unit of Academic Data Innovation, Research Administration Office, Kyoto, Japan","Iyemori T., Kyoto University, Unit of Academic Data Innovation, Academic Center for Computing and Media Studies, Kyoto, Japan; Aoki T., Kyoto University, Unit of Academic Data Innovation, Academic Center for Computing and Media Studies, Kyoto, Japan; Kajita S., Kyoto University, Unit of Academic Data Innovation, Academic Center for Computing and Media Studies, Kyoto, Japan; Motoki T., Kyoto University, Unit of Academic Data Innovation, Academic Center for Computing and Media Studies, Kyoto, Japan; Kawaguchi T., Kyoto University, Kyoto University Unit of Academic Data Innovation, Kyoto University Archives, Kyoto, Japan; Amano E., Kyoto University, Kyoto University Unit of Academic Data Innovation, Research Administration Office, Kyoto, Japan","To obtain the basic information necessary for planning a research data management (RDM) system in Kyoto University, Japan, we conducted a campus-wide survey of research datasets twice with different topics. Although only a part of campus members responded, some clear tendencies were observed. That is, (1) considerable portion of researchers are not very positive to make their data open because of a confidentiality obligation or for holding their predominance in their research fields, (2) strength of consciousness on open data is considerably different depending on their discipline, (3) the above difference may come not only from inherency of each discipline but also from the requirement of open data by the journals. (4) There exist more than fifty open data repositories, and more than half of them are managed by the institutions established for each specific field of research or by the organization outside of the campus. From the survey, it is suggested that the researchers are still conservative to make their data open, and a current practical necessity to RDM system in the campus seems to be a system for keeping and making the data open within each research group. By preparing a convenient RDM system as well as the requirement of open data by the journals in wider discipline may change the consciousness of the researchers on open data.  © 2020 IEEE.","campus; consciousness; open data; research data; survey","Information management; Surveys; Data repositories; Kyoto University , Japan; Research data managements; Research fields; Research groups; Open Data","","","","","Japan Society for the Promotion of Science, KAKEN","ACKNOWLEDGMENT This survey was announced through university office, and a survey system prepared by the Institute for Information Management and Communication, Kyoto University for the first survey and Google Forms for the second survey were used to collect answers from researchers and students. We thank all the researchers and students who spent their time for this survey. We also thank for many important/useful suggestions by the referees. This study was partially supported by JSPS KAKENHI Grant Number 20H00099 under the Japan Society for Promotion of Science (JSPS).","Ikeuchi U., Hayashi K., A survey on open research data and open access 2018, NISTEP RESEARCH MATERIAL, 268; Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorset K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLOS ONE, 10, 8, (2015); Mancilla H.A., Teperek M., Van Dijck J., Den Heijer K., Eggermont R., Plomp E., Van Der Velden Y.T., Kurapati S., On a quest for cultural change-surveying research data management practices at Delft university of technology, Liber Quarterly, 29, pp. 1-27, (2019); Iyemori T., Kawaguchi T., Kajita S., Aoki T., Motoki T., Amano E., A campus-wide survey of research datasets in a university, Submitted to Data Science Journal, 2019, Unpublished (Under Review)","","Matsuo T.; Takamatsu K.; Ono Y.; Hirokawa S.","Institute of Electrical and Electronics Engineers Inc.","","9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020","1 September 2020 through 15 September 2020","Kitakyushu","169083","","978-172817397-9","","","English","Proc. - Int. Congr. Adv. Appl. Informatics, IIAI-AAI","Conference paper","Final","","Scopus","2-s2.0-85107192348"
"Löbe M.; Matthies F.; Stäubert S.; Meineke F.A.; Winter A.","Löbe, Matthias (55938448500); Matthies, Franz (56728469000); Stäubert, Sebastian (36648060200); Meineke, Frank A. (6508217583); Winter, Alfred (7202642905)","55938448500; 56728469000; 36648060200; 6508217583; 7202642905","Problems in fairifying medical datasets","2020","Studies in Health Technology and Informatics","270","","","392","396","4","3","10.3233/SHTI200189","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086909470&doi=10.3233%2fSHTI200189&partnerID=40&md5=8baa6a1b9293f59c407ea21fb8acadcc","Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Saxony, Germany","Löbe M., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Saxony, Germany; Matthies F., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Saxony, Germany; Stäubert S., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Saxony, Germany; Meineke F.A., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Saxony, Germany; Winter A., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Saxony, Germany","Despite their young age, the FAIR principles are recognised as important guidelines for research data management. Their generic design, however, leaves much room for interpretation in domain-specific application. Based on practical experience in the operation of a data repository, this article addresses problems in FAIR provisioning of medical data for research purposes in the use case of the Leipzig Health Atlas project and shows necessary future developments. © 2020 European Federation for Medical Informatics (EFMI) and IOS Press.","Data sharing; FAIR data; Research data management","Databases, Factual; Information management; Medical problems; Data repositories; Domain-specific application; Generic design; Medical data; Medical data sets; Practical experience; Research data managements; Research purpose; conference paper; FAIR principles; plant leaf; practice guideline; factual database; Medical informatics","","","","","NMDR, (WI 1605/10-1); European Commission, EC, (H2020/824666 – FAIR4Health); Deutsche Forschungsgemeinschaft, DFG; Bundesministerium für Bildung und Forschung, BMBF, (01ZZ1803A, 031L0026)","Acknowledgements: This work was supported by the European Union (H2020/824666 – FAIR4Health), the German Ministry of Education and Research (LHA: 031L0026, SMITH: 01ZZ1803A) and the German Research Foundation (NMDR: WI 1605/10-1).","Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Dunning A., de Smaele M., Bohmer J., Are the FAIR Data Principles fair?, IJDC, 12, pp. 177-195, (2017); Meineke F.A., Lobe M., Staubert S., Introducing technical aspects of research data management in the Leipzig health atlas, Stud Health Technol Inform, 247, pp. 426-430, (2018); Wolstencroft K., Owen S., Krebs O., Seek: A systems biology data and model management platform, BMC Syst Biol, 9, (2015); Steindel S.J., OIDS: How can I express you? Let me count the ways, J Am Med Inform Assoc, 17, pp. 144-147, (2010); McMurry J.A., Juty N., Blomberg N., Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data, PLoS Biol, 15, (2017); Lobe M., User expectations of metadata repositories for clinical research, Stud Health Technol Inform, 253, pp. 60-64, (2018); Braunstein M.L., Healthcare in the age of interoperability: The promise of fast healthcare interoperability resources, IEEE Pulse, 9, pp. 24-27, (2018); Huser V., Sastry C., Breymaier M., Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM), J Biomed Inform, 57, pp. 88-99, (2015); Hripcsak G., Duke J., Shah N., Observational Health Data Sciences and Informatics (OHDSI): Opportunities for observational researchers, Stud Health Technol Inform, 216, pp. 574-578, (2015); Athey B.D., Braxenthaler M., Haas M., Transmart: An open source and community-driven informatics and data sharing platform for clinical and translational research, AMIA Jt Summits Transl Sci Proc, 2013, pp. 6-8, (2013); Hernandez-Perez T., Mendez Rodriguez E., D2.3. Guidelines for Implementing FAIR Open Data Policy in Health Research","M. Löbe; Leipzig, Härtelstraße 16-18, 04107, Germany; email: matthias.loebe@imise.uni-leipzig.de","Pape-Haugaard L.B.; Lovis C.; Madsen I.C.; Weber P.; Nielsen P.H.; Scott P.","IOS Press","","30th Medical Informatics Europe Conference, MIE 2020","28 April 2020 through 1 May 2020","Geneva","161256","09269630","978-164368082-8","","32570413","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-85086909470"
"Sinaci A.A.; Núñez-Benjumea F.J.; Gencturk M.; Jauer M.-L.; Deserno T.; Chronaki C.; Cangioli G.; Cavero-Barca C.; Rodríguez-Pérez J.M.; Pérez-Pérez M.M.; Laleci Erturkmen G.B.; Hernández-Pérez T.; Méndez-Rodríguez E.; Parra-Calderón C.L.","Sinaci, A. Anil (36905158800); Núñez-Benjumea, Francisco J. (49864153600); Gencturk, Mert (53063547700); Jauer, Malte-Levin (56575131200); Deserno, Thomas (21233724300); Chronaki, Catherine (6701349152); Cangioli, Giorgio (56690472900); Cavero-Barca, Carlos (57217671294); Rodríguez-Pérez, Juan M. (57217668979); Pérez-Pérez, Manuel M. (57217672051); Laleci Erturkmen, Gokce B. (37079175400); Hernández-Pérez, Tony (35213151400); Méndez-Rodríguez, Eva (36855954900); Parra-Calderón, Carlos L. (24332533000)","36905158800; 49864153600; 53063547700; 56575131200; 21233724300; 6701349152; 56690472900; 57217671294; 57217668979; 57217672051; 37079175400; 35213151400; 36855954900; 24332533000","From Raw Data to FAIR Data: The FAIRification Workflow for Health Research","2020","Methods of Information in Medicine","59","6","","E21","E32","11","43","10.1055/s-0040-1713684","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087472800&doi=10.1055%2fs-0040-1713684&partnerID=40&md5=1a05cab59f491e459be579bd2da06204","Srdc Software Research Development and Consultancy Corporation, ODTU Teknokent Silikon Bina K1-16, Cankaya, Ankara, 06800, Turkey; Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Inst. of Biomed. of Seville, Virgen Del Rocio Univ. Hosp., CSIC/University of Seville, Seville, Spain; Peter L. Reichertz Institute for Medical Informatics of Tu Braunschweig and Hannover Medical School, Braunschweig, Germany; Health Level Seven International Foundation, Brussels, Belgium; Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain; Department of Library and Information Sciences, Universidad Carlos Iii de Madrid, Madrid, Spain; Srdc Software Research Development and Consultancy Corporation, ODTU Teknokent Silikon Bina K1-16 06800 Cankaya, Ankara, Turkey","Sinaci A.A., Srdc Software Research Development and Consultancy Corporation, ODTU Teknokent Silikon Bina K1-16, Cankaya, Ankara, 06800, Turkey, Srdc Software Research Development and Consultancy Corporation, ODTU Teknokent Silikon Bina K1-16 06800 Cankaya, Ankara, Turkey; Núñez-Benjumea F.J., Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Inst. of Biomed. of Seville, Virgen Del Rocio Univ. Hosp., CSIC/University of Seville, Seville, Spain; Gencturk M., Srdc Software Research Development and Consultancy Corporation, ODTU Teknokent Silikon Bina K1-16, Cankaya, Ankara, 06800, Turkey; Jauer M.-L., Peter L. Reichertz Institute for Medical Informatics of Tu Braunschweig and Hannover Medical School, Braunschweig, Germany; Deserno T., Peter L. Reichertz Institute for Medical Informatics of Tu Braunschweig and Hannover Medical School, Braunschweig, Germany; Chronaki C., Health Level Seven International Foundation, Brussels, Belgium; Cangioli G., Health Level Seven International Foundation, Brussels, Belgium; Cavero-Barca C., Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain; Rodríguez-Pérez J.M., Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain; Pérez-Pérez M.M., Atos, Group of Health, Atos Research and Innovation (ARI), Madrid, Spain; Laleci Erturkmen G.B., Srdc Software Research Development and Consultancy Corporation, ODTU Teknokent Silikon Bina K1-16, Cankaya, Ankara, 06800, Turkey; Hernández-Pérez T., Department of Library and Information Sciences, Universidad Carlos Iii de Madrid, Madrid, Spain; Méndez-Rodríguez E., Department of Library and Information Sciences, Universidad Carlos Iii de Madrid, Madrid, Spain; Parra-Calderón C.L., Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Inst. of Biomed. of Seville, Virgen Del Rocio Univ. Hosp., CSIC/University of Seville, Seville, Spain","Background FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data. Objectives This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by GO FAIR which addresses the identified gaps that such process has when dealing with health datasets. Methods A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing. Results A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow. Discussion Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets. Conclusion Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture.  © 2020 Georg Thieme Verlag KG. Stuttgart New York.","data anonymization; data curation; data science; interoperability; metadata","Access to Information; Biomedical Research; Health Information Interoperability; Health Level Seven; Information Management; Metadata; Software Design; Workflow; anonymization; article; data science; FAIR principles; Fast Healthcare Interoperability Resources; funding; health level 7; medical research; metadata; workflow; access to information; data interoperability; information system; metadata; software design; workflow","","","","","Horizon 2020 Framework Programme, H2020, (824666)","This work was performed in the scope of FAIR4Health project31. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666.","Wilkinson M.D., Dumontier M., Aalbersberg I.J., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Mons B., Neylon C., Velterop J., Dumontier M., Da Silva Santos L.O., Wilkinson M.D., Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud, Inf Serv Use, 37, pp. 49-56, (2017); H2020 Programme: Guidelines on Fair Data Management in Horizon 2020; Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on Open Data and the Re-use of Public Sector Information; New Models of Data Stewardship. Program Snapshot; Go Fair Initiative; FAIRification Process; Skovgaard L.L., Wadmann S., Hoeyer K., A review of attitudes towards the reuse of health data among people in the European Union: The primacy of purpose and the common good, Health Policy, 123, 6, pp. 564-571, (2019); Federer L.M., Lu Y.L., Joubert D.J., Welsh J., Brandys B., Biomedical data sharing and reuse: Attitudes and practices of clinical and scientific research staff, PLoS One, 10, 6, (2015); Meystre S.M., Lovis C., Burkle T., Tognola G., Budrionis A., Lehmann C.U., Clinical data reuse or secondary use: Current status and potential future progress, Yearb Med Inform, 26, 1, pp. 38-52, (2017); World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects, Jama, 310, 20, pp. 2191-2194, (2013); Wma Declaration of Taipei on Ethical Considerations Regarding Health Databases and Biobanks; The Health Information Technology for Economic and Clinical Health (HITECH) Act Enforcement Interim Final Rule; Cohen I.G., Mello M.M., HIPAA and protecting health information in the 21st century, Jama, 320, 3, pp. 231-232, (2018); Recommendations on De-identification of Protected Health Information under Hipaa; Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation); Carrell D.S., Schoen R.E., Leffler D.A., Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings, J Am Med Inform Assoc, 24, 5, pp. 986-991, (2017); Pons E., Braun L.M., Hunink M.G., Kors J.A., Natural Language Processing in Radiology: A Systematic Review, Radiology, 279, 2, pp. 329-343, (2016); Liao K.P., Cai T., Savova G.K., Development of phenotype algorithms using electronic medical records and incorporating natural language processing, Bmj, 350, (2015); Chen L., Song L., Shao Y., Li D., Ding K., Using natural language processing to extract clinically useful information from Chinese electronic medical records, Int J Med Inform, 124, pp. 6-12, (2019); Ong T., Pradhananga R., Holve E., Kahn M.G., A framework for classification of electronic health data extraction-transformation-loading challenges in data network participation, Egems (Wash DC), 5, 1, (2017); Hamrouni H., Brahmia Z., Bouaziz R., A systematic approach to efficiently managing the effects of retroactive updates of time-varying data in multiversion XML databases, International Journal of Intelligent Information and Database Systems, 11, pp. 1-26, (2018); Yenni G.M., Christensen E.M., Bledsoe E.K., Developing a modern data workflow for regularly updated data, PLoS Biol, 17, 1, (2019); Wilkinson M.D., Verborgh R., Bonino Da Silva Santos L.O., Interoperability and FAIRness through a novel combination of Web technologies, PeerJ Comput Sci, 3, (2017); Collins S., Genova F., Harrower N., Turning FAIR into reality, European Commission Directorate General for Research and Innovation, 1, pp. 1-76, (2018); Ethics and Data Protection; Canham S., Ohmann C., Matei M., Et al., White Paper 4: Ethics, Supporting Document to D3.3 Draft Policy Recommendations; Ienca M., Ferretti A., Hurst S., Puhan M., Lovis C., Vayena E., Considerations for ethics review of big data health research: A scoping review, PLoS One, 13, 10, (2018); Council of the European Union Outcome of Proceedings; Floridi L., Taddeo M., What is data ethics?, Philos Trans A Math Phys Eng Sci, 374, (2016); FAIR4Health Project. FAIR4Health Project Website; HL7 Clinical Document Architecture (CDA). Health Level Seven International (HL7); HL7 Fhir; Open Industry Specifications, Models and Software for E-health (OpenEHR); Observational Health Data Sciences and Informatics. Omop Common Data Model; Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule; Burrows J.H., Secure Hash Standard, (1994); Lakshmanan T., Madheswaran M., A novel secure hash algorithm for public key digital signature schemes, Int Arab J Inf Technol, 9, pp. 262-267, (2012); Dalenius T., Finding a needle in a haystack or identifying anonymous census records, J off Stat, 2, pp. 329-336, (1986); Classification of Diseases (ICD)-11; Snomed International; The International Standard for Identifying Health Measurements, Observations, and Documents; Rauber A., Asmi A., Van Uytvanck D., Proell S., Data citation of evolving data: Recommendations of the Working Group on Data Citation (WGDC), Result of the Rda Data Citation Wg, 20, pp. 1-2, (2015); Iso 14721:2003 Space Data and Information Transfer Systems-open Archival Information System-Reference Model; Deserno T.M., Welter P., Horsch A., Towards a repository for standardized medical image and signal case data annotated with ground truth, J Digit Imaging, 25, 2, pp. 213-226, (2012); Canham S., Ohmann C., A metadata schema for data objects in clinical research, Trials, 17, 1, (2016); Cross-Enterprise Document Sharing; Digital Imaging and Communications in Medicine (DICOM); C-CDA (HL7 Cda R2 Implementation Guide: Consolidated Cda Templates for Clinical Notes-US Realm); Resource ConceptMap-Content; Oecd Principles and Guidelines for Access to Research Data from Public Funding; Science As An Open Enterprise: Open Data for Open Science; Horizon 2020, Work Programme 2018-2020, Health, Demographic Change and Wellbeing; Notice Announcing Funding Opportunity Issued for the Nih Data Commons Pilot Phase; 2016 National Research Infrastructure Roadmap; The African Open Science Platform: The Future of Science and the Science of the Future; Musen M.A., Sansone S.-A., Cheung K.-H., Et al., CEDAR: Semantic web technology to support open science, WWW'18Companion: The 2018 Web Conference Companion, (2018); Musen M.A., Bean C.A., Cheung K.H., The center for expanded data annotation and retrieval, J Am Med Inform Assoc, 22, 6, pp. 1148-1152, (2015); Thompson M., Bonino L., Wilkinson M.D., Et al., Overview of a Suite of Middle-ware Services for Implementing Fair Data Principles, (2017)","A.A. Sinaci; Srdc Software Research Development and Consultancy Corporation, Cankaya, Ankara, ODTU Teknokent Silikon Bina K1-16, 06800, Turkey; email: anil@srdc.com.tr","","Georg Thieme Verlag","","","","","","00261270","","MIMCA","32620019","English","Methods Inf. Med.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85087472800"
"Banko L.; Ludwig A.","Banko, Lars (57194499230); Ludwig, Alfred (56398991400)","57194499230; 56398991400","Fast-Track to Research Data Management in Experimental Material Science-Setting the Ground for Research Group Level Materials Digitalization","2020","ACS Combinatorial Science","22","8","","401","409","8","6","10.1021/acscombsci.0c00057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089611904&doi=10.1021%2facscombsci.0c00057&partnerID=40&md5=af9f86a7ed0c16a1b608da7da1a44ad5","Chair for Materials Discovery and Interfaces, Institute for Materials, Faculty of Mechanical Engineering, Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany; ZGH (Center for Interface-Dominated High Performance Materials), Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany; Materials Research Department, Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany","Banko L., Chair for Materials Discovery and Interfaces, Institute for Materials, Faculty of Mechanical Engineering, Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany; Ludwig A., Chair for Materials Discovery and Interfaces, Institute for Materials, Faculty of Mechanical Engineering, Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany, ZGH (Center for Interface-Dominated High Performance Materials), Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany, Materials Research Department, Ruhr-Universität Bochum, Universitätstraße 150, Bochum, 44801, Germany","Research data management is a major necessity for the digital transformation in material science. Material science is multifaceted and experimental data, especially, is highly diverse. We demonstrate an adjustable approach to a group level data management based on a customizable document management software. Our solution is to continuously transform data management workflows from generalized to specialized data management. We start up fast with a relatively unregulated base setting and adapt continuously over the period of use to transform more and more data procedures into specialized data management workflows. By continuous adaptation and integration of analysis workflows and metadata schemes, the amount and the quality of the data improves. As an example of this process, in a period of 36 months, data on over 1800 samples, mainly materials libraries with hundreds of individual samples, were collected. The research data management system now contains over 1700 deposition processes and more than 4000 characterization documents. From initially mainly user-defined data input, an increased number of specialized data processing workflows was developed allowing the collection of more specialized, quality-assured data sets. Copyright © 2020 American Chemical Society.","combinatorial material science; document management; materials informatics; research data management; scientific data management","Data Management; Materials Science; Research; Software; Workflow; information processing; materials science; research; software; workflow","","","","","Deutsche Forschungsgemeinschaft, DFG, (SFB-TR 87)","This study was funded by the German Research Foundation (DFG) as part of the Collaborative Research Centre TRR87/3 “Pulsed high power plasmas for the synthesis of nanostructured functional layers” (SFB-TR 87), project C2. ","Ludwig A., Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods, Npj Comput. Mater., 5, (2019); Himanen L., Geurts A., Foster A.S., Rinke P., Data-Driven Materials Science: Status, Challenges, and Perspectives, Advanced Science (Weinheim, Baden-Wurttemberg, Germany), 6, (2019); Green M.L., Choi C.L., Hattrick-Simpers J.R., Joshi A.M., Takeuchi I., Barron S.C., Campo E., Chiang T., Empedocles S., Gregoire J.M., Et al., Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies, Applied Physics Reviews, 4, (2017); Hill J., Mulholland G., Persson K., Seshadri R., Wolverton C., Meredig B., Materials science with large-scale data and informatics: Unlocking new opportunities, MRS Bull., 41, pp. 399-409, (2016); Soedarmadji E., Stein H.S., Suram S.K., Guevarra D., Gregoire J.M., Tracking materials science data lineage to manage millions of materials experiments and analyses, Npj Comput. Mater., 5, (2019); Zakutayev A., Wunder N., Schwarting M., Perkins J.D., White R., Munch K., Tumas W., Phillips C., An open experimental database for exploring inorganic materials, Scientific Data, 5, (2018); Zakutayev A., Perkins J., Schwarting M., White R., Munch K., Tumas W., Wunder N., Phillips C., High Throughput Experimental Materials Database, (2017); Kube S.A., Sohn S., Uhl D., Datye A., Mehta A., Schroers J., Phase selection motifs in High Entropy Alloys revealed through combinatorial methods: Large atomic size difference favors BCC over FCC, Acta Mater., 166, pp. 677-686, (2019); NIST Materials Resource Registry, (2020); Banko L., Ludwig A., Cr-Al-O-N Thin Film SEM Surface Microstructure Images, V1, (2020); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Bonino Da Silva Santos L., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, (2016); Meyer H., Meischein M., Ludwig A., Rapid Assessment of Sputtered Nanoparticle Ionic Liquid Combinations, ACS Comb. Sci., 20, pp. 243-250, (2018); Li Y.J., Savan A., Kostka A., Stein H.S., Ludwig A., Accelerated atomic-scale exploration of phase evolution in compositionally complex materials, Mater. Horiz., 5, pp. 86-92, (2018); Naujoks D., Richert J., Decker P., Weiser M., Virtanen S., Ludwig A., Phase Formation and Oxidation Behavior at 500 °c in a Ni-Co-Al Thin-Film Materials Library, ACS Comb. Sci., 18, pp. 575-582, (2016); Naujoks D., Weiser M., Salomon S., Stein H., Virtanen S., Ludwig A., Combinatorial Study on Phase Formation and Oxidation in the Thin Film Superalloy Subsystems Co-Al-Cr and Co-Al-Cr-W, ACS Comb. Sci., 20, pp. 611-620, (2018); Hattrick-Simpers J.R., Gregoire J.M., Kusne A.G., Perspective: Composition-structure-property mapping in high-throughput experiments: Turning data into knowledge, APL Materials, 4, (2016); Long C.J., Bunker D., Li X., Karen V.L., Takeuchi I., Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization, Rev. Sci. Instrum., 80, (2009); Ament S.E., Stein H.S., Guevarra D., Zhou L., Haber J.A., Boyd D.A., Umehara M., Gregoire J.M., Gomes C.P., Multi-component background learning automates signal detection for spectroscopic data, Npj Comput. Mater., 5, (2019); Stein H.S., Jiao S., Ludwig A., Expediting Combinatorial Data Set Analysis by Combining Human and Algorithmic Analysis, ACS Comb. Sci., 19, pp. 1-8, (2017); Iwasaki Y., Kusne A.G., Takeuchi I., Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries, Npj Computational Materials, 3, (2017); Oviedo F., Ren Z., Sun S., Settens C., Liu Z., Hartono N.T.P., Ramasamy S., Decost B.L., Tian S.I.P., Romano G., Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks, Npj Computational Materials, 5, (2019); Talley K.R., Bauers S.R., Melamed C.L., Papac M.C., Heinselman K.N., Khan I., Roberts D.M., Jacobson V., Mis A., Brennecka G.L., Et al., COMBIgor: Data-Analysis Package for Combinatorial Materials Science, ACS Comb. Sci., 21, pp. 537-547, (2019); Bassman L., Rajak P., Kalia R.K., Nakano A., Sha F., Sun J., Singh D.J., Aykol M., Huck P., Persson K., Et al., Active learning for accelerated design of layered materials, Npj Comput. Mater., 4, (2018); Kusne A.G., Gao T., Mehta A., Ke L., Nguyen M.C., Ho K.-M., Antropov V., Wang C.-Z., Kramer M.J., Long C., Et al., On-the-fly machine-learning for high-throughput experiments: Search for rare-earth-free permanent magnets, Sci. Rep., 4, (2014); Gubernatis J.E., Lookman T., Machine learning in materials design and discovery: Examples from the present and suggestions for the future, Phys. Rev. Materials, (2018); Banko L., Lysogorskiy Y., Grochla D., Naujoks D., Drautz R., Ludwig A., Predicting structure zone diagrams for thin film synthesis by generative machine learning, Commun. Mater., 1, (2020); Iwasaki Y., Takeuchi I., Stanev V., Kusne A.G., Ishida M., Kirihara A., Ihara K., Sawada R., Terashima K., Someya H., Et al., Machine-learning guided discovery of a new thermoelectric material, Sci. Rep., 9, (2019); Raccuglia P., Elbert K.C., Adler P.D.F., Falk C., Wenny M.B., Mollo A., Zeller M., Friedler S.A., Schrier J., Norquist A.J., Machine-learning-assisted materials discovery using failed experiments, Nature, 533, pp. 73-76, (2016)","A. Ludwig; Chair for Materials Discovery and Interfaces, Institute for Materials, Faculty of Mechanical Engineering, Ruhr-Universität Bochum, Bochum, Universitätstraße 150, 44801, Germany; email: alfred.ludwig@rub.de","","American Chemical Society","","","","","","21568952","","ACSCC","32559063","English","ACS Combi. Sci.","Article","Final","","Scopus","2-s2.0-85089611904"
"Smits D.A.B.; Teperek M.","Smits, Daen Adriaan Ben (57224092223); Teperek, Marta (36545554600)","57224092223; 36545554600","Research data management for master’s students: From awareness to action","2020","Data Science Journal","19","1","30","","","","2","10.5334/dsj-2020-030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106864849&doi=10.5334%2fdsj-2020-030&partnerID=40&md5=387b83348a741146c3fb46eda6df251e","TU Delft, Netherlands","Smits D.A.B., TU Delft, Netherlands; Teperek M., TU Delft, Netherlands","This article provides an analysis of how sixteen recently graduated master’s students from the Netherlands perceive research data management. It is important to study the master’s students’ attitudes towards this, as students in this phase prepare themselves for their career. Some of them might become future academics or policymakers, thus, potentially, the future advocates of good data management and reproducible science. In general, students were rather unsure what ‘data management’ meant and would often confuse it with data analysis, study design or methodology, or ethics and privacy. When students defined the concept, they focussed on privacy aspects. Concepts such as open data and the ‘FAIR’ principles were rarely mentioned, even though these are the cornerstones of contemporary data management efforts. In practice, the students managed their own data in an ad hoc way, and only a few of them worked with a clear data management plan. Illustrative of this is that half of the interviewees did not know where to find their data anymore. Furthermore, their study programmes had diverse approaches to data management education. Most of the classes offered were limited in scope. Nevertheless, the students seemed to be aware of the importance of data management and were willing to learn more about good data management practices. This report helps to catch an important first glimpse of how master’s students (from different scientific backgrounds) think about research data management. Only by knowing this, accurate measures can be taken to improve data management awareness and skills. The article also provides some useful recommendations on what such measures might be, and introduces some of the steps already taken by the Delft University of Technology (TU Delft). © 2020 The Author(s).","Education; FAIR Data Principles; Interview; Master Students; Reproducibility crisis; Research Data Management; Survey; University","Information management; Privacy by design; Students; Delft University of Technology; Management education; Management efforts; Management practices; Policy makers; Privacy aspects; Research data managements; Study design; Open Data","","","","","","","Akhmerov A, Steele G., TU Delft Open Data Policy of the Quantum Nanoscience Department, (2019); Baker M., 1,500 Scientists Lift the Lid on Reproducibility, 25 May 2016, Nature News, (2016); Berg J., Editorial Retraction, Science, 356, 6340, (2017); Bloom T, Ganley E, Winker M., Data Access for the Open Access Literature: PLOS’s Data Policy, PLOS Biology, 12, 2, pp. 1-3, (2014); Carlson J, Fosmire M, Miller CC, Nelson MS., Determining Data Information Literacy Needs: A Study of Students and Research Faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J, Stowell-Bracke M., Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, Portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Cruz M., Bringing Researchers Along on the Road to FAIR data, (2019); Cruz M, Dintzner N, Dunning A, Kuil A. van der, Plomp E, Teperek M, der Velden YT, Versteeg A., Policy Needs to Go Hand in Hand with Practice: The Learning and Listening Approach to Data Management, Data Science Journal, 18, 1, (2019); Dunning A., TU Delft Research Data Framework Policy, (2018); Turning FAIR into Reality, (2018); Data management – H2020 Online Manual; Fane B, Ayris P, Hahnel M, Hrynaszkiewicz I, Baynes G, Farrell E., The State of Open Data Report 2019, Digital Science, (2019); Feijen M., What Researchers Want, (2011); Given LM., The SAGE Encyclopedia of Qualitative Research Methods, (2008); Wetenschappelijke Gedragscode Integriteit [Netherlands Code of Conduct for Research Integrity], (2018); Kolb SM., Grounded Theory and the Constant Comparative Method: Valid Research Strategies for Educators, Journal of Emerging Trends in Educational Research and Policy Studies, 3, 1, pp. 83-86, (2012); Model Policy for Research Data Management (RDM) at Research Institutions/Institutes, pp. 133-136, (2017); Mancilla HA, Teperek M, van Dijck J, den Heijer K, Eggermont R, Plomp E, der Velden YT, Kurapati S., On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology, LIBER Quarterly, 29, 1, pp. 1-27, (2019); Miyakawa T., No Raw Data, No Science: Another Possible Source of the Reproducibility Crisis, Molecular Brain, 13, 24, (2020); Onderwijssysteem Nederland [Education system in The Netherlands], (2018); Open (FAIR) data; Piorun M, Kafel D, Leger-Hornby T, Najafi S, Martin E, Colombo P, LaPelle N., Teaching Research Data Management: An Undergraduate/Graduate Curriculum, JESLIB, 1, 1, pp. 46-50, (2012); Roche DG., Evaluating Science’s Open-data Policy, Science, 357, 6352, (2017); Sales L, Henning P, Veiga V, Costa MM, Sayao LF, da Silva Santos LOB, Pires LF., GO FAIR Brazil: A Challenge for Brazilian Data Science, Data Intelligence, 2, 1, pp. 238-245, (2020); Science Journals: editorial policies; Smits DAB., Research Data Attitudes of Recently Graduated Master Students (interview notes and code) [Dataset], (2020); Research Data Policies; Wat is Research Data Management?; Tongco M., Purposive Sampling as a Tool for Informant Selection, Ethnobotany Research and Applications, 5, 1, pp. 147-158, (2007); Personal Data: Personal Research Data Workflow; The Faculty of Mechanical, Maritime and Materials Engineering Research Data Management Policy, (2019); Van Reisen M, Stokmans M, Mawere M, Basajja M, Ong'ayo AO, Nakazibwe P, Kirkpatrick C, Chindoza K., FAIR Practices in Africa, Data Intelligence, 2, 1, pp. 246-256, (2020); Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Bouwman J, Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, (2016); Wittenburg P, Lautenschlager M, Thiemann H, Baldauf C, Trilsbeek P., FAIR Practices in Europe, Data Intelligence, 2, 1, pp. 257-263, (2019); ZonMw-procedure Datamanagement","D.A.B. Smits; TU Delft, Netherlands; email: daen.smits@outlook.com","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85106864849"
"Hettne K.M.; Verhaar P.; Schultes E.; Sesink L.","Hettne, Kristina Maria (8680442700); Verhaar, Peter (57193326196); Schultes, Erik (6603082428); Sesink, Laurents (6504219473)","8680442700; 57193326196; 6603082428; 6504219473","From FAIR leading practices to FAIR implementation and back: An inclusive approach to FAIR at leiden university libraries","2020","Data Science Journal","19","1","40","1","7","6","2","10.5334/DSJ-2020-040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099758880&doi=10.5334%2fDSJ-2020-040&partnerID=40&md5=9af9bc6fc15c28a9045783050250f533","Centre for Digital Scholarship, Leiden University Libraries, Leiden, Netherlands; GO FAIR International Support and Coordination Office (GFISCO), Leiden, Netherlands","Hettne K.M., Centre for Digital Scholarship, Leiden University Libraries, Leiden, Netherlands; Verhaar P., Centre for Digital Scholarship, Leiden University Libraries, Leiden, Netherlands; Schultes E., GO FAIR International Support and Coordination Office (GFISCO), Leiden, Netherlands; Sesink L., Centre for Digital Scholarship, Leiden University Libraries, Leiden, Netherlands","Leiden University (LU) adopted a data management regulation in 2016. The regulation embraces the Findable, Accessible, Interoperable and Reusable (FAIR) principles. To implement the regulation a programme was established. The focus of the programme was initially to raise awareness and to set up services to make data Findable and Accessible and to train researchers on data management planning. In 2019, the programme entered its second phase, with an increased focus on Interoperable and Reusable data, and on implementing the machine-actionable aspects of FAIR data. This step is non-trivial, however, because of the fast-developing FAIR research data international research field that requires fast adoption of leading practices by support professionals with the adequate skills. In this paper we describe how LU aims to close the feedback loop between international bottom-up organisations such as GOFAIR, the Research Data Alliance and CODATA on the one hand and university staff engaged in developing and implementing emerging FAIR leading practices on the other. During processes such as these, it is of crucial importance to focus primarily on the needs of researchers. We describe how LU builds up its support for FAIR data before, during and after research through its involvement in leading practices, training and consultancy and end with recommendations for other universities wanting to implement the FAIR principles. © 2020 The Author(s).","FAIR data; Implementation; Leading practice; Research data management","Information management; Feed-back loop; International researches; Management planning; Management regulations; Non-trivial; Research data; Second phase; University libraries; Libraries","","","","","LU Libraries; LU innovation fund","Until very recently, the CDS was partly supported financially by a LU innovation fund and partly by the LU Libraries. After a successful internal evaluation, based on a questionnaire sent out to all researchers and other users of our services, the CDS is now being funded structurally from the LU central budget. The evaluation specifically stated that the involvement in international initiatives as stated above is important for the LU, allowing for quick action in response to new developments. Specific projects have been funded either from LU research or educational grants, or by external grants, but it has always been the goal to have a core, central, structural funding for the team. This stable funding, in combination with the collaborative skills and the expertise of the members of the CDS team, who have been trained in fields such as Information Technology and Computer Science, form the conditions under which the CDS can actively participate in the development of international Leading Practice. The situation in which data stewards are employed by a specific research project only is clearly undesirable, as there will generally be fewer opportunities for them, in that case, to be involved in similar efforts. The generic tasks of a data steward are to develop RDM infrastructure, policies, and support services. To be able to do this and to further the development of the data stewardship profession itself the data steward needs to balance these activities with innovation. We argue that a data steward should possess a number basic skills in the fields Information Technology and Computer Science, next to innovation skills and collaborative skills. In addition to this, the data steward must be given the opportunity in form of time and funding to take part in leading practices activities to develop the data stewardship profession further. This is important for the less affluent institutes as well, in order to build future-proof FAIR data support. Institutes with smaller budgets can however start on a small scale by being involved in one single working group of one international leading practices organisation, to advance the support in at least one area of FAIR.","(2020); A terminology for FAIR Stewardship Skills Workshop, (2019); CoreTrustSeal, (2019); Cruz M, Et al., Policy Needs to Go Hand in Hand with Practice: The Learning and Listening Approach to Data Management, Data Science Journal, 18, 1, (2019); Erdmann C, Et al., Top 10 FAIR Data & Software Things, (2019); FAIR Data Point Design Specification, (2020); FAIR Implementation Matrix, (2019); FAIR terminology, (2019); Imming M, Et al., FAIR Data Advanced Use Cases: from principles to practice in the Netherlands, (2018); Jointly designing a data FAIRPORT, (2014); Leiden Linguistics Data, (2020); Leiden Research Data Service Catalog, (2020); Magagna B, Et al., Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence, (2020); Mushi GE, Et al., Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania, Data Science Journal, 19, 1, (2020); Research Data Management Regulations Leiden University, (2016); Schultes E, Et al., (2019); Sustkova HP, Et al., FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources, Data Intelligence, 2, 1-2, pp. 158-170, (2020); Tenopir C, Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Verhaar P., (2019); (2020); Wilkinson MD, Et al., The FAIR Guiding Principles for scientific data management and stewardship, (2016); Scientific Data, 3: 160018. Erratum, Scientific Data, 6, 1, (2019); Wittenburg P, Et al., The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data, (2019)","K.M. Hettne; Centre for Digital Scholarship, Leiden University Libraries, Leiden, Netherlands; email: k.m.hettne@library.leidenuniv.nl","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85099758880"
"Adika F.O.; Kwanya T.","Adika, Fredrick Odhiambo (57217033953); Kwanya, Tom (55345776000)","57217033953; 55345776000","Research data management literacy amongst lecturers at Strathmore University, Kenya","2020","Library Management","41","6-7","","447","466","19","5","10.1108/LM-03-2020-0043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085899556&doi=10.1108%2fLM-03-2020-0043&partnerID=40&md5=86026cbb7b30650821a1502be4d382b0","Strathmore University, Nairobi, Kenya; Department of Information and Knowledge Management, The Technical University of Kenya, Nairobi, Kenya","Adika F.O., Strathmore University, Nairobi, Kenya; Kwanya T., Department of Information and Knowledge Management, The Technical University of Kenya, Nairobi, Kenya","Purpose: The purpose of this study was to analyse the skills required by lecturers to be able to support research data management effectively; assess the research data management literacy levels amongst lecturers at Strathmore University; and suggest how research data management capacity can be strengthened to mitigate the knowledge gaps identified. Design/methodology/approach: This study was conducted as a mixed methods research. Explanatory sequential mixed methods approach was used to collect, analyse and interpret quantitative and qualitative data from lecturers at Strathmore University in Nairobi, Kenya. Quantitative data was collected using questionnaires while qualitative data was collected through focus group discussions. Quantitative data was analysed using SPSS while qualitative data was analysed thematically. Findings: The findings of this study indicate varied levels of research data management literacy amongst lecturers at Strathmore University. Lecturers understand the need of having literacy skills in managing research data. They also participate in data creation, collection, processing, validation, dissemination, sharing and archiving. This is a clear indication of good research data management. However, the study also revealed gaps in research data management skills amongst the lecturers in areas such as sharing of research data on open access journals, data legislation and securing research data. Research limitations/implications: The study has been conducted in one university in Kenya. However, the findings have been contextualised in the global landscape through suitable references. Practical implications: The findings of this study may be used to attract the attention of lecturers and librarians to research data management. The findings may also be used to develop institutional policies on research data management at Strathmore University and beyond. The suggested ways of research data capacity strengthening can be adopted or adapted by other universities to enhance research data management. Originality/value: This is an original study. © 2020, Emerald Publishing Limited.","Africa; Kenya; Research data management; Research data management literacy; Strathmore university","","","","","","","","Abok V.A., Kwanya T., Maximising the potential of social media to deliver academic library services to students: a case study of the Technical University of Kenya Library, Inkanyiso Journal of Humanity and Social Sciences, 8, pp. 147-155, (2016); Big Data Strategy – Issues Paper 12, (2013); Anane-Sarpong E., Wangmo T., Ward C.L., Sankoh O., Tanner M., Elger B.S., You cannot collect data using your own resources and put it on open access”: perspectives from Africa about public health data sharing, Developing World Bioethics, 18, pp. 394-405, (2018); Benfield J.A., Szlemko W.J., Internet-based data collection: promises and realities, Journal of Research Practice, 2, pp. 1-15, (2006); Berson A., Dubov L., Master Data Management and Data Governance, (2011); Booth A.L., Burton J., Mumford K., The position of women in UK academic economics, Economic Journal, 110, pp. 312-333, (2000); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, pp. 1059-1078, (2012); Briney K., Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success, Research Skills Series, (2015); Brooks S., Donovan P., Rumble C., Developing nations, the digital divide and research databases, Serials Review, 31, pp. 270-278, (2005); Chawinga W.D., Zinn S., Global perspectives of research data sharing: a systematic literature review, Library and Information Science Research, 41, pp. 109-122, (2019); Chilimo W., Green open access in Kenya: a review of the content, policies and usage of institutional repositories, Mousaion, 33, pp. 25-54, (2015); Coburn C.E., Turner E.O., The practice of data use: an introduction, American Journal of Education, 118, pp. 99-111, (2012); Digital dark age, (2011); Connelly R., Playford C.J., Gayle V., Dibben C., The role of administrative data in the big data revolution in social science research, Social Science Research, 59, pp. 1-12, (2016); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research. Libr, Trends, 61, pp. 636-674, (2013); Cox A.M., Verbaan E., Exploring Research Data Management, (2018); Creamer A., Morales M.E., Crespo J., Kafel D., Martin E.R., An assessment of needed competencies to promote the data curation and management librarianship of health sciences and science and technology librarians in New England, Journal of EScience Librarianship, 1, (2012); Creswell J.W., Creswell J.D., Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, (2018); Davenport T.H., Patil D.J., Data scientist: the sexiest job of the 21st century, Harvard Business Review, pp. 70-76, (2012); Denny S.G., Silaigwana B., Wassenaar D., Bull S., Parker M., Developing ethical practices for public health research data sharing in South Africa: the views and experiences from a diverse sample of research stakeholders, Journal of Empirical Research on Human Research, Ethics, 10, pp. 290-301, (2015); Doorn P., Tjalsma H., Introduction: archiving research data, Archival Science, 7, pp. 1-20, (2007); Earley S., Henderson D., DAMA-DMBOK: Data Management Body of Knowledge, (2017); Fary M., Owen K., Developing an institutional research data management plan service, Advanced Core Technology Initiative, pp. 1-24, (2013); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PloS One, 10, (2015); Ghaffarzadegan N., Xu R., Late retirement, early careers, and the aging of US science and engineering professors, PloS One, 13, (2018); Gupta S., Ethical issues in designing Internet-based research: recommendations for good practice, Journal of Research Practice, 13, (2017); Israel G.D., Determining sample size, (1992); Herzog D., Data Literacy: A User's Guide, (2016); Jahnke L.M., Asher A., The problem of data: data management and curation practices among university researchers, Council Library Information Resources, pp. 3-31, (2012); Jahnke L.M., Asher A., The problem of data: data management and curation practices among university researchers, The Problem of Data, pp. 3-31, (2012); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: how we shape up, Australian Academic and Research Libraries, 45, pp. 296-308, (2014); Kakai M., Musoke M., Okello O.C., Open access institutional repositories in universities in East Africa, Information and Learning Science, 119, pp. 667-681, (2018); Kennan M.A., Data Management: knowledge and skills required in research, scientific and technical organisations, International Federation of Library and Association Institutions IFLA, (2016); Kennan M.A., Markauskaite L., Research data management practices: a snapshot in time, International Journal of Digital Curation, 10, pp. 69-95, (2015); Kibe L., Kwanya T., Owano A., Relationship between big data analytics and organisational performance of the Technical University of Kenya and Strathmore University in Kenya, Global Knowledge Memory and Communication, (2020); Kibugi S., The use of social media in the dissemination of information in selected public and private university libraries in Kenya, Journal of Appropriate Librarianship and Information Work South Africa, 47, pp. 100-120, (2013); Koopman M.M., Data Archiving, Management Initiatives and Expertise in the Biological Sciences Department, (2015); Kothari C.R., Research Methodology: Methods and Techniques, (2014); Kurt S., Why do authors publish in predatory journals? Learn, Learned Publishing, 31, pp. 141-147, (2018); Lewis M., Libraries and the management of research data, Envisioning Future, pp. 145-168, (2010); Lipton V., Open Scientific Data-Why Choosing and Reusing the RIGHT DATA Matters, (2020); Loescher L.J., Hibler E., Hiscox H., Hla H., Harris R.B., Challenges of using the internet for behavioral research, Comput. Inform. Nurs. CIN, 29, (2011); Lyons A.C., Cude B., Lawrence F.C., Gutter M., Conducting research online: challenges facing researchers in family and consumer sciences, Family and Consumer Sciences Research Journal, 33, pp. 341-356, (2005); Marsh J.A., Interventions promoting educators' use of data: research insights and gaps, Teachers College Record, 114, pp. 1-48, (2012); Meyer W., Stockmann R., Functions, Methods and Concepts in Evaluation Research, (2013); Micheni E.M., Using the technology organization environment framework for adoption and implementation of cloud computing in institutions of higher learning in Kenya, International Journal of Engineering Science, 4, pp. 37-43, (2015); Mitroussi A., Mitroussi K., Female educational leadership in the UK and Greece, Gender in Management: An International Journal, 24, pp. 505-522, (2009); Moseti I., Digital preservation and institutional repositories: case study of universities in Kenya, Journal of South African Society of Archivists, 49, pp. 137-154, (2016); Mouton J., Valentine A., The extent of South African authored articles in predatory journals, South African Journal of Science, 113, pp. 1-9, (2017); Mukhwana E.J., Oure S., Too J., Some D.K., State of Post Graduate Research and Training in Kenya, (2016); Mulder N., Adebamowo C.A., Adebamowo S.N., Genomic research data generation, analysis and sharing - challenges in the African setting, Data Science Journal, 16, pp. 1-15, (2017); Muli E., Kimuai J., Adoption of cloud computing for education in Kenyan universities: challenges and opportunities, Int J Adv Res Comput Eng Technol IJARCET, 4, pp. 2854-2860, (2015); Munene I.I., University academics: demographic, role structure characteristics and attitudes towards merit and equity–a Kenyan case study, Research in Post-Compulsory Education, 7, pp. 247-272, (2002); Statement on open access to research publications from the National Research Foundation (NRF)-Funded Research, (2004); Scientific data: data policies, (2014); Nel E., Rich E., Morojele N., Harker B., Petersen N., Williams P., Parry C., Data collection challenges experienced while conducting the International alcohol control study (IAC) in Tshwane, South Africa. Drugs, Drugs: Education, Prevention and Policy, 24, pp. 376-383, (2017); Ng'eno E.J., Research Data Management in Kenya's Agricultural Research Institutes (Doctoral Dissertation), (2018); Ng'eno E., Mutula S., Research Data Management (RDM) in agricultural research institutes: a literature review, Inkanyiso Journal of Humanity and Social Sciences, 10, pp. 28-50, (2018); Nyaigotti-Chacha C., Reforming higher education in Kenya, (2004); Obwogi J., Factors that Affect Quality of Teaching Staff in Universities in Kenya (Doctoral Thesis), (2013); Omwansa T.K., Waema T.M., Omwenga B., Cloud Computing in Kenya (Doctoral Dissertation), (2014); Onyancha O.B., Open research data in sub-Saharan Africa: a bibliometric study using the data citation index, Publishing Research Quarterly, 32, pp. 227-246, (2016); Patterton L., Bothma T.J., Van Deventer M.J., From planning to practice: an action plan for the implementation of research data management services in resource-constrained institutions, South African Journal for Librarianship and Information Science, 84, pp. 14-26, (2018); Peersman G., Overview: data collection and analysis methods in impact evaluation, Methodological Briefs: Impact Eval. Florence UNICEF, 10, (2014); Pisani E., Aaby P., Breugelmans J.G., Carr D., Groves T., Helinski M., Mboup S., Beyond open data: realising the health benefits of sharing data, Bmj, 355, (2016); Piorun M., Kafel D., Leger-Hornby T., Najafi S., Martin E., Colombo P., LaPelle N., Teaching research data management: an undergraduate/graduate curriculum, Journal of EScience Librarianship, 1, pp. 46-50, (2012); Plotkin D., Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance, (2014); Provost F., Fawcett T., Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, (2013); Raburu P., Women Academics' Careers in Kenya (Doctoral Dissertation), (2010); Ratanya F.C., Institutional repository: access and use by academic staff at Egerton University, Kenya, Library Management, 38, pp. 276-284, (2017); Research Data Management: Practical Strategies for Information Professionals, Charleston Insights in Library, Archival, and Information Sciences, (2014); Santos G., Dang V.P.S., Gender and academic rank in the UK, Sustainability, 11, (2019); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: understanding the motivation to publish research data, Government Information Quarterly, 30, pp. 19-31, (2013); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French national research center (CNRS), Data Technol. Appl, (2018); Shabaya P., The Changing Role of Information and Communication Technologies (ICTs) for Instruction in Higher Education Institutions in Kenya, (2009); Shearer B., Schmidt K., Librarians Competencies Profile for Research Data Management, (2016); Sifuna D.N., Chege F.N., Girls' and Women's Education in Kenya: Gender Perspectives and Trends, (2006); Sivarajah U., Kamal M.M., Irani Z., Weerakkody V., Critical analysis of Big Data challenges and analytical methods, Journal of Business Research, 70, pp. 263-286, (2017); Our key milestones, Strathmore University, (2020); Tenreyro S., Royal economic society's report on the gender balance in UK economics departments and research institutes in 2016, Royal Economic Society Women's Committee, (2017); Van Noorden R., Confusion over open-data rules, Nature, 515, (2014); Wasike J.M., Njoroge L., Opening libraries to cloud computing: a Kenyan perspective, Library Hi Tech News, 3, pp. 21-24, (2015); Data management and sharing, Wellcome Trust, (2010); Whyte A., Tedds J., Making the case for research data management, DCC Briefing Papers, (2011); Xia J., Harmon J.L., Connolly K.G., Donnelly R.M., Anderson M.R., Howard H.A., Who publishes in “predatory” journals?, Journal of the Association for Information Science and Technology, 66, pp. 1406-1417, (2015)","T. Kwanya; Department of Information and Knowledge Management, The Technical University of Kenya, Nairobi, Kenya; email: tkwanya@tukenya.ac.ke","","Emerald Group Holdings Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","","Scopus","2-s2.0-85085899556"
"Hofeditz L.; Ross B.; Wilms K.; Rother M.; Rehwald S.; Brenger B.; López A.; Vogl R.; Rudolph D.","Hofeditz, Lennart (57217043574); Ross, Björn (57195061365); Wilms, Konstantin (57190278061); Rother, Marius (57193439774); Rehwald, Stephanie (57207471272); Brenger, Bela (57190878746); López, Ania (55949158100); Vogl, Raimund (6701668973); Rudolph, Dominik (56244944800)","57217043574; 57195061365; 57190278061; 57193439774; 57207471272; 57190878746; 55949158100; 6701668973; 56244944800","How to design a research data management platform? technical, organizational and individual perspectives and their relations","2020","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12185 LNCS","","","324","337","13","2","10.1007/978-3-030-50017-7_23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088746893&doi=10.1007%2f978-3-030-50017-7_23&partnerID=40&md5=bd67566222f2d5cff4e684b12ae69f88","University of Duisburg-Essen, Duisburg, 47057, Germany; RWTH, Aachen, Aachen, 52062, Germany; University of Münster, Münster, 48149, Germany","Hofeditz L., University of Duisburg-Essen, Duisburg, 47057, Germany; Ross B., University of Duisburg-Essen, Duisburg, 47057, Germany; Wilms K., University of Duisburg-Essen, Duisburg, 47057, Germany; Rother M., University of Duisburg-Essen, Duisburg, 47057, Germany; Rehwald S., University of Duisburg-Essen, Duisburg, 47057, Germany; Brenger B., RWTH, Aachen, Aachen, 52062, Germany; López A., University of Duisburg-Essen, Duisburg, 47057, Germany; Vogl R., University of Münster, Münster, 48149, Germany; Rudolph D., University of Münster, Münster, 48149, Germany","Academic research generates increasing amounts of data that needs to be shared between collaborators, made publicly accessible and/or archived for the long term, all while respecting applicable regulations on topics such as data protection. Good research data management (RDM) is challenging, and despite the growing number of available technical solutions, researchers have been reluctant to use them. We interviewed 64 academic researchers to elicit requirements and explore attitudes towards RDM. Although many funding bodies insist that each project follow published RDM guidelines, only about half the participants considered RDM relevant for their own work, and only one in three reported that they already practiced RDM or were planning to do so. The qualitative analysis of the transcripts revealed three broad categories of requirements for RDM platforms, namely technical, organizational and individual ones. We discuss how these requirements are related to, and sometimes contradict each other. © Springer Nature Switzerland AG 2020.","Design requirements; Open science; Qualitative research; RDM platform; Research data management","Data privacy; Information management; Academic research; Funding bodies; Good research; Publicly accessible; Qualitative analysis; Research data managements; Technical solutions; Human computer interaction","","","","","European Commission, EC; Deutsche Forschungsgemeinschaft, DFG; Bundesministerium für Bildung und Forschung, BMBF","Against the background of ongoing digitization in research, an increasing amount of data is being generated by researchers at higher education institutions. As this amount of data seems to grow continuously, researchers will need to change the way they manage their data in order to keep track of it and be able to collaborate effectively. In addition, public funders of research projects such as the European Commission, the German Research Foundation (DFG), and the German Federal Ministry of Education and Research (BMBF) require researchers to maintain their data and make it publicly available. Consequently, academics of all disciplines will need to rethink their data management strategies in order to be able to manage the growing and increasingly complex","Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Schopfel J., Chaudiron S., Jacquemin B., Prost H., Severo M., Thiault F., Open access to research data in electronic theses and dissertations: an overview, Libr. Hi Tech, 32, pp. 612-627, (2014); Vines T.H., Et al., The availability of research data declines rapidly with article age, Curr. Biol, 24, pp. 94-97, (2014); Agarwal R., Dhar V., Big data, data science, and analytics: the opportunity and challenge for IS research, Inf. Syst. Res, 25, pp. 443-448, (2014); Joshi M., Krag S.S., Issues in data management, Sci. Eng. Ethics, 16, pp. 743-748, (2010); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, (2007); Link G., Lumbard K., Germonprez M., Conboy K., Feller J., Contemporary issues of open data in information systems research: considerations and recommendations, Commun. Assoc. Inf. Syst, 41, pp. 587-610, (2018); Yamamoto S., HIMI 2016. LNCS, 9735, (2016); Mayring P., Qualitative Content Analysis, (2014); Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Univers. Access Inf. Soc, 16, pp. 851-862, (2017); Data Management & Sharing FAQs, (2017); Perrier L., Et al., Research data management in academic institutions: a scoping review, PLoS ONE, 12, pp. 1-14, (2017); Reuter C., Ludwig T., Kotthaus C., Kaufhold M.-A., von Radziewski E., Pipek V., Big data in a crisis?, Creating social media datasets for crisis management research, 15, (2016); Pinfield S., Cox A.M., Smith J., Research data management and libraries: relationships, activities, drivers and influences, pp. 1-28, (2014); Hicks B., UK universities put their faith in the Google cloud; Piwowar H.A., Chapman W.W., A review of journal policies for sharing research data, (2008); Campbell E.G., Clarridge B.R., Birenbaum L., Hilgartner S., Blumenthal D., Data withholding in academic genetics: evidence from a national survey, J. Am. Med. Assoc, 287, pp. 473-480, (2002); Borgmann C.L., Scholarship in the Digital Age, (2007); Tenopir C., Et al., Data sharing by scientists: practices and perceptions, PLoS ONE, 6, pp. 1-21, (2011); Kim Y., Zhang P., Understanding data sharing behaviors of STEM researchers: the roles of attitudes, norms, and data repositories, Libr. Inf. Sci. Res, 37, 3, pp. 189-200, (2015); Hilber B., Reintzsch D., Cloud Computing und Open Source-Wie groß ist die Gefahr des Copyleft bei SaaS?, Computer Und Recht: Forum für die Praxis des Rechts der Datenverargei-tung, Information und Automation, pp. 697-702, (2014); Feijen M., What researchers want-A literature study of researchers’ requirements with respect to storage and access to research data, (2011); Wilms K., Brenger B., Lopez A., Rehwald S., Open data in higher education – what prevents researchers from sharing research data?, 39th International Conference on Information System, pp. 1-9, (2018); Karkin N., Janssen M., Brooks K., Open government and data-driven policy making in the digital age, Americas Conference on Information System (AMCIS), (2018); Suptitz T., Weis S.J., Eymann T., Was müssen Virtual Research Environments leis-ten?-Ein Literaturreview zu den funktionalen und nichtfunktionalen Anforderungen, Wirtschaftsinformatik, (2013); Bankier J.G., Gleason K., Institutional repository software comparison, (2014); Deutsche Forschungsgemeinschaft: Memorandum Safeguarding Good Scientific Practice, (2013); Hevner A., Alexander B., Roles of digital innovation in design science research, Bus. Inf. Syst. Eng, 61, pp. 3-8, (2019); Mayring P., Qualitative inhaltsanalyse, Texte ver-stehen: Konzepte, Methoden, Werkzeuge, pp. 159-174, (1994); Bauer B., Et al., Forschende und ihre Daten. Ergebnisse einer österreichweiten Befragung – Report 2015. Zenodo, (2015); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists’ data-sharing behaviors: A multilevel analysis, J. Am. Soc. Inf. Sci, 67, 4, pp. 776-799, (2016); Klemm M., Liebold R., Qualitative Interviews in der Organisationsforschung, Handbuch Empirische Organisationsforschung, pp. 299-324, (2017); Jensen U., Leitlinien zum Management von Forschungsdaten: Sozialwissenschaftliche Umfragedaten, (2012); Berendt B., Vanschoren J., Gao B., Datenanalyse und-visualisierung, Handbuch Forschungsdatenmanagement, pp. 139-148, (2011); Link G., Et al., Contemporary issues of open data in information systems research: considerations and recommendations, Commun. Assoc. Inf. Syst, 41, pp. 587-610, (2017); Ribes D., Polk J.B., Flexibility relative to what? Change to research infrastructure, J. Assoc. Inf. Syst, 15, pp. 287-305, (2014); Vassilakopoulou P., Skorve E., Aanestad M., A commons perspective on genetic data governance, European Conference on Information Systems (ECIS), (2016); Biljon J. V., Pottas A., Lehong S., Platz M., Content category selection towards a maturity matrix for ICT4D knowledge sharing platforms, International Conference on Information Resources Management (Conf-IRM), (2016); Nugroho R.P., Zuiderwijk A., Janssen M., de Jong M., A comparison of national open data policies: lessons learned, Transform. Gov. People, Process Policy, 9, pp. 286-308, (2015)","B. Ross; University of Duisburg-Essen, Duisburg, 47057, Germany; email: bjoern.ross@uni-due.de","Yamamoto S.; Mori H.","Springer","","Thematic Area on Human Interface and the Management of Information, HIMI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020","Copenhagen","242419","03029743","978-303050016-0","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85088746893"
"de Araújo P.C.; de Lima K.C.R.","de Araújo, Paula Carina (57194620527); de Lima, Karolayne Costa Rodrigues (57217083765)","57194620527; 57217083765","Academic library supporting research: The case of Universidade Federal do Paraná Law Library","2020","Cases on Research Support Services in Academic Libraries","","","","167","186","19","0","10.4018/978-1-7998-4546-1.ch008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137112359&doi=10.4018%2f978-1-7998-4546-1.ch008&partnerID=40&md5=de1e4a522302efff77ebf53c5002edfb","Universidade Federal do Paraná (UFPR), Brazil","de Araújo P.C., Universidade Federal do Paraná (UFPR), Brazil; de Lima K.C.R., Universidade Federal do Paraná (UFPR), Brazil","The purpose of this chapter is to examine how the provision of research support services by the Law Library at Universidade Federal do Paraná (UFPR) in Brazil contributes to achieve the university research goals. The chapter develops a case study taking a qualitative, exploratory, and descriptive approach. The UFPR Law Library provides research support services such as classes on research support, bibliographic research support, orientation on research tools. Those research support services are not part of a formal and strategic program. It is recognized that the existence of a data repository, the UFPR Scientific Database, is an opportunity to provide scientific research data management support services at UFPR libraries. The chapter concludes that the existing research support services have an impact on research at UFPR Law School. However, there is an opportunity to create other services that will meet the user's expectations, considering the new research trends at the university. © 2021, IGI Global. All rights reserved.","","","","","","","","","Accart J.P., Serviço de referência: Do presencial ao virtual, (2012); Andrews C., Downs A., Morris-Knower J., Pacion K., Wright S.E., From ""Library as Place"" to ""Library as Platform"": Redesigning the 21st Century Academic Library, The Future of Library Space, pp. 145-167, (2016); Scholarly Communication: From Understanding to Engagement, (2020); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brown S., Alvey E., Danilova E., Morgan H., Thomas A., Evolution of Research Support Services at an Academic Library: Specialist Knowledge Linked by Core Infrastructure, New Review of Academic Librarianship, 24, 3-4, pp. 339-350, (2018); Burke P., Uma história social do conhecimento: De Gutenberg a Diderot, (2003); Curty R.G., Crowston K., Specht A., Grant B.W., Dalton E.D., Attitudes and norms affecting scientists' data reuse, PLoS One, 12, 12, (2017); Ranking Web of Universities: Latin America, (2020); de Araujo P.C., O serviço de pesquisa bibliográfica oferecido pela Biblioteca de Ciências Jurídicas do Sistema de Bibliotecas da Universidade Federal do Paraná (UFPR), Proceedings of Seminário Nacional de Bibliotecas Universitárias, 18, pp. 1-21, (2014); Drummond R., Wartho R., RIMS: The Research Impact Measurement Service at the University of New South Wales, Australian Academic and Research Libraries, 40, 2, pp. 76-87, (2009); Dutra M.L., de Macedo D.D.J., Curadoria digital: Proposta de um modelo de curadoria digital em ambientes de big data baseado numa abordagem semi-automática para seleção de objetos digitais. [Digital curation: Proposal of a digital curation model in big data environments based on a semi-automatic approach to the selection of digital objects], Informação & Informação, 21, 2, pp. 143-169, (2016); Ranking Universitário Folha 2019, (2019); Foutch L.J., A New Partner in the Process: The Role of a Librarian on a Faculty Research Team, Collaborative Librarianship, 8, 2, pp. 80-83, (2016); Fujimoto E.M.V., de Thomaz M.P., de Araujo P.C., Avaliação da qualidade dos produtos e serviços prestados pela Biblioteca De Ciências Jurídicas da Universidade Federal Do Paraná (UFPR). [Evaluation of the quality of products and services provided by the Library of Law Science of the Federal University of Paraná (UFPR)], Proceedings of Seminário Nacional de Bibliotecas Universitárias, 16, pp. 1-12, (2010); Hey A.J.G., Tansley S., Toole K., The fourth paradigm: Data-intensive scientific discovery, (2009); Kennan M.A., Corrall S., Afzal W., Making space"" in practice and education: Research support services in academic libraries, Library Management, 35, 8-9, pp. 666-683, (2014); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists' data-sharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2015); Lynch C., Jim Gray's fourth paradigm and the construction of the scientific record, The fourth paradigm: Data-intensive scientific discovery, pp. 177-183, (2009); Nickels C., Davis H., Understanding researcher needs and raising the profile of library research support, Insights: The UKSG Journal, 33, 1-4, pp. 1-13, (2020); Sewell C., Kingsley D., Developing the 21st Century Academic Librarian: The Research Support Ambassador Programme, New Review of Academic Librarianship, 23, 2-3, pp. 148-158, (2017); Spera H.B., Mugnaini R., Características da produção científica em direito: Desafios para a avaliação. [Characteristics of scientific production in law science: Challenges for evaluation], Proceedings of Encontro Nacional de Pesquisa em Ciência da Informação, 21, pp. 1-17, (2019); Tang Y., Zhang C., Development and Practice of Research Support Services in Peking University Library, International Journal of Library and Information Services, 8, 2, pp. 22-39, (2019); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Tenopir C., Birch B., Allard S., Academic Libraries and research data services: Current practices and plans for the future an ACRL white paper, (2012); Tise E., Raju R., Adam A., From research support to research partners, The quest for deeper meaning of research support, pp. 1-12, (2015); Relatório de atividades UFPR 2012 [UFPR 2012 Activity Report], (2012); Planejamento estratégico do Sistema de Bibliotecas: Gestão 2014-2018 [Library System Strategic Planning: 2014-2018 Management], (2015); Relatório de gestão 2018 [Management Report 2018], (2019); Histórico [Historical], (2020); Missão, Visão, Valores e Princípios. [Mission, Vision, Values and Principles], (2020); Indicadores institucionais UFPR. [UFPR institutional indicators], (2020)","","","IGI Global","","","","","","","978-179984547-8; 978-179984546-1","","","English","Cases on res. Support Services in Academic Libraries","Book chapter","Final","","Scopus","2-s2.0-85137112359"
"Thielen J.; Neeser A.","Thielen, Joanna (57191907253); Neeser, Amy (57203371995)","57191907253; 57203371995","Making job postings more equitable: Evidence based recommendations from an analysis of data professionals job postings between 2013-2018","2020","Evidence Based Library and Information Practice","15","3","","103","156","53","0","10.18438/eblip29674","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091379482&doi=10.18438%2feblip29674&partnerID=40&md5=6f04aa1a499a9aa14b1c40695cddf4e3","Art, Architecture and Engineering Library, University of Michigan, Ann Arbor, MI, United States; Research IT, University of California Berkeley, Berkeley, CA, United States","Thielen J., Art, Architecture and Engineering Library, University of Michigan, Ann Arbor, MI, United States; Neeser A., Research IT, University of California Berkeley, Berkeley, CA, United States","Objective - Over the last decade, many academic libraries have hired data professionals to offer research data services. As these positions often require different types of experience than traditional librarian positions, there is an increased interest in hiring professionals from outside the typical library and information science (LIS) pipeline. More broadly, there has also been an increased interest in academic libraries and higher education to incorporate the principles and practices of diversity, equity, inclusion, and accessibility (DEI&A) into their work. These phenomena allow an opportunity to examine the growing area of data professionals and library hiring practices through the lens of DEI&A. Data was collected from 180 data professional job positions, including education, experiences, and skills, to better understand the evolving and complex landscape of data professionals and to provide evidence based recommendations regarding how the profession can enact meaningful and lasting change in the areas of DEI&A. Methods - The qualifications and responsibilities listed in data professional job postings from 2013 to 2018 were examined. Prior to analyzing the job postings, a codebook of 43 variables was developed. The 177 data professional job postings (corresponding to 180 positions) were independently analyzed, noting the presence of each variable, including the locations and the degrees of complexity sought. After coding, discrepancies were mutually resolved. Overall, the coding process had 94% intercoder agreement, which indicates a high level of agreement. Results - Over one-third of postings (n = 63, 35%) did not use the word ""librarian"" in the job title. Eighty-eight percent (n = 159) required a Master's in LIS degree, but 67% (n = 119) also accepted an equivalent degree. Over half of the positions (n = 108, 60%) were also looking for an additional degree, most frequently a graduate degree. The median salary of the positions listing a quantitative value was $57,000; however, this value may not be accurate because only 26% of job positions (n = 47) gave a quantitative salary. From the research data management skills mentioned, general data management (n = 155, 86%), data repositories (n = 122, 68%), and data curation (n = 101, 56%) appeared most frequently. Libraries were also looking for traditional LIS skills and experiences, including instruction (n = 138, 77%), consultation (n = 121, 67%), and a public services perspective (n = 69, 38%). Conclusion - The results show that academic libraries are trying to recruit candidates from outside the traditional academic library pipeline. Research data activities (a non-traditional area for LIS) and traditional LIS areas were both frequently mentioned. Overall, these job positions should be written through a more intentional lens of DEI&A. This would help to make data professional positions more diverse and inclusive, while also helping academic libraries to reach their goal of recruiting outside of LIS. A set of concrete DEI&A recommendations are provided that are applicable for writing all library positions, so that readers can put these results into action and enact meaningful change within the profession. © 2020 Thielen and Neeser.","","","","","","","","","2016 top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 77, 6, pp. 274-281, (2016); 2018 top ten trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 79, 6, pp. 286-300, (2018); Association of College & Research Libraries Science & Technology Section; Brown S., Want a more diverse campus? Start at the top, The Chronicle of Higher Education, (2019); Code4Lib Jobs; Chen H., Zhang Y., Educating data management professionals: A content analysis of job descriptions, The Journal of Academic Librarianship, 43, 1, pp. 18-24, (2017); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Digital Library Federation Job Board; Educause Diversity, Equity, and Inclusion; Fernandes J.D., Sarabipour S., Smith C.T., Niemi N.M., Jadavji N.M., Kozik A.J., Holehouse A.S., Pejaver V., Symmons O., Filho A.W.B., Haage A., A survey-based analysis of the academic job market, eLife, 9, (2020); Guest G., MacQueen K., Namey E., Applied Thematic Analysis, (2012); Hall-Ellis S.D., Descriptive impressions of entry-level cataloger positions as reflected in American Libraries, AutoCAT, and the Colorado State Library Jobline, 2000-2003, Cataloging & Classification Quarterly, 40, 2, pp. 33-72, (2005); Hall-Ellis S.D., Descriptive impressions of managerial and supervisory cataloger positions as reflected in American Libraries, AutoCAT, and the Colorado State Library Jobline, 2000-2004: A content analysis of education, competencies, and experience, Cataloging & Classification Quarterly, 42, 1, pp. 55-92, (2006); About the Carnegie Classification, The Carnegie Classification of Institutions of Higher Education, (2017); International Association for Social Science Information Service & Technology Jobs Portal; Internet Archive WayBack Machine; Jardine F.M., Zerhusen E.K., Charting the course of equity and inclusion in LIS through iDiversity, The Library Quarterly, 85, 2, pp. 185-192, (2015); Kim J., Angnakoon P., Research using job advertisements: A methodological assessment, Library & Information Science Research, 38, 4, pp. 327-335, (2016); Kim J., Warga E., Moen W., Competencies required for digital curation: An analysis of job advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Mohr T.S., Why women don't apply for jobs unless they're 100% qualified, Harvard Business Review, (2014); Oh B., Kim C., Broken promise of college? New educational sorting mechanisms for intergenerational association in the 21st century, Social Science Research, 86, pp. 1-15, (2020); Research Data Access & Preservation Association; Si L., Zhuang X., Xing W., Guo W., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Silva E., Galbraith Q., Salary negotiation patterns between women and men in academic libraries, College & Research Libraries, 79, 3, pp. 324-335, (2018); Soto M., Yao C., Retention of women of color in STEM doctoral programs, Proceedings of the 29th Annual Midwest Research to Practice Conference in Adult, Continuing, Community, and Extension Education, pp. 207-213, (2010); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Kaufman J., Sandusky R., Pollock C., Research data services in academic libraries: Where are we today?, Choice, (2019); Thielen J., Neeser A., How you can write more inclusive data practitioner job postings, Journal of eScience Librarianship, 8, 2, (2019); Berkeley strategic plan, Berkeley University of California, (2018); Rackham Professional Development Diversity, Equity, and Inclusion Certificate, Rackham Graduate School, (2020); Willis D.S., Getting up to speed on diversity, Inside Higher Ed, (2017); Xia J., Wang M., Competencies and responsibilities of social science data librarians: An analysis of job descriptions, College & Research Libraries, 75, 3, pp. 362-388, (2014); Yoon K., Hulscher L., Dols R., Accessibility and diversity in library and information science: Inclusive information architecture for library websites, The Library Quarterly, 86, 2, pp. 213-229, (2016)","","","University of Alberta","","","","","","1715720X","","","","English","Evid. Based Libr. Inf. Pract.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85091379482"
"Virkus S.; Garoufallou E.","Virkus, Sirje (6507680734); Garoufallou, Emmanouel (23666959600)","6507680734; 23666959600","Data science from a library and information science perspective","2019","Data Technologies and Applications","53","4","","422","441","19","25","10.1108/DTA-05-2019-0076","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072114174&doi=10.1108%2fDTA-05-2019-0076&partnerID=40&md5=36bb1a290b7bc0d935f07c99d15c000e","School of Digital Technologies, Tallinn University, Tallinn, Estonia; Department of Library Science and Information Systems, Alexander Technological Educational Institute of Thessaloniki, Thessaloniki, Greece; Deltos Group, Thessaloniki, Greece","Virkus S., School of Digital Technologies, Tallinn University, Tallinn, Estonia; Garoufallou E., Department of Library Science and Information Systems, Alexander Technological Educational Institute of Thessaloniki, Thessaloniki, Greece, Deltos Group, Thessaloniki, Greece","Purpose: Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective. Design/methodology/approach: Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective? Findings: The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. Research limitations/implications: The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.” Originality/value: The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals. © 2019, Emerald Publishing Limited.","Bibliographic analysis; Business value; Data management; Data science; Data scientist; Information science; IoT; Library science; Literature review; Skills","Data Science; Database systems; Internet of things; Knowledge management; Publishing; Bibliographic analysis; Business value; Data scientist; Design/methodology/approach; IoT; Library and information science; Library science; Literature reviews; Skill; Web of Science; Libraries","","","","","","","Agarwal R., Dhar V., Big data, data science, and analytics: the opportunity and challenge for IS research, Information Systems Research, 25, 3, pp. 443-448, (2014); Akerkar R., Sajja P.S., Intelligent Techniques for Data Science, (2016); Amirian P., van Loggerenberg F., Lang T., Data science and analytics, Big Data in Healthcare, SpringerBriefs in Pharmaceutical Science & Drug Development, pp. 15-37, (2017); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: science librarians’ participation in data management at association of research libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Aristodemou L., Tietze F., The state-of-the-art on intellectual property analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data, World Patent Information, 55, pp. 37-51, (2018); Baskarada S., Koronios A., Unicorn data scientist: the rarest of breeds, Program, 51, 1, pp. 65-74, (2017); Bell G., Hey T., Szalay A., Beyond the data deluge, Science, 323, 5919, pp. 1297-1298, (2009); Bertolucci J., Are you recruiting a data scientist or a unicorn?, (2013); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Brunner R.J., Kim E.J., Teaching data science, Procedia Computer Science, 80, pp. 1947-1956, (2016); Cady F., The Data Science Handbook, (2017); Cao L., Data Science Thinking: The Next Scientific, Technological and Economic Revolution, (2018); Carter D., Sholler D., Data science on the ground: hype, criticism, and everyday work, Journal of the Association for Information Science and Technology, 67, 10, pp. 2309-2319, (2016); Cervone H.F., Informatics and data science: an overview for the information professional, Digital Library Perspectives, 32, 1, pp. 7-10, (2016); Chatfield A.T., Shlemoon V.N., Redublado W., Rahman F., Data scientists as game changers in big data environments, Proceedings of the 25th Australasian Conference on Information Systems, Auckland University of Technology, pp. 1-11, (2014); Chen C.P., Zhang C.-Y., Data-intensive applications, challenges, techniques and technologies: a survey on big data, Information Sciences, 275, pp. 314-347, (2014); Cleveland W.S., Data science: an action plan for expanding the technical areas of the field of statistics, International Statistical Review, 69, 1, pp. 21-26, (2001); Costa C., Santos M.Y., The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age, International Journal of Information Management, 37, 6, pp. 726-734, (2017); Cukier K., Mayer-Schonberger V., The rise of big data: how it’s changing the way we think about the world, Foreign Affairs, 92, 3, pp. 28-40, (2013); Davenport T.H., Patil D.J., Data scientist: the sexiest job of the 21st century, Harvard Business Review, 90, 5, pp. 70-76, (2012); Demchenko Y., Belloum A., Wiktorski T., EDISON data science framework: part 1. 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Virkus; School of Digital Technologies, Tallinn University, Tallinn, Estonia; email: sirje.virkus@tlu.ee","","Emerald Publishing","","","","","","25149288","","","","English","Data Technol. Appl.","Article","Final","","Scopus","2-s2.0-85072114174"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Accepted and Emerging Roles of Academic Libraries in Supporting Research 2.0","2019","Journal of Academic Librarianship","45","2","","75","80","5","29","10.1016/j.acalib.2019.01.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060303268&doi=10.1016%2fj.acalib.2019.01.001&partnerID=40&md5=4cbb89f86a62ffa2ef4a231eb30d859e","Eszterházy Károly University, Rákóczi út 53, Jászberény, Hungary","Koltay T., Eszterházy Károly University, Rákóczi út 53, Jászberény, Hungary","This paper identifies some of the tasks and roles that academic libraries have to fulfil in order to react to the appearance of Research 2.0 that materialises in data intensive research and requires supporting activities. Reacting to the appearance of Research 2.0 by becoming service providers for scholars working on data-intensive tasks will become an imperative for libraries worldwide, even though due to the differences between countries and institutions, the tasks described in this paper may not seem urgent today. On the other hand, the issues, we identified are already part of everyday best practices in several institutions. Some of them are fairly recent or have taken new characteristics. A few roles identified in this paper are on their way to become standard occupation, while there are still ones that require innovative approaches. Our argument is based on a non-exhaustive review of the recent literature, reporting both on theoretical and practical issues and presenting the results of empirical research in the field. © 2019 Elsevier Inc.","Academic libraries; Data librarians; Data literacy; Data science; Research data management; Research data services","","","","","","","","Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 6, pp. 271-289, (2017); Barbrow S., Brush D., Goldman J., Research data management and services: Resources for novice data librarians, College and Research Libraries News, 7, 85, pp. 274-278, (2017); Bates J., The politics of data friction, Journal of Documentation, 7, 42, pp. 412-429, (2017); Borgman C.L., Big data, little data, no data: Scholarship in the networked world, (2015); Borrego A., Ardanuy J., Urbano C., Librarians as research partners: Their contribution to the scholarly endeavour beyond library and information science, The Journal of Academic Librarianship, (2018); Briney K., Strategic planning for research data services, Bulletin of the Association for Information Science and Technology, 42, 4, pp. 39-41, (2016); Bryant R., Lavoie B.F., Malpas C., The realities of research data management. Part one: A tour of the Research Data Management (RDM) service space, (2017); Bugaje M., Chowdhury G., Is data retrieval different from text retrieval? An exploratory study, pp. 97-103, (2017); Burgi P.Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs, IFLA Journal, 43, 1, pp. 5-21, (2017); Burton M., Lyon L., Data science in libraries, Bulletin of the Association for Information Science and Technology, 43, 4, pp. 33-35, (2017); Cao L., Data science: nature and pitfalls, IEEE Intelligent Systems, 31, 5, pp. 66-75, (2016); Chiware E.R., Becker D.A., Research data management services in Southern Africa: A readiness survey of academic and research libraries, African Journal of Library, Archives and Information Science, 28, 1, pp. 1-16, (2018); Christensen-Dalsgaard B., van den Berg M., Grim R., Horstmann W., Jansen D., Pollard T., Roos A., Ten recommendations for libraries to get started with research data management, Final report of the LIBER working group on E-Science/Research Data Management, (2012); Cox A., Academic librarianship as a data profession, Information Today Europe/ILI365, (2018); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib Journal of Information Management, 70, 2, pp. 142-157, (2018); Cox A., Verbaan E., Exploring Research Data Management, (2018); Daraio C., Lenzerini M., Leporelli C., Naggar P., Bonaccorsi A., Bartolucci A., The advantages of an ontology-based data management approach: Openness, interoperability and data quality, Scientometrics, 108, 1, pp. 441-455, (2016); Davenport T., Patil D., Data scientist: The sexiest job of the 21st century, Harvard Business Review, 90, 5, pp. 70-76, (2012); Erway R., Rinehart A., If you build it, will they fund? Making research data management sustainable, (2016); Faniel I.M., Connaway L.S., Librarians' perspectives on the factors influencing research data management programs, College and Research Libraries, 79, 1, pp. 100-119, (2018); Federer L., Defining data librarianship: A survey of competencies, skills, and training, Journal of the Medical Library Association, 106, 3, pp. 294-303, (2018); Federer L., Taxonomy of skills and expertise for data librarians, (2018); Floridi L., Ethics in the age of information, (2016); Gabbay L.K., Shoham S., The role of academic libraries in research and teaching, Journal of Librarianship and Information Science, (2017); Grant R., Recordkeeping and research data management: A review of perspectives, Records Management Journal, 27, 6, pp. 159-174, (2017); Huling N., Dallas L.J., Kinder J.B., Whitlatch J.B., Woodard B., Professional competencies for reference and user services librarians, (2017); Johnston L.R., Carlson J., Hudson-Vitale C., Imker H., Kozlowski W., Olendorf R., Stewart C., How important are data curation activities to researchers? Gaps and opportunities for academic libraries, Journal of Librarianship and Scholarly Communication, 6, (2018); Kirkwood R.J., Collection development or data-driven content curation?, Library Management, 37, 4-5, pp. 275-284, (2016); Koltay T., Data literacy: In search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Data governance, data literacy and the management of data quality, IFLA Journal, 42, 4, pp. 303-312, (2016); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2017); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); LERU, LERU roadmap for research data, League of European Research Universities Research Data Working Group, Leuven, (2013); Lyon L., Mattern E., Education for real-world data science roles (part 2): A translational approach to curriculum development, International Journal of Digital Curation, 11, 6, pp. 13-26, (2016); Mason J., Khan K., Smith S., Literate, numerate, discriminate: Realigning 21st century skills, pp. 609-614, (2016); Maxwell D., Norton H., Wu J., The data science opportunity: Crafting a holistic strategy, Journal of Library Administration, 58, 2, pp. 111-127, (2018); Ogier A.L., Stamper M.J., Data visualization as a library service: Embedding visualization services in the library research lifecycle, Journal of eScience Librarianship, 71, (2018); Pasquetto I.V., Sands A.E., Borgman C.L., Exploring openness in data and science: What is open, to whom, when, and why?, pp. 141-144, (2015); Patel D., Research data management: A conceptual framework, Library Review, 65, 4-5, pp. 226-241, (2016); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, pp. 131-359, (2018); Pinfield S., Cox A.M., Rutter S., Mapping the future of academic libraries: A report for SCONUL, Society of College, National & University Libraries, London, (2017); RDA, 23 things: Libraries for research data. Research Data Alliance, Libraries for Research Data Interest Group, (2015); Rice R., Southall J., The data librarian's handbook, (2016); Robinson L., Information science: Communication chain and domain analysis, Journal of Documentation, 65, 4, pp. 578-591, (2009); Robinson L., Between the deluge and the dark age: Perspectives on data curation, Alexandria, 26, 6, pp. 73-76, (2016); Robinson L., Bawden D., The story of data': A socio-technical approach to education for the data librarian role in the CityLIS library school at City, University of London, Library Management, 38, 6-7, pp. 312-322, (2017); Schmidt B., Shearer K., Librarians' competencies profile for research data management. Joint task force on librarians' competencies in support of e-research and scholarly communication, (2016); Semeler A.R., Pinto A.L., Rozados H.B.F., Data science in data librarianship: Core competencies of a data librarian, Journal of Librarianship and Information Science, (2017); Silvello G., Theory and practice of data citation, Journal of the Association for Information Science and Technology, 69, 1, pp. 6-20, (2018); Solodovnik I., Budroni P., Preserving digital heritage. At the crossroads of trust and linked open data, IFLA Journal, 41, 3, pp. 251-264, (2015); Tenopir C., Pollock D., Allard S., Hughes D., Research data services in European and North American libraries: Current offerings and plans for the future, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Thomas C.V., Urban R.J., What do data librarians think of the MLIS? Professionals' perceptions of knowledge transfer, trends, and challenges, College & Research Libraries, 79, 3, pp. 401-423, (2018); Wang L., Twinning data science with information science in schools of library and information science, Journal of Documentation, 74, 6, pp. 1243-1257, (2018); Whitlatch J.B., Bodner N.E., Diefenthal M.Z., Huling N., Kluegel K.M., Professional competencies for reference and user services librarians, Reference & User Services Quarterly, 424, pp. 290-295, (2003); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: Domain-based data management training for liaison librarians, The Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College and Research Libraries, 78, 7, (2017); Yu H.H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, The Electronic Library, 35, 4, pp. 783-797, (2017)","","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85060303268"
"Higman R.; Bangert D.; Jones S.","Higman, Rosie (56835600200); Bangert, Daniel (56037329000); Jones, Sarah (57203292365)","56835600200; 56037329000; 57203292365","Three camps, one destination: The intersections of research data management, FAIR and Open","2019","Insights: the UKSG Journal","32","","","","","","19","10.1629/uksg.468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077198586&doi=10.1629%2fuksg.468&partnerID=40&md5=73c9dc8a5a0bb8c8dff0065926bcaba7","University Library, University of Göttingen, Germany; Digital Curation Centre, University of Glasgow, United Kingdom; Research Services Librarian (Data), Research Services, Red 1, University of Manchester Library, University of Manchester, Oxford Road, Manchester, M13 9PP, United Kingdom","Higman R., Research Services Librarian (Data), Research Services, Red 1, University of Manchester Library, University of Manchester, Oxford Road, Manchester, M13 9PP, United Kingdom; Bangert D., University Library, University of Göttingen, Germany; Jones S., Digital Curation Centre, University of Glasgow, United Kingdom","Open data, FAIR (findable, accessible, interoperable and reusable) and research data management (RDM) are three overlapping but distinct concepts, each emphasizing different aspects of handling and sharing research data. They have different strengths in terms of informing and influencing how research data is treated, and there is much scope for enrichment of data if they are applied collectively. This paper explores the boundaries of each concept and where they intersect and overlap. As well as providing greater definitional clarity, this will help researchers to manage and share their data, and those supporting researchers, such as librarians and data stewards, to understand how these concepts can best be used in an advocacy setting. FAIR and open both focus on data sharing, ensuring content is made available in ways that promote access and reuse. Data management by contrast is about the stewardship of data from the point of conception onwards. It makes no assumptions about access, but is essential if data are to be meaningful to others. The concepts of FAIR and open are more noble aspirations and are, this paper argues, a useful way to engage researchers and encourage good data practices from the outset. © 2019 Rosie Higman, Daniel Bangert and Sarah Jones.","FAIR; Open data; Open science; RDM; Research data management","","","","","","Australian Research Data Commons; American Geophysical Union, AGU; European Commission, EU","Although FAIR grew out of a life sciences workshop in Leiden, the principles were intentionally articulated in a broad sense to apply to all types of data. Indeed, they are being applied in various contexts; the European Commission has put the FAIR principles at the heart of their research data pilot alongside open data.16 Beyond Europe, the American Geophysical Union (AGU) has a project on Enabling FAIR Data17 and the Australian Research Data Commons (ARDC) supports a FAIR programme.18","The FAIR Data Principles; Principles and Guidelines for Access to Research Data from Public Funding, (2007); G8 Science Ministers Statement, (2013); Jones S., Open Data, FAIR Data and RDM: The Ugly Duckling, (2018); Briney K., Data Management for Researchers, (2015); Corti L., Van Den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data, (2014); The FAIR Data Principles; Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Mons B., Et al., Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud, Information Services & Use, 37, 1, pp. 49-56, (2017); Wilkinson M.D., Et al., A design framework and exemplar metrics for FAIRness, Scientific Data, 5, (2018); Fair data maturity model WG, Research Data Alliance; Introduction: Open science training handbook, FOSTER; Design issues: Linked data, Tim Berners Lee; Kim J.G., Hausenblas M., 5-Star Open Data; Turning FAIR into Reality, (2018); Open research data the new norm in H2020, OpenAIRE; Enabling FAIR data project, Coalition for Publishing Data in the Earth and Space Sciences; The FAIR data principles, Australian National Data Service; Hong N.C., Katz D.S., FAIR Enough? Can We (Already) Benefit from Applying the FAIR Data Principles to Software? (Version 2), (2018); Engaging Researchers in Good Data Management Conference 2017","R. Higman; Research Services Librarian (Data), Research Services, Red 1, University of Manchester Library, University of Manchester, Manchester, Oxford Road, M13 9PP, United Kingdom; email: rosie.higman@manchester.ac.uk","","Ubiquity Press","","","","","","20487754","","","","English","Insights UKSG J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85077198586"
"Federer L.M.; Qin J.","Federer, Lisa M. (55918619800); Qin, Jian (16023002400)","55918619800; 16023002400","Beyond the data management plan: Expanding roles for librarians in data science and open science","2019","Proceedings of the Association for Information Science and Technology","56","1","","529","531","2","2","10.1002/pra2.82","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075929639&doi=10.1002%2fpra2.82&partnerID=40&md5=cad4cce2dfb9d723b42e874314d3164a","National Library of Medicine, National Institutes of Health, Bethesda, MD, United States; School of Information Studies, Syracuse University, United States","Federer L.M., National Library of Medicine, National Institutes of Health, Bethesda, MD, United States; Qin J., School of Information Studies, Syracuse University, United States","Support for research data management (RDM) has become a popular focus for library services, and many training opportunities in RDM exist for librarians. However, fewer opportunities exist for librarians to develop the more advanced skills and expertise necessary to support data science and open science. To better understand how to develop a library workforce prepared to develop services in the emerging areas of data science and open science, the National Library of Medicine convened a workshop of 15 librarians and information professionals with a range of expertise in data and open science. This session will present the workshop's recommendations and use participatory facilitation methods to engage attendees in conversation about how to prepare information professionals to meet the evolving challenges of data science and open science. Author(s) retain copyright, but ASIS&T receives an exclusive publication license","data analytics; Data science; digital literacy; education in information sciences; library education; open science","Data Science; E-learning; Information management; Information services; Libraries; Data analytics; Digital literacies; Education in information science; Information professionals; Library education; Library services; Management plans; National library of medicines; Open science; Research data managements; Data Analytics","","","","","National Institutes of Health, NIH; U.S. National Library of Medicine, NLM"," The authors thank the workshop participants who graciously offered their time and insights, and Sarah Clarke and Maryam Zaringhalam for their assistance in workshop planning and reporting. This work was carried out by staff of the National Library of Medicine (NLM), National Institutes of Health, with support from NLM.  ","Brandenburg M.D., Joque J., Contextualizing visualization in library services, The medical library association guide to data management for librarians, pp. 139-150, (2016); Miller C., Miller R., Phillips G., (2018); Read K., LaPolla F., A new hat for librarians: providing REDCap support to establish the library as a central data hub, Journal of the Medical Library Association, 106, 1, pp. 120-126, (2018); Sayre F., Riegelman A., The reproducibility crisis and academic libraries, College & Research Libraries, 79, 1, pp. 2-9, (2018); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, College & Research Libraries, 78, 7, pp. 920-933, (2017)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85075929639"
"Read K.B.; Koos J.; Miller R.S.; Miller C.F.; Phillips G.A.; Scheinfeld L.; Surkis A.","Read, Kevin B. (57205931894); Koos, Jessica (57203942681); Miller, Rebekah S. (57209890729); Miller, Cathryn F. (57209888045); Phillips, Gesina A. (57209887940); Scheinfeld, Laurel (57202820234); Surkis, Alisa (57190153933)","57205931894; 57203942681; 57209890729; 57209888045; 57209887940; 57202820234; 57190153933","A model for initiating research data management services at academic libraries","2019","Journal of the Medical Library Association","107","3","","432","441","9","16","10.5195/jmla.2019.545","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068957423&doi=10.5195%2fjmla.2019.545&partnerID=40&md5=ac47fd8977cdef7d2747ebb66fb360c3","NYU Health Sciences Library, New York University School of Medicine, New York, NY, United States; Health Sciences Library, Stony Brook University, United States; Falk Library, Health Sciences Library System, University of Pittsburgh, Pittsburgh, PA, United States; Gumberg Library, Duquesne University, Pittsburgh, PA, United States; Hillman Library, University of Pittsburgh, Pittsburgh, PA, United States; Health Sciences Library, Stony Brook University, Stony Brook, NY, United States; Health Sciences Library, New York University (NYU) School of Medicine, New York, NY, United States","Read K.B., NYU Health Sciences Library, New York University School of Medicine, New York, NY, United States; Koos J., Health Sciences Library, Stony Brook University, United States; Miller R.S., Falk Library, Health Sciences Library System, University of Pittsburgh, Pittsburgh, PA, United States; Miller C.F., Gumberg Library, Duquesne University, Pittsburgh, PA, United States; Phillips G.A., Hillman Library, University of Pittsburgh, Pittsburgh, PA, United States; Scheinfeld L., Health Sciences Library, Stony Brook University, Stony Brook, NY, United States; Surkis A., Health Sciences Library, New York University (NYU) School of Medicine, New York, NY, United States","Background: Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM. Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) the Teaching Toolkit to teach an introductory RDM class, or (3) strategies for hosting a data class series. Case Presentation: Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules. Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes. All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library. Conclusion: The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services. The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services. The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions. © 2019, Medical Library Association. All rights reserved.","","Adult; Biomedical Research; Data Management; Female; Humans; Librarians; Libraries, Medical; Library Services; Male; Middle Aged; Pilot Projects; Research Personnel; United States; adult; article; case report; clinical article; female; human; interview; librarian; library; male; scientist; teaching; education; information processing; library; medical research; middle aged; organization and management; personnel; pilot study; procedures; United States","","","","","National Science Foundation, NSF; National Institutes of Health, NIH; U.S. National Library of Medicine, NLM, (R25LM012283, UG4LM012342); Drexel University; Temple University, TU","Funding text 1: Institutions used several methods for identifying researchers to interview, including (1) searching for researchers with active grants using National Institutes of Health (NIH) RePORTER and the National Science Foundation (NSF) funding lists, (2) mining college and department websites to identify junior and senior faculty, (3) collaborating with liaison librarians to target researchers with an existing relationship with the library, and (4) using lists of researchers who had participated in past surveys. Institutions that had the greatest success recruiting interviewees relied mainly on outreach to researchers who had previous relationships with the library. Challenges described included scheduling issues, the lack of preexisting relationships with researchers, and, for one institution, a requirement for institutional review board approval. Librarians from each institution recorded the number of interviews that they conducted and, optionally, the interviewee’s academic department. Each institution interviewed between six and fifteen researchers (Table 1).; Funding text 2: This program was supported by the National Library of Medicine (NLM), National Institutes of Health (NIH), under cooperative agreement number UG4LM012342 with the University of Pittsburgh, Health Sciences Library System, and NIH R25LM012283. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We acknowledge pilot participants of this program, specifically those from University at Buffalo: Nell Aronoff, Donna R. Berryman, AHIP, Amy Gische Lyons, AHIP, FMLA, Michelle L. Zafron, Elizabeth Stellrecht, and Linda Lohr; University of Delaware: Sarah E. Katz, Tom Melvin, Natalia Lopez, and Sandra Millard; Drexel University: Abby L. Adamczyk, AHIP, Elizabeth Ten Have, Kathleen Turner, Janice Masud-Paul, and Deborah Morley; and Temple University: Jenny Pierce, Natalie Tagge, and Nancy Turner. The authors also thank Richard McGowan for discussion and comment on the manuscript.","Brennan P.F., Prepared statement of Patricia Flatley Brennan director, National Library of Medicine [Internet], Bethesda, MD: National Library of Medicine, (2018); Tibbo H., Jones S., Research Data Management and Sharing, (2017); Martin E., Goldman J., Best Practices for Biomedical Research Data Management, (2018); Zhao S., A new training program: Biomedical and health research data management for librarians, National Network of Libraries of Medicine National Training Office Blog [Internet]: National Network of Libraries of Medicine, (2017); Martin E., Goldman J., Kafel D.M., Creamer A.T., New England Collaborative Data Management Curriculum, (2017); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: Domain-based data management training for liaison librarians, J Acad Librariansh, 44, 3, pp. 323-329, (2018); Carlson J.R., Opportunities and barriers for librarians in exploring data: Observations from the data curation profile workshops, J Esci Librariansh, 2, 2, (2013); Read K.B., Surkis A., A 2015 Survey of Health Sciences Librarians Attitudes Towards Research Data Management Education, (2019); Delaney G., Bates J., Envisioning the academic library: A reflection on roles, relevancy and relationships, New Rev Acad Librariansh, 21, 1, pp. 30-51, (2015); Lewis M.J., Libraries and the management of research data, Mcknight S, Ed. Envisioning Future Academic Library Services. London, UK: Facet Publishing, (2010); Godwin P., Parker J., Information Literacy Meets Library 2.0, (2008); Reed R.B., Diving into data: Planning a research data management event, J Esci Librariansh, 4, 1, (2015); Shaffer C.J., The role of the library in the research enterprise, J Esci Librariansh, 2, 1, (2013); Williams S.C., Gathering feedback from early-career faculty: Speaking with and surveying agricultural faculty members about research data, J Esci Librariansh, 2, 2, (2013); Read K.B., Larson C., Gillespie C., Oh S.Y., Surkis A., A two-tiered curriculum to improve data management practices for researchers, PLOS ONE, 14, 5, (2019); Read K.B., Surkis A., Larson C., McCrillis A., Graff A., Nicholson J., Xu J., Starting the data conversation: Informing data services at an academic health sciences library, J Med Libr Assoc, 103, 3, pp. 131-135, (2015); Read K.B., Surkis A., Research Data Management Teaching Toolkit. Figshare, (2018); Surkis A., Lapolla F.W.Z., Contaxis N., Read K.B., Data Day to Day: Building a community of expertise to address data skills gaps in an academic medical center, J Med Libr Assoc, 105, 2, pp. 185-191, (2017); Read K.B., Larson C., Gillespie C., Oh S.Y., Surkis A., Research Data Management Training for Information Professionals, (2017); Carlson J., Demystifying the data interview: Developing a foundation for reference librarians to talk with researchers about their data, Ref Serv Rev, 40, 1, pp. 7-23, (2012); (2019); Read K.B., Contaxis N., Surkis A., Research data management hands on workshop, Open Science Framework, (2017); Research Data Management Training [Internet], (2019)","","","Medical Library Association","","","","","","15365050","","JMLAC","31258450","English","J. Med. Libr. Assoc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85068957423"
"Boté J.-J.; Térmens M.","Boté, Juan-José (53363101000); Térmens, Miquel (14038297500)","53363101000; 14038297500","Reusing data: Technical and ethical challenges","2019","DESIDOC Journal of Library and Information Technology","39","6","","329","337","8","14","10.14429/djlit.39.6.14807","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078348113&doi=10.14429%2fdjlit.39.6.14807&partnerID=40&md5=151dbc58e1d607bda90cea7244b89962","Departament de Biblioteconomia, Documentació i Comunicació Audiovisual & Centre de Recerca en Informació, Comunicació i Cultura, Universitat de Barcelona, C/ Melcior de Palau 140, Barcelona, 08014 ES, Spain","Boté J.-J., Departament de Biblioteconomia, Documentació i Comunicació Audiovisual & Centre de Recerca en Informació, Comunicació i Cultura, Universitat de Barcelona, C/ Melcior de Palau 140, Barcelona, 08014 ES, Spain; Térmens M., Departament de Biblioteconomia, Documentació i Comunicació Audiovisual & Centre de Recerca en Informació, Comunicació i Cultura, Universitat de Barcelona, C/ Melcior de Palau 140, Barcelona, 08014 ES, Spain","Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues. © 2019, DESIDOC.","Data research management; Data reuse; Ethical challenges; FAIR principles; Library support services; Literature review; Research data management training","","","","","","","","Milne D., Watling D., Big data and understanding change in the context of planning transport systems, J. Transp. Geogr., 76, pp. 235-244, (2019); Custers B., Ursic H., Big data and data reuse: A taxonomy of data reuse for balancing big data benefits and personal data protection, Int. Data Privacy Law., 6, 1, pp. 4-15, (2016); Charamba V., Thomas B., Charamba B., Relative Importance Analysis of the Factors Influencing Maize Productivity at Olushandja and Etunda Irrigation Schemes of Namibia: A Secondary Analysis of Data from Farm Household Survey, (2017); Bote J., Dataset management as a special collection, Collect. Manage., 4, 2-4, pp. 259-276, (2019); Gregory K., Cousijn H., Groth P., Scharnhorst A., Wyatt S., Understanding data search as a socio-technical practice, J. Inf. Sci., (2019); Morrow V., Boddy J., Lamb R., The Ethics of Secondary Data Analysis: Learning from the Experience of Sharing Qualitative Data from Young People and Their Families in an International Study of Childhood Poverty, (2014); Pasquetto I., Randles B., Borgman C., On the reuse of scientific data, Data Sci. J., 16, 8, (2017); Frank R.D., Suzuka K., Yakel E., Examining the reuse of qualitative research data: Digital video in education, Archiving Conference, Archiving 2016 Final Program and Proceedings, 6, pp. 146-151; Costello M.J., Michener W.K., Gahegan M., Zhang Z., Bourne P.E., Biodiversity data should be published, cited, and peer reviewed, Trends Ecol. E, 28, 8, pp. 454-461, (2013); Majid S., Foo S., Zhang X., Research data management by academics and researchers: Perceptions, knowledge and practices, Maturity and Innovation in Digital Libraries, pp. 166-178, (2018); Johnston M.P., Secondary data analysis: A method of which the time has come, Qual. Quant. Methods in Libr., 3, 3, pp. 619-626, (2017); Pronk T.E., The time efficiency gain in sharing and reuse of research data, Data Sci. J., 18, 1, pp. 1-8, (2019); Figueiredo A.S., Data sharing: Convert Challenges into Opportunities, Front. Public Health., 5, (2017); Attard J., Orlandi F., Scerri S., Auer S., A systematic review of open government data initiatives, Gov. Inf. Q., 32, 4, pp. 399-418, (2015); Eynden V.D., Informed consent for data sharing and reuse, Creating Shareable Research Data: Managing and Archiving Social Science Research Data, (2017); Hampton S., Anderson S., Bagby S., Et al., The tao of open science for ecology, Ecosphere, 6, 7, (2015); Shorish Y., Data information literacy and undergraduates: A critical competency, College Undergrad. Libr., 22, 1, pp. 97-106, (2015); Tripathi M., Shukla A., Sonkar S.K., Research data management practices in university libraries: A study, DESIDOC J. Lib. Inf. Tech., 37, 6, pp. 417-424, (2017); Watters E.C., Cumming S., Caragata L., The lone mother resilience project: A qualitative secondary analysis, Forum Qual. Sozialforschung / Forum: Qual. Soc. Res., 19, 2, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. 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Sci. Res. Network, (2018); Curty R., Yoon A., Jeng W., Qin J., Untangling data sharing and reuse in social sciences, In Proceedings of the Association for Information Science and Technology Banner, 53, 1, pp. 1-5, (2016); Sanchez D., Martinez S., Domingo-Ferrer J., Comment on ‘Unique in the shopping mall: On the reidentifiability of credit card metadata’, Science, 351, 6279, (2016); Sugiura L., Wiles R., Pope C., Ethical challenges in online research: Public/private perceptions, Research Ethics, 13, 3-4, pp. 184-199, (2017); Thomas D., Pastrana S., Hutchings A., Clayton R., Beresford A., Ethical issues in research using datasets of illicit origin, Proceedings of IMC ’17, (2017); Slavnic Z., Research and data-sharing policy in Sweden – neoliberal courses, forces and discourses*, Prometheus, 35, 4, pp. 249-266, (2017); Helbig K., Research data management training for geographers: First impressions, ISPRS Int. J. Geo-Inf., 5, 4, (2016); Kim Y., Yoon A., Scientists’ data reuse behaviors: A multilevel analysis, J. Assoc. Inf. Sci. Technol., 68, 12, pp. 2709-2719, (2017); Hara Y., Kuwahara M., Traffic monitoring immediately after a major natural disaster as revealed by probe data – A case in Ishinomaki after the Great East Japan earthquake, Transp. Res. Part A: Policy Pract., 75, pp. 1-15, (2015); Sandbrook C., The social implications of using drones for biodiversity conservation, Ambio, 44, pp. 636-647, (2015); Kujala R., Weckstrom C., Darst R.K., Mladenovic M.N., Saramaki J., A collection of public transport network data sets for 25 cities, Scientific Data, 5, (2018); Custers B., Bachlechner D., Advancing the EU data economy: Conditions for realizing the full potential of data reuse, Information Polity, 22, 4, pp. 291-309, (2017); Bierer B.E., Crosas M., Pierce H.H., Data authorship as an incentive to data sharing, N. Eng. J. Med.., 376, pp. 1684-1687, (2017); Rolland B., Lee C.P., Beyond trust and reliability: Reusing data in collaborative cancer epidemiology research, Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 435-444, (2013)","J.-J. Boté; Departament de Biblioteconomia, Documentació i Comunicació Audiovisual & Centre de Recerca en Informació, Comunicació i Cultura, Universitat de Barcelona, Barcelona, C/ Melcior de Palau 140, 08014 ES, Spain; email: juanjo.botev@ub.edu","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Article","Final","","Scopus","2-s2.0-85078348113"
"Mohd Nor N.A.; Taib N.A.; Saad M.; Zaini H.S.; Ahmad Z.; Ahmad Y.; Dhillon S.K.","Mohd Nor, Nurul Aqilah (57205683571); Taib, Nur Aishah (57200000143); Saad, Marniza (21535141600); Zaini, Hana Salwani (57205680398); Ahmad, Zahir (57205681734); Ahmad, Yamin (45060994500); Dhillon, Sarinder Kaur (55588592200)","57205683571; 57200000143; 21535141600; 57205680398; 57205681734; 45060994500; 55588592200","Development of electronic medical records for clinical and research purposes: The breast cancer module using an implementation framework in a middle income country- Malaysia","2019","BMC Bioinformatics","19","","402","","","","5","10.1186/s12859-018-2406-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061104709&doi=10.1186%2fs12859-018-2406-9&partnerID=40&md5=e9bb10b14f220fab1b1dba63c49ab075","Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, 50603, Malaysia; Department of Surgery, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Department of Oncology, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Department of Information Technology, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia","Mohd Nor N.A., Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, 50603, Malaysia; Taib N.A., Department of Surgery, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Saad M., Department of Oncology, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Zaini H.S., Department of Information Technology, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Ahmad Z., Department of Information Technology, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Ahmad Y., Department of Information Technology, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; Dhillon S.K., Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, 50603, Malaysia","Background: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future. Results: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act. Conclusion: The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits. © 2018 The Author(s).","Breast Cancer; Database mirroring; Electronic medical record; Medical system; Quality implementation framework","Biomedical Research; Breast Neoplasms; Data Accuracy; Developing Countries; Electronic Health Records; Female; Humans; Income; Information Storage and Retrieval; Malaysia; User-Computer Interface; Data integration; Data mining; Diagnosis; Diseases; Hospital data processing; Hospitals; Information management; Integration; Interoperability; Medical computing; Quality assurance; Architectural frameworks; Breast Cancer; Electronic medical record; Epidemiological studies; Medical systems; Middle-income countries; Multi-disciplinary collaborations; Research data managements; breast tumor; computer interface; developing country; economics; electronic health record; female; human; income; information retrieval; Malaysia; measurement accuracy; medical research; pathology; Clinical research","","","","","Department of Surgery; Ministry of Higher Education, Malaysia, MOHE; Universiti Malaya, UM, (PG130-2013A, PR001-2017A, UM.C/HIR/MOHE/06)","This project was supported by University of Malaya’s Postgraduate Research Fund (PG130-2013A) to the first author. The Prototype Research Grant Scheme (PR001-2017A) and High Impact Research Grant (UM.C/HIR/MOHE/06) from the Ministry of Higher Education, Malaysia funded the design of the clinical proformas as well as the development of system. Publication charges for this article was partially funded by University of Malaya Page Charge Fund and Department of Surgery UMSC Fund. The remaining cost was funded by the University of Malaya’s Postgraduate Research Fund (PG130-2013A).","Torre L.A., Bray F., Siegel R.L., Ferlay J., Lortet-Tieulent J., Jemal A., Global Cancer statistics, 2012, CA Cancer J Clin, 65, pp. 87-108, (2015); Ong T.A., Yip C.H., Short-term survival in breast cancer: The experience of the University of Malaya Medical Centre, Asian J Surg Asian Surgical Association, 26, pp. 169-175, (2003); Mohd Taib N.A.B., Yip C.H., Mohamed I., Survival analysis of Malaysian women with breast cancer: Results from the University of Malaya Medical Centre, Asian Pac J Cancer Prev, 9, pp. 197-202, (2008); Bhoo Pathy N., Verkooijen H.M., Taib N.A., Lee S.C., Saxena N., Iau P., Et al., Association between ethnicity and survival after breast cancer in a multi-ethnic Asian setting: Results from the Singapore-Malaysia hospital-based breast cancer registry, J Univ Malaya Med Cent, 16, (2013); Abdullah N.A., Wan Mahiyuddin W.R., Muhammad N.A., Ali Z.M., Ibrahim L., Ibrahim Tamim N.S., Et al., Survival rate of breast cancer patients in Malaysia: A population-based study, Asian Pac J Cancer Prev, 14, pp. 4591-4594, (2013); Damotte V., Gourraud P.-A., Electronic medical Records in Multiple Sclerosis Research, Clin Exp Neuroimmunol, 9, pp. 13-18, (2018); Maraganore D.M., Frigerio R., Kazmi N., Meyers S.L., Sefa M., Walters S.A., Et al., Quality improvement and practice-based Resarch in neurology using the electronic medical record, Neurol Clin Pract, pp. 419-429, (2015); Narayanan J., Dobrin S., Choi J., Rubin S., Pham A., Patel V., Et al., Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy, Epilepsia, 58, pp. 68-76, (2017); Meystre S.M., Lovis C., Burkle T., Tognola G., Budrionis A., Lehmann C.U., Clinical data reuse or secondary use: Current status and potential future Progress, IMIA Yearb Med Informatics, 26, pp. 38-52, (2017); Ross M.K., Wei W., Ohno-Machado L., Big data"" and the electronic health record, IMIA Yearb. Med. Informatics., 9, pp. 97-104, (2014); Murphy E.C., Ferris F.L., O'Donnell W.R., An electronic medical records system for clinical research and the EMR-EDC interface, Investig Ophthalmol Vis Sci, 48, pp. 4383-4389, (2007); Capurro D., Yetisgen M., Van Eaton E., Black R., Tarczy-Hornoch P., Availability of Structured and Unstructured Clinical Data for Comparative Effectiveness Research and Quality Improvement: A Multi-Site Assessment, EGEMs (Generating Evid. Methods to Improv. Patient Outcomes), 2, (2014); Prokosch H.U., Ganslandt T., Perspectives for medical informatics - Reusing the electronic medical record for clinical research, Methods Inf Med, 48, pp. 38-44, (2009); Miriovsky B.J., Shulman L.N., Abernethy A.P., Importance of health information technology, electronic health records, and continuously aggregating data to comparative effectiveness research and learning health care, J Clin Oncol, 30, pp. 4243-4248, (2012); Zhou X., Chen S., Liu B., Zhang R., Wang Y., Li P., Et al., Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support, Artif Intell Med, 48, pp. 139-152, (2010); Raman S.R., Curtis L.H., Temple R., Andersson T., Ezekowitz J., Ford I., Et al., Leveraging electronic health Records for Clinical Research, Am Heart J Elsevier, Inc, 202, pp. 13-19, (2018); Niland J.C., Rouse L., Clinical research systems and integration with medical systems, Biomed. Informatics Cancer Res, (2010); Wee Y.H., It-enabled Healthcare Integration: The Case of National Electronic Health Records in Singapore, (2015); Waterson P., Health information technology and sociotechnical systems: A progress report on recent developments within the UK National Health Service (NHS), Appl Ergon, 45, pp. 150-161, (2014); The Royal Children's Hospital Melbourne. about the Electronic Medical Record (EMR) [Internet], (2016); Jagli D., Purohit S., Chandra S., Knowledge Acquisition for Electronic Health Records on cloud, Procedia Comput Sci Elsevier BV, 112, pp. 1909-1915, (2017); Epic Overview | Johns Hopkins Medicine in Baltimore, MD [Internet], (2012); De La Torre I., Martinez B., Lopez-Coronado M., Analyzing open-source and commercial EHR solutions from an international perspective, 2013 IEEE 15th Int. Conf. E-health Networking, Appl. Serv. Heal, pp. 399-403, (2013); Edsall R.L., Adler K.G., An EHR user-satisfaction survey: Advice from 408 family physicians, Fam Pract Manag Am Acad Fam Physician, 12, pp. 29-35, (2005); Ludwick D.A., Doucette J., Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries, Int J Med Inform Elsevier, 78, pp. 22-31, (2009); Hill R.G., Sears L.M., Melanson S.W., 4000 clicks: A productivity analysis of electronic medical records in a community hospital ED, Am J Emerg Med WB Saunders, 31, pp. 1591-1594, (2013); HIMS Blueprint: Towards Excellence in Health Information Management, 13, (2013); Eighth Malaysia Plan 2001-2005. Eighth Malaysia Plan 2001-2005, (2001); Ninth Malaysia Plan 2006-2010. Ninth Malaysia Plan 2006-2010, (2006); Country Health Plan 2011-2015. Ctry Heal Plan 10th Malaysia Plan 2011-2015, (2011); Anuar H.M., Sararaks S., Ismail S.A., Sapian R.A., Health Research Priorities in Malaysia for the 10th Malaysia Plan (2011-2015), Inst Heal Syst Res Natl Institutes Heal, (2014); Eleventh Plan 2016-2020 Malaysia Anchoring Growth on People. Percetakan Nasional Malaysia Berhad, (2016); Selvaraju D.D.S., Health Information Management: Malaysian Experience. Heal, Informatics Center, (2006); Tenth Malaysia Plan (2011-2015), (2011); Fuller M.J., Ahmad M.K.S., Malaysian Health Reference Data Model (MyHRDM), (2017); Malaysian Health Data Warehouse (MyHDW), (2013); Lee J., Bagheri B., Kao H.-A., Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics, Int Conf Ind Informatics, pp. 1-6, (2014); Meyers D.C., Durlak J.A., Wandersman A., The quality implementation framework: A synthesis of critical steps in the implementation process, Am J Community Psychol, 50, pp. 462-480, (2012); Laws of Malaysia Act 709 Personal Data Protection Act 2010. 10 June 2010, (2010); Kuchinke W., Aerts J., Semler S.C., Ohmann C., CDISC standard-based electronic archiving of clinical trials, Methods Inf Med, 48, pp. 408-413, (2009); Noumeir R., Integrating the healthcare enterprise process, Int J Healthc Technol Manag, 9, (2008); Goodman K., Krueger J., Crowley J., The automatic clinical trial: Leveraging the electronic medical record in multisite cancer clinical trials, Curr Oncol Rep, 14, pp. 502-508, (2012); Terry A.L., Chevendra V., Thind A., Stewart M., Neil Marshall J., Cejic S., Using your electronic medical record for research: A primer for avoiding pitfalls, Fam Pract, 27, pp. 121-126, (2009); Rogers J., EDC & EHR integration, Appl. 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EPJ Kvalitetssikring Skjema 1, (2003); University of Malaya EHealth Initiative - Where Medicine Meets ICT [Internet]; CDISC Standards in Clinical Research [Internet]; Hudson L.D., Kush R.D., Navarro Almario E., Seigneuret N., Jackson T., Jauregui B., Et al., Global standards to expedite learning from medical research data, Clin Transl Sci, 11, pp. 342-344, (2018); Majeed A., Car J., Sheikh A., Accuracy and completeness of electronic patient records in primary care, Fam Pract, 25, pp. 213-214, (2008); Point of Care Electronic Medical Records Reduce Errors in Health Care Facilities [Internet]; Ellsworth M.A., Homan J.M., Cimino J.J., Peters S.G., Pickering B.W., Herasevich V., Point-of-care knowledge-based resource needs of clinicians a survey from a large Academic Medical Center, Appl Clin Inform, 6, pp. 305-317, (2015); Gliklich R.E., Dreyer N.A., Leavy M.B., Data collection and quality assurance, Registries for Evaluating Patient Outcomes: A User's Guide [Internet], (2014); McIlwain J., A to Z Trial Integration, Appl Clin Trials, 16, pp. 1-5, (2015); Kanas G., Morimoto L., Mowat F., O'Malley C., Fryzek J., Nordyke R., Use of electronic medical records in oncology outcomes research, Clin Outcomes Res, 2, pp. 1-14, (2010); Bleicher P., Integrating Ehr With E.D.C., When two worlds collide, Appl Clin Trials, 15, pp. 6-14, (2006); Budget 2017 - Ensuring Unity and Economic Growth, Inclusive Prudent Spending, Wellbeing of the Rakyat. Budg. 2017, (2017); HHS FY 2017 Budget in Brief - National Institutes of Health (NIH), (2017); Budget 2017 -2020. Budg. 2017-2020, (2017); National Archives Act 2003, (2003)","N.A. Taib; Department of Surgery, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia; email: naisha@um.edu.my","","BioMed Central Ltd.","","","","","","14712105","","BBMIC","30717675","English","BMC Bioinform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85061104709"
"Vilar P.; Zabukovec V.","Vilar, Polona (8948648900); Zabukovec, Vlasta (24365578100)","8948648900; 24365578100","Research data management and research data literacy in Slovenian science","2019","Journal of Documentation","75","1","","24","43","19","17","10.1108/JD-03-2018-0042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053239852&doi=10.1108%2fJD-03-2018-0042&partnerID=40&md5=c0a971a30065881a26ca019fdebf2a24","Department of Library and Information Science and Book Studies, University of Ljubljana, Ljubljana, Slovenia","Vilar P., Department of Library and Information Science and Book Studies, University of Ljubljana, Ljubljana, Slovenia; Zabukovec V., Department of Library and Information Science and Book Studies, University of Ljubljana, Ljubljana, Slovenia","Purpose: The purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level. Design/methodology/approach: Purposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used. Findings: The authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare. Research limitations/implications: Research could be expanded to a wider population, include other stakeholders and use qualitative methods. Practical implications: Results are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group. Originality/value: The findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia. © 2019, Emerald Publishing Limited.","Data literacy; International studies; Research data literacy; Research data management; Researchers; Slovenia","","","","","","","","Briney K., Goben A., Zilinski L., Institutional, funder, and journal data policies, Curating Research Data, Volume One: Practical Strategies for Your Digital Repository, pp. 61-78, (2017); Buys C.M., Shaw P.L., Data management practices across an institution: survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Coates H.L., Building data services from the ground up: strategies and resources, Journal of eScience Librarianship, 3, 1, (2014); Fear K., ‘You made it, you take care of it’: data management as personal information management, The International Journal of Digital Curation, 6, 2, pp. 53-77, (2011); Henty M., Developing the capability and skills to support eResearch, Ariadne, (2008); Hey T., Hey J., e-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Hickson S., Poulton K.A., Connor M., Richardson J., Wolski M., Modifying researchers’ data management practices: a behavioural framework for library practitioners, IFLA Journal, 42, 4, pp. 253-265, (2016); Jahnke L., Asher A., Keralis S.D.C., The Problem of Data, (2012); Kennan M.A., Markauskaite L., Research data management practices: a snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 1-12, (2015); Koltay T., Data literacy: in search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Are you ready? 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Vilar; Department of Library and Information Science and Book Studies, University of Ljubljana, Ljubljana, Slovenia; email: polona.vilar@ff.uni-lj.si","","Emerald Group Holdings Ltd.","","","","","","00220418","","","","English","J. Doc.","Article","Final","","Scopus","2-s2.0-85053239852"
"Plomp E.; Dintzner N.; Teperek M.; Dunning A.","Plomp, Esther (55660168000); Dintzner, Nicolas (56100606400); Teperek, Marta (36545554600); Dunning, Alastair (56425415100)","55660168000; 56100606400; 36545554600; 56425415100","Cultural obstacles to research data management and sharing at TU Delft","2019","Insights: the UKSG Journal","32","","A30","","","","5","10.1629/uksg.484","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077181460&doi=10.1629%2fuksg.484&partnerID=40&md5=433c419b2a32bd4028ef74b4480adfeb","Faculty of Technology, Policy and Management, Delft University of Technology, Netherlands; TU Delft Library, Delft University of Technology, Netherlands; Research Data Services, Delft University of Technology, 4TU, Centre for Research Data, Netherlands; Delft University of Technology, Faculty of Applied Sciences, Lorentzweg 1, Delft, 2628 CJ, Netherlands","Plomp E., Delft University of Technology, Faculty of Applied Sciences, Lorentzweg 1, Delft, 2628 CJ, Netherlands; Dintzner N., Faculty of Technology, Policy and Management, Delft University of Technology, Netherlands; Teperek M., TU Delft Library, Delft University of Technology, Netherlands; Dunning A., Research Data Services, Delft University of Technology, 4TU, Centre for Research Data, Netherlands","Research data management (RDM) is increasingly important in scholarship. Many researchers are, however, unaware of the benefits of good RDM and unsure about the practical steps they can take to improve their RDM practices. Delft University of Technology (TU Delft) addresses this cultural barrier by appointing Data Stewards at every faculty. By providing expert advice and increasing awareness, the Data Stewardship project focuses on incremental improvements in current data and software management and sharing practices. This cultural change is accelerated by the Data Champions who share best practices in data management with their peers. The Data Stewards and Data Champions build a community that allows a discipline-specific approach to RDM. Nevertheless, cultural change also requires appropriate rewards and incentives. While local initiatives are important, and we discuss several examples in this paper, systemic changes to the academic rewards system are needed. This will require collaborative efforts of a broad coalition of stakeholders and we will mention several such initiatives. This article demonstrates that community building is essential in changing the code and data management culture at TU Delft. © 2019 Esther Plomp, Nicolas Dintzner, Marta Teperek and Alastair Dunning.","Data; Data stewardship; RDM; Software; Support; TU Delft","","","","","","","","Ioannidis J.P.A., How to make more published research true, PLoS Medicine, 11, 10, (2014); Baker M., 1,500 scientists lift the lid on reproducibility, Nature, 533, (2016); Higman R., Bangert D., Jones S., Three camps, one destination: The intersections of research data management, FAIR and Open, Insights, 32, (2019); Coates H., Ensuring research integrity: The role of data management in current crises, College & Research Libraries News, 75, 11, pp. 598-601, (2014); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services Fundamentals of Good Practice, (2014); Tenopir C., Et al., Data management education from the perspective of science educators, International Journal of Digital Curation, 11, 1, pp. 232-251, (2016); Pryor J., Whyte, Delivering Research Data Management; Stewart Lowndes J.S., Et al., Our path to better science in less time using open data science tools, Nature Ecology & Evolution, 1, 6, (2017); Wilson G., Et al., Good enough practices in scientific computing, PLOS Computational Biology, 13, 6, (2017); Houtkoop B.L., Et al., Data sharing in psychology: A survey on barriers and preconditions, Advances in Methods and Practices in Psychological Science, 1, 1, pp. 1-16, (2018); Houtkoop, Et al., Data Sharing in Psychology; De Zilwa D., Organisational culture and values and the adaptation of academic units in Australian universities, Higher Education, 54, 4, pp. 557-574, (2007); TU Delft Faculties, TU Delft; Tenopir, Et al., Data Management Education; Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Wilson, Et al., Good Enough Practices; Teperek M., Dunning A., The main obstacles to better research data management and sharing are cultural. But change is in our hands, LSE Impact Blog (Blog), (2018); Public Private Partnerships Portfolio, (2019); SURF; DMPonline; De Smaele M., Teperek M., DMPonline@TU Delft, Digital Curation Centre (Blog), (2019); Core trust seal, Core Trust Seal; Support with data funds, 4TU.ResearchData; Van Dijck J., Do as you preach: Results of 2017/2018 data management survey now published, Open Working (Blog), (2018); Mancilla H.A., Et al., On a quest for cultural change - Surveying research data management practices at Delft university of technology, LIBER Quarterly No, 29, 1, pp. 1-27, (2019); Mancilla A., Et al., On a Quest; Teperek, Dunning, LSE Impact Blog; Mancilla A., Et al., On a Quest; Mancilla A., Et al., On a Quest; Mancilla A., Et al., On a Quest; Template for Structured RDM Interviews, (2018); Teperek M., Views on Data Stewardship - Report of Preliminary Findings at the Faculty of Technology, Policy and Management (TPM) at TU Delft, (2018); Love J., A subjective assessment of research data in design, Open Working (Blog), (2019); Teperek M., Et al., Data stewardship at Tu Delft - 2018 report, Open Working (Blog), (2019); Dunning A., Changing Cultures of Research Data Management, (2018); Teperek M., Et al., Data stewardship addressing disciplinary data management needs, International Journal of Digital Curation, 13, 1, pp. 141-149, (2018); Verheul I., Et al., Data Stewardship on the Map: A Study of Tasks and Roles in Dutch Research Institutes, (2019); Gilley A., Gilley J.W., McMillan H.S., Organizational change: Motivation, communication, and leadership effectiveness, Performance Improvement Quarterly, 21, 4, pp. 75-94, (2009); Plomp E., Data champion kick off meeting, Open Working (Blog), (2019); Higman R., Teperek M., Kingsley D., Creating a community of data champions, International Journal of Digital Curation, 12, 2, pp. 96-106, (2017); Savage J.L., Cadwallader L., Establishing, developing, and sustaining a community of data champions, Data Science Journal, 18, (2019); Nust D., Et al., Reproducible research and GIScience: An evaluation using AGILE conference papers, PeerJ, 6, (2018); Juergens H., Et al., Evaluation of a novel cloud-based software platform for structured experiment design and linked data analytics, Scientific Data, 5, 1, (2018); Said A., Bellogin A., Rival: A toolkit to foster reproducibility in recommender system evaluation, Proceedings of the 8th ACM Conference on Recommender Systems - RecSys'14 (The 8th ACM Conference, pp. 371-372, (2014); Kortuem G., Bourgeois J., The internet of things for the open sharing economy, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp 2016, pp. 666-669, (2016); Zuiderwijk A., Spiers H., Sharing and re-using open data: A case study of motivations in astrophysics, International Journal of Information Management, 49, pp. 228-241, (2019); Open working: Data Champions collection, 4TU.Research Data and TU Delft Research Data Services; De Zilwa, Organisational Culture; Gilley G., McMillan, Organizational Change; Dunning A., TU Delft Research Data Framework Policy, (2018); Cruz M.J., Et al., Policy needs to go hand in hand with practice: The learning and listening approach to data management, Data Science Journal, 18, 45, pp. 1-11, (2019); Cruz, Et al., Policy needs to, Disciplinary Data Management Needs; Akhmerov A., Steele G., Open Data Policy of the Quantum Nanoscience Department, (2019); Teperek, Views on Data Stewardship; Cruz, Et al., Policy Needs to; Gilley G., McMillan, Organizational Change; Gilley G., McMillan, Organizational Change; Petters J.L., Et al., The Impact of Targeted Data Management Training for Field Research Projects - A Case Study, (2019); De Zilwa, Organisational Culture; McKiernan E.C., Et al., Meta-research: Use of the journal impact factor in academic review, promotion, and tenure evaluations, ELife, 8, (2019); Gadd E., Influencing the changing world of research evaluation, Insights, 32, (2019); Plomp E., Tu Delft's first genomics data carpentry, Open Working (Blog), (2019); Kurapati S., Teperek M., 4Tu.Centre for research data partners with the Carpentries: Impressions from the first workshop at TU Delft, Open Working (Blog), (2018); Dintzner N., Den Heijer K., Teperek M., Coding problems? Just pop over!, Open Working (Blog), (2019); Coates, Ensuring Research Integrity; McKiernan, Et al., Meta-Research: Use of the Journal Impact Factor; Gadd, Influencing the Changing World of Research Evaluation; Programmer and data manager, Academic Transfer, (2019); Teperek M., Et al., Data Stewardship at TU Delft - 2018 Report; Akhmerov A., Et al., Making Research Software a First-Class Citizen in Research, (2019); Plomp E., Cruz M.J., Versteeg A., VU Library Live talk show and podcast on the academic reward system, Open Working (Blog), (2019); Plomp E., Et al., How will we judge scientists in 2030? #wetenschapper2030, Open Working (Blog), (2019); Mancilla H.A., Et al., It's time for open science skills to count in academic careers (Part 2: Workshop and reflection), Open Working (Blog), (2018); Nationaal platform open science, Nationaal Platform Open Science; Claire C., How to Build a Community of Data Champions: Six Steps to Success, (2019)","E. Plomp; Delft University of Technology, Faculty of Applied Sciences, Delft, Lorentzweg 1, 2628 CJ, Netherlands; email: e.plomp@tudelft.nl","","Ubiquity Press","","","","","","20487754","","","","English","Insights UKSG J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85077181460"
"Smit M.","Smit, Michael (22235288500)","22235288500","Code Convention Adherence in Research Data Infrastructure Software: An Exploratory Study","2019","Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019","","","9006130","4691","4700","9","1","10.1109/BigData47090.2019.9006130","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081325273&doi=10.1109%2fBigData47090.2019.9006130&partnerID=40&md5=46a78b372370bf6c685501228daf2f10","Dalhousie University, School of Information Management, Halifax, Canada","Smit M., Dalhousie University, School of Information Management, Halifax, Canada","Science is rapidly evolving, incorporating technology like autonomous vehicles, high-throughput scientific instruments, high-fidelity numerical models, and sensor networks, all generating data with increasing frequency, variety, and volume. Scientists committed to open science are interested in sharing this data, which requires research data infrastructure (RDI). The software underlying RDI is often created and/or deployed by people who have not received formal training in software engineering, or at organizations with primary mandates that do not include software development. Our understanding of software engineering as a field and practice does not universally translate to this software. As RDI software is pushed to handle larger data sets, and used to share data more widely, it is important to understand the maintainability, the resilience of the development community, and other indicators of long-term software project health. While there is a body of research on scientific software, and on free and open source software, it is not known if existing approaches to assessing these properties are effective for RDI software. In this exploratory study, we calculate one proxy measure for maintainability (code convention adherence) for a popular ocean data management system, and compare the results with four open source projects, and with the apparent experience of users as captured in public mailing lists and an issue tracker. The results advance our limited understanding of this type of software, and inform hypothesis generation and future research design. © 2019 IEEE.","FAIR principles; ocean data management; open data; research data infrastructure; research data management; technical debt","Big data; Codes (symbols); Data Sharing; Formal methods; Maintainability; Open Data; Open source software; Open systems; Sensor networks; Software design; User experience; Development community; FAIR principles; Free and open source softwares; Hypothesis generation; Research data; Research data managements; Scientific instrument; Technical debts; Information management","","","","","ACENET; Compute Canada; Ocean Frontier Institute, OFI","This research is supported in part by the Ocean Frontier Institute and was enabled in part by support provided by ACENET and Compute Canada. The author is grateful to the co-authors of the 2011 paper leveraged extensively by this project: Barry Gergel, H. James Hoover, and Eleni Stroulia. Thank you to Bob Simons and NOOA for creating such a useful open source tool.","Costello M.J., Berghe E.V., Ocean biodiversity informatics': A new era in marine biology research and management, Marine Ecology Progress Series, (2006); Akmon D., Zimmerman A., Daniels M., Hedstrom M., The application of archival concepts to a data-intensive environment: Working with scientists to understand data management and preservation needs, Archival Science, 11, 3, pp. 329-348, (2011); Poole A.H., How has your science data grown? 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SIGITE '05, pp. 239-244, (2005); Smit M., Kelly R., Fitzsimmons S., Bruce S., Bulger C., Covey B., Davis R., Gosse R., Owens D., Pirenne B., Canadian Integrated Ocean Observing System: Cyberinfrastructure Investigative Evaluation, (2017); Baker D., Barsky E., Burpee J., Leggott M., Moon J., Sinatra M., Spencer B., Gerlitz L., Canadian National Data Services Framework: Discussion Document, (2019); Stewart A., Deyoung B., Smit M., Donaldson K., Reedman A., Bastien A., Carter B., Kelly R.J., Peterson E., Pirenne B., Et al., The development of a canadian integrated ocean observing system (cioos)), Frontiers in Marine Science, 6, pp. 431-440, (2019)","M. Smit; Dalhousie University, School of Information Management, Halifax, Canada; email: msmit@dal.ca","Baru C.; Huan J.; Khan L.; Hu X.T.; Ak R.; Tian Y.; Barga R.; Zaniolo C.; Lee K.; Ye Y.F.","Institute of Electrical and Electronics Engineers Inc.","Ankura; Baidu; IEEE; IEEE Computer Society; Very","2019 IEEE International Conference on Big Data, Big Data 2019","9 December 2019 through 12 December 2019","Los Angeles","157991","","978-172810858-2","","","English","Proc. - IEEE Int. Conf. Big Data, Big Data","Conference paper","Final","","Scopus","2-s2.0-85081325273"
"Eberhard I.","Eberhard, Igor (57201190001)","57201190001","Science between gaps and negative space. A report on the impossibility and impracticability of open science for ethnographic and sociological research; [Forschen zwischen leerstellen und negativräumen. Schwierigkeiten und unmöglichkeiten von open science bei ethnographischem und sozialwissen-schaftlichem forschen. ein erfahrungsbericht]","2019","VOEB-Mitteilungen","72","2","","516","523","7","0","10.31263/voebm.v72i2.3053","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077706811&doi=10.31263%2fvoebm.v72i2.3053&partnerID=40&md5=0fb6473ef95d612f8440eeb712ff09fa","Universität Wien, Institut für Kultur-und Sozialanthropologie/ Bibliotheks-und Archivwesen, Austria","Eberhard I., Universität Wien, Institut für Kultur-und Sozialanthropologie/ Bibliotheks-und Archivwesen, Austria","From an ideal perspective, doing ethnographic research builds on an ethical handling of research data as well as a respectful relationship between researchers and their interlocutors. However, it is in this context that Open Science has its limitations. Current examples taken from anthropological research highlight the difficulties and challenges of Open Science. © Igor Eberhard.","Cultural and socialanthropological research; History of Medicine; Limits; Medical Anthropology; Open Science; Research data management","","","","","","","","Eberhard I., Friedrich Dörbeck – Vergessen in Wien Und An-Derswo? Ein Ethnohistorischer Und Biographiegeschichtlicher Beitrag Zur Konstruktion Der Ethnologie Und Wissenschaftsgeschichte Des Rus-Sischen Fernen Ostens, (2003); Eberhard I., Wie Tätowierte zu Kriminellen gemacht wurden. Der Kriminalisierungsdiskurs von Tätowierungen am Beispiel der Hei-delberger Sammlung Schönfeld. Curare. Zeitschrift für Medizinethnolo-gie/Curare, Journal of Medical Anthropology, 40, 4, pp. 308-320, (2017); Eberhard I., Kriminelle Körper. Körper Von Kriminellen in ausge-wählten Wiener Sammlungen. Unveröffentlichter Projektbericht, (2019); Eberhard I., Kraus W., Der Elefant im Raum. Ethno-graphisches Forschungsdatenmanagement als Herausforderung für Repositorien, Mitteilungen Der Vereinigung Österreichischer Bibliothe-Karinnen Und Bibliothekare, 71, 1, pp. 41-52, (2018); Geertz C., Dichte Beschreibung. Beiträge Zum Verstehen Kulturel-Ler Systeme (12. Aufl.. Ed., Suhrkamp-Taschenbuch Wissenschaft 696, (2011); Imeri S., (2018): Archivierung und Verantwortung. Zum Stand der Debat-te über den Umgang mit Forschungsdaten in den ethnologischen Fächern, Ratswd Working Paper 267/2018, pp. 69-79","I. Eberhard; Universität Wien, Institut für Kultur-und Sozialanthropologie/ Bibliotheks-und Archivwesen, Austria; email: igor.eberhard@univie.ac.at","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85077706811"
"Heinrich S.","Heinrich, Stefan (57213185687)","57213185687","„WHO IS GOING TO PAY FOR THIS?“ COST AND OPERATIONAL MODELS FOR SUSTAINABLE RESEARCH INFRASTRUCTURES AND RESEARCH DATA MANAGEMENT SERVICES (TRIER, JUNE 12–13, 2019); [„wer soll das bezahlen?“ kosten-und betriebsmodelle für nachhaltige forschungsinfrastrukturen und fdm-services (trier, 12.–13. juni 2019)]","2019","VOEB-Mitteilungen","72","2","","585","595","10","0","10.31263/voebm.v72i2.3040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077706536&doi=10.31263%2fvoebm.v72i2.3040&partnerID=40&md5=c78d1a44d6eee923b3450e28d544792d","FWF-Projekt „Der Schreibtisch des Kaisers: ein Ort politischer Entscheidungen in der Habsburgermonarchie?“","Heinrich S., FWF-Projekt „Der Schreibtisch des Kaisers: ein Ort politischer Entscheidungen in der Habsburgermonarchie?“","From 12 to 13 June 2019 the DINI-nestor-workshop on cost and operational models took place in cooperation with the Servicecenter eScience of the University of Trier. Within the framework of four different thematic blocks, representatives of German research and service institutions addressed the problems of financing. This report summarises their presentations. © Stefan Heinrich.","2019; E-Infrastructures; Funding; Research Data Management Services; University of Trier; Workshop Report","","","","","","California Department of Fish and Game, DFG","Danach schloss Janine Felden (Universität Bremen) mit ihrem Vortrag „GFBio – A FAIR infrastructure network to assist scientists in data management“15 an. „GFBio“ steht hierbei für „German Federation for Biological Data“16 und sieht sich u.a. selbst als „authoritative, national contact point for issues concerning the management and standardisation of biological and environmental research data during the entire data life cycle (from acquisition to archiving and data publication).“17 Ursprünglich wurde diese Unternehmung 2013 als DFG-finanziertes Forschungsprojekt mit 20 Partnern ins Leben gerufen. Seit Mai 2016 ist „GFBio“ ein gemeinnütziger Verein („GFBio e.V.“), der aber selbst auch ein Förderverein ist. Er wird ebenso von der DFG gefördert und umfasst derzeit 29 Partner, wie z.B. die „Botanische Staatssammlung München“, die „Friedrich-Schiller-Universi-tät Jena“, oder das „Leibniz-Zentrum für Marine-Tropenforschung“. In ih-rem Vortrag strich die Referentin u.a. folgende zwei Problemfelder heraus: einerseits ist es das Fehlen einer bezahlbaren Versicherung: der Vereinsvor-stand haftet mit seinem Privatvermögen. Andererseits wären die Kosten erst ab 180 Projekten/Jahr gedeckt, welche von GFBio betreut werden würden. Als realistisch werden 50 Projekte/Jahr angesehen, die aber auch noch nicht wirklich erreicht werden. Die Schlussfolgerungen aus diesem Umstand lauten, dass es noch kaum Nachfrage bzw. einen Markt für FDM gibt, sowie dass Forscher kein Geld für FDM-Services investieren wollen. Sollte sich die Situation diesbezüglich ändern, so ist die Transformierung in eine gemeinnützige GmbH geplant.","","S. Heinrich; FWF-Projekt „Der Schreibtisch des Kaisers: ein Ort politischer Entscheidungen in der Habsburgermonarchie?“; email: stefanheinrich@gmx.at","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85077706536"
"Piracha H.A.; Ameen K.","Piracha, Haseeb Ahmad (57209506761); Ameen, Kanwal (23468838600)","57209506761; 23468838600","Policy and planning of research data management in university libraries of Pakistan","2019","Collection and Curation","38","2","","39","44","5","13","10.1108/CC-08-2018-0019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079786331&doi=10.1108%2fCC-08-2018-0019&partnerID=40&md5=739efd52f42df175123e5b588620cccd","University of the Punjab, Lahore, Pakistan","Piracha H.A., University of the Punjab, Lahore, Pakistan; Ameen K., University of the Punjab, Lahore, Pakistan","Purpose: This paper aims to assess the policy framework and planning regarding research data management (RDM) in university libraries of Pakistan. Design/methodology/approach: Data were collected from 30 Higher Education Commission high ranking university libraries by using mixed method explanatory sequential design. Findings: The results indicate that library heads just heard about RDM, but there was lack of knowledge and awareness. Few libraries were at the planning stage. Other major challenges including lack of willingness, motivation and coordination with researchers, non-availability of skillful professional and support staff, poor infrastructure and networking were found in this regard. Originality/value: This is the first study of its kind that explores the planning and policy development regarding RDM in university libraries of Pakistan. © 2019, Emerald Publishing Limited.","Data preservation; Pakistan; Research data curation; Research data management; Research data management policy and planning; University libraries of Pakistan","","","","","","","","Top Trends in Academic Libraries: a Review of the Trends and Issues Affecting Academic Libraries in Higher Education, pp. 274-281, (2016); Ameen K., Challenges of preparing LIS professionals for leadership roles in Pakistan, Journal of Education for Library and Information Science, 47, 3, pp. 200-217, (2006); Ameen K., Changing scenario of librarianship in Pakistan: managing with the challenges and opportunities, Library Management, 32, 3, pp. 171-182, (2011); Ameen K., Rafiq M., Research data literacy and management skills of Pakistani researchers, Paper presented at The Fifth European Conference on Information Literacy (ECIL), (2017); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: science librarians’ participation in data management at association of reference libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Arsev U.A., Guleda D., Zehra T., Research data management in Turkey: perceptions and practice, Library Hi Tech, 35, 2, pp. 271-289, (2017); ARC open access policy, (2016); Borgman C.L., Big Data, little Data, no Data: Scholarship in the Networked World, (2015); Corrall S., Research data policies: principles requirements and trends, Managing Research Data, (2012); Research Data Management: principles, practices and Prospects, (2013); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Verbaan E., Sen B.A., Upskilling liaison librarians for research data management, (2012); Cox A.M., Pinfield S., Kennan M.A., Lyon L., Research data management and libraries international survey, (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, (2016); Data Management in Perspective: Career Profile of the Academic Librarian, (2015); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Horstman W., Witt M., Libraries tackle the challenge of research data management, IFLA, 43, 1, pp. 3-4, (2017); Jones S., Pryor G., Whyte A., How to develop research data management services-a guide for HEIs, (2013); Koltay T., Research 2.0 and research data services in academic and research libraries: priority issues, Library Management, 38, 6-7, (2017); Lyon L., Patel M., Takeda K., Assessing requirements for research data management support in academic libraries: introducing a new multi-faceted capability tool, (2014); Data planning, (2017); Nasa open data portal, (2017); NHMRC open data access policy, (2017); DataScience@NIH blog: driving discover through data, (2017); Grants and findings, (2017); Neelie K., Data is the new gold, (2011); Oliver G., Harvey R., Digital Curation, (2016); (2013); Pryor G., Why manage research data, Managing Research Data, (2012); Guidance on best practice in the management of research data, (2015); Rice R., Haywood J., Research data management initiatives at university of Edinburgh, International Journal of Digital Curation, 6, 2, pp. 232-244, (2011); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith university, Electronic Library and Information Systems, 49, 4, pp. 441-446, (2015); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Smith P.L., Exploring the data management and curation (DMC) practices of scientists in research labs within a research university, (2014); Research Data Management: briefing for Library Directors, (2015); Surkis A., Read K., Research data management, Journal of the Medical Library Association : Jmla, 103, 3, pp. 154-156, (2015); Tammaro A.M., Casarosa V., Research data management in the curriculum: an interdisciplinary approach, Procedia Computer Science, 38, pp. 138-142, (2014); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers’ perception towards research data in India, IFLA, 43, 1, pp. 22-39, (2017); Verbaan E., Cox A.M., Collaboration or competition? Responses to research data management in UK higher education by librarians, IT professionals, and research administrators, Proceedings of iConference 2014, pp. 281-291, (2014); Warrad A., de Cousijn H., Aalbersberg I.J., 10 aspects of highly effective research data, (2015); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management. services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Kennan M., Corrall S., Afzal W., Making space in practice and education: research support services in academic, Library Management, 35, 8-9, pp. 666-683, (2014); (2017); Individuals using the internet (% of population), (2016); Yu S.H., Research data management: a library practitioner’s perspective, Public Services Quarterly, 13, 1, pp. 48-54, (2017)","H.A. Piracha; University of the Punjab, Lahore, Pakistan; email: haseeb.library@pu.edu.pk","","Emerald Group Holdings Ltd.","","","","","","25149326","","","","English","Collect. Curation","Article","Final","","Scopus","2-s2.0-85079786331"
"Bishop B.; Gunderman H.; Davis R.; Lee T.; Howard R.; Samors R.; Murphy F.; Ungvari J.","Bishop, Bradley (24469564000); Gunderman, Hannah (57192808657); Davis, Rowena (57214718205); Lee, Tina (57205775183); Howard, Rebecca (57212140079); Samors, Robert (57293365600); Murphy, Fiona (57215023032); Ungvari, Judit (35726098700)","24469564000; 57192808657; 57214718205; 57205775183; 57212140079; 57293365600; 57215023032; 35726098700","Data curation profiling to assess data management training needs and practices to inform a toolkit","2020","Data Science Journal","19","1","4","","","","3","10.5334/dsj-2020-004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079009440&doi=10.5334%2fdsj-2020-004&partnerID=40&md5=9b2e74e90380e3cb523819050a67a912","University of Tennessee, United States; Carnegie Mellon University, United States; Belmont Forum, Australia; Fiona Murphy and MMC Ltd, United Kingdom","Bishop B., University of Tennessee, United States; Gunderman H., Carnegie Mellon University, United States; Davis R., Belmont Forum, Australia; Lee T., Belmont Forum, Australia; Howard R., University of Tennessee, United States; Samors R., Belmont Forum, Australia; Murphy F., Fiona Murphy and MMC Ltd, United Kingdom; Ungvari J., Belmont Forum, Australia","The purpose of this paper is to explore current data management training needs and practices for Belmont Forum member agencies and researchers to inform a Toolkit. Fourteen Belmont Forum affiliated individuals were interviewed following a predetermined set of questions to create data curation profiles of their funded work. The data curation profile questionnaire includes questions related to data management, storage, stakeholders, costs, training, and credentials. The interview findings highlight gaps in existing knowledge of data management theory and practice that could impact data re-use. Results of these interviews were used to populate a Toolkit of data management training and effective practice resources specifically developed to train Belmont Forum grant awardees. The results also highlight some attitudes and behaviours of current scientists, researchers, and agency representatives, and the impact of the implementation of data management plans on the open science movement. © 2020 The Author(s).","Data curation profile; Data management plan; Open data; Open data policy; Research data management","Behavioral research; Data curation; Digital storage; Open Data; Effective practices; Impact data; Management plans; Management theory; Management training; Open science; Research data managements; Set of questions; Information management","","","","","","","E-Infrastructures and Data Management, The Data and Digital Outputs Management Plan Annex (DDOMP); Bishop B.W., Hank C.F., Data curation profiling of biocollections, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-9, (2016); Bishop B.W., Hank C.F., Curation, Digital, International Encyclopedia of Human Geography; Bishop B.W., Ungvari J., Davis R.I., Lee T., Goudeseune L., Virapongse A., Samors R.J., Belmont Forum Data Management Plan Scorecard, (2019); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Dietrich D., Adamus T., Miner A., Steinhart G., De-Mystifying the Data Management Requirements of Research Funders, 70, 1, (2012); Hey A.J.G., Trefethen A.E., The data deluge: An e-Science perspective, Grid Computing – Making the Global Infrastructure a Reality, pp. 809-824, (2003); Higgins S., The lifecycle of data management, Managing Research Data, pp. 57-61, (2012); Michener W.K., Brunt J.W., Helly J.J., Kirchner T.B., Stafford S.G., Nongeospatial metadata for the ecological sciences, Ecological Applications, 7, 1, pp. 330-342, (1997); Data Availability; Ray J., Introduction to Research Data Management, Research Data Management: Practical Strategies for Information Professionals, pp. 1-22, (2013); Smale N., Unsworth K., Denyer G., Barr D., The history, advocacy and efficacy of data management plans, Biorxiv, (2018); Sweetkind-Singer J., Larsgaard M.L., Erwin T., Digital preservation of geospatial data, Library Trends, 55, 2, pp. 304-314, (2006); Data Sharing and Mining, (2017); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing Data Curation Profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009)","B. Bishop; University of Tennessee, United States; email: wade.bishop@utk.edu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85079009440"
"Redkina N.S.","Redkina, N.S. (57210434112)","57210434112","Current Trends in Research Data Management","2019","Scientific and Technical Information Processing","46","2","","53","58","5","10","10.3103/S0147688219020035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070707302&doi=10.3103%2fS0147688219020035&partnerID=40&md5=9cb4a9ce4f1705cfb18140316499fa61","State Public Scientific and Technological Library, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630102, Russian Federation","Redkina N.S., State Public Scientific and Technological Library, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630102, Russian Federation","Abstract: This article presents an analysis of policies, guidelines and requirements set by governments of a number of countries in the field of data openness, grantors, and publishers, as well as a review of publications, which allows tracing the main trends in research data management, and the Guide to Research Data Management, which reflects the basic concepts and features of preparing a data management plan, metadata standards, data identifiers, etc. It is concluded that it is necessary to develop service management and create institutional and national research data management services. © 2019, Allerton Press, Inc.","data; FAIR; Horizon 2020; open data; research data management; research data plan","Abstracting; Information management; data; FAIR; Horizon 2020; Research data; Research data managements; Open Data","","","","","National Science Foundation, NSF; European Commission, EC; Deutsche Forschungsgemeinschaft, DFG; Bundesministerium für Bildung und Forschung, BMBF","Funding text 1: Analysis of the largest US and European grantmaker sites, international and national research councils (Arts and Humanities Research Council, AHRC; Biotechnology and Biological Sciences Research Council, BBSRC; Cancer Research UK, CRUK; Economic and Social Research Council, ESRC; Engineering and Physical Sciences Research Council, EPSRC; European Commission, EC; The Wellcome Trust; etc.) showed that there are requirements for making data management plans and sharing them, which are being prepared as an application for grants. The National Science Foundation (NSF) is requesting a two-page data management plan as part of the funding proposal process. Canadian Institutes of Health Research and Social Sciences and Humanities Research Council determine the public access to published materials, including data collections, among the requirements for obtaining grants. The Natural Sciences and Engineering Research Council of Canada includes data management for funding research projects. The German Research Foundation and the Federal Ministry of Education and Research are increasingly demanding that researchers applying for funding submit a data management plan or at least a statement describing how the data generated by the project will be managed along with their proposals. ; Funding text 2: Open access to research data is a fundamental principle of the Horizon 2020 program []. Researchers who apply for funding under this program should develop a data management plan, which should propose a strategy for collecting, storing, and making available data created in projects funded by the European Commission in accordance with official recommendations []. In addition, the strategic documents of the European Commission reflect new remuneration tools and grant financing schemes (for example, FP9) for those who practice open science []. ","(2019); Data Stewardship for Open Science: Implementing FAIR Principles, (2018); Yozwiak N.L., Schaffner S.F., Sabeti P.C., Data sharing: Make outbreak research open access, Nature, 518, pp. 477-479, (2015); Teperek M., Views on Data stewardship—report of Preliminary Findings at TPM Faculty: Preliminary Findings Report at the Faculty of Policy, (2018); Majid S., Foo S., Zhang X., Research data management by academics and researchers: Perceptions, knowledge and practices, Lect. Notes Comput. Sci., 11279, pp. 166-178, (2018); Researchers' Challenges in Sharing Data Cross Geographic Borders and Disciplines, (2019); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Boeckhout M., Gerhard A., Bredenoord L., The FAIR guiding principles for data stewardship: Fair enough?, Eur. J. Hum. Genet., 26, pp. 931-936, (2018); Donnelly M., Update to Analysis of Open Science Policies finds new activity in multiple countries, DCC News, (2018); Horizon 2020: Programme–guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020, (2017); Guidelines on FAIR Data Management in Horizon 2020, (2019); Evaluation of Research Careers Fully Acknowledging Open Science Practices: Rewards, Incentives And/Or Recognition for Researchers Practicing Open Science, (2019); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, J. Assoc. Inf. Sci. Technol., 68, pp. 2182-2200, (2017); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, ASLIB J. Inf. Manage., 70, pp. 142-157, (2018); Perrier L., Blondal E., Macdonald H., Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies, Libr. Inf. Sci. Res., 40, pp. 173-183, (2018); Bryant R., Brian L., Malpas C., A tour of the Research Data Management (RDM) Service Space, The Realities of Research Data Management, Dublin, Ohio: OCLC Research, 2017, (2019); Perrier L., Blondal E., Ayala A.P., Dearborn D., Kenny T., Lightfoot D., Et al., Research data management in academic institutions: A scoping review, Plos ONE, 12, 5, (2017); Arias-Coello A., Simon-Blas C., Arranz-Val P., Simon-Martin J., Research data management in three Spanish universities, Commun. Comput. Inf. Sci., 810, pp. 195-204, (2018); Helbig K., Research data management training for geographers: First impressions, ISPRS Int. J. Geo-Inf., 5, (2016); Juhas G., Molnar L., Ondrisova M., Juhasova A., Data, information and technology services for research and management of science, ICETA 2017—15th IEEE International Conference on Emerging Elearning Technologies and Applications: Proceedings, (2017); Liu X., Ding N., Research data management in universities of Central China: Practices at Wuhan University Library, Electron. Libr., 34, pp. 808-822, (2016); Vilar P., Zabukovec V., Research data management and research data literacy in Slovenian science, J. Doc., 75, pp. 24-43, (2019); Wiorogorska Z., Lesniewski J., Rozkosz E., Data literacy and research data management in two top universities in Poland. Raising awareness, Commun. Comput. Inf. Sci., 810, pp. 205-214, (2018); Burgi P.-Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs, IFLA J., 43, pp. 5-21, (2017); Tripathi M., Chand M., Sonkar S.K., Jeevan V.K.J., A brief assessment of researchers' perceptions towards research data in India, IFLA J., 43, pp. 22-39, (2017); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA J., 43, pp. 51-64, (2017); Pels P., Boog I., Florusbosch J.H., Data management in anthropology: The next phase in ethics governance?, Soc. Anthropol., 26, pp. 391-413, (2018); Alves C., Castro J.A., Ribeiro C., Honrado J.P., Lomba A., Research data management in the field of ecology: An overview, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 87-94, (2018); McKenzie-McHarg K., Thornton J., Introduction to Research Methodology for Specialists and Trainees, (2017); Meineke F.A., Lobe M., Staubert S., Introducing technical aspects of research data management in the Leipzig health atlas, Stud. Health Technol. Inf., 247, pp. 426-430, (2018); Wu M., Chen X., Library service design based on the needs of chemistry research data management and sharing survey, Proceedings of the Association for Information Science and Technology, 53, pp. 1-4, (2016); Zozus M.N., Lazarov A., Smith L.R., Breen T.E., Krikorian S.L., Zbyszewski P.S., Knoll S.K., Jendrasek D.A., Perrin D.C., Zambas D.N., Williams T.B., Pieper C.F., Analysis of professional competencies for the clinical research data management profession: Implications for training and professional certification, J. Am. Med. Inf. Assoc., 24, pp. 737-745, (2017)","N.S. Redkina; State Public Scientific and Technological Library, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630102, Russian Federation; email: to@spsl.nsc.ru","","Pleiades Publishing","","","","","","01476882","","","","English","Sci. Global Secur. Tech. Inf. Process.","Article","Final","","Scopus","2-s2.0-85070707302"
"Jiang J.; Huang J.; Yang Q.; Liang C.","Jiang, Jiang (57211817493); Huang, Jianwen (57207290937); Yang, Qiuyong (57208759590); Liang, Chenghui (14630550100)","57211817493; 57207290937; 57208759590; 14630550100","Research on data platform for power distribution and utilization based on CIM and OPC UA; [基于CIM和OPC统一架构的配用电网数据平台研究]","2019","Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control","47","3","","160","167","7","7","10.7667/PSPC180167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065707144&doi=10.7667%2fPSPC180167&partnerID=40&md5=a946effd33e759d477289f79fc78cd52","Information Center of Guangdong Power Grid Corporation, Guangzhou, 510600, China; Weihai CIMSTech Co., Ltd, Weihai, 264209, China","Jiang J., Information Center of Guangdong Power Grid Corporation, Guangzhou, 510600, China; Huang J., Information Center of Guangdong Power Grid Corporation, Guangzhou, 510600, China; Yang Q., Information Center of Guangdong Power Grid Corporation, Guangzhou, 510600, China; Liang C., Weihai CIMSTech Co., Ltd, Weihai, 264209, China","On the basis of the common information model and the OPC unified architecture, this paper studies the construction of distribution and utilization grid data platform to support comprehensive data analysis, including system architecture and information model research, data management, service system construction, and data aggregation. A unified information model is formed through analyzing the data in each distribution and utilization business system, tailoring the IEC TC57 common information model accordingly, and supplementing undefined elements in the CIM. The mapping rules of information model and object data to the OPC unified architecture information model are established. After the data of distribution grid and utilization system from multiple business systems have been standardized and aggregated according to the unified information model, all schema and object data are managed in the OPC unified architecture address space. Services defined by the OPC unified architecture are implemented to provide secure and efficient data access support for analysis applications. The data platform is organically coordinated with the business systems and comprehensive analysis applications, and its technical scheme has been put into practice in engineering. © 2019, Power System Protection and Control Press. All right reserved.","CIM; Data platform; Distribution grid; OPC UA; Utilization system","Information management; Information theory; Business systems; CIM; Data platform; Distribution grid; Information Modeling; Object data; OPC UA; Power distributions; Unified architecture; Utilization systems; Computer architecture","","","","","Science and Technology Project of China Southern Power Grid Ltd, (GDDWKJXM 00000025)","This work is supported by Science and Technology Project of China Southern Power Grid Ltd (No. GDDWKJXM 00000025). Key words: distribution grid; utilization system; CIM; OPC UA; data platform","Wang C., Wang S., Guo L., Prospect over the techniques of smart distribution network in China, Southern Power System Technology, 4, 1, pp. 18-22, (2010); Ding B., Zheng X., Zhou F., Et al., Research on method of capacity configuration for hybrid power in microgrid, Power System Protection and Control, 41, 16, pp. 144-148, (2013); Zhang D., Li J., Hui D., Coordinated control for voltage regulation of distribution network voltage regulation by distributed energy storage systems, Protection and Control of Modern Power Systems, 3, 3, pp. 35-42, (2018); Li S., Jiang C., Zhao Z., Et al., Study of transient voltage stability for distributed photovoltaic power plant integration into low voltage distribution network, Power System Protection and Control, 45, 8, pp. 67-72, (2017); Chen Q., Zhao X., Gan D., Active- reactive scheduling of active distribution system considering interactive load and battery storage, Protection and Control of Modern Power Systems, 2, 2, pp. 320-330, (2017); Wang P., Lin J., Guo S., Et al., Distribution system data analytics and applications, Power System Technology, 41, 10, pp. 3333-3340, (2017); Lu R., Hu R., Liu L., Design and application of power grid best dispatching intelligent data platform, East China Electric Power, 41, 1, pp. 77-80, (2013); Lin J., A unified scheme of grid operation index control based on big data platform, Power System Protection and Control, 46, 4, pp. 165-170, (2018); Li D., Wang S., Huang T., Et al., Key technologies of line loss and stealing electricity prediction analysis based on big data platform, Power System Protection and Control, 46, 5, pp. 143-151, (2018); Cao Y., Yao J., Yang S., Et al., Latest advancements of smart grid core standard IEC 61970, Automation of Electric Power Systems, 35, 17, pp. 3-4, (2011); CIM User Group, Current official CIM model release, (2017); Xie S., Yang Q., Liang C., Et al., Research of unified condition monitoring information model in data platform of power transmission equipment remote monitoring and diagnosis, Power System Protection and Control, 42, 11, pp. 86-91, (2014); Energy management system application program interface (EMS-API), part 501: common information model resource description framework (CIM RDF) schema: IEC 61970-501, (2006); Xie S., Yang Q., Xu Q., Address space management of OPC UA for common information model, Automation of Electric Power Systems, 40, 14, pp. 115-121, (2016); OPC unified architecture - part 3 address space model: IEC 62541-3, (2010); OPC unified architecture - part 4 services: IEC 62541-4, (2011)","J. Jiang; Information Center of Guangdong Power Grid Corporation, Guangzhou, 510600, China; email: j.jiang@live.cn","","Power System Protection and Control Press","","","","","","16743415","","","","Chinese","Dianli Xitong Baohu yu Kongzhi","Article","Final","","Scopus","2-s2.0-85065707144"
"Haselwanter T.","Haselwanter, Thomas (16425649600)","16425649600","“E-Infrastructures Austria plus”. short version of the final report on the HRSM project for the creation, establishment and networking of infrastructure for the preparation and management of project applications and research data; [„E-infrastructures Austria plus“. Kurzfassung des abschlussberichts über das HRSM-projekt für das schaffen, ansiedeln und vernetzen von infrastruktur zum erstellen und verwalten von projektanträgen und forschungsdaten]","2020","VOEB-Mitteilungen","73","1","","71","117","46","0","10.31263/voebm.v73i1.3373","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087300227&doi=10.31263%2fvoebm.v73i1.3373&partnerID=40&md5=8b3c921a4de9a74e0d0b93cb7159fb9c","Universität Innsbruck, Zentraler Informatikdienst, Austria","Haselwanter T., Universität Innsbruck, Zentraler Informatikdienst, Austria","In the “Orientations towards the first Strategic Plan for Horizin Europe1”, the strategy paper of the EU-Commission on the 9th EU Framework Programme for Research and Innovation, the following sentence can be found on the importance of Open Science: “Open science practices will be mainstreamed as the new modus ope-randi for EU research and innovation.“ Open Science will be the specification of how research should be carried out in the future with the help of digital tools and networks. Although the EU-Commission is a pioneer in this area, local funding bodies are alrea-dy following suit. For research institutions, this means that the institutional research infrastructure must be further developed to meet these requirements. In the project “e-Infrastructures Austria Plus”, seven work packages have begun to build up the know-how and technical infrastructures required for this. © Thomas Haselwanter.","E-Infrastructures; EScience; Open Science; Research Data Management","","","","","","Austrian Science Fund, FWF","Unabhängig vom Fachbereich sind allenfalls Ort, Zeit und die Projekt-nummer der Fördergeber möglichst automatisiert in den Metadaten anzugeben (z.B. Austrian Science Fund (FWF): project number). Für bestimmte Dateiformate sollen Möglichkeiten eruiert werden, wie auch inhaltliche Metadaten direkt aus den Dateien ausgelesen werden kön-nen. – Vermittlung von Kompetenzen zum Umgang mit Metadaten Kenntnisse zum Umgang mit Metadaten seitens der Forschenden wer-den benötigt, um die eigenen Forschungsdaten im Sinne der FAIR Prin-zipien mit den entsprechenden Metadaten auffindbar und nachnutzbar für andere Forschenden zu machen. – Barrierefreie Metadaten Metadaten sind möglichst barrierefrei für alle User zu gestalten. Dazu zählen unter anderem die Mehrsprachigkeit und die Verständlichkeit auch für Personen aus anderen Fachdisziplinen. In den Metadaten sollte außerdem ein Hinweis auf barrierefreie Inhalte enthalten sein. – Disziplinnahe Unterstützung bei der Metadatenvergabe Wichtig ist der Aufbau von Strukturen zur Unterstützung der Forschen-den bezüglich Metadaten, zum Beispiel durch die einzelnen Fachbe-reiche. Ergebnisse aus der Gruppe „Research Data Alliance“ und der Initiative „GoFAIR“ zu fachspezifischen Metadatenstandards sind hier miteinzubeziehen. – Kontrollierte Vokabularien Zur Erfüllung der FAIR-Prinzipien wird die Nutzung von kontrollierten Vokabularien oder Thesauri zur Klassifizierung der Daten nach offizi-ellen Schemata und zur Kennzeichnung des Inhalts mit einheitlichen Schlüsselwörtern empfohlen (wie zum Beispiel ÖFOS 2010).","Adam Beatrix, Lindstadt Birte, Elektronische Laborbücher im Kontext von Forschungsdatenmanagement und guter wissenschaft-licher Praxis – ein Wegweiser für die Lebenswissenschaften: ELN-Weg-weiser, (2019); Bettel Florian, Austausch mit HRSM-Projekt „Portfolio/ Showroom – Making Art Resarch Accessible, (2019); Blumesberger Susanne, Zartl Alexander, Umgang mit Metadaten in Repositorien – eine österreichweite Umfrage, Zweite Folge, (2019); Blumesberger Susanne, Preza Jose-Luis, Zartl Alexander, Cluster I: Umgang mit Metadaten in Repositorien – Eine österreichweite Umfrage, (2016); Blumesberger Susanne, Zartl Alexander, Umgang mit Meta-daten in Repositorien – Eine österreichweite Umfrage, Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen & Bibliothekare, 70, 2, pp. 249-273, (2017); Dataverse; Orientations towards the first Strategic Plan for Horizon Europe, (2019); Ferus Andreas, Gstrein Silvia, Hikl Anna-Laetitia, Kaier Christian, Krane-witter Michael, Marin Arraiza Paloma, Mayer Adelheid, Insti-tutionelle Muster-DOI-Policy, (2019); Ferus Andreas, Gstrein Silvia, Hikl Anna-Laetitia, Kaier Christian, Kra-newitter Michael, Marin Arraiza Paloma, Mayer Adelheid, Institutional Model Policy for the Registration of Digital Object Identi-fiers, (2019); Fiala Sonja, Kennzeichnung barrierefreier Dateien – eine Zu-sammenstellung am Beispiel MARC21 und MODS, (2019); Fiala Sonja, Huggle Christina, Metadatenmapping – Die Ge-genüberstellung verschiedener Metadatenschemata am Beispiel UWMETADATA>>, (2019); Gasteiner Martin, Interviews zu Forschungsdaten im Rah-men des Projekts E-Infrastructure Austria Plus, (2019); Grundhammer Veronika, Sakabe Yukiko, Zum Umgang mit Metadaten an der ÖAW: Eine erste Annäherung, (2019); Gstrein Silvia, Kaier Christian, DOI-Vergabe in Österreich: Sze-narien, (2017); Haselwanter Thomas, Miksa Tomas, Thoricht Heike, Vergleich der DMP-Tools RDMO, DMPRoadmap und Data Steward Wizard, (2019); Haselwanter Thomas, Thoricht Heike, e-Infrastructures Austria Plus Projektbericht 2017–2019, (2020); Haselwanter Thomas, Thoricht Heike, Anwendungsszenarien für Forschungsdatenrepositorien, (2019); Haselwanter Thomas, Thoricht Heike, Erstellung von Persona zur Auswahl eines institutionellen Repositoriums für Forschungsdaten, (2019); Haselwanter Thomas, Thoricht Heike, Der Ablageprozess von Forschungsdaten und was von Zenodo gelernt werden kann, (2019); Haselwanter Thomas, Thoricht Heike, Klassifizie-rung von Forschungsdaten und Speichersysteme, (2019); Heider Veronika, Raffetseder Lena, Sanchez Solis Barbara, Ulrich Xenia, DMP Template for the Social Sciences, (2018); Heindl Markus, Hikl Anna-Laetitia, Kaier Christian, ORCID Austria Workshop (Wien, 24. Mai 2018), Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare, 71, 3-4, pp. 468-474, (2018); Kalova Tereza, Maßnahmen des Forschungsdatenmanage-ments an österreichischen Wissenschaftsorganisationen, (2019); Kalova Tereza, Metadaten für Forschungsdaten: Bedürf-nisse und Anforderungen in den Naturwissenschaften, (2019); Katzmayr Michael, Seyffertitz Thomas, Leitfaden zur Er-hebung zum Forschungsdatenmanagement, (2019); Katzmayr Michael, Seyffertitz Thomas, Forschungsdaten-management an der Wirtschaftsuniversität Wien, (2019); Kennel Patrik, Sacherschließung von Forschungsdaten, (2019); Marin Arraiza Paloma, ORCID: Member API oder Public API?, (2019); Policy zum Forschungsdatenmanagement der Medizinischen Universität Wien; Miksa Thomas; Miksa Thomas; Miksa Thomas; Miksa Thomas; Miksa Thomas; Miksa Thomas; Connect and Get Connected – Linking Open Science in Aus-tria; Workshop Series „Services to Support FAIR; Sanchez Solis Barbara, Stork Christiane, Forschungsdaten-management an der Technischen Universität Wien, (2019); Practical Guide to the International Alignment of Research Data Management; Policy zum Forschungsdatenmanagement der Technischen Universität Wien; Research data lifecycle; Universität für Musik und darstellende Kunst Wien, Policy zum For-schungsdatenmanagement der Universität für Musik und darstellende Kunst Wien; Policy zum Forschungsdatenmanagement der Universität Graz; Research Lifecycle at UCF; Wien Wirtschaftsuniversitat, Policy zum Forschungsdatenmanagement der Wirtschaftsuniversität Wien; Linking Open Science in Austria, (2019); Zartl Alexander, Automatische Übertragung von Metadaten in Vi-deodateien, (2019); An dieser Stelle wird auf den ELN Wegweiser des ZB MED-Informati-onszentrums Lebenswissenschaften hingewiesen; Katzmayr Michael, Seyffertitz Thomas, Leitfaden zur Erhebung zum Forschungsdatenmanagement, (2019); Die verwendeten Icons entstammen dem Projekt; Università degli Studi di Milano, (2017); Università di Padova, (2018); FWF Datenmanagementplan (DMP) Vorlage – Guide; Practical Guide to the International Alignment of Research Data Management; Heider Veronika, Raffetseder Lena, Sanchez Solis Barbara, Ulrich Xenia, DMP Template for the Social Sciences (Version 1.0), (2018); genauere Informationen über den Piloten finden sich im Endbericht; Miksa Tomasz, Et al., (2019); Rohsmann, Katarina, (2016); Erstellung von Persona zur Auswahl eines institutionellen Repositori-ums für Forschungsdaten; Repository Platforms for Research Data Interest Group of the Research Data Alliance, (2016); Zhang Tao, Et al., (2013); (2018); Wikipedia contributors, (2019); Figshare; Wikipedia contributors, (2019); Blumesberger Susanne, Zartl Alexander, Umgang mit Meta-daten in Repositorien – Eine österreichweite Umfrage, Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen & Bibliothekare, 70, 2, pp. 249-273, (2017); Wikipedia contributors, (2019); Heindl Markus, Et al., (2018); Dieser Workshop fand nach Redaktionsschluss des Berichts statt","T. Haselwanter; Universität Innsbruck, Zentraler Informatikdienst, Austria; email: thomas.haselwanter@uibk.ac.at","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85087300227"
"Sansigolo G.; de Queiroz G.R.; Ferreira K.R.","Sansigolo, Gabriel (57205659025); de Queiroz, Gilberto R. (55438000400); Ferreira, Karine R. (55436773200)","57205659025; 55438000400; 55436773200","Defining a platform for earth observation research data sharing; [Projetando uma Plataforma para Compartilhamento de Dados Científicos de Observação da Terra]","2019","Proceedings of the Brazilian Symposium on GeoInformatics","","","","249","254","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129236057&partnerID=40&md5=674846ace5c0519ac7d362d04c6629a8","Instituto Nacional de Pesquisas, Espaciais Av. dos Astronautas, 1758, SP, São José dos Campos, CEP 12227-010, Brazil","Sansigolo G., Instituto Nacional de Pesquisas, Espaciais Av. dos Astronautas, 1758, SP, São José dos Campos, CEP 12227-010, Brazil; de Queiroz G.R., Instituto Nacional de Pesquisas, Espaciais Av. dos Astronautas, 1758, SP, São José dos Campos, CEP 12227-010, Brazil; Ferreira K.R., Instituto Nacional de Pesquisas, Espaciais Av. dos Astronautas, 1758, SP, São José dos Campos, CEP 12227-010, Brazil","The growing demand on scientific information sharing has motivated scientists and institutions to look for new computational tools for research data management and sharing. Today there are different platforms for publishing scientific data, such as Pangea or Zenodo. However, these platforms, due to their restricted characteristics, do not integrate data with tools used by Earth observation researchers. This paper presents ongoing work on defining a platform for Earth observation research data sharing, that integrates tools for storage, cataloging, management, processing and dissemination. Thus contemplating all the research activities. © 2019 National Institute for Space Research, INPE. All rights reserved.","","Digital storage; Information management; Computational tools; Data Sharing; Earth observations; Growing demand; Information sharing; Research activities; Research data; Research data managements; Scientific data; Scientific information; Observatories","","","","","","","Amorim R. C., Castro J. A., Et al., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Bezjak S., Clyburne-Sherin A., Et al., Open Science Training Handbook, (2018); Diepenbroek M., Grobe H., Et al., Pangaea—an information system for environmental sciences, Computers Geosciences, 28, 10, pp. 1201-1210, (2002); King G., An introduction to the dataverse network as an infrastructure for data sharing, Sociological Methods Research, 36, 2, pp. 173-199, (2007); Ogc standards and supporting documents, (2017); Saez R. V., Fuentes C. M., Open science now: A systematic literature review for an integrated definition, Journal of Business Research, 88, pp. 428-436, (2018); Sicilia M.-A., Garcia-Barriocanal E., Et al., Community curation in open dataset repositories: Insights from zenodo, Procedia Computer Science, 106, pp. 54-60, (2017); Wainwright M., Using ckan: storing data for re-use, (2012); Woelfle M., Olliaro P., Todd M. H., Open science is a research accelerator, Nature Chemistry, 3, (2011)","","Filho J.L.; Monteiro A.M.V.","National Institute for Space Research, INPE","","20th Brazilian Symposium on GeoInformatics, GEOINFO 2019","11 November 2019 through 13 November 2019","Sao Jose dos Campos, SP","178977","21794847","","","","Portuguese","Proc. Brazilian Symp. GeoInformatics","Conference paper","Final","","Scopus","2-s2.0-85129236057"
"Suhr M.; Umbach N.; Meyer T.; Zimmermann W.-H.; Sax U.","Suhr, Markus (57210809413); Umbach, Nadine (55891434100); Meyer, Tim (57199899467); Zimmermann, Wolfram-Hubertus (7203058782); Sax, Ulrich (8956991900)","57210809413; 55891434100; 57199899467; 7203058782; 8956991900","Cardiac tissue engineering as use case to connect biomedical research laboratories to an emerging global data infrastructure","2019","Studies in Health Technology and Informatics","264","","","363","367","4","0","10.3233/SHTI190244","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071483207&doi=10.3233%2fSHTI190244&partnerID=40&md5=a5e20371336efaed86415cb001d27839","Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany; Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany; DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Germany; University Medical Center Göttingen, Department of Medical Informatics, von-Siebold-Straße 3, Göttingen, 37075, Germany","Suhr M., Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany, University Medical Center Göttingen, Department of Medical Informatics, von-Siebold-Straße 3, Göttingen, 37075, Germany; Umbach N., Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany, DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Germany; Meyer T., Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany, DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Germany; Zimmermann W.-H., Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany, DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Germany; Sax U., Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany","Methods for cardiac tissue engineering and application in experiments are core technologies developed at the Institute of Pharmacology and Toxicology in Göttingen. As is the case in many academic research laboratories data capture and documentation may be improved to latest methods of digital research. A comprehensive information system infrastructure is the foundation of further advances toward automation of lab processes. A data management system concept is proposed and prototypically deployed that enables traceability of assets within the lab and reproducibility of published assays and results. The prototype integrates existing electronic lab notebook, experiment result database, and a newly introduced research data management system by means of a custom developed portal and integration component. The architecture concept and developed integration tools explore connection of routine experimental work in a biomedical research lab to a universal infrastructure of data. © 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).","Automation; Data Curation; Laboratory; User-Computer Interface","Biomedical Research; Knowledge Management; Laboratories; Reproducibility of Results; Tissue Engineering; Automation; Data curation; Data integration; Engineering research; Health; Laboratories; Medical informatics; Research laboratories; Tissue; Tissue engineering; Biomedical research; Cardiac tissue engineering; Comprehensive information; Data management system; Digital researches; Research data managements; System infrastructure; User-computer interfaces; knowledge management; laboratory; medical research; reproducibility; tissue engineering; Information management","","","","","German Center for Cardiovascular Research; Deutsches Zentrum für Herz-Kreislaufforschung, DZHK, (81Z7300173); Deutsche Forschungsgemeinschaft, DFG, (CRC 937); Bundesministerium für Bildung und Forschung, BMBF","This work was funded by the BMBF in the project Data and Information Management (DZHK (German Center for Cardiovascular Research)) (grant 81Z7300173) as well as by the DFG for the Collaborative Research Centers (CRC) 1002 on Modulatory Units in Heart Failure, subproject INF, and CRC 937 on Collective Behavior of Soft and Biological Matter.","Fermini B., Coyne S.T., Coyne K.P., Clinical trials in a dish: A perspective on the coming revolution in drug development, SLAS Discov. Adv. Life Sci. R D., (2018); Antman E.M., Loscalzo J., Precision medicine in cardiology, Nat. Rev. Cardiol., 13, pp. 591-602, (2016); Tiburcy M., Hudson J.E., Balfanz P., Schlick S., Meyer T., Chang Liao M.-L., Levent E., Raad F., Zeidler S., Wingender E., Riegler J., Wang M., Gold J.D., Kehat I., Wettwer E., Ravens U., Dierickx P., Van Laake L.W., Goumans M.J., Khadjeh S., Toischer K., Hasenfuss G., Couture L.A., Unger A., Linke W.A., Araki T., Neel B., Keller G., Gepstein L., Wu J.C., Zimmermann W.-H., Defined engineered human myocardium with advanced maturation for applications in heart failure modeling and repair, Circulation, 135, pp. 1832-1847, (2017); Wittenburg P., Strawn G., Common Patterns in Revolutionary Infrastructures and Data, (2018); Frohlich H., Balling R., Beerenwinkel N., Kohlbacher O., Kumar S., Lengauer T., Maathuis M.H., Moreau Y., Murphy S.A., Przytycka T.M., Rebhan M., Rost H., Schuppert A., Schwab M., Spang R., Stekhoven D., Sun J., Weber A., Ziemek D., Zupan B., From hype to reality: Data science enabling personalized medicine, BMC Med, 16, (2018); Tiburcy M., Meyer T., Soong P., Zimmermann W.-H., Collagen-based engineered heart muscle, Card. Tissue Eng., pp. 167-176, (2014); Macdonald S., Macneil R., Service integration to enhance research data management: RSpace electronic laboratory notebook case study, Int. J. Digit. Curation., 10, pp. 163-172, (2015); Corti L., Van Den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Bauer C.R., Umbach N., Baum B., Buckow K., Franke T., Grutz R., Gusky L., Nussbeck S.Y., Quade M., Rey S., Rottmann T., Rienhoff O., Sax U., Architecture of a biomedical informatics research data management pipeline, Stud. Health Technol. 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Med., 57, pp. e50-e56, (2018); Mons B., Neylon C., Velterop J., Dumontier M., Santos D.S., Bonino L.O., Wilkinson M.D., Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud, Inf. Serv. Use., 37, pp. 49-56, (2017)","M. Suhr; Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany; email: markus.suhr@med.uni-goettingen.de","Seroussi B.; Ohno-Machado L.; Ohno-Machado L.; Seroussi B.","IOS Press","","17th World Congress on Medical and Health Informatics, MEDINFO 2019","25 August 2019 through 30 August 2019","Lyon","150814","09269630","978-164368002-6","","31437946","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-85071483207"
"Borda A.; Gray K.; Fu Y.","Borda, Ann (26022902600); Gray, Kathleen (7202177088); Fu, Yuqing (57222083560)","26022902600; 7202177088; 57222083560","Research data management in health and biomedical citizen science: Practices and prospects","2020","JAMIA Open","3","1","","113","125","12","10","10.1093/JAMIAOPEN/OOZ052","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085556324&doi=10.1093%2fJAMIAOPEN%2fOOZ052&partnerID=40&md5=0e6b093f7fd8d8e3003edaee86c4c953","Health and Biomedical Informatics Centre, Melbourne Medical School, The University of Melbourne, Melbourne, Australia","Borda A., Health and Biomedical Informatics Centre, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; Gray K., Health and Biomedical Informatics Centre, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; Fu Y., Health and Biomedical Informatics Centre, Melbourne Medical School, The University of Melbourne, Melbourne, Australia","Background: Public engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results? Objective: The aim of this article is to analyze research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research. Methods: A scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers. Results: Health and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not prevalent in the review. Conclusions: There is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle. © The Author(s) 2019.","Citizen science; Crowdsourcing; Participatory health; Research data management; Self-quantification","article; citizen science; crowdsourcing; life cycle","","","","","","","Bonney R., Citizen science: a lab tradition, Living Bird, 15, 4, pp. 7-15, (1996); Bonney R, Ballard H, Jordan R, Et al., Public Participation in Scientific Research: Defining the Field and Assessing its Potential for Informal Science Education. 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Challenges and opportunities for participatory social research, First Monday, 18, 10, (2013); Hicks S, Duran B, Wallerstein N, Et al., Evaluating community-based participatory research to improve community-partnered science and community health, Prog Community Health Partnersh, 6, 3, pp. 289-299, (2012); Hogan MJ, Johnston H, Broome B, Et al., Consulting with citizens in the design of wellbeing measures and policies: lessons from a systems science application, Soc Indic Res, 123, 3, pp. 857-877, (2015); Househ M, Grainger R, Petersen C, Bamidis P, Merolli M., Balancing between privacy and patient needs for health information in the age of participatory health and social media: a scoping review, Yearb Med Inform, 27, 1, pp. 29-36, (2018); MacFarlane A, O'Reilly-de Brun M, de Brun T, Et al., Healthcare for migrants, participatory health research and implementation science–better health policy and practice through inclusion. The RESTORE project, Eur J Gen Pract, 20, 2, pp. 148-152, (2014); Okun S, Goodwin K., Building a learning health community: by the people, for the people, Learn Health Syst, 1, 3, (2017); Price A, Liew SM, Kirkpatrick J, Price J, Lopreto T, Nelken Y., Mind the gap in clinical trials: a participatory action analysis with citizen collaborators, J Eval Clin Pract, 23, 1, pp. 178-184, (2017); Ranck J., The data revolution: networks, platforms, and the data sciences in health, Disruptive Cooperation in Digital Health, pp. 103-127, (2016); Simonds VW, Wallerstein N, Duran B, Villegas M., Community-based participatory research: its role in future cancer research and public health practice, Prev Chronic Dis, 10, (2013); Tangmunarunkit H, Hsieh CK, Longstaff B, Et al., A general and extensible end-to-end participatory sensing platform, ACM Trans Intell Syst Technol, 6, 3, (2015); Thorogood A, Bobe J, Prainsack B, Et al., APPLaUD: access for patients and participants to individual level uninterpreted genomic data, Hum Genomics, 12, 1, (2018); Zusevics KL., Public health genomics: a new space for a dialogue on racism through Community Based Participatory Research, Public Health, 127, 11, pp. 981-983, (2013); Kostkova P., Disease surveillance data sharing for public health: the next ethical frontiers, Life Sci Soc Policy, 14, 1, (2018); Elliot AJ, Kara EO, Loveridge P, Et al., Internet-based remote health self-checker symptom data as an adjuvant to a national syndromic surveillance system, Epidemiol Infect, 143, 16, pp. 3416-3422, (2015); Lamas E, Salinas R, Coquedano C, Et al., The meaning of patient empowerment in the digital age: the role of online patient-communities, Stud Health Technol Inform, 244, pp. 43-47, (2017); Almalki M, Gray K, Martin-Sanchez F., Activity theory as a theoretical framework for health self-quantification: a systematic review of empirical studies, J Med Internet Res, 18, 5, (2016); Almalki M, Gray K, Martin-Sanchez F., Development and validation of a taxonomy for characterizing measurements in health self- quantification, J Med Internet Res, 19, 11, (2017); Almalki M, Gray K, Martin-Sanchezu FJ., Refining the concepts of self-quantification needed for health self-management a thematic literature review, Methods Inf Med, 56, 1, pp. 46-54, (2017); Almalki M, Gray K, Sanchez FM., The use of self-quantification systems for personal health information: big data management activities and prospects, Health Inf Sci Syst, 3, (2015); Almalki M, Sanchez MF, Gray K., Quantifying the activities of self-quantifiers: management of data, time and health, Stud Health Technol Inform, 216, pp. 333-337, (2015); Beauchet O, Launay CP, Merjagnan C, Kabeshova A, Annweiler C., Quantified self and comprehensive geriatric assessment: older adults are able to evaluate their own health and functional status, Plos One, 9, 6, (2014); Bottles K., Will the quantified self movement take off in health care?, Physician Exec, 38, 5, pp. 74-75, (2012); Campbell S., The quantified patient checks in Larry Smarr’s experiments in self-tracking for health, IEEE Pulse, 8, 4, pp. 4-10, (2017); Chen K, Zdorova M, Nathan-Roberts D., Implications of wearables, fitness tracking services, and quantified self on healthcare, Proc Hum Factors Ergon Soc Annu Meet, 61, 1, pp. 1066-1070, (2017); Chrisinger BW, King AC., Stress experiences in neighborhood and social environments (SENSE): a pilot study to integrate the quantified self with citizen science to improve the built environment and health, Int J Health Geogr, 17, 1, (2018); Daly A., The law and ethics of ‘Self-quantified’ health information: an Australian perspective, Int Data Privacy Law, 5, 2, pp. 144-155, (2015); Farnham A, Furrer R, Blanke U, Et al., The quantified self during travel: mapping health in a prospective cohort of travellers, J Travel Med, 24, 5, pp. 1-8, (2017); Fernandez-Luque L, Singh M, Ofli F, Et al., Implementing 360_ quantified self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar, BMC Med Inform Decis Mak, 17, 1, (2017); Gimpe H, Nissen M, Gorlitz R., Quantifying the quantified self: A study on the motivation of patients to track their own health, the International Conference on Information Systems, pp. 1-16, (2013); Gray K, Martin-Sanchez FJ, Campos G, Almalki M, Merolli M., Person-generated data in self-quantification a health informatics research program, Methods Inf Med, 56, 1, pp. 40-45, (2017); Li Y, Guo Y., Wiki-health: from quantified self to self- understanding, Future Gener Comput Syst, 56, pp. 333-359, (2016); Liang Z, Martell MA., Framing self-quantification for individual-level preventive health care, proceedings of HEALTHINF 2015 - 8th International Conference on Health Informatics, pp. 336-343, (2015); Meyer J, Simske S, Siek KA, Gurrin CG, Hermens H., Beyond quantified self: data for wellbeing, Extended Abstracts on Human Factors in Computing Systems, CHI’14, pp. 95-98, (2014); Palmer JE., Genetic gatekeepers: regulating direct-to-consumer genomic services in an era of participatory medicine, Food Drug Law J, 67, 4, pp. 475-524, (2012); Sharon T., Self-tracking for health and the quantified self: re- articulating autonomy, solidarity, and authenticity in an age of personalized healthcare, Philos Technol, 30, 1, pp. 93-121, (2017); Sharon T., Self-tracking technologies in the workplace: Quantifying health, behavior and productivity, Human Resour Manag Int Digest, 25, 5, pp. 10-12, (2017); Shin DH, Biocca F., Health experience model of personal informatics: the case of a quantified self, Comput Human Behav, 69, pp. 62-74, (2017); Taylor D, Murphy J, Ahmad M, Et al., Quantified-self for obesity: physical activity behaviour sensing to improve health outcomes, Medicine Meets Virtual Reality 22: Nextmed/VR22. Series: Studies in Health Technology and Informatics, 220, pp. 414-416, (2016); Vandenberghe B, Geerts D., Sleep monitoring tools at home and in the hospital: bridging quantified self and clinical sleep research, Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth’15, pp. 153-160, (2015); Vegter MW., Towards precision medicine; a new biomedical cosmology, Med Health Care and Philos, 21, 4, pp. 443-456, (2018); Wicks P, Little M., The virtuous circle of the quantified self: a human computational approach to improved health outcomes, Handbook of Human Computation, pp. 105-129, (2013); Wicks P, Vaughan TE, Massagli MP, Heywood J., Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm, Nat Biotechnol, 29, 5, (2011); Williams AD., Harnessing the quantified self movement for optimal mental health and wellbeing, the Proceedings of the first Workshop on Lifelogging Tools and Applications LTA’16, (2016); Zhang J, Dibia V, Sodnomov A, Lowry PB., Understanding the disclosure of private healthcare information within online quantified self 2.0 platforms, proceedings of the Pacific Asia Conference on Information Systems, PACIS 2015, (2015); Lopez-Campos GH, Almalki M, Martin-Sanchez F., Proposal for a standardised reporting guideline to annotate health-related self- quantification experiments, Stud Health Technol Inform, 202, pp. 79-82, (2014); General data protection regulation, Off J Eur Union, 59, pp. 1-88, (2016); Harvard biomedical data management data lifecycle model, (2018); Beck S, Brasseur TM, Poetz MK, Sauermann H., What’s the problem? How crowdsourcing contributes to identifying scientific research questions, Acad Manag Proc, 2019, (2019); Simera I, Moher D, Hoey J, Schulz KF, Altman DG., The EQUATOR Network and reporting guidelines: helping to achieve high standards in reporting health research studies, Maturitas, 63, 1, pp. 4-6, (2009); What safeguards does PatientsLikeMe have in place to secure data?, (2015); Hummel P, Braun M, Dabrock P., Data donations as exercises of sovereignty, The Ethics of Medical Data Donation. Philosophical Studies Series, 137, pp. 23-54, (2019); Tempini N, Del Savio L., Digital orphans: data closure and openness in patient-powered networks, BioSocieties, 14, 2, pp. 205-227, (2019)","A. Borda; Victorian Comprehensive Cancer Centre, Parkville, Level 13, 305 Grattan St, 3010, Australia; email: aborda@unimelb.edu.au","","Oxford University Press","","","","","","25742531","","","","English","JAMIA Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85085556324"
"Castle C.","Castle, Clair (57225740100)","57225740100","Getting the central RDM message across: A case study of central versus discipline-specific research data services (RDS) at the University of Cambridge","2019","Libri","69","2","","105","116","11","6","10.1515/libri-2018-0064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067116144&doi=10.1515%2flibri-2018-0064&partnerID=40&md5=d26b81470582b43a8e858bf16375151a","Department of Chemistry Library, University of Cambridge, Department of Chemistry, Cambridge, United Kingdom","Castle C., Department of Chemistry Library, University of Cambridge, Department of Chemistry, Cambridge, United Kingdom","RDS are usually cross-disciplinary, centralised services, which are increasingly provided at a university by the academic library and in collaboration with other RDM stakeholders, such as the Research Office. At research-intensive universities, research data is generated in a wide range of disciplines and sub-disciplines. This paper will discuss how providing discipline-specific RDM support is approached by such universities and academic libraries, and the advantages and disadvantages of these central and discipline-specific approaches. A descriptive case study on the author's experiences of collaborating with a central RDS at the University of Cambridge, as a subject librarian embedded in an academic department, is a major component of this paper. The case study describes how centralised RDM services offered by the Office of Scholarly Communication (OSC) have been adapted to meet discipline-specific needs in the Department of Chemistry. It will introduce the department and the OSC, and describe the author's role in delivering RDM training, as well as the Data Champions programme, and their membership of the RDM Project Group. It will describe the outcomes of this collaboration for the Department of Chemistry, and for the centralised service. Centralised and discipline-specific approaches to RDS provision have their own advantages and disadvantages. Supporting the discipline-specific RDM needs of researchers is proving particularly challenging for universities to address sustainably: It requires adequate financial resources and staff skilled (or re-skilled) in RDM. A mixed approach is the most desirable, cost-effective way of providing RDS, but this still has constraints. © 2019 Walter de Gruyter GmbH, Berlin/Boston.","chemistry data; discipline-specific research data services; research data management; research data services; subject librarians","","","","","","","","Scholarly Communication Toolkit, (2017); Akers K.G., Doty J., Disciplinary Differences in Faculty Research Data Management Practices and Perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Agency of the Legal Deposit Libraries; Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Needs of Researchers, (2012); Bresnahan M., Johnson A., Assessing scholarly communication and research data training needs, University Libraries Faculty & Staff Contributions., 7, (2013); Christensen-Dalsgaard B., Van Der Berg M., Grim R., Horstmann W., Jansen D., Pollard T., Roos A., Ten Recommendations for Libraries to Get Started with Research Data Management, Final Report of the LIBER Working Group on E-Science/Research Data Management (Ligue des Bibliothèques Europeénnes de Recherche-Association of European Research Libraries, (2012); Corrall S., Educating the academic librarian as a blended professional: A review and case study, Library Management, 31, 8, pp. 567-593, (2010); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Data Curation Profiles; Disciplinary RDM Training, (2018); Expectations, (2014); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the Research Data Management Landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy, pp. 82-102, (2015); FOSTER Open Science Portal; Gabridge T., The Last Mile: Liaison Roles in Curating Science and Engineering Research Data, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 265, pp. 15-21, (2009); An Introduction to GitHub for Chemists, (2018); Introduction to ORCID, (2018); Guy M., Increasing Participation in Internal RDM Training Sessions, DCC RDM Services Case Studies, (2013); Guy M., RDM Training for Librarians, DCC RDM Services Case Studies, (2013); Higman R., Teperek M., Dataset Creating a Community of Data Champions, (2017); Higman R., Teperek M., Template Research Data Management Workshop for STEM Researchers, (2017); Higman R., Teperek M., Kingsley D., Creating a Community of Data Champions, International Journal of Digital Curation, 12, 2, pp. 96-106, (2017); Hiom D., Fripp D., Gray S., Snow K., Research Data Management at the University of Bristol: Charting a Course from Project to Service, Program: Electronic Library and Information Systems, 49, 4, pp. 475-493, (2015); Kennan M.A., Data Management: Knowledge and skills required in research, scientific and technical organisations, IFLA World Library and Information Congress, 2016, (2016); Koltay T., Are You Ready Tasks and Roles for Academic Libraries in Supporting Research, New Library World, 117, 1-2, pp. 94-104, (2016); Lewis M.J., Libraries and the Management of Research Data, Envisioning Future Academic Library Services, pp. 145-168, (2010); Morais R., Borrell-Damian L., pp. 1-37, (2018); NNLM RD3: Resources for Data-Driven Discovery; Library Roles; Research Data Management Project Group, (2018); ORCID; Pinfield S., Cox A.M., Rutter S., Mapping the Future of Academic Libraries: A Report for SCONUL, (2017); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, PLos One, 9, 12, pp. 1-28, (2014); Registry of Research Data Repositories, (2018); REF 2021, (2017); Russell Group; Sewell C., Kingsley D., Developing the twenty-first Century Academic Librarian: The Research Support Ambassador Programme, New Review of Academic Librarianship, 23, 2-3, pp. 148-158, (2017); Swan A., Brown S., The Skills Roles and Career Structure of Data Scientists and Curators: An Assessment of Current Practice and Future Needs, (2008); Symplectic. Elements; Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research Data Management Services in Academic Research Libraries and Perceptions of Librarians, Library & Information Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research Data Services in European Academic Research Libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Teperek M., Higman R., Kingsley D., Is Democracy the Right System Collaborative Approaches to Building an Engaged RDM Community, International Journal of Digital Curation, 12, 2, pp. 86-95, (2017); Chemistry, (2018); About the University, Timeline, (2018); Planning and Resource Allocation Office, (2018); Department of Chemistry, (2018); Apollo-University of Cambridge Repository. Data-Chemistry, (2018); Cambridge University Library, (2018); Chemistry Library, (2018); Scholarly Communication, (2018); Research Data Management, (2018); Apollo-University of Cambridge Repository, (2018); Scholarly Communication. Open Access Project Board, (2018); Chemistry Library. Open Data FAQs for Chemists, (2018); Chemistry Library. Convert Your Files into An Open Data Format, (2018); Williamson L., Roles, Responsibilities and Skills Matrix for Research Data Management (RDM) Support. ADMIRe Working Document, (2013); Williamson L., Parsons T., ADMIRe Project. UK HEIs RDM Service Models and Skills to Support Research Data Management, (2013); Wilson J.A., Jeffreys P.W., Towards a Unified University Infrastructure: The Data Management Roll-Out at the University of Oxford, International Journal of Digital Curation, 2, 8, pp. 235-246, (2013); Wittenberg J., Elings M., Building A Research Data Management Service at the University of California, Berkeley: A Tale of Collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017)","C. Castle; Department of Chemistry Library, University of Cambridge, Department of Chemistry, Cambridge, United Kingdom; email: cmc32@cam.ac.uk","","De Gruyter Saur","","","","","","00242667","","","","English","Libri","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85067116144"
"Li Y.; Dressel W.; Hersey D.","Li, Yuan (57208286541); Dressel, Willow (57207821869); Hersey, Denise (22957791300)","57208286541; 57207821869; 22957791300","Research data management: What can librarians really help?","2019","Grey Journal","15","1","","23","30","7","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064593519&partnerID=40&md5=095ddd7939c21cc59786f9c7fef0e964","Princeton University Library, United States","Li Y., Princeton University Library, United States; Dressel W., Princeton University Library, United States; Hersey D., Princeton University Library, United States","As national government agencies continue to mandate specific data management requirements and the need for research data management (RDM) grows, many libraries are developing RDM services to help with the research mission of their institution. Research libraries’ mission and expertise have always included a variety of research services. What can the library’s role be in RDM services? This paper describes the possible roles that libraries and librarians can play throughout the data lifecycle. The Princeton University Library is presented as a case study to demonstrate these roles and the development of RDM services including advocacy, awareness, education, advisory services, data management plan development, and data repository development and promotion. In addition, this paper also discusses the challenges and opportunities associated with RDM services development in libraries and the future plans at Princeton, including the development of a RDM mini course for graduate students and a robust RDM program. © 2019, GreyNet. All rights reserved.","","","","","","","","","AAU-APLU Public Access Working Group Report and Recommendations, Association of American Universities and Association of Public and Land-Grant Universities, (2017); Brandt D.S., Purdue University Research Repository: Collaborations in Data Management; Bryant R., Et al., The Realities of Research Data Management, OCLC Research; Fearon D., Et al., Research Data Management Services, SPEC Kit, 334, (2013); Henry G., Data Curation for the Humanities: Perspectives from Rice University.” Research Data Management: Practical Strategies for Information Professionals; Holdren J., Increasing Access to the Results of Federally Funded Scientific Research, (2013); Steinhart G., An Institutional Perspective on Data Curation Services: A View from Cornell University, Research Data Management: Practical Strategies for Information Professionals, (2014); Tenopir C., Et al., Academic Libraries and Research Data Services Current Practices and Plans for the Future; Research Data Lifecycle, (2012); Westra B., Developing Data Management Services for Researchers at the University of Oregon, Research Data Management: Practical Strategies for Information Professionals, (2014)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Article","Final","","Scopus","2-s2.0-85064593519"
"Becker R.; Alper P.; Grouès V.; Munoz S.; Jarosz Y.; Lebioda J.; Rege K.; Trefois C.; Satagopam V.; Schneider R.","Becker, Regina (7402086086); Alper, Pinar (8912315800); Grouès, Valentin (25929180900); Munoz, Sandrine (57212136848); Jarosz, Yohan (55179430600); Lebioda, Jacek (57203967647); Rege, Kavita (56688288000); Trefois, Christophe (26768414800); Satagopam, Venkata (57207906980); Schneider, Reinhard (23098235400)","7402086086; 8912315800; 25929180900; 57212136848; 55179430600; 57203967647; 56688288000; 26768414800; 57207906980; 23098235400","DAISY: A Data Information System for accountability under the General Data Protection Regulation","2019","GigaScience","8","12","giz140","","","","2","10.1093/gigascience/giz140","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076014657&doi=10.1093%2fgigascience%2fgiz140&partnerID=40&md5=75abc63daacaa0a27fa9e20b0a2f9e37","Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg","Becker R., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Alper P., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Grouès V., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Munoz S., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Jarosz Y., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Lebioda J., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Rege K., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Trefois C., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Satagopam V., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg; Schneider R., Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Avenue du Swing L-4367, Belvaux, Luxembourg","Background: The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal data as well as informing the data subjects of its use. GDPR's accountability principle requires institutions, projects, and data hubs to document their data processings and demonstrate compliance with the GDPR. In response to this requirement, we see the emergence of commercial data-mapping tools, and institutions creating GDPR data register with such tools. One shortcoming of this approach is the genericity of tools, and their process-based model not capturing the project-based, collaborative nature of data processing in biomedical research. Findings: We have developed a software tool to allow research institutions to comply with the GDPR accountability requirement and map the sometimes very complex data flows in biomedical research. By analysing the transparency and record-keeping obligations of each GDPR principle, we observe that our tool effectively meets the accountability requirement. Conclusions: The GDPR is bringing data protection to center stage in research data management, necessitating dedicated tools, personnel, and processes. Our tool, DAISY, is tailored specifically for biomedical research and can help institutions in tackling the documentation challenge brought about by the GDPR. DAISY is made available as a free and open source tool on Github. DAISY is actively being used at the Luxembourg Centre for Systems Biomedicine and the ELIXIR-Luxembourg data hub. © 2019 The Author(s) 2019. Published by Oxford University Press.","accountability; data mapping; GDPR","Computer Security; Electronic Health Records; Europe; Humans; Social Responsibility; article; biomedicine; documentation; elixir; human; Luxembourg; medical research; software; computer security; electronic health record; Europe; legislation and jurisprudence; social responsibility","","","","","Luxembourg Centre for Systems Biomedicine; Luxembourg Ministry of Higher Education and Research towards the Luxembourg; Seventh Framework Programme, FP7, (623051)","Funding text 1: This work was (partially) funded by the contribution of the Luxembourg Ministry of Higher Education and Research towards the Luxembourg ELIXIR Node.; Funding text 2: The authors would like to acknowledge the Reproducible Research Results (R3) team of the Luxembourg Centre for Systems Biomedicine for support of the project and for promoting reproducible research.","Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation)., 2016; Decision of the EEA Joint Committee No 154/2018 of 6 July 2018 Amending Annex XI and Protocol 37 to the EEA Agreement [2018/1022]., (2018); Shabani M., Borry P., Rules for processing genetic data for research purposes in view of the new EU General Data Protection Regulation, Eur J Hum Genet, 26, 2, pp. 149-156, (2018); Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data., (1995); Guide to the General Data Protection Regulation: Legitimate Interests, (2018); Article 29 Working Party Opinion 06/2014 on the Notion of Legitimate Interests of the Data Controller under Article 7 of Directive 95/46/EC, (2014); Act No. 122/2013 Coll. On Protection of Personal Data and on Changing and Amending of Other Acts, Resulting from Amendments and Additions Executed by the Act. No. 84/2014 Coll., (2014); The Organic Law 3/2018 of 5 December on the Protection of Personal Data and the Guarantee of Digital Rights Act 74(12)[in Span-ish]., (2018); Morrison M., Bell J., George C., Et al., The European General Data Protection Regulation: Challenges and considerations for iPSC researchers and biobanks, Regen Med, 12, 6, pp. 693-703, (2017); Chassang G., The impact of the EU general data protection regulation on scientifc research, Ecancermedicalscience, 11, (2017); Dove E.S., The EU General Data Protection Regulation: Implications for international scientifc research in the digital era, J Law Med Ethics, 46, 4, pp. 1013-1030, (2018); Vigilant Software Datafow Mapping Tool., (2019); OneTrust Data Mapping Automation., (2019); Rumbaugh J., Jacobson I., Booch G., Unifed Modeling Language Reference Manual. 2nd Ed, (2004); Bekhuis T., Tseytlin E., EDDA Study Designs Taxonomy (Version 2.0), (2016); Kohler S., Doelken S.C., Mungall C.J., Et al., The Human Phe-notype Ontology project: Linking molecular biology and disease through phenotype data, Nucleic Acids Res, 42, pp. D966-D974, (2014); Schriml L.M., Mitraka E., Munro J., Et al., Human Disease Ontology 2018 update: Classifcation, content and workfow expansion, Nucleic Acids Res, 47, pp. D955-D962, (2019); Braschi B., Denny P., Gray K., Et al., Genenames.org: The HGNC and VGNC resources in 2019, Nucleic Acids Res, 47, pp. D786-D792, (2018); Dyke S.O.M., Philippakis A.A., Rambla De Argila J., Et al., Consent codes: Upholding standard data use conditions, PLoS Genet, 12, 1, (2016); Tang H., Jiang X., Wang X., Et al., Protecting genomic data analytics in the cloud: State of the art and opportunities, BMC Med Genomics, 9, 1, (2016); UK Data Protection Act., (2018); Law on Ethics Testing of Human-related Research. Stockholm Social Department [In Swedish]., (2018); World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects, JAMA, 310, 20, pp. 2191-2194, (2013); The NuGO Bioethics Guidelines on Human Studies., (2007); Data storage and DNA banking for biomedical research: Technical, social and ethical issues, Eur J Hum Genet, 11, 2, pp. S8-S10, (2003); Van Delden J.J.M., Van Der Graaf R., Revised CIOMS International Ethical Guidelines for Health-Related Research Involving Humans, JAMA, 317, 2, pp. 135-136, (2017); Article 29 Working Party Guidelines on Consent under Regulation 2016/679., (2017); Smoak C.G., Widel M., Truong S., The use of checksums to ensure data integrity in the healthcare industry, Pharm Program, 5, 1-2, pp. 38-41, (2012); Recommendation for File Integrity-version 1.0, (2010); Guidance for Industry: 21 CFR Part 11; Electronic Records; Electronic Signatures., (2003); Townend D., Conclusion: Harmonisation in genomic and health data sharing for research: An impossible dream?, Hum Genet, 137, 8, pp. 657-664, (2018); World Medical Association WMA Declaration of Taipei on Ethical Considerations Regarding Health Databases and Biobanks., (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientifc data management and stewardship, Sci Data, 3, (2016); ELIXIR-Luxembourg Github Repository., (2017); Ison J., Rapacki K., Menager H., Et al., Tools and data services registry: A community effort to document bioin-formatics resources, Nucleic Acids Res, 44, pp. D38-D47, (2015); Bandrowski A., Brush M., Grethe J., Et al., The Resource Iden-tifcation Initiative: A cultural shift in publishing, F1000Res, 4, (2015); Becker R., Alper P., Groues V., Et al., Supporting data for ""dAISY: A Data Information System for accountability under the General Data Protection Regulation., GigaScience Database, (2019)","R. Becker; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval 6, Belvaux, Avenue du Swing L-4367, Luxembourg; email: regina.becker@uni.lu","","Oxford University Press","","","","","","2047217X","","","31800037","English","GigaScience","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85076014657"
"Hudson-Vitale C.; Moulaison-Sandy H.","Hudson-Vitale, Cynthia (57021977800); Moulaison-Sandy, Heather (58024933100)","57021977800; 58024933100","Data management plans: A review","2019","DESIDOC Journal of Library and Information Technology","39","6","","322","328","6","6","10.14429/djlit.39.6.15086","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078409266&doi=10.14429%2fdjlit.39.6.15086&partnerID=40&md5=41b625cd3cd691c1cd9f43125dd94b14","Pennsylvania State University Libraries, University Park, 16801, United States; School of Information Science and Learning Technologies, University of Missouri, Columbia, 65211, United States","Hudson-Vitale C., Pennsylvania State University Libraries, University Park, 16801, United States; Moulaison-Sandy H., School of Information Science and Learning Technologies, University of Missouri, Columbia, 65211, United States","With increasing world-wide emphasis on providing access to research data, data management plans (DMPs) have emerged as the expected way for researchers to formalise and communicate their intentions to stakeholders, including to their funders. This review paper focuses on a thematic analysis and presentation of empirical research on DMPs, a literature that is surprisingly limited, likely due to the young age of the field. Research shows that, despite the benefits associated with data sharing, DMPs have potential that is not being realised to the fullest. Researchers in scholarly communication and information science primarily have evaluated DMPs using text analysis methodologies, often supplementing them with surveys or interviews. Future study, especially in areas of machine-actionable DMPs is promising; such research is needed to further explore how DMPs can best be utilised to support data sharing. © 2019, DESIDOC.","DART framework; Data management plans (DMPs); Data stewardship; Research data management; Review (Methodology); Scholarly communication","","","","","","National Library of Medicine, National Institutes of Health24; Institute of Museum and Library Services, IMLS; Diabetes Australia Research Trust, DART","Research on DMPs is of interest to funders, as well as to information professionals. As mentioned, the DART project was funded by the IMLS, and other research, such as the analysis undertaken by Sara Mannheimer that was funded by the National Library of Medicine, National Institutes of Health24. The overarching project was designed to assess the “impact of DMPs on grant-funded projects”24[p.2]. If there is little research into DMPs, stakeholders such as funders should consider making this kind of research a priority in order to improve the use of, as well as the perception of, DMPs.","Parham S.W., Carlson J., Hswe P., Westra B., Whitmire A., Using data management plans to explore variability in research data management practices across domains, Int. J. Digital Curation, 11, 1, pp. 53-67, (2016); Stodden V., Leisch F., Peng R.D., Preface. in Implementing Reproducible Research, (2014); Lesk M., Mattern Bially J., Moulaisonsandy H., Are papers with open data more credible? An analysis of open data availability in retracted PLoS articles, In Information in Contemporary Society: Proceedings of the Iconference 2019. Lecture Notes in Comput. Sci. (LNCS), (2019); Wiley C., Data sharing and engineering faculty: An analysis of selected publications, Sci. Tech. Libr., 37, 4, pp. 409-419, (2018); Borgman C., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol., 63, 6, pp. 1059-1078, (2011); Kassen M., Adopting and managing open data: Stakeholder perspectives, challenges and policy recommendations, Aslib J. Inf. Manage., 70, 5, pp. 518-537, (2018); Bello O., Akinwandc V., Jolaycmi O., Ibrahim A., Open data portals in africa: An analysis of open government data initiatives, Afr. J. Lib. Arch. Inf. Sci., 26, 2, pp. 97-106, (2016); Pasek J.E., Historical development and key issues of data management plan requirements for national science foundation grants: A review, Issues Sci. Technol. Libr., 87, 6, (2017); Mattern B., Janicesandyheather M., Use, ethics, and governance of data repositories: A power-sensitive sociotechnical perspective, 14Th Annual Social Informatics Research Symposium: Sociotechnical Perspective on Ethics and Governance of Emerging Information Technologies, 81St Annual Meeting of the Association for Information Science and Technology (ASIST), (2018); Pierre-Yves B., Elainem B., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs, IFLA J, 43, 1, pp. 5-21, (2017); Karen A., Jody B.F., Jaymie T., Brian S., Dealing with data: Science librarians’ participation in data management at association of research libraries institutions, College Res. Libr., 75, 4, pp. 557-574, (2014); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: Findings from a focus group in an academic health sciences library, J. Web Libr., 6, pp. 274-287, (2012); Powell R.R., Basic Research Methods for Librarians, (1997); Machi L.A., McEvoy B.T., The Literature Review: Six Steps to Success, (2016); Smale N., Unsworth K., Denyer G., Barr D., The history, advocacy and efficacy of data management plans, Biorxiv, (2018); Miksa T., Simms S., Mietchen D., Jones S., Ten principles for machine-actionable data management plans, Plos Comput. Biol., 15, 3, (2019); Proposal and Award Policies and Procedures Guide, pp. II-12, (2019); Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, J. Libr. Scholarly Commun., 3, 2, (2015); Briney K., Goben A., Zilinski L., Do you have an institutional data policy? A review of the current landscape of library data services and institutional data policies, J. Libr. Scholarly Commun., 3, 2, (2015); Berman E., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: Findings from the qualitative phase, J. Escience Libr., 6, 1, (2017); Bishoff C., Johnston L., Approaches to data sharing: An analysis of NSF data management plans from a large research university, J. Libr. Scholarly Commun., 3, 2, (2015); Mischo W.H., Schlembach M.C., O'Donnell M.N., An Analysis of Data Management Plans in University of Illinois National Science Foundation Grant Proposals, (2014); Van L., James E., Akers K.G., Hudson C., Sarkozy A., Quality evaluation of data management plans at a research university, Https://Digitalcommons.Wayne, (2017); Mannheimer S., Toward a better data management plan: The impact of DMPs on grant funded research practices, J. Escience Libr., 7, 3, (2018); van Tuyl S., Whitmire A.L., Water, water, everywhere: Defining and assessing data sharing in academia, Plos ONE, 11, 2, (2016); Mischo W.H., Schlembach M.C., O'Donnell M.N., An analysis of data management plans in University of Illinois National Science Foundation grant proposals, J. Escience Libr., 3, 1, (2014); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan?, A Snapshot of Researcher Readiness to Address Data Management Planning Requirements. J. Escience Libr, 1, 2, (2012); Using Data Management Plans as a Research Tool; Wright A., Electronic resources for developing data management skills and data management plans, J. Electron. Resour. Med. Libr., 13, 1, pp. 43-48, (2016); Samuel S.M., Grochowski P.F., Lalwani L.N., Carlson J., Analysing Data Management Plans: Where Librarians Can Make a Difference; GO FAIR Glossary; RDA DMP Common Standard for Machine-Actionable Data Management Plans; Wilkinson M.D., Michel D., Ijsbrand J.A., Gabrielle A., Dumonolivier G., Paul A.M., Baak A., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, (2016)","H. Moulaison-Sandy; School of Information Science and Learning Technologies, University of Missouri, Columbia, 65211, United States; email: moulaisonhe@missouri.edu","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Review","Final","","Scopus","2-s2.0-85078409266"
"Udod K.; Kushnarenko V.; Wesner S.; Svjatnyj V.","Udod, Kyryll (57215366417); Kushnarenko, Volodymyr (57191728675); Wesner, Stefan (22433916600); Svjatnyj, Volodymyr (26535251400)","57215366417; 57191728675; 22433916600; 26535251400","Preservation System for Scientific Experiments in High Performance Computing: Challenges and Proposed Concept","2019","Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2019","2","","8924459","809","813","4","0","10.1109/IDAACS.2019.8924459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077112955&doi=10.1109%2fIDAACS.2019.8924459&partnerID=40&md5=1dd6acc305d837b1975cf47e3d7d3391","Ulm University, Communication and Information Centre (Kiz), Ulm, 89081, Germany; Ulm University, Institute of Information Resource Management, Ulm, 89081, Germany; Donetsk National Technical University, Pokrovsk, 85300, Ukraine","Udod K., Ulm University, Communication and Information Centre (Kiz), Ulm, 89081, Germany; Kushnarenko V., Ulm University, Institute of Information Resource Management, Ulm, 89081, Germany; Wesner S., Ulm University, Institute of Information Resource Management, Ulm, 89081, Germany; Svjatnyj V., Donetsk National Technical University, Pokrovsk, 85300, Ukraine","Continuously growing amount of research experiments using High Performance Computing (HPC) leads to the questions of research data management and in particular how to preserve a scientific experiment including all related data for long term for its future reproduction. This paper covers some challenges and possible solutions related to the preservation of scientific experiments on HPC systems and represents a concept of the preservation system for HPC computations. Storage of the experiment itself with some related data is not only enough for its future reproduction, especially in the long term. For that case preservation of the whole experiment's environment (operating system, used libraries, environment variables, input data, etc.) via containerization technology (e.g. using Docker, Singularity) is proposed. This approach allows to preserve the entire environment, but is not always possible on every HPC system because of security issues. And it also leaves a question, how to deal with commercial software that was used within the experiment. As a possible solution we propose to run a preservation process outside of the computing system on the web-server and to replace all commercial software inside the created experiment's image with open source analogues that should allow future reproduction of the experiment without any legal issues. The prototype of such a system was developed, the paper provides the scheme of the system, its main features and describes the first experimental results and further research steps. © 2019 IEEE.","containerization; HPC; reproducible research; research experiments preservation","Cell proliferation; Containers; Data acquisition; Digital storage; Information management; Open source software; Packaging; Commercial software; Computing system; containerization; High performance computing; High performance computing (HPC); Reproducible research; Research data managements; Scientific experiments; Open systems","","","","","","","Schembera B., Bonisch T., Challenges of research data management for high performance computing, Research and Advanced Technology for Digital Libraries, pp. 140-151, (2017); Hunold S., A Survey on Reproducibility in Parallel Computing, (2015); Klaus Rechert P.F., Ensom T., Towards a risk model for emulation-based preservation strategies: A case study from the software-based art domain, 13th International Conference on Digital Preservation (iPRES2016), (2016); Sandve G.K., Nekrutenko A., Taylor J., Hovig E., Ten simple rules for reproducible computational research, PLOS Computational Biology, 9, 10, (2013); Younge A.J., Pedretti K., Grant R.E., Brightwell R., A tale of two systems: Using containers to deploy hpc applications on supercomputers and clouds, 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 74-81, (2017); Barba L.A., Terminologies for Reproducible Research, (2018); Peng R., The reproducibility crisis in science: A statistical counterattack, Significance, 12, 3, pp. 30-32, (2015); Collberg C., Proebsting T.A., Repeatability in computer systems research, Commun. ACM, 59, 3, pp. 62-69, (2016); Schanzel B., Leznik M., Volpert S., Domaschka J., Wesner S., Unified Container Environments for Scientific Cluster Scenarios, (2019); Sweeney K.M.D., Thain D., Efficient integration of containers into scientific workflows, Proceedings of the 9th Workshop on Scientific Cloud Computing, pp. 71-76, (2018); Hyperion Research Study Highlights-HPC Container and Industry; Hauser C.B., Domaschka J., Vice registry: An image registry for virtual collaborative environments, 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 82-89, (2017)","","","Institute of Electrical and Electronics Engineers Inc.","Ecole Nationale d'Ingenieurs de Metz (ENIM); et al.; ICS; IEEE Ukraine Section I and M/CI Joint Societies Chapter; Laboratory of Conception, Optimisation and Modelling of Systems (LCOMS); THEY","10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2019","18 September 2019 through 21 September 2019","Metz","155855","","978-172814068-1","","","English","Proc. IEEE Int. Conf. Intell. Data Acquis. Adv. Comput. Syst.: Technol. Appl., IDAACS","Conference paper","Final","","Scopus","2-s2.0-85077112955"
"Read K.B.; Larson C.; Gillespie C.; Oh S.Y.; Surkis A.","Read, Kevin B. (57205931894); Larson, Catherine (57195838190); Gillespie, Colleen (24464014000); Oh, So Young (57208689451); Surkis, Alisa (57190153933)","57205931894; 57195838190; 24464014000; 57208689451; 57190153933","A two-tiered curriculum to improve data management practices for researchers","2019","PLoS ONE","14","5","e0215509","","","","15","10.1371/journal.pone.0215509","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065527388&doi=10.1371%2fjournal.pone.0215509&partnerID=40&md5=ca91c9c5dd901d6fec7132708a6553b4","NYU Health Sciences Library, NYU Langone Health, New York, NY, United States; Institute for Innovations in Medical Education, NYU Langone Health, New York, NY, United States","Read K.B., NYU Health Sciences Library, NYU Langone Health, New York, NY, United States; Larson C., NYU Health Sciences Library, NYU Langone Health, New York, NY, United States; Gillespie C., Institute for Innovations in Medical Education, NYU Langone Health, New York, NY, United States; Oh S.Y., Institute for Innovations in Medical Education, NYU Langone Health, New York, NY, United States; Surkis A., NYU Health Sciences Library, NYU Langone Health, New York, NY, United States","Background Better research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher’s results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities. Materials and methods We created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers’ satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class. Results and discussion The online curriculum increased librarians’ self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions. © 2019 Read et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Biomedical Research; Curriculum; Data Management; Education, Distance; Humans; Job Satisfaction; Librarians; Pilot Projects; Research Personnel; adult; article; comfort; controlled study; curriculum; follow up; human; librarian; medical research; satisfaction; scientist; teaching; curriculum; education; information processing; job satisfaction; personnel; pilot study","","","","","U.S. National Library of Medicine, NLM, (R25LM012283)","","Everyone needs a data-management plan, Nature, 555, 7696, (2018); Schiermeier Q., Data management made simple, Nature, 555, 7696, pp. 403-405, (2018); Barone L., Williams J., Micklos D., Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators, PLoS Computational Biology, 13, 10, (2017); Chan A.W., Song F., Vickers A., Jefferson T., Dickersin K., Gotzsche P.C., Et al., Increasing value and reducing waste: Addressing inaccessible research, Lancet, 383, 9913, pp. 257-266, (2014); Gardner D., Goldberg D.H., Grafstein B., Robert A., Gardner E.P., Terminology for neuroscience data discovery: Multi-tree syntax and investigator-derived semantics, Neuroinformatics, 6, 3, pp. 161-174, (2008); Merelli I., Perez-Sanchez H., Gesing S., D'Agostino D., Managing, analysing, and integrating big data in medical bioinformatics: Open problems and future perspectives, BioMed Research International, 2014, (2014); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: Findings from a focus group in an academic health sciences library, Journal of Web Librarianship, 6, 4, pp. 274-287, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Et al., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PloS One, 10, 8, (2015); Read K.B., Surkis A., Larson C., McCrillis A., Graff A., Nicholson J., Et al., Starting the data conversation: Informing data services at an academic health sciences library, Journal of the Medical Library Association: JMLA, 103, 3, pp. 131-135, (2015); Deardorff A., Florance V., VanBiervliet A., Assessing the National Library of Medicine’s informationist Awards, Journal of Escience Librarianship, 5, 1, (2016); Kerby E.E., Research data services in veterinary medicine libraries, Journal of the Medical Library Association: JMLA, 104, 4, pp. 305-308, (2016); McEwen L., Li Y., Academic librarians at play in the field of cheminformatics: Building the case for chemistry research data management, Journal of Computer-Aided Molecular Design, 28, 10, pp. 975-988, (2014); Medina-Smith A., Tryka K.A., Silcox B.P., Hanisch R.J., Librarians and scientists partner to address data management: Taking collaboration to the next level, Digital Library Perspectives, 32, 3, pp. 142-152, (2016); O'Malley D., Delwiche F.A., Aligning library instruction with the needs of basic sciences graduate students: A case study, Journal of the Medical Library Association: JMLA, 100, 4, pp. 284-290, (2012); Pollock L., Data management: Librarians or science informationists?, Nature, 490, 7420, (2012); Read K.B., LaPolla F.W.Z., A new hat for librarians: Providing REDCap support to establish the library as a central data hub, Journal of the Medical Library Association: JMLA, 106, 1, pp. 120-126, (2018); Read K.B., LaPolla F.W., Tolea M.I., Galvin J.E., Surkis A., Improving data collection, documentation, and workflow in a dementia screening study, Journal of the Medical Library Association: JMLA, 105, 2, pp. 160-166, (2017); Read K.B., Surkis A., A 2015 Survey of Health Sciences Librarians Attitudes towards Research Data Management Education; Henkel H., Hutchison V., Strasser C., Rebich Hespanha S., Vanderbilt K., Wayne L., Et al., DataONE Education Modules: DataONE, (2012); Edina L.D., MANTRA: Research Data Management Training, (2013); Tibbo H., Jones S., Research Data Management and Sharing; Martin E., Goldman J., Best Practices for Biomedical Research Data Management Canvas Network, (2018); Read K.B., Larson C., Oh S., Gillespie C., Yacobucci K., Surkis A., Research Data Management Training for Information Professionals Compass Learning System, (2016); Read K.B., Surkis A., Research Data Management Teaching Toolkit: Figshare, (2018); Cook B.G., Smith G.J., Tankersley M., Evidence-based practices in education, APA Educational Psychology Handbook, Vol 1: Theories, Constructs, and Critical Issues, pp. 495-527, (2012); Mayer R.E., Moreno R., Nine ways to reduce cognitive load in multimedia learning, Educational Psychologist, 38, 1, pp. 43-52, (2003); Mayer R.E., Applying the science of learning to multimedia instruction, Psychology of Learning and Motivation, 55, pp. 77-108, (2011); Janssen-Noordman M.B., Merrienboer J.J., Van der Vleuten C.P., Scherpbier A.J., Design of integrated practice for learning professional competences, Medical Teacher, 28, 5, pp. 447-452, (2006); Sweller J., Human Cognitive Architecture: Why Some Instructional Procedures Work and Others Do Not, (2012); Bhanji F., Gottesman R., de Grave W., Steinert Y., Winer L.R., The retrospective pre-post: A practical method to evaluate learning from an educational program, Acad Emerg Med, 19, 2, pp. 189-194, (2012); Revathi M., Vijayalakshmi B., Rajaratnam N., Chandrasekar M., Analysis of the retrospective self-assessment questionnaire of a faculty development programme, South East Asian Journal of Medical Education, 9, 2, pp. 9-14, (2015); McLeod P.J., Steinert Y., Snell L., Use of retrospective pre/post assessments in faculty development, Med Educ, 42, 5, (2008); Nimon K., Zigarmi D., Allen J., Measures of program effectiveness based on retrospective pretest data: Are all created equal?, American Journal of Evaluation, 32, 1, pp. 8-28, (2010); Federer L., The Medical Library Association Guide to Data Management for Librarians, (2016); Kafel D., Creamer A.T., Martin E.R., Building the new England collaborative data management curriculum, Journal of Escience Librarianship, 3, 1, (2014); Daniel J., Making sense of MOOCs: Musings in a maze of myth, paradox and possibility, Journal of Interactive Media in Education, 2012, 3, (2012); De Freitas S.I., Morgan J., Gibson D., Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision, British Journal of Educational Technology, 46, 3, pp. 455-471, (2015); Rivard R., Measuring the MOOC dropout rate, Inside Higher Ed, 8, (2013); Zhao S., Biomedical and Health Research Data Management for Librarians: National Network of Libraries of Medicine, (2018); Surkis A., Read K.B., A pilot project to facilitate the development of data services at health sciences libraries, Medical Library Association Annual Conference, (2018)","K.B. Read; NYU Health Sciences Library, NYU Langone Health, New York, United States; email: kevin.read@nyulangone.org","","Public Library of Science","","","","","","19326203","","POLNC","31042776","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85065527388"
"Willaert T.; Cottyn J.; Kenens U.; Vandendriessche T.; Verbeke D.; Wyns R.","Willaert, Tom (56819120500); Cottyn, Jacob (57214464992); Kenens, Ulrike (55201197100); Vandendriessche, Thomas (23493972200); Verbeke, Demmy (26036598700); Wyns, Roxanne (57214473659)","56819120500; 57214464992; 55201197100; 23493972200; 26036598700; 57214473659","Research data management and the evolutions of scholarship: Policy, infrastructure and data literacy at KU leuven","2019","LIBER Quarterly","29","1","","","","","4","10.18352/lq.10272","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078721036&doi=10.18352%2flq.10272&partnerID=40&md5=c9519900c74e1a2ee59f64c74ac1dba0","Vrije Universiteit Brussel, KU Leuven, Belgium; KU Leuven, Belgium","Willaert T., Vrije Universiteit Brussel, KU Leuven, Belgium; Cottyn J., KU Leuven, Belgium; Kenens U., KU Leuven, Belgium; Vandendriessche T., KU Leuven, Belgium; Verbeke D., KU Leuven, Belgium; Wyns R., KU Leuven, Belgium","This case study critically examines ongoing developments in contemporary scholarship through the lens of research data management support at KU Leuven, and KU Leuven Libraries in particular. By means of case-based examples, current initiatives for fostering sound scientific work and scholarship are considered in three associated domains: support for policy-making, the development of research infrastructures, and digital literacy training for students, scientists and scholars. It is outlined how KU Leuven Libraries collaborates with partner services in order to contribute to KU Leuven’s research data management support network. Particular attention is devoted to the innovations that facilitate such collaborations. These accounts of initial experiences form the basis for a reflection on best practices and pitfalls, and foreground a number of pertinent challenges facing the domain of research data management, including matters of scalability, technology acceptance and adoption, and methods for effectively gauging and communicating the manifold transformations of science and scholarship. © 2019, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Data literacy; Digital scholarship; Infrastructure; Research data management; Science policy","","","","","","","","Borgman C., Scholarship in the Digital Age. Information, Infrastructure, and the Internet, (2010); Borgman C., Big data, little data, no data, Scholarship in the Networked World, (2016); Collins H., Interactional expertise as a third kind of knowledge, Phenomenology and the Cognitive Sciences, 3, 2, pp. 125-143, (2004); Collins H., Evans R., Robeiro R., Hall M., Experiments with interactional expertise, Studies in History and Philosophy of Science A, 37, 4, pp. 656-674, (2006); What is Digital Curation?, (2019); The European Cloud Initiative, (2018); Data Management Plan (DMP), (2019); Gitelman L., Raw data’ is an Oxymoron, (2013); Gitelman L., Jackson V., Introduction, Raw Data is an Oxymoron, pp. 1-14, (2013); Gradmann S., Hennicke S., Tschumpel G., Dill K., Thoden K., Pichler A., Stiller J., Modelling the scholarly domain beyond Infrastructure, Digital Humanities Im Deutschsprachigen Raum (Book of Abstracts), pp. 143-146, (2016); Krishnamurthy R., Awazu Y., Liberating data for public value: The case of Data.gov, International Journal of Information Management, 36, 4, pp. 668-672, (2016); Open Science and Its Role in Universities. a Roadmap for Cultural Change, (2018); Mons B., Data Stewardship for Open Science. Implementing FAIR Principles, (2018); Rans J., Whyte A., Using RISE, the Research Infrastructure Self-Evaluation Framework, 1, (2017); Schnapp J.T., Battles M., The Library beyond the Book, (2014); Verhaar P., Schoots F., Sesink L., Frederiks F., Fostering effective data management practices at Leiden University, LIBER Quarterly, 27, 1, pp. 1-22, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85078721036"
"Sales L.F.; Sayão L.F.","Sales, Luana Farias (25646168600); Sayão, Luís Fernando (7801523487)","25646168600; 7801523487","The big and small science: Analysis of differences in research data management; [A grande e a pequena ciência: Análise das diferenças na gestão de dados de pesquisa]","2019","Informacao e Sociedade","29","3","","151","170","19","1","10.22478/UFPB.1809-4783.2019V29N3.47615","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084134580&doi=10.22478%2fUFPB.1809-4783.2019V29N3.47615&partnerID=40&md5=8c761cc09c38f3bb9d746ecc559f7361","Instituto Brasileiro de Informação em Ciência e Tecnologia, Universidade Federal do Rio de Janeiro, Brazil","Sales L.F., Instituto Brasileiro de Informação em Ciência e Tecnologia, Universidade Federal do Rio de Janeiro, Brazil; Sayão L.F., Instituto Brasileiro de Informação em Ciência e Tecnologia, Universidade Federal do Rio de Janeiro, Brazil","The curation of research data is usually uniformly designed for all science. Profiles that characterize the big and small science are often disregarded or minimized in the planning and development of research e-infrastructure and, more specifically, in data management frameworks. While in the big science there is a homogeneity in the generation of data, in the processes of curation and an immediate demand for sharing and great investments in e-infrastructures; in small science the data are extremely heterogeneous, generated/collected by small teams in a multitude of laboratories belonging to various disciplinary domains, and rarely archived for sharing and reuse. These data need specific platforms that consider their generation flows, methodologies, sharing cultures, and reward schemes and sustainability. The present study aims to analyse these differences considering the perspective of the planning of management platforms that allows a greater visibility of small science research products and the integration of these two universes of scientific research in the context of eScience. © 2019 Universidade Federal de Campina Grande. All rights reserved.","Big science; Research data management; Research infrastructure; Small science","","","","","","","","Borgman C.L., Big Data, Little Data, no Data: Scholarship in the Networked World, (2015); Borgman C.L., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Borgman C.L., The conundrum of sharing research data, Journal of the Association for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Broad W.J., Big Science: Is It Worth the Price? A Periodic Look at the Largest New Research Projects; Heavy Costs of Major Projects Pose a Threat to Basic Science, (1990); Cragin M.H., Et al., Data sharing, small science, and institutional repositories, Philosofical Transactions of the Royal Society A, 368, pp. 4023-4038, (2010); Long Tail of Data: E-IRG Task Force Report, (2016); Erway R., Lavoie B., The Economics of Data Integrity, (2012); Ferguson A.R., Et al., Big data from small-data: Data sharing in the 'long tail' of neuroscience, Nature Neuroscience, 17, 11, pp. 1442-1447, (2014); Hedstrom M., Myers J., SEAD: Finding the Long Tail Lost in Big Data, (2014); Heidorn P.B., Shedding light on the dark data in the long tail, Library Trends, 57, 2, pp. 280-299, (2008); Hobsbawm E., Era dos Extremos: O Breve Século XX - 1914-1991, (1995); Horstmann W., Beyond the big data: The long tail of research, E-IRG Workshop, (2015); MacColl J., The role of libraries in data curation, Rlg Partnership Annual Meeting, (2010); Petsko G.A., Big science, little science, EMBO Rep., 10, 12, (2009); De Price D.J.S., O Desenvolvimento da Ciência, (1976); Rodrigues E., Saraiva R., Os Repositórios de Dados Científicos: Estado da Arte, (2010); Sayao L.F., Sales L.F., A ciência invisível: Os dados da cauda longa da pesquisa científica, Dados Científicos: Perspectivas e Desafio, (2019); Sayao L.F., Sales L.F., Dados de pesquisa: Contribuição para o estabelecimento de um modelo de curadoria digital para o país, Tendências da Pesquisa Brasileira Em Ciência da Informação, 6, 1, (2013); Science as an Open Enterprise, (2012); Weinberg A.M., Impacto of large-scale science on the United States, Science, 134, 3473, (1961); Wyborn L., Lehnert K., Exploiting the long tail of scientific data: Making small data BIG, Eresearch Austraulasia Conference, (2016)","","","Universidade Federal de Campina Grande","","","","","","01040146","","","","Portuguese","Inf. Soc.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85084134580"
"Wang Y.-Y.; Lin C.-S.","Wang, Yi-Yu (57208330722); Lin, Chi-Shiou (35220132900)","57208330722; 35220132900","A survey of data science programs and courses in the iSchools","2019","Proceedings of the Association for Information Science and Technology","56","1","","801","802","1","3","10.1002/pra2.184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075924766&doi=10.1002%2fpra2.184&partnerID=40&md5=1cf0b87eba76e1f192ba5de73e9e1cd6","Dept. of Library and Information Science, National Taiwan University, Taipei, Taiwan","Wang Y.-Y., Dept. of Library and Information Science, National Taiwan University, Taipei, Taiwan; Lin C.-S., Dept. of Library and Information Science, National Taiwan University, Taipei, Taiwan","In this poster, we examined the current data science programs and courses in the 102 iSchools members. The result shows that the program distribution was unequal by region, and the courses offered by iSchools concentrated on data analysis skills. Research data management, which concerns data management throughout research life cycle and data curation, constituted less than 10% of the courses. Author(s) retain copyright, but ASIS&T receives an exclusive publication license","data analysis; Data science education; research data services","Data handling; Information management; Life cycle; Analysis skills; Current data; Data science education; Data services; Program distribution; Research data; Research data service; Science course; Science education; Science projects; Data Science","","","","","","","Saltz J.S., Stanton J.M., An introduction to data science, (2017); Song I.-Y., Zhu Y., Big data and data science: What should we teach?, Expert Systems, 33, 4, pp. 364-373, (2016); Thomas C.V.L., Urban R.J., What do data librarians think of the MLIS? Professionals' perceptions of knowledge transfer, trends, and challenges, College & Research Libraries, (2018)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85075924766"
"Descoteaux D.; Farinelli C.; E Silva M.S.; de Waard A.","Descoteaux, Danielle (57192953379); Farinelli, Chiara (57311244400); E Silva, Marina Soares (57310523100); de Waard, Anita (8717707600)","57192953379; 57311244400; 57310523100; 8717707600","Playing well on the data fairground: Initiatives and infrastructure in research data management","2019","Data Intelligence","1","4","","350","367","17","1","10.1162/dint_a_00020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086392626&doi=10.1162%2fdint_a_00020&partnerID=40&md5=53e3dfbe36f399c071d3a75f1b04c00e","Elsevier, Inc, 50 Hampshire St, Cambridge, 02139, MA, United States; Elsevier, B.V., Radarweg 29, Amsterdam, 2043NX, Netherlands","Descoteaux D., Elsevier, Inc, 50 Hampshire St, Cambridge, 02139, MA, United States; Farinelli C., Elsevier, B.V., Radarweg 29, Amsterdam, 2043NX, Netherlands; E Silva M.S., Elsevier, B.V., Radarweg 29, Amsterdam, 2043NX, Netherlands; de Waard A., Elsevier, Inc, 50 Hampshire St, Cambridge, 02139, MA, United States","Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics. © 2019 Chinese Academy of Sciences Published under a Creative Commons Attribution 4.0 International (CC BY 4.0)","Data citation; Data sharing; Open data; Open research","Information management; Best practices; Data citation; Data preservations; Data Sharing; Elsevier; Management practises; Open datum; Open research; Research data managements; Reuse; Open Data","","","","","Adriaan Klinkenberg; Catriona Fennell; IJsbrand Jan Aalbersberg; Lorenzo Feri; Wouter Haak","The authors wish to thank and acknowledge IJsbrand Jan Aalbersberg (Senior Vice President Elsevier Research Integrity), Catriona Fennell (Director Elsevier Journal Services), and Adriaan Klinkenberg (Publishing Director, Elsevier Physics Journals) for their extended review of the manuscript. The authors also thank Maxim van Gisbergen (Senior Strategy Manager), Alberto Zigoni (Market Development Director, Research Data Management), Deborah Sweet (Vice President of Editorial at Cell Press), Lorenzo Feri (Director of Product Management) and Wouter Haak (Vice President, Research Data Management) for discussions on, among other topic relevant to this manuscript, the analytics regarding research data metrics at Elsevier.","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); G20 Leaders’ Communique Hangzhou Summit 2016, pp. 1-9, (2016); Turning FAIRInto reality: Final report and action plan from the European commission expert group on FAIR data, (2018); FAIR Author Guidelines; Wouters P., Haak W., Open data: The researcher perspective, Elsevier – Open Science, 48, (2017); Haak W., 4 principles for unlocking the full potential of research data, Elsevier Connect, (2015); Martone M., Data Citation Synthesis Group: Joint Declaration of Data Citation Principles, (2014); Cousijn H., Kenall A., Ganley E., Harrison M., Clark T., A data citation roadmap for scientific publishers, Scientific Data, 5, pp. 1-11, (2018); de Waard A., Cousijn H., Aalbersberg Ii. J., 10 Aspects of Highly Effective Research Data, pp. 1-6, (2015); Research Data Management; de Waard A., The Mendeley Data management platform: Research data management from a publisher’s perspective, (2017); Research Data-Data Citation; Transparency and Openness Promotion (TOP) Guidelines, (2014); MSU – Research Data Guidelines; Database linking; Burton A., Koers H., Manghi P., Stocker M., Schindler U., The SCHOLIX framework for interoperability in data-literature information exchange, D-Lib Magazine, 23, pp. 1-2, (2017); Burton A., Fenner M., Haak W., Manghi P., Burton A., Fenner M., Manghi P., SCHOLIXMetadata Schema for Exchange of Scholarly Communication Links, (2017); Event Data; Taylor B.M., Shillum C., New ORCID ID, pp. 1-5, (2012); Mendeley Data roadmap; WOuters P., Haak W., Open data: The researcher perspective, Elsevier – Open Science, 48, (2017); Data Seal of Approval; Stall A., Cruse P., Cousijn H., Cutcher-Gershenfeld J., de Waard A., Yarmey l., Data sharing and citations: New author guidelines promoting open and FAIR data in the earth, Science Editor, 41, 3, pp. 83-87, (2018); Pasquetto I., Do scientists reuse open data?, (2019)","D. Descoteaux; Elsevier, Inc, Cambridge, 50 Hampshire St, 02139, United States; email: d.descoteaux@elsevier.com","","MIT Press Journals","","","","","","20967004","","","","English","Data. Intell.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85086392626"
"Timm E.","Timm, Elisabeth (57200338369)","57200338369","Research data management in European ethnology: A short review of the DGV position paper; [Forschungsdatenmanagement in der europäischen ethnologie: Eine kurze kritik des DGV-positionspapiers]","2020","Zeitschrift fur Volkskunde","116","1","","88","89","1","0","10.31244/zfvk/2020/01.09","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086245592&doi=10.31244%2fzfvk%2f2020%2f01.09&partnerID=40&md5=c91e4d5426bbb70253297713671bad0f","","","[No abstract available]","","","","","","","","","","","","Waxmann Verlag GmbH","","","","","","00443700","","","","German","Z. Volkskd.","Note","Final","","Scopus","2-s2.0-85086245592"
"Tammaro A.M.; Caselli S.","Tammaro, Anna Maria (8554921900); Caselli, Stefano (7004694361)","8554921900; 7004694361","Training Data Stewards in Italy: Reflection on the FAIR RDM Summer School","2020","Communications in Computer and Information Science","1177 CCIS","","","163","172","9","0","10.1007/978-3-030-39905-4_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082106294&doi=10.1007%2f978-3-030-39905-4_16&partnerID=40&md5=dc05e83758e83aea36dadde1270a2a95","University of Parma, Parma, Italy","Tammaro A.M., University of Parma, Parma, Italy; Caselli S., University of Parma, Parma, Italy","“Fair Research Data Management” Summer School in Parma focused on the skills gap in Italy for data stewards. A distinct feature of the Summer School was its aim to bring together participants from different backgrounds and from different countries. The paper is a reflection on the organization of the Summer School and the evaluation received by the participants. © 2020, Springer Nature Switzerland AG.","Data stewards; Open Science; Research data management","Information management; Data stewards; Open science; Research data managements; Skills gaps; Summer school; Training data; Digital libraries","","","","","","","Strategic Thinking + Design Initiative Report, (2015); Awadallah R., Et al., Setting up Open Access Repositories: Challenges and Lessons From, (2019); Borgman C., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing Research Data, pp. 105-133, (2012); Mons B., Data Stewardship for Open Science: Implementing FAIR Principles, (2018); Tammaro A.M., Una proposta non sovversiva, Biblioteche Oggi, 36, pp. 65-71, (2018); Tammaro A.M., Matusiak K., Sposito A., Casarosa V., Data curator’s roles and responsibilities: An international perspective, Libri, 69, 2, pp. 89-104, (2019)","A.M. Tammaro; University of Parma, Parma, Italy; email: annamaria.tammaro@unipr.it","Ceci M.; Ferilli S.; Poggi A.","Springer","","16th Italian Research Conference on Digital Libraries, IRCDL 2020","30 January 2020 through 31 January 2020","Bari","238029","18650929","978-303039904-7","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85082106294"
"Tang R.; Hu Z.","Tang, Rong (7202299626); Hu, Zhan (57930054300)","7202299626; 57930054300","Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice","2019","Data and Information Management","3","2","","84","101","17","30","10.2478/dim-2019-0009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081004139&doi=10.2478%2fdim-2019-0009&partnerID=40&md5=242fb7281962be90ea5f9b54c2951845","School of Library and Information Science, Simmons University, Boston, Massachusetts, United States","Tang R., School of Library and Information Science, Simmons University, Boston, Massachusetts, United States; Hu Z., School of Library and Information Science, Simmons University, Boston, Massachusetts, United States","This paper reports the results of an international survey on research data management (RDM) services in libraries. More than 240 practicing librarians responded to the survey and outlined their roles and levels of preparedness in providing RDM services, challenges their libraries face, and knowledge and skills that they deemed essential to advance the RDM practice. Findings of the study revealed not only a number of location and organizational differences in RDM services and tools provided but also the impact of the level of preparedness and degree of development in RDM roles on the types of RDM services provided. Respondents' perceptions on both the current challenges and future roles of RDM services were also examined. With a majority of the respondents recognizing the importance of RDM and hoping to receive more training while expressing concerns of lack of bandwidth or capacity in this area, it is clear that, in order to grow RDM services, institutional commitment to resources and training opportunities is crucial. As an emergent profession, data librarians need to be nurtured, mentored, and further trained. The study makes a case for developing a global community of practice where data librarians work together, exchange information, help one another grow, and strive to advance RDM practice around the world. © 2019 © 2019 Rong Tang, Zhan Hu, published by Sciendo","degree of development; evolving roles; level of preparedness; locations and organizational types; RDM services and tools; research data management","","","","","","Librarian Academy; RDMLA; National Science Foundation, NSF; National Institutes of Health, NIH; U.S. Department of Energy, USDOE; U.S. Department of Education, ED; U.S. Environmental Protection Agency, EPA; Alfred P. Sloan Foundation, APSF, (2010a)","Funding text 1: This research study was a part of needs assessment project, funded by Elsevier to establish the Research Data Management Librarian Academy (RDMLA). The authors wish to thank Jean Shipman and Elaine Martin for developing questions for the online survey and Alyson Gamble for performing preliminary data processing and analysis of the survey dataset. The authors also wish to thank all the respondents who answered our survey.; Funding text 2: Around the world, research data management (RDM) has been becoming an increasingly important service that a variety of information centers and libraries provide. According to Whyte and Tedds (2011) , “Research data management concerns the organization of data, from its entry to the research cycle through to the dissemination and archiving of valuable results” (p. 1). Two years later, in the ARL (Association of Research Libraries) SPEC (Systems and Procedures Exchange Center) Kit 334 entitled “Research Data Management Services,” Fearon et al. (2013) defined RDM services as “providing information, consulting, training or active involvement in data management planning, data management guidance during research (e.g., advice on data storage or file security), research documentation and metadata, research data sharing and curation (selection, preservation, archiving, citation) of completed projects and published data” (p. 12). Within the US, a variety of federal funding agencies or foundations including National Institutes of Health (NIH), National Science Foundation (NSF), Department of Energy (DoE), Department of Education (DoED), Environmental Protection Agency (EPA), and the Alfred P. Sloan Foundation (Sloan) have started requiring the sharing of research outputs ( National Institutes of Health, 2005 ; National Institutes of Health, 2008 ) and mandating data management plan ( National Science Foundation, 2010a , 2010b ). Similar mandates from funding agencies of a variety of other countries include the UK (UKRI, n.d.) , Canada ( Government of Canada, 2016 ), Australia (ARC, 2017), European Union ( Shearer, 2015 ), Japan (JST, 2013) , and India ( Department of Science & Technology, Government of India, n.d. ). In the UK, the first data management plan (DMP henceforth) requirement was put in place by the Medical Research Council in 2006, soon followed by the Wellcome Trust in 2007 (Smale, Unsworth, Denyer, & Barr, 2018). The NSF in 2011 was the first funder in the US to implement a DMP requirement This work is licensed under the Creative Commons Attribution- ","Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science librarians' participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); ARC open access policy, (2017); Choudhury G.S., Case study in data curation at Johns Hopkins University, Library Trends, 57, 2, pp. 211-220, (2008); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Department of Science & Technology, Government of India, (n.d.). National data sharing and accessibility policy; Dietrich D., Adams T., Miner A., Steinhart G., De-mystifying the data management requirements of funders, Issues in Science and Technology Librarianship, 70, 1, pp. 1-16, (2012); Faniel I.M., Connaway L.S., Librarians' on the factors influencing research data management programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Fearon D., Gunia B., Pralle B.E., Lake S., Sallans A.L., Research data management services SPEC Kit 334, (2013); Federer L., Defining data librarianship: a survey of competencies, skills, and training, Journal of the Medical Library Association, 106, 3, pp. 294-303, (2018); Gore S.A., A librarian by any other name: The role of the informationist on a clinical research team, Journal of eScience Librarianship, 2, 1, pp. 20-24, (2013); Government of Canada, Tri-Agency Statement of Principles on Digital Data Management, (2016); Hanson K.L., Bakker T.A., Svirsky M.A., Neuman A.C., Rambo N., Informationist role: clinical data management in auditory research, Journal of eScience Librarianship, 2, 1, pp. 25-29, (2013); Hasman L., Berryman D., Mcintosh S., NLM Informationist Grant – web assisted tobacco intervention for community college students, Journal of eScience Librarianship, 2, 1, pp. 30-34, (2013); JST Policy on Open Access and Research Publications and Research Data Management, (2013); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services - a guide for HEIs. DCC How-to Guides, (2013); Martin E.R., Highlighting the informationist as a data librarian embedded in a research team, Journal of eScience Librarianship, 2, 1, pp. 1-2, (2013); National Geographic Society, United States Regions, (2009); National Institutes of Health, Policy on enhancing public access to archived publications resulting from NIH-funded research [Internet], (2005); National Institutes of Health, Revised policy on enhancing public access to archived publications resulting from NIH-funded research [Internet], (2008); National Science Foundation, Dissemination and sharing of research results [Internet], (2010); National Science Foundation, Scientists seeking NSF funding will soon be required to submit data management plans [Internet], (2010); National Science Foundation, Grant proposal guide: Chapter II - proposal preparation instructions [Internet], (2011); Memorandum for the heads of executive departments and agencies, (2013); Perrier L., Blondal E., MacDonald H., Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies, Library & Information Science Research, 40, 3-4, pp. 173-183, (2018); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); Shearer K., Comprehensive Brief on Research Data Management Policies, (2015); Smale N., Unsworth K.J., Denyer G., Barr D.P., The History, Advocacy and Efficacy of Data Management Plans, bioRxiv, pp. 443-499, (2018); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, pp. 1-21, (2015); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); UK Research and Innovation, (n.d.). Data Policy; Varner S., Hswe P., Special report: Digital humanities in libraries, (2016); Whyte A., A pathway to sustainable research data services: From scoping to sustainability, Delivering research data management services, pp. 59-88, (2014); Whyte A., Tedds J., Making the case for research data management, (2011)","R. Tang; School of Library and Information Science, Simmons University, Boston, Massachusetts, United States; email: rong.tang@simmons.edu","","Elsevier Ltd","","","","","","25439251","","","","English","Data. Inf. Manag.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85081004139"
"Wang H.; Webster K.","Wang, Huajin (23010584300); Webster, Keith (57203797966)","23010584300; 57203797966","Editorial: Artificial intelligence for data discovery and reuse demands healthy data ecosystem and community efforts","2019","ACM International Conference Proceeding Series","","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123042290&partnerID=40&md5=ec4c76999a42bde902d55dc223b26838","Carnegie Mellon University, Pittsburgh, PA, United States","Wang H., Carnegie Mellon University, Pittsburgh, PA, United States; Webster K., Carnegie Mellon University, Pittsburgh, PA, United States","There is great value embedded in reusing scientific data for secondary discoveries. However, it is challenging to find and reuse the large amount of existing scientific data distributed across the web and data repositories. Some of the challenges reside in the volume and complexity of scientific data, others pertain to the current practices and workflow of research data management. AIDR 2019 (Artificial Intelligence for Data Discovery and Reuse) is a new conference that brings together researchers across a broad range of disciplines, computer scientists, tool developers, data providers, and data curators, to share innovative solutions that apply artificial intelligence to scientific data discovery and reuse, and discuss how various stakeholders work together to create a health data ecosystem. This editorial summarizes the main themes and takeaways from the inaugural AIDR conference. © 2019 held by the owner/author(s). Publication rights licensed to ACM.","Artificial intelligence; Data discovery; Data reuse; Metadata; Research data Management","Ecosystems; Information management; Metadata; Computer scientists; Current practices; Data discovery; Data repositories; Data reuse; Innovative solutions; Research data managements; Scientific data; Artificial intelligence","","","","","National Science Foundation, NSF, (1839014); Directorate for Computer and Information Science and Engineering, CISE","This conference is supported by the National Science Foundation Directorate for Computer & Information Science & Engineering (NSF CISE) grant number 1839014. We thank members of the Program Committee, Organizing Committee, and volunteers for your hard work in making AIDR 2019 possible, and thank Carnegie Mellon University Libraries and Pittsburgh Supercomputing Center for administrative support.","","","","Association for Computing Machinery","","2019 Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019","13 May 2019 through 15 May 2019","Pittsburgh","152427","","978-145037184-1","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-85123042290"
"Bunkar A.R.; Bhatt D.D.","Bunkar, Anjana R. (57217138272); Bhatt, Dhaval D. (57217132089)","57217138272; 57217132089","Perception of researchers & academicians of parul university towards research data management system & role of library: A study","2020","DESIDOC Journal of Library and Information Technology","40","3","","139","146","7","7","10.14429/djlit.40.3.15302","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086374681&doi=10.14429%2fdjlit.40.3.15302&partnerID=40&md5=0e6fc059100d81ffc196e73b2adc5cf1","Parul Institute of Medical Science & Research, Parul University, Vadodara, 391 760, India; Parul Institute of Library and Information Science, Parul University, Vadodara, 391 760, India","Bunkar A.R., Parul Institute of Medical Science & Research, Parul University, Vadodara, 391 760, India; Bhatt D.D., Parul Institute of Library and Information Science, Parul University, Vadodara, 391 760, India","Research data management is a system that helps in archiving and retrieving of research data to reuse and preserving them for long term use. Many universities in developed countries have already started providing RDM services to their researchers and academicians. In India, it is still in the initial stage. The purpose of the present study is to investigate the perceptions of researchers and academicians of Parul University on research data management and research data sharing. It also explores the ways the researchers preserved their research data for future use. It also explores the ways the library can take initiatives to encourage and extend support to the researchers and academicians to the organisation, preservation, and sharing of research data. To investigate and study the problem 100 questionnaires were distributed. There are 88 responses we received out of 100. The study revealed that the majority of respondents were agreeing about the research data sharing and free accessibility of research data to browse and reuse. Researchers are very much interested and agreed in the library’s involvement in organizing and preservation of research data. Researchers and faculty members are more concerned about their intellectual property rights while sharing the data on the public domain. © 2020, DESIDOC.","Data management plan; Libraries; Rdm; Rdm services; Research data management system; Research data sharing; University","","","","","","","","(2019); (2019); Carlson J., Garritano J., E-science, cyberinfrastructure and the changing face of scholarship: Organizing for new models of research support at the Purdue University Libraries., (2010); Davidson J., Jones S., Emerging good practice in managing research data and research information within UK Universities, Procedia Comput. Sci., 33, pp. 215-222, (2014); Horstmann W., Witt M., Libraries tackle the challenge of research data management, IFLA Journal., 43, 1, pp. 3-4, (2017); (2019); (2019); Pinfield S., Cox A.M., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One., 9, 12, (2014); Chiwara E., Mathe Z., Academic librarians role in research data management services: A southafricanperpective, SA. J. Libr. Inf. Sci., 81, 2, (2015); Tenopir C., Sandusky R.J., Research data management services in academic research libraries and perceptions of librarians, Libr. Info. Sci. Res., 36, 2014, pp. 84-90, (2014); Cox A.M., Pinfield S., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, J. Libr. Inf. Sci., 48, 1, pp. 3-7, (2016); Surkis A., Read K., Research data management, J. Med. Libr. Assoc., 103, 3, pp. 154-156, (2015); Patel D., Research data management: A conceptual framework, Libr. Rev., 65, 4-5, pp. 226-241, (2016); Dora M., Anilkumar H., Managing research data in academic institutions: A role of libraries. Inflibnet, 10th International CALIBER 2015: Innovative Librarianship: Adapting to Digital Realities, pp. 484-495, (2015); Triptahi M., Chand M., A brief assessment of researcher’s perceptions towards research data management in India, IFLA Journal., 43, 1, pp. 22-39, (2017); Shen Y., Research data sharing and reuse practices of academic faculty researchers: A study of the Virginia Tec Data Landscape, Int. J. Digital Curation., 10, 2, pp. 157-175, (2015); Unal Y., Chowdhury G., Kurbanoglu S., Research data management anddata sharing behaviour of university researchers. Information research, Proceedings of ISIC: The information behaviour conference, 24, 1, (2018); Holly H., The role of academic libraries in research data service (RDS) provision: Opportunities and challenges, Electron. Libr., 35, 4, pp. 783-797, (2017); Schlembach M.C., Brach C.A., Research data management and the role of libraries. In special issues in data management, Am. Chem. Soc., pp. 129-144, (2012); (2019); Nishtha A.K., Reseacrh data management in India: A pilot study, EPJ web of Conferences., 186, 3002, pp. 1-10, (2018); Mannheimer S., Pienta A., Kirilova D., Elman C., Wutich A., Qualitative data sharing: Data repositories and academic libraries as key partners in addressing challenges, Am. Behav. Sci., (2018); Carroll S., Kennan M., Afzal W., Bibliometrics and research data management services: Emerging trends inlibrary support for reserach, Libr. Trends, 61, 3, pp. 636-674, (2013)","A.R. Bunkar; Parul Institute of Medical Science & Research, Parul University, Vadodara, 391 760, India; email: anjana.bunkar@paruluniversity.ac.in","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Article","Final","","Scopus","2-s2.0-85086374681"
"Saeed S.; Ali P.M.N.","Saeed, Sidra (57214145954); Ali, P. M. Naushad (57214145116)","57214145954; 57214145116","Research data management and data sharing among research scholars of life sciences and social sciences","2019","DESIDOC Journal of Library and Information Technology","39","6","","290","299","9","5","10.14429/djlit.39.06.14997","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078334115&doi=10.14429%2fdjlit.39.06.14997&partnerID=40&md5=a78477f8540b5c2ae42f0ecf7b4196d0","Department of Library and Information Science, Aligarh Muslim University, Aligarh, 202 001, India","Saeed S., Department of Library and Information Science, Aligarh Muslim University, Aligarh, 202 001, India; Ali P.M.N., Department of Library and Information Science, Aligarh Muslim University, Aligarh, 202 001, India","This study investigates perception of research scholars towards research data management and sharing. A survey was conducted among research scholars from Faculty of Life Sciences and Social Sciences, Aligarh Muslim University (AMU). In total, 352 participants filled out the questionnaire. The study shows that research scholars ofFaculty of Social Sciences are more willing to share their research data as compared to Research Scholars of Life Sciences. Contributing to scientific progress and increasing research citations and visibility were the key factors that motivated researchers to share data. However, confidentiality and data misuse were the main concerns among those who were unwilling to share. Finally, some recommendations to improve the of data management and sharing practices are presented. © 2019, DESIDOC.","Data preservation; Research data management; Research data sharing; Research scholars; Scholarly communication","","","","","","","","Cox A., Special issue: Research data management, Program: Electron. Libr. Inf. Syst., 49, 4; Averkamp S., Gu X., Rogers B., Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs, (2014); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Libr. Hi. Tech., 35, 2, pp. 271-289, (2017); Chigwada J., Research data management in research institutions in Zimbabwe, Data Sci. J., 16, 31, pp. 1-9, (2017); Cragin M.H., Palmer C.L., Carlson J.R., Et al., Data sharing, small science and institutional repositories, Philos. Trans. Royal Soc. London: A Math., Phys. Eng. Sci., 368, 1926, pp. 4023-4038, (2010); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab Universities: An exploratory study, IFLA J, 44, 4, pp. 281-299, (2018); Sharing Research Data for Journal Authors; Eynden V.V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data, (2011); Fienberg S.E., Martin M.E., Straf M.L., Sharing Research Data, (1985); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program, 49, 4, pp. 364-381, (2015); Jao I., Kombe F., Mwalukore S., Et al., Research stakeholders’ views on benefits and challenges for public health research data sharing in Kenya: The importance of trust and social relations, Plosone, 10, 9, (2015); Kim J., Data sharing and its implications for academic libraries, New Libr. World, 114, 11-12, pp. 494-506, (2013); Liu X., Ding N., Research data management in universities of central China: Practices at Wuhan University Library, The Electron. Libr., 34, 5, pp. 808-822, (2016); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study. Partnership: Can, J. Libr. Inf. Pract. Res., 13, 1; Porter J.H., A brief history of data sharing in the US long term ecological research network, Bull. Ecol. Soc. Am., 91, 1, pp. 14-20, (2010); Savage C.J., Vickers A.J., Emperical study of data sharing by authors publishing in PLoS journals, Plos ONE, 4, (2009); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the University of Lille(France) LIBREAS, Libr. Ideas, 29, (2016); Shen Y., Research data sharing and reuse practices of academic faculty researchers: A study of Virginia Tech Data Landscape, Int. J. Digital Curation, 10, 2, pp. 157-175, (2016); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Et al., Data sharing by scientists: Practices and perceptions, Plos ONE, 6, 6, (2011); Tripathi M., Chand M., Sonkar S.K., Et al., A brief assessment of researcher’s perceptions towards research data in India, IFLA J, 43, 1, pp. 22-39, (2017); Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon University, J. Libr. Scholarly Commun., 3, 3; Unal Y., Chowdhury G., Kurbanoglu S., Boustany J., Walton G., Research data management and sharing behavior of university researchers, Int. Electron. J., 24, 1, (2019); Vines T.H., Albert A.Y., Andrew R.L., Et al., The availability of research data declines rapidly with article age, Current Biol, 24, 1, pp. 94-97, (2014); Ward C., Freiman L., Jones S., Molloy L., Snow K., Making sense: Talking data management with researchers, Int. J. Digital Curation, 6, 2, pp. 265-273, (2011); Youngseek K., Jeffery S., Institutional and individual influences on scientists’ data sharing practices, J. Comput. Sci. Educ., 3, 1, pp. 47-56, (2012)","P.M.N. Ali; Department of Library and Information Science, Aligarh Muslim University, Aligarh, 202 001, India; email: naushadali.ls@amu.ac.in","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85078334115"
"Labou S.; Yoo H.J.; Minor D.; Altintas I.","Labou, Stephanie (57192364221); Yoo, Ho Jung (57216349188); Minor, David (22980662000); Altintas, Ilkay (6505855259)","57192364221; 57216349188; 22980662000; 6505855259","Sharing and archiving data science course projects to support pedagogy for future cohorts","2019","Proceedings - IEEE 15th International Conference on eScience, eScience 2019","","","9041707","644","645","1","0","10.1109/eScience.2019.00099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083214293&doi=10.1109%2feScience.2019.00099&partnerID=40&md5=38fbf386b7fd8c3f4319d858b53695e6","Library, University of California San Diego, San Diego, United States; San Diego Supercomputer Center, University of California San Diego, San Diego, United States","Labou S., Library, University of California San Diego, San Diego, United States; Yoo H.J., Library, University of California San Diego, San Diego, United States; Minor D., Library, University of California San Diego, San Diego, United States; Altintas I., San Diego Supercomputer Center, University of California San Diego, San Diego, United States","Founded in 2018, the Halicioǧlu Data Science Institute (HDSI) is a significant new organization on the UC San Diego campus. As part of their pedagogical processes, HDSI faculty desired a way to store and share student capstone projects with future cohorts, so students could easily access reusable, raw datasets and analytical workflows, with the potential to expand on work done by previous cohorts. The UC San Diego Library has been managing an institutional data repository for over a decade, with established ingest workflows and tools. The Library's Digital Collections accommodates collections of objects, complex organization of data and metadata, batch ingest options, and deposit of large datasets. Curators review data submissions and work with data depositors to enrich collections with descriptive metadata, controlled vocabularies, and persistent identifiers. HDSI faculty are working with the Library's Data Science Librarian and members of the Library's Research Data Curation program to design a custom workflow to facilitate ingest of an entire class worth of projects within a relatively quick time frame and have students prepare their materials with minimal one-on-one instruction from Library staff. The first test of this workflow ingests project materials from the Data Science & Engineering Master of Advanced Study capstone course into the Library's digital repository and makes the materials available using community-standard discoverability tools. This process represents the first ingestion of data-and computationally-intensive student projects into the repository and is intended to provide a template for a scalable workflow to accommodate other courses, ultimately creating a series of course data collections to support teaching and learning. © 2019 IEEE.","Data-repository; Data-science; Ingest-workflow; Library; Student-projects","Data Science; Digital libraries; e-Science; Large dataset; Metadata; Students; Teaching; Capstone projects; Community standards; Descriptive metadata; Digital collections; Digital repository; Organization of datum; Pedagogical process; Teaching and learning; Data curation","","","","","","","","","","Institute of Electrical and Electronics Engineers Inc.","","15th IEEE International Conference on eScience, eScience 2019","24 September 2019 through 27 September 2019","San Diego","158736","","978-172812451-3","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","","Scopus","2-s2.0-85083214293"
"da Silva J.R.; Ribeiro C.; Lopes J.C.","da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594); Lopes, João Correia (36791598000)","55496903800; 7201734594; 36791598000","Ranking Dublin Core descriptor lists from user interactions: a case study with Dublin Core Terms using the Dendro platform","2019","International Journal on Digital Libraries","20","2","","185","204","19","7","10.1007/s00799-018-0238-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045910166&doi=10.1007%2fs00799-018-0238-x&partnerID=40&md5=2104689f5c664a03d13ab4505e01d424","INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","da Silva J.R., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Ribeiro C., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Lopes J.C., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Dublin Core descriptors capture metadata in most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the requirements of different communities with the so-called Dublin Core Application Profiles that rely on the agreement within user communities, taking into account their evolving needs. In this paper, we propose an automated process to help curators and users discover the descriptors that best suit the needs of a specific research group in the task of describing and depositing datasets. Our approach is supported on Dendro, a prototype research data management platform, where an experimental method is used to rank and present DC Terms descriptors to the users based on their usage patterns. User interaction is recorded and used to score descriptors. In a controlled experiment, we gathered the interactions of two groups as they used Dendro to describe datasets from selected sources. One of the groups viewed descriptors according to the ranking, while the other had the same list of descriptors throughout the experiment. Preliminary results show that (1) some DC Terms are filled in more often than others, with different distribution in the two groups, (2) descriptors in higher ranks were increasingly accepted by users in detriment of manual selection, (3) users were satisfied with the performance of the platform, and (4) the quality of description was not hindered by descriptor ranking. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.","Collaborative; Dc terms; Dendro; Descriptor ranking; Dublin core; Linked open data; Metadata; Research data management; Usage information","","","","","","Ci?ncia e a Tecnologia; ERDF ?; National Funds; Operational Programme for Competitiveness and Internationalisation; Horizon 2020 Framework Programme, H2020; Fundação para a Ciência e a Tecnologia, FCT; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN, (POCI-01-0145-FEDER-016736); European Regional Development Fund, FEDER; Programa Operacional Temático Factores de Competitividade, POFC","Funding text 1: Acknowledgements This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-016736.; Funding text 2: This work is financed by the ERDF ? European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda??o para a Ci?ncia e a Tecnologia within project POCI-01-0145-FEDER-016736.","Allinson J., Johnston P., Powell A., A Dublin Core Application Profile for Scholarly Works, (2007); Amorim R., Castro J., Rocha J., Ribeiro C., Engaging Researchers in Data Management with LabTablet, an Electronic Laboratory Notebook, pp. 216-223, (2015); Amorim R., Castro J., Rocha J., Ribeiro C., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Univ. Access Inf. Soc., 16, 4, pp. 851-862, (2017); Ball A., Scientific Data Application Profile Scoping Study Report, (2009); Bechhofer S., Buchan I., Roure D.D., Missier P., Ainsworth J., Bhagat J., Couch P., Cruickshank D., Delderfield M., Dunlop I., Gamble M., Michaelides D., Owen S., Newman D., Sufi S., Goble C., Why linked data is not enough for scientists, Future Gen. Comput. Syst., 29, 2, pp. 599-611, (2011); Berners-Lee T., Linked Data—Design Issues, (2008); Bizer C., Heath T., Berners-Lee T., Linked data—the story so far. Special issue on linked data, Int. J. Semantic Web Inf. Syst., 5, 3, pp. 1-22, (2009); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inform. Sci. Technol., 63, 6, pp. 1059-1078, (2012); Boyko A., Kunze J., California Digital Library, Littman, J., Madden, L., Library of Congress, Vargas, B.: The Bagit File Packaging Format (V0.97), (2012); Coyle K., Baker T., Guidelines for Dublin Core Application Profiles, (2009); DCMI Metadata Terms, (2012); Dublin Core Metadata Element Set, Version 1.0: Reference Description, (2012); Directorate-General for Research & Innovation, (2016); Eynden V.V.D., Corti L., Bishop L., Horton L., Managing and Sharing Data: A guide to good practice, (2011); Gormley C., Tong Z., Elasticsearch: The Definitive Guide, (2015); Goy A., Magro D., Petrone G., Picardi C., Segnan M., Ontology-driven collaborative annotation in shared workspaces, Future Gen. Comput. Syst., 54, pp. 435-449, (2016); Greenberg J., Metadata capital: raising awareness, exploring a new concept economics of knowledge organization systems, Bull. Assoc. Inf. Sci. Technol., 40, 4, pp. 30-33, (2014); Greenberg J., Swauger S., Feinstein E., Metadata capital in a data repository, Proc. Int. Conf. Dublin Core Metadata Appl., 2013, pp. 140-150, (2013); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, Ariadne, 25, (2000); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Hodson S., ADMIRAL: A Data Management Infrastructure for Research Activities in the Life sciences, (2011); Hu R., Pu P., Acceptance Issues of Personality-based Recommender Systems, Proceedings of the Third ACM Conference on Recommender Systems (Recsys ’09), pp. 221-224, (2009); (2012); Jahnke L., Asher A., Keralis S.D.C., The Problem of Data, (2012); Joachims T., Granka L., Pan B., Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28Th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154-161, (2005); Krause E.M., Clary E., Greenberg J., Ogletree A., Evolution of an application profile: Advancing metadata best practices through the dryad data repository, Procedings of the International Conference on Dublin Core and Metadata Applications 2015, pp. 63-75, (2015); Lecarpentier D., Wittenburg P., Elbers W., Michelini A., Kanso R., Coveney P., Baxter R., EUDAT: A New Cross-Disciplinary Data Infrastructure for Science, Int. J. Digit. Curation, 8, 1, pp. 279-287, (2013); Leonelli S., Spichtinger D., Prainsack B., Sticks and carrots: encouraging open science at its source, Geo: Geogr. Environ., 2, 1, pp. 12-16, (2015); Li H., A short introduction to Learning to Rank, IEICE Trans. Inf. Syst., E94–D, 10, pp. 1854-1862, (2011); Lord P., Macdonald A., Data curation for e-Science in the UK: An audit to establish requirements for future curation and provision, (2003); Lyon L., (2007); Malta M., Baptista A., State of the Art on Methodologies for the Development of a Metadata Application Profile, pp. 61-73, (2012); Malta M., Baptista A., A Method for the Development of Dublin Core Application Profiles (Me4DCAP V0.1): A Description, In: Proceedings of the International Conference on Dublin Core and Metadata Applications 2013, pp. 90-103, (2013); Malta M., Baptista A., A panoramic view on metadata application profiles of the last decade, Int. J. Metadata Semant. Ontol., 9, 1, pp. 58-73, (2014); Martinez-Uribe L., Using the Data Audit Framework: An Oxford case study, (2009); Martinez-Uribe L., Macdonald S., User engagement in research data curation, Proceedings of the 13Th European Conference on Research and Advanced Technology for Digital Libraries, 5714, pp. 309-314, (2009); Piwowar H., Vision T., Data Reuse and the Open Data Citation Advantage, 1, (2013); Rocha J., (2016); Rocha J., Castro C., Ribeiro J., Lopes J., Dendro: Collaborative Research Data Management Built on Linked Open Data, pp. 483-487, (2014); Rocha J., Ribeiro C., Correia Lopes J., Ontology-based multi-domain metadata for research data management using triple stores, Proceedings of the 18Th International Database Engineering & Applications Symposium, IDEAS’14, pp. 105-114, (2014); Rocha J., Ribeiro C., Correia Lopes J., The Dendro Research Data Management Platform: Applying Ontologies to Long-Term Preservation in a Collaborative Environment, Proceedings of the 11Th International Conference on Digital Preservation, Ipres 2014, 2014, (2014); Rocha J., Ribeiro C., Lopes J., Managing research data at U. Porto: Requirements, technologies and services, Innovations in XML Applications and Metadata Management: Advancing Technologies, pp. 174-197, (2013); Schein A.I., Popescul A., Ungar L.H., Pennock D.M., Methods and metrics for cold-start recommendations, Proceedings of the 25Th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’02, 46, pp. 253-260, (2002); Magazine, Science: Dealing with data, Challenges and Opportunities. Introduction. Science (New York, N.Y.), 331, 6018, pp. 692-693, (2011); Silvello G., Theory and practice of data citation, J. Assoc. Inf. Sci. Technol., 69, 1, pp. 6-20, (2018); Sinha R., Swearingen K., Comparing recommendations made by online systems and friends, Proceedings of the DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries, Volume 01/W03, Dublin City University, Ireland, (2001); Sinha R., Swearingen K., The role of transparency in recommender systems, CHI ’02 Extended Abstracts on Human Factors in Computing Systems, CHI EA ’02, pp. 830-831, (2002); Strickroth S., Pinkwart N., High quality recommendations for small communities: The case of a regional parent network, Proceedings of the Sixth ACM Conference on Recommender Systems, Recsys ’12, pp. 107-114, (2012); Swanberg S., Inter-university consortium for political and social research (ICPSR), J. Med. Lib. Assoc., 105, 1, pp. 106-107, (2017); Swearingen K., Sinha R., Beyond Algorithms: An HCI perspective on recommender systems, ACM SIGIR 2001 Workshop on Recommender Systems, pp. 1-11, (2001); Implementation of the Data Seal of Approval, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","J.R. da Silva; INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; email: joaorosilva@gmail.com","","Springer Verlag","","","","","","14325012","","","","English","Int. J. Digital Libr.","Article","Final","","Scopus","2-s2.0-85045910166"
"Herr M.","Herr, Melody (57210951130)","57210951130","Responding to Research Misconduct: A Primer for LIS Professionals","2019","Science and Technology Libraries","38","3","","272","287","15","1","10.1080/0194262X.2019.1644268","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071998951&doi=10.1080%2f0194262X.2019.1644268&partnerID=40&md5=a2ac80cde2de704494a1109b2e9dd472","Office of Scholarly Communications, University of Arkansas Libraries, Fayetteville, AR, United States","Herr M., Office of Scholarly Communications, University of Arkansas Libraries, Fayetteville, AR, United States","Falsification, fabrication, plagiarism, and other forms of misconduct undermine the foundation of science–trust in the integrity of researchers and their reported results. As research team members, therefore, library and information science (LIS) professionals share responsibility for addressing research misconduct. Intended as a primer, this article defines misconduct, discusses it causes, and notes its consequences. The article then empowers LIS professionals with a set of strategies, escalating from gathering information, to engaging in conversation, to submitting formal allegations, to respond effectively when they suspect or detect misconduct. © 2019, Published with license by Taylor & Francis Group, LLC © 2019 Melody Herr. © 2019, © 2019 Melody Herr.","library and information science; research data management; research integrity; Research misconduct","Information science; Library and information science; Research data managements; Research integrities; Research misconduct; Research teams; article; conversation; human; information science; publishing; responsibility; scientific misconduct; scientist; trust; Information management","","","","","","","Ethical and professional guidelines; ACRL scholarly communication toolkit; Bedzow I., Giving voice to values as a professional physician: An introduction to medical ethics, (2019); Ben-Yehuda N., Oliver-Lumerman A., Fraud and misconduct in research: Detection, investigation, and organizational response, (2017); Bonito A.J., Titus S.L., Wright D.E., Assessing the preparedness of research integrity officers (RIOs) to appropriately handle possible research misconduct cases, Science and Engineering Ethics, 18, 4, pp. 605-619, (2012); Borrego A., Ardanuy J., Urbano C., Librarians as research partners: Their contribution to the scholarly endeavor beyond Library and information science, Journal of Academic Librarianship, 44, 5, pp. 663-670, (2018); Brainard J., Rethinking retractions, Science, 362, 6413, pp. 390-393, (2018); Butler D., Duplicated images could soon be identified by an automated test, Nature, 555, 7694, (2018); Calarco P., Shearer K., Schmidt B., Tate D., Librarians’ competencies profile for scholarly communication and open access. Accessed March 25, 2019, (2016); The ethics codes collection; Chen H., Zhang Y., Educating data management professionals: A content analysis of Job descriptions, The Journal of Academic Librarianship, 43, 1, pp. 18-24, (2017); Coates H., Ensuring research integrity: The role of data management in current crises, C&RL News, 75, 11, pp. 598-601, (2014); Fostering integrity in research, (2017); On being a scientist: A guide to responsible conduct in research, (2009); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science And Technology, 68, 9, pp. 2182-2200, (2017); DuBois J.M., Chibnall J.T., Tait R., Vander Wal J.S., Lessons from researcher rehab, Nature, 534, 7606, pp. 173-175, (2016); DuBois J.M., Chibnall J.T., Tait R., Vander Wal J.S., The professionalism and integrity in research program, Academic Medicine, 93, 4, pp. 586-592, (2018); Edwards M.A., Roy S., Academic research in the 21st century: Maintaining scientific integrity in a climate of perverse incentives and hypercompetition, Environmental Engineering Science, 34, 1, pp. 51-61, (2017); Erway R., Horton L., Nurnberger A., Otsuji R., Rushing A., Building blocks: Laying the foundation for a research data management program, (2016); Fearon D., Gunia B., Pralle B.E., Lake S., Sallans A.L., Research data management services SPEC kit 334, (2013); Foutch L.J., A new partner in the process: The role of a librarian on a faculty research team, Collaborative Librarianship, 8, 2, pp. 80-83, (2016); Fox M., Beall J., Advice for plagiarism whistleblowers, Ethics and Behavior, 24, 5, pp. 341-349, (2014); Gentile M.C., Giving voice to values: How to speak your mind when you know what’s right, (2010); Gentile M.C., Giving voice to values: A global partnership with UNGC PRME to transform management education, International Journal of Management Education, 15, 2, pp. 121-125, (2017); Gibney E., The scientist who spots fake videos, Nature News, (2017); Gorman M., Our enduring values revisited: Librarianship in an ever-changing world, (2015); Gunsalus C.K., How to blow the whistle and still have a career afterwards, Science and Engineering Ethics, 4, 1, pp. 51-64, (1998); Johnston L.R., Curating research data: A handbook of current practice, (2017); Johnston L.R., Curating research data: Practical strategies for your Digital repository, (2017); Kidder R., Moral courage, (2005); Koehler W., Ethics and values in librarianship: A history, (2015); Koppers L., Wormer H., Ickstadt K., Towards a systematic screening tool for quality assurance and semiautomatic fraud detection for images in the life sciences, Science and Engineering Ethics, 23, 4, pp. 1113-1128, (2017); Kretser A., Murphy D., Bertuzzi S., Abraham T., Allison D., Boor J., Grantham A., Harris L.J., Hollander R., Yada R., Scientific integrity principles and best practices: Recommendations from a scientific integrity consortium, Science and Engineering Ethics, 25, 2, pp. 327-355, (2019); Loikith L., Bauchwitz R., The essential need for research misconduct allegation audits, Science and Engineering Ethics, 22, 4, pp. 1027-1049, (2016); Lubalin J.S., Matheson J.L., The fallout: What happens to whistleblowers and those accused but exonerated of scientific misconduct?, Science and Engineering Ethics, 5, 2, pp. 229-250, (1999); Lyon L., Librarians in the lab: Toward radically re-engineering data curation services at the research coalface, New Review of Academic Librarianship, 22, 4, pp. 391-409, (2016); Malek J., To tell or not to tell? The ethical dilemma of the would-be whistleblower, Accountability in Research, 17, 3, pp. 115-129, (2010); Marcus A., Oransky I., The data thugs, Science, 359, 6377, pp. 731-732, (2018); McIntosh T., Higgs C., Turner M., Partlow P., Steele L., MacDougall A.E., Connelly S., Mumford M., To whistleblow or not to whistleblow: Affective and cognitive differences in reporting peers and advisors, Science and Engineering Ethics, 25, 1, pp. 171-210, (2019); Mecca J.T., Giorgini V., Medeiros K., Gibson C., Devenport L., Connelly S., Mumford M., Perspectives on whistleblowing: Faculty member viewpoints and suggestions for organizational change, Accountability in Research, 21, 3, pp. 159-175, (2014); NASIG core competencies for scholarly communication librarians, (2017); Case summaries; Case closeout memoranda; Research integrity; Plump C., Giving voice to values in the legal profession: Effective advocacy with integrity, (2018); Redman B.K., Commentary: Legacy of the Commission on Research Integrity, Science and Engineering Ethics, 23, 2, pp. 555-563, (2017); Research integrity; Resnik D.B., Elliott K.C., Soranno P.A., Smith E.M., Data-intensive science and research integrity, Accountability in Research, 24, 6, pp. 344-358, (2017); Resnik D.B., Neal T., Raymond A., Kissling G.E., Research misconduct definitions adopted by U.S. research institutions, Accountability in Research, 22, 1, pp. 14-21, (2015); Rhode D.L., Cheating: Ethics in everyday life, (2018); Satalkar P., Shaw D., Is failure to raise concerns about misconduct a breach of integrity? Researchers’ reflections on reporting misconduct, Accountability in Research, 25, 6, pp. 311-339, (2018); Schaller-Demers D.S., Responsible conduct of research: Not just for researchers, Journal of Research Administration, 46, 1, pp. 63-76, (2015); Schmidt B., Shearer K., Librarians’ competencies profile for research data management, (2016); Steneck N.H., ORI introduction to the responsible conduct of research, (2007); Stokstad E., The truth squad, Science, 361, 6408, pp. 1189-1191, (2018); Stone D., Patton B., Henn S., Difficult conversations: How to discuss what matters most, (2010); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Thomas C.V.L., Urban R.J., What do data librarians think of the MLIS? Professionals’ perceptions of knowledge transfer, trends and challenges, College & Research Libraries, 79, 3, pp. 401-423, (2018); Title 45: Public welfare, Part 689 research misconduct; Title 42: Public health, Part 93 Public Health Service policies on research misconduct; Veldkamp C.L.S., Hartgerink C.H.J., van Assen M.A.L.M., Wicherts J.M., Who believes in the storybook image of the scientist?, Accountability in Research, 24, 3, pp. 127-151, (2017); Visintini S., Boutet M., Manley A., Helwig M., Research support in health sciences libraries: A Scoping Review, The Journal of the Canadian Health Libraries Association, 39, 2, pp. 56-78, (2018); Wendelbo M., Perspectives on peer review of data: Framing standards and questions, College & Research Libraries, 78, 3, pp. 262-266, (2017); Wittenberg J., Sackmann A., Jaffe R., Situating expertise in practice: Domain-Based data management training for liaison librarians, Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018); Zhe Jin G., Feng Lu S., Retraction and reputation, Science, 361, 6408, (2018)","M. Herr; Office of Scholarly Communications, University of Arkansas Libraries, Fayetteville, MULN 225, 365 N. McIlroy Avenue, 72701-4002, United States; email: herr@uark.edu","","Routledge","","","","","","0194262X","","STELD","","English","Sci Technol Libr","Article","Final","","Scopus","2-s2.0-85071998951"
"Cox A.M.; Kennan M.A.; Lyon L.; Pinfield S.; Sbaffi L.","Cox, Andrew M. (7402563906); Kennan, Mary Anne (56001293900); Lyon, Liz (56835287100); Pinfield, Stephen (6602090850); Sbaffi, Laura (6506992618)","7402563906; 56001293900; 56835287100; 6602090850; 6506992618","Maturing research data services and the transformation of academic libraries","2019","Journal of Documentation","75","6","","1432","1462","30","50","10.1108/JD-12-2018-0211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074207179&doi=10.1108%2fJD-12-2018-0211&partnerID=40&md5=81d2d1fb03a546b918998ac9b0cd3c5e","Department of Information, University of Sheffield, Sheffield, United Kingdom; Department of Information Studies, Charles Sturt University, Albury, Australia; Department of Information Culture and Data Stewardship, University of Pittsburgh, Pittsburgh, PA, United States","Cox A.M., Department of Information, University of Sheffield, Sheffield, United Kingdom; Kennan M.A., Department of Information Studies, Charles Sturt University, Albury, Australia; Lyon L., Department of Information Culture and Data Stewardship, University of Pittsburgh, Pittsburgh, PA, United States; Pinfield S., Department of Information, University of Sheffield, Sheffield, United Kingdom; Sbaffi L., Department of Information, University of Sheffield, Sheffield, United Kingdom","Purpose: A major development in academic libraries in the last decade has been recognition of the need to support research data management (RDM). The purpose of this paper is to capture how library research data services (RDS) have developed and to assess the impact of this on the nature of academic libraries. Design/methodology/approach: Questionnaire responses from libraries in Australia, Canada, Germany, Ireland, the Netherlands, New Zealand, the UK and USA from 2018 are compared to a previous data set from 2014. Findings: The evidence supports a picture of the spread of RDS, especially advisory ones. However, future ambitions do not seem to have seen much evolution. There is limited evidence of organisational change and skills shortages remain. Most service development can be explained as the extension of traditional library services to research data. Yet there remains the potential for transformational impacts, when combined with the demands implied by other new services such as around text and data mining, bibliometrics and artificial intelligence. A revised maturity model is presented that summarises typical stages of development of services, structures and skills. Research limitations/implications: The research models show how RDS are developing. It also reflects on the extent to which RDM represents a transformation of the role of academic libraries. Practical implications: Practitioners working in the RDM arena can benchmark their current practices and future plans against wider patterns. Originality/value: The study offers a clear picture of the evolution of research data services internationally and proposes a maturity model to capture typical stages of development. It contributes to the wider discussion of how the nature of academic libraries are changing. © 2019, Andrew M. Cox, Mary Anne Kennan, Liz Lyon, Stephen Pinfield and Laura Sbaffi.","Academic libraries; Data curation; Information services; Research data management; Research data services; Scholarly communication","","","","","","","","Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Creating a Data Management Framework, (2018); Bryant R., Lavoie B., Malpas C., The Realities of Research Data Management: Part Two: Scoping the University RDM Service Bundle, (2017); Chiware E.R., Becker D.A., Research data management services in Southern Africa: a readiness survey of academic and research libraries, African Jouranl of Library and Archives and Information Science, 28, 1, pp. 1-16, (2018); Corrall S., Kennan M., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Academic librarianship as a data profession: the familiar and unfamiliar in the data role spectrum, eLucidate, 15, 1-2, pp. 7-10, (2018); Cox A.M., Kennan M.-A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data services maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Crowston K., Qin J., A capability maturity model for scientific data management: evidence from the literature, Proceedings of the American Society for Information Science and Technology, 48, 1, pp. 1-9, (2011); Dempsey L., Library collections in the life of the user: two directions, LIBER Quarterly, 26, 4, pp. 338-359, (2016); Faniel I.M., Connaway L.S., Librarians perspectives on the factors influencing research data management programs, College and Research Libraries, 79, 1, pp. 100-119, (2018); Gersick C.J., Revolutionary change theories: a multilevel exploration of the punctuated equilibrium paradigm, Academy of Management Review, 16, 1, pp. 10-36, (1991); Hayes J., The Theory and Practice of Change Management, (2014); Kellam L.M., Thompson K., Databrarianship: The Academic Data Librarian in Theory and Practice, (2016); Kenney A.R., McGovern N.Y., The five organizational stages of digital preservation, Digital Libraries: A Vision for the 21st Century, (2003); Kouper I., Fear K., Ishida M., Kollen C., Williams S.C., Research data services maturity in academic libraries, Curating Research Data, pp. 153-170, (2015); Peng G., Privette J.L., Kearns E.J., Ritchey N.A., Ansari S., A unified framework for measuring stewardship practices applied to digital environmental datasets, Data Science Journal, 13, pp. 231-253, (2015); Pickard A., Research Methods in Information, (2012); Pinfield S., Cox A., Rutter S., Mapping the future of academic libraries: a report for SCONUL, (2017); Poppelbuss J., Roglinger M., What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management, 19th European Conference on Information Systems, (2011); Proenca D., Vieira R., Borbinha J., A maturity model for information governance, International Conference on Theory and Practice of Digital Libraries, (2016); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2014); Qin J., Crowston K., Kirkland A., Pursuing best performance in research data management by using the capability maturity model and rubrics, Journal of eScience Librarianship, 6, 2, (2017); Rebouillat V., Inventory of research data management services in France2, Expanding Perspsectives on Open Science: Communities, Cultures and Diversity in Concepts and Practices, Proceedings of the 21st International Conference on Electronic Publishing, pp. 174-181, (2017); Rice R., Southall J., The Data Librarian’s Handbook, (2016); Romanelli E., Tushman M.L., Organizational transformation as punctuated equilibrium: an empirical test, Academy of Management Journal, 37, 5, pp. 1141-1166, (1994); Russell group, (2019); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future: An ACRL White Paper, (2012); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Lundeen A., Research data services in academic libraries: data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tenopir C., Pollock D., Allard S., Hughes D., Research data services in European and North American libraries: current offerings and plans for the future, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Borgman C.L., Big Data, Little Data, no Data: Scholarship in the Networked World, (2015); Kenney A.R., McGovern N.Y., The three-legged stool: institutional response to digital preservation, (2005); Si L., Xing W., Zhuang X., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015)","A.M. Cox; Department of Information, University of Sheffield, Sheffield, United Kingdom; email: a.m.cox@sheffield.ac.uk","","Emerald Group Holdings Ltd.","","","","","","00220418","","","","English","J. Doc.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85074207179"
"Rice R.","Rice, Robin (7403332002)","7403332002","SUPPORTING RESEARCH DATA MANAGEMENT AND OPEN SCIENCE IN ACADEMIC LIBRARIES: A DATA LIBRARIAN’S VIEW; [Unterstützung von forschungsdatenmanagement und offener wissenschaft in wissenschaftlichen bibliotheken: Die sicht eines data librarians]","2019","VOEB-Mitteilungen","72","2","","263","273","10","3","10.31263/voebm.v72i2.3303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077690421&doi=10.31263%2fvoebm.v72i2.3303&partnerID=40&md5=3e1024b17f3427148552457f16357d06","University of Edinburgh, Library & University Collections, United Kingdom","Rice R., University of Edinburgh, Library & University Collections, United Kingdom","The ‘data revolution’ has impacted researchers across the disciplines. As if the traditional work of teaching, competing for grants and promotion, doing research and publishing results was not challenging enough, researchers are required to make fundamental changes in the way they do all of these things. A similar shift can be seen for academic librarians. Librarians who were taught to meet the needs of their users based on information scarcity now need to retrain themselves to help users deal with information overload. Moreover, librarians increasingly find themselves ‘upstream’ in the research process, trying to assist their users in managing unwieldy amounts of data when their comfort zone is firmly ‘downstream’ in the post-publication stage. Unsettling as it may be, these are exciting developments for the library profession. © Robin Rice.","Academic Libraries; Open Science; Research data management","","","","","","European Commission, EU; Horizon 2020","Among other things, this means that data that are not appropriate to be openly shared, such as personal and sensitive data, do not have to be, or due to legislative requirements such as GDPR must not be. However, the metadata describing the research data can and should be open and discoverable, and the instructions for requesting access should be clear – and preferably even machine-actionable, with full documentation made available in order to be able to reuse the data when a request is approved. For example, the European Commission has described its data sharing policy for Horizon 2020 funded research projects as “open by default”, or “as open as possible, as closed as necessary.”6","Finnie E., Arthur M.A., Being Earnest With Collections – Voting with our Dollars: Making a New Home for the Collections Budget in the MIT Libraries, Against the Grain, 28, 4, (2016); Cox A., Et al., Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity, Journal of the Association for Information, Science and Technology, 68, 9, (2017); What is Open Science?, Introduction; Rice R., Southall J., The Data Librarian’s Handbook, pp. 152-153, (2016); FAIR Principles; HORIZON 2020 Online Manual – Funding & Tender Opportunities; Federer L., Defining data librarianship: A survey of competencies, skills, and training, Journal of the Medical Library Association, 106, 3, pp. 294-303, (2018); Paul A., Et al., Open Science and Its Role in Universities: A Roadmap for Cultural Change (LERU Advice Paper 24, (2018); Ayris P., Et al., LIBER Open Science Roadmap, Zenodo, (2018)","R. Rice; University of Edinburgh, Library & University Collections, United Kingdom; email: r.rice@ed.ac.uk","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85077690421"
"Nitecki D.A.; Davis M.E.K.","Nitecki, Danuta A. (7004392762); Davis, Mary Ellen K. (7404849596)","7004392762; 7404849596","Expanding academic librarians' roles in the research life cycle","2019","Libri","69","2","","117","125","8","1","10.1515/libri-2018-0066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067126163&doi=10.1515%2flibri-2018-0066&partnerID=40&md5=b4b2c31d67aa1aff7f799fa585d8737e","Department of Libraries, Drexel University, Philadelphia, PA, United States; Association of College and Research Libraries, Chicago, IL, United States","Nitecki D.A., Department of Libraries, Drexel University, Philadelphia, PA, United States; Davis M.E.K., Association of College and Research Libraries, Chicago, IL, United States","Research depends on prior results. The cycle of transforming research output to disseminated knowledge is changing to engage more researchers to openly discover and thereby shape future contributions to scholarship. No established framework helps librarians understand the opportunities that transition offers librarians. However, through four propositions, this paper addresses some of the changes facing academic librarians as they expand their roles: 1) Research cycles embrace interactive sharing and reuse of data; 2) Managing open research data expands librarians' roles; 3) Intellectual entrepreneurship roles provide a model to empower others; 4) Librarians demonstrate their entrepreneurial leadership by creating partnerships outside the library. Now academic librarians have opportunities to strengthen their role in how higher education shapes research by shifting greater focus toward research data management [RDM]. Two seasoned administrators and librarians illustrate pathways to prepare academic librarians for these new roles. They offer two practitioners' impressions of the demands and opportunities for librarians to extend their expertise to support RDM, and illustrate how academic librarians have begun doing so through professional association work (through the Association of College and Research Libraries (ACRL)) and at one academic library (at Drexel University). They urge academic librarians to step out of their comfort zones of organizing, preserving and servicing discovery of information resources and embrace emerging roles for which their values and expertise have prepared them. If librarians ignore these opportunities, they risk being bypassed in efforts to ensure that managing research data and scholarship are central to research protocols. © 2019 Walter de Gruyter GmbH, Berlin/Boston.","emerging librarian roles; higher education; intellectual entrepreneurship; leadership; research data management","","","","","","U.S. National Library of Medicine, NLM","3 Drexel University. Data Stewardship Committee. “Formation of University Data Management and Intelligence Infrastructure: A Proposal Toward Changing Drexel’s Culture for Innovation, Competitiveness, Risk Reduction, Observance of Privacy and Security, and Cost Savings.” Submitted by D. A. Nitecki, and K. Matuch, cochairs, edited by Priya Sankar. February 20, 2015. 4 The NNLM MAR Data Services Pilot Program is a project of Biomedical and Health Research Data Management supported by grant UG4LM012344, National Library of Medicine.","Abosede A.J., Onakoya A.B., Intellectual Entrepreneurship: Theories, Purpose and Challenges, International Journal of Business Administration, 4, 2, pp. 30-37, (2013); Framework for Information Literacy for Higher Education, (2016); Proficiencies for Assessment Librarians and Coordinators, (2017); Scholarly Communication Toolkit: Research Data Management, (2018); Brantley S., Bruns T.A., Duffin K.I., Librarians in Transition: Scholarly Communication Support as a Developing Core Competency, Journal of Electronic Resources Librarianship, 29, 3, pp. 137-150, (2017); Burke J., Cherwitz R.A., Hartelius E.J., Fixing the Fragmented University: Decentralization with Direction, (2006); Chen A., Pickle S., Waldroup H., Changing and Expanding Libraries: Exhibitions, Institutional Repositories, and the Future of Academia, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy, pp. 62-81, (2015); Cherwitz R.A., Diversifying Graduate Education: The Promise of Intellectual Entrepreneurship, Journal of Hispanic Higher Education, 4, 1, pp. 19-33, (2005); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments of Research Data Management in Academic Libraries: Towards an Understanding of Research Data Maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Dora M., Anil Kumar H., Managing Research Data in Academic Institutions: Role of Libraries, CALIBER, (2015); English Oxford Living Dictionary, (2018); Erway R., Starting the Conversation: University-Wide Research Data Management Policy, (2013); Fernandez P., Tilton K., Applying Library Values to Emerging Technology: Decision-Making in the Age of Open Access, Maker Spaces, and the Ever-Changing Library, (2018); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the Research Data Management Landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy, pp. 82-102, (2015); Open Science Definition, (2018); Godbey S., Wainscott S.B., Goodman X., Disciplinary Applications of Information Literacy Threshold Concepts, (2017); Grover R., Hale M.L., The Role of the Librarian in Faculty Research, College and Research Libraries, 49, 1, pp. 9-15, (1988); Humphrey C., Are Libraries Organized to Provide Research Data Management Services, Preserving Research Data in Canada, Blog Post, (2014); Johnston L.R., Curating Research Data 1 and 2, (2017); Kraft A., Dreyer B., Lowe P., 14 Years of PID Services at the German National Library of Science and Technology (TIB): Connected Frameworks, Research Data and Lessons Learned from a National Research Library Perspective, Data Science Journal, 16, 36, (2017); Research, (2018); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences Edited by Pascal Launois, PloS One, 9, 12, (2014); Renwick S., Winter M., Gill M., Managing Research Data at an Academic Library in a Developing Country, IFLA Journal, 43, 1, pp. 51-64, (2017); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research Data Services in Academic Libraries: Data Intensive Roles for the Future, Journal of EScience Librarianship, 4, 2, (2015); Research Data Lifecycle, (2018)","D.A. Nitecki; Department of Libraries, Drexel University, Philadelphia, United States; email: dan44@drexel.edu","","De Gruyter Saur","","","","","","00242667","","","","English","Libri","Article","Final","","Scopus","2-s2.0-85067126163"
"Williams J.P.; Williams R.D.","Williams, Justin P. (10639331600); Williams, Rachel D. (55756481700)","10639331600; 55756481700","Information science and north american archaeology: Examining the potential for collaboration","2019","Information Research","24","2","","","","","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070213535&partnerID=40&md5=9f8b6269fc75d77d0703d0e78e656fa0","Roger Williams University, RI, United States; School of Library and Information Science, Simmons University in Boston, MA, United States","Williams J.P., Roger Williams University, RI, United States; Williams R.D., School of Library and Information Science, Simmons University in Boston, MA, United States","Introduction. To understand how archaeologists produce and manage project data, we conducted a pilot survey of North American archaeologists. Method. We conducted a pilot survey using Qualtrics online survey service. The questionnaire contained fourteen questions related to project data management challenges and perspectives, along with questions regarding the potential for collaboration between archaeologists and information professionals. Additionally, we review a small vignette of archaeological research. Analysis. We used descriptive statistics and a thematic analysis of open-ended questions to identify important trends and topics related to our two research questions. Results. Our survey found that archaeologists perceive storing project data and making project data available outside their organization to be the most challenging parts of their work. We also found that most archaeologists are interested in collaborating with information professionals. Conclusion. This study identified the challenges archaeologists encounter in data management and potential for collaboration between archaeologists and information professionals in terms of research data management and sustainability and usability of research data. © the authors, 2019.","","","","","","","","","Abrahamson J.A., Fisher K.E., What's past is prologue': Towards a general model of lay information mediary behaviour, Information Research, 12, 4, (2007); Abrahamson J.A., Fisher K.E., Turner A.G., Durrance J.C., Turner T.C., Lay information mediary behavior uncovered: Exploring how nonprofessionals seek health information for themselves and others online, Journal of the Medical Library Association, 96, 4, pp. 310-323, (2008); Anderson D.G., Miller D., Yerka S., Gillam J.C., Johanson E.N., Erson D.T., Smallwood A.M., PIDBA (Paleo-Indian Database of the Americas) 2010 current status and findings, Archaeology of Eastern North America, 38, pp. 63-90, (2010); Anderson D.G., Bissett T.G., Yerka S.J., Wells J.J., Kansa E.C., Kansa S.W., Meyers K.N., White D.A., Sea-level rise and archaeological site destruction: An example from the Southeastern United States using DINAA (digital index of North American archaeology), PLOS One, 12, 11, (2017); Bates M.J., The invisible substrate of information science, Journal of the American Society for Information Science, 50, 12, pp. 1043-1050, (1999); Beaudoin J.E., Brady J.E., Finding visual information: A study of image resources used by archaeologists, architects, art historians, and artists, Art Documentation: Journal of the Art Libraries Society of North America, 30, 2, pp. 24-36, (2011); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Bradley B., Collins M.B., Hemmings A., Shoberg M., Lohse J.C., Clovis Technology, (2010); Buchanan B., Collard M., Investigating the peopling of North America through cladistic analyses of early Paleo-indian projectile points, Journal of Anthropological Archaeology, 26, 3, pp. 366-393, (2007); Caraher W., Slow archaeology: Technology, efficiency, and archaeological work, Mobilizing The past for a Digital Future: The Potential of Digital Archaeology, pp. 421-441, (2015); Ellis S.J., Mobilizing The past for a Digital Future: The Potential of Digital Archaeology, pp. 51-75, (2016); Ellis D., Haugan M., Modelling the information seeking patterns of engineers and research scientists in an industrial environment, Journal of Documentation, 53, 4, pp. 384-403, (1997); Fagan B., Ancient North America, (2005); Faniel I., Kansa E., Whitcher Kansa S., Barrera-Gomez J., Yakel E., The challenges of digging data: A study of context in archaeological data reuse, Proceedings of the 13Th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 295-304, (2013); Faniel I., Yakel E., Practices do not make perfect: Disciplinary data sharing and reuse and their implications for repository data curation, Curating Research Data. Volume One: Practical Strategies for Your Digital Repository, pp. 103-126, (2017); Hamilton M.J., Buchanan B., The accumulation of stochastic copying errors causes drift in culturally transmitted technologies: Quantifying Clovis evolutionary dynamics, Journal Anthropological Archaeology, 28, pp. 55-69, (2009); Haynes G., The Early Settlement of North America: The Clovis Era, (2002); Huvila I., The politics of boundary objects: Hegemonic interventions and the making of a document, Journal of the Association for Information Science and Technology, 62, 12, pp. 2528-2539, (2011); Huvila I., Archaeologists and their information sources, Perspectives to Archaeological Information in the Digital Society, pp. 25-54, (2014); Huvila I., Huggett J., Archaeological practices, knowledge work and digitalisation, Journal of Computer Applications in Archaeology, 1, 1, pp. 88-100, (2018); Kansa E., Kansa S.W., Arbuckle B., Publishing and pushing: Mixing models for communicating research data in archaeology, Internatinal Journal of Digital Curation, 9, 1, pp. 57-70, (2014); Kansa E.C., Kansa S.W., Wells J.J., Yerka S.J., Meyers K.N., Bissett T.G., Anderson D.G., The digital index of North American archaeology (DINAA): Networking government data to navigate an uncertain future for the past, Antiquity, 92, 363, pp. 490-506, (2018); King T.F., Federal Planning and Historic Places: The Section 106 Process, (2001); Kulasekaran S., Trelogan J., Esteva M., Johnson M., Metadata integration for an archaeology collection architecture, International Conference on Dublin Core and Metadata Applications, Austin, 2014, pp. 53-63, (2014); McManamon F.P., Kintigh K.W., Ellison L.A., Brin A., TDAR: A cultural heritage archive for twenty-first-century public outreach, research, and resource management, Advances in Archaeological Practice, 5, 3, pp. 238-249, (2017); Meltzer D.D., Peopling of North America, The Quaternary Period in the United States, 1, pp. 539-569, (2004); Morrow J.E., Morrow T.A., Geographic variation in fluted projectile points: A hemispheric perspective, American Antiquity, 64, 2, pp. 215-231, (1999); Olsson M., Making sense of the past: The embodied information practices of field archaeologists, Journal of Information Science, 42, 3, pp. 410-419, (2016); Prasciunas M., Mapping Clovis, projectile points, behavior, and bias, American Antiquity, 76, 1, pp. 107-126, (2011); Ross S., Sobotkova A., Ballsun-Stanton B., Crook P., Creating eresearch tools for archaeologists: The federated archaeological information management systems project, Australian Archaeology, 77, 1, pp. 107-119, (2013); Sanchez G., Holliday V.T., Gaines E.P., Arroyo-Cabrales J., Martinez-Taguena N., Kowler A., Lange M., Sanchez-Morales I., Human (Clovis)–gomphothere (Cuvieronius sp.) association ∼13,390 calibrated yBP in Sonora, Mexico, Proceedings of the National Academy of Sciences of the United States of America, 111, 30, pp. 10972-10977, (2014); Uildriks M., IDig-Recording archaeology: A review, Internet Archaeology, (2016); Waters M.R., Stafford T.W., Redefining the age of Clovis: Implications for peopling of the Americas, Science, 315, 5815, pp. 1122-1126, (2007); Williams J.P., Morphological Variability in Clovis Style Hafted Bifaces from across North America, (2016); Yerka S.J., Echeverry D., Erson D.G., Miller D.S., Redesigning PIDBA (The Paleoindian Database of the Americas): Enhancing the accessibility of information and the user experience, Poster Presented at the Annual Meeting of the Society for American Archaeology, (2012); Yerka S.J., Myers K.N., Demuth R.C., Erson D.G., Kansa S.W., Wells J.J., Kansa E.C., Built to Last: The Paleoindian Database of The Americas (PIDBA) and Openly-Shared Primary Data Meet The Digital Index of North American Archaeology, (2015); York J., Gutmann M., Berman F., (2016)","","","University of Boras","","","","","","13681613","","","","English","Inf. Res.","Article","Final","","Scopus","2-s2.0-85070213535"
"Jusoh Y.Y.; Abdullah R.; Sidi F.; Ishak I.; Napis S.; Marhaban M.H.; Tugiran Y.; Tajuddin N.I.I.","Jusoh, Yusmadi Yah (36147542500); Abdullah, Rusli (24483156100); Sidi, Fatimah (36139115600); Ishak, Iskandar (26422254000); Napis, Suhaimi (6507614723); Marhaban, Mohammad Hamiruce (57211599538); Tugiran, Yusnita (57209138720); Tajuddin, Nur Ilyana Ismarau (57194873355)","36147542500; 24483156100; 36139115600; 26422254000; 6507614723; 57211599538; 57209138720; 57194873355","Research data management in supporting knowledge sharing among univesity researchers","2019","International Journal of Advanced Science and Technology","28","2","","370","376","6","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081592099&partnerID=40&md5=54fa716ec12489fda5fc622ebc76b4ce","Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Faculty of Biotechnology and Biomolecular Science, Universiti Putra Malaysia, Malaysia; Faculty of Engineering, Universiti Putra Malaysia, Malaysia; Research Management Centre, Universiti Putra Malaysia, Malaysia","Jusoh Y.Y., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Abdullah R., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Sidi F., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Ishak I., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Napis S., Faculty of Biotechnology and Biomolecular Science, Universiti Putra Malaysia, Malaysia; Marhaban M.H., Faculty of Engineering, Universiti Putra Malaysia, Malaysia; Tugiran Y., Research Management Centre, Universiti Putra Malaysia, Malaysia; Tajuddin N.I.I., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia","Research data management is an important concern for institutions and several platforms to support data deposits have emerged. Thus, many research institutions have established or plan to establish research data curation services as part of their Institutional Repositories (IRs). In this paper we start by viewing the current practices in the data management workflow in one of public university in Malaysia. A survey was conducted using questionnaire for data collection among staff, researcher and postgraduate students from Public University in Malaysia. The finding shows most respondents have awareness in research data management. The finding also provided useful information to a better understanding regarding research data management. Limitation and suggestion for future research are discussed. © 2019 SERSC.","Data management; Data repository; Data sharing; Research data","","","","","","Putra University, (9558300)","The authors would like to express gratitude for the financial support provided under the Putra University Grant Scheme, Grant cost centre : 9558300","Alkhalil A., Sahandi R., John D., An exploration of the determinants for decision to migrate existing resources to cloud computing using an integrated TOE-DOI model, Journal of Cloud Computing, 6, 1, (2017); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library and Information Science Research, 38, 4, pp. 319-326, (2016); Davidson J., Jones S., Molloy L., Big Data: The Potential Role of Research Data Management and Research Data Registries, pp. 1-11, (2014); Demchenko Y., Grosso P., de Laat C., Membrey P., Addressing big data issues in Scientific Data Infrastructure, Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, pp. 48-55, (2013); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services and Use, 36, 1-2, pp. 35-43, (2016); Federer L.M., Lu Y.-L., Joubert D.J., Welsh J., Brandys B., Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff, Plos One, 10, 6, (2015); Gellerman H., Svanberg E., Barnard Y., Data Sharing of Transport Research Data, Transportation Research Procedia, 14, pp. 2227-2236, (2016); Kidwell M.C., Lazarevic L.B., Baranski E., Hardwicke T.E., Piechowski S., Falkenberg L.S., Nosek B.A., Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency, Plos Biology, 14, 5, pp. 1-15, (2016); Latham B., Research Data Management: Defining Roles, Prioritizing Services, and Enumerating Challenges, Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Lind E.M., Unified data management for distributed experiments: A model for collaborative grassroots scientific networks, Ecological Informatics, 36, pp. 231-236, (2016); Michener W.K., Ecological data sharing, Ecological Informatics, 29, P1, pp. 33-44, (2015); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos ONE, 9, 12, pp. 1-28, (2014); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Dorsett K., King James Version: Genesis: Genesis Chapter, 9, pp. 1-24, (2015)","","","Science and Engineering Research Support Society","","","","","","20054238","","","","English","Int. J. Adv. Sci. Technol.","Article","Final","","Scopus","2-s2.0-85081592099"
"Potthoff J.; Tremouilhac P.; Hodapp P.; Neumair B.; Bräse S.; Jung N.","Potthoff, Jan (55846710000); Tremouilhac, Pierre (13103602300); Hodapp, Patrick (57190569798); Neumair, Bernhard (6506635630); Bräse, Stefan (7005396290); Jung, Nicole (15064507300)","55846710000; 13103602300; 57190569798; 6506635630; 7005396290; 15064507300","Procedures for systematic capture and management of analytical data in academia","2019","Analytica Chimica Acta: X","1","","100007","","","","5","10.1016/j.acax.2019.100007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061839710&doi=10.1016%2fj.acax.2019.100007&partnerID=40&md5=d7ebb8588c0a38c73a8ba5f6e53e9a94","Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany","Potthoff J., Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Tremouilhac P., Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Hodapp P., Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Neumair B., Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Bräse S., Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany; Jung N., Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany","Data management in universities is a challenging endeavor in particular due to the diverse infrastructure of devices and software in combination with limited budget. Nevertheless, in particular the analytical measurements and data sets need to be stored if possible digitally and in a well-organized manner. This manuscript describes how scientists can achieve a data management workflow focusing on data capture and storage by small adaptions to commonly used systems. The presented method includes data transfer options from ubiquitous devices like NMR instruments, GC (MS) or LC (MS), IR and Raman, or mass spectrometers to a central server and the visualization of the available data files in an electronic lab notebook (ELN). The given instruments were chosen according to the needs of synthetic chemists, in particular devices needed in organic, inorganic and polymer chemistry where single data files in the range of several megabytes per data set are produced. Altogether, three different data transfer systems were elaborated to allow a flexible handling of different devices running with different proprietary software: The first procedure allows data capture via the use of a mail server as data exchange point. With the second procedure, data are automatically mirrored from a local file folder to a central storage server where new files are monitored and processed. The third procedure was designed to transfer data with manual support to a central server which is supervised to register new information. All components that are necessary to install and use the herein elaborated functions are available as Open Source and the designed workflows are described step by step to facilitate the adaption of procedures in other universities accordingly if desired. © 2019 The Authors","Analytical data; ELN; Information and management systems; Infrastructure; Research data management","Budget control; Data transfer; Data visualization; Digital storage; Electronic data interchange; Electronic document exchange; Mail handling; Open source software; polymer; Analytical data; Data management workflow; Infrastructure; Management systems; Proprietary software; Research data managements; Synthetic chemists; Ubiquitous devices; analytic method; Article; controlled study; data analysis; information processing; information storage; infrared spectroscopy; liquid chromatography-mass spectrometry; mass fragmentography; nuclear magnetic resonance; priority journal; Raman spectrometry; scientist; software; university; workflow; Information management","","","","","Karlsruhe Institute of Technology, KIT; Deutsche Forschungsgemeinschaft, DFG, (BR 1750/34-1, NE 1352/3-1)","We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology . This project has been funded by the German Research Foundation (DFG grants BR 1750/34-1 and NE 1352/3-1 ). This work was additionally supported by the Helmholtz program Biointerfaces in Technology and Medicine (BIFTM) . We are very thankful to the Steinbuch Centre of Computing (SCC) for the access to the Large Scale Data Facility, to the members of the Compound Platform who contributed with manifold suggestions to an ongoing improvement of the architecture, to Andreas Rapp for his support concerning the file access management of the server infrastructure and to Angelika Mösle for her contributions to coordinate the service data management. ","Bird C.L., Willoughby C., Frey J.G., Laboratory notebooks in the digital era: the role of ELNs in record keeping for chemistry and other sciences, Chem. Soc. Rev., 42, 20, pp. 8157-8175, (2013); Voegele C., Bouchereau B., Robinot N., McKay J., Damiecki P., Alteyrac L., A universal open-source electronic laboratory notebook, Bioinformatics, 29, 13, pp. 1710-1712, (2013); Coles S., Frey J., Bird C., Whitby R., Day A., First steps towards semantic descriptions of electronic laboratory notebook records, J. Cheminf., 5, (2013); Day A.E., Coles S.J., Bird C.L., Frey J.G., Whitby R.J., Tkachenko V.E., Williams A.J., ChemTrove: enabling a generic ELN to support chemistry through the use of transferable plug-ins and online data sources, J. Chem. Inf. Model., 55, 3, pp. 501-509, (2015); Frey J., Coles S., Milsted A., Willoughby C., Bird C., Sample management with the LabTrove ELN, abstracts of papers, 247th ACS national meeting & exposition, Dallas, TX, United States, March 16–20, (2014); Willoughby C., Bird C.L., Coles S.J., Frey J.G., Creating context for the experiment record. user-defined metadata: investigations into metadata usage in the LabTrove ELN, J. Chem. Inf. Model., 54, 12, pp. 3268-3283, (2014); Rudolphi F., Goossen L.J., Electronic laboratory notebook: the academic point of view, J. Chem. Inf. Model., 52, 2, pp. 293-301, (2012); Tremouilhac P., Nguyen A., Huang Y.-C., Kotov S., Lutjohann D.S., Hubsch F., Jung N., Brase S., Chemotion ELN: an Open Source electronic lab notebook for chemists in academia, J. Cheminf., 9, (2017)","N. Jung; Institute of Organic Chemistry, Karlsruhe Institute of Technology, Karlsruhe, Fritz-Haber-Weg 6, 76131, Germany; email: nicole.jung@kit.edu","","Elsevier B.V.","","","","","","25901346","","","","English","Anal. Chim. Acta  X","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85061839710"
"Marlina E.; Purwandari B.","Marlina, Ekawati (57204890242); Purwandari, Betty (56716397400)","57204890242; 56716397400","Strategy for research data management services in Indonesia","2019","Procedia Computer Science","161","","","788","796","8","9","10.1016/j.procs.2019.11.184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078892744&doi=10.1016%2fj.procs.2019.11.184&partnerID=40&md5=121fd9d7460950ab44e8dedb2fd3b050","Universitas Indonesia, Depok, 16424, Indonesia; Indonesian Institute of Sciences, Jakarta, 12710, Indonesia","Marlina E., Universitas Indonesia, Depok, 16424, Indonesia, Indonesian Institute of Sciences, Jakarta, 12710, Indonesia; Purwandari B., Universitas Indonesia, Depok, 16424, Indonesia","Research data management (RDM) ensures the availability of data access and long term data preservation. Its practices are common in developed countries. On the other hand, it is relatively premature in the developing world including Indonesia. To address this problem, we conducted a Systematic Literature Review (SLR) to study best practices for research data management globally. The results become a basis to develop a strategy for research data management services in Indonesia. The SLR synthesis identified the RDM Strengths, Weakness, Opportunities, and Threats (SWOT), which were mapped into the SWOT matrix. It was further analysed to develop strategies to implement RDM services in Indonesia, which suggest provision of national policy and IT/IS infrastructure, as well as improvement of research data awareness among reseachers. The strategies were validated by interviewing three experts in research management. © 2019 The Authors.","Research data management; SWOT analysis; Systematic literature review","Developing countries; Information systems; Information use; Data preservations; Developed countries; Research data managements; Research management; Strengths , weakness , opportunities , and threats; SWOT analysis; Systematic literature review; Systematic literature review (SLR); Information management","","","","","Universitas Indonesia, UI, (NKB-0006/UN2.R3.1/ HKP.05.00/2019)","The research was supported by the PIT 9 grant from the University of Indonesia (NKB-0006/UN2.R3.1/ HKP.05.00/2019).","Mosley M., Brackett M., Earley S., Henderson D., The DAMA Guide to the Data Management Body of Knowledge: (DAMA-DMBOK Guide), (2009); Gordon K., Principles of Data Management: Facilitating Information Sharing, (2007); Doucette L., Fyfe B., Drowning in research data: Addressing data management literacy of graduate students, Proc ACRL 2013 Conf, pp. 165-171, (2013); Briney K., Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success, (2015); Schopfel J., Ferrant C., Andre F., Fabre R., Research data management in the French National Research Center (CNRS), Data Technol Appl, 52, pp. 248-265, (2018); Gunjal B., Gaitanou P., Research data management: A practical approach to overcome challenges to boost research, IASSIST Conf. 2016, (2016); Vines T.H., Albert A.Y.K., Andrew R.L., Debarre F., Bock D.G., Franklin M.T., Gilbert K.J., Moore J.S., Renaut S., Rennison D.J., The availability of research data declines rapidly with article age, Curr Biol, 24, pp. 94-97, (2014); Mobley A., Linder S.K., Braeuer R., Ellis L.M., Zwelling L., A survey on data reproducibility in cancer research provides insights into our limited ability to translate findings from the laboratory to the clinic, PLoS One, 8, pp. 3-6, (2013); Godlee F., Smith J., Marcovitch H., Wakefield's article linking MMR vaccine and autism was fraudulent, BMJ, 342, pp. 64-66, (2011); Campos-Varela I., Ruano-Ravina A., Misconduct as the main cause for retraction. A descriptive study of retracted publications and their authors, Gac Sanit, (2018); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, Electron Libr, 33, pp. 417-449, (2015); Patel D., Research data management: A conceptual framework, Libr Rev, 65, pp. 226-241, (2016); Maryati I., Purwandari B., Solichah I., E-commerce Adoption Strategy for E-Library Development in Indonesia, ICBIM'18 Proc. 2nd Int. Conf. Bus. Inf. Manag., pp. 44-49, (2018); Hadiansyah H., Purwandari B., Satria R., Yudhoatmojo S.B., Social media strategies for public diplomacy: A case study in the ministry of foreign affairs of the Republic of Indonesia, Proc 2nd Int Conf Informatics Comput ICIC 2017, pp. 1-5, (2018); Dzulfikar M.F., Purwandari B., Sensuse D.I., Lusa J.S., Solichah I., Prima P., Wilarso I., Personalization features on business-to-consumer e-commerce: Review and future directions, 2018 4th Int Conf Inf Manag ICIM 2018, pp. 220-224, (2018); Purwandari B., Zulkarnaim N., Rahayu P., Khalid F., Budi I., Sensuse D.I., A Systematic Review of Recommender System for E-Portfolio Domain, pp. 21-26, (2017); Okoli C., Schabram K., A guide to conducting a systematic literature review of information systems research, Sprout Work. Pap. Inf. Syst., 10, (2010); Kitchenham B., Procedures for Performing Systematic Reviews, (2004); Marchewka J.T., Information Technology Project Management: Providing Measurable Organizational Value, (2015); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Med Ref Serv Q, 34, pp. 47-59, (2015); Richardson J., Nolan-Brown T., Loria P., Bradbury S., Library research support in Queensland: A survey, Aust Acad Res Libr, 43, pp. 258-277, (2012); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA J, 43, pp. 51-64, (2017); Burgi P.-Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs, IFLA J, 43, pp. 5-21, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, J Assoc Inf Sci Technol, 68, pp. 2182-2200, (2017); Kaye J., Bruce R., Fripp D., Establishing a shared research data service for UK universities, Insights UKSG J, 30, pp. 59-70, (2017); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Libr Hi Tech, 35, pp. 271-289, (2017); Lassi M., Johnsson M., Golub K., Research Data Services: An exploration of requirements at two Swedish universities, IFLA J, 42, pp. 266-277, (2016); Dierkes J., Wuttke U., The Göttingen eresearch alliance: A case study of developing and establishing institutional support for research data management, ISPRS Int J Geo-Information, 5, (2016); De Waard A., Research data management at Elsevier: Supporting networks of data and workflows, Inf Serv Use, 36, pp. 49-55, (2016); Liu X., Ding N., Research data management in universities of central China: Practices at Wuhan University Library, Electron Libr, 34, pp. 808-822, (2016); Schmidt B., Dierkes J., New alliances for research and teaching support: Establishing the Göttingen Eresearch alliance, Program, 49, pp. 461-474, (2015); Knight G., Building a research data management service for the London school of Hygiene & Tropical Medicine, Program, 49, pp. 424-439, (2015); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program, 49, pp. 364-381, (2015); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the University of Bristol: Charting a course from project to service, Program, 49, pp. 475-493, (2015); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J Librariansh Inf Sci, 46, pp. 299-316, (2014); Kruse F., Thestrup J.B., Research libraries' new role in research data management, current trends and visions in Denmark, Lib Q, 23, pp. 310-335, (2014); Van Zeeland H., Ringersma J., The development of a research data policy at Wageningen University & Research: Best practices as a framework, Lib Q, 27, pp. 153-170, (2017); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Sci J, (2017); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA J, (2018); Jackson B., The changing research data landscape and the experiences of ethics review board chairs: Implications for library practice and partnerships, J Acad Librariansh, 44, pp. 603-612, (2018); Stillerman J., Greenwald M., Wright J., Scientific data management with navigational metadata, Fusion Eng Des, 128, pp. 113-116, (2018); Grunzke R., Hartmann V., Jejkal T., Kollai H., Et al., The MASI repository service — Comprehensive, metadata-driven and multi-community research data management, Futur Gener Comput Syst, (2018); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, J Acad Librariansh, 43, pp. 346-353, (2017); Li Y.F., Kennedy G., Ngoran F., Wu P., Hunter J., An ontology-centric architecture for extensible scientific data management systems, Futur Gener Comput Syst, 29, pp. 641-653, (2013); Bechini A., Vetrano A., Management and storage of in situ oceanographic data: An ECM-based approach, Inf Syst, 38, pp. 351-368, (2013); Fan C., Shannigrahi S., DiBenedetto S., Olschanowsky C., Papadopoulos C., Newman H., Managing scientific data with named data networking, Proc. Fifth Int. Work. Network-Aware Data Manag. - NDM'15, pp. 1-7, (2015); Radchenko I., Chistyakov A., Iarkin A., Nikolaev I., Lisitsyna O., Solving data integration problems in medical imaging system: A case study in Almazov national medical research centre, Proc. 14th Cent. East. Eur. Softw. Eng. Conf. Russ. ACM, pp. 11-15, (2018); Sim H., Kim Y., Vazhkudai S.S., Vallee G.R., Lim S.-H., Butt A.R., Tagit: An integrated indexing and search service for file systems, Proc. SC17, (2017); Allen B., Ananthakrishnan R., Chard K., Foster I., Madduri R., Pruyne J., Rosen S., Tuecke S., Globus: A case study in software as a service for scientists, Proc. 8th Work. Sci. Cloud Comput. ACM, pp. 25-32, (2017); Shaon A., Smallwood E., Frances M., Cox S., Betbeder-Matibet L., Sustainable services for managing and disseminating UNSW Australia research data, Proc. - 2014 IEEE 10th Int. Conf. eScience, eScience 2014, pp. 137-144, (2014); Komiyama Y., Yamaji K., Nationwide research data management service of Japan in the open science era, Proc. - 2017 6th IIAI Int. Congr. Adv. Appl. Informatics, IIAI-AAI 2017, pp. 129-133, (2017); Matsubayashi M., Kurata K., Conceptual design for comprehensive research support platform: Successful research data management generating big data from little data, Proc - 2017 IEEE Int Conf Big Data, Big Data 2017, pp. 4407-4409, (2017); Kim J., Dong B., Byna S., Wu K., Security for the scientific data services framework, Proc. - 2015 IEEE Int. Conf. Big Data, IEEE Big Data 2015, pp. 1871-1875, (2015); Trifan A., Oliveira J.L., A fair marketplace for biomedical data custodians and clinical researchers, 2018 IEEE 31st Int. Symp. Comput. Med. Syst., pp. 188-219, (2018); Chard R., Chard K., Alt J., Parkinson D.Y., Tuecke S., Foster I., Ripple: Home automation for research data management, Proc - IEEE 37th Int Conf Distrib Comput Syst Work ICDCSW 2017, pp. 389-394, (2017); Tripathi M., Shukla A., Sonker S.K., Research data management practices in university libraries: A study, DESIDOC J Libr Inf Technol, 37, pp. 417-424, (2017); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: How we shape up, Aust Acad Res Libr, 45, pp. 296-308, (2014); Cimino J.J., Ayres E.J., Remennik L., Rath S., Freedman R., Beri A., Chen Y., Huser V., The National Institutes of Health's biomedical translational Research information System (BTRIS): Design, contents, functionality and experience to date, J Biomed Inform, 52, pp. 11-27, (2014); Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Fiore S., Aloisio G., Special section: Data management for escience, Futur Gener Comput Syst, 27, pp. 290-291, (2011); Zhang B., Pouchard L.C., Smith P.M., Gasc A., Pijanowski B.C., Data storage and sharing for the long tail of science, 2016 New York Sci Data Summit, NYSDS 2016 - Proc, (2016)","B. Purwandari; Universitas Indonesia, Depok, 16424, Indonesia; email: bettyp@cs.ui.ac.id","Younus A.","Elsevier B.V.","","5th Information Systems International Conference, ISICO 2019","23 July 2019 through 24 July 2019","Surabaya","156995","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85078892744"
"Syn S.Y.; Kim S.","Syn, Sue Yeon (22836779000); Kim, Soojung (15848471000)","22836779000; 15848471000","Professional and institutional support for RDM: A case of the National Institutes of Health (NIH)","2019","Proceedings of the Association for Information Science and Technology","56","1","","776","777","1","2","10.1002/pra2.170","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075936504&doi=10.1002%2fpra2.170&partnerID=40&md5=38bb056a69d69fa9d0f2d529c05203ad","Department of Library and Information Science, Catholic University of America, United States; Department of Library and Information Science, Chonbuk National University, South Korea","Syn S.Y., Department of Library and Information Science, Catholic University of America, United States; Kim S., Department of Library and Information Science, Chonbuk National University, South Korea","This study explored biomedical researchers' Research Data Management (RDM) practices and investigated how institute and professional services support their practices through the RDM processes with a case of NIH. It was found that institutional information technology (IT) and library services are focused on certain stages of RDM processes. Also, it was found that having a close collaboration among service units within an institute is important to maximize their resources for services, reduce duplicated services, and publicize their services to a wider range of users. Author(s) retain copyright, but ASIS&T receives an exclusive publication license","Biomedical research data; Information and data processes; Library services; Research data management","Biomedical research; Biomedical research data; Information and data process; Institutional support; Library services; Management process; National institute of healths; Professional supports; Research data; Research data managements; Information management","","","","","National Institutes of Health, NIH","Professional and Institutional Support for RDM: A Case of the National Institutes of Health (NIH)","Creamer A.T., Martin E.R., Kafel D., (2014); Federer L.M., Lu Y., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Wiley C.A., Burnette M.H., Assessing data management support needs of bioengineering and biomedical research faculty, Journal of eScience Librarianship, 8, 1, (2019)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85075936504"
"Dias P.; Rodrigues J.; Aguiar A.; David G.","Dias, Patricia (57207987994); Rodrigues, Joana (57203242279); Aguiar, Ana (57216107576); David, Gabriel (16635163900)","57207987994; 57203242279; 57216107576; 16635163900","Planning and managing data for Smart Cities: An application profile for the UrbanSense project","2019","2018 IEEE International Smart Cities Conference, ISC2 2018","","","8656835","","","","4","10.1109/ISC2.2018.8656835","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063519894&doi=10.1109%2fISC2.2018.8656835&partnerID=40&md5=04c87d757a1741739da9c3d3e1de225d","INESC TEC, Universidade Do Porto, Porto, Portugal; INESC TEC, Porto, Portugal; Instituto de Telecomunicações, Universidade Do Porto, Porto, Portugal","Dias P., INESC TEC, Universidade Do Porto, Porto, Portugal; Rodrigues J., INESC TEC, Porto, Portugal; Aguiar A., Instituto de Telecomunicações, Universidade Do Porto, Porto, Portugal; David G., INESC TEC, Universidade Do Porto, Porto, Portugal","Aiming to improve sustainability and life quality, urban space research is prompting an intensive use of communication and information technologies. With it, researchers are also facing more challenges regarding research data management and therefore seeking clear guidelines and tools for proper data organization, sharing and reuse. In the context of a smart cities research project, UrbanSense, held in the city of Porto, we proposed a data management plan, to support researchers from the moment they start to collect data up to the point of data publication. We also developed an ontology for the description of smart cities data, validated by UrbanSense researchers. Descriptions based on this ontology were evaluated by external parties, after the data was published in an institutional data repository. © 2018 IEEE.","digital curation; metadata models; research data; research data management; smart cities; UrbanSense","Information management; Ontology; Space research; Digital curation; Metadata model; Research data; Research data managements; UrbanSense; Smart city","","","","","Mobiwise, (POCI-01-0145-FEDER-016426); S2MovingCity, (CMUPERI/TIC/0010/2014); Fundação para a Ciência e a Tecnologia, FCT, (POCI-01-0145-FEDER-016736); Instituto de Telecomunicações, IT, (COMPETE2020-POCI, UID/EEA/50008/2013)","ACKNOWLEDGMENT This work was financially supported by projects S2MovingCity (CMUPERI/TIC/0010/2014) and Mobiwise (POCI-01-0145-FEDER-016426), and by Instituto de Telecomunicações, R&D Unit 50008 (UID/EEA/50008/2013), funded by FEDER funds through COMPETE2020-POCI and by national funds through FCT within project TAIL, POCI-01-0145-FEDER-016736","Hey T., Trefethen A., E-Science and Its Implications, The Royal Society, (2003); Luis Y., Santos P., Lourenco T., Perez-Penichet C., Calcada T., Aguiar A., Urbansense: An urban-scale sensing platform for the internet of things, IEEE 2nd International Smart Cities Conference: Improving the Citizens Quality of Life, ISC2 2016-Proceedings, (2016); Heidorn P., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008)","","","Institute of Electrical and Electronics Engineers Inc.","","2018 IEEE International Smart Cities Conference, ISC2 2018","16 September 2018 through 19 September 2018","Kansas City","145777","","978-153865959-5","","","English","IEEE Int. Smart Cities Conf., ISC2","Conference paper","Final","","Scopus","2-s2.0-85063519894"
"Kloe M.; Niessner C.; Woll A.; Bös K.","Kloe, M. (57210803229); Niessner, C. (13002839700); Woll, A. (6603584974); Bös, K. (8376851300)","57210803229; 13002839700; 6603584974; 8376851300","Open data in the motor test research field: Analysing the relevance and acceptance of the eResearch-infrastructure MO|RE data for motor research data; [Open Data im sportwissenschaftlichen Anwendungsfeld motorischer Tests: Eine Analyse zur Relevanz und Akzeptanz der eResearch-Infrastruktur MO|RE data für Motorikforschungsdaten]","2019","German Journal of Exercise and Sport Research","49","4","","503","513","10","3","10.1007/s12662-019-00620-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071455029&doi=10.1007%2fs12662-019-00620-2&partnerID=40&md5=909a9bfdf0b5f358b9ecfb0010c7577b","Institut für Sport und Sportwissenschaft, Karlsruher Institut für Technologie, Engler-Bunte-Ring 15, Karlsruhe, 76131, Germany","Kloe M., Institut für Sport und Sportwissenschaft, Karlsruher Institut für Technologie, Engler-Bunte-Ring 15, Karlsruhe, 76131, Germany; Niessner C.; Woll A.; Bös K.","Open access policy means free and unrestricted access to scientific knowledge. It facilitates efficient, transparent and sustainable scientific work. Research data as the basis of this knowledge is becoming more and more significant in this policy. The enormous amount of data being generated requires professional research data management. Data-sharing makes data reusable and reproducible, collaborated work between scientists increases across disciplinary boundaries. The eResearch infrastructure MO|RE data aims to provide free access to citable motor performance research data for a limited application area with high standardization possibilities. It enables an innovative use of digital object identifiers (DOI) as well as the data-pooling procedures. The present survey aims to examine the needs and relevance of MO|RE data. First this will be assessed by systematic investigation in repository databases, second by asking members of scientific institutions and members of practical fields (schools, preschools, sport clubs) who collect motor performance data using an online survey. The online survey participants were asked about their interest in using MO|RE data by themselves as well as their own disposition to access data in a free and open way in MO|RE data. Currently there is no database like MO|RE data providing access to motor research data. A total of 143 participants took part in the online survey and both groups, members of scientific institutions and members of practical fields, stated a high interest in using MO|RE data. Owners of data showed a higher interest than those participants who do not have their own data. The willingness to provide access to their own data in MO|RE data is very high (more than 70%). In summary, open data enjoys high acceptance and approval in the field of motor performance testing for the entire sample. © 2019, Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature.","Data pooling; Motor competence; Open access; Repository; Survey","adult; article; female; human; human experiment; major clinical study; male; motor performance; sport; standardization","","","","","DFG-Geschäftszeichen, (BO1551/3‑1 /SCHO 1192/6-1); Deutsche Forschungsgemeinschaft, DFG","Diese Arbeit ist im Forschungsprojekt MO|RE data eResearch-Infrastruktur für Motorikforschungsdaten entstanden. Das Projekt wurde von der Deutschen Forschungsgemeinschaft (DFG) gefördert (DFG-Geschäftszeichen: BO1551/3‑1 /SCHO 1192/6-1).","Forschungsdatenmanagement. Eine Handreichung, (2018); „Research Data Vision 2025“ – ein Schritt näher. Ein Diskussionspapier der Arbeitsgruppe Forschungsdaten, (2018); Arza V., Fressoli M., Systematizing benefits of open science practices, Information Services & Use, 37, 4, pp. 463-474, (2017); Bartling S., Friesike S., Opening science. The evolving guide on how the internet is changing research, collaboration and scholarly publishing, (2014); Blasius J., Brandt M., Repräsentativität in Online-Befragungen, Umfrageforschung. Herausforderungen und Grenzen, 9, pp. 157-177, (2009); Blumenthal D., Campbell E., Gokhale M., Yucel R., Clarridge B., Hilgartner S., Holtzman N., Data withholding in genetics and the other life sciences: prevalences and predictors, Academic Medicine, 81, 2, pp. 137-145, (2006); Bos K., Motorische Leistungsfähigkeit von Kindern und Jugendlichen, Erster Deutscher Kinder- und Jugendsportbericht, pp. 85-107, (2003); Bos K., Handbuch Motorische Tests. Sportmotorische Tests, Motorische Funktionstests, Fragebögen zur körperlich-sportlichen Aktivität und sportpsychologische Diagnoseverfahren, 3, (2017); Bos K., Oberger J., Lammle L., Opper E., Romahn N., Tittlbach S., Et al., Motorische Leistungsfähigkeit von Kindern, Zweiter Deutscher Kinder- und Jugendsportbericht. Schwerpunkt Kindheit, pp. 137-158, (2008); Bos K., Schlenker L., Albrecht C., Busch D., Lammle L., Muller H., Et al., Deutscher Motorik-Test 6-18. Manual und internetbasierte Auswertungssoftware, 186, (2016); Bos K., Schlenker L., Busch D., Lammle L., Muller H., Oberger J., Tittlbach S., Deutscher Motorik-Test 6-18, 186, (2009); Read the Budapest Open Access Initiative, (2002); Ein neuer Aufbruch für Europa, Eine Neue Dynamik für Deutschland, 19, (2018); Buttner S., Hobohm H., Muller L., Handbuch Forschungsdatenmanagement, (2011); Dawson P., Yang S., Institutional repositories, open access and copyright. What are the practices and implications?, Science & Technology Libraries, 35, 4, pp. 279-294, (2016); Einbock J., Dreyer B., Heller L., Kraft A., Niemeyer S., Plank M., Et al., Informationsbeschaffungs- und Publikationsverhalten von Wissenschaftlerinnen und Wissenschaftlern der natur- und ingenieurwissenschaftlichen Fächern: Auswertung einer Umfrage mit Schwerpunkt auf nicht-textuellen Materialien, (2017); Verordnung (EU) 2016/679 des europäischen Parlaments zum Schutz natürlicher Personen bei der Verarbeitung personenbezogener Daten, zum freien Datenverkehr und zur Aufhebung der Richtlinie 95/46/EG (Datenschutz-Grundverordnung), (2016); Open Data for a European Data Economy, (2017); Foster E., Ariel D., Open Science Framework (OSF), Journal of the Medical Library Association, 105, 2, pp. 203-206, (2017); Geukes K., Schonbrodt D., Utesch T., Geukes S., Back M., Wege aus der Vertrauenskrise. Individuelle Schritte hin zu verlässlicher und offener Forschung, Zeitschrift für Sportpsychologie, 23, 3, pp. 99-109, (2016); Groteluschen F., Fake Science, Die Anziehungskraft Der Wissenschaftlichen Fake-Journale: Deutschlandfunk, (2018); Hauck R., Kaps R., Krojanski H., Meyer A., Neumann J., Sossna V., Der Umgang mit Forschungsdaten an der Leibniz Universität Hannover: Auswertung einer Umfrage und ergänzender Interviews 2015/16, (2016); Heise C., Von Open Access zu Open Science. Zum Wandel digitaler Kulturen der wissenschaftlichen Kommunikation, (2018); Hey T., Tansley S., Tolle K., The fourth paradigm. Data-intensive scientific discovery, (2009); Riding the wave. How Europe can gain from the rising tide of scientific data, Final Report of the High Level Expert Group on Scientific Data, (2010); Hobohm H., DIKW Hierarachie, Lexikon der Bibliotheks- und Informationswissenschaft, 1, pp. 222-223, (2014); Kemper H., The Amsterdam Growth and Health Longitudinal Study (AGAHLS) 1976–1997, DANS, (2007); Gesamtkonzept für die Informationsinfrastruktur in Deutschland, Empfehlungen Der Kommission Zukunft Der Informationsinfrastruktur Im Auftrag Der Gemeinsamen Wissenschaftskonferenz Des Bundes Und Der Länder, (2011); Lakerveld J., Loyen A., Ling F., de Craemer M., van der Ploeg H., O'Gorman D., Carlin A., Et al., Identifying and sharing data for secondary data analysis of physical activity, sedentary behaviour and their determinants across the life course in Europe. General principles and an example from DEDIPAC, BMJOpen, 7, (2017); Maurer M., Jandura O., Masse statt Klasse? Einige kritische Anmerkungen zu Repräsentativität und Validität von Online-Befragungen, Sozialforschung im Internet. Methodologie und Praxis der Online-Befragung, pp. 61-73, (2009); Berlin declaration on open access to knowledge in the sciences and humanities, (2003); Multrus F., Majer S., Methodenbericht zum 13. Studierendensurvey. Vergleich Papier-Onlinebefragung. Werkstattbericht, 95, (2017); Naul R., Utesch T., Dreiskamper D., Monitoring motor development. Measuring motor competence via an online database, Changes in childhood and adolescence. Current challenges for physical education, pp. 76-80, (2018); Oberger J., Bos K., Normierung des Deutschen Motorik-Test (DMT), Informations- und Kommunikationstechnologien in der Sportmotorik. Abstractband zur 11. Tagung der dvs-Sektion Sportmotorik, pp. 92-94, (2009); Making open science a reality, 25, (2015); Pampel H., Dallmeier-Tiessen S., Open research data: from vison to practice, Opening science. The evolving guide on how the internet is changing research, collaboration and scholarly publishing, pp. 213-224, (2014); Schmidt B., Gemeinholzer B., Treloar A., Open data in global environmental research: the Belmont Forum’s open data survey, PLOS ONE, 11, 1, (2016); Scholze F., Bertelmann R., Kindling M., Pampel H., Vierkant P., Open Access und Forschungsdaten, Bibliothek der Zukunft - Zukunft der Bibliothek. Festschrift für Elmar Mittler anlässlich seines 75. Geburtstags, pp. 156-164, (2016); Schonbrodt F., Scheel A., FAQ zu Open Data und Open Science in der Sportpsychologie, Zeitschrift für Sportpsychologie, 24, 4, pp. 134-139, (2017); Schweizer G., Furley F., Die Vertrauenskrise empirischer Forschung in der Psychologie. Ausgewählte Ursachen und exemplarische Lösungsvorschläge für die sportpsychologische Forschung, Zeitschrift für Sportpsychologie, 23, 3, pp. 77-83, (2016); Sheehan K., E-mail survey response rates. A review, Journal of Computer-Mediated Communication, 6, 2, (2001); Stuart D., Baynes G., Hrynaszkiewicz I., Allin K., Penny D., Lucraft M., Astell M., Whitepaper: Practical challenges for researchers in data-sharing, figshare, (2018); Tamminen K., Poucher Z., Open science in sport and exercise psychology. Review of current approaches and considerations for qualitative inquiry, Psychology of Sport & Exercise, 36, pp. 17-28, (2018); Utesch T., Dreiskamper D., Geukes K., Open Science in der Sportwissenschaft? Ein Wegweiser zur Präregistrierung von Forschungsvorhaben und zu offenem Material, offenen Daten und offenem Code, Zeitschrift für Sportpsychologie, 24, 3, pp. 92-99, (2017); Utesch T., Strauss B., Tietjens M., Busch D., Ghanbari M., Seidel I., Die Überprüfung der Konstruktvalidität des Deutschen Motorik-Tests 6-18 für 9- bis 10-Jährige, Zeitschrift für Sportpsychologie, 22, 2, pp. 77-90, (2015); Utesch T., Zinner J., Busch D., Stabilität der physischen Fitness im Kindesalter. Konstruktvalidität der Referenzkategorien für den Deutschen Motorik-Test 6-18 im Projekt „Berlin hat Talent“ über fünf Jahre, German Journal of Exercise and Sport Research, 48, 3, pp. 141-404, (2018); Wilkinson M., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Zerback T., Maurer M., Repräsentativität in Online-Befragungen, Handbuch Online-Forschung. Sozialwissenschaftliche Datengewinnung und -auswertung in digitalen Netzen, pp. 76-103, (2014)","M. Kloe; Institut für Sport und Sportwissenschaft, Karlsruher Institut für Technologie, Karlsruhe, Engler-Bunte-Ring 15, 76131, Germany; email: meike.kloe@kit.edu","","Springer","","","","","","25093142","","","","German","Ger. J. Exerc. Sport Res.","Article","Final","","Scopus","2-s2.0-85071455029"
"Campêlo L.R.R.R.; Neto V.C.B.","Campêlo, Leonard Richard Rodrigues Rufino (57216153064); Neto, Vanderlino Coelho Barreto (57216150662)","57216153064; 57216150662","Comparing free software for creating open data repositories; [Comparando softwares gratuitos para criação de repositórios de dados abertos]; [Comparación de software libre para crear repositorios de datos abiertos]","2019","Ciencia da Informacao","48","3","","341","346","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082688996&partnerID=40&md5=c83030de1dee2fcdde55ff3ff61f4c85","Instituto Superior Fátima (ISF), DF, Brasília, Brazil; Bolsista do Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT), DF, Brasília, Brazil; Universidade de Brasília(UnB), DF, Brasília, Brazil","Campêlo L.R.R.R., Instituto Superior Fátima (ISF), DF, Brasília, Brazil, Bolsista do Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT), DF, Brasília, Brazil; Neto V.C.B., Bolsista do Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT), DF, Brasília, Brazil, Universidade de Brasília(UnB), DF, Brasília, Brazil","The study presents a comparison between free software for building Dataverse, Invenio, DSpace and CKAN repositories. From a set of criteria, it is evaluated at what level these tools have the functionalities necessary to build a repository of scientific data. © 2019, Brazilian Institute for Information in Science and Technology. All rights reserved.","Free software; Research data; Research data management","","","","","","","","Amorim R.C., Et al., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, [S.L.], 16, 4, pp. 851-862, (2017); Ckan-The Open Source Data Portal Software, (2019); The Dataverse Project, (2019); Sistema Para Construção De Repositórios Institucionais Digitais (Dspace), (2019); (2019); Martins D.L., Silva M.F., Critérios de avaliação para sistemas de bibliotecas digitais: Uma proposta de novas dimensões analíticas, CID: Revista De Ciência Da Informação E Documentação, Ribeirão Preto, 8, 1, pp. 100-121, (2017); Pavao C.M.G., Et al., Acesso Aberto a Dados De Pesquisa No Brasil: repositórios Brasileiros De Dados De Pesquisa: relatório 2018, (2018); Principe P., Et al., Estratégia Institucional para a gestão de dados de investigação na UMINHO: O papel dos SDUM, CONGRESSO NACIONAL DE BIBLIOTECÁRIOS, ARQUIVISTAS E DOCUMENTALISTAS, 13., 2018, Portugal, (2018); Home, (2019); Rocha R.P., Et al., Acesso Aberto a Dados De Pesquisa No Brasil: Soluções Tecnológicas Para Compartilhamento De Dados No Brasil repositórios Brasileiros De Dados De Pesquisa, (2018); Rodrigues E., Et al., RepositóriUM: Criação e desenvolvimento do Repositório Institucional da Universidade do Minho, CONGRESSO NACIONAL DE BIBLIOTECÁRIOS, ARQUIVISTAS E DOCUMENTALISTAS, 8., 2004, Estoril, Portugal, (2004); Semeler A.R., Et al., Ciência Da informação Em Contextos De E-Science: bibliotecários De Dados Em Tempos De Data Science, (2017)","","","Brazilian Institute for Information in Science and Technology","","","","","","01001965","","","","Portuguese","Cienc. Inf.","Article","Final","","Scopus","2-s2.0-85082688996"
"Park M.S.; Park H.","Park, Min Sook (55755517100); Park, Hyoungjoo (56441846700)","55755517100; 56441846700","An examination of metadata practices for research data reuse: Characteristics and predictive probability of metadata elements","2019","Malaysian Journal of Library and Information Science","24","3","","61","75","14","1","10.22452/mjlis.vol24no3.4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079193554&doi=10.22452%2fmjlis.vol24no3.4&partnerID=40&md5=9ea735622efd9abf95daba0fe321cb50","School of Information Studies, University of Wisconsin Milwaukee, United States","Park M.S., School of Information Studies, University of Wisconsin Milwaukee, United States; Park H., School of Information Studies, University of Wisconsin Milwaukee, United States","This study explores metadata practices in the relation to data reuse in biology. Metadata has long been viewed as a major constituent in research data management and reuse. However, the topic of whether metadata is used in a way that encourages data reuse has been understudied. The current study examined metadata elements used to describe datasets and the predictive probability of those metadata elements for data reuse under the assumption that citation frequency reflects the frequency of research data reuse. A total of 34,491 cited records from the biology category of the Clarivate Analytics Data Citation Index were analyzed using descriptive comparison and multiple regression analysis to compare usage patterns of metadata elements between data records cited more than twice and those cited only once. Of the five types of metadata elements identified and examined, metadata elements that provided descriptions about datasets and author-related information dominantly appeared across datasets, whereas DOI and ORCID identifier were scarce. Metadata related to author and funding resources were found to be positive influential factors in predicting data reuse, whereas data descriptions and identifiers appeared to have negative influences. This study contributed to a better understanding of metadata needs for data reuse. © Faculty of Computer Science and Information Technology.","Data sharing; Metadata; Research data; Research data reuse; Scholarly communication","","","","","","","","Ailamaki A., Kantere V., Dash D., Managing scientific data, Communications of the ACM, 53, 6, pp. 68-78, (2010); Ball A., Scientific data application profile scoping study report, (2009); Berger B., Daniels N.M., Yu Y.W., Computational biology in the 21st century: Scaling with compressive algorithms, Communications of the ACM, 59, 8, (2016); Blumenthal D., Campbell E.G., Anderson M.S., Causino N., Louis K.S., Withholding research results in academic life science: Evidence from a national survey of faculty, Journal of the American Medical Association, 277, 15, pp. 1224-1228, (1997); Blumenthal D., Campbell E.G., Gokhale M., Yucel R., Clarridge B., Hilgartner S., Holtzman N.A., Data withholding in genetics and the other life sciences: Prevalences and predictors, Academic Medicine, 81, 2, pp. 137-145, (2006); Borgman L.C., The conundrum of research data sharing, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brown C., The changing face of scientific discourse: Analysis of genomic and proteomic database usage and acceptance, Journal of the American Society for Information Science and Technology, 54, 10, pp. 926-938, (2003); Christel G.M., Automated metadata in multimedia information systems: Creation, refinement, use in surrogates, and evaluation, Synthesis Lectures on Information Concepts, Retrieval, and Services, 1, 1, pp. 1-74, (2009); Ensuring the integrity, accessibility, and stewardship of research data in the digital age, (2009); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Curty G.R., Beyond ""data thrifting"": An investigation of factors influencing research data reuse in the social sciences, (2015); Data Citation Synthesis Group: Joint declaration of data citation principles, (2014); DataCite metadata schema documentation for the publication and citation of research data, (2016); Davis M.H., Vickery J.N., Datasets, a shift in the currency of scholarly communication: Implications for library collections and acquisitions, Serials Review, 33, 1, pp. 26-32, (2007); Joint Data Archiving Policy (JDAP), (2018); Faniel I.M., Jacobsen T.E., Reusing scientific data: How earthquake engineering researchers assess the reusability of colleagues' data, Journal of Computer Supported Cooperative Work, 19, 3-4, pp. 355-375, (2010); Faniel I.M., Kansa E., Kansa S.W., Barrera-Gomez J., Yakel E., The challenges of digging data: A study of context in archaeological data reuse, Paper presented at the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, (2013); Faniel I.M., Yakel E., Kriesberg A., Daniels M., Can quantitative social scientists get data reuse satisfaction, Paper presented at the Research Data Access and Preservation Summit, (2013); 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Science, Technology & Human Values, 33, 5, pp. 631-652, (2008)","M.S. Park; School of Information Studies, University of Wisconsin Milwaukee, United States; email: minsook@uwm.edu","","Faculty of Computer Science and Information Technology","","","","","","13946234","","","","English","Malays. J. Libr. Inf. Sci.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85079193554"
"Borghi J.A.; Van Gulick A.E.","Borghi, John A. (57193008891); Van Gulick, Ana E. (50761335600)","57193008891; 50761335600","Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers","2019","PLoS ONE","13","7","e0200562","","","","34","10.1371/journal.pone.0200562","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050589811&doi=10.1371%2fjournal.pone.0200562&partnerID=40&md5=484f1cf701f57317569688344e8bbc12","UC Curation Center, California Digital Library, Oakland, CA, United States; University Libraries, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States","Borghi J.A., UC Curation Center, California Digital Library, Oakland, CA, United States; Van Gulick A.E., University Libraries, Carnegie Mellon University, Pittsburgh, PA, United States, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States","Neuroimaging methods such as magnetic resonance imaging (MRI) involve complex data collection and analysis protocols, which necessitate the establishment of good research data management (RDM). Despite efforts within the field to address issues related to rigor and reproducibility, information about the RDM-related practices and perceptions of neuroimaging researchers remains largely anecdotal. To inform such efforts, we conducted an online survey of active MRI researchers that covered a range of RDM-related topics. Survey questions addressed the type(s) of data collected, tools used for data storage, organization, and analysis, and the degree to which practices are defined and standardized within a research group. Our results demonstrate that neuroimaging data is acquired in multifarious forms, transformed and analyzed using a wide variety of software tools, and that RDM practices and perceptions vary considerably both within and between research groups, with trainees reporting less consistency than faculty. Ratings of the maturity of RDM practices from ad-hoc to refined were relatively high during the data collection and analysis phases of a project and significantly lower during the data sharing phase. Perceptions of emerging practices including open access publishing and preregistration were largely positive, but demonstrated little adoption into current practice. © 2018 Borghi, Van Gulick. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Female; Humans; Information Dissemination; Information Storage and Retrieval; Magnetic Resonance Imaging; Male; Neuroimaging; Reproducibility of Results; Article; clinical practice; controlled study; data analysis; health care organization; health survey; human; information processing; neuroimaging; nuclear magnetic resonance imaging; online system; perception; publishing; scientist; software; female; information dissemination; information retrieval; male; reproducibility","","","","","National Science Foundation, NSF; Directorate for Computer and Information Science and Engineering, CISE, (1349002)","","Logothetis N.K., What we can do and what we cannot do with fMRI, Nature, 453, 7197, pp. 869-878, (2008); Poldrack R.A., Farah M.J., Progress and challenges in probing the human brain, Nature, 526, 7573, pp. 371-379, (2015); 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Nichols T.E., Das S., Eickhoff S.B., Evans A.C., Glatard T., Hanke M., Et al., Best practices in data analysis and sharing in neuroimaging using MRI, Nat Neurosci, 20, 3, pp. 299-303, (2017); JASP, (2017); Carlson J., The use of lifecycle models in developing and supporting data services, Research Data Management: Practical Strategies for Information Professionals, pp. 63-86, (2014); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009); Paulk M.C., Curtis B., Chrissis M.B., Weber C.V., Capability maturity model, version 1.1, IEEE Softw, 10, 4, pp. 18-27, (1993); Crowston K., Qin J., A capability maturity model for scientific data management: Evidence from the literature, Proc Am Soc Inf Sci Technol, 48, 1, (2011); Borghi J.A., Van Gulick A.E., Survey Instrument to Assess The Research Data Management Practices and Perceptions of MRI Researchers, (2018); Borghi J.A., Van Gulick A.E., Survey Data on Research Data Management Practices and Perceptions of MRI Researchers, (2018); Dickersin K., Chan S., Chalmers T.C., Sacks H.S., Smith H., Publication bias and clinical trials, Control Clin Trials, 8, pp. 343-353, (1987); Sterling T.D., Publication decisions and their possible effects on inferences drawn from tests of significance—or vice versa, J Am Stat Assoc, 54, 285, pp. 30-34, (1959); Cohen J., The statistical power of abnormal-social psychological research: A review, J Abnorm Soc Psychol, 65, 3, pp. 145-153, (1962); Freiman J.A., Chalmers T.C., Smith H., Kuebler R.R., The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial, N Engl J Med, 299, 13, pp. 690-694, (1978); Vines T., Andrew R., Bock D., Franklin M., Gilbert K., Kane N., Et al., Mandated data archiving greatly improves access to research data, FASEB J, 27, 4, pp. 1304-1308, (2013); Vasilevsky N.A., Minnier J., Haendel M.A., Champieux R.E., Reproducible and reusable research: Are journal data sharing policies meeting the mark?, PeerJ, 5, (2017); Van Tuyl S., Whitmire A.L., Water, water, everywhere: Defining and assessing data sharing in Academia, PLOS One, 11, 2, (2016); Wolfe J.M., Kanwisher N.G., Not your parent’s NIH clinical trial, Nat Hum Behav, 2, pp. 107-109, (2018); Teeters J.L., Godfrey K., Young R., Dang C., Friedsam C., Wark B., Et al., Neurodata Without Borders: Creating a common data format for neurophysiology, Neuron, 88, 4, pp. 629-634, (2015); Ascoli G.A., Donohue D.E., Halavi M., NeuroMorpho.Org: A central resource for neuronal morphologies, J Neurosci, 27, 35, pp. 9247-9251, (2007); Lee R.Y.N., Howe K.L., Harris T.W., Arnaboldi V., Cain S., Chan J., Et al., WormBase 2017: Molting into a new stage, Nucleic Acids Res, 4, pp. 869-874, (2017); Munafo M.R., Nosek B.A., Bishop D.V.M., Button K.S., Chambers C.D., Percie du Sert N., Et al., A manifesto for reproducible science, Nat Hum Behav, 1, 1, (2017); Dunning D., Heath C., Suls J.M., Flawed self-assessment implications for health, education, and the workplace, Psychol Sci Public Interes Suppl, 5, 3, pp. 69-106, (2004); Barone L., Williams J., Micklos D., Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators, PLOS Comput Biol, 13, 10, (2017); Tenopir C., Allard S., Sinha P., Pollock D., Newman J., Dalton E., Et al., Data management education from the perspective of science educators, Int J Digit Curation, 11, 1, pp. 232-251, (2016)","A.E. Van Gulick; University Libraries, Carnegie Mellon University, Pittsburgh, United States; email: anavangulick@cmu.edu","","Public Library of Science","","","","","","19326203","","POLNC","30011302","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85050589811"
"McKenzie K.A.; Hunt S.L.; Hulshof G.; Mudaranthakam D.P.; Meyer K.; Vidoni E.D.; Burns J.M.; Mahnken J.D.","McKenzie, Katelyn A. (57205236636); Hunt, Suzanne L. (54889734100); Hulshof, Genevieve (57222706074); Mudaranthakam, Dinesh Pal (57190943512); Meyer, Kayla (57195725119); Vidoni, Eric D. (23767345900); Burns, Jeffrey M. (57044001400); Mahnken, Jonathan D. (6506062121)","57205236636; 54889734100; 57222706074; 57190943512; 57195725119; 23767345900; 57044001400; 6506062121","A semi-automated pipeline for fulfillment of resource requests from a longitudinal Alzheimer's disease registry","2019","JAMIA Open","2","4","","516","520","4","1","10.1093/jamiaopen/ooz032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103743284&doi=10.1093%2fjamiaopen%2fooz032&partnerID=40&md5=8f5e4b9ed2b3a3961b92087c31cf6733","Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States; University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States; Department of Neurology, University of Kansas, Medical Center, Kansas City, KS, United States","McKenzie K.A., Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States; Hunt S.L., Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States, University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States; Hulshof G., Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States; Mudaranthakam D.P., Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States, University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States; Meyer K., University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States; Vidoni E.D., University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States, Department of Neurology, University of Kansas, Medical Center, Kansas City, KS, United States; Burns J.M., University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States, Department of Neurology, University of Kansas, Medical Center, Kansas City, KS, United States; Mahnken J.D., Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States, University of Kansas, Alzheimer's Disease Center, Fairway, KS, United States","Objective: Managing registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer's Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data. Materials and Methods: Data was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer's Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO. Results: C3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds. Discussion: The data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur. Conclusion: We created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily. © The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.","Dynamic data; National Alzheimer's Coordinating Center; Reproducible research; Research data management","Alzheimer disease; Article; automation; cohort analysis; data analysis; disease registry; human; information processing; longitudinal study; major clinical study; priority journal; reproducibility; scoring system","","","","","","","Final NIH Statement on Sharing Research Data, (2003); Dementia: A Public Health Priority, (2012); Drazen J.M., Morrissey S., Malina D., Et al., The importance-and the complexities-of data sharing, N Engl J Med, 375, 12, pp. 1182-1183, (2016); Ng J.W., Barrett L.M., Wong A., Et al., The role of longitudinal cohort studies in epigenetic epidemiology: Challenges and opportunities, Genome Biol, 13, 6, (2012); Wang S.V., Verpillat P., Rassen J.A., Et al., Transparency and reproducibility of observational cohort studies using large healthcare databases, Clin Pharmacol Ther, 99, 3, pp. 325-332, (2016); Li K., Chan W., Doody R.S., Et al., Prediction of conversion to Alzheimer's disease with longitudinal measures and time-to-event data, J Alzheimers Dis, 58, 2, pp. 361-371, (2017); Fritz N.E., Newsome S.D., Eloyan A., Et al., Longitudinal relationships among posturography and gait measures in multiple sclerosis, Neurology, 84, 20, pp. 2048-2056, (2015); Mahmood S.S., Levy D., Vasan R.S., Et al., The framingham heart study and the epidemiology of cardiovascular disease: A historical perspective, Lancet, 383, 9921, pp. 999-1008, (2014); Freedman L.P., Cockburn I.M., Simcoe T.S., The economics of reproducibility in preclinical research, PLoS Biol, 13, 6, (2015); Laine C., Goodman S.N., Griswold M.E., Et al., Reproducible research: Moving toward research the public can really trust, Ann Intern Med, 146, 6, pp. 450-453, (2007); Peng R.D., Dominici F., Zeger S.L., Reproducible epidemiologic research, Am J Epidemiol, 163, 9, pp. 783-789, (2006); Wang X., Williams C., Liu Z.H., Croghan J., Big data management challenges in health research-a literature review, Brief Bioinform, 20, 1, pp. 156-167, (2019); Mittelstadt B.D., Floridi L., The ethics of big data: Current and foreseeable issues in biomedical contexts, Sci Eng Ethics, 22, 2, pp. 303-341, (2016); Belle A., Thiagarajan R., Soroushmehr S.M.R., Et al., Big data analytics in healthcare, Biomed Res Int, 2015, (2015); Mayer-Schonberger V., Ingelsson E., Big data and medicine: A big deal?, J Intern Med, 283, 5, pp. 418-429, (2018); Anderson N.R., Lee E.S., Brockenbrough J.S., Et al., Issues in biomedical research data management and analysis: Needs and barriers, J Am Med Inform Assoc, 14, 4, pp. 478-488, (2007); Johnson S.B., Farach F.J., Pelphrey K., Et al., Data management in clinical research: Synthesizing stakeholder perspectives, J Biomed Inform, 60, pp. 286-293, (2016); Nind T., Galloway J., McAllister G., Et al., The research data management platform (RDMP): A novel, process driven, open-source tool for the management of longitudinal cohorts of clinical data, Gigascience, 7, 7, (2018); Brembilla A., Martin B., Parmentier A.-L., Et al., How to set up a database?-a five-step process, J Thorac Dis, 10, pp. S3533-S3538, (2018); da Silva K.R., Costa R., Crevelari E.S., Et al., Glocal clinical registries: Pacemaker registry design and implementation for global and local integration-methodology and case study, PLoS One, 8, 7, (2013); Curated Clinical Cohort Phenotypes and Observations (C3PO), (2018); Research Resource Data Dictionary (R2D2), (2018); Harris P.A., Taylor R., Thielke R., Et al., Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform, 42, 2, pp. 377-381, (2009); Uniform Data Set (UDS), (2015); National Alzheimer's Coordinating Center (NACC), (1999)","K.A. McKenzie; Mail Stop 1026, Kansas City, 3901 Rainbow Blvd, 66160, United States; email: kmckenzie5@kumc.edu","","Oxford University Press","","","","","","25742531","","","","English","JAMIA Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85103743284"
"Féret R.; Cros M.","Féret, Romain (57214441205); Cros, Marie (57214468010)","57214441205; 57214468010","The embedded research librarian: A project partner","2019","LIBER Quarterly","29","1","","","","","4","10.18352/lq.10304","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078769273&doi=10.18352%2flq.10304&partnerID=40&md5=0d773b4eb7d0e1683d8e406d63f56368","Lille University Library, France; Lille University, France","Féret R., Lille University Library, France; Cros M., Lille University, France","This paper presents new services developed by the Lille University Library for European and National research project coordinators. This is a specific audience that libraries are not used to target, with a widely recognised institutional status and academic background. Supporting them in their coordination activities is an opportunity to gain a new role for libraries, which starts from the design of research at the submission stage and lasts several years after, during the project lifetime. These services help coordinators to meet their funders’ expectations on Open Access and research data management. It is also a way to develop new collaborations with research units and some university services, such as the Grant Office. The Lille University Library has already supported the writing of forty grant proposals since 2017, including about thirty since early 2019. The Library currently follows twelve projects on Open Access, research data management or both. This second figure is likely to increase in 2020 due to the number of projects supported at submission stage since the beginning of 2019. The paper describes our set of services and the lessons we learned from our approach. © 2019, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Funder mandates; Library services; Open access; Open science; Research data management","","","","","","European Horizon 20202 framework programme, (H2020); Horizon 2020 Framework Programme, H2020, (633190, 690836, 694717, 787198, 795091, 847568); Agence Nationale de la Recherche, ANR","The University of Lille is a multidisciplinary university with 3,300 researchers and 66 research units. For several years, the Lille University Library has been developing an ambitious set of support services for researchers. This article presents services1 dedicated to research projects funded by the European Horizon 20202 framework programme (H2020), or by the French Agency for Research3 (ANR). We introduce the history of our approach before presenting the three main components of this set of services and their implementation. We emphasize that this approach allowed us to develop a deeper relationship with researchers with whom we were not so used to work before. Finally, we will draw some lessons from our experience and expose new challenges we could face in the future.","Ekstrom J., Elbaek M., Erdmann C., Grigorov I., The research librarian of the future: Data scientist and co-investigator, LSE Impact of Social Sciences. [Blog, (2016); H2020 Programme — Annotated Model Grant Agreement, (2017); Background Information Note, (2018); Grigorov I., Elbaek M., Rettberg N., Davidson J., Winning Horizon 2020 with Open Science, (2015); Open Science in Practice in FP9, (2018); Lariviere V., Sugimoto C.R., Do authors comply when funders enforce open access to research?, Nature, 562, pp. 483-486, (2018); Vincent-Lamarre P., Boivin J., Gargouri Y., Lariviere V., Harnad S., Estimating open access mandate effectiveness: The MELIBEA score, Journal of the Association for Information Science and Technology, 67, 11, pp. 2815-2828, (2016)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85078769273"
"Fan Z.","Fan, Zhenjia (57193629685)","57193629685","Context-based roles and competencies of data curators in supporting research data lifecycle management: Multi-case study in China","2019","Libri","69","2","","127","137","10","2","10.1515/libri-2018-0065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067104628&doi=10.1515%2flibri-2018-0065&partnerID=40&md5=599dff4f3c469da0523e50106a0852e5","Department of Information Resources Management, Business School, Nankai University, 94 Weijin Road, Nankai District, Tianjin, China","Fan Z., Department of Information Resources Management, Business School, Nankai University, 94 Weijin Road, Nankai District, Tianjin, China","Focusing on the main research question of what the critical roles and competencies of data curation are in supporting research data life cycle management, this paper adopts a multi-case study method, with data governance frameworks, to analyze stakeholders and data curators, and their competencies, based on different contexts from cases from enterprises and academic libraries in mainland China. Via the context and business analysis on different cases, critical roles such as data supervisor, data steward, and data custodian in guaranteeing data quality and efficiency of data reuse are put forward. Based on the general factor framework summarized via existing literature, suggestions for empowering data curators' competencies are raised according to the cases. The findings of this paper are as follows: Besides digital archiving and preservation, more emphasis should be placed on data governance in the field of data curation. Data curators are closely related but not equivalent to stakeholders of data governance. The different roles of data curators would play their own part in the process of data curation and can be specified as data supervisor, data steward, and data custodian according to given contexts. The roles, competencies, and empowerment strategies presented in this paper might have both theoretical and practical significance for the fields of both data curation and data governance. © 2019 Walter de Gruyter GmbH, Berlin/Boston.","data curation; data curator; data governance; data life cycle; research data management","","","","","","Fundamental Research Funds for the Central Universities, (63172077)","Acknowledgements: This study is supported by the Fundamental Research Funds for the Central Universities (Project: Innovation\-Driven Enterprise Data Governance, No. 63172077) in Nankai University. We offer a sincere thanks to participants in the long-term field study and manuscript reviewers for their useful suggestions.","Abrams S., Curation: Buzzword or What, Information Outlook, 18, 5, pp. 25-27, (2014); Carlson J., Bracke M.S., Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, Portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Carlson J., Johnston L., Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, (2016); Corrall S., Kennan M.A., Afzal W., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research, Library Trends, 61, 3, pp. 636-674, (2013); Cragin M., Palmer C.L., Carlson J.R., Witt M., Data Sharing, Small Science and Institutional Repositories, Transactions of the Royal Society, 368, pp. 4023-4038, (1926); Fan Z., Enterprise R&D Data Governance and Curators: Case Study of NSR Practice, Library and Information Service, 61, 1, pp. 56-63, (2017); Gray J., Szalay A.S., Online Scientific Data Curation, Publication, and Archiving, Proceedings of SPIE-the International Society for Optical Engineering, 4846, pp. 103-107, (2002); Gu L., Data Governance: Opportunity for the Library, Journal of Library Science in China, 42, 9, pp. 40-56, (2016); Heidorn P.B., Tobbo H.R., Choudhury G.S., Greer C., Marciano R., Identifying Best Practices and Skills for Workforce Development in Data Curation, Proceedings of the American Society for Information Science & Technology, 44, 1, pp. 1-3, (2007); Henderson M.E., Data Management: A Practical Guide for Librarians, (2017); Hswe P., Holt A., Joining in the Enterprise of Response in the Wake of the NSF Data Management Planning Requirement, Research Library Issues, 274, pp. 11-16, (2011); Huang R., Lai T., Analysis of the Library's Participation in Scientific Data Management from the Perspective of Stakeholders, Library and Information Service, 60, 3-89, pp. 21-25, (2016); Koltay T., Data Literacy: In Search of a Name and Identity, Journal of Documentation, 72, 2, pp. 401-415, (2015); Koltay T., Data Literacy for Researchers and Data Librarians, Journal of Librarianship & Information Science, 49, 1, pp. 3-14, (2017); Krier L., Strasser C.A., Data Management for Libraries, (2014); Lesk M., Curators of the Future, New Technology of Library and Information Service, 3, pp. 1-7, (2013); Noonan D., Chute T., Data Curation and the University Archives, American Archivist, 77, 1, pp. 201-240, (2014); Oliver G., Harvey R., Data Curation: A How-To Manual, 2nd Ed, (2016); Osswald A., Skills for the Future: Educational Opportunities for Digital Curation Professionals, (2013); Otto B., Data Governance, Business & Information Systems Engineering, 3, 4, pp. 241-244, (2011); Poole A.H., The Conceptual Landscape of Digital Curation, Journal of Documentation, 72, 5, pp. 961-986, (2016); Prado J.C., Marzal A., Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents, Libri, 63, 2, pp. 123-134, (2013); Pryor G., Multi-Scale Data Sharing in the Life Sciences: Some Lessons for Policy Makers, International Journal of Digital Curation, 4, 3, pp. 71-82, (2009); Pryor G., Why Manage Research Data, Managing Research Data, pp. 1-16, (2012); Rosenbaum S., Data Governance and Stewardship: Designing Data Stewardship Entities and Advancing Data Access, Health Services Research, 45, 52, pp. 1442-1455, (2010); Sarsfield S., The Data Governance Imperative, (2009); Smith M., Data Governance: Where Technology and Policy Collide, Research Data Management: Practical Strategies for Information Professionals, pp. 45-59, (2014); Soares S., Data Governance Tools, (2014); Swan A., Brown S., The Skills, Role and Career Structure of Data Scientists and Curators: An Assessment of Current Practice and Future Needs, Nieuwsbrief Spined, 22, 7, pp. 20-22, (2008); Tammaro A.M., Ross S., Casarosa V., Research Data Curator: The Competencies Gap, BOBCATSSS 2014 Proceedings, 1, pp. 95-100, (2014); Tenopir C., Levine K., Allard S., Christian L., Volentine R., Boehm R., Nichols F., Et al., Trustworthiness and Authority of Scholarly Information in a Digital Age: Results of an International Questionnaire, Journal of the Association for Information Science and Technology, 67, 10, pp. 2344-2361, (2016); Vivarelli M., Cassella M., Valacchi F., The Digital Curator between Continuity and Change: Developing a Training Course at the University of Turin, (2013); Walters T., New Roles for New Times Digital Curation for Preservation, General Collection, 9, 2, (2011); Walters T., Assimilating Digital Repositories into the Active Research Process, Research Data Management: Practical Strategies for Information Professionals, pp. 189-201, (2014); Witt M., Institutional Repositories and Research Data Curation in a Distributed Environment, Library Trends, 57, 2, pp. 191-201, (2008); Wright S., Whitmire A., Zilinski L., Minor D., Collaboration and Tension between Institutions and Units Providing Data Management Support, Bulletin of the American Society for Information Science and Technology, 40, 6, pp. 18-21, (2014)","Z. Fan; Department of Information Resources Management, Business School, Nankai University, Nankai District, Tianjin, 94 Weijin Road, China; email: fanzhenjia@nankai.edu.cn","","De Gruyter Saur","","","","","","00242667","","","","English","Libri","Article","Final","","Scopus","2-s2.0-85067104628"
"Shipman J.P.; Tang R.","Shipman, Jean P. (7004152803); Tang, Rong (7202299626)","7004152803; 7202299626","The collaborative creation of a Research Data Management Librarian Academy (RDMLA)","2019","Information Services and Use","39","3","","243","247","4","5","10.3233/ISU-190050","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077462090&doi=10.3233%2fISU-190050&partnerID=40&md5=e3b5db17c6e13a63df18a3c8fd11876b","Global Library Relations, Global Strategic Networks, Elsevier, 7909 Desert Ridge Cove, Cottonwood Heights, UT, United States; School of Library and Information Science, Simmons University, 300 The Fenway, Boston, MA, United States","Shipman J.P., Global Library Relations, Global Strategic Networks, Elsevier, 7909 Desert Ridge Cove, Cottonwood Heights, UT, United States; Tang R., School of Library and Information Science, Simmons University, 300 The Fenway, Boston, MA, United States","With the growing momentum of research data services in libraries, a team of librarians and Library Information Science (LIS) faculty members have been working together to develop a free, online Research Data Management Librarian Academy (RDMLA) aimed primarily for practicing librarians and information professionals, but also for researchers to gain knowledge about research data management (RDM) principles and best practices. The training modules will be made available to anyone across the globe. The development team includes librarians from Harvard Medical School, Harvard University, Tufts Health Sciences, Massachusetts College of Pharmacy and Health Sciences (MCPHS), University, Boston University School of Medicine, Brown University, Northeastern University, Elsevier, and Simmons University. Simmons University's School of Library and Information Science will grant continuing education credit for those desiring such. This is a unique partnership between librarians, LIS educators, and a publisher. The RDMLA is hosted online on Canvas under the CC-BY-NC-SA (Attribution-NonCommercial-ShareAlike) licensing. The need for this training was evidenced through interviews and online surveys which identified gaps in current training offerings and highlighted what skills librarians and researchers need to contribute to their RDM success. An inventory of existing courses was prepared along with a review of job description competencies. This publication outlines the outcomes of the needs assessment, inventories, and the Academy's training unit content. © 2019 IOS Press and the authors. All rights reserved.","continuing education; librarians; online curriculum; RDM; Research data management; training","Curricula; Employment; Human resource management; Information management; Information services; Job analysis; Medicine; Personnel training; Continuing education; Harvard Medical School; Information professionals; librarians; Library and information science; Northeastern University; Online curriculum; Research data managements; Libraries","","","","","","","","J.P. Shipman; Global Library Relations, Global Strategic Networks, Elsevier, Cottonwood Heights, 7909 Desert Ridge Cove, United States; email: j.shipman@elsevier.com","","IOS Press","","","","","","01675265","","ISUSD","","English","Inf Serv Use","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85077462090"
"Fuhr J.","Fuhr, Justin (57215380207)","57215380207","“How Do I Do That?” A Literature Review of Research Data Management Skill Gaps of Canadian Health Sciences Information Professionals","2019","Journal of the Canadian Health Libraries Association","40","2","","51","60","9","4","10.29173/jchla29371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080834723&doi=10.29173%2fjchla29371&partnerID=40&md5=042409335b499ba0893522b4bbf75779","University of Alberta, Edmonton, AB, Canada","Fuhr J., University of Alberta, Edmonton, AB, Canada","Background: Research data management (RDM) services are becoming more commonplace in health sciences libraries. A review of the literature reveals numerous strategies to provide training for health sciences librarians as they provide these new services to health sciences researchers, faculty, and students. With the Tri-Agency Research Data Management Policy currently circulating for consultation, it is imperative for Canadian health sciences information professionals to offer RDM services in their libraries. Methods: A review of relevant scholarly articles were collected and analyzed. Initial searches were conducted in the University of Manitoba Libraries’ discovery service, as well as in MEDLINE, Scopus, and Web of Science. Articles were analyzed for skills necessary to provide RDM services and proposed training initiatives to fill RDM skill gaps. Results: After initial searches, 2 142 articles were identified for review. After removing duplicates and articles with only titles and abstracts, 38 articles were selected by analyzing citation counts in Web of Science and Scopus, as well as analyzing selected reference lists. Conclusion: Several suggestions for training are highlighted from the identified articles, including building a national support network, changes to post-secondary library and information studies’ curricula, and offering professional development workshops. However, no consensus emerges with respect to RDM training initiatives. As training initiatives are developed and documented, future studies will verify which initiatives have the greatest success for upskilling information professionals in managing research data in Canadian health sciences libraries. © 2019, Canadian Health Libraries Association. All rights reserved.","","consensus; health science; human; informatician; library; Manitoba; Medline; professional development; review; Scopus; skill; systematic review; Web of Science","","","","","","","Whyte A., Tedds J., Making the Case for Research Data Management [Internet], DCC Briefing Papers. Edinburgh: Digital Curation Centre, (2011); Lyon L., The Informatics Transform: Re-Engineering Libraries for the Data Decade, International Journal of Digital Curation [Internet], 7, 1, pp. 126-138, (2012); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Hey T., Trefethen A., The Data Deluge: An e-Science Perspective, Wiley Series in Communications Networking & Distributed Systems, pp. 809-824, (2003); Hey T., Hey J., E-Science and its implications for the library community, Library Hi Tech [Internet], 24, 4, pp. 515-528, (2006); Corrall S., Kennan M., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cronin B., Invisible colleges and information transfer: A review and commentary with particular reference to the social sciences, Journal of Documentation, 38, 3, pp. 212-236, (1982); Gieryn T.F., Boundary-work and the demarcation of science from non-science: Strains and interests in professional ideologies of scientists, American Sociological Review [Internet], 48, 6, pp. 781-795, (1983); Palmer C.L., Information work at the boundaries of science: Linking library services to research practices, Library Trends, 2, pp. 165-191, (1996); Hey S., Tansley S., Tolle K., Jim Gray on eScience: A transformed scientific method, The Fourth Paradigm: Data-Intensive Scientific Discovery, pp. xvii-xxxi, (2009); Surkis A., Read K., Research Data Management, J Med Libr Assoc [Internet], 103, 3, pp. 154-156, (2015); Read K., Surkis A., Larson C., McCrillis A., Graff A., Nicholson J., Xu J., Starting the Data Conversation: Informing Data Services at an Academic Health Sciences Library, J Med Libr Assoc [Internet], 103, 3, pp. 131-135, (2015); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future; an ACRL white paper, Chicago, IL: Association of College and Research Libraries, (2012); Yakel E., Digital Curation, OCLC Systems & Services: International Digital Library Perspective, 23, 4, pp. 335-340, (2007); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, PLOS ONE, 12, 3, (2017); Higgins S., The DCC curation lifecycle model, The International Journal of Digital Curation [Internet], 1, 3, pp. 134-140, (2008); Walters T., Skinner K., New Roles for New Times: Digital Curation for Preservation, Washington, DC: Association of Research Libraries, (2011); Pryor G., Donnelly M., Skilling Up to Do Data: Whose Role, Whose Responsibility, Whose Career?, International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Mar [Cited 2017 Oct 30];7(1):126–38, (2012); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, Plos ONE, 9, 12, (2014); Whyte A., A Pathway to Sustainable Research Data Services: From Scoping to Sustainability London, UK: Facet, pp. 59-88, (2014); Wittenberg J., Elings M., Building a Research Data Management Service at the University of California, IFLA Journal, 43, 1, pp. 89-97, (2017); Wang M., Fong B.L., Embedded Data Librarianship: A Case Study of Providing Data Management Support for a Science Department, Science & Technology Libraries [Internet], 34, 3, pp. 228-240, (2015); Antell K., Foote J.B., Turner J., Shults B., Dealing with Data: Science Librarians’ Participation in Data Management at Association of Research Libraries Institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Tri-Agency Open Access Policy on Publications [Internet], (2016); Tri-Agency Statement of Principles on Digital Data Management [Internet], (2016); Draft: Tri-Agency Research Data Management Policy for Consultation, (2018); Steeleworthy M., Research Data Management and the Canadian Academic Library: An Organizational Consideration of Data Management and Data Stewardship, Partnership: The Canadian Journal of Library and Information Practice and Research [Internet], 9, 1, pp. 1-11, (2014); Guindon A., Research data management at Concordia University: A survey of current practices, Feliciter [Internet], 60, 2, pp. 15-17, (2014); Witt M., Workshop C.M.I.T.I.D.R., Nov 24], Available From, (2018); (2017); Lyon L., Librarians in the Lab: Toward Radically Re-Engineering Data Curation Services at the Research Coalface, New Review of Academic Librarianship, 22, 4, pp. 391-409, (2016); Lyon L., Reflections & challenges, preparing the workforce for digital curation: The iSchool, Perspective [Internet], (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Lundeed A., Research Data Services in Academic Libraries: Data Intensive Roles for the Future?, Journal of Escience Librarianship [Internet]. 2015 Dec [Cited, 4, 2, pp. 1-21, (2017); Tenopir C., Sandusky R.J., Allard S., Birch B., Research Data Management Services in Academic Research Libraries and Perceptions of Librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Auckland M., Re-skilling for research: An investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, Research Libraries UK., (2012); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Delserone L.M., At the watershed: Preparing for research data management and stewardship at the University of Minnesota Libraries, Library Trends, 57, 2, pp. 202-210, (2008); Nicholson S.W., Bennett T.B., Data sharing: Academic libraries and the scholarly enterprise, Portal: Libraries and the Academy [Internet], 11, 1, pp. 505-516, (2011); Heidorn P.B., The Emerging Role of Libraries in Data Curation and E-science, Journal of Library Administration, 51, 7, pp. 662-672, (2011); Federer L., Defining data librarianship: A survey of competencies, skills, and training, J Med Libr Assoc [Internet], 106, 3, pp. 294-303, (2018); Lyon L., Brenner A., Bridging the data talent gap-positioning the iSchool as an agent for change, International Journal of Data Curation, 10, 1, pp. 111-122, (2015); (2018); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Oct 15], Available From, (2017); Read K., Adapting Data Management Education to Support Clinical Research Projects in an Academic Medical Center, J Med Libr Assoc [Internet], 107, 1, pp. 89-97, (2019); Biomedical & Health Research Data Management for Librarians [Internet], (2019)","J. Fuhr; University of Alberta, Edmonton, Canada; email: justin.fuhr@umanitoba.ca","","Canadian Health Libraries Association","","","","","","17086892","","","","English","J. Canadian Health Libr. Assoc.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85080834723"
"Sales L.F.; Sayão L.F.; Maranhão A.M.N.; Drumond G.M.; da Silva M.H.F.X.","Sales, Luana Farias (25646168600); Sayão, Luis Fernando (7801523487); Maranhão, Ana Maria Neves (57216201005); Drumond, Geisa Meirelles (57190564565); da Silva, Maria Helena Ferreira Xavier (57216205823)","25646168600; 7801523487; 57216201005; 57190564565; 57216205823","Librarians' competencies in research data management; [Competências dos bibliotecários na gestão dos dados de pesquisa]; [Las competencias de los bibliotecarios en la gestión de datos de investigación]","2019","Ciencia da Informacao","48","3","","303","313","10","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082821557&partnerID=40&md5=7e4d601e3e5942e1ffa6d54d26d28f5c","Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT), Rio de Janeiro, RJ, Brazil; Programa de Pós-graduação em Ciência da Informação (PPGCI), convênio Universidade Federal do Rio de Janeiro e Instituto Brasileiro de Informação em Ciência e Tecnologia (UFRJ/Ibict), Rio de Janeiro, RJ, Brazil; Universidade Federal do Rio de Janeiro (UFRJ), RJ, Brazil; Comissão Nacional de Energia Nuclear (CNEN), Rio de Janeiro, RJ, Brazil; Fundação Oswaldo Cruz (Fiocruz), RJ, Brazil; Universidade Federal Fluminense (UFF), RJ, Brazil; Universidade Federal do Estado do Rio de Janeiro (Unirio), Brazil; A Vez do Mestre (AVM), Brazil","Sales L.F., Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT), Rio de Janeiro, RJ, Brazil, Programa de Pós-graduação em Ciência da Informação (PPGCI), convênio Universidade Federal do Rio de Janeiro e Instituto Brasileiro de Informação em Ciência e Tecnologia (UFRJ/Ibict), Rio de Janeiro, RJ, Brazil; Sayão L.F., Universidade Federal do Rio de Janeiro (UFRJ), RJ, Brazil, Comissão Nacional de Energia Nuclear (CNEN), Rio de Janeiro, RJ, Brazil; Maranhão A.M.N., Universidade Federal do Rio de Janeiro (UFRJ), RJ, Brazil, Fundação Oswaldo Cruz (Fiocruz), RJ, Brazil; Drumond G.M., Universidade Federal Fluminense (UFF), RJ, Brazil; da Silva M.H.F.X., Universidade Federal Fluminense (UFF), RJ, Brazil, Universidade Federal do Estado do Rio de Janeiro (Unirio), Brazil, A Vez do Mestre (AVM), Brazil","In the scientific scenario, the intensive generation and use of research data require innovative management models, which, in turn, needs new skills for the implementation of informational infrastructures focused on data. Librarians have an important role in data management services. This fact reinforces the need to develop skills for optimize their actions in this field. This means improving librarians’ skills in supporting researchers, deploying service and literacy infrastructures, developing methodology for data sharing and reuse among other requirements. Taking as methodology the literature of the area, the study analyzes the role of librarians and the necessaries competences for the professional performance in the management of research data, outlining a professional profile of the data librarian and contributing to the debate on the subject in the area of Information ​Science. © 2019, Brazilian Institute for Information in Science and Technology. All rights reserved.","Competence of librarians; Data librarians; Data management","","","","","","","","Barbrow S., Brush D., Goldman J., Research data management and services: Resources for novice data librarians, College and Research Libraries News, [S.L.], 78, 5, pp. 274-278, (2017); Borgman C.L., Et al., Knowledge infrastructures in science: Data, diversity, and digital libraries, Int J Digit Libr, [S.L.], 16, pp. 207-227, (2015); Christensen-Dalsgaard B., Et al., Ten Recommendations for Libraries to Get Started with Research Data Management: Final Report of the LIBER Working Group on E-Science / Research Data Management. [S.L.]: Ligue Des Bibliothèqueseuropéennes De Recherche (LIBER), (2012); Correa C.F., O papel dos bibliotecários na gestão de dados científicos, Rev. Digit. Bibliotecon. Cienc. Inf., Campinas, SP, 14, 3, pp. 387-406; Dudziak E.A., Competências Do Bibliotecário Na Gestão De Dados De Pesquisa, Comunicação Científica E Acesso Aberto, (2018); Dudziak E., Dados De Pesquisa Agora Devem Ser Armazenados E Citados, (2018); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, [S.L.], 49, 1, pp. 3-14, (2017); Lage K., Losoff B., Maness J., Receptivity to library involvement in scientific data curation: A case study at the University of Colorado Boulder, Libraries and the Academy, 11, 4, pp. 915-937, (2011); Li S., Et al., The Cultivation of Scientific Data Specialists: Development of LIS Education Oriented to E-Science Service Requirements, 31, 4, pp. 700-724, (2013); Martinez-Uribe L., Fernandez P., Servicios de datos: Función estratégica de lãs bibliotecas del siglo XXI, El Profesional De La información, [S.L.], 24, 2, pp. 193-199, (2015); Martine-Zuribe L., Macdonald S., Un Nuevo Cometido Para Los Bibliotecarios académicos: Data Curation, 17, 3, pp. 273-280, (2008); Robinson L., Bawden D., The story of data: A socio-technical approach to education for the data librarian role in the CityLIS library school at City, University of London, Library Management, 38, 6/7, pp. 312-322, (2017); Sales L.F., Gestão de dados de pesquisa e o papel do bibliotecário, FÓRUM SOBRE COMPETÊNCIA EM INFORMAÇÃO: PESQUISA E PRÁTICAS NO RIO DE JANEIRO, (2018); Sayao L.F., Sales L.F., Algumas considerações sobre os repositórios digitais de dados de pesquisa, Inf. Inf., Londrina, 21, 2, pp. 90-115, (2016); Semeler A.R., Ciência Da Informação Em Contexto Da E-Science: bibliotecários De Dados Em Tempos De Data-Science, (2017); Wang M., Supporting the Research Process through Expanded Library Data Services, 47, 3, pp. 282-303, (2013)","","","Brazilian Institute for Information in Science and Technology","","","","","","01001965","","","","Portuguese","Cienc. Inf.","Article","Final","","Scopus","2-s2.0-85082821557"
"Eder C.; Jedinger A.","Eder, Christina (35147903800); Jedinger, Alexander (56574198900)","35147903800; 56574198900","FAIR national election studies: How well are we doing?","2019","European Political Science","18","4","","651","668","17","2","10.1057/s41304-018-0194-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056614016&doi=10.1057%2fs41304-018-0194-3&partnerID=40&md5=ea7f1acdf7c9cbc3d98d793e2a4d0fed","GESIS – Leibniz Institute for the Social Sciences, PO Box 12 21 55, Mannheim, 68072, Germany; GESIS – Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, Cologne, 50667, Germany","Eder C., GESIS – Leibniz Institute for the Social Sciences, PO Box 12 21 55, Mannheim, 68072, Germany; Jedinger A., GESIS – Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, Cologne, 50667, Germany","Election studies are an important data pillar in political and social science, as most political research investigations involve secondary use of existing datasets. Researchers depend on high-quality data because data quality determines the accuracy of the conclusions drawn from statistical analyses. We outline data reuse quality criteria pertaining to data accessibility, metadata provision, and data documentation using the FAIR Principles of research data management as a framework (Findability, Accessibility, Interoperability, and Reusability). We then investigate the extent to which a selection of election studies fulfils these criteria using studies from Western democracies. Our results reveal that although most election studies are easily accessible and well documented and that the overall level of data processing is satisfactory, some important deficits remain. Further analyses of technical documentation indicate that while a majority of election studies provide the necessary documents, there is still room for improvement. © 2018, European Consortium for Political Research.","Accessibility; Data; Documentation; Election studies; Findability; Interoperability; Research data management; Reusability","","","","","","","","Box-Steffensmeier J.M., Tate K., Data accessibility in political science: Putting the principle into practice, PS: Political Science and Politics, 28, 3, pp. 470-472, (1995); Carsey T.M., Making DA-RT a reality, PS: Political Science and Politics, 47, 1, pp. 72-77, (2014); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2014); Faniel I.M., Kriesberg A., Yakel E., Social scientists satisfaction with data reuse, Journal of the Association for Information Science and Technology, 67, 6, pp. 1404-1416, (2016); Gertler A.L., Bullock J.G., Reference rot: An emerging threat to transparency in political science, PS: Political Science and Politics, 50, 1, pp. 166-171, (2017); Guide to social science data preparation and archiving, (2012); King G., Replication, replication, PS: Political Science and Politics, 28, 3, pp. 444-452, (1995); Kolsrud K., Skjak K.K., Henrichsen B., Free and immediate access to data, Measuring attitudes cross-nationally. Lessons from the European Social Survey, pp. 139-156, (2007); Lupia A., Elman C., Openness in political science: Data access and research transparency: Introduction, PS: Political Science and Politics, 47, 1, pp. 19-42, (2014); Netschereder S.C., Data Processing and Documentation: Generating High Quality Research Data in Quantitative Social Science Research (GESIS Papers 2018/22), (2018); Principles and guidelines for access to research data from public funding, (2007); Putnam R.D., Bowling alone. The collapse and revival of American community, (2000); Strong D.M., Yang W.L., Yang R.Y., Data quality in context, Communication of the ACM, 40, 5, pp. 103-110, (1997); Vardigan M.B., Granda P., Archiving, documentation, and dissemination, Handbook of survey research, pp. 707-729, (2010); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, pp. 1-9, (2016)","C. Eder; GESIS – Leibniz Institute for the Social Sciences, Mannheim, PO Box 12 21 55, 68072, Germany; email: Christina.eder@gesis.org","","Palgrave Macmillan Ltd.","","","","","","16804333","","","","English","Eur. Polit. Sci.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85056614016"
"Goben A.; Griffin T.","Goben, Abigail (55849675300); Griffin, Tina (7202249123)","55849675300; 7202249123","In aggregate: Trends, needs, and opportunities from research data management surveys","2019","College and Research Libraries","80","7","","903","924","21","12","10.5860/crl.80.7.903","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074532574&doi=10.5860%2fcrl.80.7.903&partnerID=40&md5=67c3e563fdfed387dffcd4c70c1743d3","Information Services Librarian/Liaison, Library of the Health Sciences, the University of Illinois, Chicago, United States","Goben A., Information Services Librarian/Liaison, Library of the Health Sciences, the University of Illinois, Chicago, United States; Griffin T., Information Services Librarian/Liaison, Library of the Health Sciences, the University of Illinois, Chicago, United States","A popular starting point for libraries engaging in research data management (RDM) services is a needs assessment (NA); a preliminary count identified more than 50 published NA case studies. However, no overarching analysis has yet been conducted. The authors compared assessments to characterize the case study institution types; establish the target population assessed; discover cross-institutional trends both in the topics covered and the issues identified; and determine remaining gaps in the literature. Thirty-seven studies conducted in the United States were included. Twenty-five were at public, doctoral, highest-research institutions. The most frequently assessed respondents were faculty (n = 3,847). The most frequent topics involved storing, sharing, and maintaining long-term access to data. Gaps include assessing students, staff, and nonfaculty researcher needs; determining needs at various sized and degree-granting institutions; and investigating RDM needs for non-STEM disciplines. © 2019 Abigail Goben and Tina Griffin, Attribution-NonCommercial.","","","","","","","National Science Foundation Data Management Plan requirement1, (requirement1); National Science Foundation report; Office of Science and Technology Policy memorandum; National Science Foundation, NSF; National Institutes of Health, NIH; Foundation for the National Institutes of Health, FNIH; meet","Funding text 1: Academic librarians have developed research data management (RDM) services and research over the past decade as researcher demands have derived from the implementation of the National Science Foundation Data Management Plan requirement1 and the Office of Science and Technology Policy memorandum for increased access to federally funded research.2 Librarians have undertaken reskilling,3 and there has been extensive research into the preparation and engagement of librarians with research data management.4 This has led to several papers describing opportunities for librarians to engage with researchers to meet these emerging needs and requirements.5; Funding text 2: Looking at the questions topics individually, the highest number of both questions and responses relate to concerns with sharing data. Understandably, the early literature reflected uncertainty regarding the then-new National Science Foundation and National Institutes of Health data management mandates. The early focus remained on the creation of data management plans as the infrastructure for sharing data was in its infancy. Data management plan help is listed as a need in almost 50 percent of the studies reviewed here and has been consistent across the timespan of published literature. A decade later, these unfunded mandates still exist, and this issue has come to light again as increasingly high-impact journals are accepting, if not requiring, data associated with publication.24 More recently, the conversation has begun; Funding text 3: 1. National Science Foundation, “Dissemination and Sharing of Research Results,” US NSF—About (Nov. 30, 2010), available online at www.nsf.gov/bfa/dias/policy/dmp.jsp [accessed 27 January 2019]. 2. John P. Holdren, “Memorandum for the Heads of Executive Departments and Agencies: Increasing Access to the Results of Federally Funded Scientific Research,” Office of Science and Technology Policy, Executive Office of the [US] President, The White House (Feb. 22, 2013), available online at https://obamawhitehouse.archives.gov/sites/ default/files/microsites/ostp/ostp_public_access_memo_2013.pdf [accessed 7 October 2019].; Funding text 4: One potential solution for the infrastructure disparity has been identified in a recent National Science Foundation report, which calls for dedicated funding to provide sustained midscale research infrastructure development and maintenance support.18 Other possibilities may involve collaboration between institutions to pool resources as proposed by the Data Curation Network19 or expanded support for disciplinary repositories and aggregators like DataONE.20","Holdren J.P., Memorandum for the Heads of Executive Departments and Agencies: Increasing Access to the Results of Federally Funded Scientific Research, Office of Science and Technology Policy, (2013); Cox A., Verbaan E., Sen B., Upskilling Liaison Librarians for Research Data Management, Ariadne: A Web & Print Magazine of Internet Issues for Librarians & Information Specialists, 70, (2012); Cox A.M., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Tenopir C., Et al., Research Data Services in Academic Libraries: Data Intensive Roles for the Future?, Journal of Escience Librarianship, 4, 2, (2015); Tenopir C., Et al., Research Data Management Services in Academic Research Libraries and Perceptions of Librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Et al., Academic Librarians and Research Data Services: Preparation and Attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Antell K., Et al., Dealing with Data: Science Librarians Participation in Data Management at Association of Research Libraries Institutions,”, College & Research Libraries, 75, 4, pp. 557-574, (2014); Bracke M.S., Emerging Data Curation Roles for Librarians: A Case Study of Agricultural Data, Journal of Agricultural & Food Information, 12, pp. 65-74, (2011); Corrall S., Kennan M.A., Afzal W., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research, Library Trends, 61, 3, pp. 636-674, (2013); Hickson S., Et al., Modifying Researchers Data Management Practices: A Behavioural Framework for Library Practitioners,”, IFLA Journal, 42, 4, pp. 253-265, (2016)","","","Association of College and Research Libraries","","","","","","00100870","","","","English","Coll. Res. Libr.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85074532574"
"Nikolov D.; Tuna E.","Nikolov, Dimitar (55944237200); Tuna, Esen (57195154580)","55944237200; 57195154580","A lightweight framework for research data management","2019","ACM International Conference Proceeding Series","","","3333157","","","","2","10.1145/3332186.3333157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071026263&doi=10.1145%2f3332186.3333157&partnerID=40&md5=17dd6f4350867607085b34a25c0cc032","Indiana University Research Technologies, Bloomington, IN, United States","Nikolov D., Indiana University Research Technologies, Bloomington, IN, United States; Tuna E., Indiana University Research Technologies, Bloomington, IN, United States","We describe a framework for managing live research data involving two major components. First, a system for the scalable scheduling and execution of automated policies for moving, organizing, and archiving data. Second, a system for managing metadata to facilitate curation and discovery with minimal change to existing workflows. Our approach is guided by four main principles: 1) to be non-invasive and to allow for easy integration into existing workflows and computing environments; 2) to be built on established, cloud-aware, open-source tools; 3) to be easily extensible and configurable, and thus, adaptable to different academic disciplines; and 4) to integrate with and take advantage of infrastructure and services available on academic campuses and research computing environments. These principles give our solution a well-defined place along the spectrum of research data management software such as sophisticated electronic lab notebooks and science gateways. Our lightweight and flexible data management framework provides for curation and preservation of research data within a lab, department or university cyberinfrastructure. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.","Data curation; Data management; Data policies; Metadata management","Information management; Learning systems; Metadata; Open source software; Open systems; Computing environments; Cyber infrastructures; Lightweight frameworks; Management frameworks; Metadata management; Open source tools; Research computing; Research data managements; Data curation","","","","","","","Butler D., A new leaf, Nature, 436, pp. 20-21, (2005); Foster I., Globus Online: Accelerating and democratizing science through cloud-based services, IEEE Internet Computing, 15, 3, pp. 70-73, (2011); Apache Airflow, (2019); Rajasekar A., Wan M., Moore R., Schroeder W., A Prototype Rule-Based Distributed Data Management System, (2006); Watson R.W., High performance storage system scalability: Architecture, implementation and experience, 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies (MSST’05), pp. 145-159, (2005); Wilkins-Diehr N., Special issue: Science gateways — Common community interfaces to grid resources, Concurrency and Computation: Practice and Experience, 19, 6, pp. 743-749, (2007)","","","Association for Computing Machinery","","2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019","28 July 2019 through 1 August 2019","Chicago","150186","","978-145037227-5","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-85071026263"
"Cain B.; Klein M.; Finnell J.","Cain, Brian (57197858995); Klein, Martin (36721280700); Finnell, Joshua (56798726100)","57197858995; 36721280700; 56798726100","Nucleus-deploying research data management infrastructure at the los alamos national laboratory","2019","Proceedings of the ACM/IEEE Joint Conference on Digital Libraries","2019-June","","8791229","396","397","1","1","10.1109/JCDL.2019.00087","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070957332&doi=10.1109%2fJCDL.2019.00087&partnerID=40&md5=70bdc35deffa718cc127e6e293f146c8","Los Alamos National Laboratory, Los Alamos, NM, United States; Colgate University, Hamilton, NY, United States","Cain B., Los Alamos National Laboratory, Los Alamos, NM, United States; Klein M., Los Alamos National Laboratory, Los Alamos, NM, United States; Finnell J., Colgate University, Hamilton, NY, United States","Research data management (RDM) efforts, including the implementation of tools, development of best practices, and training of scholars, have taken center stage in many academic libraries. Evaluating and serving the RDM needs at federally funded organizations have also become a priority since the release of the 2013 U.S. Office of Science and Technology Policy memo. At the Los Alamos National Laboratory, a U.S. Department of Energy laboratory, the Research Library has launched a collaborative data management pilot called 'Nucleus', based on a local installation of the open source software Open Science Framework. In this poster we present a preliminary assessment of Nucleus' implementation, including user feedback and lessons learned. © 2019 IEEE.","Data Collaboration; Open Science; Research Data Management","Digital libraries; Information management; Open source software; Open systems; Data collaborations; Los Alamos National Laboratory; Office of science and technology policies; Open science; Preliminary assessment; Research data managements; Research libraries; U.S. Department of Energy; Research and development management","","","","","","","Cain B., Finnell J., Data Management Services at Los Alamos, (2019); Public Access Plan, (2014); Stebbins M., Expanding Public Access to the Results of Federally Funded Research, (2013)","","Bonn M.; Wu D.; Downie S.J.; Martaus A.","Institute of Electrical and Electronics Engineers Inc.","Association for Computing Machinery (ACM); IEEE; IEEE Computer Society","19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019","2 June 2019 through 6 June 2019","Urbana-Champaign","150613","15525996","978-172811547-4","","","English","Proc. ACM IEEE Joint Conf. Digit. Libr.","Conference paper","Final","","Scopus","2-s2.0-85070957332"
"Rezniczek A.A.; Blumesberger S.; Bargmann M.; Eberhard I.; Kaier C.","Rezniczek, Alina Adriana (57213194121); Blumesberger, Susanne (27867529800); Bargmann, Monika (8925491200); Eberhard, Igor (57201190001); Kaier, Christian (57193199587)","57213194121; 27867529800; 8925491200; 57201190001; 57193199587","The certificate course ""data librarian"" and its first implementation; [Der zertifikatskurs „data librarian“ und seine erstmalige durchführung]","2019","VOEB-Mitteilungen","72","2","","274","283","9","0","10.31263/voebm.v72i2.3176","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077679417&doi=10.31263%2fvoebm.v72i2.3176&partnerID=40&md5=13055b94b265e0bda3c3435789d77bc7","Universität Wien, Bibliotheks-und Archivwesen, Austria; Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Austria; Universität Wien, Institut für Kultur-und Sozialanthropologie / Bibliotheks-und Archivwesen, Austria; Universität Graz, Universitätsbibliothek, Austria","Rezniczek A.A., Universität Wien, Bibliotheks-und Archivwesen, Austria; Blumesberger S., Universität Wien, Bibliotheks-und Archivwesen, Austria; Bargmann M., Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Austria; Eberhard I., Universität Wien, Institut für Kultur-und Sozialanthropologie / Bibliotheks-und Archivwesen, Austria; Kaier C., Universität Graz, Universitätsbibliothek, Austria","In the digital age, libraries as service institutions are increasingly providing access to electronic resources and digital content, in addition to the printed collections on site. Digital change and rapid developments in the information sector are creating new challenges and new fields of activity. In order to meet the requirements of the professional field, permanent further education and training is required. As a result, the certificate course „Data Librarian“ was created to impart knowledge that is useful for the development and implementation of services in the field of research data management. The main focus of this certificate course is on scholarly communication and research support, policies for handling research data, data management plans, metadata in the field of repositories, data analysis, data aggregation and linking, data standards, data modelling, long-term preservation and data protection. In this article, organisers, lecturers and participants report on their experiences during the first implementation of the course. © Alina Rezniczek, Susanne Blumesberger, Monika Bargmann, Igor Eberhard, Christian Kaier Dieses Werk ist lizenziert unter einer.","Data aggregation; Data analysis; Data modelling; Repositories; Research data management; Research support; Scholarly communication","","","","","","","","van Rooi H., Snyman R., A content analysis of literature regarding knowledge management opportunities for librarians, Aslib Proceedings, 58, 3, pp. 261-271, (2006); Womack R., What is a data librarian?, Ryan Data, (2016); Khan H.R., Yunfei D., What is a data librarian? Content Analysis of Job Advertisements for Data Librarians in the United States Academic Libraries, Paper Presented at IFLA WLIC 2018, (2018)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85077679417"
"Ishak I.; Tajuddin N.I.I.; Yah Jusoh Y.; Sidi F.; Abdullah R.; Marhaban H.; Tugiran Y.; Yusof Y.","Ishak, Iskandar (26422254000); Tajuddin, Nur Ilyana Ismarau (57194873355); Yah Jusoh, Yusmadi (57211602892); Sidi, Fatimah (36139115600); Abdullah, Rusli (24483156100); Marhaban, Hamiruce (57211599538); Tugiran, Yusnita (57209138720); Yusof, Yushaida (57211604564)","26422254000; 57194873355; 57211602892; 36139115600; 24483156100; 57211599538; 57209138720; 57211604564","Influencing factors in determining research data repository infrastructure for research data management","2019","International Journal of Engineering and Advanced Technology","9","1","","1655","1660","5","0","10.35940/ijeat.A2645.109119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074595230&doi=10.35940%2fijeat.A2645.109119&partnerID=40&md5=6d71b93f790c0fae2ab9ca41409ad891","Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia","Ishak I., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Tajuddin N.I.I., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Yah Jusoh Y., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Sidi F., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Abdullah R., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Marhaban H., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Tugiran Y., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Yusof Y., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia","Increasing volumes of data are rapidly being produced by researchers with the advancement of digital technologies. In order to manage these data, a suitable research data repository infrastructure is needed by the higher learning institutions. Apart from storing the data, these data repository need to support the research data life-cycle that include the tasks of data creation, processing, analysis, preservation, access and reuse. The objective of this research is to deeply investigate the influencing factors fordata repository infrastructure in managing research data. A systematic literature review is conducted to perform the investigation where research papers are searched over three electronic journal databases. Selected papers are then analysed and a quality assessment has been conducted to identify the relevant infrastructure for research data repository. As a result, we identified the important components of research data repository infrastructure development. © BEIESP.","Data Infrastructure; Data Management; Data Repository; Research Data","","","","","","Metropolitan Museum of Art; Universiti Putra Malaysia, (UPM/700-2/1/GP-IPN/2017/9558200)","Funding text 1: A quality assessment has been performed to the 23 qualified papers based on the criteria set during the planning stage and the summary of the assessment is shown in Table 4. Based on the results, only 5 papers have met all the assessment criteria with the total 4 marks. All papers managed to meet Q1 criteria which means, these papers have clear description in terms of its aims and objectives for research data infrastructure. For Q2, only 18 papers met the requirement while 7 partially met Q2 requirement. This means that majority of the papers explained the method of analysis regarding research data infrastructure. Q3 requirement is about whether the paper showed the use of primary data that support their researches. Based on the results, only 10 papers met Q3 requirements, and 6 papers partially supported by primary data and 6 papers did not supported by primary data.; Funding text 2: This research is supported by Universiti Putra Malaysia under the Putra Grant Scheme (UPM/700-2/1/GP-IPN/2017/9558200).","Abdelrahman O.H., The status of the University of Khartoum institutional repository, DESIDOC Journal of Library & Information Technology, 7, 2, pp. 104-108, (2017); Abrizah A., Noorhidawati A., Kiran K., Global visibility of Asian universities’ Open Access institutional repositories, Malaysian Journal of Library & Information Science, 15, 3, pp. 53-73, (2017); Amorim R.C., Castro J.A., Silva J.R., Ribeiro C., A Comparative Study of Platforms for Research Data Management: Interoperability, Metadata Capabilities and Integration Potential, New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, 353, (2015); Assante M., Candela L., Castelli D., Tani A., Are Scientific Data Repositories Coping with Research Data Publishing?, Data Science Journal, 15, 6, pp. 1-24, (2016); Austin C.C., Brown S., Fong N., Humphrey C., Leahey L., Webster P., Research data repositories: Review of current features, gap analysis, and recommendations for minimum requirements, Presented at the IASSIST Annual Conference. IASSIST Quarterly Preprint, (2015); Baughman S., Roebuck G., Arlitsch K., Reporting Practices of Institutional Repositories: Analysis of Responses from Two Surveys, Journal of Library Administration, 58, 1, pp. 65-80, (2018); Brownlee R., Research data and repository metadata: Policy and technical issues at the university of Sydney library, Cataloging and Classification Quarterly, 47, 3-4, pp. 370-379, (2009); Davidson J., Jones S., Molloy L., Big data: The potential role of research data management and research data registries, Ifla, pp. 1-11, (2014); Demchenko Y., Grosso P., de LaatandMembrey C.P., Addressing big data issues in Scientific Data Infrastructure, 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, pp. 48-55, (2013); Edinburgh University Data Library Research Data Management Handbook, (2011); Eifert T., Schilling U., Bauer H.J., Kramer F., Lopez A., Infrastructure for Research Data Management as a Cross-University Project, Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration. HIMI 2017, 10274, (2017); Gordon A.S., Millman D.S., Steiger L., Adolph K.E., Gilmore R.O., Researcher-Library Collaborations: Data Repositories as a Service for Researchers, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Gray J., Gerlitz C., Bounegru L., Data infrastructure literacy, Big Data & Society, (2018); Hruby G.W., McKiernan J., Bakken S., Weng C., A centralized research data repository enhances retrospective outcomes research capacity: A case report, Journal of the American Medical Informatics Association, 20, 3, pp. 563-567, (2013); Kakai M., Musoke M.G., Okello-Obura C., Open access institutional repositories in universities in East Africa, Information and Learning Science, 119, 11, pp. 667-681, (2018); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, Plos ONE, 12, (2017); Corti L., Eynden V.V.D., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Lovett J.A., Rathemacher A.J., Boukari D., Lang C., Institutional Repositories and Academic Social Networks: Competition or Complement?, A Study of Open Access Policy Compliance Vs. Researchgateparticipation.Journal of Librarianship and Scholarly Communication, (2017); Nemati-Anaraki L., Tavassoli-Farahi M., Scholarly communication through institutional repositories: Proposing a practical model, Collection and Curation, 37, 1, pp. 9-17, (2018); Oguche D., The state of institutional repositories and scholarly communication in Nigeria, Global Knowledge, Memory and Communication, 67, 1-2, pp. 19-33, (2018); Okoli C., Schabram K., A Guide to Conducting a Systematic Literature Review of Information Systems Research. Sprouts: Working Papers on Information Systems, 10, 26, (2010); Parida P.K., Tripathi S., Odisha Spatial Data Infrastructure (OSDI) – Its Data Model, Meta Data and Sharing Policy, pp. 20-23, (2018); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Et al., Making Research Data Repositories Visible: The re3data.org Registry, PLOS ONE, 8, 11, (2013); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, Plos ONE, 9, 12, (2014); Prabhakar F., Manjula Rani S.V., Benefits And Perspectives Of Institutional Repositories in Academic Libraries, Scholarly Research Journal for Humanity Science & English Language, 5, 25, (2018); Qin J., Infrastructure, Standards, and Policies for Research Data Management, Sharing of Scientific and Technical Resources in The Era of Big Data: The Proceedings of COINFO 2013, pp. 214-219, (2013); Ridwan S.M., Institutional Repository: A Road Map to Open Access and Resources Sharing in Nigeria (Issues and Challenges), International Journal of Scientific & Engineering Research, 6, 1, pp. 598-605, (2015); Schweik C.M., Stepanov A., Grove J.M., The Open Research System: A Web-Based Metadata and Data Repository for Collaborative Research, 47, pp. 221-242, (2005); Serrano-Vicente R., Melero R., Abadal E., Evaluation of Spanish institutional repositories based on criteria related to technology, procedures, content, marketing and personnel, Data Technologies and Applications, 52, 3, pp. 384-404, (2018); Uzuegbu C.P., 2012. Academic and research institutions repository: A catalyst for access to development information in Africa, 78Th World Library and Information Congress, pp. 1-18; Wissik T., Durco M., Research Data Workflows: From Research Data Lifecycle Models To, (2015); CLARIN 2015 Selected Papers, Linköping Electronic Conference Proceedings, 123, pp. 94-107","","","Blue Eyes Intelligence Engineering and Sciences Publication","","","","","","22498958","","","","English","Int. J. Eng. Adv. Technol.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85074595230"
"Tammaro A.M.; Matusiak K.K.; Sposito F.A.; Casarosa V.","Tammaro, Anna Maria (8554921900); Matusiak, Krystyna K. (14626810000); Sposito, Frank Andreas (57189973650); Casarosa, Vittore (54939304100)","8554921900; 14626810000; 57189973650; 54939304100","Data curator's roles and responsibilities: An international perspective","2019","Libri","69","2","","89","104","15","21","10.1515/libri-2018-0090","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067131679&doi=10.1515%2flibri-2018-0090&partnerID=40&md5=4a6b1d457a5e34895611bbd4c6285b98","Information Engineering, University of Parma, Parma, Italy; Research Methods and Information Science, University of Denver Morgridge College of Education, Denver, CO, United States; Library and Archives Services, Fachhochschule Technikum Wien, Wien, Austria; Consiglio Nazionale Delle Ricerche Area della Ricerca di Pisa, Pisa, Italy","Tammaro A.M., Information Engineering, University of Parma, Parma, Italy; Matusiak K.K., Research Methods and Information Science, University of Denver Morgridge College of Education, Denver, CO, United States; Sposito F.A., Library and Archives Services, Fachhochschule Technikum Wien, Wien, Austria; Casarosa V., Consiglio Nazionale Delle Ricerche Area della Ricerca di Pisa, Pisa, Italy","The data-intensive research environment and the movement towards open science create demand for information professionals with knowledge of the research process and skills in managing and curating data. This paper is reporting the findings from a multiyear study entitled ""Data curator: Who is s/he?"" initiated by the Library Theory and Research (LTR) Section of the International Federation of Library Associations (IFLA). The study aimed to identify the roles and responsibilities of data curators around the world and also focused on the terminology used to describe the new professional roles. The following questions were posed: R1: How is data curation defined by practitioners/professional working in the field? R2: What terms are used to describe the roles for professionals in data curation area? R3: What are primary roles and responsibilities of data curators? R4: What are educational qualifications and competencies required of data curators? To answer the research questions, the research team performed a comprehensive literature review and vocabulary analysis and conducted an empirical study using mixed-methods design. The study consisted of three stages: 1. Literature review and vocabulary analysis 2. Content analysis of position announcements 3. Interviews with professionals working in data curation and research data management-Findings confirm the results from previous research about the lack of common terminology and a variability of the position titles. The concept of data lifecycle highlighted the important role of data curators. However this study also found that many positions in practice were held by non library professionals. The findings indicate that data curation is an evolving sociotechnical practice that involves not only technical systems and services structured around research data life cycle but also a range of social activities around community building. © 2019 Walter de Gruyter GmbH, Berlin/Boston.","data curator; digital curation; open science; research data management","","","","","","","","Ayris P., Berthou J.-Y., Bruce R., Lindstaedt S., Monreale A., Mons B., Murayama Y., Sodergard C., Tochetermann K., Wilkinson R., Realising the European Open Science Cloud, The Commission High Level Expert Group on the European Open Science Cloud, (2016); Bailey C.W., Research Data Curation Bibliography (Version 7), (2017); Ball A., Review of Data Management Lifecycle Models (V. 1.0), (2012); Borgman C.L., The Conundrum of Sharing Research Data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Carlson J., The Use of Life Cycle Models in Developing and Supporting Data Services, Research Data Management. Practical Strategies for Information Professionals, pp. 63-86, (2014); Center for Open Science. N.d; Connaway L.S., Introduction, The Library in the Life of the User: Engaging with People Where They Live and Learn, pp. 1-9, (2015); Corrall S., Roles and Responsibilities: Libraries, Librarians and Data, Managing Research Data, pp. 105-133, (2012); Corrall S., Designing Libraries for Research Collaboration in the Network World: An Exploratory Study, LIBER Quarterly, 24, 1, pp. 17-48, (2014); Cox A., Kennan M., Lyon L., Pinfield S., Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A., Verbaan E., Sen B., Upskilling Liaison Librarians for Research Data Management, Ariadne, (2012); Creswell J.W., Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Ed., (2013); What Is Digital Curation, (2018); Implementation Roadmap for European Open Science Cloud, (2018); Faniel I., Connaway L., Librarians' Perspectives on the Factors Influencing Research Data Management Programs, College & Research Libraries, 79, 1, (2018); Fearon D., Gunia B., Pralle B.E., Lake S., Sallans A.L., Research Data Management Services, (2013); Heidorn P.B., The Emerging Role of Libraries in Data Curation and E-Science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Hey T., Tansley S., Tolle K.M., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Higgins S., The DCC Curation Lifecycle Model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Higgins S., Digital Curation: The Emergence of a New Discipline, International Journal of Digital Curation, 6, 2, pp. 78-88, (2011); Hodson S., Jones S., Collins S., Genova F., Harrower N., Laaksonen L., Mietchen D., Petrauskaite R., Wittenburg P., Turning FAIR Data into Reality. Interim Report of the European Commission Expert Group on FAIR Data, (2018); Jaguszewski J.M., Williams K., New Roles for New Times: Transforming Liaison Roles in Research Libraries, (2013); Janke L.M., Asher A., Keralis S.D.C., The Problem of Data, (2012); Kim J., Warga E., Moen W.E., Competencies Required for Digital Curation: An Analysis of Job Advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Kraker P., Leony D., Reinhardt W., Beham G., The Case for an Open Science in Technology Enhanced Learning, International Journal of Technology Enhanced Learning, 3, 6, pp. 643-654, (2011); Matusiak K.K., Sposito F.A., Types of Research Data Management Services: An International Perspective, Proceedings of the Association for Information Science and Technology, 54, 1, pp. 754-756, (2017); McGovern N.Y., Building Capacity: Curriculum, Competencies, and Careers, The Open Date Imperative: How the Cultural Heritage Community Can Address the Federal Mandate, (2016); Molloy J.C., The Open Knowledge Foundation: Open Data Means Better Science, PLoS Biology, 9, 12, (2011); Open Knowledge International. N.d; Pryor G., Why Manage Research Data, Managing Research Data, pp. 1-16, (2012); RDA in a Nutshell, (2018); Rockenbach A.N., Mayhew M.J., Davidson J., Ofstein J., Bush R.C., Complicating Universal Definitions: How Students of Diverse Worldviews Make Meaning of Spirituality, Journal of Student Affairs Research and Practice, 52, 1, pp. 1-10, (2015); Swan A., Brown S., The Skills, Role and Career Structure of Data Scientists and Curators: An Assessment of Current Practice and Future Needs, A Report to the Joint Information Systems Committee (JISC), (2008); Tammaro A.M., Matusiak K.K., Sposito F.A., Casarosa V., Pervan A., Understanding Roles and Responsibilities of Data Curators: An International Perspective, Libellarium: Journal for the Research of Writing, Books, and Cultural Heritage Institutions, 9, 2, (2017); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services Current Practices and Plans for the Future, (2012); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research Data Services in Academic Libraries: Data Intensive Roles for the Future, Journal of EScience Librarianship, 4, 2, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research Data Management Services in Academic Research Libraries and Perceptions of Librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research Data Services in European Academic Research Libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Wilkinson M.D., Dumontier M., Aalbersberg J., Appleton G., Axton M., Baak A., Blomberg N., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, (2016); Witt M., Institutional Repositories and Research Data Curation in a Distributed Environment, Library Trends, 57, 2, pp. 191-201, (2008); Witt M., Co-Designing, Co-Developing, and Co-Implementing an Institutional Data Repository Service, Journal of Library Administration, 52, 2, pp. 172-188, (2012); Xia J., Wang M., Competencies and Responsibilities of Social Science Data Librarians: An Analysis of Job Descriptions, College & Research Libraries, 75, 3, pp. 362-388, (2014); Yoon A., Schultz T., Research Data Management Services in Academic Libraries in the US: A Content Analysis of Libraries' Websites, College & Research Libraries, 78, 7, pp. 920-933, (2017)","A.M. Tammaro; Information Engineering, University of Parma, Parma, Italy; email: annamaria.tammaro@UNIPR.IT","","De Gruyter Saur","","","","","","00242667","","","","English","Libri","Review","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85067131679"
"Kansa S.W.; Atici L.; Kansa E.C.; Meadow R.H.","Kansa, Sarah W. (35272151600); Atici, Levent (55175537300); Kansa, Eric C. (24801999000); Meadow, Richard H. (7007071574)","35272151600; 55175537300; 24801999000; 7007071574","Archaeological analysis in the information age: Guidelines for maximizing the reach, comprehensiveness, and longevity of data","2020","Advances in Archaeological Practice","8","1","","40","52","12","19","10.1017/aap.2019.36","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078916465&doi=10.1017%2faap.2019.36&partnerID=40&md5=d9639fa353fa9635199edd9721fafd5d","Open Context, 125 El Verano Way, San Francisco, 94127, CA, United States; Department of Anthropology, University of Nevada, 4505 S. Pkwy, Las Vegas, 89154, NV, United States; Department of Anthropology, Harvard University, 11 Divinity Avenue, Cambridge, 02138, MA, United States","Kansa S.W., Open Context, 125 El Verano Way, San Francisco, 94127, CA, United States; Atici L., Department of Anthropology, University of Nevada, 4505 S. Pkwy, Las Vegas, 89154, NV, United States; Kansa E.C., Open Context, 125 El Verano Way, San Francisco, 94127, CA, United States; Meadow R.H., Department of Anthropology, Harvard University, 11 Divinity Avenue, Cambridge, 02138, MA, United States","With the advent of the Web, increased emphasis on research data management, and innovations in reproducible research practices, scholars have more incentives and opportunities to document and disseminate their primary data. This article seeks to guide archaeologists in data sharing by highlighting recurring challenges in reusing archived data gleaned from observations on workflows and reanalysis efforts involving datasets published over the past 15 years by Open Context. Based on our findings, we propose specific guidelines to improve data management, documentation, and publishing practices so that primary data can be more efficiently discovered, understood, aggregated, and synthesized by wider research communities. 2019 © Society for American Archaeology.","Data documentation; Data management; Data reuse; Guidelines; Reproducible research; Zooarchaeology","","","","","","National Endowment for the Humanities, NEH, (PK-50072-08, PR-234235-16)","This research has been supported in part by a grant from the National Endowment for the Humanities (PK-50072-08). Many of the topics discussed in this paper are futher explored in ongoing research, funded by another National Endowment for the Humanities grant (PR-234235-16). Any views, findings, conclusions, or recommendations expressed in this article do not necessarily represent those of the National Endowment for the Humanities. We thank the reviewers for their helpful comments and suggestions.","Altschul J.H., Fostering collaborative synthetic research in archaeology, Advances in Archaeological Practice, 6, pp. 19-29, (2018); Anderson D.G., Sea-level rise and archaeological site destruction: An example from the southeastern united states using dinaa (digital index of north american archaeology), PLoS ONE, 12, 11, (2017); Arbuckle B.S., Data sharing reveals complexity in the westward spread of domestic animals across neolithic turkey, PLoS ONE, 9, 6, (2014); Archaeological institute of america (aia), Considerations Regarding the Tenure and Promotion of Classical Archaeologists Employed in Colleges and Universities, with Addendum, Guidelines for the Evaluation of Digital Technology and Scholarship in Archaeology, (2018); Atici L., Chogha Mish Fauna, (2013); Atici L., Other people's data: A demonstration of the imperative of publishing primary data, Journal of Archaeological Method and Theory, 20, pp. 663-681, (2013); Beebe C., Standard descriptive vocabulary and archaeology digital data collection, Advances in Archaeological Practice, 5, pp. 250-264, (2017); Behrensmeyer A.K., Taphonomic and ecologic information from bone weathering, Paleobiology, 4, pp. 150-162, (1978); Berners-Lee T., Linked data, Electronic document, (2006); Boessneck J., Osteological differences between sheep (ovis aries lineé) and goat (capra hircus lineé), Science in Archaeology, pp. 331-358, (1969); Dibble W.F., Data collection in zooarchaeology: Incorporating touch-screen, speech-recognition, barcodes, and gis, Ethnobiology Letters, 6, pp. 249-257, (2015); Driesch A.V.D., Peabody museum of archaeology and ethnology, harvard university, cambridge, massachusetts, A Guide to the Measurement of Animal Bones from Archaeological Sites, (1976); Driver J.C., Identification, classification and zooarchaeology, Circaea, 9, pp. 35-47, (1992); Driver J.C., Identification, classification and zooarchaeology, Ethnobiology Letters, 2, pp. 19-39, (2011); Driver J., (2018); Faniel I., Beyond the archive: Bridging data creation and reuse in archaeology, Advances in Archaeological Practice, 6, pp. 105-116, (2018); Faniel I., JCDL 2013 Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 295-304, (2013); Faniel I.M., Yakel E., Practices do not make perfect: disciplinary data sharing and reuse practices and their implications for repository data curation, Curating Research Data, Volume One: Practical Strategies for Your Digital Repository, pp. 103-126, (2017); Grigson C., Towards a blueprint for animal bone reports in archaeology, Research Problems in Zooarchaeology, pp. 121-128, (1978); Hammer E., Near eastern landscapes and declassified u2 aerial imagery, Advances in Archaeological Practice, 7, pp. 107-126, (2019); Huggett J., Promise and paradox: Accessing open data in archaeology, Proceedings of the Digital Humanities Congress 2012: Studies in the Digital Humanities, (2014); Jones E.L., The promise and peril of older collections: Meta-Analyses and the zooarchaeology of late prehistoric/early historic new mexico, Open Quaternary, 1, 1, (2015); Jones E.L., Hurley D.A., Relational databases and zooarchaeology education, SAA Archaeological Record, 11, 1, pp. 19-21, (2011); Kansa E., New directions for the digital past, Archaeology 2.0: New Approaches to Communication and Collaboration, pp. 1-26, (2011); Kansa E., Openness and archaeology's information ecosystem, World Archaeology, 44, pp. 498-520, (2012); Kansa E.C., Kansa S.W., We all know that a 14 is a sheep: Data publication and professionalism in archaeological communication, Journal of Eastern Mediterranean Archaeology and Heritage Studies, 1, pp. 88-98, (2013); Kansa Eric. C., Publishing and pushing: Mixing models for communicating research data in archaeology, International Journal of Digital Curation, 9, 1, pp. 57-70, (2014); Kansa E.C., Jason Schultz, Ahrash N. Bissell, Protecting traditional knowledge and expanding access to scientific data: juxtaposing intellectual property agendas via a some rights reserved model, International Journal of Cultural Property, 12, pp. 285-314, (2005); Kansa S.W., Using linked open data to improve data reuse in zooarchaeology, Ethnobiology Letters, 6, pp. 224-231, (2015); Kimbrough J.L., Laura N. Gasaway, Publication of government-funded research, open access, and the public interest, Vanderbilt Journal of Entertainment and Technology Law, 18, pp. 267-302, (2015); Grand challenges for archaeology, American Antiquity, 79, pp. 5-24, (2014); Kintigh K.W., Jeffrey H. Altschul, Mary C. Beaudry, Robert D. Drennan, Ann P. Kinzig, Timothy A. Kohler, W. Fredrick Limp, Herbert D. G. Maschner, William K. Michener, Timothy R. Pauketat, Peter Peregrine, Jeremy A. Sabloff, Tony J. Wilkinson, Henry T. Wright, Melinda A. Zeder, Grand challenges for archaeology, Proceedings of the National Academy of Sciences, 111, 3, pp. 879-880, (2014); Kintigh K.W., Jeffrey H. Altschul, Ann P. Kinzig, W.Fredrick Limp, William K. Michener, Jeremy A. Sabloff, Edward J. Hackett, Timothy A. Kohler, Bertram Ludascher, Clifford A. Lynch, Cultural dynamics, deep time, and data: Planning cyberinfrastructure investments for archaeology, Advances in Archaeological Practice, 3, pp. 1-15, (2015); Lau H.K., Sarah W. Kansa, Zooarchaeology in the era of big data: Contending with interanalyst variation and best practices for contextualizing data for informed reuse, Journal of Archaeological Science, 95, pp. 33-39, (2018); Marwick B., Computational reproducibility in archaeological research: basic principles and a case study of their implementation, Journal of Archaeological Method and Theory, 24, pp. 424-450, (2017); Marwick B., Birch S.E.P., A standard for the scholarly citation of archaeological data as an incentive to data sharing, Advances in Archaeological Practice, 6, pp. 125-143, (2018); Meadow R.H., Bonecode-a system of numerical coding for faunal data from middle eastern sites, Approaches to Faunal Analysis in the Middle East, pp. 169-186, (1978); Meadow R.H., Melinda A. Zeder, Peabody museum, Approaches to Faunal Analysis in the Middle East, (1978); Michelle J.L., Laura Brenskelle, John Wieczorek, Sarah W. Kansa, Eric C. Kansa, Neill J. Wallis, Jessica N. King, Kitty F. Emery, Robert Guralnick, Zooarchnet: Connecting zooarchaeological specimens to the biodiversity and archaeology data networks, PLoS ONE, 14, 4, (2019); Payne S., A metrical distinction between sheep and goat, The Domestication and Exploitation of Plants and Animals, pp. 295-305, (1969); Peres T.M., Methodological issues in zooarchaeology, Integrating Zooarchaeology and Paleoethnobotany: A Consideration of Issues, Methods, and Cases, pp. 15-36, (2010); Redding R.H., Melinda A. Zeder, John McArdle, Bonesort ii-a system for the computer processing of identifiable faunal material, Approaches to Faunal Analysis in the Middle East, pp. 135-147, (1978); Reitz E.J., Elizabeth S. Wing, Zooarchaeology, (2008); Richards J., Twenty years preserving data: A view from the united kingdom, Advances in Archaeological Practice, 5, pp. 227-237, (2017); (2019); Styles B.W., Mona Colburn, Taphonomic, environmental, and cultural influences on archaic faunal assemblages at modoc rock shelter, illinois, usa, Quarternary International, (2019); Uerpmann H.-P., The knocod system for processing data on animal bones from archaeological sites, Approaches to Faunal Analysis in the Middle East, pp. 149-167, (1978); White house, Executive Order: Making Open and Machine Readable the New Default for Government Information. Electronic document, (2013); Wolverton S., Data quality in zooarchaeological faunal identification, Journal of Archaeological Method and Theory, 20, pp. 381-396, (2013); Yakel E., Ixchel M. Faniel, Zachary J. Maiorana, Virtuous and vicious circles in the data life-cycle, Information Research, 24, 2, (2019)","","","Cambridge University Press","","","","","","23263768","","","","English","Adv. Archaeol. Pract.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85078916465"
"Smith P.L.; Gonzalez S.; Bossart J.","Smith, Plato L. (24475610400); Gonzalez, Sara (55427098000); Bossart, Jean (57190810212)","24475610400; 55427098000; 57190810212","Data management and the role of librarians","2019","Grey Journal","15","1","","31","38","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064661443&partnerID=40&md5=a4fd95739fe9b8e455a91a530406eb02","George A. Smathers Libraries, University of Florida, United States","Smith P.L., George A. Smathers Libraries, University of Florida, United States; Gonzalez S., George A. Smathers Libraries, University of Florida, United States; Bossart J., George A. Smathers Libraries, University of Florida, United States","‘Research Data Science’ is defined by Committee on Data of the International Council for Science Research Data Alliance (CODATA-RDA) as an ensemble of (a) Open Science principles and practices (FAIR) and research data management and curation skills, (b) the use of a range of data platforms and infrastructures, (c) large scale analysis, (d) statistics, (e) visualization and modeling techniques, (f) software development and annotation, and (g) more. Data management and the role of librarians must now include developing expertise and training with faculty, students, and staff on “research data science” directly and/or indirectly through collaborative library/faculty partnerships. To meet this need, librarians at the University of Florida have developed a new research support service called Academic Research Consulting & Services (ARCS) to assist faculty, students, and staff with their data management and research needs. The library-centered, campus-wide focused UF Data Management and Curation Working (DMCWG) and ARCS work in collaborative partnerships with the campus units such as the UF Informatics Institute, UF Data Carpentry Club (https://github.com/UF-Carpentry), and UF Data Science & Informatics (DSI) undergraduate student organization to provide support to pre-and post-grant research and teaching. This new role of librarians is to facilitate library/faculty collaborations and broker resources that contribute to the facilitation of promulgating ‘research data science’ skills at scale for their respective institutions. This paper will discuss the developing outreach activities, interdepartmental collaborations, some initial outcomes, and future goals of leveraging capacity, infrastructure, and resources to develop data management efforts across communities of practice within an institution’s current organizational culture. One aim of this paper is to highlight the importance and significance of developing good library and faculty partnerships built on character, integrity, and humility as the cornerstones for the roles of librarians as collaborators in promoting socio-technical data management programs. © 2019, GreyNet. All rights reserved.","","","","","","","UFII","SQL workshops, and developing learning pathways for students. UFII fellows and students attend of help at workshops. DSI is funded by UFII and collaborates with UF Libraries/MSL.","Data Governance Policy, Appendix 1 -Data Management Life Cycle, 7, (2017); University of Glasgow Humanities Advanced Technology & Information Institute (HATII), and Digital Curation Center, Data Asset Framework [Formerly Data Audit Framework] Implementation Guide, (2009); Davis M.C., Challenger R., Jayewardene D.N.W., Clegg C.W., Advancing socio-technical systems thinking: A call for bravery, Applied Ergonomics, 45, 2, pp. 171-180, (2014); CESSDA Saw Archive Development Canvas (Detailed Version), (2017); JISC Circular 6/03 (Revised). an Invitation for Expressions of Interest to Establish a New Digital Curation Centre for Research into and Support of the Curation and Preservation of Digital Data and Publications, (2003); (2013); (2013); Jones S., Pryor G., Whyte A., How to Develop Research DataManagement Services: A Guide for HEIs’. DCC How-to Guides [Internet], Edinburgh, (2013); Antell K., Foote J.B., Turner J., Shults B., Dealing with Data: Science Librarians’ Participation in Data Management at Association of Research Libraries Institutions, Coll Res Libr, 75, 4, (2014); Diekema A.R., Wesolek A., Walters C.D., The NSF/NIH Effect: Surveying the Effect of Data Management Requirements on Faculty, Sponsored Programs, and Institutional Repositories, J Acad Librariansh, 40, 3-4, (2014); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J Librariansh Inf Sci, 46, 4, (2014); Saunders L., Academic Libraries’ Strategic Plans: Top Trends and Under-Recognized Areas, J Acad Librariansh, 41, 3, (2015); Hickson S., Poulton K.A., Connor M., Richardson J., Wolski M., Modifying researchers data management practices: A behavioural framework for library practitioners, IFLA J, 42, 4, (2016); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, Plos One, 10, 2, (2015); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos One, 10, 8, (2015); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Libr Hi Tech, 32, 3, (2014); Macmillan D., Data sharing and discovery: What librarians need to know, Journal of Academic Librarianship, 40, (2014); Wallis J.C., Rolando E., Borgman C.L., If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology, Plos One, 8, 7, (2013); Marshall B., O'Bryan K., Qin N., Vernon R., Organizing, contextualizing, and storing legacy research data: A case study of data management for librarians, Issues Sci Technol Librariansh, (2013); Van Tuyl S., Whitmire A.L., Water, water, everywhere: Defining and assessing data sharing in Academia, Plos One, 11, 2, (2016); Kratz J.E., Strasser C., Researcher perspectives on publication and peer review of data, Plos One, 10, 2, (2015); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, Peerj, (2013); Michener W.K., Ten Simple Rules for Creating a Good Data Management Plan, Plos Comput Biol, 11, 10, (2015); Jorn Nielsen H., Hjorland B., Curating research data: The potential roles of libraries and information professionals, J Doc, 70, 2, (2014); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos One, 9, 12, pp. 1-28, (2014); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Libr Inf Sci Res [Internet], 38, 4, pp. 319-326, (2016); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Libr Inf Sci Res [Internet], 36, 2, pp. 84-90, (2014); Kennan M.A., Corrall S., Afzal W., Making space” in practice and education: Research support services in academic libraries, Libr Manag, 35, 8-9, (2014); Patel D., Research data management: A conceptual framework, Libr Rev, 65, 4-5, (2016); Greene J.C., Caracelli V.J., Graham W.F., Toward a conceptual framework for mixed-method evaluation designs, Educational Evaluation and Policy Analysis, 11, 3, pp. 255-274, (1989); Creswell J.W., Klassen A.C., Plano Clark V.L., Smith K.C., Best Practices for Mixed Methods Research in the Health Sciences, pp. 541-545, (2013)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Article","Final","","Scopus","2-s2.0-85064661443"
"Bhardwaj R.K.","Bhardwaj, Raj Kumar (55295645900)","55295645900","Research data management in higher educational institutions","2019","DESIDOC Journal of Library and Information Technology","39","6","","269","270","1","3","10.14429/djlit.39.06.15281","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078349900&doi=10.14429%2fdjlit.39.06.15281&partnerID=40&md5=b628eed59c31da997e8f7f55d47a0f1f","St. Stephen’s College, University of Delhi, India","Bhardwaj R.K., St. Stephen’s College, University of Delhi, India","[No abstract available]","","","","","","","","","Gorgolewski K., Margulies D.S., Milham M.P., Making data sharing count: A publication-based solution, Front. Neurosci., 7, (2013); “Data Policies, (2018)","R.K. Bhardwaj; St. Stephen’s College, University of Delhi, India; email: raajchd@gmail.com","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Editorial","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85078349900"
"Napis S.; Michael V.A.; Jusoh Y.Y.; Abdullah R.; Sidi F.; Ishak I.; Marhaban M.H.; Tugiran Y.","Napis, Suhaimi (6507614723); Michael, Valerie Anak (57215665239); Jusoh, Yusmadi Yah (36147542500); Abdullah, Rusli (24483156100); Sidi, Fatimah (36139115600); Ishak, Iskandar (26422254000); Marhaban, Mohammad Hamiruce (57211599538); Tugiran, Yusnita (57209138720)","6507614723; 57215665239; 36147542500; 24483156100; 36139115600; 26422254000; 57211599538; 57209138720","A prelimenary study on the development of research data management (RDM) policy","2019","International Journal of Advanced Science and Technology","28","2","","377","384","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081539931&partnerID=40&md5=6ffd4d880ac4af77f10c5d94f0b0f108","Faculty Biotechnology and Bimolecular Sciences, Universiti Putra Malaysia, Malaysia; Faculty of Design and Architecture, Universiti Putra Malaysia, Malaysia; Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Faculty of Engineering, Universiti Putra Malaysia, Malaysia; Research Management Centre, Universiti Putra Malaysia, Malaysia","Napis S., Faculty Biotechnology and Bimolecular Sciences, Universiti Putra Malaysia, Malaysia; Michael V.A., Faculty of Design and Architecture, Universiti Putra Malaysia, Malaysia; Jusoh Y.Y., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Abdullah R., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Sidi F., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Ishak I., Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia; Marhaban M.H., Faculty of Engineering, Universiti Putra Malaysia, Malaysia; Tugiran Y., Research Management Centre, Universiti Putra Malaysia, Malaysia","Data sharing is an important pre-requisite towards enhancing knowledge in science and technology in recent years. The need to systematically collect, collate and manage research data will further facilitate knowledge archiving for future reference and re-use. This paper describes the development of a policy framework for data repository in one of Malaysian public university with a focus on the awareness of data sharing among researchers. Questionnaires were distributed to 164 respondents focusing on the benefit of data sharing, lack of data sharing requirement and types of data to be included in data repository management. From the finding, the respondents are willing to share and provide data to the other researchers provided there is a proper policy document. This research also discusses the recommended guidelines for the data repository policy and also on how to promote data sharing among researchers. © 2019 SERSC.","Data repository; Data sharing; Policy and governance; Research Data Management (RDM)","","","","","","Putra University, (9558500)","The authors would like to express gratitude for the financial support provided under the Putra University Grant Scheme, Grant cost centre: 9558500","Fecher B., Friesike S., Hebing M., What drives academic data sharing?, Plos One, 10, 2, (2015); Harvard Research Data Security Policy, (2017); Higgins E., Taylor M., Lisboa P., Arshad F., Developing a data sharing framework: A case study, Transforming Government: People, Process and Policy, 8, 1, pp. 151-164, (2014); Monash University Policy, (2017); ICSSR Data Service, (2015); Kim Y., Adler M., Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories, International Journal of Information Management, 35, 4, pp. 408-418, (2015); Kim Y., Zhang P., Understanding data sharing behaviors of STEM researchers: The roles of attitudes, norms, and data repositories, Library & Information Science Research, 37, 3, pp. 189-200, (2015); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 3, 43, pp. 263-265, (2017); Macmillan D., Data sharing and discovery: What librarians need to know, The Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); NTU Research Data Policy, (2019); Neuman W.L., Qualitative and Quantitative Research Designs. Social Research Methods. Qualitative and Quantitative Approaches. Fifth Edition, pp. 137-168, (2003); Rice R., Ekmekcioglu C., Haywood J., Jones S., Lewis S., Macdonald S., Weir T., Implementing the research data management policy: University of Edinburgh roadmap, International Journal of Digital Curation, 8, 2, pp. 194-204, (2013); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Government Information Quarterly, 30, pp. S19-S31, (2013); Research Data Management. Introduction, (2019); Research Data Management Policy, (2018); Zhu B., Marciano R., Moore R., Herr L., Schulze J., Digital repository: Preservation environment and policy implementation, International Journal on Digital Libraries, 12, 1, pp. 41-49, (2012)","","","Science and Engineering Research Support Society","","","","","","20054238","","","","English","Int. J. Adv. Sci. Technol.","Article","Final","","Scopus","2-s2.0-85081539931"
"Parciak M.; Bender T.; Sax U.; Bauer C.R.","Parciak, Marcel (57204583259); Bender, Theresa (57204583786); Sax, Ulrich (8956991900); Bauer, Christian Robert (57200871605)","57204583259; 57204583786; 8956991900; 57200871605","Applying FAIRness: Redesigning a Biomedical Informatics Research Data Management Pipeline","2019","Methods of information in medicine","58","6","","229","234","5","6","10.1055/s-0040-1709158","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084169387&doi=10.1055%2fs-0040-1709158&partnerID=40&md5=605247ce8a86439a0e75774dcfb5ff85","Department of Medical Informatics, University Medical Center Göttingen, Niedersachsen, Göttingen, Germany","Parciak M., Department of Medical Informatics, University Medical Center Göttingen, Niedersachsen, Göttingen, Germany; Bender T., Department of Medical Informatics, University Medical Center Göttingen, Niedersachsen, Göttingen, Germany; Sax U., Department of Medical Informatics, University Medical Center Göttingen, Niedersachsen, Göttingen, Germany; Bauer C.R., Department of Medical Informatics, University Medical Center Göttingen, Niedersachsen, Göttingen, Germany","BACKGROUND:  Managing research data in biomedical informatics research requires solid data governance rules to guarantee sustainable operation, as it generally involves several professions and multiple sites. As every discipline involved in biomedical research applies its own set of tools and methods, research data as well as applied methods tend to branch out into numerous intermediate and output data objects, making it very difficult to reproduce research results. OBJECTIVES:  This article gives an overview of our implementation status applying the Findability, Accessibility, Interoperability and Reusability (FAIR) Guiding Principles for scientific data management and stewardship onto our research data management pipeline focusing on the software tools that are in use. METHODS:  We analyzed our progress FAIRificating the whole data management pipeline, from processing non-FAIR data up to data usage. We looked at software tools for data integration, data storage, and data usage as well as how the FAIR Guiding Principles helped to choose appropriate tools for each task. RESULTS:  We were able to advance the degree of FAIRness of our data integration as well as data storage solutions, but lack enabling more FAIR Guiding Principles regarding Data Usage. Existing evaluation methods regarding the FAIR Guiding Principles (FAIRmetrics) were not applicable to our analysis of software tools. CONCLUSION:  Using the FAIR Guiding Principles, we FAIRificated relevant parts of our research data management pipeline improving findability, accessibility, interoperability and reuse of datasets and research results. We aim to implement the FAIRmetrics to our data management infrastructure and-where required-to contribute to the FAIRmetrics for research data in the biomedical informatics domain as well as for software tools to achieve a higher degree of FAIRness of our research data management pipeline. Georg Thieme Verlag KG Stuttgart · New York.","","Biomedical Research; Data Management; Health Information Interoperability; Health Services Accessibility; Humans; Informatics; Software; data interoperability; health care delivery; human; information processing; information science; medical research; software","","","","","","","","","","NLM (Medline)","","","","","","2511705X","","","32349157","English","Methods Inf Med","Article","Final","","Scopus","2-s2.0-85084169387"
"Kaari J.","Kaari, Jennifer (57195723131)","57195723131","Researchers at Arab universities hold positive views on research data management and data sharing","2020","Evidence Based Library and Information Practice","15","2","","168","170","2","1","10.18438/eblip29746","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087568474&doi=10.18438%2feblip29746&partnerID=40&md5=0f1e8fa1dcc6240caf11869e226fe727","East Orange Public Library, East Orange, NJ, United States","Kaari J., East Orange Public Library, East Orange, NJ, United States","Objective-To investigate researchers' practices and attitudes regarding research data management and data sharing. Design-Email survey. Setting-Universities in Egypt, Jordan, and Saudi Arabia. Subjects-Surveys were sent to 4,086 academic faculty researchers. Methods-The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices. Main Results-The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported Objective-To investigate researchers' practices and attitudes regarding research data management and data sharing. Design-Email survey. Setting-Universities in Egypt, Jordan, and Saudi Arabia. Subjects-Surveys were sent to 4,086 academic faculty researchers. Methods-The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices. Main Results-The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported. © 2020 Kaari.","","","","","","","","","Glynn L., A critical appraisal tool for library and information research, Library Hi Tech, 24, 3, pp. 387-399, (2006); Perrier L., Barnes L., Developing research data management services and support for researchers: A mixed methods study, Partnership: The Canadian Journal of Library and Information Practice and Research, 13, 1, (2018); Tenopir C., Dalton E., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists wWorldwide, PLOS ONE, 10, 8, (2015)","J. Kaari; East Orange Public Library, East Orange, United States; email: jkaari@eopl.org","","University of Alberta","","","","","","1715720X","","","","English","Evid. Based Libr. Inf. Pract.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85087568474"
"Monteiro G.; Lucas E.R.O.","Monteiro, Gabriela (58086848100); Lucas, Elaine Rosangela de Oliveira (58086538700)","58086848100; 58086538700","Open scientific data: identifying the role of management policies and development agencies; [Dados científicos abertos: identificando o papel das políticas de gestão e das agências de fomento]","2019","AtoZ","8","1","","13","20","7","1","10.5380/atoz.v8i1.67253","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141669908&doi=10.5380%2fatoz.v8i1.67253&partnerID=40&md5=a546795b2162750be423f72d4e4cb198","Universidade Federal de Santa Catarina, SC, Florianópolis, Brazil","Monteiro G., Universidade Federal de Santa Catarina, SC, Florianópolis, Brazil; Lucas E.R.O., Universidade Federal de Santa Catarina, SC, Florianópolis, Brazil","The transformations of scientific practices point out that science lives a moment of attention to collaborative work and the sharing of research data, in order to accelerate the construction of new knowledge and increase the efficiency of investments. International and Brazilian development agencies have already initiated recommendations and requirements for funded projects to include a research data management plan for open access sharing. In the short or medium term, each university or research center will need to be prepared to meet the requirements of funding agencies. Thus, this research has the main objective of developing a mapping of the main institutional policies of national and international development agencies in the promotion of open access to the data of scientific research developed in Brazil. The initial review of the literature contemplates the themes of scientific communication and the open access movement, as well as concepts about open scientific data and a brief contextualization of the promotion of scientific research in Brazil. Regarding the methodological procedures outlined, the research is considered social applied in what refers to its purpose. Regarding the central objective, it will have an exploratory and descriptive characteristic and, in relation to the technical procedures to obtain the data, a bibliographical-documentary research will be carried out. The collection and processing of the data to identify the development agencies that will be part of the documentary research began in April of this year. The data were obtained based on the list of Research Productivity Scholarships of the National Council of Scientific and Technological Development in July 2017 and through the extraction of information on the financing of their projects, which are based on the academic curriculum of the Plataforma Lattes. The research is in the phase of submission to the qualification examination. © 2019 Monteiro & Lucas.","Agencies of Promotion; Open Data Policy; Open Scientific Data; Research Data Management","","","","","","","","Alves V. B. A., Open Archives: via verde ou via dourada?, Ponto de Acesso, 2, 2, pp. 127-137, (2008); Baptista A. A., Costa S. M., Kuramoto H., Rodrigues E., Comunicação científica: o papel da Open Archives Initiative no contexto do Acesso Livre, Pesquisa Brasileira Em Ciência da Informação e Biblioteconomia, 2, 2, pp. 1-17, (2007); Bardin L., Análise de Conteúdo, (2011); Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, (2003); Declaração de Budapest, (2002); (2017); Ministério da Educação, (2017); Ministério da Ciência, Tecnolo-gia, Inovações e Comunicações, (2017); Brasília: Centro de Documentação e Informação (CEDI), (1988); Curty R. G. A., As diferentes dimensões do reuso de dados científicos, Tendências da Pesquisa Brasileira em Ciência da Informação, 9, 2, (2016); Gil A. C., Métodos e Técnicas de Pesquisa Social, (2008); Manifesto brasileiro de apoio ao acesso livre à informação científica, (2005); Kuramoto H., Informação científica: proposta de um modelo para o Brasil, Ciência da Informação. Brasília, 35, 2, pp. 91-102, (2006); Leite F. C. L., Como gerenciar e ampliar a visibilidade da informação científica brasileira: Repositórios Institucionais de Acesso Aberto, (2009); Estratégia Nacional de Ciência, Tecnologia e Inovação 2016-2010, (2016); Institucional, (2017); Mueller S. P. M., A comunicação científica e o movimento de acesso livre ao conhecimento, Ciência da Informação, 35, 2, pp. 27-38, (2006); OECD Principles and guidelines for access to research data from public funding, (2007); Oliveira A. C. S., Silva E. M., Ciência aberta: dimensões para um novo fazer científico, Informação & In-formação, 21, 2, pp. 05-39, (2016); Prodanov C. C., Freitas E. C., Metodologia do Trabalho Científico: métodos e técnicas da pesquisa e do trabalho acadêmico (2a ed.), (2013); Sayao L. S. F., Sales L. F., Dados de pesquisa: contribuição para o estabelecimento de um modelo de curadoria digital para o país, Tendências da Pesquisa Brasileira em Ciência da Informação, 6, 1, (2013); Sayao L. F., Sales L. F., Dados abertos de pesquisa: ampliando o conceito de acesso livre, RECIIS: Revista Eletrônica de Comunicação, Informação & Inovação em Saúde, Rio de Janeiro, 8, 2, pp. 76-92, (2014); Sayao L. F., Sales L. F., Guia de Gestão de Dados de Pesquisa para Bibliotecários e Pesquisadores, (2015); Targino M., das G., Comunicação Científica: uma revisão de seus elementos básicos, Informação & Socie-dade, João Pessoa, 10, 2, pp. 67-85, (2000)","G. Monteiro; Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil; email: gabriela.monteiro@udesc.br","","Programa de Pos-Graduacao em Gestao da Informacao, Universidade Federal do Parana","","","","","","2237826X","","","","Portuguese","AtoZ.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85141669908"
"Mosconi G.; Li Q.; Randall D.; Karasti H.; Tolmie P.; Barutzky J.; Korn M.; Pipek V.","Mosconi, Gaia (57194540455); Li, Qinyu (57204177188); Randall, Dave (7202208815); Karasti, Helena (55912546300); Tolmie, Peter (6602706528); Barutzky, Jana (57209203099); Korn, Matthias (36668656500); Pipek, Volkmar (8541973000)","57194540455; 57204177188; 7202208815; 55912546300; 6602706528; 57209203099; 36668656500; 8541973000","Three Gaps in Opening Science","2019","Computer Supported Cooperative Work: CSCW: An International Journal","28","3-4","","749","789","40","11","10.1007/s10606-019-09354-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066801122&doi=10.1007%2fs10606-019-09354-z&partnerID=40&md5=e0f1c6991674afc45543257e1897478a","University of Siegen, Siegen, Germany","Mosconi G., University of Siegen, Siegen, Germany; Li Q., University of Siegen, Siegen, Germany; Randall D., University of Siegen, Siegen, Germany; Karasti H., University of Siegen, Siegen, Germany; Tolmie P., University of Siegen, Siegen, Germany; Barutzky J., University of Siegen, Siegen, Germany; Korn M., University of Siegen, Siegen, Germany; Pipek V., University of Siegen, Siegen, Germany","The Open Science (OS) agenda has potentially massive cultural, organizational and infrastructural consequences. Ambitions for OS-driven policies have proliferated, within which researchers are expected to publish their scientific data. Significant research has been devoted to studying the issues associated with managing Open Research Data. Digital curation, as it is typically known, seeks to assess data management issues to ensure its long-term value and encourage secondary use. Hitherto, relatively little interest has been shown in examining the immense gap that exists between the OS grand vision and researchers’ actual data practices. Our specific contribution is to examine research data practices before systematic attempts at curation are made. We suggest that interdisciplinary ethnographically-driven contexts offer a perspicuous opportunity to understand the Data Curation and Research Data Management issues that can problematize uptake. These relate to obvious discrepancies between Open Research Data policies and subject-specific research practices and needs. Not least, it opens up questions about how data is constituted in different disciplinary and interdisciplinary contexts. We present a detailed empirical account of interdisciplinary ethnographically-driven research contexts in order to clarify critical aspects of the OS agenda and how to realize its benefits, highlighting three gaps: between policy and practice, in knowledge, and in tool use and development. © 2019, Springer Nature B.V.","Collaborative research practices; Digital curation; Ethnographic approach; Open Data policy; Open research Data; Open Science; Research Data management; Research Data practices","Data curation; Information management; Collaborative research; Digital curation; Ethnographic approaches; Open science; Research data; Research data managements; Open Data","","","","","Deutsche Forschungsgemeinschaft, DFG, (SFB 1187)","Funding text 1: In our institution this process is still at a very early stage. The IT service provider of the university struggles to develop solutions that could support data sharing and reuse for the CRC context. Very few Bbest practices^ can be shared so far among other INF projects funded by the DFG. From how to construct a Research Data Management Plan to how to develop solutions for long-term preservation and data reuse is left to each INF project to discover independently (no suggestions are provided from the funders). On the one hand, funders and IT service providers are at the very beginning of this process and they have yet to develop the requisite know-how concerning OS strategy. On the other hand, the researchers have just started to realize and reflect upon the potential impact of OS over their work.; Funding text 2: This research has been possible thanks to the engagement of many scholars, the CRC BMedia of Cooperation^ organization board and the IT service provider with whom we have worked with and learned from. The findings in this paper originate from the project INF funded by a grant of the DFG (SFB 1187).","Daisy A., What is Digital Curation? DCC briefing papers: Introduction to curation, Edinburgh: Digital Curation Centre, (2008); Arzberger P., Schroeder P., Beaulieu A., Bowker G., Casey K., Laaksonen L., Moorman D., Uhlir P., Wouters P., Promoting access to public research Data for scientific, economic, and social development, Data Science Journal, 3, pp. 135-152, (2006); Asherjahnke A.M., Curating the ethnographic moment, Archive Journal, 3, (2013); Bechhofer S.D., Roure M., Gamble C., Gobleiain B., Research objects: Towards exchange and reuse of digital knowledge, FWCS 2010. 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Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, San Francisco, California, USA, 27 February – 02 March 2016, pp. 194-204, (2016); Zimmerman A., Not by metadata alone: The use of diverse forms of knowledge to locate data for reuse, International Journal on Digital Libraries, 7, 1-2, pp. 5-16, (2007)","G. Mosconi; University of Siegen, Siegen, Germany; email: gaia.mosconi@uni-siegen.de","","Springer Netherlands","","","","","","09259724","","CSCWE","","English","Comput Supported Coop Work CSCW Int J","Article","Final","","Scopus","2-s2.0-85066801122"
"Payal; Awasthi S.; Tripathi M.","Payal (57214149681); Awasthi, Shipra (57208174830); Tripathi, Manorama (7007046122)","57214149681; 57208174830; 7007046122","A selective review of literature on research data management in academic libraries","2019","DESIDOC Journal of Library and Information Technology","39","6","","338","345","7","4","10.14429/djlit.39.06.14451","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078330185&doi=10.14429%2fdjlit.39.06.14451&partnerID=40&md5=5c2dc1f983e20699ac2493a0dc0e7096","Central Library, Jawaharlal Nehru University, New Delhi, 110 067, India","Payal, Central Library, Jawaharlal Nehru University, New Delhi, 110 067, India; Awasthi S., Central Library, Jawaharlal Nehru University, New Delhi, 110 067, India; Tripathi M., Central Library, Jawaharlal Nehru University, New Delhi, 110 067, India","The present paper dwells on Research Data Management (RDM), its need, importance, the behaviour of the researchers in different disciplines towards data sharing and the role of libraries in extending data management services. The policies of publishers and funding bodies for sharing research data have also been described. It underlines that the library professionals should have a comprehensive understanding of the emerging issues, trends and challenges about the research data management to deploy appropriate services for the researchers. It highlights various resources on research data management, which may serve as guidelines for library professionals and researchers. The study will prove beneficial to library professionals and inspire them to extend RDM services in their organisations The paper concludes by advocating that the library professionals must update and upskill themselves with new trends, tools and techniques to provide RDM services. They should sensitise researchers to make their datasets accessible for reuse and sharing. © 2019, DESIDOC.","Data sharing; Research data; University libraries","","","","","","","","Koltay T., Data governance, data literacy and the management of data quality, IFLA J, 42, 4, pp. 303-312, (2016); Kuipers T., Hoeven J., Insight into digital preservation of research output in Europe, Survey Report, (2009); Childs S., Managing and sharing research data, A Guide to Good Practice. Rec. Manage. J., 24, 3, pp. 252-253, (2014); 2018 top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Amp; Research Libraries News, 79, 6; Galeto M., Data Management, (2016); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, (2016); (2018); Publisher Research Data Policies, (2017); Cox A.M., Verbaan E., Sen B.A., Up skilling liaison librarians for research data management, Ariadne, 2012, 70; Guidelines on the Implementation of Open Access to Scientific Publications and Research Data in Projects Supported by the European Research Council under Horizon 2020, (2017); Poole A.H., Garwood D.A., Digging into data management in public‐funded, international research in digital humanities, J. Assoc. Inf. Sci. Technol., (2019); Toups M., Hughes M., When data curation isn‘t: A redefinition for liberal arts universities, J. Libr. Adm., 53, 4, pp. 223-233, (2013); Latham B., Poe J.W., The library as partner in university data curation: A case study in collaboration, J. Web Libr.., 6, 4, pp. 288-304, (2012); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curationbehaviors and attitudes at a teaching-centered university, College Res. Libr., 73, 4, pp. 349-365, (2012); Medeiros N., A public trust: Libraries and data curation, OCLC Syst. Serv.: Int. Digital Libr. Perspect., 29, 4, pp. 192-194, (2013); David L.T., Alayon S.B., Digital curation projects: A study of selected academic and research repositories in the Philippines, LIBRES: Libr. Inf. Sci. Res. Electron. J, 26, 1, (2016); Chao T.C., Et al., Data practices and curation vocabulary (DPCVocab): An empirically derived framework of scientific data practices and curatorial processes, Wiley Online Libr, (2015); Harris-Pierce R.L., Quan Liu Y., Is data curation education at library and information science schools in North America adequate?, New Libr. World, 113, 11-12, pp. 598-613, (2012); Weber N.M., Palmer C.L., Chao T.C., Current trends and future directions in data curation research and education, J. Web Libr., 6, 4, pp. 305-320, (2012); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists ‘data-sharing behaviors: A multilevel analysis, Inf. Sci. Fac. Publ., 16, (2016); Markauskaite, Et al., Investigating eResearch: Collaboration practices and future challenges, 2012, In Collaborative and Distributed E-Research: Innovations in Technologies, Strategies and Applications; Kurata K., Et al., Identifying the complex position of research data and data sharing among researchers in natural science, Sage J, 3, (2017); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists’ data-sharing behaviors: A multilevel analysis, Inf. Sci. Fac. Publ., 16, (2016); Nguyen V.M., Et al., To share or not to share in the emerging era of big data: Perspectives from fish telemetry researchers on data sharing, Res. Gate, (2017); Joo S., Peters C., User needs assessment for research data services in a research university, J. Libr. Inf. Sci., (2019); Vidal-Infer A., Tarazona B., Alonso-Arroyo A., Public availability of research data in dentistry journals indexed in Journal Citation Reports, Springer Link. Clin Oral Invest, 22, (2018); Alawialsheikh-Ali A., Et al., Public availability of published research data in high-impact journals, Plos ONE, 6, 9, (2011); Polona V., Vlasta Z., Research data management and research data literacy in Slovenian science, J. Doc., 75, 1, (2019); Lassi M., Et al., Research data services: An exploration of requirements at two Swedish universities, Sage J, pp. 266-277, (2016); Piwowar H.A., Chapman W.W., Mine, yours, ours? Sharing data on human genetic variation, Plos One, 2010; Mayernik M.S., Research Data and Metadata Curation as Institutional Issues. Willey Online Library, (2016); Amanda L., Whitmire M.B., Shan C.S., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electron. Libr. Inf. Syst., 49, 4, pp. 382-407, (2015); Barsky E., Three UBC Research Data Management (RDM) Surveys: Science and Engineering, Humanities and Social Sciences, and Health Sciences: Summary Report, (2017); Borycz J., Et al., Assisting geophysicists in data management: Perceptions and opportunities, In AGU Fall Meeting Abstracts, (2018); Mancille H.A., Teperek M., van Dijck J., Den Heijer K., Eggermont R., Plomp E., Kurapati S., On a quest for cultural change-surveying research data management practices at Delft University of Technology, Libr. Q, 29, 1, (2019); Park J., Gabbard J.L., Factors that affect scientists‘ knowledge sharing behavior in health and life sciences research communities: Differences between explicit and implicit knowledge, Comp. Human Behav., 78, pp. 326-335, (2018); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J. Libr. Inf. Sci., 46, 4, pp. 299-316, (2014); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos ONE, 10, 8, (2015); Surkus A., Read K., Research data management, J. Med. Libr. Assoc.: JMLA, 103, 3, (2015); Li Y., Dressel W., Hersey D., Research data management: What can librarians really help?, Grey J. (TGJ), 15, 1, (2019); Giarlo M.J., Academic libraries as data quality hubs, J. Libr. Scholarly Commun., 1, 3, (2013); Zilinskinelson L.; Defining and Implementing a Pragmatic Data Governance Process with Oracle Metadata Management and Oracle Data Quality Solutions. Oracle Fusion Middleware, (2015); Koning D.M., Meyer B., Moors A., & amp; Pels, P. Guidelines for anthropological research: Data management, ethics, and integrity, Ethnography, 20, 2, pp. 170-174, (2019); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College Res. Libr., 78, 7, pp. 920-933, (2017); Tenopir C., Et al., Research data management services in academic research libraries and perceptions of librarians, Libr. Inf. Sci. Res., 36, 2, pp. 84-90, (2014); Tenopir C., Et al., Research data services in European academic research libraries, Liber Q, 27, 1, pp. 23-44, (2017); Antell K., Et al., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College Res. Libr., 75, 4, pp. 557-574, (2014); Corrall S., Et al., Bibliometrics and research data management services: Emerging trends in library support for research, Libr. Trends, 61, 3, pp. 636-674, (2013); Creamer A., Et al., An assessment of needed competencies to promote the data curation and management librarianship of health sciences and science and technology librarians in New England, J. Escience Libr., 1, 1, pp. 18-26, (2012); Castle C., Getting the Central RDM Message Across: A Case Study of Central versus Discipline-Specific Research Data Services (RDS), 69, 2, (2019); Verbakal E., Grootveld M., Essential for data support: Five years’ experience for data management training, IFLA J, pp. 278-283, (2016); Briney K., Goben A., Zilinski L., Do You have an Institutional Data Policy?, (2015); Read K.B., Koos J., Miller R.S., Miller C.F., Phillips G.A., Scheinfeld L., Surkis A., A model for initiating research data management services at academic libraries, J. Med. Libr. Assoc.: JMLA, 107, 3, (2019); Grunzke R., Hartmann V., Jejkal T., Kollai H., Prabhune A., Herold H., Hoffmann A., The MASi repository service—Comprehensive, metadata-driven and multi-community research data management, Future Gener. Comp. Syst., 94, pp. 879-894, (2019); (2019); Fitschen T., Et al., Caos DB-Research data management for complex, changing, and automated research workflows, Data, 4, 2, (2019); Wilkinson M.D., Et al., Scientific Data, 2016, 3. the FAIR Guiding Principles for Scientific Data Management and Stewardship, (2019); (2015)","M. Tripathi; Central Library, Jawaharlal Nehru University, New Delhi, 110 067, India; email: manoramatripathi2@yahoo.com","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Review","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85078330185"
"de Koning M.; Meyer B.; Moors A.; Pels P.","de Koning, Martijn (24167831800); Meyer, Birgit (8041752100); Moors, Annelies (6603872960); Pels, Peter (7801594642)","24167831800; 8041752100; 6603872960; 7801594642","Guidelines for anthropological research: Data management, ethics, and integrity","2019","Ethnography","20","2","","170","174","4","14","10.1177/1466138119843312","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064937408&doi=10.1177%2f1466138119843312&partnerID=40&md5=0b970d35c0292fd0da864d30c5ffc6ea","University of Amsterdam, Netherlands; Utrecht University, Netherlands; Universiteit Leiden, Netherlands","de Koning M., University of Amsterdam, Netherlands; Meyer B., Utrecht University, Netherlands; Moors A., University of Amsterdam, Netherlands; Pels P., Universiteit Leiden, Netherlands","As anthropologists we are increasingly confronted with attempts – be it by employers, the media, or policy makers – to regulate our work in ways that are both epistemologically and ethically counterproductive and threaten our scientific integrity. This document is written out of concern about the problems that occur when protocols for data management, integrity, and ethics, developed for sciences that employ a positivistic, hypothesis-testing and replicable style of research, are applied to different scientific practices, such as social and cultural anthropology, that are more explorative, intersubjective and interpretative. In social and cultural anthropology, issues of scientific governance and its ethics are strongly case-specific. Still, concerns about the imposition of scientific protocols from other disciplines require anthropologists to develop some general guidelines for data management, integrity and ethics of anthropological research. Rather than fixed rules, these are broad principles to guide work and adapt it to specific cases. © The Author(s) 2019.","anonymity; consent; data management; ethics; harm; integrity; privacy; protection","","","","","","","","(2018); Pels P., Boog I., Florusbosch J.H., Kripe Z., Minter T., Postma M., Sleeboom-Faulkner M., Simpson B., Dilger H., Schonhuth M., Von Poser A., Cordillera A., Castillo R., Lederman R., Richards-Rissetto H., Data management in anthropology: The next phase in ethics governance?, Social Anthropology, 26, 3, pp. 391-413, (2018)","M. de Koning; University of Amsterdam, Netherlands; email: M.J.M.deKoning@uva.nl","","SAGE Publications Ltd","","","","","","14661381","","","","English","Ethnography","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85064937408"
"Cardoso J.; Proença D.; Borbinha J.","Cardoso, João (57217569103); Proença, Diogo (55734670700); Borbinha, José (15041751700)","57217569103; 55734670700; 15041751700","Machine-actionable data management plans: A knowledge retrieval approach to automate the assessment of funders’ requirements","2020","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12036 LNCS","","","118","125","7","5","10.1007/978-3-030-45442-5_15","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084185574&doi=10.1007%2f978-3-030-45442-5_15&partnerID=40&md5=b381ad4576b03d6a87d47bd1fd056a82","INESC-ID, Lisbon, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal","Cardoso J., INESC-ID, Lisbon, Portugal, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Proença D., INESC-ID, Lisbon, Portugal, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Borbinha J., INESC-ID, Lisbon, Portugal, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal","Funding bodies and other policy-makers are increasingly more concerned with Research Data Management (RDM). The Data Management Plan (DMP) is one of the tools available to perform RDM tasks, however it is not a perfect concept. The Machine-Actionable Data Management Plan (maDMP) is a concept that aims to make the DMP interoperable, automated and increasingly standardised. In this paper we showcase that through the usage of semantic technologies, it is possible to both express and exploit the features of the maDMP. In particular, we focus on showing how a maDMP formalised as an ontology can be used automate the assessment of a funder’s requirements for a given organisation. © Springer Nature Switzerland AG 2020.","Data Management Plan; Machine Actionable Data Management Plan; Semantic technologies","Semantics; Funding bodies; Knowledge retrieval; Management plans; Policy makers; Research data managements; Semantic technologies; Information management","","","","","Data Management Plan; National Science Foundation, NSF; European Commission, EC; Fundação para a Ciência e a Tecnologia, FCT, (LISBOA-01-0145-FEDER-016394, UID/CEC/50021/2019)","Funding text 1: Funding bodies and other policy-makers are increasingly more concerned with Research Data Management (RDM). One of the contributing factors is the general perception that research data should be a public good [16]. In order to guide researchers through the process of managing their data, many funding agencies (e.g. the National Science Foundation (NSF), the European Commission (EC), or the Funda¸cão para a Ciência e Tecnologia (FCT) have created and published their own open access policies, as well as requiring that any grant proposals be accompanied by a Data Management Plan (DMP).; Funding text 2: Acknowledgements. This work was supported by national funds through FCT with reference UID/CEC/50021/2019, and by project PRECISE (LISBOA-01-0145-FEDER-016394).","Antunes G., Analysis of Enterprise Architecture Models: An Application of Ontologies to the Enterprise Architecture Domain, (2015); Baader F., Calvanese D., McGuinness D., Patel-Schneider P., Nardi D., The Description Logic Handbook: Theory, (2003); Bakhshandeh M., Ontology-Driven Analysis of Enterprise Architecture Models, (2016); Breitman K., Casanova M.A., Truszkowski W., Semantic Web: Concepts, Technologies and Applications, Springer, Heidelberg, (2007); Lenzerini M., Milano D., Poggi A., Ontology representation & reasoning. Technical report, Noe Interop (IST-508011), (2004); McGuinness D.L., van Harmelen F., Et al., Owl web ontology language overview, W3C Recommendation, 10, 10, (2004); Michener W.K., Ten simple rules for creating a good data management plan, Plos Comput. Biol., 11, 10, (2015); Miksa T., Neish P., Walk P., WG DMP Common Standards Case Statement, (2017); Miksa T., Vieira R.J.C., Barateiro J., Rauber A., Vplan-Ontology for Collection of Process Verification Data, (2014); Proenca D., Maturity Assessment Support with Conceptual Modelling Methods and Semantic Techniques, (2018); Sheth A.P., Changing focus on interoperability in information systems: From system, syntax, structure to semantics, Interoperating Geographic Information Systems. the Springer International Series in Engineering and Computer Science, 495, pp. 5-29, (1999); Sheth A.P., Ramakrishnan C., Semantic (Web) technology in action: Ontology driven information systems for search, integration, and analysis, IEEE Data Eng. Bull., 26, 4, (2003); Simms S., Jones S., Mietchen D., Miksa T., Machine-actionable data management plans (MaDMPs), Res. Ideas Outcomes, 3, (2017); Studer R., Benjamins V.R., Fensel D., Et al., Knowledge engineering: Principles and methods, Data Knowl. Eng., 25, 1, pp. 161-198, (1998); Uschold M., Gruninger M., Ontologies: Principles, methods and applications, Knowl. Eng. Rev., 11, 2, pp. 93-136, (1996); Whyte A., Tedds J., Making the Case for Research Data Management, (2011)","J. Cardoso; INESC-ID, Lisbon, Portugal; email: joao.m.f.cardoso@tecnico.ulisboa.pt","Jose J.M.; Yilmaz E.; Magalhães J.; Martins F.; Castells P.; Ferro N.; Silva M.J.","Springer","","42nd European Conference on IR Research, ECIR 2020","14 April 2020 through 17 April 2020","Lisbon","239129","03029743","978-303045441-8","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85084185574"
"Si L.; Zeng Y.; Guo S.; Zhuang X.","Si, Li (35812698800); Zeng, Yueliang (57209261054); Guo, Sicheng (57209252558); Zhuang, Xiaozhe (55909737400)","35812698800; 57209261054; 57209252558; 55909737400","Investigation and analysis of research support services in academic libraries","2019","Electronic Library","37","2","","281","301","20","18","10.1108/EL-06-2018-0125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067048773&doi=10.1108%2fEL-06-2018-0125&partnerID=40&md5=a95a473d0f260eefb9ef379e6c122b2f","Center for the Studies of Information Resources, Wuhan University, Wuhan, China; Department of Library Science, School of Information Management, Wuhan University, Wuhan, China; School of Information Management, Wuhan University, Wuhan, China; Library of Wuhan University of Technology, Wuhan, China; School of Information Management, Wuhan University, Wuhan, China","Si L., Center for the Studies of Information Resources, Wuhan University, Wuhan, China, School of Information Management, Wuhan University, Wuhan, China; Zeng Y., Department of Library Science, School of Information Management, Wuhan University, Wuhan, China; Guo S., School of Information Management, Wuhan University, Wuhan, China; Zhuang X., Library of Wuhan University of Technology, Wuhan, China","Purpose: This paper aims at understanding the current situation of research support services offered by academic libraries in world-leading universities and providing useful implications and insights for other academic libraries. Design/methodology/approach: Of the top 100 universities listed in the QS World University Rankings in 2017, 76 libraries were selected as samples and a website investigation was conducted to explore the research support services. The statistical method and visualization software was used to generalize the key services, and the text analysis and case analysis were applied to reveal the corresponding implementation. Findings: Research support service has become one of the significant services of academic libraries in the context of e-research and data-intensive research. The research support services can be generally divided into seven aspects, as follows: research data management (62, 81.58 per cent), open access (64, 84.21 per cent), scholarly publishing (59, 77.63 per cent), research impact measurement (32, 42.11 per cent), research guides (47, 61.84 per cent), research consultation (59, 77.63 per cent) and research tools recommendation (38, 50.00 per cent). Originality/value: This paper makes a comprehensive investigation of research support services in academic libraries of top-ranking universities worldwide. The findings will help academic libraries improve research support services; thus, advancing the work of researchers and promoting scientific discovery. © 2019, Emerald Publishing Limited.","Academic libraries; Data-intensive research; E-research; Research support","article; case study; consultation; financial management; human; library; publishing; scientist; software; statistical analysis","","","","","National Natural Science Foundation of China, NSFC, (71573198)","This paper is one of the research outcomes of the project supported by The National Natural Science Foundation of P.R. China (Project Name: Research on the Formation Mechanism and Service of the Federation of Institutional Research Data Repository in Big Data Environment, Project No. 71573198).","Environmental scan 2015, (2015); Environmental scan 2017, (2017); Barga R.S., Andrews S., Parastatidis S., A virtual research environment (VRE) for bioscience researchers, International Conference on Advanced Engineering Computing and Applications in Sciences, pp. 31-38, (2007); Bladek M., Bibliometrics services and the academic library: meeting the emerging needs of the campus community, College and Undergraduate Libraries, 21, 3-4, pp. 330-344, (2014); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, 3, pp. 224-234, (2015); Read the budapest open access initiative, (2002); Butler K., Byrd J., Research consultation assessment: perceptions of students and librarians, Journal of Academic Librarianship, 42, 1, pp. 83-86, (2016); Chadwell F., Sutton S.C., The future of open access and library publishing, New Library World, 115, 5-6, pp. 225-236, (2014); (2011); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); E-research, (2006); Gessner G.G., Eldermire E., Tang N., Tancheva K., The research lifecycle and the future of research libraries: a library of apps, Proceedings of the ACRL 2017 Conference, pp. 533-543, (2017); Geuna A., Martin B.R., University research evaluation and funding: an international comparison, Minerva, 41, 4, pp. 277-304, (2003); Gosh S.B., Das A.K., Open access and institutional repositories a developing country perspective: a case study of India, IFLA Journal, 33, 3, pp. 229-250, (2007); Haddow G., Mamtora J., Research support in australian academic libraries: services, resources, and relationships, New Review of Academic Librarianship, 23, 6, pp. 1-21, (2017); Hansson J., Johannesson K., Librarians’ views of academic library support for scholarly publishing: an every-day perspective, The Journal of Academic Librarianship, 39, 3, pp. 232-240, (2013); Hensley M.K., Shreeves S.L., Davis-Kahl S., A survey of library support for formal undergraduate research programs, College and Research Libraries, 75, 4, pp. 422-441, (2013); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the university of bristol: charting a course from project to service, Program: Electronic Library and Information Systems, 49, 4, pp. 475-493, (2015); Hoffman S., Dynamic Research Support in Academic Libraries, (2016); Hoodless C., Pinfield S., Subject v. functional: should subject librarians be replaced by functional specialists in academic libraries?, Journal of Librarianship and Information Science, 50, 4, pp. 345-360, (2018); Huang H., Jiang Y., Qiu X., Study on research plans in Australian University Libraries, Library Development, 281, 4, pp. 77-83, (2017); 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McMurry N., Holdsworth L., Research support services for agriculture, (2017); McRostie D., The only constant is change: evolving the library support model for research at the University of Melbourne, Library Management, 37, 6-7, pp. 363-372, (2016); Robyn D., Richard W., RIMS: the research impact measurement service at the University of New South Wales, Australian Academic and Research Libraries, 40, 2, pp. 76-87, (2009); Santillan-Aldana J., The open access movement and the library world seen from the experience of the E-LIS project, OCLC Systems and Services: International Digital Library Perspectives, 25, 2, pp. 135-147, (2009); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Sinkinson C., Alexander S., Hicks A., Guiding design: Exposing librarian and student mental models of research guides, Portal: Libraries and the Academy, 12, 1, pp. 63-84, (2012); Thomas J., Future-proofing: the academic library’s role in e-research support, Library Management, 32, 1-2, pp. 37-47, (2011); Winston M., The US urban university library: supporting research related to crime, New Library World, 111, 3-4, pp. 125-145, (2010); Xiao L., Zhang C., Theories and developments of research support service in academic universities: with Peking University library’s practices, Journal of Academic Library, 34, 6, pp. 35-42, (2016); Xue J., Jiao K., Zhang X., Et al., Research support service of foreign academic libraries based on research lifecycle, Information Studies: Theory and Application, 39, 5, pp. 110-114, (2016); Zhao L., Riding the wave of open access: providing library research support for scholarly publishing literacy, Australian Academic and Research Libraries, 45, 1, pp. 3-18, (2014)","Y. Zeng; Department of Library Science, School of Information Management, Wuhan University, Wuhan, China; email: zengyueliang2014@163.com","","Emerald Group Holdings Ltd.","","","","","","02640473","","ELLID","","English","Electron. Libr.","Article","Final","","Scopus","2-s2.0-85067048773"
"Zhang L.; Eichmann-Kalwara N.","Zhang, Li (55936403400); Eichmann-Kalwara, Nickoal (57194658593)","55936403400; 57194658593","Mapping the Scholarly Literature Found in Scopus on Research Data Management: A Bibliometric and Data Visualization Approach","2019","Journal of Librarianship and Scholarly Communication","7","1","eP2226","","","","17","10.7710/2162-3309.2266","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150926583&doi=10.7710%2f2162-3309.2266&partnerID=40&md5=a34dd8576b183e5fbac9bb6c0aa17bb7","Mississippi State University, United States; University of Colorado Boulder, United States","Zhang L., Mississippi State University, United States; Eichmann-Kalwara N., University of Colorado Boulder, United States","INTRODUCTION Since the 2000s, interest in research data management (RDM) has grown considerably. As a result, a large body of literature has discussed a broad variety of aspects related to data management. But, few studies have examined and also interpreted from visual perception the intellectual structure and progressive development of the existing literature on RDM. METHODS Guided by five research questions, this study employed bibliometric techniques and a visualization tool (CiteSpace) to identify and analyze the patterns of the scholarly publications about RDM. RESULTS Through CiteSpace’s modeling and computing, the knowledge (or network) structures, significant studies, notable topics, and development trends in the literature of RDM were revealed. DISCUSSION The majority of the literature pertinent to RDM was published after 2002. Major research clusters within this interdisciplinary field include “scientific collaboration,” “research support service,” and “data literacy,” while the “scientific collaboration” research cluster was the most active. Topics such as “digital curation” and “information processing” appeared most frequently in the RDM literature. Additionally, there was a sharp increase in several specific topics, such as “digital library,” “big data,” and “data sharing.” CONCLUSION By looking into the “profile” of the literature on RDM, in terms of knowledge structure, evolving trends, and important topics in the domain, this work will add new information to current discussions about RDM, new service development, and future research focuses in this area. © 2019 Zhang & Eichmann-Kalwara.","","","","","","","Office of Science and Technology policies; National Science Foundation, NSF; National Institutes of Health, NIH","forums for discussions concerning RDM. PLoS ONE, another interdisciplinary journal, has the highest citation counts (n=164). As is seen in Figure 5, researchers in biological sciences, library science, computer science, and health sciences are heavily engaged in this particular field, which is expected given recent Office of Science and Technology policies, and National Institutes of Health (NIH) and National Science Foundation (NSF) mandates for research data management plans. Since this study searched only one database (Scopus), we cannot say that research on RDM practices emphasizes STEM fields. But Akers and Doty","Akers K. G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Arzberger P., Schroeder P., Beaulieu A., Bowker G., Casey K, Laaksonen L., Wouters P., Promoting access to public research data for scientific, economic, and social development, Data Science Journal, 3, pp. 135-153, (2004); Borgman C. L., Wallis J. C., Mayernik M. S., Pepe A., Drowning in data: Digital library architecture to support scientific use of embedded sensor networks, Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital libraries, pp. 269-277, (2007); Borgman C. L., Wallis J. C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal of Digital Libraries, 7, pp. 17-30, (2007); Borgman C. L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borner K., Chen C., Boyack K. W., Visualizing knowledge domains, Annual Review of Information Science & Technology, 37, 1, pp. 179-255, (2003); Carlson J., Fosmire M., Miller C., Sapp Nelson M., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Chen C., Searching for intellectual turning points: Progressive knowledge domain visualization, Proceedings of the National Academy of Sciences of the United States of America, 101, pp. 5303-5310, (2004); Chen C., Ibekwe-SanJuan F., Hou J., The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis, Journal of the American Society for Information Science and Technology, 61, pp. 1386-1409, (2010); Corrall S, Kennan M. A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A. M., Kennan M. A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A. M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Dotsika F., Watkins A., Identifying potentially disruptive trends by means of keyword network analysis, Technological Forecasting and Social Change, 119, pp. 114-127, (2017); Durieux V., Gevenois P. A., Bibliometric indicators: Quality measurements of scientific publications, Radiology, 255, 2, pp. 342-351, (2010); Edwards P. N., Mayernik M. S., Batcheller A. L., Geoffrey C. B., Borgman C. L., Science friction: Data, metadata, and collaboration, Social Studies of Science, 41, 5, pp. 667-690, (2011); Flanders J., Munoz T., An introduction to humanities data curation, DH curation guide: A community resource guide to data curation in the digital humanities; Gherghina S., Katsanidou A., Data availability in political science journals, European Political Science, 12, 3, pp. 333-349, (2013); Gold A., Cyberinfrastructure, data, and libraries, Part 2: Libraries and the data challenge: Roles and actions for libraries, D-Lib Magazine, 13, (2007); Heidorn P. B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-289, (2008); Hey T., Tansley S., Tolle K., The fourth paradigm: Data-intensive scientific discovery, (2009); Kraus S., Filser M., Eggers F., Hills G. E., Hultman C. M., The entrepreneurial marketing domain: A citation and co-citation analysis, Journal of Research in Marketing and Entrepreneurship, 14, 1, pp. 6-26, (2012); Kirk A., Data visualization: A successful design process, (2012); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Lynch C., Big data: How do your data grow?, Nature, 455, pp. 28-29, (2008); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Molloy L., Digital curation skills in the performing arts: An investigation of practitioner awareness and knowledge of digital object management and preservation, International Journal of Performance Arts and Digital Media, 10, 1, pp. 7-20, (2014); Piwowar H. A., Day R. S., Fridsma D. B., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, (2007); Pritchard A., Statistical bibliography or bibliometrics?, Journal of Documentation, 25, pp. 348-349, (1969); Pryor G., Managing research data, (2012); Ray J. M., Research data management: Practical strategies for information professionals, (2014); Rodrigues S. P., van Eck N. J., Waltman L., Jansen F. W., Mapping patient safety: A large-scale literature review using bibliometric visualisation techniques, BMJ Open, 4, 3, pp. 1-8, (2014); Small H, Boyack K.W., Klavans R., Identifying emerging topics in science and technology, Research Policy, 43, 8, pp. 1450-1467, (2014); Thelwall M., Kousha K., Figshare: A universal repository for academic resource sharing?, Online Information Review, 40, 3, pp. 333-346, (2016); Tenopir C., Allard S., Douglass K., Aydinoglu A. U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Sandusky R. J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Towns J., Cockerill T., Dahan M., Foster I., Gaither K., Grimshaw A, Wilkins-Diehr N., XSEDE: Accelerating scientific discovery, Computing in Science & Engineering, 16, 5, pp. 62-74, (2014); Verbeek A., Debackere K., Luwel M., Zimmermann E., Measuring progress and evolution in science and technology I: The multiple uses of bibliometric indicators, International Journal of Management Reviews, 4, 2, pp. 179-211, (2002); White H. D., McCain K. W., Visualizing a discipline: An author co-citation analysis of information science, 1972–1995, Journal of the American Society for Information Science and Technology, 49, 4, pp. 327-355, (1998); Williams M., Bagwell J., Nahm Zozus M., Data management plans: The missing perspective, Journal of Biomedical Informatics, 71, pp. 130-142, (2017); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008)","L. Zhang; 395 Hardy Rd., 39762, United States; email: lzhang@library.msstate.edu","","Iowa State University Digital Press","","","","","","21623309","","","","English","J. Librariansh. Sch. Commun.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85150926583"
"Grunzke R.; Hartmann V.; Jejkal T.; Kollai H.; Prabhune A.; Herold H.; Deicke A.; Dressler C.; Dolhoff J.; Stanek J.; Hoffmann A.; Müller-Pfefferkorn R.; Schrade T.; Meinel G.; Herres-Pawlis S.; Nagel W.E.","Grunzke, Richard (37096970200); Hartmann, Volker (7005982861); Jejkal, Thomas (24478712700); Kollai, Helen (56668667900); Prabhune, Ajinkya (56534978900); Herold, Hendrik (57199151343); Deicke, Aline (57190859263); Dressler, Christiane (57201417472); Dolhoff, Julia (57201422140); Stanek, Julia (57190610770); Hoffmann, Alexander (55706038800); Müller-Pfefferkorn, Ralph (8774342800); Schrade, Torsten (57190125722); Meinel, Gotthard (6506545059); Herres-Pawlis, Sonja (9277407800); Nagel, Wolfgang E. (9435404200)","37096970200; 7005982861; 24478712700; 56668667900; 56534978900; 57199151343; 57190859263; 57201417472; 57201422140; 57190610770; 55706038800; 8774342800; 57190125722; 6506545059; 9277407800; 9435404200","The MASi repository service — Comprehensive, metadata-driven and multi-community research data management","2019","Future Generation Computer Systems","94","","","879","894","15","10","10.1016/j.future.2017.12.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044718212&doi=10.1016%2fj.future.2017.12.023&partnerID=40&md5=2f98c778d768e8202db85cb0fedce2ab","Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Germany; Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Germany; Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Germany; Institut für Anorganische Chemie, Rheinisch-Westfälische Technische Hochschule Aachen, Germany","Grunzke R., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Hartmann V., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Germany; Jejkal T., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Germany; Kollai H., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Germany; Prabhune A., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Germany; Herold H., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Germany; Deicke A., Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Germany; Dressler C., Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Germany; Dolhoff J., Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Germany; Stanek J., Institut für Anorganische Chemie, Rheinisch-Westfälische Technische Hochschule Aachen, Germany; Hoffmann A., Institut für Anorganische Chemie, Rheinisch-Westfälische Technische Hochschule Aachen, Germany; Müller-Pfefferkorn R., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Schrade T., Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Germany; Meinel G., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Germany; Herres-Pawlis S., Institut für Anorganische Chemie, Rheinisch-Westfälische Technische Hochschule Aachen, Germany; Nagel W.E., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany","Nowadays, the daily work of many research communities is characterized by an increasing amount and complexity of data. This makes it increasingly difficult to manage, access and utilize the data to ultimately gain scientific insights based on it. At the same time, domain scientists want to focus on their science instead of IT. The solution is research data management to store data in a structured way enabling easy discovery for future reference and usage. An integral part is the use of metadata. With it, data becomes accessible by its content and context instead of its name and location only. The use of metadata shall be as automatic and seamless as possible in order to foster a high usability. Here, we present the architecture and developments of the Metadata Management for Applied Sciences (MASi) project that is currently building a comprehensive research data management service. MASi extends the existing KIT Data Manager framework by a generic metadata programming interface and a generic graphical web interface. Furthermore, MASi is OAI compliant and supports the OAI-PMH protocol while providing support for provenance information using ProvONE, a well-established and accepted provenance model. To illustrate the practical applicability of the MASi service, we present the adoption of initial use cases within geography, chemistry and digital humanities. The MASi research data management service is currently being prepared to go into production to satisfy the complex and varying requirements in an efficient, useable and sustainable way. © 2018 The Authors","Communities; Metadata; Research data management","Ecosystems; Metadata; Community researches; Comprehensive research; Digital humanities; Metadata management; Programming interface; Provenance models; Research communities; Research data managements; Information management","","","","","Helmholtz Association of German Research Centres; Deutsche Forschungsgemeinschaft, DFG, (GE 2481/1-1, HE 5480/11-1, ME 1592/4-1, NA 711/9-1, STO 397/4-1)","This work was supported by the German Research Foundation via the MASi project (NA 711/9-1, GE 2481/1-1, HE 5480/11-1, ME 1592/4-1, STO 397/4-1) and the Helmholtz Association of German Research Centres via the LSDMA project.","Landesman B., Seeing standards: A visualization of the metadata universe, Tech Serv. 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Theory Comput., 10, 6, pp. 2232-2245, (2014); (2017); Bray T., Paoli J., Sperberg-McQueen C.M., Maler E., Yergeau F., Extensible markup language (XML), World Wide Web J., 2, 4, pp. 27-66, (1997); (2017); Folk M., Cheng A., Yates K., HDF5: A file format and I/O library for high performance computing applications, (1999); Mattmann C., Zitting J., Tika in Action, (2011); Tika A., Supported Document Formats, (2017); (2017); (2017); (2017); (2017); (2017); (2017); (2017); Grunzke R., Neumann M., Ilsche T., Hartmann V., Jejkal T., Stotzka R., Knupfer A., Nagel W.E., Design evaluation of a performance analysis trace repository, Tools for Program Development and Analysis in Computational Science, (2017); Grunzke R., Adolph T., Biardzki C., Bode A., Borst T., Bungartz H.-J., Busch A., Frank A., Grimm C., Hasselbring W., Kazakova A., Latif A., Limani F., Neumann M., (2017); (2017); Benedyczak K., Schuller B., Petrova M., Rybicki J., Grunzke R., UNICORE 7 - Middleware services for distributed and federated computing, in: International Conference on High Performance Computing Simulation, HPCS, (2016); Allcock W., Bresnahan J., Kettimuthu R., Link M., Dumitrescu C., Raicu I., Foster I., The Globus striped GridFTP framework and server, Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, (2005); Schuller B., Pohlmann T., UFTP: High-performance data transfer for UNICORE, pp. 135-142, (2011); Haslhofer B., Warner S., Lagoze C., Klein M., Sanderson R., Nelson M.L., Van de Sompel H., Resourcesync: leveraging sitemaps for resource synchronization, Proceedings of the 22nd International Conference on World Wide Web, pp. 11-14, (2013)","R. Grunzke; Technische Universität Dresden, Dresden, 01069, Germany; email: richard.grunzke@tu-dresden.de","","Elsevier B.V.","","","","","","0167739X","","FGCSE","","English","Future Gener Comput Syst","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85044718212"
"Iosifescu Enescu I.; Plattner G.-K.; Bont L.; Fraefel M.; Meile R.; Kramer T.; Espona-Pernas L.; Haas-Artho D.; Hägeli M.; Steffen K.","Iosifescu Enescu, I. (35191718800); Plattner, Gian-Kasper (6602819138); Bont, L. (55044694100); Fraefel, M. (26428126200); Meile, R. (55640042700); Kramer, T. (57213929160); Espona-Pernas, L. (56040085700); Haas-Artho, D. (57204477076); Hägeli, M. (25654817700); Steffen, K. (57206225739)","35191718800; 6602819138; 55044694100; 26428126200; 55640042700; 57213929160; 56040085700; 57204477076; 25654817700; 57206225739","OPEN SCIENCE, KNOWLEDGE SHARING and REPRODUCIBILITY AS DRIVERS for the ADOPTION of FOSS4G in ENVIRONMENTAL RESEARCH","2019","International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives","42","4/W14","","107","110","3","0","10.5194/isprs-archives-XLII-4-W14-107-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074364289&doi=10.5194%2fisprs-archives-XLII-4-W14-107-2019&partnerID=40&md5=2261ce442236532e6602f4e97e4c951b","Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","Iosifescu Enescu I., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Plattner G.-K., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Bont L., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Fraefel M., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Meile R., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Kramer T., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Espona-Pernas L., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Haas-Artho D., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Hägeli M., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Steffen K., Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","Support for open science is a highly relevant user requirement for the environmental data portal EnviDat. EnviDat, the institutional data portal and publication data repository of the Swiss Federal Research Institute WSL, actively implements the FAIR (Findability, Accessibility, Interoperability and Reusability) principles and provides a range of services in the area of research data management. Open science, with its requirements for improved knowledge sharing and reproducibility, is driving the adoption of free and open source software for geospatial (FOSS4G) in academic research. Open source software can play a key role in the proper documentation of data sets, processes and methodologies, because it supports the transparency of methods and the precise documentation of all steps needed to achieve the published results. EnviDat actively supports these activities to enhance its support for open science. With EnviDat, WSL contributes to the ongoing cultural evolution in research towards open science and opportunities for distant collaboration. © 2019. CC BY 4.0 License.","envidat; environmental data repository; environmental research; FOSS4G; knowledge sharing; Open science; reproducibility; research data management; research data publication","Interoperability; Knowledge management; Open Data; Reusability; envidat; Environmental data; Environmental researches; FOSS4G; Knowledge-sharing; Open science; Reproducibilities; Research data; Research data managements; Open source software","","","","","","","Baker M., 1,500 scientists lift the lid on reproducibility, Nature, 533, pp. 452-454, (2016); Bont L.G., Moll P.E., Seilaplan. EnviDat, (2018); Bont L.G., Fraefel M., Iosifescu Enescu I., Sample geodata and software for demonstrating geospatial preprocessing for forest accessibility and wood harvesting at foss4g2019, EnviDat, (2019); Fraefel M., Dataset for ogrs 2018 publication, EnviDat, (2018); Iosifescu Enescu I., Iosifescu Enescu C., Pachaud N.H., Tsorlini A., Hurni L., A decade of geoinformation sharing at eth zurich, Procedings of the 27th International Cartographic Conference: Spatial data infrastructures, standards, open source and open data for geospatial (SDIOpen 2015, (2015); Iosifescu Enescu I., Plattner G.-K., Espona Pernas L., Haas-Artho D., Bischof S., Lehning M., Steffen K., The envidat concept for an institutional environmental data portal, Data Science Journal, 17, (2018); Iosifescu Enescu I., Fraefel M., Plattner G.-K., Espona-Pernas L., Haas-Artho D., Lehning M., Steffen K., Fostering open science at wsl with the envidat environmental data portal. In proceedings of the, 5th Open Source Geospatial Research and Education Symposium (OSGRS 2018, (2018); Wilkinson D.M., Et al., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","Brovelli M.A.; Andreea Florentina A.F.","International Society for Photogrammetry and Remote Sensing","","2019 Free and Open Source Software for Geospatial, FOSS4G 2019","26 August 2019 through 30 August 2019","Bucharest","152383","16821750","","","","English","Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85074364289"
"Sampaio M.; Ferreira A.L.; Castro J.A.; Ribeiro C.","Sampaio, Marcelo (57212528011); Ferreira, Ana Luís (57212532287); Castro, João Aguiar (55977255100); Ribeiro, Cristina (7201734594)","57212528011; 57212532287; 55977255100; 7201734594","Training Biomedical Researchers in Metadata with a MIBBI-Based Ontology","2019","Communications in Computer and Information Science","1057 CCIS","","","28","39","11","0","10.1007/978-3-030-36599-8_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076961711&doi=10.1007%2f978-3-030-36599-8_3&partnerID=40&md5=e16c5691989ab0379a843d4162131deb","INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Sampaio M., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ferreira A.L., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Castro J.A., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Recent initiatives in data management recognize that involving the researchers is one of the more problematic issues and that taking into account the practices of each domain can ease this process. We describe here an experiment in the adoption of data description by researchers in the biomedical domain. We started with a generic lightweight ontology based on the Minimum Information for Biological and Biomedical Investigations (MIBBI) standard and presented it to researchers from the Institute of Innovation and Investigation in Health (I3S) in Porto. This resulted in seven interviews and four data description sessions using a RDM platform. The feedback from researchers shows that this intentionally restricted ontology favours an easy entry point into RDM but does not prevent them from identifying the limitations of the model and pinpointing their specific domain requirements. To complete the experiment, we collected the extra descriptors suggested by the researchers and compared them to the full MIBBI. Part of these new descriptors can be obtained from the standard, reinforcing the importance of common metadata models for broad domains such as biomedical research. © 2019, Springer Nature Switzerland AG.","Biomedical research; FAIR; Metadata; MIBBI; Research data management","Data description; Information management; Ontology; Semantics; Biomedical investigations; Biomedical research; Domain requirements; FAIR; Lightweight ontology; MIBBI; Minimum information; Research data managements; Metadata","","","","","Fundação para a Ciência e a Tecnologia, FCT, (PD/BD/114143/2015, POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF","This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT-Fundação para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT-Fundação para a Ciência e a Tecnologia.","Assante M., Et al., Are scientific data repositories coping with research data publishing?, Data Sci. J., 15, 6, pp. 1-24, (2016); Bandrowski A., Et al., The ontology for biomedical investigations, Plos ONE, 11, 4, pp. 1-19, (2016); Brazma A., Et al., Minimum information about a microarray experiment (MIAME)-toward standards for microarray data, Nat. Genet., 29, 4, (2001); Castro J.A., Et al., Involving data creators in an ontology-based design process for metadata models, Developing Metadata Application Profiles, pp. 181-213, (2017); Cech T.R., Fostering innovation and discovery in biomedical research, JAMA, 294, 11, pp. 1390-1393, (2005); Turning FAIR into reality, Technical Report, pp. 1-78, (2018); Gonzalez-Beltran A., Et al., LinkedISA: Semantic representation of ISA-Tab experimental metadata, BMC Bioinform, 15, 14, (2014); Hey A.J.G., Tansley S., Tolle K.M., Et al., The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, (2009); Hoehndorf R., Schofield P.N., Gkoutos G.V., The role of ontologies in biological and biomedical research: A functional perspective, Brief. Bioinform., 16, 6, pp. 1069-1080, (2015); Mayer G., Et al., Controlled vocabularies and ontologies in proteomics: Overview, principles and practice, Biochim. Biophys. Acta (Bba)-Proteins Proteomics, 1844, 1, pp. 98-107, (2014); Qin J., Ball A., Greenberg J., Functional and architectural requirements for metadata: Supporting discovery and management of scientific data, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 62-71, (2012); Read K., Common Metadata Elements for Cataloging Biomedical Datasets; Sansone S.-A., Et al., FAIRsharing as a community approach to standards, repositories and policies, Nat. Biotechnol., 37, 4, (2019); da Silva J.R., Ribeiro C., Lopes J.C., Ranking Dublin Core descriptor lists from user interactions: A case study with Dublin Core terms using the Dendro platform, Int. J. Digit. Libr., 20, 2, (2019); Taylor C.F., Et al., Promoting coherent minimum reporting guidelines for biological and biomedical investigations: The MIBBI project, Nat. Biotechnol., 26, 8, pp. 889-896, (2009); Taylor C.F., Et al., The minimum information about a proteomics experiment (MIAPE), Nat. Biotechnol., 25, 8, pp. 887-893, (2007); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos ONE, 10, 8, pp. 26-40, (2015); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, J. Am. Soc. Inf. Sci. Technol., 63, 8, pp. 1505-1520, (2012); Willoughby C., Et al., Creating context for the experiment record. User-defined metadata: Investigations into metadata usage in the LabTrove ELN, J. Chem. Inf. Model., 54, pp. 3268-3283, (2014)","J.A. Castro; INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: joaoaguiarcastro@gmail.com","Garoufallou E.; Fallucchi F.; William De Luca E.","Springer","","13th International Conference on Metadata and Semantic Research, MTSR 2019","28 October 2019 through 31 October 2019","Rome","235199","18650929","978-303036598-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85076961711"
"Estevão J.S.B.; Arns E.M.; Do Rocio Strauhs F.","Estevão, Janete Saldanha Bach (56102897800); Arns, Elaine Mandelli (57216956276); Do Rocio Strauhs, Faimara (35932607400)","56102897800; 57216956276; 35932607400","Research data management: A practice to open the black-box of scientific research; [GESTÃO DE DADOS DE PESQUISA: UMA PRÁTICA PARA ABRIR A CAIXA PRETA DA PESQUISA CIENTÍFICA]","2019","Revista Digital de Biblioteconomia e Ciencia da Informacao","17","","8656239","","","","2","10.20396/RDBCI.V17I0.8656239","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085502358&doi=10.20396%2fRDBCI.V17I0.8656239&partnerID=40&md5=f5a840554536050b5951ab5ebd65dd31","Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil","Estevão J.S.B., Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil; Arns E.M., Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil; Do Rocio Strauhs F., Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil","This paper discusses how the Open Science has constituted a new approach to the scientific knowledge generation process, based on the collaborative form that scientific production is being created and outreach. Traditional research outcomes such as papers, dissertations and theses, although available in open access, can be compared to a black box, under the Actor-Network Theory (ANT) perspective, since they are considered the final and closed result of a determined researcher's or a research group's analysis. This paper, mainly bibliographic, uses bibliographical and bibliometric research as methods of data treatment, followed by a systematic review of the literature. The aim is to highlight how the research data availability has agency within the science process and the scholarly communication. To this end, some of ANT's concepts are used to identify the role of research data within the network, built of human and nonhuman elements together. As results, this paper evidence how the research data availability provides new information resources, allowed mainly by the scientific activity feedback. © 2019 Universidade Estadual de Campinas. All rights reserved.","Actor-Network Theory; Data - Life cycle; Scientific data; Scientific information","","","","","","Natural Sciences and Engineering Research Council of Canada, NSERC","TRI-AGENCY Statement of Principles on Digital Data Management. 2014. 5 p. Canadian Institutes of Health Research (CIHR). The Natural Sciences and Engineering Research Council of Canada (NSERC). The Social Sciences and Humanities Research Council of Canada (SSHRC): Alberta, 2014. Disponível em: http://www.science.gc.ca/eic/site/063.nsf/vwapj/statement_of_principles_data_management.p","Dos Anjos R.L., Dias G.A., Atuação dos profissionais da informação no ciclo de vida dos dados - Dataone: um estudo comparado, Informação & Informação, Londrina, 24, 1, pp. 80-101, (2019); Monograph & Serial Costs in ARL Libraries, 1986-2011, (2012); Ball A., A Review of Data Management Lifecycle Models (Version 1.0)., (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Bosch S., Henderson K., Coping with the terrible twins: Periodicals price survey 2012, Library Journal, (2012); Callon M., Society in the making: The study of technology as a tool for sociological analysis, The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology, pp. 77-97, (1993); Callon M., Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay, Power, Action and Belief: A New Sociology of Knowledge?, pp. 1-29, (1986); Callon M., The sociology of an actor-network: The case of the electric vehicle, Mapping the Dynamics of Science and Technology: Sociology of Science in the Real World., pp. 72-102, (1986); Callon M., Law J., Rip A., How to study the force of science, Mapping the Dynamics of Science and Technology, pp. 3-15, (1986); Les Compétences Transversales. 2004. Programme de Formation de l'École Québécoise., (2004); Carlson J., The use of life cycle models in developing and supporting data services, Research Data Management: Practical Strategies for Information Professionals, pp. 63-86, (2014); Corti L., Van Den Eynden V., Bishop L., Woollard M., The research data lifecycle, Managing and Sharing Research Data: A Guide to Good Practice, pp. 18-23, (2014); Curty R.G., As diferentes dimensões do reuso de dados científicos, Anais.., pp. 1-23, (2016); Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2016); HORIZON 2020 em Breves Palavras: O Programa-quadro de Investigação e Inovação da UE., (2014); H2020 Programme. H2020 AGA - Annotated Model Grant Agreement. Version 2.1.1, pp. 216-219, (2016); Open Innovation, Open Science, Open to the World. A Vision for Europe, (2016); Study on the Economic and Technical Evolution of the Scientific Publication Markets in Europe: Final Report, (2006); Fecher B., Friesike S., Open science: One term five schools of thoughts, Opening Science, pp. 17-47, (2014); Feenberg A., Racionalização Subversiva: Tecnologia, Poder e Democracia, A Teoria Crítica de Andrew Feenberg: Racionalização Democrática, Poder e Tecnologia, pp. 67-95, (2010); De Franca A.L.D., De Pinho Neto J.A.S., Dias G.A., A Ciência da informação e o pensamento de Bruno Latour: implicações para a análise de redes sociais, Informação & Sociedade: Estudos, João Pessoa, 25, 1, pp. 137-144, (2015); De Freitas M.A., Leite F.C.L., Atores do sistema de comunicação científica: apontamentos para discussão de suas funções, Informação & Informação, Londrina, 24, 1, pp. 273-299, (2019); Green A.G., Gutmann M.P., Building partnerships among social science researchers, institution-based repositories and domain specific data archives, OCLC Systems & Services, International Digital Library Perspectives, 23, 1, pp. 35-53, (2007); Grigg K.S., Data in the Sciences, Kristi Datalibrarianship: The Academic Data Librarian in Theory and Practice., pp. 179-192, (2015); Guertin H., Bernhard P., Les 6 Étapes d'Un Projet de Recherche d'Information. École de Bibliothéconomie et des Sciences de l'Information (EBSI), (2005); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, Edinburgh, 3, 1, pp. 134-140, (2008); I2S2 Idealised Sceintific Research Activity Lifecycle Model., (2011); Seizing Canada's Moment: Moving Forward in Science, Technology and Innovation., (2014); Guide to Social Science Data Preparation and Archiving: Best Practice throughout the Data Life Cycle. 5. Ed., (2012); Latour B., Ciência em Ação: Como Seguir Cientistas e Engenheiros Sociedade Afora., (2000); Latour B., Como terminar uma tese de sociologia, Cadernos de Campo, 14, 15, pp. 339-352, (2006); Latour B., Give me a laboratory and i will raise the world, Science Observed., pp. 141-169, (1983); Latour B., La Tecnología es la sociedad hecha para que dure, Sociologia Simétrica. Ensayos Sobre Ciencia, Tecnología y Sociedad, pp. 109-142, (1998); Latour B., One more turn after the social turn, The Social Dimension of Science., pp. 272-294, (1992); Latour B., Os objetos tem história? Encontro de Pasteur com Withehead num banho de ácido lático, História, Ciências, Saúde: Manguinhos, 2, 1, pp. 7-26, (1995); Latour B., Um coletivo de humanos e não-humanos: No labirinto de Dédalo, A Esperança de Pandora: Ensaios Sobre A Realidade Dos Estudos Científicos, pp. 201-246, (2001); Latour B., We Have Never Been Modern, (1993); Latour B., Callon M., Unscrewing the big Leviathan: How actors macro-structure reality and how sociologists help them to do so, Advances in Social Theory and Methodology: Toward and Integration of Micro- And Macro-Sociologies., pp. 277-303, (1981); Law J., Technology and heterogeneous engineering: The case of portuguese expansion, The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology, pp. 105-127, (1993); Lyon L., EBank UK: Building the links between research data, scholarly communication and learning, Ariadne, Leicestershire, 36, (2003); Da Silva Medeiros J., Uma investigação sobre a autoria de dados científicos: teias de uma rede em construção, Rev. Digit. Bibliotecon. Cienc. Inf., 14, 2, pp. 298-317, (2016); Michener W.K., Jones M.B., Ecoinformatics: Supporting ecology as a data-intensive science, Trends in Ecology & Evolution [S.l.], 27, 2, pp. 85-93, (2012); Mooers C.N., Encyclopedia of Library and Information Science, 7, (1972); PMC and research funder policies, The Library, (2018); PMC policies, The Library, (2018); NIH Data Sharing Policy and Implementation Guidance., (2003); NIH Grants Policy Statement (10/10) - Part II: Terms and Conditions of NIH Grant Awards, Subpart A: General - File 6 of 6. 15 Out., (2010); Long-lived digital data collections: Enabling research and education in the 21st century, National Science Foundation, (2005); Cyberinfrastructure Vision for 21st Century Discovery., (2007); Dissemination and Sharing of Research Results., (2010); Business Process Model and Notation (BPMN)., 20, (2011); Memorandum for the Heads of Executive Departments and Agencies. 6p. 22 Fev., (2013); The Open Science and Research Handbook., (2014); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Making Open Science A Reality. (OECD Science, Technology and Industry Policy Papers, 25), (2015); Pavao C.G., Da Rocha R.P., Gabriel R.F., Proposta de criação de uma rede de dados abertos da pesquisa brasileira. Rev, Digit. Bibliotecon. Cienc. Inf., 16, 2, pp. 329-343, (2018); Pinheiro L.V.R., Mutações na ciência da informação e reflexos nas mandalas interdisciplinares, Informação & Sociedade: Estudos, João Pessoa, 28, 3, pp. 115-134, (2018); Pontika N., Knoth P., Cancellieri M., Pearce S., Fostering Open Science to Research using a Taxonomy and an eLearning Portal, IKnow: 15th International Conference on Knowledge Technologies and Data Driven Business, (2015); Making Open Science A Reality. OECD Science, Technology and Industry Policy Papers, N. 25, (2015); Research Data Management in Canadian Universities: A Statement of Principles., (2016); Janine R., Gries C., Ben B., Bowen G.J., Felzer B.S., McIntyre N.E., Soranno P.A., Vanderbilt K.L., Weathers K.C., Completing the data life cycle: Using information management in macrosystems ecology research, Frontiers in Ecology and the Environment, 12, 1, pp. 24-30, (2014); Sayao L.F., Sales L.F., Algumas considerações sobre os Repositórios digitais de dados de Pesquisa, Informação & Informação Londrina, 21, 2, pp. 90-115, (2016); Sayao L.F., Sales L.F., O impacto da curadoria digital dos dados de pesquisa na comunicação científica, Encontros Bibli: Revista Eletrônica de Biblioteconomia e Ciência da Informação Florianópolis, 17, 2, pp. 118-135, (2012); Sayao L.F., Sales L.F., Curadoria digital: um novo patamar para preservação de dados digitais de pesquisa, Informação & Sociedade: Estudos, João Pessoa, 22, 3, pp. 179-192, (2012); Sayao L.F., Sales L.F., Ciberinfraestrutura de informação para a pesquisa: uma proposta de arquitetura para integração de repositórios e sistemas CRIS, Brazil, Informacao & Sociedade: Estudos, João Pessoa, 25, 3, pp. 163-184, (2015); Setenareski L.E., Repositórios Digitais Abertos: Um Movimento Do Livre Acesso Alternativo À Estrutura Oligopolizada das Editoras., (2013); Sismondo S., Actor-network theory, An Introduction to Science and Technology Studies., pp. 81-92, (2010); Research Data Archiving Policy., (1990); DDI version 3.0 conceptual model, Data Documentation Initiative Alliance., (2014); EPSRC Policy Framework on Research Data., (2018); Policy on Data, Software and Materials Management and Sharing., (2017); TRI-AGENCY Statement of Principles on Digital Data Management., (2014); Research Lifecycle at University of Central Florida, (2012)","J.S.B. Estevão; Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil; email: janeteest@gmail.com","","Universidade Estadual de Campinas","","","","","","1678765X","","","","Portuguese","Rev. Digit. Bibliotecon. Cienc. Inf.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85085502358"
"de Figueiredo G.B.; Moreira J.L.R.; de Faria Cordeiro K.; Campos M.L.M.","de Figueiredo, Glaucia Botelho (57213142553); Moreira, João Luiz Rebelo (56970579400); de Faria Cordeiro, Kelli (49361148000); Campos, Maria Luiza Machado (7202803675)","57213142553; 56970579400; 49361148000; 7202803675","Aligning DMBOK and open government with the FAIR data principles","2019","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11787 LNCS","","","13","22","9","2","10.1007/978-3-030-34146-6_2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077681636&doi=10.1007%2f978-3-030-34146-6_2&partnerID=40&md5=91677451310870f1ab6d63d042cdc7e7","Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-916, RJ, Brazil; Vrije Universiteit Amsterdam, De Boelelaan 1081, Amsterdam, 1081 HV, Netherlands; Center of Naval System Analysis of Brazilian Navy, Rio de Janeiro, 20091-000, RJ, Brazil","de Figueiredo G.B., Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-916, RJ, Brazil; Moreira J.L.R., Vrije Universiteit Amsterdam, De Boelelaan 1081, Amsterdam, 1081 HV, Netherlands; de Faria Cordeiro K., Center of Naval System Analysis of Brazilian Navy, Rio de Janeiro, 20091-000, RJ, Brazil; Campos M.L.M., Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-916, RJ, Brazil","In enterprise organizations, the value of data has been considered on strategic level for a long time. As valuable assets, data need to be managed from source to disposal, considering their whole life cycle. To guide the data managing needs of enterprise organizations, the non-profit organization DAMA promotes the development and practice of data management as key enterprise assets. In 2017, DAMA has published the second edition of the DAMA International Guide to Data Management Body of Knowledge (DAMA DMBOK®). While the DAMA DMBOK focuses on corporate data, the FAIR data principles target research data management involving researchers and publishers in Academia. Data management is also a core issue in the Government sector, which has a great relevance in the open government initiatives, supporting the civil society to follow the actions of government bodies. This article makes a systematic analysis of these three data natures – research data, corporate data and government data – and the respective sets of principles that act as a basis for their management. These principles are correlated to identify similarities and possible complementarities focusing on the improvement of research data management, represented by the FAIR initiative, proposing an initial framework to support it. © Springer Nature Switzerland AG 2019.","Corporate data; Data management; DMBOK; FAIR; FAIR principles; Government data; Open government data; Research data","Life cycle; Metadata; Nonprofit organization; Ontology; Open Data; Corporate data; DMBOK; FAIR; FAIR principles; Government data; Open government data; Research data; Information management","","","","","","","Henderson D., Earley S., The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK2), (2017); Mission, Vision, Purpose and Goals | DAMA; Attard J., Orlandi F., Scerri S., Auer S., A systematic review of open government data initiatives, Gov. Inf. Q., 32, 4, pp. 399-418, (2015); Rice R., DISC-UK Datashare Project: Final Report, (2009); Arzberger P., Et al., Promoting access to public research data for scientific, economic, and social development, Data Sci. J., 3, pp. 135-152, (2006); Singh N.K., Monu H., Dhingra N., Research data management policy and institutional framework, 5Th International Symposium on Emerging Trends and Technologies in Libraries and Information Services (ETTLIS), pp. 111-115, (2018); van den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data: Best Practice Or Researchers, (2011); Peng G., Et al., A conceptual enterprise framework for managing scientific data stewardship, Data Sci. J., 17, (2018); Otto B., Wende K., Schmidt A., Osl P., Towards a framework for corporate data quality management, ACIS 2007 Proceedings, 109, (2007); Ubaldi B., Open government data: Towards empirical analysis of open government data initiatives, OECD Working Papers on Public Governance, No. 22. OECD Publishing, (2013); Geiger C.P., Lucke J., Open Government and (Linked) (Open) (Government) (Data), Jedem Ejournal Edemocracy Open Gov., 4, 2, pp. 265-278, (2012); The 8 Principles of Open Government Data (Opengovdata.Org); Wilkinson M., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016)","G.B. de Figueiredo; Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-916, Brazil; email: glaucia.botelho@ufrj.br","Guizzardi G.; Gailly F.; Suzana Pitangueira Maciel R.","Springer","","Workshop on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data, FAIR 2019, 6th Workshop on Conceptual Modeling in Requirements Engineering and Business Analysis, MREBA 2019, 2nd International Workshop on Empirical Methods in Conceptual Modeling, EmpER 2019, 8th International Workshop on Modeling and Management of Big Data, MoBiD19 2019 and 7th International Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th International Conference on Conceptual Modeling, ER 2019","4 November 2019 through 7 November 2019","Salvador","235549","03029743","978-303034145-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85077681636"
"Langer A.; Vu Nguyen Hai D.; Gaedke M.","Langer, André (57193121679); Vu Nguyen Hai, Dang (57217293271); Gaedke, Martin (8905803700)","57193121679; 57217293271; 8905803700","Solidrdp: Applying solid data containers for research data publishing","2020","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12128 LNCS","","","399","415","16","0","10.1007/978-3-030-50578-3_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087035971&doi=10.1007%2f978-3-030-50578-3_27&partnerID=40&md5=b93b853852b096f9f34f89227b6f6fd8","Chemnitz University of Technology, Chemnitz, Germany","Langer A., Chemnitz University of Technology, Chemnitz, Germany; Vu Nguyen Hai D., Chemnitz University of Technology, Chemnitz, Germany; Gaedke M., Chemnitz University of Technology, Chemnitz, Germany","In the context of Open Science, researchers are encouraged to publish their research datasets in digital data repositories so that others can find and reuse it. However, this process is commonly conducted via centralized data management platforms. Research data has to be uploaded to such a platform and this imposes the risk to become dependent from the access control and data exposure capabilities of the platform provider. Semantic technologies are one approach to improve this situation and manage research datasets in a decentralized way with an interdisciplinary focus. We are particularly interested in Linked Data Platform - based approaches and how good Solid in particular fits for research data publishing (RDP) activities. In this paper, we therefore present a conceptual RDP model and we assess a container-based approach to publish research data in a Solid environment in a decentralized manner, both from a researcher and developer perspective. © Springer Nature Switzerland AG 2020.","Data container; Data publishing; Decentralization; Linked Data; Research data management; Solid","Access control; Information management; Semantics; Data container; Data exposure; Data platform; Digital datas; Management platforms; Open science; Research data; Semantic technologies; Containers","","","","","Deutsche Forschungsgemeinschaft, DFG, (416228727 – SFB 1410)","This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 416228727 – SFB 1410.","Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Univers. Access Inf. Soc, 16, 4, pp. 851-862, (2017); Charalabidis Y., Zuiderwijk A., Alexopoulos C., Janssen M., Lampoltshammer T., Ferro E., The multiple life cycles of open data creation and use, The World of Open Data. PAIT, 28, pp. 11-31, (2018); Crosas M., The dataverse network: an open-source application for sharing, discovering and preserving data, D-Lib Mag, 17, (2011); Curdt C., Hoffmeister D., Waldhoff G., Jekel C., Bareth G., Development of a metadata manegement system for an interdisciplinary research project, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, 4, pp. 7-12, (2012); Khan S., Dspace or Fedora: which is a better solution?, SRELS J. Inf. Manag, 56, 1, pp. 45-50, (2019); Kim S.H., Berlocher I., Lee T., RDF based linked open data management as a DaaS platform LODaaS (linked open data as a service), ALLDATA 2015, (2015); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists’ data-sharing behaviors: a multilevel analysis, J. Assoc. Inf. Sci. Technol, 67, 4, pp. 776-799, (2016); Langer A., PIROL: cross-domain research data publishing with linked data technologies, Proceedings of the Doctoral Consortium Papers Presented at the 31st CAiSE 2019, pp. 43-51, (2019); Langer A., Bilz E., Gaedke M., Analysis of current RDM applications for the interdisciplinary publication of research data, SEM4TRA-AMAR@SEMANTICS, (2019); Mansour E., Sambra A.V., Hawke S., Et al., A demonstration of the solid platform for social web applications, Proceedings of the 25th International Conference Companion on World Wide Web, WWW 2016 Companion, pp. 223-226, (2016); Nielsen J., Landauer T.K., A mathematical model of the finding of usability problems, Proceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems, CHI 1993, pp. 206-213, (1993); Robinson D.C., Hand J.A., Madsen M.B., McKelvey K.R., The Dat Project, an open and decentralized research data tool, Sci. Data, 5, 1, (2018); Sambra A.V., Mansour E., Hawke S., Et al., Solid: a platform for decentralized social applications based on linked data, (2016); Stufflebeam D., Evaluation model, New Dir. Eval, 2001, 89, pp. 7-98, (2001); Wang W., Gopfert T., Stark R., Data management in collaborative interdisciplinary research projects—conclusions from the digitalization of research in sustainable manufacturing, ISPRS Int. J. Geo-Inf, 5, 4, (2016); Website of the European Commission: Open Science Monitor, (2018); Wiley: Global Data Sharing Trends, (2016); Wiljes C., Jahn N., Lier F., Et al., Towards linked research data: an institutional approach, 3rd Workshop on Semantic Publishing (SePublica), 994, pp. 27-38, (2013); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, 1, (2016); Wissik T., Durco M., Research data workflows: from research data lifecycle models to institutional solutions, Selected Papers from the CLARIN Annual Conference 2015, 123, pp. 94-107, (2015)","A. Langer; Chemnitz University of Technology, Chemnitz, Germany; email: andre.langer@informatik.tu-chemnitz.de","Bielikova M.; Mikkonen T.; Pautasso C.","Springer","","20th International Conference on Web Engineering, ICWE 2020","9 June 2020 through 12 June 2020","Helsinki","240999","03029743","978-303050577-6","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85087035971"
"Vogl R.; Rudolph D.; Thoring A.","Vogl, Raimund (6701668973); Rudolph, Dominik (56244944800); Thoring, Anne (55758236000)","6701668973; 56244944800; 55758236000","Bringing Structure to Research Data Management Through a Pervasive, Scalable and Sustainable Research Data Infrastructure","2019","The Art of Structuring: Bridging the Gap between Information Systems Research and Practice","","","","501","512","11","1","10.1007/978-3-030-06234-7_47","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118432929&doi=10.1007%2f978-3-030-06234-7_47&partnerID=40&md5=bd81effc8e6238c5798b90779e073a98","University of Münster, Münster, Germany","Vogl R., University of Münster, Münster, Germany; Rudolph D., University of Münster, Münster, Germany; Thoring A., University of Münster, Münster, Germany","One of the key fields of digitalization at universities is the management of an ever increasing amount of digital research data. Based on several surveys amongst researchers, demands and knowledgeability on the subject are varying widely. Services for research data management and underlying infrastructures are called for and are a currently very actively discussed subject. To create demand oriented, future proof, scalable and financially and operationally sustainable infrastructures and services, a structured approach to demand assessment and infrastructure architecture is key. Based on user surveys and conceptual (technical) design workshops of university IT infrastructure providers starting in 2016, a consortium of five universities in North Rhine-Westphalia (NRW) has formed to pursue together an open source and joint operations approach for creating a multisite integrated storage and compute platform (primarily using open source/freeware community standards Ceph and OpenStack) as a research data infrastructure, providing the operational basis for the actual research data services and software (envisioned to be containerized or virtualized appliances). A joint funding proposal, set in the context of the German National Research Data Infrastructure Initiative (NFDI) has been submitted, aiming at the creation of this 33 Petabyte storage and 4, 500 CPU core compute environment, with the joint operations team already having been formed. Additionally, tools for data management and curation shall be made available on this infrastructure. The development of these tools is progressing under the project title sciebo. RDS (Research Data Services), which aims at adding research data management workflows to the well-established sciebo sync and share cloud storage platform, which is already widely used for collaboration on research data and will, in the future, also be operated in an OpenStack/Ceph setting. With DFG providing funding for this development, the aim is for a wider adoption of these tools in the German research community. Workpackages within this project for empiric analysis of user demands have been designed to ensure that these research data services will find users beyond the project partners’ institutions. © Springer Nature Switzerland AG 2019.","Cloud computing; Open source; Research data management","","","","","","","","Apel J., FAIR data principles, (2018); Lopez A., Vogl R., Roller S., Research data infrastructures-A perspective for the state of North Rhine-Westphalia in Germany, EUNIS 2017, Münster, Book of Proceedings, pp. 105-112, (2017); Mons B., Tochtermann K., Realising the European open science cloud. First report and recommendations of the commission high level expert group on the European open science cloud, (2016); About, (2018); Paten B., A data biosphere for biomedical research, (2017); Vogl R., Rudolph D., Thoring A., Angenent H., Stieglitz S., Meske C., How to build a cloud storage service for half a million users in higher education: Challenges met and solutions found, Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 5328-5337, (2016); Grundsätze zum Umgang mit Forschungsdaten an der Westfälischen Wilhelms-Universität Münster, (2017)","R. Vogl; University of Münster, Münster, Germany; email: r.vogl@wwu.de","","Springer International Publishing","","","","","","","978-303006234-7; 978-303006233-0","","","English","The Art of Structuring: Bridging the Gap between Information Systems Research and Practice","Book chapter","Final","","Scopus","2-s2.0-85118432929"
"Mohammed M.S.; Ibrahim R.","Mohammed, Mahdi Salah (57214156113); Ibrahim, Rafea (57214143478)","57214156113; 57214143478","Challenges and practices of research data management in selected Iraq universities","2019","DESIDOC Journal of Library and Information Technology","39","6","","308","314","6","10","10.14429/djlit.39.6.14443","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078336233&doi=10.14429%2fdjlit.39.6.14443&partnerID=40&md5=ad4867d1b531dc40436c9a1f351f1fc2","Al-sharea Department, College of Islamic Sciences, Al Iraqia University, Baghdad, Iraq; Usol al-Fiqh Department, College of Islamic Sciences, Al Iraqia University, Baghdad, Iraq","Mohammed M.S., Al-sharea Department, College of Islamic Sciences, Al Iraqia University, Baghdad, Iraq; Ibrahim R., Usol al-Fiqh Department, College of Islamic Sciences, Al Iraqia University, Baghdad, Iraq","Research emphasises the fundamental role of research data management (RDM) in enhancing academic and scientific research. This paper intended to examine RDM in Iraqi Universities, identify the current challenges of RDM and propose influential RDM practices. Data collection employed a self-administered questionnaires distributed to 155 postgraduate students and 20 faculty members from five universities in Iraq. Research findings revealed that there is a lack of proper RDM. Postgraduate students and researchers were managing their own research data. Main challenges of maintaining a good RDM involve lack of guidelines on effective RDM practices, insufficient of adequate human resources, technological obsolescence, insecure and inefficient infrastructure, lack of financial resources, absence of research data management policies and lack of support by institutional authorities and researchers negatively influenced on research data management. Postgraduate students and researchers recommend building research data repositories and collaboration with other universities and research organisations. © 2019, DESIDOC.","Academic libraries; Challenges; Iraq universities; Research data management; Research data management practices","","","","","","Al Iraqia University","Authors acknowledge the support and assistance provided by Al Iraqia University in conducting this research.","Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, J. Libr. Scholarly Commun., 3, 2, (2015); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Sci. J., 16, (2017); Carole S., Managing and sharing research data: A guide to good practice, J. Royal Stat. Soc.: Series a (Statistics in Society, 180, 3, pp. 940-941, (2017); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA Journal, 43, 1, pp. 51-64, (2017); Manorama T., Archana S., Sharad Kumar S., Research data management practices in university libraries: A study, DESIDOC J. Libr. Inf. Technol., 37, 6, (2017); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, Int. J. Digital Curation, 10, 2, pp. 69-95, (2015); Munafo M.R., Brian A., Nosek D.V.M., Bishop K.S.B., Christopher D.C., Nathalie P.D.S., Simonsohn U., Wagenmakers E.-J., Jennifer J.W., Ioannidis J.P.A., A manifesto for reproducible science, Nature Human Behav, 1, 1, (2017); Koltay T., Research 2.0 and research data services in academic and research libraries: Priority issues, Libr. Manage., 38, 6-7, pp. 345-353, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, J. Assoc. Inf. Sci. Technol., 68, 9, pp. 2182-2200, (2017); Tenopir C., Et al., Research data management services in academic research libraries and perceptions of librarians, Libr. Inf. Sci. Res., 36, 2, pp. 84-90, (2014); Surkis A., Read K., Research data management, J. Med. Libr. Assoc.:Jmla, 103, 3, pp. 154-156, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Hughes D., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, J. Escience Libr., 4, 2, (2015); Perrier L., Leslie B., Developing research data management services and support for researchers: A mixed methods study, Partnership: Can. J. Libr. Inf. Prac. Res., 13, 1, (2018); Catherine A.W., Towards Best Practice in Research Data Management in the Humanities, (2017); Strong M.A., Digital curation: A how-to-do-it manual, J. Med. Libr. Assoc.: JMLA, 100, 2, (2012); Tenopir C., Hughes D., Barid L., Andrew L., Academic Libraries Follow-Up Dataset [Data Set], (2016); Goldman J., Kafel D., Martin E., Assessment of data management services at new England region resource libraries, J. Escience Libr., 4, 1, (2015); Mannheimer S., Ready, engage! Outreach for library data services, Bulletin of the Association for Information Science and Technology, 41, 1, pp. 42-44, (2014); Forrest W., Cohabitation, relationship quality, and desistance from crime, J. Marriage Family, 76, 3, pp. 539-556, (2014); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos One, 9, 12, (2014); Peer L., Green A., Building an open data repository for a specialised research community: Process, challenges and lessons, Int. J. Digital Curation, 7, 1, pp. 151-162, (2012); Australian Code for Responsible Research, (2015); Erway R., Starting the Conversation: University-Wide Research Data Management Policy, (2013); Middlebrough L., Identifying Ways Forward within Higher Education in Iraq, (2019); Chiware E.R.T., Mathe Z., Academic libraries’ role in research data management services: A South African perspective, S. Afr. J. Libr. Inf. Sci., 81, 2, (2016); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries’ websites, College Res. Libr., 78, 7, (2017); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in european academic research libraries, Liber Q, 27, 1, pp. 23-44, (2017); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib J. Inf. Manage., 70, 2, pp. 142-157, (2018); Lowndes J.S.S., Best B.D., Scarborough C., Afflerbach J.C., Frazier M.R., O'Hara C.C., Jiang N., Halpern B.S., Our path to better science in less time using open data science tools, Nature Ecol. Evol., 1, 6, (2017); John B., Abrams S., Lowenberg D., Simms S., Chodacki J., Support your data: A research data management guide for researchers, Res. Ideas Outcomes, 4, (2018); Federer L.M., Ya-Ling L., Joubert D.J., Data literacy training needs of biomedical researchers, J. Med. Libr. Assoc.:Jmla, 104, 1, pp. 52-57, (2016); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data Curation: A Study Of Researcher Practices And Needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Keil D.E., Research data needs from academic libraries: The perspective of a faculty researcher, J. Libr. Adm., 54, 3, pp. 233-240, (2014); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, Aust. Libr. J., 64, 3, pp. 224-234, (2015); Kim J., Data sharing and its implications for academic libraries, New Libr. World, 114, 11-12, pp. 494-506, (2013); Creamer A., Current issues and approaches to curating student research data, Bulletin of the Association for Information Science and Technology, 41, 6, pp. 22-25, (2015)","M.S. Mohammed; Al-sharea Department, College of Islamic Sciences, Al Iraqia University, Baghdad, Iraq; email: mahdi.alani@yahoo.com","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Article","Final","","Scopus","2-s2.0-85078336233"
"Zielinski T.; Hay J.; Millar A.J.","Zielinski, Tomasz (56181435000); Hay, Johnny (57211125119); Millar, Andrew J. (7201856684)","56181435000; 57211125119; 7201856684","The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation","2019","Wellcome Open Research","4","","104","","","","0","10.12688/wellcomeopenres.15341.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091116929&doi=10.12688%2fwellcomeopenres.15341.2&partnerID=40&md5=9cbc2cbbb965bfc911dc35df7113a1b5","SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom; EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom","Zielinski T., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom; Hay J., EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Millar A.J., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom","Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering. PlaSMo stands for 'Plant Systems-biology Modelling' and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting until 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative, public repository with secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in a similar situation. We share our reflections on the sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies. © 2019 Zielinski T et al.","Data sharing; Exit strategy; Research data management; Research funding; Sustainable data infrastructure","","","","","","European Commission’s FP7; UK Centre for Mammalian Synthetic Biology, (BB/M018040); Wellcome Trust, WT, (204804); Biotechnology and Biological Sciences Research Council, BBSRC","Funding text 1: Grant information: This work was funded by the Wellcome Trust through a Wellcome Institutional Strategic Support Fund (ISSF3) [204804]. This work was also supported by the Biotechnology and Biological Sciences Research Council (BBSRC) through the UK Centre for Mammalian Synthetic Biology [BB/M018040].; Funding text 2: PlaSMo stands for ‘Plant Systems-biology Modelling’ and the PlaSMo portal (plasmo.ed.ac.uk) was envisioned by the plant systems biology community during a BBSRC and GARNet workshop in July 2005. The initial 2-year development was funded as part of BBSRC’s Bioinformatics and Biological Resources call in 2008 and then supported by the European Commission’s FP7 Collaborative Project TiMet (2010–2015).","Concordat On Open Research Data, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Wittig U., Rey M., Weidemann A., Et al., Data management and data enrichment for systems biology projects, J Biotechnol, 261, pp. 229-237, (2017); Stuart D., Baynes G., Hrynaszkiewicz I., Et al., Practical Challenges for Researchers in Data Sharing, Whitepaper, (2018); Funding research data management and related infrastructures, (2016); Figshare; Zenodo; Dryad; Edinburgh DataShare; UK Data Archive; BioModels; Glont M., Nguyen T.V.N., Graesslin M., Et al., BioModels: expanding horizons to include more modelling approaches and formats, Nucleic Acids Res, 46, pp. D1248-D1253, (2018); Wolstencroft K., Krebs O., Snoep J.L., Et al., FAIRDOMHub: a repository and collaboration environment for sharing systems biology research, Nucleic Acids Res, 45, pp. D404-D407, (2017); Wolstencroft K., Owen S., Krebs O., Et al., SEEK: a systems biology data and model management platform, BMC Syst Biol, 9, 1, (2015); Troup E., Clark I., Swain P., Et al., Practical evaluation of SEEK and OpenBIS for biological data management in SynthSys; first report, (2015); Rocca-Serra P., Brandizi M., Maguire E., Et al., ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level, Bioinformatics, 26, 18, pp. 2354-2356, (2010); SEEK REST API; Littlejohn J., jsonschema2pojo [Internet]; Reiser L., Berardini T.Z., Li D., Et al., Sustainable funding for biocuration: The Arabidopsis Information Resource (TAIR) as a case study of a subscription-based funding model, Database (Oxford), 2016, (2016); Gabella C., Durinx C., Appel R., Funding knowledgebases: Towards a sustainable funding model for the UniProt use case [version 2; peer review: 3 approved], F1000Res, 6, (2017); Chandras C., Weaver T., Zouberakis M., Et al., Models for financial sustainability of biological databases and resources, Database (Oxford), 2009, (2009); Wilcox A., Randhawa G., Embi P., Et al., Sustainability considerations for health research and analytic data infrastructures, EGEMS (Wash DC), 2, 2, (2014); Ozdemir V., Badr K.F., Dove E.S., Et al., Crowd-funded micro-grants for genomics and ""big data"": an actionable idea connecting small (artisan) science, infrastructure science, and citizen philanthropy, OMICS, 17, 4, pp. 161-172, (2013); Kitchin R., Collins S., Frost D., Funding models for Open Access digital data repositories, Online Inform Rev, 39, 5, pp. 664-681, (2015); Business Models for Sustainable Data Repositories, (2017); Dillo I., Hodson S., Et al., Income Streams for Data Repositories, (2016); Protein Data Bank; Berman H.M., Battistuz T., Bhat T.N., Et al., The Protein Data Bank, Acta Crystallogr Sect D Biol Crystallogr, 58, 1, pp. 899-907, (2002); GeneBank; Benson D.A., Cavanaugh M., Clark K., Et al., GenBank, Nucleic Acids Res, 41, DATABASE ISSUE, pp. D36-42, (2013); ArrayExpress; Brazma A., Parkinson H., Sarkans U., Et al., ArrayExpress--a public repository for microarray gene expression data at the EBI, Nucleic Acids Res, 31, 1, pp. 68-71, (2003); Van den Eynden V., Knight G., Vlad A., Et al., Towards Open Research: practices, experiences, barriers and opportunities [Internet], (2016); BioDare; Zielinski T., Moore A.M., Troup E., Et al., Strengths and limitations of period estimation methods for circadian data, PLoS One, 9, 5, (2014); Kilic S., Sagitova D.M., Wolfish S., Et al., From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF, Database (Oxford), 2016, (2016); Leonelli S., Smirnoff N., Moore J., Et al., Making open data work for plant scientists, J Exp Bot, 64, 14, pp. 4109-4117, (2013); Bauch A., Adamczyk I., Buczek P., Et al., openBIS: a flexible framework for managing and analyzing complex data in biology research, BMC Bioinformatics, 12, (2011); Research Enrichment - Open Research; Zielinski T., Hay J., SynthSys/Seek-Java-RESTClient: Java RestClient for SEEK API 1.7.0 (Version v1.0.0), Zenodo, (2019); Zielinski T., Hay J., SynthSys/Seek-Bulk-Update: Bulk Update For Seek API 1.7.0 (Version v.1.0.0), Zenodo, (2019); Zielinski T., Tindal C., SynthSys/PlasmoPortal: The last working version of PlaSMo portal (Version v2.1.5), Zenodo, (2019)","A.J. Millar; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom; email: andrew.millar@ed.ac.uk","","F1000 Research Ltd","","","","","","2398502X","","","","English","Wellcome Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85091116929"
"Schöpfel J.; Farace D.; Prost H.; Zane A.","Schöpfel, Joachim (14619562900); Farace, Dominic (26432473400); Prost, Hélène (15069878000); Zane, Antonella (57204030261)","14619562900; 26432473400; 15069878000; 57204030261","Data papers as a new form of knowledge organization in the field of research data","2019","Knowledge Organization","46","8","","622","638","16","8","10.5771/0943-7444-2019-8-622","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081574402&doi=10.5771%2f0943-7444-2019-8-622&partnerID=40&md5=04262307d9f1585cea241d4b905f8e22","University of Lille, GERiiCO laboratory, France; GreyNet International, Amsterdam, Netherlands; Cnrs, GERiiCO Laboratory, France; University of Padova, Italy","Schöpfel J., University of Lille, GERiiCO laboratory, France; Farace D., GreyNet International, Amsterdam, Netherlands; Prost H., Cnrs, GERiiCO Laboratory, France; Zane A., University of Padova, Italy","Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are pardy blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing. © 2019 International Society for Knowledge Organization. All rights reserved.","Data; Journals; Metadata; Papers; Research","","","","","","","","Belter C.W., Measuring the value of research data: A citation analysis of oceanographic data sets, PLoS One, 9, 3, (2014); Bordelon D., Grothkopf U., Meakins S., Sterzik M., Trends and developments in VLT data papers as seen through telbib, Observatory Operations: Strategies, Processes, and Systems, (2016); Callaghan S., Donegan S., Pepler S., Et al., Making data a first-class scientific output: Data citation and publication by NERC'S environmental data centres, International Journal of Digital Curation, 7, pp. 107-113, (2012); Candela L., Castelli D., Manghi P., Tani A., Data journals: A survey, Journal of the Association for Information Science and Technology, 66, pp. 1747-1762, (2015); Chavan W., Penev L., The data paper: A mechanism to incentivize data publishing in biodiversity science, Bmc Bioinformatics, 12, (2011); Costello M.J., Michener W.K., Gahegan M., Zhang Z.-Q., Bourne P.E., Biodiversity data should be published, cited, and peer reviewed, Trends in Ecology & Evolution, 28, pp. 454-461, (2013); Davis G.H., Payne E., Sih A., Commentary: Four ways in which data-free papers on animal personality fail to be impactful, Frontiers in Ecology and Evolution, 3, 102, pp. 1-3, (2015); De Waard A., The future of the journal? Integrating research data with scientific discourse, Logos, 21, 1-2, pp. 7-11, (2010); Farace D.J., Frantzen J., Smith P.L., Data papers are witness to trusted resources in grey literature: A project use case, Grey Journal, 14, 1, pp. 31-36, (2018); Friedman R., Psaki S., Bingenheimer J.B., Announcing a new journal section: Data papers, Studies in Family Planning, 48, pp. 291-292, (2017); Garcia-Garcia A., Borrul A.L., Peset F., Data journals: Eclosion de nuevas revistas especializadas en datos, Elprofesional de la Information, 24, pp. 845-854, (2015); Huang X., Hawkins B.A., Qiao G., Biodiversity data sharing: Will peer-reviewed data papers work?, BioStience, 63, pp. 5-6, (2013); Le Deuff O., Une nouvelle rubrique pour la rfsic : Le data paper, Revue Franfaise des Sciences de i'In-Formation et de la Communication 15, (2018); Li K., Greenberg J., Dunic J., Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers, Journal of the Association for Information Science and Technology Early View, (2019); National Plan for Open Science, (2018); Michener W.K., Helly J.W.B.J.J., Kirchner T.B., Stafford S.G., Nongeospatial metadata for the ecological sciences, Ecological Archives, 1, pp. 330-342, (1997); Neuroth H., Strathmann S., Osswald A., Ludwig J., Digital Curation of Research Data, (2013); Newman P., Corke P., Data papers-peer reviewed publication of high quality data sets, The International Journal of Robotics Research, 28, (2009); Partel M., Data availability for macroecology: How to get more out of regular ecological papers, Acta Oecologjca, 30, pp. 97-99, (2006); Penev L., Chavan W., Georgiev T., Stoev P., Data Papers As Incentives for Opening Biodiversity Data: One Year of Experience and Per-spectives for the Future, (2012); Reymonet N., Ameliorer i'Exposition des Donnees de la Recherche: La Publication de Data Papers, (2017); Rowley J., The wisdom hierarchy: Representations of the DIKW hierarchy, Journal of Information Science, 33, pp. 163-180, (2007); Rowley J., Hardey R., Organising Knowledge: An Introduction to Man Aging a Ccess to Information, (2008); Senderov V., Georgiev T., Penev L., Online direct import of specimen records into manuscripts and automatic creadon of data papers from biological databases, Research Ideas and Outcomes, 2, (2016); Smith M., Data papers in the network era, Somethings Gotta Give: Charleston Conference Proceedings, pp. 13-20, (2012); Star S.L., Grisemer J.R., Institutional ecology, 'translations' and boundary objects: Amateurs and professionals in Berkeley's museum of vertebrate zoology, 1907-39, Social Studies of Science, 19, pp. 387-420, (1989); Wilkinson M.D., Et al., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","International Society for Knowledge Organization","","","","","","09437444","","","","English","Knowl. Organ.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85081574402"
"Ohaji I.K.; Chawner B.; Yoong P.","Ohaji, Isaac K. (57223194831); Chawner, Brenda (15058644000); Yoong, Pak (6603346227)","57223194831; 15058644000; 6603346227","The role of a data librarian in academic and research libraries","2019","Information Research","24","4","844","","","","4","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085763727&partnerID=40&md5=d416c61e31835bd20e63f0c945dfcb95","College of Medicine, University of Nigeria, Ituku-Ozalla, Enugu, Nigeria; School of Information Studies, Victoria University of Wellington, New Zealand; School of Information Management, Victoria University of Wellington, New Zealand","Ohaji I.K., College of Medicine, University of Nigeria, Ituku-Ozalla, Enugu, Nigeria; Chawner B., School of Information Studies, Victoria University of Wellington, New Zealand; Yoong P., School of Information Management, Victoria University of Wellington, New Zealand","Introduction. This paper presents a data librarian role blueprint (the blueprint) in order to facilitate an understanding of the academic and research librarian’s role in research data management and e-research.. Method. The study employed a qualitative ase research approach to investigate the dimensions of the role of a data librarian in New Zealand research organizations, using semi-structured interviews as the main data collection instrument. Analysis.A data analysis spiral was used to analyse the interview data, with the addition of a job analysis framework to organize the role performance components of a data librarian. Results. The influencing factors, performance components and training needs for a data librarian role form the basis of the blueprint. Conclusions. The findings which are reflected in the blueprint provide a conceptual understanding of the data librarian role which may be used to inform and enhance practice, or to develop relevant education and training programmes. © the author, 2019.","","","","","","","","","Abbott A., The system of professions: an essay on the division of expert labour, (1988); Allard S., Mack T. R., Feltner-Reichert M., The librarian’s role in institutional repository: a content analysis of the literature, Reference Services Review, 33, 3, pp. 325-336, (2005); Alvaro E., Brooks H., Ham M., Poegel S., Rosencrans S., E-Science librarianship: field undefined, Issues in Science and Technology Librarianship, 66, (2011); An X., An integrated approach to records management, The Information Management Journal, 37, 4, pp. 24-30, (2003); Ashley K., Research data and libraries: who does what, Insights, 25, 2, pp. 155-157, (2012); Atkins D. E., Droegemeier K. K., Feldman S. I., Garcia-Molina H., Klein M. 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B., The emerging role of libraries in data curation and e-Science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Henty M., Dreaming of data: the library’s role in supporting e-Research and data management, Paper presented at the Australia Library and Information Association Biennial Conference, (2008); Hey T., Hey J., E-Science and its implications for the library community, Library Hi Tech, 21, 4, pp. 515-528, (2006); Hey T., Trefethen A. E., The data deluge: an eScience perspective, Grid computing: making the global infrastructure a reality, pp. 809-824, (2003); Higgins S., The DCC curation lifecycle model, The International Journal of Digital Curation, 1, 3, pp. 134-140, (2008); Hunter J., Cook R., Pope S., E-research middleware: the missing link in Australia’s e-research agenda, (2004); Jankowski N. W., Exploring e-Science: an introduction, Journal of Computer-Mediated Communication, 12, 2, pp. 549-562, (2007); Johnson A. M., Bresnahan M. 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J., Data services in academic libraries: assessing needs and promoting services, Reference & User Services Quarterly, 46, 3, pp. 61-75, (2007); In Texas A&M University Libraries Research Guides, (2019); Reznik-Zellen R. C., Adamick J., McGinty S., Tiers of research data support services, Journal of e-Science Librarianship, 1, 1, pp. 27-35, (2012); Salo D., Retooling libraries for the data challenge, (2010); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program: Electronic Library and Information Systems, 49, 4, pp. 440-460, (2015); Simons N., Searle S., Redefining ‘the librarian’ in the context of emerging eResearch services, Paper presented at Victorian Association for Library Automation Conference, VALA2014, (2014); Smith M., Communicating with data: new roles for scientists, publishers and librarians, Learned Publishing, 24, 3, pp. 203-205, (2011); Soehner C., Steeves C., Ward J., E-Science and data support services: a study of ARL member institutions, (2010); Stake R. E., The case study method in social inquiry, Educational Researcher, 7, 2, pp. 5-8, (1978); Swan A., Brown S., The skills, role and career structure of data scientists and curators: an assessment of current practice and future needs (Report to the JISC), (2008); Tenopir C., Sandusky R. J., Allard S., Birch B., Academic librarians and research data services: preparation and attitudes, IFLA World Library and Information Congress, (2012); Thomas J., Future-proofing: the academic library’s role in e-research support, Library Management, 32, 1, pp. 37-47, (2011); Verbaan E., Cox A. M., Collaboration or competition? Responses to research data management in UK higher education by librarians, IT professionals, and research administrators, iConference 2014 Proceedings, pp. 281-292, (2014); Verbaan E., Cox A. M., Occupational sub-cultures, jurisdictional struggle and third space: theorising professional service responses to research data management, Journal of Academic Librarianship, 40, 3, pp. 211-219, (2014); Voss C., Tsikirktsis N., Frohlich M., Case research in operations management, International Journal of Operations & Production Management, 22, 2, pp. 195-219, (2002); Wang M., Supporting the research process through expanded library data services, Program: Electronic Library and Information Systems, 47, 3, pp. 282-303, (2013); Wong G. K. W., Exploring research data hosting at the HKUST institutional repository, Serials Review, 35, 3, pp. 125-132, (2009); Yin R. K., Case study research: design and methods, 5, (2009); Yu S., Research Data Management: a library practitioner’s perspective, Public Services Quarterly, 13, 1, pp. 48-54, (2017)","","","University of Boras","","","","","","13681613","","","","English","Inf. Res.","Article","Final","","Scopus","2-s2.0-85085763727"
"Stodden V.; Ferrini V.; Gabanyi M.; Lehnert K.; Morton J.; Berman H.","Stodden, Victoria (15623425400); Ferrini, Vicki (8957567700); Gabanyi, Margaret (25931953400); Lehnert, Kerstin (57206148394); Morton, John (57212145853); Berman, Helen (7101628162)","15623425400; 8957567700; 25931953400; 57206148394; 57212145853; 7101628162","Open access to research artifacts: Implementing the next generation data management plan","2019","Proceedings of the Association for Information Science and Technology","56","1","","481","485","4","1","10.1002/pra2.51","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076014636&doi=10.1002%2fpra2.51&partnerID=40&md5=da2ae20c6d83f452b0ac700077494fca","School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, United States; Earth Observatory of Columbia University, New York, NY, United States; Research Collaboratory for Structural Biology, Rutgers University, Piscataway, NJ, United States; Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, United States","Stodden V., School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, United States; Ferrini V., Earth Observatory of Columbia University, New York, NY, United States; Gabanyi M., Research Collaboratory for Structural Biology, Rutgers University, Piscataway, NJ, United States; Lehnert K., Earth Observatory of Columbia University, New York, NY, United States; Morton J., Earth Observatory of Columbia University, New York, NY, United States; Berman H., Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, United States","We describe a new vision for a Data Management Plan (DMP) that incorporates controlled vocabularies and semantic descriptions of the scholarly objects to be produced by the proposed project. We implement this vision in an open-source web-based DMP tool, called ezDMP, at ezdmp.org. The integrated use of structured information in ezDMP permits several important goals. First, with minimal additional effort, researchers can create DMPs with more complete information about the scholarly objects to be produced. Second, research funders can productively query this structured information to learn about repository use and other patterns of scholarly objects creation. Finally, ezDMP puts a structure in place that can support the integration of information about digital scholars objects, in an organized and systematic way, into research data management environments. Author(s) retain copyright, but ASIS&T receives an exclusive publication license","Code Policy; Code Sharing; Cyberinfrastructure for Research; Data Management Plan; Data Policy; Data Sharing; Digital Repositories; Open Access; Reproducible Research","Information use; Open systems; Semantics; Code policy; Code sharing; Cyberinfrastructure; Cyberinfrastructure for research; Data management plan; Data policy; Data Sharing; Digital repository; Management plans; OpenAccess; Reproducible research; Information management","","","","","NIH Funded, (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-19-014.html); National Science Foundation, NSF, (1649703); Foundation for the National Institutes of Health, FNIH; National Sleep Foundation, NSF, (1649545, 1649555); Rural Development Administration, RDA","Funding text 1:  In this article, we have described the implementation of a next generation DMP and the motivation for the two key goals it addresses in facilitating greater access and transparency in research: To communicate policy priorities regarding artifact availability to the research community; and to enable funders and community stakeholders to learn about research artifact creation, archiving, and reuse practices by researchers and other stakeholders. ). Funding agencies are continuing to implement Data Management Plans (see e.g., the October 2018 Request for Information by the National Institutes for Health entitled “Request for Information (RFI) on Proposed Provisions for a Draft Data Management and Sharing Policy for NIH Funded or Supported Research” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-19-014.html ). We anticipate extending the tool to accommodate other funding sources in a customized way in the future. Within NSF, data and artifact policies are advancing, especially with respect to enabling reproducibility of results (see e.g., https://www.nsf.gov/pubs/2019/nsf19022/nsf19022.pdf and https://www.nsf.gov/cise/oac/ci2030/ACCI_CI2030Report_Approved_Pub.pdf ). Recommendation 6‐5 of the recent National Academies report on reproducibility exhorts the NSF to “[c]onsider extending NSF's current data‐management plan to include other digital artifacts, such as software” (National Academies,  We believe a next generation Data Management Plan, generated using a tool that produces structured, machine readable output using controlled vocabularies and semantic descriptions of the scholarly objects produced, will permit a greater understanding of practices regarding artifact creation, and availability, allowing for improved credit and recognition of these efforts. In addition, the approach pioneered by ezDMP will encourage greater development of artifact standards and interoperability and facilitate the incorporation of the Data Management Plan in future data management environments.  ; Funding text 2: We thank the stakeholders who provided feedback and completed our evaluation rubric. We also thank participants at RDA and other workshops for comments. NSF awards 1649555, 1649545, and 1649703 supported this work.","Lynch L., Nusser S., Brown S., Chasen J., Dutta D., Wheeler B., (2017); Ahokas M., Kuusniemi M.E., Friman J., Tuuli Project: Accelerating data management planning in Finnish Research Organisations, International Journal of Digital Curation, 12, 2, pp. 107-115, (2017); Buckheit J.B., Donoho D.L., WaveLab and reproducible research, Wavelets and statistics, pp. 55-81, (1995); Claerbout J., Karrenback M., Electronic documents give reproducible research a new meaning, Proceedings of 62nd Annual International Meeting Society of Exploration Geophysics, pp. 601-604, (1992); Donoho D.L., Maleki A., Shahram M., Rahman I.U., Stodden V., Reproducible research in computational harmonic analysis, Computing in Science & Engineering, 11, 1, pp. 8-18, (2009); Gabanyi M., (2019); Gil Y., Ratnakar V., Kim J., Gonzalez-Calero P.A., Groth P., Moody J., Deelman E., Wings: Intelligent workflow-based design of computational experiments, IEEE Intelligent Systems, 26, 1, (2011); Reproducibility and replicability in science, (2019); Sallans A., Donnelly M., DMP online and DMPTool: Different strategies towards a shared goal, International Journal of Digital Curation, 7, 2, pp. 123-129, (2012); Santana-Perez I., Ferreira da Silva R., Rynge M., Deelman E., Perez-Hernandez M.S., Corcho O., Reproducibility of execution environments in computational science using semantics and clouds, Future Generation Computer Systems, 67, pp. 354-367, (2017); Shreeves S.L., Presenting the new and improved DMPTool, Paper presented at Open Repositories, (2014); Stodden V., (2013); Stodden V., McNutt M., Bailey D.H., Deelman E., Gil Y., Hanson B., Taufer M., Enhancing reproducibility for computational methods, Science, 354, 6317, pp. 1240-1241, (2016)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85076014636"
"Mushi G.E.; Pienaar H.; Deventer M.","Mushi, Gilbert Exaud (57493936700); Pienaar, Heila (14322260600); Deventer, Martie van (57213686776)","57493936700; 14322260600; 57213686776","Identifying and implementing relevant research data management services for the library at the university of dodoma, Tanzania","2020","Data Science Journal","19","1","1","","","","11","10.5334/dsj-2020-001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077960449&doi=10.5334%2fdsj-2020-001&partnerID=40&md5=7e25a88dccb9c5cff663b20824c77dde","University of Dodoma, Tanzania; University of Pretoria, South Africa","Mushi G.E., University of Dodoma, Tanzania; Pienaar H., University of Pretoria, South Africa; Deventer M., University of Pretoria, South Africa","Research Data Management (RDM) services are increasingly becoming a subject of interest for academic and research libraries globally – this is also the case in developing countries. The interest is motivated by a need to support research activities through data sharing and collaboration both locally and internationally. Many institutions, especially in the developed countries, have implemented RDM services to accelerate research and innovation through e-Research but extensive RDM is not so common in developing countries. In reality many African universities and research institutions are yet to implement the most basic of data management services. We believe that the absence of political will and national government mandates on data management often hold back the development and implementation of RDM services. Similarly, research funding agencies are not yet applying sufficient pressure to ensure that Africa complies with the requirement to deposit research data in trusted repositories. While the context was acknowledged the University of Dodoma library staff realized that it is urgent to prepare for the inevitable – the time when RDM will be a requirement for research funding support. This paper presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community. This paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country academic institution. The paper also mentions the requirements for the sustainability of these initiatives. © 2020 The Author(s).","Data curation; Research data; Research data management; Tanzania; University library; University of Dodoma","Developing countries; Information management; Libraries; Research data; Research data managements; Tanzania; University libraries; University of Dodoma; Data curation","","","","","National Science Foundation, NSF; Carnegie Corporation of New York, CCNY; Center for Outcomes Research and Evaluation, Yale School of Medicine, CORE; Australian National Data Service, ANDS; National Kidney Foundation of South Africa, NKF; University of Pretoria, UP","Funding text 1: In Africa, the National Research Foundation in South Africa has enforced the retention of data for its funded research and provided a framework for data management services by academic and research institutions there. However, universities and research institutions in many other African countries (and most probably also other developing countries) are yet to implement data management services. The absence of research funding agency policies and national government mandates on data management further obstruct the development and implementation of RDM services.; Funding text 2: The National Science Foundation in the United States of America (USA), the Australian National Data Service in Australia [now the Australian Research Data Commons] and the e-Science Core Programme in the United Kingdom (UK) have all been involved in enforcing mandates and advocating for national legislative instruments on data retention and frameworks on responsible conduct of research (Chiware & Mathe 2016).; Funding text 3: Sincere gratitude goes to the University of Pretoria and Carnegie New York Cooperation for funding the studies that led to this paper.","Antell K., Et al., Dealing with Data: Science Librarians’ Participation in Data Management at Association of Research Libraries Institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Chiware E.R.T., Mathe Z., Academic libraries’ role in Research Data Management Services: A South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2016); Coates H.L., Building Data Services From the Ground Up: Strategies and Resources Building Data Services From the Ground Up: Strategies and Resources, Journal of Escience Librarianship, 3, 1, pp. 52-59, (2014); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Davidson J., Introduction to Data Management Planning, (2016); EPSRC Policy Framework on Research Data – EPSRC Website. Engineering and Physical Sciences Research Council, (2011); Erway R., Starting the Conversation: University-wide Research Data Management Policy, OCLC Research, (2013); Erway R., Et al., Building Blocks: Laying the Foundation for a Research Data Management Program, 23, (2016); Erway R., Rinehart A., If You Build It, Will they Fund? Making Research Data Management Sustainable, (2016); Fary M., Owen K., Developing an Institutional Research Data Management Plan Service, (2013); Flores J.R., Et al., Libraries and The Research Data Management Landscape. The Process of Discovery: The CLIR Postdoctoral Fellowship Program and The Future of The Academy, 2010, pp. 82-102, (2015); Grynoch T., Implementing Research Data Management Services in a Canadian Context, Dalhousie Journal of Interdisciplinary Management, 12, pp. 1-24, (2016); Jones S., How to develop a data management and sharing plan, DCC How-To Guides, (2011); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services – a guide for HEIs, Digital Curation Centre, pp. 1-22, (2013); Kelly T., Hodgett M., Managing Research Data, (2015); Krier L., Strasser C.A., Data Management for Libraries: A LITA Guide, (2014); Liu X., Ding N., Research data management in universities of Central China: Practices at Wuhan University Library Introduction, The Electronic Library, 34, 5, (2016); Mboera L.E.G., The Management of Health and Biomedical Data in Tanzania: Need for a National Scientific Data Policy, (2015); Mushi G.E., Identifying Relevant Research Data Management Services for the Library at the University of Dodoma, (2017); Mushi G.E., Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, (2017); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos ONE, 9, 12, pp. 1-28, (2014); Reed R.B., Diving into data: Planning a research data management event, Journal of Escience Librarianship, 4, 1, (2015); RCUK Common Principles on Data Policy, (2015); Research Data Management: Briefing for Library Directors. March., (2015); Searle S., University libraries and research data management: Developing knowledge, skills and careers, Council of Australian University Librarians, (2011); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services, (2012); van Deventer M., Pienaar H., Research Data Management in a Developing Country: A Personal Journey, International Journal of Digital Curation, 10, 2, pp. 33-47, (2015); van Tuyl S., Michalek G., Assessing Research Data Management Practices of Faculty at Carnegie Mellon University, Journal of Librarianship and Scholarly Communication, 3, 3, (2015); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices, Program, 49, 4, pp. 382-407, (2015)","G.E. Mushi; University of Dodoma, Tanzania; email: ray19mushi@gmail.com","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85077960449"
"Steel K.M.; Thompson H.; Wright W.","Steel, Kathryn M. (55055283200); Thompson, Helen (48661629800); Wright, Wendy (15046312900)","55055283200; 48661629800; 15046312900","Opportunities for intra-university collaborations in the new research environment","2019","Higher Education Research and Development","38","3","","638","652","14","9","10.1080/07294360.2018.1549537","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058711117&doi=10.1080%2f07294360.2018.1549537&partnerID=40&md5=f7dd689844b4bfd85eadbda1a72d6709","Library Services, Federation University Australia, Churchill, Australia; Collaborative Research Centre in Australian Studies, Federation University Australia, Churchill, Australia; Centre for eResearch and Digital Innovation, Federation University Australia, Ballarat, Australia; School of Health & Life Sciences, Federation University Australia, Churchill, Australia","Steel K.M., Library Services, Federation University Australia, Churchill, Australia, Collaborative Research Centre in Australian Studies, Federation University Australia, Churchill, Australia; Thompson H., Centre for eResearch and Digital Innovation, Federation University Australia, Ballarat, Australia; Wright W., School of Health & Life Sciences, Federation University Australia, Churchill, Australia","New opportunities for research collaborations within universities are explored through reflection on a recent collaboration between an academic researcher, the library and the eResearch Centre at a regional Australian university. Such opportunities arise from significant changes to the research landscape, including increased emphasis on open access publication of research outputs and the growth of eResearch capabilities. The latter has resulted in increases in data size and complexity and provides opportunities for collaboration across research institutions. This article reflects on the dynamics and assesses the outcomes of a collaboration formed during an externally funded open research data project. This project and a precursor project are briefly described, together with the specific contribution of each collaborator. Collaboration dynamics and the reasons for project success are assessed, as are implications for future research practice. Outcomes from eResearch collaborations may provide broader benefits to universities, as well as rewards to academic researchers. © 2018, © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Collaboration; eResearch; open access; reflective practice; research data management","","","","","","Australian National Data Service, ANDS","ANDS has provided a focus for transforming Australia’s research data environment, and is an international exemplar (Davidson, Jones, Molloy, & Kejser, 2014). ANDS is funded by the Australian government via the National Collaborative Research Infrastructure Strategy (NCRIS), which supports national research capability and collaborative infrastructure. ANDS has funded a series of projects via a partnership approach, beginning with the Seeding the Commons program, in order to build institutional capacity to manage and preserve Australian research data, provide rich metadata to aid discovery, and increase access to open data and related infrastructure (Groenewegen & Treloar, 2013). ANDS has played a major role in research data discoverability and potential re-use, facilitated by its RDA portal which contains searchable metadata and links to research datasets.","Adams T.M., Bullard K.A., A case study of librarian outreach to scientists: Collaborative research and scholarly communication in conservation biology, College & Undergraduate Libraries, 21, 3-4, pp. 377-395, (2014); Atkins D., Bindoff N., Borgman C., Ellisman M., Feldman S., Foster I., Ynnerman A., RCUK review of e-science 2009: Building a UK foundation for the transformative enhancement of research and innovation, (2010); Auckland M., Re-skilling for research: An investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Baldwin R.G., Austin A.E., Toward greater understanding of faculty research collaboration, The Review of Higher Education, 19, 1, pp. 45-70, (1995); Bedi S., Walde C., Transforming libraries: Canadian academic librarians embedded in faculty research projects, College & Research Libraries, 78, 3, pp. 314-327, (2017); Borgman C.L., Research data: Who will share what, with whom, when, and why?, (2010); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Bossio D., Loch B., Schier M., Mazzolini A., A roadmap for forming successful interdisciplinary education research collaborations: A reflective approach, Higher Education Research & Development, 33, 2, pp. 198-211, (2014); Carr L., Swan A., Arthur S., Oppenheim C., Brody T., Hitchcock S., Harnad S., Repositories for institutional open access: Mandated deposit policies, (2006); Chitty T., McRostie D., Better together: The ESRC in the university research library of the twenty-first century, The Australian Library Journal, 65, 3, pp. 157-166, (2016); National innovation and science agenda, (2016); Cook R., eResearch services and advanced IT - the next generation, (2010); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing research data, pp. 105-133, (2012); Creamer E.G., Exploring the link between inquiry paradigm and the process of collaboration, The Review of Higher Education, 26, 4, pp. 447-465, (2003); Davidson J., Jones S., Molloy L., Kejser U.B., Emerging good practice in managing research data and research information within UK universities, Procedia Computer Science, 33, pp. 215-222, (2014); De Silva P.U.K., Vance C.K., Scientific scholarly communication: The changing landscape, (2017); Donham J., Green C.W., Perspectives on … developing a culture of collaboration: Librarian as consultant, The Journal of Academic Librarianship, 30, 4, pp. 314-321, (2004); Einfalt J., Turley J., Partnerships for success: A collaborative support model to enhance the first year student experience, The International Journal of the First Year in Higher Education, 4, 1, pp. 73-84, (2013); Groenewegen D., The state of Australian research data–Systems are ready but where are the incentives?, The state of open data: A selection of analyses and articles about open data, curated by Figshare, pp. 34-35, (2016); Groenewegen D., Treloar A., Adding value by taking a national and institutional approach to research data: The ANDS experience, International Journal of Digital Curation, 8, 2, pp. 89-98, (2013); Haddow G., Xia J.C., Willson M., Collaboration in the humanities, arts and social sciences in Australia, Australian Universities Review, 59, 1, pp. 24-36, (2017); Hains-Wesson R., Young K., A collaborative autoethnography study to inform the teaching of reflective practice in STEM, Higher Education Research & Development, 36, 2, pp. 297-310, (2017); Hara N., Solomon P., Kim S.-L., Sonnenwald D., An emerging view of scientific collaboration: Scientists’ perspectives on collaboration and factors that impact collaboration, Journal of the American Society for Information Science and Technology, 54, 10, pp. 952-965, (2003); Haynes E.B., Librarian-faculty partnerships in instruction, Advances in Librarianship, 20, pp. 191-222, (1996); Huggard S., Pigram P., Williams A., Fisch E., The connected researcher–Research data management at La Trobe University, (2016); Ivey R., Information literacy: How do librarians and academics work in partnership to deliver effective learning programs?, Australian Academic & Research Libraries, 34, 2, pp. 100-113, (2003); Jones S., Research data policies: Principles, requirements and trends, Managing research data, pp. 47-66, (2012); Kahn P., Goodhew P., Murphy M., Walsh L., The scholarship of teaching and learning as collaborative working: A case study in shared practice and collective purpose, Higher Education Research & Development, 32, 6, pp. 901-914, (2013); Liffers M., Brown A., McInnes B., Major open data: The Digital Mineral Library at Curtin University, (2015); Lynch C., The institutional challenges of cyberinfrastructure and e-research, Educause Review, 43, 6, pp. 74-88, (2008); Malthouse R., Roffey-Barentsen J., Watts M., Reflectivity, reflexivity and situated reflective practice, Professional Development in Education, 40, 4, pp. 597-609, (2014); Marquis E., Healey M., Vine M., Fostering collaborative teaching and learning scholarship through an international writing group initiative, Higher Education Research & Development, 35, 3, pp. 531-544, (2016); McAlpine K., Chang J., McLean J., Albone C., Embedding research data management: Case studies from the University of Sydney, (2016); McClintock D., Ison R., Armson R., Metaphors for reflecting on research practice: Researching with people, Journal of Environmental Planning and Management, 46, 5, pp. 715-731, (2003); McKiernan E.C., Bourne P.E., Brown C.T., Buck S., Kenall A., Lin J., Yarkoni T., How open science helps researchers succeed, eLife, 5, (2016); Milne C., Thompson E.E., De Vine L., Getting research data out there: Collaborative solutions to identifying, describing and making research data more visible, (2011); Montiel-Overall P., Teacher and librarian collaboration: A qualitative study, Library & Information Science Research, 30, 2, pp. 145-155, (2008); Science & engineering indicators 2014, (2014); Pham H.T., Tanner K., Collaboration between academics and library staff: A structurationist perspective, Australian Academic & Research Libraries, 46, 1, pp. 2-18, (2015); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Knowledge, networks and nations: Global scientific collaboration in the 21st century, (2011); Saltiel D., Teaching reflective research and practice on a post qualifying child care programme, Social Work Education, 22, 1, pp. 105-111, (2003); Schrage M., No more teams! Mastering the dynamics of creative collaboration, (1995); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program, 49, 4, pp. 440-460, (2015); Smith L., Monash University Library and learning: A new paradigm for a new age, Australian Academic & Research Libraries, 42, 3, pp. 246-263, (2011); Suber P., Open access, (2012); Van den Eynden V., Bishop L., Incentives for sharing research data: Evidence from five European case studies, (2014); Ware M., Mabe M., The STM report: An overview of scientific and scholarly publishing, (2015); Wilkes J., Godwin J., Gurney L.J., Developing information literacy and academic writing skills through the collaborative design of an assessment task for first year engineering students, Australian Academic & Research Libraries, 46, 3, pp. 164-175, (2015); Wise S., Sefton P., Linking data, linking disciplines, (2015)","K.M. Steel; Library Services, Federation University Australia, Churchill, Australia; email: kay.steel@federation.edu.au","","Routledge","","","","","","07294360","","","","English","High. Educ Res. Dev.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85058711117"
"Cauchick-Miguel P.A.; Moro S.R.; Rivera R.; Amorim M.","Cauchick-Miguel, Paulo A. (8392535100); Moro, Suzana R. (57190390759); Rivera, Roberto (57217285024); Amorim, Marlene (55250827300)","8392535100; 57190390759; 57217285024; 55250827300","Data management plan in research: characteristics and development","2020","Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST","319 LNICST","","","3","14","11","1","10.1007/978-3-030-50072-6_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087010726&doi=10.1007%2f978-3-030-50072-6_1&partnerID=40&md5=2deaa00a2d33120941367d9eefa8430a","Universidade Federal de Santa Catarina, Florianópolis, 88040-900, SC, Brazil; Universidade de Aveiro, Aveiro, 3810-193, Portugal","Cauchick-Miguel P.A., Universidade Federal de Santa Catarina, Florianópolis, 88040-900, SC, Brazil; Moro S.R., Universidade Federal de Santa Catarina, Florianópolis, 88040-900, SC, Brazil; Rivera R., Universidade de Aveiro, Aveiro, 3810-193, Portugal; Amorim M., Universidade de Aveiro, Aveiro, 3810-193, Portugal","Data science is an interdisciplinary field that extracts value from data. One of the relevant areas is its application in research in order to define requirements of the data life cycle. Thus, data should be managed before, during, and after a research project completion. A robust data management plan (DMP) is a relevant and useful instrument to establish data-related requirements. In this context, this paper aims at highlighting some characteristics associated to research data management. To conduct this study peer-reviewed literature and secondary data are methodologically employed to fulfil the paper objective. The results discuss the development of DMP, provide some examples of documents and a check list related to data management, and present some recommendations for developing a suitable data management plan from the literature. The data management plan is one of the important instruments that should be considered with care when designing and applying it. Future work may consider providing a structure and guidance for research students in the field of industrial engineering as a valuable avenue to explore. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.","Data planning; Research Data Management; Research plan","Engineering research; Industrial research; Information management; Life cycle; Check-lists; Data life cycle; Interdisciplinary fields; ITS applications; Project completion; Research data managements; Secondary datum; Research and development management","","","","","","","Michener W.F., Ten simple rules for creating a good data management plan, PLoS Comput. Biol, 11, 10, (2015); Davis H.M., Cross W.M., Using a data management plan review service as a training ground for librarians, J. Librariansh. Sch. Commun, 3, 2, (2015); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: science librarians’ participation in data management at an association of research libraries institutions, Coll. Res. Libr, 75, 4, pp. 557-574, (2014); Surkis A., Read K., Research data management, J. Med. Libr. Assoc. JMLA, 103, 3, pp. 154-156, (2015); Cox A.M., Tam W.W.T., A critical analysis of lifecycle models of the research process and research data management, Aslib J. Inf. Manage, 70, 2, pp. 142-157, (2018); Vieira R., Ferreira F., Barateiro J., Borbinha J., Data management with risk management in engineering and science projects, New Rev. Inf. Netw, 19, 2, pp. 49-66, (2014); Nature editorial-making plans, Nature, 555, 7696, (2018); Mannheimer S., Toward a better data management plan: the impact of DMPs on grant funded research practices, J. eSci. Librariansh, 7, 3, (2018); Bellgard M.I., ERDMAS: an exemplar-driven institutional research data management and analysis strategy, Int. J. Inf. Manage, 50, pp. 337-340, (2020); Wright A., Electronic resources for developing data management skills and data management plans, J. Electron. Resour. Med. Libr, 13, 1, pp. 43-48, (2016); Holles J.H., Schmidt M.L., Graduate research data management course content: teaching the Data Management Plan (DMP), 2018 ASEE Annual Conference and Exposition, (2018); Reilly M., Dryden A.R., Building an online data management plan tool, J. Librariansh. Sch. Commun, 1, 3, (2013); Van Loon J.E., Akers K.G., Hudson C., Sarkozy A., Quality evaluation of data management plans at a research university, IFLA J, 43, 1, pp. 98-104, (2017); European Commission – European Union; Willaert T., Cottyn J., Kenens U., Vandendriessche T., Verbeke D., Wyns R., Research data management and the evolutions of scholarship: policy, infrastructure and data literacy at KU Leuven, LIBER Q, 29, pp. 1-19, (2019); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Booth A., Sutton A., Papaioannou D., Systematic approaches to a successful literature review, (2012)","P.A. Cauchick-Miguel; Universidade Federal de Santa Catarina, Florianópolis, 88040-900, Brazil; email: paulo.cauchick@ufsc.br","Mugnaini R.","Springer","","1st EAI International Conference on Data and Information in Online Environments, DIONE 2020","19 March 2020 through 20 March 2020","Florianopolis","241069","18678211","978-303050071-9","","","English","Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.","Conference paper","Final","","Scopus","2-s2.0-85087010726"
"Fitschen T.; Schlemmer A.; Hornung D.; Tom Wörden H.; Parlitz U.; Luther S.","Fitschen, Timm (57210467537); Schlemmer, Alexander (47861369300); Hornung, Daniel (49963587800); Tom Wörden, Henrik (26427841800); Parlitz, Ulrich (56211906500); Luther, Stefan (7005503111)","57210467537; 47861369300; 49963587800; 26427841800; 56211906500; 7005503111","CaosDB—research data management for complex, changing, and automated research workflows","2019","Data","4","2","83","","","","2","10.3390/data4020083","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070841332&doi=10.3390%2fdata4020083&partnerID=40&md5=79195358bed5b6d96f0a5daa6b89de67","Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany; Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, 37075, Germany; Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, 37075, Germany; Indiscale GmbH i.G., Göttingen, 37075, Germany","Fitschen T., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany; Schlemmer A., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, 37075, Germany; Hornung D., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Indiscale GmbH i.G., Göttingen, 37075, Germany; Tom Wörden H., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, Indiscale GmbH i.G., Göttingen, 37075, Germany; Parlitz U., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, 37075, Germany; Luther S., Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany, Institute for the Dynamics of Complex Systems, Georg-August-Universität, Göttingen, 37077, Germany, German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, 37075, Germany, Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, 37075, Germany","We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.","ACID; Database; FAIR; RDMS; Research data management","Bioinformatics; Data acquisition; Data handling; Digital storage; Information management; Life cycle; Query processing; Semantics; Data management system; Data repositories; Data-source; FAIR; Research data; Research data management system; Research data managements; Seamless integration; Work-flows; Query languages","","","","","Deutsches Zentrum für Herz-Kreislaufforschung, DZHK; Deutsche Forschungsgemeinschaft, DFG, (SFB 937); Bundesministerium für Bildung und Forschung, BMBF, (FKZ 031A147)","Funding: We acknowledge support from the German Federal Ministry of Education and Research (BMBF) (project FKZ 031A147, GO-Bio), the German Research Foundation (DFG) (Collaborative Research Centers SFB 1002 Project C3 and SFB 937 Project A18) and the German Center for Cardiovascular Research (DZHK e.V.).","Nelson E.K., Piehler B., Eckels J., Rauch A., Bellew M., Hussey P., Ramsay S., Nathe C., Lum K., Krouse K., Et al., LabKey Server: An open source platform for scientific data integration, analysis and collaboration, BMC Bioinform, 12, (2011); Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in Biomedical Research Data Management and Analysis: Needs and Barriers, J. Am. Med. Inform. Assoc., 14, pp. 478-488, (2007); Wruck W., Peuker M., Regenbrecht C.R., Data management strategies for multinational large-scale systems biology projects, Brief. Bioinform., 15, pp. 65-78, (2014); Rocha da Silva J., Aguiar Castro J., Ribeiro C., Correia Lopes J., Dendro: Collaborative Research Data Management Built on Linked Open Data, The Semantic Web: ESWC 2014 Satellite Events, pp. 483-487, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Marcus D.S., Olsen T., Ramaratnam M., Buckner R.L., XNAT: A software framework for managing neuroimaging laboratory data, Proceedings of the 12Th Annual Meeting of the Organization for Human Brain Mapping, (2006); Kanza S., Willoughby C., Gibbins N., Whitby R., Frey J.G., Erjavec J., Zupancic K., Hren M., Kovac K., Electronic lab notebooks: Can they replace paper?, J. Cheminform., 9, (2017); Schweiger D., Trajanoski Z., Pabinger S., SPARQLGraph: A web-based platform for graphically querying biological Semantic Web databases, BMC Bioinform, 15, (2014); Haag F., Lohmann S., Siek S., Ertl T., QueryVOWL: A Visual Query Notation for Linked Data, The Semantic Web: ESWC 2015 Satellite Events, pp. 387-402, (2015); Chochiang K., Grabmann B., Betbeder M.L., Lapayre J.C., Sa-Ngiamsak C., OntoQuer: A Tool for Building SPARQL Query Automatically Applying with Our Ontologies, J. Softw., 12, pp. 145-152, (2017); Fielding R.T., Architectural Styles and the Design of Network-Based Software Architectures, (2000); Fitschen T., Hornung D., Schlemmer A., Tom Worden H., Caos DB, (2018); Follesdal D., Hilpinen R., Deontic Logic: An Introduction, Deontic Logic: Introductory and Systematic Readings, pp. 1-35, (1971); GNU Affero General Public License, (2018)","A. Schlemmer; Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37077, Germany; email: alexander.schlemmer@ds.mpg.de","","MDPI","","","","","","23065729","","","","English","Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85070841332"
"Brochu L.; Burns J.","Brochu, Lauren (57208905354); Burns, Jane (56479851300)","57208905354; 56479851300","Librarians and Research Data Management–A Literature Review: Commentary from a Senior Professional and a New Professional Librarian","2019","New Review of Academic Librarianship","25","1","","49","58","9","11","10.1080/13614533.2018.1501715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066080663&doi=10.1080%2f13614533.2018.1501715&partnerID=40&md5=2e68e3940e0771585faa81914f8d6c41","Library & Web Services, Irish Hospice Foundation, Dublin, Ireland; School of Information & Communication Studies, University College Dublin, Dublin, Ireland; Institute Librarian, Athlone Institute of Technology, Athlone, Co. Westmeath, Ireland","Brochu L., Library & Web Services, Irish Hospice Foundation, Dublin, Ireland, School of Information & Communication Studies, University College Dublin, Dublin, Ireland; Burns J., School of Information & Communication Studies, University College Dublin, Dublin, Ireland, Institute Librarian, Athlone Institute of Technology, Athlone, Co. Westmeath, Ireland","In the changing landscape of libraries and the roles of librarians, the area of Research Data Management (RDM) is emerging with new opportunities and challenges. This literature review identifies the current levels of publication that deal with the relationship of the librarian and their role in the research data management process and provides an examination of institutional research policies supporting collaboration of librarians as part of the research team. © 2018, Published with license by Taylor & Francis Group, LLC.","Librarians; RDM; research cycle; research data management; research data management plan; research data management tools; research management","","","","","","","","Bryant R., Lavoie B., Malpas C., A Tour of the Research Data Management (RDM) Service Space. The Realities of Research Data Management, Part 1, (2017); Cox A.M., Verbaan E., Sen B., A Spider, an Octopus, or an Animal Just Coming into Existence? Designing a Curriculum for Librarians to Support Research Data Management, Journal of eScience Librarianship, (2014); (2017); (2017); (2017); (2017); Deards K., (2013); Ekstrom J., Elbaek M., Erdmann C., Grigorov I., (2014); Tenopir C., Sandusky R., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, (2014)","J. Burns; Dublin 18, Apt. 103, The Cubes 3, South Beacon Quarter, Sandyford Industrial Estate, Ireland; email: laurene.brochu@gmail.com","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Review","Final","","Scopus","2-s2.0-85066080663"
"Schöpfel J.; Prost H.; Zane A.","Schöpfel, Joachim (14619562900); Prost, Hélène (15069878000); Zane, Antonella (57204030261)","14619562900; 15069878000; 57204030261","Data from: “data papers as a new form of knowledge organization in the field of research data”","2019","Grey Journal","15","3","","170","172","2","0","10.17026/dans-zk3-jkyb","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075717852&doi=10.17026%2fdans-zk3-jkyb&partnerID=40&md5=51226f1a80545da2890db27d1d849a54","University of Lille, France; CNRS, GERiiCO laboratory, France; Antonio Vallisneri Bio-medical Library, France","Schöpfel J., University of Lille, France; Prost H., CNRS, GERiiCO laboratory, France; Zane A., Antonio Vallisneri Bio-medical Library, France","Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e. disciplines, publishers and business models, and about their structure, length, formats, metadata and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e. description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing. © 2019, GreyNet. All rights reserved.","Academic publishing; Data journals; Data papers; FAIR principles; Knowledge organization; Open science; Research data","","","","","","","","Belter C.W., Measuring the Value of Research Data: A Citation Analysis of Oceanographic Data Sets, Plos One, (2014); Callaghan S., Donegan S., Pepler S., Et al., Making data a first-class scientific output: Data citation and publication by NERC's environmental data centres, International Journal of Digital Curation, 7, 1, pp. 107-113, (2012); Bordelon D., Grothkopf U., Meakins S., Sterzik M., Trends and developments in VLT data papers as seen through telbib, Proc. SPIE 9910, Observatory Operations: Strategies, Processes, and Systems VI, 99102B, (2016); Candela L., Castelli D., Manghi P., Tani A., Data Journals: A Survey, JASIST, 66, 9, pp. 1747-1762, (2015); Chavanpenev W., The data paper: A mechanism to incentivize data publishing in biodiversity science, BMC Bioinformatics, 12, (2011); Costello Mark J., Michener William K., Gahegan M., Zhang Z.-Q., Bourne Philipp E., Biodiversity data should be published, cited, and peer reviewed, Trends in Ecology & Evolution, 28, 8, pp. 454-461, (2013); Davis Grace H., Payne E., Andrew S.I.H., Commentary: Four ways in which data-free papers on animal personality fail to be impactful, Frontiers in Ecology and Evolution, 3, 102, pp. 1-3, (2015); Farace D., Frantzen J., Smith P., Data Papers are Witness to Trusted Resources in Grey Literature: A Project Use Case, The Grey Journal, 14, 1, pp. 31-36, (2018); Friedman R., Psaki S., Bingenheimer J., Announcing a New Journal Section: Data Papers, Studies in Family Planning, 48, 3, pp. 291-292, (2017); Data Journals: eclosión De Nuevas Revistas Especializadas En Datos. El Profesional De La información, 24, 6, pp. 845-854, (2015); Xiaolei H.U.A.N.G., Hawkins Bradford A., Gexia Q.I.A.O., Biodiversity Data Sharing: Will Peer-Reviewed Data Papers Work?, Bioscience, 63, 1, pp. 5-6, (2013); Le Deuff O., Une Nouvelle Rubrique Pour La RFSIC: Le Data Paper. Revue française Des Sciences De l’information Et De La Communication, (2018); Li K., Greenberg J., Dunic J., Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers, Preprint Accepted by JASIST, (2019); National Plan for Open Science. Paris, Ministère De l’Enseignement Supérieur, De La Recherche Et De l’Innovation, (2018); Michener William K., Brunt James W., Helly John J., Kirchner Thomas B., Stafford Susan G., Nongeospatial metadata for the ecological sciences, Ecological Archives, 7, 1, pp. 330-342, (1997)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Data paper","Final","","Scopus","2-s2.0-85075717852"
"Cho J.","Cho, Jane (15833851000)","15833851000","Study of Asian RDR based on re3data","2019","Electronic Library","37","2","","302","313","11","2","10.1108/EL-01-2019-0016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066907413&doi=10.1108%2fEL-01-2019-0016&partnerID=40&md5=a90acbd0c293ea84e6e7368023fd1c5f","Department of Library and Information Science, Institute of Social Science, University of Incheon, Incheon, South Korea","Cho J., Department of Library and Information Science, Institute of Social Science, University of Incheon, Incheon, South Korea","Purpose: RDR has become an essential academic infrastructure in an atmosphere that facilitates the openness of research output granted by public research funds. This study aims to understand operational status of 152 Asian data repositories on re3data and cluster repositories into four groups according to their operational status. In addition, identify the main subject areas of RDRs in Asian countries and try to understand what topic correlations exist between data archived in Asian countries. Design/methodology/approach: This study extracts metadata from re3data and analyzes it in various ways to grasp the current status of research data repositories in Asian countries. The author clusters the repositories into four groups using hierarchical cluster analysis according to the level of operation. In addition, for identifying the main subject areas of RDRs in Asian countries, extracted the keywords of the subject field assigned to the each repository, and Pathfinder Network (PFNET) analysis is performed. Findings: About 70 per cent of the Asian-country repositories are those where licenses or policies are declared but not granted permanent identifiers and international-level certification. As a result of the subject domain analysis, eight clusters are formed centering on life sciences and natural sciences. Originality/value: The research output in developing countries, especially non-English-speaking countries, tends not to be smoothly circulated in the international community due to the immaturity of the open-access culture, as well as linguistic and technical problems. This study has value, in that it investigates the status of Asian countries’ research data management and global distribution infrastructure in global open-science trends. © 2019, Emerald Publishing Limited.","Asia; Open access; Open science; RDR; Research data repositories","","","","","","National Science Foundation, NSF; Research Councils UK, RCUK; Deutsche Forschungsgemeinschaft, DFG","Funding text 1: Trustworthy research data repositories (RDRs) are needed to ensure that research data are stored and published reliably. An RDR is a sustainable information infrastructure that provides long-term storage and research data access. It can be said that it is an essential element of the research infrastructure used by the scientific community to carry out the highest level of research in each field. Recently, it has become essential in an atmosphere that facilitates the openness of research data granted by public research funds (OECD, 2013). The National Science Foundation (NSF), Research Councils UK (RCUK) and other research support agencies are strengthening policies to deposit research results output granted from public funds in reliable repositories (STEPI, 2017). Therefore, the criteria and the certification system for securing the reliability of RDRs are becoming important issues.; Funding text 2: The re3data.org is a registry for repositories in 67 countries around the world. This is the result of a research project funded by the German Research Foundation (DFG) from 2012 to 2015. Since January 2016, it has been operated as a service of DataCite. Re3data has a comprehensive set of metadata for 42 attributes to index and describe RDRs.","Business models principles, (2012); Kim S., Global data repository status and analysis: based on Korea, China and Japan data in re3data.org, International Journal of Knowledge Content Development and Technology, 8, 1, pp. 79-89, (2018); Kim S., Choi M., Functional requirements for research data repositories, International Journal of Knowledge Content Development and Technology, 27, 2, pp. 41-51, (2017); Kindling M., Pampel H., Sandt S., Rucknagel J., Vierkant P., Kloska G., Witt M., Et al., The landscape of research data repositories in 2015: a re3data analysis, D-Lib Magazine, 23, 3-4, (2017); Lee J.Y., A study on the network generation methods for examining the intellectual structure of knowledge domains, Journal of the Korean Society for Library and Information Science, 40, 2, pp. 333-355, (2006); Lee J.Y., A novel clustering method for examining and analyzing the intellectual structure of a scholarly field, Korea Society for Information Management, 23, 4, pp. 215-231, (2006); OECD principles and guidelines for access to research data from public funding, (2013); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Goebelbecker H., Et al., Making research data repositories visible: the re3data.org registry, PLoS One, 8, 11, (2013); Expansion of open science policy and implications, STEPI Insight, 216, pp. 2-38, (2017); Zhang S., Huang G., Geng Q., Research on UK scientific data publishing platforms based on Re3data, Digital Library Forum, 6, pp. 16-24, (2017)","J. Cho; Department of Library and Information Science, Institute of Social Science, University of Incheon, Incheon, South Korea; email: chojane123@naver.com","","Emerald Group Holdings Ltd.","","","","","","02640473","","ELLID","","English","Electron. Libr.","Article","Final","","Scopus","2-s2.0-85066907413"
"Pasek J.E.; Mayer J.","Pasek, Judith E. (6701746555); Mayer, Jennifer (57211579209)","6701746555; 57211579209","Education needs in research data management for science-based disciplines: Self-assessment surveys of graduate students and faculty at two public universities","2019","Issues in Science and Technology Librarianship","2019","92","","","","","11","10.29173/istl12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074473519&doi=10.29173%2fistl12&partnerID=40&md5=4fa73ea5fad7f821a92deeebc511863c","STEM Liaison Librarian, University of Wyoming, Laramie, WY, United States; University of Northern Colorado, Greeley, CO, United States","Pasek J.E., STEM Liaison Librarian, University of Wyoming, Laramie, WY, United States; Mayer J., University of Northern Colorado, Greeley, CO, United States","Research data management is a prominent and evolving consideration for the academic community, especially in scientific disciplines. This research study surveyed 131 graduate students and 79 faculty members in the sciences at two public doctoral universities to determine the importance, knowledge, and interest levels around research data management training and education. The authors adapted 12 competencies for measurement in the study. Graduate students and faculty ranked the following areas most important among the 12 competencies: ethics and attribution, data visualization, and quality assurance. Graduate students indicated they were least knowledgeable and skilled in data curation and re-use, metadata and data description, data conversion and interoperability, and data preservation. Their responses generally matched the perceptions of faculty. The study also examined how graduate students learn research data management, and how faculty perceive that their students learn research data management. Results showed that graduate students utilize self-learning most often and that faculty may be less influential in research data management education than they perceive. Responses for graduate students between the two institutions were not statistically different, except in the area of perceived deficiencies in data visualization competency. Introduction. © 2019, Association of College and Research Libraries. All rights reserved.","","","","","","","National Science Foundation, NSF, (NSF 2013)","The broad classifications of the National Science Foundation (NSF 2013) fields of study","(2018); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Bracke M.S., Fosmire M., Teaching data information literacy skills in a library workshop setting: A case study in agricultural and biological engineering, Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, pp. 129-148, (2015); Carlson J., Bracke M., Planting the seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, 1, pp. 95-110, (2015); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Jeffryes J., Johnston L.R., Nichols M., Westra B., Wright S.J., An exploration of the data information literacy competencies: Findings from the project interviews, Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, pp. 51-70, (2015); Carlson J., Johnston L., Westra B., Nichols M., Developing an approach for data management education: A report from the Data Information Literacy Project, International Journal of Data Curation, 8, 1, pp. 204-217, (2013); Carlson J., Johnston L.R., Westra B., Developing the data information literacy project: Approach and methodology, Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, pp. 35-50, (2015); Carlson J., Nelson M.S., Johnston L.R., Koshoffer A., Developing data literacy programs: Working with faculty, graduate students and undergraduates, Bulletin of the American Society for Information Science and Technology, 41, 6, pp. 14-17, (2015); Carlson J., Stowell-Bracke M., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality field station, Portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Fernandez P., Eaker C., Swauger S., Davis M.L.E.S., Public progress, data management and the land grant mission: A survey of agriculture researchers’ practices and attitudes at two land-grant institutions, Issues in Science and Technology Librarianship, 83, (2016); Frank E.P., Pharo N., Academic librarians in data information literacy instruction: A case study in meteorology, College & Research Libraries, 77, 4, pp. 536-552, (2016); Jahnke L., Asher A., Keralis S.D.C., The Problem of Data [Internet], (2012); Johnston L., Jeffryes J., Data management skills needed by structural engineering students: Case study at the University of Minnesota, Journal of Professional Issues in Engineering Education and Practice, 140, 2, (2014); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data curation: A study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Mischo W.H., Wiley C.A., Schlembach M.C., Imker H.J., An integrated data management plan instructional program [Internet]. [cited 2018 Jul 27]. 2017 ASEE Annual Conference & Exposition; 2017 Jun 24; Columbus (OH), American Society for Engineering Education, (2017); O'Kelly M., Garrison J., Merry B., Torreano J., Building a peer-learning service for students in an academic library, Portal: Libraries and the Academy, 15, 1, pp. 163-182, (2015); Peters C., Vaughn P., Initiating data management instruction to graduate students at the University of Houston using the New England Collaborative Data Management Curriculum, Journal of Escience Librarianship, 3, 1, (2014); Piorun M., Kafel D., Leger-Hornby T., Najafi S., Martin E., Colombo P., Lapelle N., Teaching research data management: An undergraduate/graduate curriculum, Journal of Escience Librarianship, 1, 1, pp. 46-50, (2012); Pouchard L., Bracke M.S., An analysis of selected data practices: A case study of the Purdue College of Agriculture, Issues in Science and Technology Librarianship, 85, (2016); Schmidt L., Holles J.H., Teaching research data management: It takes a team to do it right! [Internet]. [cited 2018 Jul 16]. 2018 ASEE Annual Conference & Exposition; 2018 Jun 23; Salt Lake City (UT), American Society for Engineering Education, (2018); Sheehan J., Kenning A., Mannheimer S., Knobel C., Llovet P., Data-Intensive Science and Campus IT [Internet]. EDUCAUSE Review [2015 Sep 28; Cited 2019 Apr 18], (2015); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future [Internet], pp. 1-54, (2012); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos ONE, 10, 8, (2015); Thielen J., Samuel S.M., Carlson J., Moldwin M., Developing and teaching a two-credit data management course for graduate students in climate and space sciences, Issues in Science and Technology Librarianship, 86, (2017); University of Wyoming Enrollment Summary Spring 2018, (2018); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program, 49, 4, pp. 382-407, (2015); Wiley C., Mischo W.H., Data management practices and perspectives of atmospheric scientists and engineering faculty, Issues in Science and Technology Librarianship, 85, (2016); Wiley C.A., Kerby E.E., Managing research data: Graduate student and postdoctoral researcher perspectives, Issues in Science and Technology Librarianship, 89, (2018)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85074473519"
"Timmermann M.","Timmermann, Marie (57213192291)","57213192291","A collective challenge: Open science from the perspective of science Europe; [Eine kollektive herausforderung: Open science aus der perspektive von science Europe]","2019","VOEB-Mitteilungen","72","2","","424","430","6","1","10.31263/voebm.v72i2.2831","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077701776&doi=10.31263%2fvoebm.v72i2.2831&partnerID=40&md5=de03f3003b021c322037a9049e472d08","Science Europe, Belgium","Timmermann M., Science Europe, Belgium","Open Science aims to enhance the quality of research by making research and its outputs openly available, reproducible and accessible. Science Europe, the association of major Research Funding Organisations and Research Performing Organisations, advocates data sharing as one of the core aspects of Open Science and promotes a more harmonised approach to data sharing policies. Good research data management is a prerequisite for Open Science and data management policies should be aligned as much as possible, while taking into account discipline-specific differences. Research data management is a broad and complex field with many actors involved. It needs collective efforts by all actors to work towards aligned policies that foster Open Science. © Marie Timmermann.","Funders; Open Science; Research data management; Research institutions","","","","","","","","Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific datamanagement and stewardship, Scientific Data, 3, (2016); Practical Guide to the International Alignment of Research Data Management, (2018); Data Management Plan; Data Management Plan; H2020 Programme, (2019); Science Europe, (2018)","M. Timmermann; Science Europe, Belgium; email: marie.timmermann@scienceeurope.org","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85077701776"
"Aoki T.; Kajita S.; Motoki T.; Iyemori T.; Kawaguchi T.","Aoki, Takaaki (25647110200); Kajita, Shoji (7006312319); Motoki, Tamaki (11438913600); Iyemori, Toshihiko (6701604107); Kawaguchi, Tomoko (57215419417)","25647110200; 7006312319; 11438913600; 6701604107; 57215419417","Promoting Common Understanding on Research Data Management using Rubric","2019","Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019","","","8992756","387","390","3","1","10.1109/IIAI-AAI.2019.00085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080879143&doi=10.1109%2fIIAI-AAI.2019.00085&partnerID=40&md5=327caecb9584f418743cb6e9db3cd03e","Academic Center for Computing and Media, StudiesKyoto University, Kyoto, Japan","Aoki T., Academic Center for Computing and Media, StudiesKyoto University, Kyoto, Japan; Kajita S., Academic Center for Computing and Media, StudiesKyoto University, Kyoto, Japan; Motoki T., Academic Center for Computing and Media, StudiesKyoto University, Kyoto, Japan; Iyemori T., Academic Center for Computing and Media, StudiesKyoto University, Kyoto, Japan; Kawaguchi T., Academic Center for Computing and Media, StudiesKyoto University, Kyoto, Japan","Research data management (RDM) is the activity to describe and practice what kind of data is to be used/obtained/generated, and how that data is analyzed/saved/shared or published from the plan to completion of the research project. The RDM consists ambiguous concept so that the understanding of RDM is diverse according to each researcher. In order to foster a common recognition of RDM in a Japanese university, a workshop that researchers utilize a rubric to evaluate and review their attitudes to RDM was held. In this paper, we report the process of the development of RDM rubric and the workshop. © 2019 IEEE.","capability maturity model; fair principle; open science; research data management; rubric","Software engineering; Capability maturity models; fair principle; Open science; Research data managements; rubric; Information management","","","","","","","Concordat on Open Resear Ch Data, (2016); Guidance on Best Practice in the Management of Research Data, (2015); Guidelines for Responding to Misconduct in Research, (2014); Aoki T., Kajita S., Akasaka H., Takeda H., Development and deployment of research data preservation policy at a Japanese research university in 2016, 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 120-123, (2017); JST Policy on Open Access to Research Publications and Research Data Management, (2017); The Obligations of the Submission of the Data Management Plan, (2018); NEDO [Data Management Policy of the Projects Awarded by NEDO], (2019); Guideline for Developing Data Policy at National Research and Develop Agency], (2018); Kurata K., Matsubayashi M., Takeda M., Research data management in Japanese universities and research institutions, Joho Kanri, 60, 2, pp. 119-127, (2017); RDM Training Tools, (2017); Furukawa M., Ojiro K., Yamaji K., Development and Analysis of RDM Training Online Course, pp. 84-89, (2018); Qin J., Crowston K., Kirkland A., Pursuing best performance in research data management by using the capability maturity model and rubrics, J. EScience Librariansh, 6, 2, (2017); A Capability Maturity Model for Research Data Management; Creating A Data Management Framework; Borghi J., Abrams S., Lowenberg D., Simms S., Chodacki J., Support your data: A research data management guide for researchers, Res. Ideas Outcomes, 4, (2018); Wilkinson M.D., Et al., The fair guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Kyoto University Acadmic Data Innovation Unit","","","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics (IIAI)","8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019","7 July 2019 through 11 July 2019","Toyama","157792","","978-172812627-2","","","English","Proc. - Int. Congr. Adv. Appl. Inf., IIAI-AAI","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85080879143"
"Mancilla H.A.; Teperek M.; Dijck J.; Heijer K.D.; Eggermont R.; Plomp E.; Velden Y.T.; Kurapati S.","Mancilla, Heather Andrews (57210646956); Teperek, Marta (36545554600); Dijck, Jasper van (57210638960); Heijer, Kees Den (57199344195); Eggermont, Robbert (15050177200); Plomp, Esther (55660168000); Velden, Yasemin Turkyilmaz-van der (57205742015); Kurapati, Shalini (56005569500)","57210646956; 36545554600; 57210638960; 57199344195; 15050177200; 55660168000; 57205742015; 56005569500","On a quest for cultural change — surveying research data management practices at Delft University of Technology","2019","LIBER Quarterly","29","1","","1","27","26","12","10.18352/lq.10287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071153962&doi=10.18352%2flq.10287&partnerID=40&md5=b308dbc3fb1daef527f1cf4d2c9a39e6","Delft University of Technology, Delft, Netherlands","Mancilla H.A., Delft University of Technology, Delft, Netherlands; Teperek M., Delft University of Technology, Delft, Netherlands; Dijck J., Delft University of Technology, Delft, Netherlands; Heijer K.D., Delft University of Technology, Delft, Netherlands; Eggermont R., Delft University of Technology, Delft, Netherlands; Plomp E., Delft University of Technology, Delft, Netherlands; Velden Y.T., Delft University of Technology, Delft, Netherlands; Kurapati S., Delft University of Technology, Delft, Netherlands","The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties. The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward,’ embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018. The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty. Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories. The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results. Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels. © 2019, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Data management; Data management plans; FAIR principles; Library; Research support; University","","","","","","Dutch Research Council; LIBER Executive Board; ZonMw; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO","Funding text 1: We also acknowledge the continuing support from the LIBER Executive Board, whose financial contribution allows us to publish this journal in Open Access.; Funding text 2: The importance of effective research data management (RDM) and sharing practices in research is nowadays highly recognised by funding bodies, governments, publishers and research institutions. The commitment to the Findable, Accessible, Interoperable and Re-usable (FAIR) principles (Wilkinson et al., 2016) is not only a requirement for all projects funded by the European Commission’s Horizon 2020 funding scheme (European Commission, 2017), but they are also one of the fundamental principles of the European Open Science Cloud (European Commission, 2018). In addition to that, in the Netherlands, the Dutch government declared that Open Science and Open Access should be the norm (Regeerakkoord, 2017–2021). The two major national funding bodies, the Dutch Research Council (NWO) and the Netherlands Organisation for Health Research and Development (ZonMW), have detailed requirements for data management and data sharing as part of their research grant conditions (NWO, 2016; ZonMW, 2018). In parallel, more and more journals and publishers require that research data supporting research articles are made available (e.g., Nature research, 2016; PLOS, 2014). Last but not least, research institutions have also recognised the importance and necessity of good data management and transparency in research. In the Netherlands, this has been reflected in the National Plan Open Science1 (NPOS), signed in 2017 by the Association of Universities in the Netherlands (VSNU), and in the Netherlands Code of Conduct for Research Integrity published in October 2018.2","H2020 programme, Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon, 2020, (2017); Implementation Roadmap for the European Open Science Cloud, (2018); Johnson R., Parsons T., Chiarelli A., Jisc Data Asset Framework Toolkit 2016, Zenodo, (2016); Krause J., Lambeng N., Andrews H., Boehmer J., Cruz M., Dijck J., Teperek M., Quantitative assessment of research data management practice (Version 3) [Data set], Zenodo, (2018); Data Availability Statements and Data Citations Policy: Guidance for Authors, (2016); Open (FAIR) Data, (2016); Data Availability, (2014); Rans J., Whyte A., Using RISE, the Research Infrastructure Self-Evaluation Framework, (2017); Vertrouwen in de toekomst, VVS, CDA, D66 En Christenunie., (2017); Teperek M., Krause J., Lambeng N., Blumer E., van Dijck J., Eggermont R., der Velden Y.T., Quantitative Assessment of Research Data Management Practice, (2019); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Zonmw is Changing to a New Approach for Data Management, (2018)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85071153962"
"Kansa S.W.; Kansa E.C.","Kansa, Sarah Whitcher (35272151600); Kansa, Eric C. (24801999000)","35272151600; 24801999000","Data beyond the Archive in Digital Archaeology: An Introduction to the Special Section","2018","Advances in Archaeological Practice","6","2","","89","92","3","23","10.1017/aap.2018.7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055629576&doi=10.1017%2faap.2018.7&partnerID=40&md5=d5f3259abbec7537a9fb3eea88e7551e","Alexandria Archive Institute, Open Context, 125 El Verano Way, San Francisco, 94127, CA, United States","Kansa S.W., Alexandria Archive Institute, Open Context, 125 El Verano Way, San Francisco, 94127, CA, United States; Kansa E.C., Alexandria Archive Institute, Open Context, 125 El Verano Way, San Francisco, 94127, CA, United States","This special section stems from discussions that took place in a forum at the Society for American Archaeology's annual conference in 2017. The forum, Beyond Data Management: A Conversation about Digital Data Realities, addressed challenges in fostering greater reuse of the digital archaeological data now curated in repositories. Forum discussants considered digital archaeology beyond the status quo of data management to better situate the sharing and reuse of data in archaeological practice. The five papers for this special section address key themes that emerged from these discussions, including: challenges in broadening data literacy by making instructional uses of data; strategies to make data more visible, better cited, and more integral to peer-review processes; and pathways to create higher-quality data better suited for reuse. These papers highlight how research data management needs to move beyond mere check-box compliance for granting requirements. The problems and proposed solutions articulated by these papers help communicate good practices that can jumpstart a virtuous cycle of better data creation leading to higher impact reuses of data. © Copyright 2018 Society for American Archaeology.","","","","","","","","","Anderson D.G., Bissett T.G., Yerka S.J., Wells J.J., Kansa E.C., Kansa S.W., Myers K.N., Carl Demuth R., White D.A., Sea-Level Rise and Archaeological Site Destruction: An Example from the Southeastern United States Using DINAA (Digital Index of North American Archaeology), PLOS ONE, 12, 11, (2017); Arbuckle B.S., Kansa S.W., Kansa E., Orton D., Cakirlar C., Gourichon L., Atici L., Galik A., Marciniak A., Mulville J., Buitenhuis H., Carruthers D., De Cupere B., Demirergi A., Frame S., Helmer D., Martin L., Peters J., Pollath N., Pawlowska K., Russell N., Twiss K., Wurtenberger D., Data Sharing Reveals Complexity in the Westward Spread of Domestic Animals across Neolithic Turkey, PLOS ONE, 9, 6, (2014); Atici L., Kansa S.W., Lev-Tov J., Kansa E.C., Other People's Data: A Demonstration of the Imperative of Publishing Primary Data, Journal of Archaeological Method and Theory, 1, 3, pp. 1-19, (2012); Atici L., Birch Pilaar S.E., Erdogu B., Spread of Domestic Animals across Neolithic Western Anatolia: New Zooarchaeological Evidence from Uǧurlu Hoyuk, the Island of Gokceada, Turkey, PLOS ONE, 12, 10, (2017); The World's Most Valuable Resource Is No Longer Oil, but Data, (2017); Huggett J., Digital Data Realities, Introspective Digital Archaeology (Blog), (2016); Kansa E.C., Kansa S.W., Arbuckle B., Publishing and Pushing: Mixing Models for Communicating Research Data in Archaeology, International Journal of Digital Curation, 9, 1, pp. 57-70, (2014); Kansa S.W., Beyond Data Management: A Conversation about ""digital Data Realities"" Tweets Covering a Forum at the 2017 Society for American Archaeology Conference (March 31, 2017, Vancouver, BC), Storify.com, (2017); Kintigh K., Spielmann K.A., Brin A., Selcuk Candan K., Clark T.C., Peeples M., Data Integration in the Service of Synthetic Research, (2017); McManamon F.P., Kintigh K.W., Ellison L., Brin A., TDAR: A Cultural Heritage Archive for Twenty-First-Century Public Outreach, Research, and Resource Management, Advances in Archaeological Practice, 5, 3, pp. 238-249, (2017); Molteni M., Diehard Coders Just Rescued NASA's Earth Science Data, Wired, (2017); Richards J.D., Twenty Years Preserving Data: A View from the United Kingdom, Advances in Archaeological Practice, 5, 3, pp. 227-237, (2017)","S.W. Kansa; Alexandria Archive Institute, Open Context, San Francisco, 125 El Verano Way, 94127, United States; email: sarahkansa@gmail.com","","Cambridge University Press","","","","","","23263768","","","","English","Adv. Archaeol. Pract.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85055629576"
"Tripathi D.P.; Pandy S.R.","Tripathi, D.P. (57192093875); Pandy, Shri Ram (57204619057)","57192093875; 57204619057","Developing a Conceptual Framework of Research Data Management for Higher Educational Institutions","2018","IEEE 5th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, ETTLIS 2018","","","8485193","105","110","5","2","10.1109/ETTLIS.2018.8485193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056383729&doi=10.1109%2fETTLIS.2018.8485193&partnerID=40&md5=1ec0a73d41b3a03a6e314380dd71e519","Biju Patnaik Central Library, National Institute of Technology Rourkela, Rourkela, Sundargarh, Odisha, India; Department of Library Information Science, Banaras Hindu University, Uttar Pradesh, India","Tripathi D.P., Biju Patnaik Central Library, National Institute of Technology Rourkela, Rourkela, Sundargarh, Odisha, India; Pandy S.R., Department of Library Information Science, Banaras Hindu University, Uttar Pradesh, India","The purpose of this paper is to describe a conceptual framework for managing research data in higher educational institutes. The framework presents the workflow of the data life-cycle in its various phase right from its creation, storage, organization, sharing and usage. It also attempts to address crucial issues in Research Data Management (RDM) such as data privacy, data security, copyright and licensing. The framework may help the educational institutions in managing the research data in a more efficient and effective manner. © 2018 IEEE.","Copyright and Licensing; Data Privacy; Data Repositories; Data Security; Framework of RDM; RDM Policy; Research Data Management","Copyrights; Digital storage; Information management; Information services; Libraries; Life cycle; Security of data; Conceptual frameworks; Data life cycle; Data repositories; Educational Institutes; Educational institutions; Framework of RDM; Research data; Research data managements; Data privacy","","","","","","","Guidelines for Data Classification-computing Services ISO - CARNEGIE Mellon University"", (2016); Patel D., Research data management: A conceptual framework, Libr. Rev, 65, 4-5, pp. 226-241, (2016); Indian Copyright Act (1957), Section 17, (1957); Sweeney L., Datafly: A System for Providing Anonymity in Medical Data; Research Data Management Toolkit: Research Lifecycle","","Anbu K J.P.; Sandhu G.; Kataria S.; Gartner R.","Institute of Electrical and Electronics Engineers Inc.","","5th IEEE International Symposium on Emerging Trends and Technologies in Libraries and Information Services, ETTLIS 2018","21 February 2018 through 23 February 2018","Greater Noida","140707","","978-153860828-9","","","English","IEEE Int. Symp. Emerg. Trends Technol. Libr. Inf. Serv., ETTLIS","Conference paper","Final","","Scopus","2-s2.0-85056383729"
"Engel F.; Schlager S.","Engel, Felix (57211199820); Schlager, Stefan (48662907600)","57211199820; 48662907600","RDFBones - making research explicit: An extensible digital standard for research data","2019","Anthropologischer Anzeiger","76","3","","245","257","12","1","10.1127/anthranz/2019/0882","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072986793&doi=10.1127%2fanthranz%2f2019%2f0882&partnerID=40&md5=f15022ab1a467399af360e7404eb55b0","Biological Anthropology - Faculty of Medicine, Albert Ludwig University Freiburg, Hebelstraße 29, Breisgau, Freiburg, 79104, Germany","Engel F., Biological Anthropology - Faculty of Medicine, Albert Ludwig University Freiburg, Hebelstraße 29, Breisgau, Freiburg, 79104, Germany; Schlager S., Biological Anthropology - Faculty of Medicine, Albert Ludwig University Freiburg, Hebelstraße 29, Breisgau, Freiburg, 79104, Germany","A fundamental impediment to the adoption of digital standards in physical anthropology is the vast diversity of this area of research. Even within osteology, many investigations require some modification of existing standards to suit their specific study designs. This might be a reason for researchers not to use database software based exclusively on one particular standard. It also makes it difficult to keep track of research data compatibility and to process data from different investigations in one database system. Up to now, comprehensive and monolithic data standards have failed to address these issues. We propose a different approach, concentrating on the exact definition of individual data items. These are the building blocks researchers can use to describe the various aspects of their research, like skeletal inventories, research methods and work flows, resulting data and their processing employing mathematical transformations or textual conclusions. Because the building blocks of these descriptions are defined beforehand, the degree of compatibility between different investigations becomes evident. Our data standard, RDFBones, is an RDF (Resource Description Framework) ontology, containing a number of classes and properties for describing anthropological research and materials. Individual researchers can use these elements to define their methodology. That way, RDFBones helps to build standards, instead of prescribing them. Once a standard is formulated, however, it can be published and shared otherwise, supporting uniform methodology. RDFBones also creates a perfect means for sustained long-term data storage. © 2019 E. Schweizerbart’sche Verlagsbuchhandlung, 70176 Stuttgart, Germany.","Data modelling; Data sharing; Osteology; Research data management","Anthropology, Physical; Data Analysis; Humans; Software; data analysis; human; physical anthropology; software","","","","","Metaphacts GmbH Walldorf; American Academy of Forensic Sciences, AAFS; Deutsche Forschungsgemeinschaft, DFG; German-Israeli Foundation for Scientific Research and Development, GIF","Funding text 1: Acknowledgements: The authors thank the Metaphacts GmbH Walldorf (Germany), represented by Peter Haase and Johannes Trame, for providing their metaphactory framework for testing purposes. This article profited from valuable input by participants to an RDFBones workshop in Freiburg (Germany) in October 2016, a symposium on standardized data at the American Association of Physical Anthropology (AAPA) meeting in New Orleans (USA) in April 2017 and a workshop on data standards, archiving and analytics at the American Academy of Forensic Sciences (AAFS) meeting in February 2018 in Seattle (USA). We also thank two anonymous reviewers for improving this paper with valuable suggestions. The development of RDFBones was funded by a grant from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG).; Funding text 2: The authors thank the Metaphacts GmbH Walldorf (Germany), represented by Peter Haase and Johannes Trame, for providing their metaphactory framework for testing purposes. This article profited from valuable input by participants to an RDFBones workshop in Freiburg (Germany) in October 2016, a symposium on standardized data at the American Association of Physical Anthropology (AAPA) meeting in New Orleans (USA) in April 2017 and a workshop on data standards, archiving and analytics at the American Academy of Forensic Sciences (AAFS) meeting in February 2018 in Seattle (USA). We also thank two anonymous reviewers for improving this paper with valuable suggestions. The development of RDFBones was funded by a grant from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG).","Alexiev V., Kostadinov S., Parvanova J., RDF data and image annotations in researchspace, Proceedings of the 1st International Workshop on Collaborative Annotations in Shared Environment metadata, vocabularies and techniques in the Digital Humanities DH-Case 2013. ACM. Article 18., (2013); Allemang D., Hendler J., Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. 2nd, (2011); Amith M., He Z., Bian J., Lossio-Ventura J.A., Tao C., Assessing the practice of biomedical ontology evaluation: Gaps and opportunities, Journal of Biomedical Informatics, 80, pp. 1-13, (2018); Austin A.E., Digitizing standards with OsteoSurvey: A case study in open access data collection at Deir el-Medina, Egypt, American Journal of Physical Anthropology, 156, S60, (2015); Austin A.E., OsteoSurvey: An open-source data collection tool for studying commingled human remains, American Journal of Physical Anthropology, 162, S64, (2017); Baker C.J.O., Cheung K.-H., SEMANTIC WEB: Revolutionizing Knowledge Discovery in the Life Sciences, (2007); Bandrowski A., Brinkman R., Brochhausen M., Brush M.H., Bug B., Chibucos M.C., Zheng J., The ontology for biomedical investigations, PLoS One, 11, 4, (2016); Biagetti M.T., Un modello ontologico per l’integrazione delle informazioni del patrimonio culturale: CIDOC-CRM, Italian Journal of Library, 7, 3, pp. 43-77, (2016); Borner K., Conlon M., Corson-Rikert J., Ding Y., VIVO: A Semantic Web Approach to Scholarly Networking and Discovery. Synthesis Lectures on Semantic Web: Theory and Technology, (2012); Brickley M., McKinley J.I., Guidelines to the Standards for Recording Human Remains, (2004); Buikstra J.E., Ubelaker D.H., Standards for Data Collection from Human Skeletal Remains., (1994); Burger A., Davidson D., Baldock R., Anatomy Ontologies for Bioinformatics: Principles and Practice, (2008); Cardoso S.D., Pruski C., Da Silveira M., Supporting biomedical ontology evolution by identifying outdated concepts and the required type of change, Journal of Biomedical Informatics, 87, pp. 1-11, (2018); Cherny E., Haase P., Mouromtsev D., Andreev A., Pavlov D., Application of CIDOC-CRM for the Russian Heritage Cloud platform, New Trends in Databases and Information Systems, pp. 448-457, (2015); Damann F.E., Lynch J.J., Pawaskar S., Stephan C.N., Ousley S.D., Herrmann N.P., Frye A., Engel F., Schlager S., Data standards, archiving and analytics in forensic anthropology, Proceedings of the American Academy of Forensic Sciences, 70th Annual Scientific Meeting, pp. 1-7, (2018); Dengel A., Semantische Technologien: Grundlagen -Konzepte - Anwendungen, (2012); DuCharme B., Learning SPARQL, (2011); Dudar C., Ousley S., Jones E., Wilczak C., Hefner J., Gwyn M., Mulhern D., Osteoware: Standardized skeletal documentation software at the Smithsonian Institution, American Journal of Physical Anthropology, 162, S64, (2017); Dudar J.C., Searching and extracting reports from the Osteoware database, American Journal of Physical Anthropology, 144, S52, (2011); Engel F., Schlager S., A digital framework for managing research data in skeletal collections, American Journal of Physical Anthropology, 156, S60, (2015); Engel F., Schlager S., Make research explicit using RDFBones, an extensible digital standard for research data, American Journal of Physical Anthropology, 162, S64, (2017); Engel F., Schlager S., Drotziger S., Integration of contextual information with bioanthropological data from skeletal collections, American Journal of Physical Anthropology, 159, S62, pp. 139-140, (2016); Harbeck M., Anleitung zur standardisierten Skelettdokumentation in der Staatssammlung für Anthropologie und Paläoanthropologie München., (2014); Hefner J.T., Cranial non-metrics and macromorphoscopics in OsteoWare, American Journal of Physical Anthropology, 144, S52, (2011); Herold P., Data sharing among ecology, evolution, and natural resources scientists: An analysis of selected publications, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Hinton J., Photographs, radiographs and summary paragraphs in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); Hitzler P., Krotzsch M., Rudolph S., Sure Y., Semantic web, (2008); Hoehndorf R., Schofield P.N., Gkoutos G.V., The role of ontologies in biological and biomedical research: A functional perspective, Briefings in Bioinformatics, 16, 6, pp. 1069-1080, (2015); Jett J., Nurmikko-Fuller T., Cole T.W., Page K.R., Downie J.S., Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies, 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL), pp. 35-44, (2016); Joger U., Research collections in Germany: Modern trends in methods of sorting, preserving, and research, Zoological Collections of Germany: The Animal Kingdom in its Amazing Plenty at Museums and Universities, pp. 17-28, (2018); Jones E.B., Documenting dental inventories, development, and wear in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); Kaiser J.E., BADaBooM - a new database solution for bioarchaeology, American Journal of Physical Anthropology, 156, S60, (2015); Kaltenthaler D., Lohrer J.-Y., The historic development of the zooarchaeological database OssoBook and the xBook framework for scientific databases, (2018); Kaltenthaler D., Lohrer J.-Y., Kroger P., Obermaier H., A framework for supporting the workflow for archaeo-related sciences: Managing, synchronizing and analyzing data, Datenbanksysteme für Business, Technologie und Web (BTW 2017) Workshopband, pp. 89-98, (2017); Kasse P., Tsolis D., Semantic archeological sites/monuments management system, IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications, pp. 217-221, (2014); Konkoly D., Configurable schema-aware RDF data input forms, (2017); Konopka B.M., Biomedical ontologies: A review, Biocybernetics and Biomedical Engineering, 35, 2, pp. 75-86, (2015); Lammerhirt D., Disciplinary differences in opening research data, (2016); Langley N.R., Jantz L.M., Ousley S.D., Jantz R.L., Milner G., Data Collection Procedures for Forensic Skeletal Material 2.0, (2016); London M.R., Documentation of pathological conditions in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); Lynch J.J., Stephan C.N., Computational Tools in Forensic Anthropology: The Value of Open-Source Licensing as a Standard, Forensic Anthropology, 1, 4, pp. 228-243, (2018); Madden G.D., Documenting age and sex related morphology in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); Mejino J.L., Agoncillo A.V., Rickard K.L., Rosse C., Representing complexity in part-whole relationships within the foundational model of anatomy (p. 450), AMIA Annual Symposium Proceedings, volume, (2003); Moller D., Die Alexander-Ecker-Sammlung in Freiburg, Sammeln, Erforschen, Zurückgeben? Menschliche Gebeine aus der Kolonialzeit in akademischen und musealen Sammlungen, pp. 106-120, (2013); Moore-Jansen P.M., Ousley S.D., Jantz R.L., Data Collection Procedures for Forensic Skeletal Material, (1994); Mouromtsev D., Haase P., Cherny E., Pavlov D., Andreev A., Spiridonova A., Towards the Russian Linked Culture Cloud: Data enrichment and publishing, The Semantic Web. Latest Advances and New Domains, pp. 637-651, (2015); Mulhern D.M., Dental pathology and dental morphology in Osteoware, American Journal of Physical Anthropology, 144, S52, pp. 220-221, (2011); Sharing Publication-related Data and Materials: Responsibilities of Authorship in the Life Sciences., (2003); Noy N.F., Shah N.H., Whetzel P.L., Dai B., Dorf M., Griffith N., Musen M.A., BioPortal: Ontologies and integrated data resources at the click of a mouse, Nucleic Acids Research, 37, pp. W170-W173, (2009); O'Brien C., Documenting taphonomy and cranial modification in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); Ousley S., Recording cranial and postcranial measurements in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); R: A language and environment for statistical computing, (2016); Rumpel S., Der Lebenszyklus von Forschungsdaten, Handbuch Forschungsdatenmanagement, (2011); Salvadores M., Alexander P.R., Musen M.A., Noy N.F., BioPortal as a Dataset of Linked Biomedical Ontologies and Terminologies in RDF, Semantic Web, 4, 3, pp. 277-284, (2013); Schlegel K., Bayerl S., Zwicklbauer S., Stegmaier F., Seifert C., Granitzer M., Kosch H., Trusted facts: Triplifying primary research data enriched with provenance information, The Semantic Web: ESWC 2013 Satellite Events, pp. 268-270, (2013); Sholts S.B., Bell J.A., Rick T.C., Ecce Homo: Science and Society Need Anthropological Collections, Trends in Ecology & Evolution, 31, 8, pp. 580-583, (2016); Smith B., Ashburner M., Rosse C., Bard J., Bug W., Ceusters W., Lewis S., The OBO Foundry: Coordinated evolution of ontologies to support biomedical data integration, Nature Biotechnology, 25, 11, pp. 1251-1255, (2007); Splendiani A., Donato M., Draghici S., Ontologies for Bioinformatics, Springer Handbook of Bio-/Neuroinformatics, pp. 441-461, (2014); Steckel R.H., Larsen C.S., Sciulli P.W., Walker P.L., Data collection codebook, (2006); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Triebel D., Hagedorn G., Rambold G., An appraisal of megascience platforms for biodiversity information, MycoKeys, 5, pp. 45-63, (2012); Wehrle D., Wiebelt B., Suchodoletz D.V., Design eines FDM-fähigen Speichersystems, 10. DFN-Forum Kommunikationstechnologien, 30.-31. Mai, pp. 115-124, (2017); Wilczak C.A., Inventories, adding individuals and tracking skeletal elements in Osteoware, American Journal of Physical Anthropology, 144, S52, (2011); Williams L.L., Combining multiple osteological recording standards in a single database: Applications for international research, American Journal of Physical Anthropology, 162, S64, (2017); Zhao M., Zhang S., Li W., Chen G., Matching biomedical ontologies based on formal concept analysis, Journal of Biomedical Semantics, 9, 1, (2018)","F. Engel; Biological Anthropology - Faculty of Medicine, Albert Ludwig University Freiburg, Freiburg, Hebelstraße 29, Breisgau, 79104, Germany; email: felix.engel@anthropologie.uni-freiburg.de","","E. Schweizerbart'sche Verlagsbuchhandlung","","","","","","00035548","","AANZA","30816408","English","Anthropol. Anz.","Article","Final","","Scopus","2-s2.0-85072986793"
"Politze M.; Bensberg S.; Müller M.S.","Politze, Marius (57195741179); Bensberg, Sarah (57209348417); Müller, Matthias S. (35249260000)","57195741179; 57209348417; 35249260000","Managing discipline-specific metadata within an integrated research data management system","2019","ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems","2","","","253","260","7","1","10.5220/0007725002530260","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067472416&doi=10.5220%2f0007725002530260&partnerID=40&md5=0dee7068bddb8bda3c03a383dbf6db55","IT Center, RWTH Aachen University, Templergraben 55, Aachen, Germany","Politze M., IT Center, RWTH Aachen University, Templergraben 55, Aachen, Germany; Bensberg S., IT Center, RWTH Aachen University, Templergraben 55, Aachen, Germany; Müller M.S., IT Center, RWTH Aachen University, Templergraben 55, Aachen, Germany","Our university intends to improve the central IT-support for management of research data. A core demand is supporting FAIR guiding principles. In order to make research data findable for future research projects, an application for the creation and storage of structured meta data for research data was developed. The created meta data repository enable creating, maintaining and querying research data based on discipline-specific properties. Since large number of meta data standards exist for different scientific domains, technologies from the areas of Linked Data and Semantic Web are used to process and store meta data. This work describes the requirements, the design and the implementation a the meta data application that can be integrated into existing research work flows and gives an overview of technical backgrounds used for creating the meta data repository. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Data Repository; Knowlegde Graph; Linked Data; Semantic Web; Service Oriented Architecture; Web Service","Data handling; Digital storage; Information services; Information systems; Information use; Linked data; Management information systems; Metadata; Semantic Web; Service oriented architecture (SOA); Web services; Data repositories; Guiding principles; Integrated research; Knowlegde Graph; Meta data repositories; Research data; Specific properties; Technical background; Information management","","","","","","","Beckett D., Berners-Lee T., Prud'hommeaux E., Carothers G., RDF 1.1 Turtle. W3C, (2014); Bizer C., Heath T., Berners-Lee T., Linked data - The story so far, International Journal on Semantic Web and Information Systems, 5, 3, pp. 1-22, (2009); Curdt C., Hoffmeister D., Jekel C., Udelhoven K., Waldhoff G., Bareth G., Implementation of a centralized data management system for the CRC Transregio 32’Patterns in Soil-Vegetation-Atmosphere-Systems, Proceedings of the 2nd Data Management Workshop, pp. 27-33, (2016); DataCite Metadata Schema Documentation for the Publication and Citation of Research Data V4.1, (2017); Decker S., Rethinking Access to Scientific Knowledge: Knowledge Graphs, (2017); Galkin M., Auer S., Vidal M.-E., Scerri S., Enterprise knowledge graphs: A semantic approach for knowledge management in the next generation of enterprise information systems, Proceedings of the 19th International Conference on Enterprise Information Systems, pp. 88-98, (2017); Gandon F., Schreiber G., RDF 1.1 XML Syntax, (2014); Information and Documentation - The Dublin Core Metadata Element Set, (2017); Kalman T., Kurzawe D., Schwardmann U., European Persistent Identifier Consortium - PIDs für die Wissenschaft, Langzeitarchivierung Von Forschungsdaten, pp. 151-164, (2012); Kirsten T., Kiel A., Wagner J., Ruhle M., Loffler M., Selecting, packaging, and granting access for sharing study data, INFORMATIK 2017: Digitale Kulturen, pp. 1381-1392, (2017); Klar J., Enke H., Projekt RADIESCHEN: Rahmenbedingungen Einer Disziplinübergreifenden Forschungsdateninfrastruktur, (2013); W3C, (2017); Kraft A., Razum M., Potthoff J., Porzel A., Engel T., Lange F., Furtado F., The RADAR project - A service for research data archival and publication, ISPRS International Journal of Geo-Information, 5, 3, (2016); W3C, (2014); Matthews B., Fisher S., CSMD: The Core Scientific Metadata Model, (2013); W3C, (2009); Politze M., Decker B., Ontology based semantic data management for pandisciplinary research projects, Proceedings of the 2nd Data Management Workshop, (2016); Politze M., Decker B., Eifert T., PstaiX - A process-aware architecture to support research processes, INFORMATIK 2017: Digitale Kulturen, pp. 1369-1380, (2017); Shape Expressions Language 2.0, (2017); Research Data Lifecycle, (2012); Schmitz D., Politze M., Forschungsdaten man-agen – Bausteine für eine dezentrale, forschungsnahe Unterstützung. O-bib, Das Offene Bibliotheksjournal, 5, 3, pp. 76-91, (2018); W3C, (2013); Sporny M., Longley D., Kellogg G., Lanthaler M., Lindstrom N., W3C, (2014); Van Garderen P., Archivematica: Using mi-croservices and open-source software to deliver a comprehensive digital curation solution, Proceedings of the 7th International Conference on Preservation of Digital Objects, pp. 145-149, (2010); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","Filipe J.; Smialek M.; Brodsky A.; Hammoudi S.","SciTePress","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","21st International Conference on Enterprise Information Systems, ICEIS 2019","3 May 2019 through 5 May 2019","Heraklion, Crete","148445","","978-989758372-8","","","English","ICEIS - Proc. Int. Conf. Enterp. Inf. Syst.","Conference paper","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85067472416"
"Barabucci G.; Eschweiler M.; Speer A.","Barabucci, Gioele (35309411100); Eschweiler, Mark (57205736849); Speer, Andreas (26322085400)","35309411100; 57205736849; 26322085400","TI-one: Active research data management in a modern philosophy department","2018","Proceedings - IEEE 14th International Conference on eScience, e-Science 2018","","","8588689","314","315","1","1","10.1109/eScience.2018.00070","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061352096&doi=10.1109%2feScience.2018.00070&partnerID=40&md5=03722e36714ddd285c6510c9cee51c98","Cologne Center for EHumanities, University of Cologne, Cologne, Germany; Thomas-Institut, Faculty of Philosophy, University of Cologne, Cologne, Germany","Barabucci G., Cologne Center for EHumanities, University of Cologne, Cologne, Germany, Thomas-Institut, Faculty of Philosophy, University of Cologne, Cologne, Germany; Eschweiler M., Thomas-Institut, Faculty of Philosophy, University of Cologne, Cologne, Germany; Speer A., Thomas-Institut, Faculty of Philosophy, University of Cologne, Cologne, Germany","When it comes to managing their digital data, researchers are often left to their own devices, with little guidance from their hosting institution. These problems are exacerbated in the humanities, in which each project is seen as a separate world that needs special solutions, leading to data losses and an accumulation of technical debt. This paper presents our vision and progress on TI-One: a department-wide system that guides the management of the data of the whole Thomas-Institut, part of the Philosophy Faculty of the University of Cologne. The novel features of TI-One are 1) a department-wide set of guidelines and conventions, 2) the materialization of live data from non-file sources (e.g., DBs), 3) a versioning system with extended metadata that creates an almost effortless path from automated backups to proper long-term archival of research data. © 2018 IEEE.","Data lake; Heterogeneity; Humanities; Research data management","Digital devices; Heterogeneity; Humanities; Long term archival; Research data; Research data managements; Special solutions; Technical debts; Versioning systems; Information management","","","","","","","Spiro L., This is why we fight"": Defining the values of the Digital Humanities, Debates in the Digital Humanities, (2012); Fang H., Managing data lakes in big data era: What's a data lake and why has it became popular in data management ecosystem, 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) 820-824, (2015); Barabucci G., Spadini E., Turska M., Data vs presentation. What is the core of a scholarly digital edition?, Advances in Digital Scholarship Editing: Papers, (2017); Barabucci G., Funktionale und deklarative Programmierung-basierte Methode für nachhaltige reproduzierbare und verifizierbare Datenkuration, DHd Konferenz 2018: Kritik der Digitalen Vernunft, (2018)","","","Institute of Electrical and Electronics Engineers Inc.","","14th IEEE International Conference on eScience, e-Science 2018","29 October 2018 through 1 November 2018","Amsterdam","144041","","978-153869156-4","","","English","Proc. - IEEE Int. Conf. eScience, e-Science","Conference paper","Final","","Scopus","2-s2.0-85061352096"
"Savage J.L.; Cadwallader L.","Savage, James L. (54984724300); Cadwallader, Lauren (37065748500)","54984724300; 37065748500","Establishing, developing, and sustaining a community of data champions","2019","Data Science Journal","18","1","23","","","","6","10.5334/dsj-2019-023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069459576&doi=10.5334%2fdsj-2019-023&partnerID=40&md5=d29607667ffdbb18f2398898e7fcd1ff","Department of Zoology, University of Cambridge, Cambridge, United Kingdom; Office of Scholarly Communication, Cambridge University Library, University of Cambridge, Cambridge, United Kingdom","Savage J.L., Department of Zoology, University of Cambridge, Cambridge, United Kingdom; Cadwallader L., Office of Scholarly Communication, Cambridge University Library, University of Cambridge, Cambridge, United Kingdom","Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines. While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit. One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally. This article focuses on an ongoing example of this approach: The Data Champion Programme at the University of Cambridge, UK. We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term. © 2019 The Author(s).","Academic Libraries; Community of practice; Data Curation; RDM; Research support; SciDataCon","Information management; Libraries; Academic libraries; Community of practice; Good practices; Higher education institutions; Research data managements; Research support; SciDataCon; University of Cambridge; Data curation","","","","","Clair Castle; Marta Teperek","We thank Clair Castle for initial discussion and Danny Kingsley for comments on the manuscript. We are grateful to Marta Teperek for convening the session that included our original presentation at SciDataCon 2018, and for helpful discussion during and after the conference.","Awre C., Baxter J., Clifford B., Colclough J., Cox A., Dods N., Drummond P., Fox Y., Gill M., Gregory K., Gurney A., Harland J., Khokhar M., Lowe D., O'Beirne R., Proudfoot R., Schwamm H., Smith A., Verbaan E., Waller L., Williamson L., Wolf M., Zawadzki M., Research Data Management as a “wicked problem. Library Review, Emerald Group Publishing Limited, 64, 4-5, pp. 356-371, (2015); Bryant R., Lavoie B., Malpas C., Scoping the University RDM Service Bundle. the Realities of Research Data Management, Part 2, (2017); Higman R., Teperek M., Kingsley D., Creating a Community of Data Champions, International Journal of Digital Curation, 12, 2, pp. 96-106, (2017); Ingram C., How and Why You Should Manage Your Research Data: A Guide for Researchers, JISC, (2016); Markowetz F., Five selfish reasons to work reproducibly, Genome Biology, 16, (2015); Call for Data Champions, (2018); Sewell C., Kingsley D., Developing the 21st Century Academic Librarian: The Research Support Ambassador Programme. New Review of Academic Librarianship, Routledge, 23, 2-3, pp. 148-158, (2017); Studer S., Von Schnurbein G., Organizational Factors Affecting Volunteers: A Literature Review on Volunteer Coordination, Voluntas, (2013); Teperek M., Cruz M.J., Verbakel E., Bohmer J., Dunning A., Data Stewardship addressing disciplinary data management needs, International Journal of Digital Curation, 13, 1, pp. 141-149, (2018); Teperek M., Higman R., Kingsley D., Is Democracy the Right System? Collaborative Approaches to Building an Engaged RDM Community, International Journal of Digital Curation, 12, 2, pp. 86-95, (2018); Common Principles on Data Policy, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M., Thompson M., Van Der Lei J., Van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","J.L. Savage; Department of Zoology, University of Cambridge, Cambridge, United Kingdom; email: james.savage@cantab.net","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85069459576"
"Fazal F.A.; Chakravarty R.","Fazal, Fathima Azra (57211160718); Chakravarty, Rupak (36674495400)","57211160718; 36674495400","Role of Library in Research Support: A study of Bharathiar University","2019","Library Philosophy and Practice","2019","","2780","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072842056&partnerID=40&md5=3461a030406fe857cc790382abeff69b","Department of Library and Information Science, Panjab University, India","Fazal F.A., Department of Library and Information Science, Panjab University, India; Chakravarty R., Department of Library and Information Science, Panjab University, India","The pressure for increased research productivity to raise institutional ranking has shifted priorities for Indian universities. The role libraries can play in this scenario is of vital importance. Research Support from libraries needs to be examined along with how researchers are responding to this. The purpose of the study was to understand how aware and satisfied the researchers in AHSS (Arts, Humanities, Social Sciences) were regarding the Research Support provision of an Indian University Library (Anna Arignar Library). The authors surveyed the relationship between researchers and Library Research Support at an Indian university which had a high research ranking in the National Institute Ranking Framework (Bharathiar University). A structured questionnaire was used to collect data for the study. It was f ound that although researchers were comparatively satisfied with most of the traditional services and resources, they were unaware of newer, researcher-specific services like bibliometrics and Research Data Management. On the basis of findings, the authors recommended proactive participation of libraries in research process, and publicizing their services. The study helps understand needs of researchers with respect to the library, and how satisfied they are with the status quo. © 2019, Library Philosophy and Practice.","Arts; Doctoral research; Humanities; Library Research Support; Library satisfaction; Research development; Research Support; Social sciences","","","","","","","","Adeniran P., User satisfaction with academic libraries services: Academic staff and students perspectives, International Journal of Library and Information Science, 3, 10, pp. 209-216, (2012); Akmal Ahmat M., Fadly Misaridin S., Adilah Azmi N., Muhammadan L., Hassan Basri J., Mohamed Ghazali M., Nasir Hj Mohd Rashid M., The Establishment of Strategic Program in Research Support Service (Spiress), in USM Library, (2016); Budd J.M., Faculty publishing productivity: Comparisons over time, (2006), College & Research Libraries, 67, 3, pp. 230-239; Finlayson A., Mitha S.B., Research support for academic excellence: a case study. (2014) ACU/INASP Case Studies, pp. 1-3; Grover R., Hale M.L., The role of the librarian in faculty research, College & Research Libraries, 49, 1, pp. 9-15, (1988); Hollister C.V., Schroeder R., The impact of library support on education faculty research productivity: An exploratory study, Behavioral & Social Sciences Librarian, 34, 3, pp. 97-115, (2015); Keller A., Research Support in Australian University Libraries: An Outsider View (2015), Australian Academic & Research Libraries, 46, 2, pp. 73-85; Kuhn R., Research Libraries Consortium and research support, (2008); Larsen A.V., Dorch B., Nyman M., Thomsen Kirsten., Drachen T.M., Analysis of Research Support Services at international Best Practice Institutions, (2010); Raju R., Schoombee L., Research support through the lens of transformation in academic libraries with reference to the case of Stellenbosch University Libraries, South African Journal of Libraries and Information Science, 79, 2, pp. 27-38, (2014); The value of libraries for research and researchers, (2011); Wiklund G., Voog H., It takes two to tango-making way for relevant research support services at Lund University Libraries (LUB), ScieCom Info, 9, 1, (2013)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85072842056"
"Tenopir C.; Christian L.; Allard S.; Borycz J.","Tenopir, C. (7005106498); Christian, L. (56435776500); Allard, S. (7006450046); Borycz, J. (57207788260)","7005106498; 56435776500; 7006450046; 57207788260","Research Data Sharing: Practices and Attitudes of Geophysicists","2018","Earth and Space Science","5","12","","891","902","11","29","10.1029/2018EA000461","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058445878&doi=10.1029%2f2018EA000461&partnerID=40&md5=fcf8dc29eeb81c89c645a0c314b65ee8","Univeristy of Tennessee, School of Information Science, Knoxville, TN, United States","Tenopir C., Univeristy of Tennessee, School of Information Science, Knoxville, TN, United States; Christian L., Univeristy of Tennessee, School of Information Science, Knoxville, TN, United States; Allard S., Univeristy of Tennessee, School of Information Science, Knoxville, TN, United States; Borycz J., Univeristy of Tennessee, School of Information Science, Knoxville, TN, United States","Open data policies have been introduced by governments, funders, and publishers over the past decade. Previous research showed a growing recognition by scientists of the benefits of data-sharing and reuse, but actual practices lag and are not always compliant with new regulations. The goal of this study is to investigate motives, attitudes, and data practices of the community of Earth and planetary geophysicists, a discipline believed to have accepting attitudes toward data sharing and reuse. A better understanding of the attitudes and current data-sharing practices of this scientific community could enable funders, publishers, data managers, and librarians to design systems and services that help scientists understand and adhere to mandates and to create practices, tools, and services that are scientist-focused. An online survey was distributed to the members of the American Geophysical Union, producing 1,372 responses from 116 countries. The attitudes of researchers to data sharing and reuse were generally positive, but in practice, scientists had concerns about sharing their own research data. These concerns include the possibility of potential data misuse and the need for assurance of proper citation and acknowledgement. Training and assistance in good data management practices are lacking in many scientific fields and might help to alleviate these doubts. ©2018. The Authors.","attitudes of scientists; barriers to data sharing; data management; data sharing; open science; research data curation","attitudinal survey; data management; design; Earth; geophysics","","","","","Center for Information and Communication Studies; National Institute of Health; Welcome Trust; National Science Foundation, NSF, (080944); American Geophysical Union, AGU; University of Tennessee, UT; European Commission, EC","Funding text 1: Interest in sharing and reusing data has increased over the last decade as funders, publishers, and governments have begun to implement more open data policies or mandates (Putri et al., 2015; van den Van Den Eynden et al., 2016; Zuiderwijk & Janssen, 2014), reproducibility of science has become a growing concern (McNutt, 2014; Yaffe & Koch, 2015), and scientists increasingly recognize that there are real benefits to open data (Lowndes et al., 2017; McKiernan et al., 2016). Sharing research data in open repositories is now required for research funded by the European Commission and the Welcome Trust and is recommended by U.S. Federal agencies such as the National Institute of Health (n.d.) and National Science Foundation (n.d.). However, many scientists are still not yet entirely compliant with local, regional, or international data-sharing requirements. Scientists also have some concerns about open data that negatively impact compliance (Chatfield & Reddick, 2018; Putri et al., 2015; Tenopir, Dalton, et al., 2015).; Funding text 2: This project, Data Observation Network for Earth (DataONE), is funded by the National Science Foundation (NSF), award 080944, under a cooperative agreement, William Michener, Principal Investigator. NSF had no role in the research design, data collection, data analysis, nor in the writing of this paper or the decision where to publish. The authors of this article have no conflicts of interest to declare. We would like to thank the American Geophysical Union for their assistance, in particular Brooks Hanson, AGU Executive Vice President of Science. We would also like to thank Natalie Rice of the Center for Information and Communication Studies (University of Tennessee), and graduate research assistants Paris Whalon and Hannah Blanco, also of the University of Tennessee. The data for this study can be found in the Dryad data repository at the time of publication.","Ahonen-Rainio P., Kraak M.-J., Deciding on fitness for use: Evaluating the utility of sample maps as an element of geospatial metadata, Cartography and Geographic Information Science, 32, 2, pp. 101-112, (2005); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, The International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Aleixandre-Benavent R., Moreno-Solano L.M., Ferrer Sapena A., Sanchez Perez E.A., Correlation between impact factor and public availability of published research data in information science and library science journals, Scientometrics, 107, 1, pp. 1-13, (2016); AGU demographics. [online Image], (2014); Demographics for 2017 honors cycle. Author, (2017); Bezuidenhout L., Chakauya E., Hidden concerns of sharing research data by low/middle-income country scientists, Global Bioethics, 29, 1, pp. 39-54, (2018); Bierer B.E., Crosas M., Pierce H.H., Data authorship as an incentive to data sharing, The New England Journal of Medicine, 376, 17, pp. 1684-1687, (2017); Burwell S.M., VanRoekel S., Park T., Mancini D.J., M-13-13—Memorandum for the heads of executive departments and agencies: Open data policy, (2013); Chatfield A.T., Reddick C.G., The role of policy entrepreneurs in open government data policy innovation diffusion: An analysis of Australian federal and state governments, Government Information Quarterly, 35, 1, pp. 123-134, (2018); DOE policy for digital research data management: Glossary, (2018); Douglass K., Allard S., Tenopir C., Wu L., Frame M., Managing scientific data as public assets: Data sharing practices and policies among full-time government employees, Journal of the Association for Information Science and Technology, 65, 2, pp. 251-262, (2014); Faniel I.M., Kriesberg A., Yakel E., Social scientists' satisfaction with data reuse, Journal of the Association for Information Science and Technology, 67, 6, pp. 1404-1416, (2016); Herold P., Data sharing among ecology, evolution, and natural resources scientists: An analysis of selected publications, Journal of Librarianship & Scholarly Communication, 3, 2, pp. 1-23, (2015); Kim Y., Burns C.S., Norms of data sharing in biological sciences: The roles of metadata, data repository, and journal and funding requirements, Journal of Information Science, 42, 2, pp. 230-245, (2016); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists' data-sharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2016); Lowndes J.S.S., Best B.D., Scarborough C., Afflerbach J.C., Frazier M.R., O'Hara C.C., Jiang N., Halpern B.S., Our path to better science in less time using open data science tools, Nature Ecology & Evolution, 1, 6, (2017); McKiernan E.C., Bourne P.E., Brown C.T., Buck S., Kenall A., Lin J., McDougall D., Nosek B.A., Ram K., Soderberg C.K., Spies J.R., Thaney K., Updegrove A., Woo K.H., Yarkoni T., How open science helps researchers succeed, eLife, 5, (2016); McNutt M., Reproducibility, Science, 343, 6168, (2014); NIH data sharing policy and implementation guidance; Dissemination and sharing of research results. Retrieved May 9, 2018; Nugroho R.P., Zuiderwijk A., Janssen M., de Jong M., A comparison of national open data policies: Lessons learned, Transforming Government: People, Process and Policy, 9, 3, pp. 286-308, (2015); Pampel H., Dallmeier-Tiessen S., Opening science, pp. 213-224, (2014); Putri N.R., Anneke Z., Marijn J., Martin D.J., A comparison of national open data policies: Lessons learned, Transforming Government: People, Process, and Policy, 9, 3, pp. 286-308, (2015); Schmidt B., Gemeinholzer B., Treloa A., Open data in global environmental research: The Belmont Forum's open data survey, PLoS One, 11, 1, (2016); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research data services in academic libraries: Data intensive roles for the future, Journal of eScience Librarianship., 4, 2, (2015); Van Den Eynden V., Knight G., Vlad A., Radler B., Tenopir C., Leon D., Manista F., Whitworth J., Corti L., Survey of Wellcome researchers and their attitudes to open research, Technical Report, (2016); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); Yaffe M.B., Koch D.H., Reproducibility in science, Science Signaling, 8, 371, (2015); Yoon A., Data reusers' trust development, Journal of the Association for Information Science and Technology, 68, 4, pp. 946-956, (2017); Yoon A., Schultz T., Research data management services in academic libraries in the US: A content analysis of libraries' websites, (2017); Zuiderwijk A., Janssen M., Open data policies, their implementation, and impact: A framework for comparison, Government Information Quarterly, 31, 1, pp. 17-29, (2014)","C. Tenopir; Univeristy of Tennessee, School of Information Science, Knoxville, United States; email: ctenopir@utk.edu","","Wiley-Blackwell Publishing Ltd","","","","","","23335084","","","","English","Earth Space Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85058445878"
"Holles J.H.; Schmidt L.","Holles, Joseph H. (6602237719); Schmidt, Larry (22136252700)","6602237719; 22136252700","Graduate research data management course content: Teaching the data management plan (DMP)","2018","ASEE Annual Conference and Exposition, Conference Proceedings","2018-June","","","","","","6","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051213703&partnerID=40&md5=9065c4d95253bb19ae3cee037af753d4","University of Wyoming, Department of Chemical Engineering, United States; University of Wyoming, Brinkerhoff Geology Library, United States","Holles J.H., University of Wyoming, Department of Chemical Engineering, United States; Schmidt L., University of Wyoming, Brinkerhoff Geology Library, United States","[No abstract available]","","","","","","","","","Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Schmidt L.O., Holles J.H., A graduate class in research data management, Chemical Engineering Education, 52, 1, pp. 52-59, (2018); Fong B.L., Wang M., Required data management training for graduate students in an earth and environmental sciences department, Journal of EScience Librarianship, 4, 1, (2015); Wright S.J., Andrews C., Data Information Literacy Case Study Directory, 2, 1, (2013); Thielen J., Samuel S.M., Carlson J., Moldwin M., Developing and teaching a two-credit data management course for graduate students in climate and space science, Issues in Science and Technology Librarianship, 86, (2017); Johnston L., Jeffryes J., Data Information Literacy Case Study Directory, 3, 1, (2012); Holles J.H., Schmidt L.O., Implementing a graduate class in research data management for science/Engineering students, 2018 ASEE Annual Conference & Exposition, (2018); Data Management Plan Tool, (2014); Carlson J., The data curation profiles toolkit: User guide, Data Curation Profiles Toolkit, (2010); Carlson J., The data curation profiles toolkit: Interviewer's manual, Data Curation Profiles Toolkit, (2010); Carlson J., The data curation profiles toolkit: Interview worksheet, Data Curation Profiles Toolkit, (2010); Carlson J., The data curation profiles toolkit: The profile template, Data Curation Profiles Toolkit, (2010); Qin J., D'Ignazio J., The central role of metadata in a science data literacy course, Journal of Library Metadata, 10, 2-3, pp. 188-204, (2010); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data managment pilot course at an academic library, Journal of EScience Librarianship, 3, 1, (2014); Briney K., Data Managment for Researchers: Organize, Maintain and Share Your Data for Research Success, (2015); DMPTool can Make Applying for Grants Easier, (2018); Writing Data Management Plans, (2018); Introduction to Data Management and Data Management Planning Fall 2015, (2015); Library Workshops: Data Management Planning and DMPTool, (2018); McLure M., Level A.V., Crabston C.L., Oehlerts B., Culbertson M., Data curation: A study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014)","","","American Society for Engineering Education","","125th ASEE Annual Conference and Exposition","23 June 2018 through 27 December 2018","Salt Lake City","138114","21535965","","","","English","ASEE Annu. Conf. Expos. Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85051213703"
"Anilkumar N.","Anilkumar, Nishtha (55697673900)","55697673900","Research Data Management in India: A Pilot Study","2018","EPJ Web of Conferences","186","","03002","","","","7","10.1051/epjconf/201818603002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057756073&doi=10.1051%2fepjconf%2f201818603002&partnerID=40&md5=7a5d03e8e4ad4f9207d5f7806302226a","Physical Research Laboratory, Ahmedabad, India","Anilkumar N., Physical Research Laboratory, Ahmedabad, India","Data is a by-product of the research process where published results are the output. More and more research institutes are getting interested in this by-product. Data could be in the form of statistics, experimental results, observational data, interview recordings, etc. Organizing the varied forms of data is a challenge for any institute. This is particularly so in the present scenario of constantly changing technologies for data storage and retrieval. The funding agencies are making it mandatory to archive these datasets so that these can be preserved for the posterity and / or re-used by others. Libraries, as a part of the research institutes, seem to be well equipped to organize and manage these datasets. The author undertook the present study to find the level of involvement of libraries in 'data management' in India. A survey was done to assess the awareness about data curation, data archival policies, infrastructure required, technologies used, etc. The survey sample consisted of 15 national research / academic institutes in India. The study showed that libraries' role in data management in research / academic institutes was still at a very early stage of development in India. © The Authors, published by EDP Sciences, 2018.","","","","","","","Ministry of Statistics and; Pro-gramme Implementation; Indian Council of Social Science Research; Ministry of Coal, Government of India","Funding text 1: 3. ICSSR Data Service (INFLIBNET) http://www.icssrdataservice.in/ The “ICSSR Data Service” is culmination of signing of Memorandum of Understanding (MoU) between Indian Council of Social Science Research (ICSSR) and Ministry of Statistics and Pro-gramme Implementation (MoSPI). The MoU provides for setting-up of “ICSSR Data Service: Social Science Data Repository” and host NSS and ASI datasets generated by MoSPI. The ICSSR Data Service is set-up with an aim to support researchers, teachers and policymakers who heavily rely on high-quality social and economic data for their research. The ICSSR Data; Funding text 2: The above survey results show that in India RDM carried out by libraries is almost non-existent or at the very least on the idea plane only. As part of the study, I also tried to find standalone data centers funded by the Government of India. I found quite a few data centers in India dedicated to different subject disciplines and catering to researchers, the funding agencies and the general public. These are:","Borgman C.L., Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); OMB Circular 110, Offce of Management & Budget; Tenopir C., Birch B., Allard S., ACRL White Paper, (2012); College & Research Libraries News, 73, 6, pp. 311-320, (2012); Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Norman B., Stanton K.V., International Journal of Digital Curation, 9, 1, pp. 253-262, (2014); Erway R., Research Data Management Policy. Dublin, (2013); Auckland M., RLUK Report, (2012); Pinfield S., Cox A.M., Smith J., PLoS ONE, 9, 12, (2014); Briney K., Goben A., Zilinski L., Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Doiphode M., Nimje R., Alex S., Data Science Journal, 12, (2013); Chad K., Enright S., Insights: The UKSG Journal, 27, 2, pp. 147-153, (2014)","N. Anilkumar; Physical Research Laboratory, Ahmedabad, India; email: nishtha@prl.res.in","D'Abrusco R.; Harvard�Smithsonian Center for Astrophysics, 60 Garden St. MS 67, Cambridge, MA; Lesteven S.; Centre de Donnees Astronomique de Strasbourg(CDS), Observatoire Astronomique de Strasbourg, 11, Rue de l�Universite, Strasbourg; Dorch B.; Kern B.","EDP Sciences","American Astronomical Society; Astronomy and Astrophysics; Centre de Donnees Astronomique de Strasbourg (CDS); et al.; MNRAS; SPIE Digital Library","8th Library and Information Services in Astronomy: ""Astronomy Librarianship in the Era of Big Data and Open Science"", LISA 2018","6 June 2017 through 9 June 2017","Strasbourg","142663","21016275","978-275989054-5","","","English","EPJ Web Conf.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85057756073"
"","","","ACM International Conference Proceeding Series","2018","ACM International Conference Proceeding Series","","","","","","77","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064822997&partnerID=40&md5=391c68c19f88886b19822017d7f29b98","","","The proceedings contain 15 papers. The topics discussed include: exploring purchase and repurchase behavior in online mobile games: a preliminary study; offering free trials with consumer online reviews; analysis on the development mode of e-commerce enterprises from the perspective of space-time concept; research on interaction modeling of cross-border e-commerce and import and export trade; framing effect of online store information presentation on consumer ’s purchasing decisions; analysis on the feasibility of strategic development of short-term rent industry across the Taiwan straits in the era of sharing economy; adopting data analysis and visualization technology to construct clinical research data management and analysis system; a smart system for elderly care using IoT and mobile technologies; and the effect of icon size and grid size on smartphone menu selection.","","","","","","","","","","","","Association for Computing Machinery","","2nd International Conference on Software and e-Business, ICSEB 2018","18 December 2018 through 20 December 2018","Zhuhai","147476","","978-145036127-9","","","English","ACM Int. Conf. Proc. Ser.","Conference review","Final","","Scopus","2-s2.0-85064822997"
"Costa L.; da Silva J.R.","Costa, Lázaro (57211169862); da Silva, João Rocha (55496903800)","57211169862; 55496903800","Dendro: A FAIR, Open-Source Data Sharing Platform","2019","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11799 LNCS","","","384","387","3","1","10.1007/978-3-030-30760-8_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072874158&doi=10.1007%2f978-3-030-30760-8_39&partnerID=40&md5=2f4202b69c3f6965fa98f73d2fde6b7e","INESC TEC/Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Costa L., INESC TEC/Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; da Silva J.R., INESC TEC/Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Dendro, a research data management (RDM) platform developed at FEUP/INESC TEC since 2014, was initially targeted at collaborative data storage and description in preparation for deposit in any data repository (CKAN, Zenodo, ePrints or B2Share). We implemented our own data deposit and dataset search features, consolidating the whole RDM workflow in Dendro: dataset exporting, automatic DOI attribution, and a dataset faceted search, among other features. We discuss the challenges faced when implemented these features and how they make Dendro more FAIR. © Springer Nature Switzerland AG 2019.","Data citation; Dendro; FAIR; Repositories; Research data","Deposits; Digital libraries; Digital storage; Distributed computer systems; Information management; Data citation; Dendro; FAIR; Repositories; Research data; Open Data","","","","","Fundação para a Ciência e a Tecnologia, FCT, (POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF","This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Inter-nationalisation-COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT-Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-016736.","Altman M., Crosas M., The evolution of data citation: From principles to implementation, IASSIST Q, 37, pp. 62-70, (2013); Amorim R., Castro J., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, (2017); Costello M.J., Motivating online publication of data, Bioscience, (2009); Leonelli S., Spichtinger D., Prainsack B., Sticks and carrots: Encouraging open science at its source, Geo: Geography and Environment, (2015); Pontika N., Knoth P., Cancellieri M., Pearce S., Fostering open science to research using a taxonomy and an eLearning portal, Proceedings of the 15Th International Conference on Knowledge Technologies and Data-Driven Business-I-Know 2015, (2015); Ross-Hellauer T., Deppe A., Schmidt B., Survey on open peer review: Attitudes and experience amongst editors, authors and reviewers, Plos ONE, (2017); da Silva J.R., Ribeiro C., Lopes J.C., Ranking Dublin Core Descriptor Lists from User Interactions: A Case Study with Dublin Core Terms Using the Dendro Platform, (2018); Silvello G., Theory and Practice of Data Display, (2017); Wilkinson M.D., The FAIR Guiding Principles for Scientific Data Management and Stewardship, pp. 1-9, (2016)","L. Costa; INESC TEC/Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; email: lazaroosta@hotmail.com","Doucet A.; Isaac A.; Golub K.; Aalberg T.; Jatowt A.","Springer Verlag","","23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019","9 September 2019 through 12 September 2019","Oslo","231979","03029743","978-303030759-2","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85072874158"
"Latif A.; Limani F.; Tochtermann K.","Latif, Atif (25927157000); Limani, Fidan (57190967994); Tochtermann, Klaus (16053767400)","25927157000; 57190967994; 16053767400","A generic research data infrastructure for long tail research data management","2019","Data Science Journal","18","1","17","","","","7","10.5334/dsj-2019-017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066756648&doi=10.5334%2fdsj-2019-017&partnerID=40&md5=9f227d1a045a87920c3f33da41804fec","ZBW – Leibniz Information Center for Economics, Kiel/Hamburg, Germany","Latif A., ZBW – Leibniz Information Center for Economics, Kiel/Hamburg, Germany; Limani F., ZBW – Leibniz Information Center for Economics, Kiel/Hamburg, Germany; Tochtermann K., ZBW – Leibniz Information Center for Economics, Kiel/Hamburg, Germany","The advent of data intensive science has fueled the generation of digital scientific data. Undoubtedly, digital research data plays a pivotal role in transparency and re-producibility of scientific results as well as in steering the innovation in a research process. However, the main challenges for science policy and infrastructure projects are to develop practices and solutions for research data management which in compliance with good scientific standards make the research data discoverable, citeble and accessible for society potential reuse. GeRDI – the Generic Research Data (RD) Infrastructure – is such a research data management initiative which targets long tail content that stems from research communities belonging to different domain and research practices. It provides a generic and open software which connects research data infrastructures of communities to enable the investigation of multidisciplinary research questions. © 2019 The Author(s).","Generic Research Data Infrastructure; Long Tail Content; Research Data; Research Data Management","Regulatory compliance; Data intensive science; Infrastructure project; Long tail; Multi-disciplinary research; Research communities; Research data; Research data managements; Scientific standards; Information management","","","","","Deutsche Forschungsgemeinschaft, DFG","This work was supported by the DFG (German Research Foundation) with the GeRDI project (Grants No. BO818/16-1 and HA2038/6-1). All project partners i.e., ZBW – Leibniz Information Center for Economics, CAU – Kiel University, TUD – Technical University of Dresden, LRZ – Leibniz Supercomputing Centre Munich and DFN – German National Research and Education Network have contributed in the realization of GeRDI project. Specifically, the software architecture has been designed by the software engineering group of Prof. Wilhelm Hasselbring from Kiel University. TUD contributed in design of metadata harvester and results. ZBW contributes in requirement gathering and metadata schema on going development. LRZ supports the operation and infrastructure issues during the development and provide the pilot operation while DFN is working for the project sustainability with operational model.","Bobby Vocile W., Open Science Trends You Need to Know About, (2017); Brophy E., Razum M., Radar: A Research Data Management Repository for Long Tail Data. Tage 2017, (2017); Buckland M., Data management as bibliography, Bulletin of the American Society for Information Science and Technology, 37, 6, pp. 34-37, (2011); de Sousa N.T., Hasselbring W., Weber T., Kranzlmuller D., Designing a generic research data infrastructure architecture with continuous software engineering, 3Rd Workshop on Continuous Software Engineering (Cse 2018), (2018); Long Tail of Data, E-Irg Task Force Report 2016, (2016); Grunzke R., Adolph T., Biardzki C., Bode A., Borst T., Bungartz H.J., Et al., Challenges in creating a sustainable generic research data infrastructure, Softwaretechnik-Trends, 37, 2, pp. 74-77, (2017); Hey T., The fourth paradigm – data-intensive scientific discovery, Communications in Computer and Information Science, (2012); Horstmann W., Nurnberger A., Shearer K., Wolski M., Addressing the Gaps: Recommendations for Supporting the Long Tail of Research Data, (2017); Linne M., Zenk-Moltgen W., Strengthening Institutional Data Management and Promoting Data Sharing in the Social and Economic Sciences, 27, 1, (2017); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Dierolf U., Et al., Making research data repositories visible: The re3data.org registry, Plos ONE, 8, 11, (2013); Smith Rumsey A., Sustainable Economics for a Digital Planet: Ensuring Long-Term Access to Digital Information: Final Report of the Blue Ribbon Task Force on Sustainable Digital Preservation and Access, (2010); Starr J., Ammann N., Ashton J., Barton A., Elliott J., Jacquemot-Perbal M.C., Ziedorn F., Et al., Datacite Metadata Schema for the Publication and Citation of Research Data, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","A. Latif; ZBW – Leibniz Information Center for Economics, Kiel/Hamburg, Germany; email: a.latif@zbw.eu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85066756648"
"Jetten M.; Simons E.; Rijnders J.","Jetten, Mijke (57194546253); Simons, Ed (57194549505); Rijnders, Jan (57207357769)","57194546253; 57194549505; 57207357769","The role of CRIS's in the research life cycle. A case study on implementing a FAIR RDM policy at Radboud University, the Netherlands","2019","Procedia Computer Science","146","","","156","164","8","7","10.1016/j.procs.2019.01.090","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062456816&doi=10.1016%2fj.procs.2019.01.090&partnerID=40&md5=97cc67c823efcd88a7c67e26b1267f31","Radboud University, Comeniuslaan 4, Nijmegen, 6525 HP, Netherlands","Jetten M., Radboud University, Comeniuslaan 4, Nijmegen, 6525 HP, Netherlands; Simons E., Radboud University, Comeniuslaan 4, Nijmegen, 6525 HP, Netherlands; Rijnders J., Radboud University, Comeniuslaan 4, Nijmegen, 6525 HP, Netherlands","In 2015, Radboud University (Nijmegen, the Netherlands) started a project to extend its CRIS (Metis) with functionalities that allow researchers to register (metadata) and archive (uploading files) their research data, while at the same time making the data available for reuse in a FAIR way (via national Dutch data archive DANS). The new functionality was integrated with already existing functions in the CRIS, thus offering a one-stop-shop interface to researchers in which registration and archiving of data is combined with registration of publications, the uploading of full text to the university's repository, the linking of datasets and publications and the creation of researcher's profile (CV) pages. Next to the functional extension of the CRIS, the project also included an organizational element: the establishment of support and management structures and workflows, including data curation processes, in order to assure the quality of the data registration process and to foster the FAIRness of the research data. In the period up to now, we continued to transform the university's CRIS, by bringing it in line with the research life cycle perspective and policy changes in Research Data Management (RDM), including a Data Management Plan (DMP) module and FAIR data. In this paper, it will be argued and demonstrated that both for researchers and research institutes, a CRIS oriented approach to RDM brings added value. We also point to future use cases that put a central role for CRIS's even earlier within the research life cycle, e.g. at pre-registration of research questions and informed consent/ethics approval procedures. We further envision our CRIS to play a linking pin function between storage and service locations of data during research and at publication. The paper will use Radboud University as a good practice of past, present and future use of CRIS's in the research life cycle that universities and research institutes as well as researchers and research support desks are currently dealing with in the FAIR data era. © 2019 The Authors. Published by Elsevier B.V.","Archiving data; CRIS; DANS data archive; Data life cycle; Data management plans; DMP; Donders repository; FAIR; Metis; Narcis; Project registration; RDM; Research Data Management; Research data policy","Data curation; Digital storage; Information systems; Information use; Life cycle; Sounding apparatus; Archiving datum; CRIS; Data archives; Data life cycle; Donders repository; FAIR; Management plans; Metis; Narcis; Project registration; Research data; Research data managements; Information management","","","","","","","Schopfel J., Prost H., Rebouillat V., Research Data in Current Research Information Systems, Procedia Computer Science, 106, pp. 305-320, (2017); Mons B., Neylon C., Velterop J., Dumontier M., Da Silva Santos L., Wilkinson M., Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud, Information Services & Use, 37, pp. 49-56, (2017); (2016); Bittner S., Muller A., Social networking tools and research information systems: Do they compete?, Webology, 8, 1, pp. 1-8, (2011); Clifford A., Institutional Repositories: Essential Infrastructure for Scholarship in the Digital Age, Lynch Libraries and the Academy, 3, 2, pp. 327-336, (2003); Prosser D., Institutional repositories and Open Access: The future of scholarly communication, Information Services & Use, 23, 2, pp. 167-170, (2003); Simons E., Jetten M., Messelink M., Van Berchum M., Schoonbrood H., Wittenberg M., The important role of CRIS's for registering and archiving research data, The RDS-project at Radboud University (The Netherlands) in Cooperation with Data-archive DANS. Procedia Computer Science, 106, pp. 321-328, (2017); Wilkinson M.D., Dumontier M., Et al., The Fair Guiding Principles for scientific data management and stewardship, Nature Scientific Data, 3, (2016); Verhaar P., Schoots F., Sesink L., Frederiks F., Fostering Effective Data Management Practices at Leiden University, Liber Quarterly, 27, 1, pp. 1-22, (2017); Azerouala O., Saake G., Schallehn E., Analyzing data quality issues in research information systems via data profiling, International Journal of Information Management, 41, pp. 50-56, (2018); Hornbostel S., From CRIS to CRIS: Integration and Interoperability, Proceedings of the 8th International Conference on Current Research Information Systems, pp. 29-38, (2006); Azeroual O., Abuosba M., Improving the data quality in the research information systems, International Journal of Computer Science and Information Security, 15, 11, pp. 82-86, (2017); Nabavi M., Jeffery K., Jamali H., Added value in the context of research information systems, Program, 50, 3, pp. 325-339, (2016); Jeffery K., Asserson A., Institutional Repositories and Current Research Information Systems, New Review of Information Networking, 14, pp. 71-83, (2008)","M. Jetten; Radboud University, Nijmegen, Comeniuslaan 4, 6525 HP, Netherlands; email: m.jetten@ubn.ru.nl","Clements A.; de Castro P.; Sicilia M.-A.; Simons E.; Bergstrom J.","Elsevier B.V.","","14th International Conference on Current Research Information Systems: FAIRness of Research Information, CRIS 2018","14 June 2018 through 16 June 2018","Umea","145383","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85062456816"
"Hart A.H.","Hart, Amy Hatfield (57208690534)","57208690534","Academic librarian and practitioner collaborative research model: A diagrammatic metaphor","2018","Advances in Library Administration and Organization","39","","","117","131","14","0","10.1108/S0732-067120180000039009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065554145&doi=10.1108%2fS0732-067120180000039009&partnerID=40&md5=f68f384775be4277746e466274839177","Purdue University, IN, United States","Hart A.H., Purdue University, IN, United States","This chapter explores specializations within academic librarian practices, focusing on librarian research and collaboration. Academic librarian roles are transitioning from service providers to specialists, researchers, and collaborators. Roles have shifted to incorporate interdisciplinary research and collaboration; embedded librarianship; research data management expertise; information literacy instruction; and core curriculum development. In order to understand this shift in roles, a mixed methods research project undertaken with a Purdue University researcher and Purdue Libraries faculty that prompted the development of a research diagrammatic metaphor modeling the components of librarian-faculty collaboration. The model demonstrates the dynamics and roles in academic collaboration and interdisciplinary research. A generalization of the model applied to two librarian-faculty collaboration scenarios exemplifies how these components facilitate engagement and project management. Potentially the model could be operationalized to understand disciplinary differences and provide a framework of accountability for both faculty and librarians engaged in research projects. © 2018 by Emerald Publishing Limited All rights of reproduction in any form reserved.","Diagrammatic metaphor; Interdisciplinary research; Librarians; Library science; Makerspace; Mixed methods research; Research; Universities and colleges faculty; Work breakdown structure","","","","","","","","Presidential Committee on Information Literacy, (1989); Barton A., Bracke P.J., Clark A.M., Digitization, data curation, and human rights documents: Case study of a library-researcher-practitioner collaboration, IASSIST Quarterly, 40, 1, pp. 27-34, (2016); Brooks A.W., Information literacy and the flipped classroom: Examining the impact of a one-shot flipped class on student learning and perceptions, Communications in Information Literacy, 8, 2, pp. 225-235, (2014); Carlson J., Kneale R., Embedded librarianship in the research context navigating new waters, College & Research Libraries News, 72, 3, pp. 167-170, (2011); Clark A.M., Diplomacy of Conscience: Amnesty International and Changing Human Rights Norms, (2001); Dearborn C., Barton A., Harmeyer N., The Purdue University Research Repository: HUBzero customization for dataset publication and digital preservation, OCLC Systems & Services: International Digital Library Perspectives, 30, 1, pp. 15-27, (2014); Haigh A.E., Kinsella C.J., Bringing in the librarians: Rethinking collaboration for political science research projects, Behavioral & Social Sciences Librarian, 35, 1, pp. 19-31, (2016); Huckaby M., Wilmeth Active Learning Center to Offer Advanced Learning Environment, (2017); (2013); Johnson R.B., Onwuegbuzie A.J., Turner L.A., Toward a definition of Mixed Methods Research, Journal of Mixed Methods Research, 1, 2, pp. 112-133, (2007); Kotter W.R., Bridging the great divide: Improving relations between librarians and classroom faculty, The Journal of Academic Librarianship, 25, 4, pp. 294-303, (1999); Overn K.M., Faculty-library collaboration: Two pedagogical approaches, Journal of Information Literacy, 8, 2, pp. 36-55, (2014); Rushmer R., Pallis G., Inter-professional working: The wisdom of integrated working and the disaster of blurred boundaries, Public Money & Management, 23, 1, pp. 59-66, (2003); Shields K., Research partners, teaching partners: A collaboration between FYC faculty and librarians to study students’ research and writing habits, Internet Reference Services Quarterly, 19, pp. 207-218, (2014); (2012)","","","Emerald Group Publishing Ltd.","","","","","","07320671","","","","English","Adv. Libr. Adm. Organ.","Book chapter","Final","","Scopus","2-s2.0-85065554145"
"Bowman M.A.; Maxwell R.A.","Bowman, Marjorie A. (7202503031); Maxwell, Rose A. (7201687384)","7202503031; 7201687384","A beginner's guide to avoiding Protected Health Information (PHI) issues in clinical research – With how-to's in REDCap Data Management Software","2018","Journal of Biomedical Informatics","85","","","49","55","6","6","10.1016/j.jbi.2018.07.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050524034&doi=10.1016%2fj.jbi.2018.07.008&partnerID=40&md5=dce088ab2c53c49d76a27437e76720a9","Departments of Family Medicine and Population & Public Health Sciences at the Wright State University Boonshoft School of Medicine, Dayton, OH, United States; Department of Obstetrics & Gynecology at the Wright State University Boonshoft School of Medicine, Dayton, OH, United States","Bowman M.A., Departments of Family Medicine and Population & Public Health Sciences at the Wright State University Boonshoft School of Medicine, Dayton, OH, United States; Maxwell R.A., Department of Obstetrics & Gynecology at the Wright State University Boonshoft School of Medicine, Dayton, OH, United States","Protecting personally identifiable information is important in clinical research. The authors, two faculty members involved in developing and implementing research infrastructure for a medical school, observed challenges novice researchers encountered in recognizing, collecting, and managing Protected Health Information (PHI) for clinical research. However, we had difficulty finding resources that provide practical strategies for novice clinical researchers for this topic. Common issues for beginners were: 1. Recognition of PHI, e.g. lack of recognition of ‘indirect’ PHI, i.e., that the combination of two or more non-PHI data types or other specific information could result in identifiable data requiring protection; 2. Collection of PHI, e.g., proposed collection of data not necessary for fulfillment of the project's objectives or potential inadvertent collection of PHI in free text response items; and 3. Management of PHI, e.g., proposed use of coding systems that directly included PHI, or proposed data collection techniques, electronic data storage, or software with inadequate protections. From these observations, the authors provide the following in this paper: 1. A brief review of the elements of PHI, particularly ‘indirect’ PHI; 2. Sample data management plans for common project types relevant to novice clinical researchers to ensure planning for data security; 3. Basic techniques for avoiding issues related to the collection of PHI, securing and limiting access to collected PHI, and management of released PHI; and 4. Methods for implementing these techniques in the Research Electronic Data Capture (REDCap) system, a commonly used and readily available research data management software system. © 2018 Elsevier Inc.","","Clinical Protocols; Computational Biology; Computer Security; Curriculum; Database Management Systems; Education, Medical; Health Information Management; Health Insurance Portability and Accountability Act; Humans; Software; United States; clinical research; computer security; human; identifiable information; note; scientist; software; biology; clinical protocol; curriculum; database management system; education; health insurance; medical education; medical information system; United States","","","","","","","(2018); (2018); Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G., Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support, J. Biomed. Inform., 42, 2, pp. 377-381, (2009); Amgad M., Man Kin Tsui M., Liptrott S.J., Shash E., Medical student research: an integrated mixed-methods systematic review and meta-analysis, PLoS One, 10, 6, (2015); Brannan G.D., Dumsha J.Z., Yens D.P., A research primer: basic guidelines for the novice researcher, J. Am. Osteopath. Assoc., 113, 7, pp. 556-563, (2013); Seehusen D.A., Weaver S.P., Resident research in family medicine: where are we now?, Fam. Med., 41, 9, pp. 663-668, (2009); Wickramasinghe D.P., Perera C.S., Senarathna S., Samarasekera D.N., Patterns and trends of medical student research, BMC Med. Educ., 13, (2013); (2018); Jolly I., (2018); Information Shield I., (2018); (2018); Neale A.V., Schwartz K.L., A primer of the HIPAA privacy rule for practice-based researchers, J. Am. Board Fam. Pract., 17, 6, pp. 461-465, (2004); Department of Health and Human Services, 45 CFR subtitle A (10–1–02 edition) § 164.514, Code of Federal Regulations, pp. 718-723, (2002); (2018); Hrynaszkiewicz I., Norton M.L., Vickers A.J., Altman D.G., Preparing raw clinical data for publication: Guidance for journal editors, authors, and peer reviewers, BMJ, 340, (2010); Tucker K., Branson J., Dilleen M., Et al., Protecting patient privacy when sharing patient-level data from clinical trials, BMC Med. Res. Methodol., 16, (2016); Malin B., Sweeney L., How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems, J. Biomed. Inform., 37, 3, pp. 179-192, (2004); Malin B., Sweeney L., Re-identification of DNA through an automated linkage process, Proc AMIA Symp., pp. 423-427, (2001); Source Media I, PRIVACY: re-identification of PHI is still a risk, Health Data Manage., 23, 10, (2015); Greengard S., Privacy matters, Commun. ACM, 51, 9, pp. 17-18, (2008); (2018); Williams M., Bagwell J., Nahm Z.M., Data management plans: the missing perspective, J. Biomed. Inform., 71, pp. 130-142, (2017); Neamatullah I., Douglass M.M., Lehman L.W., Et al., Automated de-identification of free-text medical records, BMC Med. Inform. Decis. Mak., 8, (2008); Stubbs A., Uzuner O., Annotating longitudinal clinical narratives for de-identification: the 2014 i2b2/UTHealth corpus, J. Biomed. Inform., 58, pp. S20-S29, (2015); Deleger L., Lingren T., Ni Y., Et al., Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research, J. Biomed. Inform., 50, pp. 173-183, (2014); Sabharwal R., Holve E., Rein A., Segal C., Approaches to using Protected Health Information (PHI) for Patient-Centered Outcomes Research (PCORI): Regulatory Requirements, De-identification Strategies, and Policy, (2012)","R.A. Maxwell; Department of Obstetrics & Gynecology, Wright State University Boonshoft School of Medicine, Dayton, 128 E. Apple Street, Suite 3800, 45409, United States; email: rose.maxwell@wright.edu","","Academic Press Inc.","","","","","","15320464","","JBIOB","30017974","English","J. Biomed. Informatics","Note","Final","","Scopus","2-s2.0-85050524034"
"Radchenko I.; Nikolaev I.; Chistyakov A.; Lisitsyna O.; Iarkin A.","Radchenko, Irina (56724746000); Nikolaev, Igor (57205433941); Chistyakov, Alexander (57031540300); Lisitsyna, Olga (25959134700); Iarkin, Anton (57205434711)","56724746000; 57205433941; 57031540300; 25959134700; 57205434711","Solving data integration problems in medical imaging system: A case study in Almazov national medical research centre","2018","ACM International Conference Proceeding Series","","","","","","","0","10.1145/3290621.3290625","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060032881&doi=10.1145%2f3290621.3290625&partnerID=40&md5=26e53db6d1d6249cc1dcce7da84f9b1d","ITMO University, St.Petersburg, Russian Federation; St.Petersburg State University, St.Petersburg, Russian Federation; University of Turku, Turku, Finland","Radchenko I., ITMO University, St.Petersburg, Russian Federation; Nikolaev I., St.Petersburg State University, St.Petersburg, Russian Federation; Chistyakov A., ITMO University, St.Petersburg, Russian Federation; Lisitsyna O., University of Turku, Turku, Finland; Iarkin A., ITMO University, St.Petersburg, Russian Federation","This research is dedicated to creating a data transformation and unification layer between a number of medical systems (which are essentially lab equipment such as CT, MRI, X-rays scanners and so on) and centralized data management system called PACS (picture archiving and communication system). Source data are present in different (even proprietary) formats and can't be fed to PACS as is. Another challenge is that the data provided by source medical information systems contain personal user information, so it needs to be anonymized prior to analysis. Authors of this paper created a methodology and a set of services to depersonalize, process, store and retrieve medical user data. The system tracks global user identifiers in depersonalized form, so it has the ability to group different samples related to the same user. The system retrieves the data from heterogeneous sources (namely, computer tomography storage, magnetic resonance imaging storage, X-rays storage, angiography storage and ultrasonic investigation storage), processes it using a predefined set of rules and transfers the data to the PACS. PACS allows computer scientists and engineers to perform research on depersonalised data and to generate a set of results. These results are stored in both depersonalized and personalized forms. They are fed to a special service which enriches them with personal user information and stores them in the medical system for a doctor to observe. Also, they are stored in the depersonalized form and can be presented to a general audience. A doctor validates the results of the research. Our research group is in charge of creating the new PACS service which can act as a proxy between the aforementioned medical systems and the old PACS. We are also developing our own DICOM storage (DICOM is an industrial standard of medical imaging information representation) as a backend to our PACS. © 2018 Copyright is held by the owner/author(s).","Data Integration; Data Management; Heterogeneous Data Sources; Medical Imaging Systems; Research Data Management","Computer aided diagnosis; Computerized tomography; Data communication equipment; Data integration; Digital storage; Imaging systems; Magnetic resonance imaging; Magnetic storage; Medical imaging; Medical information systems; Medical problems; Metadata; Picture archiving and communication systems; Software engineering; Ultrasonic testing; X rays; Computer scientists; Data management system; Heterogeneous data sources; Heterogeneous sources; Industrial standards; Integration problems; Research data managements; Ultrasonic investigations; Information management","","","","","","","Alalawi Z.M., Eid M.M., Albarrak A.I., Assessment of picture archiving and communication system (PACS) at three of ministry of health hospitals in Riyadh region – Content analysis, J. Infect. Public Health, 9, 6, pp. 713-724, (2016); Nunes A.A., De Mello L.M., Coelho E.B., De Souza J.P., Martinez E.Z., Ana L.W., Do Valle Lessa Dallora M.E., Filho A.P., De Azevedo Marques P.M., Analyses of budget impact considering the use of the picture archiving and communication system, Value Health Reg. Issues, 8, pp. 62-68, (2015); Aryanto K.Y.E., Broekema A., Langenhuysen R.G.A., Oudkerk M., Van Ooijen P.M.A., A web-based institutional DICOM distribution system with the integration of the clinical trial processor (CTP), J. Med. Syst., 39, (2015); Didden H.W., De Valk J.P.J., Bakker A.R., Top-down design of a picture archiving and communications system (PACS) by means of simulation, Comput. Methods Programs Biomed., 26, 1, pp. 85-95, (1988); Golubev A., Bogatencov P., Secrieru G., Updating DICOM network architecture for its integration at international level, RoEduNet Conference: Networking in Education and Research, (2016); Huang H.K., PACS and Imaging Informatics: Basic Principles and Applications, (2010); Hwang I.-C., Lee K.W., Park S.S., Chanthanoulay S., Sisavanh M., Rajpho V., Kim M., Billamay S., Phangmanixay S., Oudavong B., The first picture archiving and communication system in Lao People’s Democratic Republic: Changes in the utilization rate of imaging tests in the first year after implementation, Int. J. Med. Inf., 94, pp. 31-38, (2016); Johnson L.J., Cope M.R., Shahrokhi S., Tamblyn P., Measuring tip–apex distance using a picture archiving and communication system (PACS), Injury, 39, 7, pp. 786-790, (2008); Jorritsma W., Cnossen F., Dierckx R.A., Oudkerk M., Van Ooijen P.M.A., Pattern mining of user interaction logs for a post-deployment usability evaluation of a radiology PACS client, Int. J. Med. Inf., 85, 1, pp. 36-42, (2016); Kapoor D., Picture archiving and communication systems (PACS)–A new paradigm in healthcare, Apollo Med, 7, 3, pp. 181-184, (2010); Lee Y.H., Park E.H., Suh J.-S., Simple and efficient method for region of interest value extraction from picture archiving and communication system viewer with optical character recognition software and macro program, Acad. Radiol., 22, 1, pp. 113-116, (2015); Mahlaola T.B., Van Dyk B., Reasons for picture archiving and communication system (pacs) data security breaches: Intentional versus non-intentional breaches, Health SA Gesondheid, 21, pp. 271-279, (2016); Mansoori B., Erhard K.K., Sunshine J.L., Picture archiving and communication system (PACS) implementation, integration & Benefits in an integrated health system, Acad. Radiol., 19, 2, pp. 229-235, (2012); Van Ooijen P.M.A., Bongaerts A.H.H., Witkamp R., Wijker A., Tukker W., Oudkerk M., Multi-detector computed tomography and 3-dimensional imaging in a multi-vendor picture archiving and communications systems (PACS) environment1, Acad. Radiol., 11, 6, pp. 649-660, (2004); Prabhakar A.M., Benjamin Harvey H., Brinegar K.N., Raja A.S., Kelly J.R., Brink J.A., Saini S., Oklu R., Critical access hospital ED to quaternary medical center: Successful implementation of an integrated Picture Archiving and Communications System for patient transfers by air and sea, Am. J. Emerg. Med., 34, 8, pp. 1427-1430, (2016); Ratib O., Roduit N., Nidup D., De Geer G., Rosset A., Geissbuhler A., PACS for Bhutan: A cost effective open source architecture for emerging countries, Insights Imaging, 7, 5, pp. 747-753, (2016); Ravichandran D., Praveenkumar P., Balaguru Rayappan J.B., Amirtharajan R., Chaos based crossover and mutation for securing DICOM image, Comput. Biol. Med., 72, pp. 170-184, (2016); Silberzweig J.E., Khorsandi A.S., El-Shayal T., Abiri M.M., The use of a picture archiving and communication system to catalogue visible-light photographic images, J. Vasc. Interv. Radiol., 18, 4, pp. 577-579, (2007); Stut W.J.J., De Valk J.P.J., Didden H.W., Bakker A.R., Ter Haar Romeny B.M., First experiences with the modelling and simulation packagemiracles applied to a picture archiving and communication system (PACS) in a clinical environment, Comput. Methods Programs Biomed., 28, 1, pp. 63-70, (1989); Tan S.L., Lewis R.A., Picture archiving and communication systems: A multicentre survey of users experience and satisfaction, Eur. J. Radiol., 75, 3, pp. 406-410, (2010); Watkins J., Weatherburn G., Bryan S., The impact of a picture archiving and communication system (PACS) upon an intensive care unit, Eur. J. Radiol., 34, 1, pp. 3-8, (2000); Yepes-Calderon F., Wihardja F., Melamed E., Song M., Paladini G., Lepore N., Nelson M., Erberich S., Bluml S., Gordon McComb J., Extending PACS Functionality: Towards Facilitating The Conversion of Clinical Necessities into Research-Derived Applications, (2017); Dexter A.P., Various aspects of e-health solutions based on PACS/RIS and the challenges they may pose for the chief medical executive, Diagnostic Radiology and Radiotherapy, 4, pp. 97-100, (2015)","","","Association for Computing Machinery","ACM Special Interest Group on Software Engineering (SIGSOFT); Association for Computing Machinery (ACM); RUSSOFT Association","14th Central and Eastern European Software Engineering Conference Russia, CEE-SECR 2018","12 October 2018 through 13 October 2018","Moscow","142911","","978-145036176-7","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-85060032881"
"Akhoon I.U.H.; Ganaie S.A.; Khazir M.","Akhoon, Irfan Ul Haq (57204622327); Ganaie, Shabir Ahmad (55745889700); Khazir, Mudasir (57204623600)","57204622327; 55745889700; 57204623600","Research Data Management in Open Access Journals by Developed Countries A Comparative Study between United States of America and United Kingdom","2018","IEEE 5th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, ETTLIS 2018","","","8485197","116","120","4","1","10.1109/ETTLIS.2018.8485197","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056381032&doi=10.1109%2fETTLIS.2018.8485197&partnerID=40&md5=180e7f196a94c66bad98b896646eba53","Dept. of Lib. Information Science, University of Kashmir, 190006, India","Akhoon I.U.H., Dept. of Lib. Information Science, University of Kashmir, 190006, India; Ganaie S.A., Dept. of Lib. Information Science, University of Kashmir, 190006, India; Khazir M., Dept. of Lib. Information Science, University of Kashmir, 190006, India","The present study measures quantitatively the current position and growth of Open Access archives, at the first stage it gives a quantitative overview of Open Access archives established and published from different developing and developed countries. Further it focuses on a comparative study between United States of America (USA) and United Kingdom (UK) the two developed nations. The study is exclusively based on the data collected from the Scopus database an Elsevier's abstract and citation database upto July 2017. With about 5927 (26%) of total number of active journals indexed by Scopus, USA has built significant influences headed for the progress of Open Access Scholarly publishing. © 2018 IEEE.","Open Access Scholarly Publishing-Developed textbf Nations; Open Access Journals; Open Access-USA; Open AccessUK; Scopus Indexing Open Access Journals; Research Data Management","Information management; Information services; Libraries; Comparative studies; Developed countries; Open access archives; Open access journals; Research data managements; Scholarly publishing; Scopus database; United States of America; Publishing","","","","","","","Hirwade M., Rajyalakshmi D., Open Access: India Is Moving Towards Third World Super Power, (2006); Nashipudi M., Ravi B., Indian research going global: A study on the status of open access publishing, Int. J. Inf. Res, (2014); Sengupta S., Open Access Repositories: The Asian Scenario with Special Reference to Library & Information Science, (2012); Brown C., Abbas J.M., Institutional Digital Repositories for Science and Technology: A View from the Laboratory, J. Libr. Adm, 50, 3, pp. 181-215, (2010); Bhat M.H., Community Engagement in Indian Open Access Repositories: A Deposit Activity Profile, , Chinese Librarianship: An Int. Elec. J, 29, (2012); Wani Z.A., Gul S., Rah J.A., Open access repositories: A global perspective with an emphasis on Asia, Chinese Librarianship: An Int. Elec. J, 27, (2009); Wang X., Chang S., Open access- philosophy, policy and practice: A comparative study, Chinese Librarianship: An Int. Elec. J, 23, (2007); Rajashekhar T., Open Access Initiatives in India, pp. 201-207, (2004)","","Anbu K J.P.; Sandhu G.; Kataria S.; Gartner R.","Institute of Electrical and Electronics Engineers Inc.","","5th IEEE International Symposium on Emerging Trends and Technologies in Libraries and Information Services, ETTLIS 2018","21 February 2018 through 23 February 2018","Greater Noida","140707","","978-153860828-9","","","English","IEEE Int. Symp. Emerg. Trends Technol. Libr. Inf. Serv., ETTLIS","Conference paper","Final","","Scopus","2-s2.0-85056381032"
"Cox A.M.; Tam W.W.T.","Cox, Andrew Martin (7402563906); Tam, Winnie Wan Ting (35106222600)","7402563906; 35106222600","A critical analysis of lifecycle models of the research process and research data management","2018","Aslib Journal of Information Management","70","2","","142","157","15","36","10.1108/AJIM-11-2017-0251","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045617008&doi=10.1108%2fAJIM-11-2017-0251&partnerID=40&md5=48281ffea3f012f10ef9b969d72a3e83","Information School, University of Sheffield, Sheffield, United Kingdom; Centre for Information Management, School of Business and Economics, Loughborough University, Loughborough, United Kingdom","Cox A.M., Information School, University of Sheffield, Sheffield, United Kingdom; Tam W.W.T., Centre for Information Management, School of Business and Economics, Loughborough University, Loughborough, United Kingdom","Purpose: Visualisations of research and research-related activities including research data management (RDM) as a lifecycle have proliferated in the last decade. The purpose of this paper is to offer a systematic analysis and critique of such models. Design/methodology/approach: A framework for analysis synthesised from the literature presented and applied to nine examples. Findings: The strengths of the lifecycle representation are to clarify stages in research and to capture key features of project-based research. Nevertheless, their weakness is that they typically mask various aspects of the complexity of research, constructing it as highly purposive, serial, uni-directional and occurring in a somewhat closed system. Other types of models such as spiral of knowledge creation or the data journey reveal other stories about research. It is suggested that we need to develop other metaphors and visualisations around research. Research limitations/implications: The paper explores the strengths and weaknesses of the popular lifecycle model for research and RDM, and also considers alternative ways of representing them. Practical implications: Librarians use lifecycle models to explain service offerings to users so the analysis will help them identify clearly the best type of representation for particular cases. The critique offered by the paper also reveals that because researchers do not necessarily identify with a lifecycle representation, alternative ways of representing research need to be developed. Originality/value: The paper offers a systematic analysis of visualisations of research and RDM current in the Library and Information Studies literature revealing the strengths and weaknesses of the lifecycle metaphor. © 2018, Emerald Publishing Limited.","Lifecycle; Metaphor; Research; Research data management; Research process; Visualization","Data visualization; Flow visualization; Information management; Research; Visualization; Critical analysis; Design/methodology/approach; Knowledge creations; Library and information studies; Metaphor; Research data managements; Research process; Systematic analysis; Life cycle","","","","","","","Al-Omar M., Cox A.M., Scholars’ research-related personal information collections: a study of education and health researchers in a Kuwaiti University, Aslib Journal of Information Management, 68, 2, pp. 155-173, (2016); Ball A., Review of Data Management Lifecycle Models, (2012); Bates J., Lin Y.-W., Goodale P., Data journeys: capturing the socio-material constitution of data objects and flows, Big Data & Society, 3, 2, (2016); Berry K., Research as bricolage: embracing relationality, multiplicity and complexity, Doing Educational Research: A Handbook, pp. 87-116, (2006); Introduction, Mixing Methods: Qualitative and Quantitative Research, pp. 11-16, (1992); Brew A., Conceptions of research: a phenomenographic study, Studies in Higher Education, 26, 3, pp. 271-285, (2001); Briney K., Data Management for Researchers: Organize, Maintain and Share your Data for Research Success, (2015); Burgi P., Roos J., Images of strategy, European Management Journal, 21, 1, pp. 69-78, (2003); Carlson J., The use of life cycle models in developing and supporting data services, Research Data Management: Practical Strategies for Information Professionals, pp. 63-86, (2014); Data life cycle models and concepts version 1.0, (2011); Introduction, The Library in the Life of the User: Engaging with People Where They Live and Learn, pp. 1-9, (2015); Corrall S., Designing libraries for research collaboration in the network world: an exploratory study, LIBER Quarterly, 24, 1, pp. 17-48, (2014); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Creswell J.W., Clark V.L.P., Designing and Conducting Mixed Methods Research, (2011); Dempsey L., Malpas C., Lavoie B., Collection directions: the evolution of library collections and collecting, Portal: Libraries and the Academy, 14, 3, pp. 393-423, (2014); Grigorov I., Carvalho J., Davidson J., Donnelly M., Elbaek M., Franck G., Jones S., Melero R., Knoth P., Kuchma I., Orth A., Pontika N., Rodrigues E., Schmidt B., Research lifecycle enhanced by an ‘open science by default’ workflow, (2014); Haider J., Kjellberg S., Data in the making: temporal aspects in the construction of research data, New Big Science in Focus: Perspectives on ESS and MAX IV, pp. 143-164, (2016); Harburg E., The Island of Research, American Scientist, 54, 4, (1966); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Humphrey C., E-science and the life cycle of research, (2006); Jeng W., Mattern E., He D., Lyon L., Unpacking the ‘black box’: a preliminary study of visualizing humanists and social science scholars’ data and research processes, Philadelphia, PA, 20-23 March, (2016); L'Hours H., Workflows and lifecycles: the long-term view from a national data centre, (2014); Lyon L., eBank UK: building the links between research data, scholarly communication and learning, Ariadne, (2003); Ma F., Wang J., A literature review of studies on information lifecycle I: the perspective of value, Journal of the China Society for Scientific and Technical Information, 29, 5, pp. 939-947, (2010); Ma F., Wang J., The review of studies on information lifecycle II: the perspective of management, Journal of the China Society for Scientific and Technical Information, 29, 6, pp. 1080-1086, (2010); Mattern E., Jeng W., He D., Lyon L., Brenner A., Using participatory design and visual narrative inquiry to investigate researchers’ data challenges and recommendations for library research data services, Program, 49, 4, pp. 408-423, (2015); Moller K., Lifecycle models of data-centric systems and domains, Semantic Web, 4, 1, pp. 67-88, (2013); Nonaka I., Toyama R., Konno N., SECI, ba and leadership: a unified model of dynamic knowledge creation, Long Range Planning, 33, 1, pp. 5-34, (2000); Palmer C.L., Cragin M.H., Scholarship and disciplinary practices, Annual Review of Information Science and Technology, 42, 1, pp. 163-212, (2008); Patel M., Idealised Scientific research activity lifecycle model, (2011); Pepe A., Mayernik M., Borgman C.L., Sompel H.V.D., From artifacts to aggregations, Modeling Scientific Life Cycles on the Semantic Web, 61, 3, pp. 567-582, (2010); Pickard A.J., Research Methods in Information, (2013); Patterns of Information use and exchange: case studies of researchers in the life sciences, (2009); Open to all? Case studies of openness in research, (2010); The research lifecycle at UCF, (2012); Data management support for researchers, (2017); Unsworth J., Scholarly primitives: what methods do humanities researchers have in common, and how might our tools reflect this?, (2000); Waddington S., Green R., Awre C., CLIF: Moving repositories upstream in the content lifecycle, Journal of Digital Information, 13, 1, (2012); Williams C., Managing Archives: Foundations, Principles and Practice, (2006); Wilson J., Mapping life-cycle models to the university – in theory and in practice, (2014); Wissik T., Durco M., Research data workflows: from research data lifecycle models to institutional solutions, CLARIN 2015 Selected Papers, Linköping Electronic Conference Proceedings, Annual Conference 2015 Linköping University Electronic Press, Linköpings universitet, pp. 94-107, (2015); Dooley J., The Archival Advantage: Integrating Archival Expertise into Management of Born-Digital Library Materials, (2015); Lenhardt W.C., Ahalt S., Blanton B., Christopherson L., Data management lifecycle and software lifecycle management in the context of conducting science, Journal of Open Research Software, 2, 1, pp. 1-4, (2014)","A.M. Cox; Information School, University of Sheffield, Sheffield, United Kingdom; email: a.m.cox@sheffield.ac.uk","","Emerald Group Holdings Ltd.","","","","","","20503806","","","","English","Aslib J. Inf. Manage.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85045617008"
"Piracha H.A.; Ameen K.","Piracha, Haseeb Ahmad (57209506761); Ameen, Kanwal (23468838600)","57209506761; 23468838600","Research data management practices of faculty members","2018","Pakistan Journal of Information Management and Libraries","20","","","60","75","15","4","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068000099&partnerID=40&md5=47c3a3407c9fe838f1b4f96635922843","University of the Punjab, Lahore, Pakistan","Piracha H.A., University of the Punjab, Lahore, Pakistan; Ameen K., University of the Punjab, Lahore, Pakistan","The study aimed to explore the Research Data Management (RDM) practices of university faculty members through qualitative research design. The data was collected through semi-structured, in-depth interviews from purposively selected ten faculty members from the University of Punjab (PU). The study discovered some significant factors including RDM and curation practices, the amount of research data produced, the support needed for data curation and their willingness to share it. In addition, the study explored issues the researchers face with regards to RDM. The findings reveal that respondents need assistance regarding storage and security of data, improving the quality of backup, support for storage and preservation. They agreed with a need for a central repository of the University. © 2018, University of the Punjab. All rights reserved.","Academics rdm skills; Research data management; Research data management services; University of the punjab","","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Alexogiannopoulos E., McKenney S., Pickton M., Research data management project: A DAF investigation of research data management practices, (2010); Ameen K., Rafiq M., Research data literacy and management skills of pakistani researchers., (2017); Averkamp S., Gu X., Rogers B., Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs., (2014); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Berman E., An exploratory sequential mixed methods approach to understanding researchers’ data management practices at UVM: Integrated Findings to Develop Research Data Services, Journal of escience librarianship, (2017); Carlson J., Nelson M.S., Johnston L.R., Koshoffer A., Developing data literacy programs: Working with faculty, graduate students and undergraduates, Bulletin of the Association for Information Science and Technology, 41, 6, pp. 14-17, (2015); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library trends, 61, 3, pp. 636-674, (2013); Cox, Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox, Verban E., Exploring research data management., (2018); Delasalle J., Research Data Management at the University of Warwick: Recent steps towards a joined-up approach at a UK university, LIBREAS. Library Ideas, 23, (2013); Drachen T., Ellegaard O., Larsen A., Dorch S., Sharing data increases citations, Liber Quarterly, 26, 2, (2016); Henty M., Weaver B., Bradbury S., Porter S., Investigating data management practices in Australian universities., (2008); Kim J., A study on the perceptions of university researchers on data management and sharing, Journal of the Korean Society for Library and Information Science, 49, 3, pp. 413-436, (2015); Koltay T., Data literacy: In search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); MacMillan D., Data sharing and discovery: What librarians need to know, The Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); Procter R., Halfpenny P., Voss A., Research data management: Opportunities and challenges for HEIs, Managing research data., (2014); Read K.B., Surkis A., Larson C., McCrillis A., Graff A., Nicholson J., Xu J., Starting the data conversation: Informing data services at an academic health sciences library, Journal of the Medical Library Association: JMLA, 103, 3, (2015); Rice R., Southall J., The data librarian’s handbook, (2016); Searle S., Best practice guidelines for researchers: Managing research data and primary material, (2015); Shen Y., Research data sharing and reuse practices of academic faculty researchers: A Study of the Virginia Tech Data Landscape, International Journal of Digital Curation, 10, 2, pp. 157-175, (2015); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Smith P.L., Exploring the data management and curation (DMC) practices of scientists in research labs within a research university., (2014); Steinhart G., An institutional perspective on data curation services: A view from Cornell University, Research data management: Practical strategies for information professionals., (2014); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of escience librarianship, 1, 2, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PloS one, 6, 6, (2011); Tenopir C., Birch B., Allard S., Academic libraries and research data services, Current Practices and Plans for the Future. Chicago, IL: Association of College and Research Libraries., (2012); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, (2017); Van Tuyl S., Michalek G., Assessing research data management practices of faculty at Carnegie Mellon University., (2015); Ward C., Freiman L., Jones S., Molloy L., Snow K., Making sense: Talking data management with researchers, International Journal of Digital Curation, 6, 2, pp. 265-273, (2011); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic library and information systems, 49, 4, pp. 382-407, (2015); Whyte A., Tedds, Making the case for research data management. Edinburgh: Digital Curation Centre., (2011)","","","University of the Punjab","","","","","","24097462","","","","English","Pak. J. Inf. Manag. Libr.","Article","Final","","Scopus","2-s2.0-85068000099"
"Furukawa M.; Ojiro K.; Yamaji K.","Furukawa, Masako (56937819900); Ojiro, Koichi (57205506179); Yamaji, Kazutsuna (7102241687)","56937819900; 57205506179; 7102241687","Development and Analysis of Online RDM Training Course","2018","2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018","","","8574478","640","644","4","0","10.1109/GCCE.2018.8574478","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060305288&doi=10.1109%2fGCCE.2018.8574478&partnerID=40&md5=9cc4601676b5bace86985d685c89e63d","National Institute of Informatics, Tokyo, Japan","Furukawa M., National Institute of Informatics, Tokyo, Japan; Ojiro K., National Institute of Informatics, Tokyo, Japan; Yamaji K., National Institute of Informatics, Tokyo, Japan","Research data management (RDM) is important to support research data sharing in open science, as well as to enhance the transparency of the research process. However, there are still very few staff trained in research data management in Japan. Therefore, the Open Access Repository Promotion Association, known as JPCOAR, organized a research data task force and developed a training tool to teach research data management skills. National Institute of Informatics (NII) collaborated with the JPCOAR to launch an online course 'Research data management in the open science era' on November 15, 2017. In this paper, we discuss development and analysis of the online RDM training course. © 2018 IEEE.","MOOC; Research Data Management","Curricula; Human resource management; Information management; MOOC; Online course; Open science; Research data; Research data managements; Research process; Training course; Training tools; E-learning","","","","","National Science Foundation, NSF","Regarding sharing, LearnSphere [17] was developed under the auspices of the National Science Foundation (NSF). LearnSphere is an infrastructure for learning analysis. By simple drag-and-drop operations, processing the designation of storage location, selection of statistical model and visualization of learning data can be performed. However, responding to international standardization and interoperability of tools and data are future subjects.","A Joint Statement by the G8 Science Ministers on Making Research Data open(G8); Guidelines for Responding to Misconduct in Research Adopted August 26, 2014 by Ministry of Education, Culture, Sports, Science and Technology MEXT; Recommendations Concerning An Approach to Open Science That Will Contributes to Open Innovation; Data Sharing Policy for the Realization of Genomic Medicine; JST Policy on Open Access to Research Publications and Research Data Management; Research Data Policy Types; Research Data Management Policy; Learning Analytics and Adaptive Learning. NMC Horizon Report: 2016 Higher Education Edition, pp. 38-39, (2016); Experience API Specification","","","Institute of Electrical and Electronics Engineers Inc.","et al.; IEEE; IEEE Consumer Electronics Society; Institute of Electronics, Information and Communication Engineers (EIC); The Institute of Electrical Engineers of Japan (IEEJ); The Institute of Image Information and Television Engineers (ITE)","7th IEEE Global Conference on Consumer Electronics, GCCE 2018","9 October 2018 through 12 October 2018","Nara","143651","","978-153866309-7","","","English","IEEE Glob. Conf. Consum. Electron., GCCE","Conference paper","Final","","Scopus","2-s2.0-85060305288"
"Enescu I.I.; Plattner G.-K.; Pernas L.E.; Haas-Artho D.; Bischof S.; Lehning M.; Steffen K.","Enescu, Ionuț Iosifescu (35191718800); Plattner, Gian-Kasper (6602819138); Pernas, Lucia Espona (23392815200); Haas-Artho, Dominik (57204477076); Bischof, Sandro (55354625200); Lehning, Michael (6603785154); Steffen, Konrad (57206225739)","35191718800; 6602819138; 23392815200; 57204477076; 55354625200; 6603785154; 57206225739","The EnviDat concept for an institutional environmental data portal","2018","Data Science Journal","17","","28","","","","6","10.5334/dsj-2018-028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055752487&doi=10.5334%2fdsj-2018-028&partnerID=40&md5=8cc9587b1c90b36bd22d94e5a989ff28","Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; WSL Institute for Snow and Avalanche Research SLF, Switzerland; School of Architecture, Civil and Environmental Engineering, EPFL, Switzerland; ETH Zurich, Switzerland","Enescu I.I., Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; Plattner G.-K., Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; Pernas L.E., Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; Haas-Artho D., Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; Bischof S., Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; Lehning M., WSL Institute for Snow and Avalanche Research SLF, Switzerland, School of Architecture, Civil and Environmental Engineering, EPFL, Switzerland; Steffen K., Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland, School of Architecture, Civil and Environmental Engineering, EPFL, Switzerland, ETH Zurich, Switzerland","EnviDat is the environmental data portal developed by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. The strategic initiative EnviDat highlights the importance WSL lays on Research Data Management (RDM) at the institutional level and demonstrates the commitment to accessible research data in order to advance environmental science. EnviDat focuses on registering and publishing environmental data sets and provides unified and efficient access to the WSL’s comprehensive reservoir of environmental monitoring and research data. Research data management is organized in a decentralized manner where the responsibility to curate research data remains with the experts and the original data providers. EnviDat supports data producers and data users in registration, documentation, storage, publication, search and retrieval of a wide range of heterogeneous data sets from the environmental domain. Innovative features include (i) a flexible, three-layer metadata schema, (ii) an additive data discovery model that considers spatial data and (iii) a DataCRediT mechanism designed for specifying data authorship. In addition, the overall user-friendly appearance in EnviDat provides an important opportunity for showcasing WSL research activities and results. The EnviDat portal builds on a conceptual system consisting of a core system, a set of guiding principles and a number of key services. Its development closely follows the conceptual framework, being guided by principles towards the ultimate goal of providing useful services for researchers. © 2018 The Author(s).","Data portal; Data sharing; EnviDat; Environmental data; Environmental science; Metadata; Research data management","Environmental engineering; Environmental management; Information management; Metadata; Data portal; Data Sharing; EnviDat; Environmental data; Environmental science; Research data managements; Digital storage","","","","","","","Adams J., Collaborations: The fourth age of research, Nature, 497, 7451, pp. 557-560, (2013); Beck K., Beedle M., van Bennekum A., Cockburn A., Cunningham W., Fowler M., Grenning J., Highsmith J., Hunt A., Jeffries R., Kern J., Marick B., Martin R.C., Mallor S., Shwaber K., Sutherland J., The Agile Manifesto. the Agile Alliance, (2001); Bizer C., Heath T., Berners-Lee T., Linked Data – the story so far, International Journal on Semantic Web and Information Systems, 5, 3, pp. 1-22, (2009); Credit – CASRAI, (2018); CCES Research Platforms, (2018); Open Data Zürich – Stadt Zürich, (2018); About CKAN. Ckan, (2012); Welcome to Datacite, (2018); Datacite Metadata Schema Documentation for the Publication and Citation of Research Data, (2016); Dawes N., Kumar K.A., Michel S., Aberer K., Lehning M., Sensor Metadata Management and Its Application in Collaborative Environmental Research, 2008 IEEE Fourth International Conference on Escience, pp. 143-150, (2008); Dawes N.M., Lehning M., Bavay M., Sarni S., Iosifescu I., Gwadera R., Scipion D.E., Blanchet J., Davison A., Berne A., Hurni L., Parlange M.B., Aberer K., Open Support Platform for Environmental Research (OSPER) – tools for the discovery and exploitation of environmental data, AGU Fall Meeting Abstracts, 51, (2012); Dbpedia, (2018); DCMI Specifications, (2018); Eawag – Research Data Management, (2016); GEOSS Portal, (2018); Science DMZ, (2018); ETH Zurich DOI Desk, (2018); Guidelines on FAIR Data Management in Horizon 2020, (2016); Fecher B., Friesike S., Open Science: One Term, Five Schools of Thought, Opening Science, pp. 17-47, (2014); Open Science and Research Handbook, (2014); Frosio G., Open Access Publishing: A Literature Review (SSRN Scholarly Paper No. ID 2697412), (2014); GDAL: GDAL – Geospatial Data Abstraction Library, (2018); Project Info. Geodata4edu.Ch, (2018); (2018); Geotools the Open Source Java GIS Toolkit — Geotools, (2018); Iosifescu-Enescu I., Geodata Download Service Technology Overview, (2016); Iosifescu Enescu I., Vescoukis V., Iosifescu Enescu C.M., Muller F., Panchaud N.H., Hurni L., Hypercube-Based Visualization Architecture for Web-Based Environmental Geospatial Information Systems, The Cartographic Journal, 52, 2, pp. 137-148, (2015); Isni|How ISNI Works, (2018); ISO/TS 19139:2007 – Geographic Information – Metadata – XML Schema Implementation, (2007); ISO 19115-1:2014 – Geographic Information – Metadata – Part 1: Fundamentals, (2014); Jeung H., Sarni S., Paparrizos I., Sathe S., Aberer K., Dawes N., Papaioannou T.G., Lehning M., Effective Metadata Management in Federated Sensor Networks, 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp. 107-114, (2010); Johnson C., Top scientific visualization research problems, IEEE Computer Graphics and Applications, 24, 4, pp. 13-17, (2004); Kellenberger B., Iosifescu Enescu I., Nicola R., Iosifescu Enescu C.M., Panchaud N.H., Walt R., Hotea M., Piguet A., Hurni L., The wheel of design: Assessing and refining the usability of geoportals, International Journal of Cartography, 2, 1, pp. 95-112, (2016); Nakamura Y., Cochrane G., Karsch-Mizrachi I., The International Nucleotide Sequence Database Collaboration, Nucleic Acids Research, 41, D1, pp. D21-D24, (2013); International Nucleotide Sequence Database Collaboration, (2018); Tree Ring|National Centers for Environmental Information (NCEI) Formerly Known as National Climatic Data Center (NCDC), (2018); Nielsen J., Designing Web Usability: The Practice of Simplicity, (1999); Norman D.A., Emotional Design, (2003); National Snow and Ice Data Center, (2018); Making Open Science a Reality, OECD Science, Technology and Industry Policy, (2015); Open Knowledge International, (2018); Structure of the ORCID Identifier – Feedback & Support for ORCID, (2018); Ott T., Swiaczny F., Time-Integrative Geographic Information Systems: Management and Analysis of Spatio-Temporal Data, (2012); PANGAEA. Data Publisher for Earth & Environmental Science, (2018); Perrino T., Howe G., Sperling A., Beardslee W., Sandler I., Shern D., Pantin H., Kaupert S., Cano N., Cruden G., Bandiera F., Brown C.H., Advancing Science Through Collaborative Data Sharing and Synthesis, Perspectives on Psychological Science, 8, 4, pp. 433-444, (2013); Project Jupyter: Computational Narratives as the Engine of Collaborative Data Science, Jupyter Blog, (2015); Project Jupyter, (2018); QGIS as OGC Data Server, (2018); Rda|Research Data Sharing without Barriers, (2018); About Us|Researchgate, the Professional Network for Scientists, (2018); Shen H., Interactive notebooks: Sharing the code, Nature News, 515, 7525, (2014); Shotton D., Peroni S., The Datacite Ontology, (2018); Slocum T.A., McMaster R.B., Kessler F.C., Howard H.H., Thematic Cartography and Geovisualization, (2008); Greenland Climate Network (Gc-Net), (2018); About – Opendata.Swiss, (2018); Envidat: The Environmental Data Portal, (2018); Long-Term Forest Ecosystem Research (LWF), (2018); Swiss National Forest Inventory NFI, (2018); SNF. Swiss National Science Foundation, (2018); Apache Solr, (2017); Postgresql: The world’s Most Advanced Open Source Database, (2018); Reuters T., Researcherid.Com, (2018); TOP 10 Most Interesting UX Design Case Studies to Inspire Your Service Reinvention in 2018, (2017); van Dam A., Forsberg A.S., Laidlaw D.H., Laviola J.J., Simpson R.M., Immersive VR for scientific visualization: A progress report, IEEE Computer Graphics and Applications, 20, 6, pp. 26-52, (2000); Vierkant P., Pampel H., Kindling M., Witt M., Scholze F., Re3data.Org – Registry of Research Data Repositories, (2015); Resource Description Framework (RDF), (2014); (2018); Cascading Style Sheets, (2018); Javascript Web Apis – W3C, (2018); The RDF Data Cube Vocabulary, (2018); Waterman M., Noble J., Allan G., How Much Up-front?: A Grounded Theory of Agile Architecture, Proceedings of the 37Th International Conference on Software Engineering, 1, pp. 347-357, (2015); Wikipedia, (2018); Wilkinson M.D., Dumontier M., Aalbersberg I., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Santos L.B.S., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 'T Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, (2016); The Global Risks Report 2018, (2018); Datacredit – Contributor Roles Taxonomy for Data, (2018)","I.I. Enescu; Swiss Federal Institute for Forest, Snow and Landscape WSL, Switzerland; email: ionut.iosifescu@wsl.ch","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85055752487"
"Karimova Y.; Castro J.A.; Ribeiro C.","Karimova, Yulia (57195369729); Castro, João Aguiar (55977255100); Ribeiro, Cristina (7201734594)","57195369729; 55977255100; 7201734594","Data Deposit in a CKAN Repository: A Dublin Core-Based Simplified Workflow","2019","Communications in Computer and Information Science","988","","","222","235","13","4","10.1007/978-3-030-11226-4_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060755717&doi=10.1007%2f978-3-030-11226-4_18&partnerID=40&md5=8a99743f39396c690d9e430691bc7b7a","INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Karimova Y., INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Castro J.A., INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Researchers are currently encouraged by their institutions and the funding agencies to deposit data resulting from projects. Activities related to research data management, namely organization, description, and deposit, are not obvious for researchers due to the lack of knowledge on metadata and the limited data publication experience. Institutions are looking for solutions to help researchers organize their data and make them ready for publication. We consider here the deposit process for a CKAN-powered data repository managed as part of the IT services of a large research institute. A simplified data deposit process is illustrated here by means of a set of examples where researchers describe their data and complete the publication in the repository. The process is organised around a Dublin Core-based dataset deposit form, filled by the researchers as preparation for data deposit. The contacts with researchers provided the opportunity to gather feedback about the Dublin Core metadata and the overall experience. Reflections on the ongoing process highlight a few difficulties in data description, but also show that researchers are motivated to get involved in data publication activities. © Springer Nature Switzerland AG 2019.","CKAN; Data publication; Dublin Core; Metadata; Research data management","Deposits; Information management; Metadata; Publishing; CKAN; Data publications; Data repositories; Dublin Core; Funding agencies; Limited data; Research data managements; Research institutes; Digital libraries","","","","","European Regional; Funda¸cão para a Ciência e a Tec-nologia; Operational Programme for Competitiveness and Internationalisation; Fundação para a Ciência e a Tecnologia, FCT, (SFRH/BD/136332/2018); Federación Española de Enfermedades Raras, FEDER, (016736); Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN, (PD/BD/114143/2015, POCI-01-0145-FEDER-016736); European Regional Development Fund, FEDER; Programa Operacional Temático Factores de Competitividade, POFC","Funding text 1: Acknowledgements. This work is financed by the ERDF – European Regional; Funding text 2: This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Inter-nationalisation-COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT-Fundação para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT-Fundação para a Ciência e a Tecnologia. Yulia Karimova is supported by research grant SFRH/BD/136332/2018, provided by the FCT-Fundação para a Ciência e a Tecnologia.; Funding text 3: Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸cão para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT - Funda¸cão para a Ciência e a Tec-nologia. Yulia Karimova is supported by research grant SFRH/BD/136332/2018, provided by the FCT - Funda¸cão para a Ciência e a Tecnologia.","Amorim R., Et al., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univers. Access Inf. Soc., 16, pp. 851-862, (2017); Assante M., Et al., Are scientific data repositories coping with research data publishing?, Data Sci. J., 15, (2016); Bishoff C., Johnston L., Approaches to data sharing: An analysis of NSF data management plans from a Large Research University, J. Libr. Sch. Commun., 3, 2, (2015); Castro J.A., Et al., Involving data creators in an ontology-based design process for metadata models, Developing Metadata Application Profiles, pp. 181-214, (2017); Accompanying the Document Proposal for a Directive of the European Parliament and of the Council on the Re-Use of Public Sector Information, (2018); Horizon 2020. Work Programme 2016-2017, (2017); van den Eynden V., Et al., Managing and sharing data-best practice for researchers, UK Data Archive, pp. 1-40, (2011); Farnel S., Shiri A., Metadata for research data: Current practices and trends, International Conference on Dublin Core and Metadata Applications, pp. 74-82, (2014); Gartner R., Metadata Becomes Digital, pp. 27-39, (2016); Hudson Vitale C.R., The current state of meta-repositories for data, Curating Research Data, Volume One: Practical Strategies for Your Digital Repository, pp. 251-261, (2017); Karimova Y., Vocabulários Controlados Na descrição De Dados De investigação No Dendro, (2016); Karimova Y., Castro J.A., Silva J.R., Pereira N., Ribeiro C., Promoting semantic annotation of research data by their creators: A use case with B2NOTE at the end of the RDM workflow, MTSR 2017. CCIS, 755, pp. 112-122, (2017); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, Plos ONE, 12, 3, pp. 1-44, (2017); Qin J., Ball A., Greenberg J., Functional and architectural requirements for metadata: Supporting discovery and management of scientific data, Proceedings of the DCIM International Conference on Dublin Core and Metadata Applications, pp. 62-71, (2012); Qin J., Li K., How portable are the metadata standards for scientific data? A proposal for a metadata infrastructure, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 25-34, (2013); Ribeiro C., Et al., Projeto TAIL-Gestão De Dados De investigação Da produção Ao depósito E à Partilha (Resultados Preliminares), pp. 256-264, (2016); Rocha J., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Proceedings of the 11Th International Conference on Digital Preservation, Ipres, (2014); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Gov. Inf. Q., 30, pp. 19-31, (2013); Shearer K., Furtado F., COAR Survey of Research Data Management: Results. Confederation of Openaccess Repositories, (2017); Swan A., Brown S., To share or not to share: Publication and quality assurance of research data outputs, A Report Commissioned by the Research Information Network, (2008); Tani A., Candela L., Castelli D., Dealing with metadata quality: The legacy of digital library efforts, Inf. Process. Manag., 49, 6, pp. 1194-1205, (2013); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos ONE, 10, 8, (2015); Vardigan M., Heus P., Thomas W., Data documentation initiative: Toward a standard tor the social sciences, Int. J. Digit. Curation, 3, 1, pp. 107-113, (2008); Winn J., Open data and the academy: An evaluation of CKAN for research data management, IASSIST 2013, pp. 28-31, (2013)","Y. Karimova; INESC TEC, Faculty of Engineering, University of Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: ylaleo@gmail.com","Manghi P.; Candela L.; Silvello G.","Springer Verlag","","15th Italian Research Conference on Digital Libraries, IRCDL 2019","31 January 2019 through 1 February 2019","Pisa","223069","18650929","978-303011225-7","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85060755717"
"Grasse M.; López A.; Winter N.","Grasse, Marleen (57204480904); López, Ania (55949158100); Winter, Nina (57204479460)","57204480904; 55949158100; 57204479460","Landesinitiative NFDI – A central point of contact for RDM for higher education institutions in the German state of north Rhine-Westphalia","2018","Data Science Journal","17","","25","","","","1","10.5334/dsj-2018-025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055730674&doi=10.5334%2fdsj-2018-025&partnerID=40&md5=1a56e7c7290d389f59cce4e7523cc1b6","University Duisburg-Essen, Germany","Grasse M., University Duisburg-Essen, Germany; López A., University Duisburg-Essen, Germany; Winter N., University Duisburg-Essen, Germany","This paper gives an overview of activities regarding RDM in Germany including the national political context as well as initiatives on federal state level. The knowledge about Germany’s federal system, which also entails the autonomy of the federal states regarding the higher education system, is fundamental to understand the different approaches towards RDM in Germany. The state initiatives of Thuringia, Baden-Wuerttemberg and Hesse are described to compare them to the state initiative (Landesinitiative NFDI) of Germany’s most populous state of North-Rhine Westphalia (NRW). The aim of the initiative in NRW is to initiate the collaboration between institutions, to link current RDM activities in NRW and to prepare the local institutions for the participation in a National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur, NFDI). © 2018 The Author(s).","German higher education system; National research data infrastructure; RDM; RDM initiative; RDM template policy; Research data management; State initiative","Computer applications; Computer science; Higher education system; RDM initiative; Research data; Research data managements; State initiative; Information management","","","","","Digitale Hochschule NRW; Ministry for Science and Arts; Ministry of Culture and Science; Ministry of Culture and Science of the Federal Government of NRW; Natural Resources Wales, NRW; Universität Duisburg-Essen, UDE; Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen, MKW","Funding text 1: To foster RDM activities in Hesse, the HeFDI initiative is funded by Hesse’s Ministry for Science and Arts with 3,4 Mio Euro between 2016 and 2020. The work of HeFDI builds upon the work of a local RDM center of competence at the university of Marburg, which was funded by the state ministry from 2013 to 2015 and can be considered as a pilot project for the joint RDM activities in Hesse. The main goals of HeFDI are the establishment of RDM policies, RDM training offers, advice for researchers regarding data management plans, legal issues, licensing and tools in the framework of data as well as shared infrastructure and repositories at the participating institutions.; Funding text 2: The work of the Landesinitiative NFDI is funded by the Ministry of Culture and Science of the Federal Government of NRW (Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen) and the Digitale Hochschule NRW (DH-NRW). For this publication, we acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.; Funding text 3: First efforts for collaboration in the sector of information infrastructure is made: Three consortia of universities in NRW are currently applying for grants for new storage infrastructure, which in a first stage aim to cover the demand for storage for all full universities and in a second stage aim to cover the demand of all universities within NRW. For the long-term availability of research data the federal government of NRW funded a common license for the ‘Rosetta’ software of the Exlibris group for universities in NRW. A collaborative working environment for research data is developed in form of the campus cloud ‘sciebo’ by the university of Münster and made available to all universities in NRW (see also López, Vogl & Roller 2017). Further RDM projects funded by varying sources are carried out by different partners in NRW. In addition, several universities are starting to employ data managers and RDM experts at their institutions, which are building up local RDM competence. Thus, there is a great demand for knowledge exchange between the individual institutions and RDM projects in NRW. To stimulate the coordination of the different RDM activities in NRW, the Landesinitiative NFDI, as a central point of contact for universities in the context of RDM, is funded by the Ministry of Culture and Science of the Federal Government of NRW and the Digitale Hochschule NRW (DH-NRW) with about 400 000 Euro for two years. The DH-NRW is a federation of 42 universities3 with participation of the Ministry of Culture and Science of the German state of NRW with the aim to promote digitalisation in research, teaching and infrastructure and to foster collaboration between the participating institutions. The project team of the Landesinitiative NFDI reports to the program committee of the DH-NRW, which can be considered as the ‘think tank’ of the institution and comprises vice presidents and CIOs of the participating universities. The Landesinitiative NFDI is supported by a group of RDM experts with various institutional backgrounds including research libraries, IT centers, research support offices, and extramural research institutions. The RDM expert group comprises ten representatives of different types of higher education institutions and non-university research institutes. Their input is pivotal for gaining insights into the demands concerning RDM at the different institutions as well as for gathering information on current developments in the state of NRW. In September 2017, the project Landesinitiative NFDI, located at the university library of the University of Duisburg-Essen, officially started with a project team of three persons. The activities of the Landesinitiative NFDI are based on the work of the former ‘Fachteam FDM’ of the DH-NRW, which consisted of six RDM experts from scientific libraries and IT centers who drafted a pilot study on the status of RDM in NRW (DV-ISA 2016). With the establishment of the Landesinitiative NFDI the group was extended to the current RDM expert group.","Brand O., Stille W., Schachtner J., Hefdi – Die Landesweite Initiative Zum Aufbau Von Forschungsdateninfrastrukturen in Hessen, 2, (2018); Digitale Agenda 2014–2017, (2014); Curdt C., Grasse M., Hess V., Kasties N., Lopez A., Magrean B., Perry A., Quast A., Rudolph D., Stork S., Vompras J., Winter N., Zur Rolle Der Hochschulen – Positionspapier Der Landesinitiative NFDI Und Der Expertengruppe FDM Der Digitalen Hochschule NRW Zum Aufbau Einer Nationalen Forschungsdateninfrastruktur, (2018); Umgang Mit Digitalen Daten in Der Wissenschaft: Forschungsdatenmanagement in NRW – Eine Erste Bestandsaufnahme, (2016); European Cloud Initiative – Building a Competitive Data and Knowledge Economy in Europe, (2016); (2016); Grasse M., Lopez A., Winter N., (2018); Model Policy for Research Data Management (RDM) at Research Institutions/Institutes, LEARN Toolkit of Best Practice for Research Data Management, pp. 133-136, (2017); Lopez A., Vogl R., Roller S., Research Data Infrastructures – A Perspective for the State of North Rhine-Westphalia in Germany, EUNIS 23Rd Annual Congress – Shaping the Digital Future of Universities, pp. 105-112, (2017); Ministerium für Innovation, Wissenschaft Und Forschung Des Landes Nordrhein-Westfalen, (2017); Leistung Aus Vielfalt – Empfehlungen Zu Strukturen, Prozessen Und Finanzierung Des Forschungsdatenmanagements in Deutschland, (2016); Bildung Und Kultur, Studierende an Hochschulen, Wintersemester 2017/18, (2018)","N. Winter; University Duisburg-Essen, Germany; email: nina.winter@uni-due.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85055730674"
"Grunzke R.; Hartmann V.; Jejkal T.; Kollai H.; Dressler C.; Dolhoff J.; Stanek J.; Herold H.; Hoffmann A.; Muller-Pfefferkorn R.; Schrade T.; Herres-Pawlis S.; Meinel G.; Nagel W.E.","Grunzke, Richard (37096970200); Hartmann, Volker (7005982861); Jejkal, Thomas (24478712700); Kollai, Helen (56668667900); Dressler, Christiane (57201417472); Dolhoff, Julia (57201422140); Stanek, Julia (57190610770); Herold, Hendrik (57199151343); Hoffmann, Alexander (55706038800); Muller-Pfefferkorn, Ralph (8774342800); Schrade, Torsten (57190125722); Herres-Pawlis, Sonja (9277407800); Meinel, Gotthard (6506545059); Nagel, Wolfgang E. (9435404200)","37096970200; 7005982861; 24478712700; 56668667900; 57201417472; 57201422140; 57190610770; 57199151343; 55706038800; 8774342800; 57190125722; 9277407800; 6506545059; 9435404200","Performance Evaluation of the Metadata-Driven MASi Research Data Management Repository Service","2018","Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018","","","","334","338","4","0","10.1109/PDP2018.2018.00059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048800929&doi=10.1109%2fPDP2018.2018.00059&partnerID=40&md5=b6e90563bf35ac470a99220196f3c976","Center for Information Services and High Performance Computing, Technische Universitat Dresden, Dresden, Germany; Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany; Institut fur Anorganische Chemie, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany; Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Digitale Akademie Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany","Grunzke R., Center for Information Services and High Performance Computing, Technische Universitat Dresden, Dresden, Germany; Hartmann V., Center for Information Services and High Performance Computing, Technische Universitat Dresden, Dresden, Germany; Jejkal T., Center for Information Services and High Performance Computing, Technische Universitat Dresden, Dresden, Germany; Kollai H., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany; Dressler C., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany; Dolhoff J., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany; Stanek J., Institut fur Anorganische Chemie, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany; Herold H., Institut fur Anorganische Chemie, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany; Hoffmann A., Institut fur Anorganische Chemie, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany; Muller-Pfefferkorn R., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Schrade T., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Herres-Pawlis S., Digitale Akademie Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany; Meinel G., Digitale Akademie Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany; Nagel W.E., Digitale Akademie Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany","Research data is increasingly important in order to gain insights from scientific data. To optimally foster this, the management of research data is required to be usable, customizable and fast. We enable this by building up the MASi research data management repository service, based on the KIT DM framework. The aim is on utilizing a single repository instance to serve multiple arbitrary community use cases. Due to their diverse data characteristics the performance of the MASi service has to be fitting across the different cases. We evaluate the performance along three initial heterogeneous use cases. Various aspects are investigated; First, the object insertion and query performance of the database along the object fill level. Second and third, the ingest and download performance of digital objects using real-life data sets. Highly favorable performance characteristics are shown. © 2018 IEEE.","Performance; Repository; Research Data Management","Query processing; Data characteristics; Performance; Performance characteristics; Performance evaluations; Query performance; Real life datasets; Repository; Research data managements; Information management","","","","","","","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Scientific Data, 3, (2016); Metadata Management for Applied Sciences, (2017); Grunzke R., Hartmann V., Jejkal T., Prabhune A., Herres-Pawlis S., Hoffmann A., Deicke A., Schrade T., Herold H., Meinel G., Stotzka R., Nagel W.E., Towards a metadata-driven multicommunity research data management service, Proc. of IWSG 2016 (8th International Workshop on Science Gateways), (2017); Jejkal T., Vondrous A., Kopmann A., Stotzka R., Hartmann V., The Repository Architecture Enabling Cross-Disciplinary Research, pp. 9-11, (2014); KIT Data Manager (Website, (2017); Grunzke R., Hartmann V., Jejkal T., Kollai H., Prabhune A., Herold H., Deicke A., Dressler C., Dolhoff J., Stanek J., Hoffmann A., Muller-Pfefferkorn R., Schrade T., Meinel G., Herres-Pawlis S., Nagel W.E., The MASi Repository Service-Comprehensive, Metadata-driven and Multi-community Research Data Management, (2018); Meinel G., Hecht R., Herold H., Analyzing building Stock Using Topographic Maps and GIS, Building Research & Information, 37, 5-6, pp. 468-482, (2009); Liu J., Chakraborty S., Hosseinzadeh P., Yu Y., Tian S., Petrik I., Bhagi A., Lu Y., Metalloproteins containing cytochrome, iron-sulfur, or copper redox centers, Chemical Reviews, 114, 8, pp. 4366-4469, (2014); Hoffmann A., Stanek J., Dicke B., Peters L., Grimm-Lebsanft B., Wetzel A., Jesser A., Bauer M., Gnida M., Meyer-Klaucke W., Et al., Implications of guanidine substitution on copper complexes as entatic-state models, European Journal of Inorganic Chemistry, 29, pp. 4731-4743, (2016); Project: Corpus Vitrearum Deutschland, (2017); KIT Data Manager Manual, (2017); Allcock W., Bresnahan J., Kettimuthu R., Link M., Dumitrescu C., Raicu I., Foster I., The Globus striped GridFTP framework and server, Proceedings of the 2005 ACM/IEEE Conference on Supercomputing. IEEE Computer Society, (2005); Schuller B., Pohlmann T., UFTP: High-performance data transfer for unicore, UNICORE Summit 2011 Proceedings, pp. 135-142, (2011)","","Kotenko I.; Merelli I.; Lio P.","Institute of Electrical and Electronics Engineers Inc.","","26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018","21 March 2018 through 23 March 2018","Cambridge","136952","","978-153864975-6","","","English","Proc. - Euromicro Int. Conf. Parallel, Distrib., Network-Based Process., PDP","Conference paper","Final","","Scopus","2-s2.0-85048800929"
"Singh N.K.; Monu H.; Dhingra N.","Singh, Neeraj Kumar (55458871500); Monu, Harpreet (57204616915); Dhingra, Navjyoti (57204628300)","55458871500; 57204616915; 57204628300","Research Data Management Policy and Institutional Framework","2018","IEEE 5th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, ETTLIS 2018","","","8485259","111","115","4","5","10.1109/ETTLIS.2018.8485259","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056382640&doi=10.1109%2fETTLIS.2018.8485259&partnerID=40&md5=df5072b88e13b82ac021dca0fa53fdec","A C Joshi Library, Panjab University, Chandigarh, India; IISER, Mohali, India","Singh N.K., A C Joshi Library, Panjab University, Chandigarh, India; Monu H., IISER, Mohali, India; Dhingra N., A C Joshi Library, Panjab University, Chandigarh, India","Data created from research is a valuable resource and usually requires much time and money to be produced. Research data is the data that is collected, observed, or created for purposes of analysis to produce original research results. Research data has significant value in responsible research, which refers to the ability to justify conclusions on the basis of the data acquired and generated through research and that is furnished to other researchers for scrutiny and/or verification. This data can be used and reused for future scientific and educational purposes. Research data management is becoming increasingly important and it is beneficial for the institutions as well as for the researchers. It helps institutions to increase impact and visibility of their research and simultaneously helps researchers to keep their data secure, improve the opportunities for collaboration for further research, more research publications and increased citations. This paper discusses some of the key issues related to research data management, roles and responsibilities of the institutions as well as the research scholar, cost and infrastructure, data sharing policies, legal and ethical issues, copyright issues, etc. An attempt has been made to depict and present suitable policies with a policy framework. Work flow chart with adoption of best practices in RDM is given, that will boost the research process in an organisation. The main objective of this paper is to provide suggestions and recommendations for successful research data management based on some of the best practices and principles of RDM adopted by the institutions worldwide. © 2018 IEEE.","Managing Research; RDM; Research Data Management; Responsible Research","Information services; Libraries; Best practices; Ethical issues; Institutional framework; Policy framework; Research data managements; Research process; Research results; Research scholars; Information management","","","","","","","What is Research Data Management - University of Leicester, Www2.le.ac.uk, (2017); What is RDM? | Library | Lancaster University, Lancaster.ac.uk, (2017); King's College London - Introduction to research data management, Kcl.ac.uk, (2017); Whyte A., Tedds J., Making the Case for Research Data Management, DCC Briefing Papers, (2011); Research data lifecycle - NUI Galway, Library.nuigalway.ie, (2017); The Australian Research Data Infrastructure Strategy | Department of Education and Training - Document library, Australian Government, Docs.education.gov.au, (2014)","","Anbu K J.P.; Sandhu G.; Kataria S.; Gartner R.","Institute of Electrical and Electronics Engineers Inc.","","5th IEEE International Symposium on Emerging Trends and Technologies in Libraries and Information Services, ETTLIS 2018","21 February 2018 through 23 February 2018","Greater Noida","140707","","978-153860828-9","","","English","IEEE Int. Symp. Emerg. Trends Technol. Libr. Inf. Serv., ETTLIS","Conference paper","Final","","Scopus","2-s2.0-85056382640"
"Schmidt L.; Holies J.H.","Schmidt, Larry (22136252700); Holies, Joseph H. (57189500264)","22136252700; 57189500264","Teaching research data management: It takes a team to do it right!","2018","ASEE Annual Conference and Exposition, Conference Proceedings","2018-June","","","","","","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051214784&partnerID=40&md5=2e543466c93c30c2793f0dd6c7fd95b6","University of Wyoming, Department of Chemical Engineering, United States","Schmidt L., University of Wyoming, Department of Chemical Engineering, United States; Holies J.H., University of Wyoming, Department of Chemical Engineering, United States","[No abstract available]","","","","","","","","","Proposal and Award Policies and Procedures Guide Part I-Grant Proposal Guide, (2013); NIH Data Sharing Policy, (2007); DOE Policy for Digital Research Data Management; U.S.G. USGS Data Management: Data Management Plans, (2017); Brantley S., Vidie R., Brasier K., Yoxtheimer D., Pollak J., Wilderman C., Wen T., Engaging over data on fracking and water quality: Data alone aren't the solution, but they bring people together, Science, 359, 6374, (2018); Informatics, (2018); Krier L., Strasser C., Data Management for Libraries. A LITA Guide, (2014); Briney K., Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success, (2015); Carlson J., Fosmire M., Millier C.C., Sapp Nelson M., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, 2, (2011); Fong B., Wang M., Required data management training for graduate students in an earth and environmental sciences department, Journal of EScience Librarianship, 4, 1, (2015); Schmidt L., Holies J., A graduate course in research data management, Chemical Engineering Education, 52, 1, (2018); Holies J., Schmidt L., Implementing a graduate class in research data management for science/Engineering students, 2018 ASEE Annual Conference & Exposition, (2018); Whitmire A., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Wright S., Andrews C., Data Information Literacy Case Study Directory, 2, 1, (2013); Thielen J., Samuel S., Carlson J., Moldwin M., Developing and teaching a two-credit data management course for graduate students in climate and space sciences, Issues in Science and Technology Librarianship, 86, (2017); Data Management Plan Tool, (2014); Carlson J., The data curation profiles toolkit: User guide, Data Curation Profiles Toolkit, (2010); Westra B., Walton D., Data Information Literacy Case Study Directory, 4, 1, (2012); Johnston L., Jeffryes J., Data Information Literacy Case Study Directory, 3, 1, (2012); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at a small liberal arts school, International Federation of Library Associations and Institutions, 43, 1, (2017); Adamick J., Reznik-Zellen R., Sheridan M., Data management training for graduate students at a large research university, Journal of E Science Librarianship, 1, 3, (2013); Corti L., Van Den Eyden V., Learning to manage and share data: Jump-starting the research methods curriculum, International Journal of Social Research Methodology, 18, 5; Piorun M., Kafel D., Leger-Hornby T., Najafi S., Martin E., Colombo P., LaPelle N., Teaching research data management: An undergraduate / graduate curriculum, Journal of EScience Librarianship, 1, 1, (2012); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data management pilot course at an academic library, Journal of EScience Librarianship, 3, 1, (2014); Carlson J., Bracke M., Data Management and Sharing from the Perspective of Graduate Students: An Examination of Culture and Practice at the Water Quality Field Station, (2013); Valentino M., Boock M., Data Management for Graduate Students: A Case Study at Oregon State University, (2015); Borgman C., Syllabus for Data Management and Practice, (2015)","","","American Society for Engineering Education","","125th ASEE Annual Conference and Exposition","23 June 2018 through 27 December 2018","Salt Lake City","138114","21535965","","","","English","ASEE Annu. Conf. Expos. Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85051214784"
"Li Y.; Dressel W.; Hersey D.","Li, Yuan (57208286541); Dressel, Willow (57207821869); Hersey, Denise (22957791300)","57208286541; 57207821869; 22957791300","Research data management: What can librarians really help?","2019","GL-Conference Series: Conference Proceedings","2019-December","","","67","74","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062960063&partnerID=40&md5=843ffb6d2b81174ab2cc46b010199f5a","Princeton University Library, United States","Li Y., Princeton University Library, United States; Dressel W., Princeton University Library, United States; Hersey D., Princeton University Library, United States","As national government agencies continue to mandate specific data management requirements and the need for research data management (RDM) grows, many libraries are developing RDM services to help with the research mission of their institution. Research libraries' mission and expertise have always included a variety of research services. What can the library's role be in RDM services? This paper describes the possible roles that libraries and librarians can play throughout the data lifecycle. The Princeton University Library is presented as a case study to demonstrate these roles and the development of RDM services including advocacy, awareness, education, advisory services, data management plan development, and data repository development and promotion. In addition, this paper also discusses the challenges and opportunities associated with RDM services development in libraries and the future plans at Princeton, including the development of a RDM mini course for graduate students and a robust RDM program. © TextRelease, 2019.","","Information management; Students; Advisory services; Data repositories; Graduate students; National governments; Princeton University; Research data managements; Research libraries; Services development; Libraries","","","","","","","AAU-APLU Public Access Working Group Report and Recommendations, (2017); Scott B.D., Purdue university research repository: Collaborations in data management, Research Data Management: Practical Strategies for Information Professionals, (2014); Bryant R., Et al., The realities of research data management, OCLC Research; Fearon D., Et al., Research Data Management Services, SPEC Kit 334 (July 2013), (2013); Henry G., Data curation for the humanities: Perspectives from rice university, Research Data Management: Practical Strategies for Information Professionals, (2014); Holdren J., Increasing Access to the Results of Federally Funded Scientific Research, (2013); Steinhart G., An institutional perspective on data curation services: A view from cornell university, Research Data Management: Practical Strategies for Information Professionals, (2014); Tenopir C., Et al., Academic Libraries and Research Data Services Current Practices and Plans for the Future; Research data lifecycle, UK Data Service, (2012); Westra B., Developing data management services for researchers at the university of oregon, Research Data Management: Practical Strategies for Information Professionals, (2014)","","","TextRelease","EBSCO; et al.; Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR); Korea Institute of Science and Technology Information (KISTI); Nuclear Information Section; International Atomic Energy Agency (NIS-IAEA); Slovak Centre of Scientific and Technical Information (CVTISR)","20th International Conference on Grey Literature: Research Data Fuels and Sustains Grey Literature, GL 2018","3 December 2018 through 4 December 2018","New Orleans","145765","13862316","978-907748433-3","","","English","GL-Conf. Series: Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85062960063"
"Vaziri E.; Naghshineh N.; Chakoli A.N.","Vaziri, Esmaeil (57194052976); Naghshineh, Nader (34877299900); Chakoli, Abdolreza Noroozi (57221727991)","57194052976; 34877299900; 57221727991","Data sharing: International and national approaches","2018","Iranian Journal of Information Processing Management","33","3","","1023","1052","29","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049892925&partnerID=40&md5=0d6c9bfe2b20419776eb31dff2679069","Information Science and Knowledge Studies Department, University of Tehran, Iran; Shahed University, Iran","Vaziri E., Information Science and Knowledge Studies Department, University of Tehran, Iran; Naghshineh N., Information Science and Knowledge Studies Department, University of Tehran, Iran; Chakoli A.N., Shahed University, Iran","Information and Communication Technologies (ICTs) have significant role in producing research data in different scientific fields. These data not only lead to generate fields based on research data, but also cause to produce a new paradigm or approach in research which is called Fourth Paradigm or Data Intensive Researches. These researches are based on data sharing by researchers, organizations and scientific societies. These data can be reused by other researchers. Data sharing is considered as a norm in some of scientific fields. The present article, that uses library research method, investigates the approaches, actions, policies and relevant regulations on data sharing in papers, regulations and stakeholder's websites in scientific and non-scientific databases. Besides national and international organizations, and scientific publishers in the world require data sharing as a condition of publications. Because of the universal agreements and accepting data sharing by many organizations and scientific fields and due to its benefits and applications, it seems that development of this issue can be an important step on science and research policy making and lead to more research impact in the country. © 2018 Iranian Research Institute for Scientific Information and Documentation. All rights reserved.","Data intensive researches; Data sharing; Research data; Research data management","","","","","","National Science Foundation, NSF","1. Repositories 2. Organization for Economic Co-operation and Development (OECD) 3. National Science Foundation (NSF) 4. National Institute of Health (NIH) 5. Budapest 6. Bethesda 7. Berlin 8. UK National Research Council (NRC) 9. European Commission 10. European Commission 11. Science 12. Public Library of Science (PLOS)","Arzberger P., Schroeder P., Beaulieu A., Bowker G., Casey K., Laaksonen L., Moorman D., Uhlir P., Wouters P., Promoting access to public research data for scientific, economic, and social development, Data Science Journal, 3, pp. 135-152, (2004); Berlin Declaration on Open Access to Knowledge in The Sciences and Humanities, (2004); Bishoff C., Johnston L., Approaches to data sharing: An analysis of nsf data management plans from a large research university, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Borgman C.L., Research Data: Who Will Share What, with Whom, When, and Why?, (2010); Shared Responsibilities in Sharing Research Data: Policies and Partnerships, (2008); Shared Responsibilities in Sharing Research Data: Policies and Partnerships, (2008); H2020 Programmed: Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2016); Ganley E., PLOS data policy: Catalyst for a better research process, College & Research Libraries News, 75, 6, pp. 305-308, (2014); Guedj D., Ramjoue C., European commission policy on open-access to scientific publications and research data in horizon 2020, Biomedical Data Journal, 1, 1, pp. 11-14, (2015); Hampton S.E., Strasser C.A., Tewksbury J.J., Gram W.K., Budden A.E., Batcheller A.L., Duke C.S., Porter J.H., Big data and the future of ecology, Frontiers in Ecology and The Environment, 11, 3, pp. 156-162, (2013); Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Kim Y., Institutional and Individual Influences on Scientists' Data Sharing Behaviors, (2013); Kim Y., Adler M., Social scientists' data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories, International Journal of Information Management, 35, 4, pp. 408-418, (2015); Kim Y., Stanton J.M., Institutional and individual influences on scientists' data sharing practices, Journal of Computational Science Education, 3, 1, pp. 47-56, (2012); Kim Y., Zhang P., Understanding data sharing behaviors of STEM researchers: The roles of attitudes, norms, and data repositories, Library & Information Science Research, 37, 3, pp. 189-200, (2015); Michener W.K., Ecological data sharing, Ecological Informatics, 29, 1, pp. 33-44, (2015); NIH Data Sharing Policy and Implementation Guidance, (2003); Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans, (2010); National science board task force on data policies, Digital Research Data Sharing and Management, (2011); Scientific Data: Data Policies; OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Pampel H., Dallmeier-Tiessen S., Open research data: From vision to practice, Opening Science, (2014); PLOS' New Data Policy: Public Access to Data, (2014); PLOS' New Data Policy: Public Access to Data; Concordat on Open Research Data, (2016); Stewardship of digital research data: A framework of principles and guidelines, Responsibilities of Universities and Colleges, Research Institutions and Research Funders, (2015); Rodriguez V., Access to data and material for research: Putting empirical evidence into perspective, New Genetics and Society, 28, 1, pp. 67-86, (2009); Spencer H., Thoughts on The Sharing of Data and Research Materials and The Role of Journal Policies, (2010); Swan A., Policy Guidelines for The Development and Promotion of Open Access, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Et al., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PloS One, 10, 8, (2015); Taylor P.L., Research sharing, ethics and public benefit, Nature Biotechnology, 25, 4, pp. 398-401, (2007); Van Den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data: Best Practices for Researchers, (2011); Whitlock M.C., Data archiving in ecology and evolution: Best practices, Trends in Ecology & Evolution, 26, 2, pp. 61-65, (2011)","E. Vaziri; Information Science and Knowledge Studies Department, University of Tehran, Iran; email: evaziri@ut.ac.ir","","Iranian Research Institute for Scientific Information and Documentation","","","","","","22518223","","","","Persian","Iranian J. Info. Pro. Manag.","Article","Final","","Scopus","2-s2.0-85049892925"
"Schirrwagen J.; Cimiano P.; Ayer V.; Pietsch C.; Wiljes C.; Vompras J.; Pieper D.","Schirrwagen, Jochen (55485372400); Cimiano, Philipp (15838793700); Ayer, Vidya (57193209865); Pietsch, Christian (56548514200); Wiljes, Cord (55532719500); Vompras, Johanna (23390824000); Pieper, Dirk (15077496300)","55485372400; 15838793700; 57193209865; 56548514200; 55532719500; 23390824000; 15077496300","Expanding the research data management service portfolio at bielefeld university according to the three-pillar principle towards data FAIRness","2019","Data Science Journal","18","1","6","","","","2","10.5334/dsj-2019-006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060883591&doi=10.5334%2fdsj-2019-006&partnerID=40&md5=8ad422fef625a3891fedc6f9ec5eefbe","Bielefeld University, Germany","Schirrwagen J., Bielefeld University, Germany; Cimiano P., Bielefeld University, Germany; Ayer V., Bielefeld University, Germany; Pietsch C., Bielefeld University, Germany; Wiljes C., Bielefeld University, Germany; Vompras J., Bielefeld University, Germany; Pieper D., Bielefeld University, Germany","Research Data Management at Bielefeld University is considered as a cross-cutting task among central facilities and research groups at the faculties. While initially started as project “Bielefeld Data Informium” lasting over seven years (2010–2015), it is now being expanded by setting up a Competence Center for Research Data. The evolution of the institutional RDM is based on the three-pillar principle: 1. Policies, 2. Technical infrastructure and 3. Support structures. The problem of data quality and the issues with reproducibility of research data is addressed in the project Conquaire. It is creating an infrastructure for the processing and versioning of research data which will finally allow publishing of research data in the institutional repository. Conquaire extends the existing RDM infrastructure in three ways: with a Collaborative Platform, Data Quality Checking, and Reproducible Research. © 2019 The Author(s).","Continuous integration; Reproducibility; Research data management","Information services; Bielefeld University; Collaborative platform; Continuous integrations; Institutional repositories; Reproducibilities; Reproducible research; Research data managements; Technical infrastructure; Information management","","","","","Massachusetts Department of Fish and Game, DFG","This research/work was supported by the German Research Foundation (DFG) and the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).","Ayer V., Pietsch C., Vompras J., Schirrwagen J., Wiljes C., Et al., Conquaire: Towards an architecture supporting continuous quality control to ensure reproducibility of research, D-Lib Magazine, CNRI, 23, 1-2, (2017); Ayer V., Pietsch C., Vompras J., Schirrwagen J., Wiljes C., Et al., Enabling Git Based Research Data Quality Control for Institutional Repositories, (2017); Bibliotheksverband D., Wissenschaftliche Bibliotheken 2025, (2018); Cimiano P., McCrae J., Jahn N., Pietsch C., Schirrwagen J., Et al., CONQUAIRE: Continuous Quality Control for Research Data to Ensure Reproducibility: An Institutional Approach, (2015); DFG Guidelines on the Handling of Research Data, (2015); Guidelines Collaborative Research Centres, (2017); Edler S., Meyermann A., Gebel T., Liebig S., Diewald M., The German Data Service Center for Business and Organizational Data (DSC-BO), Schmollers Jahrbuch, 132, 4, pp. 619-634, (2012); Directorate-General for Research & Innovation, (2016); Management of Research Data a Key Strategic Challenge for University Management, (2014); Kehm B.M., To be or not to be? The impacts of the excellence initiative on the German system of higher education, Institutionalization of World-Class University in Global Competition, pp. 81-97, (2013); Knorn B., Strategieprozess der Universitätsbibliothek Bielefeld und seine Folgen, Pro Libris, 22, 3, (2017); Meier Zu Verl C., Horstmann W., Studies on Subject-Specific Requirements for Open Access Infrastructure, (2011); Pasquetto I., Randles B., Borgman C., On the reuse of scientific data, Data Science Journal, 16, (2017); Peng R.D., Reproducible research and biostatistics, Biostatistics, 10, 3, pp. 405-408, (2009); Ram K., Git can facilitate greater reproducibility and increased transparency in science, Source Code for Biology and Medicine, 8, 1, (2013); Shaping digital transformation in science. “Digital Information” Initiative by the Alliance of Science Organizations in Germany, Mission Statement, pp. 2018-2022, (2017); Stodden V., Guo P., Ma Z., Toward reproducible computational research: An empirical analysis of data and code policy adoption by journals, Plos One, 8, 6, (2013); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Et al., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Vogl R., Rudolph D., Thoring A., Angenent H., Stieglitz S., Et al., How to Build a Cloud Storage Service for Half a Million Users in Higher Education: Challenges Met and Solutions Found, 2016 49Th Hawaii International Conference on System Sciences (HICSS), pp. 5328-5337, (2016); Wiljes C., Jahn N., Lier F., Paul-Stueve T., Vompras J., Et al., Towards Linked Research Data: An Institutional Approach, 994, pp. 27-38, (2013); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wilms K., Meske C., Stieglitz S., Rudolph D., Vogl R., How to Improve Research Data Management, Human Interface and the Management of Information: Applications and Services, 434–442, (2016)","J. Schirrwagen; Bielefeld University, Germany; email: jochen.schirrwagen@uni-bielefeld.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85060883591"
"Avuglah B.K.; Underwood P.G.","Avuglah, Bright K. (57208904773); Underwood, Peter G. (8587090400)","57208904773; 8587090400","Research Data Management (RDM) capabilities at the University of Ghana, Legon","2019","Library Philosophy and Practice","2019","","2258","","","","6","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066066748&partnerID=40&md5=52b10dfefaa0f4cef9146b7930947bea","University of Ghana, Legon, Ghana; University of Pretoria, South Africa","Avuglah B.K., University of Ghana, Legon, Ghana; Underwood P.G., University of Pretoria, South Africa","The purpose of this study was to assess Research Data Management (RDM) capabilities at the University of Ghana (UG). The study focused on four key capability elements: policy framework, technological infrastructure, skills and knowledge, and support services. It explored the extent to which RDM is embedded in research practices at UG and provides insight into the preparedness of UG to develop RDM. A qualitative case study method was adopted for the study and data was gathered using semi-structured interviews and document analysis. The instrument for the assessment was informed by the Collaborative Assessment for Research Data Infrastructure and Objectives (CARDIO) Matrix tool and respondents were drawn from the Library, IT department, Research Office and senior researchers. The results of the study show that RDM at UG is currently underdeveloped but with immense potential for growth. Though there is no formal RDM infrastructure in place, RDM is considered an essential research integrity issue. Capabilities were generally found to be limited, uncoordinated and not officially instituted. The study recommends that a clear and comprehensive policy framework for RDM should be developed to articulate RDM aspirations and express management's commitment. It also recommends that research support staff should be supported to build their capacity for RDM promotion and support. © 2019 Library Philosophy and Practice.","RDM capability; Research data management; University of Ghana","","","","","","","","Research data management in practice, Caulfield East, Victoria, Australia: Australian National Data Service, (2013); Creating a data management framework: ANDS Guide, Caulfield East, Victoria, Australia: Australian National Data Service, (2017); Avuglah B.K., Developing an implementation plan for Research Data Management (RDM) at the University of Ghana, (2016); Awre C., Baxter J., Clifford B., Colclough J., Cox A., Dods N., Drummond P., Fox Y., Et al., Research data management as a ""wicked problem, Library Review, 64, 4-5, pp. 356-371, (2015); Ball J., Research data management for libraries: getting started, Insights, 26, 3, pp. 256-260, (2013); Beitz A., Groenewegen D., Harboe-Ree C., Macmillan W., Searle S., Case study 3: Monash University, a strategic approach, Delivering research data management services: fundamentals of good practice, pp. 163-190, (2014); Brewerton A., Re-skilling for research: investigating the needs of researchers and how library staff can best support them, New Review of Academic Librarianship, 18, 1, pp. 96-110, (2012); Chigwada J., Chiparausha B., Kasiroori J., Research data management in research institutions in Zimbabwe, Data Science Journal, 16, 31, pp. 1-9, (2017); Chiware E.R., Becker D.A., Research data management services in southern Africa: a readiness survey of academic and research libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA journal, 43, 1, pp. 65-80, (2017); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library & Information Science Research, 38, 4, pp. 319-326, (2016); Creswell J.W., Research design: qualitative, quantitative, and mixed methods approaches, (2009); Creswell J.W., Qualitative inquiry and research design: choosing among five approaches, (2013); Crowston K., Qin J., A capability maturity model for scientific data management: evidence from the literature, Proceedings of the Association for Information Science and Technology, 48, 1, pp. 1-9, (2011); Davidson J., Jones S., Molloy L., Kejser U.B., Emerging good practice in managing research data and research information within UK universities, Procedia Computer Science, 33, pp. 215-222, (2014); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Hey T., Tansley S., Tolle K.M., The fourth paradigm: data-intensive scientific discovery, (2009); Higman R., Pinfield S., Research data management and openness: the role of data sharing in developing institutional policies and practices, Program: Electronic Library and Information Systems, 49, 4, pp. 364-381, (2015); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the University of Bristol: charting a course from project to service, Program: Electronic Library and Information Systems, 49, 4, pp. 475-493, (2015); Hodson S., Molloy L., Case study 5: Development of institutional RDM services by projects in the JISC managing research data programmes, Delivering Research Data Management Services, pp. 205-237, (2014); Jones S., Duke M., Rans J., Welgert V., Meeting the requirements of the EPSRC research data policy, JISC, (2015); Jones S., Pryor G., Whyte A., Developing research data management capability: the view from a national support service, In: Proceedings of the 9th International Conference on Preservation of Digital Objects (iPRES), pp. 142-149, (2012); Jones S., Pryor G., Whyte A., How to develop research data management services-a guide for HEIs, DCC how-to guides, (2013); Jones S., CARDIO: Collaborative Assessment of Research Data Infrastructure and Objectives, SlideShare, (2014); Kahn M., Higgs R., Davidson J., Jones S., Research data management in South Africa: how we shape up, Australian Academic & Research Libraries, 45, 4, pp. 296-308, (2014); Khokhar M., Schwamm H., Krug J., Albin-Clark A., Data Management Administration Online (DMAOnline), Procedia Computer Science, 106, pp. 291-298, (2017); Lynch C., Carleton D.E., Lecture: impact of digital scholarship on research libraries, Journal of Library Administration, 49, 3, pp. 227-244, (2009); Lyon L., Ball A., Duke M., Day M., Developing a community capability model framework for data-intensive research, In: Proceedings of the 9th International Conference on Preservation of Digital Objects (iPRES), pp. 9-16, (2012); Pickard A.J., Research methods in information, (2008); Procter R., Halfpenny P., Voss A., Research data management: opportunities and challenges for HEIs, Managing Research Data, pp. 135-150, (2012); Pryor G., A maturing process of engagement: raising data capabilities in UK higher education, International Journal of Digital Curation, 8, 2, pp. 181-193, (2013); Pryor G., A patchwork of change, Delivering research data management services: fundamentals of good practice, pp. 1-19, (2014); Rans J., Jones S., RDM strategy: moving from plans to action, (2013); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, International Journal of Digital Curation, 6, 2, pp. 232-244, (2011); Sallans A., Lake S., Data management assessment and planning tools, Research data management: practical strategies for information professionals. Series: Charleston Insights in Library, Information, and Archival Sciences, pp. 87-108, (2014); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program: electronic library and information systems, 49, 4, pp. 440-460, (2015); Ssebulime J.B., van Deventer M., Pienaar H., The role academic libraries could play in developing research data management services: a case of Makerere University (Preprint), (2018); Takeda K., Brown M., Coles S., Carr L., Earl G., Frey J., Hancock P., White W., Et al., Data management for all: the institutional data management blueprint project, Presentation at the 6th International Digital Curation Conference, (2010); University of Ghana Strategic Plan, 2014-2024, (2014); van Deventer M., Pienaar H., Research data management in a developing country: a personal journey, International Journal of Digital Curation, 10, 2, pp. 33-47, (2015); Wang M., Academic Library, e-Science/e-Research, and data services in a broader context, In: Proceedings of the ACRL 2013 Conference in Indianapolis, (2013); Whyte A., Emerging infrastructure and services for research data management and curation in the UK and Europe, Managing Research Data, pp. 205-234, (2012); Whyte A., Allard S., How to discover requirements for research data management services, (2014); Whyte A., Molloy L., Beagrie N., Houghton J., What to measure?. Towards metrics for research data management, Research data management: practical strategies for information professionals, pp. 275-302, (2014); Wilson J.A., Martinez-Uribe L., Fraser M.A., Jeffreys P., An institutional approach to developing research data management infrastructure, International Journal of Digital Curation, 6, 2, pp. 274-287, (2011); Wong G.K., Exploring research data hosting at the HKUST institutional repository, Serials Review, 35, 3, pp. 125-132, (2009); Yin R.K., Case study research: Design and methods, 5, (2009)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85066066748"
"Rupp K.K.","Rupp, Kortney K. (57411685200)","57411685200","There's an app for that: Electronic research notebooks: A piece of the research data management puzzle","2018","Issues in Science and Technology Librarianship","2018","90","","","","","0","10.5062/F4TX3CMM","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055737219&doi=10.5062%2fF4TX3CMM&partnerID=40&md5=d0705c9e9fecc823e254f1bf65ac49b8","Chemical Information Librarian and LBNL Liaison, University of California Berkeley, Berkeley, CA, United States","Rupp K.K., Chemical Information Librarian and LBNL Liaison, University of California Berkeley, Berkeley, CA, United States","[No abstract available]","","","","","","","","","Bird C.L., Frey J.G., Chemical information matters: An e-research perspective on information and data sharing in the chemical sciences, Chemical Society Reviews, 42, 16, pp. 6754-6776, (2013); Kihlen M., Waligorski M., Electronic lab notebooks - a crossroads is passed, Drug Discovery Today, 8, 22, pp. 1007-1009, (2003); Nesdill D., Introducing Labarchives to the University of Utah, (2018); Rubacha M., Rattan A.K., Hosselet S.C., A review of electronic laboratory notebooks available in the market today, Journal of the Association for Laboratory Automation, 16, 1, pp. 90-98, (2011)","K.K. Rupp; Chemical Information Librarian and LBNL Liaison, University of California Berkeley, Berkeley, United States; email: kortneyrupp@berkeley.edu","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Note","Final","","Scopus","2-s2.0-85055737219"
"Sharif N.; Ritter W.; Davidson R.L.; Edmunds S.C.","Sharif, Naubahar (14822416300); Ritter, Waltraut (55505736700); Davidson, Robert L. (57209848928); Edmunds, Scott C. (36719422500)","14822416300; 55505736700; 57209848928; 36719422500","An open science ‘state of the art’ for Hong Kong: Making open research data available to support Hong Kong innovation policy","2018","Journal of Contemporary Eastern Asia","17","2","","200","221","21","1","10.17477/jcea.2018.17.2.200","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068914007&doi=10.17477%2fjcea.2018.17.2.200&partnerID=40&md5=f595e99decdb59932e19b762c100d291","Division of Social Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China; Knowledge Dialogues, Hong Kong, China; Open Data Hong Kong, China; TwoCloaks Ltd, Glasgow, United Kingdom; Open Data Hong Kong, BGI HK Limited, 16 Dai Fu Street, Tai Po Industrial Estate, Hong Kong, China","Sharif N., Division of Social Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China; Ritter W., Knowledge Dialogues, Hong Kong, China; Davidson R.L., Open Data Hong Kong, China, TwoCloaks Ltd, Glasgow, United Kingdom; Edmunds S.C., Open Data Hong Kong, BGI HK Limited, 16 Dai Fu Street, Tai Po Industrial Estate, Hong Kong, China","Open Science is an umbrella term that involves various movements aiming to remove the barriers to sharing any kind of output, resources, methods or tools at any stage of the research process. While the study of open science is relatively advanced in Western countries, we know of no scholarship that attempts to understand open science in Hong Kong. This paper provides a broad-based background on the major research data management organisations, policies and institutions with the intention of laying a foundation for more rigorous future research that quantifies the benefits of open access and open data policies. We explore the status and prospects for open science (open access and open data) in the context of Hong Kong and how open science can contribute to innovation in Hong Kong. Surveying Hong Kong’s policies and players, we identify both lost research potential and provide positive examples of Hong Kong’s contribution to scientific research. Finally, we offer suggestions regarding what changes can be made to address the gaps we identify. © 2018 World Association for Triple helix and Future strategy studies. All rights reserved.","Hong Kong; Innovation Policy; Open Access; Open Data; Open Science; Research Data Management","","","","","","BGI; China National Genebank; Departments of Biotechnology and Science & Technology; Wellcome Trust DBT; National Science Foundation, NSF; National Institutes of Health, NIH; Bill and Melinda Gates Foundation, BMGF; Wellcome Trust, WT; Fourth Framework Programme, FP4; Department of Biotechnology, Ministry of Science and Technology, India, DBT; National Natural Science Foundation of China, NSFC; Chinese Academy of Sciences, CAS; The Wellcome Trust DBT India Alliance, WTDBT India Alliance","Funding text 1: In 2014 the National Natural Science Foundation of China (NSFC) and the Chinese Academy of Sciences (CAS) announced that researchers they support should deposit their papers into online repositories and make them publicly accessible within 12 months of publication (Van Noorden, 2014). Mainland China has also started to make progress in the area of data policies, with the National Science and Technology Infrastructure Program requiring national research projects they fund in areas including meteorology (CMA, 2017), earth science (Geodata.cn, 2017) and agriculture and forestry (China Forest Science Data Centre, 2003) to submit and share data. The Chinese Ministry of Science and Technology (MOST) recently announced new policies and the formation of a new science data centre that will be ‘promoting open access to, and sharing of, science data’(MOST, 2018).; Funding text 2: Another government agency in Hong Kong that can advocate for the sharing of research data is the Innovation and Technology Commission (ITC). The ITC administers the Innovation Technology Fund (ITF), which provides financial support to projects that contribute to innovation and technology upgrading in Hong Kong, including both R&D and non-R&D projects. The ITF guidelines require fund recipients to make R&D outcomes available to industry and the public, but only ‘aggregated summary information’ of funded projects, including project abstracts, deliverables, implementation organisations and contact persons, are required to be published on the website dedicated to the ITF (Innovation and Technology Commission, 2017).; Funding text 3: In India, the Departments of Biotechnology and Science & Technology have jointly adopted an open access policy for publicly funded research (DBT India, 2014) and the Wellcome Trust/DBT India Alliance (the ‘India Alliance’) supports ‘unrestricted access to the published output of research’ (Wellcome Trust DBT India Alliance, 2016).; Funding text 4: Funding agencies such as the NIH and the NSF in the US and the European Union Framework Programmes, following the lead of independent funders such as the Wellcome Trust and the Gates Foundation, have been leading the way in mandating open access to published research outputs and have been developing parallel policies specifically for data access.","(2015); AFCD Hong Kong Biodiversity Online, (2018); Bobrow M., Funders must encourage scientists to share, Nature, 522, 7555, (2015); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Census and Statistics Department Homepage, (2017); China Academy of Forestry Science and Technology Office of Science and Technology Letter, (2003); China Meteorological Administration Data Centre Policies, (2017); Request for Information on Electronic Search Services Charge Statistics - A FOI Request to Companies Registry, (2016); The Global Innovation Index 2016: Winning with Global Innovation, Ithaca, Fontainebleau, and, (2016); DBT and DST Open Access Policy Policy on Open Access to DBT and DST Funded Research, (2014); Earthwatch Institute Hong Kong, (2016); Accessinfo.Hk FOI Request: EB Research Funding and Policies Information - A FOI Request to Environment Bureau, (2017); ERAC Opinion on Open Research Data, (2016); Fang M., Yao X., Chan C.-K., Lau N.T., Lau A.P.S., Street level air quality data collected by the mobile real-time air monitoring platform, DataSpace@HKUST, (2009); FHD Research Funding and Policies Information - A FOI Request to Food and Health Bureau, (2017); National Earth System Science Data Sharing Infrastructure - About Us, (2017); Gibb M., CityU Scholars: A Hub of Research Excellence, (2017); Harnad S., Australia is not maximising the return on its research investment, Proceedings National Scholarly Communications Forum, (2005); HKBWS Ringed BFS Record System — Ringed BFS Sightings, (2011); Hong Kong Birdwatching Society - Hong Kong Bird Report, (2014); Hong Kong Code on Access to Information, (2016); Data Studio@SP, (2017); Research Data and Records Management, (2016); Holdren J.P., Increasing Access to the Results of Federally Funded Scientific Research, (2013); Guide to Filling in the Application Forms Innovation and Technology Commission Innovation and Technology Fund Innovation and Technology Support Programme Guide to Filling in The Application Forms, (2017); Kao E., Boosting R&D in Hong Kong requires more than just funds, scientists say, South China Morning Post, (2017); Accessinfo.Hk FOI Request: Information on Costs, Charges and Revenue for Map Data, (2015); Accessinfo.Hk FOI Request: Data Access Charges for Land Registry Data - A FOI Request to Land Registry, (2018); Leonelli S., Bezuidenhout L., Impact of Social Sciences – The Politics of Data: The Rising Prominence of A Data-Centric Approach to Scientific Research, (2015); Leyden A., Hong Kong Air Pollution on the App Store, (2012); Liang H., Fu K., Data from: Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science, (2015); Opinions on Further Strengthening The Construction of Scientific Research Integrity -, (2018); Making Open Science A Reality, (2015); STEM Australia’s Future, (2014); Choosing CKAN for Research Data Management, (2012); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Community Weather Information Network (CoWIN), (2018); Research Data Oxford» University of Oxford Policy on the Management of Research Data and Records, (2013); RGC HK Annual Report 2011, (2011); Agency for Science, Technology & Research (A*STAR) ROARMAP Policy Page, (2014); National Chung Hsing University ROARmaps Policy Page, (2014); Science as An Open Enterprise, (2012); Safecast API, (2016); Shearer K., Repanas K., Yamaji K., Asian Open Access: Regional Survey, (2017); Stringer R., Predicting ‘Red Tide’ Risks: Citizen Science Leader Data Published in Hong Kong Scientific Paper | Earthwatch Freshwater Watch, (2014); Taiwan Forestry Department Data Policy, (2016); Taiwan Ocean Data Bank Repository Data Policy V7, (2014); Taiwan MOST, (2018); Making Federal Research Results Available to All | Whitehouse.Gov, (2017); UNESCO Science Report: Towards 2030. Regional Overview: Asia and the Pacific, (2015); Annual Review of Common Data Collection Format (CDCF) - A FOI Request to UGC Secretariat, (2015); Accessinfo.Hk FOI Request: Request for Statistics on Data Charges and Revenue, (2016); Van Noorden R., Chinese agencies announce open-access policies, Nature, (2014); Wellcome DBT - Award Policies, (2016); (2014)","","","World Association for Triple helix and Future strategy studies","","","","","","23839449","","","","English","J. Contemp. East. Asia","Article","Final","","Scopus","2-s2.0-85068914007"
"Christopher J.; McCaffrey D.; Culich A.; Neeser A.; Dombrowski Q.; Wiedlea A.","Christopher, Jason (57195153951); McCaffrey, Debra (57203371839); Culich, Aaron (57195153581); Neeser, Amy (57203371995); Dombrowski, Quinn (55511282700); Wiedlea, Andrew (55900223800)","57195153951; 57203371839; 57195153581; 57203371995; 55511282700; 55900223800","Research facilitation on a budget","2018","ACM International Conference Proceeding Series","","","a72","","","","0","10.1145/3219104.3219137","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051418191&doi=10.1145%2f3219104.3219137&partnerID=40&md5=fcf4cec1543177e7f13a90eb23b897ce","UC Berkeley, United States; University of Michigan, United States; Lawrence Berkeley Lab, United States","Christopher J., UC Berkeley, United States; McCaffrey D., University of Michigan, United States; Culich A., UC Berkeley, United States; Neeser A., UC Berkeley, United States; Dombrowski Q., UC Berkeley, United States; Wiedlea A., UC Berkeley, United States, Lawrence Berkeley Lab, United States","Achieving broad uptake of research computing services is a tremendous challenge when funding for staff positions is constrained. Outreach and ongoing engagement with researchers is both essential and time-consuming, leading to a tension between supporting day-to-day operations and building the kinds of partnerships that ensure ongoing support for the program. Since 2015, Berkeley Research Computing (BRC) has been hiring primarily graduate students into a part-time “domain consultant” role (influenced by ACI-REF job descriptions and Campus Champion activities) that addresses the program's staffing needs in an affordable way, while providing those graduate students with the technical training and professional work experience required for professional research facilitator positions. The domain consultant program has evolved from an hourly student position into a codified set of practices informed by IT service management, addressing needs including: in-person consulting, tier 2 triage of HPC troubleshooting tickets, support for cloud computing and compute in virtualized analytics environments, and user training. In addition, consulting engagements are reviewed and discussed regularly, both in team meetings and in one-on-one meetings with the service manager, to provide opportunities for consultants to hone their skills. This paper will also highlight the value of programs such as BRC's for addressing gaps in graduate education practices that can hinder PhD recipients' success when applying for research facilitator positions. It will also illustrate the value of thinking broadly about partnerships when developing a consulting program, by describing the program's recent expansion into research data management, and at the Lawrence Berkeley Lab. © 2018 Association for Computing Machinery.","Research facilitation","Budget control; Employment; Human resource management; Information management; Job analysis; Personnel training; Day-to-day operations; Graduate education; Graduate students; IT service management; Research computing; Research data managements; Service managers; Technical training; Students","","","","","","","Watts D., Fassler N., Now Rolling Out in Beta: Track Anything in Asana, with Custom Fields, (2016); Towns J., Cockerill T., Dahan M., Foster I., Gaither K., Grimshaw A., Hazlewood V., Lathrop S., Lifka D., Peterson G.D., Roskies R., Ray Scott J., Wilkins-Diehr N., XSEDE: Accelerating scientific discovery, Computing in Science & Engineering, 16, 5, pp. 62-74, (2014); Anisi D.A., Optimal Motion Control of A Ground Vehicle, (2003); Bottum J., Dougherty M., Jacobs G., Yockel S., Michael L., Cuff J., Wilson P., Advanced Cyberinfrastructure - Research and Educational Facilitation: Campus-Based Computational Research Support; Michael L., Maas B., Research computing facilitators: The missing human link in needs-based research cyberinfrastructure, ECAR Research Bulletin, (2016)","","","Association for Computing Machinery","","2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018","22 July 2017 through 26 July 2017","Pittsburgh","138224","","978-145036446-1","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85051418191"
"Trippel T.; Zinn C.","Trippel, Thorsten (55237014700); Zinn, Claus (57196765933)","55237014700; 57196765933","Lessons learned: On the challenges of migrating a research data repository from a research institution to a university library","2019","LREC 2018 - 11th International Conference on Language Resources and Evaluation","","","","146","150","4","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059877706&partnerID=40&md5=39e87f24773fabe88093c6699330dfe8","University of Tübingen, Wilhelmstrasse 19, Tübingen, 72074, Germany","Trippel T., University of Tübingen, Wilhelmstrasse 19, Tübingen, 72074, Germany; Zinn C., University of Tübingen, Wilhelmstrasse 19, Tübingen, 72074, Germany","The transfer of research data management from one institution to another infrastructural partner is all but trivial, but can be required, for instance, when an institution faces reorganisation or closure. In a case study, we describe the migration of all research data, identify the challenges we encountered, and discuss how we addressed them. It shows that the moving of research data management to another institution is a feasible, but potentially costly enterprise. Being able to demonstrate the feasibility of research data migration supports the stance of data archives that users can expect high levels of trust and reliability when it comes to data safety and sustainability. © LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.","Data Migration; Data Repositories; Research Data Management","Data archives; Data migration; Data repositories; Data safeties; Reorganisation; Research data managements; Research institutions; University libraries; Information management","","","","","Deutsche Forschungsgemeinschaft, DFG, (75650358, 88614379, SFB 833); Bundesministerium für Bildung und Forschung, BMBF","This work has been supported by the German Research Foundation (DFG reference no. 88614379), and the SFB 833 data management project INF (DFG reference no. 75650358). The data centre cooperates closely with the CLARIN-D centre in Tübingen which is funded by the German Federal Ministry of Education and Research (BMBF). We would like to thank the anonymous reviewers for their valuable feedback.","Dima E., Henrich V., Hinrichs E., Hinrichs M., Hoppermann C., Trippel T., Zastrow T., Zinn C., A repository for the sustainable management of research data, Proceedings of the 8th. International Conference on Language Resources and Evaluation (LREC'12)., (2012); Dima E., Hoppermann C., Hinrichs E., Trippel T., Zinn C., A metadata editor to support the description of linguistic resources, Proceedings of the 8th. International Conference on Language Resources and Evaluation (LREC'12)., (2012); Language Resource Management - Persistent Identification and Sustainable Access (PISA), (2011); Language Resource Management - Component MetaData Infrastructure - Part 1: the Component Metadata Model, (2015); Lyse G.I., Meurer P., Smedt K.D., ComeDI: A component metadata editor, Selected Papers from the CLARIN 2014 Conference, 116, 8, pp. 82-98, (2015); Trippel T., Zinn C., Enhancing the quality of metadata by using authority control, 5th. Workshop on Linked Data in Linguistic (LDL-2016) at LREC'16, (2016); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Zinn C., Trippel T., Kaminski S., Dima E., Crosswalking from CMDI to Dublin core and Marc 21, Proceedings of the 10th. International Conference on Language Resources and Evaluation (LREC'16)., (2016)","","Isahara H.; Maegaard B.; Piperidis S.; Cieri C.; Declerck T.; Hasida K.; Mazo H.; Choukri K.; Goggi S.; Mariani J.; Moreno A.; Calzolari N.; Odijk J.; Tokunaga T.","European Language Resources Association (ELRA)","","11th International Conference on Language Resources and Evaluation, LREC 2018","7 May 2018 through 12 May 2018","Miyazaki","143414","","979-109554600-9","","","English","LREC - Int. Conf. Lang. Resour. Evaluation","Conference paper","Final","","Scopus","2-s2.0-85059877706"
"Wilcox D.","Wilcox, David (57208480729)","57208480729","Supporting fair data principles with fedora","2018","LIBER Quarterly","28","1","","","","","1","10.18352/lq.10247","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064906319&doi=10.18352%2flq.10247&partnerID=40&md5=021566a2a5eaf5348b59f02158ca8849","DuraSpace, Beaverton, OR, Canada","Wilcox D., DuraSpace, Beaverton, OR, Canada","Making data findable, accessible, interoperable, and re-usable is an important but challenging goal. From an infrastructure perspective, repository technologies play a key role in supporting FAIR data principles. Fedora is a flexible, extensible, open source repository platform for managing, preserving, and providing access to digital content. Fedora is used in a wide variety of institutions including libraries, museums, archives, and government organizations. Fedora provides native linked data capabilities and a modular architecture based on well-documented APIs and ease of integration with existing applications. As both a project and a community, Fedora has been increasingly focused on research data management, making it well-suited to supporting FAIR data principles as a repository platform. Fedora provides strong support for persistent identifiers, both by minting HTTP URIs for each resource and by allowing any number of additional identifiers to be associated with resources as RDF properties. Fedora also supports rich metadata in any schema that can be indexed and disseminated using a variety of protocols and services. As a linked data server, Fedora allows resources to be semantically linked both within the repository and on the broader web. Along with these and other features supporting research data management, the Fedora community has been actively participating in related initiatives, most notably the Research Data Alliance. Fedora representatives participate in a number of interest and working groups focused on requirements and interoperability for research data repository platforms. This participation allows the Fedora project to both influence and be influenced by an international group of Research Data Alliance stakeholders. This paper will describe how Fedora supports FAIR data principles, both in terms of relevant features and community participation in related initiatives. © 2018, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Fair data; Fedora; Linked data; Open source; Repository","","","","","","Institutions","Fedora is stewarded by DuraSpace,4 a not-for-profit organization funded primarily through membership. Institutions join DuraSpace and direct annual funding to support the project(s) of their choice. In 2017, 74 DuraSpace member institutions supported Fedora with $562,300 in funding (Fedora Community, 2018). This funding pays for 2 full time equivalent (FTE) staff members, as well as travel for conferences, workshops, and user groups, marketing and communication, and other priorities as determined by the project governance group. Fedora is designed, built, and maintained by the community; DuraSpace provides support but the majority of the development is done by members of the community.","Fedora API Extension Framework, (2018); Next Generation Repositories, (2017); Fedora and Digital Preservation, (2018); (2018); The FAIR Data Principles; Library of Congress Subject Headings, (2011); Preservation Metadata Maintenance Activity, (2018); Research Data Repository Interoperability WG Final Recommendations, (2018); Retrieved from Fedora Website, (2018); Retrieved from W3C Wiki, (2018); Wilcox D., Fedora Annual Report, (2018); Woods A., Restful HTTP API, (2018)","D. Wilcox; DuraSpace, Beaverton, Canada; email: dwilcox@duraspace.org","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85064906319"
"Wiljes C.; Cimiano P.","Wiljes, Cord (55532719500); Cimiano, Philipp (15838793700)","55532719500; 15838793700","Teaching research data management for students","2019","Data Science Journal","18","1","38","1","9","8","9","10.5334/dsj-2019-038","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073281395&doi=10.5334%2fdsj-2019-038&partnerID=40&md5=aeea99bbfdc4735371150d93bdf30f86","Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Germany","Wiljes C., Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Germany; Cimiano P., Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Germany","Sound skills in managing research data are a fundamental requirement in any discipline of research. Therefore, research data management should be included in academic education of students as early as possible. We have been teaching an interdisciplinary full semester’s course on research data management for six years. We report how we established the course. We describe our competency-based approach to teaching research data management and the curriculum of topics that we consider essential. We evaluate our approach by a survey done among the participants of the course and summarize the lessons we learned in teaching the course. © 2019 The Author(s).","RDM; Research data management; Teaching","Information management; Surveys; Academic education; Research data; Research data managements; Teaching researches; Teaching","","","","","CITEC; Cluster of Excellence Cognitive Interaction Technology; Cluster of Excellence Cognitive Interaction Technology 'CITEC; Cluster of Excellence Cognitive Interaction Technology ‘CITEC, (EXC 277); Deutsche Forschungsgemeinschaft, DFG; Universität Bielefeld","Funding text 1: This work was supported by the German Research Foundation (DFG) and the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).; Funding text 2: This work was supported by the German Research Foundation (DFG) and the Cluster of Excellence Cognitive Interaction Technology 'CITEC' (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).; Funding text 3: Bielefeld University1 is a rather young university in Germany and has about 24,000 students, 2,700 employees and 260 professors. The Cluster of Excellence Cognitive Interaction Technology (CITEC)2 is an institute funded by the German Excellence Initiative. In 2012 CITEC released the CITEC Open Science Manifesto3 to express its strong commitment to the ideals of Open Science. Regarding teaching, the Open Science Manifesto stated:","Baker M., Penny D., Is there a reproducibility crisis?, Nature, 533, 7604, pp. 452-454, (2016); Biernacka K., Dolzycka D., Helbig K., Buchholz P., Train-The-Trainer Konzept Zum Thema Forschungsdatenmanagement, (2018); Briney K., Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success; Corti L., Van Den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice; Enke L., Leitfaden Zum Forschungsdaten-Management: Handreichungen Aus Dem Wissgrid-Projekt, (2013); Muilenburg J., Lebow M., Rich J., Lessons Learned From a Research Data Management Pilot Course at an Academic Library, Journal of Escience Librarianship, 3, 1, pp. 67-73, (2014); Nielsen M., Reinventing Discovery: The New Era of Networked Science; Pryor G., Managing Research Data; Ray J.M., Research Data Management: Practical Strategies for Information Professionals; Wiljes C., Research Data Management Course: Survey Data, (2018); Wiljes C., Research Data Management Course: Survey Results, (2018)","C. Wiljes; Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Germany; email: cwiljes@cit-ec.uni-bielefeld.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85073281395"
"Bangani S.; Moyo M.","Bangani, Siviwe (57196458975); Moyo, Mathew (57040910700)","57196458975; 57040910700","Data sharing practices among researchers at south african universities","2019","Data Science Journal","18","1","28","","","","8","10.5334/dsj-2019-028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070943412&doi=10.5334%2fdsj-2019-028&partnerID=40&md5=0161fccb835e67499ad0aba9547fb525","North-West University, South Africa","Bangani S., North-West University, South Africa; Moyo M., North-West University, South Africa","Research data management practices have gained momentum the world over. This is due to increased demands by governments and other funding agencies to have research data archived and shared as widely as possible. This paper sought to establish the data sharing practices of researchers in South Africa. The study further sought to establish the level of collaboration among researchers in sharing research data at the university level. The outcomes of the survey will help the researchers to develop appropriate data literacy awareness programmes meant to stimulate growth in data sharing practices for the benefit of research, not only in South Africa, but the world at large. A survey research method was used to gather data from willing public universities in South Africa. A similar study was conducted in other countries such as the United Kingdom, France and Turkey but the Researchers believe that circumstances in the developed world may differ with the South African research environment, hence the current study. The major finding of this study was that most researchers preferred to use data produced by others but less keen on sharing their own data. This study is the first of its kind in South Africa which investigates data sharing practices of researchers from multi-disciplinary fields at the university level and will contribute immensely to the growing body of literature in the area of research data management. © 2019 The Author(s).","Data management; Data preservation; Data reuse; Data services; Ethics; Legislation","Laws and legislation; Surveys; Awareness programmes; Data preservations; Data reuse; Data services; Data-sharing practices; Ethics; Research data managements; Research environment; Information management","","","","","National Institutes of Health, NIH, (P01GM091743)","","Bezuidenhout L., Chakauya E., Hidden concerns of sharing research data by low/middle-income country scientists, Global Bioethics, 29, 1, pp. 39-54, (2018); Borgman C.L., Big data, little data, no data, (2015); Buys C.M., Shaw P.L., Data Management Practices Across an Institution: Survey and Report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Chen X., Wu M., Survey on the needs for chemistry research data management and sharing, The Journal of Academic Librarianship, 43, 4, pp. 346-353, (2017); Chiware E., Becker D., Research Data Management Services in Southern Africa: A Readiness Survey of Academic and Research Libraries, African Journal of Library Archives and Information Science, 28, 1, pp. 1-16, (2018); Cleary M., Jackson D., Walter G., Editorial. Research data ownership and dissemination: Is it too simple to suggest that ‘possession is nine-tenths of the law’?, Journal Of Clinical Nursing, 22, 15, pp. 2087-2089, (2013); Dietrich S., Van der Ham J., Pras A., Van Rijswijk-Deij R., Shou D., Sperotto A., Van Wynsberghe A., Zuck L.D., Ethics in data sharing: Developing a model for best practice, IEEE Security and Privacy Workshops, (2014); Elsayed A.M., Saleh E.I., Research data management and sharing among researchers in Arab universities: An exploratory study, IFLA Journal, (2018); Elsevier, Sharing research data, (2018); (2018); Key Data on Education in Europe 2012 - Final Report - European, (2012); Howie C.T., Neethling J., Louw A., Privacy and data protection, (2006); Research data assessment support Findings of the 2016 data assessment framework (DAF) surveys, (2016); Koopman M.M., De Jager K., Archiving South African digital research data: How ready are we?, South African Journal of Science, 112, 7-8, pp. 1-7, (2016); Kvale L.H., Data Sharing in the life sciences: A study of researchers at the Norwegian University of Life Sciences, (2012); Maluleka J.R., Onyancha O.B., Research collaboration among Library and Information Science schools in South Africa (1991-2012): An informetrics study, Mousaion, 34, 3, pp. 36-59, (2016); Martone M.E., Garcia-Castro A., Van den Bos G.R., Data sharing in psychology, American Psychologist, 73, 2, pp. 111-125, (2018); Michener W.K., Ecological data sharing, Ecological Informatics, 29, 1, pp. 33-44, (2015); Statement on Open Access to Research Publications from the National Research Foundation (NRF)-Funded Research, (2015); (2018); Onyancha O.B., Open research data in Sub-Saharan Africa: A bibliometric study using the Data Citation Index, Publishing Research Quarterly, 32, 3, pp. 227-246, (2016); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Parker M., Exploring the Ethical Imperative for Data Sharing. In: O’Connell, ME and Plewes, TJ, Sharing Research Data to Improve Public Health in Africa: A Workshop Summary, (2015); Patterton L., Bothma T.J.D., Van Deventer M.J., From planning to practice: An action plan for the implementation of research data management services in resource-constrained institutions, South African Journal of Libraries and Information Science, 84, 2, pp. 14-26, (2018); Renaut S., Budden A.E., Gravel D., Poisot D., Peres-Neto P., Data management, archiving, and sharing for biologists and the role of research institutions in the technology-oriented age, BioScience, 68, 6, pp. 400-411, (2018); Legal interoperability of research data: Principles and implementation guidelines, RDA-CODATA Legal Interoperability Interest Group, (2016); Roos A., Personal data protection in New Zealand: Lesson for South Africa?, Potchefstroom Electronic Law Journal, 11, 4, pp. 61-109, (2008); Ross M.W., Iguchi M.Y., Panicker S., Ethical aspects of data sharing and research participant protections, American Psychologist, 73, 2, pp. 138-145, (2018); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the University of Lille (France), LIBREAS, 29, pp. 97-112, (2016); Seto B., Luo J., Biomedical data sharing, security and standards, Data Science Journal, 6, 17, pp. 54-57, (2007); Sieber J.E., Ethics of sharing scientific and technological data: A heuristic for coping with complexity & uncertainty, Data Science Journal, 4, 22, pp. 165-170, (2005); Plant Breeders’ Act, 1976, (1976); South African Copyright Act Pretoria: Government Communication and Information System, (1978); National Research Foundation ACT 23 OF 1998, (1998); The Promotion of Access to Information Act, 2000 (PAIA), (2000); Electronic Communications and Transactions Act, 2002, (2002); Trade Marks Act, No. 194 of 1993, (2008); The Protection of Personal Information Act, (2013); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data Sharing by Scientists: Practices and Perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide, PLoS ONE, 10, 8, (2015); Code of academic and research ethics, (2007); Research data management policy, (2017); Van den Eynden V., Knight G., Vlad A., Radler B., Tenopir C., Leon D., Manista F., Whitworth J., Corti L., Towards open research: Practices, experiences, barriers and opportunities, (2016); Wiley, Research Data Sharing Insights, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., Hoen P.A.C.T., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., Van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Wouters P., Haak W., (2017)","S. Bangani; North-West University, South Africa; email: Siviwe.Bangani@gmail.com","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85070943412"
"Avdis A.; Candy A.S.; Hill J.; Kramer S.C.; Piggott M.D.","Avdis, Alexandros (26537012900); Candy, Adam S. (23975666700); Hill, Jon (7404771349); Kramer, Stephan C. (36473510900); Piggott, Matthew D. (7102871137)","26537012900; 23975666700; 7404771349; 36473510900; 7102871137","Efficient unstructured mesh generation for marine renewable energy applications","2018","Renewable Energy","116","","","842","856","14","39","10.1016/j.renene.2017.09.058","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031726729&doi=10.1016%2fj.renene.2017.09.058&partnerID=40&md5=d9109d03ef9fa876eeccd9d4262b709a","Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, United Kingdom; Environmental Fluid Mechanics, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Netherlands; Environment Department, University of York, United Kingdom","Avdis A., Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, United Kingdom; Candy A.S., Environmental Fluid Mechanics, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Netherlands; Hill J., Environment Department, University of York, United Kingdom; Kramer S.C., Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, United Kingdom; Piggott M.D., Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, United Kingdom","Renewable energy is the cornerstone of preventing dangerous climate change whilst maintaining a robust energy supply. Tidal energy will arguably play a critical role in the renewable energy portfolio as it is both predictable and reliable, and can be put in place across the globe. However, installation may impact the local and regional ecology via changes in tidal dynamics, sediment transport pathways or bathymetric changes. In order to mitigate these effects, tidal energy devices need to be modelled, to predict hydrodynamic changes. Robust mesh generation is a fundamental component required for developing simulations with high accuracy. However, mesh generation for coastal domains can be an elaborate procedure. Here, we describe an approach combining mesh generators with Geographical Information Systems. We demonstrate robustness and efficiency by constructing a mesh with which to examine the potential environmental impact of a tidal turbine farm installation in the Orkney Islands. The mesh is then used with two well-validated ocean models, to compare their flow predictions with and without a turbine array. The results demonstrate that it is possible to create an easy-to-use tool to generate high-quality meshes for combined coastal engineering, here tidal turbines, and coastal ocean simulations. © 2017 The Authors","Geographical Information Systems; Mesh generation; Pentland Firth; Renewable energy generation; Research Data Management; Tidal turbine arrays","Orkney; Scotland; United Kingdom; Climate change; Coastal engineering; Environmental impact; Geographic information systems; Information management; Information systems; Information use; Marine applications; Mesh generation; Sediment transport; Turbines; Dangerous climate changes; Marine renewable energy; Pentland Firth; Renewable energy generation; Research data managements; Sediment transport pathways; Tidal turbines; Unstructured mesh generation; array; climate change; data management; energy efficiency; environmental impact; GIS; model validation; power generation; renewable resource; research work; sediment transport; tidal power; turbine; Tidal power","","","","","Engineering and Physical Sciences Research Council, EPSRC, (EP/J010065/1, EP/K503733/1, EP/L000407/1, EP/M011054/1)","This paper was supported by an EPSRC Impact Acceleration Award (EP/K503733/1) and EPSRC grants EP/J010065/1, EP/M011054/1, EP/L000407/1. The authors would like to acknowledge the support of the Imperial College High Performance Computing Service .  ","Obama B., The irreversible momentum of clean energy, Science, 355, 6321, pp. 126-129, (2017); I. Dincer, Renewable energy and sustainable development: a crucial review, Renew. Sustain. Energy Rev., 4, 2, pp. 157-175, (2000); Johansson B., Security aspects of future renewable energy systems–a short overview, Energy, 61, pp. 598-605, (2013); Luo L., Wang J., Schwab D., Vanderploeg H., Leshkevich G., Bai X., Hu H., Wang D., Simulating the 1998 spring bloom in Lake Michigan using a coupled physical-biological model, J. Geophys. Res. C Oceans, 117, C10, (2012); Hunter N.M., Bates P.D., Neelz S., Pender G., Villanueva I., Wright N.G., Liang D., Falconer R.A., Lin B., Waller S., Crossley A.J., Mason D., Benchmarking 2D hydraulic models for urban flood simulations, Proc. Inst. Civ. Eng. Water Manag., 161, 1, pp. 13-30, (2008); Hill J., Collins G.S., Avdis A., Kramer S.C., Piggott M.D., How does multiscale modelling and inclusion of realistic palaeobathymetry affect numerical simulation of the Storegga Slide tsunami?, Ocean. Model., 83, pp. 11-25, (2014); Wells M.R., Allison P.A., Piggott M.D., Hampson G.J., Pain C.C., Gorman G.J., Tidal modelling of an ancient tide-dominated seaway, Part 1: model validation and application to global Cretaceous (Aptian) tides, J. Sediment. Res., 80, pp. 393-410, (2010); Mitchell A.J., Allison P.A., Gorman G.J., Piggott M.D., Pain C.C., Tidal calculation in an ancient epicontinental sea: the early jurassic laurasian seaway, Geology, 39, 3, pp. 207-210, (2011); Funke S., Farrell P., Piggott M.D., Tidal turbine array optimisation using the adjoint approach, Renew. Energy, 63, pp. 658-673, (2014); Angeloudis A., Falconer R.A., Sensitivity of tidal lagoon and barrage hydrodynamic impacts and energy outputs to operational characteristics, Renew. Energy, 114, pp. 337-351, (2017); Perez-Ortiz A., Borthwick A.G.L., McNaughton J., Avdis A., Characterization of the tidal resource in Rathlin sound, Renew. Energy, 114, pp. 229-243, (2017); Hasegawa D., Sheng J., Greenberg D.A., Thompson K.R., Far-field effects of tidal energy extraction in the Minas Passage on tidal circulation in the Bay of Fundy and Gulf of Maine using a nested-grid coastal circulation model, Ocean. Dyn., 61, 11, pp. 1845-1868, (2011); Neill S.P., Jordan J.R., Couch S.J., Impact of tidal energy converter (TEC) arrays on the dynamics of headland sand banks, Renew. Energy, 37, 1, pp. 387-397, (2012); Martin-Short R., Hill J., Kramer S.C., Avdis A., Allison P.A., Piggott M.D., Tidal resource extraction in the Pentland Firth, UK: potential impacts on flow regime and sediment transport in the inner sound of stroma, Renew. Energy, 76, pp. 596-607, (2015); Angeloudis A., Falconer R.A., Bray S., Ahmadian R., Representation and operation of tidal energy impoundments in a coastal hydrodynamic model, Renew. Energy, 99, pp. 1103-1115, (2016); Gorman G.J., Piggott M.D., Pain C.C., de Oliveira C.R.E., Umpleby A.P., Goddard A.J.H., Optimisation based bathymetry approximation through constrained unstructured mesh adaptivity, Ocean. Model, 12, 3-4, pp. 436-452, (2006); Deleersnijder E., Lermusiaux P.F.J., Multi–scale modeling: nested-grid and unstructured-mesh approaches, Ocean. Dyn., 58, 5-6, pp. 335-336, (2008); Deleersnijder E., Legat V., Lermusiaux P.F.J., Multi–scale modelling of coastal, shelf and global ocean dynamics, Ocean. Dyn., 60, 6, pp. 1357-1359, (2010); Lermusiaux P.F.J., Schroter J., Danilov S., Iskandarani M., Pinardi N., Westerink J.J., Multiscale modeling of coastal, shelf, and global ocean dynamics, Ocean. Dyn., 63, 11-12, pp. 1341-1344, (2013); Castillo J.E., Mathematical aspects of numerical grid generation, Soc. Ind. Appl. Math., (1991); Knupp P., Steinberg S., Fundamentals of Grid Generation, (1993); Lo D.S.H., Finite Element Mesh Generation, (2017); George P.L., Automatic Mesh Generation: Application to Finite Element Methods, (1991); Gorman G.J., Piggott M.D., Wells M.R., Pain C.C., Allison P.A., A systematic approach to unstructured mesh generation for ocean modelling using GMT and Terreno, Comput. Geosci., 34, 12, pp. 1721-1731, (2008); Heinzer T.J., Williams M.D., Dogrul E.C., Kadir T.N., Brush C.F., Chung F.I., Implementation of a feature-constraint mesh generation algorithm within a GIS, Comput. 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Shelf Res., 48, pp. 50-60, (2012); Easton M.C., Harendza A., Woolf D.K., Jackson A., Characterisation of a tidal energy site: hydrodynamics and seabed structure, 9th European Wave and Tidal Energy Conference (EWTEC), (2011); McIlvenny J., Tamsett D., Gillibrand P., Goddijn-Murphy L., On the sediment dynamics in a tidally energetic channel: the inner sound, northern scotland, J. Mar. Sci. Eng., 4, 2, (2016); Avdis A., Hill J., Domain Boundaries Around Orkney and Shetland Islands for Hydrodynamic Simulations, figshare, (2017); Avdis A., Hill J., Triangular Unstructured Mesh Around Orkney and Shetland Islands for Hydrodynamic Simulations, figshare, (2017); Avdis A., Hill J., Qmesh Version 1.0 Source Code, (2017); Pugh D.T., Tides, Surges and Mean Sea Levels, (1996); Gayathri R., Murty P.L.N., Bhaskaran P.K., Srinivasa Kumar T., A numerical study of hypothetical storm surge and coastal inundation for AILA cyclone in the bay of bengal, Environ. 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Energy, 80, pp. 755-769, (2015); Lewis M., Neill S.P., Robins P.E., Hashemi M.R., Resource assessment for future generations of tidal–stream energy arrays, Energy, 83, pp. 403-415, (2015); Lloyd P.M., Stansby P.K., Shallow-water flow around model conical islands of small side slope. I: surface piercing, J. Hydraul. Eng., 123, 12, pp. 1057-1067, (1997); Lloyd P.M., Stansby P.K., Shallow-water flow around model conical islands of small side slope. II: submerged, J. Hydraul. Eng., 123, 12, pp. 1068-1077, (1997); Stansby P., Chini N., Lloyd P., Oscillatory flows around a headland by 3D modelling with hydrostatic pressure and implicit bed shear stress comparing with experiment and depth-averaged modelling, Coast. Eng., 116, pp. 1-14, (2016)","A. Avdis; Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, United Kingdom; email: a.avdis@imperial.ac.uk","","Elsevier Ltd","","","","","","09601481","","","","English","Renew. Energy","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85031726729"
"Zenk-Möltgen W.; Akdeniz E.; Katsanidou A.; Naßhoven V.; Balaban E.","Zenk-Möltgen, Wolfgang (55781093800); Akdeniz, Esra (57202368275); Katsanidou, Alexia (55318696600); Naßhoven, Verena (57202361803); Balaban, Ebru (57202369300)","55781093800; 57202368275; 55318696600; 57202361803; 57202369300","Factors influencing the data sharing behavior of researchers in sociology and political science","2018","Journal of Documentation","74","5","","1053","1073","20","25","10.1108/JD-09-2017-0126","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048060884&doi=10.1108%2fJD-09-2017-0126&partnerID=40&md5=e603919fd0bdf1c09d2f484a084c6b4a","Data Archive for the Social Sciences, GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany; Leibniz Institute for Educational Trajectories, Bamberg, Germany","Zenk-Möltgen W., Data Archive for the Social Sciences, GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany; Akdeniz E., Data Archive for the Social Sciences, GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany; Katsanidou A., Data Archive for the Social Sciences, GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany; Naßhoven V., Data Archive for the Social Sciences, GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany; Balaban E., Leibniz Institute for Educational Trajectories, Bamberg, Germany","Purpose: Open data and data sharing should improve transparency of research. The purpose of this paper is to investigate how different institutional and individual factors affect the data sharing behavior of authors of research articles in sociology and political science. Design/methodology/approach: Desktop research analyzed attributes of sociology and political science journals (n=262) from their websites. A second data set of articles (n=1,011; published 2012-2014) was derived from ten of the main journals (five from each discipline) and stated data sharing was examined. A survey of the authors used the Theory of Planned Behavior to examine motivations, behavioral control, and perceived norms for sharing data. Statistical tests (Spearman’s ρ, χ2) examined correlations and associations. Findings: Although many journals have a data policy for their authors (78 percent in sociology, 44 percent in political science), only around half of the empirical articles stated that the data were available, and for only 37 percent of the articles could the data be accessed. Journals with higher impact factors, those with a stated data policy, and younger journals were more likely to offer data availability. Of the authors surveyed, 446 responded (44 percent). Statistical analysis indicated that authors’ attitudes, reported past behavior, social norms, and perceived behavioral control affected their intentions to share data. Research limitations/implications: Less than 50 percent of the authors contacted provided responses to the survey. Results indicate that data sharing would improve if journals had explicit data sharing policies but authors also need support from other institutions (their universities, funding councils, and professional associations) to improve data management skills and infrastructures. Originality/value: This paper builds on previous similar research in sociology and political science and explains some of the barriers to data sharing in social sciences by combining journal policies, published articles, and authors’ responses to a survey. © 2018, Emerald Publishing Limited.","Data availability; Data policy; Data sharing; Political science; Replication; Research data management; Research transparency; Sociology; Theory of Planned Behaviour","","","","","","","","Abrams S., Cruse P., Strasser C., Willet P., Boushey G., Kochi J., Laurance M., Rizk-Jackson A., DataShare: empowering researcher data curation, International Journal of Digital Curation, 9, 1, pp. 110-118, (2014); Agosti M., Ferro N., Silvello G., Digital libraries: from digital resources to challenges in scientific data sharing and re-use, Comprehensive Guide through the Italian Database Research Over the Last 25 Years, Studies in Big Data, 31, pp. 27-41, (2017); Ajzen I., The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50, 2, pp. 179-211, (1991); Ajzen I., Constructing a TpB Questionnaire: conceptual and methodological considerations, (2002); Ajzen I., Attitudes, Personality and Behaviour, (2005); Ajzen I., Fishbein M., Understanding attitudes and predicting social behaviour, (1980); Altman M., The role of research funding and policy community in data citation – rewards, incentives, and infrastructure, (2016); Code of ethics of the ASA Committee on professional ethics, (1999); Berman F., Wilkinson R., Wood J., Building global infrastructure for data sharing and exchange through the research data alliance: guest editorial, D-Lib Magazine, 20, pp. 1-2, (2014); Brase J., Sens I., Lautenschlager M., The tenth anniversary of assigning DOI names to scientific data and a five year history of DataCite, D-Lib Magazine, 21, pp. 1-2, (2015); Joint declaration of data citation principles – final, (2014); Dewald W.G., Thursby J.G., Anderson R.G., Replication in empirical economics: the journal of money, credit and banking project, American Economic Review, 76, 4, pp. 587-603, (1986); Sharing research data, (2017); Enke N., Thessen A., Bach K., Bendix J., Seeger B., Gemeinholzer B., The user’s view on biodiversity data sharing – investigating facts of acceptance and requirements to realize a sustainable use of research data, Ecological Informatics, 11, pp. 25-33, (2012); Fecher B., Friesike S., Hebing M., What drives academic data sharing, PLoS One, 10, 2, pp. 1-25, (2015); Fishbein M., Ajzen I., Predicting and Changing Behavior: The Reasoned Action Approach, (2010); Gherghina S., Katsanidou A., Data availability in political science journals, European Political Science, 12, 4, pp. 333-349, (2013); Gherghina S., Katsanidou A., Data availability policies in political science journals, (2014); Godin G., Kok G., The theory of planned behavior: a review of its applications to health-related behaviors, American Journal of Health Promotion, 11, 2, pp. 87-98, (1996); Gregory K., Cousijn H., Groth P., Scharnhorst A., Wyatt S., Understanding data retrieval practices: a social informatics perspective, (2018); Groves R.M., The promise of collaborative data sharing across research sectors, The Palgrave Handbook of Survey Research, pp. 297-300, (2018); Harding T.S., Mayhew M.J., Finelli C.J., Carpenter D.D., The theory of planned behavior as a model of academic dishonesty in engineering and humanities undergraduates, Ethics & Behavior, 17, 3, pp. 255-279, (2007); Horton L., Katsanidou A., Purposing your survey: archives as a market regulator, or how can archives connect supply and demand?, IASSIST Quarterly, 35, 4, pp. 18-23, (2011); Quick guide to data citation, (2012); Kankanhalli A., Tan B.C.Y., Wei K.-K., Contributing knowledge to electronic knowledge repositories: an empirical investigation, Management Information Systems Research, 29, 1, pp. 113-144, (2005); Katsanidou A., Horton L., Jensen U., Data policies, data management and the quality of academic writing, International Studies Perspectives, 17, 4, pp. 379-391, (2016); Kim Y., Stanton J.M., Institutional and individual influences on scientists’ data sharing practices, Journal of Computational Science Education, 3, 1, pp. 47-56, (2012); Kim Y., Zhang P., Understanding data sharing behaviors of STEM researchers: the roles of attitudes, norms, and data repositories, Library & Information Science Research, 37, 3, pp. 189-200, (2015); King G., Publication, publication, Political Science & Politics, 39, 1, pp. 119-125, (2006); Kling R., Spector L., Academic rewards for scholarly research communication via electronic publishing, (2002); Leiner D.J., SoSci Survey: computer software, (2014); Mongeon P., Robinson-Garcia N., Jeng W., Costas R., Incorporating data sharing to the reward system of science: linking DataCite records to authors in the web of science, Aslib Journal of Information Management, 69, 5, pp. 545-556, (2017); Moss E., Cave C., Lyle J., Big Data, Big Challenges in Evidence-Based Policy Making, (2015); New data for understanding the human condition: international perspectives, (2013); Peng J., Zhi-cai J., Lin-jie G., Application of the expanded theory of planned behavior in intercity travel behavior, Discrete Dynamics in Nature and Society, 2014, 2, pp. 1-10, (2014); Piwowar H., Altmetrics: value all research products, Nature, 493, 7431, (2013); Forschungsdatenmanagement in den Sozial-, Verhaltens- und Wirtschaftswissenschaften: Orientierungshilfen für die Beantragung und Begutachtung datengenerierender und datennutzender Forschungsprojekte, (2016); Rauber A., Asmi A., van Uytvanck D., Proll S., Data citation of evolving data: recommendations of the Working Group on Data Citation (WGDC), (2015); Savage C.J., Vickers A.J., Empirical study of data sharing by authors publishing in PLoS Journals, PLoS One, 4, 9, (2009); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); Social sciences citation index: journal list, (2013); Zenk-Moltgen W., Lepthien G., Data from: data sharing in sociology journals, (2014); Zenk-Moltgen W., Lepthien G., Data sharing in sociology journals, Online Information Review, 38, 6, pp. 709-722, (2014); Zenk-Moltgen W., Katsanidou A., Akdeniz E., Nasshoven V., Balaban E., Replication data for: factors influencing the data sharing behavior of researchers in sociology and political sciences, (2017)","W. Zenk-Möltgen; Data Archive for the Social Sciences, GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany; email: wolfgang.zenk-moeltgen@gesis.org","","Emerald Group Holdings Ltd.","","","","","","00220418","","","","English","J. Doc.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85048060884"
"Cox J.","Cox, John (7404023358)","7404023358","Positioning the Academic Library within the Institution: A Literature Review","2018","New Review of Academic Librarianship","24","3-4","","219","243","24","38","10.1080/13614533.2018.1466342","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054359490&doi=10.1080%2f13614533.2018.1466342&partnerID=40&md5=fe309ed9aeb803638413aab1548408a6","Library, National University of Ireland Galway, Galway, Ireland","Cox J., Library, National University of Ireland Galway, Galway, Ireland","A strong position in the institution is vital for any academic library and affects its recognition, resourcing, and prospects. Higher education institutions are experiencing radical change, driven by greater accountability, stronger competition, and increased internationalization. They prioritize student success, competitive research, and global reputation. This has significant implications for library strategy, space, structures, partnerships, and identity. Strategic responses include refocusing from collections to users, reorganizing teams and roles, developing partnerships, and demonstrating value. Emphasis on student success and researcher productivity has generated learning commons buildings, converged service models, research data management services, digital scholarship engagement, and rebranding as partners. Repositioning is challenging, with the library no longer perceived as the heart of the campus but institutional leadership often holding traditional perceptions of its role. This review discusses literature on how academic libraries have been adapting or might adapt, functionally, physically, strategically, and organizationally to position themselves effectively within the institution. © 2018, Published with license by Taylor & Francis Group, LLC.","Collaboration; leadership; management; roles; strategy","","","","","","","","Adams Becker S., Cummins M., Davis A., Freeman A., Hall Giesinger C., NMC Horizon report: 2017 higher education edition, (2017); Adams Becker S., Cummins M., Davis A., Freeman A., Hall Giesinger C., NMC Horizon report: 2017 library edition, (2017); Adema J., Stone G., The surge in new university presses and academic-led publishing: An overview of a changing publishing ecology in the UK, Liber Quarterly, 27, 1, pp. 97-126, (2017); Alexander B., Adams Becker S., Cummins M., Hall Geisinger C., Digital literacy in higher education, Part II: An NMC Horizon project strategic brief, (2017); Alexander L., Case B.D., Downing K.E., Gomis M., Maslowski E., Librarians and scholars: Partners in digital humanities, Educause Review, 22, 2-3, (2014); Allen S., Towards a conceptual map of academic libraries' role in student retention: A literature review, The Christian Librarian, 57, 1, pp. 7-19, (2014); Altman M., Bernhardt M., Horowitz L., Lu W., Shapiro R., SPEC Kit 348: Rapid fabrication/makerspace services, (2015); Anderson R., Can't buy us love: The declining importance of library books and the rising importance of special collections, (2013); Anne K., Carlisle T., Dombrowski Q., Glass E., Gniady T., Jones J., Sipher J., Building capacity for digital humanities: A framework for institutional planning, (2017); Appleton L., Assuring quality using “moments of truth” in super‐converged services, Library Management, 33, 6-7, pp. 414-420, (2012); Appleton L., Sharing space in university libraries, University libraries and space in the digital world, pp. 119-130, (2013); Appleton L., Stevenson V., Boden D., Developing learning landscapes: Academic libraries driving organisational change, Reference Services Review, 39, 3, pp. 343-361, (2011); Armann-Keown V., Bolefski A., SPEC Kit 355: Campus-wide entrepreneurship, (2017); Framework for information literacy for higher education, (2015); Auckland M., Re-skilling for research: An investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Bains S., Teaching ‘old’ librarians new tricks, SCONUL Focus, 58, pp. 8-11, (2013); Baker D., Allden A., Leading libraries: Leading in uncertain times: A literature review, (2017); Baker D., Allden A., Leading libraries: The view from above, (2017); Bell S., Dempsey L., Fister B., New roles for the road ahead: Essays commissioned for ACRL's 75th anniversary, (2015); Bergstrom T.C., Digital scholarship centres: Converging space and expertise, Developing digital scholarship: Emerging practices in academic libraries, pp. 105-120, (2016); Blummer B., Kenton J.M., Learning commons in academic libraries: Discussing themes in the literature from 2001 to the present, New Review of Academic Librarianship, 23, 4, pp. 329-352, (2017); Bonn M., Furlough M., Getting the word out: Academic libraries as scholarly publishers, (2015); Bordonaro K., Internationalization and the North American university library, (2013); Brown K., Malenfant K.J., Academic library impact on student learning and success: Findings from Assessment in Action team projects, (2017); Brown S., Bennett C., Henson B., Valk A., SPEC Kit 342: Next-gen learning spaces, (2014); Bryant R., Clements A., Feltes C., Groenewegen D., Huggard S., Mercer H., Wright J., Research information management: Defining RIM and the library's role, (2017); Bryant R., Lavoie B., Malpas C., Scoping the university RDM service bundle. The realities of research data management, Part 2, (2017); Bryant R., Lavoie B., Malpas C., A tour of the research data management (RDM) service space. The realities of research data management, Part 1, (2017); Bulpitt G., Leading the student experience: Super-convergence of organisation, structure and business processes, (2012); Church-Duran J., Distinctive roles: Engagement, innovation, and the liaison model, portal: Libraries and the Academy, 17, 2, pp. 257-271, (2017); Connaway L.S., The library in the life of the user: Engaging with people where they live and learn, (2015); Connaway L.S., Harvey W., Kitzie V., Mikitish S., Academic library impact: Improving practice and essential areas to research, (2017); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, Liber Quarterly, 24, 1, pp. 17-48, (2014); Cowan S.M., Information literacy: The battle we won that we lost?, Portal: Libraries and the Academy, 14, 1, pp. 23-32, (2014); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox J., Communicating new library roles to enable digital scholarship: A review article, New Review of Academic Librarianship, 22, 2-3, pp. 132-147, (2016); Cox J., New directions for academic libraries in research staffing: A case study at National University of Ireland Galway, New Review of Academic Librarianship, 23, 2-3, pp. 110-124, (2017); Day A., Research information management: How the library can contribute to the campus conversation, New Review of Academic Librarianship, 24, 1, pp. 23-34, (2018); DeGroff H., Preparing for the research excellence framework: Examples of open access good practice across the United Kingdom, The Serials Librarian, 71, 2, pp. 96-111, (2016); Delaney G., Bates J., Envisioning the academic library: A reflection on roles, relevancy and relationships, New Review of Academic Librarianship, 21, 1, pp. 30-51, (2015); Dempsey L., Intra-institutional boundaries: New contexts of collaboration on campus, New roles for the road ahead: Essays commissioned for ACRL's 75th birthday, pp. 80-82, (2015); Dempsey L., Library collections in the life of the user: Two directions, Liber Quarterly, 26, 4, pp. 338-359, (2016); Denda K., Study abroad programs: A golden opportunity for academic library engagement, The Journal of Academic Librarianship, 39, 2, pp. 155-160, (2013); Eldridge J., Fraser K., Simmonds T., Smyth N., Strategic engagement: New models of relationship management for academic librarians, New Review of Academic Librarianship, 22, 2-3, pp. 160-175, (2016); Fister B., Student learning, lifelong learning and partner in pedagogy, New roles for the road ahead: Essays commissioned for ACRL's 75th birthday, pp. 58-62, (2015); Franklin B., Surviving to thriving: Advancing the institutional mission, Journal of Library Administration, 52, 1, pp. 94-107, (2012); Fruin C., Sutton S., Strategies for success: Open access policies at North American educational institutions, College & Research Libraries, 77, 4, pp. 469-499, (2016); Givens M., Macklin L.A., Mangiafico P., Faculty profile systems: New services and roles for libraries, portal: Libraries and the Academy, 17, 2, pp. 235-255, (2017); Godfrey I., Rutledge L., Mowdood A., Reed J., Bigler S., Soehner C., Supporting student retention and success: Including family areas in an academic library, portal: Libraries and the Academy, 17, 2, pp. 375-388, (2017); Green H., Libraries across land and sea: Academic library services on international branch campuses, College & Research Libraries, 74, 1, pp. 9-23, (2013); Gremmels G.S., Staffing trends in college and university libraries, Reference Services Review, 41, 2, pp. 233-252, (2013); Groenewegen D., Yesterday and today: Reflecting on past practice to help build and strengthen the researcher partnership at Monash University, New Review of Academic Librarianship, 23, 2-3, pp. 171-184, (2017); Gwyer R., Identifying and exploring future trends impacting on academic libraries: A mixed methodology using journal content analysis, focus groups, and trend reports, New Review of Academic Librarianship, 21, 3, pp. 269-285, (2015); Haddow G., Mamtora J., Research support in Australian academic libraries: Services, resources, and relationships, New Review of Academic Librarianship, 23, 2-3, pp. 89-109, (2017); Holmgren R., Spencer G., The changing landscape of library and information services: What presidents, provosts, and finance officers need to know, (2014); Hoodless C., Pinfield S., Subject vs. functional: Should subject librarians be replaced by functional specialists in academic libraries?, Journal of Librarianship and Information Science, 23, 2-3; Howard R., Fitzgibbons M., Librarian as partner: In and out of the library, Developing digital scholarship: Emerging practices in academic libraries, pp. 43-60, (2016); Hudson-Vitale C., Imker H., Johnston L.R., Carlson J., Kozlowski W., Olendorf R., Stewart C., SPEC Kit 354: Data curation, (2017); Jackson H.A., Collaborating for student success: An e-mail survey of U.S. libraries and writing centers, The Journal of Academic Librarianship, 43, 4, pp. 281-296, (2017); Jaguszewski J.M., Williams K., New roles for new times: Transforming liaison roles in research libraries, (2013); Jeal Y., Strategic alignment at the University of Manchester Library: Ambitions, transitions, and new values, New Review of Academic Librarianship, 20, 3, pp. 278-295, (2014); Kenney A.R., Leveraging the liaison model: From defining 21st century research libraries to implementing 21st century research universities, (2014); Kenney A.R., Li X., Rethinking research libraries in the era of global universities, (2016); Lavoie B., Malpas C., Stewardship of the evolving scholarly record: From the invisible hand to conscious coordination, (2015); Lewis D.W., Reimagining the academic library, (2016); Lewis R., Sarli C.C., Suiter A.M., SPEC kit 346: Scholarly output assessment activities, (2015); Lippincott J.K., Goldenberg-Hart D., CNI workshop report. Digital scholarship centers: Trends and good practice, (2014); MacKenzie A., Digital scholarship: Scanning library services and spaces, Developing digital scholarship: Emerging practices in academic libraries, pp. 23-40, (2016); MacKenzie A., Martin L., Developing digital scholarship: Emerging practices in academic libraries, (2016); Mackey T.P., Jacobson T.E., Reframing information literacy as a metaliteracy, College & Research Libraries, 72, 1, pp. 62-78, (2011); Martin L., The university library and digital scholarship: A review of the literature, Developing digital scholarship: Emerging practices in academic libraries, pp. 3-22, (2016); Matthews G., Walton G., Strategic development of university library space: Widening the influence, New Library World, 115, 5-6, pp. 237-249, (2014); Matthews G., Walton G., University libraries and space in the digital world, (2013); Maxwell D., The research lifecycle as a strategic roadmap, Journal of Library Administration, 56, 2, pp. 111-123, (2016); McRostie D., The only constant is change: Evolving the library support model for research at the University of Melbourne, Library Management, 37, 6-7, pp. 363-372, (2016); Melling M., Collaborative service provision through super-convergence, Collaboration in libraries and learning environments, pp. 149-166, (2013); Melling M., Weaver M., The Teaching Excellence Framework: What does it mean for academic libraries?, Insights, 30, 3, pp. 152-160, (2017); Miller R.K., Pressley L., SPEC Kit 349: Evolution of library liaisons, (2015); Mulligan R., SPEC Kit 350: Supporting digital scholarship, (2016); Murray A., Ireland A., Communicating library impact on retention: A framework for developing reciprocal value propositions, Journal of Library Administration, 57, 3, pp. 311-326, (2017); Murray A., Ireland A., Provosts' perceptions of academic library value & preferences for communication: A national study, College & Research Libraries, 79, 3, pp. 336-365, (2018); Nichols J., Melo M., Dewland J., Unifying space and service for makers, entrepreneurs, and digital scholars, portal: Libraries and the Academy, 17, 2, pp. 363-374, (2017); Oakleaf M., The value of academic libraries: A comprehensive research review and report for the Association of College and Research Libraries, (2010); Okerson A., Holzman A., The once and future publishing library, (2015); Oliveira S.M., The academic library's role in student retention: A review of the literature, Library Review, 66, 4-5, pp. 310-329, (2017); Pagowsky N., Hammond J., A programmatic approach: Systematically tying the library to student retention efforts on campus, College & Research Libraries News, 73, 10, pp. 582-585, (2012); Pinfield S., Making open access work: The ‘state-of-the-art’ in providing open access to scholarly literature, Online Information Review, 39, 5, pp. 604-636, (2015); Pinfield S., Cox A.M., Rutter S., Mapping the future of academic libraries: A report for SCONUL, (2017); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); Posner M., No half measures: Overcoming common challenges to doing digital humanities in the library, Journal of Library Administration, 53, 1, pp. 43-52, (2013); Pun R., Collard S., Parrott J., Bridging worlds: Emerging models and practices of U.S. academic libraries around the globe, (2016); Roberts S., Esson R., Leadership skills for collaboration: Future needs and challenges, Collaboration in libraries and learning environments, pp. 87-102, (2013); Robertson M., Perceptions of Canadian provosts on the institutional role of academic libraries, College & Research Libraries, 76, 4, pp. 490-511, (2015); Saunders L., Room for improvement: Priorities in academic libraries' strategic plans, Journal of Library Administration, 56, 1, pp. 1-16, (2016); Schonfeld R.C., Organizing the work of the research library, (2016); Leadership challenges. Some views from those in the hot seat, SCONUL Focus, 66, pp. 4-13, (2016); Sinclair B., The university library as incubator for digital scholarship, Educause Review, 22, 2-3, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., R esearch data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Tyrer G., Ives J., Corke C., Employability skills, the student path, and the role of the academic library and partners, New Review of Academic Librarianship, 19, 2, pp. 178-189, (2013); Vandegrift M., Varner S., Evolving in common: Creating mutually supportive relationships between libraries and the digital humanities, Journal of Library Administration, 53, 1, pp. 67-78, (2013); Vinopal J., McCormick M., Supporting digital scholarship in research libraries: Scalability and sustainability, Journal of Library Administration, 53, 1, pp. 27-42, (2013); Walton G., Matthews G., Exploring informal learning space in the university: A collaborative approach, (2017); Weaver M., Student journey work: A review of academic library contributions to student transition and success, New Review of Academic Librarianship, 19, 2, pp. 101-124, (2013); Witt S.W., Kutner L., Cooper L., Mapping academic library contributions to campus internationalization, College & Research Libraries, 76, 5, pp. 587-608, (2015); Wolff-Eisenberg C., Ithaka S+R US library survey 2016, (2017); Wolff-Eisenberg C., Rod A.B., Schonfeld R.C., Ithaka S+R | Jisc | RLUK UK survey of academics 2015, (2016); Wolff-Eisenberg C., Rod A.B., Schonfeld R.C., Ithaka S+R US faculty survey 2015, (2016); Wynne B., Dixon S., Donohue N., Rowlands I., Changing the library brand: A case study, New Review of Academic Librarianship, 22, 2-3, pp. 337-349, (2016)","J. Cox; Library, National University of Ireland Galway, Galway, H91 REW4, Ireland; email: john.cox@nuigalway.ie","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Review","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85054359490"
"Coetzer G.; Botha R.; Jacobs L.","Coetzer, Glenda (57204899386); Botha, Roelf (54986800200); Jacobs, Lorette (57204897331)","57204899386; 54986800200; 57204897331","Progress with implementing the new research data management system at HartRAO","2018","EPJ Web of Conferences","186","","12002","","","","0","10.1051/epjconf/201818612002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057738073&doi=10.1051%2fepjconf%2f201818612002&partnerID=40&md5=1120f6b4ad22a4ea7851308557f8702a","Hartebeesthoek Radio Astronomy Observatory, South Africa; University of South Africa, South Africa","Coetzer G., Hartebeesthoek Radio Astronomy Observatory, South Africa, University of South Africa, South Africa; Botha R., Hartebeesthoek Radio Astronomy Observatory, South Africa; Jacobs L., University of South Africa, South Africa","The Hartebeesthoek Radio Astronomy Observatory (HartRAO) participates in global radio astronomy and fundamental astronomy (space geodesy) research activities. Data and data products produced by HartRAO's expanding range of on-site and off-site instrumentation must be archived and stored at HartRAO and made accessible to the scientific community. The data management and storage systems currently being used for managing fundamental astronomy data are not capable of handling the large volumes of data and have become obsolete. This necessitated the design and implementation of a next-generation Geodetic Research Data Management System (GRDMS), which complies with internationally accepted data service standards. We present the top-level conceptual model of the GRDMS and progress to date with developments of various sub-systems, data structuring and organisation within the sub-systems. © The Authors, published by EDP Sciences, 2018.","","","","","","","National Research Foundation; National Research Foundation, NRF","The authors would like to acknowledge funding awarded by the National Equipment Programme (NEP) of the National Research Foundation (NRF) for funding the development of the co-located academic network.","Coetzer G.C., Botha R.C., Combrinck L., Et al., A new geodetic research data management system at the hartebeesthoek radio astronomy observatory in open science at the frontiers of librarianship, Astronomical Society of the Pacific Conference Series, 492, pp. 22-30, (2015); Behrend D., Data Science Journal, 12, pp. WDS81-WDS84, (2013); Noll C., CDDIS. IVS2015-2016 Biennial Report, (2017); UNAVCO GSAC WS: Web Services for Geodesy Data Repositories, (2013)","","D'Abrusco R.; Harvard�Smithsonian Center for Astrophysics, 60 Garden St. MS 67, Cambridge, MA; Lesteven S.; Centre de Donnees Astronomique de Strasbourg(CDS), Observatoire Astronomique de Strasbourg, 11, Rue de l�Universite, Strasbourg; Dorch B.; Kern B.","EDP Sciences","American Astronomical Society; Astronomy and Astrophysics; Centre de Donnees Astronomique de Strasbourg (CDS); et al.; MNRAS; SPIE Digital Library","8th Library and Information Services in Astronomy: ""Astronomy Librarianship in the Era of Big Data and Open Science"", LISA 2018","6 June 2017 through 9 June 2017","Strasbourg","142663","21016275","978-275989054-5","","","English","EPJ Web Conf.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85057738073"
"Holles J.H.; Schmidt L.","Holles, Joseph H. (6602237719); Schmidt, Larry (22136252700)","6602237719; 22136252700","Implementing a graduate class in research data management for science and engineering students","2018","ASEE Annual Conference and Exposition, Conference Proceedings","2018-June","","","","","","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051212346&partnerID=40&md5=f5efbd7ae3465e9586b3f8f1dcfca4a7","University of Wyoming, Department of Chemical Engineering, United States; University of Wyoming, United States","Holles J.H., University of Wyoming, Department of Chemical Engineering, United States; Schmidt L., University of Wyoming, United States","[No abstract available]","","","","","","","","","Proposal and Award Policies and Procedures Guide Part I - Grant Proposal Guide, (2013); NIH Data Sharing Policy, (2017); USGS Data Management: Data Management Plans, (2017); Carlson J., Frosmire M., Miller C.C., Sapp Nelson M., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Johnston L., Westra B., Nichols M., Developing an approach for data management education: A report from the data information literacy project, The International Journal of Digital Curation, 8, 1, pp. 204-207, (2013); McLure M., Level A.V., Crabston C.L., Oehlerts B., Culbertson M., Data curation: A study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Borgman C.L., Syllabus for Data Management and Practice, Part I, Winter 2015, (2015); Qin J., D'Ignazio J., The Science Data Literacy Project: Educator Resources, (2017); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data managment pilot course at an academic library, Journal of EScience Librarianship, 3, 1, (2014); Johnston L., Jeffryes J., Data Information Literacy Case Study Directory, 3, 1, (2012); Adamick J., Reznik-Zellen R.C., Sheridan M., Data management training for graduate students at a large research university, Journal of EScience Librarianship, 1, 3, (2013); Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Wright S.J., Andrews C., Data Information Literacy Case Study Directory, 2, 1, (2013); Thielen J., Samuel S.M., Carlson J., Moldwin M., Developing and teaching a two-credit data management course for graduate students in climate and space science, Issues in Science and Technology Librarianship, 86, (2017); Schmidt L.O., Holles J.H., A graduate class in research data management, Chemical Engineering Education, 52, 1, pp. 52-59, (2018); Briney K., Data Managment for Researchers: Organize, Maintain and Share Your Data for Research Success, (2015); Krier L., Strasser C.A., Data Management for Libraries: A LITA Guide, (2014); ORCID: Connecting Research and Researchers, (2018); Hake R.R., Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanical test data for introductory physics courses, American Journal of Physics, 66, 1, pp. 64-74, (1998); Corti L., Van Den Eynden V., Bishop L., Woolard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Data Management Plan Tool, (2014); Carlson J., The data curation profiles toolkit: User guide, Data Curation Profiles Toolkit, (2010); Carlson J., The data curation profiles toolkit: Interviewer's manual, Data Curation Profiles Toolkit, (2010); Carlson J., The data curation profiles toolkit: Interview worksheet, Data Curation Profiles Toolkit, (2010); Carlson J., The data curation profiles toolkit: The profile template, Data Curation Profiles Toolkit, (2010); Russo Martin E., New England Collaborative Data Management Curriculum, (2017); Holles J.H., Schmidt L.O., Graduate research data management course content: Teaching the data management plan (DMP), 2018 ASEE Annual Conference & Exposition, (2018); Madihally S., Reviving graduate seminar series through non-technical presentations, Chemical Engineering Education, 45, 4, (2011); Burrows V., Beaudoin S., A graduate course in research methods, Chemical Engineering Education, 35, 4, (2001); Ollis D., The research proposition, Chemical Engineering Education, 29, 4, (1995); Ollis D., Catalyzing the student-to-researcher transition: Research initiation and professional development for new graduate students, Chemical Engineering Education, 50, 4, pp. 221-229, (2016); Holles J.H., A graduate course in theory and methods of research, Chemical Engineering Education, 41, 4, pp. 226-232, (2007)","","","American Society for Engineering Education","","125th ASEE Annual Conference and Exposition","23 June 2018 through 27 December 2018","Salt Lake City","138114","21535965","","","","English","ASEE Annu. Conf. Expos. Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85051212346"
"Langer A.","Langer, André (57193121679)","57193121679","PIROL: Cross-domain research data publishing with linked data technologies","2019","CEUR Workshop Proceedings","2370","","","43","51","8","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067205281&partnerID=40&md5=0e682f0efedae35fb74afef1560c5033","Chemnitz University of Technology, Germany","Langer A., Chemnitz University of Technology, Germany","Effective research data management for traceability, preservation and reuse is an important part of good scientific practice and is already under discussion over a long period of time. However, the digital transformation in science also led to new challenges for researchers on how to describe, publish and share their research data. This includes the interdisciplinary annotation and discovery of research data, data privacy issues in exposure of data with trends to decentralized platforms as well as sophisticated automatisms to ensure data quality and compliance aspects. Only limited tool support exists for these processes so far. The following research project will use Linked Data principles to improve the current situation in this problem domain. It will first focus on components and services, that assist researchers in the annotation process of their research data. Next, it will investigate how this research data can be stored and discovered in decentralized, multi-user scenarios to allow data reuse under respect of data privacy concerns. In a third step, meta data descriptions will be used to apply automated data conformance and quality assessment operations on scientific data. © 2019 CEUR-WS All rights reserved.","Data annotation; Data publishing; Data quality; Decentralization; Linked data; Open research; SoLiD","Information management; Information systems; Information use; Linked data; Metadata; Solids; Systems engineering; Data annotation; Data publishing; Data quality; Decentralization; Digital transformation; Good scientific practices; Linked Data principles; Research data managements; Data privacy","","","","","Bundesministerium für Bildung und Forschung, BMBF","In order to achieve the defined objectives in the problem domain of research data management, expertise in information management, semantic technologies and Web Engineering is needed. The first year of this early-stage PhD project focused on the familiarization with the State of the Art in Linked Data technologies. In the following, we list already achieved preliminary result that will already contribute to the PIROL project. A national growth-core project on Linked Enterprise Data Services7 in Germany funded by the BMBF supported this initial phase. The primary focus was set on data coherence and data quality assessment operations for general-purpose Linked Data sets accessible via existing web services or placed in a data lake.","Bizer C., Heath T., Berners-Lee T., Linked data - The story so far, IJSWIS, 5, 3, pp. 1-22, (2009); Griffin P.C., Khadake J., LeMay K.S., Et al., Best practice data life cycle approaches for the life sciences, F1000Research, 6, (2018); Khalili A., Loizou A., Van Harmelen F., Adaptive linked data-driven web components: Building flexible and reusable semantic web interfaces, Lecture Notes in Computer Science, 9678, pp. 677-692, (2016); Langer A., Gaedke M., Fame.Q - A formal approach to master quality in enterprise linked data, Proceedings of the 15th International Conference WWW/Internet 2016, (2016); Langer A., Gaedke M., DaQAR - An ontology for the uniform exchange of comparable LD quality assessment requirements, Lecture Notes in Computer Science, pp. 234-242, (2018); Langer A., Gopfert C., Gaedke M., URI-aware user input interfaces for the unobtrusive reference to linked data, IADIS International Journal on Computer Science and Information Systems, 13, 2, (2018); Langer A., Siegert V., Gopfert C., Gaedke M., SemQuire - Assessing the Data Quality of Linked Open Data Sources Based on DQV, (2018); Mansour E., Sambra A.V., Hawke S., Et al., A demonstration of the solid platform for social web applications, Proceedings of the 25th International Conference Companion on World Wide Web, pp. 223-226, (2016); Sousa R.B., Cugler D.C., Malaverri J.E.G., Medeiros C.B., A provenance-based approach to manage long term preservation of scientific data, 2014 IEEE 30th ICDE Workshops, (2014); Steinhof C., Erfolgskriterien von Forschungsdatenrepositorien und Deren Relevanz für Verschiedene Stakeholder-Gruppen, (2017); Umbrich J., Hogan A., Polleres A., Decker S., IOS press link traversal querying for a diverse web of data, Semantic Web Interoperability, Usability, Applicability, (2014); Zaveri A., Rula A., Maurino A., Et al., Quality assessment for linked open data: A survey, Semantic Web Journal, 1, pp. 1-31, (2014)","A. Langer; Chemnitz University of Technology, Germany; email: andre.langer@informatik.tu-chemnitz.de","La Rosa M.; Reichert M.; Plebani P.","CEUR-WS","","Doctoral Consortium Papers Presented at the 31st International Conference on Advanced Information Systems Engineering, CAiSE-DC 2019","3 June 2019 through 7 June 2019","Rome","148447","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85067205281"
"Lefebvre A.; Spruit M.","Lefebvre, Armel (57169815200); Spruit, Marco (16178767900)","57169815200; 16178767900","Designing Laboratory Forensics","2019","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11701 LNCS","","","238","251","13","2","10.1007/978-3-030-29374-1_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072884606&doi=10.1007%2f978-3-030-29374-1_20&partnerID=40&md5=e8243c82dccc97f79733f77b2c42ff96","Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, Utrecht, 3584CC, Netherlands","Lefebvre A., Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, Utrecht, 3584CC, Netherlands; Spruit M., Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, Utrecht, 3584CC, Netherlands","Recently, the topic of research data management (RDM) has emerged at the forefront of Open Science. Funders and publishers posit new expectations on data management planning and transparent reporting of research. At the same time, laboratories rely upon undocumented files to record data, process results and submit manuscripts which hinders repeatable and replicable management of experimental resources. In this study, we design a forensic process to reconstruct and evaluate data management practices in scientific laboratories. The process we design is named Laboratory Forensics (LF) as it combines digital forensic techniques and the systematic study of experimental data. We evaluate the effectiveness and usefulness of Laboratory Forensics with laboratory members and data managers. Our preliminary evaluation indicates that LF is a useful approach for assessing data management practices. However, LF needs further developments to be integrated into the information systems of scientific laboratories. © 2019, IFIP International Federation for Information Processing.","Design science; Laboratory forensics; Open science; Reproducibility","Design; Digital forensics; Electronic commerce; Laboratories; Design science; Forensic Techniques; Further development; Management planning; Management practices; Open science; Reproducibilities; Research data managements; Information management","","","","","","","Commission: Access to and preservation of scientific information in, Europe, (2015); Lefebvre A., Schermerhorn E., Spruit M., How Research Data Management Can Contribute to Efficient and Reliable Science, (2018); Bechhofer S., Buchan I., Et al., Why linked data is not enough for scientists, Futur. Gener. Comput. Syst., 29, pp. 599-611, (2013); Federer L.M., Belter C.W., Et al., Data sharing in PLOS ONE: An analysis of data availability statements, Plos ONE, 13, (2018); Collberg C., Proebsting T.A., Repeatability in computer systems research, Commun. ACM, 59, pp. 62-69, (2016); Peng R.D., Dominici F., Zeger S.L., Reproducible Epidemiologic Research, (2006); Stevens H., Life out of Sequence: a Data-Driven History of Bioinformatics, (2013); Ince D.C., Hatton L., Graham-Cumming J., The case for open computer programs, Nature, 482, pp. 485-488, (2012); Hevner A.R., A three cycle view of design science research, Scand. J. Inf. Syst., 19, pp. 87-92, (2007); Gregor S., Hevner A.R., Positioning and presenting design science for maximum impact, MIS Q, 37, pp. 337-355, (2013); Palmer G., A road map for digital forensic research, Proceedings of the 2001 Digital Forensic Research Workshop Conference, (2001); Casey E., Katz G., Lewthwaite J., Honing digital forensic processes, Digit. Investig., 10, pp. 138-147, (2013); Rowlingson R., A Ten Step Process for Forensic Readiness, Int. J. Digit. Evid., 2, (2004); Ayris P., Berthou J.-Y., Et al., Towards a FAIR Internet of data, services and things for practicing open, Science, 3, (2018); Arnes A., Digital Forensics, (2017); Franklin A., Perovic S., Experiment in Physics, (2016); Weber M., Experiment in biology, The Stanford Encyclopedia of Philosophy, (2018); Radder H., Experimentation in the Natural Sciences, Presented at The, (2012); ACM: Artifact Review and Badging; Latour B., Woolgar S., Laboratory Life the Construction of Scientific Facts, (1986); Borgman C.L., Data, disciplines, and scholarly publishing, Learned Publishing, Pp. 29– 38. Wiley, (2008); Nosek B.A., Alter G., Et al., Promoting an Open Research Culture, (2015); Mabey M., Doupe A., Zhao Z., Ahn G.-J., Challenges, opportunities and a framework for web environment forensics, Advances in Digital Forensics XIV. IAICT, 532, pp. 11-33, (2018); Huang Y., Gottardo R., Comparability and reproducibility of biomedical data, Brief. Bioinform., 14, pp. 391-401, (2013); Hoehndorf R., Dumontier M., Gkoutos G.V., Evaluation of research in biomedical ontologies, Brief. Bioinform., 14, pp. 696-712, (2013)","A. Lefebvre; Department of Information and Computing Sciences, Utrecht University, Utrecht, Princetonplein 5, 3584CC, Netherlands; email: a.e.j.lefebvre@uu.nl","Pappas I.O.; Pappas I.O.; Krogstie J.; Jaccheri L.; Mikalef P.; Dwivedi Y.K.; Mäntymäki M.","Springer Verlag","","18th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2019","18 September 2019 through 20 September 2019","Trondheim","231769","03029743","978-303029373-4","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85072884606"
"Langer A.; Bilz E.; Gaedke M.","Langer, André (57193121679); Bilz, Ellen (57211124392); Gaedke, Martin (8905803700)","57193121679; 57211124392; 8905803700","Analysis of current RDM applications for the interdisciplinary publication of research data","2019","CEUR Workshop Proceedings","2447","","","","","","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072737690&partnerID=40&md5=3148a8e9a831da532a8135aff71d29f4","Chemnitz University of Technology, Germany","Langer A., Chemnitz University of Technology, Germany; Bilz E., Chemnitz University of Technology, Germany; Gaedke M., Chemnitz University of Technology, Germany","The digital transformation of science allows researchers nowadays to expose Research Objects through many different publishing channels, so that other interested stakeholders can find and reuse it. Linked Data is an accepted mean in these meta descriptions to enhance Findability, Accessibility, Interoperability and Reusability (FAIR). But researchers face a large variety of established publishing applications, where they have to select between general-purpose or domain-specific platforms and user interfaces of varying quality and feature set. In order to improve interoperability aspects, we want to analyze which publishing systems currently exist and to which extent they support Linked Data annotations from the very beginning. We therefore concentrated on research data and conducted a systematic mapping of general-purpose research data management (RDM) systems currently in use, and summarize them in a tabular resource. The obtained results were then evaluated against their current support for semantic, interdisciplinary data annotation and exchange. We show, that a large set of established research data publishing solutions already exists, but that their support for Linked Data is still limited and can be improved. Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).","Data Publishing; FAIR; Linked Data; Research Data Management; Systematic Mapping","Data handling; Interoperability; Linked data; Mapping; Reusability; Semantics; User interfaces; Data annotation; Data publishing; Digital transformation; Domain specific; FAIR; Publishing systems; Research data managements; Systematic mapping; Information management","","","","","","","Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Assante M., Candela L., Castelli D., Tani A., Are scientific data repositories coping with research data publishing?, Data Science Journal, 15, pp. 1-24, (2016); Becker P.N., Erstgutachter D., Paschke A., Schulte C., Repositorien und Das Semantic Web – Repositorieninhalte Als Linked Data Bereitstellen, (2014); Bornmann L., Mutz R., Growth rates of modern science: A bibliometric analysis, CoRR, (2014); Charalabidis Y., Zuiderwijk A., Alexopoulos C., Et al., The Multiple Life Cycles of Open Data Creation and Use, pp. 11-31, (2018); Dallmeier-Tiessen S., Khodiyar V., Murphy F., Et al., Connecting data publication to the research workflow: A preliminary analysis, International Journal of Digital Curation, 12, 1, pp. 88-105, (2017); Jianhui L., Chao W., Lili Z., Chengzan L., Lianglin H., Survey and analysis of scientific data publishing, China Scientific Data, 1, 1, (2016); Karam N., Muller-Birn C., Gleisberg M., Et al., A terminology service supporting semantic annotation, integration, discovery and analysis of interdisciplinary research data, Datenbank-Spektrum, 16, 3, pp. 195-205, (2016); Kim Y., Nah S., Internet researchers’ data sharing behaviors: An integration of data reuse experience, attitudinal beliefs, social norms, and resource factors, Online Information Review, 42, 1, pp. 124-142, (2018); Kokorceny M., Bodnarova A., Comparison of digital libraries systems, Advances in Data Networks, Communications, Computers, pp. 97-100, (2010); Langer A., Pirol: Cross-domain Research Data Publishing with Linked Data technologies, Proceedings of the Doctoral Consortium Papers Presented at the 31st CAiSE 2019, pp. 43-51, (2019); Langer A., Gopfert C., Gaedke M., URI-aware user input interfaces for the unobtrusive reference to Linked Data, IADIS International Journal on Computer Science and Information Systems, 13, 2, (2018); Parsons M.A., Godoy O., Ledrew E., Et al., A conceptual framework for managing very diverse data for complex, interdisciplinary science, Journal of Information Science, 37, 6, pp. 555-569, (2011); Poline J., Chodacki J., Gulick A.E.V., Data Sharing to Data Publishing, pp. 1-14, (2019); Poole A.H., How has your science data grown? Digital curation and the human factor: A critical literature review, Archival Science, 15, 2, pp. 101-139, (2015); Sousa R.B., Cugler D.C., Malaverri J.E.G., Medeiros C.B., A provenance-based approach to manage long term preservation of scientific data, Proceedings - International Conference on Data Engineering, pp. 126-133, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The fair guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","Kaffee L.-A.; Kaffee L.-A.; Endris K.M.; Endris K.M.; Vidal M-E.; Comerio M.; Sadeghi M.; Chaves-Fraga D.; Colpaert P.","CEUR-WS","","Joint 1st International Workshop on Semantics for Transport and the 1st International Workshop on Approaches for Making Data Interoperable, SEM4TRA-AMAR 2019","9 September 2019","Karlsruhe","151821","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85072737690"
"Goben A.; Nelson M.S.","Goben, Abigail (55849675300); Nelson, Megan Sapp (16402823900)","55849675300; 16402823900","Teaching librarians about data: The ACRL research data management roadshow","2018","College and Research Libraries News","79","7","","354","357","3","5","10.5860/crln.79.7.354","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052541936&doi=10.5860%2fcrln.79.7.354&partnerID=40&md5=3f92a58fbf38e88701f08163cdefae53","University of Illinois-Chicago, United States; Purdue University, United States","Goben A., University of Illinois-Chicago, United States; Nelson M.S., Purdue University, United States","[No abstract available]","","","","","","","","","Wiggins G., McTighe J., Understanding by Design. 2Nd Expanded Edition, (2005); Goben A., Nelson M.S., Engaging Liaisons Thru Education: The First Year Results of The Roadshow, (2018)","","","Association of College and Research Libraries","","","","","","00990086","","","","English","Coll. Res. Libr. News","Note","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85052541936"
"Haglund L.; Roos A.; Wallgren-Björk P.","Haglund, Lotta (7003790031); Roos, Annikki (25031560200); Wallgren-Björk, Petra (57203203647)","7003790031; 25031560200; 57203203647","Health science libraries in Sweden: new directions, expanding roles","2018","Health Information and Libraries Journal","35","3","","251","255","4","6","10.1111/hir.12229","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050915250&doi=10.1111%2fhir.12229&partnerID=40&md5=7e30c22494dd5ac1f07f052063193ac6","Swedish School of Sport and Health Sciences, Stockholm, Sweden; Karolinska Institutet University Library, Stockholm, Sweden; Danderyd University Hospital, Stockholm, Sweden","Haglund L., Swedish School of Sport and Health Sciences, Stockholm, Sweden; Roos A., Karolinska Institutet University Library, Stockholm, Sweden; Wallgren-Björk P., Danderyd University Hospital, Stockholm, Sweden","Librarians in Sweden are facing huge challenges in meeting the demands of their organisations and users. This article looks at four key areas: coping with open science/open access initiatives; increasing demands from researchers for support doing systematic reviews; understanding user experiences in Swedish health science libraries; and the consequences of expanding roles for recruitment and continuing professional development. With regard to changing roles, there is an increasing shift from the generalist towards the expert role. The authors raise the issue as to how to prepare those new to the profession to the changing environment of health science libraries. © 2018 The Authors. Health Information and Libraries Journal published by John Wiley & Sons Ltd on behalf of Health Libraries Group","careers; collaboration; library and information professionals; licensing; national strategies; nordic states; open access (OA); professional development; research data (management); review; systematic","Access to Information; Cooperative Behavior; Humans; Information Services; Librarians; Libraries, Medical; Library Services; Professional Role; Staff Development; Sweden; article; career; health science; human; library; licensing; professional development; scientist; Sweden; systematic review; access to information; cooperation; information service; librarian; personnel management; professional standard; Sweden","","","","","Vetenskapsrådet, VR","Developments within the other areas of open science (open data/tools/methods) are quite decentralised compared to OA. These are coordinated by the Swedish Research Council, but there are several actors in this field (‘SND Swedish National Data Service, 2017; SUNET, 2015; Swedish National Infrastructure for Computing). The Government has given the Swedish Research Council the task of evaluating the extent to which publicly financed data fulfils the FAIR principles. This task must be finished in December 2018.","Appleton L., User experience (UX) in libraries: Let's get physical (and digital), Insights, 29, 3, pp. 224-227, (2016); Bell S., Design thinking: Librarians are incorporating it into their practice (The 2.018 Conference), (2018); Beverley C.A., Booth A., Bath P.A., The role of the information specialist in the systematic review process: A health information case study, Health Information & Libraries Journal, 20, 2, pp. 65-74, (2003); (2018); Bjork B.-C., Scholarly journal publishing in transition- from restricted to open access, Electronic Markets, 27, 2, pp. 101-109, (2017); (2018); (2018); (2018); Recommendation on access to and preservation of Scientific Information. Brussels, (2018); Foster M., Systematic reviews training for librarians: Planning, developing and evaluating, Journal of EAHIL, 14, 1, pp. 4-8, (2018); Jonson R., Towards a Competitive and Sustainable OA Market in Europe – A Study of the Open Access Market and Policy Environment OpenAIREproject: European Commission, (2017); (2016); Kronman U., Open Access i SwePub 2010–2016, (2016); Kronman U., Evaluation of offset agreements – report 3: Springer Compact, (2018); Utbildningsplan Biblioteks och informationsvetenskap, 180 högskolepoäng Library and Information Science, 180 credits, (2016); Arkivvetenskap, biblioteks- och informationsvetenskap respektive museologi (ABM) - Masterprogram, (2018); Meert D., Torabi N., Costella J., Impact of librarians on reporting of the literature searching component of pediatric systematic reviews, Journal of the Medical Library Association, 104, 4, pp. 267-277, (2016); Kunskap i samverkan – för samhällets utmaningar och stärkt konkurrenskraft, (2016); Profession, Utbildning, Forskning Biblioteks- och informationsvetenskap för en stärkt bibliotekarieprofession Nationell biblioteksstrategi, (2018); Open APC Sweden A national open repository of publication costs for open access articles, (2018); (2018); Nicholson J., McCrillis A., Williams J., Collaboration challenges in systematic reviews: A survey of health sciences librarians, Journal of the Medical Library Association, 105, 4, pp. 385-393, (2017); Priestner A., The Speaking Wall, (2015); Konsten att synliggöra bibliotek – ett halländskt projekt 2009-2010, pp. 1-68, (2011); Why do we need scientific assessment?, (2018); (2017); (2015); Bibliotekarie 180 högskolepoäng, heltid 100%, (2018); Masterprogram i Biblioteks- och informationsvetenskap, distansutbildning 120 högskolepoäng, heltid 100%, (2018); Biblioteks- och informationsvetenskap – Masterprogram i ABM 2018/2019; (2016); (2013); (2016); (2017); Vetenskaplig publicering 7,5 högskolepoäng, deltid 50% – distans, (2018)","","","Blackwell Publishing Ltd","","","","","","14711834","","","30006988","English","Health Inf. Libr. J.","Review","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85050915250"
"Adeniran P.O.; Oyovwevotu L.","Adeniran, Pauline Oghenekaro (57199212065); Oyovwevotu, Luke (57211187778)","57199212065; 57211187778","Academic library research support services: A review of Redeemer's University and the Nigeria natural medicine development agency's research activities","2019","Library Philosophy and Practice","2019","","2753","","","","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072930155&partnerID=40&md5=9f47d9adad36b44174b880689e3e1840","Tekena Tamuna Library, Redeemer's University, Nigeria; The Nigeria Natural Medicine Development Agency, Nigeria","Adeniran P.O., Tekena Tamuna Library, Redeemer's University, Nigeria; Oyovwevotu L., The Nigeria Natural Medicine Development Agency, Nigeria","The focus of academic libraries is to support teaching, learning and research in their immediate institutions. Academic libraries support research by providing research collections, services, data literacy training and research data management. This study examined academic libraries research support and the challenges associated with the utilisation of such services by researchers in Redeemer's University and the Nigeria Natural Medicine Development Agency both in Nigeria. A questionnaire was used to collect data from researchers in the two institution and findings revealed the research activities of the respondents and the varying levels of engagement in different types of research support services offered by academic libraries. Findings also revealed that the researchers moderately utilised these services. Recommendations were given based on the findings of the study. © 2019 Library Philosophy and Practice.","Academic libraries; Library resources; Library services; Library usage; Research support","","","","","","","","EFA (Nigeria) Report Card 2007, (2007); Onaolapo S.A., Evaluating the use of Polytechnic Libraries in Nigeria: A Case Study of Federal Polytechnic, Offa, Library, Kwara State, Nigeria, Library Philosophy and Practice (e-journal), (2016); Onwudinjo O.T., Law Journal Collections: Accreditation Issues and Imperatives for Law, Library Philosophy and Practice, 7, 5, pp. 148-152, (2015); Singh D., The Role of the Academic Library in Facilitating Research: Perceptions of Postgraduate Students, (2007); Genevieve H., Lynn K., The role of an academic library in research: researchers' perspectives at a South African University of Technology, SA Jnl, Libs & Info Sci, 77, 1, (2011); Hamblin Y., Library portals case studies, Assignation, 22, 3, pp. 26-29, (2005); Azad A.N., Seyyed F.J., Factors influencing faculty research productivity: evidence from AACSB Accredited Schools in the GCC countries, Journal of International Business Research, (2007)","","","University of Idaho Library","","","","","","15220222","","","","English","Libr. Philos. Pract.","Article","Final","","Scopus","2-s2.0-85072930155"
"","","","GL-Conference Series: Conference Proceedings","2019","GL-Conference Series: Conference Proceedings","2019-December","","","","","161","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062998858&partnerID=40&md5=525207db140f9497c93539c63610bf31","","","The proceedings contain 15 papers. The topics discussed include: legal issues surrounding the collection, use and access to grey data in the university setting; how data policies reflect the political will of organizations; on open access to research data: experiences and reflections from DANS; the data librarian: myth, reality or utopia?; research data management: what can librarians really help?; data management and the role of librarians; measuring reuse of institutionally-hosted grey literature; published electronic media are becoming grey; semantic query analysis from the global science gateway; open data engages citation and reuse: a follow-up study on enhanced publication; and when the virtual becomes reality: an environmental scan of the presence of virtual reality and artificial intelligence in health and cancer care environments.","","","","","","","","","","","","TextRelease","EBSCO; et al.; Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR); Korea Institute of Science and Technology Information (KISTI); Nuclear Information Section; International Atomic Energy Agency (NIS-IAEA); Slovak Centre of Scientific and Technical Information (CVTISR)","20th International Conference on Grey Literature: Research Data Fuels and Sustains Grey Literature, GL 2018","3 December 2018 through 4 December 2018","New Orleans","145765","13862316","978-907748433-3","","","English","GL-Conf. Series: Conf. Proc.","Conference review","Final","","Scopus","2-s2.0-85062998858"
"Yazdi M.A.","Yazdi, M. Amin (57190273762)","57190273762","Enabling operational support in the research data life cycle","2019","CEUR Workshop Proceedings","2432","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071748777&partnerID=40&md5=aacea011a2f4271b9be4851760fadc14","IT Center, RWTH Aachen University, Aachen, 52074, Germany","Yazdi M.A., IT Center, RWTH Aachen University, Aachen, 52074, Germany","Since 2015 a set of preliminary design studies were started on how to promote the stewardship of research data at RWTH Aachen University. This has resulted in a bottom-up software architecture approach that has created fundamentals for interconnection of Research Data Management (RDM) services. It has facilitated the development of essential services for the collection of structured data-sets with unique persistent identifiers. However, this service-oriented architecture has to be complemented by a set of web technologies to support the exploration and discovery of relevant data or, track and trace data within the research life cycle. With respect to lessons learned from the RDM project and literature reviews, besides technical improvements, investigation on scientists’ research process and providing means for operational support (data detection, prediction, and recommendations) are essential. Thus, this research project plans to enable operational support for RDM services across the research data life cycle while at the same time, keeping an eye on data privacy concerns. The goal is to build control-flow models, predict deviations and recommend personalized solutions by analyzing and discovering users’ process model with the help of process intelligence techniques. © 2019 CEUR-WS. All rights reserved.","Operational support; Process discovery; Process mining; Research data management","Data mining; Data privacy; Information services; Life cycle; Service oriented architecture (SOA); Essential services; Literature reviews; Process Discovery; Process intelligence; Process mining; Research data managements; Research life cycles; Technical improvement; Information management","","","","","","","van der Aalst W., Process Mining Discovery, Conformance and Enhancement of Business Processes, 2, (2011); van der Aalst W., Process Mining: Data Science in Action, (2016); van der Aalst W., Adriansyah A., van Dongen B., Replaying history on process models for conformance checking and performance analysis, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2, 2, pp. 182-192, (2012); van der Aalst W., Gunther C.W., Finding structure in unstructured processes: The case for process mining, Application of Concurrency to System Design, 2007. ACSD 2007. Seventh International Conference on, pp. 3-12, (2007); Agrawal R., Gunopulos D., Leymann F., Mining process models from workflow logs, International Conference on Extending Database Technology, pp. 467-483, (1998); Belfiore J.C., Rekaya G., Viterbo E., The golden code: A 2 x 2 full-rate space-time code with non-vanishing determinants, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings., (2004); Chen J., Zhou X., Jin Q., Recommendation of optimized information seeking process based on the similarity of user access behavior patterns, Personal and Ubiquitous Computing, 17, 8, pp. 1671-1681, (2013); Donoho D., 50 years of data science, Journal of Computational and Graphical Statistics, 26, 4, pp. 745-766, (2017); van Eck M.L., Lu X., Leemans S.J., van der Aalst W.M., PM<sup>2</sup>: A process mining project methodology, International Conference on Advanced Information Systems Engineering, pp. 297-313, (2015); Groeger C., Schwarz H., Mitschang B., Prescriptive analytics for recommendation-based business process optimization, International Conference on Business Information Systems, pp. 25-37, (2014); Huang X., Lu T., Ding X., Gu N., Enabling data recommendation in scientific workflow based on provenance, 2013 8th ChinaGrid Annual Conference, pp. 117-122, (2013); Kim Y., Adler M., Social scientists data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories, International Journal of Information Management, 35, 4, pp. 408-418, (2015); Kokolakis S., Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon, Computers & Security, 64, pp. 122-134, (2017); Kumar M., Thomas L., Annappa B., Distilling lasagna from spaghetti processes, Proceedings of the 2017 International Conference on Intelligent Systems, Meta-Heuristics & Swarm Intelligence, pp. 157-161, (2017); Press G., A Very Short History of Data Science, (2013); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Government Information Quarterly, 30, pp. S19-S31, (2013); Skulimowski A.M., Kacprzyk J., Knowledge, information and creativity support systems: Recent trends, advances and solutions, KICSS2013-8th International Conference on Knowledge, Information, and Creativity Support Systems, 364, (2016); Terragni A., Hassani M., Analyzing customer journey with process mining: From discovery to recommendations, The IEEE International Conference on Future IOT and Cloud, 2018. FiCloud 2018. 6th International Conference on, (2018); Yazdi M., Valdez A.C., Lichtschlag L., Ziefle M., Borchers J., Visualizing opportunities of collaboration in large research organizations, International Conference on HCI in Business, Government and Organizations, pp. 350-361, (2016)","M.A. Yazdi; IT Center, RWTH Aachen University, Aachen, 52074, Germany; email: yazdi@itc.rwth-aachen.de","Claes J.; van Dongen B.","CEUR-WS","","2019 International Conference on Process Mining Doctoral Consortium, ICPM-DC 2019","23 June 2019","Aachen","151005","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85071748777"
"Revez J.","Revez, Jorge (57192803532)","57192803532","Opening the heart of science: A review of the changing roles of research libraries","2018","Publications","6","1","9","","","","7","10.3390/PUBLICATIONS6010009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062278519&doi=10.3390%2fPUBLICATIONS6010009&partnerID=40&md5=8fc3461a008035cc71a62d5d41a6b36b","Alameda da Universidade, Centro de Estudos Clássicos, Faculdade de Letras, Universidade de Lisboa, Lisboa, 1600-214, Portugal","Revez J., Alameda da Universidade, Centro de Estudos Clássicos, Faculdade de Letras, Universidade de Lisboa, Lisboa, 1600-214, Portugal","In a world of information overload and data deluge, is opening science a research library's duty? Or is the openness of science deeply changing libraries, ultimately converting them into something else? The purpose of the review is to highlight the challenging issues stemming from the relationship between research and libraries. A broad literature analysis was performed focused on the intersection of three different perspectives: (1) the future of research libraries, (2) the emerging new roles, and (3) the ongoing openness of science. Libraries are still at the heart of science but challenged by several stakeholders within the complexity of present science production and communication. Research support services, research data management, or research information management are emerging roles, among others, sustaining an open path where libraries thrive to be more collaborative while looking forward to establishing new partnerships. © 2019 by the authors.","Open science; Research libraries; Research support services","","","","","","Carlos Guardado da Silva","This research had no funding but as part of an ongoing Ph.D. study, I want to show gratitude to my thesis supervisors for all their support: Maria Manuel Borges (University of Coimbra, Portugal) and Carlos Guardado da Silva (University of Lisbon, Portugal). I also acknowledge Carla Esteves who helped me with the English version and the peer-reviewers.","Wilson L.R., The Service of Libraries in Promoting Scholarship and Research, Libr. Q. Inf. Community Policy, 3, pp. 127-145, (1933); Vandegrift M., Designing Digital Scholarship Ecologies, LIS Scholarsh. Arch. Prepr, (2018); Garvey W.D., Communication: The Essence of Science: Facilitating Information Exchange among Librarians, Scientists, Engineers and Students, (1979); Science as an Open Enterprise, (2012); Bartling S., Friesike S., Opening Science: The Evolving Guide on How the Internet is Changing Research, Collaboration and Scholarly Publishing, (2014); Borges M.M., Reflexos da Tecnologia Digital no Processo de Comunicação da Ciência, Una Mirada a la Ciencia de la Información Desde los Nuevos Contextos Paradigmáticos de la Posmodernidad, pp. 179-196, (2017); Dewey B.I., Transforming Research Libraries for the Global Knowledge Society, (2010); Earnshaw R., Vince J., Digital Convergence-Libraries of the Future Rae Earnshaw and John Vince, (2008); Brown J.S., Changing How We Think About and Lead Change, Library Workforce for 21st Century Research Libraries, (2012); Dempsey L., Library Collections in the Life of the User: Two Directions, Liber Q, 26, pp. 338-359, (2017); Pinfield S., Cox A.M., Rutter S., Mapping the Future of Academic Libraries: A Report for SCONUL, (2017); Dempsey L., The Network Reshapes the Library: Lorcan Dempsey on Libraries, Services, and Networks, (2014); Preliminary Report, (2016); Hoffman S., Dynamic Research Support for Academic Libraries, (2016); Powering Scholarship: RLUK Strategy 2014-17, (2014); Report of the Association of Research Libraries Strategic Thinking and Design Initiative, (2014); Strategic Thinking and Design Initiative: Extended and Updated Report, (2016); Research Libraries Powering Sustainable Knowledge in the Digital Age: LIBER Europe Strategy 2018-2022, (2017); Lougee W.P., Diffuse Libraries: Emergent Roles for the Research Library in the Digital Age, (2002); Lougee W.P., The diffuse library revisited: Aligning the library as strategic asset, Libr. Hi Tech, 27, pp. 610-623, (2009); Marcum D.B., George G., The Data Deluge: Can Libraries Cope with e-Science?, (2010); Lincoln Y.S., Research Libraries in the Twenty-First Century, InHigher Education: Handbook of Theory and Research, 25, pp. 425-448, (2010); Koltay T., Are you ready?, Tasks and roles for academic libraries in supporting Research 2.0. New Libr. World, 117, pp. 94-104, (2016); Cox J., Communicating New Library Roles to Enable Digital Scholarship: A Review Article, New Rev. Acad. Librariansh, 22, pp. 132-147, (2016); Anglada L., Son las bibliotecas sostenibles en un mundo de información libre, digital y en red?, Prof. Inf, 23, pp. 603-611, (2014); Borgman C.L., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Webster D., Strategic Challenges Facing Research Libraries, Report and Proceedings of a Seminar on Managing University Libraries, Held on 26-27 August 2002 at the OECD Headquarters in Paris, (2002); Case M.M., Partners in Knowledge Creation: An Expanded Role for Research Libraries in the Digital Future, J. Libr. Adm, 48, pp. 141-156, (2008); No Brief Candle: Reconceiving Research Libraries for the 21st Century, (2008); Smith A., The Research Library in the 21st Century: Collecting, Preserving, and Making Accessible Resources for Scholarship, No Brief Candle: Reconceiving Research Libraries for the 21st Century, pp. 13-20, (2008); Luce R.E., A New Value Equation Challenge: The Emergence of eResearch and Roles for Research Libraries, No Brief Candle: Reconceiving Research Libraries for the 21st Century, pp. 42-50, (2008); Heidorn P.B., The Emerging Role of Libraries in Data Curation and E-science, J. Libr. Adm, 51, pp. 662-672, (2011); Gold A., Cyberinfrastructure, Data, and Libraries, Part 2: Libraries and the Data Challenge: Roles and Actions for Libraries, D-Lib Mag, (2007); Harris S., Moving Towards an Open Access Future: The Role of Academic Libraries, (2012); Rodrigues E., GRL2020 Position Paper, Paving the Way for a Collaborative Global Research Environment: Outcomes of GRL2020 Europe, (2008); Griffin S., New Roles for Libraries in Supporting Data-Intensive Research and Advancing Scholarly Communication, IJHAC, 7, pp. 59-71, (2013); Paving the Way for a Collaborative Global Research Environment: Outcomes of GRL2020 Europe, (2008); Corrall S., Designing Libraries for Research Collaboration in the Network World: An Exploratory Study, Liber Q, 24, pp. 17-48, (2014); Tancheva K., Gessner G.C., Tang N., Eldermire E., Furnas H., Branchini D., Steinhart G., A Day in the Life of a (Serious) Researcher: Envisioning the Future of the Research Library, (2016); Gessner G.C., Eldermire E., Tang N., Tancheva K., The Research Lifecycle and the Future of Research Libraries: A Library of Apps, At the Helm: Leading Transformation: The Proceedings of the ACRL 2017 Conference, pp. 533-543, (2017); Hartsell-Gundy A., Braunstein L., Golomb L., Digital Humanities in the Library: Challenges and Opportunities for Subject Specialists, (2015); White J.W., Gilbert H., Laying the Foundation: Digital Humanities in Academic Libraries, (2016); Kamposiori C., The Role of Research Libraries in the Creation, Archiving, Curation, and Preservation of Tools for the Digital Humanities, (2017); Bryant R., Clements A., Feltes C., Groenewegen D., Huggard S., Mercer H., Missingham R., Oxnam M., Rauh A., Wright J., Research Information Management: Defining RIM and the Library's Role, (2017); Johnson I.M., The intelligent university library: Developing a more comprehensive option for the researcher, Inf. Dev, 33, pp. 219-223, (2017); Kingsley D., ""Become Part of the Research Process""-Observations from RLUK2017; Lynch C., Updating the Agenda for Academic Libraries and Scholarly Communications, Coll. Res. Libr, 78, pp. 126-130, (2017); Next Generation Repositories: Behaviours and Technical Recommendations of the COAR Next Generation Repositories Working Group, (2017); Nicholas D., Boukacem-Zeghmouri C., Rodriguez-Bravo B., Xu J., Watkinson A., Abrizah A., Herman E., Swigon M., Where and how early career researchers find scholarly information, Learn. Publ, pp. 1-11, (2017); Nicholas D., Publish or perish thwarts young researchers' urge to innovate, Res. Eur, 440, pp. 7-8, (2016); Bush V., Science: The Endless Frontier, (1945); The Value of Libraries for Research and Researchers: A RIN and RLUK Report, (2011); Vaughan K.T.L., Hayes B.E., Lerner R.C., McElfresh K.R., Pavlech L., Romito D., Reeves L.H., Morris E.N., Development of the research lifecycle model for library services, J. Med. Libr. Assoc, 101, pp. 310-314, (2013); Vinopal J., McCormick M., Supporting Digital Scholarship in Research Libraries: Scalability and Sustainability, J. Libr. Adm, 53, pp. 27-42, (2013); Bjork B.-C., A model of scientific communication as a global distributed information system, Inf. Res, 12, (2007); Carlson J., Kneale R., Embedded librarianship in the research context: Navigating new waters, Coll. Res. Libr. News, 72, pp. 167-170, (2011); Brewerton A., Re-Skilling for Research: Investigating the Needs of Researchers and How Library Staff Can Best Support Them, New Rev. Acad. Librariansh, 18, pp. 96-110, (2012); Plutchak T.S., A Librarian out of the Library, J. eSci. Librariansh, 5, pp. 1-5, (2016); Anderson R., A quiet culture war in research libraries-and what it means for librarians, researchers and publishers, Insights, 28, pp. 21-27, (2015)","J. Revez; Alameda da Universidade, Centro de Estudos Clássicos, Faculdade de Letras, Universidade de Lisboa, Lisboa, 1600-214, Portugal; email: jrevez@campus.ul.pt","","MDPI AG","","","","","","23046775","","","","English","Publ.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85062278519"
"de León M.A.P.; de Ferrer L.A.I.","de León, Mireia Alcalá Ponce (57155939400); de Ferrer, Lluís Anglada I. (38261370100)","57155939400; 38261370100","From open access to open data: Collaborative work in the university libraries of Catalonia","2018","LIBER Quarterly","28","1","","1","14","13","2","10.18352/lq.10253","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064928929&doi=10.18352%2flq.10253&partnerID=40&md5=949bfc31f8e81d9394ebf46d33a752b0","Àrea de Ciència Oberta, Consorci de Serveis Universitaris de Catalunya, Barcelona, Spain","de León M.A.P., Àrea de Ciència Oberta, Consorci de Serveis Universitaris de Catalunya, Barcelona, Spain; de Ferrer L.A.I., Àrea de Ciència Oberta, Consorci de Serveis Universitaris de Catalunya, Barcelona, Spain","In the last years, the scientific community and funding bodies have paid attention to data collected, generated or used in different research activities, because the dissemination of these data can be seen as a constituent of Open Science. For this reason, many funders are requiring or promoting the development of Data Management Plans, and depositing open data following the FAIR (Findable, Accessible, Interoperable and Reusable) data management principles. Libraries and research offices of Catalan universities that work in a coordinated way within the Open Science Area of the University Services Consortium of Catalonia offer support services to research data management. The different activities carried out at the Consortium level, as well as the implementation of the service in each university are presented. © 2018, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","FAIR data; Open science; Research data management; Research support; University libraries","","","","","","","","Abadal E., Olle C., Redondo S., Open access monographs published by university presses in Spain, El Profesional De La información, 27, 2, pp. 300-311, (2018); (2018); Australian Libraries: The Digital Economy within everyone’s Reach, (2017); Anglada L., Abadal E., ¿Qué es la ciencia abierta?, Anuario Thinkepi, 12, pp. 292-298, (2018); CBUC 2004–2013: Els últims Deu Anys d’activitats I De presència pública, (2015); Centre De Serveis Científics I Acadèmics De Catalunya, (2018); Gestió De Les Dades De Recerca: Resultats De l’enquesta Prospectiva a Gener De 2016, (2016); Gestió de les dades de recerca: Resultats de l’enquesta prospectiva a gener de 2016 [Dataset], Zenodo, (2016); Plans De gestió De Dades: Versió 2, (2016); Data Management Plans: Version 2, December 2016, (2016); Recomanacions per Seleccionar Un Repositori per Al dipòsit De Dades De Recerca: Versió 3, Maig 2017, (2017); Research Data Management Plan/Pla De gestió De Dades De Recerca, (2018); Infografia Research Data Management Plan, (2018); DMP, (2016); LEARN Toolkit of Best Practice for Research Data Management, (2017); Apoyo a Centros De Excelencia “Severo Ochoa” Y a Unidades De Excelencia “María De Maeztu, (2018); Plan Estatal De investigación científica Y técnica De innovación 2017–2020, (2018); Parusel I., Reoyo S., Portal De La Recerca De Catalunya: Agregant informació De procedència I Institucions Diverses, (2018); Peset F., Aleixandre-Benavent R., Gonzalvo S., Ferrer-Sapena A., Experiencias y lecciones aprendidas en datos abiertos en investigación, El Grupo Datasea. Evista PH, 92, pp. 224-225, (2017); Policy Recommendations for Open Access to Research Data in Europe, (2014); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, Plos One, 6, 6, (2011); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research Data Services in European Academic Research Libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85064928929"
"Nind T.; Galloway J.; McAllister G.; Scobbie D.; Bonney W.; Hall C.; Tramma L.; Reel P.; Groves M.; Appleby P.; Doney A.; Guthrie B.; Jefferson E.","Nind, Thomas (35189331700); Galloway, James (57193279789); McAllister, Gordon (57203180578); Scobbie, Donald (57203190291); Bonney, Wilfred (26649829000); Hall, Christopher (57193279809); Tramma, Leandro (57191271137); Reel, Parminder (26639748200); Groves, Martin (56884028000); Appleby, Philip (57203186868); Doney, Alex (6602412421); Guthrie, Bruce (10143419400); Jefferson, Emily (56203564900)","35189331700; 57193279789; 57203180578; 57203190291; 26649829000; 57193279809; 57191271137; 26639748200; 56884028000; 57203186868; 6602412421; 10143419400; 56203564900","The research data management platform (RDMP): A novel, process driven, open-source tool for the management of longitudinal cohorts of clinical data","2018","GigaScience","7","7","giy060","","","","10","10.1093/gigascience/giy060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050861658&doi=10.1093%2fgigascience%2fgiy060&partnerID=40&md5=dd9aac62a4de876a248da2ddec2be4a5","Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Edinburgh Parallel Computing Centre, James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, United Kingdom","Nind T., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Galloway J., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; McAllister G., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Scobbie D., Edinburgh Parallel Computing Centre, James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, United Kingdom; Bonney W., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Hall C., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Tramma L., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Reel P., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Groves M., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Appleby P., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Doney A., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Guthrie B., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom; Jefferson E., Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Mail Box 15, Dundee, DD1 9SY, United Kingdom","Background: The Health Informatics Centre at the University of Dundee provides a service to securely host clinical datasets and extract relevant data for anonymized cohorts to researchers to enable them to answer key research questions. As is common in research using routine healthcare data, the service was historically delivered using ad-hoc processes resulting in the slow provision of data whose provenance was often hidden to the researchers using it. This paper describes the development and evaluation of the Research Data Management Platform (RDMP): an open source tool to load, manage, clean, and curate longitudinal healthcare data for research and provide reproducible and updateable datasets for defined cohorts to researchers. Results: Between 2013 and 2017, RDMP tool implementation tripled the productivity of data analysts producing data releases for researchers from 7.1 to 25.3 per month and reduced the error rate from 12.7% to 3.1%. The effort on data management reduced from a mean of 24.6 to 3.0 hours per data release. The waiting time for researchers to receive data after agreeing a specification reduced from approximately 6 months to less than 1 week. The software is scalable and currently manages 163 datasets. A total 1,321 data extracts for research have been produced, with the largest extract linking data from 70 different datasets. Conclusions: The tools and processes that encompass the RDMP not only fulfil the research data management requirements of researchers but also support the seamless collaboration of data cleaning, data transformation, data summarization and data quality assessment activities by different research groups. © The Author(s) 2018. Published by Oxford University Press.","Clinical datasets; Data catalogue; Health informatics; Record linkage; Research data management; Translational research","Computer Systems; Databases, Factual; Humans; Internet; Longitudinal Studies; Medical Informatics; Programming Languages; Quality Control; Reproducibility of Results; Research; Scotland; Software; Universities; cleaning; cohort analysis; human; medical informatics; note; productivity; publication; quality control; scientist; software; translational research; computer language; computer system; factual database; Internet; longitudinal study; medical informatics; procedures; reproducibility; research; Scotland; university","","","","","Farr Institute of Health Informatics Research and Dundee University Medical School; Scottish Health Informatics Programme; Wellcome Trust, WT, (WT086113); Horizon 2020 Framework Programme, H2020, (633983); Medical Research Council, MRC, (MR/M501633/1)","The authors acknowledge the support from the Farr Institute of Health Informatics Research and Dundee University Medical School. This work was supported by the Medical Research Council (MRC) grant number MR/M501633/1 (PI: Andrew Morris) and the Wellcome Trust grant number WT086113 through the Scottish Health Informatics Programme (SHIP) (PI: Andrew Morris). SHIP is a collaboration between the Universities of Aberdeen, Dundee, Edinburgh, Glasgow, and St Andrews, and the Information Services Division of NHS Scotland. This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 633 983 (PI: Maria-Christina Zennaro).","Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J Libr Inf Sci, 46, 4, pp. 299-316, (2014); Ball A., Reviewof the State of the Art of the Digital Curation of Research Data; Ball A., Review of data management lifecycle models; Corti L., Van den Eynden V., Bishop L., Et al., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Poschen M., Finch J., Procter R., Et al., Development of a pilot data management infrastructure for biomedical researchers at university of manchester-approach, findings, challenges and outlook of the MaDAM project, Int J Digit Curation, 7, 2, pp. 110-122, (2012); Murphy S.N., Weber G., Mendis M., Et al., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), J Am Med Inform Assoc, 17, 2, pp. 124-130, (2010); Lowe H.J., Ferris T.A., Hernandez P.M., Et al., STRIDE-An integrated standards-based translational research informatics platform, AMIA Annu Symp Proc, 2009, pp. 391-395, (2009); Brandt C.A., Morse R., Matthews K., Et al., Metadata-driven creation of data marts from an EAV-modeled clinical research database, Int J Med Inform, 65, 3, pp. 225-241, (2002); Li Z., Wen J., Zhang X., Et al., ClinData Express-a metadata driven clinical research data management system for secondary use of clinical data, AMIA Annu Symp Proc, 2012, pp. 552-557, (2012); Harris P.A., Taylor R., Thielke R., Et al., Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform, 42, 2, pp. 377-381, (2009); Athey B.D., Braxenthaler M., Haas M., Et al., tranSMART: An open source and Community-Driven informatics and data sharing platform for clinical and translational research, AMIA Jt Summits Transl Sci Proc, 2013, pp. 6-8, (2013); Scheufele E., Aronzon D., Coopersmith R., Et al., tranSMART: An open source knowledge management and high content data analytics platform, AMIA Jt Summits Transl Sci Proc, 2014, pp. 96-101, (2014); Kimball P., Verbeke S., Flattery M., Et al., Influenza vaccination does not promote cellular or humoral activation among heart transplant recipients, Transplantation, 69, 11, pp. 2449-2451, (2000); Nind T.; Kimball R., Ross M., Thorthwaite W., Et al., The DataWarehouse Lifecycle Toolkit, (2008); Fuchsberger C., Flannick J., Teslovich T.M., Et al., The genetic architecture of type 2 diabetes, Nature, 536, 7614, pp. 41-47, (2016); Dreischulte T., Donnan P., Grant A., Et al., Safer Prescribing-A trial of education, informatics, and financial incentives, N Engl J Med, 374, 11, pp. 1053-1064, (2016); Stitziel N.O., Et al., Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease, N Engl J Med, 374, 12, pp. 1134-1144, (2016); Gaulton K.J., Ferreira T., Lee Y., Et al., Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci, Nat Genet, 47, 12, pp. 1415-1425, (2015); A Charter for Safe Havens in Scotland, (2015); (2012); Hebert H.L., Shepherd B., Milburn K., Et al., Cohort profile: Genetics of diabetes audit and research in Tayside Scotland (GoDARTS), Int J Epidemiol, 47, 2, (2018); Bonney W., Galloway J., Hall C., Et al., Mapping local codes to read codes, Stud Health Technol Inform, 234, pp. 29-36, (2017); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a 'wicked' problem, J Librarianship Info Sci, 48, 1, pp. 3-17, (2016); Idso S.B., Kimball B.A., Pettit Iii G.R., Et al., Effects of atmospheric CO2 enrichment on the growth and development of Hymenocallis littoralis (Amaryllidaceae) and the concentrations of several antineoplastic and antiviral constituents of its bulbs, Am J Bot, 87, 6, pp. 769-773, (2000); Nind T., Galloway J., McAllister G., Et al., Supporting data for ""The Research Data Management Platform (RDMP), Giga-Science Database, (2018)","E. Jefferson; Health Informatics Centre, University of Dundee, Ninewells Hospital and Medical School, Dundee, Mail Box 15, DD1 9SY, United Kingdom; email: e.r.jefferson@dundee.ac.uk","","Oxford University Press","","","","","","2047217X","","","29790950","English","GigaScience","Note","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85050861658"
"Funamori M.; Hayashi M.; Komiyama Y.; Tsuchiya M.; Yamaji K.","Funamori, Miho (55960871900); Hayashi, Masaharu (57208574827); Komiyama, Yusuke (57208828375); Tsuchiya, Masatoshi (13104548600); Yamaji, Kazutsuna (7102241687)","55960871900; 57208574827; 57208828375; 13104548600; 7102241687","Requirements Analysis of System for Research Data Management to Prevent Scientific Misconduct","2018","Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018","","","8693373","382","389","7","1","10.1109/IIAI-AAI.2018.00083","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065173976&doi=10.1109%2fIIAI-AAI.2018.00083&partnerID=40&md5=2037eb0f6d246f43df4d6e6448b91900","Research Center for Open Science and Data Platform, National Institute of Informatics, Tokyo, Japan; Information and Media Center, Toyohashi University of Technology, Toyohashi, Japan","Funamori M., Research Center for Open Science and Data Platform, National Institute of Informatics, Tokyo, Japan; Hayashi M., Research Center for Open Science and Data Platform, National Institute of Informatics, Tokyo, Japan; Komiyama Y., Research Center for Open Science and Data Platform, National Institute of Informatics, Tokyo, Japan; Tsuchiya M., Information and Media Center, Toyohashi University of Technology, Toyohashi, Japan; Yamaji K., Research Center for Open Science and Data Platform, National Institute of Informatics, Tokyo, Japan","With the pressing need for research data management to prevent scientific misconduct (RDM-PSM) in Japan, our goal in this study was to identify the basic functionalities and requirements of an RDM-PSM system. To achieve it, we first extracted the core elements of the guidelines on preserving research materials set forth by the Science Council of Japan and reorganized the elements to form the minimum requirements and two basic functionalities, 'Content Management' and 'Institutional Management,' of an RDM-PSM system. Next, the minimum requirements for RDM-PSM were scrutinized in order to formulate the system requirements and associated functions needed to develop the system. Finally, the functions were mapped onto an assumed RDM-PSM workflow model at academic institutions to evaluate the consistency and usability of these functions. Even though this requirements analysis was conducted on the basis of Japanese guidelines, the identified requirements and analysis procedure are useful in international contexts to meet the increasing demand worldwide for RDM-PSM. © 2018 IEEE.","Data policy; Institutional repository; Open Science; Research data management; Research materials preservation policy; Scientific misconduct","Information services; Requirements engineering; Institutional repositories; Materials preservation; Open science; Research data managements; Scientific misconduct; Information management","","","","","","","Data Sharing Policy and Implementation Guidance, (2003); Declaration on Access to Research Data from Public Funding, (2004); Juliet Statistics, (2018); Meadows A., To Share or Not to Share? That Is the (Research Data) Question., (2014); Fang F.C., Steen R.G., Casadevall A., Misconduct accounts for the majority of retracted scientific publications, Proc Natl Acad Sci USA, 109, 42, pp. 17028-17033, (2012); Kaiser J., Former U. S. Research Fraud Chief Speaks Out on Resignation, 'Frustrations, (2014); University of California Curation Center, (2018); Advice and Answers from the PURR Team; Information Services: Research Data Service, (2018); Guideline for Dealing with Scientific Misconduct, (2006); Guideline for Dealing with Scientific Misconduct, (2014); Reply: For the Enhancement of Soundness of Scientific Research, (2015); Basic Principles at JST for the Promotion of Open Science of Research Outputs, (2017); Japan Genomic Medicine Project: Data Sharing Policy for the Realization of Genomic Medicine, (2016); Open Access Implementation Policy for the Funding Programs of the Japan Society for the Promotion of Science, (2017); Model Case Study for the Promotion of Research Integrity at Research Institutions, (2017); Policy for the Prevention of Research Misconduct, (2014); F. Y. 2016 Survey of the Implementation of Guideline for Dealing with Research Misconduct, (2015); Policy for the Preservation of Research Materials, (2016); FAIR Data Principles","","","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics (IIAI)","7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018","8 July 2018 through 13 July 2018","Yonago","147517","","978-153867447-5","","","English","Proc. - Int. Congr. Adv. Appl. Inf., IIAI-AAI","Conference paper","Final","","Scopus","2-s2.0-85065173976"
"Wasner C.; Barkow I.; Odoni F.","Wasner, Catharina (57202996228); Barkow, Ingo (57190193183); Odoni, Fabian (57193623967)","57202996228; 57190193183; 57193623967","Enhancing the research data management of computer-based educational assessments in Switzerland","2018","Data Science Journal","17","","18","","","","0","10.5334/dsj-2018-018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050126160&doi=10.5334%2fdsj-2018-018&partnerID=40&md5=4fc1c90f8981395ca0aa633cf824fe78","Swiss Institute for Information Science, University of Applied Sciences, Pulvermühlestrasse 57, Chur, CH-7000, China","Wasner C., Swiss Institute for Information Science, University of Applied Sciences, Pulvermühlestrasse 57, Chur, CH-7000, China; Barkow I., Swiss Institute for Information Science, University of Applied Sciences, Pulvermühlestrasse 57, Chur, CH-7000, China; Odoni F., Swiss Institute for Information Science, University of Applied Sciences, Pulvermühlestrasse 57, Chur, CH-7000, China","Since 2006 the education authorities in Switzerland have been obliged by the Constitution to harmonize important benchmarks in the educational system throughout Switzerland. With the development of national educational objectives in four disciplines an important basis for the implementation of this constitutional mandate was created. In 2013 the Swiss National Core Skills Assessment Program (in German: ÜGK – Überprüfung der Grundkompetenzen) was initiated to investigate the skills of students, starting with three of four domains: mathematics, language of teaching and first foreign language in grades 2, 6 and 9. ÜGK uses a computer-based test and a sample size of 25.000 students per year. A huge challenge for computer-based educational assessment is the research data management process. Data from several different systems and tools existing in different formats has to be merged to obtain data products researchers can utilize. The long term preservation has to be adapted as well. In this paper, we describe our current processes and data sources as well as our ideas for enhancing the data management. © 2018, Ubiquity Press Ltd. All rights reserved.","Computer-based assessments; Educational sciences; Metadata; OAIS; Research data management","Information management; Metadata; Students; Computer-based assessments; Computer-based tests; Educational assessment; Educational objectives; Educational science; Long-term preservation; OAIS; Research data managements; E-learning","","","","","","","Barkow I., The Challenges of Metadata Management in Computer-Based Surveys and Assessments, Dissertation. Szeged., (2016); Barkow I., Block W., Greenfield J., Gregory A., Hebing M., Hoyle L., Zenk-Moltgen W., Generic Longitudinal Business Process Model. DDI Working Paper Series – Longitudinal Best Practices, No. 5, (2013); Reference Model for an Open Archival Information System (OAIS), (2012); Mission Statement DARIS, (2017); Preservation Policy DARIS, (2017)","C. Wasner; Swiss Institute for Information Science, University of Applied Sciences, Chur, Pulvermühlestrasse 57, CH-7000, China; email: catharina.wasner@htwchur.ch","","Ubiquity Press Ltd","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85050126160"
"Zielinski T.; Hay J.; Millar A.J.","Zielinski, Tomasz (56181435000); Hay, Johnny (57211125119); Millar, Andrew J. (7201856684)","56181435000; 57211125119; 7201856684","The grant is dead, long live the data-migration as a pragmatic exit strategy for research data preservation [version 1; peer review: 2 approved]","2019","Wellcome Open Research","4","","104","","","","2","10.12688/wellcomeopenres.15341.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072736095&doi=10.12688%2fwellcomeopenres.15341.1&partnerID=40&md5=fe395faae9f6312b72a60bc3547aa53e","SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom; EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom","Zielinski T., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom; Hay J., EPCC, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom; Millar A.J., SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom","Open research, data sharing and data re-use have become a priority for publicly-and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering. PlaSMo stands for ‘Plant Systems-biology Modelling’ and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting till 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative public repository of secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in similar situation. We share our reflections on sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies. © 2019 Zielinski T et al.","Data sharing; Exit strategy; Research data management; Research funding; Sustainable data infrastructure","","","","","","UK Centre for Mammalian Synthetic Biology, (BB/M018040); Wellcome Trust, WT, (204804, ISSF3); Biotechnology and Biological Sciences Research Council, BBSRC, (BB/M018040/1)","This work was funded by the Wellcome Trust through a Wellcome Institutional Strategic Support Fund (ISSF3) [204804]. This work was also supported by the Biotechnology and Biological Sciences Research Council (BBSRC) through the UK Centre for Mammalian Synthetic Biology [BB/M018040].","Concordat on Open Research Data, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, (2016); Wittig U., Rey M., Weidemann A., Et al., Data management and data enrichment for systems biology projects, J Biotechnol, 261, pp. 229-237, (2017); Stuart D., Baynes G., Hrynaszkiewicz I., Et al., Practical Challenges for Researchers in Data Sharing, Whitepaper, (2018); Knowledge Exchange Research Data, Expert Group and Science Europe Working Group, on Research Data: Funding Research Data Management and Related Infrastructures, (2016); Glont M., Nguyen T.V.N., Graesslin M., Et al., BioModels: Expanding horizons to include more modelling approaches and formats, Nucleic Acids Res, 46, D1, pp. D1248-D1253, (2018); Wolstencroft K., Krebs O., Snoep J.L., Et al., FAIRDOMHub: A repository and collaboration environment for sharing systems biology research, Nucleic Acids Res, 45, D1, pp. D404-D407, (2017); Wolstencroft K., Owen S., Krebs O., Et al., SEEK: A systems biology data and model management platform, BMC Syst Biol, 9, 1, (2015); Troup E., Clark I., Swain P., Et al., Practical Evaluation of SEEK and Openbis for Biological Data Management in Synthsys, (2015); Rocca-Serra P., Brandizi M., Maguire E., Et al., ISA software suite: Supporting standards-compliant experimental annotation and enabling curation at the community level, Bioinformatics, 26, 18, pp. 2354-2356, (2010); Littlejohn J., Jsonschema2pojo [Internet].; Berman H.M., Battistuz T., Bhat T.N., Et al., The Protein Data Bank, Acta Crystallogr Sect D Biol Crystallogr, 58, 1, pp. 899-907, (2002); Benson D.A., Cavanaugh M., Clark K., Et al., Genbank. Nucleic Acids Res., 41, Database, pp. D36-D42, (2013); Brazma A., Parkinson H., Sarkans U., Et al., ArrayExpress--a public repository for microarray gene expression data at the EBI, Nucleic Acids Res, 31, 1, pp. 68-71, (2003); van den Eynden V., Knight G., Vlad A., Et al., Towards Open Research: Practices, Experiences, Barriers and Opportunities [Internet]., (2016); Zielinski T., Moore A.M., Troup E., Et al., Strengths and limitations of period estimation methods for circadian data, Plos One, 9, 5, (2014); Bauch A., Adamczyk I., Buczek P., Et al., OpenBIS: A flexible framework for managing and analyzing complex data in biology research, BMC Bioinformatics, 12, (2011); Research Enrichment – Open Research; Business Models for Sustainable Data Repositories, (2017); Dillo I., Hodson S., Et al., Income Streams for Data Repositories, (2016); Zielinski T., Hay J., SynthSys/Seek-Java-RESTClient: Java RestClient for SEEK API 1.7.0 (Version v1.0.0), Zenodo, (2019); Zielinski T., Hay J., Synthsys/Seek-Bulk-Update: Bulk Update for Seek API 1.7.0 (Version V.1.0.0). Zenodo, (2019); Zielinski T., Tindal C., Synthsys/Plasmoportal: The Last Working Version of Plasmo Portal (Version V2.1.5). Zenodo, (2019)","","","F1000 Research Ltd","","","","","","2398502X","","","","English","Wellcome Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85072736095"
"","","","EPJ Web of Conferences","2018","EPJ Web of Conferences","186","","","","","340","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057733710&partnerID=40&md5=452f3824ee727fce4e5fbda6f699f3b3","","","The proceedings contain 46 papers. The topics discussed include:  bibliography, catalogs, pixel data: management of heterogeneous big data at CDS by the documentalists; associated data: indexation, discovery, challenges and roles; VizieR catalogue system certified by the data seal of approval; COSIM: the necessary evolution of a cross-identification tool along with data evolution; evolution of the NASA/IPAC extragalactic database (NED) into a data mining discovery engine. II. current contents and future plans; research data management in India: a pilot study; the astrolabe project: identifying and curating astronomical �dark data� through development of cyberinfrastructure re-sources; growing a bibliography; shared nomenclature and identifiers for telescopes and instruments; and a bibliometric analysis of observatory publications 2011-2015.","","","","","","","","","","","D'Abrusco R.; Harvard�Smithsonian Center for Astrophysics, 60 Garden St. MS 67, Cambridge, MA; Lesteven S.; Centre de Donnees Astronomique de Strasbourg(CDS), Observatoire Astronomique de Strasbourg, 11, Rue de l�Universite, Strasbourg; Dorch B.; Kern B.","EDP Sciences","American Astronomical Society; Astronomy and Astrophysics; Centre de Donnees Astronomique de Strasbourg (CDS); et al.; MNRAS; SPIE Digital Library","8th Library and Information Services in Astronomy: ""Astronomy Librarianship in the Era of Big Data and Open Science"", LISA 2018","6 June 2017 through 9 June 2017","Strasbourg","142663","21016275","978-275989054-5","","","English","EPJ Web Conf.","Conference review","Final","","Scopus","2-s2.0-85057733710"
"Aigner V.S.; Andrae M.; Bauer B.; Blumesberger S.; Kaiser O.; Stumpf M.","Aigner, Von Sebastian (57189698067); Andrae, Magdalena (57195640529); Bauer, Bruno (57200436821); Blumesberger, Susanne (27867529800); Kaiser, Olivia (56277864300); Stumpf, Markus (37010089500)","57189698067; 57195640529; 57200436821; 27867529800; 56277864300; 37010089500","Cooperative report of the 7TH German librarians’ congress: “Libraries For Change” (Leipzig, march 18-21, 2019); [Kooperativer Bericht Vom 7. Deutschen Bibliothekskongress: “Bibliotheken Verändern” (Leipzig, 18.-21. märz 2019)]","2019","VOEB-Mitteilungen","72","1","","184","213","29","0","10.31263/voebm.v72i1.2286","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068766035&doi=10.31263%2fvoebm.v72i1.2286&partnerID=40&md5=cda2d1f8aac7077f610a454d0ade152e","Die Österreichische Bibliothekenverbund und Service GmbH (OBVSG), Austria; Technische Universität Wien, Universitätsbibliothek, Austria; Medizinische Universität Wien, Universitätsbibliothek, Austria; Universität Wien, Bibliotheks- und Archivwesen, Austria","Aigner V.S., Die Österreichische Bibliothekenverbund und Service GmbH (OBVSG), Austria; Andrae M., Technische Universität Wien, Universitätsbibliothek, Austria; Bauer B., Medizinische Universität Wien, Universitätsbibliothek, Austria; Blumesberger S., Universität Wien, Bibliotheks- und Archivwesen, Austria; Kaiser O., Universität Wien, Bibliotheks- und Archivwesen, Austria; Stumpf M., Universität Wien, Bibliotheks- und Archivwesen, Austria","The 7th German Librarians’ Congress took place from 18 to 21 March 2019 in Leipzig. The motto of the conference, which was attended by more than 4.000 people (including 95 from Austria), was “Libraries for change”. This cooperative report covers the following topics: Specialised Information Services programme, long-term archiving, repositories, research data and research data management, open access, NS provenance research, quality management and accessibility. © Sebastian Aigner, Magdalena Andrae, Bruno Bauer, Susanne Blumesberger, Olivia Kaiser und Markus Stumpf.","108<sup>th</sup> German Librarians’ Day; 7<sup>th</sup> German Librarians’ Congress; Cooperative report; Leipzig 2019","","","","","","","","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85068766035"
"Elsayed A.M.; Saleh E.I.","Elsayed, Amany M. (36600229600); Saleh, Emad I. (56669527900)","36600229600; 56669527900","Research data management and sharing among researchers in Arab universities: An exploratory study","2018","IFLA Journal","44","4","","281","299","18","26","10.1177/0340035218785196","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050200116&doi=10.1177%2f0340035218785196&partnerID=40&md5=8e819a0b898d9f27a2479b7ca35ea4c7","King Abdulaziz University, Saudi Arabia; Helwan University, Egypt","Elsayed A.M., King Abdulaziz University, Saudi Arabia, Helwan University, Egypt; Saleh E.I., King Abdulaziz University, Saudi Arabia, Helwan University, Egypt","This study investigates researchers’ current practices for managing and sharing research data. An online survey was conducted among researchers from three Arab universities in Egypt, Jordan, and Saudi Arabia. In total, 337 participants filled out the questionnaire. The study shows that 97% of researchers were responsible for their research data, and 64.4% of researchers shared their data. Contributing to scientific progress and increasing research citations and visibility were the key factors that motivated researchers to share data. However, confidentiality and data misuse were the main concerns among those who were reluctant to share. Finally, some recommendations regarding the improvement of data management and sharing practices are presented. © The Author(s) 2018.","Arab researchers; open science; research data management; research data sharing; scholarly communication","","","","","","Deanship of Scientific Research; European Commission, EC; European Research Council, ERC; King Abdulaziz University, KAU, (G-65-246-38); Horizon 2020","Funding text 1: Recently, there has been a growing awareness of the management and reuse of research data, due to the shift from open access to scientific publications alone to open access to both publications and research data. In March 2001, during the OECD Committee on Scientific and Technology Policy meeting, scientists from the Netherlands recommended that a working group on issues of access to digital forms of research data be established (Arzberger et al., 2004). At the same time, funding bodies encouraged researchers to openly publish their data. For example, the National Institutes of Health added a data management plan requirement in 2003 for grants over $500,000 (Borgman, 2012). Also, the European Commission launched the Open Research Data Pilot initiative in 2015 to have scientific publications and research data in projects supported by the European Research Council under Horizon 2020 freely accessible for reuse (OpenAIRE2020 project, 2016).; Funding text 2: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This Project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (G-65-246-38). The authors, therefore, acknowledge with thanks DSR technical and financial support.; Funding text 3: Despite the foregoing, there is light at the end of the tunnel. First, there is the Research Output Management through Open Access Institutional Repositories in Palestinian Higher Education (ROMOR) (2016), a recently funded research project that was launched in 2016 under the auspices of the European Union’s Erasmus Plus program. The current phase of the project involves a research data pilot study to assess research data and output management at the managerial levels of Palestine’s higher education institutions. Second, two Arab data repositories were listed in ‘re3data.org’ – the first being a government data repository in Egypt named Egypt’s information portal, and the second being Open data for Africa in Tunisia.","Adams J., King C., Pendlebury D., Global research report Middle East: Exploring the changing landscape of Arabian, Persian, and Turkish research, Thomson Reuters, (2011); El-Hadi database, (2017); Aksnes D.W., Rorstad K., Piro F., Et al., Are female researchers less cited? A large-scale study of Norwegian scientists, Journal of the American Society for Information Science and Technology (JASIST), 62, 4, pp. 628-636, (2011); Arab Strategy for Scientific and Technical Research and Innovation, (2016); Arzberger P., Schroeder P., Beaulieu A., Et al., Promoting access to public research data for scientific, economic, and social development, Data Science Journal, 3, pp. 135-153, (2004); Averkamp S., Gu X., Rogers B., Data management at the University of Iowa: A University Libraries report on campus research data needs, (2014); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology (JASIST), 63, 6, pp. 1059-1078, (2012); (2017); Cheah P.Y., Tangseefa D., Somsaman A., Et al., Perceived benefits, harms, and views about how to share data responsibly: A qualitative study of experiences with and attitudes toward data sharing among research staff and community representatives in Thailand, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 278-289, (2015); Chretien J.P., Rivers C.M., Johansson M.A., Make data sharing routine to prepare for public health emergencies, PLoS Medicine, 13, 8, (2016); Cinkosky M.J., Fickett J.W., Gilna P., Et al., Electronic data publishing and GenBank, Science, 252, 5010, pp. 1273-1277, (1991); Cragin M.H., Palmer C.L., Carlson J.R., Et al., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 368, 1926, pp. 4023-4038, (2010); Curty R.G., Beyond ‘data thrifting’: An investigation of factors influencing research data reuse in the social sciences, (2015); Dar A., EduSearch, (2017); Dar A., HumanIndex in Arabic, (2017); Guidelines on open access to scientific publications and research data in Horizon 2020, (2016); Elsayed A.M., The use of academic social networks among Arab researchers: A survey, Social Science Computer Review, 34, 3, pp. 378-391, (2016); Ferguson L., How and why researchers share data (and why they don’t), Wiley Exchanges, (2014); Fienberg S.E., Martin M.E., Straf M.L., Sharing Research Data, (1985); Griffiths A., The publication of research data: Researcher attitudes and behavior, International Journal of Digital Curation, 4, 1, pp. 46-56, (2009); Technology and Innovation (2013–2017); Jao I., Kombe F., Mwalukore S., Research stakeholders’ views on benefits and challenges for public health research data sharing in Kenya: The importance of trust and social relations, PloS one, 10, 9, (2015); (2017); Koopman M.M., De Jager K., Archiving South African digital research data: How ready are we?, South African Journal of Science, 112, 7-8, pp. 1-7, (2016); Kvale L., Sharing of research data: A study among researchers at UMB, The 7th Munin Conference on Scientific Publishing 2012–New Trends, (2014); Lammerhirt D., PASTEUR4OA Briefing Paper: Disciplinary differences in opening research data, (2016); (2017); Martinez-Uribe L., Digital repository services for managing research data: What do Oxford researchers need, IASSIST Quarterly, 31, 3-4, pp. 28-33, (2007); Meadows A., To share or not to share? That is the (research data) question, The Scholarly Kitchen, (2014); National strategy for science, technology and innovation 2015–2030; Reporting checklist for life sciences articles, (2013); What is the Open Research Data Pilot?, (2016); Pampel H., re3data.org reaches a milestone and begins offering badges, (2016); Parsons T., Grimshaw S., Williamson L., Research data management survey: Report, (2013); Perry C.M., Archiving of publicly funded research data: A survey of Canadian researchers, Government Information Quarterly, 25, 1, pp. 133-148, (2008); Piwowar H., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Porter J.H., A brief history of data sharing in the US Long Term Ecological Research Network, Bulletin of the Ecological Society of America, 91, 1, pp. 14-20, (2010); Top 10 Universities in the Arab Region 2016, (2016); Stewardship of digital research data: Principles and guidelines. London: RIN, (2008); (2016); Review of literature on scientists’ research productivity: En studie inom IVAs projekt Agenda för forskning, (2012); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the University of Lille (France) LIBREAS, Library Ideas, (2016); Shen Y., Research data sharing and reuse practices of academic faculty researchers: A study of the Virginia Tech Data Landscape, International Journal of Digital Curation, 10, 2, pp. 157-175, (2016); Steinhart G., Chen E., Arguillas F., Et al., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of eScience Librarianship, 1, 2, pp. 63-78, (2012); Tenopir C., Allard S., Douglass K., Data sharing by scientists: Practices and perceptions, PloS one, 6, 6, (2011); Tripathi M., Chand M., Sonkar S.K., Et al., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); In 2017 the UK Data Archive celebrates 50 years of data archiving, (2017); Science, Technology & Innovation Policy in the United Arab Emirates, (2015); Van den Eynden V., Bishop L., Sowing the seed: Incentives and motivations for sharing research data, a researcher’s perspective, A Knowledge Exchange Report, (2014); Van den Eynden V., Corti L., Woollard M., Et al., Managing and sharing data: A best practice guide for researchers, (2011); Vasilevsky N.A., Minnier J., Haendel M.A., Reproducible and reusable research: Are journal data sharing policies meeting the mark?, PeerJ, 5, (2017); Vines T.H., Albert A.Y., Andrew R.L., Et al., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Youngseek K., Jeffery S., Institutional and individual influences on scientists’ data sharing practices, Journal of Computational Science Education, 3, 1, pp. 47-56, (2012); Youngseek K., Jeffery S., Institutional and individual factors affecting scientists’ data sharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology (JASIST), 67, 4, pp. 776-799, (2016); Zou'bi M.R., Mohamed-Nour S., El-Kharraz J., The Arab States. UNESCO science report: Towards 2030, (2015)","A.M. Elsayed; King Abdulaziz University, Helwan University, Saudi Arabia, Egypt; email: amany03@gmail.com","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85050200116"
"Cruz M.; Dintzner N.; Dunning A.; Kuil A.; Plomp E.; Teperek M.; Velden Y.T.; Versteeg A.","Cruz, Maria (57205738070); Dintzner, Nicolas (56100606400); Dunning, Alastair (56425415100); Kuil, Annemiek van der (57211288785); Plomp, Esther (55660168000); Teperek, Marta (36545554600); Velden, Yasemin Turkyilmaz-van der (57205742015); Versteeg, Anke (57211290324)","57205738070; 56100606400; 56425415100; 57211288785; 55660168000; 36545554600; 57205742015; 57211290324","Policy needs to go hand in hand with practice: The learning and listening approach to data management","2019","Data Science Journal","18","1","45","","","","6","10.5334/dsj-2019-045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073374944&doi=10.5334%2fdsj-2019-045&partnerID=40&md5=a9d6b714814ff0969cc4d0e55c99c82d","Vrije Universiteit Amsterdam, Netherlands; Delft University of Technology, Netherlands; Utrecht University, Netherlands","Cruz M., Vrije Universiteit Amsterdam, Netherlands; Dintzner N., Delft University of Technology, Netherlands; Dunning A., Delft University of Technology, Netherlands; Kuil A., Utrecht University, Netherlands; Plomp E., Delft University of Technology, Netherlands; Teperek M., Delft University of Technology, Netherlands; Velden Y.T., Delft University of Technology, Netherlands; Versteeg A., Delft University of Technology, Netherlands","In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), "" Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment”. However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft. © 2019 The Author(s).","Code; Data archive; Data champions; Data repository; Data stewardship; Open science; Policy; Policy development; Policy implementation; Rdm; Research data management; Tu delft","Information management; Investments; Public policy; Code; Data archives; Data champions; Data repositories; Data stewardship; Open science; Policy development; Policy implementations; Research data managements; Tu delft; Research and development management","","","","","","","4TU.Federation, (2019); 4TU.ResearchData, (2019); Akhmerov A., Steele G., Open Data Policy of the Quantum Nanoscience Department, (2019); Andrews Mancilla H., Teperek M., van Dijck J., den Heijer K., Eggermont R., Plomp E., Turkyilmaz-van der Velden Y., Kurapati S., On a Quest for Cultural Change - Surveying Research Data Management Practices at Delft University of Technology, LIBER Quarterly, 29, 1, pp. 1-27, (2019); Ball M., Bloemers M., Carr D., Cavalli V., Haglund M., Kalaitzi V., Papadopoulou E., Quattroni P., Robertson D., Stojanovski J., Teperek M., Turkyilmaz-van der Velden Y., Vandevelde K., A Vision for Open Science, (2018); Benedictus R., Miedema F., Ferguson M.W.J., Fewer numbers, better science, Nature, 538, 7626, pp. 453-455, (2016); Byrne M., Making Progress Toward Open Data: Reflections on Data Sharing at PLOS ONE, (2017); TOP Guidelines, (2019); Chen X., Dallmeier-Tiessen S., Dasler R., Feger S., Fokianos P., Gonzalez J.B., Hirvonsalo H., Kousidis D., Lavasa A., Mele S., Rodriguez D.R., Simko T., Smith T., Trisovic A., Trzcinska A., Tsanaktsidis I., Zimmermann M., Cranmer K., Heinrich L., Watts G., Hildreth M., Lloret Iglesias L., Lassila-Perini K., Neubert S., Open is not enough, Nature Physics, 15, 2, pp. 113-119, (2019); Data Seal of Approval 2013 for Repository 4TU.Datacentrum, (2016); Cruz M., Take research data management seriously and organize discipline specific support, (2018); Cruz M., Gramsbergen E., NetCDF At The 4TU.Centre For Research Data, (2018); Cruz M.J., Dunning A., Research Data Management within the 4TU Research Centres, (2018); Templated For Structured RDM Interviews, (2018); Dintzner N., Den Heijer K., Teperek M., Coding problems?, (2019); Domingus M., Graag niet al te generiek, Gewetensvol datamanagement. Th&ma 2017-3 Zwemmen of verzuipen: Zeeën van data, 2017, 3, pp. 54-59, (2017); Dunning A., TU Delft Research Data Framework Policy, (2018); Dunning A., Research Data Policy for PhDs at TU Delft, (2019); H2020 Programme: Guidelines on FAIR Data Management, (2016); Figueras i Ventura J., Russchenberg H.W.J., IDRA, IRCTR drizzle radar: First results, (2008); Gunawan D., Amalia A., The Implementation of open data in Indonesia, (2016); Higman R., Teperek M., Kingsley D., Creating a Community of Data Champions, International Journal of Digital Curation, 12, 2, pp. 96-106, (2018); Hrynaszkiewicz I., Birukou A., Astell M., Swaminathan S., Kenall A., Khodiyar V., Standardising and Harmonising Research Data Policy in Scholary Publishing, International Journal of Digital Curation, 12, 1, pp. 65-71, (2017); Jahnke L., Asher A.D., Keralis S.D.C., The problem of data, (2012); Johnson R., Chiarelli A., Monitoring sector progress towards compliance, (2017); Kurapati S., Teperek M., 4TU.Centre for Research Data partners with The Carpentries: Impressions from the first workshop at TU Delft, (2018); RDM in de praktijk, (2019); Love J.S., A Subjective Assessment of Research Data in Design, (2019); Love J.S., Design Research Data - A Subjective, (2019); Lowndes J.S.S., Best B.D., Scarborough C., Afflerbach J.C., Frazier M.R., O'Hara C.C., Jiang N., Halpern B.S., Our path to better science in less time using open data science tools, Nature Ecology & Evolution, 1, 6, (2017); Molenbroek J.F.M., DINED - anthropometric database, (2018); (2019); Reporting standards and availability of data, (2019); Naughton L., Kernohan D., Making sense of journal research data policies, Insights, 29, 1, pp. 84-89, (2016); Journal open-data policies, (2018); Advanced Software for Remote Data Retrieval, (2019); Otto T., Russchenberg H.J.W., Reinoso Rondinel R.R., Unal C.M.H., Yin J., IDRA weather radar measurements - all data, (2010); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Plomp E., Data Champion kick off meeting, (2019); Data Availability, (2019); Pryor G., Jones S., Whyte A., Options and approaches to RDM service provision, Delivering Research Data Management Services: Fundamentals of Good Practice. Facet Publishing, (2013); Rans J., Jones S., RDM strategy: Moving from plans to action, (2013); Teperek M., Views on Data Stewardship - report of preliminary findings at the Faculty of Technology, Policy and Management (TPM) at TU Delft, (2018); Teperek M., Cruz M., Verbakel E., Bohmer J., Dunning, Data Stewardship Addressing Disciplinary Data Management Needs, International Journal of Digital Curation, 13, 1, pp. 141-149, (2018); Teperek M., Higman R., Kingsley D., Is Democracy the Right System? Collaborative Approaches to Building an Engaged RDM Community, International Journal of Digital Curation, 12, 2, pp. 86-95, (2017); Become a Member Organisation, (2019); DANS: Dutch Song Database and DINED receive Dutch Data Prize, (2014); Open (FAIR) data, (2019); Krause J., Lambeng N., Andrews H., Boehmer J., Cruz M., Van Dijck J., Den Heijer K., Van Der Kruyk M., Teperek M., Quantitative assessment of research data management practice, (2018); Data Champions, (2018); Our Data Champions, (2019); Utrecht University, (2016); van Belle J.P., Africa Data Revolution Report 2018: Status and Emerging Impact to Open Data in Africa, (2018); Vandewalle P., Code Sharing Is Associated with Research Impact in Image Processing, Computing in Science & Engineering, 14, 4, pp. 42-47, (2012); van Dijck J., Do as you preach: Results of 2017/2018 data management survey now published, (2018); van Wezenbeek W., Touwen H., Versteeg A., van Wesenbeeck A., Nationaal plan open science, (2017); van Zeeland H., Ringersma J., The Development of a Research Data Policy at Wageningen University & Research: Best Practices as a Framework, Liber Quarterly, 27, 1, pp. 153-170, (2017); Verhaar P., Schoots F., Sesink L., Frederiks F., Fostering Effective Data Management Practices at Leiden University, Liber Quarterly, 27, 1, pp. 1-22, (2017); Versteeg A., van der Kuil A., Dunning A.C., Motivations for Sharing Research Data at Delft University of Technology, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","M. Teperek; Delft University of Technology, Netherlands; email: M.Teperek@tudelft.nl","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85073374944"
"Khan H.R.; Chang H.-C.; Kim J.","Khan, Hammad Rauf (57202600911); Chang, Hsia-Ching (34881443200); Kim, Jeonghyun (37106972000)","57202600911; 34881443200; 37106972000","Unfolding Research Data Services: An Information Architecture Perspective","2018","Proceedings of the ACM/IEEE Joint Conference on Digital Libraries","","","","353","354","1","3","10.1145/3197026.3203887","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048873526&doi=10.1145%2f3197026.3203887&partnerID=40&md5=8335f90ba8ef351682c8972a57535e11","University of North Texas, Denton, TX, United States","Khan H.R., University of North Texas, Denton, TX, United States; Chang H.-C., University of North Texas, Denton, TX, United States; Kim J., University of North Texas, Denton, TX, United States","With the exponential growth in digital research data, libraries are beginning to find opportunities to assist researchers with planning, maintaining, sharing, and accessing data through research data services. Using a content analysis with the lens of information architecture, this study sought to better understand how these services are organized in North American academic library websites and to what extent the research data lifecycle is supported within research data services. 50 academic library websites were studied and results yielded three provisions that make up research data services: Information Access, Technical Support, and Personalized Consultation. The data lifecycle was found to be strongly supported in research data services for planning, data curation, and data access stages. © 2018 Authors.","data lifecycle; research data management (rdm); research data services (rds)","Information management; Information retrieval; Websites; Academic libraries; Data lifecycle; Digital researches; Exponential growth; Information access; Information architectures; Research data; Research data managements; Digital libraries","","","","","","","Cohen J., A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 1, pp. 37-46, (1960); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Fearon D., Guina B., Pralle B., Lake S., Sallans A., Research Data Management Services, SPEC KIT 334, (2013); Rosenfield L., Morville P., Arango J., Information Architecture: For the Webs and beyond (4th Ed, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014)","","","Institute of Electrical and Electronics Engineers Inc.","ACM SIGIR; ACM SIGWEB; IEEE TCDL","18th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2018","3 June 2018 through 7 June 2018","Fort Worth","136862","15525996","978-145035178-2","","","English","Proc. ACM IEEE Joint Conf. Digit. Libr.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85048873526"
"Auge T.; Heuer A.","Auge, Tanja (57194833958); Heuer, Andreas (9533312500)","57194833958; 9533312500","ProSA—Using the CHASE for Provenance Management","2019","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11695 LNCS","","","357","372","15","4","10.1007/978-3-030-28730-6_22","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072835297&doi=10.1007%2f978-3-030-28730-6_22&partnerID=40&md5=92783267b34d50cfa36a997434219350","University of Rostock, Rostock, 18051, Germany","Auge T., University of Rostock, Rostock, 18051, Germany; Heuer A., University of Rostock, Rostock, 18051, Germany","Collecting, storing, tracking, and archiving scientific data is the main task of research data management, being the basis for scientific evaluations. In addition to the evaluation (a complex query in the case of structured databases) and the result itself, the important part of the original database used has also to be archived. To ensure reproducible and replicable research, the evaluation queries can be processed again at a later point in time in order to reproduce the result. Being able to calculate the origin of an evaluation is the main problem in provenance management, particularly in why and how data provenance. We are developing a tool called ProSA which combines data provenance and schema/data evolution using the CHASE for the different database transformations needed. Besides describing the main ideas of ProSA, another focus of this paper is the concrete use of our CHASE tool ChaTEAU for invertible query evaluation. © 2019, Springer Nature Switzerland AG.","Annotation; CHASE; Data curation; Provenance; Temporal databases; Theoretical foundations of databases","Data curation; Information management; Information systems; Information use; Query processing; Annotation; CHASE; Provenance; Temporal Database; Theoretical foundations; Database systems","","","","","CHASE","We thank our students Fabian Renn and Frank R?ger for their comparison of different CHASE tools like Llunatic and PDQ as well as Martin Jurklies for the basic implementation of our CHASE tool ChaTEAU.","Aho A.V., Beeri C., Ullman J.D., The theory of joins in relational databases, ACM Trans. Database Syst., 4, 3, pp. 297-314, (1979); Amarilli A., Bourhis P., Senellart P., Provenance circuits for trees and treelike instances, Extended Version). Corr Abs/1511, 8723, (2015); Amsterdamer Y., Deutch D., Tannen V., Provenance for aggregate queries, PODS, pp. 153-164, (2011); Auge T., Heuer A., Combining provenance management and schema evolution, IPAW 2018. LNCS, 11017, pp. 222-225, (2018); Auge T., Heuer A., Inverses in research data management: Combining provenance management, schema and data evolution (inverse im forschungsdatenman-agement), Grundlagen Von Datenbanken. CEUR Workshop Proceedings, 2126, pp. 108-113, (2018); Auge T., Heuer A., The theory behind minimizing research data: Result equivalent CHASE-inverse mappings, CEUR Workshop Proceedings of the LWDA, 2191, pp. 1-12, (2018); Benczur A., Kiss A., Markus T., On a general class of data dependencies in the relational model and its implication problems, Comput. Math. Appl., 21, 1, pp. 1-11, (1991); Benedikt M., Et al., Benchmarking the chase, PODS, pp. 37-52, (2017); Benedikt M., Leblay J., Tsamoura E., PDQ: Proof-driven query answering over web-based data, PVLDB, 7, 13, pp. 1553-1556, (2014); Bonifati A., Ileana I., Linardi M., ChaseFUN: A data exchange engine for functional dependencies at scale, EDBT, pp. 534-537, (2017); Bruder I., Heuer A., Schick S., Spors S., Konzepte für das Forschungsdatenman-agement an der Universität Rostock (Concepts for the Management of Research Data at the University of Rostock), CEUR Workshop Proceedings of the LWDA, 1917, (2017); Bruder I., Et al., Daten wie Sand am Meer-Datenerhebung,-strukturierung,-management und Data Provenance für die Ostseeforschung, Datenbank-Spektrum, 17, 2, pp. 183-196, (2017); Bruder I., Et al., Daten wie Sand am Meer-Datenerhebung,-strukturierung,-management und Data Provenance für die Ostseeforschung, Datenbank-Spektrum, 17, 2, pp. 183-196, (2017); Deutsch A., Hull R., Provenance-directed Chase&Backchase, In Search of Elegance in the Theory and Practice of Computation. LNCS, 8000, pp. 227-236, (2013); Deutsch A., Popa L., Tannen V., Query reformulation with constraints, SIGMOD Rec, 35, 1, pp. 65-73, (2006); Fagin R., Kolaitis P.G., Miller R.J., Popa L., Data exchange: Semantics and query answering, Theor. Comput. Sci., 336, 1, pp. 89-124, (2005); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Quasi-inverses of schema mappings, ACM Trans. Database Syst., 33, 2, pp. 1-11, (2008); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema mapping evolution through composition and inversion, Schema Matching and Mapping. Data-Centric Systems and Applications, pp. 191-222, (2011); Geerts F., Mecca G., Papotti P., Santoro D., That’s all folks! LLUNATIC goes open source, PVLDB, 7, 13, pp. 1565-1568, (2014); Greco S., Molinaro C., Spezzano F., Incomplete Data and Data Dependencies in Relational Databases. Synthesis Lectures on Data Management, (2012); Green T.J., Karvounarakis G., Tannen V., Provenance semirings, PODS, pp. 31-40, (2007); Green T.J., Tannen V., The semiring framework for database provenance, PODS, pp. 93-99, (2017); Grunert H., Heuer A., Datenschutz im PArADISE, Datenbank-Spektrum, 16, 2, pp. 107-117, (2016); Grunert H., Heuer A., Privacy protection through query rewriting in smart environments, EDBT, pp. 708-709, (2016); Grunert H., Heuer A., Rewriting complex queries from cloud to fog under capability constraints to protect the users’ privacy, OJIOT, 3, 1, pp. 31-45, (2017); Grunert H., Heuer A., Query rewriting by contract under privacy constraints, OJIOT, 4, 1, pp. 54-69, (2018); Halevy A.Y., Answering queries using views: A survey, VLDB J, 10, 4, pp. 270-294, (2001); Herschel M., Diestelkamper R., Ben Lahmar H., A survey on provenance: what for? What form? What from?, VLDB J, 26, 6, pp. 881-906, (2017); Ileana I., Cautis B., Deutsch A., Katsis Y., Complete yet practical search for minimal query reformulations under constraints, SIGMOD Conference, pp. 1015-1026, (2014); Jurklies M., CHASE Und BACKCHASE: Entwicklung Eines Universal-Werkzeugs für Eine Basistechnik Der Datenbankforschung, (2018); Kohler S., Ludascher B., Zinn D., First-Order Provenance Games, (2013); Maier D., The Theory of Relational Databases, (1983); Maier D., Mendelzon A.O., Sagiv Y., Testing implications of data dependencies, ACM Trans. Database Syst., 4, 4, pp. 455-469, (1979)","T. Auge; University of Rostock, Rostock, 18051, Germany; email: tanja.auge@uni-rostock.de","Welzer T.; Podgorelec V.; Kamišalic Latific A.; Eder J.","Springer Verlag","","23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019","8 September 2019 through 11 September 2019","Bled","231209","03029743","978-303028729-0","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85072835297"
"Todorova T.; Krasteva R.; Tsvetkova E.","Todorova, Tania (55467619800); Krasteva, Rositza (57193269281); Tsvetkova, Elisaveta (57193263546)","55467619800; 57193269281; 57193263546","Data Literacy and Research Data Management: The Case at ULSIT","2019","Communications in Computer and Information Science","989","","","535","544","9","1","10.1007/978-3-030-13472-3_50","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062288443&doi=10.1007%2f978-3-030-13472-3_50&partnerID=40&md5=688c09e1cd0af40d226080362edbedc6","University of Library Studies and Information Technologies, Sofia, Bulgaria","Todorova T., University of Library Studies and Information Technologies, Sofia, Bulgaria; Krasteva R., University of Library Studies and Information Technologies, Sofia, Bulgaria; Tsvetkova E., University of Library Studies and Information Technologies, Sofia, Bulgaria","In 2017 a team of researchers from the University of Library Studies and Information Technologies (ULSIT) joined an international research group to conduct a scientific survey on Data Literacy and Research Data Management. In the research paper we examine the current state of data literacy, data sharing and reuse perceptions and practices among researchers at ULSIT. The research, which found gaps in understanding of data management best practices, provides recommendations for data literacy training to be provided in the near future. The implementation of the survey was timely, necessary, and useful in light of current amendments to the Act for the Development of the Academic Staff in the Republic of Bulgaria (2018). Analysis of the current levels of awareness and gaps in knowledge among the survey population has resulted in the creation of a document with recommendations for changes to the university’s data management policy that also includes specific improvements for the university’s Quality of Education Management System. © 2019, Springer Nature Switzerland AG.","Data literacy; Higher education; Open access; Research data management; Training; ULSIT","Human resource management; Open access; Personnel training; Population statistics; Surveys; Data literacy; Higher education; International researches; Management policy; Quality of education; Research data managements; Research papers; ULSIT; Information management","","","","","European Commission"," Bulgarian OpenAIRE Repository (incl. Bulgarian publications funded by FP7/ERC and EC projects)","Chowdhury G., Walton G., Kurbanoglu S., Unal Y., Boustany J., Information practices for sustainability: Information, data and environmental literacy, The Fourth European Conference on Information Literacy (ECIL): Abstracts, (2016); Schneider R., Research data literacy, ECIL 2013. CCIS, 397, pp. 134-140, (2013); Better Science for Better Bulgaria, Republic of Bulgaria; Bulgaria National Roadmap for Research Infrastructure 2017–2023, Ministry of Education and Science, Republic of Bulgaria; Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program, 49, 4, pp. 382-407, (2015); Todorova T., Krasteva R., Tsvetkova E., Data literacy survey implementation at ULSIT: Presentation, The Fifth European Conference on Information Literacy (ECIL), Saint-Malo, (2017); 2010 Information and Documentation-Guidelines for Bibliographic References and Citations to Information Resources; EU Council Conclusions on the Transition Towards an Open Science System; National Research Network; Strategy for Development of Higher Education in the Republic of Bulgaria for the 2014– 2020 Period; National Strategy for Development of Scientific Research in the Republic of Bulgaria 2017– 2030, Better Science for Better Bulgaria, Republic of Bulgaria; Diagnostic Review Mapping of Research Infrastructures and Equipment in Bulgaria, Ministry of Education and Science Republic of Bulgaria, (2017); Operational Programme of EC “Science and Education for Smart Growth 2014–2020”; The Act for the Development of the Academic Staff in the Republic of Bulgaria, Latest Amendments through № 30 (03.05.2018), (2010)","T. Todorova; University of Library Studies and Information Technologies, Sofia, Bulgaria; email: t.todorova@unibit.bg","Huotari M.L.; Mizrachi D.; Kurbanoglu S.; Ünal Y.; Grassian E.; Roy L.; Boustany J.; Špiranec S.","Springer Verlag","","6th European Conference on Information Literacy, ECIL 2018","24 September 2018 through 27 September 2018","Oulu","223839","18650929","978-303013471-6","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85062288443"
"Schöpfel J.; Ferrant C.; André F.; Fabre R.","Schöpfel, Joachim (14619562900); Ferrant, Coline (56895992200); André, Francis (57191732594); Fabre, Renaud (57203119054)","14619562900; 56895992200; 57191732594; 57203119054","Research data management in the French National Research Center (CNRS)","2018","Data Technologies and Applications","52","2","","248","265","17","14","10.1108/dta-01-2017-0005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051246310&doi=10.1108%2fdta-01-2017-0005&partnerID=40&md5=8e288e9af81608fe845684a311229e12","GERiiCO Laboratory, University of Lille, Villeneuve d’Ascq, France; Sociological Observatory of Change, Sciences Po, CNRS, Paris, France; INRA, Paris, France; National Scientific Research Center, CNRS, DIST, Paris, France; Northwestern University, Evanston, IL, United States","Schöpfel J., GERiiCO Laboratory, University of Lille, Villeneuve d’Ascq, France; Ferrant C., Sociological Observatory of Change, Sciences Po, CNRS, Paris, France, INRA, Paris, France, Northwestern University, Evanston, IL, United States; André F., National Scientific Research Center, CNRS, DIST, Paris, France; Fabre R., National Scientific Research Center, CNRS, DIST, Paris, France","Purpose: The purpose of this paper is to present empirical evidence on the opinion and behaviour of French scientists (senior management level) regarding research data management (RDM). Design/methodology/approach: The results are part of a nationwide survey on scientific information and documentation with 432 directors of French public research laboratories conducted by the French Research Center CNRS in 2014. Findings: The paper presents empirical results about data production (types), management (human resources, IT, funding, and standards), data sharing and related needs, and highlights significant disciplinary differences. Also, it appears that RDM and data sharing is not directly correlated with the commitment to open access. Regarding the FAIR data principles, the paper reveals that 68 per cent of all laboratory directors affirm that their data production and management is compliant with at least one of the FAIR principles. But only 26 per cent are compliant with at least three principles, and less than 7 per cent are compliant with all four FAIR criteria, with laboratories in nuclear physics, SSH and earth sciences and astronomy being in advance of other disciplines, especially concerning the findability and the availability of their data output. The paper concludes with comments about research data service development and recommendations for an institutional RDM policy. Originality/value: For the first time, a nationwide survey was conducted with the senior research management level from all scientific disciplines. Surveys on RDM usually assess individual data behaviours, skills and needs. This survey is different insofar as it addresses institutional and collective data practice. The respondents did not report on their own data behaviours and attitudes but were asked to provide information about their laboratory. The response rate was high (>30 per cent), and the results provide good insight into the real support and uptake of RDM by senior research managers who provide both models (examples for good practice) and opinion leadership. © 2018, Emerald Publishing Limited.","Data curation; Data preservation; Data sharing; FAIR principles; Open Science; Research data management","Behavioral research; Earth (planet); Human resource management; Information management; Research laboratories; Data curation; Data preservations; Data production; Data Sharing; FAIR principle; Management level; Open science; Research center; Research data managements; Senior management; Surveys","","","","","European Commission, EC; Agence Nationale de la Recherche, ANR","Approximatively, half of the laboratories (48 per cent) produce their databases together with other research structures, often with funding from the French National Research Agency ANR[11] (67 per cent) and/or from the European Commission (45 per cent).","(2017); Andre F., Déluge des données de la recherche?, Big Data: Nouvelles Partitions De L’Information, pp. 77-95, (2015); Awre C., Baxter J., Clifford B., Colclough J., Cox A., Dods N., Drummond P., Fox Y., Gill M., Gregory K., Gurney A., Harland J., Khokhar M., Lowe D., O'Beirne R., Proudfoot R., Schwamm H., Smith A., Verbaan E., Waller L., Williamson L., Wolf M., Zawadzki M., Research data management as a ‘wicked problem’, Library Review, 64, 4-5, pp. 356-371, (2015); Aydinoglu A.U., Dogan G., Taskin Z., Research data management in Turkey: perceptions and practices, Library Hi Tech, 35, 2, pp. 271-289, (2017); Barsky E., Adam S., Farrar P., Meredith-Lobay M., Mitchell M., Naslund J.-A., Sylka C., Research data management survey, UBC: humanities and social sciences, (2017); Bauer B., Ferus A., Gorraiz J., Gumpenberger C., Grundhammer V., Maly N., Muhlegger J.M., Preza J.L., Sanchez Solis B., Schmidt N., Steineder C., Researchers and their data: results of an Austrian survey, (2015); Brown S., Bruce R., Kernohan D., Directions for research data management in UK universities, (2015); Burgi P.-Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland. national efforts to guarantee the sustainability of research outputs, IFLA Journal, 43, 1, pp. 5-21, (2017); Research data management: principles, practices, and prospects, (2013); Livre blanc: une science ouverte dans une république numérique, (2016); The ethical challenges of the sharing of scientific data, (2015); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Curdt C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project, Program, 49, 4, pp. 494-512, (2015); Curty R.G., Crowston K., Specht A., Grant B., Dalton E.W., Attitudes and norms affecting scientists’ data reuse, (2017); Higman R., Pinfield S., Research data management and openness, Program, 49, 4, pp. 364-381, (2015); Humphrey C., Shearer K., Whitehead M., Towards a collaborative national research data management network, International Journal of Digital Curation, 11, 1, pp. 195-207, (2016); Klump J., Data as social capital and the gift culture in research, Data Science Journal, 16, 14, pp. 1-8, (2017); Knight G., Building a research data management service for the London School of hygiene & tropical medicine, Program, 49, 4, pp. 424-439, (2015); Funding research data management and related infrastructures, (2016); Kowalczyk S., Shankar K., Data sharing in the sciences, Annual Review of Information Science and Technology, 45, 1, pp. 247-294, (2011); Krumholz H.M., Waldstreicher J., The yale open data access (YODA) project – a mechanism for data sharing, New England Journal of Medicine, 375, 5, pp. 403-405, (2016); Ten Recommendations for Libraries to Get Started With Research Data Management, (2012); Martin C., Cadiou C., Jannes-Ober E., Data management: new tools, new organization, and new skills in a French research institute, LIBER Quaterly, 27, 1, pp. 73-88, (2017); Mons B., Neylon C., Velterop J., Dumontier M., da Silva Santos L.O., Wilkinson M.D., Cloudy, increasingly FAIR; revisiting the FAIR data guiding principles for the European Open Science Cloud, Information Services & Use, 37, 1, pp. 49-56, (2017); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2014); Reilly S., Schallier W., Schrimpf S., Smit E., Wilkinson M., Report on integration of data and publications, (2011); Schmidt B., Dierkes J., New alliances for research and teaching support: establishing the Göttingen eresearch alliance, Program, 49, 4, pp. 461-474, (2015); Schopfel J., Prost H., Research data management in social sciences and humanities: a survey at the university of lille 3 (France), LIBREAS. Library Ideas, 29, pp. 98-112, (2016); Schopfel J., Ferrant C., Andre F., Fabre R., Ready for the future? A survey on open access with scientists from the French national research center (CNRS), Interlending & Document Supply, 44, 4, pp. 141-149, (2016); Simukovic E., Kindling M., Schirmbacher P., Unveiling research data stocks: a case of humboldt-Universität zu Berlin, iConference, pp. 742-748, (2014); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Tenopir C., Pollock D., Allard S., Hughes D., Research data services in European and North American libraries: current offerings and plans for the future, Proceedings of the Association for Information Science and Technology, 53, 1, pp. 1-6, (2016); Thanos C., Research data reusability: conceptual foundations, impediments and enabling technologies, Publications, 5, 1, (2017); Science as an open enterprise: summary report, (2012); Ward C., Freiman L., Jones S., Molloy L., Snow K., Making sense: talking data management with scientists, International Journal of Digital Curation, 6, 2, pp. 265-273, (2011); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic scientists, Library Hi Tech, 32, 3, pp. 467-482, (2014); Whyte A., Tedds J., Making the case for research data management, (2011); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Groth P., Goble C., Grethe J.S., Heringa J., 't Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.-A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","J. Schöpfel; GERiiCO Laboratory, University of Lille, Villeneuve d’Ascq, France; email: joachim.schopfel@univ-lille3.fr","","Emerald Publishing","","","","","","25149288","","","","English","Data Technol. Appl.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85051246310"
"Steinerová J.; Ondrišová M.","Steinerová, Jela (8337751700); Ondrišová, Miriam (56538463800)","8337751700; 56538463800","Research Data Literacy Perception and Practices in the Information Environment","2019","Communications in Computer and Information Science","989","","","545","555","10","1","10.1007/978-3-030-13472-3_51","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062291602&doi=10.1007%2f978-3-030-13472-3_51&partnerID=40&md5=71a579a2ca13a3b574ef247281b3e64e","Comenius University, Bratislava, Slovakia","Steinerová J., Comenius University, Bratislava, Slovakia; Ondrišová M., Comenius University, Bratislava, Slovakia","The paper addresses conceptual contexts of research data, research data literacy and research data perception and practices. The concepts of research data and research data literacy are explained in the context of the information environment. A typology of research data is presented based on a qualitative study among Slovak researchers. Research data types are represented by a concept map. Results of a survey among Slovak scholars are presented as part of a multinational online questionnaire survey on Data Literacy and Research Data Management (257 subjects). Findings confirm positive perception of data management issues among researchers. Majority of them are willing to share their data, but express concerns regarding misuse and misinterpretation of data. Most researchers are interested in research data management trainings. In conclusion, we propose the implications for LIS education with regard to data management, including the courses on data analysis and visualization and for development and maintenance of research data infrastructures in the information environment. © 2019, Springer Nature Switzerland AG.","Research data literacy perception; Research data management; Research data management courses; Research data practices; Slovak researchers","Curricula; Data visualization; Information management; Surveys; Information environment; LIS educations; Management issues; Online questionnaire; Qualitative study; Research data; Research data managements; Slovak researchers; Research and development management","","","","","","","Borgman C.L., Big Data, Little Data, No Data. Scholarship in the Networked World, (2015); Koltay T., Data literacy for researchers and data librarians, J. Librarianship Inf. Sci., 49, 1, pp. 3-14, (2017); Schneider R., Research data literacy, ECIL 2013. CCIS, 397, pp. 134-140, (2013); Koltay T., Spiranec S., Karvalics L.Z., Research 2.0 and the Future of Information Literacy, Amsterdam, Chandos, (2016); Carlson J., Johnston L., Westra B., Nichols M., Developing an approach for data management education: A report from data literacy project, Int. J. Digital Curation, 8, 1, pp. 204-217, (2013); Martin E.R., What is data literacy?, J. Esci. Librarianship, 3, 1, pp. 1-2, (2014); Sonnenwald D.H., Visioning the future of ınformation and library science: challenges and opportunities, Informationswissenschaft Zwischen Virtuellen Infrastruktur Und Materialen Lebenswelt, ISI 2013, pp. 22-34, (2013); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worlwide, Plos ONE, 10, 8, pp. 1-24, (2015); Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Furner J., “Data”: The data, Information Cultures in the Digital Age, pp. 287-306, (2016); Steinerova J., Informačné Prostredie a Vedecká Komunikácia, Informačné Ekológie. Vyd. UK, Bratislava, (2018); Schneider R., Training trainers for research data literacy: A content-and method-oriented approach, ECIL 2017. CCIS, 810, pp. 139-147, (2018); Steinerova J., Open science and the research ınformation literacy framework, ECIL 2016. CCIS, 676, pp. 277-285, (2016); Chowdhury G., Walton G., Kurbanoglu S., Unal Y., Boustany J., Information practices for sustainability: ınformation, data and environmental literacy, The 4Th ECIL Conference on Information Literacy, 22, (2016); Palsdottir A., Data literacy, collaboration and sharing of research data among academics at the University of Iceland, ECIL 2017. CCIS, 810, pp. 178-185, (2018); Insight into Digital Preservation of Research Output in Europe: Survey Report, (2009); If You Build It, Will they Come? How Researchers Perceive and Use Web 2.0, (2010); Kim Y., Stanton J.M., Institutional and ındividul factors affecting scientist´s data-sharing behaviors: A multilevel analysis, J. Assoc. Inf. Sci. Technol., 67, pp. 776-799, (2016); Carlson J., Stowell B.M., Planting the seeds for data literacy: Lessons learned from a student-centered education program, Int. J. Digital Curation, 10, 1, pp. 95-110, (2015); Library of Congress Preservation Outreach and Communication – Baseline Digital Preservation Curriculum, (2018)","J. Steinerová; Comenius University, Bratislava, Slovakia; email: jela.steinerova@uniba.sk","Boustany J.; Špiranec S.; Grassian E.; Mizrachi D.; Roy L.; Huotari M.L.; Kurbanoglu S.; Ünal Y.","Springer Verlag","","6th European Conference on Information Literacy, ECIL 2018","24 September 2018 through 27 September 2018","Oulu","223839","18650929","978-303013471-6","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85062291602"
"Qin J.; Coll I.S.; Zeng M.L.; Dobreski B.","Qin, Jian (16023002400); Coll, Imma Subirats (57212110451); Zeng, Marcia Lei (7101778742); Dobreski, Brian (57041206800)","16023002400; 57212110451; 7101778742; 57041206800","Technical and policy underpinnings of FAIR data principles","2019","Proceedings of the Association for Information Science and Technology","56","1","","569","571","2","1","10.1002/pra2.93","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075910270&doi=10.1002%2fpra2.93&partnerID=40&md5=df706c9d3d32184ea1f45bf29631e02d","Syracuse University, Syracuse, NY, United States; AIMS, Food and Agriculture Organization (FAO) of the United Nations, United States; Kent State University, Kent, OH, United States; University of Tennessee, Knoxville, TN, United States","Qin J., Syracuse University, Syracuse, NY, United States; Coll I.S., AIMS, Food and Agriculture Organization (FAO) of the United Nations, United States; Zeng M.L., Kent State University, Kent, OH, United States; Dobreski B., University of Tennessee, Knoxville, TN, United States","At the trajectory of exponential data growth and FAIR (Findable, Accessible, Interoperable and Re-usable) requirements, organizations face the data management and publishing challenges in support of effective data discovery and publishing activities. This panel brings researchers and practitioners who have researched and practiced in managing datasets and providing discovery services to share their experience and expertise in addressing the challenges and proactively responding to trends in research data management, curation, sharing, and reuse. Author(s) retain copyright, but ASIS&T receives an exclusive publication license","Data curation; Knowledge representation for data; Metadata interoperability; Research Data Management","Information management; Interoperability; Data curation; Data discovery; Data publishing; Data reuse; Exponentials; Knowledge representation for data; Knowledge-representation; Metadata interoperability; Research data managements; Knowledge representation","","","","","","","(2019); (2019); (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85075910270"
"Lefebvre A.; Schermerhorn E.; Spruit M.","Lefebvre, Armel (57169815200); Schermerhorn, Elizabeth (57197727046); Spruit, Marco (16178767900)","57169815200; 57197727046; 16178767900","How research data management can contribute to efficient and reliable science","2018","26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018","","","","","","","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061324253&partnerID=40&md5=9258dbd42cf087b75378601bfe1862d3","University of Utrecht, Utrecht, Netherlands","Lefebvre A., University of Utrecht, Utrecht, Netherlands; Schermerhorn E., University of Utrecht, Utrecht, Netherlands; Spruit M., University of Utrecht, Utrecht, Netherlands","Research data management (RDM) is an emergent discipline which is increasingly receiving attention from universities, funding agencies and academic publishers. While data management (DM) benefits from a large corpus of data governance and management frameworks adapted to industry, its academic counterpart RDM still struggles at identifying, organizing and implementing the main functions of RDM. In this study we explore the status of research data management at two research organizations in the Netherlands. We identify the main roles and tasks involved in research data governance, services and research. We show that, while the application of the DAMA-DMBOK functions and RDM structures are overlapping, RDM is coping with fundamentally different organizational structures and roles than the roles and tasks listed in professional DM frameworks. As RDM is developed to make science more efficient and reliable, it is questionable whether its current structure is effective. Based on interviews with data managers, researchers and librarians we identified several issues. For instance, at the moment, researchers are responsible for tasks that depend on DM expertise that they, generally, do not possess. At the same time, research data governance as currently implemented fails to capture the complexity of (professional) data management. Similarly, research data support is not well integrated with the wide diversity of research projects. If not addressed, these issues may impede any progress towards open, efficient and reliable science. © 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018. All Rights Reserved.","Data stewardship; Open Data; Open Science; Research Data Governance; Research Data management; Research infrastructure","Information management; Information systems; Information use; Data stewardship; Open science; Research data; Research data managements; Research infrastructure; Open Data","","","","","","","Ayris P., Et al., Realising the European Open Science Cloud, (2016); Belter C.W., Measuring the value of research data: A citation analysis of oceanographic data sets, PLoS ONE, 9, 3, (2014); Benbasat I., Goldstein D.K., Mead M., The case research strategy in studies of information systems, MIS Quarterly, 11, 3, (1987); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, pp. 1059-1078, (2012); Chesbrough H., Open innovation: Where we've been and where we're going, Research-Technology Management, 55, 4, pp. 20-27, (2012); Corti L., Et al., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Open Research Data as the Default: Frequently Asked Questions About the Exte Nsion of the Open Research Data Pilot, (2016); EU Open Innovation, Open Science, Open to the World, European Comission, (2016); Guidelines on Data Management in Horizon 2020, (2016); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS ONE, 10, 2, (2015); Heath H., Cowley S., Developing a grounded theory approach: A comparison of Glaser and Strauss, International Journal of Nursing Studies, 41, pp. 141-150, (2004); Higgins S., DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Horton L., Overview of UK Institution RDM Policies, (2016); Klein H.H.H.K., Myers M.D., A set of principles for conducting and evaluating interpretive field studies in information systems, MIS Quarterly, 23, 1, pp. 67-93, (1999); Korhonen J.J., Et al., Designing data governance structure: An organizational perspective, Journal on Computing, 2, 4, pp. 11-17, (2013); Lefebvre A., Spruit M., Omta W., Towards reusability of computational experiments capturing and sharing research objects from knowledge discovery processes, IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, (2015); Link G., Et al., Contemporary issues of open data in information systems research: Considerations and recommendations, Communications of the Association for Information Systems, (2017); Manieri A., Et al., Data science professional uncovered: How the EDISON project will contribute to a widely accepted profile for data scientists, Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015, pp. 588-593, (2016); Mannheimer S., Sterman L.B., Borda S., Discovery and Reuse of Open Datasets: An Exploratory Study, 5, (2016); Mosley M., Et al., DAMA Guide to the Data Management Body of Knowledge, (2010); Data Management Protocol, (2017); Otto B., A morphology of the organisation of data governance, ECIS 2011 Proceedings, (2011); Peng R.D., Reproducible research in computational science, Science. NIH Public Access, 334, 6060, pp. 1226-1227, (2011); Ponte D., Enabling an open data ecosystem, ECIS 2015 Research-in-Progress Papers, (2015); Pryor G., Managing Research Data, (2012); Shoshni A., Rotem D., Scientific Data Management: Challenges, Technology, and Deployment, (2009); Simms S., Et al., The future of data management planning: Tools, policies, and players, International Digital Curation Conference (IDCC16), (2016); Tenopir C., Et al., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Tenopir C., Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research. JAI, 36, 2, pp. 84-90, (2014); Tsai A.C., Et al., Promises and pitfalls of data sharing in qualitative research, Social Science & Medicine, (2016); Wallis J.C., Et al., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, 7, (2013); Wende K., Otto B., A contingency approach to data governance, Proceedings, 12th International Conference on Information Quality (ICIQ-07), (2007); Wilkinson M.D., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Zillner S., Et al., European Big Data Value Strategic Research and Innovation Agenda, (2017)","","Frank U.; Kautz K.; Bednar P.M.","Association for Information Systems","","26th European Conference on Information Systems, ECIS 2018","23 June 2018 through 28 June 2018","Portsmouth","143975","","","","","English","Eur. Conf. Inf. Syst.: Beyond Digitization - Facets Socio-Tech. Change, ECIS","Conference paper","Final","","Scopus","2-s2.0-85061324253"
"Küsters U.; Klages T.","Küsters, Ulrike (57207359833); Klages, Tina (49961782100)","57207359833; 49961782100","Fostering Open Science at Fraunhofer","2019","Procedia Computer Science","146","","","39","52","13","1","10.1016/j.procs.2019.01.078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062446829&doi=10.1016%2fj.procs.2019.01.078&partnerID=40&md5=9e034d8a43f09efa0ce26eab43e81dda","Fraunhofer Informationszentrum Raum und Bau IRB, Nobelstr.12, Stuttgart, D-70565, Germany","Küsters U., Fraunhofer Informationszentrum Raum und Bau IRB, Nobelstr.12, Stuttgart, D-70565, Germany; Klages T., Fraunhofer Informationszentrum Raum und Bau IRB, Nobelstr.12, Stuttgart, D-70565, Germany","Open science as a paradigm shift in science concerns university or non-university research organizations from the field of not only basic research but also applied research, i.e., research and technology organizations. However, these have a completely different starting point than the other research organizations for the implementation of open science. This is due to their unique position in the innovation system and their close connection with business. In other words, they must meet the demands of both the scientific and economic systems as well as fulfill their overriding mission of conducting research for society. In the interplay between science and business, Fraunhofer has developed a balanced system of control mechanisms and research management topics as well as an infrastructure specifically adapted to the needs of the organization, which is now subject to considerable challenges and changing tendencies due to digitization. These changes affect control processes and related organizational issues as well as the infrastructure around the core business of Fraunhofer - the generation of research output - and the associated infrastructure. This paper discusses how the Fraunhofer-Gesellschaft deals with these requirements with a special focus on dissemination management. The current activities in the field of the Fraunhofer infrastructure and the introduction of a Current Research Information System (CRIS) as an indicator system to measure scientific excellence will be discussed, along with the activities for the implementation of open science. © 2019 The Authors. Published by Elsevier B.V.","applied Science; Current Research Information System (CRIS); Data Curation; DSpace-CRIS; Exploitation; Innovation System; Intellectual Property (IP); Open Access; Open Access Rate; Open Data; Open Innovation; Open Science; Open Science Infrastruktur; Publication Output; Research; Research Data Management; Research Infrastructure; Technology Organizations (RTO); Technology Readiness Level (TRL)","Data curation; Information systems; Information use; Open access; Open Data; Research; Sounding apparatus; Applied science; D-space; Exploitation; Innovation system; Open innovation; Open science; Publication Output; Research data managements; Research information systems; Research infrastructure; Technology readiness levels; Information management","","","","","Horizon 2020 Framework Programme, H2020, (709747)","","(2017); The Fraunhofer Group for Microelectronics; Fraunhofer-Gesellschaft Zur Förderung der Angewandten Forschung E.V. High Performance Centers; (2014); (2015); Bruggemann J., Crosetto P., Meub L., Bizer K., Intellectual property rights hinder sequential innovation, Experimental Evidence. Research Policy, 45, 10, pp. 2054-2068, (2016); Heller M.A., Can Patents Deter Innovation? the Anticommons in Biomedical Research, Science, 280, 5364, pp. 698-701, (1998); Elsabry E., Sumikura K., Who needs Access to Research? the Case of Pharmaceutical Industry, Septentrio Conference Series, 1, (2016); Felin T., Zenger T.R., Closed or open innovation?, Problem Solving and the Governance Choice. Research Policy, 43, 5, pp. 914-925, (2014); Laursen K., Salter A.J., The paradox of openness: Appropriability, External Search and Collaboration. Research Policy, 43, 5, pp. 867-878, (2014); Elsabry E., Who needs access to research?, Exploring the Societal Impact of Open Access. Revue Française des Sciences de l'Information et de la Communication, 11, (2017); Chesbrough H., From Open Science to Open Innovation, (2015); Zimmermann H.-D., Pucihar A., Open Innovation, Open Data and new Business Models, IDIMT - Interdisciplinary Information and Management Talks, pp. 449-458, (2015); Ferro E., Osella M., Eight Business Model Archetypes for PSI Re-Use. In: Google Campus, editor, Open Data on the Web Workshop, (2013); Research: Open Data Means Business, (2018); Howard A., Open Data Economy: Eight Business Models for Open Data and Insight from Deloitte UK, (2018); Magalhaes G., Roseira C., Manley L., Business models for open government data, ICEGOV 2014: Proceedings Guimarães, pp. 365-370, (2014); Guides: How to Make A Business Case for Open Data, (2018); Scistarter - Science We Want Do Together, (2018); Zooniverse, (2018); The OpenAIRE2020 Project, (2018); European Commission. Discover Great EU-funded Innovations: The Innovation Radar Is A European Commission Initiative to Identify High Potential Innovations and Innovators in EU-funded Research and Innovation Framework Programmes [0.06.2018]; McAdam M., Debackere K., Beyond 'triple helix' toward 'quadruple helix' models in regional innovation systems: Implications for theory and practice, R&D Management, 48, 1, pp. 3-6, (2018); Fraunhofer Open Access-Strategie 2020, (2018); Today's Scholarly Journals: Open, Re-usable, Sustainable, (2018); Broschinski C., Fraunhofer Society Joins OpenAPC, (2018); Open Access in Deutschland: Die Strategie des Bundesministeriums für Bildung und Forschung, (2016); Joining Efforts for Responsible Research and Innovation, (2018); Moedas C., Embracing An ERA of Change: German National ERA Conference, (2015); Recommendation on Access to and Preservation of Scientific Information: Commission Recommendation, (2018); Open Science Skills Working Group Report, (2017)","U. Küsters; Fraunhofer Informationszentrum Raum und Bau IRB, Stuttgart, Nobelstr.12, D-70565, Germany; email: ulrike.kuesters@irb.fraunhofer.de","Simons E.; Clements A.; de Castro P.; Sicilia M.-A.; Bergstrom J.","Elsevier B.V.","","14th International Conference on Current Research Information Systems: FAIRness of Research Information, CRIS 2018","14 June 2018 through 16 June 2018","Umea","145383","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85062446829"
"Rodrigues J.; Castro J.A.; da Silva J.R.; Ribeiro C.","Rodrigues, Joana (57203242279); Castro, João Aguiar (55977255100); da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","57203242279; 55977255100; 55496903800; 7201734594","Hands-On Data Publishing with Researchers: Five Experiments with Metadata in Multiple Domains","2019","Communications in Computer and Information Science","988","","","274","288","14","4","10.1007/978-3-030-11226-4_22","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060760462&doi=10.1007%2f978-3-030-11226-4_22&partnerID=40&md5=344071215eec2b9a52e58c43108a1fbb","Faculty of Engineering of the University of Porto, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Rodrigues J., Faculty of Engineering of the University of Porto, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Castro J.A., Faculty of Engineering of the University of Porto, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; da Silva J.R., Faculty of Engineering of the University of Porto, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., Faculty of Engineering of the University of Porto, INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","The current requirements for open data in the EU are increasing the awareness of researchers with respect to data management and data publication. Metadata is essential in research data management, namely on data discovery and reuse. Current practices tend to either leave metadata definition to researchers, or to assign their creation to curators. The former typically results in ad-hoc descriptors, while the latter follows standards but lacks specificity. In this exploratory study, we adopt a researcher-curator collaborative approach in five data publication cases, involving researchers in data description and discussing the use of both generic and domain-oriented metadata. The study shows that researchers working on familiar datasets can contribute effectively to the definition of metadata models, in addition to the actual metadata creation. The cases also provide preliminary evidence of cross-disciplinary descriptor use. Moreover, the interaction with curators highlights the advantages of data management, making researchers more open to participate in the corresponding tasks. © Springer Nature Switzerland AG 2019.","Data publication; Dendro; Metadata; Research data management","Digital libraries; Information management; Metadata; Collaborative approach; Cross-disciplinary; Current practices; Data publications; Dendro; Exploratory studies; Multiple domains; Research data managements; Open Data","","","","","Operational Programme for Competitiveness and Internationalisation; Fundação para a Ciência e a Tecnologia, FCT, (POCI-01-0145); Federación Española de Enfermedades Raras, FEDER, (016736); Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN, (PD/BD/114143/2015, POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","Funding text 1: Acknowledgements. This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸cão para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT - Funda¸cão para a Ciência e a Tecnologia.; Funding text 2: This work is financed by the ERDF-European Regional Development Fund through the Operational Programme for Competitiveness and Interna-tionalisation-COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT-Fundação para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT-Fundação para a Ciência e a Tecnologia.","Castro J.A., Et al., Involving data creators in an ontology-based design process for metadata models, Developing Metadata Application Profiles, pp. 181-213, (2017); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, Int. J. Digit. Curation, 8, 2, pp. 5-26, (2013); Assante M., Et al., Are scientific data repositories coping with research data publishing?, Data Sci. J, 15, (2016); Bergold J., Thomas S., Participatory research methods: A methodological approach in motion, Forum Qual. Soc. Res., 13, 1, (2012); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a “wicked” problem, J. Librarianship Inf. Sci., 48, 1, pp. 3-17, (2016); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Kim Y., Institutional and Individual Influences on scientists’ Data Sharing Behaviors. the School of Information Studies-Dissertations, (2013); Palmer C.L., Et al., Site-based data curation based on hot spring geobiology, Plos One, 12, 3, (2017); Qin J., Ball A., Greenberg J., Functional and architectural requirements for metadata: Supporting discovery and management of scientific data, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 62-71, (2012); Directorate-General for Research and Innovation, Guidelines on FAIR Data Management in Horizon 2020, (2016); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, Int. J. Digit. Curation, 6, 2, pp. 232-244, (2011); Silva J.R., Ribeiro C., Lopes J.C., Ranking Dublin Core descriptor lists from user interactions: A case study with Dublin Core Terms using the Dendro platform, Int. J. Digit. Libr., (2018); Silvello G., Theory and practice of data citation, J. Assoc. Inf. Sci. Technol., 69, 1, pp. 6-20, (2018); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos ONE, 10, 8, (2015); Thanos C., Research data reusability: Conceptual foundations, barriers and enabling technologies, Publications, 5, 1, (2017); White H.C., Considering personal organization: Metadata practices of scientists, J. Libr. Metadata, 10, 2-3, pp. 156-172, (2010); White H.C., Descriptive metadata for scientific data repositories: A comparison of information scientist and scientist organizing behaviors, J. Libr. Metadata, 14, 1, pp. 24-51, (2014); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, J. Am. Soc. Inf. Sci. Technol., 63, 8, pp. 1505-1520, (2012); Yoon A., Red flags in data: Learning from failed data reuse experiences, Proc. Assoc. Inf. Sci. Technol., 53, 1, pp. 1-6, (2016)","J. Rodrigues; Faculty of Engineering of the University of Porto, INESC TEC, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: joanasousarodrigues.14@gmail.com","Manghi P.; Candela L.; Silvello G.","Springer Verlag","","15th Italian Research Conference on Digital Libraries, IRCDL 2019","31 January 2019 through 1 February 2019","Pisa","223069","18650929","978-303011225-7","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85060760462"
"Ayris P.; Ignat T.","Ayris, Paul (22233574500); Ignat, Tiberius (57204049125)","22233574500; 57204049125","Defining the role of libraries in the Open Science landscape: A reflection on current European practice","2018","Open Information Science","2","1","","1","22","21","16","10.1515/opis-2018-0001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064936833&doi=10.1515%2fopis-2018-0001&partnerID=40&md5=cb4b9adc2f5c8b62170991b4b6f475ff","UCL, University College, London, United Kingdom; Scientific Knowledge Services, Lausanne, Switzerland","Ayris P., UCL, University College, London, United Kingdom; Ignat T., Scientific Knowledge Services, Lausanne, Switzerland","This collaborative paper looks at how libraries can engage with and offer leadership in the Open Science movement. It is based on case studies and the results of an EU-funded research project on Research Data Management taken from European research-led universities and their libraries. It begins by analysing three recent trends in Science, and then links component parts of the research process to aspects of Open Science. The paper then looks in detail at four areas and identifies roles for libraries: Open Access and Open Access publishing, Research Data Management, E-Infrastructures (especially the European Open Science Cloud), and Citizen Science. The paper ends in suggesting a model for how libraries, by using a 4-step test, can assess their engagement with Open Science. This 4-step test is based on lessons drawn from the case studies.  © 2018 Paul Ayris, Tiberius Ignat, published by De Gruyter 2018.","Citizen Science; EOSC; European Open Science Cloud; Open Access; Open Access Publishing; Open Science; RDM; Research Data Management","","","","","","","","Achard P., Ayris P., Fdida S., Gradmann S., Horstmann W., Labastida I., Smit A., LERU Roadmap for Research Data, (2013); Ayris P., Berthou J.-Y., Bruce R., Lindstaedt S., Manreale A., Mons B., Wilkinson R., Realising the European open science cloud, First Report and Recommendations of the Commission High Level Expert Group on the European Open Science Cloud, (2016); Bennett S.H., The scope and limitations of science, Zigon Journal of Religion and Science, 3, 3, pp. 343-353, (1968); Australian Guide to Running A BioBlitz, (2015); Bonney R., Cooper C.B., Dickinson J., Kelling S., Phillips T., Rosenberg K.V., Shirk J., Citizen Science: A developing tool for expanding science knowledge and scientific literacy, BioScience, 59, 11, pp. 977-984, (2009); Bunge E., Citizen Science in der Bibliotheksarbeit-Möglichkeiten und Chancen, (2017); Grey F., Et al., Citizen Science at Universities: Trends, Guidelines and Recommendations, (2016); Nielsen M., Reinventing Discovery: The New Era of Networked Science, (2013); RCUK Policy on Open Access and Supporting Guidance, (2013); Green Paper on Citizen Science-Citizen Science for Europe-Towards A Better Society of Empowered Citizens and Enhanced Research, (2013); A haplotype map of the human genome, Nature, 437, pp. 1299-1320, (2005); Wilsdon J., The road to REF 2021: Why i welcome Lord Stern's blueprint for research assessment, Guardian, (2016)","P. Ayris; UCL, University College, London, United Kingdom; email: p.ayris@ucl.ac.uk","","Walter de Gruyter GmbH","","","","","","24511781","","","","English","Open Inf. Sci.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85064936833"
"Cruz M.J.; Kurapati S.; Turkyilmaz-Van Der Velden Y.","Cruz, Maria J. (57212380253); Kurapati, Shalini (56005569500); Turkyilmaz-Van Der Velden, Yasemin (57205742015)","57212380253; 56005569500; 57205742015","The role of data stewardship in software sustainability and reproducibility","2018","Proceedings - IEEE 14th International Conference on eScience, e-Science 2018","","","8588628","1","8","7","1","10.1109/eScience.2018.00009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061393016&doi=10.1109%2feScience.2018.00009&partnerID=40&md5=f77549c11e73a87d41256085458090f5","TU Delft Library, Delft University of Technology, Delft, Netherlands; Technology, Policy and Management, Delft University of Technology, Delft, Netherlands; Applied Sciences/ Mechanical, Materials, and Maritime Engineering, Delft University of Technology, Delft, Netherlands","Cruz M.J., TU Delft Library, Delft University of Technology, Delft, Netherlands; Kurapati S., Technology, Policy and Management, Delft University of Technology, Delft, Netherlands; Turkyilmaz-Van Der Velden Y., Applied Sciences/ Mechanical, Materials, and Maritime Engineering, Delft University of Technology, Delft, Netherlands","Software and computational tools are instrumental for scientific investigation in today's digitized research environment. Despite this crucial role, the path towards implementing best practices to achieve reproducibility and sustainability of research software is challenging. Delft University of Technology has begun recently a novel initiative of data stewardship - disciplinary support for research data management, one of the main aims of which is achieving reproducibility of scientific results in general. In this paper, we aim to explore the potential of data stewardship for supporting software reproducibility and sustainability as well. Recently, we gathered the key stakeholders of the topic (i.e. researchers, research software engineers, and data stewards) in a workshop setting to understand the challenges and barriers, the support required to achieve software sustainability and reproducibility, and how all the three parties can efficiently work together. Based on the insights from the workshop, as well as our professional experience as data stewards, we draw conclusions on possible ways forward to achieve the important goal of software reproducibility and sustainability through coordinated efforts of the key stakeholders. © 2018 IEEE.","Data Stewardship; Research Software Engineering; Software Reproducibility; Software Sustainability","Engineering research; Information management; Software engineering; Computational tools; Data stewardship; Delft University of Technology; Professional experiences; Reproducibilities; Research data managements; Research environment; Scientific investigation; Sustainable development","","","","","European Commission and the Netherlands Organisation for Scientific Research; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","Funding bodies such as the European Commission and the Netherlands Organisation for Scientific Research (NWO), among others, have already implemented substantial policy changes to promote open science and the FAIR data principles. FAIR data is connected to improving reproducibility of research results and refers to a dataset being Findable, Accessible, Interoperable, and Reusable according to a set of 15 guiding principles [10]. The objectives of the open science policy of the European Commission are four-fold [11].","Begley C.G., Ioannidis J.P.A., Reproducibility in science: Improving the standard for basic and preclinical research, Circ. Res., 116, 1, pp. 116-126, (2015); PSYCHOLOGY. Estimating the reproducibility of psychological science, Science, 349, 6251, (2015); Donoho D.L., Maleki A., Rahman I.U., Shahram M., Stodden V., Reproducible research in computational harmonic analysis, Comput. Sci. Eng., 11, 1, pp. 8-18, (2009); Stodden V., Miguez S., Best practices for computational science: Software infrastructure and environments for reproducible and extensible research, J. Open Res. Softw., 2, 1, (2014); Hutton C., Wagener T., Freer J., Han D., Duffy C., Arheimer B., Most computational hydrology is not reproducible, so is it really science?, Water Resour. Res., 52, 10, pp. 7548-7555, (2016); Teperek M., Cruz M.J., Verbakel E., Bohmer J.K., Dunning A., Data Stewardship-Addressing Disciplinary Data Management Needs; EEMCS-4 Months' Data Stewardship Progress Report | Open Working; Cruz M.J., Dunning A., Research Data Management Within the 4TU Research Centres; Baker M., 1, 500 scientists lift the lid on reproducibility, Nature, 533, 7604, pp. 452-454, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, (2016); Open Science | Digital Single Market; Could Open Research Benefit Cambridge University Researchers? | Unlocking Research; Data Stewardship in the Age of Big Data; Howison J., Bullard J., Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature, J. Assoc. Inf. Sci. Technol., 67, 9, pp. 2137-2155, (2016); It's Impossible to Conduct Research Without Software, Say 7 out of 10 UK Researchers | Software Sustainability Institute; Nangia U., Katz D.S., Track 1 Paper: Surveying the U.S. National Postdoctoral Association Regarding Software Use and Training in Research; Beyond Data: Reproducibility in Scientific Software and the Role of Digital Preservation-Council on Library and Information Resources; Doorn P.K., Aerts P., A Conceptual Approach to Data Stewardship and Software Sustainability: Scientists in Charge, with A Little Help from Their Friends, pp. 1-12, (2016); Sharing Publication-Related Data and Materials: Responsibilities of Authorship in the Life Sciences, (2003); Wilson G., Bryan J., Cranston K., Kitzes J., Nederbragt L., Teal T.K., Good enough practices in scientific computing, PLoS Comput. Biol., 13, 6, (2017); Hettrick S., Research Software Sustainability: Report on A Knowledge Exchange Workshop, (2016); Katerbow M., Feulner G., Recommendations on the Development, Use and Provision of Research Software; Hut R.W., Vande Giesen N.C., Drost N., Comment on 'Most computational hydrology is not reproducible so is it really science?' by Christopher Hutton et al.: Let hydrologists learn the latest computer science by working with Research Software Engineers (RSEs) and not reinvent the waterwheel our, Water Resour. Res., 53, 5, pp. 4524-4526, (2017); Is Software Reproducibility Possible and Practical? | Software Sustainability Institute; Do As You Preach: Results of 2017 Data Management Survey Now Published | Open Working; Teperek M., Krause J., Lambeng N., Blumer E., Van Dijck J., Eggermont R., Van Der Kruyk M., Den Heijer K., Andrews H., Bohmer J.K., Cruz M.J., Busse-Wicher M., Survey: Research Data Management Practice; Stewards T.D.D., Krause J., Lambeng N., Andrews H., Boehmer J., Cruz M., Van Dijck J., Den Heijter K., Vander Kruyk M., Teperek M., Quantitative assessment of research data management practice, Zenodo; Event Report: Towards Cultural Change in Data Management-Data Stewardship in Practice | Open Working; Cruz M.J., Kurapati S., Der Velden Y.T., Software Reproducibility: How to Put It into Practice?; Buckheit J.B., Donoho D.L., Wavelab and reproducible research, Wavelets and Statistics, 103, pp. 55-81, (1995); TU Delft Research Data Framework Policy Now Approved | Open Working","","","Institute of Electrical and Electronics Engineers Inc.","","14th IEEE International Conference on eScience, e-Science 2018","29 October 2018 through 1 November 2018","Amsterdam","144041","","978-153869156-4","","","English","Proc. - IEEE Int. Conf. eScience, e-Science","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85061393016"
"Curdt C.","Curdt, Constanze (36681871300)","36681871300","Supporting the interdisciplinary, long-term research project ‘patterns in soil-vegetation-atmosphere-systems’ by data management services","2019","Data Science Journal","18","1","5","","","","7","10.5334/dsj-2019-005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060703153&doi=10.5334%2fdsj-2019-005&partnerID=40&md5=f154d90e8958e6cf00436a3e78c04bed","Institute of Geography, University of Cologne, Germany; Regional Computing Centre (RRZK), University of Cologne, Germany","Curdt C., Institute of Geography, University of Cologne, Germany, Regional Computing Centre (RRZK), University of Cologne, Germany","Science conducted in cross-institutional, interdisciplinary, long-term research projects requires active sharing of data, documents and further information. Thus, within the Collaborative Research Centre/Transregio 32 ‘Patterns in Soil-Vegetation-Atmosphere Systems’, funded by the German Research Foundation, research data management (RDM) services have been available since early 2007. These services were established to support all researchers during their entire individual research studies. They cover provision of general guidance, support and training for RDM. To fulfil the scientists’ needs and requests with regard to storage, backup, documentation, search and sharing of data with other project members, a project-specific RDM system was designed and implemented. This system was developed and continuously modified in collaboration with the scientists to facilitate their system acceptance. Besides the mentioned services, the system supports further common services such as controlled access to data, rights management, data publication with DOI and data statistics (on repository and single data level). All RDM services provided for the scientists are thus bundled and available to the users in one system: a ‘one-stop-shop’. After more than ten years of RDM service provision for the CRC/TR32, the repository statistics clearly visualize the use of the diverse RDM system services. Furthermore, it has been shown that an RDM adapted to the needs of interdisciplinary researchers can be fruitful and indispensable when scientists conduct their research study e.g. with a time lag. RDM services established at an early stage can contribute to a successful long-term research project. © 2019 The Author(s).","Data repository; Geosciences; Research data management; Services","Digital storage; Search engines; Vegetation; Collaborative research; Data management services; Data repositories; Geosciences; German research foundations; Research data managements; Services; Soil vegetation atmospheres; Information management","","","","","","","Cremer F., Engelhardt C., Neuroth H., Embedded Data Manager – Integriertes Forschungsdaten-management: Praxis, Perspektiven und Potentiale, Bibliothek Forschung Und Praxis, 39, 1, (2015); Curdt C., Metadata Management in an Interdisciplinary, Project-Specific Data Repository: A Case Study from Earth Sciences, Metadata and Semantics Research: 10Th International Conference, MTSR 2016, Göttingen, Germany, pp. 357-368, (2016); Curdt C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DB project database, Program, 49, 4, pp. 494-512, (2015); Jones S., The range and components of RDM infrastrucutre and services, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 89-114, (2014); Klar J., Enke H., Forschungsdaten in der Gruppendomäne – Zwischen individuellen Anforderungen und übergreifenden Infrastrukturen, Zeitschrift für Bibliothekswesen Und Bibliographie (Zfbb), 60, 6, pp. 316-324, (2013); Lotz T., Nieschulze J., Bendix J., Dobbermann M., Konig-Ries B., Diverse or uniform? — Intercomparison of two major German project databases for interdisciplinary collaborative functional biodiversity research, Ecological Informatics, 8, pp. 10-19, (2012); Muckschel C., Weist C., Kohler W., Central data management in environmental research projects – selected problems and solutions, Gil Jahrestagung, 28, pp. 101-104, (2008); Overpeck J.T., Meehl G.A., Bony S., Easterling D.R., Climate Data Challenges in the 21st Century, Science, 331, 6018, pp. 700-702, (2011); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2014); Redohl B., The DFG Perspective: Research Data Management with a Focus on Collaborative Research Centres (SFB), Proceedings of the 2Nd Data Management Workshop, 96, pp. 77-81, (2016); Simmer C., Et al., Monitoring and Modeling the Terrestrial System from Pores to Catchments: The Transregional Collaborative Research Center on Patterns in the Soil–Vegetation–Atmosphere System, Bulletin of the American Meteorological Society, 96, 10, pp. 1765-1787, (2015); Ulbricht D., Elger K., Bertelmann R., Klump J., PanMetaDocs, eSciDoc, and DOIDB—An Infrastructure for the Curation and Publication of File-Based Datasets for GFZ Data Services, ISPRS International Journal of Geo-Information, 5, 3, (2016); Wang W., Gopfert T., Stark R., Data Management in Collaborative Interdisciplinary Research Pro-jects—Conclusions from the Digitalization of Research in Sustainable Manufacturing, ISPRS International Journal of Geo-Information, 5, 4, (2016); Weber A., Piesche C., Requirements on Long-Term Accessibility and Preservation of Research Results with Particular Regard to Their Provenance, ISPRS International Journal of Geo-Information, 5, 4, (2016); Whyte A., Molloy L., Beagrie N., Houghton J., What to Measure? Toward Metrics for Research Data Management, Research Data Management: Practical Strategies for Information Professionals, pp. 275-300, (2014); Willmes C., Kurner D., Bareth G., Building Research Data Management Infrastructure using Open Source Software, Transactions in GIS, 18, 4, pp. 496-509, (2014)","C. Curdt; Institute of Geography, University of Cologne, Germany; email: c.curdt@uni-koeln.de","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85060703153"
"Federer L.","Federer, Lisa (55918619800)","55918619800","Providing meaningful information: Part C—Data management and visualization","2018","A Practical Guide for Informationists: Supporting Research and Clinical Practice","","","","49","62","13","0","10.1016/B978-0-08-102017-3.00005-X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137464707&doi=10.1016%2fB978-0-08-102017-3.00005-X&partnerID=40&md5=e8d27698457d3243e442908b04383a76","National Institutes of Health Library, Bethesda, MD, United States","Federer L., National Institutes of Health Library, Bethesda, MD, United States","This chapter provides an introduction to how informationists can support one of the biggest challenges that researchers and medical professionals now face: how to deal with the rapidly increasing data deluge. With the size of research and clinical data growing exponentially, and with new policies from funders and journals, researchers and clinicians need help to ensure that they are able to work effectively with their data in ways that comply with requirements but do not present an undue burden. Specifically, this chapter considers how informationists can provide support for data management and visualization. Many of the skills that informationists already have are applicable to these problems, and this chapter also provides suggestions for how informationists can get started with gaining new skills and designing data services. © 2018 Elsevier Ltd. All rights reserved.","Data management; Data services; Data visualization; Informationist services; Research data management; Research support services","","","","","","","","Anscombe F.J., Graphs in statistical analysis, The American Statistician, 27, 1, (1973); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science Librarians’ participation in data management at Association of Research Libraries Institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Begley C.G., Ioannidis J.P.A., Reproducibility in science: Improving the standard for basic and preclinical research, Circulation Research, 116, 1, pp. 116-126, (2014); Brandenburg M., Joque J., Contextualizing visualization in library services, The Medical Library Association Guide to Data Management for Librarians, pp. 139-150, (2016); Eligible Hospital Medicaid EHR Incentive Program Stage 3 Objectives and Measures Objective 8 of 8, (2016); Clark K., Grove M., Shneiderman B., Chute C.G., Towards event sequence representation, reasoning and visualization for EHR data, pp. 801-805, (2012); Collins F.S., Tabak L.A., Policy: NIH plans to enhance reproducibility, Nature, 505, 7485, pp. 612-613, (2014); Dewdney S.B., Lachance J., Electronic records, registries, and the development of “Big Data”: Crowd-sourcing quality toward knowledge, Frontiers in Oncology, 6, (2017); Data Visualization, (2017); Ehrenstein V., Nielsen H., Pedersen A.B., Johnsen S.P., Pedersen L., Clinical epidemiology in the era of big data: New opportunities, familiar challenges, Journal of Clinical Epidemiology, 9, pp. 245-250, (2017); Federer L.M., Lu Y.-L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, (2016); Felten P., Visual literacy, Change: The Magazine of Higher Learning, 40, 6, pp. 60-64, (2008); Hanson K., Bakker T.A., Svirsky M.A., Neuman A.C., Rambo N., Informationist role: Clinical data management in auditory research, Journal of eScience Librarianship, 2, 1, pp. 25-29, (2013); Komorowski M., A History of Storage Cost (Update), (2014); Martin E.R., Creamer A.T., Kafel D.M., New England Collaborative Data Management Curriculum, (2013); Monroe M., Lan R., Lee H., Plaisant C., Shneiderman B., Temporal event sequence simplification, IEEE Transactions on Visualization and Computer Graphics, 19, 12, pp. 2227-2236, (2013); National Institutes of Health Plan for Increasing Access to Scientific Publications and Digital Scientific Data from NIH Funded Scientific Research, (2015); NIH Data Sharing Policy, (2016); NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources, (2016); NIH Genomic Data Sharing Policy, (2017); Dissemination and Sharing of Research Results, (2010); Visualization, (2017); Schofield P., Big data in mental health research—do the n s justify the means? Using large data-sets of electronic health records for mental health research, BJPsych Bulletin, 41, 3, pp. 129-132, (2017); Browse Article and Data Sharing Requirements by Federal Agency, (2016); Stephens Z.D., Lee S.Y., Faghri F., Campbell R.H., Zhai C., Efron M.J., Robinson G.E., Big data: Astronomical or genomical?, PLOS Biology, 13, 7, (2015); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Turkay C., Jeanquartier F., Holzinger A., Hauser H., On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, pp. 117-140, (2014); Data Management Planning Tool, (2017); Journal of eScience Librarianship, (2017); REDCap, (2017); Vanian J., Data Is The New Oil, (2016); Weissgerber T.L., Garovic V.D., Savic M., Winham S.J., Milic N.M., Punjabi N., From static to interactive: Transforming data visualization to improve transparency, PLoS Biology, 14, 6, (2016); Whipple E.C., Odell J.D., Ralston R.K., Liu G.C., When informationists get involved: The CHICA-GIS Project, Journal of eScience Librarianship, 2, 1, (2013)","","","Elsevier","","","","","","","978-008102017-3; 978-008102016-6","","","English","A Practical Guide for Informationists: Supporting Research and Clinical Practice","Book chapter","Final","","Scopus","2-s2.0-85137464707"
"Schembera B.; Iglezakis D.","Schembera, Björn (56829559000); Iglezakis, Dorothea (22334176800)","56829559000; 22334176800","The Genesis of EngMeta - A Metadata Model for Research Data in Computational Engineering","2019","Communications in Computer and Information Science","846","","","127","132","5","6","10.1007/978-3-030-14401-2_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062666906&doi=10.1007%2f978-3-030-14401-2_12&partnerID=40&md5=4c472f7153c9f7a69aaefde4b93f51fc","High Performance Computing Center, Nobelstr. 19, Stuttgart, 70569, Germany; University Library Stuttgart, Holzgartenstr. 16, Stuttgart, 70174, Germany","Schembera B., High Performance Computing Center, Nobelstr. 19, Stuttgart, 70569, Germany; Iglezakis D., University Library Stuttgart, Holzgartenstr. 16, Stuttgart, 70174, Germany","In computational engineering, numerical simulations produce huge amounts of data. To keep this research data findable, accessible, inter-operable and reusable, a structured description of the data is indispensable. This paper outlines the genesis of EngMeta – a metadata model designed to describe engineering simulation data with a focus on thermodynamics and aerodynamics. The metadata model, developed in close collaboration with engineers, is based on existing standards and adds discipline-specific information as the main contribution. Characteristics of the observed system offer researchers important search criteria. Information on the hardware and software used and the processing steps involved helps to understand and replicate the data. Such metadata are crucial to keeping the data FAIR and bridging the gap to a sustainable research data management in computational engineering. © 2019, Springer Nature Switzerland AG.","Big data; Computational engineering; High performance computing; Metadata; Research data management; Simulation","Big data; Information management; Semantics; Thermodynamics; Computational engineering; Engineering simulation; Hardware and software; High performance computing; Processing steps; Research data managements; Simulation; Specific information; Metadata","","","","","Bundesministerium für Bildung und Forschung, BMBF, (16FMD008)","2 In contrast, climate sciences offer much more advanced research data management possibilities making use of well-established metadata models [8]. 3 The project DIPL-ING (http://www.ub.uni-stuttgart.de/dipling) is funded by the Federal Ministry of Education and Research (BMBF) under grant no 16FMD008.","Arora R., Data management: State-of-the-practice at open-science data centers, Handbook on Data Centers, pp. 1095-1108, (2015); Belhajjame K., Et al., PROV-DM: The PROV Data Model, (2013); Chalk S., The Experiment Markup Language, (2014); Cuevas-Vicenttin V., Et al., Provone: A PROV Extension Data Model for Scientific Workflow Provenance, (2016); Datacite Metadata Schema for the Publication and Citation of Research Data, Version 4.1, (2017); Iglezakis D., Schembera B., Anforderungen der Ingenieurwissenschaften an das Forschungsdatenmanagement der Universität Stuttgart-Ergebnisse der Bedarfs-analyse des Projektes DIPL-ING. O-bib, Das Offene Bibliotheksjournal, 5, 3, pp. 46-60, (2018); Jones M.B., Et al., Codemeta: An Exchange Schema for Software Metadata, Version 2.0, (2017); Lautenschlager M., Toussaint F., Thiemann H., Reinke M., The CERA-2 Data Model, (1998); Schembera B., Bonisch T., Challenges of research data management for high performance computing, TPDL 2017. LNCS, 10450, pp. 140-151, (2017); Wilkinson M.D., Et al., The fair guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016)","B. Schembera; High Performance Computing Center, Stuttgart, Nobelstr. 19, 70569, Germany; email: schembera@hlrs.de","Zervas M.; Sartori F.; Garoufallou E.; Siatri R.","Springer Verlag","","12th International Conference on Metadata and Semantics Research, MTSR 2018","23 October 2018 through 26 October 2018","Limassol","224109","18650929","978-303014400-5","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85062666906"
"Wilms K.; López A.; Brenger B.; Rehwald S.","Wilms, Konstantin (57190278061); López, Ania (55949158100); Brenger, Bela (57190878746); Rehwald, Stephanie (57207471272)","57190278061; 55949158100; 57190878746; 57207471272","Open data in higher education - What prevents researchers from sharing research data?","2018","International Conference on Information Systems 2018, ICIS 2018","","","","","","","4","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062556172&partnerID=40&md5=b3edb18fff652ab8ee2fb43db4cd533a","University of Duisburg-Essen, Forsthausweg 2, Duisburg, 47057, Germany; University of Duisburg-Essen, Universitätsstr. 9-11, Essen, 45141, Germany; RWTH Aachen, Seffenter Weg 23, Aachen, 52074, Germany","Wilms K., University of Duisburg-Essen, Forsthausweg 2, Duisburg, 47057, Germany; López A., University of Duisburg-Essen, Universitätsstr. 9-11, Essen, 45141, Germany; Brenger B., RWTH Aachen, Seffenter Weg 23, Aachen, 52074, Germany; Rehwald S., University of Duisburg-Essen, Universitätsstr. 9-11, Essen, 45141, Germany","Open data - a concept, where researchers not only publish their findings in form of research publications but share the corresponding (raw) data sets - has gained increasing attention within the past years. One reason for the increasing popularity is the emergence of new e-science technologies in higher education (HE) making the exchange of data more available. However, the adoption rate of open data technologies remains low compared to the topics' significance in research. While Information Systems (IS) research has majorly focused on the technical perspective of e-science technologies, this work tries to emphasize non-technical factors which impact researchers' acceptance towards the concept of open data. Grounded on the value-based theory, this research-in-progress proclaims that most potential users in academia conduct open data if the personal advantages outweigh the disadvantages. Simultaneously, uncertainty factors impact the decision-making process. This research-in-progress work presents the primarily results of an ongoing quantitative analysis including n=280 researchers from two large universities in Germany. The results indicate that new technologies diminish the perceived effort occurring during the data preparation process, as well as researchers perceived personal benefit regarding data exchange. Implications of these findings and future enhancements are discussed. © International Conference on Information Systems 2018, ICIS 2018.All rights reserved.","Data Management; Research Data Management; Sharing Research Data","Decision making; Electronic data interchange; Information management; Information systems; Information use; Data preparation; Data technologies; Decision making process; Non-technical factors; Research data; Research data managements; Uncertainty factors; Value-based theory; Open Data","","","","","National Science Foundation, NSF; Australian Research Council, ARC; Deutsche Forschungsgemeinschaft, DFG","Open data, as a component of research data management (RDM), got increased priority especially since several international research funding institutions, schuas the National Science Foundation (NSF), the Australian Research Council (ARC), or the German Research Foundation (Deutsche Forschungsgemeinschaft 2013) set research data accessibility on their primary agenda (Wilms et al. 2016). The NSF defined open data as “publicly available data structured in a way to be fully accessible and usable” (National Science Foundation 2016). Open data covers “original scientific research results, raw data and metadata, source mterialsa, digital representations of pictorial and graphical materials and scholarly multimedia mterial” a(Berlin Declaration, 2003, p. 1).","Amorim R.C., Castro J.A., Rocha Da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 4, pp. 851-862, (2017); Bauer B., Ferus A., Gorraiz J., Grundhammer V., Gumpenberger C., Maly N., Muhlegger J.M., Preza J.L., Sanchez Solis B., Schmidt N., Steineder C., Forschende und ihre Daten, Ergebnisse Einer Österreichweiten Befragung - Report 2015, (2015); Bender W.C., Consumer purchase costs-do retailers recognize them?, Journal of Retailing, 40, 1, pp. 1-8, (1964); Bhattacherjee A., Park S.C., Why end-users move to the cloud: A migration-theoretic analysis, European Journal of Information Systems, 23, 3, pp. 357-372, (2014); Campbell E.G., Clarridge B.R., Birenbaum L., Hilgartner S., Blumenthal D., Evidence from a national survey, Journal of the American Medical Association, 287, 4, pp. 473-480, (2002); Carleton R., Sharpe D., Asmundson G., Anxiety sensitivity and intolerance of uncertainty: Requisites of the fundamental fears?, Behaviour Research and Therapy, 45, 10, pp. 2307-2316, (2007); Cenfetelli R.T., Inhibitors and enablers as dual factor concepts in technology usage, Journal of the Association for Information Systems, 5, 11-12, pp. 472-492, (2004); Cenfetelli R.T., Schwarz A., Identifying and testing the inhibitors of technology usage intentions, Information Systems Research, 22, 4, pp. 808-823, (2011); Memorandum Safeguarding Good Scientific Practice, (2013); Dodds W.B., Monroe K.B., Grewal D., Effects of price, brand, and store information on buyers' product evaluations, Journal of Marketing Research, 28, 3, (1991); Enke N., Thessen A., Bach K., Bendix J., Seeger B., Gemeinholzer B., The user's view on biodiversity data sharing - Investigating facts of acceptance and requirements to realize a sustainable use of research data -, Ecological Informatics, 11, pp. 25-33, (2012); Featherman M., Extending the technology acceptance model by inclusion of perceived risk, AMCIS 2001 Proceedings, (2001); Featherman M.S., Pavlou P.A., Predicting e-services adoption: A perceived risk facets perspective, International Journal of Human-Computer Studies, 59, 4, pp. 451-474, (2003); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS ONE, 10, 2, (2015); Feijen M., What Researchers Want, (2011); Fornell C., Larcker D.F., Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, 1, (1981); Foster N.F., Gibbons S., Understanding faculty to improve content recruitment for institutional repositories, D-Lib Magazine, (2005); Gutman J., Means-end chains as goal hierarchies, Psychology and Marketing, 14, 6, pp. 545-560, (1997); Hsu M.-H., Ju T.L., Yen C.-H., Chang C.-M., Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations, Int. J. Human-Computer Studies, 65, 2, pp. 153-169, (2007); Kahneman D., Tversky A., Prospect theory: An analysis of decision under risk, Econometrica: Journal of the Econometric Society, 47, pp. 263-291, (1979); Kim H.-W., Chan H.C., Gupta S., Value-based Adoption of Mobile Internet: An empirical investigation, Decision Support Systems, 43, 1, pp. 111-126, (2007); Kim H.-W., Kankanhalli A., Investigating user resistance to information systems implementation: A status quo bias perspective, MIS Quarterly, 33, 3, pp. 567-582, (2009); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists' data-sharing behaviors: A multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2012); Kim Y., Zhang P., Understanding data sharing behaviors of STEM researchers: The roles of attitudes, norms, and data repositories, Library & Information Science Research, 37, 3, pp. 189-200, (2015); Lagrange R.L., Ferraro K.F., Supancic M., Perceived risk and fear of crime: Role of social and physical incivilities, Journal of Research in Crime and Delinquency, (1992); Lending D., Straub D., Impacts of an integrated information center on faculty end-users: A qualitative assessment, Journal of the American Society for Information Science, 48, 5, pp. 466-471, (1997); Link G., Lumbard K., Conboy K., Feldman M., Feller J., George J., Germonprez M., Goggins S., Jeske D., Kiely G., Schuster K., Willis M., Contemporary issues of open data in information systems research: Considerations and recommendations, Communications of the Association for Information Systems, 41, 25, pp. 587-610, (2017); Lynch C., Big data: How do your data grow?, Nature, pp. 28-29, (2008); Lyytinen K., Data matters in IS theory building, Journal of the Association for Information Systems, 10, 10, pp. 715-720, (2009); Moore G.C., Benbasat I., Development of an instrument to measure the perceptions of adopting an information technology innovation, Information Systems Research, 2, 3, pp. 192-222, (1991); Nahm A.Y., Rao S.S., Solis-Galvan L.E., Ragu-Nathan T.S., The Q-sort method: Assessing reliability and construct validity of questionnaire items at a pre-testing stage, Journal of Modern Applied Statistical Methods, 1, 1, pp. 114-125, (2002); Data Management & Sharing FAQs | NSF - National Science Foundation, (2016); Nunnally J.C., Bernstein I.H., Psychometric Theory, (1994); Ortoleva P., Status quo bias, multiple priors and uncertainty aversion, Games and Economic Behavior, 69, 2, pp. 411-424, (2010); Perrier L., Blondal E., Ayala A.P., Dearborn D., Kenny T., Lightfoot D., Reka R., Thuna M., Trimble L., MacDonald H., Research data management in academic institutions: A scoping review, PLoS ONE, 12, 5, pp. 1-14, (2017); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, 3, (2007); Piwowar H.A., Vision T., Data reuse and the open data citation advantage, PeerJ, 1, 1, (2013); Przybylski A.K., Murayama K., Dehaan C.R., Gladwell V., Motivational, emotional, and behavioral correlates of fear of missing out, Computers in Human Behavior, 29, 4, pp. 1841-1848, (2013); Ranganathan C., Seo D., Babad Y., Switching behavior of mobile users: Do users' relational investments and demographics matter?, European Journal of Information Systems, 15, 3, pp. 269-276, (2006); Renzl B., Trust in management and knowledge sharing: The mediating effects of fear and knowledge documentation, Omega, 36, 2, pp. 206-220, (2008); Ribes D., Polk J., Flexibility relative to what? Change to research infrastructure, Journal of the Association for Information Systems, 15, 5, pp. 287-305, (2014); Rountree P.W., Land K.C., Perceived risk versus fear of crime: Empirical evidence of conceptually distinct reactions in survey data, Social Forces, 74, 4, pp. 1353-1376, (1996); Savage C.J., Vickers A.J., Empirical study of data sharing by authors publishing in PLOS journals, PloS One, 4, 9, pp. 70-78, (2009); Schwartz S.H., Are there universal aspects in the structure and contents of human values?, Journal of Social Issues, 50, 4, pp. 19-45, (1994); Sirdeshmukh D., Singh J., Sabol B., Consumer trust, value, and loyalty in relational exchanges, Journal of Marketing, 66, 1, pp. 15-37, (2002); Tsolakidis A., Triperina E., Sgouropoulou C., Christidis N., Measuring academic research impact based on open data: A case of engineering faculties, Education Engineering (EDUCON), pp. 1611-1618, (2017); Wilms K., Meske C., Stieglitz S., Rudolph D., Vogl R., How to improve research data management, Human Interface and the Management of Information: Applications and Services. HIMI 2016. Lecture Notes in Computer Science, 9735, pp. 434-442, (2016); Wilms K., Stieglitz S., Buchholz A., Vogl R., Rudolph D., Do researchers dream of research data management?, Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 4411-4420, (2018); Zeithaml V., Consumer perceptions of price, quality, and value: A means-, Journal of Marketing, 52, 3, (1988)","","","Association for Information Systems","Auburn University; et al.; Georgia State University, J. Mack Robinson College of Business; Oklahoma State University; Prospect Press; The University of Arizona","39th International Conference on Information Systems, ICIS 2018","13 December 2018 through 16 December 2018","San Francisco","145376","","978-099668317-3","","","English","Int. Conf. Inf. Syst., ICIS","Conference paper","Final","","Scopus","2-s2.0-85062556172"
"Voß V.; Hamrin G.","Voß, Viola (57208525001); Hamrin, Göran (57208525594)","57208525001; 57208525594","Establishing RDM services for small-scale data producers at big universities","2018","LIBER Quarterly","28","1","","","","","1","10.18352/lq.10255","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065035552&doi=10.18352%2flq.10255&partnerID=40&md5=99779050ebd01cdd9fdbfb87f3f5f49e","Universitäts-und Landesbibliothek (ULB), Münster, Germany; Kungliga Tekniska Högskolan (KTH) Library, Stockholm, Sweden","Voß V., Universitäts-und Landesbibliothek (ULB), Münster, Germany; Hamrin G., Kungliga Tekniska Högskolan (KTH) Library, Stockholm, Sweden","During an international library conference in 2017 the authors had many productive exchanges about similarities and differences in Swedish and German higher-education libraries. Since research data management (RDM) is an emerging topic on both sides of the Baltic Sea, we find it valuable to compare strategies, services, and workflows to learn from each other’s practices. Aim: In this paper, we aim to compare the practices and needs of small-scale data producers in engineering and the humanities. In particular, we try to answer the following research questions: • What kind of data do the small-scale data producers produce? • What do these producers need in terms of RDM support? • What then can we librarians help them with? Hypothesis: Our research hypothesis is that small-scale data producers have similar needs in engineering and the humanities. This hypothesis is based on the similarities in demands from funding agencies on (open) research data and on the assumption that research in different subjects often creates results which are different in content but similar in structure. Method: We study the current strategies, practices, and services of our respective universities (KTH Royal Institute of Technology Stockholm and West-fälische Wilhelms-Universität Münster). We also study the work and initiatives done on a more advanced level by universities, libraries, and other organisations in Sweden and Germany. Results: The paper will give an overview of how we did the groundwork for the initial services provided by our libraries. We focus on what we are doing and why we are doing it. We find that we are following in the leading footsteps of other university libraries. The experiences shared by colleagues help us to adapt their best practices to our local demands, making them better practices for KTH and WWU researchers. Limitation: We restrict ourselves to studying only researchers who create data on a small scale, since the large-scale data producers handle the RDM on their own. © 2018, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Academic support services; Germany; Research data management; Sweden; University libraries","","","","","","","","Grundsätze Zum Umgang Mit Forschungsdaten, (2010); Forschungsdatenmanagement, Eine Handreichung, (2018); Research Data Vision 2025 – Ein Schritt näher, (2018); Andorfer P., Forschungsdaten in den (Digitalen) Geisteswissenschaften: Versuch einer Konkretisierung, DARIAH-DE Working Papers, 14, (2015); Bohle Carbonell K., Enigmas, Networks, and People at Work — the Underwear of Data Science, (2018); Bollen J., Data models for theatre research: People, places, and performance, Theatre Journal, 68, 4, pp. 615-632, (2016); Burghardt M., Digital Humanities in der Musikwissenschaft – Computergestützte Erschließungsstrategien und Analyseansätze für handschriftliche Liedblätter, Bibliothek – Forschung Und Praxis, 42, 2, pp. 324-332, (2018); Burghardt M., Music Information Retrieval Meets Digital Humanities: Computergestützte Erschließung Und Analyse Von Handschriftlichen Volksliedblättern, (2018); Cremer F., Klaffki L., Steyer T., Der Chimäre auf der Spur: Forschungsdaten in den Geisteswissenschaften, O-Bib, 5, 2, pp. 142-162, (2018); Curdt C., Kramer F., Hess V., Lopez A., Magrean B., Rudolph D., Vompras J., Einführung in Forschungsdatenmanagement, (2016); Curdt C., Hess V., Lopez A., Magrean B., Rudolph D., Vompras J., Herausforderung Forschungsdatenmanagement – Unterstützung Der Hochschulen Durch Eine einrichtungsübergreifende Kooperation in NRW, pp. 95-103, (2017); Curdt C., Grasse M., Hess V., Kasties N., Lopez A., Magrean B., Winter N., Zur Rolle Der Hochschulen: Positionspapier Der Landesinitiative NFDI Und Expertengruppe FDM Der Digitalen Hochschule NRW Zum Aufbau Einer Nationalen Forschungsdateninfrastruktur, (2018); Wissenschaftliche Bibliotheken 2025, 4, (2018); Empfehlungen Zur Gesicherten Aufbewahrung Und Bereitstellung Digitaler Forschungsprimärdaten, (2009); Sicherung Guter Wissenschaftlicher Praxis, Safeguarding Good Scientific Practice, (2013); Stärkung Des Systems Wissenschaftlicher Bibliotheken in Deutschland, (2018); Geisteswissenschaftliche Datenzentren im deutschsprachigen Raum, Grundsatzpapier Zur Sicherung Der Langfristigen Verfügbarkeit Von Forschungsdaten, (2018); Dierkes J., Curdt C., Von der Idee zum Konzept – Forschungsdatenmanagement an der, Universität Zu Köln. O-Bib, 5, 2, pp. 28-46, (2018); Thesen Zur Informations-Und Kommunikationsinfrastruktur Der Zukunft, (2018); Dressel W.F., Research data management instruction for digital humanities, Journal of Escience Librarianship, 6, 2, pp. 1-7, (2017); Faniel I.M., Connaway L.S., Librarians’ perspectives on the factors influencing research data management programs, College & Research Libraries, 79, 1, pp. 100-119, (2018); Formas Policy för öppen tillgång till Forskningsdata. (Policy Decision Made at the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning.) 2016-06-30, (2016); Gaillard M., CERN Data Centre Passes the 200-Petabyte Milestone., (2017); Goble C., Building the FAIR Research Commons: A Data Driven Society of Scientists, Talk Given at the Symposium “The Future of a Data-Driven Society”, (2018); Gradl T., Henrich A., Plutte C., Heterogene Daten in den Digital Humanities: Eine Architektur zur forschungsorientierten Föderation von Kollektionen. Zeitschrift für digitale Geisteswissenschaften, Sonderband 1, Grenzen Und Möglichkeiten Der Digital Humanities, (2015); Hahn H., Kalman T., Kolbmann W., Kollatz T., Neuschafer M., Pielstrom S., Tonne D., Dariah-De-Handbuch Digital Humanities: Anwendungen, Forschungsdaten Und Projekte, (2015); Hansson S.O., What is technological science?, Studies in History and Philosophy of Science Part A, 38, pp. 523-527, (2007); Hodson S., African open science platform, Talk Given at the 11Th Research Data Alliance Meeting Berlin, 21, (2018); Management Von Forschungsdaten – Eine Zentrale Strategische Herausforderung für Hochschulleitungen, (2014); Wie Hochschulleitungen Die Entwicklung Des Forschungsdatenmanagements Steuern können: Orientierungspfade, Handlungsoptionen, Szenarien, (2016); It-Empfehlungen für Den Nachhaltigen Umgang Mit Digitalen Daten in Den Altertumswissenschaften, (2017); Ingram C., Audiovisual Research Data: A Guide to Managing Digital Media for Anyone Using It as an Integral Part of Carrying out Research, 21, 1, pp. 36-41, (2017); Jensen U., Leitlinien Zum Management Von Forschungsdaten. Sozialwissenschaftliche Umfragedaten. TECHNICAL Reports. 2012|07, (2012); Johnson K., Steeves V., Research data management among life sciences faculty: Implications for library service, LIS Scholarship Archive, (2018); Kaden B., Warum Forschungsdaten Nicht Publiziert Werden, (2018); Kaden B., Was sind Scholarly Makerspaces? – eine erste Überlegung [Blog post], Future E-Research Support in the Humanities, (2018); Kaden B., Kleineberg M., Anforderungen Und Trends Werkzeugbasierter Forschung in Den Geisteswissenschaften, (2018); Katerbow M., Feulner G., Recommendations on the Development, Use and Provision of Research Software, (2018); Kronenwett S., Dienstleistungsangebote für Digital Arbeitende Geisteswissenschaftlerinnen, (2017); Linek S.B., Fecher B., Friesike S., Hebing M., Data sharing as social dilemma: Influence of the researcher’s personality, Plos One, 12, 8, pp. 1-24, (2017); Lopez A., Forschungsdatenmanagement an Deutschen Universitäten—ein Neues Arbeitsfeld für Bibliotheken? [Slideshow], (2015); Ludwig J., Enke H., Leitfaden Zum Forschungsdaten-Management, Handreichungen Aus Dem Wissgrid-Projekt, (2013); Meyer A., Neumann J., Sossna V., Service durch Kompetenzbündelung – Das institutionelle Konzept zum Forschungsdatenmanagement der Leibniz Universität Hannover, E-Science-Tage 2017: Forschungsdaten Managen, pp. 13-21, (2017); Meyer-Doerpinghaus U., Troger B., Forschungsdatenmanagement als Herausforderung für Hochschulen und Hochschulbibliotheken, O-Bib, 2, 4, pp. 65-72, (2015); Moritz C., Smart R., Retteen A., Hunter M., Stanley S., Soper D., Vandegrift M., De-Centering and Recentering Digital Scholarship: A Manifesto, (2017); Nestor Als Kern Einer Langzeitarchivierungs-Und Langzeitverfügbarkeits-Infrastruktur für Digitale Daten Und Dokumente in Deutschland, (2018); Neuroth H., Forschungsdaten(Management) in Den Digital Humanities – Eine Annäherung. Talk Given at the Lecture Series “Digital Humanities: Die Digitale Transformation Der Geisteswissenschaften” at Humboldt University Berlin, 30.1.2018, (2018); Ottersen O.P., The Circuitous Road Towards Open Access: Swedish Universities to Pull the Plug on Elsevier [Blog Post], (2018); Owens T., Defining data for humanists: Text, artifact, information or evidence, Journal of Digital Humanities, 1, 1, pp. 6-8, (2011); Peukert H., Curating humanities research data: Managing workflows for adjusting a repository framework, International Journal of Digital Curation, 12, 2, pp. 234-245, (2017); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos One, 9, 12, pp. 1-28, (2014); Pursula A., Hedlund J., Jareborg N., Kaunisvaara J., Azab A., Syed A., Tryggve – Nordic Collaboration for Sensitive Data. Poster Presented at ELIXIR All Hands Meeting 4.-7.6.2018, (2018); Leistung Aus Vielfalt. Empfehlungen Zu Strukturen, Prozessen Und Finanzierung Des Forschungsdatenmanagements in Deutschland, (2016); Schritt für Schritt – oder: Was bringt wer mit?, Ein Diskussionsimpuls Zu Zielstellung Und Voraussetzungen für Den Einstieg in Die Nationale Forschungsdateninfrastruktur (NFDI), (2017); Zusammenarbeit als Chance, Zweiter Diskussionsimpuls Zur Ausgestaltung Einer Nationalen Forschungsdateninfrastruktur (NFDI) für Die Wissenschaft in Deutschland, (2018); Schirmbacher P., Dimensionen des Forschungsdatenmanagements im digitalen Zeitalter, Bibliothek – Forschung für Die Praxis. Festschrift für Konrad Umlauf Zum 65. Geburtstag, pp. 389-405, (2017); Schoch C., Big? Smart? Clean? Messy? Data in the humanities, Journal of Digital Humanities, 2, 3, pp. 2-13, (2013); Schonbrodt F., Gollwitzer M., Abele-Brehm A., Der Umgang mit Forschungsdaten im Fach Psychologie: Konkretisierung der DFG-Leitlinien im Auftrag des DGPs Vorstands, Psychologische Rundschau, 68, pp. 20-35, (2016); Sesartic A., Dieude A., Research data management training and support services at both ETH Zurich and EPF Lausanne. Lessons learned, best practices and the way forward, E-Science-Tage 2017: Forschungsdaten Managen, pp. 5-12, (2017); Snow C.P., The two cultures, The Two Cultures, pp. 1-51, (1959); Snow C.P., The two cultures: A second look, The Two Cultures, pp. 53-107, (1963); Stacker T., Steenweg H., Forschungsdaten – Aufgabe und Herausforderung für Bibliotheken, Editorial. O-Bib, 5, 2, pp. IV-V, (2018); Thalinger C., Twitter’s quest for a wholly Graal runtime [Video], Talk Given at the Conference Javaone, 2017, (2017); Thoegersen J.L., “Yeah, I guess that’s data”: Data practices and conceptions among humanities faculty, Portal: Libraries and the Academy, 18, 3, pp. 491-504, (2018); Towe M., Wie Forschungsdaten die Bibliothek verändern, Erfahrungen Aus Der Eth-Bibliothek. B.I.T.Online, 20, 5, pp. 361-370, (2017); Vandegrift M., Designing Digital Scholarship Ecologies, (2018); Förslag till Nationella Riktlinjer för öppen tillgång till Vetenskaplig Information, (2015); Weisbrod D., Kaden B., Kleineberg M., EDissPlus – Optionen für die Langzeitarchivierung dissertationsbezogener Forschungsdaten aus Sicht von Bibliotheken und Forschenden, E-Science-Tage 2017: Forschungsdaten Managen, pp. 189-198, (2017); Empfehlungen Zur Weiterentwicklung Der Wissenschaftlichen Informationsinfrastrukturen in Deutschland Bis 2020, (2012); Zenk-Moltgen W., Akdeniz E., Katsanidou A., Nasshoven V., Balaban E., Factors influencing the data sharing behavior of researchers in sociology and political science, Journal of Documentation, 74, 5, pp. 1053-1073, (2018)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85065035552"
"Bradley-Ridout G.","Bradley-Ridout, Glyneva (57215381098)","57215381098","Preferred but not required: Examining research data management roles in health science librarian positions","2018","Journal of the Canadian Health Libraries Association","39","3","","138","145","7","3","10.29173/JCHLA29368","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080833732&doi=10.29173%2fJCHLA29368&partnerID=40&md5=addabd1965f874a17b8c42c58731e4aa","University of Toronto, Toronto, ON, Canada","Bradley-Ridout G., University of Toronto, Toronto, ON, Canada","Introduction: Research data management (RDM) is being recognized as an increasingly important role for librarians, but little work has been done to investigate whether RDM tasks are being asked of librarians in their professional roles. In this paper, current job postings in health science librarianship are examined to investigate whether research data management job duties are being included in advertised health science librarian positions. Methods: Job postings available as of April 5th, 2018 on the University of Toronto’s Faculty of Information (iSchool) jobsite were collected and analyzed to identify positions related to health science librarianship. The job responsibilities and descriptions were further examined to identify instances where research data management was mentioned. Results: Thirty-two job descriptions were identified as meeting the inclusion criteria. Of these thirty-two health science librarian postings, eight included supporting research data management services, in some capacity, as part of the position description. Discussion/Conclusion: Through the job posting analysis, a trend emerged where RDM is not consistently seen as a role for health science librarians. However, the literature indicates that in many instances, research data management is already being done by health science librarians, and is a service which is likely to continue in the future. As such, it is important that research data management roles start being acknowledged and reflected in education and job description opportunities. © 2018, Canadian Health Libraries Association. All rights reserved.","","article; education; health science; human; human experiment; librarian; library science; professional standard; responsibility; work","","","","","","","Read K.B., Surkis A., Larson C., Et al., Starting the data conversation: Informing data services at an academic health sciences library, J Med Libr Assoc, 103, 3, pp. 131-135, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Cheek F.M., Bradigan P.S., Academic health sciences library research support, Journal of the Medical Library Association: JMLA, 98, 2, pp. 167-171, (2010); (2015); Noh Y., A study comparing public and medical librarians’ perceptions of the role and duties of health information""providing librarians, Health Information & Libraries Journal, 32, 4, pp. 300-321, (2015); Cooper I.D., Crum J.A., New activities and changing roles of health sciences librarians: A systematic review, 1990–2012, Journal of the Medical Library Association: JMLA, 101, 4, (2013); (2016); Holst R., Funk C.J., Adams H.S., Et al., Vital pathways for hospital librarians: Present and future roles, Journal of the Medical Library Association: JMLA, 97, 4, (2009); Spencer A.J., Eldredge J.D., Roles for librarians in systematic reviews: A scoping review, Journal of the Medical Library Association: JMLA, 106, 1, (2018); Weightman A.L., Williamson J., The value and impact of information provided through library services for patient care: A systematic review, Health Information & Libraries Journal, 22, 1, pp. 4-25, (2005); Read K., Lapolla F.W.Z., A new hat for librarians: Providing REDCap support to establish the library as a central data hub, Journal of the Medical Library Association: JMLA, 106, 1, pp. 120-126, (2018); Choi S.-H., An analysis on information technological factors in job qualifications of librarians, Journal of Korean Library and Information Science Society, 39, (2008); Corrall S., Kennan M.A., Afzal W.J., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research, 61, 3, pp. 636-674, (2013); Peters C., Dryden A.R., Assessing the Academic Library's Role in Campus-Wide Research Data Management: A First Step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Creamer A., Morales M.E., Crespo J., Et al., An Assessment of Needed Competencies to Promote the Data Curation and Management Librarianship of Health Sciences and Science and Technology Librarians in New England, 1, 1, (2012); Awre C., Baxter J., Clifford B., Et al., Research Data Management as a “wicked problem"", 64, 4-5, pp. 356-371, (2015)","G. Bradley-Ridout; University of Toronto, Toronto, Canada; email: glyneva.bradley.ridout@mail.utoronto.ca","","Canadian Health Libraries Association","","","","","","17086892","","","","English","J. Canadian Health Libr. Assoc.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85080833732"
"Wittenberg J.; Sackmann A.; Jaffe R.","Wittenberg, Jamie (57193527016); Sackmann, Anna (57201680799); Jaffe, Rick (57201679228)","57193527016; 57201680799; 57201679228","Situating Expertise in Practice: Domain-Based Data Management Training for Liaison Librarians","2018","Journal of Academic Librarianship","44","3","","323","329","6","15","10.1016/j.acalib.2018.04.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045701107&doi=10.1016%2fj.acalib.2018.04.004&partnerID=40&md5=4aa54d99478dd11f9c1a9dd1bd2d9823","Indiana University Libraries, Indiana University Bloomington, 1320 East Tenth Street, Bloomington, 47405, IN, United States; Kresge Engineering Library, 110 Bechtel Engineering Center, University of California, Berkeley, 94720, United States; Research IT, Earl Warren Hall, 2195 Hearst Ave, Berkeley, 94720, CA, United States","Wittenberg J., Indiana University Libraries, Indiana University Bloomington, 1320 East Tenth Street, Bloomington, 47405, IN, United States; Sackmann A., Kresge Engineering Library, 110 Bechtel Engineering Center, University of California, Berkeley, 94720, United States; Jaffe R., Research IT, Earl Warren Hall, 2195 Hearst Ave, Berkeley, 94720, CA, United States","The research data management team at the University of California, Berkeley implemented a domain-based Librarian Training Program in order to upskill liaison librarians in research data management principles and create a community of practice among librarians providing research data support. The training program partnered with representatives from each subject division of the Library to integrate content from relevant disciplines. The training model emphasized scaffolding and concrete deliverables, teaching specific tools and concepts, and creating learning objects useful for instruction and outreach. Employing a situated, learning-based, pedagogical model, the program was more successful than previous attempts at library-wide research data management training at Berkeley. This analysis details the program management, curricular design, instruction, and outcomes that made the Library Training Program successful. © 2018 Elsevier Inc.","","","","","","","","","Antell K., Foote J., Turner J., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, (2014); Bresnahan M., Johnson A., Assessing scholarly communication and research data training needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Byatt D., De Luca F., Gibbs H., Patrick M., Rumsey S., White W., Supporting researchers with their research data management: Professional service training requirements—a DataPool Project Report, (2013); Carlson J., Stowell-Bracke M., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality Field Station, Portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); California, University of, University of California, Berkeley library strategic plan, (2017); Cox A., Verbaan E., Sen B., A spider, an octopus, or an animal just coming into existence? Designing a curriculum for librarians to support research data management, Journal of eScience Librarianship, 3, 1, (2014); Cyber Infrastructure Council, Cyberinfrastructure vision for 21st century discovery, (2007); De Cagna J., Tending the garden of knowledge: A look at communities of practice with Etienne Wenger, Information Outlook, 5, 7, pp. 6-12, (2001); Delserone L.M., At the watershed: Preparing for research data management and stewardship at the University of Minnesota Libraries, Library Trends, 57, 2, pp. 202-210, (2008); Farrell R., Badke W., Situating information literacy in the disciplines: A practical and systematic approach for academic librarians, Reference Services Review, 43, 2, pp. 319-340, (2015); Jaguszewski J.M., Williams K., New roles for new times: Transforming liaison roles in research libraries, (August), 1–17, (2013); Johnson A., Bresnahan M., DataDay!: Designing and assessing a research data workshop for subject librarians, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 1-19, (2015); Johnston L.R., Carlson J.R., Hswe P., Hudson-Vitale C., Imker H., Kozlowski W., Stewart C., Data curation network: How do we compare? A snapshot of six academic library institutions’ data repository and curation services, Journal of eScience Librarianship, 6, 1, (2017); Johnston L.R., Olendorf R., Stewart C., Carlson J., Hudson-vitale C., Imker H., Kozlowski W., Data curation network: A cross-institutional staffing model for curating research, (2017); Kezar A., Bottom-up/top-down leadership: Contradiction or hidden phenomenon, The Journal of Higher Education, 83, 5, pp. 725-760, (2012); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, The Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Lave J., Wenger E., Situated learning: Legitimate peripheral participation, (1991); Lloyd A., Learning to put out the red stuff: Becoming information literate through ciscursive practice, The Library Quarterly, 77, 2, pp. 181-198, (2007); Lyon L., Librarians in the lab: Toward radically re-engineering data curation services at the research coalface, New Review of Academic Librarianship, 22, 4, pp. 391-409, (2016); Mattern E., Brenner A., Lyon L., Learning by teaching about RDM: An active learning model for internal library education, International Journal of Digital Curation, 11, 2, pp. 27-38, (2016); Peterson R.A., Asking specific questions, Constructing Effective Questionnaires, pp. 83-100, (2017); Pryor G., Donnelly M., Skilling up to do data: Whose role, whose responsibility, whose career?, International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Rice R., Research data MANTRA: A labour of love, Journal of eScience Librarianship, 3, 1, (2014); de Smaele M., Verbakel E., Potters N., Data intelligence training for library staff, International Journal of Digital Curation, 8, 1, pp. 218-228, (2013); Spillane J., Halverson R., Diamond J., Towards a theory of leadership practice: A distributed perspective, Journal of Curriculum Studies, 36, 1, pp. 3-34, (2004); Tibbo H., Jones S., Research data management and sharing, (2015); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017)","J. Wittenberg; Indiana University Libraries, Indiana University Bloomington, Bloomington, 1320 East Tenth Street, 47405, United States; email: jvwitten@iu.edu","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85045701107"
"De Meulemeester A.; Schietse B.; Vermeeren B.; Ghesquière E.; Declève G.; Buysse H.; Discart I.; Alewaeters K.; Durieux N.; Peleman R.; Pauwels N.","De Meulemeester, Ann (56192600200); Schietse, Bérengère (57204550181); Vermeeren, Bruno (57204549470); Ghesquière, Elke (57204544709); Declève, Ghislaine (36622248800); Buysse, Heidi (6506149624); Discart, Inge (57203635577); Alewaeters, Katrien (6508000854); Durieux, Nancy (56604979600); Peleman, Renaat (7006805617); Pauwels, Nele (55440353100)","56192600200; 57204550181; 57204549470; 57204544709; 36622248800; 6506149624; 57203635577; 6508000854; 56604979600; 7006805617; 55440353100","Current and future directions in Belgian medical and health sciences librarianship: a user-tailored approach","2018","Health Information and Libraries Journal","35","4","","336","340","4","5","10.1111/hir.12237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056132505&doi=10.1111%2fhir.12237&partnerID=40&md5=3798deaf44d1a7490219462f59a93030","Knowledge Centre for Health Ghent, Ghent, Belgium; Health Science Library, Université libre de Bruxelles, Brussels, Belgium; VVBAD, Antwerp, Belgium; KU Leuven Libraries – 2Bergen, Leuven, Belgium; Library for Health Sciences, Université catholique de Louvain, Brussels, Belgium; Medical Library, Vrije Universiteit Brussel, Brussels, Belgium; Life Sciences Library, University of Liège, Liège, Belgium","De Meulemeester A., Knowledge Centre for Health Ghent, Ghent, Belgium; Schietse B., Health Science Library, Université libre de Bruxelles, Brussels, Belgium; Vermeeren B., VVBAD, Antwerp, Belgium; Ghesquière E., KU Leuven Libraries – 2Bergen, Leuven, Belgium; Declève G., Library for Health Sciences, Université catholique de Louvain, Brussels, Belgium; Buysse H., Knowledge Centre for Health Ghent, Ghent, Belgium; Discart I., KU Leuven Libraries – 2Bergen, Leuven, Belgium; Alewaeters K., Medical Library, Vrije Universiteit Brussel, Brussels, Belgium; Durieux N., Life Sciences Library, University of Liège, Liège, Belgium; Peleman R., Knowledge Centre for Health Ghent, Ghent, Belgium; Pauwels N., Knowledge Centre for Health Ghent, Ghent, Belgium","This article is part of a new series in this regular feature. The series intend to serve as a road map by sharing expertise and drawing together trends that are relevant to both health science librarians and health informatics professionals. The present article is a collaboration of six medical and health sciences libraries in Belgium and the Flemish library and archive association (VVBAD, n.d., https://www.vvbad.be/). It aims to elucidate the extended, user-tailored approach provided by medical and health sciences libraries in Belgium motivated by the recent changes in user expectations and behaviour. © 2018 Health Libraries Group","education and training; Europe, Western; information literacy; libraries, health science; libraries, medical; professional associations; research data (management); review, systematic","Belgium; Humans; Information Literacy; Library Science; Universities; article; Belgium; drawing; education; expectation; health science; human; human experiment; information center; information literacy; librarian; library; medical informatics; Belgium; information literacy; library science; organization and management; trends; university","","","","","","","Adriaenssens J., Eyssen M., Mertens R., Benahmed N., Paulus D., Ameye F., Walraevens M., EBP plan, 291, (2017); Buysse H., Peleman R., De Meulemeester A., Information literacy in health sciences education: Proposal of a new model in a multi-perspectivism setting, Journal of the European Association for Health Information and Libraries, 14, pp. 15-20, (2018); Corens D., Merkur S., Jemiai N., Belgium: Health system review, 9, (2007); De Meulemeester A., Pauwels N., Peleman R., Buysse H., Self-reported information literacy skills among researchers within a medical and health science faculty, Information literacy: Key to an inclusive society: 4th European Conference, ECIL 2016, Prague, Czech Republic, October 10–13, 2016, revised selected papers, 676, pp. 422-427, (2016); De Meulemeester A., Peleman R., Buysse H., Impact of purposefully designed learning activities in the case of information literacy self-efficacy, Information literacy in everyday life: 6th European Conference, ECIL 2018, Oulu, Finland, Septebmer 24–27, revised selected papers; Montgomery S.E., Library space assessment: User learning behaviors in the library, Journal of Academic Librarianship, 40, pp. 70-75, (2014); Page M.J., Shamseer L., Altman D.G., Tetzlaff J., Sampson M., Tricco A.C., Moher D., Epidemiology and reporting characteristics of systematic reviews of biomedical research: A cross-sectional study, PLoS medicine, 13, (2016); Pauwels N.S., Mertens M., Peleman R., De Meulemeester A., Supporting research data management: Challenges and approach from an academic health library perspective, Workplace Information Literacy: 5th European conference, ECIL 2017, Saint-Malo, France, September 18-21, abstracts, (2017); Vrijens F., Renard F., Camberlin C., Desomer A., Dubois C., Jonckheer P., Meeus P., Performance of the Belgian Health System - Report 2015, 259, (2016)","","","Blackwell Publishing Ltd","","","","","","14711834","","","30387540","English","Health Inf. Libr. J.","Article","Final","","Scopus","2-s2.0-85056132505"
"Weber T.; Kranzlmuller D.","Weber, Tobias (57201379351); Kranzlmuller, Dieter (26643233300)","57201379351; 26643233300","How FAIR can you get? image retrieval as a use case to calculate FAIR metrics","2018","Proceedings - IEEE 14th International Conference on eScience, e-Science 2018","","","8588646","114","124","10","4","10.1109/eScience.2018.00027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061379432&doi=10.1109%2feScience.2018.00027&partnerID=40&md5=2624269a9ae0f3e94ab2693beecb8ad8","Leibniz Supercomputing Centre (LRZ), Bavarian Academy of Sciences and Humanities, Garching, Germany","Weber T., Leibniz Supercomputing Centre (LRZ), Bavarian Academy of Sciences and Humanities, Garching, Germany; Kranzlmuller D., Leibniz Supercomputing Centre (LRZ), Bavarian Academy of Sciences and Humanities, Garching, Germany","Many providers of research data services officially embrace the FAIR guiding principles for scientific data management and stewardship. To assess the compliance of their services to these principles and to indicate possible improvements, use-case-centric metrics are needed as an addendum to existing approaches. The retrieval of spatially and temporally annotated images can exemplify such a use case. A prototypical benchmark based on that use case indicates that currently no research data repository achieves the full score according to the proposed metric. Suggestions on how to increase the score include automatic annotation based on the metadata inside the image file and support for content negotiation to retrieve the research data. This can lead to an improvement of data integration workflows, resulting in a better and more FAIR approach to manage research data. © 2018 IEEE.","FAIR Guiding Principles; Research Data Management","Data integration; Image retrieval; Automatic annotation; Guiding principles; Image files; Research data; Research data managements; Scientific data management; Work-flows; Information management","","","","","Deutsche Forschungsgemeinschaft, DFG, (BO818/16-1)","This work was supported by the DFG (German Research Foundation) with the GeRDI project (Grant No. BO818/16-1).","Ayris P., Berthou J., Bruce R., Lindstaedt S., Monreale A., Mons B., Murayama Y., Sodergard C., Tochtermann K., Wilkinson R., Realising the european open science cloud, First Report and Recommendations of the Commission High Level Expert Group on the European Open Science Cloud, (2016); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services & Use, 36, 1-2, pp. 35-43, (2016); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); Cox S., Yu J., OzNome 5-start Tool: A Rating System for Making Data FAIR and Trustable (Presentation Given at the 2017 EResearch Australasia Conference), (2017); Wilkinson M.D., Sansone S.-A., Schultes E., Doorn P., Bonino Dasilvasantos L.O., Dumontier M., A Design Framework and Exemplar Metrics for Fairness, (2017); Wilkinson M., Bonino L.O., Nichols N., Leinweber K., FAIRMetrics/Metrics: Proposed FAIR Metrics and Results of the Metrics Evaluation Questionnaire, (2018); Boehmer J., Dunning A., De Smaele M., Are the fair data principles fair?, 12th International Digital Curation Conference, 1, (2017); Evaluation of Data Repositories Based on the FAIR Principles for IDCC 2017 Practice Paper; Wilkinson M.D., Verborgh R., Bonino Dasilvasantos L.O., Clark T., Swertz M.A., Kelpin F.D., Gray A.J., Schultes E.A., Van Mulligen E.M., Ciccarese P., Kuzniar A., Gavai A., Thompson M., Kaliyaperumal R., Bolleman J.T., Dumontier M., Interoperability and fairness through a novel combination of web technologies, PeerJ Computer Science, 3, (2017); Research Data Repository Interoperability Primer, (2017); Gregory K., Groth P., Cousijn H., Scharnhorst A., Wyatt S., Searching Data: A Review of Observational Data Retrieval Practices, (2017); Devarakonda R., Palanisamy G., Green J.M., Wilson B.E., Data sharing and retrieval using OAI-PMH, Earth Science Informatics, 4, 1, pp. 1-5, (2011); Sompel H.V.D., Nelson M.L., Lagoze C., Warner S., Resource harvesting within the OAI-PMH framework, D-Lib Magazine, 10, 12, (2004); Kindling M., Pampel H., Van De Sandt S., Rucknagel J., Vierkant P., Kloska G., Witt M., Schirmbacher P., Bertelmann R., Scholze F., The Landscape of Research Data Repositories in 2015: A re3data analysis, D-Lib Magazine, 23, 3-4, (2017); Jagadish H., Gehrke J., Labrinidis A., Papakonstantinou Y., Patel J.M., Ramakrishnan R., Shahabi C., Big data and its technical challenges, Communications of the ACM, 57, 7, pp. 86-94, (2014); Lynch C., Big data: How do your data grow?, Nature, 455, 7209, pp. 28-29, (2008); Demchenko Y., Grosso P., De Laat C., Membrey P., Addressing big data issues in scientific data infrastructure, Collaboration Technologies and Systems (CTS), 2013 International Conference on, pp. 48-55, (2013); Luscier J.D., Thompson W.L., Wilson J.M., Gorham B.E., Dragut L.D., Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots, Frontiers in Ecology and the Environment, 4, 8, pp. 408-413, (2006); Johnson C.R., Hendriks E., Berezhnoy I.J., Brevdo E., Hughes S.M., Daubechies I., Li J., Postma E., Wang J.Z., Image processing for artist identification, IEEE Signal Processing Magazine, 25, 4, pp. 37-48, (2008); De Sousa N.T., Hasselbring W., Weber T., Kranzlmuller D., Designing a Generic Research Data Infrastructure Architecture with Continuous Software Engineering, Software Engineering Workshops 2018, Ser. CEUR Workshop Proceedings, 2066, pp. 85-88, (2018); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Goebelbecker H.-J., Gundlach J., Schirmbacher P., Dierolf U., Making research data repositories visible: The re3data.org registry, PLOS ONE, 8, 11, pp. 1-10, (2013); Databib and re3data.org Merge, (2015); Gudgin M., Nielsen H.F., Karmarkar A., Mendelsohn N., Moreau J.-J., Lafon Y., Hadley M., SOAP version 1.2 part 1: Messaging framework (second edition), W3C, W3C Recommendation, (2007); Harris S., Seaborne A., SPARQL 1.1 query language, W3C, W3C Recommendation, (2013); Verborgh R., Dumontier M., A Web Api Ecosystem Through Feature-based Reuse, (2016); Covitz P.A., Hartel F., Schaefer C., De Coronado S., Fragoso G., Sahni H., Gustafson S., Buetow K.H., Cacore: A common infrastructure for cancer informatics, Bioinformatics, 19, 18, pp. 2404-2412, (2003); Fielding R., Reschke J., Hypertext transfer protocol (http/1.1): Message syntax and routing, Internet Requests for Comments, RFC Editor, RFC, 7230, (2014); Nottingham M., Web linking, Internet Requests for Comments, RFC Editor, RFC, 5988, (2010); Backer A., Pietsch C., Summann F., Wolf S., BASE (Bielefeld Academic Search Engine). Eine Suchmaschinenl ösung zur Indexierung wissenschaftlicher Metadaten, Datenbank-Spektrum, 17, 1, pp. 5-13, (2017); Vierkant P., Spier S., Rucknagel J., Pampel H., Fritze F., Gundlach J., Fichtmuller D., Kindling M., Kirchhoff A., Goebelbecker H.-J., Klump J., Kloska G., Reuter E., Semrau A., Schnepf E., Skarupianski M., Bertelmann R., Schirmbacher P., Scholze F., Kramer C., Ulrich R., Witt M., Fuchs C., Schema for the Description of Research Data Repositories-RFC Version 2.2, (2014); Klein M., Sanderson R., Van De Sompel H., Warner S., Haslhofer B., Nelson M., Lagoze C., Resourcesync Framework Specification, (2016); Xie Y., Implementing Reproducible Research, 1, (2014)","","","Institute of Electrical and Electronics Engineers Inc.","","14th IEEE International Conference on eScience, e-Science 2018","29 October 2018 through 1 November 2018","Amsterdam","144041","","978-153869156-4","","","English","Proc. - IEEE Int. Conf. eScience, e-Science","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85061379432"
"Jackson B.","Jackson, Brian (56583406700)","56583406700","The Changing Research Data Landscape and the Experiences of Ethics Review Board Chairs: Implications for Library Practice and Partnerships","2018","Journal of Academic Librarianship","44","5","","603","612","9","7","10.1016/j.acalib.2018.07.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050145354&doi=10.1016%2fj.acalib.2018.07.001&partnerID=40&md5=6da142b4cbedebbbd01ba8658e45764e","Mount Royal University, 4825 Mount Royal Gate SW, Calgary, T3E 6K6, Alberta, Canada","Jackson B., Mount Royal University, 4825 Mount Royal Gate SW, Calgary, T3E 6K6, Alberta, Canada","Academic libraries have to a large extent taken the lead in facilitating new approaches to research data management, but changes to the research data landscape have had an impact on numerous areas of academic work, including ethics review. Using interpretive phenomenological analysis of interviews with chairs of Canadian research ethics boards, this study explores how ethics review boards have experienced changes to data policy and related technologies in order to describe the ethical implications of new approaches to data management and to explore ways in which the library, ethics review boards, and other campus partners might harmonize efforts to support emerging data practices. While ethics review boards in Canada are keenly aware of open data policies, data publishing in practice is still nascent. There is uncertainty about the adoption of changing technologies for research and their impacts on privacy protection. Where responsibility lies for addressing these uncertainties is often unclear. Academic libraries and research ethics boards are well-suited to engage in mutual knowledge transfer and to integrate data management planning and ethics review processes. Institutional-level oversight that includes all campus departments impacted by changes to the research data landscape may facilitate improved communication and reduce role ambiguity. © 2018 Elsevier Inc.","Research data management; Research ethics","","","","","","","","Antes A.L., Walsh H.A., Strait M., Hudson-Vitale C.R., Dubois J.M., Examining data repository guidelines for qualitative data sharing, Journal of Empirical Research on Human Research Ethics, 13, 1, pp. 61-73, (2018); van Baalen S., ‘Google wants to know your location’: The ethical challenges of fieldwork in the digital age, Research Ethics, pp. 1-17, (2018); Bell K., The more things change, the more they stay the same: The TCPS 2 and institutional ethical oversight of social science research in Canada, The ethics rupture: Exploring alternatives to formal research ethics review, pp. 189-205, (2016); Bialobrzeski A., Ried J., Dabrock P., Differentiating and evaluating common good and public good: Making implicit assumptions explicit in the contexts of consent and duty to participate, Public Health Genomics, 15, 5, pp. 285-292, (2012); Bishop L., Ethical sharing and reuse of qualitative data, Australian Journal of Social Issues, 44, 3, pp. 255-272, (2009); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Buchanan E., Ess C., Internet research ethics and the institutional review board: Current practices and issues, ACM SIGCAS Computers and Society, 39, 3, pp. 43-49, (2009); Budd J.M., Phenomenology and information studies, Journal of Documentation, 61, 1, pp. 44-59, (2005); Tri-council policy statement: Ethical conduct for research involving humans, (2014); Carusi A., Jirotka M., From data archive to ethical labyrinth, Qualitative Research, 9, 3, pp. 285-298, (2009); Childs S., McLeod J., Lomas E., Cook G., Opening research data: Issues and opportunities, Records Management Journal, 24, 2, pp. 142-162, (2014); Clement A., Obar J.A., Canadian internet “boomerang” traffic and mass NSA surveillance: Responding to privacy and network sovereignty challenges, Law, privacy, and surveillance in Canada in the post-Snowden era, pp. 13-44, (2015); Cook K., Snyder J., Calvert J., Canadian research ethics board members’ attitudes toward benefits from clinical trials, BMC Medical Ethics, 16, 1, (2015); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Creswell J.W., Poth C.N., Qualitative inquiry & research design: Choosing among five approaches, (2018); Hardy L.J., Hughes A., Hulen E., Schwartz A.L., Implementing qualitative data management plans to ensure ethical standards in multi-partner centers, Journal of Empirical Research on Human Research Ethics, 11, 2, pp. 191-198, (2016); Heidegger M., Being and time, (1962); Hern A., University College London hit by ransomware attack, The Guardian, (2017); Holland F., Teaching in higher education: An interpretive phenomenological analysis, (2014); Ioannidis J.P.A., Informed consent, big data, and the oxymoron of research that is not research, American Journal of Bioethics, 13, 4, pp. 40-42, (2013); Jones S., Pryor G., Whyte A., How to develop research data management services - a guide for HEIs, (2013); Kaye J., de Vries J., Heeney C., Hawkins N., Boddington P., Data sharing in genomics - re-shaping scientific practice, Nature Reviews Genetics, 10, 5, pp. 331-335, (2009); Kozlakidis Z., Cason R.J., Mant C., Cason J., Human tissue biobanks: The balance between consent and the common good, Research Ethics, 8, 2, pp. 113-123, (2012); Latham B., Research data management: Defining roles, prioritizing services, and enumerating challenges, Journal of Academic Librarianship, 43, 3, pp. 263-265, (2017); Leary T.D., The lived faculty experience with formalized assessment initiatives: An interpretive phenomenological analysis (doctoral thesis), (2017); Lunshof J., Chadwick R., Vorhaus D.B., Church G.M., From genetic privacy to open consent, Nature Reviews Genetics, 9, 5, pp. 406-411, (2008); Mannheimer S., Pienta A., Kirilova D., Elman C., Wutich A., Qualitative data sharing: Data repositories and academic libraries as key partners in addressing challenges, The American Behavioral Scientist, pp. 1-22, (2018); Mauthner N.S., Should data be regulated?, The ethics rupture: Exploring alternatives to formal research ethics review, pp. 206-229, (2016); Mauthner N.S., Parry O., Open access digital data sharing: Principles, policies and practices, Social Epistemology, 27, 1, pp. 47-67, (2013); Metcalf J., Crawford K., Where are human subjects in big data research? The emerging ethics divide, Big Data & Society, 3, 1, pp. 1-14, (2016); Meyers C.A., Bagnall R.G., The challenges of undergraduate online learning experienced by older workers in career transition, International Journal of Lifelong Education, 36, 4, pp. 442-457, (2017); Parry O., Mauthner N.S., Whose data are they anyway? Practical, legal and ethical issues in archiving qualitative research data, Sociology, 38, 1, pp. 139-152, (2004); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Schrag Z.M., Ethical imperialism: Institutional review boards and the social sciences, 1965–2009, (2010); Smith J.A., Beyond the divide between cognition and discourse: Using interpretative phenomenological analysis in health psychology, Psychology & Health, 11, 2, pp. 261-271, (1996); Smith J.A., Flowers P., Larkin M., Interpretative phenomenological analysis: Theory, method and research, (2009); Social Sciences and Humanities Research Ethics Special Working Committee, Extending the spectrum: The TCPS and ethical issues in internet-based research, (2008); Tenenberg J., Learning through observing peers in practice, Studies in Higher Education, 41, 4, pp. 756-773, (2016); Tenopir C., Sandusky R., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); VanScoy A., Evenstad S.B., Interpretative phenomenological analysis for LIS research, Journal of Documentation, 71, 2, pp. 338-357, (2015); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, 3-4, pp. 211-219, (2014); Warrell J.G., Jacobsen M., Internet research ethics and the policy gap for ethical practice in online research settings, The Canadian Journal of Higher Education, 44, 1, pp. 22-37, (2014); Wood C., Farmer M.D., Goodall D., Changing professional identity in the transition from practitioner to lecturer in higher education: An interpretive phenomenological analysis, Research In Post-Compulsory Education, 21, 3, pp. 229-245, (2016); Yardley S.J., Watts K.M., Pearson J., Richardson J.C., Ethical issues in the reuse of qualitative data: Perspectives from literature, practice, and participants, Qualitative Health Research, 24, 1, pp. 102-113, (2014); Yeung P., Bennett R., University secrets are stolen by cybergangs: Scientific research targeted by hackers, (2017); Zarate O.A., Brody J.G., Brown P., Ramirez-Andreotta M.D., Perovich L., Matz J., Balancing benefits and risks of immortal data: Participants’ views of open consent in the personal genome project, Hastings Center Report, 46, 1, pp. 36-45, (2016)","","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85050145354"
"Turco M.L.; Rinaudo F.; Piumatti P.; Calvano M.; Spreafico A.; Patrucco G.","Turco, Massimiliano Lo (57208392384); Rinaudo, Fulvio (6505516700); Piumatti, Paolo (55503850700); Calvano, Michele (55791217200); Spreafico, Alessandra (57202446578); Patrucco, Giacomo (57194543351)","57208392384; 6505516700; 55503850700; 55791217200; 57202446578; 57194543351","The digitisation of museum collections for research, management and enhancement of tangible and intangible heritage","2018","Proceedings of the 2018 3rd Digital Heritage International Congress, Digital Heritage 2018 - Held jointly with the 2018 24th International Conference on Virtual Systems and Multimedia, VSMM 2018","","","8810128","","","","7","10.1109/DigitalHeritage.2018.8810128","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072373581&doi=10.1109%2fDigitalHeritage.2018.8810128&partnerID=40&md5=85418585c25de80dd4239bdca9a62c88","Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy","Turco M.L., Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy; Rinaudo F., Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy; Piumatti P., Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy; Calvano M., Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy; Spreafico A., Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy; Patrucco G., Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy","This research project aims to define a new methodology that employs Building Information Modelling (BIM) tools, normally used in the Architecture, Engineering, and Construction (AEC) sector, in an unconventional manner, to create 3D databases for small objects: Those belonging to museum collections inaccessible to the public. The project will develop a methodology to virtually reproduce 3D objects through the integration of geometric and alphanumeric information. The new procedure will exploit some different levels of knowledge concerning objects in a museum: research, data management, setting up of virtual communication platforms, applying it to some objects that are a part of the collection of the Egyptian museum in Turin. © 2018 IEEE.","BIM; Data Management; Metric Survey; Modelling and Simulation; Museums","Architectural design; Information management; 3D database; Architecture , engineering , and constructions; Building Information Modelling; Digitisation; Modelling and simulations; Museum collections; Small objects; Virtual communication; Museums","","","","","Compagnia di San Paolo; Politecnico di Torino, POLITO","1 The project is a part of the pilot initiative “Create a network around your research idea”, funded by Politecnico di Torino and Compagnia di San Paolo, and is carried on in collaboration with Museo Egizio of Turin.","Apollonio F.I., Gaiani M., Zheng S., BIM-based modeling and data enrichment of classical architectural buildings, SCIRES-IT, 2, 2, pp. 41-62, (2012); Lopez F.J., Lerones P.M., Llamas J., Gomez-Garcia-Bermejo J., Zalama E., A Review of Heritage Building Information Modeling (HBIM), Multimodal Technologies and Interaction, 21, 2, (2018); Hervy B., Billen R., Laroche F., Carre C., Servieres M., Et al., A generalized approach for historical mock-up acquisition and data modelling: Towards historically enriched 3D city models., 3u3d2012: Usage, Usability, and Utility of 3D City Models, (2012); Hervy B., Laroche F., Kerouanton J.-L., Bernard A., Courtin C., Dhaene L., Guillet B., Waels A., Augmented historical scale model for museums: From curation to multi-modal promotion, Laval Virtual VRIC14, (2014); Barsanti S.G., Guidi G., 3d digitization of museum content within the 3dicons project, SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II, 5, 1, pp. 151-156, (2013); Carboni N., De Luca L., Towards a conceptual foundation for documenting tangible and intagible elements of a cultural object, Digital Applications in Archaeology and Cultural Heritage, 2016, 3, pp. 108-116; Bruseker G., Carboni N., Guillem A., Cultural Heritage Data Management: The Role of Formal Ontology and CIDOC CRM, Heritage and Archaeology in the Digital Age, Cham, pp. 93-131, (2017); Meghini C., Scopigno R., Richards J., Wright H., Geser G., Cuy S., Fihn J., Fanini B., Hollander H., Niccolucci F., Felicetti A., Ronzino P., Nurra F., Papatheodorou C., Gavrilis D., ARIADNE: A Research Infrastructure for Archaeology, Journal on Computing and Cultural Heritage (JOCCH), 10, 3, (2017); Bruwier M.-C., Claes W., Quertinmont A., La Description de l'Egypte de Jean-Jacques Rifaud 1813-1826, (2014); Einaudi S., Drovetti e i modellini del Museo Egizio di Torino, Studi Piemontesi XLV, 2, pp. 501-506; Kersten T., Lindstaedt M., Potential of Automatic 3D object reconstruction from multiple Images for applications in Architecture, Cultural Heritage and Archaeology, International Journal of Heritage in the Digital Era, 1, 3, pp. 399-420, (2012); Di Pietra V., Donadio E., Picchi D., Sambuelli L., Spano A.T., Multi-source 3D models supporting ultrasonic test to investigate an egyptian sculpture of the archaeological museum in Bologna, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5, 1, pp. 259-266, (2017); Guidi G., Malik U.S., Frischer B., Barandoni C., Paolucci F., The Indiana University-Uffizi project: Metrologica! challenges and workflow for massive 3D digitization of sculptures, Virtual System & Multimedia (VSMM), 2017 23rd International Conference, pp. 1-8, (2017); Samaan M., Heno R., Pierrot-Deseilligny M., Close-range photogrammetric tools for small 3d archaeological objects, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5, 2, pp. 549-553, (2013); Felicetti A., Scarselli T., Mancinelli M.L., Niccolucci F., Mapping ICCD Archaeological Data to CIDOC-CRM: The RA Schema, CRMEX 2013. Practical Experiences with CIDOC CRM and Its Extensions, (2013)","","Addison A.C.; Thwaites H.","Institute of Electrical and Electronics Engineers Inc.","","3rd Digital Heritage International Congress, Digital Heritage 2018","26 October 2018 through 30 October 2018","San Francisco","151277","","978-172810292-4","","","English","Proc. Digit. Herit. Int. Congr., Digital Heritage - Held jointly Int. Conf. Virtual Syst. Multimed., VSMM","Conference paper","Final","","Scopus","2-s2.0-85072373581"
"Perrier L.; Blondal E.; MacDonald H.","Perrier, Laure (56470673600); Blondal, Erik (56401098900); MacDonald, Heather (7202132515)","56470673600; 56401098900; 7202132515","Exploring the experiences of academic libraries with research data management: A meta-ethnographic analysis of qualitative studies","2018","Library and Information Science Research","40","3-4","","173","183","10","18","10.1016/j.lisr.2018.08.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052948340&doi=10.1016%2fj.lisr.2018.08.002&partnerID=40&md5=5c03a768a76393e27f8f0d77819d232f","University of Toronto Libraries, University of Toronto, 130 St. George Street, Toronto, M5S 1A5, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College Street, Toronto, M5T 3M6, Ontario, Canada; MacOdrum Library, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Ontario, Canada","Perrier L., University of Toronto Libraries, University of Toronto, 130 St. George Street, Toronto, M5S 1A5, Ontario, Canada; Blondal E., Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College Street, Toronto, M5T 3M6, Ontario, Canada; MacDonald H., MacOdrum Library, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Ontario, Canada","Taking on responsibilities in research data management (RDM) has proven to be a significant challenge as libraries have adopted new roles within higher education institutions. A qualitative review using the meta-ethnographic approach was conducted that examined the experiences of academic libraries and provided clarity on contextual influences associated with achievements, as well as illuminating the reasons for deficiencies. Libraries experienced uncertainty around roles and relationships related to RDM yet were recognized positively as a neutral, centralized space within academic institutions. This perception, combined with the current approach of fostering partnerships and collaborations, may prove to be useful for libraries as they strategically consider how best to provide continued support and services in RDM. Understanding the perspectives of academic libraries on how they respond and support the demands related to RDM offers a fuller, more robust insight that is essential for planning and decision-making. © 2018 Elsevier Inc.","","","","","","","","","Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, pp. 557-574, (2014); Association of College & Research Libraries, 2016 top trends in academic libraries, College & Research Libraries News, 77, pp. 274-281, (2016); Association of College & Research Libraries, Environmental scan 2017, (2017); Borgman C.L., Big data, little data, no data: Scholarship in the networked world, (2015); Campbell R., Pound P., Morgan M., Daker-White G., Britten N., Pill R., Donovan J., Evaluating meta-ethnography: Systematic analysis and synthesis of qualitative research, Health Technology Assessment, 15, 43, pp. 1-164, (2011); Canadian Association of Research Libraries, Strategic directions May 2016 to May 2019, (2016); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Technology and Science, 68, pp. 2182-2200, (2017); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, 70, (2012); Data sharing, CASRAI dictionary, (2015); Emerge Project, What is a meta-ethnography, (2018); Goldman J., Kafel D., Martin E.R., Assessment of data management services at New England region resource libraries, Journal of eScience Librarianship, 4, 1, (2015); International Federation of Library Associations and Institutions, Trends in research libraries 2017, (2017); McGowan J., Sampson M., Salzwedel D.M., Cogo E., Foerster V., Lefebvre C., PRESS–Peer review of electronic search strategies: 2015 guideline explanation and elaboration, (2016); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data curation: A study of researcher practices and needs, portal: Libraries and the Academy, 14, pp. 139-164, (2014); Noblit G.W., Hare R.D., Meta-ethnography: Synthesizing qualitative studies, (1988); Noblit G.W., Hare R.D., Meta-ethnography: Synthesizing qualitative studies, Counterpoints, 44, pp. 99-123, (1999); Perrier L., Blondal E., Ayala A.P., Dearborn D., Kenny T., Lightfoot D., MacDonald H., Research data management in academic institutions: A scoping review, PLoS One, 12, 5, (2017); Perrier L., Blondal E., Ayala A.P., Dearborn D., Kenny T., Lightfoot D., MacDonald H., Research data management in academic institutions: A scoping review [data file], (2017); Pope C., Mays N., Popay J., Synthesizing qualitative and quantitative health evidence: A guide to methods, (2007); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice, (2014); Ray J., Research data management: Practical strategies for information professionals, (2014); Research data management, In Original RDC glossary, (2017); Schutz A., Collected papers, vol 1. Leiden, (1962); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future [White paper], (2012); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Lundeen A., Research data services in academic libraries: Data intensive roles for the future, Journal of eScience Librarianship, 4, 2, pp. 1-21, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, International Federation of Library Associations and Institutions, 39, 1, pp. 70-78, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017); Tong A., Sainsbury P., Craig J., Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups, International Journal for Quality in Health Care, 19, 6, pp. 349-357, (2007); Whyte A., Final results from DCC RDM 2014 survey, (2014)","L. Perrier; University of Toronto Libraries, University of Toronto, Toronto, 130 St. George Street, M5S 1A5, Canada; email: l.perrier@utoronto.ca","","Elsevier Ltd","","","","","","07408188","","LISRD","","English","Libr. Inf. Sci. Res.","Article","Final","","Scopus","2-s2.0-85052948340"
"Akinsolu F.T.; Balczár E.; Kovács N.; Gáll T.; Harangi M.; Varga O.","Akinsolu, Folahanmi Tomiwa (57197711962); Balczár, Eszter (57203124053); Kovács, Nóra (56624220900); Gáll, Tibor (57200501767); Harangi, Mariann (6602349828); Varga, Orsolya (16432987900)","57197711962; 57203124053; 56624220900; 57200501767; 6602349828; 16432987900","Developing a database for Rett syndrome research performed in the European Union: A resource for researchers and stakeholders","2018","Child: Care, Health and Development","44","5","","794","800","6","0","10.1111/cch.12595","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050641215&doi=10.1111%2fcch.12595&partnerID=40&md5=c8619111143a8ef137f820ff9efe361a","Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary; Faculty of Medicine, University of Debrecen, Debrecen, Hungary; Department of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary","Akinsolu F.T., Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary; Balczár E., Faculty of Medicine, University of Debrecen, Debrecen, Hungary; Kovács N., Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary; Gáll T., Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary; Harangi M., Department of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary; Varga O., Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary","Background: For most rare diseases, which are often significantly under-resourced, sufficient information on funding landscape is missing, which may prevent effective use of research resources and be an obstacle to making effective decisions on research. The objective of this research was to create a database of Rett syndrome research projects carried out in the European Union (EU) and to provide a research landscape analysis. Method: Websites of organizations funding research projects were identified and systematically checked. Projects were analysed by date, place, funder types, and research topics. Results: The analysis revealed that the total expenditure on Rett syndrome research was almost €70 million, allocated among 247 projects mostly performed in Italy and the United Kingdom. The main research sponsor was the European Commission. Highlighting research trends and gaps, this work facilitates changes in rare disease research data management. Conclusion: This work demonstrates the feasibility of creating an EU-based research database on Rett syndrome projects. It provides a source of information on research development which is useful for individuals, organizations and key players in the private and public sector to make progressive decisions on Rett syndrome research. © 2018 John Wiley & Sons Ltd","project databases; rare diseases; research support; Rett syndrome","Biomedical Research; Databases as Topic; Databases, Factual; European Union; Humans; Program Development; Research Support as Topic; Rett Syndrome; Stakeholder Participation; data base; European Union; factual database; financial management; genetics; human; medical research; organization and management; program development; Rett syndrome; stakeholder engagement","","","","","Rett Syndrome Association of Australia, RSAA; National Research Council, NRC; European Commission, EC; Fondazione Telethon; Consiglio Nazionale delle Ricerche, CNR","Funding text 1: FIGURE 2 Research topics based on ORPHANET research categories [Colour figure can be viewed at wileyonlinelibrary. com] Most projects received support from Italy through Italian Rett Syndrome Association (Associazione Italiana Rett, 2017), National Research Council of Italy (Consiglio Nazionale delle Ricerche, 2017), and Telethon Foundation (Fondazione Telethon, 2017), which funded 26, 22, and 19 projects, respectively.; Funding text 2: 2) To provide a landscape analysis by showing the magnitude of financial support from public and private organizations, by pre-senting trends in research funding through identifying funded research topics, and evaluating the role of different funding sectors.","Sostieni i progetti AIRETT, (2017); Progetti di ricerca, (2017); de Oliveira C., Nguyen V.H., Wijeysundera H.C., Wong W.W., Woo G., Liu P.P., Krahn M.D., How much are we spending? The estimation of research expenditures on cardiovascular disease in Canada, BMC Health Services Research, 12, (2012); Sources of funding, (2015); Projects & Results Service Available at, (1994); La ricerca, (2017); Giles J., Finding philanthropy: Like it? Pay for it, Nature, 481, pp. 252-253, (2012); Guy J., Gan J., Selfridge J., Cobb S., Bird A., Reversal of neurological defects in a mouse model of Rett syndrome, Science, 315, pp. 1143-1147, (2007); Rett Syndrome Research Landscape, (2008); Izsak K., Markianidou P., Lukach R., Wastyn A., The impact of the crisis on research and innovation policies, (2013); Rett Syndrome in Online Mendelian Inheritance in Man, (2014); Research Portfolio Online Reporting Tools (RePORT), Glossary, (2012); Neul J.L., Kaufmann W.E., Glaze D.G., Christodoulou J., Clarke A.J., Bahi-Buisson N., Percy A.K., Rett syndrome: Revised diagnostic criteria and nomenclature, Annals of Neurology, 68, pp. 944-950, (2010); Search by research category, (2014); Rath A., Olry A., Dhombres F., Brandt M.M., Urbero B., Ayme S., Representation of rare diseases in health information systems: The Orphanet approach to serve a wide range of end users, Human Mutation, 33, pp. 803-808, (2012); Member organizations, (2011); Stehr F., Forkel M., Funding resources for rare disease research, Biochimica et Biophysica Acta, 1832, pp. 1910-1912, (2013); Taruscio D., Agresta L., Amato A., Bernardo G., Bernardo L., Braguti F., Vittozzi L., The Italian National Centre for Rare Diseases: Where research and public health translate into action, Blood Transfusion, 12, pp. s591-s605, (2014); Varga O., Akinsolu F.T., Balczar E., Kovacs N., Tibor G., Rett syndrome research, (2014); Pharmaceutical Industry, (2015)","O. Varga; Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary; email: varga.orsolya@sph.unideb.hu","","Blackwell Publishing Ltd","","","","","","03051862","","CCHDD","30033519","English","Child Care Health Dev.","Article","Final","","Scopus","2-s2.0-85050641215"
"Kun W.; Tong L.; Xiaodan X.","Kun, Wang (57208888021); Tong, Liu (57208886283); Xiaodan, Xie (57208879758)","57208888021; 57208886283; 57208879758","Application of Big Data Technology in Scientific Research Data Management of Military Enterprises","2019","Procedia Computer Science","147","","","556","561","5","7","10.1016/j.procs.2019.01.221","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066015310&doi=10.1016%2fj.procs.2019.01.221&partnerID=40&md5=48384674610aa0d3b7d1f243edfcf936","Science and Technology on Optical Radiation Laboratory, Beijing, 100854, China","Kun W., Science and Technology on Optical Radiation Laboratory, Beijing, 100854, China; Tong L., Science and Technology on Optical Radiation Laboratory, Beijing, 100854, China; Xiaodan X., Science and Technology on Optical Radiation Laboratory, Beijing, 100854, China","Scientific research data has an important strategic position for the development of enterprises and countries, and is an important basis for management to conduct strategic research and decision-making. Compared with the Internet industry, big data technology started late in the military enterprises, while military enterprises research data often has the characteristics of decentralization, low relevance, and diverse data types. It cannot fully utilize the advantages of data resources to enhance the core competitiveness of enterprises. To this end, this paper deeply explores the application methods of big data technology in military scientific research data management, and lays a foundation for the construction of scientific research big data platform. © 2019 The Author(s).","big data technology; data analysis; decision; scientific research data","Big data; Competition; Data reduction; Decision making; Information management; Internet of things; Core competitiveness; Data technologies; decision; Internet industries; Military enterprise; Scientific research datum; Scientific researches; Strategic research; Research and development management","","","","","","","Jonathan V., Big Data Technology Literature Review[J], Computer Science, 12, (2015); Rajeshwari, State of the Art of Big Data Analytics: A Survey[J], International Journal of Computer Applications, 22, (2015); Xueqi C., Overview of big data systems and analysis techniques[J], Journal of Software, 9, pp. 56-60, (2014); Donovan S., Big data: Teaching must evolve to keep up with advances [J], Nature, 455, 7212, (2008); Wienhofen L.W., Empirical Big Data Research: A Systematic Literature Mapping [J], Preprint Submitted to Information Systems, 10, pp. 1-11, (2015); Jake L., Big Data application in Biomedical research and Health care: A literature review[J], Biomedical Informatics Insights, 8, pp. 1-5, (2016); Jun H., Analysis of the impact of big data on enterprise management decisions[J], Scientific and Technological Progress and Countermeasures, 4, (2014)","","Sun Y.; Yu J.; Bie R.","Elsevier B.V.","","7th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018","19 October 2018 through 21 October 2018","Beijing","147937","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85066015310"
"Read K.B.","Read, Kevin B. (57205931894)","57205931894","Adapting data management education to support clinical research projects in an academic medical center","2019","Journal of the Medical Library Association","107","1","","89","97","8","10","10.5195/jmla.2019.580","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059286285&doi=10.5195%2fjmla.2019.580&partnerID=40&md5=a0bd27490103cff2bdbdfd892a492bfd","NYU Health Sciences Library, New York University School of Medicine, 577 First Avenue, New York, 10016, NY, United States","Read K.B., NYU Health Sciences Library, New York University School of Medicine, 577 First Avenue, New York, 10016, NY, United States","Background: Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown. Case Presentation: Leveraging my academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop. Conclusions: The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing. © 2019, Medical Library Association. All rights reserved.","","Academic Medical Centers; Adult; Biomedical Research; Data Analysis; Education; Female; Humans; Libraries, Medical; Male; Middle Aged; New York; Research Personnel; Young Adult; article; case report; clinical article; clinical research; coordination; human; human experiment; librarian; library; practice guideline; scientist; university hospital; adult; data analysis; education; female; male; medical research; middle aged; New York; organization and management; personnel; procedures; university hospital; young adult","","","","","","","Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in biomedical research data management and analysis: Needs and barriers, J Am Med Inform Assoc., 14, 4, pp. 478-488, (2007); Wang X., Williams C., Liu Z.H., Croghan J., Big data management challenges in health research-a literature review, Briefings Bioinform., (2017); Barone L., Williams J., Micklos D., Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators, PLoS Comput Biol., 13, 10, (2017); Johansson B., Fogelberg-Dahm M., Wadensten B., Evidence-based practice: The importance of education and leadership, J Nurs Manag., 18, 1, pp. 70-77, (2010); Federer L.M., Lu Y.L., Joubert D.J., Data literacy training needs of biomedical researchers, J Med Libr Assoc., 104, 1, pp. 52-57, (2016); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, Coll Res Libr., 73, 4, pp. 349-365, (2012); Carlson J., Johnston L., Westra B., Nichols M., Developing an approach for data management education: A report from the data information literacy project, Int J Digit Curation., 8, 1, pp. 204-217, (2013); Macmillan D., Developing data literacy competencies to enhance faculty collaborations, LIBER Q., 24, 3, pp. 140-160, (2015); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA J., 43, 1, pp. 89-97, (2017); Piorun M.E., Kafel D., Leger-Hornby T., Najafi S., Martin E.R., Colombo P., LaPelle N., Teaching research data management: An undergraduate/graduate curriculum, J eSci Libr., 1, 1, (2012); Reisner B.A., Vaughan K.T.L., Shorish Y.L., Making data management accessible in the undergraduate chemistry curriculum, J Chem Educ., 91, 11, pp. 1943-1946, (2014); Adamick J., Reznik-Zellen R.C., Sheridan M., Data management training for graduate students at a large research university, J eSci Libr., 1, 3, (2013); Fransson J., Lagunas P.T., Kjellberg S., Toit M.D., Developing integrated research data management support in close relation to doctoral students’ research practices, Proc Assoc Inf Sci Technol., 53, 1, pp. 1-4, (2016); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at small liberal arts colleges, IFLA J., 43, 1, pp. 105-118, (2017); Johnston L., Jeffryes J., Steal this idea: A library instructors’ guide to educating students in data management skills, Coll Res Libr News., 75, 8, pp. 431-434, (2014); Johnston L., Lafferty M., Petsan B., Training researchers on data management: A scalable, cross-disciplinary approach, J eSci Libr., 1, 2, (2012); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data management pilot course at an academic library, J eSci Libr., 3, 1, (2014); Southall J., Scutt C., Training for research data management at the Bodleian Libraries: National contexts and local implementation for researchers and librarians, New Rev Acad Libr., 23, 2-3, pp. 303-322, (2017); Tammaro A.M., Casarosa V., Research data management in the curriculum: An interdisciplinary approach, Procedia Computer Science, (2014); Verbakel E., Grootveld M., ‘Essentials 4 Data Support’: Five years’ experience with data management training, IFLA J., 42, 4, pp. 278-283, (2016); DeBose K.G., Haugen I., Miller R.K., Information literacy instruction programs: Supporting the college of agriculture and life sciences community at Virginia Tech, Libr Trends., 65, 3, pp. 316-338, (2017); Fong B.L., Wang M., Required data management training for graduate students in an earth and environmental sciences department, J eSci Libr., 4, 1, (2015); Hou C.Y., Meeting the needs of data management training: The federation of Earth Science Information Partners (ESIP) data management for scientists short course, Issues Sci Technol Libr., 2015, 80, (2015); Thielen J., Hess A.N., Advancing research data management in the social sciences: Implementing instruction for education graduate students into a doctoral curriculum, Behav Soc Sci Libr., pp. 1-15, (2018); Dressel W.F., Research data management instruction for digital humanities, J eSci Libr., 6, 2, (2017); Bruland P., Breil B., Fritz F., Dugas M., Interoperability in clinical research: From metadata registries to semantically annotated CDISC ODM, Studies Health Technol Inform., 180, pp. 564-568, (2012); Gaddale J.R., Clinical data acquisition standards harmonization importance and benefits in clinical data management, Perspect Clin Res., 6, 4, pp. 179-183, (2015); Krishnankutty B., Bellary S., Kumar N.B., Moodahadu L.S., Data management in clinical research: An overview, Indian J Pharm., 44, 2, pp. 168-172, (2012); Leroux H., Metke-Jimenez A., Lawley M.J., Towards achieving semantic interoperability of clinical study data with FHIR, J Biomed Semantics., 8, 1, (2017); Arthofer K., Girardi D., Data quality- and master data management-a hospital case, Stud Health Technol Inform., 236, pp. 259-266, (2017); Callahan T., Barnard J., Helmkamp L., Maertens J., Kahn M., Reporting data quality assessment results: Identifying individual and organizational barriers and solutions., 5, 1, (2017); Houston L., Probst Y., Yu P., Martin A., Exploring data quality management within clinical trials, Appl Clin Inform., 9, 1, pp. 72-81, (2018); Teunenbroek T.V., Baker J., Dijkzeul A., Towards a more effective and efficient governance and regulation of nanomaterials, Particle Fibre Toxicol., 14, 1, (2017); Ohmann C., Banzi R., Canham S., Battaglia S., Matei M., Ariyo C., Becnel L., Bierer B., Bowers S., Clivio L., Dias M., Druml C., Faure H., Fenner M., Galvez J., Ghersi D., Gluud C., Groves T., Houston P., Karam G., Kalra D., Knowles R.L., Krleza-Jeric K., Kubiak C., Kuchinke W., Kush R., Lukkarinen A., Marques P.S., Newbigging A., O'Callaghan J., Ravaud P., Schlunder I., Shanahan D., Sitter H., Spalding D., Tudur-Smith C., van Reusel P., van Veen E.B., Visser G.R., Wilson J., Demotes-Mainard J., Sharing and reuse of individual participant data from clinical trials: Principles and recommendations, BMJ Open., 7, 12, (2017); Polancich S., James D.H., Miltner R.S., Smith G.L., Moneyham L., Building DNP essential skills in clinical data management and analysis, Nurse Educ., 43, 1, pp. 37-41, (2018); Sirgo G., Esteban F., Gomez J., Moreno G., Rodriguez A., Blanch L., Guardiola J., Gracia R., De Haro L., Bodi M., Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment, Int J Med Inform., 112, pp. 166-172, (2018); Good clinical data management practices., (2017); Sylvia M., Terhaar M., An approach to clinical data management for the doctor of nursing practice curriculum, J Prof Nurs., 30, 1, pp. 56-62, (2014); Read K.B., LaPolla F.W.Z., Tolea M.I., Galvin J.E., Surkis A., Improving data collection, documentation, and workflow in a dementia screening study, J Med Libr Assoc., 105, 2, pp. 160-166, (2017); Read K., LaPolla F.W.Z., A new hat for librarians: Providing REDCap support to establish the library as a central data hub, J Med Libr Assoc., 106, 1, pp. 120-126, (2018); (2016); Developing data management plans [course]., (2017); Surkis A., LaPolla F.W.Z., Contaxis N., Read K.B., Data Day to Day: Building a community of expertise to address data skills gaps in an academic medical center, J Med Libr Assoc., 105, 2, pp. 185-191, (2017); Stuckey H., The second step in data analysis: Coding qualitative research data, J Soc Health Diabetes., 3, 1, pp. 7-10, (2015); Bardyn T.P., Patridge E.F., Moore M.T., Koh J.J., Health sciences libraries advancing collaborative clinical research data management in universities, J eSci Libr., 7, 2, (2018)","K.B. Read; NYU Health Sciences Library, New York University School of Medicine, New York, 577 First Avenue, 10016, United States; email: kevin.read@nyumc.org","","Medical Library Association","","","","","","15365050","","JMLAC","30598653","English","J. Med. Libr. Assoc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85059286285"
"Rudžioniene J.; Grigas V.; Enwald H.; Kortelainen T.","Rudžioniene, Jurgita (55998420700); Grigas, Vincas (57193269176); Enwald, Heidi (8912978700); Kortelainen, Terttu (6508164877)","55998420700; 57193269176; 8912978700; 6508164877","Drop out factors in data literacy and research data management survey: Experiences from Lithuania and Finland","2018","Informacijos Mokslai","82","","","115","130","15","1","10.15388/Im.2018.82.8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062481644&doi=10.15388%2fIm.2018.82.8&partnerID=40&md5=8892fa12b84cbc2db6fb47575833e03d","Vilnius University, Vilnius, Lithuania; University of Oulu, Oulu, Finland","Rudžioniene J., Vilnius University, Vilnius, Lithuania; Grigas V., Vilnius University, Vilnius, Lithuania; Enwald H., University of Oulu, Oulu, Finland; Kortelainen T., University of Oulu, Oulu, Finland","The purpose of this paper is to develop an understanding of factors that affect the respondents to drop out of an already started survey on research data management. We decided to take a questionnaire on data management survey at Vilnius University and Oulu University implemented in 2017 as a case study. The data for the analysis was collected using the questionnaire, which was used in multinational research for Data Literacy and Research Data Management, performed by a group of researchers in more than ten countries, initiated by Serap Kurbanoğlu and Joumana Boustany. This paper describes the analysis of 1 185 survey samples, of which 515 were unfinished and 670 finished in both universities. For the analysis of the data, we used Framework for Web Survey Participation created by Andy Peytchev (2009). The collected data was analyzed using IBM SPSS Statistics ver. 19 with descriptive and inferential statistical tests. The most significant factors on deciding not to finish the survey were the length of the survey, the scientific field, experience, age and the topic of the survey. No statistically significant difference was measured between those who finished the survey and unfinished evaluating the data by gender and job position. An important factor in not finishing the survey was the design of the survey. © 2018, Informacijos mokslai.; Straipsnio tikslas - nustatyti ir išanalizuoti veiksnius, turinčius itakos respondentu sprendimui nutraukti klausimyno pildyma. Šiam tikslui gyvendinti pasirinkta Informacinio raštingumo ir moksliniu duomenu valdymo tyrimo anketa, kuri buvo pateikiama Vilniaus universiteto (Lietuva) ir Oulu universiteto (Suomija) mokslininkams, atliekant moksliniu duomenu valdymo tyrima abiejuose universitetuose 2017 m. Šis tyrimas buvo tarptautinio (atlikto keliasdešimtyje valstybiu) mokslinio tyrimo apie duomenu raštinguma ir moksliniu duomenu valdyma (angl. Data Literacy and Research Data Management) dalis. Tyrimo vadove˙s - Serap Kurbanoğlu ir Joumana Boustany. Šiame straipsnyje analizuojami 1 185 anketu duomenys iš abieju universitetu, iš kuriu 515 - prade˙tos ir nebaigtos pildyti anketos, o 670 - baigtos pildyti anketos. Duomenu analize˙ atlikta taikant Framework for Web Survey Participation (Andy Peytchev, 2009). Gautieji duomenys apdoroti taikant IBM SPSS Statistics 19 versija, naudojant aprašomosios ir inferencine˙s statistikos testus. Tyrimo rezultatai atskleide˙, kad svarbiausi veiksniai, nule˙mȩ nebaigta anketos pildyma, yra anketos apimtis, respondentu atstovaujama mokslo sritis, mokslinio darbo patirtis, amžius ir tyrimo tematika. Einamos pareigos ir respondentu lytis reikšmingos takos tam neture˙jo. Svarbus veiksnys, lemiantis anketos pildymo neužbaigima, yra ir klausimyno dizaino bei struktūros sprendimai. © 2018, Informacijos mokslai.","Apklausa; Informacinis raštingumas; Information literacy; Moksliniu duomenu valdymas; Oulu universitetas (Suomija); Research data management; Survey; University of Oulu (Finland); Vilniaus universitetas (Lietuva); Vilnius University (Lithuania)","","","","","","","","Armstrong J.S., Overton T.S., Estimating Nonresponse Bias in Mail Surveys, Journal of Marketing Research [online], 14, 14, (1977); Banks M., Zeitlyn D., Visual methods in social research, (2015); Baruch Y., Response Rate in Academic Studies-A Comparative Analysis, (1999); Bosnjak M., Poggio T., Becker K.R., Funke F., Wachenfeld A., Fischer B., Online survey participation via mobile devices, In: The American Association for Public Opinion Research (AAPOR) 68th Annual Conference. 2013, (2013); Bosnjak M., Tuten T.L., Classifying Response Behaviors in Web-based Surveys, Journal of Computer-Mediated Communication [online], 6, 6, pp. 0-0, (2001); Chaiken S., Heuristic versus sys-tematic information processing and the use of source versus message cues in persuasion, Journal of Personality and Social Psychology [online], 39, 39, pp. 752-766, (1980); Chudoba B., How much time are respondents willing to spend on your survey?, (2018); Couper Mick P., Kreuter F., Using paradata to explore item level response times in surveys, Journal of the Royal Statistical Society: Series A (Statistics in Society) [online], 176, 176, pp. 271-286, (2013); De Bruijne M., Wijnant A., Improving Response Rates and Questionnaire Design for Mobile Web Surveys, Public Opinion Quarterly [online], 78, 78, pp. 951-962, (2014); Dillman D.A.S., Jolene D.C., Leah M., Dillman D.A., Internet, mail, and mixed-mode surveys: the tailored design method, (2009); Ding D., Poquet O., Williams Joseph Jay N., Radhika C.S.R., Increasing Response Rates to Email Surveys in MOOCs, In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization-UMAP '18 [online], pp. 203-206, (2018); Edwards P., Roberts I., Clarke M., Diguiseppi C., Pratap S., Wentz R., Kwan I., Increasing response rates to postal questionnaires: systematic review, BMJ (Clinical research ed.) [online], 324, 324, (2002); Fan W., Yan Z., Factors affecting response rates of the web survey: A systematic review, Computers in Human Behavior 2010, (2010); Fan W., Yan Z., Factors affecting response rates of the web survey: A systematic review, Computers in Human Behavior [online], 26, 26, pp. 132-139, (2010); Frick A., Bachtinger M.T., Reips U.-D., Financial incentives, personal information and drop-out rate in online studies, In: Current Internet science. Trends, techniques, results, (1999); Frick A., Bachtiger M.T., Reips U.D., Dimensions of Internet Science [online], (2001); Galesic M., Bosnjak M., Effects of Questionnaire Length on Participation and Indicators of Response Quality in a Web Survey, Public Opinion Quarterly [online], 73, 73, pp. 349-360, (2009); Galesic M., Dropouts on the Web: Effects of Interest and Burden Experienced During an Online Survey, Journal of Official Statistics [online], 22, 2, pp. 313-328, (2006); Ganassali S., The Influence of the Design of Web Survey Questionnaires on the Quality of Responses, Survey Research Methods [online], 2, 2, pp. 21-32, (2008); Heerwegh D., Loosveldt G., An Evaluation of the Effect of Response Formats on Data Quality in Web Surveys, Social Science Computer Review [online], 20, 20, pp. 471-484, (2002); Joinson A.N., Alan W., Reips U.-D., Personalization, authenti cation and self-disclosure in self-administered Internet surveys, Computers in Human Behavior [online], 23, 23, pp. 275-285, (2007); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services-a guide for HEIs, (2013); Keusch F., Why do people participate in Web surveys? Applying survey participation theory to Internet survey data collection, Management Review Quarterly [online], 65, 65, pp. 183-216, (2015); Klofstad C.A.B., Shelley B.D., Matching the Message to the Medium, Social Science Computer Review [online], 26, 26, pp. 498-509, (2008); Lenzner T., Kaczmirek L., Lenzner A., Cognitive burden of survey questions and response times: A psycholinguistic experiment, Applied Cognitive Psychology [online], 24, 24, pp. 1003-1020, (2010); Liu M., Wronski L., Examining Completion Rates in Web Surveys via Over 25, 000 Real-World Surveys, Social Science Computer Review [online], 36, 36, pp. 116-124, (2018); Lugtig P., Toepoel V., The Use of PCs, Smartphones, and Tablets in a Probability-Based Panel Survey, Social Science Computer Review [online], 34, 34, pp. 78-94, (2016); Malhotra N., Completion Time and Response Order Effects in Web Surveys [online], (2008); Manfreda Katja Lozar B., Jernej V., Vasja B., Michael H.I., Web Surveys versus other Survey Modes: A Meta-Analysis Comparing Response Rates, International Journal of Market Research [online], 50, 50, pp. 79-104, (2008); Matzat U., Snijders C., Van Der Horst W., Effects of Different Types of Progress Indicators on Drop-Out Rates in Web Surveys, Social Psychology [online], 40, 40, pp. 43-52, (2009); O'neil Kevin M., Penrod S.D., Methodological variables in Web-based research that may affect results: Sample type, monetary incentives, and personal information, Behavior Research Methods, Instruments, & Computers [online], 33, 33, pp. 226-233, (2001); O'neil K.M.P., Steven D., Bornstein B.H., Web-based research: Methodological variables' effects on dropout and sample characteristics, Behavior Research Methods, Instruments, & Computers [online], 35, 35, pp. 217-226, (2003); Open Science and Research. [online], (2018); Petty R.E., Cacioppo J.T., The effects of involvement on responses to argument quantity and quality: Central and peripheral routes to persuasion, Journal of Personality and Social Psychology [online], 46, 46, pp. 69-81, (1984); Peytchev A., Survey Breakoff [online], (2009); Porter Stephen R., Whitcomb M.E., The Impact of Contact Type on Web Survey Response Rates [online], (2003); Rath J.M.W., Valerie F.V., Andrea C.G., Molly P.M., Paul D., Vallone D.M., Boosting Online Response Rates Among Nonresponders, Social Science Computer Review [online], 35, 35, pp. 619-632, (2017); Schlosser S., Mays A., Mobile and Dirty, Social Science Computer Review [online], 36, 36, pp. 212-230, (2018); Schneider R., Training Trainers for Research Data Literacy: A Content-and Method-Oriented Approach, pp. 139-147, (2018); Spiranec S., Kos D., Data Literacy and Research Data Management: The Croatian State of Affairs, pp. 148-157, (2018); Strasser C., Research Data Management, (2015); Tuten Tracy L.B., Michael B.W., Banner-advertised Web surveys, Marfketing Research [online], 11, 4, (1999); Research data policy at the University of Oulu. [online], (2018); Vincent C.E., Socioeconomic Status and Familial Variables in Mail Questionnaire Responses, American Journal of Sociology [online], 69, 69, pp. 647-653, (1964); Wells T., Bailey Justin T., Link M.W., Comparison of Smartphone and Online Computer Survey Administration, Social Science Computer Review [online], 32, 32, pp. 238-255, (2014); Yan T., Ryan L., Becker Sandra E., Smith J., Assessing Quality of Answers to a Global Subjective Well-being Question Through Response Times, Survey research methods [online], 9, 2, pp. 101-109, (2015); Yan T., Tourangeau R., Fast times and easy questions: the effects of age, experience and question complexity on web survey response times, Applied Cognitive Psychology [online], 22, 22, pp. 51-68, (2008); Zhang C., Conrad F., Speeding in Web Surveys: The tendency to answer very fast and its association with straightlining, Survey Research Methods [online], 8, 8, pp. 127-135, (2014)","","","Vilnius University - Faculty of Communication","","","","","","13920561","","","","English","Inf. Moksl.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85062481644"
"Siddaiah D.K.","Siddaiah, Dinesh K. (57195126514)","57195126514","Enriching library user’s experience with Evernote","2018","Library Hi Tech News","35","7","","11","12","1","1","10.1108/LHTN-06-2018-0035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055544391&doi=10.1108%2fLHTN-06-2018-0035&partnerID=40&md5=3271035276682022c6d072d8c7a2a5c3","Indian Institute of Technology Ropar, Rupnagar, India","Siddaiah D.K., Indian Institute of Technology Ropar, Rupnagar, India","Purpose: Digital information management tools (DIMTs) are used widely for varieties of digital information management. In academics, these tools are being used for the advancement of student productivity, research data management, research consultation, etc. The libraries of universities and institutions use DIMTs for organizing and retrieving varieties of digital information. Design/methodology/approach: The author studied the various emerging DIMTs and picked Evernote for further study based on the popularity and usage of the tool in the academic system. Findings: This paper explains how and to what extent Evernote can be used as a DIMT in academics and in the digital library management. Evernote is one of the best options for implementation in the library, especially to train the students to use and enrich their academic and research experience. Evernote definitely adds value to the libraries. Originality/value: It is found from the study that, Evernote is very handy in handling the research consultation service and manage research projects in libraries. © 2018, Emerald Publishing Limited.","Digital information management; Digital library management; DIMTs; Evernote; Information management; Student productivity","","","","","","","","Hall K., Evernote: 77 Steps to Help You Master Evernote and Organize Your Life Better (Evernote, Evernote Essentials, Evernote for Beginners), (2015); Kani J., Evernote in the research consultation: a feasibility study, Reference Services Review, 45, 1, pp. 67-78, (2017); Melinda K., Lawrence C., Advancing student productivity: an introduction to Evernote, Information Systems Education Journal, 14, 2, pp. 19-26, (2016); Smith C., 20 amazing Evernote statistics and facts, (2018); Lifehack productivity, (2018)","D.K. Siddaiah; Indian Institute of Technology Ropar, Rupnagar, India; email: dinesh.ncet@gmail.com","","Emerald Group Holdings Ltd.","","","","","","07419058","","","","English","Libr. Hi Tech News","Article","Final","","Scopus","2-s2.0-85055544391"
"","","","CEUR Workshop Proceedings","2019","CEUR Workshop Proceedings","2370","","","","","82","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067184278&partnerID=40&md5=802de3a558f0c8975d5f5b9bc58a6645","","","The proceedings contain 8 papers. The topics discussed include: a big data perspective on cyber-physical systems for industry 4.0: modernizing and scaling complex event processing; a conceptual modeling framework for software-enabled enterprise transformation; providing privacy guarantees in process mining; a methodology for integrating user experience methods and techniques into agile software development; cross-domain research data management with linked data technologies; traceability links recovery in BPMN models; a data-driven framework to facilitate automated requirements engineering; and the quest for a database selection and design method.","","","","","","","","","","","Reichert M.; Plebani P.; La Rosa M.","CEUR-WS","","Doctoral Consortium Papers Presented at the 31st International Conference on Advanced Information Systems Engineering, CAiSE-DC 2019","3 June 2019 through 7 June 2019","Rome","148447","16130073","","","","English","CEUR Workshop Proc.","Conference review","Final","","Scopus","2-s2.0-85067184278"
"Glusker A.; Exner N.","Glusker, Ann (6508190234); Exner, Nina (7801566844)","6508190234; 7801566844","Responding to change: Reinventing librarian identities in the age of research mandates","2018","Advances in Library Administration and Organization","39","","","91","115","24","3","10.1108/S0732-067120180000039007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065541225&doi=10.1108%2fS0732-067120180000039007&partnerID=40&md5=b1f48eadfead1c00965e4e642b921888","National Network of Libraries of Medicine, Pacific Northwest Region, United States; Virginia Commonwealth University Library, Richmond, VA, United States","Glusker A., National Network of Libraries of Medicine, Pacific Northwest Region, United States; Exner N., Virginia Commonwealth University Library, Richmond, VA, United States","This chapter outlines libraries’ (and librarians’) changing identities in the new world of research mandates from funders, institutions, and publishers. As libraries respond to the demands of these mandates on their users at the individual, departmental, and institutional levels, they need to revise their approaches to relationship building and user engagement, as well as maintain flexibility in the face of changing roles and skill requirements. This chapter will (1) outline the changing scholarly ecosystem; (2) summarize major terms and concepts to understand the process of producing research outputs; (3) discuss the perspectives of the major players in the research enterprise; (4) present some of the challenges that research mandates and the changing research environment have brought to libraries; and finally (5) review ways in which libraries have successfully addressed them. The focus here is on the academic research setting, although many of the strategies outlined can be equally applicable in both non-academic research and non-research funding contexts. © 2018 by Emerald Publishing Limited All rights of reproduction in any form reserved.","Data sharing; Funder research mandates; Institutional research mandates; Open access publishing; Research data management; Scholarly communication","","","","","","","","High-impact educational practices: A brief overview, High-Impact Educational Practices: What they Are, Who has Access to Them, and Why they Matter, (2008); Becker J.E., Krumholz H.M., Ben-Josef G., Ross J.S., Reporting of results in ClinicalTrials.Gov and high-impact journals, Journal of the American Medical Association, 311, 10, pp. 1063-1065, (2014); Briney K., Goben A., Zilinski L., Institutional, funder and journal data policies, Curating Research Data: Volume 1: Practical Strategies for Your Digital Repository, (2017); Bruns T., Brantley S., Duffin K., Scholarly communication coaching: Liaison librarians’ shifting roles, Partnerships and New Roles in the 21St-Century Academic Library: Collaborating, Embedding, and Cross-Training for the Future, (2015); Chadwell F., Sutton S.C., The future of open access and library publishing, New Library World, 115, 5-6, pp. 225-236, (2014); Chow A.S., Shaw T.L., Gwynn D., Martensen D., Howard M., Changing times and requirements: Implications for LIS education, LIBRES Library and Information Science Research Electronic Journal, 21, 1, pp. 1-23, (2011); Day J., Casey A.M., Wolfe C., The library as publishing house, Creating Research Infrastructures in the 21St-Century Academic Library, (2015); Deng S., Dotson L., Redefining scholarly services in a research lifecycle, Creating Research Infrastructures in the 21St-Century Academic Library, (2015); Holmes K.L., Lyon J.A., Johnson L.M., Sarli C.C., Tennant M.R., Library-based clinical and translational research support, Journal of the Medical Library Association, 101, 4, (2013); Jubb M., The scholarly ecosystem, Academic and Professional Publishing, (2012); A comparative review of research assessment regimes in five countries and the role of libraries in the research assessment process, Report Commissioned by OCLC Research, (2009); Kouper I., Fear K., Ishida M., Kollen C., Williams S.C., Research data services maturity in academic libraries, Practical Strategies for Your Digital Repository, 1, (2017); Lange J., Scholarly management publication and open access funding mandates: A review of publisher policies, Ticker: The Academic Business Librarianship Review, 1, 3, pp. 15-27, (2016); Lauer M., FY 2016 by the numbers, National Institutes of Health Office of Extramural Research, (2017); Lippincott S.K., The library publishing coalition: Organizing libraries to enhance scholarly publishing, Insights, 29, 2, pp. 188-191, (2016); Maccoll J., Library roles in university research assessment, Library Quarterly, 20, 2, pp. 152-168, (2010); Mangiafico P., Smith K.L., Reason, risk and reward: Models for libraries and other stakeholders in an evolving scholarly publishing ecosystem, Cultural Anthropology, 29, 2, pp. 216-235, (2014); Neugebauer T., Murray A., The critical role of institutional services in open access advocacy, International Journal of Digital Curation, 8, 1, (2013); Partlo K., From data to the creation of meaning Part II: Data librarian as translator, IASSIST Quarterly, 38, 2, pp. 12-15, (2014); Partridge P., Lee J., Munro C., Becoming “Librarian 2.0”: The skills, knowledge, and attributes required by library and information science professionals in a web 2.0 world (and beyond), Library Trends, 59, 1-2, pp. 315-335, (2010); Pinfield S., Making open access work: The “state-of-the-art” in providing open access to scholarly literature, Online Information Review, 39, 5, (2015); Plutchak T.S., Kaplan L., A library perspective: Data wranglers in libraryland: Finding opportunities in the changing policy landscape, The Serials Librarian, 70, 1-4, pp. 14-25, (2016); Reilly S.K., Rounding up the data: Libraries pushing new frontiers, Learned Publishing, 27, pp. S33-S34, (2014); Rice R., Southall J., The Data librarian’s Handbook, (2016); Ross J.S., Krumholz H.M., Ushering in a new era of open science through data sharing: The wall must come down, Journal of the American Medical Association, 309, 13, (2013); Open Access and Research Funders: A Report on Challenges, Opportunities, and Collaboration, (2016); Smith M., Data governance: Where technology and policy collide, Research Data Management, (2014); Steele C., Scholarly communication, scholarly publishing and university libraries: Plus ca change?, Australian Academic & Research Libraries, 45, 4, (2014); OMB Circular A-21 Cost Principles for Educational Institutions, (2004); Walters D., Managing mandates, Serials Review, 42, 2, (2016); Webster K., The evolving role of libraries in the scholarly ecosystem, Academic and Professional Publishing, (2012); Zbylut D.J., Indirect costs: The past, present, and a possible institutional response, SRA Journal, 24, 3, pp. 13-18, (1992)","","","Emerald Group Publishing Ltd.","","","","","","07320671","","","","English","Adv. Libr. Adm. Organ.","Book chapter","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85065541225"
"Wilms K.L.; Stieglitz S.; Buchholz A.; Vogl R.; Rudolph D.","Wilms, Konstantin L. (57190278061); Stieglitz, Stefan (8982225800); Buchholz, Alina (57224766304); Vogl, Raimund (6701668973); Rudolph, Dominik (56244944800)","57190278061; 8982225800; 57224766304; 6701668973; 56244944800","Do researchers dream of research data management?","2018","Proceedings of the Annual Hawaii International Conference on System Sciences","2018-January","","","4411","4420","9","9","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062555532&partnerID=40&md5=e19895a983db1d43023a3852e3cfb9b0","University of Duisburg-Essen, Germany; University of Münster, Germany","Wilms K.L., University of Duisburg-Essen, Germany; Stieglitz S., University of Duisburg-Essen, Germany; Buchholz A., University of Duisburg-Essen, Germany; Vogl R., University of Münster, Germany; Rudolph D., University of Münster, Germany","The ongoing digitalization of academic work processes has led to a shift in academic work culture where researchers are supposed to take on more responsibility in term of adequate data management. Third party funding institutions as well as high class journals are increasingly asking for standardized data management processes and started to set up policies which should guide researchers to manage their data properly. In this work, we deal with the highly IS relevant topic of research data management (RDM) and provide an overview of the different existing research data management guidelines of the eight biggest governmental funded institutions and the biggest politically-independent institution. All existing guidelines of those institutions were considered in a qualitative analysis, summarized and evaluated. It has been found that non-technical requirements evolve to non-technical barriers, which institutions need to address to a greater extent within their guidelines to promote scientific research. This work shows the shift in the understanding of RDM and provides the present perspective which help researchers to better understand the ongoing trend of RDM within science. © 2018 IEEE Computer Society. All rights reserved.","","Systems science; Academic work; Management process; Non-technical barriers; Qualitative analysis; Research data managements; Scientific researches; Technical requirement; Third parties; Information management","","","","","European Commission H2020 Program; National Science Foundation, NSF; National Institutes of Health, NIH; Wellcome Trust, WT; National Research Council Canada, NRC; Australian Research Council, ARC; Deutsche Forschungsgemeinschaft, DFG; Bundesministerium für Bildung und Forschung, BMBF; Horizon 2020","For the discovery of already existing international standards towards research data management guidelines and to alert on possibly missing guidelines, the biggest and most influential institutions regarding research data management within the scientific community were selected. Thus, the existing guidelines of the eight largest and widest known governmental funded institutions were collected. Additionally, Wellcome Trust, as a widely known, but politically independent institution, was consulted within the list as well. “Large” as a requirement connotes the number of the institutions’ salaried employees, as well as its international influence. The considered institutions are the following: The Australian Research Council (ARC)[41], the Bundesministerium für Bildung und Forschung (BMBF) [42], the Deutsche Forschungsgemeinschaft (DFG) [43], the European Commission H2020 Program (Horizon 2020) [44], the National Institutes of Health (NIH) [45], the National Research Council Canada (NRC) [46], the National Science Foundation (NSF) [47], the Organisation for Economic Co-operation and Development (OECD) [48] and the Wellcome Trust [49] as the only politically and financially independent institution within this list is settled in London, United Kingdom (around 2000 employees).","Bell G., Hey T., Szalay A., Computer science: Beyond the data deluge, Science (80-.), 323, 5919, pp. 1297-1298, (2009); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol., 63, 6, pp. 1059-1078, (2012); Whyte A., Tedds J., Making the case for research data management, Digital Curation Centre, (2011); Devarakonda R., Palanisamy G., Green J.M., Wilson B.E., Data sharing and retrieval using OAI-PMH, Earth Sci. Informatics, 4, 1, pp. 1-5, (2011); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional policies and practices, Program, 49, 4, pp. 364-381, (2015); Corti L., van den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data, (2014); Bell G., Foreword, The Fourth Paradigm, (2009); Becker J., Heide T., Knackstedt R., Steinhorst M., Supporting knowledge management and collaboration in research communities using automatically created research portals, Int. J. Web Portals, 5, 2, pp. 1-16, (2013); Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univers. Access Inf. Soc., pp. 1-12, (2016); Savage C.J., Vickers A.J., Empirical study of data sharing by authors publishing in PLOS journals, PLoS One, 4, 9, pp. 70-78, (2009); Childs S., McLeod J., Lomas E., Cook G., Opening research data: Issues and opportunities, Rec. Manag. J., 24, 2, pp. 142-162, (2014); Wilms K., Meske C., Stieglitz S., Rudolph D., Vogl R., How to improve research data management, Human Interface and the Management of Information: Applications and Services. HIMI 2016. Lecture Notes in Computer Science, pp. 434-442, (2016); Schopfel J., Chaudiron S., Jacquemin B., Prost H., Severo M., Thiault F., Open access to research data in electronic theses and dissertations: An overview, Libr. Hi Tech, 32, 4, pp. 612-627, (2014); Wilms K., Et al., Digital transformation in higher education – New cohorts, new requirements?, AMCIS 2017 Proc, (2017); Mayring P., Fenzl T., Qualitative Inhaltsanalyse, Handbuch Methoden Der Empirischen Sozialforschung, pp. 543-556, (2014); Ackoff R.L., From data to wisdom, J. Appl. Syst. Anal., 16, 1, pp. 3-9, (1989); Jennex M.E., Bartczak S.E., A revised knowledge pyramid, Int. J. Knowl. Manag., 9, pp. 19-30, (2013); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, 3, (2007); Palmer C.L., Et al., Data curation for the long tail of science: The case of environmental sciences, Third International Digital Curation Conference, pp. 1-6, (2007); Witt M., Institutional repositories and research data curation in a distributed environment, Libr. Trends, 57, 2, pp. 191-201, (2008); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists’ data-sharing behaviors: A multilevel analysis, J. Assoc. Inf. Sci. Technol- Ogy, 67, 4, pp. 776-799, (2012); Joshi M., Krag S.S., Issues in data management, Sci. Eng. Ethics, 16, 4, pp. 743-748, (2010); Koslow S.H., Should the neuroscience community make a paradigm shift to sharing primary data?, Nat. Neurosci., 3, 9, pp. 863-866, (2000); Nelson B., Data sharing: Empty archives, Nature, 461, 7261, pp. 160-163, (2009); Tenopir C., Et al., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Bryan Heidorn P., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Faniel I.M., Jacobsen T.E., Reusing scientific data: How earthquake engineering researchers assess the reusability of colleagues’ data, Comput. Support. Coop. Work, 19, 3, pp. 355-375, (2010); Gardner D., Et al., Towards effective and rewarding data sharing, Neuroinformatics, 1, 3, pp. 289-295, (2003); Zimmermann A.S., New knowledge from old data: The role of standards in the sharing and reuse of ecological data, Sci. Technol. Hum. Values, 33, 5, pp. 631-652, (2008); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice, Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Stockle G., A checklist for planning research data management, Astrostatistics Data Min, pp. 247-251, (2012); Liang S., Holmes V., Antoniou G., Higgins J., Icurate: A research data management system. Multi-disciplinary Trends in Artificial Intelligence, Lect. Notes Comput. Sci., pp. 39-47, (2015); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J. Librariansh. Inf. Sci., 46, 4, pp. 299-316, (2014); Nariani R., Fernandez L., Open access publishing: What authors want, Coll. Res. Libr., 73, 2, pp. 182-195, (2012); Giffels J., Sharing data is a shared responsibility, Sci. Eng. Ethics, 16, 4, pp. 801-803, (2010); Hanson B., Sugden A., Alberts B., Making data maximally available, Science (80-.), 331, 6018, (2011); Arzberger P., Et al., Promoting access to public research data for scientific, economic, and social development, Data Sci. J., 3, pp. 135-152, (2004); Smit E., van der Hoeven J., Giaretta D., Avoiding a Digital Dark Age for data: Why publishers should care about digital preservation, Learn. Publ., 24, 1, pp. 35-49, (2011); Soranno P.A., Cheruvelil K.S., Elliott K.C., Montgomery G.M., It’s good to share: Why environmental scientists’ ethics are out of date, Bioscience, 65, 1, pp. 69-73, (2015); Rudolph D., Thoring A., Vogl R., Research data management: Wishful thinking or reality?, PIK - Prax. Der Informationsverarbeitung Und Kommun, 38, 3-4, pp. 113-120, (2015); Research Data Management – Australian Code for the Responsible Conduct of Research, (2017); RFII Empfehlungen 2016: Leistung Aus Vielfalt, (2017); Memorandum Safeguarding Good Scientific Practice, (2013); H2020 Programme - Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020, (2017); NIH Data Sharing Policy and Implementation Guidance, (2017); Data Analytics Centre, (2017); Data Management & Sharing FAQs | NSF - National Science Foundation; Data Management Procedures, (2016); Policy on Data Management and Sharing; Viangteeravat T., Nagisetty V.R., Anyanwu M.N., Kuscu E., Brooks I.M., McDonald C.S., Protected Research Information Management Environment (PRIME) provides a secure open source data management option for clinical and scientific research, BMC Bioinformatics, 12, 7, (2011); Berman F., Cerf V., Who will pay for public access to research data?, Science (80-.), 341, 6146, pp. 616-617, (2013); Deutsche Forschergemeinschaft, (2017); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Gov. Inf. Q., 30, pp. S19-S31, (2013); Feijen M., Horstmann W., Manghi P., Robinson M., Russell R., Driver: Building the network for accessing digital repositories across Europe, Ariadne, 53, (2007); Pearce N., Smith A.H., Data sharing: Not as simple as it seems, Environ. Heal., 10, 10, pp. 1-7, (2011); Campbell E.G., Clarridge B.R., Birenbaum L., Hilgartner S., Blumenthal D., Evidence from a national survey, J. Am. Med. Assoc., 287, 4, pp. 473-480, (2002); Carusi A., Jirotka M., From data archive to ethical labyrinth, Qual. Res., 9, 4, pp. 285-298, (2009); Piwowar H.A., Who shares? Who doesn’t? Factors associated with openly archiving raw research data, PLoS One, 6, 7, (2011); Meske C., Brockmann T., Wilms K., Social collaboration and gamification, Gamification, Progress in IS, pp. 93-109, (2017); Meske C., Brockmann T., Wilms K., Stieglitz S., Gamify employee collaboration - A critical review of gamification elements in social software, Australas. Conf. Inf. Syst., pp. 1-15, (2015)","","Bui T.X.","IEEE Computer Society","AIS; Bizgenics Foundation; IBM; Pacific Research Institute for Information Systems and Management (PRIISM); The International Society of Service Innovation Professionals; University of Hawai'i at Manoa, Shidler College of Business","51st Annual Hawaii International Conference on System Sciences, HICSS 2018","2 January 2018 through 6 January 2018","Big Island","169516","15301605","978-099813311-9","","","English","Proc. Annu. Hawaii Int. Conf. Syst. Sci.","Conference paper","Final","","Scopus","2-s2.0-85062555532"
"Pontika N.","Pontika, Nancy (56548850900)","56548850900","Roles and jobs in the open research scholarly communications environment: Analysing job descriptions to predict future trends","2019","LIBER Quarterly","29","1","","","","","12","10.18352/lq.10282","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069858908&doi=10.18352%2flq.10282&partnerID=40&md5=95902da97da35a9a98f1aa00c6701650","CORE, Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom","Pontika N., CORE, Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom","During the past two-decades academic libraries updated current staff job responsibilities or created brand new roles. This allowed them to adapt to scholarly communication developments and consequently enabled them to offer efficient services to their users. The global calls for openly accessible research results has shifted the institutional, national and international focus and their constant evolvement has required the creation of new research positions in academic libraries. This study reports on the findings of an analysis of job descriptions in the open research services as advertised by UK academic libraries. Method: From March 2015 to March 2017, job advertisements relating to open access, repositories and research data management were collected. Results: The analysis of the data showed that the primary responsibilities of the open research support staff were: to ensure and facilitate compliance with funders’ open access policies, maintain the tools that enable compliance, create reports and collect statistics that measure compliance rates and commit to continuous liaising activities with research stakeholders. Discussion: It is clear that the open research services is a complex environment, requiring a variety of general and subject specific skill sets, while often a role may involve more than one area of expertise. Conclusion: The results of this study could benefit prospective employees and universities that wish to embed open research skills in their curriculum. © 2019, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Competencies; Open access; Repositories; Research data; Scholarly communications; Skills","","","","","","","","Allard S., Mack T.R., Feltner-Reichert M., The librarian’s role in institutional repositories: A content analysis of the literature, Reference Services Review, 33, 3, pp. 325-336, (2005); Ashcroft L., Developing competencies, critical analysis and personal transferable skills in future information professionals, Library Review, 53, 2, pp. 82-88, (2004); Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Biddiscombe R., The development of information professionals’ needs for Internet and IT skills: Experiences at the University of Birmingham, Program, 35, 2, pp. 157-166, (2001); Blumenthal J., Martinez I., Murthy V., Silver L., Position Descriptions in Health Sciences Libraries, (2006); Borgman C.L., What are digital libraries? Competing visions, Information Processing and Management, 35, 3, pp. 227-243, (1999); Bychowski B.K.H., Costa M.C., Sudhakaran J., Caffrey C.M., Moore A.D., Zhang Y., Old worlds, new meanings: A study of trends in science librarian job ads, Issues in Science and Technology Librarianship, 63, (2010); Cassella M., Morando M., Fostering new roles for librarians: Skills set for repository managers – Results of a survey in Italy, LIBER Quarterly, 21, 3-4, pp. 407-428, (2012); Chan D.L.H., Kwok C.S.Y., Yip S.K.F., Changing roles of reference librarians: The case of the HKUST institutional repository, Reference Services Review, 33, 3, pp. 268-282, (2005); Choi Y., Rasmussen E., What qualifications and skills are important for digital librarian positions in academic libraries? A job advertisement analysis, The Journal of Academic Librarianship, 35, 5, pp. 457-467, (2009); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Gabridge T., The last mile: Liaison roles in curating science and engineering research data, Research Library Issues, 265, pp. 15-21, (2009); Gerolimos M., Konsta R., Librarians’ skills and qualifications in modern informational environment, Library Management, 29, 8-9, pp. 691-699, (2008); Henty M., Dreaming of Data: The library’s Role in Supporting E-Research and Data Management, (2008); Jenkins B., Breakstone E., Hixson C., Content in, content out: The dual roles of reference librarian in institutional repositories, Reference Services Review, 33, 3, pp. 312-324, (2005); Jones R., Andrew T., Maccoll J., The Institutional Repository, (2006); Jones S., Pryor G., Whyte A., How to Develop RDM Services – a Guide for Heis. DCC How to Guides, (2013); Kim J., Warga E., Moen W., Digital curation in the academic library job market, Proceedings of the American Society for Information Science and Technology, 49, 1, pp. 1-4, (2013); Kwasik H., Qualifications for a serials librarian in an electronic environment, Serials Review, 28, 1, pp. 33-37, (2013); Lewis M.J., Libraries and the management of research data, Envisioning Future Academic Library Services, pp. 145-168, (2010); Marion L., Digital librarian, cybrarian, or librarian with specialized skills: Who will staff digital libraries?, Crossing the Divide: Proceedings of the Tenth National Conference of the Association of College and Research Libraries, pp. 143-149, (2001); McMullen K.D., Felicia Y., Adapting to change: A survey of evolving job descriptions in medical librarianship, Journal of Hospital Librarianship, 13, 3, pp. 246-257, (2013); Monastersky R., Publishing frontiers: The library reboot, Nature, 495, 7442, pp. 430-432, (2013); Ottaviani J., Hank C., Libraries should lead the institutional repository initiative and development at their institutions, Bulletin of the American Society for Information Science and Technology, 35, 4, pp. 17-21, (2009); Park J.R., Lu C., Marion L., Cataloging professionals in the digital environment: A content analysis of job descriptions, Journal of the American Society for Information Science and Technology, 60, 4, pp. 844-857, (2009); Peek R., Newby G.B., Scholarly Publishing: The Electronic Frontier, (1996); Picarra M., Monitoring Compliance with Open Access Policies. Pasteur40a., (2012); Pinfield S., Salter J., Bath P.A., Hubbard B., Millington P., Anders J.H.S., Hussain A., Open Access repositories worldwide, 2005–2012: Past growth, current characteristics and future responsibilities, Journal for the American Society for Information Science and Technology, 65, 12, pp. 2404-2421, (2014); Pontika N., Rozenberga D., Developing strategies to ensure compliance with funders’ open access policies, Insights, 28, 1, pp. 32-36, (2015); Robinson M., Institutional repositories: Staff and skills set, SHERPA Document, (2008); Schwartz C., Digital libraries: An overview, The Journal of Academic Librarianship, 26, 6, pp. 385-393, (2000); Sharp K., Internet librarianship: Traditional roles in a new environment, IFLA Journal, 27, 2, pp. 78-81, (2001); Simons N., Richardson J., New roles, new responsibilities: Examining training needs of repository staff, Journal of Librarianship and Scholarly Communication, 1, 2, pp. 1-16, (2012); Suber P., Open Access, (2012); Swan A., Institutional repositories – now and next, University Libraries and Digital Learning Environments, pp. 119-134, (2011); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services. Current Practices and Plans for the Future., (2012); Common Principles on Data Policy, (2019); Walters T.O., Reinventing the library – how repositories are causing librarians to rethink their professional roles, Portal: Libraries and the Academy, 7, 2, pp. 213-225, (2007); Whyte A., Tedds J., Making the case for research data management, Digital Curation Centre and Jisc, (2011); Wickham J., Repository Management: An Emerging Profession in the Information Sector, (2010)","N. Pontika; CORE, Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom; email: pontika.nancy@gmail.com","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85069858908"
"Pittman J.M.; Bajwa G.; Joseph J.; Keller N.","Pittman, Jason M. (57219478234); Bajwa, Garima (55843512100); Joseph, Joshua (57210816881); Keller, Nicholas (57210813855)","57219478234; 55843512100; 57210816881; 57210813855","Curating Research Data - Cyber security perspective from a nascent Brain Machine Interface Laboratory","2018","2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018","","","8796827","89","94","5","1","10.1109/UEMCON.2018.8796827","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071540984&doi=10.1109%2fUEMCON.2018.8796827&partnerID=40&md5=eac796cb54aec5f54c1b4446b412d350","Capitol Technology University, Laurel, MD, United States","Pittman J.M., Capitol Technology University, Laurel, MD, United States; Bajwa G., Capitol Technology University, Laurel, MD, United States; Joseph J., Capitol Technology University, Laurel, MD, United States; Keller N., Capitol Technology University, Laurel, MD, United States","Researchers across disciplines are in dissonance between data curation and open access versus data security. To accelerate new discoveries, preserving scientific artifacts such as research protocols and raw data is a rational expectation. Brain-machine interface research is particularly sensitive to data curation as data sets are large, archived in disparate formats, and bereft of useful metadata associated with mode of capture, number of channels, or even activities performed by participants during capture. Moreover, curating such data in a secure fashion is little discussed and requires expertise not shared across all fields. Time and labor costs for research data curation can be exorbitant without the addition concern of security. Such factors amplify further when a laboratory is young and resident at a small school with limited resources. Thus, this paper describes the development of a secure research data curation model within a nascent brain-machine interface laboratory at a small, private four-year institution in the mid-Atlantic region of the United States. We utilized a case study design to analyze existing data curation and cybersecurity literature for best practices. Using five unique search strings, we identified eight thematic best practices across three cyber security dimensions (integrity, availability, and access control). Recommendations and future work are discussed in response to the execution of the case research and the findings. © 2018 IEEE.","brain-machine interface; cyber security; data curation; research artifacts","Access control; Brain; Brain computer interface; Interface states; Mobile telecommunication systems; Ubiquitous computing; Wages; Best practices; Brain machine interface; Cyber security; Mid-Atlantic; Rational expectations; Research data; Research protocol; Study design; Data curation","","","","","DoD Information Assurance Scholarship Program; International Association for Suicide Prevention, IASP, (H98230-17-1-0363)","This work was supported by DoD Information Assurance Scholarship Program (IASP) under capacity-building grant H98230-17-1-0363.","Abbott D., Dcc Briefing Paper: What is Digital Curation, (2008); Walters T.O., Data curation program development in us universities: The Georgia institute of technology example, International Journal of Digital Curation, 4, 3, pp. 83-92, (2009); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); McDonough B.E., Warren C.A., Don N.S., A third replication of event-related brain potential (erp) indicators of unconscious psi, Proceedings of the 41st Annual Convention of the Parapsychological Association, pp. 64-75, (1998); Birbaumer N., Breaking the silence: Brain-computer interfaces (bci) for communication and motor control, Psychophysiology, 43, 6, pp. 517-532, (2006); Carrubba S., Frilot C., Chesson A.L., Marino A.A., Nonlinear EEG activation evoked by low-strength low-frequency magnetic fields, Neuroscience Letters, 417, 2, pp. 212-216, (2007); Denzin N.K., Lincoln Y.S., The Sage Handbook of Qualitative Research, (2005); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Lebedev M.A., Nicolelis M.A., Brain-machine interfaces: Past, present and future, TRENDS in Neurosciences, 29, 9, pp. 536-546, (2006); Millan J.D.R., Carmena J., Invasive or noninvasive: Understanding brain-machine interface technology, IEEE Engineering in Medicine and Biology Magazine, 29, pp. 16-22, (2010); Andersen R.A., Musallam S., Pesaran B., Selecting the signals for a brain-machine interface, Current Opinion in Neurobiology, 14, 6, pp. 720-726, (2004); Millan J.D.R., Rupp R., Muller-Putz G., Murray-Smith R., Giugliemma C., Tangermann M., Vidaurre C., Cincotti F., Kubler A., Leeb R., Et al., Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges, Frontiers in Neuroscience, 4, (2010); Homan R.W., Herman J., Purdy P., Cerebral location of international 10-20 system electrode placement, Electroencephalography and Clinical Neurophysiology, 66, 4, pp. 376-382, (1987); Basar E., Basar-Eroglu C., Guntekin B., Yener G.G., Brain's alpha, beta, gamma, delta, and theta oscillations in neuropsychiatric diseases: Proposal for biomarker strategies, Supplements to Clinical Neurophysiology. Elsevier, 62, pp. 19-54, (2013); Bache K., Lichman M., Uci Machine Learning Repository, (2013); Bnci Horizon 2020; Moody G.B., Mark R.G., Goldberger A.L., Physionet: A webbased resource for the study of physiologic signals, IEEE Engineering in Medicine and Biology Magazine, 20, 3, pp. 70-75, (2001); Cavelty M.D., Cyber-security, The Routledge Handbook of New Security Studies, pp. 154-162, (2010); Qadir S., Quadri S., Information availability: An insight into the most important attribute of information security, Journal of Information Security, 7, 3, (2016); Jeng W., He D., Chi Y., Social science data repositories in data deluge: A case study of icpsrs workflow and practices, The Electronic Library, 35, 4, pp. 626-649, (2017); Mullins J.L., Enabling International Access to Scientific Data Sets: Creation of the Distributed Data Curation Center (d2c2), (2007); Heidorn P.B., The emerging role of libraries in data curation and escience, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Myers J., Hedstrom M., Akmon D., Payette S., Plale B.A., Kouper I., McCaulay S., McDonald R., Suriarachchi I., Varadharaju A., Et al., Towards sustainable curation and preservation: The sead project's data services approach, E-Science (E-Science), 2015 IEEE 11th International Conference On. IEEE, pp. 485-494, (2015); Kurata K., Matsubayashi M., Mine S., Identifying the complex position of research data and data sharing among researchers in natural science, SAGE Open, 7, 3, (2017); Whitman M.E., Mattord H.J., Principles of information security, Cengage Learning, (2011); Baxter P., Jack S., Qualitative case study methodology: Study design and implementation for novice researchers, The Qualitative Report, 13, 4, pp. 544-559, (2008); Merriam S.B., Qualitative research and case study applications in education. Revised and expanded from, Case Study Research in Education ERIC, (1998); Yin R.K., Case Study Research and Applications: Design and Methods, (2017); Hancock D.R., Algozzine B., Doing Case Study Research: A Practical Guide for Beginning Researchers, (2016); Tellis W.M., Application of a case study methodology, The Qualitative Report, 3, 3, pp. 1-19, (1997); Landwehr C.E., Formal models for computer security, ACM Computing Surveys (CSUR), 13, 3, pp. 247-278, (1981); Pfleeger C.P., Pfleerer S.L., Security in Computing. Prentice Hall Professional Technical Reference, (2002); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008); Gray J., Szalay A.S., Thakar A.R., Stoughton C., Et al., Online scientific data curation, publication, and archiving, Virtual Observatories, 4846, pp. 103-108, (2002); Assante M., Candela L., Castelli D., Tani A., Are scientific data repositories coping with research data publishing, Data Science Journal, 15, (2016); Pricing F.; Gorton I., Sivaramakrishnan C., Black G., White S., Purohit S., Lansing C., Madison M., Schuchardt K., Liu Y., Velo: A knowledgemanagement framework for modeling and simulation, Computing in Science & Engineering, 14, 2, pp. 12-23, (2012); Velo: The Smart Destination for Total Data Man-agement; Jacobs C., Avdis A., Git-rdm: A research data management plugin for the git version control system, The Journal of Open Source Software, 1, 2, pp. 1-2, (2016)","","Chakrabarti S.; Saha H.N.","Institute of Electrical and Electronics Engineers Inc.","Columbia University; IEEE New York Section; IEEE Region 1; IEEE USA; Institute of Engineering and Management (IEM); University of Engineering and Management (UEM)","9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018","8 November 2018 through 10 November 2018","New York City","150821","","978-153867693-6","","","English","IEEE Annu. Ubiquitous Comput., Electron. Mob. Commun. Conf., UEMCON","Conference paper","Final","","Scopus","2-s2.0-85071540984"
"Smith P.L.; Gonzalez S.; Bossart J.","Smith, Plato L. (24475610400); Gonzalez, Sara (55427098000); Bossart, Jean (57190810212)","24475610400; 55427098000; 57190810212","Data management and the role of librarians","2019","GL-Conference Series: Conference Proceedings","2019-December","","","75","82","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062987390&partnerID=40&md5=1160143863502895cfa45bc66776f409","George A. Smathers Libraries, University of Florida, United States","Smith P.L., George A. Smathers Libraries, University of Florida, United States; Gonzalez S., George A. Smathers Libraries, University of Florida, United States; Bossart J., George A. Smathers Libraries, University of Florida, United States","'Research Data Science' is defined by Committee on Data of the International Council for Science Research Data Alliance (CODATA-RDA) as an ensemble of (a) Open Science principles and practices (FAIR) and research data management and curation skills, (b) the use of a range of data platforms and infrastructures, (c) large scale analysis, (d) statistics, (e) visualization and modeling techniques, (f) software development and annotation, and (g) more. Data management and the role of librarians must now include developing expertise and training with faculty, students, and staff on ""research data science"" directly and/or indirectly through collaborative library/faculty partnerships. To meet this need, librarians at the University of Florida have developed a new research support service called Academic Research Consulting & Services (ARCS) to assist faculty, students, and staff with their data management and research needs. The library-centered, campus-wide focused UF Data Management and Curation Working (DMCWG) and ARCS work in collaborative partnerships with the campus units such as the UF Informatics Institute, UF Data Carpentry Club (https://github.com/UF-Carpentry), and UF Data Science & Informatics (DSI) undergraduate student organization to provide support to pre- and post-grant research and teaching. This new role of librarians is to facilitate library/faculty collaborations and broker resources that contribute to the facilitation of promulgating 'research data science' skills at scale for their respective institutions. This paper will discuss the developing outreach activities, interdepartmental collaborations, some initial outcomes, and future goals of leveraging capacity, infrastructure, and resources to develop data management efforts across communities of practice within an institution's current organizational culture. One aim of this paper is to highlight the importance and significance of developing good library and faculty partnerships built on character, integrity, and humility as the cornerstones for the roles of librarians as collaborators in promoting socio-technical data management programs. © TextRelease, 2019.","","C (programming language); Data Science; Data visualization; Human resource management; Libraries; Personnel training; Research and development management; Software design; Students; Collaborative library; Collaborative partnerships; Communities of Practice; Organizational cultures; Principles and practices; Research data managements; Undergraduate students; University of Florida; Information management","","","","","UFII","SQL workshops, and developing learning pathways for students. UFII fellows and students attend of help at workshops. DSI is funded by UFII and collaborates with UF Libraries/MSL.","Data Governance Policy, Appendix 1 - Data Management Life Cycle, (2017); University of Glasgow Humanities Advanced Technology & Information Institute (HATII), and Digital Curation Center, (2009); Davis M.C., Challenger R., Jayewardene D.N.W., Clegg C.W., Advancing socio-technical systems thinking: A call for bravery, Applied Ergonomics, 45, 2, pp. 171-180, (2014); CESSDA SaW Archive Development Canvas (Detailed Version), (2017); JISC Circular 6/03 (Revised). An Invitation for Expressions of Interest to Establish a New Digital Curation Centre for Research into and Support of the Curation and Preservation of Digital Data and Publications, (2003); Checklist for a Data Management Plan [Internet]. Version 4.0, (2013); USGS Data Management [Internet], (2013); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services: A Guide for HEIs', (2013); Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science librarians' participation in data management at association of research libraries institutions, Coll Res Libr., 75, 4, (2014); Diekema A.R., Wesolek A., Walters C.D., The NSF/NIH effect: Surveying the effect of data management requirements on faculty, sponsored programs, and institutional repositories, J Acad Librariansh., 40, 3-4, (2014); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J Librariansh Inf Sci., 46, 4, (2014); Saunders L., Academic libraries' strategic plans: Top trends and under-recognized areas, J Acad Librariansh., 41, 3, (2015); Hickson S., Poulton K.A., Connor M., Richardson J., Wolski M., Modifying researchers data management practices: A behavioural framework for library practitioners, IFLA J., 42, 4, (2016); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS One, 10, 2, (2015); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Libr Hi Tech., 32, 3, (2014); MacMillan D., Data sharing and discovery: What librarians need to know, Journal of Academic Librarianship, 40, (2014); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); Marshall B., O'Bryan K., Qin N., Vernon R., Organizing, contextualizing, and storing legacy research data: A case study of data management for librarians, Issues Sci Technol Librariansh, (2013); Van Tuyl S., Whitmire A.L., Water, water, everywhere: Defining and assessing data sharing in academia, PLoS One, 11, 2, (2016); Kratz J.E., Strasser C., Researcher perspectives on publication and peer review of data, PLoS One, 10, 2, (2015); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, Peer J., (2013); Michener W.K., Ten simple rules for creating a good data management plan, PLoS Comput Biol., 11, 10, (2015); Jorn Nielsen H., Hjorland B., Curating research data: The potential roles of libraries and information professionals, J Doc., 70, 2, (2014); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, pp. 1-28, (2014); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Libr Inf Sci Res [Internet], 38, 4, pp. 319-326, (2016); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Libr Inf Sci Res [Internet], 36, 2, pp. 84-90, (2014); Kennan M.A., Corrall S., Afzal W., Making space"" in practice and education: Research support services in academic libraries, Libr Manag., 35, 8-9, (2014); Patel D., Research data management: A conceptual framework, Libr Rev., 65, 4-5, (2016); Greene J.C., Caracelli V.J., Graham W.F., Toward a conceptual framework for mixed-method evaluation designs, Educational Evaluation and Policy Analysis, 11, 3, pp. 255-274, (1989); Creswell J.W., Klassen A.C., Plano Clark V.L., Smith K.C., Best practices for mixed methods research in the health sciences, Bethesda (Maryland): National Institutes of Health, 2013, pp. 541-545, (2011)","","","TextRelease","EBSCO; et al.; Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR); Korea Institute of Science and Technology Information (KISTI); Nuclear Information Section; International Atomic Energy Agency (NIS-IAEA); Slovak Centre of Scientific and Technical Information (CVTISR)","20th International Conference on Grey Literature: Research Data Fuels and Sustains Grey Literature, GL 2018","3 December 2018 through 4 December 2018","New Orleans","145765","13862316","978-907748433-3","","","English","GL-Conf. Series: Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85062987390"
"Witt M.; Stall S.; Duerr R.; Plante R.; Fenner M.; Dasler R.; Cruse P.; Hou S.; Ulrich R.; Kinkade D.","Witt, Michael (15119883100); Stall, Shelley (57194149447); Duerr, Ruth (22233540500); Plante, Raymond (14825812700); Fenner, Martin (7006600825); Dasler, Robin (57190961692); Cruse, Patricia (28767472400); Hou, Sophie (57205608389); Ulrich, Robert (57192199990); Kinkade, Danie (55945661600)","15119883100; 57194149447; 22233540500; 14825812700; 7006600825; 57190961692; 28767472400; 57205608389; 57192199990; 55945661600","Connecting Researchers to Data Repositories in the Earth, Space, and Environmental Sciences","2019","Communications in Computer and Information Science","988","","","86","96","10","3","10.1007/978-3-030-11226-4_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060766346&doi=10.1007%2f978-3-030-11226-4_7&partnerID=40&md5=44d2b35bad24b55339c505a7e052085e","Purdue University, West Lafayette, IN, United States; American Geophysical Union, Washington DC, United States; Ronin Institute, Montclair, NJ, United States; National Institute of Standards and Technology, Gaithersburg, MD, United States; DataCite, Hannover, Germany; National Center for Atmospheric Research, University Corporation for Atmospheric Research, Boulder, CO, United States; Karlsruher Institut für Technologie, Karlsruhe, Germany; Woods Hole Oceanographic Institution, Woods Hole, MA, United States","Witt M., Purdue University, West Lafayette, IN, United States; Stall S., American Geophysical Union, Washington DC, United States; Duerr R., Ronin Institute, Montclair, NJ, United States; Plante R., National Institute of Standards and Technology, Gaithersburg, MD, United States; Fenner M., DataCite, Hannover, Germany; Dasler R., DataCite, Hannover, Germany; Cruse P., DataCite, Hannover, Germany; Hou S., National Center for Atmospheric Research, University Corporation for Atmospheric Research, Boulder, CO, United States; Ulrich R., Karlsruher Institut für Technologie, Karlsruhe, Germany; Kinkade D., Woods Hole Oceanographic Institution, Woods Hole, MA, United States","The Repository Finder tool was developed to help researchers in the domain of Earth, space, and environmental sciences to identify appropriate repositories where they can deposit their research data and to promote practices that implement the FAIR Principles, encouraging progress toward sharing data that are findable, accessible, interoperable, and reusable. Requirements for the design of the tool were gathered through a series of workshops and working groups as a part of the Enabling FAIR Data initiative led by the American Geophysical Union that included the development of a decision tree that researchers may follow in selecting a data repository, interviews with domain repository managers, and usability testing. The tool is hosted on the web by DataCite and enables a researcher to query all data repositories by keyword or to view a list of domain repositories that accept data for deposit, support open access, and provide persistent identifiers. Metadata records from the re3data.org registry of research data repositories and the returned results highlight repositories that have achieved trustworthy digital repository certification through a formal procedure such as the CoreTrust Seal. © Springer Nature Switzerland AG 2019.","Data facilities; FAIR principles; Geosciences; Recommender systems; Repositories; Research data management","Decision trees; Deposits; Digital libraries; Earth (planet); Environmental engineering; Open access; Recommender systems; Trees (mathematics); American Geophysical Union; Data repositories; Digital repository; Environmental science; FAIR principles; Geosciences; Repositories; Research data managements; Information management","","","","","Lunds Universitet","Enabling FAIR Data was made possible by a grant from the Laura and John Arnold Foundation to the American Geophysical Union; program manager, Shelley Stall. Sabbatical support for Michael Witt was provided in part by the Pufendorf Institute of Advanced Studies at Lund University.","Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016); Cruse P., Servilla M., Workflow Recommendations for Enabling FAIR Data in the Earth, Space, and Environmental Sciences, (2018); Buddenbohm S., de Jong M., Priddy M., Moranville Y., Ribbe P., Open Data in the Humanities Platform: Humanities at Scale: Evolving the DARIAH ERIC, DARIAH, (2017); 2012-Space Data and Information Transfer Systems – Audit and Certification of Trustworthy Digital Repositories, (2012); Core Trustworthy Data Repositories Extended Guidance, (2018); Witt M., Co-designing, co-developing, and co-implementing an institutional data repository service, J. Libr. Adm., 52, 2, pp. 172-188, (2012); Whyte A., Where to Keep Research Data: DCC Checklist for Evaluating Data Repositories Version 1.1, (2016); Plante R., Witt M., Interview Questions for Determining Data Repository FAIR Compliance, (2018); Rucknagel J., Et al., Metadata Schema for the Description of Research Data Repositories: Version 3.0, (2015); Nielsen J., 10 Heuristics for User Interface Design, Nielsen Norman Group, (1995)","M. Witt; Purdue University, West Lafayette, United States; email: mwitt@purdue.edu","Manghi P.; Candela L.; Silvello G.","Springer Verlag","","15th Italian Research Conference on Digital Libraries, IRCDL 2019","31 January 2019 through 1 February 2019","Pisa","223069","18650929","978-303011225-7","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85060766346"
"Tang H.; Gao K.; Zhou Y.; Zheng W.; Yang X.; Zhao J.","Tang, Haijing (55819011100); Gao, Keyan (57207318233); Zhou, Yangdong (57205019782); Zheng, Wenhao (57205587443); Yang, Xu (57697579500); Zhao, Jinfeng (57205572297)","55819011100; 57207318233; 57205019782; 57205587443; 57697579500; 57205572297","Adopting data analysis and visualization technology to construct clinical research data management and analysis system","2018","ACM International Conference Proceeding Series","","","","49","53","4","0","10.1145/3301761.3301767","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064838869&doi=10.1145%2f3301761.3301767&partnerID=40&md5=92c1918070f39e53eda4271965edc3eb","Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China","Tang H., Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China; Gao K., Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China; Zhou Y., Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China; Zheng W., Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China; Yang X., Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China; Zhao J., Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China","With the development of information technology, information systems have been widely used in medical institutions, and more and more clinical research data has been digitized, which provides the possibility to carry out clinical research with data as the source. However, the complexity and multi-dimensionality of clinical data make medical scientists' progress slow, and comprehensive use of various big data technologies is needed to help improve the efficiency of clinical research. Visualization technology can display data in an intuitive and easy-to-read way, helping medical researchers understand data, while parallel computing can greatly improve computing efficiency. Therefore, this paper explores the application strategies of data analysis technology and visualization technology in the management and analysis of clinical research data, and builds a set of clinical research data management analysis system, which combines various technologies to help effectively promotemedical clinical. © 2018 AssociationforComputingMachinery.","Clinical data; Data analysis; Visualization","Clinical research; Data handling; Data reduction; Data visualization; Efficiency; Electronic commerce; Flow visualization; Information analysis; Medical information systems; Visualization; Application strategies; Clinical data; Computing efficiency; Medical institutions; Medical researchers; Research data managements; Various technologies; Visualization technologies; Information management","","","","","","","Yanbo X., Yang G., Pan J., Construction of specialist clinical information systembased on clinical data center[J], Electronic Technology and Software Engineering, 16, pp. 206-207, (2015); Yifu W., Chuan L., Design and implementation of clinical medical research system based on data mining[J], Sichuan Journal of Physiological Sciences, 38, 2, pp. 93-95, (2016); Feifei W., Design and Implementation of a Scalable Clinical Data Center System [D], (2017); Hongliang C., Design and Implementation of Electronic Medical Record System Based on CDR Platform [D], (2014); Jieying F., Research and System Implementation of Clinical Document Structure Processing [D], (2016); Zhiwei S., Design and Implementation of Clinical Document Structure System Based on Cloud Service [D], (2017); Chunxi W., Research and Implementation of Medical Data Mining Visualization System [D], (2017); Kebin J., Hanzhen L., Ye Y., Application of data mining based on apriori algorithm in mobile medical system[J], Journal of Beijing University of Technology, 43, 3, pp. 394-401, (2017); Wei Z., Yu F., Haibin W., Et al., Design and implementation of clinical heart sound management diagnosis system based on SSH framework[J], Modern Electronic Technology, 39, 21, pp. 145-149, (2016); Wei W., Data transmission encryption technology based on clinical image information system[J], Fujian Computer, 32, 4, (2016)","X. Yang; Sch. of Comp. Sci. and Tech, Beijing Institute of Technology, Beijing, 100081, China; email: yangxu@tsinghua.edu.cn","","Association for Computing Machinery","","2nd International Conference on Software and e-Business, ICSEB 2018","18 December 2018 through 20 December 2018","Zhuhai","147476","","978-145036127-9","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-85064838869"
"Petters J.L.; Brooks G.C.; Smith J.A.; Haas C.A.","Petters, Jonathan L. (57214762189); Brooks, George C. (57209984792); Smith, Jennifer A. (56609449500); Haas, Carola A. (7202619970)","57214762189; 57209984792; 56609449500; 7202619970","The impact of targeted data management training for field research projects - A case study","2019","Data Science Journal","18","1","43","","","","7","10.5334/dsj-2019-043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073341198&doi=10.5334%2fdsj-2019-043&partnerID=40&md5=ac780c06260c5131ec714058d943a414","Data Services, University Libraries, Virginia Tech, Blacksburg, VA, United States; Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, United States; Department of Environmental Science and Ecology, The University of Texas at San Antonio, San Antonio, TX, United States","Petters J.L., Data Services, University Libraries, Virginia Tech, Blacksburg, VA, United States; Brooks G.C., Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, United States; Smith J.A., Department of Environmental Science and Ecology, The University of Texas at San Antonio, San Antonio, TX, United States; Haas C.A., Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, United States","We present a joint effort at Virginia Tech between a research group in the Department of Fish and Wildlife Conservation and Data Services in the University Libraries to improve data management for long-term ecological field research projects in the Florida Panhandle. Consultative research data management support from Data Services in the University Libraries played an integral role in the development of the training curriculum. Emphasizing the importance of data quality to the field workers at the beginning of this training curriculum was a vital part of its success. Also critical for success was the research group’s investment of time and effort to work with field workers and improve data management systems. We compare this case study to three others in the literature to compare and contrast data management processes and procedures. This case study serves as one example of how targeted training and efforts in data and project management for a research project can lead to substantial improvements in research data quality. © 2019 The Author(s).","Data management; Ecology; Field research; Libraries; Wildlife conservation","Animals; Conservation; Curricula; Ecology; Human resource management; Libraries; Project management; Research and development management; Data management system; Field research; Management process; Management training; Research data managements; Training curriculum; University libraries; Wildlife conservation; Information management","","","","","Virginia Tech University Libraries","We thank Kelly Jones and Steve Goodman (current and former research associates at Virginia Tech) for bringing the field crew perspective to campus and leading the implementation of the training of the field workers, and thank Brandon Rincon and Vivian Porter for ongoing efforts to improve the data acquisition and management process. We thank Mark Parsons of Rensselaer Polytechnic Institute for his encouragement to publish this case study. We also thank Mark, Nicholas Caruso (currently in Carola Haas’ research group), and five anonymous reviewers for comments that led to substantial improvements of this manuscript. Publication was funded by Virginia Tech University Libraries through the VT Open Access Subvention Fund.","Brase J., Socha Y., Callaghan S., Borgman C., Uhlir P., Caroll B., Data Citation: Principles and Practice, Research Data Management: Practical Strategies for Information Professionals, pp. 167-186, (2014); Bryant R., Lavoie B., Malpas C., A Tour of the Research Data Management (RDM) Service Space, (2017); Burnette H.M., Williams S.C., Imker H.J., From Plan to Action: Successful Data Management Plan Implementation in a Multidisciplinary Project, Journal of eScience Librarianship, 5, 1, (2016); Curdt C., Supporting the Interdisciplinary, Long-Term Research Project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services, Data Science Journal, 18, 1, (2019); DataONE Education Module: Why Data Management?, (2016); DataONE Education Module: Data Entry and Manipulation, (2016); DataONE Education Module: Data Quality Control and Assurance, (2016); DataONE Education Module: Metadata, (2016); Fearon D., Gunia B., Pralle B.E., Lake S., Sallans A.L., Research Data Management Services, (2013); Gilley A., Gilley J.W., McMillan H.S., Organizational change: Motivation, communication, and leadership effectiveness, Performance Improvement Quarterly, 21, 4, pp. 75-94, (2009); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); NSF Grant Proposal Guide, (2011); Ogier A.L., Brown A.M., Petters J., Hilal A., Porter N., Enhancing Collaboration Across the Research Ecosystem: Using Libraries as Hubs for Discipline-Specific Data Experts, (2018); Parsons M.A., Brodzik M.J., Rutter N.J., Data management for the Cold Land Processes Experiment: Improving hydrological science, Hydrological Processes, 18, 18, pp. 3637-3653, (2004); Petters J.L., Haas C.A., Brooks G., Smith J., Eglin AFB Field Projects Data Management Training Curriculum, (2017); Tenopir C., Allard S., Sinha P., Pollock D., Newman J., Dalton E., Baird L., Et al., Data management education from the perspective of science educators, International Journal of Digital Curation, 11, 1, pp. 232-251, (2016); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Teperek M., Cruz M.J., Verbakel E., Bohmer J.K., Dunning A., Data Stewardship-addressing disciplinary data management needs, (2018); Whitlock M.C., Data archiving in ecology and evolution: Best practices, Trends in Ecology and Evolution, 26, 2, pp. 61-65, (2011); Wittenberg J., Sackmann A., Jaffe R., Situating Expertise in Practice: Domain-Based Data Management Training for Liaison Librarians, The Journal of Academic Librarianship, 44, 3, pp. 323-329, (2018); Yin R.K., Case Study Research: Design and Methods, (2009)","J.L. Petters; Data Services, University Libraries, Virginia Tech, Blacksburg, United States; email: jpetters@vt.edu","","Ubiquity Press","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85073341198"
"Shelly M.; Jackson M.","Shelly, Marita (12243664200); Jackson, Margaret (7404069045)","12243664200; 7404069045","Research data management compliance: is there a bigger role for university libraries?","2018","Journal of the Australian Library and Information Association","67","4","","394","410","16","11","10.1080/24750158.2018.1536690","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057961210&doi=10.1080%2f24750158.2018.1536690&partnerID=40&md5=6df1023fca19be72c4bb8ce6d042749e","University Library and College of Business, RMIT University, Melbourne, Australia","Shelly M., University Library and College of Business, RMIT University, Melbourne, Australia; Jackson M., University Library and College of Business, RMIT University, Melbourne, Australia","This article explores how 13 Australian universities are assisting their researchers to manage the growing expectation to make research data more accessible. It identifies which university groups are supporting staff with research data management (RDM) activities and queries whether university libraries might have a bigger role to play in this space. We found that there was not a consistent approach to RDM at the 13 universities and that while there was generally strong encouragement to store research data securely during and after the project, there was overall a lack of practical support in how to undertake this activity. From our findings, a question of whether library staff have the appropriate experience, training and professional development to enable academic libraries in Australia to expand their RDM role arises and warrants further research. © 2018, © Australian Library & Information Association 2018.","Academic libraries; Australian universities; open research data; RDM activities; research data; research data management; training","","","","","","","","Implementing FAIR data principles: The role of libraries, (2017); Research data management policy, (2017); NCRIS committee, strategic roadmap, 2005, (2006); The FAIR data principles; Discovery program funding rules, (2017); Research data management, (2018); Barbrow S., Brush D., Goldman J., Research data management and services resources for novice data librarians, College and Research Libraries News, 78, 5, (2017); Bird C., Continued adventures in open access: 2009 perspective, Learned Publishing, 23, pp. 107-116, (2010); Bobrow M., Balancing privacy with public benefit, (2013); Bryant R., Lavoie B., Malpas C., The realities of research data management part one a tour of the research data management (RDM) service space, (2017); Read the Budapest open access initiative, (2002); Chambers C., The seven deadly sins of psychology: a manifesto for reforming the culture of scientific practice, (2017); Cheshire L., Broom A., Emmison M., Archiving qualitative data in Australia: An introduction, Australian Journal of Social Issues, 44, 3, pp. 239-254, (2009); Childs S., McLeod J., Lomas E., Cook G., Opening research data: Issues and opportunities, Records Management Journal, 24, 2, pp. 142-162, (2014); Corti L., The European landscape of qualitative social research archives: Methodological and practical issues, Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 12, 3, (2011); Researcher needs survey conducted by University of Newcastle library, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developing in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Dunning A., de Smaele M., Bohmer J., Are the FAIR principles fair?, International Journal of Digital Curation, 12, 1, pp. 177-194, (2017); Guidelines on open access to scientific publications and research data in horizon 2020, (2013); Fitzgerald A., Pappalardo K., Building the infrastructure for data access and reuse in collaborative research: An analysis of the legal context, (2007); Fitzgerald A., Pappalardo K., Austin A., Practical data management: A legal and policy guide, (2008); Management of research data and primary materials policy, (2016); Groenewegen D., Yesterday and today: Reflecting on past practice to help build and strengthen the researcher partnership at Monash University, New Review of Academic Librarianship, 23, 2-3, pp. 171-184, (2017); Policies for sharing research data in social sciences and humanities: A survey about research funders’ data policies, (2014); Kidwell M.C., Lazarevic L.B., Baranski E., Hardwicke T.E., Piechowski S., Falkenberg L.S., Nosek B.A., Badges to acknowledge open practices: A simple, low cost, effective method for increasing transparency, Plos Biology, 14, 5, pp. 1-15, (2016); Review of the Australian code for the responsible conduct of research, (2016); Australian code for the responsible conduct of research, (2018); National Health Medical Research Council open access policy, (2018); National statement on ethical conduct in human research, (2007); Nuijten M.B., Borghuis J., Veldkamp C.L.S., Dominguez-Alvarez L., van Assen M.A.L.M., Wicherts J.M., Journal data sharing policies and statistical reporting inconsistencies in psychology, (2017); Declaration on access to research data from public funding, (2004); OECD principles and guidelines for access to research data from public funding, (2007); Patel D., Research data management: A conceptual framework, Library Review, 65, 4-5, pp. 226-241, (2016); Pinfield S., Cox A.M., Rutter S., Mapping the future of academic libraries: A New paragraph: Use this style when you need to begin a new paragraph, Report for sconul, (2017); Pryor G., A patchwork of change, Delivering research data management services: Fundamentals of good practice, pp. 1-20, (2014); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice; 23 things: Libraries for research data, (2015); Research data management policy process, (2016); Schroeder R., e-research infrastructures and open science: Towards a new system of knowledge production?, Prometheus, 25, 1, pp. 1-17, (2007); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program: Electronic Library and Information Systems, 49, 4, pp. 440-460, (2015); Data management, (2017); What does open access mean, (2017); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future, (2012); Thomas C.V.L., Urban R.J., What do data librarians think of the MLIS? Professionals’ perception of knowledge transfer, trends and challenges, College & Research Libraries, 79, 3, pp. 401-423, (2018); Open access policy, (2017); Research data management procedure, (2016); Research data management & discovery, (2018); Whyte A., Tedds J., Making the case for research data management, (2011); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., Comment: The FAIR guiding principles for scientific data management and stewardship, (2016)","M. Shelly; University Library and College of Business, RMIT University, Melbourne, Australia; email: marita.shelly@rmit.edu.au","","Australian Library and Information Association","","","","","","24750158","","","","English","J. Aust. Libr. Inf. Assoc.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85057961210"
"Latham B.","Latham, Bethany (35077098600)","35077098600","Research Data Management: Defining Roles, Prioritizing Services, and Enumerating Challenges","2017","Journal of Academic Librarianship","43","3","","263","265","2","23","10.1016/j.acalib.2017.04.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019003901&doi=10.1016%2fj.acalib.2017.04.004&partnerID=40&md5=6524dabd368354675cfeeb05ae2a2d7c","Jacksonville State University, United States","Latham B., Jacksonville State University, United States","[No abstract available]","","","","","","","","","Akers K.G., Going beyond data management planning, College & Research Libraries News, 75, 8, pp. 435-436, (2014); Cox A., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library and Information Science Research, 38, pp. 319-326, (2016); Cox A., Verbaan E., Sen B., A new role for academic librarians? Research data management, Multimedia Information & Technology, 38, 4, pp. 29-30, (2012); Engineering and Physical Sciences Research Council, Engineering and physical sciences research council website, (2017); Rolando L., Carlson J., Hswe P., Parham S.W., Westra B., Whitmire A.L., Data management plans as a research tool, Bulletin of the Association for Information Science and Technology, 41, 6, pp. 43-44, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, pp. 84-90, (2014); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: a tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017)","","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85019003901"
"Eberhard I.; Kraus W.","Eberhard, Igor (57201190001); Kraus, Wolfgang (57214301131)","57201190001; 57214301131","The elephant in the room: Challenges for ethnographic data management repositories; [Der elefant im raum. Ethnographisches forschungsdatenmanagement als herausforderung für repositorien]","2018","VOEB-Mitteilungen","71","1","","41","52","11","4","10.31263/voebm.v71i1.2018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054139136&doi=10.31263%2fvoebm.v71i1.2018&partnerID=40&md5=f2980b373440b32f9a02a29af799f8e5","Universität Wien, Institut für Kultur- und Sozialanthropologie, Austria","Eberhard I., Universität Wien, Institut für Kultur- und Sozialanthropologie, Austria; Kraus W., Universität Wien, Institut für Kultur- und Sozialanthropologie, Austria","Today’s scientific research funding is often inextricably linked to data management which in turn presupposes data accessibility and sustainability. Common criteria are FAIR Data Principles and open access dissemination of research data. Yet, the University of Vienna-based pilot project ‘Ethnographic data archiving’ highlights the limitations which arise in the process of establishing ethnographic data management repositories. These do not only result from practical and subject-specific challenges but also concern current legal and ethical requirements to organizing repositories. The article aims to discuss some of the challenges and limitations of creating and sustaining repositories for ethnographic fieldwork data. © 2018, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Ethnographic data archiving; Ethnography; Repositories; Research data management; Research ethics; Social and cultural anthropology","","","","","","","","Ethnografie: Die Praxis Der Feldforschung. 2. Aufl, (2015); Caton H., The Samoa Reader: Anthropologists Take Stock, (1990); (2008); (2017); Imeri S., Open Data? Zum Umgang mit Forschungsdaten in den ethnologischen Fächern, in Kratzke, Jonas und Heuveline, Vincent (Hg.):, E-Science-Tage, (2017); TCPS 2 – Chapter 9: Research Involving the First Nations, Inuit and Métis Peoples of Canada, (2018); Forschungsdatenmanagement in Den Sozial-, Verhaltens- Und Wirtschaftswissenschaften: Orientierungshilfen für Die Beantragungund Begutachtung Datengenerierender Und Datennutzender Forschungsprojekte, (2018); Workshop „Archivierung Und Zugang Zu Qualitativen Daten, (2018); Smioski A., Wegweiser Qualitative Datenarchivierung. Infrastruktur, Datenakquise, Dokumentation Und Weitergabe, Sws-Rundschau, 51, 2, pp. 219-238, (2011); Smioski A., Archivierung Und Sekundärnutzung Qualitativer Daten, (2012); Sterzer W., Imeri S., Harbeck M., (2018); (2016)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85054139136"
"Agnew W.; Fischer M.; Foster I.; Chard K.","Agnew, William (57193702693); Fischer, Michael (57193699314); Foster, Ian (35572232000); Chard, Kyle (9132950200)","57193702693; 57193699314; 35572232000; 9132950200","An Ensemble-Based Recommendation Engine for Scientific Data Transfers","2017","Proceedings of DataCloud 2016: 7th International Workshop on Data-Intensive Computing in the Clouds - Held in conjunction with SC 2016: The International Conference for High Performance Computing, Networking, Storage and Analysis","","","7845276","9","16","7","0","10.1109/DataCloud.2016.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015910336&doi=10.1109%2fDataCloud.2016.005&partnerID=40&md5=5e20ebc95b1074d4b352c200e09f3a80","Georgia Inst. of Tech, 30332 North Ave NW, Atlanta, GA, United States; University of Wisconsin-Milwaukee, Milwaukee, WI, United States; University of Chicago, 5801 S Ellis Ave, Chicago, IL, United States","Agnew W., Georgia Inst. of Tech, 30332 North Ave NW, Atlanta, GA, United States; Fischer M., University of Wisconsin-Milwaukee, Milwaukee, WI, United States; Foster I., University of Chicago, 5801 S Ellis Ave, Chicago, IL, United States; Chard K., University of Chicago, 5801 S Ellis Ave, Chicago, IL, United States","Big data scientists face the challenge of locating valuable datasets across a network of distributed storage locations. We explore methods for recommending storage locations ('endpoints') for users based on a range of prediction models including collaborative filtering and heuristics that consider available information such as user, institution, access history, endpoint ownership, and endpoint usage. We combine the strengths of these models by training a deep recurrent neural network on their predictions. Collectively we show, via analysis of historical usage from the Globus research data management service, that our approach can predict the next storage location accessed by users with 80.3% and 95.3% accuracy for top-1 and top-3 recommendations, respectively. Additionally, our heuristics can predict the endpoints that users will use in the future with over 75% precision and recall. © 2016 IEEE.","","Collaborative filtering; Data transfer; Digital storage; Forecasting; Heuristic methods; Information filtering; Information management; Location; Recurrent neural networks; Access history; Distributed storage; Precision and recall; Prediction model; Research data managements; Scientific data; Storage location; Big data","","","","","National Science Foundation, NSF, (NSF-1461260); U.S. Department of Energy, USDOE, (DE-AC02-06CH11357)","This work was supported in part by National Science Foundation grant NSF-1461260 (BigDataX REU) and Department of Energy contract DE-AC02-06CH11357.","Ranking Factorization Recommender; Allen B., Bresnahan J., Childers L., Foster I., Kandaswamy G., Kettimuthu R., Kordas J., Link M., Martin S., Pickett K., Tuecke S., Software as a service for data scientists, Communications of the ACM, 55, 2, pp. 81-88, (2012); Altintas I., Berkley C., Jaeger E., Jones M., Ludascher B., Mock S., Kepler: An extensible system for design and execution of scientific workflows, 16th International Conference on Scientific and Statistical Database Management, pp. 423-424, (2004); Ansari A., Essegaier S., Kohli R., Internet recommendation systems, Journal of Marketing Research, 37, 3, pp. 363-375, (2000); Cao J., Jarvis S.A., Saini S., Nudd G.R., Gridflow: Workflow management for grid computing, 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 198-205, (2003); Chard K., Lidman M., McCollam B., Bryan J., Ananthakrishnan R., Tuecke S., Foster I., Globus Nexus: A platform-as-A-service provider of research identity, profile, group management, Future Generation Computer Systems, 56, pp. 571-583, (2016); Chard K., Pruyne J., Blaiszik B., Ananthakrishnan R., Tuecke S., Foster I., Globus data publication as a service: Lowering barriers to reproducible science, 11th IEEE International Conference on E-Science, pp. 401-410, (2015); Chard K., Tuecke S., Foster I., Efficient and secure transfer, synchronization, sharing of big data, IEEE Cloud Computing, 1, 3, pp. 46-55, (2014); Churches D., Gombas G., Harrison A., Maassen J., Robinson C., Shields M., Taylor I., Wang I., Programming scientific and distributed workflow with Triana services, Concurrency and Computation: Practice and Experience, 18, 10, pp. 1021-1037, (2006); Deelman E., Gannon D., Shields M., Taylor I., Workflows and e-science: An overview of workflow system features and capabilities, Future Generation Computer Systems, 25, 5, pp. 528-540, (2009); Doraimani S., Iamnitchi A., File grouping for scientific data management: Lessons from experimenting with real traces, 17th International Symposium on High Performance Distributed Computing, pp. 153-164, (2008); Foster I., Globus Online: Accelerating and democratizing science through cloud-based services, IEEE Internet Computing, 15, 3, (2011); Glorot X., Bordes A., Bengio Y., Deep sparse rectifier neural networks, 14th International Conference on Artificial Intelligence and Statistics, (2011); Graves A., Generating Sequences with Recurrent Neural Networks, (2013); Jung E.-S., Kettimuthu R., Vishwanath V., Toward optimizing disk-to-disk transfer on 100g networks, IEEE International Conference on Advanced Networks and Telecommunications Systems, pp. 1-6, (2013); Karpathy A., The Unreasonable Effectiveness of Recurrent Neural Networks, (2015); Kettimuthu R., Vardoyan G., Agrawal G., Sadayappan P., Foster I., An elegant sufficiency: Load-aware differentiated scheduling of data transfers, International Conference for High Performance Computing, Networking, Storage and Analysis, (2015); Lee C., Abe H., Hirotsu T., Umemura K., Predicting network throughput for grid applications on network virtualization areas, 1st International Workshop on Network-aware Data Management, pp. 11-20, (2011); Low Y., Bickson D., Gonzalez J., Guestrin C., Kyrola A., Hellerstein J.M., Distributed graphlab: A framework for machine learning and data mining in the cloud, Proceedings of the VLDB Endowment, 5, 8, pp. 716-727, (2012); Swany M., Wolski R., Multivariate resource performance forecasting in the Network Weather Service, ACM/IEEE Conference on Supercomputing, (2002); Tuecke S., Ananthakrishnan R., Chard K., Lidman M., McCollam B., Foster I., Globus Auth: A research identity and access management platform, 12th IEEE International Conference on E-Science, (2016); Vazhkudai S., Schopf J.M., Predicting sporadic grid data transfers, 11th IEEE International Conference on High Performance Distributed Computing, pp. 188-196, (2002); Wolski R., Experiences with predicting resource performance on-line in computational grid settings, SIGMETRICS Performance Evaluation Review, 30, 4, pp. 41-49, (2003); Yu J., Buyya R., A taxonomy of workflow management systems for grid computing, Journal of Grid Computing, 3, 3-4, pp. 171-200, (2005); Yun D., Wu C.Q., Rao N.S., Settlemyer B.W., Lothian J., Kettimuthu R., Vishwanath V., Profiling transport performance for big data transfer over dedicated channels, International Conference on Computing, Networking and Communications, pp. 858-862, (2015); Zhang J., Tan W., Alexander J., Foster I., Madduri R., Recommend-as-you-go: A novel approach supporting services-oriented scientific workflow reuse, IEEE International Conference on Services Computing, pp. 48-55, (2011); Zheng Z., Ma H., Lyu M.R., King I., Wsrec: A collaborative filtering based web service recommender system, IEEE International Conference on Web Services (ICWS), pp. 437-444, (2009); Zhou Y., Wilkinson D., Schreiber R., Pan R., Large-scale parallel collaborative filtering for the Netflix prize, 4th International Conference on Algorithmic Aspects in Information and Management, pp. 337-348, (2008)","","","Institute of Electrical and Electronics Engineers Inc.","","7th International Workshop on Data-Intensive Computing in the Clouds, DataCloud 2016","14 November 2016","Salt Lake City","126434","","978-150906158-7","","","English","Proc. DataCloud: Int. Workshop Data-Intensive Comput. Clouds - Held conjunction SC: Int. Conf. High Perform. Comput., Netw., Storage Anal.","Conference paper","Final","","Scopus","2-s2.0-85015910336"
"Price S.; Boateng R.; Loader B.; Suleman H.; Hall W.; Earl G.; Tiropanis T.; Tinati R.; Wang X.; Gandolfi E.; Denemark D.; Schmidt M.; Billings M.; Tsoi K.; Xu J.; Birkin M.; Gatewood J.; Groflin A.; Spanakis G.; Wessels B.","Price, Simon (56270406000); Boateng, Richard (23003527600); Loader, Brian (9332771700); Suleman, Hussein (6602649208); Hall, Wendy (55326967000); Earl, Graeme (34879699600); Tiropanis, Thanassis (16053715500); Tinati, Ramine (55217808400); Wang, Xin (56728940800); Gandolfi, Eleonora (57205501943); Denemark, David (6602342613); Schmidt, Maxine (57205501336); Billings, Marilyn (56851515900); Tsoi, Kelvin (16065259000); Xu, Jie (57050877700); Birkin, Mark (6701762517); Gatewood, Jane (57205503991); Groflin, Alexander (56926820400); Spanakis, Gerasimos (35749092700); Wessels, Bridgette (10046373200)","56270406000; 23003527600; 9332771700; 6602649208; 55326967000; 34879699600; 16053715500; 55217808400; 56728940800; 57205501943; 6602342613; 57205501336; 56851515900; 16065259000; 57050877700; 6701762517; 57205503991; 56926820400; 35749092700; 10046373200","Worldwide universities network (WUN) web observatory:Applying lessons from the web to transform the research data ecosystem","2017","26th International World Wide Web Conference 2017, WWW 2017 Companion","","","","1665","1667","2","2","10.1145/3041021.3051691","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048407525&doi=10.1145%2f3041021.3051691&partnerID=40&md5=a5289ba3ccefbe018576bae7cb3ece7f","University of Bristol, United Kingdom; University of Ghana, Ghana; University of York, United Kingdom; University of Cape Town, South Africa; University of Southampton, United Kingdom; University of Western Australia, Australia; University of Massachusetts Amherst, United States; Chinese University of Hong Kong, China; University of Leeds, United Kingdom; University of Rochester, United States; University of Basel, Switzerland; Universiteit Maastricht, Netherlands; Newcastle University, United Kingdom","Price S., University of Bristol, United Kingdom; Boateng R., University of Ghana, Ghana; Loader B., University of York, United Kingdom; Suleman H., University of Cape Town, South Africa; Hall W., University of Southampton, United Kingdom; Earl G., University of Southampton, United Kingdom; Tiropanis T., University of Southampton, United Kingdom; Tinati R., University of Southampton, United Kingdom; Wang X., University of Southampton, United Kingdom; Gandolfi E., University of Southampton, United Kingdom; Denemark D., University of Western Australia, Australia; Schmidt M., University of Massachusetts Amherst, United States; Billings M., University of Massachusetts Amherst, United States; Tsoi K., Chinese University of Hong Kong, China; Xu J., University of Leeds, United Kingdom; Birkin M., University of Leeds, United Kingdom; Gatewood J., University of Rochester, United States; Groflin A., University of Basel, Switzerland; Spanakis G., Universiteit Maastricht, Netherlands; Wessels B., Newcastle University, United Kingdom","The ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is time-consuming; sharing 'live' and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.","Data Science; Research Data Management; Social Machines","Data Science; Information management; Observatories; World Wide Web; Current generation; Global challenges; Inter-disciplinary researches; International networks; Research data; Research data managements; Sensitive datas; Streaming data; Data Sharing","","","","","OECD; United Nations; World Bank Group; World Health Organization","The Worldwide Universities Network (WUN) is the most active global higher education and research network with 90 active research initiatives, engaging over 2,000 researchers and students collaborating on a diverse range of projects. These initiatives are committed to addressing some of the world’s most urgent challenges and are supported by prolific partners such as the United Nations Foundation, World Bank, OECD and World Health Organization. WUN research focuses on four themes that address the following globally significant challenges.","Hall W., Tiropanis T., Web evolution and web science, Computer Networks, 56, 18, pp. 3859-3865, (2012); Phethean C., Simperl E., Tiropanis T., Tinati R., Hall W., The role of data science in web science, IEEE Intelligent Systems, 31, 3, pp. 102-107, (2016); Tiropanis T., Hall W., Hendler J., De Larrinaga C., The web observatory: A middle layer for broad data, Big Data, 2, 3, pp. 129-133, (2014); Tiropanis T., Hall W., Shadbolt N., De Roure D., Contractor N., Hendler J., The web science observatory, IEEE Intelligent Systems, 28, 2, pp. 100-104, (2013)","","","International World Wide Web Conferences Steering Committee","Bankwest; Curtin University; Edith Cowan University (ECU); et al.; Murdoch University; The University of Western Australia","26th International World Wide Web Conference, WWW 2017 Companion","3 April 2017 through 7 April 2017","Perth","143622","","978-145034914-7","","","English","Int. World Wide Web Conf. , WWW Companion","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85048407525"
"Bugaje M.; Chowdhury G.","Bugaje, Maryam (57197719494); Chowdhury, Gobinda (7006058701)","57197719494; 7006058701","Identifying design requirements of a user-centered research data management system","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11279 LNCS","","","335","347","12","2","10.1007/978-3-030-04257-8_35","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057246625&doi=10.1007%2f978-3-030-04257-8_35&partnerID=40&md5=c719d49b6c25ed5b27d32a5b0ec6a9a4","Department of Computer and Information Sciences, Northumbria University, Newcastle, United Kingdom","Bugaje M., Department of Computer and Information Sciences, Northumbria University, Newcastle, United Kingdom; Chowdhury G., Department of Computer and Information Sciences, Northumbria University, Newcastle, United Kingdom","Research data repositories perform many useful functions, the key ones being the storage of research datasets, and making the same discoverable for potential reuse. Over the years, various criteria for assessing the user-centeredness of information systems have been developed and standards have gradually been improved. However, there has been less development in case of research data management (RDM) systems. By means of a combination of user-focused research methods viz. questionnaire surveys, face-to-face interviews, a systematic appraisal of existing services and a technical experiment, we have sought to understand the meaning of user-centeredness pertaining to research data repositories, and identify some key indicators of it. We have furthermore translated our findings into design requirements based on which we propose to develop and test a prototype of a user-centered RDM system. This paper reports on how we identified the design requirements that would make the RDM systems more user-centered. © Springer Nature Switzerland AG 2018.","Information retrieval; Metadata; Research data management; Research data repositories; Scientific data; User-Centered design","Digital libraries; Digital storage; Information retrieval; Metadata; Research and development management; Surveys; User centered design; Face-to-face interview; Key indicator; Questionnaire surveys; Research data; Research data managements; Scientific data; Technical experiments; User-centered; Information management","","","","","","","Arend D., Lange M., Chen J., Et al., E! DAL-a framework to store, share and publish research data, BMC Bioinform, 15, 1, (2014); Curdt C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DB project database, Program Electron. Libr. Inf. Syst., 49, 4, pp. 494-512, (2015); Cox A., Pinfield S., Research data management and libraries: Current activities and future priorities, J. Libr.; Amorim R., Castro J., da Silva R.J., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univers. Access Inf. Soc., 16, 4, pp. 851-862, (2016); Patel D., Research data management: A conceptual framework, Libr. Rev., 65, 4-5, pp. 226-241, (2016); Perrino T., Et al., Advancing Science Through Collaborative Data Sharing and Synthesis, Perspect. Psychol. Sci., 8, 4, pp. 433-444, (2013); (2012); Borgman C., The conundrum of sharing research data, SSRN Electron. J., (2011); Costello M., Motivating online publication of data, Bioscience, 59, 5, pp. 418-427, (2009); Faniel I., Jacobsen T., Reusing scientific data: How earthquake engineering researchers assess the reusability of colleagues’ data, Comput. Support. Coop. Work. (CSCW), 19, 3-4, pp. 355-375, (2010); Carlson J., Demystifying the data interview: Developing a foundation for reference librarians to talk with researchers about their data, Ref. Serv. Rev., 40, 1, pp. 7-23, (2012); Muckschel C., Nieschulze J., Weist C., Sloboda B., Kohler W., Herausforderungen, Probleme Und Lösungsansätze Im Datenmanagement Von Sonderforschungsbereichen. In: Ezai (Elektronische Zeitschrift für Agrarinformatik), 2, pp. 1-16, (2007); Curdt C., Hoffmeister D., Waldhoff G., Jekel C., Bareth G., Scientific research data management for soil-vegetation-atmosphere data – the TR32DB, Int. J. Digit. Curation, 7, 2, pp. 68-80, (2012); | Biomedical and Healthcare Data Discovery and Indexing Ecosystem; Research Data Discovery Service: Laying the Firm Foundations for a Jisc UK Research Data Discovery Service, (2018); Bugaje M., Chowdhury G., Is data retrieval different from text retrieval? An exploratory study, ICADL 2017. LNCS, 10647, pp. 97-103, (2017); Bugaje M., Chowdhury G., Data retrieval = text retrieval?, Iconference 2018. LNCS, 10766, pp. 253-262, (2018); Whyte A., Tedds J., Making the case for research data management, Digital Curation Centre, Dccacuk, (2011); Santos C., Blake J., States D., Supplementary data need to be kept in public repositories, Nature, 438, 7069, (2005); Sallans A., Lake S., Data management assessment and planning tools, Research Data Management: Practical Strategies for Information Professionals, pp. 87-107, (2014); Dumontier M., Gray A., Marshall M., Et al., The health care and life sciences community profile for dataset descriptions, Peerj, 4, (2016); (2016); Boeckhout M., Zielhuis G., Bredenoord A., The FAIR guiding principles for data stewardship: Fair enough?, Eur. J. Hum. Genet., 26, 7, pp. 931-936, (2018); Starr J., Castro E., Crosas M., Et al., Achieving human and machine accessibility of cited data in scholarly publications, Peerj Comput. Sci., 1, (2015); Alsos O.A., Svanaes D., Designing for the secondary user experience, INTERACT 2011. LNCS, 6949, pp. 84-91, (2011); Bugaje M., Chowdhury G., Towards a more user-centered design of research data management (RDM) systems [abstract], Information: Interactions and Impact (I3), pp. 53-55, (2017); Taylor R., Question-negotiation and information seeking in libraries, Coll. Res. Libr., 76, 3, pp. 251-267, (2015); Morris R., Toward a user-centered information service, J. Am. Soc. Inf. Sci, 45, 1, pp. 20-30, (1994); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, J. Am. Soc. Inf. Sci. Technol., 63, 8, pp. 1505-1520, (2012); van Noorden R., Data-sharing: Everything on display, Nature, 500, 7461, pp. 243-245, (2013); Chowdhury G., Walton G., Bugaje M., Research data management: Practices, skills and training needs of university researchers in the UK, 2017 Fifth European Conference on Information Literacy (ECIL), (2017); Bugaje M., Chowdhury G., Disciplinary contexts in research data management: A case-study of three disciplines (accepted contribution), Fifth European Conference on Information Literacy (ECIL), (2018); Borgman C., Big Data, Little Data, No Data, pp. 81-161, (2015); Boru D., Kliazovich D., Granelli F., Bouvry P., Zomaya A.Y., Energy-efficient data replication in cloud computing datacenters, Clust. Comput., 18, 1, pp. 385-402, (2015); Chowdhury G.G., Sustainability of Scholarly Information, (2014); Weber A., Piesche C., Requirements on long-term accessibility and preservation of research results with particular regard to their provenance, ISPRS Int. J. Geo-Inf., 5, (2016); To Share Or Not to Share: Publication and Quality Assurance of Research Data Outputs, (2008); Rumsey S., Jefferies N., Challenges in building an institutional research data catalogue, Int. J. Digit. Curation, 8, 2, pp. 205-214, (2013); Weibel S., The Dublin Core: A simple content description model for electronic resources, Bull. Am. Soc. Inf. Sci. Technol., 24, 1, pp. 9-11, (2005)","G. Chowdhury; Department of Computer and Information Sciences, Northumbria University, Newcastle, United Kingdom; email: gobinda.chowdhury@northumbria.ac.uk","Žumer M.; Hinze A.; Dobreva M.","Springer Verlag","","20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018","19 November 2018 through 22 November 2018","Hamilton","221079","03029743","978-303004256-1","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85057246625"
"Schöpfel J.; Prost H.","Schöpfel, Joachim (14619562900); Prost, Hélène (15069878000)","14619562900; 15069878000","D4Humanities: Deposit of dissertation data in social sciences & humanities – A project in digital humanities","2018","International Conference Series on Grey Literature","2017-October","","","121","126","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045438550&partnerID=40&md5=dacff5e2033d07b765d9ed8b7ad60500","GERiiCO Laboratory, University of Lille, France; CNRS, GERiiCO Laboratory, France","Schöpfel J., GERiiCO Laboratory, University of Lille, France; Prost H., CNRS, GERiiCO Laboratory, France","Following our work on research data and electronic theses and dissertations since 2013, we are conducting a new research project between 2017 and 2018 called D4Humanities with three objectives – to develop the research data management and stewardship on our campus, to gain better insight into the nature of research data in social sciences and humanities and to produce empirical evidence on the development of dissertations. In particular, the project contains three components: 1. Qualitative survey on behaviours and knowledge in the field of research data with 50 scientists from the University of Lille Social Sciences and Humanities Department, with a special focus on the FAIR guiding principles of scientific data management and stewardship. 2. The creation of a workflow for the submission of research data related to PhD dissertations (deposit, preservation and dissemination of data via the NAKALA service Huma-Num) 3. Two conceptual studies on the definition and typology of research data in SSH and on the development of dissertations in the environment of e-Science and Open Science (content, format, structure, requirements). In the following we present some preliminary results, in particular from the survey and from the conceptual studies, in order to enhance the understanding of research data in SSH and of the development of dissertations. Acknowledgment: The project receives funding from the European Institute of Social Sciences and Humanities (MESHS Lille) and from the Regional Council (Conseil Régional Hauts-de-France). We would like to thank the D4Humanities project team for their contribution to the research underlying this paper, in particular Cécile Malleret, Eric Kergosien and Leslie Hyacinthe. © 2018 Oriental Scientific Publishing Company. All rights reserved.","Digital humanities; Humanities; Open access; Open science; PhD dissertations; Research data; Social sciences","Behavioral research; Deposits; Information management; Social sciences; Surveys; Digital humanities; Humanities; Open Access; Open science; PhD dissertations; Research data; Research and development management","","","","","Horizon 2020 Framework Programme, H2020; European Commission, EC; Agence Nationale de la Recherche, ANR","In 2015, we conducted a campus-wide survey at the University of Lille on research data management in social sciences and humanities. The survey received 270 responses, equivalent to 15% of all scientists, scholars, PhD students and administrative and technical staff; all disciplines were represented. The responses showed a wide variety of data, practice and usage; some differences seem related to job status and disciplines. Generally, 20-25% of the sample can be considered as pioneers in data management and sharing, and 25-30% are motivated; only 5-10% appear reluctant to make their data available (Schöpfel & Prost 2016). On the basis of the results of this first survey, we prepared a small qualitative survey with academic “volunteers” on the Lille SSH campus, among researchers and PhD students from various disciplines. We wanted to gain more insight in personal research data management behaviour and data literacy, in particular those contributing to the compliance with the FAIR principles for data management (Wilkinson et al. 2016). The investigation is not over; for the moment, we have conducted 27 interviews with researchers from history, archaeology, literature and language studies, psychology and information sciences. First results and comments: Interest and motivation: finding volunteers on the campus was not easy this time; obviously, for many colleagues RDM is not a “hot topic” to spend one hour or more in a semi-directive interview on data practice and literacy. At least, it does not appear as priority or relevant. Funding agencies: one half of the volunteering respondents (14) has conducted or participated in one or more research projects funded by the European Commission (H2020 program) and/or the French National Research Agency (ANR programs). But only 10 have knowledge of requirements (such as of the H2020 program), guidelines or recommendations for RDM. Privacy: 13 respondents use or produce personal data as defined by the French CNIL commission, or confidential data. 6 submitted a research protocol to the university's ethics committee. Standards, description: 8 participants reported assigning codes to their data, 9 people have already drafted a data management plan, and 5 participants follow standards for describing their data. Dissemination and sharing: data collection, analysis and storage are often carried out by the researcher him/herself or together with the research team. 16 participants agree to share their data with others, which means above all with other colleagues from the project team. 10 participants have already submitted their data to an online server, 2 others intend to do so; only one refuses for security reasons. Need for advice: generally, the respondents need advice on querying databases, formatting and naming data; they seek advice on licensing and legal protection of sensitive data; they want to know more about the services offered by the deposit platforms. So far, they have been seeking advice on RDM not at the library but with people from the IT department (system security, storage) and from the ethics committee. Need for data services: the services requested by the researchers relate mainly to the different aspects related to data storage: to know what data to store, under which formats, on which server, with which guarantees of duration and security. They want to encourage exchanges between researchers and information professionals. So far, we have observed very large differences between disciplines and research domains, but also between research methods and tools in the same field. Some scientists have a long experience with RDM and apply standard and transparent data procedure, even if they do not always call it RDM. This data literacy can mainly be explained by legal issues (privacy laws, especially in psychology, education, sociology, and projects in public health) or ethics rules, less (up to now) by requirements from funding agencies. However, application of standards in RDM remains exceptional, such as data publishing and sharing. We did not encounter significant reluctance or even opposition to RDM and data sharing, but rather ignorance or lack of interest.","Chaudiron S., Maignant C., Schopfel J., Westeel I., Livre Blanc Sur Les Données De La Recherche dans Les Thèses De Doctorat, (2015); Jacquemin B., Prost H., Schopfel J., Severo M., Thiault F., Ouvrir les données de la recherche pour la veille scientifique. Le cas des thèses électroniques, VSST'2013, (2013); Kindling M., Et al., The landscape of research data repositories in 2015: A re3data analysis, D-Lib Magazine, 23, 3-4, (2017); De Mauro A., Greco M., Grimaldi M., A formal definition of big data based on its essential features, Library Review, 65, 3, pp. 122-135, (2016); Paillassard P., Schopfel J., Stock C., How to get a French doctoral thesis, especially when you aren't French, Publishing Research Quaterly, 21, 1, pp. 73-93, (2005); Prost H., Malleret C., Schopfel J., Hidden treasures. Opening data in PhD dissertations in social sciences and humanities, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Prost H., Schopfel J., Les Données De La Recherche En SHS. Une Enquête À L'Université De Lille 3, (2015); Schopfel J., Adding value to electronic theses and dissertations in institutional repositories, D-Lib Magazine, 19, 3-4, (2013); Schopfel J., Prost H., Degrees of secrecy in an open environment. The case of electronic theses and dissertations, ESSACHESS - Journal for Communication Studies, 6, 2, (2013); Schopfel J., Chaudiron S., Jacquemin B., Prost H., Severo M., Thiault F., Open access to research data in electronic theses and dissertations: An overview, Library Hi Tech, 32, 4, pp. 612-627, (2014); Schopfel J., Juznic P., Prost H., Malleret C., Cesarek A., Koler-Povh T., Dissertations and data (keynote address), GL17 International Conference on Grey Literature, (2015); Schopfel J., Prost H., Malleret C., Making data in PhD dissertations reusable for research, 8th Conference on Grey Literature and Repositories, (2015); Schopfel J., Prost H., Piotrowski M., Hilf E.R., Severiens T., Grabbe P., A French-German survey of electronic theses and dissertations: Access and restrictions, D-Lib Magazine, 21, 3-4, (2015); Schopfel J., Kergosien E., Chaudiron S., Jacquemin B., Dissertations as data, ETD2016, (2016); Schopfel J., Prost H., Research data management in social sciences and humanities: A survey at the university of Lille 3 (France), LIBREAS. Library Ideas, 29, pp. 98-112, (2016); Schopfel J., Prost H., Rebouillat V., Research data in current research information systems, CRIS 2016, (2016); Schopfel J., Kergosien E., Prost H., Pour commencer, pourriez-vous définir 'données de la recherche' ?» une tentative de réponse?», Atelier VADOR: Valorisation Et Analyse Des Données De La Recherche, INFORSID 2017, (2017); Schopfel J., Prost H., Malleret C., Research and development in the field of research data and dissertations. The D4Humanities project at the university of Lille (France), 10th Conference on Grey Literature and Repositories, (2017); Schopfel J., Rasuli B., Are electronic theses and dissertations (still) grey literature in a digital age? a FAIR debate, The Electronic Library, 35, 4, (2017); Vompras J., Schirrwagen J., Repository workflow for interlinking research data with grey literature, 8th Conference on Grey Literature and Repositories, (2015); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","TextRelease","Biblioteca Centrale 'G. Marconi', National Research Council of Italy, CNR; et al.; Institute of Information Science and Technologies (ISTI), National Research Council of Italy, CNR; Korea Institute of Science and Technology Information (KISTI); Slovak Centre of Scientific and Technical Information (CVTISR); The New York Academy of Medicine","19th International Conference on Grey Literature: Public Awareness and Access to Grey Literature, GL 2017","23 October 2017 through 24 October 2017","Rome","135094","13862316","978-907748431-9","","","English","Int. Conf. Ser.  Grey Lit.","Conference paper","Final","","Scopus","2-s2.0-85045438550"
"Klas C.-P.; Hopt O.","Klas, Claus-Peter (6603013670); Hopt, Oliver (55480889500)","6603013670; 55480889500","An operationalized ddi infrastructure to document, publish, preserve and search social science research data","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11057 LNCS","","","94","99","5","0","10.1007/978-3-030-00066-0_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053822136&doi=10.1007%2f978-3-030-00066-0_8&partnerID=40&md5=c6fe6e15765ad7ee5c25178c2931e5bd","GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany","Klas C.-P., GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany; Hopt O., GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany","The social sciences are here in a very privileged position, as there is already an existing meta-data standard defined by the Data Documentation Initiative (DDI) to document research data such as empirical surveys. But even so the DDI standard already exists since the year 2000, it is not widely used because there are almost no (open source) tools available. In this article we present our technical infrastructure to operationalize DDI, to use DDI as living standard for documentation and preservation and to support the publishing process and search functions to foster re-use and research. The main contribution of this paper is to present our DDI architecture, to showcase how to operationalize DDI and to show the efficient and effective handling and usage of complex meta-data. The infrastructure can be adopted and used as blueprint for other domains. © 2018, Springer Nature Switzerland AG.","DDI; Research data; Research data management; SKOS; Standards and interoperability","Behavioral research; Information management; Metadata; Data documentation; Empirical surveys; Publishing process; Research data; Research data managements; SKOS; Social science research; Technical infrastructure; Digital libraries","","","","","","","(2014); Klas C.-P., Hopt O., Zenk-Moltgen W., Muhlbauer A., Ddi-Flatdb: Efficient Access to DDI, (2016); Klas C.-P., Hopt O., Zenk-Moltgen W., Muhlbauer A., Ddi-Flat-Db: A Lightweight Framework for Heterogeneous DDI Sources, (2016); Blumenberg M., Zenk-Moltgen W., Klas C.-P., (2015); Vardigan M., Heus P., Thomas W., Data documentation initiative: Toward a standard for the social sciences, Int. J. Digit. Curation, 3, 1, pp. 107-113, (2008); Hopt O., Klas C.-P., Muhlbauer A., Ddi-Flatdb: Next Steps, (2017); Hopt O., Klas C.-P., Zenk-Moltgen W., Muhlbauer A., Efficient and Flexible DDI Handling for the Development of Multiple Applications, (2017)","C.-P. Klas; GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany; email: Claus-Peter.Klas@gesis.org","Mendez E.; Ribeiro C.; David G.; Lopes J.C.; Crestani F.","Springer Verlag","","22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018","10 September 2018 through 13 September 2018","Porto","218159","03029743","978-303000065-3","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85053822136"
"Schmidt L.O.; Holles J.H.","Schmidt, Lawrence O. (22136252700); Holles, Joseph H. (6602237719)","22136252700; 6602237719","A graduate course in research data management","2017","Education Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting","2017-October","","","623","631","8","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052073497&partnerID=40&md5=0c739511d12f37bf68fa08b9a9d29ad4","Brinkerhoff Geology Library, University of Wyoming, Laramie, 82071, WY, United States; Department of Chemical Engineering, University of Wyoming, Laramie, 82071, WY, United States","Schmidt L.O., Brinkerhoff Geology Library, University of Wyoming, Laramie, 82071, WY, United States; Holles J.H., Department of Chemical Engineering, University of Wyoming, Laramie, 82071, WY, United States","[No abstract available]","","","","","","","","","","","","AIChE","","Education Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting","29 October 2017 through 3 November 2017","Minneapolis","136244","","978-151085793-3","","","English","Educ. Div. - Core Program. Area AIChE Annu. Meet.","Conference paper","Final","","Scopus","2-s2.0-85052073497"
"Castro J.A.; Amorim R.C.; Gattelli R.; Karimova Y.; Da Silva J.R.; Ribeiro C.","Castro, João Aguiar (55977255100); Amorim, Ricardo Carvalho (56442184300); Gattelli, Rúbia (57195367829); Karimova, Yulia (57195369729); Da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","55977255100; 56442184300; 57195367829; 57195369729; 55496903800; 7201734594","Involving data creators in an ontology-based design process for metadata models","2017","Developing Metadata Application Profiles","","","","181","213","32","8","10.4018/978-1-5225-2221-8.ch008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027511938&doi=10.4018%2f978-1-5225-2221-8.ch008&partnerID=40&md5=baad159d62ae38ffcb20c6a71dd0af9a","Faculty of Engineering, University of Porto, Portugal; University of Porto, Portugal; Department of Informatics Engineering, University of Porto, INESC-Porto, Portugal","Castro J.A., Faculty of Engineering, University of Porto, Portugal; Amorim R.C., University of Porto, Portugal; Gattelli R., Faculty of Engineering, University of Porto, Portugal; Karimova Y., Faculty of Engineering, University of Porto, Portugal; Da Silva J.R., Faculty of Engineering, University of Porto, Portugal; Ribeiro C., Department of Informatics Engineering, University of Porto, INESC-Porto, Portugal","Research data are the cornerstone of science and their current fast rate of production is disquieting researchers. Adequate research data management strongly depends on accurate metadata records that capture the production context of the datasets, thus enabling data interpretation and reuse. This chapter reports on the authors' experience in the development of the metadata models, formalized as ontologies, for several research domains, involving members from small research teams in the overall process. This process is instantiated with four case studies: vehicle simulation; hydrogen production; biological oceanography and social sciences. The authors also present a data description workflow that includes a research data management platform, named Dendro, where researchers can prepare their datasets for further deposit in external data repositories. © 2017, IGI Global.","","Bioinformatics; Information management; Marine biology; Metadata; Photobiological hydrogen production; Biological oceanography; Data interpretation; Data repositories; Ontology-based; Overall process; Research data managements; Research domains; Vehicle simulation; Ontology","","","","","","","Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., A Comparative Study of Platforms for Research Data Management: Interoperability, Metadata Capabilities and Integration Potential, New Contributions in Information Systems and Technologies., (2015); Amorim R.C., Castro J.A., Rocha da Silva J., Ribeiro C., Engaging researchers in data management with LabTablet, an electronic laboratory notebook, Proceedings of the Symposium on Languages, Applications and Technologies (SLATE '15)., (2015); Assante M., Candela L., Castelli D., Tani A., Are Scientific Data Repositories Coping with Research Data Publishing?, Data Science Journal, 15, 6, pp. 1-24, (2016); Ball A., Chen S., Greenberg J., Perez C., Jeffery K., Koskela R., Building a disciplinary metadata standards directory, Proceedings of IDCC '14., (2014); Bartha G., Kocsis S., Standardization of Geographic Data: The European INSPIRE Directive, European Journal of Geography, 22, pp. 79-89, (2011); Berners-Lee T., Hendler J., Lassila O., The Semantic Web, Scientific American, 284, 5, pp. 34-43, (2001); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Candela L., Castelli D., Manghi P., Tani A., Data Journals: A survey., pp. 90-103, (2015); Castro J.A., Rocha da Silva J., Ribeiro C., Creating lightweight ontologies for dataset description. Practical applications in a cross-domain research data management workflow, Proceedings of theIEEE/ACM Joint Conference on Digital Libraries (JCDL)., (2014); Corcho O., Ontology based document annotation: trends and open research problems, International Journal of Metadata, Semantics and Ontologies, 1, 1, pp. 47-57, (2006); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Crystal A., Greenberg J., Usability of a metadata creation application for resource authors, Library & Information Science Research, 27, 2, pp. 177-189, (2005); Multi-beneficiary General Model Grant Agreement., (2013); Faniel I.M., Yakel E., Significant Properties as Contextual Metadata, Journal of Library Metadata, 11, 3-4, pp. 155-165, (2011); Content standard for digital geospatial metadata, Version 2.0., (1998); Fegraus E., Andelman S., Jones M.B., Schildhauer M., Maximizing the value of ecological data with structured metadata: An introduction to Ecological Metadata Language (EML) and principles for metadata creation, Bulletin of the Ecological Society of America, 86, 3, pp. 158-168, (2005); Ferreira M.J.F., Gales L., Fernandes V.R., Rangel C.M., Pinto M.F.R., Alkali free hydrolysis of sodium borohydride for hydrogen generation under pressure, International Journal of Hydrogen Energy, 35, 18, pp. 9869-9878, (2010); Gore S.A., e-Science and data management resources on the Web, Medical Reference Services Quarterly, 30, 2, pp. 167-177, (2011); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, Ariadne, (2000); Heidorn P.B., The Emerging Role of Libraries in Data Curation and Escience, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Heidorn P.B.P.B.H., Shedding Light on the Dark Data in the Long Tail of Science, Library Trends, 57, 2, pp. 280-299, (2008); Jorg B., CERIF: The Common European Research Information Format Model, Data Science Journal, 9, pp. 24-31, (2010); Karimova Y., Vocabulários controlados na descrição de dados de investigação no Dendro, (2016); Lassila O., Mcguinness D., The Role of Frame-Based Representation on the Semantic Web., (2001); Leegard R., Keegan J., Ward K., In-depth Interviews, Qualitative research practice: A guide for social science students and researchers, pp. 138-169, (2003); Lewis S.C., Zamith R., Hermida A., Content Analysis in an Era of Big Data: A Hybrid Approach to Computational and Manual Methods, Journal of Broadcasting & Electronic Media, 57, 1, pp. 34-52, (2013); Li Y., Kennedy G., Ngoran F., Wu P., Hunter J., An ontology-centric architecture for extensible scientific data management systems, Future Generation Computer Systems, 29, 2, pp. 641-653, (2013); Madin J., Bowers S., Schildhauer M., Krivov S., Pennington D., Villa F., An ontology for describing and synthesizing ecological observation data, Ecological Informatics, 2, 3, pp. 279-296, (2007); Martinez-Uribe L., Macdonald S., User engagement in research data curation, Research and Advanced Technology for Digital Libraries, (2009); Martone M.E., Brain and Behavior: We want you to share your data, Brain and Behavior, 4, 1, (2013); Mena-Garces E., Garcia-Barriocanal E., Sicilia M.-A., Sanchez-Alonso S., Moving from dataset metadata to semantics in ecological research: A case in translating EML to OWL, Procedia Computer Science, 4, pp. 1622-1630, (2011); Understanding Metadata., (2004); Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century., (2005); Grants.Gov Application Guide A Guide for Preparation and Submission of NSF Applications via Grants.gov., (2011); Nilsson M., Baker T., Johnston P., The Singapore Framework for Dublin Core Application Profiles., (2008); Noy N.F., Mcguinness D.L., Ontology Development 101: A Guide to Creating Your First Ontology., (2000); Perrotta D., Macedo J.L., Rossetti R.J.F., De Sousa J.F., Kokkinogenis Z., Ribeiro B., Afonso J.L., Route Planning for Electric Buses: A Case Study in Oporto, Procedia: Social and Behavioral Sciences, 111, pp. 1004-1014, (2014); Perrotta D., Teixeira A., Silva H., Ribeiro B., Afonso J., Electrical Bus Performance Modeling for Urban Environments, SAE Int. J. Alt. Power, 1, 1, pp. 34-45, (2012); Piwowar H.A., Day R.B., Fridsma D.S., Sharing detailed research data is associated with increased citation rate. PLoS ONE, 2, 3, (2007); Pouchard L., Cinquini L., Strand G., The Earth System Grid Discovery and Semantic Web Technologies, Workshop for Semantic Web Technologies for Searching and Retrieving Scientific Data - 2nd International Semantic Web Conference, pp. 1-6, (2003); Qin J., Li K., How Portable Are the Metadata Standards for Scientific Data? A Proposal for a Metadata Infrastructure, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 25-34, (2013); Qin J., Ball A., Greenberg J., Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 62-71, (2012); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, International Journal of Digital Curation, 6, 2, pp. 232-244, (2011); Rocha da Silva J., Usage-driven Application Profile Generation Using Ontologies, (2016); Rocha da Silva J., Castro J.A., Ribeiro C., Honrado J., Lomba A., Goncalves J., Beyond INSPIRE: An Ontology for Biodiversity Metadata Records, On the Move to Meaningful Internet Systems: OTM 2014 Workshops, 8842, pp. 597-607, (2014); Rocha da Silva J., Castro J.A., Ribeiro C., Lopes J.C., Dendro: collaborative research data management built on linked open data, Proceedings of the 11th European Semantic Web Conference., (2014); Rocha da Silva J., Ribeiro C., Barbosa J., Gouveia M., Lopes J.C., UPBox and Data Notes: a collaborative data management environment for the long tail of research data, Proceedings of the iPres 2013 Conference., (2013); Rocha da Silva J.R., Cristina R., Lopes J.C., Semi-automated application profile generation for research data assets, Proceedings of the Metadata and Semantics Research Conference (MTSR '12)., (2012); Soldatova L.N., King R.R.D., An ontology of Scientific Experiments, Journal of the Royal Society, Interface, 3, 11, pp. 795-803, (2006); Stemler S., An overview of Content Analysis, Practical Assessment, Research & Evaluation, 7, 17, pp. 137-146, (2001); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Et al., Data Sharing by Scientists: Practices and Perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Birch B., Allard S., Academic libraries and research data services., (2012); Treloar A., Wilkinson R., Rethinking Metadata Creation and Management in a Data-Driven Research World, Proceedings of the IEEE Fourth International Conference on eScience, pp. 782-789, (2008); Vardigan M., Heus P., Thomas W., Data Documentation Initiative:Toward a Standard tor the Social Sciences, The International Journal of Digital Curation, 3, 1, pp. 107-113, (2008); Vines T.H., Albert A.Y.K., Andrew R.L., Debarre F., Bock D.G., Franklin M.T., Rennison D., Et al., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Wessels B., Finn R.L., Linde P., Mazzetti P., Nativi S., Riley S., Wyatt S., Et al., Issues in the development of open access to research data. Prometheus. Critical Studies in Innovation, 32, 1, pp. 49-66, (2014); Wieczorek J., Bloom D., Guralnick R., Blum S., Doring M., Giovanni R., Vieglais D., Et al., Darwin core: An evolving community-developed biodiversity data standard, PLoS ONE, 7, 1, (2012); Willis C., Greenberg J., White H., Analysis and Synthesis of Metadata Goals for Scientific Data, Journal of the American Society for Information Science and Technology, 63, 8, pp. 1505-1520, (2012); Wilson A.J., Toward Releasing the Metadata Bottleneck A Baseline Evaluation of Contributor-supllied Metadata, Library Resources & Technical Services, 51, 1, pp. 16-28, (2007)","","","IGI Global","","","","","","","978-152252223-2; 1522522212; 978-152252221-8","","","English","Dev. Met. Appl. Profil.","Book chapter","Final","","Scopus","2-s2.0-85027511938"
"Majid S.; Foo S.; Zhang X.","Majid, Shaheen (7006566241); Foo, Schubert (7102988160); Zhang, Xue (35217437800)","7006566241; 7102988160; 35217437800","Research data management by academics and researchers: Perceptions, knowledge and practices","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11279 LNCS","","","166","178","12","7","10.1007/978-3-030-04257-8_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057251537&doi=10.1007%2f978-3-030-04257-8_16&partnerID=40&md5=c4325fddf7d50a3dcf388e9947b846ae","Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, 637718, Singapore","Majid S., Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, 637718, Singapore; Foo S., Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, 637718, Singapore; Zhang X., Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, 637718, Singapore","The purpose of this study was to investigate research data management RDM) activities undertaken by academics, researchers, and research students at Nanyang Technological University Singapore. An online questionnaire was used and 241 respondents participated in this study. It was found that most of research data produced were in MS Office (text, spreadsheet, presentation) format, images, and structured statistical data. In addition to generating own research data, over one-half of the respondents were using data produced by other team members. They were storing their research data in their personal storage devices and assigning certain additional information to their files for fast and accurate retrieval. Overall, the respondents exhibited a positive attitude towards collaborative research and data sharing although a majority of them preferred data sharing with their own team members. The major concerns expressed for sharing research data were legal and ethical issues, data misuse, and misinterpretation of data. A majority of the respondents showed interest in attending research data management training. The paper suggests certain measures for promoting research data management by academics and researchers. © Springer Nature Switzerland AG 2018.","Data security; Data sharing; Research data management; Singapore","Information management; Security of data; Virtual storage; Collaborative research; Data Sharing; Nanyang Technological University; Online questionnaire; Positive attitude; Research data managements; Singapore; Statistical datas; Digital libraries","","","","","","","Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos ONE, 9, 12, pp. 1-28, (2014); Yoon A., Schultz T., Research data management services in academic libraries in the US, College Res. Libr., 78, 7, pp. 920-933, (2017); Tripathi M.M., Shukla A., Sonker S.K., Research data management practices in university libraries, DESIDOC J. Libr. Inf. Technol., 37, 6, pp. 417-424, (2017); Poole A.H., How has your science data grown? Digital curation and the human factor: A critical literature review, Arch. Sci., 15, 2, pp. 101-139, (2015); Lee D.J., Stvilia B., Practices of research data curation in institutional repositories: A qualitative view from repository staff, Plos ONE, 12, 3, pp. 1-44, (2017); Chiware E., Mathe Z., Academic libraries’ role in research data management services, South Afr. J. Libr. Inf. Sci., 81, 2, pp. 1-10, (2015); Surkis A., Read K., Research data management, J. Med. Libr. Assoc., 103, 3, pp. 154-156, (2015); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J. Libr. Inf. Sci., 46, 4, (2014); Fei Y., Deuble R., Morgan H., Designing research data management services based on the research lifecycle: A consultative leadership approach, J. Aust. Libr. Information Assoc., 66, 3, pp. 287-298, (2017); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic libraries and perceptions of librarians, Libr. Inf. Sci. Res., 36, pp. 84-90, (2014); Morgan A., Duffield N., Walkley Hall L., Research data management support: Sharing our experiences, J. Aust. Libr. Inf. Assoc., 66, 3, pp. 299-305, (2017); Javier C.P., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Nanyang Technological University, Research Data Management; Chowdhury G., Walton G., Kurbanoglu S., Unal Y., Boustany J., Information practices for sustainability: Information, data and environmental literacy, The Fourth European Conference on Information Literacy (ECIL), (2016); CITI Program: Research Ethics and Compliance Training","S. Majid; Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, 637718, Singapore; email: asmajid@ntu.edu.sg","Žumer M.; Hinze A.; Dobreva M.","Springer Verlag","","20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018","19 November 2018 through 22 November 2018","Hamilton","221079","03029743","978-303004256-1","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85057251537"
"Dugenie P.; Freire N.; Broeder D.","Dugenie, Pascal (6507509839); Freire, Nuno (15042329400); Broeder, Daan (23471772100)","6507509839; 15042329400; 23471772100","Building new knowledge from distributed scientific corpus: HERBADROP & EUROPEANA: Two concrete case studies for exploring big archival data","2017","Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017","2018-January","","","2231","2239","8","2","10.1109/BigData.2017.8258174","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047796758&doi=10.1109%2fBigData.2017.8258174&partnerID=40&md5=9befc76670af5efad2174daf6a976a2a","CINES, Centre Informatique National de l'Enseignement Superieur, Montpellier, France; INESC-ID/Europeana DSI, Den Haag, Netherlands; MEERTENS Institut (Afdeling Technische Ontwikkeling), Amsterdam, NL, Netherlands","Dugenie P., CINES, Centre Informatique National de l'Enseignement Superieur, Montpellier, France; Freire N., INESC-ID/Europeana DSI, Den Haag, Netherlands; Broeder D., MEERTENS Institut (Afdeling Technische Ontwikkeling), Amsterdam, NL, Netherlands","This paper presents approaches for building new knowledge using emerging methods and big data technologies together with archival practices. Two cases studies have been considered. The first one called HERBADROP is concerned with preservation and analysis of herbarium images. The second one called EUROPEANA investigates how to facilitate the re-use of cultural heritage language resources for research purposes. The common point between these two case studies is that they are both concerned with the use of valuable heritage resources within the EUDAT (European Data) infrastructure. HERBADROP leverages on the data services provided by EUDAT for long-term preservation, while EUROPEANA leverages on EUDAT to achieve citability and persistent identification of cultural heritage datasets. EUDAT1 is an initiative of some of the main European data centers and together with community research infrastructure organisations, to build a common eInfrastructure for general research data management. In this paper, we show how technologcal trends may offer some new research potential in the domain of computational archival science in particular appraising the challenges of producing quality, meaning, knowledge and value from quantity, tracing data and analytic provenance across complex big data platforms and knowledge production ecosystems. © 2017 IEEE.","","Concretes; Historic preservation; Information management; Community researches; Cultural heritages; Knowledge production; Language resources; Long-term preservation; Persistent Identification; Research data managements; Research potential; Big data","","","","","Fundac¸ão para a Ciência e a Tecnologia; European Commission, EC, (CEF-TC-2015-1-01); Fundação para a Ciência e a Tecnologia, FCT, (UID/CEC/50021/2013)","1EUDAT initiative is funded by a series of European projects, wherof the latest EUDAT2020 that is also funding the Pilot projects working on the described use-cases. See http://eudat.eu Fig. 1. A layered view of provision of services and facilities to preserve and exploit cultural and natural heritage data.","Ang Y., Puniamoorthy J., Pont A.C., Bartak M., Blanckenhorn W.U., Eberhard W.G., Puniamoorthy N., Silva V.C., Munari L., Meier R., A plea for digital reference collections and other science-based digitisation initiatives in taxonomy: Sepsidnet as exemplar, Systematic Entomology, 38, 3, pp. 637-644, (2013); Beaman R., Cellinese N., Mass Digitization of Scientific Collections: New Opportunities to Transform the Use of Biological Specimens and Underwrite Biodiversity Science, 209, pp. 7-17, (2012); Berendsohn W.G., Guntsch A., Creating a cross-domain pipeline for natural history data, ZooKeys, 209, pp. 47-52, (2012); Vasily B., De Casanove Alexia, Pascal D., Van Horik Rene, Simon L., Javier Q., Linda R., Data curation policies for EUDAT collaborative data infrastructure, Proceedings of the XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID), (2017); Simon C., Herbadrop-15 months using a pilot infrastructure, User Workshop, (2017); Mark C., Collaboration is the thing, IEEE Big Data 2016: 1st CAS Workshop, (2016); Rob C., Herbadrop-feedback from the royal botanic garden of edimburgh, User Workshop, (2017); Drinkwater R.E., Cubey R.W.N., Haston E.M., The use of optical character recognition (OCR) in the digitization of herbarium specimens labels, PhytoKeys, 38, pp. 15-30, (2014); DSA. Data Seal of Approval; Pascal D., Lorene B., Eudat Data Pilot Herbadrop, (2017); Pascal D., Simon C., Eudat Data Pilot Herbadrop, (2016); Collaborative Data Infrastructure; Haston E., Cubey R., Pullan M., Atkins H., Harris D.J., Developing integrated workflows for the digitization of herbarium specimens using a modular and scalable approach, ZooKeys, 209, pp. 93-102, (2012); Elspeth H., Simon C., Pascal D., Herbadrop-a longterm preservation of herbarium specimen images, Proceedings of the Second Eudat User Forum, (2016); Valerie J., Sonia R., David T., Size matters: The implications of volume for the digital archive of tomorrow a case study from the UK national archives, Records Management Journal, 24, 3, pp. 224-237, (2014); Lehtonen J., Heiska S., Pajari M., Tegelberg R., Saarenmaa H., The process of digitizing natural history collection specimens at Digitarium, Proceedings of the Environmental Information Management Conference 2011 (EIM), (2011); Open Archival Information System; The Research Data Alliance Practical Policy Working Group; Sandusky Robert J., Computational provenance: Dataone and implications for cultural heritage institutions, IEEE Big Data 2016: 1st CAS Workshop, (2016); Hengchen S., Coeckelbergs M., Van Hooland S., Verborgh R., Steiner T., Exploring archives with probabilistic models: Topic modelling for the valorisation of digitised archives of the european commission, IEEE Big Data 2016: 1st CAS Workshop, (2016); Sonia R., Traces through time: A probabilistic approach to connected archival data, IEEE Big Data 2016: 1st CAS Workshop, (2016); Tegelberg R., Haapala J., Mononen T., Pajari M., Saarenmaa H., The development of a digitising service centre for natural history collections, ZooKeys, 209, pp. 75-86, (2012); Tesseract. OCR Analysis Tool; The herbonauts website:. Recruiting the general public to acquire the data from herbarium labels, Proceedings of the UNESCO International Conference, Botanists of the Twenty-first Century: Roles, Challenges and Opportunities, (2014); Xu W., Huang R., Esteva M., Song J., Walls R., Content-based Comparison for Collections Identification, IEEE Big Data 2016: 1st CAS Workshop, (2016)","","Nie J.-Y.; Obradovic Z.; Suzumura T.; Ghosh R.; Nambiar R.; Wang C.; Zang H.; Baeza-Yates R.; Baeza-Yates R.; Hu X.; Kepner J.; Cuzzocrea A.; Tang J.; Toyoda M.","Institute of Electrical and Electronics Engineers Inc.","Cisco; Elsevier; IEEE; IEEE Computer Society; The Mit Press","5th IEEE International Conference on Big Data, Big Data 2017","11 December 2017 through 14 December 2017","Boston","134260","","978-153862714-3","","","English","Proc. - IEEE Int. Conf. Big Data, Big Data","Conference paper","Final","","Scopus","2-s2.0-85047796758"
"Yu S.H.","Yu, Siu Hong (57193063032)","57193063032","Research Data Management: A Library Practitioner's Perspective","2017","Public Services Quarterly","13","1","","48","54","6","3","10.1080/15228959.2016.1223475","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010469840&doi=10.1080%2f15228959.2016.1223475&partnerID=40&md5=522125e6920f834c01e7fe63bb3bb895","University of Western Ontario, London, ON, Canada","Yu S.H., University of Western Ontario, London, ON, Canada","Column description. The Future Voices in Public Services column is a forum for students in graduate library and information science programs to discuss key issues they see in academic library public services, to envision what they feel librarians in public service have to offer to academia, to tell us of their visions for the profession, or to tell us of research that is going on in library schools. Interested students can contact Nancy Dewald. © 2017, Published with license by Taylor & Francis © 2017, © Siu Hong Yu.","","","","","","","","","Carlson J., Nelson M.S., Johnston L.R., Koshoffer A., Developing data literacy programs: Working with faculty, graduate students and undergraduates, Bulletin of the Association for Information Science and Technology, 41, 6, pp. 14-17, (2015); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing research data, pp. 105-133, (2012); Cox A.M., Verbaan E., Sen B., (2012); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); (2016); Guindon A., Research data management at Concordia University: A survey of current practices, Feliciter, 60, 2, pp. 15-17, (2014); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Kroes N., (2011); Malone T., (2015); Pryor G., Why manage research data?, Managing research data, pp. 1-16, (2012); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice, (2014); Ray J.M., Research data management: Practical strategies for information professionals, (2014); Rumsey A.S., When we are no more: How digital memory is shaping our future, (2016); Shearer K., (2015); Steeleworthy M., Research data management and the Canadian academic library: An organizational consideration of data management and data stewardship, Partnership: The Canadian Journal of Library and Information Practice and Research, 9, 1, pp. 1-11, (2014); Szigeti K., Keys S., (2016); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Whyte A., Tedds J., (2011)","S.H. Yu; London, 434 Stonehaven Place, N6H 5N3, Canada; email: syu333@uwo.ca","","Routledge","","","","","","15228959","","","","English","Public Serv. Q.","Note","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85010469840"
"Raboudi A.","Raboudi, Amel (57205574619)","57205574619","A study of Information System mutations (changes) using Knowledge Organization System (KOS) applied to biomedical research study","2018","CEUR Workshop Proceedings","2306","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060624314&partnerID=40&md5=1113a85ea1982111051e4bc278044904","FEALINX, 37 rue Adam Ledoux, Courbevoie, 92400, France; INSERM, UMR970, Paris-Cardiovascular Research Center at HEGP, Paris, France; Université de Technologie de Compiègne (UTC), UMR 7337 Roberval, Compiègne, France","Raboudi A., FEALINX, 37 rue Adam Ledoux, Courbevoie, 92400, France, INSERM, UMR970, Paris-Cardiovascular Research Center at HEGP, Paris, France, Université de Technologie de Compiègne (UTC), UMR 7337 Roberval, Compiègne, France","A biomedical research study is a collaboration process where several experts, institutions, disciplines and data sources are involved. Traceability of data provenance and efficient data management are essential in order to guarantee results integrity. Researchers, however, use different Information Systems (IS) in order to collect, process and analyze their increasingly complex datasets. Accordingly, Research Data Management (RDM) is seen as a tedious, time consuming and error prone task. In a previous work, an IS based on Product Lifecycle Management (PLM) technology was proposed to manage data heterogeneity and provenance throughout the biomedical research lifecycle. However, the fast-changing context of biomedical research causes data, information and knowledge changes, that we hereby call mutations. Mutations can affect IS components and impact IS consistency. In a collaboration context, it is important to have the same shared knowledge for all actors. Therefore, the use of a Knowledge Organization Systems (KOS) is proposed in order to model shared knowledge and enable dealing with knowledge level mutations. © 2018 CEUR-WS. All rights reserved.","Change management; Information System; Knowledge Organization System; Mutation; Ontology evolution; Research Data Management","Engineering research; Information systems; Information use; Life cycle; Change management; Knowledge organization systems; Mutation; Ontology evolution; Research data managements; Knowledge management","","","","","","","Allanic M., Et al., PLM as a strategy for the management of heterogeneous information in bio-medical imaging field, International Journal of Information Technology and Management, 16, 1, pp. 5-30, (2017); Anderson R., Mansingh G., Migrating MIS to KMS: A case of social welfare systems, Knowledge Management for Development: Domains, Strategies and Technologies for Developing Countries, pp. 93-109, (2014); Bergman M., An Intrepid Guide to Ontologies, (2007); Beynon-Davies P., The ‘language’ of informatics: The nature of information systems, International Journal of Information Management, 29, 2, pp. 92-103, (2009); Dos Reis J.C., Et al., Analyzing and supporting the mapping maintenance problem in biomedical knowledge organization systems, Proc. SIMI Workshop at ESWC., pp. 25-36, (2012); Directorate-General for Research and Innovation: Realising the European Open Science Cloud: First Report and Recommendations of the Commission High Level Expert Group on the European Open Science Cloud, (2016); Flouris G., Et al., Ontology change: Classification and survey, The Knowledge Engineering Review, 23, 2, pp. 117-152, (2008); Gibbon G.A., A brief history of LIMS, Laboratory Automation & Information Management, 32, 1, pp. 1-5, (1996); Gruber T.R., A translation approach to portable ontology specifications, Knowledge Acquisition, 5, 2, pp. 199-220, (1993); Haase P., Semantic Technologies for Distributed Information Systems, (2006); Haller K., Information system maintenance costs: The ""in-between “challenge, Workshop Software-Reengineering, Germany, (2010); Hodge G., Systems of knowledge organization for digital libraries: Beyond traditional authority files, Digital Library Federation, Council on Library and Information Resources, 1755, (2000); Jacob F., Evolution and tinkering, Science, 196, 4295, pp. 1161-1166, (1977); Kondylakis H., Papadakis N., EvoRDF: Evolving the exploration of ontology evolution, The Knowledge Engineering Review, 33, (2018); Lebo T., Et al., Prov-o: The prov ontology, W3C Recommendation, 30, (2013); Noy N.F., Klein M., Ontology evolution: Not the same as schema evolution, Know. Inf. Sys., 6, 4, pp. 428-440, (2004); Raboudi A., Et al., Integration and provenance control of proteomics data using SWOMed, a product lifecycle management framework for biomedical research, SMMAP Congress October, (2017); Raboudi A., Et al., Traçabilité de l’intégration de données biomédicales hétérogènes dans le système SWOMed de gestion du cycle de vie des études biomédicales, Actes Du Symposium SIIM 2017, (2017); Stojanovic L., Et al., User-driven ontology evolution management, Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web, pp. 285-300, (2002); Turban E., Et al., Introduction to Information Technology, (2004); Xuan D.N., Et al., Ontology Evolution and Source Autonomy in Ontology-Based Data Warehouses, (2006); Zablith F., Evolva: Towards Automatic Ontology Evolution, (2008); Zablith F., Et al., Ontology evolution: A process-centric survey, The Knowledge Engineering Review, 30, 1, pp. 45-75, (2015); Zurawski M., Distributed multi-contextual ontology evolution–a step towards semantic autonomy, International Conference on Knowledge Engineering and Knowledge Management, pp. 198-213, (2006)","A. Raboudi; FEALINX, Courbevoie, 37 rue Adam Ledoux, 92400, France; email: amel.raboudi@utc.fr","Osborne F.; Hollink L.","CEUR-WS","","2018 EKAW Doctoral Consortium, EKAW-DC 2018","13 November 2018","Nancy","144243","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85060624314"
"Auge T.; Heuer A.","Auge, Tanja (57194833958); Heuer, Andreas (9533312500)","57194833958; 9533312500","Combining provenance management and schema evolution","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11017 LNCS","","","222","225","3","6","10.1007/978-3-319-98379-0_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053830701&doi=10.1007%2f978-3-319-98379-0_24&partnerID=40&md5=ec3938b3e6d9bda78fcfbe5265bf567f","University of Rostock, Rostock, Germany","Auge T., University of Rostock, Rostock, Germany; Heuer A., University of Rostock, Rostock, Germany","The combination of provenance management and schema evolution using the CHASE algorithm is the focus of our research in the area of research data management. The aim is to combine the construction of a CHASE inverse mapping to calculate the minimal part of the original database — the minimal sub-database — with a CHASE-based schema mapping for schema evolution. © Springer Nature Switzerland AG 2018.","CHASE algorithm; CHASE inverse; Data evolution; Data provenance; Schema evolution; Schema mapping","Artificial intelligence; Computer science; Computers; Chase algorithm; CHASE inverse; Data evolution; Data provenance; Schema evolution; Schema mappings; Information management","","","","","","","Auge T., Heuer A., Inverse im Forschungsdatenmanagement, Proceedings of 30Th Workshop Grundlagen Von Datenbanken, (2018); Auge T., Umsetzung Von Provenance-Anfragen in Big-Data-Analytics-Umgebungen, (2017); Cheney J., Chiticariu L., Tan W.C., Provenance in databases: Why, how and where, Found. Trends Databases, 1, 4, pp. 379-474, (2009); Buneman P., Khanna S., Tan W.C., Why and where: A characterization of data provenance, ICDT 2001. LNCS, 1973, pp. 316-330, (2001); Fagin R., Inverting schema mappings, ACM Trans. Database Syst., 32, 4, pp. 25-31, (2007); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema mapping evolution through composition and inversion, Schema Matching and Mapping. DCSA, pp. 191-222, (2011); Johnston T., Bitemporal Data: Theory and Practice, Morgan Kaufmann, Burling-Ton, (2014); Bruder I., Klettke M., Moller M.L., Meyer F., Heuer A., Jurgensmann S., Feis-Tel S., Daten wie Sand am Meer – Datenerhebung,-strukturierung,-management und Data Provenance für die Ostseeforschung, Datenbank-Spektrum, 17, 2, pp. 183-196, (2017); Heuer A., METIS in PArADISE: Provenance Management bei der Auswertung von Sensordatenmengen für die Entwicklung von Assistenzsystemen, BTW Workshops. LNI, 242, pp. 131-136, (2015); Green T.J., Karvounarakis G., Tannen V., Provenance semirings, PODS, pp. 31-40, (2007)","T. Auge; University of Rostock, Rostock, Germany; email: tanja.auge@uni-rostock.de","Belhajjame K.; Gehani A.; Alper P.","Springer Verlag","","7th International Provenance and Annotation Workshop, IPAW 2018","9 July 2018 through 10 July 2018","London","218129","03029743","978-331998378-3","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85053830701"
"Van Wyk B.; Geldenhuys H.","Van Wyk, Brenda (55747191900); Geldenhuys, Hermien (57203169845)","55747191900; 57203169845","Learn 3.0 meets library 3.0: A case study","2018","Proceedings of the International Conference on e-Learning, ICEL","2018-July","","","479","484","5","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050814363&partnerID=40&md5=bf5019573531df349f91ff67728ac6f2","Independent Institute of Education, Johannesburg, South Africa","Van Wyk B., Independent Institute of Education, Johannesburg, South Africa; Geldenhuys H., Independent Institute of Education, Johannesburg, South Africa","The global uptake of eLearning systems in higher education is improving rapidly. One of the burning questions is whether eLearning courses provide learners with adequate access to academic quality information and readings to aid in the development of critical thinking skills, and to support independent learning. An evaluation of recent research and Web developments alludes to the underutilisation of available information sources and support services in eLearning. Tschirhart, Hamm, Perpich, Powell, and Reiman-Send (2013) state that even the latest and more flexible online learning management systems (LMS’s) fail to embed access to academic information sources. ELearning 2.0 and 3.0 developments are familiar concepts and are being incorporated in online learning programmes. Similarly, leading academic library and information services (LIS) offer Library 2.0 and Library 3.0 services and tools for improved access to academic information (Kwanya, Stilwell, & Underwood, 2015). LIS Services include access to known databases and platforms and offer further support services for the development of digital literacy skills, research data management, and digital rights management (DRM). Although these services are geared to enhance learning experiences and academic skills, they lack planned and meaningful integration with LMS’s. Realising this gap in alignment between the LMS and LIS, a private higher education institution in South Africa embarked on an inter-departmental collaboration project to embed and integrate existing digital library service and systems in their LMS, to assist content developers, lecturers and learners. This initiative forms part of the institutional eStrategy and is developed and managed by the institution’s Central Academic Team (CAT) according to uniform standards across programmes and faculties. This case study reports on how the project, informed by teaching and learning theories, supports and enriches eLearning. Collaboration across departments and disciplines within the institution promoted the integration of information support services and allowed access to academic information via the LMS, for a better student experience. © Academic Conferences Limited. All rights reserved.","ELearning; Embedded digital library services; Learn 3.0; Library 3.0; LMS; Web 3.0","Copyrights; Digital libraries; Information management; Information services; Learning systems; Online systems; Research and development management; Teaching; Collaboration projects; Critical thinking skills; Digital Library Services; Digital Rights Management; Higher education institutions; Learn 3.0; Research data managements; Web 3.0; E-learning","","","","","","","Adams P.N.A., Role of policies in collaborative design process for digital libraries within african Higher Education, Library Hi Tech, 29, 4, pp. 678-696, (2011); Akerlind G., Trevitt C., Enhancing Learning Through Technology: When Students Resist The Change, (2011); ALA Digital Content Tip Sheet, (2012); Asuncion R.R., Effects of addie model on the performance of B.E.E.D. sophomore students in the project-Based Multimedia Learning Environment, International Journal of Multidisciplinary Approach & Studies, 3, 3, pp. 119-129, (2016); Blaschke L.M., Hase S., Heutagogy: A Holistic Framework for Creating Twenty-First-Century Self-Determined Learners, (2016); Carlson J., Kneale R., Embedded librarianship in the research context: Navigating new waters, College & Research Libraries News, 72, 3, pp. 167-169, (2011); Dene J., Embedded librarianship at the claremont colleges, Embedded Librarians: Moving Beyond One-Shot Instruction, pp. 219-228, (2011); Dewey B.I., Social, intellectual, and cultural spaces: Creating compelling library environments for the digital age, Journal of Library Administration, 48, 1, (2008); Farkas M.G., Libraries in The Learning Management System, (2015); Flammia M., Cleary Y., Slattery D.M., Virtual Teams in Higher Education: A Handbook for Students and Teachers, (2016); Gonzalez C., What do university teachers think elearning is good for their teaching?, Studies in Higher Education, 35, 1, pp. 61-78, (2010); Hussain F., Elearning 3.0 = elearning 2.0 + web 3.0?, IADIS International Conference on Cognition and Exploratory Learning in The Digital Age, (CELDA), (2012); Hyman J.A., Moser T.M., Segala L.N., Electronic reading and digital library technologies: Understanding learner expectation and usage intent for mobile learning, Education Tech Research Development, 62, pp. 35-52, (2014); Johnson S., Evensen O.L., Gelfand J., Lammers G., Sipe L., Zilper N., Key Issues for E-Resources Collection Development: A Guide for Libraries, (2012); Khalil S.M., From resistance to acceptance and use of technology in academia, Open Praxis, 5, 2, pp. 151-163, (2013); Kwanya T., Stilwell C., Underwood P., Library 3.0: Intelligent Libraries and Apomediation, (2015); Mlitwa N., Van Belle J., Mediators for lecturer perspectives on learning management systems at universities in the Western Cape, South Africa, Association for Information Systems, PACIS 2011 Proceedings, (2011); Pawar S.S., Kaur P., Need of Change in Library and Information Science Education in India and Skills in The Changing Knowledge Era: A Study, (2015); Roberts M.S., Applying The Andragogical Model of Adult Learning: A Case Study of The Texas Comptroller’S Fiscal Management Division, (2007); Tschirhart L., Hamm B., Perpich D., Powell C., Reiman-Sendi K.A., They Came for The Carbs, and Stayed for The Collaboration: Engaging Library Workers Across Units to Deliver Meaningful Learning Objects, (2013); Vassiliou M., Rowley J., Progressing the definition of “e-book, Library Hi Tech, 26, 3, pp. 355-368, (2008); Wiebe E., Durepos G., Mills A.J., Encyclopedia of Case Study Research, (2010)","","Ivala E.","Academic Conferences Limited","","13th International Conference on e-Learning, ICEL 2018","5 July 2018 through 6 July 2018","Cape Town","138027","20488882","978-191121890-6","","","English","Proc. Int. Conf. e-Lear., ICEL","Conference paper","Final","","Scopus","2-s2.0-85050814363"
"Hüsler A.; Haas S.; Parry L.; Romero M.; Nisisako T.; Williams P.; Wildman R.D.; Alexander M.R.","Hüsler, Amanda (56241337900); Haas, Simon (57201776407); Parry, Luke (56432494400); Romero, Manuel (7202431331); Nisisako, Takasi (6506344251); Williams, Paul (7404955994); Wildman, Ricky D. (7006057613); Alexander, Morgan R. (7402334369)","56241337900; 57201776407; 56432494400; 7202431331; 6506344251; 7404955994; 7006057613; 7402334369","Correction: Effect of surfactant on: Pseudomonas aeruginosa colonization of polymer microparticles and flat films (RSC Advances (2018) 8 (15352-15357) DOI: 10.1039/C8RA01491D)","2018","RSC Advances","8","34","","19278","","","0","10.1039/c8ra90042f","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047534094&doi=10.1039%2fc8ra90042f&partnerID=40&md5=5c557c3e71463dafe4ede6fd8ffa30c8","Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Centre for Additive Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan; Centre for Biomolecular Sciences, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, United Kingdom","Hüsler A., Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, United Kingdom, Centre for Additive Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan; Haas S., Centre for Additive Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Parry L., Centre for Additive Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Romero M., Centre for Biomolecular Sciences, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Nisisako T., Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan; Williams P., Centre for Biomolecular Sciences, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Wildman R.D., Centre for Additive Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Alexander M.R., Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, United Kingdom","The authors regret that the following information was not included in the original manuscript. All relevant data are available from the University of Nottinghams Research Data Management Repository under the DOI: 10.17639/nott.352, available at the following address: http://dx.doi.org/10.17639/nott.352 The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers. © The Royal Society of Chemistry 2018.","","","","","","","","","","M.R. Alexander; Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; email: morgan.alexander@nottingham.ac.uk","","Royal Society of Chemistry","","","","","","20462069","","RSCAC","","English","RSC Adv.","Erratum","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85047534094"
"Aoki T.; Kajita S.; Akasaka H.; Takeda H.","Aoki, Takaaki (25647110200); Kajita, Shoji (7006312319); Akasaka, Hirokazu (57200286239); Takeda, Hagane (57200272453)","25647110200; 7006312319; 57200286239; 57200272453","Development and Deployment of Research Data Preservation Policy at a Japanese Research University in 2016","2017","Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","","","8113223","120","123","3","1","10.1109/IIAI-AAI.2017.129","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040614628&doi=10.1109%2fIIAI-AAI.2017.129&partnerID=40&md5=62688ac4b36dccacbfda41425e7618d5","Institute of Information Management and Communication, Kyoto University, Sakyo, Kyoto, 6068501, Japan; Planning and Information Management Department, Kyoto University, Sakyo, Kyoto, 6068501, Japan","Aoki T., Institute of Information Management and Communication, Kyoto University, Sakyo, Kyoto, 6068501, Japan; Kajita S., Institute of Information Management and Communication, Kyoto University, Sakyo, Kyoto, 6068501, Japan; Akasaka H., Planning and Information Management Department, Kyoto University, Sakyo, Kyoto, 6068501, Japan; Takeda H., Planning and Information Management Department, Kyoto University, Sakyo, Kyoto, 6068501, Japan","This paper reviews the policy development and deployment for research data preservation at Kyoto University, Japan, during fiscal year (FY) 2016. The universitys regulations and guidelines for research integrity and data preservation were formulated in FY2014 and 2015, in response to statements from the Ministry of Education, Culture, Sports, Science and Technology, and supplemental comments by Science Council of Japan (in FY2014). In FY2016, several departments at KU developed a prototype system for research data preservation using open source web frameworks with preference to agility of deployment rather than robustness. The prototype system also worked as the proof of concept for a full-service research data preservation system, which is due to be deployed by KUs central IT department in FY2017 for university-wide use. © 2017 IEEE.","Archive System; Data Preservation; Research Data Management; Research Integrity","Open systems; Archive systems; Data preservations; Kyoto University , Japan; Ministry of Education; Research data managements; Research integrities; Science and Technology; Science council of japans; Information management","","","","","","","Guidelines for Responding to Misconduct in Research' (Adopted August 26, 2014 by Ministry of Education, Culture, Sports, Science and Technology (Mext), (2014); [Enhancing the Integrity of Scientific Research (Response)], (2015); Promoting Research Integrity Regulations of Kyoto University, (2015); Matters Ruled for the Preservation and Disclosure of Research Data As Defined in Article 7-2 of the Regulations Regarding Promoting Research Integrity of Kyoto University, (2015); Plone CMS: Open Source Content Management-Site; Ruby on rails- A web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern; What is Enterprise Content Management (ECM); ECM Enterprise Content Management, (2006); Oracle WebCenter Content; FUJITSU Storage ETERNUS DA700 Data Archiver","","Hashimoto K.; Fukuta N.; Matsuo T.; Hirokawa S.; Mori M.; Mori M.","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics","6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","9 July 2017 through 3 July 2017","Hamamatsu, Shizuoka","132541","","978-153860621-6","","","English","Proc. - IIAI Int. Congr. Adv. Appl. Inf., IIAI-AAI","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85040614628"
"Harrison R.","Harrison, Ruth (57202156864)","57202156864","The Academic Library and the Research Office: Providing Scholarly Communications Support at Imperial College London—A Case Study","2018","Collaboration and the Academic Library: Internal and External, Local and Regional, National and International","","","","143","150","7","1","10.1016/B978-0-08-102084-5.00013-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047356392&doi=10.1016%2fB978-0-08-102084-5.00013-4&partnerID=40&md5=a36d8d8ff6b2fccf56c94239fd12449a","Imperial College London, London, United Kingdom","Harrison R., Imperial College London, London, United Kingdom","The roles of academic libraries and research offices in facilitating scholarly communications support have been traditionally split between providing access to research literature and ensuring policy compliance and governance respectively. From 2012 onwards (following the publication of the Finch report and implementation of the Research Councils UK open access policy) in the United Kingdom, this has changed as both departments have sought to collaborate to enable shared delivery of open access (OA) and research data management (RDM) support to researchers. This case study outlines how Imperial College London Library Services and the Research Office successfully achieved effective and efficient researcher support. Endorsement of collaborative working from senior management, the development of OA and RDM policies at the College level, and agreement between department heads that priority should be given to building up staff resource, expertise, and service development all contributed to this success. © 2018 Ruth Harrison Published by Elsevier Ltd. All rights reserved.","Collaboration; Open access; Research data management; Research offices; Research support; Scholarly communications support; University libraries","","","","","","","","Intersections of scholarly communication and information literacy: Creating strategic collaborations for a changing academic environment, (2013)","","","Elsevier","","","","","","","978-008102084-5; 978-008102288-7","","","English","Collaboration and the Academic Library: Internal and External, Local and Regional, National and International","Book chapter","Final","","Scopus","2-s2.0-85047356392"
"","","","20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11279 LNCS","","","","","361","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057226323&partnerID=40&md5=2388daae4b88c64ce30d45d8e443a805","","","The proceedings contain 37 papers. The special focus in this conference is on Asia-Pacific Digital Libraries. The topics include: Where the dead blogs are: A disaggregated exploration of web archives to reveal extinct online collectives; a method for retrieval of tweets about hospital patient experience; rewarding, but not for everyone: Interaction acts and perceived post quality on social Q&A sites; towards recommending interesting content in news archives; a twitter-based culture visualization system by analyzing multilingual geo-tagged tweets; development of content-based metadata scheme of classical poetry in thai national historical corpus; research data management by academics and researchers: Perceptions, knowledge and practices; examining Japanese American digital library collections with an ethnographic lens; exploring information needs and search behaviour of Swahili speakers in Tanzania; bilingual Qatar digital library: Benefits and challenges; measuring the semantic world – how to map meaning to high-dimensional entity clusters in pubMed?; digital preservation effort of manuscripts collection: case studies of pustakabudaya.id as Indonesia heritage digital library; The general data protection regulation (GDPR, 2016/679/EE) and the (Big) personal data in cultural institutions: Thoughts on the GDPR compliance process; a recommender system in ukiyo-e digital archives for Japanese art novices; tertiary students’ preferences for library search results pages on a mobile device; book recommendation beyond the usual suspects: Embedding book plots together with place and time information; users’ responses to privacy issues with the connected information ecologies created by fitness trackers; multiple level enhancement of children’s picture books with augmented reality; towards semantic quality enhancement of user generated content.","","","","","","","","","","","Žumer M.; Hinze A.; Dobreva M.","Springer Verlag","","20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018","19 November 2018 through 22 November 2018","Hamilton","221079","03029743","978-303004256-1","","","English","Lect. Notes Comput. Sci.","Conference review","Final","","Scopus","2-s2.0-85057226323"
"Cox A.M.; Kennan M.A.; Lyon L.; Pinfield S.","Cox, Andrew M. (7402563906); Kennan, Mary Anne (56001293900); Lyon, Liz (56835287100); Pinfield, Stephen (6602090850)","7402563906; 56001293900; 56835287100; 6602090850","Developments in research data management in academic libraries: Towards an understanding of research data service maturity","2017","Journal of the Association for Information Science and Technology","68","9","","2182","2200","18","107","10.1002/asi.23781","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016435639&doi=10.1002%2fasi.23781&partnerID=40&md5=236c7cbb176ae34266b27a0933991440","Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom; School of Information Studies, Charles Sturt University – Sydney, Locked Bag 450, Silverwater, 2128, NSW, Australia; School of Information Sciences, University of Pittsburgh, Information Sciences Building, 135 North Bellefield Avenue, Pittsburgh, 15260, PA, United States","Cox A.M., Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom; Kennan M.A., School of Information Studies, Charles Sturt University – Sydney, Locked Bag 450, Silverwater, 2128, NSW, Australia; Lyon L., School of Information Sciences, University of Pittsburgh, Information Sciences Building, 135 North Bellefield Avenue, Pittsburgh, 15260, PA, United States; Pinfield S., Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom","This article reports an international study of research data management (RDM) activities, services, and capabilities in higher education libraries. It presents the results of a survey covering higher education libraries in Australia, Canada, Germany, Ireland, the Netherlands, New Zealand, and the UK. The results indicate that libraries have provided leadership in RDM, particularly in advocacy and policy development. Service development is still limited, focused especially on advisory and consultancy services (such as data management planning support and data-related training), rather than technical services (such as provision of a data catalog, and curation of active data). Data curation skills development is underway in libraries, but skills and capabilities are not consistently in place and remain a concern. Other major challenges include resourcing, working with other support services, and achieving “buy in” from researchers and senior managers. Results are compared with previous studies in order to assess trends and relative maturity levels. The range of RDM activities explored in this study are positioned on a “landscape maturity model,” which reflects current and planned research data services and practice in academic libraries, representing a “snapshot” of current developments and a baseline for future research. © 2017 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals, Inc. on behalf of Association for Information Science and Technology","","Education; Information management; Microcellular radio systems; Academic libraries; Consultancy services; International studies; Management planning; Policy development; Research data managements; Service development; Skills development; human; information processing; landscape; leadership; manager; maturity; model; publication; scientist; skill; Libraries","","","","","","","Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, pp. 171-191, (2014); Research data management framework: Capability maturity guide, (2011); Auckland M., Re-skilling for research: An investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Beagrie N., Keeping research data safe: User guide version 2, (2011); Borgman C.L., Big data, little data, no data: Scholarship in the networked world, (2015); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, pp. 77-101, (2006); Brewerton A., Re-skilling for research: Investigating the needs of researchers and how library staff can best support them, New Review of Academic Librarianship, 18, pp. 96-110, (2012); Carlson S., (2006); Case D., Looking for information: A survey of research on information seeking, needs and behavior, (2012); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing research data, pp. 105-133, (2012); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, LIBER Quarterly, 24, pp. 17-48, (2014); Corrall S., Kennan M., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, pp. 299-316, (2014); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a “wicked” problem, Journal of Librarianship and Information Science, 48, pp. 3-17, (2016); Cox A.M., Verbaan E., Sen B.A., Upskilling liaison librarians for research data management, Ariadne, 70, (2012); Crowston K., Qin J., A capability maturity model for scientific data management: Evidence from the literature, Proceedings of the American Society for Information Science and Technology, 48, pp. 1-9, (2011); (2016); Freiman L., Ward C., Jones S., Molloy L., Snow K., Incremental scoping study and implementation plan: A pilot project for supporting research data management, (2010); Hey T., Hey J., E-Science and its implications for the library community, Library Hi Tech, 24, pp. 515-528, (2006); Hodson S., Molloy L., Case study 5: Development of institutional RDM services by projects in the Jisc Managing Research Data programmes, Delivering research data management services, pp. 205-237, (2013); Houghton J.W., Gruen N., Open research data: Report to the Australian National Data Service (ANDS), (2014); Kennan M., Corrall S., Afzal W., Making space” in practice and education: Research support services in academic, Library Management, 35, pp. 666-683, (2014); Kenney A.R., McGovern N.Y., The five organizational stages of digital preservation, Digital libraries: A vision for the 21st century, (2003); Knight G., (2013); Lewis M., Libraries and the management of research data, Envisioning future academic library services: Initiatives, ideas and challenges, pp. 145-168, (2010); Lyon L., Dealing with data: Roles, rights, responsibilities and relationships, (2007); Lyon L., Open science at web-scale: Optimising participation and predictive potential, (2009); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, pp. 126-138, (2012); Lyon L., Librarians in the lab: Towards radically re-engineering research data services at the research coalface, New Review of Academic Librarianship, (2016); Lyon L., Ball A., Duke M., Day M., pp. 9-16, (2012); Lyon L., Brenner A., Bridging the data talent gap – Positioning the iSchool as an agent for change, Internation Journal of Digital Curation, 10, pp. 111-122, (2015); Lyon L., Mattern E., Acker A., Langmead A., (2016); (2016); Martin E., Highlighting the informationist as a data librarian embedded in a research team, Journal of eScience Librarianship, 2, (2013); Mayernik M.S., Thompson C.A., Williams V., Allard S., Palmer C.L., Tenopir C., Enriching education with exemplars in practice: Iterative development of data curation internships, International Journal of Digital Curation, 10, pp. 123-134, (2015); Michener W.K., Brunt J.W., Helly J.J., Kirchner T.B., Stafford S.G., Non-geospatial metadata for the ecological sciences, Ecological Applications, 7, 1997, pp. 330-342, (1997); Myatt G., Making sense of data: A practical guide to exploratory data analysis and data mining, (2007); Australian code for the responsible conduct of research, (2007); Paulk M.C., Curtis B., Chrissis M.B., Weber C.V., Capability maturity model, version 1.1, IEEE Software, 10, pp. 18-27, (1993); Pickard A., Research methods in information, (2012); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, (2014); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice, (2014); Ray J., Research data management: Practical strategies for information professionals, (2014); Shadbolt A., Konstantelos L., Lyon L., Guy M., Delivering innovative RDM training: The immersiveInformatics pilot programme, International Journal of Digital Curation, 9, pp. 313-323, (2014); Si L., Xing W., Zhuang X., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, pp. 417-449, (2015); (2016); Swan A., Brown S., The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs, (2008); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future: an ACRL white paper, (2012); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, pp. 70-78, (2013); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, pp. 211-219, (2014); Whyte A., A pathway to sustainable research data services: From scoping to sustainability, Delivering research data management services, pp. 59-88), (2014); Whyte A., (2014); Wilson J.A.J., Martinez-Uribe L., Fraser M.A., Jeffreys P., An institutional approach to developing research data management infrastructure, International Journal of Digital Curation, 6, pp. 274-287, (2011)","","","John Wiley and Sons Inc.","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85016435639"
"Arias-Coello A.; Simon-Blas C.; Arranz-Val P.; Simon-Martin J.","Arias-Coello, Alicia (24528583800); Simon-Blas, Clara (57196422360); Arranz-Val, Pablo (55480264600); Simon-Martin, Jose (18635399100)","24528583800; 57196422360; 55480264600; 18635399100","Research Data Management in Three Spanish Universities","2018","Communications in Computer and Information Science","810","","","195","204","9","3","10.1007/978-3-319-74334-9_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041751507&doi=10.1007%2f978-3-319-74334-9_21&partnerID=40&md5=39a15f694fac871e0d8b170f0716aa2f","Instituto Universitario de Evaluación, Universidad Complutense Madrid, Madrid, Spain; Universidad Rey Juan Carlos, Madrid, Spain; Universidad de Burgos, Burgos, Spain","Arias-Coello A., Instituto Universitario de Evaluación, Universidad Complutense Madrid, Madrid, Spain; Simon-Blas C., Universidad Rey Juan Carlos, Madrid, Spain; Arranz-Val P., Universidad de Burgos, Burgos, Spain; Simon-Martin J., Instituto Universitario de Evaluación, Universidad Complutense Madrid, Madrid, Spain","Research Data Management (RDM) is an important ability required in the global knowledge environment and essential in the university research context. In this work we will present the results of the application of a web survey of faculties and doctoral students from three Spanish universities, with the aim of knowing their current levels of awareness and gaps in different issues of RDM. This study is part of an international survey on “Base Data Literacy” led by Professor Joumana Boustany from Paris Descartes University. The questionnaire was sent to the research academic staff and student research fellows of the three universities. We received a total of 828 responses, 591 of which were completely filled out. In accordance to the results of this survey we have detected a growing need among research academic staff and research students for RDM skills. © 2018, Springer International Publishing AG.","Data management plan; Metadata; Research Data Management; Spanish’s Universities","Information management; Metadata; Societies and institutions; Students; Surveys; Academic staff; Current levels; Global knowledge; International survey; Research data managements; Student research; University research; Web surveys; Education","","","","","","","Schneider R., Research data literacy, ECIL 2013. CCIS, 397, pp. 134-140, (2013); The EU Framework Programme for Research and Innovation; Directorate-General for Research & Innovation, (2017); Aydinoglu A.-U., Dogan G., Taskin Z., Research data management in Turkey: Perceptions and practices, Libr. Hi Tech, 35, 2, pp. 271-289, (2017); Research and Innovation, Participant Portal.; Leber J., In a Data Deluge, Companies Seek to Fill a New Role, (2013); Roman-Molina J., Bernal I., Prácticas En La gestión, difusión Y preservación De Datos De investigación En El CSIC, (2014); Piorun M.E., E-science as a catalyst for transformational change in university research libraries: A dissertation, University of Massachusetts Medical School, (2013); Grupo De Trabajo De Apoyo a La Investigación; Pagoda: Consorcio Madroño","A. Arias-Coello; Instituto Universitario de Evaluación, Universidad Complutense Madrid, Madrid, Spain; email: aarias@ucm.es","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85041751507"
"Berber F.; Yahyapour R.","Berber, Fatih (57191252863); Yahyapour, Ramin (15066204200)","57191252863; 15066204200","DNS as resolution infrastructure for persistent identifiers","2017","Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017","","","8104688","1085","1094","9","0","10.15439/2017F114","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039917292&doi=10.15439%2f2017F114&partnerID=40&md5=fd1591cd7c25c6c97690766fcad700d6","Gesellschaft für Wissenschaftliche Datenverarbeitung Gcóttingen, Germany; University of Gottingen, Gottingen, Germany","Berber F., Gesellschaft für Wissenschaftliche Datenverarbeitung Gcóttingen, Germany; Yahyapour R., Gesellschaft für Wissenschaftliche Datenverarbeitung Gcóttingen, Germany, University of Gottingen, Gottingen, Germany","The concept of persistent identification is increasingly important for research data management. At the beginnings it was only considered as a persistent naming mechanism for research datasets, which is achieved by providing an abstraction for addresses of research datasets. However, recent developments in research data management have led persistent identification to move towards a concept which realizes a virtual global research data network. The base for this is the ability of persistent identifiers of holding semantic information about the identified dataset itself. Hence, community-specific representations of research datasets are mapped into globally common data structures provided by persistent identifiers. This ultimately enables a standardized data exchange between diverse scientific fields. Therefore, for the immense amount of research datasets, a robust and performant global resolution system is essential. However, for persistent identifiers the number of resolution systems is in comparison to the count of DNS resolvers extremely small. For the Handle System for instance, which is the most established persistent identifier system, there are currently only five globally distributed resolvers available. The fundamental idea of this work is therefore to enable persistent identifier resolution over DNS traffic. On the one side, this leads to a faster resolution of persistent identifiers. On the other side, this approach transforms the DNS system to a data dissemination system. © 2017 PTI.","","Electronic data interchange; Information dissemination; Information systems; Internet protocols; Semantics; Data dissemination; Naming mechanisms; Persistent Identification; Research data; Research data managements; Resolution systems; Scientific fields; Semantic information; Information management","","","","","","","De Sompel H.V., Sanderson R., Shankar H., Klein M., Persistent identifiers for scholarly assets and the web: The need for an unambiguous mapping, IJDC, 9, 1, (2014); Kuhn T., Dumontier M., Making digital artifacts on the web verifiable and reliable, IEEE Transactions On Knowledge and Data Engineering, 27, 9, pp. 2390-2400, (2015); Bellini E., Luddi C., Cirinna C., Lunghi M., Felicetti A., Bazzanella B., Bouquet P., Interoperability knowledge base for persistent identifiers interoperability framework, Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference On. IEEE, pp. 868-875, (2012); Weigel T., Kindermann S., Lautenschlager M., Actionable persistent identifier collections, Data Science Journal, 12, pp. 191-206, (2014); Karakannas A., Zhao Z., Information Centric Networking for Delivering Big Data with Persistent Identifiers, (2014); Evrard A.E., Erdmann C., Holmquist J., Damon J., Dietrich D., Persistent, Global Identity for Scientists Via Orcid, (2015); Liu C.H., Yang B., Liu T., Efficient naming, addressing and profile services in internet-of-things sensory environments, Ad Hoc Networks, 18, pp. 85-101, (2014); Berber F., Wieder P., Yahyapour R., A high-performance persistent identification concept, 2016 IEEE International Conference On Networking, Architecture and Storage (NAS, pp. 1-10, (2016); Jung J., Sit E., Balakrishnan H., Morris R., Dns performance and the effectiveness of caching IEEE, ACM Trans. Netw., 10, 5, pp. 589-603, (2002); Cohen E., Kaplan H., Proactive caching of dns records: Addressing a performance bottleneck, Comput. Netw., 41, 6, pp. 707-726, (2003); Yu Y., Wessels D., Larson M., Zhang L., Authority server selection in dns caching resolvers, SIGCOMM Comput. Commun. Rev., 42, 2, pp. 80-86, (2012); Sarat S., Pappas V., Terzis A., On the use of anycast in dns, Proceedings of 15th International Conference On Computer Communications and Networks, pp. 71-78, (2006); Pan J., Hou Y.T., Li B., An overview of dns-based server selections in content distribution networks, Comput. Netw., 43, 6, pp. 695-711, (2003); Su A.J., Choffnes D.R., Kuzmanovic A., Bustamante F.E., Drafting behind akamai: Inferring network conditions based on cdn redirections IEEE, ACM Transactions On Networking, 17, 6, pp. 1752-1765, (2009); Handle Software Package; Dynamic Delegation Discovery System (Ddds); Dns Txt Resource Record; Handlednsresolver Source Code","","Ganzha M.; Maciaszek L.; Paprzycki M.","Institute of Electrical and Electronics Engineers Inc.","","2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017","3 September 2017 through 6 September 2017","Prague","131962","","978-839462537-5","","","English","Proc. Fed. Conf. Comput. Sci. Inf. Syst., FedCSIS","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85039917292"
"Teodoro D.; Mottin L.; Gobeill J.; Gaudinat A.; Vachon T.; Ruch P.","Teodoro, Douglas (35270547800); Mottin, Luc (57190048338); Gobeill, Julien (15841431700); Gaudinat, Arnaud (55916001700); Vachon, Thérèse (24767148600); Ruch, Patrick (57201506313)","35270547800; 57190048338; 15841431700; 55916001700; 24767148600; 57201506313","Improving average ranking precision in user searches for biomedical research datasets","2017","Database","2017","","bax083","","","","5","10.1093/database/bax083","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050199513&doi=10.1093%2fdatabase%2fbax083&partnerID=40&md5=8c3eafc0ed8a44a9f1d32f5e3a6a33db","Text Mining Group, SIB Swiss Institute of Bioinformatics, Geneva, 1227, Switzerland; Department of Information Science, HEG Geneva HES-SO, Geneva, 1227, Switzerland; Novartis Institutes for BioMedical Research–Text Mining Services (NIBR Informatics/TMS), Novartis Pharma AG Postfach, Basel, 4002, Switzerland","Teodoro D., Text Mining Group, SIB Swiss Institute of Bioinformatics, Geneva, 1227, Switzerland, Department of Information Science, HEG Geneva HES-SO, Geneva, 1227, Switzerland; Mottin L., Text Mining Group, SIB Swiss Institute of Bioinformatics, Geneva, 1227, Switzerland, Department of Information Science, HEG Geneva HES-SO, Geneva, 1227, Switzerland; Gobeill J., Text Mining Group, SIB Swiss Institute of Bioinformatics, Geneva, 1227, Switzerland, Department of Information Science, HEG Geneva HES-SO, Geneva, 1227, Switzerland; Gaudinat A., Department of Information Science, HEG Geneva HES-SO, Geneva, 1227, Switzerland; Vachon T., Novartis Institutes for BioMedical Research–Text Mining Services (NIBR Informatics/TMS), Novartis Pharma AG Postfach, Basel, 4002, Switzerland; Ruch P., Text Mining Group, SIB Swiss Institute of Bioinformatics, Geneva, 1227, Switzerland, Department of Information Science, HEG Geneva HES-SO, Geneva, 1227, Switzerland","Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries, and provided competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP, being þ22.3% higher than the median infAP of the participant’s best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system’s performance increasing our baseline up to þ5.0% and þ3.4% for the infAP and infNDCG metrics, respectively. The similarity measure algorithm showed robust performance in different training conditions, with small performance variations compared to the Divergence from Randomness framework. Finally, the result categorization did not have significant impact on the system’s performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results. © The Author(s) 2017. Published by Oxford University Press.","","Biomedical Research; Data Curation; Databases, Factual; Information Storage and Retrieval; factual database; information processing; information retrieval; medical research","","","","","Novartis Institutes; Novartis Institutes for BioMedical Research?Text Mining Services; Novartis; Novartis Institutes for BioMedical Research, NIBR; European Commission, EC","Funding text 1: Aware of the needs for data sharing in scientific research, several systems are being investigated and implemented to provide flexible and scalable information management that meets the scale and variety of data produced by the biomedical community (6). For example, dbGaP provides public access to large-scale genetic and phenotypic datasets required for wide association study designs (7). PhenDisco brings standardization of phenotype variables and of study metadata, and result ranking to dbGaP to improve search performance of phenotypes (8). GigaDB not only hosts research datasets but also tools, such as executable workflows, and assigns a Digital Object Identifier to datasets, which can be then used and cited by other researchers (9). OpenAIRE, a large-scale initiative funded by the European Commission, provides an open data infrastructure service that enables collection, interlink and access to research publications and datasets, and to projects of the European Commission and other national funding schemes (10). Finally, the biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium, funded by the US National Institute of Health Big Data to Knowledge program (11), aims at building a data discovery index that makes data findable, accessible, interoperable and reusable allowing thus biomedical researchers to more easily find, reanalyse and reuse data (12). A common characteristic of these systems is that they are all powered by an information retrieval engine that enables indexing of the dataset metadata and content, and allows end users to locate the appropriate research data from the set of indexed repositories.; Funding text 2: This work was supported by Novartis Institutes for BioMedical Research–Text Mining Services (NIBR Informatics/TMS), Novartis Pharma AG.; Funding text 3: This work was supported by Novartis Institutes for BioMedical Research?Text Mining Services (NIBR Informatics/TMS), Novartis Pharma AG.","Mervis J., Agencies rally to tackle big data, Science, 336, pp. 22-22, (2012); Alsheikh-Ali A.A., Qureshi W., Al-Mallah M.H., Et al., Public availability of published research data in high-impact journals, PloS One, 6, (2011); Yang H., Support the Manchester Manifesto: a case study of the free sharing of human genome data, Prometheus, 29, pp. 337-341, (2011); Anagnostou P., Capocasa M., Milia N., Et al., When data sharing gets close to 100%: what human paleogenetics can teach the open science movement, PloS One, 10, (2015); Bishop D., Reproducibility and reliability of biomedical research: improving research practise, The Academy of Medical Sciences, Symposium report, (2015); Teodoro D., Choquet R., Pasche E., Et al., Biomedical data management: a proposal framework, Stud Health Technol Inform, 150, pp. 175-179, (2009); Mailman M.D., Feolo M., Jin Y., Et al., The NCBI dbGaP database of genotypes and phenotypes, Nat. Genet, 39, pp. 1181-1186, (2007); Doan S., Lin K.W., Conway M., Et al., PhenDisco: phenotype discovery system for the database of genotypes and phenotypes, J. Am. Med. Inform. Assoc, 21, pp. 31-36, (2014); Edmunds S.C., Li P., Hunter C.I., Et al., Experiences in integrated data and research object publishing using GigaDB, International Journal on Digital Libraries, 18, pp. 99-111, (2017); Manghi P., Bolikowski L., Manold N., Et al., Openaireplus: the European scholarly communication data infrastructure, D-Lib Magazine, 18, pp. 9-10, (2012); Bourne P.E., Bonazzi V., Dunn M., Et al., The NIH Big Data to Knowledge (BD2K) initiative, J. Am. Med. Inform. Assoc, 22, pp. 1114-1114, (2015); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, 3, (2016); Ohno-Machado L., Sansone S.A., Alter G., Et al., Finding useful data across multiple biomedical data repositories using DataMed, Nat Genet, 49, pp. 816-819, (2016); Sansone S.A., Gonzalez-Beltran A., Rocca-Serra P., Et al., DATS, The Data Tag Suite to Enable Discoverability of Datasets, Scientific data, 4, (2017); Wilkinson R., Zobel J., Sacks-Davis R., Similarity measures for short queries, Fourth Text Retrieval Conference (TREC-4), pp. 277-285, (1995); Hearst M.A., Improving full-text precision on short queries using simple constraints, Proceedings of the Symposium on Document analysis and Information Retrieval, pp. 217-228, (1996); Crouch C.J., Crouch D.B., Chen Q., Et al., Improving the retrieval effectiveness of very short queries, Inform. Process. Manage, 38, pp. 1-36, (2002); Gobeill J., Pasche E., Teodoro D., Et al., Question answering for biology and medicine, Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine (ITAB 2009), pp. 1-5, (2009); Pasche E., Teodoro D., Gobeill J., Et al., QA-driven guidelines generation for bacteriotherapy, AMIA Annu Symp Proc, 2009, pp. 509-513, (2009); Gobeill J., Pasche E., Teodoro D., Et al., Answering gene ontology terms to proteomics questions by supervised macro reading in Medline, EMBnet, 18, (2012); Gobeill J., Gaudinat A., Pasche E., Et al., Deep Question Answering for protein annotation. Deep question answering for protein annotation, Database (Oxford), (2015); Roberts K., Gururaj A., Chen X., Et al., Information Retrieval for Biomedical Datasets: The 2016 bioCADDIE Dataset Retrieval Challenge, (2017); Bengio Y., Ducharme R., Vincent P., Et al., A neural probabilistic language model, J. Machine Learn. Res, 3, pp. 1137-1155, (2003); Mikolov T., Chen K., Corrado G., Et al., Efficient Estimation of Word Representations in Vector Space, (2013); Diaz F., Mitra B., Craswell N., Query Expansion with Locally-Trained Word Embeddings, (2016); Aydin F., Husunbeyi Z.M., Ozgur A., Automatic query generation using word embeddings for retrieving passages describing experimental methods, Database: J. Biol. Databases Curation, (2017); Teodoro D., Gobeill J., Pasche E., Et al., Automatic prior art searching and patent encoding at CLEF-IP’10, Proceedings of the Conference on Multilingual and Multimodal Information Access Evaluation (CLEF 2010), (2010); Teodoro D., Pasche E., Vishnyakova D., Et al., Automatic IPC encoding and novelty tracking for effective patent mining, Proceedings of the 8th NTCIR Workshop Meeting on Evaluation of Information Access Technologies (NTCIR-8), pp. 309-317, (2010); Teodoro D., Mottin L., Gobeill J., Et al., Assessing text embedding models to assign UniProt classes to scientific literature, F1000Research, 6, (2017); Levy O., Goldberg Y., Dagan I., Improving Distributional Similarity with Lessons Learned from Word Embeddings, Transactions of the Association for Computational Linguistics, 3, pp. 211-225, (2015); Rehurek R., Sojka P., Software framework for topic modelling with large corpora, Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, (2010); He B., Ounis I., Term frequency normalisation tuning for BM25 and DFR models, European Conference on Information Retrieval, pp. 200-214, (2005); Le Q.V., Mikolov T., 2014Distributed Representations of Sentences and Documents, Proceedings of the 31st International Conference on Machine Learning, 32, 2, pp. 1188-1196; Cohen T., Roberts K., Gururaj A., Et al., A Publicly Available Benchmark for Biomedical Dataset Retrieval: The Reference Standard for the 2016 bioCADDIE Dataset Retrieval Challenge, (2017); Yilmaz E., Kanoulas E., Aslam J.A., A simple and efficient sampling method for estimating AP and NDCG, Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 603-610, (2008); Amati G., Van Rijsbergen C.J., Probabilistic models of information retrieval based on measuring the divergence from randomness, ACM Trans. Inform. Syst. (TOIS), 20, pp. 357-389, (2002); Ounis I., Amati G., Plachouras V., Et al., Terrier information retrieval platform, European Conference on Information Retrieval, pp. 517-519, (2005); Amigo E., Gonzalo J., Artiles J., Et al., Combining evaluation metrics via the unanimous improvement ratio and its application to clustering tasks, J. Artif. Intel. Res, 42, pp. 689-718, (2011); Teodoro D., Pasche E., Gobeill J., Et al., Building a transnational biosurveillance network using semantic web technologies: requirements, design, and preliminary evaluation, J. Med. Internet Res, 14, (2012); Pasche E., Gobeill J., Teodoro D., Et al., An advanced search engine for patent analytics in medicinal chemistry, MIE, pp. 204-209, (2012); Azaria A., Ekblaw A., Vieira T., Lippman A., MedRec: Using Blockchain for Medical Data Access and Permission Management, Proceedings of the International Conference on Open and Big Data (OBD), pp. 25-30, (2016); Lappalainen I., Almeida-King J., Kumanduri V., Et al., The European Genome-phenome Archive of human data consented for biomedical research, Nat. Genet, 47, pp. 692-695, (2015); Gupta Y., Saini A., Saxena A.K., A new fuzzy logic based ranking function for efficient information retrieval system, Expert Systems Appl, 42, pp. 1223-1234, (2015)","D. Teodoro; Text Mining Group, SIB Swiss Institute of Bioinformatics, Geneva, 1227, Switzerland; email: douglas.teodoro@sib.swiss","","Oxford University Press","","","","","","17580463","","","29220475","English","Database","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85050199513"
"Dunie M.","Dunie, Matt (57196286833)","57196286833","The importance of research data management: The value of electronic laboratory notebooks in the management of data integrity and data availability","2017","Information Services and Use","37","3","","355","359","4","1","10.3233/ISU-170843","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032570744&doi=10.3233%2fISU-170843&partnerID=40&md5=373cbea4bb6b6b754ed73dd3b7966e86","LabArchives, 1915 Aston Ave, Carlsbad, 92008, CA, United States","Dunie M., LabArchives, 1915 Aston Ave, Carlsbad, 92008, CA, United States","Laboratory data - the data produced in the practice of the scientific method - is not consistently managed among academic labs within or external to many academic research institutions. As data on which research is based upon becomes more openly available, Data Management Plans will more often be enforced. Thus, Data integrity, Data lifecycle, Data Security, Perpetual Revision History, Permanence, and unchangeable time stamps, will be concerns that will evolve into in the management of laboratory research data. Proof of research and discovery is a major concern among researchers and there are more instances of research fraud, unintended or intentional, than most people realize. The use of a Digital Lab Notebook can help prove discoveries, protect intellectual property, and provide the tools necessary to defend or audit research activities and preserve research integrity. As the collaborative nature of scientific research continues to become more easily executed with the continual advancement of research technology, it becomes essential for researchers, funding agencies, publishers, and institutions to protect and defend the work produced in research laboratories.","Data integrity; Data management; Digital laboratory notebooks","Availability; Information management; Research laboratories; Societies and institutions; Data integrity; Digital laboratory notebook; Electronic laboratory notebook; Research activities; Research data managements; Research integrities; Research technologies; Scientific researches; Laboratories","","","","","","","Two Manifestos for Better Science, (2017); Ware M., Mabe M., The STM Report: An Overview of Scientific and Scholarly Journal Publishing, (2015)","M. Dunie; LabArchives, Carlsbad, 1915 Aston Ave, 92008, United States; email: mdunie@labarchives.com","Armbruster C.; Lawlor B.","IOS Press","","APE 2017 and NFAIS 2017","16 January 2017 through 18 January 2017","Berlin","131215","01675265","","ISUSD","","English","Inf Serv Use","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85032570744"
"Clement R.; Blau A.; Abbaspour P.; Gandour-Rood E.","Clement, Ryan (57020956400); Blau, Amy (52963105700); Abbaspour, Parvaneh (57193528920); Gandour-Rood, Eli (57193541976)","57020956400; 52963105700; 57193528920; 57193541976","Team-based data management instruction at small liberal arts colleges","2017","IFLA Journal","43","1","","105","118","13","18","10.1177/0340035216678239","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014659158&doi=10.1177%2f0340035216678239&partnerID=40&md5=4177151b69ac8a2875c73cd6f504a83a","Middlebury College, United States; Whitman College, United States; Lewis & Clark College, United States; University of Puget Sound, United States","Clement R., Middlebury College, United States; Blau A., Whitman College, United States; Abbaspour P., Lewis & Clark College, United States; Gandour-Rood E., University of Puget Sound, United States","This paper describes a collaborative approach taken by librarians at five small, regional liberal arts colleges to developing/enhancing research data management services on their campuses. The five colleges collectively belong to a consortium known as the Northwest Five Consortium. Over 10 months, librarians from the five schools collaborated to plan a data management and curation workshop with the goals of developing relationships with researchers working with data, developing their own research data management skills and services, and building a model for future training and outreach around institutional research data management services. This workshop brought together research teams including faculty, students, and librarians, and incorporated active learning modules as well as in-depth pre-workshop discussion. This article will discuss the context and background for this workshop, the model itself, and the outcomes and possibilities for future developments. © 2016, © The Author(s) 2016.","Communities of practice; data services; information literacy and instruction; preservation and conservation; research data management","","","","","","","","23 (Research Data) Things; Antell K., Foote J.B., Turner J., Et al., Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Ball R., Medeiros N., Teaching integrity in empirical research: A protocol for documenting data management and analysis, Journal of Economic Education, 43, 2, pp. 182-189, (2012); Reinventing Undergraduate Education: A Blueprint for America’s Research Universities, (1998); Brandt D.S., Librarians as partners in e-research: Purdue University Libraries promote collaboration, College & Research Libraries News, 68, 6, pp. 365-396, (2007); Bresnahan M.M., Johnson A.M., Assessing scholarly communication and research data training needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Calzada Prado J., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri: International Journal of Libraries & Information Services, 63, 2, pp. 123-134, (2013); Carlson J., Data Curation Profiles Toolkit, (2010); Carlson J., Fosmire M., Miller C.C., Et al., Determining data information literacy needs: A study of students and research faculty, portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Sapp Nelson M., Bracke M., Et al., The Data Information Literacy Toolkit, (2015); Choudhury G.S., Case study in data curation at Johns Hopkins University, Library Trends, 57, 2, pp. 211-220, (2008); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Creamer A.T., Morales M.E., Kafel D., Et al., A sample of research data curation and management courses, Journal of eScience Librarianship, 1, 2, (2012); DataONE Education Modules, (2015); DIY Research Data MANTRA Training Kit for Librarians; Goldstein S., Oelker S., Planning for data curation in the small liberal arts college environment, Sci-Tech News, 65, 3, (2011); Guy M., RDM Training for Librarians, (2013); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Hensley M.K., A survey of instructional support for undergraduate research programs, portal: Libraries and the Academy, 15, 4, pp. 719-762, (2015); Hunter A.-B., Laursen S.L., Seymour E., Becoming a scientist: The role of undergraduate research in students’ cognitive, personal, and professional development, Science Education, 91, 1, pp. 36-74, (2007); Hwse P., Holt A., Joining in the enterprise of response in the wake of the NSF data management planning requirement, Research Library Issues, 274, pp. 11-17, (2011); Definitions, The Carnegie Classification of Institutions of Higher Education; Keralis S.D.C., Data curation education: A snapshot, The Problem of Data, pp. 32-43, (2012); High-impact Educational Practices: What They Are, Who Has Access to Them, and Why They Matter, (2008); New England Collaborative Data Management Curriculum; MacMillan D., Sequencing genetics information: Integrating data into information literacy for undergraduate biology students, Issues in Science & Technology Librarianship, (2010); Mooney H., Collie W.A., Nicholson S., Et al., Collaborative approaches to undergraduate research training: Information literacy and data management, Advances in Social Work, 15, 2, pp. 368-389, (2014); Piorun M., Kafel D., Leger-Hornby T., Et al., Teaching research data management: An undergraduate/graduate curriculum, Journal of eScience Librarianship, 1, 1, (2012); Pryor G., Donnelly M., Skilling up to do data: Whose role, whose responsibility, whose career?, International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Qin J., D'Ignazio J., Lessons learned from a two-year experience in science data literacy education, International Association of Scientific and Technological University Libraries, 31st annual conference, (2010); Reisner B.A., Vaughan K.T.L., Shorish Y.L., Making data management accessible in the undergraduate chemistry curriculum, Journal of Chemical Education, 91, 11, pp. 1943-1946, (2014); Essentials 4 Data Support, (2016); Rice R., Research data MANTRA: A labour of love, Journal of eScience Librarianship, 3, 1, (2014); Rowlett R.S., Blockus L., Larson S., Characteristics of Excellence in Undergraduate Research (COEUR), pp. 2-19, (2012); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Shadbolt A., Konstantelos L., Lyon L., Et al., Delivering innovative RDM training: The immersive informatics pilot programme, International Journal of Digital Curation, 9, 1, pp. 313-323, (2014); Shorish Y., Data curation is for everyone! The case for Master’s and Baccalaureate institutional engagement with data curation, Journal of Web Librarianship, 6, 4, pp. 263-273, (2012); Shorish Y., Data information literacy and undergraduates: A critical competency, College & Undergraduate Libraries, 22, 1, pp. 97-106, (2015); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions. Report, (2010); Stamatoplos A., The role of academic libraries in mentored undergraduate research: A model of engagement in the academic community, College & Research Libraries, 70, 3, pp. 235-249, (2009); Stamatoplos A., Neville T., Henry D., Analyzing the data management environment in a Master’s-level institution, Journal of Academic Librarianship, 42, 2, pp. 154-160, (2016); Stephenson E., Caravello P.S., Incorporating data literacy into undergraduate information literacy programs in the social sciences: A pilot project, Reference Services Review, 35, 4, pp. 525-540, (2007); Strasser C.A., Hampton S.E., The fractured lab notebook: Undergraduates and ecological data management training in the United States, Ecosphere, 3, 12, pp. 1-18, (2012); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future. Report, (2012); Tenopir C., Sandusky R.J., Allard S., Et al., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Sandusky R.J., Allard S., Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Toups M., Hughes M., When data curation isn’t: A redefinition for liberal arts universities, Journal of Library Administration, 53, 4, pp. 223-233, (2013); Verbakel E., Noordegraaf M., de Smaele M., Et al., Data-intelligence training for library staff, (2013); Walton G., Data curation and the academic library, New Review of Academic Librarianship, 16, 1, pp. 1-3, (2010); Watkins W., Hamilton E., Boyko E., Et al., Creating a national peer-to-peer training program for data librarians in Canada, IASSIST Quarterly, 28, 2-3, (2004); Zilinski L.D., Nelson M.S., Van Epps A.S., Developing professional skills in STEM students: Data information literacy, Issues in Science and Technology Librarianship, (2014)","R. Clement; Middlebury College, Middlebury, Vermont, 110 Storrs Ave, 05753, United States; email: rclement@middlebury.edu","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85014659158"
"Chigwada J.; Chiparausha B.; Kasiroori J.","Chigwada, Josiline (57193754137); Chiparausha, Blessing (57193754178); Kasiroori, Justice (57194939608)","57193754137; 57193754178; 57194939608","Research data management in research institutions in Zimbabwe","2017","Data Science Journal","16","","31","","","","23","10.5334/dsj-2017-031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024376421&doi=10.5334%2fdsj-2017-031&partnerID=40&md5=4dc46bc8747d473a2416cd4c98ae502b","Bindura University of Science Education, Zimbabwe","Chigwada J., Bindura University of Science Education, Zimbabwe; Chiparausha B., Bindura University of Science Education, Zimbabwe; Kasiroori J., Bindura University of Science Education, Zimbabwe","The research was aimed at evaluating how research data are being managed in research institutions in Zimbabwe. The study also sought to assess the challenges that are faced in research data management by research institutions in Zimbabwe. Twenty five institutions of higher learning and other organisations that deal with research were selected using purposive sampling to participate in the study. An online questionnaire on SurveyMonkey was sent to the selected participants and telephone interviews were done to follow up on participants who failed to respond on time. Data that were collected using interviews were entered manually into SurveyMonkey for easy analysis. It was found out that proper research data management is not being done. Researchers were managing their own research data. Most of the research data were in textual and spreadsheet format. Graphical, audio, video, database, structured text formats and software applications research data were also available. Lack of guidelines on good practice, inadequate human resources, technological obsolescence, insecure infrastructure, use of different vocabulary between librarians and researchers, inadequate financial resources, absence of research data management policies and lack of support by institutional authorities and researchers negatively impacted on research data management. Authors recommend the establishment of research data repositories and use of existing research data repositories that are registered with the Registry of Research Data Repositories to ensure that research data standards are adhered to when doing research. © 2017 The Author(s).","Data sharing; Research data; Research data management; Research data services; Research institutions","Application programs; Obsolescence; Societies and institutions; Surveys; Data Sharing; Financial resources; Online questionnaire; Research data; Research data managements; Research institutions; Software applications; Technological obsolescence; Information management","","","","","","","Australian Code for Responsible Research, (2015); Buys C.M., Shaw P.L., Data Management Practices Across an Institution: Survey and Report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Corti L., Van Den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2011); Faryowen, Developing an Institutional Research Data Management Plan Service, June, (2013); Grace S., Whyte A., Rans J., Building UEL’s Research Data Repository Capabilities with Eprint, (2015); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); (2013); Research Data Management Policy, (2016); Re3data.Org Reaches a Milestone and Begins Offering Badges, (2016); Rice R., DISC-UK Datashare Project: Final Report, (2009); Surkis A., Read K., Research data management, Journal of the Medical Library Association: JMLA, 103, 3, pp. 154-156, (2015); Tenopir C., Hughes D., Allard S., Fram M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research Data Services in Academic Libraries: Data Intensive Roles for the Future?, Journal of Escience Librarianship, 4, 2, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research Data Services in European Academic Research Libraries, Submitted to LIBER QUARTERLY, (2016); (2017); University of Leeds Research Data Management Policy, (2016); The University of Manchester Research Data Management Policy, (2016); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Whyte A., Tedds J., Making the Case for Research Data Management, DCC Briefing Papers, (2011); Wilson F., Martinez-Uribe P., Akram M., Developing Infrastructure for Research Data Management at the University of Oxford, (2010)","J. Chigwada; Bindura University of Science Education, Zimbabwe; email: josyphiri@gmail.com","","Ubiquity Press Ltd","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85024376421"
"Müller J.; Heiss K.I.; Oberhoffer R.","Müller, Jan (35208780300); Heiss, Kirsten Ingmar (18433983200); Oberhoffer, Renate (6701686659)","35208780300; 18433983200; 6701686659","Implementation of an open adoption research data management system for clinical studies","2017","BMC Research Notes","10","1","252","","","","8","10.1186/s13104-017-2566-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022200027&doi=10.1186%2fs13104-017-2566-0&partnerID=40&md5=3199e0f5cf1c6f61ce4772e4085cf3ea","Institute of Preventive Pediatrics, Technische Universität München, Uptown München-Campus D, Georg-Brauchle-Ring 60/62, Munich, 80992, Germany; OpenCampus GmbH, Kastenbauerstraße 2, Munich, 81677, Germany","Müller J., Institute of Preventive Pediatrics, Technische Universität München, Uptown München-Campus D, Georg-Brauchle-Ring 60/62, Munich, 80992, Germany; Heiss K.I., OpenCampus GmbH, Kastenbauerstraße 2, Munich, 81677, Germany; Oberhoffer R., Institute of Preventive Pediatrics, Technische Universität München, Uptown München-Campus D, Georg-Brauchle-Ring 60/62, Munich, 80992, Germany","Background: Research institutions need to manage multiple studies with individual data sets, processing rules and different permissions. So far, there is no standard technology that provides an easy to use environment to create databases and user interfaces for clinical trials or research studies. Therefore various software solutions are being used - from custom software, explicitly designed for a specific study, to cost intensive commercial Clinical Trial Management Systems (CTMS) up to very basic approaches with self-designed Microsoft® databases. Findings: The technology applied to conduct those studies varies tremendously from study to study, making it difficult to evaluate data across various studies (meta-analysis) and keeping a defined level of quality in database design, data processing, displaying and exporting. Furthermore, the systems being used to collect study data are often operated redundantly to systems used in patient care. As a consequence the data collection in studies is inefficient and data quality may suffer from unsynchronized datasets, non-normalized database scenarios and manually executed data transfers. Conclusions: With OpenCampus Research we implemented an open adoption software (OAS) solution on an open source basis, which provides a standard environment for state-of-the-art research database management at low cost. © 2017 The Author(s).","Clinical trials; Data management; Open-source software","Biomedical Research; Clinical Studies as Topic; Humans; Information Storage and Retrieval; Medical Informatics Applications; Software; clinical study; human; information retrieval; medical informatics; medical research; procedures; software","","","","","Deutsche Forschungsgemeinschaft, DFG; Technische Universität München, TUM","This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.","OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Lang T., Cheah P.Y., White N.J., Clinical research: Time for sensible global guidelines, Lancet, 377, 9777, pp. 1553-1555, (2011); Yusuf S., Bosch J., Devereaux P.J., Collins R., Baigent C., Granger C., Et al., Sensible guidelines for the conduct of large randomized trials, Clin Trials., 5, 1, pp. 38-39, (2008); Lang T., Siribaddana S., Clinical trials have gone global: Is this a good thing?, PLoS Med., 9, 6, (2012); Hemminki A., Kellokumpu-Lehtinen P.L., Harmful impact of EU clinical trials directive, BMJ, 332, 7540, pp. 501-502, (2006); Shah J., Rajgor D., Pradhan S., McCready M., Zaveri A., Pietrobon R., Electronic data capture for registries and clinical trials in orthopaedic surgery: Open source versus commercial systems, Clin Orthop Relat Res., 468, 10, pp. 2664-2671, (2010); Leroux H., McBride S., Gibson S., On selecting a clinical trial management system for large scale, multi-centre, multi-modal clinical research study, Stud Health Technol Inform., 168, pp. 89-95, (2011)","J. Müller; Institute of Preventive Pediatrics, Technische Universität München, Uptown München-Campus D, Munich, Georg-Brauchle-Ring 60/62, 80992, Germany; email: j.mueller@tum.de","","BioMed Central Ltd.","","","","","","17560500","","","28683771","English","BMC Res. Notes","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85022200027"
"Flathers E.; Kenyon J.; Gessler P.E.","Flathers, Edward (57193311465); Kenyon, Jeremy (55531964300); Gessler, Paul E (7004083483)","57193311465; 55531964300; 7004083483","A service-based framework for the OAIS model for earth science data management","2017","Earth Science Informatics","10","3","","383","393","10","2","10.1007/s12145-017-0297-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012935784&doi=10.1007%2fs12145-017-0297-3&partnerID=40&md5=dd7882861aca2bc517908ea9243b3ea8","College of Natural Resources, University of Idaho, Moscow, ID, United States; University Libraries, University of Idaho, Moscow, ID, United States","Flathers E., College of Natural Resources, University of Idaho, Moscow, ID, United States; Kenyon J., University Libraries, University of Idaho, Moscow, ID, United States; Gessler P.E., College of Natural Resources, University of Idaho, Moscow, ID, United States","The Consultative Committee for Space Data Systems (CCSDS), in 2002, released their first version of a Reference Model for an Open Archival Information System (OAIS). In 2003, the model was adopted by the International Standards Organization (ISO) as ISO 14721:2003. The CCSDS document was updated in 2012 with additional focus on verifying the authenticity of data and developing concepts of access rights and a security model. The OAIS model is the basis of research data management systems across institutions and disciplines around the world. The Organization for the Advancement of Structured Information Standards (OASIS), in 2006, released their first version of a Reference Model for Service Oriented Architecture (SOA). OASIS defines the SOA as “a paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains.” Systems designed around the SOA model benefit from improved scalability, flexibility, and agility. This paper applies the SOA model to the OAIS repository to describe how repositories can be implemented and extended through the use of services that may be internal or external to the host institution, including the consumption of network- or cloud-based services and resources. We use the Service Oriented Architecture (SOA) design paradigm to describe a set of potential extensions to OAIS Reference Model: purpose and justification for each extension, where and how each extension connects to the model, and an example of a specific service that meets the purpose. © 2017, The Author(s).","Data management; Open archival information system (OAIS); Repositories; Service oriented architecture (SOA)","","","","","","U.S. Department of Agriculture, USDA, (2011-68002-30191); National Institute of Food and Agriculture, NIFA","This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2011-68002-30191.","Ahrens J., Hendrickson B., Long G., Et al., Data-intensive science in the US DOE: case studies and future challenges, Comput Sci Eng, 13, 6, pp. 14-24, (2011); Amorim R.C., Castro J.A., da Silva J.R., Ribeiro C., A comparison of research data management platforms: architecture, flexible metadata and interoperability, Univ Access Inf Soc, pp. 1-12, (2016); Barnett W., Stewart C.A., Walsh A., Welch V., A roadmap for using NSF cyberinfrastructure with InCommon, (2011); Bhatti R., Bertino E., Ghafoor A., Federated identity and privilege management, Commun ACM, 50, 2, pp. 81-88, (2007); Brown W.J., Malveau R.C., McCormick H.W., Et al., AntiPatterns: refactoring software, architectures, and projects in crisis, (1998); Audit and certification of trustworthy repositories, (2011); CCSDS: Consultative Committee for Space Data Systems.reference model for an Open Archival Information System (OAIS), (2012); Channabasavaiah K., Holley K., Tuggle E., Migrating to a service-oriented architecture, Part, (2003); Clarke L., Zheng-Bradley X., Smith R., Et al., The 1000 genomes project: data management and community access, Nat Methods, 9, 5, pp. 459-462, (2012); Cohen S., Ontology and taxonomy of services in a service-oriented architecture, Microsoft Architecture J, 11, pp. 30-35, (2007); Duerr R.E., Downs R.R., Tilmes C., Et al., On the utility of identification schemes for digital earth science data: an assessment and recommendations, Earth Sci Inf, 4, (2011); Goodchild M.F., Beyond metadata: Towards user-centric description of data quality. Keynote paper, Proceedings, 5th Int. Symposium Spatial Data Quality, ITC, Netherlands, 13–15 June, (2007); Gunelius S., The Data Explosion in 2014 Minute by Minute, (2014); Haak L.L., Fenner M., Paglione L., Et al., ORCID: a system to uniquely identify researchers, Learned Publishing, 25, 4, pp. 259-264, (2012); Haeberli C., dos Anjos A., Becket H.P., Et al., ATLAS TDAQ data collection software, IEEE Trans Nucl Sci, 51, 3, pp. 585-590, (2004); Haendel M.A., Vasilevsky N.A., Wirz J.A., Dealing with data: a case study on information and data management literacy, PLoS Biol, 10, 5, (2012); Han H., Giles L., Zha H., Et al., Two supervised learning approaches for name disambiguation in author citations, Proceedings of the 2004 joint ACM/IEEE conference on Digital Libraries, pp. 296-305, (2004); Hey A.J.G., Trefethen A.E., The data deluge: an e-science perspective, Grid computing: making the global infrastructure a reality, pp. 809-824, (2003); Hilbert M., Lopez P., The world’s technological capacity to store, communicate, and compute information, Science, 332, 6025, pp. 60-65, (2011); DOI Handbook, (2012); ISO/TS 19139:2007: Geographic information–Metadata–XML schema implementation, (2007); Josuttis N.M., Versioning, St. Laurent S (ed) SOA in practice: the art of distributed system design. O'Reilly Media, Sebastopol CA, pp. 145-157, (2007); Kunze J., Boyko A., Littman J., Et al., The bagit file packaging format, (2011); Lavoie B.F., The Open Archival Information System (OAIS) reference model: introductory guide (2nd Edition), (2014); Liakos P., Koltsida P., Kakaletris G., Et al., A distributed infrastructure for earth-science big data retrieval, Int J Coop Inf Syst, 24, 2, (2015); Marcial L.H., Hemminger B.M., Scientific data repositories on the web: an initial survey, J Am Soc Inf Sci Technol, 61, 10, pp. 2029-2048, (2010); Nezhad H.R.M., Benatallah B., Casati F., Toumani F., Web services interoperability specifications, Computer, 39, 5, pp. 24-32, (2006); Reference model for service oriented architecture version, (2006); Palma F., Nayrolles M., Moha N., Et al., SOA antipatterns: an approach for their specification and detection, Int J Coop Inf Syst, 22, 4, (2013); Papazoglou M.P., Van Den Heuvel W.J., Service-oriented design and development methodology, Int J Web Eng Technol, 2, 4, pp. 412-442, (2006); Pessoa R.M., Silva E., van Sinderen M., Et al., Enterprise interoperability with SOA: a survey of service composition approaches, 12th Enterprise Distributed Object Computing Conference Workshops, (2008); PREMIS data dictionary for preservation metadata version, (2008); (2016); Reichman O.J., Jones M.B., Schildhauer M.P., Challenges and opportunities of open data in ecology, Science, 331, 6018, pp. 703-705, (2011); Ren M., Lyytinen K.J., Building enterprise architecture agility and sustenance with SOA, Commun Assoc Inf Syst, 22, 1, (2008); Sen A., Metadata management: past, present and future, Decis Support Syst, 37, 1, pp. 151-173, (2004); Shokouhi M., Federated search, Found Trends Inf Retr, 5, 1, pp. 1-102, (2011); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); Tsai W.T., Service-oriented system engineering: a new paradigm. Proceedings of the 2005 I.E. International Workshop on Service-Oriented System, Engineering, pp. 3-6, (2005); Wong-Bushby I., Egan R., Isaacson C., A case study in SOA and re-architecture at company ABC. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, (2006); Yarmey L., Khalsa S.L., Building on the international polar year: discovering interdisciplinary data through federated search, Data Sci J, 13, pp. PDA79-PDA82, (2014); Zikopoulos P., Eaton C., deRoos D., Et al., Understanding big data: analytics for enterprise class hadoop and streaming data, (2011)","E. Flathers; College of Natural Resources, University of Idaho, Moscow, United States; email: flathers@uidaho.edu","","Springer Verlag","","","","","","18650473","","","","English","Earth Sci. Informatics","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85012935784"
"Renwick S.; Winter M.; Gill M.","Renwick, Shamin (9638076500); Winter, Marsha (57202624014); Gill, Michelle (57193537603)","9638076500; 57202624014; 57193537603","Managing research data at an academic library in a developing country","2017","IFLA Journal","43","1","","51","64","13","16","10.1177/0340035216688703","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014716788&doi=10.1177%2f0340035216688703&partnerID=40&md5=c31199b823a3bd939cd878a225706af1","University of the West Indies, St Augustine, Trinidad and Tobago","Renwick S., University of the West Indies, St Augustine, Trinidad and Tobago; Winter M., University of the West Indies, St Augustine, Trinidad and Tobago; Gill M., University of the West Indies, St Augustine, Trinidad and Tobago","Managing research data has become an issue for many universities. In the Caribbean, the St Augustine Campus Libraries at the University of the West Indies are keenly aware of the need to support researchers in this regard. The objectives of this study were to identify current practices in managing research data on the campus and to determine a possible role for the Campus Libraries. A pilot study of 100 researchers on the campus was conducted. Analysis of the 65 valid responses revealed that while researchers owned data sets they had little knowledge or experience in managing such. This low level of awareness is instructive and validates a role for the Campus Libraries to play in supporting researchers on campus. The Campus Libraries need to sensitize researchers about what data planning and managing research data entail as well as provide technical assistance with actual data storage. © 2017, © The Author(s) 2017.","Academic libraries; Caribbean; data management; research data management; research data service; University of the West Indies","","","","","","","","Andaur G., Research data management in Latin America and the Caribbean: An overview, (2016); Breeding M., Systems librarian, Computers in Libraries, 36, 3, pp. 15-17, (2016); Buys C.M., Shaw P.L., Data management practices across an institution: Survey and report, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Clairbourn M.P., Bigger on the inside: Building research data services at the University of Virginia, Insights, 28, 2, pp. 100-106, (2015); Coates H., Ensuring research integrity: The role of data management in current crises, College & Research Libraries News, 75, 11, pp. 598-601, (2014); Creamer A., Current issues and approaches to curating student research data, Bulletin of the Association for Information Science and Technology, 41, 6, pp. 22-25, (2015); Doty J., Et al., Making student research data discoverable: A pilot program using Dataverse, JLSC, 3, 2, pp. 1-25, (2015); Erway R., Starting the Conversation: University-Wide Research Data Management Policy, (2013); Fearon D., Gunia B., Pralle B., Et al., SPEC Kit 334: Research Data Management Services, Association of Research Libraries, (2013); Goldman J., Kafel D., Martin E.R., Assessment of data management services at New England Region Resource Libraries, Journal of eScience Librarianship, 4, 1, pp. 1-17, (2015); Grynoch T., Implementing research data management services in a Canadian context, Dalhousie Journal of Interdisciplinary Management, 12, (2016); Henderson M.E., Knott T.L., Starting a research data management program based in a university library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Mannheimer S., Ready, engage! Outreach for library data services, Bulletin of the Association for Information Science and Technology, 41, 1, pp. 42-44, (2014); Pinfield S., Cox A.M., Smith J., Research data and libraries: Relationships, activities, drivers and influences, PLoS ONE, 12, pp. 1-28, (2014); Pryor G., Managing Research Data, (2012); Shen Y., Varvel V.E., Developing data management services at the Johns Hopkins University, Journal of Academic Librarianship, 39, 6, pp. 552-557, (2013); Swanson J., Reinhart A.K., Data in context: Using case studies to generate a common understanding of data in academic libraries, Journal of Academic Librarianship, 42, 1, pp. 97-101, (2016); Tenopir C., Sandusky R.J., Allard S., Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Tenopir C., Hughes D., Allard S., Et al., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Strategic Plan, (2012); RDI Fund: The UWI-Trinidad and Tobago Research and Development Impact Fund, (2015); About UWI, (2015); Research and Information Management System, (2016); van Teijlingen E.R., Hundley V., The importance of pilot studies, Social Research UPDATE, (2001); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library and information Systems, 49, 4, pp. 382-407, (2015)","S. Renwick; Alma Jordan Library, University of the West Indies, Trinidad and Tobago; email: shamin.renwick@sta.uwi.edu","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85014716788"
"Thielen J.; Hess A.N.","Thielen, Joanna (57191907253); Hess, Amanda Nichols (55960905300)","57191907253; 55960905300","Advancing Research Data Management in the Social Sciences: Implementing Instruction for Education Graduate Students Into a Doctoral Curriculum","2017","Behavioral and Social Sciences Librarian","36","1","","16","30","14","13","10.1080/01639269.2017.1387739","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046069935&doi=10.1080%2f01639269.2017.1387739&partnerID=40&md5=0a451b955ed20a040fbc67db28068f8b","Oakland University Libraries, Rochester, MI, United States","Thielen J., Oakland University Libraries, Rochester, MI, United States; Hess A.N., Oakland University Libraries, Rochester, MI, United States","Research data management (RDM) skills are vital yet often untaught in graduate programs, especially in the social sciences. In this article, the authors present a case study of how a research data librarian and an education librarian partnered to provide targeted RDM instruction for a previously unconsidered student group: education doctoral students. They discuss the design, development, and implementation of this focused RDM support. Assessment data from a workshop and in-class sessions are presented and contextualized. From this information, the authors offer practical suggestions that other social science librarians can use to create similar workshops at their institutions. © 2018, © 2018 Published with license by Taylor & Francis Group, LLC. © 2018, © Joanna Thielen and Amanda Nichols Hess.","data literacy; education students; graduate students; outreach; research data management","article; case report; clinical article; controlled study; curriculum; graduate student; human; human experiment; information processing; librarian; literacy; sociology","","","","","","","Adamick J., Reznik-Zellen R., Sheridan M., Data management training for graduate students at a large research university, Journal of eScience Librarianship, 1, 3, (2012); Information Literacy Competency Standards for Higher Education, (2000); Characteristics of programs of information literacy that illustrate best practices, (2012); Framework for information literacy for higher education, (2016); (2016); Information literacy standards for teacher education, (2011); 2012 Top ten trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 73, 6, pp. 311-320, (2012); Borgman C., Data, disciplines, and scholarly publishing, Learned Publishing, 21, 1, pp. 29-38, (2008); Brandt D.S., Librarians as partners in e-research: Purdue University libraries promote collaboration, College & Research Libraries News, 68, 6, pp. 365-367, (2007); Bhavnagri N.P., Bielat V., Faculty-librarian collaboration to teach research skills: Electronic symbiosis, Reference Librarian, 43, 89-90, pp. 121-138, (2005); Blummer B., Watulak S., Kenton J., The research experience for education graduate students: a phenomenographic study, Internet Reference Services Quarterly, 17, 3-4, pp. 117-146, (2012); Briney K., Data management for researchers, (2015); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty, portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Nelson M.S., Johnston L.R., Koshoffer A., Developing data literacy programs: Working with faculty, graduate students and undergraduates, Bulletin of the American Society for Information Science and Technology, 41, 6, pp. 14-17, (2015); Carlson J., Stowell-Bracke M., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality field station, portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Corti L., denEynden V.V., Learning to manage and share data: Jump-starting the research methods curriculum, International Journal of Social Research Methodology, 18, 5, pp. 545-559, (2015); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Crouse W.F., Kasbohm K.E., Information literacy in teacher education: A collaborative model, Educational Forum, 69, 1, pp. 44-52, (2005); Duke T.S., Ward J.D., Preparing information literate teachers: A metasynthesis, Library and Information Science Research, 31, 4, pp. 247-256, (2009); Eaker C., Planning data management education initiatives: Process, feedback, and future directions, Journal of eScience Librarianship, 3, 1, (2014); Emmons M., Keefe E.B., Moore V.M., Sanchez R.M., Mals M.M., Neely T.Y., Teaching information literacy skills to prepare teachers who can bridge the research-to-practice gap, Reference & User Services Quarterly, 49, 2, pp. 140-150, (2009); Federer L.M., Lu Y.-L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Frugoli J., Etgen A., Kuhar M., Developing and communicating responsible data management policies to trainees and colleagues, Science and Engineering Ethics, 16, 4, pp. 753-762, (2010); Hollister C., Schroeder R., The impact of library support on education faculty research productivity: An exploratory study, Behavioral & Social Sciences Librarian, 34, 3, pp. 97-115, (2015); Johnston L., Jeffryes J., Steal this idea: A library instructors' guide to educating students in data management skills, College & Research Library News, 75, 8, pp. 431-434, (2014); Johnston L., Jeffryes J., Data management skills needed by structural engineering students: Case study at the University of Minnesota, Journal of Professional Issues in Engineering Education & Practice, 140, 2, (2013); Kovalik C.L., Jensen M.L., Schloman B., Tipton M., Information literacy, collaboration, and teacher education, Communications in Information Literacy, 4, 2, pp. 145-169, (2010); Kumar S., Ochoa M., Edwards M., Considering information literacy skills and needs: Designing library instruction for the online learner, Communications in Information Literacy, 6, 1, pp. 91-106, (2012); McMillen P.S., Garcia J., Bolin D.A., Promoting professionalism in master's level teachers through research based writing, Journal of Academic Librarianship, 36, 5, pp. 427-439, (2010); Doctoral research degrees—School of Education and Human Services; Graduate enrollment by program (Fall)—10 Sources, (2016); Piorun M., Kafel D., Leger-Hornby T., Teaching research data management: An undergraduate/graduate curriculum, Journal of eScience Librarianship, 1, 1, (2012); Savin-Baden M., Tombs G., Research methods for education in the digital age, (2017); Whitmire A., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Witt S.W., Dickinson J.B., Teaching teachers to teach: Collaborating with a university education department to teach skills in information literacy pedagogy, Behavioral and Social Sciences Librarian, 22, 1, pp. 75-95, (2003)","J. Thielen; Research Data and Science Librarian, Oakland University Libraries, Rochester, 100 Library Dr, 48309, United States; email: jthielen@oakland.edu","","Routledge","","","","","","01639269","","BSSLD","","English","Behav. Soc. Sci. Libr.","Article","Final","","Scopus","2-s2.0-85046069935"
"Matsubayashi M.; Kurata K.","Matsubayashi, Mamiko (16029220300); Kurata, Keiko (7101658552)","16029220300; 7101658552","Conceptual design for comprehensive research support platform: Successful research data management generating big data from little data","2017","Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017","2018-January","","","4407","4409","2","3","10.1109/BigData.2017.8258475","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047771871&doi=10.1109%2fBigData.2017.8258475&partnerID=40&md5=34050a69b136b36baee9860459c30455","Faculty of Library, Information and Media Science, University of Tsukuba, Ibaraki, Japan; Faculty of Letters, Keio University, Tokyo, Japan","Matsubayashi M., Faculty of Library, Information and Media Science, University of Tsukuba, Ibaraki, Japan; Kurata K., Faculty of Letters, Keio University, Tokyo, Japan","Data sharing, which is hot issues in scholarly communication, is regarded as generating big data from little data in little science. In this article, a conceptual framework for research support platform in university is proposed, by the survey of two cases of representative and subject-based data archives in Japan; Data Integration and Analysis System Program (DIAS) and Inter-university Upper atmosphere Global Observation Network (IUGONET). © 2017 IEEE.","data archive; data sharing; open science; research data management; scholarly communication","Big data; Conceptual design; Information management; Upper atmosphere; Data archives; Data Sharing; Open science; Research data managements; Scholarly communication; Data integration","","","","","Japan Society for the Promotion of Science, JSPS, (JP26280121)","ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number JP26280121.","Fiscutean A., Inside the large hadron collider: How IT powers the greatest experiment in history, ZDnet, (2015); Kurata K., Matsubayashi M., Mine S., Identifying the complex position of research data and data sharing among researchers in natural science, SAGE Open, 7, 3, (2017); Kaye J., Bruce R., Fripp D., Establishing a shared research data service for UK universities, Insights, 30, 1, pp. 59-70; Kurata K., Matsubayashi M., Takeda M., Research data management in Japanese universities and research institutions: Status report based on questionnaire survey, J of Inf Proc Mngt, 60, 2, pp. 119-127, (2017)","","Nie J.-Y.; Obradovic Z.; Suzumura T.; Ghosh R.; Nambiar R.; Wang C.; Zang H.; Baeza-Yates R.; Baeza-Yates R.; Hu X.; Kepner J.; Cuzzocrea A.; Tang J.; Toyoda M.","Institute of Electrical and Electronics Engineers Inc.","Cisco; Elsevier; IEEE; IEEE Computer Society; The Mit Press","5th IEEE International Conference on Big Data, Big Data 2017","11 December 2017 through 14 December 2017","Boston","134260","","978-153862714-3","","","English","Proc. - IEEE Int. Conf. Big Data, Big Data","Conference paper","Final","","Scopus","2-s2.0-85047771871"
"Harvey M.J.; McLean A.; Rzepa H.S.","Harvey, Matthew J. (8855686600); McLean, Andrew (56765222800); Rzepa, Henry S. (7005542267)","8855686600; 56765222800; 7005542267","A metadata-driven approach to data repository design","2017","Journal of Cheminformatics","9","1","9","","","","9","10.1186/s13321-017-0190-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010952844&doi=10.1186%2fs13321-017-0190-6&partnerID=40&md5=e8d574100b5eb42434f815c2ab5658f6","High Performance Computing Service, Imperial College London, London, United Kingdom; ICT Division, Imperial College London, London, United Kingdom; Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom","Harvey M.J., High Performance Computing Service, Imperial College London, London, United Kingdom; McLean A., ICT Division, Imperial College London, London, United Kingdom; Rzepa H.S., Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom","The design and use of a metadata-driven data repository for research data management is described. Metadata is collected automatically during the submission process whenever possible and is registered with DataCite in accordance with their current metadata schema, in exchange for a persistent digital object identifier. Two examples of data preview are illustrated, including the demonstration of a method for integration with commercial software that confers rich domain-specific data analytics without introducing customisation into the repository itself. © 2017 The Author(s).","Data preview; Data repository; DataCite; Metadata-driven; Mpublish","","","","","","","","Downing J., Murray-Rust P., Tonge A.P., Morgan P., Rzepa H.S., Cotterill F., Day N., Harvey M.J., SPECTRa: The deposition and validation of primary chemistry research data in digital repositories, J Chem Inf Mod, 48, pp. 1571-1581, (2008); Harvey M.J., Mason N.J., McLean A., Rzepa H.S., Digital repository curation. SWORD-endpoint creation and Data Visualisations using metadata exchange standards, J Cheminformatics, 7, (2015); Rzepa H.S., McLean A., Harvey M.J., InChI as a research data management tool, Chem Int, 38, 3-4, pp. 24-26, (2016); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci Data, 3, (2016); Metadata Can Be Retrieved Using; Metadata Can Be Retrieved Using; The Specification and Implementation of the InChI Algorithm Is Managed by the InChI Trust; O'Boyle N.M., Banck M., James C.A., Morley C., Vandermeersch T., Hutchison G.R., Open babel: An open chemical toolbox, J. Cheminformatics, 3, (2011); InChI.js. InChI for the Web Browser; Open Archives Initiative Object Reuse and Exchange; Harvey M.J., Mason N.J., McLean A., Rzepa H.S., Standards-based metadata procedures for retrieving data for display or mining utilizing Persistent (data-DOI) Identifiers, J Cheminformatics, 7, (2015); DOI Content Negotiation; Harvey M.J., Mason N.J., Rzepa H.S., Digital data repositories in chemistry and their integration with journals and electronic laboratory notebooks, J Chem Inf Model, 54, pp. 2627-2635, (2014); Registry of Research Data Repositories; Imperial College High Performance Computing Service Data Repository; Rzepa H.S., McLean A., Harvey M.J., XML Registration with re3data, (2016); Rzepa H.S., McLean A., Harvey M.J., Data Repository Project, (2016); Richer J., User Authentication with OAuth 2.0; DataCite Metadata Schema for the Publication and Citation of Research Data. Version 4.0, (2016); Harvey M.J.; Boney K., Braddock D.C., Clarke J., Rzepa H.S., Yaqoob M., White A.J., Epimeric face-selective oxidations and diastereodivergent transannular oxonium ion formation-fragmentations: Computational modelling and total syntheses of 12-epoxyobtusallene IV, 12-epoxyobtusallene II, Obtusallene X, Marilzabicycloallene C and Marilzabicycloallene D. Imperial College HPC Data Repository, (2016); Boney K., Braddock D.C., Clarke J., Rzepa H.S., Yaqoob M., White A.J., Epimeric face-selective oxidations and diastereodivergent transannular oxonium ion formation-fragmentations: Computational modelling and total syntheses of 12-epoxyobtusallene IV, 12-epoxyobtusallene II, obtusallene X, marilzabicycloallene C and marilzabicycloallene D, J Org Chem, 81, pp. 9539-9552, (2016); Hanson R.M., Prilusky J., Zhou R., Nakane T., Sussman J.L., JSmol and the next-generation web-based representation of 3D molecular structure as applied to proteopedia, Israel J Chem, 53, pp. 207-216, (2013); Boney K., Braddock D.C., Clarke J., Rzepa H.S., Yaqoob M., White A.J., FAIR Data Table. Computed Relative Reaction Free Energies (kcal/mol-1) of Obtusallene Derived Oxonium and Chloronium Cations, (2016)","H.S. Rzepa; Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; email: rzepa@imperial.ac.uk","","BioMed Central Ltd.","","","","","","17582946","","","","English","J. Cheminformatics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85010952844"
"Ball A.; Darlington M.; McMahon C.","Ball, Alexander (55796629584); Darlington, Mansur (7004413270); McMahon, Christopher (55887655100)","55796629584; 7004413270; 55887655100","The minimum mandatory metadata sets for the KIM project and RAIDmap","2017","Developing Metadata Application Profiles","","","","37","64","27","0","10.4018/978-1-5225-2221-8.ch003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027511550&doi=10.4018%2f978-1-5225-2221-8.ch003&partnerID=40&md5=1178a0448a79d2f4660fa10aa577574c","University of Bath, United Kingdom; Department of Mechanical Engineering, University of Bristol, United Kingdom","Ball A., University of Bath, United Kingdom; Darlington M., University of Bath, United Kingdom; McMahon C., Department of Mechanical Engineering, University of Bristol, United Kingdom","A Minimum Mandatory Metadata Set (M3S) was devised for the KIM (Knowledge and Information Management Through Life) Project to address two challenges. The first was to ensure the project's documents were sufficiently self-documented to allow them to be preserved in the long term. The second was to trial the M3S and supporting templates and tools as a possible approach that might be used by the aerospace, defence and construction industries. A different M3S was devised along similar principles by a later project called REDm-MED (Research Data Management for Mechanical Engineering Departments). The aim this time was to help specify a tool for documenting research data records and the associations between them, in support of both preservation and discovery. In both cases the emphasis was on collecting a minimal set of metadata at the time of object creation, on the understanding that later processes would be able to expand the set into a full metadata record. © 2017, IGI Global.","","Construction industry; Information management; Mechanical Engineering Department; Object creation; Research data; Research data managements; Metadata","","","","","","","Adobe XMP developer center.; Ball A., Darlington M., Howard T., McMahon C., Culley S., Visualizing research data records for their better management, Journal of Digital Information, 13, 1, (2012); Ball A., Patel M., McMahon C., Green S., Clarkson J., Culley S., A grand challenge: Immortal information and through-life knowledge management (KIM), International Journal of Digital Curation, 1, 1, pp. 53-59, (2006); Ball A., Thangarajah U., RAIDmap application developer guide., (2012); Caplan P., Preservation metadata, DCC Digital Curation Manual., (2006); Reference model for an Open Archival Information System (OAIS), (2002); Reference model for an Open Archival Information System (OAIS), (2012); Darlington M., REDm-MED project final report to JISC., (2012); Darlington M., Thangarajah U., Ball A., RAIDmap application user guide., (2012); Day M., Preservation metadata, Metadata applications and management, pp. 253-273, (2004); Duranti L., Eastwood T., MacNeil H., The preservation of the integrity of electronic records., (1997); Metadata for digital preservation:The Cedars Project outline specification., (2000); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, Ariadne, (2000); Hunt A., Thomas D., The pragmatic programmer: From journeyman to master., (2000); PREMIS: Preservation metadata maintenance activity., (2016); Lupovici C., Masanes J., Metadata for the long-term preservation of electronic publications., (2000); McMahon C., Design informatics: Supporting engineering design processes with information technology, Journal of the Indian Institute of Science, 95, 4, pp. 365-378, (2015); Recordkeeping metadata standard for commonwealth agencies., (1999); Australian government recordkeeping metadata standard, version 2.2., (2015); Preservation metadata for digital collections., (1999); Metadata standards framework - preservation metadata (revised)., (2003); Data dictionary for preservation metadata., (2005); Preservation metadata for digital objects: A review of the state of the art., (2001); Preservation metadata and the oais information model: A metadata framework to support the preservation of digital objects., (2002); PREMIS data dictionary for preservation metadata, version 2.1., (2011); PROS 99/007 Standard for the management of electronic records, version 1.2., (2000); Final report., (1998); Rogers C., Tennis J.T., General Study 15 - Application profile for authenticity metadata., (2016); Starr J., Ashton J., Brase J., Bracke P., Gastl A., Gillet J., Ziedorn F., DataCite metadata schema for the publication and citation of research data, version 2.2., (2011); Metadata specifications derived from the fundamental requirements: A reference model for business acceptable communications., (1996); Wolf M., Wicksteed C., Date and time formats., (1998)","","","IGI Global","","","","","","","978-152252223-2; 1522522212; 978-152252221-8","","","English","Dev. Met. Appl. Profil.","Book chapter","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85027511550"
"","","","Information Services and Use","2017","Information Services and Use","37","3","","","","114","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032581162&partnerID=40&md5=79529a705b9a4455c64c57ec309f9f09","","","The proceedings contain 17 papers. The topics discussed include: leveraging information and collaboration to cure disease; the importance of research data management: the value of electronic laboratory notebooks in the management of data integrity and data availability; open science towards reproducible research; the Napster moment: access and innovation in academic publishing; open access policies and science Europe: state of play; embracing digital: key considerations for publishers, marketers and customers; the journey from excellence to innovation; publishing in the doghouse: protecting copyright, enabling access, or both?; are STM publishers doing the right thing?; scholarly triage: advances in manuscript submission using text analytics techniques; reprinted from STM membership matters February 2017 2017 STM; peer review in the 21st century; data-first manifesto: shifting priorities in scholarly communications; preprints as a complement to the journal system in biology; and advancing library cyberinfrastructure for big data sharing and reuse.","","","","","","","","","","","Armbruster C.; Lawlor B.","IOS Press","","APE 2017 and NFAIS 2017","16 January 2017 through 18 January 2017","Berlin","131215","01675265","","ISUSD","","English","Inf Serv Use","Conference review","Final","","Scopus","2-s2.0-85032581162"
"Bugaje M.; Chowdhury G.","Bugaje, Maryam (57197719494); Chowdhury, Gobinda (7006058701)","57197719494; 7006058701","Data retrieval = Text retrieval?","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10766 LNCS","","","253","262","9","2","10.1007/978-3-319-78105-1_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044433080&doi=10.1007%2f978-3-319-78105-1_29&partnerID=40&md5=dbef4aac2bb48608b3b80e99e0fde8d3","Faculty of Engineering and Environment, iSchool, Northumbria University, Newcastle, United Kingdom","Bugaje M., Faculty of Engineering and Environment, iSchool, Northumbria University, Newcastle, United Kingdom; Chowdhury G., Faculty of Engineering and Environment, iSchool, Northumbria University, Newcastle, United Kingdom","Due to the comparatively more recent emergence of data retrieval systems than text-based search engines, the former have still yet to achieve similar maturity in terms of standards and techniques. Most of the existing solutions for data retrieval are more or less makeshift adaptations of text retrieval systems rather than purpose-built solutions specially designed to cater to the particular peculiarities, subtleties, and unique requirements of research datasets. In this paper we probe into the key differences between text and data retrieval that bear practical relevance to the retrieval question; these differences we demonstrate by evaluating some representative examples of research data repositories as well as presenting findings from previous studies. © Springer International Publishing AG, part of Springer Nature 2018.","Data retrieval; Research data management; Research data repositories; Text retrieval","Information management; Information retrieval; Data retrieval; Research data; Research data managements; Text retrieval; Text retrieval system; Search engines","","","","","","","Borgman C., Big Data, Little Data, No Data: Scholarship in the Networked World., (2015); Weber A., Piesche C., Requirements on long-term accessibility and preservation of research results with particular regard to their provenance, ISPRS Int. J. Geo-Inf., 5, (2016); Bugaje M., Chowdhury G., Is data retrieval different from text retrieval? An exploratory study, ICADL 2017. LNCS, 10647, pp. 97-103, (2017); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol, 63, 6, pp. 1059-1078, (2012); Borgman C.L., Wallis J.C., Mayernik M.S., Who’s got the data? Interdependencies in science and technology collaborations, Comput. Support. Coop. Work, 21, 6, pp. 485-523, (2012); An RDA Europe Report, (2014); Boru D., Kliazovich D., Granelli F., Bouvry P., Zomaya A.Y., Energy-efficient data replication in cloud computing datacenters, Clust. Comput., 18, 1, pp. 385-402, (2015); Chowdhury G.G., Sustainability of Scholarly Information, (2014); Chowdhury G.G., How to improve the sustainability of digital libraries and information services?, J. Assoc. Inf. Sci. Technol., 67, 10, pp. 2379-2391, (2016)","M. Bugaje; Faculty of Engineering and Environment, iSchool, Northumbria University, Newcastle, United Kingdom; email: maryam.bugaje@northumbria.ac.uk","Chowdhury G.; McLeod J.; Gillet V.; Willett P.","Springer Verlag","","13th International Conference on Transforming Digital Worlds, iConference 2018","25 March 2018 through 28 March 2018","Sheffield","211969","03029743","978-331978104-4","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85044433080"
"Barbrow S.; Brush D.; Goldman J.","Barbrow, Sarah (55735341200); Brush, Denise (16199642000); Goldman, Julie (57651093600)","55735341200; 16199642000; 57651093600","Research data management and services: Resources for novice data librarians","2017","College and Research Libraries News","78","5","","274","278","4","11","10.5860/crln.78.5.274","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019430960&doi=10.5860%2fcrln.78.5.274&partnerID=40&md5=b85b8d102887e0c8e64f2ec1694d07e0","Wellesley College, United States; Rowan University, United States; National Network of Libraries of Medicine, New England Region, United States","Barbrow S., Wellesley College, United States; Brush D., Rowan University, United States; Goldman J., National Network of Libraries of Medicine, New England Region, United States","[No abstract available]","","","","","","","","","Kafel D., Morales M., Hart R.V., Gore S., Creamer A., Crespo J., Martin E., Building an e-science portal for librarians: A model of collaboration, Journal of eScience Librarianship, 1, 1, (2012); Read K., Creamer A., Kafel D., Hart R.V., Martin E., Building an escience thesaurus for librarians: A collaboration between the national network of libraries of medicine, New England Region and an associate fellow at the national library of medicine, Journal of eScience Librarianship, 2, 2, (2013)","","","Association of College and Research Libraries","","","","","","00990086","","","","English","Coll. Res. Libr. News","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85019430960"
"Conrad S.; Shorish Y.; Whitmire A.L.; Hswe P.","Conrad, Suzanna (55532551100); Shorish, Yasmeen (55532864600); Whitmire, Amanda L. (24463842400); Hswe, Patricia (15725232500)","55532551100; 55532864600; 24463842400; 15725232500","Building professional development opportunities in data services for academic librarians","2017","IFLA Journal","43","1","","65","80","15","18","10.1177/0340035216678237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014600429&doi=10.1177%2f0340035216678237&partnerID=40&md5=d8f230e74147b098d8a0153dcae74184","California State University, United States; James Madison University, United States; Stanford University, United States; Andrew W. Mellon Foundation, United States","Conrad S., California State University, United States; Shorish Y., James Madison University, United States; Whitmire A.L., Stanford University, United States; Hswe P., Andrew W. Mellon Foundation, United States","Research data management represents a significant professional development area for academic librarians – significant for its growing importance to the profession, since researchers are increasingly expected to comply with research data management requirements, and for the extent of competence needed by librarians to support researchers in research data management practices and plans. This article recounts how the Association of College and Research Libraries is fostering professional development opportunities in research data management. The authors describe two key endeavors: (1) the development and deployment of a needs assessment survey, which allowed insight into the types of librarians expressing the most need; and (2) planning and implementation of a pre-conference workshop for ACRL 2015, intended to prototype a future professional development offering. The article concludes by discussing additional assessment that was done following the workshop and how the pre-conference laid the foundation for proposing a “roadshow” for research data management, similar to what the Association of College and Research Libraries sponsors for scholarly communication. © 2016, © The Author(s) 2016.","Academic libraries; data management; data services; professional development; professional organizations","","","","","","","","Creation of ACRL Digital Curation Interest Group, (2010); 2012 top ten trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 73, pp. 311-320, (2012); Intersections of Scholarly Communication and Information Literacy: Creating Strategic Collaborations for a Changing Academic Environment, (2013); ACRL Digital Curation Interest Group; Immersion Program; 23 (Research Data) Things; 23 (Research Data) Things: Community Groups; Borgman C.L., Syllabus for Data Management and Practice, Part I, Winter 2015, (2015); Portage Home; Coates H.L., Developing a data management lab: Teaching effective methods for health and social sciences research. Poster presented at the Data Information Literacy Symposium, West Lafayette, IN, (2013); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, (2012); Creamer A., Scientific Data Management LIS 532G-01 Fall; E-Science Institute Course Description; Science Boot Camp; Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, pp. 662-672, (2011); Hswe P., Peering outward: Data curation services in academic libraries and scientific data publishing, Getting the Word Out: Academic Libraries as Scholarly Publishers, pp. 221-248, (2015); Hswe P., Holt A., Joining in the enterprise of response in the wake of the NSF data management planning requirement, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 274, pp. 11-17, (2011); Jaguszewski J., Williams K., New Roles for New Times: Transforming Liaison Roles in Research Libraries, (2013); Johnston L., Jeffryes J., Teaching Civil Engineering Data Information Literacy Skills: An e-Learning Approach. Data Information Literacy Case Study Directory 3, (2015); Enabling Open Science: Scholarly Communication & Research Infrastructures; Little G., Managing the data deluge, Journal of Academic Librarianship, 38, 5, pp. 263-264, (2012); Muilenburg J., Lebow M., Rich J., Lessons learned from a research data management pilot course at an academic library, Journal of eScience Librarianship, 3, (2015); Nicholson S.W., Bennett T.B., Data sharing: Academic libraries and the scholarly enterprise, portal: Libraries and the Academy, 11, 1, pp. 505-516, (2011); RDM Educational Efforts - V2; Reisner B.A., Vaughan K.T.L., Shorish Y., Making data management accessible in the undergraduate Chemistry curriculum, Journal of Chemical Education, 91, 11, pp. 1943-1946, (2014); Rockenbach B., Ruttenberg J., Tancheva K., Et al., Association of Research Libraries/Columbia University/Cornell University/University of Toronto Pilot Library Liaison Institute. Report, (2015); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Shorish Y., Data curation is for everyone! The case for Master’s and Baccalaureate institutional engagement with data curation, Journal of Web Librarianship, 6, pp. 263-273, (2012); Shorish Y., Data information literacy and undergraduates: A critical competency, College & Undergraduate Libraries, 22, 1, pp. 97-106, (2015); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2012); An Introduction to Research Data Management; Whitmire A.L., Thoughts on ‘eResearch’: A scientist’s perspective, Journal of eScience Librarianship, 2, pp. 68-72, (2013); Whitmire A., GRAD 521 Research Data Management Syllabus and Lesson Plans, (2014); Whitmire A.L., Implementing a graduate-level research data management course: Approach, outcomes, and lessons learned, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Witt M., Co-designing, co-developing, and co-implementing an institutional data repository service, Journal of Library Administration, 52, 2, pp. 172-188, (2012); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008); Wright S., LibGuides: NTRES 6600: Research Data Management Seminar: Home","Y. Shorish; James Madison University, Harrisonburg, MSC 4601, 22807, United States; email: shorisyl@jmu.edu","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85014600429"
"Nicholson S.W.; Bennett T.B.","Nicholson, Shawn W. (7101734060); Bennett, Terrence B. (7202878211)","7101734060; 7202878211","The good news about bad news: Communicating data services to cognitive misers","2017","Journal of Electronic Resources Librarianship","29","3","","151","158","7","2","10.1080/1941126X.2017.1340720","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029364116&doi=10.1080%2f1941126X.2017.1340720&partnerID=40&md5=009ffad9b4dbf0e0e4d6dc2978e5d80f","Business / Economics Librarian, The College of New Jersey, 2000 Pennington Rd, Ewing, 08628, NJ, United States","Nicholson S.W., Business / Economics Librarian, The College of New Jersey, 2000 Pennington Rd, Ewing, 08628, NJ, United States; Bennett T.B., Business / Economics Librarian, The College of New Jersey, 2000 Pennington Rd, Ewing, 08628, NJ, United States","This article considers how to communicate and interact with researchers about data management services. Using the premise that bad information is processed more thoroughly than good, the authors integrate that premise into an exploration of the alignment (or nonalignment) of library-emanating data management communications with the divergent expectations of researchers in different academic domains. How might the notion that bad information resonates better with researchers be used to inform the ways that libraries promote data services and communicate about data management practices? And could these approaches differ across disciplines? Insights concerning possible misalignment of current communications could lead to corrective action. © 2017, Published with license by Taylor & Francis © Shawn W. Nicholson and Terrence B. Bennett.","individual motivation; institutional pressure; negative messages; Research data management; social communication","","","","","","","","Baumeister R.F., Bratslavsky E., Finkenauer C., Vohs K.D., Bad is stronger than good, Review of General Psychology, 5, 4, pp. 323-370; Becher T., Academic tribes and territories: Intellectual enquiry and the cultures of disciplines; Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 368, 1926, pp. 4023-4038; Donohew L., Activation theory of information exposure, Encyclopedia of communication theory, 2, pp. 12-14; Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PloS ONE, 10, 2; Gold A., Cyberinfrastructure, data, and libraries, part 2: Libraries and the data challenge: Roles and actions for libraries, D-Lib Magazine, 13, 9-10; Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672; Hickson S., Poulton K.A., Connor M., Richardson J., Wolski M., Modifying researchers' data management practices: A behavioural framework for library practitioners, IFLA Journal, 42, 4, pp. 253-265; Hovland C., Social communication, Proceedings of the American Philosophical Society, 92, 5, pp. 371-375; Journal Citation Reports®, (2016); Kim Y., Adler M., Social scientists' data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories, International Journal of Information Management, 35, 4, pp. 408-418; Liu B., Sentiment analysis: Mining opinions, sentiments, and emotions; Nicholson S.W., Bennett T.B., Data sharing: Academic libraries and the scholarly enterprise, Libraries and the Academy, 11, 1, pp. 505-516; Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12; Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6; Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future [white paper]; Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2","T.B. Bennett; Business / Economics Librarian, The College of New Jersey, Ewing, 2000 Pennington Rd, 08628, United States; email: tbennett@tcnj.edu","","Routledge","","","","","","1941126X","","","","English","J. Electron. Resour. Libr.","Article","Final","","Scopus","2-s2.0-85029364116"
"Burgi P.-Y.; Blumer E.; Makhlouf-Shabou B.","Burgi, Pierre-Yves (7004194024); Blumer, Eliane (55360099300); Makhlouf-Shabou, Basma (57193530932)","7004194024; 55360099300; 57193530932","Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs","2017","IFLA Journal","43","1","","5","21","16","14","10.1177/0340035216678238","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014585268&doi=10.1177%2f0340035216678238&partnerID=40&md5=243bb65971da997d3b8c8d82653020cb","University of Geneva, Switzerland; Geneva School of Business Administration, Switzerland","Burgi P.-Y., University of Geneva, Switzerland; Blumer E., University of Geneva, Switzerland; Makhlouf-Shabou B., Geneva School of Business Administration, Switzerland","In this article, the authors report on an ongoing data life cycle management national project realized in Switzerland, with a major focus on long-term preservation. Based on an extensive document analysis as well as semi-structured interviews, the project aims at providing national services to respond to the most relevant researchers’ data life cycle management needs, which include: guidelines for establishing a data management plan, active data management solutions, long-term preservation storage options, training, and a single point of access and contact to get support. In addition to presenting the different working axes of the project, the authors describe a strategic management and lean startup template for developing new business models, which is key for building viable services. © 2016, © The Author(s) 2016.","Business model; data life cycle management; preservation of digital data; research data management services; value proposition canvas","","","","","","","","Abrams S., Cruse P., Kunze J., Et al., Total Cost of Preservation (TCP): Cost Modeling for Sustainable Services, (2012); Bairavasundaram L.N., Goodson G.R., Schroeder B., Et al., An analysis of data corruption in the storage stack, pp. 223-238, (2008); Ball A., Review of the State of the Art of the Digital Curation of Research Data (version 1.2), (2010); Beagrie N., Chruszcz J., Lavoie B., Keeping Research Safe. A Cost Model and Guidance for UK Universities. Final Report, (2008); Beagrie N., Lavoie B., Woollard M., Keeping Research Safe 2. Final Report, (2010); Bihouix P., L’Âge des low-tech, (2014); Borgman C.L., Big Data, Little Data, No Data, (2015); Data Life Cycle Models and Concepts, (2011); What is Digital Curation?, (2007); Preserving Digital Objects with Restricted Resources, (2014); Goodman A., Pepe A., Blocker A.W., Et al., Ten simple rules for the care and feeding of scientific data, PLoS Comput Biol, 10, 4, (2014); Kim J., Warga E., Moen W.E., Competencies required for digital curation: An analysis of job advertisements, International Journal of Digital Curation, 8, 1, pp. 66-83, (2013); Klindt M., Amrhein K., One core preservation system for all your data. No exception!, pp. 101-108, (2015); Lamport L., Shostak R., Pease M., The Byzantine generals problem, ACM Transactions on Programming Language and Systems, 4, 3, pp. 382-401, (1982); Lee C.A., A framework for contextual information in digital collections, Journal of Documentation, 67, 1, pp. 95-143, (2011); Maniatis P., Roussopoulos M., Giuli T.J., Et al., The LOCKSS peer-to-peer digital preservation system, ACM Transactions on Computer Systems, 23, 1, pp. 2-50, (2005); Long-Lived Digital Data Collections Enabling Research and Education in the 21st Century, (2005); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); Osterwalder A., Pigneur Y., Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, (2010); Osterwalder A., Pigneur Y., Bernarda G., Et al., Value Proposition Design: How to Create Products and Services Customers Want, (2014); Pigneur Y., Osterwalder A., Strategyzer; Pouchard L., Revisiting the data lifecycle with big data curation, International Journal of Digital Curation, 10, 2, pp. 176-192, (2015); Rosenthal D.S.H., Bit preservation: A solved problem?, International Journal of Digital Curation, 5, 1, pp. 134-148, (2010); Schumacher J., Et al., From Theory to Action: ‘Good Enough’ Digital Preservation Solutions for Under-Resourced Cultural Heritage Institutions, (2014); Tenopir C., Dalton E.D., Allard S., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PloS ONE, 10, 8, (2015); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, 7, (2013); Wallis J.C., Data producers courting data reusers: Two cases from modeling communities, International Journal of Digital Curation, 9, 1, pp. 98-109, (2014)","E. Blumer; University of Geneva, Geneva, Rue du Général Dufour 24, 1204, Switzerland; email: eliane.blumer@unige.ch","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85014585268"
"Sewell C.; Kingsley D.","Sewell, Claire (57194275658); Kingsley, Danny (27267610200)","57194275658; 27267610200","Developing the 21st Century Academic Librarian: The Research Support Ambassador Programme","2017","New Review of Academic Librarianship","23","2-3","","148","158","10","24","10.1080/13614533.2017.1323766","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019642041&doi=10.1080%2f13614533.2017.1323766&partnerID=40&md5=6af86c4a984887cf9f766d71d3e4ab14","Office of Scholarly Communication, Cambridge University Library, Cambridge, United Kingdom","Sewell C., Office of Scholarly Communication, Cambridge University Library, Cambridge, United Kingdom; Kingsley D., Office of Scholarly Communication, Cambridge University Library, Cambridge, United Kingdom","The nature of academic librarianship is changing as librarians move away from the curation of material and into research support roles. Although this creates new opportunities it can be difficult for staff to learn the skills needed. The Office of Scholarly Communication at Cambridge University seeks to address this issue with the Research Support Ambassadors Programme, an initiative which skills staff in areas such as Research Data Management and Open Access. This case study outlines the evolution of the program from its pilot through to its recently completed second run in 2016. The challenges associated with running a cross-library training program are discussed and solutions highlighted. Also discussed is the impact that the program has had on participants. This case study will be of interest to those aiming to pursue a career in this area of librarianship and those looking at preparing staff for the future of the academic library. © 2017 The Author(s). Published with license by Taylor & Francis © 2017, © Claire Sewell and Danny Kingsley.","Library staff development; research support; scholarly communication; university libraries","","","","","","","","Auckland M., Re-skilling for research: An investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Bonn M., Tooling up scholarly communication education and training, College & Research Libraries News, 75, 3, pp. 132-135, (2014); Bresnahan M.M., Johnson A.M., Assessing scholarly communication and research data training needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Brewerton A., Reskilling for research: Investigating the needs of researchers and how library staff can best support them, New Review of Academic Librarianship, 18, 1, pp. 96-110, (2012); Bruns T., Brantley S., Duffin K., Scholarly communication coaching: Liaison librarians' shifting roles,  century academic library, pp. 9-31, (2015); Christensen-Dalsgaard B., van den Berg M., Grim R., Horstmann W., Jansen D., Pollard T., Roos A., Ten recommendations for libraries to get started with research data management, Final report of the LIBER working group on E-Science/Research Data Management, (2012); Corrall S., Educating the academic librarian as a blended professional: A review and case study, Library Management, 31, 8-9, pp. 567-593, (2010); Guy M., RDM Training for Librarians, DCC RDM Services Case Studies, (2013); Kennan M.A., Data Management: Knowledge and skills required in research, scientific and technical organisations, (2016); Kennan M.A., Corral S., Waseem A., Making space” in practice and education: research support services in academic libraries, Library Management, 35, 8-9, pp. 666-683, (2014); Kirchner J., Scholarly communications: Planning for the integration of liaison librarian roles, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 265, (2009); Koltay T., Data literacy for researchers and data librarian, Journal of Librarianship and Information Science, 49, 1, pp. 3-147, (2015); Marcum D., Educating the research librarian: are we falling short? [Blog post], (2015); Peet L., Harvard's Copyright First Responders to the rescue [Blog post], (2014); Robinson L., O'Neil M., Simon A., Bates J., Shankar K., Matthews P., Reid P., Developing the professionals of the future: Views from experts in “library schools, SCONUL Focus, 67, pp. 5-17, (2016); Rodriguez J.E., Scholarly communications competencies: Open access training for librarians, New Library World, 116, 7-8, pp. 397-405, (2015); Schmidt B., Calarco P., Kuchma I., Shearer K., Time to adopt: Librarians' new skills and competency profiles, Positioning and power in academic publishing: Players, agents and agendas, pp. 1-8, (2016); Shirkey C., Hoover J., Building a scholarly communication boot camp for East Carolina University liaisons, (2015); Wirth A.A., Chadwell F.A., Rights well: An authors' rights workshop for librarians, Portal: Libraries and the Academy, 10, 3, pp. 337-354, (2010)","C. Sewell; Office of Scholarly Communication, Cambridge University Library, Cambridge, West Road, CB3 9DR, United Kingdom; email: ces43@cam.ac.uk","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85019642041"
"Znamirowski B.","Znamirowski, Barbara (24469446600)","24469446600","GIS trends","2017","Association of Canadian Map Libraries and Archives Bulletin","2017-Winter","155","","41","43","2","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018416321&partnerID=40&md5=d8893bbddfde66a3e1c9889c06408451","","","This article looks at some of the latest developments that impact the data publishing practices, focusing specifically on Research Data Management (RDM) and data archiving Portage Network has plays a key role in Canada for establishing for research data management in libraries and associated stakeholders. It has released the DMP Assistant, which is designed to help researchers develop and implement research data management plans.","","Canada; data management; design; GIS; research and development; stakeholder; trend analysis","","","","","","","Canada's Tri-Agency Statement of the Principles on Digital Data Management, (2017); Barsky E., Brosz J., Leahey A., Research data discovery and the scholarly ecosystem in Canada; a white paper, The Portage Network, Data Discovery Working Group on Behalf of the Canadian Association of Research Libraries (CARL), (2016); Fry J., Doiron J., Letourneau D., Perrier L., Perry C., Watkins W., Research data management training landscape in Canada; a white paper, The Portage Training Expert Group on Behalf of the Canadian Association of Research Libraries (CARL), (2017); Scholars Portal Research Data Repositories; Crosas M., Dataverse 4.0: Defining Data Publishing","B. Znamirowski; email: bznamirowski@trentu.ca","","Association of Canadian Map Libraries and Archives","","","","","","08409331","","","","English","Assoc. Can. Map Libr. Arch. Bull.","Article","Final","","Scopus","2-s2.0-85018416321"
"Bauer B.; Blumesberger S.; Kann B.; Steiner C.; Stumpf M.; Wödl U.","Bauer, Bruno (57200436821); Blumesberger, Susanne (27867529800); Kann, Bettina (57189698102); Steiner, Christoph (57205265064); Stumpf, Markus (37010089500); Wödl, Ute (57205268125)","57200436821; 27867529800; 57189698102; 57205265064; 37010089500; 57205268125","Cooperative report of the 107th German librarians’ day: ""open & networked"" (Berlin, June 12–15 2018); [�kooperativer bericht vom 107. Deutschen bibliothekartag: „offen & vernetzt“ (Berlin, 12.–15. Juni 2018)]","2018","VOEB-Mitteilungen","71","3-4","","475","507","32","0","10.31263/voebm.v71i3-4.2170","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059329130&doi=10.31263%2fvoebm.v71i3-4.2170&partnerID=40&md5=b6c73b753cb59cfb75c36859ed1cfe9c","Medizinische Universität Wien, Universitätsbibliothek, Austria; Universität Wien, Bibliotheks-und Archivwesen, Austria; Die Österreichische Bibliothekenverbund und Service GmbH (OBVSG); Österreichische Nationalbibliothek; Universität Wien, Bibliotheks-und Archivwesen, Fachbereichsbibliothek Zeitgeschichte, Austria; Arbeiterkammer Wien, Abt. Bibliothek – Wissen – Information, Austria","Bauer B., Medizinische Universität Wien, Universitätsbibliothek, Austria; Blumesberger S., Universität Wien, Bibliotheks-und Archivwesen, Austria; Kann B., Die Österreichische Bibliothekenverbund und Service GmbH (OBVSG); Steiner C., Österreichische Nationalbibliothek; Stumpf M., Universität Wien, Bibliotheks-und Archivwesen, Fachbereichsbibliothek Zeitgeschichte, Austria; Wödl U., Arbeiterkammer Wien, Abt. Bibliothek – Wissen – Information, Austria","The 107th German Librarians’ Day took place from 12 to 15 June 2018 in Berlin. The motto of the conference, which was attended by 4.050 people, was ""open and connected"". This cooperative report covers the following topics: machine indexing, long-term archiving, requirements for future library management systems, library education, open access, research data and research data management, NS provenance research and quality management. © Bruno Bauer, Susanne Blumesberger, Bettina Kann, Christoph Steiner, Markus Stumpf, Ute Wödl.","107th German librarians’ day; Berlin 2018; Cooperative report","","","","","","","","Heidrun Wiesenmüller (Hochschule Der Medien Stuttgart): Maschinelle Indexierung am Beispiel Der DNB – Analyse Und Entwicklungsmöglich-keiten; Beckmann R.; Hermann K., Sächsische Landesbibliothek – Staats-und Universitätsbibliothek Dresden): Automatisierte Sacherschließung. Verga-be von Notationen der Regensburger Verbundklassifikation (Vortrag, 107, Deutscher Bibliothekartag, 12, 6, (2018); Kohn K.K., Deutsche Nationalbibliothek, Frankfurt A. M.): Maschinelle Sacherschließungsverfahren Bei Medizinischen Publikationen. Erfahrungen an Der DNB (Vortrag, 107. Deutscher Bibliothekartag, 12.6.2018; Busse F., Deutsche Nationalbibliothek, Frankfurt A. M.): Ddc-Kurz-Notationen. Der Weg Zu Einer Maschinellen Klassifikatorischen Erschließung (Vortrag, 107. Deutscher Bibliothekartag, 12.6.2018; Vorndran A., Deutsche Nationalbibliothek, Frankfurt A. M.): Hervorholen was in Unseren Daten Steckt! Mehrwerte Durch Analysen großer Bibliotheksdatenbestände (Vortrag, 107. Deutscher Bibliothekartag, 13.6.2018); Neumann M., Technische Hochschule Köln): Webscraping für Die Metadatengewinnung – Das Dfg-Projekt Smart Harvesting II (Vortrag, 107. Deutscher Bibliothekartag, 13.6.2018; Pohl A., (Hochschulbibliothekszentrum Des Landes Nordrhein-Westfalen, Köln): Lobid – Offene, Webbasierte Infrastruktur für Zentrale Bibliothekarische Daten (Vortrag, 107. Deutscher Bibliothekartag, 14.6.2018); Kett J., Deutsche Nationalbibliothek, Frankfurt A. Main): Die GND Als Plattform für Publizierende – Das Projekt GND4P (Vortrag, 107. Deutscher Bibliothekartag, 14.6.2018)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85059329130"
"Serrano D.; Stroulia E.; Lau D.; Ng T.","Serrano, Diego (35318636000); Stroulia, Elen (6603883706); Lau, Diana (55935002300); Ng, Tinny (56493941100)","35318636000; 6603883706; 55935002300; 56493941100","Linked REST APIs: A Middleware for Semantic REST API Integration","2017","Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017","","","8029755","138","145","7","18","10.1109/ICWS.2017.26","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032336659&doi=10.1109%2fICWS.2017.26&partnerID=40&md5=fa1318f5985987946736ae3e457473cc","Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Center for Advanced Studies, IBM Canada Lab, Toronto, Canada","Serrano D., Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Stroulia E., Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Lau D., Center for Advanced Studies, IBM Canada Lab, Toronto, Canada; Ng T., Center for Advanced Studies, IBM Canada Lab, Toronto, Canada","Over the last decade, an exponentially increasing number of REST services have been providing a simple and straightforward syntax for accessing rich data resources. To use these services, however, developers have to understand 'information-use contracts' specified in natural language, and, to build applications that benefit from multiple existing services they have to map the underlying resource schemas in their code. This process is difficult and error-prone, especially as the number and overlap of the underlying services increases, and the mappings become opaque, difficult to maintain, and practically impossible to reuse. The more recent advent of the Linked Data formalisms can offer a solution to the challenge. In this paper, we propose a conceptual framework for REST-service integration based on Linked Data models. In this framework, the data exposed by REST services is mapped to Linked Data schemas, based on these descriptions, we have developed a middleware that can automatically compose API calls to respond to data queries (in SPARQL). Furthermore, we have developed a RDF model for characterizing the access-control protocols of these APIs and the quality of the data they expose, so that our middleware can develop 'legal' compositions with desired qualities. We report our experience with the implementation of a prototype that demonstrates the usefulness of our framework in the context of a research-data management application. © 2017 IEEE.","data integration; Linked Data; REST APIs","Access control; Data handling; Information management; Middleware; Semantic Web; Semantics; Web services; Websites; Access control protocol; Conceptual frameworks; Data resources; Linked datum; Natural languages; Research data managements; REST APIs; Rest services; Data integration","","","","","","","Schmachtenberg M., Bizer C., Paulheim H., State of the Lod Cloud 2014, 30, (2014); Martin D., Burstein M., Hobbs J., Lassila O., McDermott D., McIlraith S., Narayanan S., Paolucci M., Parsia B., Payne T., Et al., OWL-S: Semantic markup for web services, W3C Member Submission, 22, (2004); Feier C., Polleres A., Dumitru R., Domingue J., Stollberg M., Fensel D., Towards Intelligent Web Services: The Web Service Modeling Ontology, (2005); Akkiraju R., Farrell J., Miller J.A., Nagarajan M., Sheth A.P., Verma K., Web Service Semantics-wsdl-s, (2005); Kopecky J., Vitvar T., Bournez C., Farrell J., Sawsdl: Semantic annotations for wsdl and xml schema, Internet Computing, IEEE, (2007); Kopecky J., Vitvar T., Fensel D., Gomadam K., Hrests & Microwsmo, (2009); Vitvar T., Kopecky J., Viskova J., Fensel D., The Semantic Web: Research and Applications, (2008); Pedrinaci C., Domingue J., Toward the next wave of services: Linked services for the web of data, Journal of Universal Computer Science, 16, 13, (2010); Patil A.A., Oundhakar S.A., Sheth A.P., Verma K., Meteor-s web service annotation framework, 13th International Conference On World Wide Web, (2004); Wolstencroft K., Haines R., Fellows D., Williams A., Withers D., Owen S., Soiland-Reyes S., Dunlop I., Nenadic A., Fisher P., Et al., The taverna workflow suite: Designing and executing workflows of web services on the desktop, web or in the cloud, Nucleic Acids Research, (2013); Hobold G.C., Siqueira F., Discovery of semantic web services compositions based on sawsdl annotations, 19th International Conference On Web Services (ICWS), (2012); Sbodio M.L., Martin D., Moulin C., Discovering semantic web services using sparql and intelligent agents, Web Semantics: Science, Services and Agents On the World Wide Web, 8, 4, (2010); Speiser S., Harth A., Integrating linked data and services with linked data services, The Semantic Web: Research and Applications, (2011); Corson-Rikert J., Mitchell S., Lowe B., Rejack N., Ding Y., Guo C., The vivo ontology, Synthesis Lectures On Semantic Web: Theory and Technology, (2012); Lanthaler M., Gutl C., Hydra: A Vocabulary for Hypermedia-driven Web Apis, 996, (2013); Maleshkova M., Pedrinaci C., Domingue J., Alvaro G., Martinez I., Using semantics for automating the authentication of web apis, International Semantic Web Conference, (2010); Debattista J., Lange C., Auer S., Daq, an ontology for dataset quality information, LDOW, (2014); Arenas M., Perez J., Querying semantic web data with sparql, 13th SIGMOD-SIGACT-SIGART Symposium On Principles of Database Systems, (2011); Ullmann J.R., An algorithm for subgraph isomorphism, Journal of the ACM (JACM), 23, 1, (1976); Alrifai M., Risse T., Combining global optimization with local selection for efficient qos-aware service composition, 18th International Conference On World Wide Web, (2009); Borgman C., Furner J., Scholarly communication and bibliometrics, Annual Review of Information Science and Technology, 36, (2002); Katz J.S., Martin B.R., What is research collaboration, Research Policy, 26, 1, (1997); Rodriguez-Mier P., Pedrinaci C., Lama M., Mucientes M., An integrated semantic web service discovery and composition framework, IEEE Transactions On Services Computing, 9, 4, (2016)","","Chen S.; Altintas I.","Institute of Electrical and Electronics Engineers Inc.","","24th IEEE International Conference on Web Services, ICWS 2017","25 June 2017 through 30 June 2017","Honolulu","130852","","978-153860752-7","","","English","Proc. - IEEE Int. Conf. Web Serv., ICWS.","Conference paper","Final","","Scopus","2-s2.0-85032336659"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Data literacy for researchers and data librarians","2017","Journal of Librarianship and Information Science","49","1","","3","14","11","68","10.1177/0961000615616450","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014601476&doi=10.1177%2f0961000615616450&partnerID=40&md5=1db2f64e2af4e7c777cad8f8ea5089ed","Szent István University, Hungary","Koltay T., Szent István University, Hungary","This paper describes data literacy and emphasizes its importance. Data literacy is vital for researchers who need to become data literate science workers and also for (potential) data management professionals. Its important characteristic is a close connection and similarity to information literacy. To support this argument, a review of literature was undertaken on the importance of data, and the data-intensive paradigm of scientific research, researchers’ expected and real behaviour, the nature of research data management, the possible roles of the academic library, data quality and data citation, Besides describing the nature of data literacy and enumerating the related skills, the application of phenomenographic approaches to data literacy and its relationship to the digital humanities have been identified as subjects for further investigation. © 2015, © The Author(s) 2015.","Data citation; data curation; data librarian; data literacy; data quality; data sharing; research data management","","","","","","","","Intersections of Scholarly Communication and Information Literacy: Creating Strategic Collaborations for a Changing Academic Environment, (2013); ACRL Research Planning and Review Committee. Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 75, 6, pp. 294-302, (2014); Framework for Information Literacy for Higher Education, (2015); Final Report, American Library Association Presidential Commission on Information Literacy, (1989); Altman M., Crosas M., The evolution of data citation: From principles to implementation, IASSIST Quarterly, 37, pp. 62-70, (2013); Bawden D., Origins and concepts of digital literacy, Digital Literacies: Concepts, Policies and Practices, pp. 17-32, (2008); Bonn M., Tooling up. Scholarly communication education and training, College and Research Libraries News, 75, 3, pp. 132-135, (2014); Borgman C., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Borgman C., Research data: Who will share what, with whom, when, and why?, (2010); Borgman C., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Bosanquet L., Building relevance amidst the content revolution, Library Management, 31, 3, pp. 133-144, (2010); Boyd D., Crawford K., Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon, Information, Communication and Society, 15, 5, pp. 662-679, (2012); Bradford P., Wurman R.S., Information Architects, (1996); Bruce C., The Seven Faces of Information Literacy, (1997); Buckland M., Data management as bibliography, Bulletin of the American Society for Information Science and Technology, 37, 6, pp. 34-37, (2011); Bundy A., Australian and New Zealand Information Literacy Framework. Principles, Standards and Practice, (2004); Calzada Prado J., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Carlson J., Fosmire M., Miller C.C., Et al., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Johnston L., Westra B., Et al., Developing an approach for data management education: A report from the Data Information Literacy Project, International Journal of Digital Curation, 8, 1, pp. 204-217, (2013); Carlson J., Stowell Bracke M., Planting the seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, 1, pp. 95-110, (2015); Christensen-Dalsgaard B., van den Berg M., Grim R., Et al., Ten Recommendations for Libraries to get Started with Research Data Management, (2012); Data Citation Standards and Practices, (2010); Coiro J., Knobel M., Lankshear C., Et al., Central issues in new literacies and new literacies research, The Handbook of Research on New Literacies, pp. 25-32, (2008); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Corrall S., Evolving academic library specialties, Journal of the American Society for Information Science and Technology, 64, 8, pp. 1526-1542, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’problem, Journal of Librarianship and Information Science, (2014); Joint Declaration of Data Citation Principles, (2014); Davis-Kahl S., Fishel T.A., Hensley M.K., Weaving the threads Scholarly communication and information literacy, College and Research Libraries News, 75, 8, pp. 441-444, (2014); Erway R., Starting the conversation: University-wide research data management policy, Educause Review Online, (2013); Federer L., The librarian as research informationist: A case study, Journal of the Medical Library Association, 101, 4, pp. 298-302, (2013); Giarlo M., Academic libraries as quality hubs, Journal of Librarianship and Scholarly Communication, 1, 3, pp. 1-10, (2013); Goodman A., Et al., Ten simple rules for the care and feeding of scientific data, PLoS Computational Biology, 10, 4, (2014); Haendel M.A., Et al., Dealing with data: A case study on information and data management literacy, PLoS Biology, 10, 5, (2012); Hey T., Hey J., e-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Hswe P., Holt A., A New Leadership Role for Libraries, (2012); Hunt K., The challenges of integrating data literacy into the curriculum in an undergraduate institution, IASSIST Quarterly, 28, 2, pp. 12-15, (2004); Quick Guide to Data Citation, (2012); Jahnke L., Asher A., Keralis S.D., The Problem of Data, (2012); Johnson C.A., The Information Diet: A Case for Conscious Consumption, (2012); Koltay T., Data literacy: In search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Kruse F., Thestrup J.B., Research libraries’ new role in research data management, current trends and visions in Denmark, Liber Quarterly, 23, 4, pp. 310-335, (2014); Li S., Xiaozhe Z., Wenming X., Et al., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Limberg L., Sundin O., Talja S., Three theoretical perspectives on information literacy, HUMAN IT, 11, 2, pp. 93-130, (2012); Livingstone S., Wijnen C.W., Papaioannou T., Et al., Situating media literacy in the changing media environment: Critical insights from European research on audiences, Audience Transformations: Shifting Audience Positions in Late Modernity. Routledge Studies in European Communication Research and Education, pp. 210-227, (2014); Loukides M., The Evolution of Data Products, (2011); Lynch C., Jim Gray’s Fourth Paradigm and the construction of the scientific record, The Fourth Paradigm: Data-Intensive Scientific Discovery, pp. 177-183, (2009); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Maceviciute E., Research libraries in a modern environment, Journal of Documentation, 70, 2, pp. 282-302, (2014); MacMillan D., Data sharing and discovery: What librarians need to know, Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); MacMillan D., Developing data literacy competencies to enhance faculty collaborations, LIBER Quarterly, 24, 3, pp. 140-160, (2015); Madrid M.M., A study of digital curator competences: A survey of experts, International Information and Library Review, 45, 3-4, pp. 149-156, (2013); Mandinach E.B., Gummer E.S., A systemic view of implementing data literacy in educator preparation, Educational Researcher, 42, 1, pp. 30-37, (2013); Marcum D., Educating the Research Librarian: Are We Falling Short?, (2015); Merrill A., Library+, Public Services Quarterly, 7, 3-4, pp. 144-148, (2011); Mooney H., A practical approach to data citation: The Special Interest Group on Data Citation and development of the Quick Guide to Data Citation, IASSIST Quarterly, 37, pp. 71-77, (2013); Mooney H., Newton M.P., The anatomy of a data citation: Discovery, reuse, and credit, Journal of Librarianship and Scholarly Communication, 1, 1, pp. 1-14, (2012); Nielsen H.J., Hjorland B., Curating research data: The potential roles of libraries and information professionals, Journal of Documentation, 70, 2, pp. 221-240, (2014); Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century, (2005); NMC Horizon Report: 2014 Library Edition, (2014); Peroni S., Gray T., Dutton A., Et al., Setting our bibliographic references free: Towards open citation data, Journal of Documentation, 71, 2, pp. 253-277, (2015); Perry G.J., Roderer N.K., Assar S., A current perspective on medical informatics and health sciences librarianship, Journal of the Medical Library Association, 93, 2, pp. 199-205, (2005); Pryor G., Why manage research data?, Managing Research Data, pp. 1-16, (2012); Qin J., D'Ignazio J., Lessons learned from a two-year experience in science data literacy education, 2, (2010); Ramirez M.L., Opinion: Whose role is it anyway? A library practitioner’s appraisal of the digital data deluge, Bulletin of the American Society for Information Science and Technology, 37, 5, pp. 21-23, (2011); RECODE Policy Recommendations for Open Access to Research Data, (2014); Reilly S.K., Rounding up the data: Libraries pushing new frontiers, Learned Publishing, 27, 5, pp. 33-34, (2014); Rieh S.Y., Judgment of information quality and cognitive authority in the Web, Journal of the American Society for Information Science and Technology, 53, 2, pp. 145-161, (2002); Schield M., Information literacy, statistical literacy and data literacy, IASSIST Quarterly, 28, 2-3, pp. 6-11, (2004); Schnapp J., Presner P., Digital Humanities Manifesto 2.0, (2009); Schneider R., Research data literacy, Worldwide Commonalities and Challenges in Information Literacy Research and Practice, pp. 1-16, (2013); The SCONUL Seven Pillars of Information Literacy. Core Model for Higher Education, (2011); Seadle M., Library hi tech and information science, Library Hi Tech, 30, 2, pp. 205-209, (2012); Shorish Y., Data information literacy and undergraduates: A critical competency, College and Undergraduate Libraries, 22, 1, pp. 97-106, (2015); Si L., Zhuang X., Xing W., Et al., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Smith S., Is data the new media?, EContent, 36, 2, pp. 14-19, (2013); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Stephenson E., Schifter Caravello P., Incorporating data literacy into undergraduate information literacy programs in the social sciences: A pilot project, Reference Services Review, 35, 4, pp. 525-540, (2007); Stuart D., Facilitating access to the Web of Data, (2011); Sula C.A., Digital humanities and libraries: A conceptual model, Journal of Library Administration, 53, 1, pp. 10-26, (2013); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services. Current Practices and Plans for the Future, (2012); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Tenopir C., Et al., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Et al., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Torres-Salinas D., Martin-Martin A., Fuente-Gutierrez E., Analysis of the coverage of the Data Citation Index – Thomson Reuters: Disciplines, document types and repositories, Revista Española de Documentación Científica, 37, 1, (2014); Vandergrift M., What is digital humanities and what’s it doing in the library?, In the library with a lead pipe, (2012); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and Third Space: Theorising professional service responses to Research Data Management, Journal of Academic Librarianship, 40, 3-4, pp. 211-219, (2014); Researcher Development Framework, (2011); Vlaeminck S., Wagner G.G., On the role of research data centres in the management of publication-related research data, Liber Quarterly, 23, 4, pp. 336-357, (2014); Wong G.K., Facilitating students’ intellectual growth in information literacy teaching, Reference and User Services Quarterly, 50, 2, pp. 114-118, (2010); Yates C., Partridge H., Bruce C., Exploring information experiences through phenomenography, Library and Information Research, 36, 112, pp. 96-119, (2012)","T. Koltay; Szent István University, Jászberény, Rákóczi u. 53., H-5100, Hungary; email: koltay.tibor@abpk.szie.hu","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Review","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85014601476"
"Ganguly V.R.","Ganguly, Von Raman (56702193000)","56702193000","Sustainable software development: Code4research; [Nachhaltige softwareentwicklung: code4research]","2018","VOEB-Mitteilungen","71","1","","171","180","9","0","10.31263/voebm.v71i1.2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054128196&doi=10.31263%2fvoebm.v71i1.2019&partnerID=40&md5=92ec7b1bb867fa7acbb2df804cb9bb6f","Universität Wien, Zentraler Informatikdienst, Austria","Ganguly V.R., Universität Wien, Zentraler Informatikdienst, Austria","With the step into the digital age, research processes increasingly process data in digital form, which are generated, analyzed and presented with the help of software. The range of commercial solutions is reaching its limits when it comes to research requirements, and software is increasingly being developed as part of a research project. In many cases, the developed programs will continue to run after the project ends as they serve as a basis for further research or the results will be published in an alternative form. This poses new challenges for many scientists, who have little to do with their actual task, research. Software development is just one of many other challenges that come with increasing digitization. This article focuses on the development process of software taking into account sustainability and the aspect of archiving. Among other things, it raises the question of whether and how research can be supported centrally and what role research data management plays in this. © 2018, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Archiving; Research data management; Software development; Sustainable software","","","","","","","","Der Umgang Mit Requirements-Engineering an Wissenschaftlichen Bibliotheken. Master-Thesis (ULG), (2017)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85054128196"
"Weiß J.-P.; Rauch J.; Hüsers J.; Liebe J.-D.; Teuteberg F.; Hübner U.","Weiß, Jan-Patrick (57194135616); Rauch, Jens (57195714880); Hüsers, Jens (56072061100); Liebe, Jan-David (54581263800); Teuteberg, Frank (9038835200); Hübner, Ursula (23496620100)","57194135616; 57195714880; 56072061100; 54581263800; 9038835200; 23496620100","Development of a data model for comprehensive research data management for flexible analysis of longitudinal data; [Entwicklung eines Datenmodells für ein umfassendes Forschungsdatenmanagement zur flexiblen Analyse longitudinaler Daten]","2017","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","275","","","1357","1368","11","0","10.18420/in2017_136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083242445&doi=10.18420%2fin2017_136&partnerID=40&md5=f4d6403c2ffcac2cd2afc485342c3f76","Hochschule Osnabrück, Informatik im Gesundheitswesen, Postfach 19 40, Osnabrück, 49009, Germany; Universität Osnabrück, Unternehmensrechnung und Wirtschaftsinformatik, Katharinenstr. 1, Osnabrück, 49069, Germany","Weiß J.-P., Hochschule Osnabrück, Informatik im Gesundheitswesen, Postfach 19 40, Osnabrück, 49009, Germany; Rauch J., Hochschule Osnabrück, Informatik im Gesundheitswesen, Postfach 19 40, Osnabrück, 49009, Germany; Hüsers J., Hochschule Osnabrück, Informatik im Gesundheitswesen, Postfach 19 40, Osnabrück, 49009, Germany; Liebe J.-D., Hochschule Osnabrück, Informatik im Gesundheitswesen, Postfach 19 40, Osnabrück, 49009, Germany; Teuteberg F., Universität Osnabrück, Unternehmensrechnung und Wirtschaftsinformatik, Katharinenstr. 1, Osnabrück, 49069, Germany; Hübner U., Hochschule Osnabrück, Informatik im Gesundheitswesen, Postfach 19 40, Osnabrück, 49009, Germany","[No abstract available]","","","","","","","","","Agarwal R., Et al., Research commentary —The digital transformation of healthcare. Current status and the road ahead, Information Systems Research, 21, pp. 796-809, (2010); Amarasingham R., Et al., Clinical information technologies and inpatient outcomes: A multiple hospital study, Archives of Internal Medicine, 169, pp. 108-114, (2009); Bauer A., Data-Warehouse-Systeme. Architektur, Entwicklung, Anwendung, (2013); Bojicic I., Et al., A comparative analysis of data warehouse data models, 2016 6th International Conference on Computers Communications & Control (ICCCC), pp. 151-159, (2016); Buntin M.B., Et al., The benefits of health information technology: A review of the recent literature shows predominantly positive results, Health Affairs (Project Hope), 30, pp. 464-471, (2011); Chamoni P., Analytische informationssysteme, Business Intelligence-Technologien Und -Anwendungen, (2010); Dinu V., Nadkarni P., Guidelines for the effective use of entity-attribute-value modeling for biomedical databases, International Journal of Medical Informatics, 76, pp. 769-779, (2007); Forschungsgruppe Informatik Im Gesundheitswesen Der Hochschule Osnabrück: IT-Report Gesundheitswesen, (2017); Friedman C.P., Wong A.K., Blumenthal D., Achieving a nationwide learning health system, Science Translational Medicine, 2, (2010); Hey T., The Fourth Paradigm. Data-Intensive Scientific Discovery, (2009); Hubner U., Et al., ROSE – the learning health care system in the Osnabrück-Emsland / ROSE – Das lernende Gesundheitssystem in der Region Osnabrück-Emsland, International Journal of Health Professions, 3, (2016); Inmon W.H., Building the Data Warehouse, (2005); Jovanovic V., Subotic D., Mrdalj S., Data modeling styles in data warehousing, 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1458-1463, (2014); Kimball R., Ross M., The data warehouse toolkit, The Definitive Guide to Dimensional Modeling, (2013); Lenz R., Beyer M., Kuhn K.A., Semantic integration in healthcare networks, International Journal of Medical Informatics, 76, pp. 201-207, (2007); Loper D., Et al., Integrating healthcare-related information using the entity-attribute-value storage model, Health Information Science. First International Conference, HIS 2012, pp. 13-24, (2012); Linstedt D., Olschimke M., Building A Scalable Data Warehouse with Data Vault 2.0, (2016); Meineke F., Et al., Medizinische Forschungsdatenbanken Als Baustein Des Forschungsdatenmanagements An Der Universität Leipzig, (2012); Murphy S.N., Et al., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), Journal of the American Medical Informatics Association JAMIA, 17, pp. 124-130, (2010); Olsen L., Aisner D., McGinnis J.M., Olsen L., Aisner D., McGinnis J.M., Roundtable on evidence-based medicine, The Learning Healthcare System. Workshop Summary, (2007); Pommerening K., Et al., Der Impact der Medizinischen Informatik, Informatik-Spektrum, 38, pp. 347-369, (2015); Thye J., Et al., IT-benchmarking of clinical workflows: Concept, implementation, and evaluation, Studies in Health Technology and Informatics, 198, pp. 116-124, (2014); Wickham H., Chang W., Ggplot2: Create Elegant Data Visualisation Using the Grammar of Graphics, (2016); Wade T.D., Hum R.C., Murphy J.R., A Dimensional Bus model for integrating clinical and research data, Journal of the American Medical Informatics Association JAMIA, 18, pp. 96-102, (2011); Yamamoto K., Et al., A pragmatic method for electronic medical record-based observational studies: Developing an electronic medical records retrieval system for clinical research, BMJ Open, 2, (2012)","","Eibl M.; Gaedke M.","Gesellschaft fur Informatik (GI)","","47. Jahrestagung der Gesellschaft fur Informatik, Informatik 2017 - 47th  Annual Meeting of the German Informatics Society (GI), Informatics 2017","25 September 2017 through 29 September 2017","Chemnitz","158762","16175468","978-388579669-5","","","German","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85083242445"
"Monteiro C.; Lopes C.T.; Silva J.R.","Monteiro, Cláudio (8397804400); Lopes, Carla Teixeira (57194455159); Silva, João Rocha (55496903800)","8397804400; 57194455159; 55496903800","Supporting description of research data: Evaluation and comparison of term and concept extraction approaches","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11057 LNCS","","","377","380","3","0","10.1007/978-3-030-00066-0_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053842629&doi=10.1007%2f978-3-030-00066-0_44&partnerID=40&md5=05251706ca3b721e26cbf42ff5e46229","Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Monteiro C., Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Lopes C.T., Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Silva J.R., Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal, INESC TEC, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","The importance of research data management is widely recognized. Dendro is an ontology-based platform that allows researchers to describe datasets using generic and domain-specific descriptors from ontologies. Selecting or building the right ontologies for each research domain or group requires meetings between curators and researchers in order to capture the main concepts of their research. Envisioning a tool to assist curators through the automatic extraction of key concepts from research documents, we propose 2 concept extraction methods and compare them with a term extraction method. To compare the three approaches, we use as ground truth an ontology previously created by human curators. © 2018, Springer Nature Switzerland AG.","Ontology learning; Research data management; Term extraction","Digital libraries; Extraction; Information management; Ontology; Text processing; Automatic extraction; Concept extraction; Domain specific; Ontology learning; Ontology-based; Research data managements; Research domains; Term extraction; Data mining","","","","","","","Amorim R.C., Castro J.A., da Silva J.R., Ribeiro C., A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential, New Contributions in Information Systems and Technologies. AISC, 353, pp. 101-111, (2015); Castro J.A., Perrotta D., Amorim R.C., da Silva J.R., Ribeiro C., Ontologies for research data description: A design process applied to vehicle simulation, MTSR 2015. CCIS, 544, pp. 348-354, (2015); Cimiano P., Madche A., Staab S., Volker J., Ontology learning, Handbook on Ontologies. IHIS, pp. 245-267, (2009); Frantzi K.T., Ananiadou S., Tsujii J., The C-value/NC-value method of automatic recognition for multi-word terms, ECDL 1998. LNCS, 1513, pp. 585-604, (1998); Rocha J., Ribeiro C., Lopes J., Ranking Dublin Core descriptor lists from user interactions: A case study with Dublin Core Terms using the Dendro platform, Int. J. Digital Libr., (2018); Wong W., Liu W., Bennamoun M., Ontology learning from text, ACM Comput. Surv., 44, 4, pp. 1-36, (2012)","C. Monteiro; Faculty of Engineering, University of Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: claudio.monteiro@fe.up.pt","Mendez E.; Ribeiro C.; David G.; Lopes J.C.; Crestani F.","Springer Verlag","","22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018","10 September 2018 through 13 September 2018","Porto","218159","03029743","978-303000065-3","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85053842629"
"","","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","2018","Communications in Computer and Information Science","810","","","","","843","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041711267&partnerID=40&md5=df05556bb41b29d81662143fc409e020","","","The proceedings contain 84 papers. The special focus in this conference is on Information Literacy in the Workplace. The topics include: Information Literacy Quest. In Search of Graduate Employability; Information Literacy (IL) in the Academic Context: Is There a Gap Between Employability Competencies and Student Information Literacy Skills?; professional Practice: Using Case Studies in Information Literacy Instruction Towards Career Readiness; the Role of Sense of Coherence in Knowledge Sharing; training Trainers for Research Data Literacy: A Content- and Method-Oriented Approach; data Literacy and Research Data Management: The Croatian State of Affairs; data Literacy Education Design Based on Needs of Graduate Students in University of Chinese Academy of Sciences; data Literacy Among Charles University PhD Students: Are They Prepared for Their Research Careers?; data Literacy, Collaboration and Sharing of Research Data Among Academics at the University of Iceland; implementing Library Strategies and Values as a Part of the Workplace Information Literacy; date Literacy as Requirement for China’s Library and Information Profession: A Preliminary Research on Recruitment Data; research Data Management in Three Spanish Universities; data Literacy and Research Data Management in Two Top Universities in Poland. Raising Awareness; research Data Reshaping Cultural Society: Case of the Lebanese University; news, Fake News, and Critical Authority; ICT Access and Use by Teachers and Information Professionals: Perspectives and Constraints for the Development of Media and Information Literacy in Brazil; a Method Combining Deductive and Inductive Principles to Define Work-Related Digital Media Literacy Competences; copyright Literacy Among the Literacies in Hungary; search Engine Literacy.","","","","","","","","","","","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference review","Final","","Scopus","2-s2.0-85041711267"
"Chard R.; Chard K.; Alt J.; Parkinson D.Y.; Tuecke S.; Foster I.","Chard, Ryan (55588066400); Chard, Kyle (9132950200); Alt, Jason (57195364962); Parkinson, Dilworth Y. (10341232700); Tuecke, Steve (6602740450); Foster, Ian (35572232000)","55588066400; 9132950200; 57195364962; 10341232700; 6602740450; 35572232000","Ripple: Home Automation for Research Data Management","2017","Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017","","","7979852","389","394","5","26","10.1109/ICDCSW.2017.30","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027527060&doi=10.1109%2fICDCSW.2017.30&partnerID=40&md5=294613f48729fc468537f335b06caf52","Computing, Environment, and Life Sciences, Argonne National Laboratory, United States; Computation Institute, University of Chicago and Argonne National Laboratory, United States; Advanced Light Source Division, Lawrence Berkeley National Laboratory, United States","Chard R., Computing, Environment, and Life Sciences, Argonne National Laboratory, United States; Chard K., Computation Institute, University of Chicago and Argonne National Laboratory, United States; Alt J., Computation Institute, University of Chicago and Argonne National Laboratory, United States; Parkinson D.Y., Advanced Light Source Division, Lawrence Berkeley National Laboratory, United States; Tuecke S., Computation Institute, University of Chicago and Argonne National Laboratory, United States; Foster I., Advanced Light Source Division, Lawrence Berkeley National Laboratory, United States","Exploding data volumes and acquisition rates, plus ever more complex research processes, place significant strain on research data management processes. It is increasingly common for data to flow through pipelines comprised of dozens of different management, organization, and analysis steps distributed across multiple institutions and storage systems. To alleviate the resulting complexity, we propose a home automation approach to managing data throughout its lifecycle, in which users specify via high-level rules the actions that should be performed on data at different times and locations. To this end, we have developed Ripple, a responsive storage architecture that allows users to express data management tasks via a rules notation. Ripple monitors storage systems for events, evaluates rules, and uses serverless computing techniques to execute actions in response to these events. We evaluate our solution by applying Ripple to the data lifecycles of two real-world projects, in astronomy and light source science, and show that it can automate many mundane and cumbersome data management processes. © 2017 IEEE.","Responsive storage; Serverless; Software defined cyberinfrastructure","Digital storage; Distributed computer systems; Life cycle; Light sources; Storage management; Acquisition rates; Computing techniques; Cyber infrastructures; Management process; Real world projects; Research data managements; Serverless; Storage architectures; Information management","","","","","","","Atkins D., Hey T., Hedstrom M., National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Data and Visualization Final Report, National Science Foundation, Tech. Rep, (2011); Birnholtz J.P., Bietz M.J., Data at work: Supporting sharing in science and engineering, International ACM SIGGROUP Conference on Supporting Group Work. ACM, pp. 339-348, (2003); Chard K., Tuecke S., Foster I., Efficient and secure transfer, synchronization, and sharing of big data, IEEE Cloud Computing, 1, 3, pp. 46-55, (2014); Rajasekar A., Wan M., Moore R., Schroeder W., A prototype rulebased distributed data management system, HPDC Workshop on Next Generation Distributed Data Management, 102, (2003); Rajasekar A., Moore R., Hou C., Lee C.A., Marciano R., De Torcy A., Wan M., Schroeder W., Chen S.-Y., Gilbert L., Tooby P., Zhu B., IRODS Primer: Integrated rule-oriented data system, Synthesis Lectures on Information Concepts, Retrieval, and Services, 2, 1, pp. 1-143, (2010); Schuler R., Kesselman C., Czajkowski K., Data centric discovery with a data-oriented architecture, 1st Workshop on the Science of Cyberinfrastructure: Research, Experience, Applications and Models, Ser. SCREAM '15, pp. 37-44, (2015); Leibovici T., Taking Back Control of HPC File Systems with Robinhood Policy Engine, (2015); SPADE; Deslippe J., Essiari A., Patton S.J., Samak T., Tull C.E., Hexemer A., Kumar D., Parkinson D., Stewart P., Workflow management for real-time analysis of lightsource experiments, 9th Workshop on Workflows in Support of Large-Scale Science, pp. 31-40, (2014); Juric M., Kantor J., Lim K., Lupton R.H., Dubois-Felsmann G., Jenness T., Axelrod T.S., Aleksic J., Allsman R.A., AlSayyad Y., Et al., The LSST Data Management System, (2015); If This Then That; Ur B., Pak Yong Ho M., Brawner S., Lee J., Mennicken S., Picard N., Schulze D., Littman M.L., Trigger-action programming in the wild: An analysis of 200, 000 IFTTT recipes, CHI Conference on Human Factors in Computing Systems. ACM, pp. 3227-3231, (2016); Chard K., D'Arcy M., Heavner B., Foster I., Kesselman C., Madduri R., Rodriguez A., Soiland-Reyes S., Goble C., Clark K., Deutsch E.W., Dinov I., Price N., Toga A., I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets, IEEE International Conference on Big Data, (2016)","","Ferreira J.E.; Higashino T.; Musaev A.","Institute of Electrical and Electronics Engineers Inc.","IEEE Technical Committee on Distributed Processing (TCDP); National Science Foundation (NSF)","37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017","5 June 2017 through 8 June 2017","Atlanta","129181","","978-153863292-5","","","English","Proc. - IEEE Int. Conf. Distrib. Comput. Syst. Workshops, ICDCSW","Conference paper","Final","","Scopus","2-s2.0-85027527060"
"Auge T.; Heuer A.","Auge, Tanja (57194833958); Heuer, Andreas (9533312500)","57194833958; 9533312500","The theory behind minimizing research data — Result equivalent CHASE-inverse mappings","2018","CEUR Workshop Proceedings","2191","","","1","12","11","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053610913&partnerID=40&md5=dc3436c9dcc7d16d6c720755f2bc1fc7","University of Rostock, Rostock, Germany","Auge T., University of Rostock, Rostock, Germany; Heuer A., University of Rostock, Rostock, Germany","In research data management and other applications, the primary research data have to be archived for a longer period of time to guarantee the reproducibility of research results. How can we minimize the amount of data to be archived, especially in the case of constantly changing databases or database schemes and permanently performing new evaluations on these data? In this article, we will consider evaluation queries given in an extended relational algebra. For each of the operations, we will decide whether we can compute an inverse mapping to automatically compute a (minimal) subdatabase of the original research database when only the evaluation query and the evaluation result is stored. We will distinguish between different types of inverses from exact inverses to data exchange equivalent inverses. If there is no inverse mapping, especially for aggregation operations, we will derive the necessary provenance information to be able to perform the calculation of this subdatabase. The theory behind this minimization of research data, that has to be archived to guarantee reproducible research, is based on the CHASE&BACKCHASE technique, the theory of schema mappings and their inverses, and the provenance polynomials to be used for how provenance. © 2018 CEUR-WS. All Rights Reserved.","CHASE; Data exchange equivalence; Data provenance; Inverse schema mappings; Research data management","Algebra; Database systems; Electronic data interchange; Information management; Query processing; Aggregation operation; CHASE; Data provenance; Evaluation results; Relational algebra; Reproducible research; Research data managements; Schema mappings; Mapping","","","","","","","Aho A.V., Beeri C., Ullman J.D., The Theory of Joins in Relational Databases, ACM TODS, 4, 3, pp. 297-314, (1979); Auge T., Umsetzung Von Provenance-Anfragen in Big-Data-Analytics-Umgebungen, (2017); Auge T., Heuer A., Combining Provenance Management and Schema Evolution. To be published in Provenance and Annotation of Data and Processes, Proceedings of The 7th International Provenance and Annotation Workshop (IPAW), (2018); Auge T., Heuer A., Inverse im Forschungsdatenmanagement — Eine Kombination aus Provenance Management, Schema- Und Daten-Evolution, Proceedings of The 30th Workshop on “Grundlagen Von Datenbanken, pp. 108-113, (2018); Amsterdamer Y., Deutch D., Tannen V., Provenance for Aggregate Queries, ACM PODS, pp. 153-164, (2011); Bruder I., Klettke M., Moller M.L., Meyer F., Heuer A., Jurgensmann S., Feistel S., Daten wie Sand am Meer - Datenerhebung, -strukturierung, -management und Data Provenance für die Ostseeforschung, Datenbank-Spektrum, 17, 2, pp. 183-196, (2017); Buneman P., Khanna S., Tan W.C., Why and Where: A Characterization of Data Provenance, ICDT, 1, pp. 316-330, (2001); Cheney J., Chiticariu L., Tan W.C., Provenance in Databases: Why, How, and Where, Foundations and Trends in Databases, 1, 4, pp. 379-474, (2009); Deutsch A., Popa L., Tannen V., Physical Data Independence, Constraints, and Optimization with Universal Plans, Proceedings of 25th International Conference on Very Large Data Bases, pp. 459-470, (1999); Fagin R., Inverting Schema Mappings, ACM TODS, 32, 4, (2007); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Quasi-Inverses of Schema Mappings, ACM TODS, 33, 2, (2008); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema Mapping Evolution Through Composition and Inversion, Schema Matching and Mapping, (2011); Green T.J., Karvounarakis G., Tannen V., Provenance semirings, Proceedings of The 26th ACM Symposium on PODS, pp. 31-40, (2007); Greco S., Molinaro C., Datalog and Logic Databases, Synthesis Lectures on Data Management, (2015); Green T.J., Tannen V., The Semiring Framework for Database Provenance, Proceedings of The 36th ACM Symposium on PODS, pp. 93-99, (2017); Herschel M., A Hybrid Approach to Answering Why-Not Questions on Relational Query Results, J. Data and Information Quality, 5, 3, (2015); Mohapatra A., Genesereth M., Aggregation in Datalog Under Set Semantics Stanford University, (2012); Marten D., Heuer A., Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements, OJDB, 4, pp. 22-42, (2017); Maier D., Mendelzon A.O., Sagiv Y., Testing Implications of Data Dependencies, ACM TODS, 4, 4, pp. 455-469, (1979)","","Ponzetto S.P.; Bizer C.; Keuper M.; Stuckenschmidt H.; Gemulla R.","CEUR-WS","BridgingIT GmbH; et al.; Fraunhofer-Institut fur Intelligente Analyse- und Informationssysteme (IAIS); German Management Consulting GmbH; Institut fur Enterprise Systems (InES); mayato GmbH","2018 Conference ""Learning, Knowledge, Data, Analytics"", LWDA 2018","22 August 2018 through 24 August 2018","Mannheim","138957","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85053610913"
"Auge T.; Heuer A.","Auge, Tanja (57194833958); Heuer, Andreas (9533312500)","57194833958; 9533312500","Inverses in research data management: Combining provenance management, schema and data evolution; [Inverse im Forschungsdatenmanagement Eine Kombination aus Provenance Management, Schema- und Daten-Evolution]","2018","CEUR Workshop Proceedings","2126","","","108","113","5","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049803962&partnerID=40&md5=ea4008c76aceaaece02cd4849845c452","Lehrstuhl für Datenbank- und Informationssysteme, Institut für Informatik, Universität Rostock, Germany","Auge T., Lehrstuhl für Datenbank- und Informationssysteme, Institut für Informatik, Universität Rostock, Germany; Heuer A., Lehrstuhl für Datenbank- und Informationssysteme, Institut für Informatik, Universität Rostock, Germany","Collecting, recording, storing, tracking, and archiving scientific data is the task of research data management, which is the basis for scientific evaluations on these data. In addition to the evaluation and the result itself, the section of the original database used has to be archived too. This evaluation usually corresponds to a complex database query. Thus, to ensure reproducible and replicable research, the evaluation queries can be processed again later on in time to reproduce the result. If the data or the schema of the research database changes frequently, the original database would now have to be permanently stored (frozen) after every evaluation carried out on the database. In order to avoid this and in order to avoid massively replicated databases, we want to use provenance management techniques to calculate the minimal part of the database that must be frozen in order to be able to generate the query result again. For this, we want to combine techniques of why and how provenance with the theory of schema mappings for data integration and data exchange, especially the inverse schema mappings of Fagin. These inverse schema mappings have been extended by a new chase inverse, the result equivalent chase inverse. In this article we present an overview of the entire research project and then concentrate on the first concrete partial results: the classification of internal requests in the calculation of why provenance. We will distinguish between the cases, (1) whether we can calculate the inverse only with the query result and the evaluation query, (2) whether the inverse calculates a sub-database that can be homomorphically mapped to the original database, or (3) whether additional provenance information (polynomials of how provenance) must be stored to be able to calculate the inverse. Copyright is held by the author/owner(s). © 2018 CEUR-WS. All rights reserved.","Algorithmus; CHASE; CHASE; Data Provenance; Daten; Ergebnisäquivalenz; Evolution; Evolution; Inverse; Schema","Data integration; Database systems; Electronic data interchange; Information management; Algorithmus; CHASE; Data provenance; Daten; Evolution; Inverse; Schema; Query processing","","","","","","","Aho A.V., Beeri C., Ullman J.D., The theory of joins in relational databases, ACM TODS, 43, pp. 297-314, (1979); Amsterdamer Y., Deutch D., Tannen V., Provenance for aggregate queries, PODS, pp. 153-164, (2011); Auge T., Umsetzung von Provenance-Anfragen in BigData-Analytics-Umgebungen, (2017); Bruder I., Heuer A., Schick S., Spors S., Konzepte fur das Forschungsdatenmanagement an der Universität Rostock, LWDA, 1917, pp. 165-175, (2017); Bruder I., Klettke M., Moller M.L., Meyer F., Heuer A., Jurgensmann S., Feistel S., Daten wie Sand am Meer - Datenerhebung -strukturierung -management und Data Provenance fur die Ostseeforschung, Datenbank-Spektrum, 172, pp. 183-196, (2017); Buneman P., Khanna S., Tan W.C., Why and where: A characterization of data provenance, ICDT, 1, pp. 316-330, (2001); Cheney J., Chiticariu L., Tan W.C., Provenance in databases: Why, how, and where, Foundations and Trends in Databases, 14, pp. 379-474, (2009); Curino C., Moon H.J., Deutsch A., Zaniolo C., Update rewriting and integrity constraint maintenance in a schema evolution support system: PRISM++, PVLDB, 42, pp. 117-128, (2010); Fagin R., Kolaitis P.G., Miller R.J., Popa L., Data exchange: Semantics and query answering, Theor. Comput. Sci., pp. 89-124, (2005); Fagin R., Inverting schema mappings, ACM TODS, 324, (2007); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Quasi-inverses of schema mappings, ACM TODS, 332, pp. 111-1152, (2008); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema mapping evolution through composition and inversion, Schema Matching and Mapping, pp. 191-222, (2011); Geerts F., Mecca G., Papotti P., Santoro D., That's all folks! LLUNATIC goes open source, PVLDB, 713, pp. 1565-1568, (2014); Green T.J., Karvounarakis G., Tannen V., Provenance semirings, PODS, pp. 31-40, (2007); Green T.J., Tannen V., The semiring framework for database provenance, PODS, 2017, pp. 93-99, (2017); Herschel M., A hybrid approach to answering whynot questions on relational query results, J. Data and Information Quality, 53, pp. 101-1029, (2015); Heuer A., METIS in PArADISE: Provenance Management bei der Auswertung von Sensordatenmengen fur die Entwicklung von Assistenzsystemen, BTW Workshops., 242, pp. 131-136, (2015); Johnston T., Bitemporal Data - Theory and Practice., (2014); Kohler S., Ludascher B., Zinn D., First-order provenance games, CoRR abs/1309.2655, (2013); Maier D., Mendelzon A.O., Sagiv Y., Testing implications of data dependencies, ACM TODS, 44, pp. 455-469, (1979); Marten D., Heuer A., Machine learning on large databases: Transforming hidden markov models to SQL statements, OJDB, 41, pp. 22-42, (2017); Svacina J., Intensional Answers for Provenance Queries in Big Data Analytics, (2016)","","Klassen G.; Heinrich-Heine-Universitat Dusseldorf, Institut fur Informatik, Universitatsstr. 1, Dusseldorf; Conrad S.; Heinrich-Heine-Universitat Dusseldorf, Institut fur Informatik, Universitatsstr. 1, Dusseldorf","CEUR-WS","","30th GI-Workshop Grundlagen von Datenbanken, GvDB 2018 - 30th GI-Workshop on the Foundations of Databases, GvDB 2018","22 May 2018 through 25 May 2018","Wuppertal","137501","16130073","","","","German","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85049803962"
"De Sousa N.T.; Hasselbring W.; Weber T.; Kranzlmüller D.","De Sousa, Nelson Tavares (57201380782); Hasselbring, Wilhelm (26643500000); Weber, Tobias (57201379351); Kranzlmüller, Dieter (26643233300)","57201380782; 26643500000; 57201379351; 26643233300","Designing a generic research data infrastructure architecture with continuous software engineering","2018","CEUR Workshop Proceedings","2066","","","85","88","3","9","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044520304&partnerID=40&md5=a04fac4baf74a8ec23118cbb6d9dc84e","Software Engineering Group, Kiel University, Kiel, Germany; Leibniz Supercomputing Centre, Bavarian Academy of Sciences and Humanities, Garching, Germany","De Sousa N.T., Software Engineering Group, Kiel University, Kiel, Germany; Hasselbring W., Software Engineering Group, Kiel University, Kiel, Germany; Weber T., Leibniz Supercomputing Centre, Bavarian Academy of Sciences and Humanities, Garching, Germany; Kranzlmüller D., Leibniz Supercomputing Centre, Bavarian Academy of Sciences and Humanities, Garching, Germany","Long-living software systems undergo a continuous development including adaptions due to altering requirements or the addition of new features. This is an even greater challenge if neither all users nor requirements are known at an initial design phase. In such a context, complex restructuring activities are much more probable, if the challenges are not taken into account from the beginning. We introduce a combination of the concepts of domain-driven design and self-contained systems to meet these challenges within the system's architecture design. We show the merits of this approach by designing an architecture for a generic research data infrastructure, a use case where the mentioned challenges can be found. Embedding this approach within continuous software engineering, allows to implement and integrate changes continuously, without neglecting other crucial properties such as maintainability and scalability. © 2018 CEUR-WS. All rights reserved.","Continuous software engineering; Microservice; Research data management; Self-contained system; Systemoriented architecture","Computer architecture; Software engineering; Architecture designs; Continuous development; Continuous software engineerings; Domain-driven designs; Microservice; Research data managements; Self-contained systems; Software systems; Information management","","","","","Deutsche Forschungsgemeinschaft, DFG, (BO818/16-1, HA2038/6-1)","ACKNOWLEDGEMENTS This work was supported by the DFG (German Research Foundation) with the GeRDI project (Grants No. BO818/16-1 and HA2038/6-1).","Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., Da Silva Santos L.B., Bourne P.E., Et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016); C. H. L. E. G. on the european open science cloud, Realising the European Open Science Cloud, (2013); Grunzke R., Adolph T., Biardzki C., Bode A., Borst T., Bungartz H.-J., Busch A., Frank A., Grimm C., Hasselbring W., Kazakova A., Latif A., Limani F., Neumann M., De Sousa N.T., Tendel J., Thomsen I., Tochtermann K., Muller-Pfefferkorn R., Nagel W.E., Challenges in Creating a Sustainable Generic Research Data Infrastructure, Softwaretechnik-Trends, 37, 2; Quaas M., Hoffmann J., Kamin K., Kleemann L., Schacht K., Fishing for proteins, WWF, (2016); Evans E., Domain-Driven Design: Tackling Complexity in the Heart of Software, (2004); Lewis J., Fowler M., Microservices, (2014); Hasselbring W., Microservices for scalability: Keynote talk abstract, Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering (ICPE 2016), pp. 133-134, (2016); Hasselbring W., Steinacker G., Microservice architectures for scalability, agility and reliability in e-commerce, 2017 IEEE International Conference on Software Architecture Workshops (ICSAW), pp. 243-246, (2017); Shahin M., Babar M.A., Zhu L., Continuous integration, delivery and deployment: A systematic review on approaches, tools, challenges and practices, IEEE Access, 5, 2017, pp. 3909-3943","","Steghofer J.-P.; Schmieders E.; Tessmer J.; Schneider K.; Lucke U.; Lichter H.; Steffens A.; Konersmann M.; Krusche S.; Kuhrmann M.; Striewe M.; Hottger R.; Jung R.; Strickroth S.; Riehle D.; Heinrich R.","CEUR-WS","","2018 Combined Workshops of the German Software Engineering Conference, SE-WS 2018","6 March 2018","Ulm","135060","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85044520304"
"Lee D.J.; Stvilia B.","Lee, Dong Joon (56133692500); Stvilia, Besiki (22836964300)","56133692500; 22836964300","Practices of research data curation in institutional repositories: A qualitative view from repository staff","2017","PLoS ONE","12","3","e0173987","","","","42","10.1371/journal.pone.0173987","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015653263&doi=10.1371%2fjournal.pone.0173987&partnerID=40&md5=e9b95d988286a79f20b3748db22d267c","University Libraries, Texas AandM University, College Station, TX, United States; School of Information, Florida State University, Tallahassee, FL, United States","Lee D.J., University Libraries, Texas AandM University, College Station, TX, United States; Stvilia B., School of Information, Florida State University, Tallahassee, FL, United States","The importance of managing research data has been emphasized by the government, funding agencies, and scholarly communities. Increased access to research data increases the impact and efficiency of scientific activities and funding. Thus, many research institutions have established or plan to establish research data curation services as part of their Institutional Repositories (IRs). However, in order to design effective research data curation services in IRs, and to build active research data providers and user communities around those IRs, it is essential to study current data curation practices and provide rich descriptions of the sociotechnical factors and relationships shaping those practices. Based on 13 interviews with 15 IR staff members from 13 large research universities in the United States, this paper provides a rich, qualitative description of research data curation and use practices in IRs. In particular, the paper identifies data curation and use activities in IRs, as well as their structures, roles played, skills needed, contradictions and problems present, solutions sought, and workarounds applied. The paper can inform the development of best practice guides, infrastructure and service templates, as well as education in research data curation in Library and Information Science (LIS) schools. © 2017 Lee, Stvilia.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Academies and Institutes; Data Curation; Research; United States; human; human experiment; information processing; information science; interview; skill; staff; United States; university; organization; organization and management; research","","","","","","","Expanding Public Access to the Results of Federally Funded Research | the White House, (2013); Specifications for Projects That Develop Digital Products, (2011); NIH Data Sharing Policy and Implementation Guidance, (2010); Grant Proposal Guide (Gpg 11001), (2010); Aalbersberg I.J.J., Kahler O., Supporting science through the interoperability of data and articles, Lib Mag, 17, 1-2, (2011); Thomson Reuters Unveils Data Citation Index for Discovering Global Data Sets, (2012); Simmhan Y.L., Plale B., Gannon D., A survey of data provenance in e-science, SIGMOD Rec., 34, 3, pp. 31-36, (2005); Tenopir C., Birch B., Allard S., Academic libraries and research data services, Association of College and Research Libraries, (2012); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Et al., Research data services in academic libraries: Data intensive roles for the future?, J EScience Librariansh, 4, 2, (2015); Lee D.J., Stvilia B., Identifier schemas and research data, Proc Am Soc Inf Sci Technol., 49, 1, pp. 1-4, (2012); Witt M., Cragin M., Introduction to institutional data repositories workshop, Libr Res Publ, (2008); Lynch C., Institutional repositories: Essential infrastructure for scholarship in the digital age, Association of Research Libraries, (2003); Markey K., Rieh S.Y., St Jean B., Kim J., Yakel E., Census of Institutional Repositories in the United States: MIRACLE Project Research Findings, (2007); Rieger O.Y., Select for success: Key principles in assessing repository models, Lib Mag, 13, 7-8, (2007); Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans (NSF 10-077), (2010); Witt M., Co-designing, Co-developing, and Co-implementing an Institutional Data Repository Service, J Libr Adm., 52, 2, pp. 172-188, (2012); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr Trends., 57, 2, pp. 280-299, (2008); Westell M., Institutional repositories: Proposed indicators of success, Libr Hi Tech., 24, 2, pp. 211-226, (2006); Foster I., Jennings N.R., Kesselman C., Brain meets brawn:why grid and agents need each other, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004 AAMAS 2004, pp. 8-15, (2004); Latif A., Borst T., Tochtermann K., Exposing Data from an Open Access Repository for Economics As Linked Data, Lib Mag, 20, 9-10, (2014); Park O.N., Development of linked data for archives in Korea, Lib Mag, 21, 3-4, (2015); Warner S., Author Identifiers in Scholarly Repositories, (2010); Shreeves S.L., Knutson E.M., Stvilia B., Palmer C.L., Twidale M.B., Cole T.W., Is ""quality"" metadata ""shareable "" metadata? the implications of local metadata practices for federated collections, Proceedings of the Association of College and Research Libraries (ACRL) 12th National Conference Minneapolis, MN, pp. 223-237, (2005); Stvilia B., Gasser L., Twidale M.B., Shreeves S.L., Cole T.W., Metadata Quality for Federated Collections, (2004); Cragin M.H., Heidorn P.B., Palmer C.L., Smith L.C., An Educational Program on Data Curation, (2007); Curry E., Freitas A., O'Riain S., The role of community-driven data curation for enterprises, Linking Enterprise Data, pp. 25-47, (2010); Lord P., Macdonald A., E-Scicence Curation Report: Data Curation for E-Science in the UK: An Audit to Establish Requirements for Future Curation and Provision, (2003); Qin J., Ball A., Greenberg J., Functional and architectural requirements for metadata: Supporting discovery and management of scientific data, Int Conf Dublin Core Metadata Appl., pp. 62-71, (2012); Stvilia B., Hinnant C.C., Wu S., Worrall A., Lee D.J., Burnett K., Et al., Research project tasks, data, and perceptions of data quality in a condensed matter physics community, J Assoc Inf Sci Technol., 66, 2, pp. 246-263, (2015); Stvilia B., Hinnant C.C., Wu S., Worrall A., Lee D.J., Burnett K., Et al., Studying the data practices of a scientific community, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 425-426, (2013); Higgins S., The DCC curation lifecycle model, Int J Digit Curation., 3, 1, pp. 134-140, (2008); Burton A., Treloar A., Designing for Discovery and Re-Use: The ""ANDS Data Sharing Verbs"" Approach to Service Decomposition, Int J Digit Curation., 4, 3, pp. 44-56, (2009); Reference Model for An Open Archival Information System (OAIS), (2012); Welcome to DRAMBORA Interactive: Log in or Register to Use the Toolkit, (2008); Jones S., Ross S., Ruusalepp R., The Data Audit Framework: A Toolkit to Identify Research Assets and Improve Data Management in Research Led Institutions, (2008); Trustworthy Repositories Audit and Certification (TRAC): Criteria and Checklist, (2007); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Et al., Data sharing by scientists: Practices and perceptions, PLoS ONE., 6, 6, (2011); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, Int J Digit Libr., 7, 1-2, pp. 17-30, (2007); Wu S., Worrall A., Stvilia B., Exploring data practices of the earthquake engineering community, IConference Proceedings, (2016); Parham S.W., Bodnar J., Fuchs S., Supporting tomorrow's research Assessing faculty data curation needs at Georgia Tech, Coll Res Libr News., 73, 1, pp. 10-13, (2012); Van Tuyl S., Michalek G., Assessing Research Data Management Practices of Faculty at Carnegie Mellon University, (2015); Kim Y., Addom B.K., Stanton J.M., Education for eScience Professionals: Integrating Data Curation and Cyberinfrastructure, Int J Digit Curation., 6, 1, pp. 125-138, (2011); Lee D.J., Stvilia B., Developing a data identifier taxonomy, Cat Classif Q., 52, 3, pp. 303-336, (2014); Engestrom Y., Learning by Expanding: An Activity-theoretical Approach to Developmental Research, (1987); Leontiev A., Activity, Consciousness, Personality, (1978); Lord P., Macdonald A., Lyon L., Giaretta D., From data deluge to data curation, Proc UK E-Sci Hands Meet, (2004); Schutt R., Investigating the Social World, (2009); Gasser L., The integration of computing and routine work, ACM Trans Inf Syst., 4, 3, pp. 205-225, (1986); Starr J., Information politics: The story of an emerging metadata standard, First Monday., 8, 7, (2003); Stvilia B., Twidale M., Smith L.C., Gasser L., Information quality work organization in Wikipedia, Journal of the American Society for Information Science and Technology., 59, 6, pp. 983-1001, (2008); Dublin core metadata initiative, Dublin Core Metadata Element Set, Version 1.1, (2012); California digital library, NOID-Curation-Confluence, (2013); Berners-Lee T., Linked data, W3C, (2006); California digital library, CDL Digital File Format Recommendations: Master Production Files, (2011); Curation Reference Manual | Digital Curation Centre, (2007); Wilson T.D., Activity theory and information seeking, Annu Rev Inf Sci Technol., 42, 1, pp. 119-161, (2008); Berners-Lee T., Hendler J., Lassila O., The semantic web, Sci Am., 284, 5, (2001); Huang H., Stvilia B., Jorgensen C., Bass H.W., Prioritization of data quality dimensions and skills requirements in genome annotation work, Journal of the American Society for Information Science and Technology., 63, 1, pp. 195-207, (2012); Wu S., Exploring the Data Work Organization of the Gene Ontology, (2014); Strasser C., Cook R., Michener W., Budden A., Primer on data management: What you always wanted to know, DataONE, (2012)","","","Public Library of Science","","","","","","19326203","","POLNC","28301533","English","PLoS ONE","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85015653263"
"","","","Proceedings of the International Conference on Dublin Core and Metadata Applications","2018","Proceedings of the International Conference on Dublin Core and Metadata Applications","2018-September","","","","","136","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056865253&partnerID=40&md5=4552bc4aa80d1e2f009fe5cb34107e00","","","The proceedings contain 11 papers. The topics discussed include: an approach to enabling RDF data in querying to invoke REST API for complex calculating; experiments in operationalizing metadata quality interfaces: a case study at the University of north Texas libraries; designing a multilingual knowledge graph as a service for cultural heritage – some challenges and solutions; validation of a metadata application profile domain model; research data management in the field of ecology: an overview; metadata models for organizing digital archives on the web: metadata-centric projects at Tsukuba and lessons learned; linked data publishing and ontology in Korea libraries; author identifier analysis: name authority control in two institutional repositories; visualizing library metadata for discovery; building a framework to encourage the use of metadata in modern web-design; analysis of user-supplied metadata in a health sciences institutional repository; linking knowledge organization systems via Wikidata; a study of multilingual semantic data integration; metadata as content: navigating the intersection of repositories, documentation, and legacy futures; and why build custom categorizers using Boolean queries instead of machine learning? Robert Wood Johnson foundation case study.","","","","","","","","","","","","Dublin Core metadata initiative","","2018 International Conference on Dublin Core and Metadata Applications, DCMI 2018","10 September 2018 through 13 September 2018","Porto","141884","19391358","","","","English","Proc. Int. Conf. Dublin Core Metadata Appl.","Conference review","Final","","Scopus","2-s2.0-85056865253"
"Saade G.; Rahme D.","Saade, Gladys (7006942862); Rahme, Dalal (57200573892)","7006942862; 57200573892","Research Data Reshaping Cultural Society: Case of the Lebanese University","2018","Communications in Computer and Information Science","810","","","215","224","9","0","10.1007/978-3-319-74334-9_23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041750298&doi=10.1007%2f978-3-319-74334-9_23&partnerID=40&md5=16a298ce75c86d59b957cf22e7d7e475","Faculty of Information Science, Lebanese University, Bauchriyeh, Lebanon; Data Services Librarian, American University of Beirut, Beirut, Lebanon","Saade G., Faculty of Information Science, Lebanese University, Bauchriyeh, Lebanon; Rahme D., Faculty of Information Science, Lebanese University, Bauchriyeh, Lebanon, Data Services Librarian, American University of Beirut, Beirut, Lebanon","Research Data Management (RDM) is a new practice in Lebanese academic institutions. The purpose of this study is to assess technological and organizational needs of one of those institutions, the Lebanese University (LU). A questionnaire was sent to academics from all faculties and branches at LU. The survey measured the demographics of survey respondents, their use of data, the degree of openness and the readiness of researchers to manage, share, and preserve datasets. Results described the concerns, challenges and level of commitment of the researchers to data management and sharing. They revealed a moderate level of awareness of information literacy and RDM practices and gave a preliminary figure of the quantity of data generated at LU allowing participants to express their needs. The paper offers guidance for developing a collection of services to support different activities in this area. © 2018, Springer International Publishing AG.","Academic universities in Lebanon; Data sharing; Institutional repositories; Lebanese University; Open access; Research data management; Scholarly communication","Education; Information management; Information services; Societies and institutions; Data Sharing; Institutional repositories; Lebanese University; Lebanon; Open Access; Research data managements; Scholarly communication; Surveys","","","","","","","Bouquillion P., Matthews J., Le Web Collaboratif: Mutations Des Industries De La Culture Et De La Communication, (2010); Dagnaud M., Le Modèle Californien: Comment l’Esprit Collaboratif Change Le Monde, (2016); Rieffel R., Revolution Numerique, Revolution Culturelle?, (2014); Surkis A., Read K., Research data management, J. Med. Libr. Assoc., 103, 3, pp. 154-156, (2015); Del Castillo D., The Arab World’s Scientific Desert. The Chronical of, Higher Education, (2004); Allatif A.L., Mouawiqat al bahth al ilmi, Al Saouddiya, (2008); Maadan S.H., Waqeh al bahth al ilmi fi al watan al arabi fi dil al fajwa al raqmiyya, Majallat Al Ouloum Al Insaniyya, 38, (2012); Zahlan A., Al arab wa tahadiyat al asr. Markaz dirassat al wihda al Arabiya, Beirut, (1990); Badran A., Les Etats Arabes, (2006); Stephan M., Recherche et Communication, Bahithat, pp. 76-95, (1997); Hotet F., Al mouwassafat al bahthiya lilrasael al jamiiya fi elm al nafs, Bahithat, pp. 24-40, (1997); Mourtada A., The Conditions of Academic Research in the Arab World, Bahithat, (1997); Funari M., Research data and humanities: A European context. Ital, J. Libr. Inf. Sci, 5, 1, pp. 209-236, (2014); Hakim Rahme D., Co-Authorship Patterns at the Medical School of the American University of Beirut between 2004 and 2014. Paper presented at: IFLA WLIC 2016 – Columbus, OH – Connections. Collaboration, Community in Session 101 - Poster Sessions, (2016); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E.L., Libraries and the research data management landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and The Future of The Academy, pp. 82-102, (2015); Eschenfelder K.R., Johnson A., Managing the Data Commons: Controlled Sharing of Scholarly Data, University Libraries Faculty and Staff Contributions, 15, (2014); Sixth Arabic Report on Education and Development, (2017); (2017); (2017); Questionnaire National Research Data Survey, (2017); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Et al., Data sharing by scientists: Practices and perceptions, Plos ONE, 6, 6, (2011)","D. Rahme; Faculty of Information Science, Lebanese University, Bauchriyeh, Lebanon; email: drahmeh@gmail.com","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85041750298"
"Horstmann W.; Witt M.","Horstmann, Wolfram (57190480179); Witt, Michael (15119883100)","57190480179; 15119883100","Libraries tackle the challenge of research data management","2017","IFLA Journal","43","1","","3","4","1","4","10.1177/0340035216688787","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014649842&doi=10.1177%2f0340035216688787&partnerID=40&md5=242b302956172270757dd69175644fab","University of Goettingen, Germany; Purdue University, United States","Horstmann W., University of Goettingen, Germany; Witt M., Purdue University, United States","[No abstract available]","","","","","","","","","Witt M., Horstmann W., International approaches to research data services in libraries, IFLA Journal, 42, 4, pp. 251-252, (2016)","W. Horstmann; Goettingen State and University Library, University of Goettingen, Goettingen, Germany; email: horstmann@sub.uni-goettingen.de","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Editorial","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85014649842"
"Kriesberg A.; Huller K.; Punzalan R.; Parr C.","Kriesberg, Adam (55504251900); Huller, Kerry (57191212467); Punzalan, Ricardo (18438320500); Parr, Cynthia (8629593400)","55504251900; 57191212467; 18438320500; 8629593400","An analysis of federal policy on public access to scientific research data","2017","Data Science Journal","16","","27","","","","11","10.5334/dsj-2017-027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020537776&doi=10.5334%2fdsj-2017-027&partnerID=40&md5=f526ad16154f928077c29f8a39d2fbf9","College of Information Studies, University of Maryland, College Park, MD, United States; National Agricultural Library, US Department of Agriculture, Beltsville, MD, United States","Kriesberg A., College of Information Studies, University of Maryland, College Park, MD, United States; Huller K., College of Information Studies, University of Maryland, College Park, MD, United States; Punzalan R., College of Information Studies, University of Maryland, College Park, MD, United States; Parr C., National Agricultural Library, US Department of Agriculture, Beltsville, MD, United States","The 2013 Office of Science and Technology Policy (OSTP) Memo on federally-funded research directed agencies with research and development budgets above $100 million to develop and release plans to increase and broaden access to research results, both published literature and data. The agency responses have generated discussion and interest but are yet to be analyzed and compared. In this paper, we examine how 19 federal agencies responded to the memo, written by John Holdren, on issues of scientific data and the extent of their compliance to the directives outlined in the memo. We present a varied picture of the readiness of federal science agencies to comply with the memo through a comparative analysis and close reading of the contents of these responses. While some agencies, particularly those with a long history of supporting and conducting science, scored well, other responses indicate that some agencies have only taken a few steps towards implementing policies that comply with the memo. These results are of interest to the data curation community as they reveal how different agencies across the federal government approach their responsibilities for research data management, and how new policies and requirements might continue to affect scientists and research communities. © 2017 The Author(s).","Data access; Metadata; Office of science and techology policy; Open data","Budget control; Metadata; Comparative analysis; Data access; Office of science and technology policies; Open datum; Research communities; Research data managements; Research-and-development budget; Scientific research datum; Information management","","","","","USDA National Agricultural Library","The authors wish to acknowledge the USDA National Agricultural Library for supporting this work through a Cooperative Agreement.","Barata K.J., Managing Intellectual Assets: The Identification, Capture, Maintenance, and Use of the Records of Federally Sponsored Scientific Research, Archival Issues, 21, pp. 129-143, (1996); Berman F., Cerf V., Who Will Pay for Public Access to Research Data?, Science, 341, pp. 616-617, (2013); Bishoff C., Johnston L., Approaches to Data Sharing: An Analysis of NSF Data Management Plans from a Large Research University, Ournal of Librarianship and Scholarly Communication, 3, (2015); Canada and Treasury Board 2014 Canada’s Action Plan on Open Government; CENDI 2016 Implementation of Public Access Programs in Federal Agencies [WWW Document]; Corneliussen S.T., New York Times commentary argues that all research papers should be free, Physics Today, (2016); English R., Raphael M., The next big library legislative issue, American Libraries, (2006); Fleming J.R., Meteorology at the Smithsonian Institution, 1847-1874: The natural history connection*, Archives of Natural History, 16, pp. 275-284, (1989); Food and Drug Administration 2015 Plan to Increase Access to Results of Fda-Funded Scientific Research; Franceschi-Bicchierai L., White House Directive Expands Access to Scientific Research, (2013); Frankel M.S., Public access to data, Science, 283, (1999); (2016); Harden V.A., A Short History of the NIH [WWW Document], (2009); Notes on the Fair Access to Science and Technology Research Act [WWW Document], (2016); Heafey E., Public Access to Science: The New Policy of the National Institutes of Health in Light of Copyright Protections in National and International Law, UCLA Journal of Law and Technology, 14, (2011); Holdren J.P., Memorandum for the Heads of Executive Departments and Agencies: Increasing Access to the Results of Federally Funded Scientific Research, (2013); Holdren J.P., Public Access-Report to Congress-July 2016, (2016); Holdren J.P., Public Access-Report to Congress-October 2016, (2016); European Commission [WWW Document], (2016); (2016); Howard J., White House Delivers New Open-Access Policy That Has Activists Cheering, The Chronicle of Higher Education, (2013); Kaiser J., Bill Would Require Free Public Access to Research Papers, Science, 312, pp. 828a-828a, (2006); Killian J.R., Sputnik, Scientists, and Eisenhower: A Memoir of the First Special Assistant to the President for Science and Technology, (1977); Lurie N., Manolio T., Patterson A.P., Collins F., Frieden T., Research as a Part of Public Health Emergency Response, New England Journal of Medicine, 368, pp. 1251-1255, (2013); Michelini A., De Simoni B., Amato A., Boschi E., Collecting, Digitizing, and Distributing Historical Seismological Data, 86, (2005); Miles M.B., Huberman A.M., Qualitative Data Analysis: An Expanded Sourcebook, (1994); Murphy K., Should All Research Papers Be Free?, The New York Times, (2016); NASA Plan: Increasing Access to the Results of Scientific Research, (2014); Plan for Providing Public Access to the Results of Federally Funded Research, (2015); Analysis of Comments and Implementation of the NIH Public Access Policy, (2008); NSF at a Glance [WWW Document], (2016); Nickum L.S., Elusive No Longer? Increasing Accessibility to the Federally Funded Technical Report Literature, The Reference Librarian, 45, pp. 33-51, (2006); NOAA Research Council 2015 NOAA Plan for Increasing Public Access to Research Results; About OSTP [WWW Document], (2016); Office of the Assistant Secretary for Preparedness and Response 2014 Pandemic and All Hazards Preparedness Act [WWW Document]; Parham S.W., Carlson J., Hswe P., Westra B., Whittier A., Using data management plans to explore variability in research data management practices across domains, International Journal of Digital Curation, 11, (2016); Parham S.W., Doty C., NSF DMP Content Analysis: What are Researchers Saying?, (2012); Peek R., A Potential OA Policy for U.S. Agencies, 26, (2009); Rolando L., Carlson J., Hswe P., Parham S.W., Westra B., Whitmire A.L., Data Management Plans as a Research Tool, Bul. Am. Soc. Info. Sci. Tech, 41, pp. 43-45, (2015); Scholarly Publishing and Academic Resources Coalition 2016 Browse Data Sharing Requirements by Federal Agency [WWW Document]; Shelby R., Accountability and Transparency: Public Access to Federally Funded Research Data Policy Essay, Harvard Journal on Legislation, 37, pp. 369-390, (2000); (2015); Public Access to Results of Federally Funded Research at the U.S, (2016); Van Noorden R., US science to be open to all, Nature, 494, pp. 414-415, (2013); Van Tuyl S., Whitmire A.L., Water, Water, Everywhere: Defining and Assessing Data Sharing in Academia, PLOS ONE, 11, (2016); Open Access Publishing [WWW Document], (2016); Whitmire A., Briney K., Nurnberger A., Henderson M., Atwood T., Janz M., Kozlowski W., Lake S., Vandegrift M., Zilinski L., A Table Summarizing the Federal Public Access Policies Resulting from the US Office of Science and Technology Policy Memorandum of February 2013, (2015)","A. Kriesberg; College of Information Studies, University of Maryland, College Park, United States; email: akriesberg@gmail.com","","Ubiquity Press Ltd","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85020537776"
"Chard K.; Foster I.; Tuecke S.","Chard, Kyle (9132950200); Foster, Ian (35572232000); Tuecke, Steven (6602740450)","9132950200; 35572232000; 6602740450","Globus: Research data management as service and platform","2017","ACM International Conference Proceeding Series","Part F128771","","a26","","","","5","10.1145/3093338.3093367","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025839238&doi=10.1145%2f3093338.3093367&partnerID=40&md5=dcaeb5bc994c21bfd17aad32f69bc5c7","University of Chicago, 5735 S Ellis Ave, Chicago, 60637, IL, United States","Chard K., University of Chicago, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Foster I., University of Chicago, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Tuecke S., University of Chicago, 5735 S Ellis Ave, Chicago, 60637, IL, United States","Globus o.ers a suite of data and user management capabilities to the research community, encompassing data transfer and sharing, user identity and authorization, and data publication. Globus capabilities are accessible via both a web browser and REST APIs. Web access allows Globus to address the needs of research labs through a so.ware-As-A-service model; the newer REST APIs address the needs of developers of research services, who can now use Globus as a platform, outsourcing complex user and data management tasks to Globus cloud-hosted services. Here we review Globus capabilities and outline how it is being applied as a platform for scientific services. © 2017 ACM.","Globus; Research data management; Science as a service","Data transfer; Information management; Data publications; Globus; Management tasks; Research communities; Research data managements; Science as a service; Scientific service; User management; Outsourcing","","","","","","","2017, (2017); 2017; Allcock W., Bresnahan J., Keimuthu R., Link M., Dumitrescu C., Raicu I., Foster I., Globus striped gridftp framework and server, ACM IEEE Conference on Supercomputing (SC 05). IEEE Computer Society, 54, (2005); Allen B., Bresnahan J., Childers L., Foster I., Kandaswamy G., Keimuthu R., Kordas J., Link M., Martin S., Picke K., Tuecke S., So.ware as a service for data scientists, Commun ACM, 52, 2012, pp. 81-88, (2012); Ananthakrishnan R., Chard K., Foster I., Tuecke S., Globus platform-As-A-service for collaborative science applications, Concurrency-Practice and Experience 27 2014, 2, pp. 290-305, (2014); Basney J., Humphrey M., Von Welch, MyProxy Online Credential Repository. So.ware: Practice and Experience 2005, 35, 9, pp. 801-816, (2005); Blaiszik B., Chard K., Pruyne J., Ananthakrishnan R., Tuecke S., Foster I., Materials data facility: Data services to advance materials science research, Journal of the Minerals, Metals & Materials Society (JOM), 68, 8, pp. 2045-2052, (2016); Chard K., Lidman M., McCollam B., Bryan J., Ananthakrishnan R., Tuecke S., Foster I., Globus nexus: A platform-Asa-service provider of research identity, proffle, and group management, Future Generation Computer Systems 56 2016, pp. 571-583, (2016); Chard K., Pruyne J., Blaiszik B., Ananthakrishnan R., Tuecke S., Foster I., Globus Data Publication as a Service: Lowering Barriers to Reproducible Science, 11th IEEE International Conference on E-Science 2015, pp. 401-410, (2015); Chard K., Tuecke S., Foster I., Ecient and secure transfer synchronization and sharing of big data, IEEE Cloud Computing 1 2014, 3, pp. 46-55, (2014); Crosas M., Dataverse Network: An Open-Source Application for Sharing, Discovering and Preserving Data. D-Lib Magazine 2011, 17, 12, (2011); Dart E., Rotman L., Tierney B., Hester M., Zurawski J., Science DMZ: A Network Design Pa.ern for Data-intensive Science, International Conference on High Performance Computing, Networking, Storage and Analysis (SC 13, (2013); Dubey A., Wagle D., Delivering so.ware as a service, McKinsey .Arterly, 2007, (2007); Foster I., Globus online: Accelerating and democratizing science through cloud-based services, Internet Computing IEEE, 15, pp. 70-73, (2011); Foster I., Vasiliadis V., Tuecke S., So.ware As A Service As A Path to So.ware Sustainability, (2013); Hardt D., OAuth 2.0 Authorization Framework 2012, (2012); Sakimura N., Bradley J., Jones M., De Medeiros B., Mortimore C., OpenID Connect Core 1.0, (2014); Smith M., Barton M., Bass M., Branschofsky M., McClellan G., Stuve D., Tansley R., Harford Walker J., DSpace: An open source dynamic digital repository, D-Lib Magazine, 9, 2003, (2003); Tuecke S., Ananthakrishnan R., Chard K., Lidman M., McCollam B., Rosen S., Foster I., Globus Auth: A research identity and access management platform, 12th IEEE International Conference on E-Science (E-Science, pp. 203-212, (2016)","","","Association for Computing Machinery","","2017 Practice and Experience in Advanced Research Computing, PEARC 2017","9 July 2017 through 13 July 2017","New Orleans","128771","","978-145035272-7","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85025839238"
"Karimova Y.; Castro J.A.; Da Silva J.R.; Pereira N.; Rodrigues J.; Ribeiro C.","Karimova, Yulia (57195369729); Castro, Joĩo Aguiar (55977255100); Da Silva, Joĩo Rocha (55496903800); Pereira, Nelson (57195715570); Rodrigues, Joana (57203242279); Ribeiro, Cristina (7201734594)","57195369729; 55977255100; 55496903800; 57195715570; 57203242279; 7201734594","Description + annotation: Semantic data publication workflow with Dendro and B2NOTE","2017","International Journal of Metadata, Semantics and Ontologies","12","4","","182","194","12","1","10.1504/IJMSO.2017.093645","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051042617&doi=10.1504%2fIJMSO.2017.093645&partnerID=40&md5=c38338f6e57dfaa18c411665a431f6db","INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal","Karimova Y., INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal; Castro J.A., INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal; Da Silva J.R., INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal; Pereira N., INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal; Rodrigues J., INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., INESC TEC, Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal","Metadata puts research data in their context, making data intelligible and apt to sustain technology evolution and to be reused, in compliance with the FAIR principles. The workflow proposed in this work includes metadata generation in the context of research projects, created with the Dendro platform, and metadata originated in the interaction of people with the deposited data, created with the B2NOTE service from EUDAT. In our experiments, datasets are prepared with Dendro, taking into consideration general-purpose descriptors and domain-specific ones, then transparently deposited in B2SHARE. After publication, B2NOTE provides an environment where authors, other researchers, and any interested party can enrich the description with less formal comments, tags or keywords. This work contributes with (a) a set of use cases in several domains, (b) details on the descriptors used by authors in each case, and (c) reflections on the use of data after publication, using the B2NOTE contributions. © Copyright 2017 Inderscience Enterprises Ltd.","B2NOTE; B2SHARE; Dendro; Metadata; Research data management; Semantic annotation","Information management; Semantics; B2NOTE; B2SHARE; Dendro; Research data managements; Semantic annotations; Metadata","","","","","Fundação para a Ciência e a Tecnologia, FCT, (PD/BD/114143/2015, POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation – COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT – Fundação para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by FCT – Fundação para a Ciência e a Tecnologia. We thank Yann Le Frank and the B2NOTE team for the availability of the beta version of B2NOTE and the helpful remarks. The authors of this paper are part of the TAIL project at INESC TEC. “TAIL: Research data management from creation to deposit and sharing” runs from 2016 to 2019 and proposes research data management workflows using the Dendro platform and mobile platforms for data and metadata collection.","Addis M., RDM Workflows and Integrations for Higher Education Institutions Using Hosted Services, Arkivum Report, (2015); Amorim R., Et al., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Universal Access in the Information Society, 16, 851, pp. 851-862, (2017); Assante M., Et al., Are scientific data repositories coping with research data publishing?, Data Science Journal, 15, 6, (2016); Awre C., Et al., Research data management as a ""wicked problem, Library Review, 64, 4-5, pp. 356-371, (2015); Castro J.A., Et al., Involving data creators in an ontologybased design process for metadata models, Developing Metadata Application Profiles, IGI Global, pp. 181-214, (2017); Castro J.A., Da Silva J.R., Ribeiro C., Designing an application profile using qualified Dublin Core: A case study with fracture mechanics datasets, International Conference on Dublin Core and Metadata Applications, pp. 47-52, (2013); Castro J.A., Da Silva J.R., Ribeiro C., Creating lightweight ontologies for dataset description. Practical applications in a cross-domain research data management workflow, IEEE/ACM Joint Conference on Digital Libraries (JCDL), pp. 313-316, (2014); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ""wicked"" problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Da Silva J.R., Et al., Dendro: Collaborative research data management built on linked open data, The Semantic Web: ESWC 2014 Satellite Events. ESWC 2014. Lecture Notes in Computer Science, 8798, pp. 483-487, (2014); Annotate your research data with B2NOTE, EUDAT News, (2017); EUDAT as a long-term repository for the University of Porto, EUDAT Data Pilot, (2017); It all started with a data pilot which then paved the way for a long-term research data management relationship, EUDAT Data Pilot, (2017); Fortuna P., Hate Speech Dataset Annotated for Portuguese, (2017); Goldfarb D., Le Franc Y., Enhancing the discoverability and interoperability of multi-disciplinary semantic repositories, Proceedings of the 2nd International Workshop on Semantics for Biodiversity Co-located with 16th International Semantic Web Conference (ISWC 2017), (2017); Goncalves F., Recolha, Classificação, Adaptaçãao e Seleção de Indicadores de Inovação, (2017); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, Project MUSE, 57, 2, pp. 280-299, (2008); Huang S.-L., Lin S.-C., Chan Y.-C., Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing, Information Processing and Management, 48, 4, pp. 599-617, (2012); Karimova Y., Castro J.A., Vocabulários controlados na descrição de dados de investigação no Dendro, Cadernos BAD, 2, pp. 241-255, (2016); Karimova Y., Et al., Promoting semantic annotation of research data by their creators: A use case with B2NOTE at the end of the RDM workflow, Metadata and Semantic Research. MTSR 2017. Communications in Computer and Information Science, 755, pp. 112-122, (2017); Lassi M., Johnsson M., Golub K., Research data services: An exploration of requirements at two Swedish universities, IFLA Journal, 42, 4, pp. 266-277, (2016); Latif A., EUDAT: Research data infrastructure and European Open Science Cloud vision, Team ZBW Mediatalk, (2017); Le Franc Y., Organise, retrieve and aggregate data using annotations with B2NOTE, EUDAT Webinar, (2017); Matias M., Descriptive Statistics on Children's Emotion Regulation, Parents Work-family Conflict and Parent's Psychological Availability, Dataset, (2017); Mayernik M.S., Metadata realities for cyberinfrastructure: Data authors as metadata creators, Social Science Research Network, (2011); Moreira M., Multicamera System for Automatic Positioning of Objects in Game Sports, Dataset, (2016); Pereira N., Da Silva J.R., Ribeiro C., Social Dendro: Social network techniques applied to research data description, Research and Advanced Technology for Digital Libraries. TPDL 2017. Lecture Notes in Computer Science, (2017); Pires A., HAREM NER Models for OpenNLP, Stanford CoreNLP, SpaCy, NLTK. Dataset, (2017); Ribeiro C., Et al., Projeto TAIL - Gestão de dados de investigação da produção ao depósito e à partilha (resultados preliminares), Cadernos BAD, 2, pp. 256-264, (2016); Rocha J., Ribeiro C., Lopes J.C., Ontology-based multi-domain metadata for research data management using triple stores, Proceedings of the 18th International Database Engineering & Applications Symposium. IDEAS'14, Porto, Portugal, (2014); Rocha J., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Proceedings of the 11th International Conference on Digital Preservation, IPRES, (2014); Rocha J., Ribeiro C., Lopes J.C., Ranking Dublin Core descriptor lists from user interactions: A case study with Dublin Core terms using the Dendro platform, International Journal on Digital Libraries, pp. 1-20, (2018); Rodriguez A., B2NOTE - The EUDAT Annotation Service. Semantic Services in EOSC, (2018); Shearer K., Furtado F., COAR Survey of Research Data Management: Results, Confederation of Open Access Repositories, (2017); Silva F., Et al., End-to-end research data management workflows: A case study with Dendro and EUDAT, Metadata and Semantics Research. MTSR 2016. Communications in Computer and Information Science, 672, pp. 369-375, (2016); Silvello G., Theory and practice of data citation, Journal of the Association for Information Science and Technology, 69, 1, pp. 6-20, (2018); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS ONE, 10, 8, (2015); Thanos C., Research data reusability: Conceptual foundations, barriers and enabling technologies, Publications, 5, 1, (2017); Van Den Eynden V., Et al., Managing and sharing data - Best practice for researchers, UK Data Archive, pp. 1-40, (2011); Vines T.H., Et al., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Web Annotation Data Model, (2015); White H.C., Descriptive metadata for scientific data repositories: A comparison of information scientist and scientist organizing behaviors, Journal of Library Metadata, 14, 1, pp. 24-51, (2014); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, Journal of the Association for Information Science and Technology, 63, 8, pp. 1505-1520, (2012); Zervas P., Sampson D.G., The effect of users' tagging motivation on the enlargement of digital educational resources metadata, Computers in Human Behavior, 32, pp. 292-300, (2014); Zhang Y., Et al., Controlled vocabularies for scientific data: Users and desired functionalities, Proceedings of the Association for Information Science and Technology, 52, 1, pp. 1-8, (2015)","Y. Karimova; INESC TEC, Faculty of Engineering, University of Porto, Porto, Dr. Roberto Frias, 4200-465, Portugal; email: ylaleo@gmail.com","","Inderscience Publishers","","","","","","17442621","","","","English","Int. J. Metadata Semant. Ontol.","Conference paper","Final","","Scopus","2-s2.0-85051042617"
"Southall J.; Scutt C.","Southall, John (35769907000); Scutt, Catherine (57194183201)","35769907000; 57194183201","Training for Research Data Management at the Bodleian Libraries: National Contexts and Local Implementation for Researchers and Librarians","2017","New Review of Academic Librarianship","23","2-3","","303","322","19","9","10.1080/13614533.2017.1318766","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019163051&doi=10.1080%2f13614533.2017.1318766&partnerID=40&md5=4d03f971ec9b005a1e67c07c69b58c67","Social Science Library, University of Oxford, Oxford, United Kingdom; Education Library, University of Oxford, Oxford, United Kingdom","Southall J., Social Science Library, University of Oxford, Oxford, United Kingdom; Scutt C., Education Library, University of Oxford, Oxford, United Kingdom","This article outlines the involvement of the Bodleian Libraries at the University of Oxford in developing new services for research data management. It offers reflections on what such additional support means for academic librarians, specifically considering support offered by subject consultants and a series of research data management (RDM) training workshops. The need to reshape library roles, teams and collections to accommodate developments in support for research data and its management is discussed. Additional actions being carried out within the Bodleian Libraries to help further meet these needs are outlined and include the development of the role of a Data Librarian and engagement of a network of librarians with this expanding area of professional knowledge. This review of what has been achieved so far provides fellow practitioners with valuable lessons and pointers to consider when reviewing the support required within their own institutions. © 2017, Published with license by Taylor & Francis Group, LLC © 2017, © John Southall and Catherine Scutt.","Library staff development; researchers; roles; scholarly communication; university libraries","","","","","","","","Auckland M., Re-skilling for Research, (2012); Cole G., Evans J., University of Exeter research data management and open access training for staff, ALISS Quarterly, 10, 1, pp. 22-25, (2014); Cox A., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, 70, (2012); Cox A., Verbaan E., Sen B., Rising to the research data management challenge, SCONUL Focus, 60, pp. 42-44, (2014); Dietrich D., Adamus T., Miner A., Steinhart G., De-mystifying the data management requirements of research funders, Issues in Science & Technology Librarianship, 70, (2012); Federer L., Research data management in the age of big data: Roles and opportunities for librarians, Information Services & Use, 36, pp. 35-43, (2016); Hickson S., Poulton K.A., Connor M., Richardson J., Wolski M., Modifying researchers' data management practices, IFLA Journal, 42, pp. 253-265, (2016); MacMillan D., Developing data literacy competencies to enhance faculty collaborations, LIBER Quarterly, 24, 3, pp. 140-160, (2015); Research data management, (2017); Pryor G., Jones S., Whyte A., Delivering research data management services: Fundamentals of good practice, (2013); RCUK common principles on data policy, (2015); Rice R., Southall J., The data librarian's handbook, (2016); Sawnhney S.C., New directions: Research data management, big data and librarians, ALISS Quarterly, 9, 2, pp. 21-25, (2014); Research data management policy, (2015); Research support team; Whyte A., Tedds J., Making the case for research data management (DCC Briefing Papers), (2011)","C. Scutt; University of Oxford, Education Library, Oxford, 15 Norham Gardens, OX2 6PY, United Kingdom; email: catherine.scutt@bodleian.ox.ac.uk","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85019163051"
"Schöpfel J.; Prost H.; Malleret C.","Schöpfel, Joachim (14619562900); Prost, Hélène (15069878000); Malleret, Cécile (57077016400)","14619562900; 15069878000; 57077016400","Research and development in the field of research data and dissertations. The D4humanities project at the University of lille (France)","2018","Grey Journal","14","Special Winter Issue","","30","36","6","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040723383&partnerID=40&md5=334f2871e7f7958615a8703adf67368e","GERiiCO laboratory, University of Lille SHS, France; INIST (CNRS), France; Academic library, University of Lille SHS, France","Schöpfel J., GERiiCO laboratory, University of Lille SHS, France; Prost H., INIST (CNRS), France; Malleret C., Academic library, University of Lille SHS, France","The paper presents the research project D4Humanities conducted by the GERIICO laboratory at the University of Lille in the field of research data management (RDM). In particular, it describes the development of a local workflow for the submission of research data related to PhD dissertations and the connection to the national RDM infrastructure Huma-Num (deposit, preservation and dissemination of research data via the NAKALA service), along with the RDM training program for PhD students provided by the Graduate School in Social Sciences and Humanities at the University of Lille. © 2018 TextRelease.","Data Literacy; Data Management; Data Sharing; Electronic Theses and Dissertations; PhD Training Program; Research Data; Social Sciences and Humanities","","","","","","","","Awre C., Et al., Research Data Management as a “wicked problem, Library Review, 64, 4-5, pp. 356-371, (2015); Chaudiron S., Maignant C., Schopfel J., Westeel I., Les données De La Recherche Dans Les thèses De Doctorat - Livre Blanc: Rapport De Recherche, (2015); Kindling M., Et al., The Landscape of Research Data Repositories in 2015: A Re3data Analysis, (2017); Neuroth H., Digital Curation of Research Data Experiences of a Baseline Study in Germany, (2013); Prost H., Malleret C., Schopfel J., Treasures H., Opening Data in PhD Dissertations in Social Sciences and Humanities, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Schopfel J., Et al., Open Access to Research Data in Electronic Theses and Dissertations: An Overview, Library Hi Tech, 32, 4, pp. 612-627, (2014); Schopfel J., Prost H., Malleret C., Making data in PhD dissertations reusable for research, Conference on Grey Literature and Repositories; Schopfel J., Prost H., 29, pp. 98-112, (2016); Schopfel J., Prost H., Rebouillat V., Research Data in Current Research Information Systems, (2016); Schopfel J., Kergosien E., Helene P., Pour commencer, pourriez-vous définir 'données de la recherche'? » Une tentative de réponse, Atelier VADOR: Valorisation Et Analyse Des Données De La Recherche, (2017); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos ONE, 10, 8, (2015); Vompras J., Schirrwagen J., Repository workflow for interlinking research data with grey literature, Conference on Grey Literature and Repositories, (2015); Ward C., Et al., Making sense: Talking data management with scientists, International Journal of Digital Curation, 6, 2, pp. 265-273, (2011)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Article","Final","","Scopus","2-s2.0-85040723383"
"Surkis A.; LaPolla F.W.Z.; Contaxis N.; Read K.B.","Surkis, Alisa (57190153933); LaPolla, Fred Willie Zametkin (55933740600); Contaxis, Nicole (57193864914); Read, Kevin B. (57205931894)","57190153933; 55933740600; 57193864914; 57205931894","Data Day to Day: Building a community of expertise to address data skills gaps in an academic medical center","2017","Journal of the Medical Library Association","105","2","","185","191","6","19","10.5195/jmla.2017.35","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017274057&doi=10.5195%2fjmla.2017.35&partnerID=40&md5=537038608c8846b2dcb298db1cd6186f","Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States","Surkis A., Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States; LaPolla F.W.Z., Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States; Contaxis N., Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States; Read K.B., Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States","Background: The New York University Health Sciences Library data services team had developed educational material for research data management and data visualization and had been offering classes at the request of departments, research groups, and training programs, but many members of the medical center were unaware of these library data services. There were also indications of data skills gaps in these subject areas and other data-related topics. Case Presentation: The data services team enlisted instructors from across the medical center with data expertise to teach in a series of classes hosted by the library. We hosted eight classes branded as a series called “Data Day to Day.” Seven instructors from four units in the medical center, including the library, taught the classes. A multipronged outreach approach resulted in high turnout. Evaluations indicated that attendees were very satisfied with the instruction, would use the skills learned, and were interested in future classes. Conclusions: Data Day to Day met previously unaddressed data skills gaps. Collaborating with outside instructors allowed the library to serve as a hub for a broad range of data instruction and to raise awareness of library services. We plan to offer the series three times in the coming year with an expanding roster of classes. © 2017, Medical Library Association. All rights reserved.","","Academic Medical Centers; Humans; Information Storage and Retrieval; Learning; Library Services; New York; Professional Competence; awareness; case report; health science; human; information processing; library; New York; skill; training; information retrieval; learning; library; professional competence; university hospital","","","","","National Center for Advancing Translational Sciences, NCATS, (UL1TR001445)","","Hanson K., Surkis A., Introducing researchers to data management: Pedagogy and strategy, 113Th Annual Meeting of the Medical Library Association, (2013); Crummett C., One academic library’s response to the data dilemma, MLA’13, 113Th Annual Meeting of the Medical Library Association, (2013); Laurance M., Building blocks: Contributing to open data initiatives by teaching data reuse workshops, 114Th Annual Meeting of the Medical Library Association, (2014); Norton H.F., Garcia-Milian R., Tennant M., Botero C.E., Supporting the local research data environment via cross-campus collaboration and leveraging of national expertise, MLA’13, 113Th Annual Meeting of the Medical Library Association, (2013); Wirz J., More than a pretty picture: Data visualization and research communication skills, MLA’15, 115Th Annual Meeting of the Medical Library Association, (2015); Federer L.M., Joubert D.J., Davis M., Expanding and enhancing library data and GIS services: Implementing an information visualization service, MLA’15, 115Th Annual Meeting of the Medical Library Association, (2015); Federer L., Beyond data management: Developing a comprehensive data science support program in the library, MLA’16, 116Th Annual Meeting of the Medical Library Association, (2016); Teal T.K., Cranston K.A., Lapp H., White E., Wilson G., Ram K., Pawlik A., Data carpentry: Workshops to increase data literacy for researchers, Int J Digital Curation, 10, 1, pp. 135-143, (2015); Ragon B., Where is my data scientist?, MLA’15, 115Th Annual Meeting of the Medical Library Association, (2015); Magle C.T., Bringing bioinformatics into the library with an informatics workshop series, MLA’16, 116Th Annual Meeting of the Medical Library Association, (2016); Surkis A., Read K., Building data management services at an academic medical center: An entrepreneurial approach, The Medical Library Association Guide to Data Management for Librarians, (2016); Williams J.D., Rambo N.H., It’s the end of the world and we feel fine, J Med Libr Assoc, 103, 4, pp. 213-215, (2015); Murphy S.N., Weber G., Mendis M., Gainer V., Chueh H.C., Churchill S., Kohane I., Serving the enterprise and beyond with informatics for integrating biology and the bedside (I2b2), J am Med Inform Assoc, 17, 2, pp. 124-130, (2010); Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G., Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform, 42, 2, pp. 377-381, (2009)","","","Medical Library Association","","","","","","15365050","","JMLAC","28377684","English","J. Med. Libr. Assoc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85017274057"
"Krochmal M.; Cisek K.; Husi H.","Krochmal, Magdalena (57190291124); Cisek, Katryna (23098973500); Husi, Holger (6602990839)","57190291124; 23098973500; 6602990839","Database Creation and Utility","2017","Integration of Omics Approaches and Systems Biology for Clinical Applications","","","","286","300","14","0","10.1002/9781119183952.ch17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050223995&doi=10.1002%2f9781119183952.ch17&partnerID=40&md5=45e817fbe22ec1efdbdc7633ad448c35","Proteomics Laboratory, Biomedical Research Foundation, Academy of Athens, Athens, Greece; Mosaiques Diagnostics GmbH, Hannover, Germany; Institute of Cardiovascular and Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom; Department of Diabetes and Cardiovascular Science, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom","Krochmal M., Proteomics Laboratory, Biomedical Research Foundation, Academy of Athens, Athens, Greece; Cisek K., Mosaiques Diagnostics GmbH, Hannover, Germany; Husi H., Institute of Cardiovascular and Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom, Department of Diabetes and Cardiovascular Science, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom","Data storage is an essential task in research data management allowing for efficient data utilization and analysis. Due to an exponential increase in data generation (primarily owing to employment of new high-throughput -omics technologies), databases became a crucial element in everyday scientific practice and an important tool to support the discovery process. This chapter introduces basic technical aspects of database systems, data models, and database development. In particular, relational databases are discussed. Moreover, biological databases and their application in research are presented. © 2018 John Wiley & Sons, Inc.","Data life cycle; Data storage; Database design; Database development; Database systems; Relational databases","","","","","","","","Curbelo R.J., Loza E., De Yebenes M.J., Carmona L., Databases and registers: useful tools for research, no studies., Rheumatol Int, 34, pp. 447-452, (2014); Zou D., Ma L., Yu J., Zhang Z., Biological databases for human research., Genomics Proteomics Bioinformatics, 13, pp. 55-63, (2015); Stein L., Creating Databases for Biological Information: An Introduction., (2013); Wynn M.L., Consul N., Merajver S.D., Schnell S., Logic-based models in systems biology: a predictive and parameter-free network analysis method., Integr Biol (Camb), 4, pp. 1323-1337, (2012); Connolly T.M., Begg C.E., Database Systems: A Practical Approach to Design, Implementation, and Management., (2005); Lenhardt W.C., Ahalt S., Blanton B., Christopherson L., Idaszak R., Data management lifecycle and software lifecycle management in the context of conducting science, J Open Res Softw, 2, 1, (2014); Foster E.C., Godbole S.V., Database Systems: A Pragmatic Approach, (2010); Stein L., Creating Databases for Biological Information: An Introduction., (2002); Strauch C., NoSQL databases, Lecture Selected Topics on Software-Technology Ultra-Large Scale Sites., (2011); Pokorny J., NoSQL databases: a step to database scalability in web environment., Int J Web Inf Syst, 9, pp. 69-82, (2013); Watt A., Database Design, (2015); Keim D.A., Kriegel H.-P., Using visualization to support data mining of large existing databases, Proceedings of the IEEE Visualization '93 Workshop, 871, pp. 210-229, (1994); Kopanakis I., Theodoulidis B., Visual data mining modeling techniques for the visualization of mining outcomes., J Vis Lang Comput, 14, pp. 543-589, (2003); Janert P.K., Data Analysis with Open Source Tools, (2010); Benson D.A., Clark K., Karsch-Mizrachi I., Lipman D.J., Ostell J., Sayers E.W., GenBank., Nucleic Acids Res, 42, pp. D32-D37, (2014); Safran M., Chalifa-Caspi V., Shmueli O., Olender T., Lapidot M., Rosen N., Shmoish M., Peter Y., Glusman G., Feldmesser E., Adato A., Peter I., Khen M., Atarot T., Groner Y., Lancet D., Human gene-centric databases at the Weizmann Institute of Science: GeneCards, UDB, CroW 21 and HORDE., Nucleic Acids Res, 31, pp. 142-146, (2003); RNAcentral: an international database of ncRNA sequences., Nucleic Acids Res, 43, pp. D123-D129, (2015); Kozomara A., Griffiths-Jones S., miRBase:annotating high confidence microRNAs using deep sequencing data., Nucleic Acids Res, 42, pp. D68-D73, (2014); Bateman A., Martin M.J., O'Donovan C., UniProt: a hub for protein information., Nucleic Acids Res, 43, pp. D204-D212, (2015); Rose P.W., Beran B., Bi C., Bluhm W.F., Dimitropoulos D., Goodsell D.S., Prlic A., Quesada M., Quinn G.B., Westbrook J.D., Young J., Yukich B., Zardecki C., Berman H.M., Bourne P.E., The RCSB Protein Data Bank: redesigned web site and web services., Nucleic Acids Res, 39, pp. D392-D401, (2011); Ponten F., Schwenk J.M., Asplund A., Edqvist P.H., The Human Protein Atlas as a proteomic resource for biomarker discovery., J Intern Med, 270, pp. 428-446, (2011); Liu X., Yu X., Zack D.J., Zhu H., Qian J., TiGER: a database for tissue-specific gene expression and regulation., BMC Bioinformatics, 9, (2008); Croft D., Mundo A.F., Haw R., Milacic M., Weiser J., Wu G., Caudy M., Garapati P., Gillespie M., Kamdar M.R., Jassal B., Jupe S., Matthews L., May B., Palatnik S., Rothfels K., Shamovsky V., Song H., Williams M., Birney E., Hermjakob H., Stein L., D'Eustachio P., The reactome pathway knowledgebase., Nucleic Acids Res, 42, pp. D472-D477, (2014); Okuda S., Yamada T., Hamajima M., Itoh M., Katayama T., Bork P., Goto S., Kanehisa M., KEGG Atlas mapping for global analysis of metabolic pathways., Nucleic Acids Res, 36, pp. W423-W426, (2008); Rappaport N., Nativ N., Stelzer G., Twik M., Guan-Golan Y., Stein T.I., Bahir I., Belinky F., Morrey C.P., Safran M., Lancet D., MalaCards: an integrated compendium for diseases and their annotation., Database (Oxford), 2013, (2013); Fernandes M., Husi H., FP222 the chronic kidney disease database (CKDdb)., Nephrol Dial Transplant, 30, (2015); Krochmal M., Fernandes M., Filip S., Pontillo C., Husi H., Zoidakis J., Mischak H., Vlahou A., Jankowski J., PeptiCKDdb-peptide- and proteincentric database for the investigation of genesis and progression of chronic kidney disease, Database (Oxford), (2016); University of Michigan, Ann Arbor, (2017); Europe PMC: a full-text literature database for the life sciences and platform for innovation., Nucleic Acids Res, 43, pp. D1042-D1048, (2015); Blake J.A., Christie K.R., Dolan M.E., Gene Ontology Consortium: going forward., Nucleic Acids Res, 43, pp. D1049-D1056, (2015); Gray K.A., Yates B., Seal R.L., Wright M.W., Bruford E.A., Genenames.org: the HGNC resources in 2015., Nucleic Acids Res, 43, pp. D1079-D1085, (2015); Antezana E., Blonde W., Egana M., Rutherford A., Stevens R., De Baets B., Mironov V., Kuiper M., BioGateway: a semantic systems biology tool for the life sciences., BMC Bioinformatics, 10, (2009); Wright J., Wagner A., The Systems Biology Research Tool: evolvable open-source software., BMC Syst Biol, 2, (2008); Huber W., Carey V.J., Gentleman R., Anders S., Carlson M., Carvalho B.S., Bravo H.C., Davis S., Gatto L., Girke T., Gottardo R., Hahne F., Hansen K.D., Irizarry R.A., Lawrence M., Love M.I., Macdonald J., Obenchain V., Oles A.K., Pages H., Reyes A., Shannon P., Smyth G.K., Tenenbaum D., Waldron L., Morgan M., Orchestrating highthroughput genomic analysis with Bioconductor., Nat Methods, 12, pp. 115-121, (2015); Zhu Y., Davis S., Stephens R., Meltzer P.S., Chen Y., GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus., Bioinformatics, 24, pp. 2798-2800, (2008); Ji Z., Ji H., GEOsearch: GEOsearch, (2015); Zhang X., Kuivenhoven J.A., Groen A.K., Forward individualized medicine from personal genomes to interactomes., Front Physiol, 6, (2015); Yugi K., Kubota H., Hatano A., Kuroda S., Trans-omics: how to reconstruct biochemical networks across multiple ""omic"" layers, Trends Biotechnol, 34, 4, pp. 276-290, (2016); Pesce F., Pathan S., Schena F.P., From -omics to personalized medicine in nephrology: integration is the key., Nephrol Dial Transplant, 28, pp. 24-28, (2013); Chavan S.S., Shaughnessy J.D.J.R., Edmondson R.D., Overview of biological database mapping services for interoperation between different ""omics"" datasets., Hum Genomics, 5, pp. 703-708, (2011); Kim T.Y., Kim H.U., Lee S.Y., Data integration and analysis of biological networks., Curr Opin Biotechnol, 21, pp. 78-84, (2010); Kuo T.C., Tian T.F., Tseng Y.J., 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data., BMC Syst Biol, 7, (2013); Su G., Morris J.H., Demchak B., Bader G.D., Biological network exploration with cytoscape 3., Curr Protoc Bioinformatics, 47, pp. 8.13.1-8.13.24, (2014); Harel A., Dalah I., Pietrokovski S., Safran M., Lancet D., Omics data management and annotation., Methods Mol Biol, 719, pp. 71-96, (2011); Chowdhury S., Sarkar R.R., Comparison of human cell signaling pathway databases-evolution, drawbacks and challenges, Database (Oxford), (2015); Alyass A., Turcotte M., Meyre D., From big data analysis to personalized medicine for all: challenges and opportunities., BMC Med Genomics, 8, (2015); Gomez-Cabrero D., Abugessaisa I., Maier D., Teschendorff A., Merkenschlager M., Gisel A., Ballestar E., Bongcam-Rudloff E., Conesa A., Tegner J., Data integration in the era of omics: current and future challenges, BMC Syst Biol, 8, (2014)","","","wiley","","","","","","","978-111918395-2; 978-111918114-9","","","English","Integr. of Omics Approaches and Syst. Biol. for Clin. Appl.","Book chapter","Final","","Scopus","2-s2.0-85050223995"
"","","","Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","2017","Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","","","","","","1028","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040596078&partnerID=40&md5=21f8a4301fe59bde333bb023ebe71a23","","","The proceedings contain 215 papers. The topics discussed include: pricing strategies and decisions in a Bertrand competition with Markov process; competitive advantages through CSV: quantitative analysis of CSV-promoting companies; the association of product metaphors with emotionally durable design; proposal of framework for concept design in a team: results of application to 43 teams; text-mining application on CSR report analytics: a study of petrochemical industry; study on educational effect inspection of new subject 'information security and morals': significance and problem of the curriculum revision judging from class evaluation analysis; the possibility for the active use of smart devices in university education; development and deployment of research data preservation policy at a Japanese research university in 2016; the open search.org in open science era: a communication platform for everyone building their repositories and using others; dominating centrality set: a new measure on the network coverage of influential center nodes; utilization of the student e-portfolio in the graduate education: the case report of 'Nitobe school program' in Hokkaido university; recent program of AIST innovation school: for the future innovation leader in global society; women's university and institutional research: the potential of women's education captured in data; a data-driven approach to dropout prevention: Kyoto Koka women's university case; the study of global mobility of the technology university students; and a new way of visualizing curricula using competencies: cosine similarity, multidimensional scaling methods, and scatter plotting.","","","","","","","","","","","Hashimoto K.; Fukuta N.; Matsuo T.; Hirokawa S.; Mori M.; Mori M.","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics","6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","9 July 2017 through 3 July 2017","Hamamatsu, Shizuoka","132541","","978-153860621-6","","","English","Proc. - IIAI Int. Congr. Adv. Appl. Inf., IIAI-AAI","Conference review","Final","","Scopus","2-s2.0-85040596078"
"da Silva J.R.; Pereira N.; Dias P.; Barros B.","da Silva, João Rocha (55496903800); Pereira, Nelson (57195715570); Dias, Pedro (57203975405); Barros, Bruno (57214535638)","55496903800; 57195715570; 57203975405; 57214535638","Grassroots meets grasstops: Integrated research data management with eudat b2 services, dendro and labtablet","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11057 LNCS","","","359","362","3","1","10.1007/978-3-030-00066-0_40","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053840719&doi=10.1007%2f978-3-030-00066-0_40&partnerID=40&md5=3bdb655d6aa4aceaa6776a230106f108","INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","da Silva J.R., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Pereira N., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Dias P., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Barros B., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","We present an integrated research data management (RDM) workflow that captures data from the moment of creation until its deposit. We integrated LabTablet, our electronic laboratory notebook, Dendro, our data organisation and description platform aimed at collaborative management of research data, and EUDAT’s B2DROP and B2SHARE platforms. This approach combines the portability and automated metadata production abilities of LabTablet, Dendro as a collaborative RDM tool for dataset preparation, with the scalable storage of B2DROP and the long-term deposit of datasets in B2SHARE. The resulting workflow can be put to work in research groups where laboratorial or field work is central. © 2018, Springer Nature Switzerland AG.","Data curation; Data repositories; Electronic laboratory notebooks; Ontologies; Research data management","Deposits; Digital storage; Information management; Ontology; Collaborative management; Data curation; Data repositories; Electronic laboratory notebook; Integrated research; Research data managements; Research groups; Scalable storage; Digital libraries","","","","","Funda¸cão para a Ciência e a Tecnologia, (POCI-01-0145-FEDER-016736); Fundação para a Ciência e a Tecnologia, FCT; European Regional Development Fund, ERDF","Acknowledgements. This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸cão para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-016736. This work is also funded in the context of an extended pilot by EUDAT and carried out by INESC TEC. We also thank Daan Broeder and Damien Lecarpentier for the assistance provided during our collaboration with EUDAT.","Amorim R.C., Castro J.A., Silva J.R., Ribeiro C., LabTablet: Semantic metadata collection on a multi-domain laboratory notebook, MTSR 2014. CCIS, 478, pp. 193-205, (2014); Silva J.R., Ribeiro C., Lopes J.C., Ranking Dublin Core descriptor lists from user interactions: A case study with Dublin Core Terms using the Dendro platform, Int. J. Digital Libr., (2018)","J.R. da Silva; INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: joaorosilva@gmail.com","Mendez E.; Ribeiro C.; David G.; Lopes J.C.; Crestani F.","Springer Verlag","","22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018","10 September 2018 through 13 September 2018","Porto","218159","03029743","978-303000065-3","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85053840719"
"Wiorogórska Z.; Leśniewski J.; Rozkosz E.","Wiorogórska, Zuzanna (56502178300); Leśniewski, Jędrzej (57505994300); Rozkosz, Ewa (56500665800)","56502178300; 57505994300; 56500665800","Data Literacy and Research Data Management in Two Top Universities in Poland. Raising Awareness","2018","Communications in Computer and Information Science","810","","","205","214","9","7","10.1007/978-3-319-74334-9_22","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041710043&doi=10.1007%2f978-3-319-74334-9_22&partnerID=40&md5=dd75c9551a676acae5acee23007a30b1","University of Warsaw, Warsaw, Poland; Wrocław University of Science and Technology, Wrocław, Poland; University of Lower Silesia, Wrocław, Poland","Wiorogórska Z., University of Warsaw, Warsaw, Poland; Leśniewski J., Wrocław University of Science and Technology, Wrocław, Poland; Rozkosz E., University of Lower Silesia, Wrocław, Poland","We present the results of the Polish part of a study conducted within the framework of the international research project named ReDaM coordinated by the Information Literacy Association (InLitAs). It was a quantitative study based on use of a questionnaire consisted of 25 open-ended and multiple choice questions that was translated from English into Polish. Data were collected from February to the end of April 2017. The target groups were doctoral students and faculty employed at the University of Warsaw and Wrocław University of Science and Technology. The study revealed that a significant number of respondents knew the basic concepts related to Research Data Management (RDM). At the same time, they had not used institutional solutions elaborated in their parent institutions. We did not notice any differences in RDM practices between the fields of study at the two universities. However, we did notice significant differences between academic staff and research students. © 2018, Springer International Publishing AG.","Data information literacy; Data literacy; Poland; Research Data Management; Scholarly information literacy; Science data literacy","Information management; Societies and institutions; Data informations; Data literacy; Information literacy; Poland; Research data managements; Science-data; Education","","","","","","","Carlson J., Fosmire M., Miller C.C., Sapp Nelson M., Determining data information literacy needs: A study of students and research faculty, Libr. Acad., 11, 2, pp. 629-657, (2011); Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA J, 43, 1, pp. 65-80, (2017); Johnson L., Adams Becker S., Estrada V., Freeman A., NMC Horizon Report: 2014 Higher Education Edition, Austin, (2014); Bryant R., Lavoie B., Malpas C., A Tour of the Research Data Management (RDM) Service Space, (2017); Koltay T., Data governance, data literacy and the management of data quality, IFLA J, 42, 4, pp. 303-312, (2016); Qin J., D'Ignazio J., Lessons learned from a two-year experience in science data literacy education, Proceedings of the 31St Annual IATUL Conference, (2010); Research & Innovation: Open Science; Makani J., Knowledge management, research data management, and university scholarship: Towards an integrated institutional research data management support-system framework, VINE, 45, 3, pp. 344-359, (2015); Burgi P.-Y., Blumer E., Makhlouf-Shabou B., Research data management in Switzerland: National efforts to guarantee the sustainability of research outputs, IFLA J, 43, 1, pp. 5-21, (2017); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA J, 43, 1, pp. 51-64, (2017); Awre C., Baxter J., Clifford B., Colclough J., Cox A., Dods N., Drummond P., Fox Y., Gill M., Gregory K., Gurney A., Harland J., Khokhar M., Lowe D., O'Beirne R., Proudfoot R., Schwamm H., Smith A., Verbaan E., Waller L., Williamson L., Wolf M., Zawadzki M., Research data management as a “wicked problem’, ’. Libr. Rev., 64, 4-5, (2015); Buddenbohm S., Cretin N., Dijk E., Gaiffe B., De Jong M., Le Teiller-Becquart N., Minel J.-L., State of the Art Report on Open Access Publishing of Research Data in the Humanities, (2016); Higman R., Pinfield S., Research data management and openness: The role of data sharing in developing institutional polices and practices, Program, 49, 4, pp. 364-381, (2015); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: A tale of collaboration, IFLA J, 43, 1, pp. 89-97, (2017); Zilinski L.D., Sapp Nelson M., Van Epps A.S., Developing professional skills in STEM students: Data information literacy, ISTL, (2014); Tarkpea T., Seiler V., Integrating data literacy into information literacy e-course for PhD students, The Fourth European Conference on Information Literacy, (2016); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the University of Bristol: Charting a course from project to service, Program, 49, 4, pp. 475-793, (2015); Ranking Uczelni Akademickich, (2016); Wrocław University of Science and Technology: General Information; Politechnika Wrocławska: HR Strategy for Researchers (HRS4R); Christensen-Dalsgaard B., Ten recommendations for libraries to get started with research data management, LIBER, (2012); Politechnika Wrocławska, (2017); Uchwała Nr 68 Senatu Uniwersytetu Warszawskiego Z Dnia 22 Marca, (2017)","Z. Wiorogórska; University of Warsaw, Warsaw, Poland; email: z.d.wiorogorska@uw.edu.pl","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85041710043"
"Komiyama Y.; Yamaji K.","Komiyama, Yusuke (57208828375); Yamaji, Kazutsuna (7102241687)","57208828375; 7102241687","Nationwide Research Data Management Service of Japan in the Open Science Era","2017","Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","","","8113225","129","133","4","6","10.1109/IIAI-AAI.2017.144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040598133&doi=10.1109%2fIIAI-AAI.2017.144&partnerID=40&md5=ebc5c21d5ed36d588b8d27e7e13238d1","Digital Content and Media Sciences Research Division, National Institute of Informatics, Chiyoda Ward, Tokyo, Japan","Komiyama Y., Digital Content and Media Sciences Research Division, National Institute of Informatics, Chiyoda Ward, Tokyo, Japan; Yamaji K., Digital Content and Media Sciences Research Division, National Institute of Informatics, Chiyoda Ward, Tokyo, Japan","Recently, in Japan, there has been a great need in universities and research institutions to archive their research data for ten years because they need to maintain the reproducibility of data for ensuring research integrity and the promotion of open science. However, a research data management (RDM) service does not exist in Japan. Therefore, at the National Institute of Informatics (NII), we developed the next-generation RDM on a national scale by using the open science framework (OSF). We combined the RDM with the existing NII worldwide cyberinfrastructure services (SINET and GakuNin) and OSF add-ons for external Cloud services and institutional repositories. Finally, we displayed the first closed trial for nationwide RDM services in seven universities and a national research institute for research integrity; this helped promote open science in Japan in the fourth quarter of fiscal year 2016. NII and participant organizations obtained some RDM operating know-how and discovered new issues by follow-up meetings. The source code is available on GitHub at (http://doi.org/10.5281/zenodo.546481); (http://doi.org/10.5281/zenodo.546480). © 2017 IEEE.","Open science; RDM; Research data archive; Research data management service; Research integrity; Research support service","Information management; Information services; Technology transfer; Open science; Research data; Research data managements; Research integrities; Research support; Societies and institutions","","","","","Japan Society for the Promotion of Science, KAKEN; Ministry of Education, Culture, Sports, Science and Technology, MEXT","Kazutsuna Yamaji Digital Content and Media Sciences Research Division, National Institute of Informatics Chiyoda Ward, Tokyo, JAPAN yamaji@nii.ac.jp factors of cyberinfrastructures, which includes the management infrastructure, publication infrastructure, and discovery infrastructure for research data. Besides, all universities and research institutions need to archive research data from each laboratory for ten years because this will maintain the research integrity. Research data archives will be required for projects of the Grants-in-Aid for Scientific Research by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) or the Japan Society for the Promotion of Science (JSPS). Besides this, the Japan Science and Technology agency (JST) required the submission of a data management plan for the adoption of researchers in the CREST and SAKIGAKE grant programs from the 2016 fiscal year, on an experimental basis.","Tsukuba Communiqué, (2016); Recommendations Concerning An Approach to Open Science That Will Contribute to Open Innovation, (2016); McKiernan E.C., Bourne P.E., Brown C.T., Buck S., Kenall A., Lin J., McDougall D., Nosek B.A., Ram K., Soderberg C.K., Spies J.R., Thaney K., Updegrove A., Woo K.H., Yarkoni T., How open science helps researchers succeed, Elife, 5, (2016); McNutt M., Lehnert K., Hanson B., Nosek B.A., Ellison A.M., King J.L., RESEARCH integrity. Liberating field science samples and data, Science, 351, 6277, pp. 1024-1026, (2016); Nosek B.A., Center for open science: Strategic plan, Open Science Framework, (2017); European open science cloud, Nat. Genet., 48, 8, (2016); Ardestani S.B., Hakansson C.J., Laure E., Livenson I., Stranak P., Dima E., Blommesteijn D., Van De Sanden M., B2SHARE: An open escience data sharing platform, 2015 IEEE 11th International Conference on E-Science, pp. 448-453, (2015); Position Paper: European Open Science Cloud for Research, (2015); Memon A.S., Jensen J., Cernivec A., Benedyczak K., Riedel M., Federated authentication and credential translation in the eudat collaborative data infrastructure, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 726-731, (2014); Yamaji K., Kataoka T., Nakamura M., Orawiwattanakul T., Sonehara N., Attribute aggregating system for shibboleth based access management federation, 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet, pp. 281-284, (2010); Naito H., Kajita S., Hirano Y., Mase K., Multiple-tiered security hierarchy for web applications using central authentication and authorization service, 2007 International Symposium on Applications and the Internet Workshops, (2007); Kurimoto T., Urushidani S., Yamada H., Yamanaka K., Nakamura M., Abe S., Fukuda K., Koibuchi M., Ji Y., Takakura H., Yamada S., A fully meshed backbone network for dataintensive sciences and SDN services, 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 909-911, (2016); King G., An introduction to the dataverse network as an infrastructure for data sharing, Sociol. Methods Res., 36, 2, pp. 173-199, (2007); Mosweunyane G., Carr L.A., Direct desktop-repository deposits with SWORD, 2014 IST-Africa Conference Proceedings, pp. 1-8, (2014); Yamaji K., Aoyama T., Furukawa M., Yamada T., Development and Deployment of the Open Access Repository and Its Application to the Open Educational Recourses, pp. 395-403, (2016); Yokoyama S., Matsumoto A., Yoshioka N., Network traffic optimization architecture for scalability in academic inter-cloud computing environments, Proceedings of the 2nd International Workshop on Hot Topics in Cloud Service Scalability-HotTopiCS, 14, pp. 1-6, (2014)","","Hashimoto K.; Fukuta N.; Matsuo T.; Hirokawa S.; Mori M.; Mori M.","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics","6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","9 July 2017 through 3 July 2017","Hamamatsu, Shizuoka","132541","","978-153860621-6","","","English","Proc. - IIAI Int. Congr. Adv. Appl. Inf., IIAI-AAI","Conference paper","Final","","Scopus","2-s2.0-85040598133"
"Funamori M.","Funamori, Miho (55960871900)","55960871900","Open Science and the Academy: A Theoretical Discussion","2017","Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","","","8113221","109","115","6","3","10.1109/IIAI-AAI.2017.19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040545961&doi=10.1109%2fIIAI-AAI.2017.19&partnerID=40&md5=1106bce6b3b13af8423373113cfe5f0c","Information and Society Research Division, National Institute of Informatics, Tokyo, Japan","Funamori M., Information and Society Research Division, National Institute of Informatics, Tokyo, Japan","The Open Science movement is gaining traction since the G8 Science Ministers made a joint statement in 2013 on open scientific research data and increasing access to the peerreviewed published results of scientific research. Major policy developments can be seen in funding agencies mandating open access to scientific results. However, the idea of Open Science represents an alternate approach to the scientific process enabled by digital technologies encompassing the entire research cycle, including how researchers are being evaluated and rewarded. Still, even though the idea of Open Science is envisioned as a systematic change in the way science is performed, the scientific communities remain largely ignorant on this issue.This paper presents a theoretical discussion on how academics might adopt the idea of Open Science by scrutinizing the background and evolution of this policy agenda and the issues arising from it, and by analyzing academias perception of and behavior toward Open Science. The misalignment of Open Science purposes and the researchers value is pointed out as the major impediment to realization of Open Science concept. © 2017 IEEE.","Data sharing; Digital technology; Incentive; Open access; Open Science; Research data management; Research evaluation; Researcher","Information management; Data Sharing; Digital technologies; Incentive; Open Access; Open science; Research data managements; Research evaluation; Researcher; Behavioral research","","","","","Global Research Council; Horizon2020; US Mega-Foundations on Higher Education Policy Formation; National Institutes of Health; Research and Development; European Metrology Programme for Innovation and Research; European Commission; Japan Society for the Promotion of Science, (15K13175)","Funding text 1: Governments and research funding agencies also started to demand open access to research publications that were publicly funded. A medical patient in the United States who wanted to study his illness in research articles claimed that it was unfair to also be charged for the research outputs, as the research itself was already funded by tax-payers [4]. Governments and funding agencies charged with accountability and transparency issues followed the claim. In 2000, the National Institutes of Health (NIH) in the US created a repository called PubMed Central where research publications can be deposited and shared online. In 2007, the deposit of research articles funded by NIH into the repository was mandated, and similar open access policies started to be adopted by other funding agencies. In 2012, Research Information Network in the United Kingdom issued a report, commonly called the Finch Report, clearly recommending gold OA publishing, whereby publishers receive their revenue from authors rather than readers [5]. And in 2013, the Global Research Council, comprised of the heads of funding agencies from around the world, announced the “Action Plan towards Open Access to Publications,” following which funding agencies around the world started to pursue open access policy measures [6].; Funding text 2: After the mandates on research publications, funding agencies also started to ask for research data to be made publicly available. In 2003, the NIH adopted the Data Sharing Policy, which required investigators submitting an application to include a plan for data sharing or state why data sharing was not possible [7]. In 2004, the Declaration on Access to Research Data from Public Funding was adopted by the Organisation for Economic Co-operation and Development (OECD) and in three other countries [8][9]. In the UK, the Biotechnology and Biological Sciences Research Council (BBSRC) was the first to announce the Data Sharing Policy, which became a common rule among all UK Research Councils in 2011 [10]. In the US, the White House issued the Executive Directive on Increasing Access to the Results of Federally Funded Scientific Research in 2013, which required U.S. funding agencies with annual expenditures in research and development over $100 million to develop a plan to support increased public access to the results of their funded research. This included both research publications and data [11]. The European Commission started the Open Research Data Pilot for selected projects of Horizon2020, the European Union Research and Innovation program for the years 2014 to 2020 [12]. In 2013, the G8 Science Ministers made a joint statement on open scientific research data and increasing the access to peer-reviewed published results [13]. In 2014, the Australian Research Council (ARC) started to require researchers to outline how research data will be managed for ARC-funded research. Commercial publishers also started working on research data sharing by establishing data journals where research data can be published. In 2014, the Nature Publishing Group launched “Scientific Data,” and Elsevier launched “Data in Brief.” In 2015, a policy concept named Open Science became widely recognized. Open Science includes the idea of making research outputs—both publications and research data—openly available, but is also a broader concept encompassing the entire research cycle.. The European Commission (EC) adopted the term “Science 2.0.” However, they renamed it after the term Open Science was suggested through public consultation. Science 2.0 is described as “the on-going evolution in the modus operandi of doing research and organizing science…enabled by digital technologies and driven by the globalization of the scientific community, as well as the need to address the grand challenges of our times” [14][15]. Also in 2015, the OECD published the report “Making Open Science a Reality” [2]. In 2016, the EC announced “Open Innovation, Open Science, Open to the World – a Vision for Europe” in order to realize the priority policy “Digital Single Market” [16]. Under the “Amsterdam Call for Action on Open Science,” policy actions are underway such as the establishment of the European Open Science Policy Platform, which gives advice to the EC, and the development of the European Open Science Cloud for hosting and processing research data in a trusted environment [17][18].; Funding text 3: ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number 15K13175 “The Impact of US Mega-Foundations on Higher Education Policy Formation.”","Pontika N., Pearce S., Knoth P., Fostering open science to research using a taxonomy and an elearning portal, IKnow: 15th International Conference on Knowledge Technologies and Data Driven Business, (2015); Making Open Science A Reality OECD Science, Technology and Industry Policy Papers, (2015); Monograph & Serial Costs in ARL Libraries, 19862011, (2010); Taxpayers Support Open Access to NIH Research-Public Interest Advocates Join Forces to Support Congress and NIH Leadership, (2004); Accessibility, Sustainability, Excellence: How to Expand Access to Research Publications, (2012); Action Plan towards open access to publications, Annual Global Meeting, (2013); NIH Data Sharing Policy and Implementation Guidance, (2003); Declaration on Access to Research Data from Public Funding, (2004); Principles and Guidelines for Access to Research Data from Public Funding, (2007); RCUK Common Principles on Data Policy, (2011); Increasing Access to the Results of Federally Funded Scientific Research, (2013); Scientific Data: Open Access to Research Results Will Boost Europe's Innovation Capacity, (2012); Consultation on 'Science 2. 0': Science in Transition, (2014); Validation of the Results of the Public Consultation on Science 2. 0: Science in Transition, (2015); Open Innovation, Open Science, Open to the World- A Vision for Europe, (2016); Amsterdam Call for Action on Open Science, (2016); Realising the European Open Science Cloud, (2016); David P.A., The economic logic of open science and the balance between private property rights and the public domain in scientific data and information: A primer, National Research Council on the Role of the Public Domain in Science, (2003); Nielsen M., Reinventing Discovery: The New Era of Networked Science, (2011); SPARC Honors Michael Nielsen As Innovator for Bringing Open Science into the Mainstream, (2012); Gray J., The Fourth Paradigm: Data-intensive Scientific Discovery, (2009); Baker M., 1, 500 scientists lift the lid on reproducibility, Nature, 533, 7604, (2016); Lee K., Bero L., Ethics: Increasing accountability, Nature, (2006); Concordat on Open Research Data, (2016); Science As An Open Enterprise, (2012); Open Data in A Big Data World, (2015); Baker M., Over half of psychology studies fail reproducibility test, Nature News, (2017)","M. Funamori; Information and Society Research Division, National Institute of Informatics, Tokyo, Japan; email: funamori@nii.ac.jp","Hashimoto K.; Fukuta N.; Matsuo T.; Hirokawa S.; Mori M.; Mori M.","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics","6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017","9 July 2017 through 3 July 2017","Hamamatsu, Shizuoka","132541","","978-153860621-6","","","English","Proc. - IIAI Int. Congr. Adv. Appl. Inf., IIAI-AAI","Conference paper","Final","","Scopus","2-s2.0-85040545961"
"Groenewegen D.","Groenewegen, David (23567996300)","23567996300","Yesterday and Today: Reflecting on Past Practice to Help Build and Strengthen the Researcher Partnership at Monash University","2017","New Review of Academic Librarianship","23","2-3","","171","184","13","4","10.1080/13614533.2017.1336637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021254619&doi=10.1080%2f13614533.2017.1336637&partnerID=40&md5=dfc8624efd78da45e05a2e6ac33dc321","Library, Monash University, Melbourne, Australia","Groenewegen D., Library, Monash University, Melbourne, Australia","Librarians at universities continue to seek new ways to engage and partner with researchers in order to enhance and enrich the way that they work. This article looks at some of the ways that libraries have been doing this in recent years, what has worked, and what needs to be done to continue to develop these important partnerships. These recommendations will be based on the experiences of Monash University Library, in particular its recent implementation of Figshare for Institutions. The article includes discussion of such topics where libraries have sought partnerships with researchers, such as Open Access, research data management, and capability building, and will focus on the importance of working with researchers to understand their needs. © 2017, Published with license by Taylor & Francis Group, LLC © 2017, © David Groenewegen.","Librarianship; partnerships; research data management","","","","","","","","Beitz A., Groenewegen D., Harboe-Ree C., Macmillan W., Searle S., Pryor G., Jones S., Case Study 3: Monash University, a strategic approach, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 163-190, (2014); Buckle A., Dunstone M.A., Law R.H.P., Whisstock J.C., A common fold mediates vertebrate defense and bacterial attack (2QP2), (2007); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, (2017); D'Onfro J., 15 Steve Jobs quotes that will leave you feeling inspired, Business Insider, (2016); Dombrowski Q., What ever happened to project Bamboo?, Literary and Linguistic Computing, 29, 3, pp. 326-339, (2014); Federer L., The librarian as research informationist: A case study, Journal of the Medical Library Association: JMLA, 101, 4, pp. 298-302, (2013); Federer L., Exploring new roles for librarians: The research informationist, Synthesis Lectures on Emerging Trends in Librarianship, 1, 2, pp. 1-47, (2014); Gordon A.S., Millman D.S., Steiger L., Adolph K.E., Gilmore R.O., Researcher-library collaborations: Data repositories as a service for researchers, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Groenewegen D., A comment on open access: The whipping boy for problems in scholarly publishing, Communications of the Association for Information Systems, 37, pp. 378-382, (2015); Groenewegen D., Research Data Ecosystem, (2015); Groenewegen D., Splawa-Neyman P., (2016); Groenewegen D., Treloar A., Adding value by taking a national and institutional approach to research data: The ANDS experience, International Journal of Digital Curation, 8, 2, pp. 89-98, (2013); Matarazzo J.M., Pearlstein T., Academic libraries: A soft analysis, a warning and the road ahead, IFLA Journal, 41, 1, pp. 5-12, (2015); Mazure E.S., Alpi K.M., Librarian readiness for research partnerships, Journal of the Medical Library Association, 103, 2, pp. 91-95, (2015); McCluskey C., Being an embedded research librarian: Supporting research by being a researcher, Journal of Information Literacy, 7, 2, pp. 4-14, (2013); Meulemans Y.N., Carr A., Not at your service: Building genuine faculty-librarian partnerships, Reference Services Review, 41, 1, pp. 80-90, (2013); (2001); Morris M., Boruff J.T., Gore G.C., Scoping reviews: Establishing the role of the librarian, Journal of the Medical Library Association: JMLA, 104, 4, pp. 346-354, (2016); Nicholas D., Watkinson A., Abdullah A., Boukace-Zeghmouri C., Rodriguez Bravo B., Swigon M., Herman E., Early career researchers: The harbingers of change?, (2016); Pajewski A., Aho M.K., Bennett E., Slybrarianship: Building alliances through user engagement and outreach, The Machiavellian Librarian: Winning allies, combating budget cuts, and influencing stakeholders, pp. 275-283, (2014); Pinfield S., Cox A.M., Smith J., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, PLOS ONE, 9, 12, (2014); Posner M., No half measures: Overcoming common challenges to doing digital humanities in the library, Journal of Library Administration, 53, 1, pp. 43-52, (2013); Rosado C.J., Buckle A.M., Law R.H.P., Butcher R.E., Kan W.-T., Bird C.H., Whisstock J.C., A common fold mediates vertebrate defense and bacterial attack, Science, 317, 5844, pp. 1548-1551, (2007); Schaffner J.R.E., Does every research library need a digital humanities center?: OCLC Research, (2014); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program, 49, 4, pp. 440-460, (2015); Sharp H.S., Library and laboratory: Partners in research, IRE Transactions on Engineering Writing and Speech, 4, 2, pp. 58-61, (1961); Speaks P.C., Whittle S.B., Farinelli C., Cambiano R.L., Cambiano R.M., Shifting the perspective of the research librarian: A coresearch paradigm, Reference Librarian, 56, 4, pp. 259-273, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Van den Eynden V., Knight G., Vlad A., Radler B., Tenopir C., Leon D., Corti L., Survey of Wellcome researchers and their attitudes to open research, (2016); Vassilakaki E., Moniarou-Papaconstantinou V., A systematic literature review informing library and information professionals' emerging roles, New Library World, 116, 1-2, pp. 37-66, (2015); Wang M., Supporting the research process through expanded library data services, Program, 47, 3, pp. 282-303, (2013); Wolff C., Rod A.B., Schonfeld R.C., UK Survey of Academics 2015, (2016)","D. Groenewegen; Monash University, 40 Exhibition Walk, 3800, Australia; email: David.Groenewegen@monash.edu","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85021254619"
"McRostie D.; Konstantelos L.","McRostie, Donna (56205846700); Konstantelos, Leo (36608320800)","56205846700; 36608320800","Supporting Digital Scholarship and the Digital Humanities: A Collaboration on Concept, Space, and Services Between the Library and the Faculty of Arts at the University of Melbourne","2018","Collaboration and the Academic Library: Internal and External, Local and Regional, National and International","","","","117","129","12","0","10.1016/B978-0-08-102084-5.00011-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047368450&doi=10.1016%2fB978-0-08-102084-5.00011-0&partnerID=40&md5=0314b5896ff939231cc33b75c1c9f716","University of Melbourne, Melbourne, VIC, Australia","McRostie D., University of Melbourne, Melbourne, VIC, Australia; Konstantelos L., University of Melbourne, Melbourne, VIC, Australia","Digital Humanities is inherently a collaborative endeavour. At the University of Melbourne, the Faculty of Arts and the University Library have collaborated on the establishment of a Digital Studio to support digital humanities research and partnership. Launched in late 2016, the Digital Studio has a direct physical connection between a new Arts Faculty building and the University’s flagship Baillieu Library. The project involved the establishment of a facility that provides services and infrastructure to support University researchers, professionals, and select industry experts and students working on Humanities, Arts, and Social Sciences (HASS) digital projects. The Library has been integrally involved in planning for this facility and longer term is expected to make a significant contribution to its ongoing operation and future success. The Digital Studio will be a front-of-house venue for the Library to provide training and delivery of a range of research support services in areas such as informatics, research data management, digitisation, digital preservation, and data mining. © 2018 L. Konstantelos Published by Elsevier Ltd. All rights reserved.","Collaboration; Digital humanities; Digital scholarship; Innovation space; University libraries","","","","","","","","Kroll S., Forsman R., A slice of research life: Information support for research in the United States, (2010); Lamb R., Sawyer S., Kling R., A social informatics perspective on socio-technical networks, (2000); Leu D.J., Kinzer C.K., The convergence of literacy instruction with networked technologies for information and communication, Reading Research Quarterly, 35, 1, pp. 108-127, (2000); McCarthy G., Konstantelos L., University of Melbourne Digital Preservation Strategy 2015–2025: Vision, mandate and principles, (2013); Moed H.F., Citation analysis in research evaluation, (2005); Pearce N., Weller M., Scanlon E., Ashleigh M., Digital scholarship considered: How new technologies could transform academic work, in education, 16, 1, pp. 33-44, (2010); Sinclair B., The university library as incubator for digital scholarship, (2014); Weller M., The digital scholar: How technology is transforming scholarly practice, (2011)","","","Elsevier","","","","","","","978-008102084-5; 978-008102288-7","","","English","Collaboration and the Academic Library: Internal and External, Local and Regional, National and International","Book chapter","Final","","Scopus","2-s2.0-85047368450"
"Meinhardt H.","Meinhardt, Haike (56012947000)","56012947000","Informationsinfrastrukturen im Wandel","2017","Zeitschrift fur Bibliothekswesen und Bibliographie","64","5","","261","267","6","0","10.3196/186429501764544","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032375129&doi=10.3196%2f186429501764544&partnerID=40&md5=e9ac4799727d09c498587e0a183c1a14","Technische Hochschule Köln, Fakultät für Informations- und Kommunikationswissenschaften, Institut für Informationswissenschaft, Gustav-Heinemann-Ufer 54, Köln, 50968, Germany","Meinhardt H., Technische Hochschule Köln, Fakultät für Informations- und Kommunikationswissenschaften, Institut für Informationswissenschaft, Gustav-Heinemann-Ufer 54, Köln, 50968, Germany","The changes which digitalisation and technical advances are bringing about to the sciences are raising increasingly urgent questions regarding management of the ever-growing quantities of highly heterogeneous research data which the sciences are producing. The long-anticipated German Council for Scientific Information Infrastructures (Rfll) was finally founded in 2014. In summer 2016 it published recommendations for the expansion and development of research data management in Germany. A discussion paper followed in April 2017. The purpose of the Council is to maintain an overview of the system as a whole. Based on comprehensive analysis it is designing a network model which integrates the various players in the scientific system. It also puts forward proposals for new incentive systems as a means of initiating partnerships and bringing about a change of culture. One of its main tasks is to demand adequate funding procedures and reliable interinstitutional funding from policymakers. Roughly a year after the recommendation paper was published, a number of players have already taken up the recommendations of the Council and are developing services in the area of FDM. Similar developments are also being observed at the international level.","","","","","","","","","Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanage-ments in Deutschland, pp. A-15, (2016); Das ZfBB-Themenheft Forschungsinfrastruktur, ZfBB, 61, pp. 4-5, (2014); Analyse Von Drees B., Zukunft der Informationsinfrastrukturen: das deutsche Bibliothekswesen im digitalen Zeitalter, Perspektive Bibliothek, 5, pp. 25-48, (2016); Die Umfassende und Noch Weiter Zurückreichende Betrachtung In: Rfll Berichte No. 2: Konzepte Seit Den 1960er Jahren, (2016); Burger T., Der Rat für Informationsinfrastrukturen, Stand der Empfehlungen Zur Nationalen Forschungsdateninfrastruktur; Kommission Zukunft der Informationsinfrastruktur: Gesamtkonzept für Die Informationsinfrastruktur in Deutschland, (2011); Brunger-Weilandt S., Informationsinfrastruktur -eine Standortbestimmung, Bibliotheken: Innovation Aus Tradition, (2015); CDU Deutschlands, CSU-Landesleitung SPD: Deutschlands Zukunft Gestalten. Koalitionsvertrag Zwischen CDU CSU und SPD. 18; Die Bundesregierung: Digitale Agenda 2014-2017, (2014); Rat für Informationsinfrastrukturen: Der Rat; Rfll - Rat für Informationsinfrastrukturen: Leistung aus Vielfalt. Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland, (2016); WGL: Die Forschungsdatenzentren der Leibniz-Gemeinschaft; DFG-Förderprogramm «informationsinfrastrukturen für Forschungsdaten»; RADAR FIZ Karlsruhe: Ober Uns; GA Gauß Allianz: Startschuss für Die Helmholtz Data Federation; Forschungsdaten.info; RatSWD: Forschungsdateninfrastruktur: Standards Setzen und Qualität Sichern; Ventzke M., Informationsangebote Zum Thema Forschungsdatenmanagement Auf Internetseiten Deutscher Universitäten; O'Donnell B., Europe Joins Forces to Create Largest Ever Shared Data Repository for Researchers, (2017); Europe's Future: Reflections of the RISE Group; Die RDA Ist Eine Internationale Community-basierte Initiative, Die Vor Allem Den Weltweiten Austausch von Forschungsdaten Zum Ziel Hat. 2013 Gegründet, Kann Sie Bereits Beachtlichen Output für Viele Disziplinen Vorweisen","H. Meinhardt; Technische Hochschule Köln, Fakultät für Informations- und Kommunikationswissenschaften, Institut für Informationswissenschaft, Köln, Gustav-Heinemann-Ufer 54, 50968, Germany; email: haike.meinhardt@th-koeln.de","","Vittorio Klostermann","","","","","","00442380","","","","German","Z. Bibliothekswes. Bibliogr.","Review","Final","","Scopus","2-s2.0-85032375129"
"Daniel C.; Ouagne D.; Sadou E.; Paris N.; Hussain S.; Jaulent M.-C.; Kalra D.","Daniel, Christel (24334555100); Ouagne, David (23498447600); Sadou, Eric (36676701500); Paris, Nicolas (57204777182); Hussain, Sajjad (57192440913); Jaulent, Marie-Christine (7003346504); Kalra, Dipak (56107739200)","24334555100; 23498447600; 36676701500; 57204777182; 57192440913; 7003346504; 56107739200","Cross border semantic interoperability for learning health systems: The EHR4CR semantic resources and services","2017","Learning Health Systems","1","1","e10014","","","","7","10.1002/lrh2.10014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059109769&doi=10.1002%2flrh2.10014&partnerID=40&md5=8aab2d4d46989136525a9908ba7d7ffa","Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France; AP-HP, Paris, France; EUROREC Institute, Sint-Martens-Latem, Belgium","Daniel C., Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France, AP-HP, Paris, France; Ouagne D., Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France; Sadou E., Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France, AP-HP, Paris, France; Paris N., AP-HP, Paris, France; Hussain S., Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France; Jaulent M.-C., Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France; Kalra D., EUROREC Institute, Sint-Martens-Latem, Belgium","With the development of platforms enabling the integration and use of phenome, genome, and exposome data in the context of international research, data management challenges are increasing, and scalable solutions for cross border and cross domain semantic interoperability need to be developed. Reusing routinely collected clinical data, especially, requires computable portable phenotype algorithms running across different electronic health record (EHR) products and healthcare systems. We propose a framework for describing and comparing mediation platforms enabling cross border phenotype identification within federated EHRs. This framework was used to describe the experience gained during the EHR4CR project and the evaluation of the platform developed for accessing semantically equivalent data elements across 11 European participating EHR systems from 5 countries. Developers of semantic interoperability platforms are beginning to address a core set of requirements in order to reach the goal of developing cross border semantic integration of data. © 2016 The Authors. Learning Health Systems published by Wiley Periodicals, Inc. on behalf of the University of Michigan","biomedical research; data integration and standardization; electronic health records; interoperability; knowledge representation; terminology as topic","","","","","","","","Friedman C.P., Wong A.K., Blumenthal D., Achieving a nationwide learning health system, Sci Transl Med, 2, 57, (2010); Martin Sanchez F., Gray K., Bellazzi R., Lopez-Campos G., Exposome informatics: considerations for the design of future biomedical research information systems, J Am Med Inform Assoc, 21, 3, pp. 386-390, (2014); Moreno-Conde A., Moner D., da Cruz W.D., Et al., Clinical Information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis, J Am Med Inform Assoc., 22, 4, pp. 925-934, (2015); Doods J., Bache R., McGilchrist M., Daniel C., Dugas M., Fritz F., Work package 7. Piloting the EHR4CR feasibility platform across Europe, Methods Inf Med, 53, 4, pp. 264-268, (2014); Cuggia M., Besana P., Glasspool D., Comparing semi-automatic systems for recruitment of patients to clinical trials, Int J Med Inform, 80, pp. 371-388, (2011); Schreiweis B., Trinczek B., Kopcke F., Et al., Comparison of electronic health record system functionalities to support the patient recruitment process in clinical trials, Int J Med Inform, 83, 11, pp. 860-868, (2014); Niland J., ASPIRE: agreement on standardized protocol inclusion requirements for eligibility, (2007); Eea C., caMATCH: a patient matching tool for clinical trials, (2005); Musen M.A., Tu S.W., Das A.K., Shahar Y., EON: a component-based approach to automation of protocol-directed therapy, J Am Med Inform Assoc, 3, pp. 367-388, (1996); Ohno-Machado L., Parra E., Henry S., Tu S., Musen M., AIDS2: a decision-support tool for decreasing physicians' uncertainty regarding patient eligibility for HIV treatment protocols, pp. 429-433, (1993); Sutton D.R., Fox J., The syntax and semantics of the PROforma guideline modeling language, J Am Med Inform Assoc, 10, pp. 433-443, (2003); Shahar Y., Miksch S., Johnson P., The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines, Artif Intell Med, 14, pp. 29-51, (1998); Boxwala A., GLIF3: a representation format for sharable computer interpretable clinical practice guidelines, J Biomed Inform, 37, pp. 147-161, (2004); Voss E.A., Makadia R., Matcho A., Et al., Feasibility and utility of applications of the common data model to multiple, disparate observational health databases, J Am Med Inform Assoc, 22, 3, pp. 553-564, (2015); Quaglini S., Stefanelli M., Lanzola G., Caporusso V., Panzarasa S., Flexible guideline-based patient careflow systems, Artif Intell Med, 22, pp. 65-80, (2001); Delaney B.C., Curcin V., Andreasson A., Et al., Translational medicine and patient safety in Europe: TRANSFoRm-architecture for the learning health system in Europe, Biomed Res Int, 2015, (2015); De Moor G., Sundgren M., Kalra D., Et al., Using electronic health records for clinical research: the case of the EHR4CR project, J Biomed Inform, 53, pp. 162-173, (2015); El Fadly A., Rance B., Lucas N., Et al., Integrating clinical research with the healthcare enterprise: from the RE-USE project to the EHR4CR platform, J Biomed Inform, 44, pp. S94-102, (2011); Jiang G., Evans J., Oniki T.A., Et al., Harmonization of detailed clinical models with clinical study data standards, Methods Inf Med, 54, 1, pp. 65-74, (2015); Margolis R., Derr L., Dunn M., Et al., The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data, J Am Med Inform Assoc, 21, 6, pp. 957-958, (2014); Schneeweiss S., Learning from big health care data, N Engl J Med, 370, 23, pp. 2161-2163, (2014); Mo H., Thompson W.K., Rasmussen L.V., Et al., Desiderata for computable representations of electronic health records-driven phenotype algorithms, J Am Med Inform Assoc, 22, 6, pp. 1220-1230, (2015); Gottesman O., Kuivaniemi H., Tromp G., Et al., The Electronic Medical Records and Genomics (eMERGE) network: past, present, and future, Genet Med, 15, 10, pp. 761-771, (2013); Newton K.M., Peissig P.L., Kho A.N., Et al., Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network, J Am Med Inform Assoc, 20, e1, pp. e147-e154, (2013); Pathak J., Wang J., Kashyap S., Et al., Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience, J Am Med Inform Assoc, 18, pp. 376-386, (2011); Gennari J., Sklar D., Silva J., Cross-tool communication: from protocol authoring to eligibility determination, pp. 199-203, (2001); Nammuni K., Pickering C., Modgil S., Et al., Design-a-trial: a rule-based decision support system for clinical trial design, Knowl-Based Syst, 17, pp. 121-129, (2004); ERGO: a template-based expression language for encoding eligibility criteria, (2009); Jenders R., Sujansky W., Broverman C., Chadwick M., Towards improved knowledge sharing: assessment of the HL7 reference information model to support medical logic module queries, pp. 308-312, (1997); Hammond W.E., Jaffe C., Kush R.D., Healthcare standards development. The value of nurturing collaboration, J AHIMA, 80, pp. 44-50, (2009); Bache R., Taweel A., Miles S., Delaney B.C., An eligibility criteria query language for heterogeneous data warehouses, Methods Inf Med, 54, 1, pp. 41-44, (2015); Doods J., Botteri F., Dugas M., Fritz F., EHR4CR WP7. A European inventory of common electronic health record data elements for clinical trial feasibility, Trials, 15, (2014); Soto-Rey I., Trinczek B., Girardeau Y., Et al., Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system, BMC Med Res Methodol, 15, (2015); Weng C., Tu S.W., Sim I., Et al., Formal representation of eligibility criteria: a literature review, J Biomed Inform, 43, pp. 451-467, (2010); Sahoo S.S., Lhatoo S.D., Gupta D.K., Et al., Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care, J Am Med Inform Assoc, 21, 1, pp. 82-89, (2014); Ahn S., Huff S.M., Kim Y., Kalra D., Quality metrics for detailed clinical models, Int J Med Inform, 82, 5, pp. 408-417, (2013); Curtis L.H., Et al., Design considerations, architecture, and use of the MiniSentinel distributed data system, Pharmacoepidem Drug Saf, 21, pp. 23-31, (2012); Kohane I.S., Churchill S.E., Murphy S.N., A translational engine at the national scale: informatics for integrating biology and the bedside, J Am Med Inform Assoc, 19, pp. 181-185, (2012); McMurry A.J., Murphy S.N., MacFadden D., Et al., SHRINE: enabling nationally scalable multi-site disease studies, PLoS One, 8, 3, (2013); Lowe H.J., Et al., STRIDE – an integrated standards-based translational research informatics platform, AMIA Annu Symp Proc, 2009, pp. 391-395, (2009); Herr T.M., Et al., Practical considerations in genomic decision support: the eMERGE experience, J Pathol Inform, 6, (2015); Pathak J., Bailey K.R., Beebe C.E., Et al., Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium, J Am Med Inform Assoc, 20, e2, pp. e341-e348, (2013); Rea S., Pathak J., Savova G., Et al., Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project, J Biomed Inform, 45, 4, pp. 763-771, (2012); Shivade C., Raghavan P., Fosler-Lussier E., Et al., A review of approaches to identifying patient phenotype cohorts using electronic health records, J Am Med Inform Assoc, 21, 2, pp. 221-230, (2014); Sinaci A.A., Laleci Erturkmen G.B., A federated semantic metadata registry framework for enabling interoperability across clinical research and care domains, J Biomed Inform, 46, 5, pp. 784-794, (2013); Schober D., Boeker M., Bullenkamp J., Et al., The DebugIT core ontology: semantic integration of antibiotics resistance patterns, Stud Health Technol Inform, 160, pp. 1060-1064, (2010); Coorevits P., Sundgren M., Klein G.O., Et al., Electronic health records: new opportunities for clinical research, J Intern Med, 274, 6, pp. 547-560, (2013)","C. Daniel; Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICS, Paris, F-75006, France; email: christel.daniel@aphp.fr","","John Wiley and Sons Inc","","","","","","23796146","","","","English","Learning Health Syst.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85059109769"
"Read K.; LaPolla F.W.Z.","Read, Kevin (57205931894); LaPolla, Fred Willie Zametkin (55933740600)","57205931894; 55933740600","A new hat for librarians: Providing REDCap support to establish the library as a central data hub","2018","Journal of the Medical Library Association","106","1","","120","126","6","16","10.5195/jmla.2018.327","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040942008&doi=10.5195%2fjmla.2018.327&partnerID=40&md5=02471cf3210e28a3e84305275b277c36","New York University (NYU) Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States","Read K., New York University (NYU) Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States; LaPolla F.W.Z., New York University (NYU) Health Sciences Library, NYU School of Medicine, 577 First Avenue, New York, 10016, NY, United States","Background: REDCap, an electronic data capture tool, supports good research data management, but many researchers lack familiarity with the tool. While a REDCap administrator provided technical support and a clinical data management support unit provided study design support, a service gap existed. Case Presentation: Librarians with REDCap expertise sought to increase and improve usage through outreach, workshops, and consultations. In collaboration with a REDCap administrator and the director of the clinical data management support unit, the role of the library was established in providing REDCap training and consultations. REDCap trainings were offered to the medical center during the library’s quarterly data series, which served as a springboard for offering tailored REDCap support to researchers and research groups. Conclusions: Providing REDCap support has proved to be an effective way to associate the library with data-related activities in an academic medical center and identify new opportunities for offering data services in the library. By offering REDCap services, the library established strong partnerships with the Information Technology Department, Clinical Data Support Department, and Compliance Office by filling in training gaps, while simultaneously referring users back to these departments when additional expertise was required. These new partnerships continue to grow and serve to position the library as a central data hub in the institution. © 2018, Medical Library Association. All rights reserved.","Clinical data management; Consultations; Data collection; Data governance; Education; REDCap; Training; Workshops","Biomedical Research; Database Management Systems; Evidence-Based Practice; Humans; Internet; Librarians; Libraries, Medical; Library Services; Professional Competence; User-Computer Interface; administrative personnel; article; consultation; human; information processing; information technology; librarian; scientist; university hospital; computer interface; database management system; evidence based practice; Internet; library; medical research; organization and management; professional competence","","","","","","","Vanderbilt University, (2004); Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G., Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform, 42, 2, pp. 377-381, (2009); Filkins B.L., Kim J.Y., Roberts B., Armstrong W., Miller M.A., Hultner M.L., Castillo A.P., Ducom J.C., Topol E.J., Steinhubl S.R., Privacy and security in the era of digital health: What should translational researchers know and do about it?, Am J Transl Res, 8, 3, pp. 1560-1580, (2016); Zozus M.N., Lazarov A., Smith L.R., Breen T.E., Krikorian S.L., Zbyszewski P.S., Knoll S.K., Jendrasek D.A., Perrin D.C., Zambas D.N., Williams T.B., Pieper C.F., Analysis of professional competencies for the clinical research data management profession: Implications for training and professional certification, J Am Med Inform Assoc, 24, 4, pp. 737-745, (2017); Dugas M., Neuhaus P., Meidt A., Doods J., Storck M., Bruland P., Varghese J., Portal of medical data models: Information infrastructure for medical research and healthcare, Database (Oxford), (2016); Lyon J.A., Garcia-Milian R., Norton H.F., Tennant M.R., The use of Research Electronic Data Capture (REDCap) software to create a database of librarian-mediated literature searches, Med Ref Serv Q., 33, 3, pp. 241-252, (2014); Patridge E., Bardyn T., Change leadership: Partnering with institutional stakeholders to address the need for collaboration spaces and data management support for research success, (2017); Read K.B., LaPolla F.W.Z., Tolea M.I., Galvin J.E., Surkis A., Improving data collection, documentation, and workflow in a dementia screening study, J Med Libr Assoc., 105, 2, pp. 160-166, (2017); Williams J., McCrillis A., McGowan R., Nicholson J., Surkis A., Thompson H., Thompson H., Vieira D., Leveraging technology and staffing in developing a new liaison program, Med Ref Serv Q., 33, 2, pp. 157-166, (2014); Surkis A., LaPolla F.W.Z., Contaxis N., Read K.B., Data Day to Day: Building a community of expertise to address data skills gaps in an academic medical center, J Med Libr Assoc, 105, 2, pp. 185-191, (2017); Partners HealthCare System, (2004)","","","Medical Library Association","","","","","","15365050","","JMLAC","29339942","English","J. Med. Libr. Assoc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85040942008"
"Chen X.; Wu M.","Chen, Xiujuan (57193630334); Wu, Ming (57193621897)","57193630334; 57193621897","Survey on the Needs for Chemistry Research Data Management and Sharing","2017","Journal of Academic Librarianship","43","4","","346","353","7","23","10.1016/j.acalib.2017.06.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020920435&doi=10.1016%2fj.acalib.2017.06.006&partnerID=40&md5=3584f2446c655d74ffc5a4ebe097c79d","Chengdu Library and Information Center, Chinese Academy of Sciences, No. 16, South Section 2, Yihuan Road, Chengdu, 610041, Sichuan, China; National Science Library, Chinese Academy of Sciences, No. 33 Beisihuan Xilu, Haidian District, Beijing, 100190, China; University of Chinese Academy of Science, No. 19A, Yuquan Road, Shijingshan District, Beijing, 100049, China","Chen X., Chengdu Library and Information Center, Chinese Academy of Sciences, No. 16, South Section 2, Yihuan Road, Chengdu, 610041, Sichuan, China, University of Chinese Academy of Science, No. 19A, Yuquan Road, Shijingshan District, Beijing, 100049, China; Wu M., National Science Library, Chinese Academy of Sciences, No. 33 Beisihuan Xilu, Haidian District, Beijing, 100190, China, University of Chinese Academy of Science, No. 19A, Yuquan Road, Shijingshan District, Beijing, 100049, China","This paper aims to reveal the situation of research data in chemistry research process and chemistry researchers’ need for data management support from five perspectives, i.e., data generation and collection, data recording and processing, data preservation and backup, data publication and sharing, needs for data management and sharing services. Our survey is based on a questionnaire carried out among 119 subjects, i.e., researchers and graduate students in chemistry of Chinese Academy of Science. The analysis results provide us with a better understanding on the current attitudes and needs of researchers and graduate students about data management and sharing in chemistry. Although this survey was implemented in chemistry, it could provide us with some inspirations for designing a range of library services for other disciplines, particularly in promotion, consulting and training of research data management and sharing, and research data storage. © 2017 Elsevier Inc.","Chemistry; Data management and sharing; Data management lifecycle; Library data service; Needs survey","","","","","","","","Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in biomedical research data management and analysis: Needs and barriers, Journal of the American Medical Informatics Association, 14, 4, pp. 478-488, (2007); Bardyn T.P., Resnick T., Camina S.K., Translational researchers’ perceptions of data management practices and data curation needs: Findings from a focus group in an academic health sciences library, Journal of Web Librarianship, 6, 4, pp. 274-287, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, 1, pp. 17-30, (2007); Carlson J., Demystifying the data interview, Reference Services Review, 40, 1, pp. 7-23, (2012); Carlson J., Stowell-Bracke M., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality field station, Portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Gray J., Liu D.T., Nieto-Santisteban M., Szalay A., DeWitt D.J., Heber G., Scientific data management in the coming decade, SIGMOD Rec., 34, 4, pp. 34-41, (2005); Hall N.F., Environmental studies faculty attitudes towards sharing of research data, Paper presented at the proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries, Indianapolis, Indiana, USA, (2013); Huang X., Hawkins B.A., Lei F., Miller G.L., Favret C., Zhang R., Qiao G., Willing or unwilling to share primary biodiversity data: Results and implications of an international survey, Conservation Letters, 5, 5, pp. 399-406, (2012); Kim J., A study on the perceptions of University researchers on data management and sharing, Journal of the Korean Society for Library and Information Science, 49, 3, pp. 413-436, (2015); Peters C., Dryden A.R., Assessing the academic library's role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Williams S.C., Using a bibliographic study to identify faculty candidates for data services, Science & Technology Libraries, 32, 2, pp. 202-209, (2013)","M. Wu; National Science Library, Chinese Academy of Sciences, Beijing, No. 33 Beisihuan Xilu, Haidian District, 100190, China; email: wum@mail.las.ac.cn","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-85020920435"
"Berber F.; Yahyapour R.","Berber, Fatih (57191252863); Yahyapour, Ramin (15066204200)","57191252863; 15066204200","A High-Performance Persistent Identifier Management Protocol","2017","2017 IEEE International Conference on Networking, Architecture, and Storage, NAS 2017 - Proceedings","","","8026839","","","","3","10.1109/NAS.2017.8026839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031724731&doi=10.1109%2fNAS.2017.8026839&partnerID=40&md5=a559c9f90486bac6eb903e7a23235269","Gesellschaft fur Wissenschaftliche Datenverarbeitung Gottingen, Germany; University of Gottingen, Germany","Berber F., Gesellschaft fur Wissenschaftliche Datenverarbeitung Gottingen, Germany; Yahyapour R., Gesellschaft fur Wissenschaftliche Datenverarbeitung Gottingen, Germany, University of Gottingen, Germany","Persistent identifiers are well acknowledged for providing an abstraction for addresses of research datasets. However, due to the explosive growth of research datasets the view onto the concept of persistent identification moves towards a much more fundamental component for research data management. The ability of attaching semantic information into persistent identifier records, in principle enables the realization of a virtual global research data network by means of persistent identifiers. However, the increased importance of persistent identifiers has at the same time led to a steadily increasing load at persistent identifier systems. Therefore, the focus of this paper is to propose a high performance persistent identifier management protocol. In contrast to the DNS system, persistent identifier systems are usually subjected to bulky registration requests originating from individual research data repositories. Thus, the fundamental approach in this work is to implement a bulk registration operation into persistent identifier systems. Therefore, in this work we provide an extended version of the established Handle protocol equipped with a bulk registration operation. Moreover, we provide a specification and an efficient data model for such a bulk registration operation. Finally, by means of a comprehensive evaluation, we show the profound speedup achieved by our extended version of the Handle System. This is also highly relevant for various other persistent identifier systems, which are based on the Handle System. © 2017 IEEE.","","Information management; Network architecture; Semantics; Comprehensive evaluation; Explosive growth; Extended versions; Fundamental component; Management protocols; Persistent Identification; Research data managements; Semantic information; Digital storage","","","","","","","De Sompel H.V., Sanderson R., Shankar H., Klein M., Persistent identifiers for scholarly assets and the web: The need for an unambiguous mapping, IJDC, 9, 1, (2014); Kuhn T., Dumontier M., Making digital artifacts on the web verifiable and reliable, IEEE Transactions On Knowledge and Data Engineering, 27, 9, pp. 2390-2400, (2015); Hilse H.-W., Kothe J., Implementing Persistent Identifiers, (2006); Kevin Richards R., A beginners guide to persistent identifiers, Gbif; Hakala J., Et al., Persistent Identifiers: An Overview; Tonkin E., Persistent identifiers: Considering the options, Ariadne, 56, (2008); Bellini E., Cirinna C., Lunghi M., Damiani E., Fugazza C., Persistent identifiers distributed system for cultural heritage digital objects, IPRES, 2008, (2008); Bellini E., Luddi C., Cirinna C., Lunghi M., Felicetti A., Bazzanella B., Bouquet P., Interoperability knowledge base for persistent identifiers interoperability framework, Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference On, pp. 868-875, (2012); Weigel T., Kindermann S., Lautenschlager M., Actionable persistent identifier collections, Data Science Journal, 12, pp. 191-206, (2014); Karakannas A., Zhao Z., Information Centric Networking for Delivering Big Data with Persistent Identifiers, (2014); Schmitt O., Majchrzak T.A., Bingert S., Experimental realization of a persistent identifier infrastructure stack for named data networking, Networking, Architecture and Storage (NAS), 2015 IEEE International Conference On, pp. 33-38, (2015); Berber F., Wieder P., Yahyapour R., A high-performance persistent identification concept, 2016 IEEE International Conference On Networking, Architecture and Storage (NAS), pp. 1-10, (2016); Jung J., Sit E., Balakrishnan H., Morris R., Dns performance and the effectiveness of caching, IEEE/ACM Trans. Netw., 10, 5, pp. 589-603, (2002); Cohen E., Kaplan H., Proactive caching of dns records: Addressing a performance bottleneck, Comput. Netw., 41, 6, pp. 707-726, (2003); Yu Y., Wessels D., Larson M., Zhang L., Authority server selection in dns caching resolvers, SIGCOMM Comput. Commun. Rev., 42, 2, pp. 80-86, (2012); Wiener J.L., Naughton J.F., Oodb bulk loading revisited: The partitioned-list approach, Proceedings of the 21th International Conference On Very Large Data Bases, Ser. VLDB '95, pp. 30-41, (1995); Jagadish H.V., Narayan P.P.S., Seshadri S., Sudarshan S., Kanneganti R., Incremental organization for data recording and warehousing, Proceedings of the 23rd International Conference On Very Large Data Bases, Ser. VLDB '97, pp. 16-25, (1997); Silberstein A., Cooper B.F., Srivastava U., Vee E., Yerneni R., Ramakrishnan R., Efficient Bulk Insertion into a Distributed Ordered Table, Proceedings of the 2008 ACM SIGMOD International Conference On Management of Data, Ser. SIGMOD '08, pp. 765-778, (2008); Sun S., Lannom L., Boesch B., Handle System Overview, (2003)","","","Institute of Electrical and Electronics Engineers Inc.","et al.; IEEE; IEEE Computer Society; IEEE Technical Committee on Computer Architecture (TCCA); IEEE Technical Committee on Distributed Processing (TCDP); IEEE Technical Committee on Parallel Processing (TCPP)","2017 IEEE International Conference on Networking, Architecture, and Storage, NAS 2017","7 August 2017 through 9 August 2017","Shenzhen","130440","","978-153863486-8","","","English","IEEE Int. Conf. Netw., Archit., Storage, NAS - Proc.","Conference paper","Final","","Scopus","2-s2.0-85031724731"
"Politze M.; Decker B.; Eifert T.","Politze, Marius (57195741179); Decker, Bernd (57204932477); Eifert, Thomas (6508209571)","57195741179; 57204932477; 6508209571","PstaiX - A process-aware architecture to support research processes","2017","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","275","","","1369","1380","11","3","10.18420/in2017_137","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067434623&doi=10.18420%2fin2017_137&partnerID=40&md5=9131b2fd4355769ee0b2a941575c2a82","RWTH Aachen University, IT Center, Seffenter Weg 23, Aachen, 52080, Germany","Politze M., RWTH Aachen University, IT Center, Seffenter Weg 23, Aachen, 52080, Germany; Decker B., RWTH Aachen University, IT Center, Seffenter Weg 23, Aachen, 52080, Germany; Eifert T., RWTH Aachen University, IT Center, Seffenter Weg 23, Aachen, 52080, Germany","Universities IT service providers are faced with rising demands on existing IT systems and higher degrees of individualization. The challenge thus is to provide services that researchers can use today but that are flexible and sustainable enough to also support tomorrows’ research processes. Emerging from previous projects supporting administrative and learning processes, a reference architecture is proposed that aims at providing a general guideline to build process-aware services supporting eResearch. The proposed architecture gives guidance on structuring development and operation of services and formalizes how existing IT systems transition into process-aware services. © 2017 Gesellschaft fur Informatik (GI). All rights reserved.","EResearch; OAuth2; Process modelling; Process support; Research data management; SOA; Software architecture","Development and operations; E-research; IT service providers; IT system; Learning process; Proposed architectures; Reference architecture; Research process; Architecture","","","","","","","Bischof C., Bunsen G., Hinzelmann S., Gigamove – Einfach und schnell große Dateien austauschen, DFN Mitteilungen (Ausgabe 80), pp. 30-32, (2011); Barkhuus L., Dourish P., Everyday encounters with context-aware computing in a campus environment, UbiComp 2004: Ubiquitous Computing, 3205 of Lecture Notes in Computer Science, pp. 232-249, (2004); Cantor S., Kemp J., Philpott R., Maler E., Security Assertion Markup Language (SAML) V2.0, (2005); Crouch S., Hong N.C., Hettrick S., Jackson M., Pawlik A., Sufi S., Carr L., de Roure D., Goble C., Parsons M., The software sustainability Institute: Changing research software attitudes and practices, Computing in Science & Engineering, 15, 6, pp. 74-80, (2013); Curdt C., Design and Implementation of A Research Data Management System: The CRC/TR32 Project Database (TR32DB), (2014); Eifert T., Bunsen G., Grundlagen und Entwicklung von Identity Management an der RWTH Aachen, PIK - Praxis Der Informationsverarbeitung Und Kommunikation, 36, 2, (2013); Eifert T., Muckel S., Schmitz D., Introducing research data management as a service suite at RWTH Aachen University, 9. DFN-Forum Kommunikationstechnologien, 257 of GI Edition Lecture Notes in Informatics Proceedings (LNI), pp. 55-66, (2016); Fielding R.T., Architectural Styles and the Design of Network-Based Software Architectures, (2000); Grabatin M., Hommel W., Metzger S., Pohn D., Improving the scalability of identity federations through levelofassurance management automation, 9. DFN-Forum Kommunikationstechnologien, 257 of GI Edition Lecture Notes in Informatics Proceedings (LNI), pp. 67-76, (2016); Hardt D., The OAuth 2.0 Authorization Framework, (2012); Juling W., Vom Rechnernetz zu e-Science, PIK - Praxis Der Informationsverarbeitung Und Kommunikation, 32, 1, pp. 33-36, (2009); Kraft A., Razum M., Potthoff J., Porzel A., Engel T., Lange F., van den Broek K., Furtado F., The RADAR project - A service for research data archival and publication, ISPRS International Journal of Geo-Information, 5, 3, (2016); Kuppers B., Dondorf T., Willemsen B., Pflug H.J., Vonhasselt C., Magrean B., Muller M.S., Bischof C., The scientific programming integrated degree program – A pioneering approach to join theory and practice, Procedia Computer Science, 80, pp. 1957-1967, (2016); Namiot D., Sneps-Sneppe M., On Micro-services Architecture, International Journal of Open Information Technologies, (2014); Politze M., Decker B., Ontology based semantic data management for pandisciplinary research projects, Proceedings of the 2nd Data Management Workshop, 96 of Kölner Geographische Arbeiten, (2016); Politze M., Schaffert S., Decker B., A secure infrastructure for mobile blended learning applications, European Journal of Higher Education IT 2016-1, (2016); Rathfelder C., Groenda H., ISOAMM: An independent SOA maturity model, Distributed Applications and Interoperable Systems, 5053 of Lecture Notes in Computer Science, pp. 1-15, (2008); Welke R., Hirschheim R., Schwarz A., Service-oriented architecture maturity, Computer, 44, 2, pp. 61-67, (2011); Zimmermann A., Sandkuhl K., Pretz M., Falkenthal M., Jugel D., Wissotzki M., Towards an integrated service-oriented reference enterprise architecture, Proceedings of the 2013 International Workshop on Ecosystem Architectures, pp. 26-30, (2013)","","Eibl M.; Gaedke M.","Gesellschaft fur Informatik (GI)","","47. Jahrestagung der Gesellschaft fur Informatik, Informatik 2017 - 47th  Annual Meeting of the German Informatics Society (GI), Informatics 2017","25 September 2017 through 29 September 2017","Chemnitz","158762","16175468","978-388579669-5","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85067434623"
"Zhang W.; Ying J.","Zhang, Wei (57542043600); Ying, Jun (35228239700)","57542043600; 35228239700","Strategies for data management in clinical researches","2017","Fudan University Journal of Medical Sciences","44","1","","122","126","4","0","10.3969/j.issn.1672-8467.2017.01.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015620144&doi=10.3969%2fj.issn.1672-8467.2017.01.021&partnerID=40&md5=981f5ac23fe91a0b54fb568944bcbe9c","Fudan University Library, Shanghai, 200032, China; Center of Evidence-based Medicine, Fudan University, Shanghai, 200032, China","Zhang W., Fudan University Library, Shanghai, 200032, China; Ying J., Fudan University Library, Shanghai, 200032, China, Center of Evidence-based Medicine, Fudan University, Shanghai, 200032, China","Clinical researchers have begun to recognize the importance of long-term preservation and sharing of data because of the emergency of numerous medical data in clinical studies, so the concept and methods of research data management emerged. Data management in clinical researches can accelerate the accuracy and efficiency of clinical research and meet the expectations and requirements of researchers, institutions and research sponsors. This paper discussed the key points of clinical data management through two aspects, data management in individual clinical research and data management in organizational clinical researches. It will help clinical researchers and scientific research governors to understand the process of data management in clinical researches and promote standardized clinical research activities. © 2017, Editorial Department of Fudan University Journal of Medical Sciences. All right reserved.","Clinical research; Data management; Data warehouse; Institutional repository","","","","","","","","Chan A.W., Tetzlaff J.M., Gotzsche P.C., Et al., SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials, BMJ, 346, (2013); A service of the U.S. National Institutes of Health; International Clinical Trials Registry Platform; NIH Data Sharing Policy and Implementation Guidance; Dissemination and sharing of research results; Hrynaszkiewicz I., Norton M.L., Vickers A.J., Et al., Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers, BMJ, 340, (2010)","J. Ying; Fudan University Library, Shanghai, 200032, China; email: jjunying@fudan.edu.cn","","Fudan University","","","","","","16728467","","FXYUA","","Chinese","Fudan Univ. J. Med. Sci.","Review","Final","","Scopus","2-s2.0-85015620144"
"Siemensma G.; Ritchie A.; Lewis S.","Siemensma, Gemma (56503921200); Ritchie, Ann (57533035200); Lewis, Suzanne (37064551700)","56503921200; 57533035200; 37064551700","Shaping the professional landscape through research, advocacy and education – an Australian perspective","2017","Health Information and Libraries Journal","34","2","","171","176","5","5","10.1111/hir.12180","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017479766&doi=10.1111%2fhir.12180&partnerID=40&md5=ceb507ceb00bd696a7ec8b4e7d524c0f","Ballarat Health Services, Ballarat, VIC, Australia; Barwon Health, Geelong, VIC, Australia; Central Coast Local Health District, Sydney, NSW, Australia","Siemensma G., Ballarat Health Services, Ballarat, VIC, Australia; Ritchie A., Barwon Health, Geelong, VIC, Australia; Lewis S., Central Coast Local Health District, Sydney, NSW, Australia","This article is the first in a new series in this regular feature. The intention of the series is to look at important global developments in health science libraries. Librarians will be invited to share with HILJ readers key initiatives in their country or region. These articles should serve as a road map, describing the key changes in the field and exploring factors driving these changes. We initiate this series with an article by three Australian librarians who use research findings to depict the evolving professional landscape in their country. The starting point of their analysis is a report completed in 2011 which looked into likely future workforce and education requirements for health library professionals. The authors trace the achievements since then, most notably in the areas of research, advocacy and education. Clearly, a great deal has been achieved leading to a greater return on investment. The authors maintain that the key to shaping the profession and enhancing the status of librarians is ongoing professional development. To this end, Australia is promoting a systematic, competency based health specialist certification. Finally, they identify trends impacting on health librarianship, such as the growing importance of research data management and consumer health literacy. JM. © 2017 Health Libraries Group","Australia; case reports; clinical librarians; competencies; education and training; evidence based library and information practice; national strategies; professional development","Australia; Consumer Advocacy; Health Literacy; Humans; Librarians; Libraries, Medical; Library Science; Research; Australia; consumer advocacy; health literacy; human; librarian; library; library science; research","","","","","","","Browne R., Lasserre K., McTaggart J., Bayley L., McKibbon A., Clark M., Perry G.J., Murphy J., International trends in health science librarianship: part 1–the English speaking world, Health Information and Libraries Journal, 29, pp. 75-80, (2012); Health Librarianship Workforce and Education: Research to Plan the Future [Internet], (2011); Kammermann M., The Census of Australian Health Libraries and Health Librarians Working Outside the Traditional Library Setting: the Final Report of the 2012 Anne Harrison Award project conducted Between October 2014-February 2015 [Internet], (2016); Resources to Implement the NSQHS Standards, (2017); The Community Returns Generated by Australian Health Libraries, (2013); MLA's Competencies for Lifelong Learning and Professional Success, (2007); Health Specialisation Competencies, (2013); Ritchie A., Future Requirements for Health Librarianship Workforce and Education: Outcomes of the Health Libraries Australia Research Project 2009–11, (2011); Ritchie A., Kuusniemi M.E., Developing Research Data Management Practices at a University Hospital Library in Australia – An International Collaborative Project, (2016)","","","Blackwell Publishing Ltd","","","","","","14711834","","","28383165","English","Health Inf. Libr. J.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85017479766"
"Yoon A.; Schultz T.","Yoon, Ayoung (55755842200); Schultz, Teresa (57196390790)","55755842200; 57196390790","Research data management services in academic libraries in the US: A content analysis of libraries' websites","2017","College and Research Libraries","78","7","","920","933","13","60","10.5860/crl.78.7.920","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032810413&doi=10.5860%2fcrl.78.7.920&partnerID=40&md5=de66784abc4813b90cc874fd35557611","Indiana University, Purdue University, Indianapolis, United States; University of Nevada, Reno, United States","Yoon A., Indiana University, Purdue University, Indianapolis, United States; Schultz T., University of Nevada, Reno, United States","Examining landscapes of research data management services in academic libraries is timely and significant for both those libraries on the front line and the libraries that are already ahead. While it provides overall understanding of where the research data management program is at and where it is going, it also provides understanding of current practices and data management recommendations and/or tool adoptions as well as revealing areas of improvement and support. This study examined the research data (management) services in academic libraries in the United States through a content analysis of 185 library websites, with four main areas of focus: service, information, education, and network. The results from the content analysis of these webpages reveals that libraries need to advance and engage more actively to provide services, supply information online, and develop educational services. There is also a wide variation among library data management services and programs according to their web presence. © 2017 Ayoung Yoon and Teresa Schultz.","","","","","","","Ece Tur-nator; Future of the Academy, (82–102)","9. Jodi Reeves Flores, Jason J. Brodeur, Morgan G. Daniels, Natsuko Nichools, and Ece Tur-nator, “Libraries and the Research Data Management Landscape,” The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy (2015), 82–102, available online at www.clir.org/pubs/reports/pub167/pub167.pdf [accessed 22 August 2015]. 10. Ibid., 88. 11. Ibid., 88–89.","Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and the Plans for the Future, Association of College and Research Libraries, (2012); Holden J.P., Memorandum for the Heads of Executive Departments and Agencies, Office of Science and Technology Policy (2013), Available Online at https://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp-public-access-memo-2013.pdf [Accessed 20 August 2015]; Federer L.M., Lu Y., Joubert D.J., Data Literacy Training Needs of Biomedical Researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Keil D.E., Research Data Needs from Academic Libraries: The Perspective of a Faculty Researcher, Journal of Library Administration, 54, 3, pp. 233-240, (2014); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data Curation: A Study of Researcher Practices and Needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Keil, Research Data Needs from Academic Libraries; Tenopir C., Sandusky R.J., Allard S., Birch B., Academic Librarians and Research Data Services: Preparation and Attitudes, IFLA Journal, 39, 1, (2013); Tenopir, Birch, Allard, Academic Libraries and Research Data Services; Top Trends in Academic Libraries: A Review of the Trends and Issues Affecting Academic Libraries in Higher Education, College & Research Libraries News, 75, 6, (2014); Flores J.R., Brodeur J.J., Daniels M.G., Nichools N., Turnator E., Libraries and the Research Data Management Landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy, pp. 82-102, (2015); Reinhalter L., Wittman R.J., The Library: Big Data's Boomtown, Serials Librarian, 67, 4, (2014); Tenopir, Sandusky, Allard, Birch, Academic Librarians and Research Data Services; Antell K., Foote J.B., Turner J., Shults B., Dealing with Data: Science Librarians' Participation in Data Management at Association of Research Libraries Institutions, College & Research Libraries, 75, 4, (2014); Kennan M.A., Cortall S., Afzal W., Making Space' in Practice and Education: Research Support Services in Academic Libraries, Library Management, 35, 8-9, pp. 666-683, (2014); Erway R., Rinehart A., If You Build It, Will They Fund? Making Research Data Management Sustainable, Online Computer Library Center, (2015); Gold A., Data Curation and Libraries: Short-Term Development, Long-Term Prospects, Digital Commons@Cal Poly, (2010); Mullins J.L., Enabling International Access to Scientific Data Sets: Creation of the Distributed Data Curation Center (D2C2), Purdue University, (2007); Knight G., Building a Research Data Management Service for the London School of Hygiene & Tropical Medicine, Program: Electronic Library and Information Systems, 49, 4, pp. 424-439, (2015); Coates H., Building Data Services from the Ground Up: Strategies and Resources, Journal of EScience Librarianship, 3, 1, pp. 52-59, (2014); Varvel V.E., Sheh Y., Data Management Consulting at the Johns Hopkins University, New Review of Academic Librarianship, 19, 3, pp. 224-245, (2013); Addison A., Moore J., Hudson-Vitale C., Forging Partnerships: Foundations of Geospatial Data Stewardship, Journal of Map & Geography Libraries, 11, 3, pp. 359-375, (2015); Cox A., Hiom D., Fripp D., Gray S., Snow K., Steer D., Librarians as Partners in Research Data Service Development at Griffith University, Program: Electronic Library and Information Services, 49, 4, pp. 440-460, (2015); Ball J., Research Data Management for Libraries: Getting Started, Insights, 26, 3, pp. 256-260, (2013); Henderson M.E., Knott T.L., Starting a Research Data Management Program Based in a University Library, Medical Reference Services Quarterly, 34, 1, pp. 47-59, (2015); Kiel R., O'Neil F., Gallagher A., Mohammad C., The Library in the Research Culture of the University: A Case Study of Victoria University Library, IFLA Journal, 41, 1, pp. 40-52, (2015); Chad K., Enright S., The Research Cycle and Research Data Management (RDM): Innovating Approaches at the University of Westminster, Insights, 27, 2, pp. 147-153, (2014); Xia J., Wang M., Competencies and Responsibilities of Social Science Data Librarians: An Analysis of Job Descriptions, College & Research Libraries, 75, 3, pp. 362-388, (2014); Tenopir, Birch, Allard, Academic Libraries and Research; Tenopir C., Sandusky R.J., Allard S., Birch B., Research Data Management Services in Academic Research Libraries and Perceptions of Librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Antell, Foote, Turner, Shults, Dealing with Data; Briney K., Goben A., Zilinski L., Do You Have an Institutional Data Policy? A Review of the Current Landscape of Library Data Services and Institutional Data Policies, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 1-25, (2015); Steinhart G., Libraries as Distributors of Geospatial Data: Data Management Policies as Tools for Managing Partnerships, Library Trends, 55, 2, pp. 264-284, (2006); Cox A.M., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities, Journal of Librarianship & Information Science, 46, 4, pp. 299-316, (2014); Kennan, Cortall, Afzal, Making Space' in Practice and Education"";; Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and Analysis of Research Data Services in University Libraries, Electronic Library, 33, 3, pp. 417-449, (2015); Xing S., Zhuang, Hua, Zhou, Investigation and Analysis of Research Data Services; Prasad B.D., Content Analysis: A Method in Social Science Research, In Research Methods for Social Work, pp. 173-193, (2008); Berelson B., Content Analysis in Communication Research, (1952); Kim I., Kuljis J., Applying Content Analysis to Web-Based Content, Journal of Computing and Information Technology, 18, 4, pp. 369-375, (2010); Strasser C., Research Data Management, National Information Standards Organization, (2015); Antell, Foote, Turner, Shults, Dealing with Data; Briney, Goban, Zilinski, Do You Have An Institutional Data Policy?; Tenopir, Birch, Allard, Academic Libraries and Research Data Services; Strasser, Research Data Management; Tenopir, Birch, Allard, Academic Libraries and Research Data Services""; Briney, Goban, and Zilinski, ""do You Have An Institutional Data Policy?","","","Association of College and Research Libraries","","","","","","00100870","","","","English","Coll. Res. Libr.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85032810413"
"Wittenberg J.; Elings M.","Wittenberg, Jamie (57193527016); Elings, Mary (16174703000)","57193527016; 16174703000","Building a Research Data Management Service at the University of California, Berkeley: A tale of collaboration","2017","IFLA Journal","43","1","","89","97","8","27","10.1177/0340035216686982","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014588509&doi=10.1177%2f0340035216686982&partnerID=40&md5=5671111898bf1d95a6c09145f0267e18","Indiana University, Bloomington, United States; University of California, Berkeley, United States","Wittenberg J., Indiana University, Bloomington, United States; Elings M., University of California, Berkeley, United States","University of California, Berkeley’s Library and the central Research Information Technologies unit have collaborated to develop a research data management program that leverages each organization’s expertise and resources to create a unified service. The service offers a range of workshops, consultation, and an online resource. Because of this collaboration, service areas that are often fully embedded in IT, like backup and secure storage, as well as services in the Library domain, like resource discovery and instruction, are integrated into a single research data management program. This case study discusses the establishment of the program, the obstacles in implementing it, and outcomes of the collaborative model. © 2017, © The Author(s) 2017.","Academic libraries; data services; LIS as a profession","","","","","","","","An Interpretation of the Library Bill of Rights, (2002); The 50 Top Research Universities; Cox A., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Davis H.M., Cross W.M., Using a data management plan review service as a training ground for librarians, Journal of Librarianship & Scholarly Communication, 3, 2, pp. 1-20, (2015); Ferguson A.R., Nielson J.L., Cragin M.H., Et al., Big data from small data: Data-sharing in the ‘long tail’ of neuroscience, Nature Neuroscience, 17, 11, (2014); Nardine J., Moyo L., Learning Community as a Model for Cultivating Teaching Proficiencies among Library Instructors: A Case Study, (2013); Soehner C., Steeves C., Ward J., Et al., E-science and data support services: A study of ARL Member Institutions, Association of Research Libraries, (2010); Tenopir C., Sandusky R.J., Allard S., Et al., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); By the Numbers; Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, 3-4, pp. 211-219, (2014); Wilson J.A.J., Jefferies P., Towards a unified university infrastructure: The data management roll-out at the University of Oxford, International Journal of Digital Curation, 8, 2, pp. 235-246, (2013); Witteveen A., Better together: The cohort model of professional development, Library Journal, (2015); Zorich D., Waibel G., Erway R., Beyond the silos of the LAMs: Collaboration among libraries, archives, and museums, OCLC Research, (2008)","J. Wittenberg; Indiana University, Bloomington, 107 S Indiana Ave, 47405, United States; email: jamie.wittenberg@gmail.com","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85014588509"
"Alves C.; Castro J.A.; Ribeiro C.; Honrado J.P.; Lomba A.","Alves, Cristiana (57198312822); Castro, João Aguiar (55977255100); Ribeiro, Cristina (7201734594); Honrado, João Pradinho (8241417300); Lomba, Angela (8241417600)","57198312822; 55977255100; 7201734594; 8241417300; 8241417600","Research data management in the field of Ecology: An overview","2018","Proceedings of the International Conference on Dublin Core and Metadata Applications","2018-September","","","87","94","7","7","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056867975&partnerID=40&md5=a3289f6f74b267ed10aa8f86eb6bd0f8","CIBIO, InBIO, Portugal; INESC TEC, Portugal; INESC TEC, FEUP, Portugal; CIBIO, InBIO, FCUP, Portugal","Alves C., CIBIO, InBIO, Portugal; Castro J.A., INESC TEC, Portugal; Ribeiro C., INESC TEC, FEUP, Portugal; Honrado J.P., CIBIO, InBIO, FCUP, Portugal; Lomba A., CIBIO, InBIO, Portugal","The diversity of research topics and resulting datasets in the field of Ecology (the scientific study of ecological systems and their biodiversity) has grown in parallel with developments in research data management. Based on a meta-analysis performed on 93 scientific references, this paper presents a comprehensive overview of the use of metadata tools in the Ecology domain through time. Overall, 40 metadata tools were found to be either referred or used by the research community from 1997 to 2018. In the same period, 50 different initiatives in ecology and biodiversity research were conceptualized and implemented to promote effective data sharing in the community. A relevant concern that stems from this analysis is the need to establish simple methods to promote data interoperability and reuse, so far limited by the production of metadata according to different standards. With this study, we also highlight challenges and perspectives in research data management in the domain of Ecology towards best practice guidelines. © 2018 CEUR-WS. All rights reserved.","Biodiversity; Ecology; Literature review; Metadata tools; Research Data Management","Biodiversity; Ecology; Metadata; Best practice guidelines; Data interoperability; Ecological systems; Literature reviews; Research communities; Research data managements; Scientific references; Scientific studies; Information management","","","","","FCT-Portuguese Science Foundation; European Regional Development Fund, FEDER","This research is a result of the project TAIL – Research data management from creation to deposit and sharing - POCI-01-0145-FEDER-016736, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by National funds through FCT-Portuguese Science Foundation. (PTDC/EEI-ESS/1672/2014/2014).","Aloisio G., Milillo G., Williams R.D., An XML architecture for high-performance web-based analysis of remote-sensing archives, Future Generation Computer Systems, 16, 1, pp. 91-100, (1999); Baker E., Rycroft S., Smith V.S., Linking multiple biodiversity informatics platforms with Darwin Core Archives, Biodiversity Data Journal, 2, (2014); Ball A., Greenberg J., Jeffery K., Koskela R., RDA Metadata Standards Directory Working Group, (2016); Berkley C., Bowers S., Jones M.B., Madin J.S., Schildhauer M., Improving Data Discovery in Metadata Repositories Through Semantic Search, (2009); Berkley C., Jones M., Bojilova J., Higgins D., M Etacat: A Schema-Indep Endent XM L Database Sy Stem, (2001); Buchadas A., Vaz A.S., Honrado J.P., Alagador D., Bastos R., Cabral J.A., Vicente J.R., Dynamic models in research and management of biological invasions, Journal of Environmental Management, 196, pp. 594-606, (2017); Costello M.J., Michener W.K., Gahegan M., Zhang Z.-Q., Bourne P.E., Biodiversity data should be published, cited, and peer reviewed, Trends in Ecology & Evolution, 28, 8, pp. 454-461, (2013); Cushing J.B., Nadkarni N., Finch M., Fiala A., Murphy-Hill E., Delcambre L., Maier D., Component-based end-user database design for ecologists, Journal of Intelligent Information Systems, 29, 1, pp. 7-24, (2007); Da Silva J.R., Castro J.A., Ribeiro C., Honrado J., Lomba A., Goncalves J., Beyond inspire: An ontology for biodiversity metadata records, On The Move to Meaningful Internet Systems: Otm 2014 Workshops, 8842, pp. 597-607, (2014); Guidelines on FAIR Data M Anagement in Horizon 2020, (2016); Filetti M., Gnauck A., A concept of a virtual research environment for long-term ecological projects with free and open source software, Environmental Software Systems: Frameworks of Eenvironment, 359, pp. 235-244, (2011); Gil I.S., Hutchison V., Frame M., Palanisamy G., Metadata activities in biology, Journal of Library Metadata, 10, 2-3, pp. 99-118, (2010); Guralnick R., Walls R., Jetz W., Humboldt Core - Toward a standardized capture of biological inventories for biodiversity monitoring, modeling and assessment, Ecography, (2017); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Henshaw D.L., Spycher G., Remillard S.M., Transition from A Legacy Databank to An Integrated Ecological Information System, (2002); Higgins D., Berkley C., Jones M.B., Managing Heterogeneous Ecological Data Using Morpho, (2002); Higgins J.P., Green S., Cochrane Handbook for Systematic Reviews of Interventions, 4, (2011); Jetz W., McPherson J.M., Guralnick R.P., Integrating biodiversity distribution knowledge: Toward a global map of life, Trends in Ecology & Evolution, 27, 3, pp. 151-159, (2012); Jones C., Blanchette C., Brooke M., Harris J., Jones M., Schildhauer M., A metadata-driven framework for generating field data entry interfaces in ecology, Ecological Informatics, 2, 3, pp. 270-278, (2007); Leigh C., Laporte B., Bonada N., Fritz K., Pella H., Sauquet E., Datry T., IRBAS: An online database to collate, analyze, and synthesize data on the biodiversity and ecology of intermittent rivers worldwide, Ecology and Evolution, 7, 3, pp. 815-823, (2017); Lyon L., Dealing with data: Roles, rights, responsibilities and relationships, Consultancy Report, (2007); Malaverri J.G., Vilar B., Medeiros C.B., A TOOL BASED ON WEB SERVICES TO QUERY BIODIVERSITY INFORM ATION, (2009); Mayernik M., Metadata Realities for Cyberinfrastructure: Data Authors as Metadata Creators, (2011); Michener W.K., Beach J.H., Jones M.B., Ludascher B., Pennington D.D., Pereira R.S., Schildhauer M., A knowledge environment for the biodiversity and ecological sciences, Journal of Intelligent Information Systems, 29, 1, pp. 111-126, (2007); Michener W.K., Brunt J.W., Helly J.J., Kirchner T.B., Stafford S.G., Nongeospatial metadata for the ecological sciences, Ecological Applications, 7, 1, pp. 330-342, (1997); Michener W.K., Brunt J.W., Vanderbilt K.L., Ecological Informatics: A Long-Term Ecological Research Perspective, (2002); Michener W.K., Jones M.B., Ecoinformatics: Supportingecology as a data-intensive science, Trends in Ecology & Evolution, 27, 2, pp. 85-93, (2012); Michener W.K., Porter J., Servilla M., Vanderbilt K., Long term ecological research and information management, Ecological Informatics, 6, 1, pp. 13-24, (2011); Nadrowski K., Ratcliffe S., Bonisch G., Bruelheide H., Kattge J., Liu X., Wirth C., Harmonizing, annotating and sharing data in biodiversity-ecosystem functioning research, Methods in Ecology and Evolution, 4, 2, pp. 201-205, (2013); Nilsson M., The Singapore Framework for Dublin Core Application Profiles, (2008); Palmer C.L., Thomer A.K., Baker K.S., Wickett K.M., Hendrix C.L., Rodman A., Fouke B.W., Site-based data curation based on hot spring geobiology, PLOS ONE, 12, 3, (2017); Pfeifer M., Lefebvre V., Gardner T.A., Arroyo-Rodriguez V., Baeten L., Banks-Leite C., Ewers R.M., BIOFRAG - A new database for analyzing BIOdiversity responses to forest FRAGmentation, Ecology and Evolution, 4, 9, pp. 1524-1537, (2014); Qin J., Li K., How portable are the metadata standards for scientific data? A proposal for a metadata infrastructure, The International Conference on Dublin Core and Metadata Applications, (2013); Reichman O.J., Jones M.B., Schildhauer M.P., Challenges and opportunities of open data in ecology, Science, 331, 6018, pp. 703-705, (2011); Tani A., Candela L., Castelli D., Dealing with metadata quality: The legacy of digital library efforts, Information Processing & Management, 49, 6, pp. 1194-1205, (2013); Thanos C., Research data reusability: Conceptual foundations, barriers and enabling technologies, Publications, 5, 1, (2017); Veiga A.K., Saraiva A.M., Chapman A.D., Morris P.J., Gendreau C., Schigel D., Robertson T.J., A conceptual framework for quality assessment and management of biodiversity data, PLOS ONE, 12, 6, (2017); Weibel S., Kunze J., Lagoze C., Wolf M., Dublin Core Metadata for Resource Discovery, (1998); White H.C., Descriptive metadata for scientific data repositories: A comparison of information scientist and scientist organizing behaviors, Journal of Library Metadata, 14, 1, pp. 24-51, (2014); Wieczorek J., Bloom D., Guralnick R., Blum S., Doring M., Giovanni R., Vieglais D., Darwin Core: An evolving community-developed biodiversity data standard, PLOS ONE, 7, 1, (2012)","","","Dublin Core metadata initiative","","2018 International Conference on Dublin Core and Metadata Applications, DCMI 2018","10 September 2018 through 13 September 2018","Porto","141884","19391358","","","","English","Proc. Int. Conf. Dublin Core Metadata Appl.","Conference paper","Final","","Scopus","2-s2.0-85056867975"
"Kranjec I.; Glavica M.; Vodopijevec A.","Kranjec, Irena (55902437100); Glavica, Marijana (55912861100); Vodopijevec, Alen (14049319700)","55902437100; 55912861100; 14049319700","Research data and academic libraries; [Istraživački podaci i visokoškolske knjižnice]","2018","Vjesnik Bibliotekara Hrvatske","61","1","","611","626","15","0","10.30754/vbh.61.1.635","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060552709&doi=10.30754%2fvbh.61.1.635&partnerID=40&md5=1ec7ffcfe615e3d9aaef81893655aa80","Filozofski fakultet Zagreb, Knjižnica, Croatia; Centar za znanstvene informacije Instituta Ruđer Bošković, Croatia","Kranjec I., Filozofski fakultet Zagreb, Knjižnica, Croatia; Glavica M., Filozofski fakultet Zagreb, Knjižnica, Croatia; Vodopijevec A., Centar za znanstvene informacije Instituta Ruđer Bošković, Croatia","Purpose. The paper deals with the role of academic libraries in the context of open science, especially in supporting researchers, faculty and students in the research data management process, publishing and sharing research data. It examines research data as a new library material, the features and differences from the “traditional” library materials and services, as well as new skills and knowledge needed for research data services. Approach. An overview is given of scientific and professional papers and examples of good practice regarding scientific data services in academic libraries. Findings. Academic libraries should respond to the rapid changes in the scientific landscape and to the new needs of their users by developing research data services. Some of these services could be considered as expansion of the existing information services (for example searching and finding the resources and also citing of the research data sets), but in most cases this kind of services demands new skills and knowledge. Furthermore, the cooperation among library staff is of vital importance for the high-quality research data services development, as well as the cooperation with other faculty departments and other institutions dealing with research data (especially research data archives). Practical implications. Academic libraries can consider introducing research data services in their strategic plans, missions, and planned activities according to the recommendations presented in the paper. © VBH 2018.","Academic libraries; Data management plan; Research data; Research data management; Research data services","","","","","","","","Value of Academic Libraries: A Comprehensive Research Review and Report. Researched by Megan Oakleaf, (2010); Ten Recommendations for Libraries to Get Started with Research Data Management: Final Report of the LIBER Working Group on E-Science/Research Data Management, (2012); (2003); (2003); (2002); Data Publishing Routes.// Expert Tour Guide to Data Management; Expert Tour Guide on Data Management; Research Data.// Expert Tour Guide to Data Management; Conrad S., Shorish Y., Whitmire A.L., Hswe P., Building professional development opportunities in data services for academic librarians, IFLA Journal, 43, 1, pp. 65-80, (2017); Cox A.M., Kennan M.A., Lyon L., Pinfield S., Developments in research data management in academic libraries: Towards an understanding of research data service maturity, Journal of the Association for Information Science and Technology, 68, 9, pp. 2182-2200, (2017); H2020 Programme: Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020; Geraci D., Jacobs J., Humphrey C., Data Basics: An Introductory Text, (2012); Gold A., Cyberinfrastructure, data, and libraries. Part 1: A cyberinfrastructure primer for librarians, D-Lib Magazine, 13, pp. 9-10, (2007); Gold A., Cyberinfrastructure, Data, and Libraries, 13, pp. 9-10, (2007); Henneken E., Unlocking and sharing data in astronomy, Bulletin of the Association for Information Science and Technology, 41, 48, pp. 40-43, (2015); Inter-University Consortium for Political and Social Research (ICPSR); Linde P., Noorman M., Wessels B.A., Sveinsdottir T., How can libraries and other academic stakeholders engage in making data open?, Information Services & Use, 34, pp. 211-219, (2014); Što Je Obzor 2020?; Principles and guidelines for access to research data from public funding, OECD, (2007); Open Research Data Pilot; Pampel H., Dallmeier-Tiessen S., Open Research Data: From Vision to Practice.// Opening Science: the Evolving Guide on How the Web is Changing Research, Collaboration and Scholarly Publishing, pp. 139-153, (2014); Rice R., Southall J., The Data librarian’s Handbook, (2016); Reeves Flores J., Libraries and the research data management landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and The Future of The Academy, (2015); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perception of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R.J., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Unidata: Data Services and Tools for Geoscience; Wilkinson M.D., Dumontier M., Aalsbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Et al., The FAIR Guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Zenodo","","","Hrvatsko Knjiznicarsko Drustvo","","","","","","05071925","","","","English","Vjesn. Bibl. Hrvat.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85060552709"
"Chard R.; Chard K.; Tuecke S.; Foster I.","Chard, Ryan (55588066400); Chard, Kyle (9132950200); Tuecke, Steve (6602740450); Foster, Ian (35572232000)","55588066400; 9132950200; 6602740450; 35572232000","Software defined cyberinfrastructure for data management","2017","Proceedings - 13th IEEE International Conference on eScience, eScience 2017","","","8109174","456","457","1","3","10.1109/eScience.2017.69","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043765310&doi=10.1109%2feScience.2017.69&partnerID=40&md5=16fd6d512807a512fa6dd6e092d6abda","Computing, Environment, and Life Sciences, Argonne National Laboratory, United States; Computation Institute, University of Chicago, Argonne National Laboratory, United States","Chard R., Computing, Environment, and Life Sciences, Argonne National Laboratory, United States; Chard K., Computation Institute, University of Chicago, Argonne National Laboratory, United States; Tuecke S., Computation Institute, University of Chicago, Argonne National Laboratory, United States; Foster I., Computing, Environment, and Life Sciences, Argonne National Laboratory, United States, Computation Institute, University of Chicago, Argonne National Laboratory, United States","Scientific research is data-centric, relying on the acquisition, management, movement, analysis, and sharing of data. Proficiently managing the end-to-end lifecycle of scientific data is non-trivial and comprises many time consuming and mundane tasks. While individual tasks are not prohibitive, when done repeatedly and frequently they represent a significant strain on researchers. We posit that a better approach is to automate these tasks through a Software Defined Cyberinfrastructure. We have developed R IPPLE to provide such capabilities by automating research data management activities via a programmable and event-based cyber-environment. Users specify high-level management policies, such as data movement and metadata extraction, using intuitive If-Trigger-Then-Action rules. These rules are then autonomously, and reliably, executed and managed by Ripple. © 2017 IEEE.","Data Management; Ripple; Software Defined Cyberinfrastructure","Cyber infrastructures; Data movements; Level management; Meta-data extractions; Research data managements; Ripple; Scientific data; Scientific researches; Information management","","","","","","","Foster I., Et al., Software defined cyberinfrastructure, The 37th IEEE International Conference on Distributed Computing Systems, (2017); Chard R., Et al., Ripple Home automation for research data management, The 37th IEEE International Conference on Distributed Computing Systems, (2017); Chard K., Et al., Efficient and secure transfer, synchronization, and sharing of big data, IEEE Cloud Computing, 1, 3, pp. 46-55, (2014); Leibovici T., Taking Back Control of HPC File Systems with Robinhood Policy Engine, (2015); Rajasekar A., Et al., IRODS Primer Integrated rule-oriented data system, Synthesis Lectures on Information Concepts, Retrieval, and Services, 2, 1, pp. 1-143, (2010); AbdelBaky M., Et al., Software-defined environments for science and engineering, The International Journal of High Performance Computing Applications, (2017)","","","Institute of Electrical and Electronics Engineers Inc.","","13th IEEE International Conference on eScience, eScience 2017","24 October 2017 through 27 October 2017","Auckland","132556","","978-153862686-3","","","English","Proc. - IEEE Int. Conf. eSci., eScience","Conference paper","Final","","Scopus","2-s2.0-85043765310"
"Špiranec S.; Kos D.","Špiranec, Sonja (35312018400); Kos, Denis (56502002400)","35312018400; 56502002400","Data Literacy and Research Data Management: The Croatian State of Affairs","2018","Communications in Computer and Information Science","810","","","148","157","9","2","10.1007/978-3-319-74334-9_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041706274&doi=10.1007%2f978-3-319-74334-9_16&partnerID=40&md5=def0d22be9c1e8e3a62cf3d8c919aaa6","Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia","Špiranec S., Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia; Kos D., Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia","This report is a part of an international survey on data literacy and research data management and is concerned with the Croatian state of affairs. A key aim of this study is to elicit the practices of Croatian researchers and PhD students regarding the production, dissemination, provision, storage, and description of research data, as well as portraying institutional attitudes towards those issues. By contributing Croatian data this study widens the international perspective on research data management. Findings of this study expose the current levels of awareness and gaps in knowledge and allow creation of structured and well-focused educational activities. © 2018, Springer International Publishing AG.","Academic staff; Croatia; Data literacy; Research data management","Education; Human resource management; Information management; Academic staff; Croatia; Current levels; Data literacy; Educational activities; International perspective; International survey; Research data managements; Digital storage","","","","","","","Hey T., The fourth paradigm – data-intensive scientific discovery, IMCW 2012. CCIS, Vol. 317, P. 1., (2012); Gold A., Libraries, process, and data, Proceedings of the ASIST, 50, 1, pp. 1-9, (2013); European Commission: Open Innovation, Open Science, (2016); Making Open Science a Reality, (2015); The Royal Society: Science as an Open Enterprise, (2012); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J. Librariansh. Inf. Sci., 46, 4, pp. 299-316, (2014); Koltay T., Spiranec S., Libraries meet research 2.0, Research 2.0 and the Impact of Digital Technologies on Scholarly Inquiry, pp. 32-52, (2017); McLure M., Et al., Data curation: A study of researcher practices and needs, Libr. Acad., 14, 2, pp. 139-164, (2014); Tenopir C., Et al., Data sharing by scientists, Plos One, 6, 6, (2011); SERSCIDA: Analysis of Existing Potentials for the Establishment of a Social Sciences Digital Data Base Archive in Croatia, (2012); (2015)","S. Špiranec; Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia; email: sspiran@ffzg.hr","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85041706274"
"Amorim R.C.; Castro J.A.; Rocha da Silva J.; Ribeiro C.","Amorim, Ricardo Carvalho (56442184300); Castro, João Aguiar (55977255100); Rocha da Silva, João (56442622200); Ribeiro, Cristina (7201734594)","56442184300; 55977255100; 56442622200; 7201734594","A comparison of research data management platforms: architecture, flexible metadata and interoperability","2017","Universal Access in the Information Society","16","4","","851","862","11","57","10.1007/s10209-016-0475-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032293174&doi=10.1007%2fs10209-016-0475-y&partnerID=40&md5=4fb6167ebc9ce54313c8c33e980422a4","INESC TEC—Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Amorim R.C., INESC TEC—Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Castro J.A., INESC TEC—Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Rocha da Silva J., INESC TEC—Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Ribeiro C., INESC TEC—Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Research data management is rapidly becoming a regular concern for researchers, and institutions need to provide them with platforms to support data organization and preparation for publication. Some institutions have adopted institutional repositories as the basis for data deposit, whereas others are experimenting with richer environments for data description, in spite of the diversity of existing workflows. This paper is a synthetic overview of current platforms that can be used for data management purposes. Adopting a pragmatic view on data management, the paper focuses on solutions that can be adopted in the long tail of science, where investments in tools and manpower are modest. First, a broad set of data management platforms is presented—some designed for institutional repositories and digital libraries—to select a short list of the more promising ones for data management. These platforms are compared considering their architecture, support for metadata, existing programming interfaces, as well as their search mechanisms and community acceptance. In this process, the stakeholders’ requirements are also taken into account. The results show that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools. Nevertheless, depending on the context, some platforms can meet all or part of the stakeholders’ requirements. © 2016, Springer-Verlag Berlin Heidelberg.","","Data description; Digital libraries; Information services; Metadata; Societies and institutions; Data organization; Different domains; Institutional repositories; Management platforms; Programming interface; Research data managements; Research management; Search mechanism; Information management","","","","","Fundac¸ão para a Ciência e a Tec-nologia; National Strategic Reference Framework; Fundação para a Ciência e a Tecnologia, FCT, (SFRH/BD/77092/2011); European Regional Development Fund, ERDF","Acknowledgments This work is supported by the Project NORTE-07-0124-FEDER000059, financed by the North Portugal Regional Operational Programme (ON.2-O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundac¸ão para a Ciência e a Tec-nologia (FCT). João Rocha da Silva is also supported by research grant SFRH/BD/77092/2011, provided by the Portuguese funding agency, Fundac¸ão para a Ciência e a Tecnologia (FCT).","Alam A.W., Muller S., Schumann N., Datorium: sharing platform for social science data. In: Proceedings of the 14th International Symposium on Information Science, ISI, pp. 244-249, (2015); Amorim R.C., Castro J.A., Rocha da Silva, J., Ribeiro, C.: Labtablet: semantic metadata collection on a multi-domain laboratory notebook. In: Springer Communications in Computer and Information, Science, 478, pp. 193-205, (2014); Armbruster C., Romary L., Comparing repository types: challenges and barriers for subject-based repositories, research repositories, national repository systems and institutional repositories in serving scholarly communication, Int. J. Digit. Libr. Syst., 1, 4, pp. 61-73, (2010); Assante M., Candela L., Castelli D., Manghi P., Pagano P., Science 2.0 repositories: time for a change in scholarly communication, D-Lib Mag, 21, 1-2, (2015); Ball A., Tools for Research Data Management, (2012); Bankier J., Institutional repository software comparison, UNESCO Communication and Information, vol, (2014); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol., 63, 6, pp. 1059-1078, (2012); Burns C.S., Lana A., Budd J., Institutional repositories: exploration of costs and value, D-Lib Mag., 19, 1, (2013); Candela L., Castelli D., Manghi P., Tani A., Data journals: a survey, Int. Rev. Res. Open Distance Learn., 66, pp. 1747-1762, (2015); Coles S.J., Frey J.G., Bird C.L., Whitby R.J., Day A.E., First steps towards semantic descriptions of electronic laboratory notebook records, J. Cheminform., 5, pp. 1-10, (2013); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: a guide to good practice, Rec. Manag. J., 24, 3, pp. 252-253, (2014); Systems: Reference Model for an Open Archival Information System (OAIS), Technical Report, (2002); Devarakonda R., Palanisamy G., Data sharing and retrieval using OAI-PMH, Earth Sci. Inf., 4, 1, pp. 1-5, (2011); Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, Technical Report, (2013); Fay E., Repository software comparison: building digital library infrastructure at LSE, Ariadne, 64, 2009, pp. 1-11, (2010); Green A., Macdonald S., Rice R., Policy-making for Research Data in Repositories: A Guide, (2009); Heidorn P., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Hodson S., ADMIRAL: A Data Management Infrastructure for Research Activities in the Life Sciences, (2011); Hoxha J., Brahaj A., Open government data on the web: a semantic approach, International Conference on Emerging Intelligent Data and Web Technologies, pp. 107-113, (2011); Kucera J., Chlapek D., Mynarz J., Czech CKAN repository as case study in public sector data cataloging, Syst. Integr., 19, 2, pp. 95-107, (2012); Lagoze C., Sompel H.V.D., Nelson M., Warner S., The open archives initiative protocol for metadata harvesting, (2001); Lynch C.A., Institutional repositories: essential infrastructure for scholarship in the digital age, Portal Libr. Acad., 3, 2, pp. 327-336, (2003); Lyon L., Dealing with Data: Roles, Rights, Responsibilities and Relationships, (2007); McNutt M., Improving scientific communication, Science, 342, 6154, (2013); Grants.gov Application Guide: A Guide for Preparation and Submission of National Science Foundation Applications via Grants.gov, Technical Report, (2011); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Poschen M., Finch J., Procter R., Goff M., McDerby M., Collins S., Besson J., Beard L., Grahame T., Development of a pilot data management infrastructure for biomedical researchers at University of Manchester-approach, findings, challenges and outlook of the MaDAM project, Int. J. Digit. Curation, 7, pp. 110-122, (2012); Rafes K., Germain C., A platform for scientific data sharing, BDA2015 Bases de Donées Avancées, (2015); Ramalho J.C., Ferreira M., Faria L., Castro R., Barbedo F., Corujo L., RODA and CRiB a Service-Oriented Digital Repository, In: iPres Conference Proceedings, (2008); Rocha da Silva J., Barbosa J., Gouveia M., Correia Lopes J., Ribeiro C., UPBox and DataNotes: a collaborative data management environment for the long tail of research data, iPres Conference Proceedings, (2013); Rocha da Silva J., Castro J.A., Ribeiro C., Correia Lopes J., Dendro: Collaborative Research Data Management Built on Linked Open Data, (2014); Rocha da Silva J., Ribeiro C., Correia Lopes J., UPData—a data curation experiment at U.Porto using DSpace, iPres Conference Proceedings, pp. 224-227, (2011); Rocha da Silva J., Ribeiro C., Correia Lopes J., Managing multidisciplinary research data: extending DSpace to enable long-term preservation of tabular datasets, iPres Conference Proceedings, pp. 105-108, (2012); Rocha da Silva J., Ribeiro C., Correia Lopes J., Ontology-based multi-domain metadata for research data management using triple stores, Proceedings of the 18th International Database Engineering AND Applications Symposium, (2014); Rocha da Silva J., Ribeiro C., Correia Lopes J., The Dendro research data management platform: applying ontologies to long-term preservation in a collaborative environment, iPres Conference Proceedings, (2014); Vanden Eynden V., Corti L., Bishop L., Horton L., Managing and Sharing Data, (2014); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, J. Assoc. Inf. Sci. Technol., 63, 8, pp. 1505-1520, (2012); Winn J., Open data and the academy: an evaluation of CKAN for research data management. In: International Association dor Social Science Information Services and, Technology, (2013)","R.C. Amorim; INESC TEC—Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; email: ricardo.amorim3@gmail.com","","Springer Verlag","","","","","","16155289","","","","English","Univers. Access Inf. Soc.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85032293174"
"Zozus M.N.; Lazarov A.; Smith L.R.; Breen T.E.; Krikorian S.L.; Zbyszewski P.S.; Knoll S.K.; Jendrasek D.A.; Perrin D.C.; Zambas D.N.; Williams T.B.; Pieper C.F.","Zozus, Meredith N. (56744770800); Lazarov, Angel (57195235545); Smith, Leigh R. (57195232535); Breen, Tim E. (54790564900); Krikorian, Susan L. (57195232155); Zbyszewski, Patrick S. (57195227724); Knoll, Shelly K. (57195229646); Jendrasek, Debra A. (57195229405); Perrin, Derek C. (57195225900); Zambas, Demetris N. (57195230406); Williams, Tremaine B. (57195233283); Pieper, Carl F. (56869874600)","56744770800; 57195235545; 57195232535; 54790564900; 57195232155; 57195227724; 57195229646; 57195229405; 57195225900; 57195230406; 57195233283; 56869874600","Analysis of professional competencies for the clinical research data management profession: Implications for training and professional certification","2017","Journal of the American Medical Informatics Association","24","4","ocw179","737","745","8","11","10.1093/jamia/ocw179","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026368301&doi=10.1093%2fjamia%2focw179&partnerID=40&md5=25ed7ef2f9cb9887cc4d868f67ceee96","Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Boston Biomedical, Boston, MA, United States; Merck GP3S, Philadelphia, PA, United States; Hoosier Cancer Research Network, Indianapolis, IN, United States; Relypsa Inc, Redwood City, CA, United States; Onconova Therapeutics Inc, Newtown, PA, United States; Roche, Toronto, ON, Canada; Chiltern International Limited, Wilmington, NC, United States; Astellas Pharma, Northbrook, IL, United States; Novartis, East Hanover, NJ, United States; Duke University Medical Center, Durham, NC, United States","Zozus M.N., Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Lazarov A., Boston Biomedical, Boston, MA, United States; Smith L.R., Merck GP3S, Philadelphia, PA, United States; Breen T.E., Hoosier Cancer Research Network, Indianapolis, IN, United States; Krikorian S.L., Relypsa Inc, Redwood City, CA, United States; Zbyszewski P.S., Onconova Therapeutics Inc, Newtown, PA, United States; Knoll S.K., Roche, Toronto, ON, Canada; Jendrasek D.A., Chiltern International Limited, Wilmington, NC, United States; Perrin D.C., Astellas Pharma, Northbrook, IL, United States; Zambas D.N., Novartis, East Hanover, NJ, United States; Williams T.B., Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Pieper C.F., Duke University Medical Center, Durham, NC, United States","Objective: To assess and refine competencies for the clinical research data management profession. Materials and Methods: Based on prior work developing and maintaining a practice standard and professional certification exam, a survey was administered to a captive group of clinical research data managers to assess professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge. Results: Respondents confirmed a set of 91 professional competencies. As expected, differences were seen in job tasks between early- to mid-career and mid- to late-career practitioners. Respondents indicated growing variability in types of studies for which they managed data and types of data managed. Discussion: Respondents adapted favorably to the separate articulation of professional competencies vs foundational knowledge. The increases in the types of data managed and variety of research settings in which data are managed indicate a need for formal education in principles and methods that can be applied to different research contexts (ie, formal degree programs supporting the profession), and stronger links with the informatics scientific discipline, clinical research informatics in particular. Conclusion: The results document the scope of the profession and will serve as a foundation for the next revision of the Certified Clinical Data ManagerTM exam. A clear articulation of professional competencies and necessary foundational knowledge could inform the content of graduate degree programs or tracks in areas such as clinical research informatics that will develop the current and future clinical research data management workforce. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.","Clinical data management; Clinical research informatics; Professional competencies","Biomedical Research; Certification; Clinical Trials as Topic; Data Collection; History, 20th Century; Medical Informatics; Professional Competence; United States; career; certification; clinical research; female; human; human experiment; information processing; information science; male; manager; physician; clinical trial (topic); education; history; information processing; medical informatics; medical research; professional competence; standards; United States","","","","","National Institute on Aging, NIA, (P30AG028716)","","McBride R., Singer S.W., Introduction [to the 1995 Clinical Data Management Special Issue of Controlled Clinical Trials]., Controlled Clinical Trials., 16, pp. 1S-3S, (1995); Good Clinical Data Management Practices (GCDMP); Bloom B.S., Krathwohl D.R., Taxonomy of educational objectives: the classification of educational goals, by a committee of college and university examiners., Handbook I: CognitiveDomain., (1956); Anderson L.W., Krathwohl D.R., Airasian P.W., Cruikshank K.A., Mayer R.E., Pintrich P.R., Et al., A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives., (2001); Miller G.E., The assessment of clinical skills/competence/performance, Acad Med., 63, (1990); A Report from the Heart Special Project Committee to the National Advisory Heart Council, May 1967., Controlled Clinical Trials., 9, pp. 137-148, (1988); Collen M.F., Clinical research databases: a historical review, J Med Sys., 14, (1990); McBride R., Singer S.W., Interim reports, participant closeout, and study archives., Con Clin Trials., 16, pp. 1S-3S, (1995); Gassman J.J., Owen W.W., Kuntz T.E., Martin J.P., Amoroso W.P., Data quality assurance, monitoring, and reporting, Cont Clin Trials., 16, pp. 1S-3S, (1995); Hosking J.D., Newhouse M.M., Bagniewska A., Hawkins B.S., Data collection and transcription, Cont Clin Trials., 16, pp. 1S-3S, (1995); McFadden E.T., LoPresti F., Bailey L.R., Clarke E., Wilkins P.C., Approaches to data management, Cont Clin Trials., 16, pp. 1S-3S, (1995); Blumenstein B.A., James K.E., Lind B.K., Mitchell H.E., Functions and organization of coordinating centers for multicenter studies, Cont Clin Trials., 16, pp. 1S-3S, (1995); Cont Clin Trials., 16, pp. 1S-3S, (1995); Cont Clin Trials., 16, pp. 1S-3S, (1995); Ittenbach R., Howard K., Nahm M., Training the next generation of clinical datamanagers: the industry/academia interface., Society for Clinical DataManagement (SCDM) Annual Conference; Bornstein S., Clinical data management task list, Data Basics., 5, pp. 8-10, (1999); Appendix 4 to the CCDM Certification Handbook.; Nahm M., Zhang J., Operationalization of the UFuRT methodology for usability analysis in the clinical research data management domain, J Biomed Inform., 42, 2, pp. 327-333, (2009); Nahm M., Johnson C., Walden A., Johnson T., Zhang J., Clinical research data management tasks and definitions., Poster presented at the AMIA Summits Transl Sci., (2010); Wand Y., Weber R., On the ontological expressiveness of information systems analysis and design grammars., Inf Syst J., 3, 4, pp. 217-237, (1993)","M.N. Zozus; Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, 4301 W. Markham St. #782, 72205-7199, United States; email: mzozus@uams.edu","","Oxford University Press","","","","","","10675027","","JAMAF","28339721","English","J. Am. Med. Informatics Assoc.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85026368301"
"Allen B.; Ananthakrishnan R.; Chard K.; Foster I.; Madduri R.; Pruyne J.; Rosen S.; Tuecke S.","Allen, Bryce (54981342100); Ananthakrishnan, Rachana (14051682800); Chard, Kyle (9132950200); Foster, Ian (35572232000); Madduri, Ravi (6505672275); Pruyne, Jim (6602252957); Rosen, Stephen (55626318300); Tuecke, Steve (6602740450)","54981342100; 14051682800; 9132950200; 35572232000; 6505672275; 6602252957; 55626318300; 6602740450","Globus: A case study in software as a service for scientists","2017","ScienceCloud 2017 - Proceedings of the 8th Workshop on Scientific Cloud Computing, co-located with HPDC 2017","","","","25","32","7","10","10.1145/3086567.3086570","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025823938&doi=10.1145%2f3086567.3086570&partnerID=40&md5=5e472f92f42bddfc096144fc64f5e869","Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States","Allen B., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Ananthakrishnan R., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Chard K., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Foster I., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Madduri R., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Pruyne J., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Rosen S., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States; Tuecke S., Computation Institute, University of Chicago, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, 60637, IL, United States","While some properties of SaaS have long been leveraged in science, particularly in science gateways, there is yet to be widespread adoption of SaaS models. For example, few scientific SaaS providers are publicly available, few leverage elastic cloud platforms, and none-with the exception of Globus-implement subscription-based models to recoup operations costs. Globus has employed the SaaS model for seven years and is fast approaching subscription levels that will support long-term sustainability. In this paper we discuss the SaaS paradigm and explore its suitability to scientific domains. We then describe how production Globus SaaS services are implemented, deployed, and operated. © 2017 ACM.","Globus; Research data management; Science as a service","Cloud computing; Information management; Web services; Cloud platforms; Globus; Long-term sustainability; Operations cost; Research data managements; Science as a service; Science gateway; Software as a service (SaaS)","","","","","US Department of Energy, (DE-AC02-06CH11357); National Science Foundation, NSF, (ACI-1148484)","We thank Globus subscribers for supporting the operation and development of Globus, and users of Globus services for their continued support. This research was supported in part by NSF grant ACI-1148484 (SciDaaS) and US Department of Energy contract DE-AC02-06CH11357.","Allcock W., Bresnahan J., Kettimuthu R., Link M., Dumitrescu C., Raicu L., Foster I., The globus striped GridFTP framework and server, ACM/IEEE Conference on Supercomputing (SC '05), (2005); Allen B., Bresnahan J., Childers L., Foster I., Kandaswamy G., Kettimuthu R., Kordas J., Link M., Martin S., Pickett K., Tuecke S., Software as a service for data scientists, Commun ACM, 55, 2, pp. 81-88, (2012); Blaiszik B., Chard K., Pruyne J., Ananthakrishnan R., Tuecke S., Foster I., The materials data facility: Data services to advance materials science research, Journal of the Minerals, Metals & Materials Society (JOM), 68, 8, pp. 2045-2052, (2016); Chard K., Lidman M., McCollam B., Bryan J., Anan-Thakrishnan R., Tuecke S., Foster I., Globus Nexus: A Platform-asa-Service provider of research identity, profile, and group management, Future Generation Computer Systems, 56, pp. 571-583, (2016); Chard K., Pruyne J., Blaiszik B., Ananthakrishnan R., Tuecke S., Foster I., Globus data publication as a service: Lowering barriers to reproducible science, 11th IEEE International Conference on E-Science, pp. 401-410, (2015); Chard K., Tuecke S., Foster I., Efficient and secure transfer, synchronization, and sharing of big data, IEEE Cloud Computing, 1, 3, pp. 46-55, (2014); Chard R., Chard K., Bubendorfer K., Lacinski L., Madduri R., Foster I., Cost-Aware cloud provisioning, 11th IEEE International Conference on E-Science, pp. 136-144, (2015); Chard R., Chard K., Ng B., Bubendorfer K., Rodriguez A., Madduri R., Foster I., An automated tool profiling service for the cloud, 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 223-232, (2016); Czyzyk J., Mesnier M.P., More J.J., The NEOS server, IEEE Computational Science and Engineering, 5, 3, pp. 68-75, (1998); Foster I., Globus online: Accelerating and democratizing science through cloud-based services, Internet Computing IEEE, 15, 3, pp. 70-73, (2011); Foster I., Kesselman C., The history of the grid, High Performance Computing: From Grids and Clouds to Exascale, pp. 3-30, (2011); Software As A Service (SaaS), (2017); Goecks J., Nekrutenko A., Taylor J., Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences, Genome Biol, 11, 8, (2010); Klimeck G., McLennan M., Brophy S.P., Adams G.B., Lundstrom M.S., Nanohub.org: Advancing education and research in nanotechnology, Computing in Science & Engineering, 10, 5, pp. 17-23, (2008); Lawrence K.A., Zentner M., Wilkins-Diehr N., Wernert J.A., Pierce M., Marru S., Michael S., Science gateways today and tomorrow: Positive perspectives ofnearly 5000 members of the research community, Concurrency and Computation: Practice and Experience, 27, 16, pp. 4252-4268, (2015); Liu Y., Padmanabhan A., Wang S., CyberGIS Gateway for enabling data-rich geospatial research and education, Concurrency and Computation: Practice and Experience, 27, 2, pp. 395-407, (2015); Madduri R., Chard K., Chard R., Lacinski L., Rodriguez A., Sulakhe D., Kelly D., Dave U., Foster I., The globus galaxies platform: Delivering science gateways as a service, Concurrency and Computation: Practice and Experience, 27, 16, pp. 4344-4360, (2015); Madduri R.K., Sulakhe D., Lacinski L., Liu B., Rodriguez A., Chard K., Dave U.J., Foster I.T., Experiences building globus genomics: A next-generation sequencing analysis service using galaxy, globus, and amazon web services, Concurrency and Computation: Practice and Experience, 26, 13, pp. 2266-2279, (2014); Meyer F., Paarmann D., Robert Olson M., Glass E.M., Kubal M., Paczian T., Rodriguez A., Stevens R., Wilke A., Et al., The metagenomics RAST server-Apublic resource for the automatic phylogenetic and functionalanalysis ofmetagenomes, BMC Bioinformatics, 9, 1, (2008); Miller M.A., Pfeiffer W., Schwartz T., Creating the CIPRES Science Gateway for inference of large phylogenetic trees, Gateway Computing Environments Workshop, pp. 1-8, (2010); Newman S., Building Microservices (1st Ed.), (2015); Stevens R., Woodward P., DeFanti T., Catlett C., From the I-WAY to the national technology grid, Commun ACM, 40, 11, pp. 50-60, (1997); Tansley R., Bass M., Stuve D., Branschofsky M., Chudnov D., McClellan G., Smith M., The DSpace institutional digital repository system: Current functionality, 3rd ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '03), pp. 87-97, (2003); Thain D., Tannenbaum T., Livny M., Distributed computing in practice: The Condor experience, Concurrency and Computation: Practice and Experience, 17, 2-4, pp. 323-356, (2005); Tuecke S., Ananthakrishnan R., Chard K., Lidman M., McCollam B., Rosen S., Foster I., Globus Auth: A research identity and access management platform, 12th IEEE International Conference on E-Science (E-Science), pp. 203-212, (2016); Waldrop M.M., The Dream Machine: JCR Licklider and the Revolution That Made ComputingPersonal, (2001); Wilkins-Diehr N., Gannon D., Klimeck G., Oster S., Pamidighantam S., TeraGrid science gateways and their impact on science, Computer, 41, (2008)","","","Association for Computing Machinery, Inc","ACM SIGARCH; University of Arizona","8th Workshop on Scientific Cloud Computing, ScienceCloud 2017","26 June 2017 through 30 June 2017","Washington","128545","","978-145035021-1","","","English","ScienceCloud - Proc. Workshop Sci. Cloud Comput., Co-located HPDC","Conference paper","Final","","Scopus","2-s2.0-85025823938"
"Ball A.; Darlington M.; McMahon C.","Ball, Alexander (55796629584); Darlington, Mansur (7004413270); McMahon, Christopher (55887655100)","55796629584; 7004413270; 55887655100","The minimum mandatory metadata sets for the KIM project and RAIDmap","2018","Digital Curation: Breakthroughs in Research and Practice","","","","391","412","21","0","10.4018/978-1-5225-6921-3.ch019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059729413&doi=10.4018%2f978-1-5225-6921-3.ch019&partnerID=40&md5=5e7a5b08583229608f9bb6c9777d079d","University of Bath, United Kingdom; University of Bristol, United Kingdom","Ball A., University of Bath, United Kingdom; Darlington M., University of Bath, United Kingdom; McMahon C., University of Bristol, United Kingdom","A Minimum Mandatory Metadata Set (M3S) was devised for the KIM (Knowledge and Information Management Through Life) Project to address two challenges. The first was to ensure the project’s documents were sufficiently self-documented to allow them to be preserved in the long term. The second was to trial the M3S and supporting templates and tools as a possible approach that might be used by the aerospace, defence and construction industries. A different M3S was devised along similar principles by a later project called REDm-MED (Research Data Management for Mechanical Engineering Departments). The aim this time was to help specify a tool for documenting research data records and the associations between them, in support of both preservation and discovery. In both cases the emphasis was on collecting a minimal set of metadata at the time of object creation, on the understanding that later processes would be able to expand the set into a full metadata record. © 2019, IGI Global.","","Construction industry; Information management; Mechanical Engineering Department; Object creation; Research data; Research data managements; Metadata","","","","","","","Adobe XMP Developer Center, (2016); Ball A., Darlington M., Howard T., McMahon C., Culley S., Visualizing research data records for their better management, Journal of Digital Information, 13, 1, (2012); Ball A., Patel M., McMahon C., Green S., Clarkson J., Culley S., A grand challenge: Immortal information and through-life knowledge management (KIM), International Journal of Digital Curation, 1, 1, pp. 53-59, (2006); Ball A., Thangarajah U., RAIDmap Application Developer Guide, (2012); Caplan P., Preservation metadata, DCC Digital Curation Manual, (2006); Reference Model for an Open Archival Information System (OAIS) (Blue Book No. CCSDS 650.0-B-1), (2002); Reference Model for an Open Archival Information System (OAIS) (Magenta Book No. CCSDS 650.0-M-2), (2012); Darlington M., REDm-MED Project Final Report to JISC, (2012); Darlington M., Thangarajah U., Ball A., RAIDmap Application User Guide, (2012); Day M., Preservation metadata, Metadata Applications and Management, pp. 253-273, (2004); Duranti L., Eastwood T., MacNeil H., The Preservation of the Integrity of Electronic Records, (1997); Metadata for Digital Preservation: The Cedars Project Outline Specification, (2000); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, Ariadne, 25, (2000); Hunt A., Thomas D., The Pragmatic Programmer: From Journeyman to Master, (2000); Project Overview; PREMIS: Preservation Metadata Maintenance Activity, (2016); Lupovici C., Masanes J., Metadata for the Long-Term Preservation of Electronic Publications, (2000); McMahon C., Design informatics: Supporting engineering design processes with information technology, Journal of the Indian Institute of Science, 95, 4, pp. 365-378, (2015); Recordkeeping Metadata Standard for Commonwealth Agencies, (1999); Australian Government Recordkeeping Metadata Standard, (2015); Preservation Metadata for Digital Collections, (1999); Metadata Standards Framework – Preservation Metadata (Revised), (2003); Data Dictionary for Preservation Metadata, (2005); Preservation Metadata for Digital Objects: A Review of the State of the Art, (2001); Preservation Metadata and the Oais Information Model: A Metadata Framework to Support the Preservation of Digital Objects, (2002); REMIS Data Dictionary for Preservation Metadata, (2011); PROS 99/007 Standard for the Management of Electronic Records, (2000); Final Report, (1998); Rogers C., Tennis J.T., General Study 15 – Application Profile for Authenticity Metadata, (2016); Starr J., Ashton J., Brase J., Bracke P., Gastl A., Gillet J., Ziedorn F., Datacite Metadata Schema for the Publication and Citation of Research Data, (2011); Metadata Specifications Derived from the Fundamental Requirements: A Reference Model for Business Acceptable Communications, (1996); Wolf M., Wicksteed C., Date and Time Formats, (1998)","","","IGI Global","","","","","","","978-152256922-0; 1522569219; 978-152256921-3","","","English","Digital Curation: Breakthroughs in Research and Practice","Book chapter","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85059729413"
"Bakos A.; Miksa T.; Rauber A.","Bakos, Asztrik (57203972387); Miksa, Tomasz (55260160000); Rauber, Andreas (57074846700)","57203972387; 55260160000; 57074846700","Research data preservation using process engines and machine-actionable data management plans","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11057 LNCS","","","69","80","11","3","10.1007/978-3-030-00066-0_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053837813&doi=10.1007%2f978-3-030-00066-0_6&partnerID=40&md5=ea3bc476e553ffce158be58b211f7f3b","SBA Research and TU Wien, Favoritenstrasse 16, Wien, 1040, Austria","Bakos A., SBA Research and TU Wien, Favoritenstrasse 16, Wien, 1040, Austria; Miksa T., SBA Research and TU Wien, Favoritenstrasse 16, Wien, 1040, Austria; Rauber A., SBA Research and TU Wien, Favoritenstrasse 16, Wien, 1040, Austria","Scientific experiments in various domains require nowadays collecting, processing, and reusing data. Researchers have to comply with funder policies that prescribe how data should be managed, shared and preserved. In most cases this has to be documented in data management plans. When data is selected and moved into a repository when project ends, it is often hard for researchers to identify which files need to be preserved and where they are located. For this reason, we need a mechanism that allows researchers to integrate preservation functionality into their daily workflows of data management to avoid situations in which scientific data is not properly preserved. In this paper we demonstrate how systems used for managing data during research can be extended with preservation functions using process engines that run pre-defined preservation workflows. We also show a prototype of a machine-actionable data management plan that is automatically generated during this process to document actions performed. Thus, we break the traditional distinction between platforms for managing data during research and repositories used for preservation afterwards. Furthermore, we show how researchers can easier comply with funder requirements while reducing their effort. © 2018, Springer Nature Switzerland AG.","BPMN; Data managament; Data management plans; Digital preservation; Machine-actionable DMPs; Repositories","Digital libraries; Digital storage; Engines; BPMN; Data managament; Digital preservation; Management plans; Repositories; Information management","","","","","Vienna Business Agency; WAW; Österreichische Forschungsförderungsgesellschaft, FFG","Acknowledgments. This research was carried out in the context of the Austrian COMET K1 program and publicly funded by the Austrian Research Promotion Agency (FFG) and the Vienna Business Agency (WAW).","H2020 programme guidelines on fair data management in horizon 2020, EC Directorate General for Research and Innovation, (2016); Bankier J.G., Institutional Repository Software Comparison, (2014); Castagne M., Institutional Repository Software Comparison: Dspace, Eprints, Digital Commons, (2013); Chen X., Et al., CERN analysis preservation: A novel digital library service to enable reusable and reproducible research, TPDL 2016. LNCS, 9819, pp. 347-356, (2016); European Open Science Cloud Declaration, (2017); (2013); Darema F., Dynamic data driven applications systems: A new paradigm for application simulations and measurements, ICCS 2004. LNCS, 3038, pp. 662-669, (2004); Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Nature Sci. Data, 3, (2016); Miksa T., Rauber A., Mina E., Identifying impact of software dependencies on replicability of biomedical workflows, J. Biomed. Inform., 64, C, pp. 232-254, (2016); Otto B., Data Governance. Microsoft Res, 4, pp. 241-246, (2011); Proell S., Meixner K., Rauber A., Precise data identification services for long tail research data, In: Ipres, 2016, (2016); Rauber A., Miksa T., Ganguly R., Budroni P., Information Integration for Machine Actionable Data Management Plans, (2017); Rosa C.A., Craveiro O., Domingues P., Open source software for digital preservation repositories: A survey, Int. J. Comput. Sci. Eng. Surv. (IJCSES), 8, 3, pp. 21-39, (2017); Schembera B., Bonisch T., Challenges of research data management for high performance computing, TPDL 2017. LNCS, 10450, pp. 140-151, (2017); Simms S., Jones S., Mietchen D., Miksa T., Machine-actionable data management plans, Res. Ideas Outcomes, 3, (2017)","T. Miksa; SBA Research and TU Wien, Wien, Favoritenstrasse 16, 1040, Austria; email: miksa@ifs.tuwien.ac.at","Mendez E.; Ribeiro C.; David G.; Lopes J.C.; Crestani F.","Springer Verlag","","22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018","10 September 2018 through 13 September 2018","Porto","218159","03029743","978-303000065-3","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85053837813"
"Ming W.; Hui H.","Ming, Wu (57193621897); Hui, Hu (57209486617)","57193621897; 57209486617","Data Literacy Education Design Based on Needs of Graduate Students in University of Chinese Academy of Sciences","2018","Communications in Computer and Information Science","810","","","158","168","10","0","10.1007/978-3-319-74334-9_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041703296&doi=10.1007%2f978-3-319-74334-9_17&partnerID=40&md5=dbdd09f56c643ba9c2f044c70ef7db67","National Science Library, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China","Ming W., National Science Library, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China; Hui H., National Science Library, Chinese Academy of Sciences, Beijing, China","In the new data-intensive research environment, research data is an important part of scientific findings and every researcher will face sophisticated data management problems during their research life. Solving these data problems requires researchers and students have new skill sets and competencies, which ensure their outputs are accessible, discoverable and reusable. Using the online questionnaire survey method, we conducted a data literacy survey among 59 graduate students of life science in University of Chinese Academy of Sciences (UCAS). The current situation and needs of graduate students’ data literacy competences are revealed. On the basis of demand investigation, the data literacy education model of teachers, students and curriculum is constructed, the education content is based on research data lifecycle and includes three levels of learning modes. In addition, the data literacy education implementation scenes for graduate students in UCAS were also designed, and provide evidence for libraries to implement data literacy education services better. © 2018, Springer International Publishing AG.","Data literacy; Data literacy education; Graduate students; Research data management","Curricula; Education; Education computing; Information management; Surveys; Teaching; Chinese Academy of Sciences; Data literacy; Data management problems; Education designs; Graduate students; Online questionnaire; Research data managements; Scientific findings; Students","","","","","","","Working Group on Intersections of Scholarly Communication and Information Literacy, (2013); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty. Portal, Libr. Acad, 11, 2, pp. 629-657, (2011); Carlson J., Johnston L., Westra B., Nichols M., Developing an approach for data management education: A report from the data information literacy project, Int. J. Digit. Curation, 8, 1, pp. 204-217, (2013); Data Information Literacy; MANTRA Research Data Management Training","W. Ming; National Science Library, Chinese Academy of Sciences, Beijing, China; email: wum@mail.las.ac.cn","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85041703296"
"Bradley C.","Bradley, Cara (26767491000)","26767491000","Research support priorities of and relationships among Librarians and research administrators: A content analysis of the professional literature","2018","Evidence Based Library and Information Practice","13","4","","15","30","15","4","10.18438/eblip29478","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059363965&doi=10.18438%2feblip29478&partnerID=40&md5=351153af39585a328411df0b84643523","University of Regina, Regina, SK, Canada","Bradley C., University of Regina, Regina, SK, Canada","Objective - This research studied the recent literature of two professions, library and information studies (LIS) and research administration (RA), to map the priorities and concerns of each with regard to research support. Specifically, the research sought to answer these research questions: (1) What are the similarities and differences emerging from the LIS and RA literatures on research support? (2) How do librarians and research administrators understand and engage with each other's activities through their professional literatures? (3) Do Whitchurch's (2008a, 2008b, 2015) concepts of bounded-cross-boundary-unbounded professionals and theory of the ""third space"" provide a useful framework for understanding research support? Methods - The research method was a content analysis of journal articles on research-related topics published in select journals in the LIS (n = 195) and RA (n = 95) fields from 2012-2017. The titles and abstracts of articles to be included were reviewed to guide the creation of thematic coding categories. The coded articles were then analyzed to characterize and compare the topics and concerns addressed by the literature of each profession. Results - Only two (2.2%) RA articles referred to librarians and libraries in their exploration of research support topics, while six (3.1%) LIS articles referred to the research office or research administrators in a meaningful way. Of these six, two focused on undergraduate research programs, two on research data management, and two on scholarly communications. Thematic coding revealed five broad topics that appeared repeatedly in both bodies of literature: research funding, research impact, research methodologies, research infrastructure, and use of research. However, within these broad categories, the focus varied widely between the professions. There were also several topics that received considerable attention in the literature of one field without a major presence in that of the other, including research collaboration in the RA literature, and institutional repositories, research data management, citation analysis or bibliometrics, scholarly communication, and open access in the LIS literature. Conclusion - This content analysis of the LIS and RA literature provided insight into the priorities and concerns of each profession with respect to research support. It found that, even in instances where the professions engaged on the same broad topics, they largely focused on different aspects of issues. The literature of each profession demonstrated little awareness of the activities and concerns of the other. In Whitchurch's (2008a) taxonomy, librarians and research administrators are largely working as ""bounded"" professionals, with occasional forays into ""cross-boundary"" activities (p. 377). There is not yet evidence of ""unbounded"" professionalism or a move to a ""third space"" of research support activity involving these professions (Whitchurch, 2015, p. 85). Librarians and research administrators will benefit from a better understanding of the current research support landscape and new modes of working, like the third space, that could prove transformative. © 2018 Bradley.","","","","","","","Canadian Association of Research Libraries, CARL","The author gratefully acknowledges the Canadian Association of Research Libraries for providing a CARL Research Grant for Practicing Librarians, which made this research possible. She would also like to acknowledge the coding work of student assistant John Kapp. A preliminary version of this research was presented at the Research Libraries UK Conference in March 2018.","Antell K., Foote J.B., Turner J., Shults B., Dealing with data: Science librarians' participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, pp. 557-574, (2014); New roles for new times: Transforming liaison roles in research libraries, (2013); Bhabha H.K., The location of culture, (1994); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, Liber Quarterly, 24, 1, pp. 17-48, (2014); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2013); Cox A.M., Verbaan E., How academic librarians, IT staff, and research administrators perceive and relate to research, Library & Information Science Research, 38, pp. 319-326, (2016); Ferguson C., Metz T., Finding the third space: On leadership issues related to the integration of library and computing, Leadership, higher education, and the information age: A new era for information technology and libraries, pp. 95-112, (2003); Hansson J., Johannesson K., Librarians' views of academic library support for scholarly publishing: An every-day perspective, Journal of Academic Librarianship, 39, pp. 232-240, (2013); Hensley M.K., Shreeves S.L., Davis-Kahl S., A survey of library support for formal undergraduate research programs, College & Research Libraries, 75, pp. 422-441, (2014); Hensley M.K., Shreeves S.L., Davis-Kahl S., A survey of campus coordinators of undergraduate research programs, College & Research Libraries, 76, pp. 975-995, (2015); Hockey J., Allen-Collinson J., Occupational knowledge and practice amongst UK university research administrators, Higher Education Quarterly, 63, 2, pp. 141-159, (2009); MacColl J., Jubb M., Supporting research: Environments, administration and libraries, (2011); Masango C.A., Combating inhibitors of quality research outputs at the University of Cape Town, Journal of Research Administration, 46, 1, pp. 11-24, (2015); McAlpine L., Hopwood N., Third spaces': A useful developmental lens?, International Journal for Academic Development, 14, pp. 159-162, (2009); Nariani R., PubMed Central Canada: Beyond an open access repository, Journal of Academic Librarianship, 39, pp. 76-83, (2013); O'Brien L., Richardson J., Supporting research through partnership, Partnerships and new roles in the 21st-century academic library: Collaborating, embedding, and cross-training for the future, pp. 191-212, (2015); Research support services in UK libraries, (2010); Re-skilling for research, (2012); Russell-Simmons H.N., Anthony C., Ballard M., Coffman J., Gilbreath D., Keys T.L., Vanderford N.L., Enhancing faculty productivity through a centralized communications and project management infrastructure: A case study at the University of Kentucky Markey Cancer Center, Journal of Research Administration, 47, 2, pp. 68-79, (2016); Sproles C., Detmering R., Johnson A.M., Trends in the literature on library instruction and information literacy, 2001-2010, Reference Services Review, 41, pp. 395-412, (2013); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, pp. 211-219, (2014); Whitchurch C., Beyond administration and management: Reconstructing the identities of professional staff in UK higher education, Journal of Higher Education Policy and Management, 30, pp. 375-386, (2008); Whitchurch C., Shifting identities and blurring boundaries: The emergence of Third Space professionals in UK higher education, Higher Education Quarterly, 62, pp. 377-396, (2008); Whitchurch C., The rise of Third Space professionals: Paradoxes and dilemmas, Forming, recruiting and managing the academic profession, pp. 79-99, (2015)","C. Bradley; University of Regina, Regina, Canada; email: cara.bradley@uregina.ca","","University of Alberta","","","","","","1715720X","","","","English","Evid. Based Libr. Inf. Pract.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85059363965"
"Perrier L.; Blondal E.; Ayala A.P.; Dearborn D.; Kenny T.; Lightfoot D.; Reka R.; Thuna M.; Trimble L.; MacDonald H.","Perrier, Laure (56470673600); Blondal, Erik (56401098900); Ayala, A. Patricia (56173075100); Dearborn, Dylanne (57194270861); Kenny, Tim (56174202200); Lightfoot, David (56174194600); Reka, Roger (57194282854); Thuna, Mindy (14424273300); Trimble, Leanne (56694241900); MacDonald, Heather (7202132515)","56470673600; 56401098900; 56173075100; 57194270861; 56174202200; 56174194600; 57194282854; 14424273300; 56694241900; 7202132515","Research data management in academic institutions: A scoping review","2017","PLoS ONE","12","5","e0178261","","","","36","10.1371/journal.pone.0178261","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019645195&doi=10.1371%2fjournal.pone.0178261&partnerID=40&md5=c5585d06eb5249895051c040e1aa9989","Gerstein Science Information Centre, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Gibson D. Lewis Health Science Library, UNT Health Science Center, Fort Worth, TX, United States; St. Michael's Hospital Library, St. Michael's Hospital, Toronto, ON, Canada; Faculty of Information, University of Toronto, Toronto, ON, Canada; Engineering and Computer Science Library, University of Toronto, Toronto, ON, Canada; Map and Data Library, University of Toronto, Toronto, ON, Canada; MacOdrum Library, Carleton University, Ottawa, ON, Canada","Perrier L., Gerstein Science Information Centre, University of Toronto, Toronto, ON, Canada; Blondal E., Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Ayala A.P., Gerstein Science Information Centre, University of Toronto, Toronto, ON, Canada; Dearborn D., Gerstein Science Information Centre, University of Toronto, Toronto, ON, Canada; Kenny T., Gibson D. Lewis Health Science Library, UNT Health Science Center, Fort Worth, TX, United States; Lightfoot D., St. Michael's Hospital Library, St. Michael's Hospital, Toronto, ON, Canada; Reka R., Faculty of Information, University of Toronto, Toronto, ON, Canada; Thuna M., Engineering and Computer Science Library, University of Toronto, Toronto, ON, Canada; Trimble L., Map and Data Library, University of Toronto, Toronto, ON, Canada; MacDonald H., MacOdrum Library, Carleton University, Ottawa, ON, Canada","Objective: The purpose of this study is to describe the volume, topics, and methodological nature of the existing research literature on research data management in academic institutions. Materials and methods: We conducted a scoping review by searching forty literature databases encompassing a broad range of disciplines from inception to April 2016. We included all study types and data extracted on study design, discipline, data collection tools, and phase of the research data lifecycle. Results: We included 301 articles plus 10 companion reports after screening 13,002 titles and abstracts and 654 full-text articles. Most articles (85%) were published from 2010 onwards and conducted within the sciences (86%). More than three-quarters of the articles (78%) reported methods that included interviews, cross-sectional, or case studies. Most articles (68%) included the Giving Access to Data phase of the UK Data Archive Research Data Lifecycle that examines activities such as sharing data. When studies were grouped into five dominant groupings (Stakeholder, Data, Library, Tool/Device, and Publication), data quality emerged as an integral element. Conclusion: Most studies relied on self-reports (interviews, surveys) or accounts from an observer (case studies) and we found few studies that collected empirical evidence on activities amongst data producers, particularly those examining the impact of research data management interventions. As well, fewer studies examined research data management at the early phases of research projects. The quality of all research outputs needs attention, from the application of best practices in research data management studies, to data producers depositing data in repositories for long-term use. © 2017 Perrier et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Data Collection; Data Curation; Information Dissemination; Information Management; Information Services; Information Storage and Retrieval; Research; Universities; attention; data base; human; human experiment; information center; information processing; interview; publication; screening; self report; study design; information dissemination; information processing; information retrieval; information service; information system; research; university","","","","","","","Gonzalez A., Peres-Neto P.R., Data curation: Act to staunch loss of research data, Nature, 520, 7548, (2015); Vines T.H., Albert A.Y., Andrew R.L., Debarre F., Bock D.G., Franklin M.T., Et al., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Holdren J.P., Increasing Access to the Results of Federally Funded Scientific Research, (2013); Declaration on Access to Research Data from Public Funding, (2004); Research Data, (2011); Overview of Funders' Data Policies; Borgman C.L., The conundrum of sharing research data, Advances in Information Science, 63, 6, pp. 1059-1078, (2012); Hayes J., The data-sharing policy of the world meteorological organization: The case for international sharing of scientific data, Committee on the Case of International Sharing of Scientific Data: A Focus on Developing Countries, pp. 29-31, (2012); Ivezic Z., Data sharing in astronomy, Committee on the Case of International Sharing of Scientific Data: A Focus on Developing Countries, pp. 41-45, (2012); Van Noorden R., Gates foundation announces world's strongest policy on open access research, Nature Newsblog; Butler D., Zika researchers release real-time data on viral infection study in monkeys, Nature, 530, (2016); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLoS One, 10, 2, (2015); Higgins J.P.T., Green S., Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011], The Cochrane Collaboration, (2011); Bull S., Roberts N., Parker M., Views of ethical best practices in sharing individual-level data from medical and public health research: A systematic scoping review, Journal of Empirical Research on Human Research Ethics, 10, 3, pp. 225-238, (2015); Shearer K., Comprehensive Brief on Research Data Management Policies, (2015); Arksey H., O'Malley L., Scoping studies: Towards a methodological framework, International Journal of Social Research Methodology, 8, 1, pp. 19-32, (2005); Joanna briggs institute reviewers' manual: 2015 edition, Methodology for JBI Scoping Reviews; Moher D., Liberati A., Tetzlaff J., Altman D.G., Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement, BMJ, (2009); PRESS-Peer Review of Electronic Search Strategies: 2015 Guideline Explanation and Elaboration (PRESS E & E), (2016); Glossary-Research Data Management; Research Data Lifecycle; Rodgers M., Sowden A., Petticrew M., Arai L., Roberts H., Britten N., Et al., Testing methodological guidance on the conduct of narrative synthesis in systematic reviews: Effectiveness of interventions to promote smoke alarm ownership and function, Evaluation, 15, 1, pp. 49-74, (2009); Arai L., Britten N., Popay J., Roberts H., Petticrew M., Rodgers M., Et al., Testing methodological guidance on the conduct of narrative synthesis in systematic reviews: Effectiveness of interventions to promote smoke alarm ownership and function, Evaluation, 15, 1, pp. 49-73, (2009); Rosenthal R., Combining results of independent studies, Psychological Bulletin, 85, 1, pp. 185-193, (1978); Hurwitz B., Greenhalgn T., Skultans V., Meta-narrative mapping: A new approach to the systematic review of complex evidence, Narrative Research in Health and Illness, pp. 349-381, (2008); Den Besten M., Thomas A.J., Schroeder R., Life science research and drug discovery at the turn of the 21st century: The experience of SwissBioGrid, J Biomed Discov Collab, 4, (2009); Diekmann F., Data practices of agricultural scientists: Results from an exploratory study, Journal of Agricultural and Food Information, 13, 1, pp. 14-34, (2012); Varvel V.E., Shen Y., Data management consulting at the john hopkins University, New Review of Academic Librarianship, 19, 3, pp. 224-245, (2013); Wynholds L.A., Wallis J.C., Borgman C.L., Sands A., Data, data use, and scientific inquiry: Two case studies of data practices, Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 19-22, (2012); Averkamp S., Gu X., Report on the University Libraries' Data Management Need Survey, (2012); McKay D., Oranges are not the only fruit: An institutional case study demonstrating why data digital libraries are not the whole answer to e-research, ICADL 2010: The Role of Digital Libraries in a Time of Global Change, 6102, pp. 236-249, (2010); Roos A., Case study: Developing research data management training and support at helsinki University library, Association of European Research Libraries, (2014); Kansa E.C., Kansa S.W., Arbuckle B., Publishing and pushing: Mixing models for communicating research data in archaeology, International Journal of Digital Curation, 9, 1, pp. 57-70, (2014); Pawson R., Greenhalgh T., Harvey G., Walshe K., Realist review - A new method of systematic review designed for complex policy interventions, J Health Serv Res Policy, 10, pp. 21-34, (2005); Greenhalgh T., Wong G., Jagosh J., Greenhalgh J., Manzano A., Westhorp G., Et al., Protocol - The RAMESES II study: Developing guidance and reporting standards for realist evaluation, BMJ Open, 5, 8, (2015); Murad M.H., Asi N., Alsawas M., Alahdab F., New evidence pyramid, Evid Based Med., 21, 4, pp. 125-127, (2016); Hruby G.W., McKiernan J., Bakken S., Weng C., A centralized research data repository enhances retrospective outcomes research capacity: A case report, J Am Med Inform Assoc, 20, 3, pp. 563-567, (2013); Willoughby C., Bird C.L., Coles S.J., Frey J.G., Creating context for the experiment record. User-defined metadata: Investigations into metadata usage in the LabTrove ELN, Journal of Chemical Information and Modeling, 54, 12, pp. 3268-3283, (2014); Piwowar H.A., Chapman W.W., Public sharing of research datasets: A pilot study of associations, Journal of Informetrics, 4, 2, pp. 148-156, (2010); Ioannidis J.P., Greenland S., Hlatky M.A., Khoury M.J., Macleod M.R., Moher D., Et al., Increasing value and reducing waste in research design, conduct, and analysis, Lancet, 383, 9912, pp. 166-175, (2014)","","","Public Library of Science","","","","","","19326203","","POLNC","28542450","English","PLoS ONE","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85019645195"
"Drobikova B.; Jarolimkova A.; Soucek M.","Drobikova, Barbora (56278562500); Jarolimkova, Adela (27567887500); Soucek, Martin (56114541200)","56278562500; 27567887500; 56114541200","Data Literacy Among Charles University PhD Students: Are They Prepared for Their Research Careers?","2018","Communications in Computer and Information Science","810","","","169","177","8","2","10.1007/978-3-319-74334-9_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041724531&doi=10.1007%2f978-3-319-74334-9_18&partnerID=40&md5=60b5740149e1f10e5339bff2210cfe12","Faculty of Arts, Institute of Information Studies and Librarianship, Charles University, Prague, Czech Republic","Drobikova B., Faculty of Arts, Institute of Information Studies and Librarianship, Charles University, Prague, Czech Republic; Jarolimkova A., Faculty of Arts, Institute of Information Studies and Librarianship, Charles University, Prague, Czech Republic; Soucek M., Faculty of Arts, Institute of Information Studies and Librarianship, Charles University, Prague, Czech Republic","The goal of our study, based on an extensive survey, is to discover the attitudes toward data sharing and research data management among Charles University doctoral students. The research was carried out as a part of a Data Literacy Multinational Study cooperation project. We have used a Czech version of the data literacy questionnaire presented by Gobinda Chowdhury et al. at the ECIL conference 2016. Results show that doctoral students from all disciplines are willing to share data, but they are not aware of open access principles. Doctoral students have confirmed they share their data at least among their research teams. The results do not show a significant difference in this practice between disciplines. Doctoral students prefer their research data to be preserved for further research. © 2018, Springer International Publishing AG.","Data literacy; Data sharing; Data sharing practices; Information literacy","Information management; Surveys; Data literacy; Data Sharing; Data-sharing practices; Information literacy; Open Access; Research data; Research data managements; Research teams; Students","","","","","Univerzita Karlova v Praze, UK","Acknowledgements. The publication was supported by the Charles University programme Progres Q15 “Life course, lifestyle and quality of life from the perspective of individual adaptation and the relationship of the actors and institutions.”","Tenopir C., Dalton E.D., Allard S., Frame M., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos One, 10, (2015); Calzada Prado J., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, pp. 123-134, (2013); Facts and Figures; Koltay T., Data literacy: In search of a name and identity, J. Doc, 71, pp. 401-415, (2015); Koltay T., Data literacy for researchers and data librarians, J. Libr. Inf. Sci, 49, pp. 3-14, (2017); Carlson J., Fosmire M., Miller C.C., Sapp Nelson M., Determining data information literacy needs: A study of students and research faculty, Libr. Acad., 11, pp. 629-657, (2011); Qin J., D'Ignazio J., The central role of metadata in a science data literacy course, J. Libr. Metadata, 10, pp. 188-204, (2010); Haendel M.A., Vasilevsky N.A., Wurz J.A., Dealing with data: A case study on information and data management literacy, Plos Biol, 10, (2012); Schneider R., Research data literacy, ECIL 2013. CCIS, 397, pp. 134-140, (2013); Hunt K., The challenges of integrating data literacy into the curriculum in an undergraduate institution, Iassist Q. (Summer/Fall), pp. 12-16, (2004); Shield M., Information literacy, statistical literacy, data literacy, IASSIST Q. Summer/Fall, pp. 6-11, (2004); Frank E.P., Pharo N., Academic librarians in data information literacy instruction: A case study in meteorology, Coll. Res. Libr., 77, pp. 536-552, (2016); Sapp Nelson M.R., A pilot competency matrix for data management skills: A step toward the development of systematic data information literacy programs, J. Escience Libr, (2017); Pavlaskova E., (2016); Chowdhury G., Walton G., Kurbanoglu S., Unal Y., Boustany J., Information practices for sustainability: Information, data and environmental literacy, The Fourth European Conference on Information Literacy (ECIL), (2016); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists’ data-sharing behaviors: A multilevel analysis, JASIST, 97, pp. 776-799, (2016)","A. Jarolimkova; Faculty of Arts, Institute of Information Studies and Librarianship, Charles University, Prague, Czech Republic; email: adela.jarolimkova@ff.cuni.cz","Roy L.; Spiranec S.; Boustany J.; Kurbanoglu S.; Grassian E.; Mizrachi D.","Springer Verlag","","5th European Conference on Information Literacy in the Workplace, ECIL 2017","18 September 2017 through 21 September 2017","Saint Malo","210239","18650929","978-331974333-2","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85041724531"
"Yatcilla J.K.; Bracke M.S.","Yatcilla, Jane Kinkus (35777133500); Bracke, Marianne Stowell (16644670800)","35777133500; 16644670800","Investigating the Needs of Agriculture Scholars: The Purdue Case","2017","Journal of Agricultural and Food Information","18","3-4","","293","305","12","2","10.1080/10496505.2017.1336094","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025142683&doi=10.1080%2f10496505.2017.1336094&partnerID=40&md5=cf877adcd7d05de0fec28b2484dc1511","Purdue University Libraries, West Lafayette, IN, United States","Yatcilla J.K., Purdue University Libraries, West Lafayette, IN, United States; Bracke M.S., Purdue University Libraries, West Lafayette, IN, United States","To better understand the complexity of agricultural research, two Purdue University Libraries’ faculty members conducted a series of interviews with subjects from across the College of Agriculture. Interview questions addressed research methodologies and outputs, research data management, primary information resources, and other aspects of the research cycle and the researchers’ professional lives. Interviews were recorded, transcribed, and analyzed using nVivo software. The results are summarized and presented here. © 2017, Published with license by Taylor & Francis © 2017, © Jane Kinkus Yatcilla and Marianne Stowell Bracke.","","","","","","","","","Our story, (2017)","J.K. Yatcilla; Purdue University Libraries, Health&Life Sciences Division, West Lafayette, 504 West State Street, 47907, United States; email: janeyat@purdue.edu","","Taylor and Francis Inc.","","","","","","10496505","","","","English","J. Agric. Food inf.","Article","Final","","Scopus","2-s2.0-85025142683"
"Meineke F.A.; Löbe M.; Stäubert S.","Meineke, Frank A (6508217583); Löbe, Matthias (55938448500); Stäubert, Sebastian (36648060200)","6508217583; 55938448500; 36648060200","Introducing technical aspects of research data management in the leipzig health atlas","2018","Studies in Health Technology and Informatics","247","","","426","430","4","10","10.3233/978-1-61499-852-5-426","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046552089&doi=10.3233%2f978-1-61499-852-5-426&partnerID=40&md5=3177d845724ee2a88ff9b23f0a8c7dd6","Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany","Meineke F.A., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany; Löbe M., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany; Stäubert S., Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany","Medical research is an active field in which a wide range of information is collected, collated, combined and analyzed. Essential results are reported in publications, but it is often problematic to have the data (raw and processed), algorithms and tools associated with the publication available. The Leipzig Health Atlas (LHA) project has therefore set itself the goal of providing a repository for this purpose and enabling controlled access to it via a web-based portal. A data sharing concept in accordance to FAIR and OAIS is the basis for the processing and provision of data in the LHA. An IT architecture has been designed for this purpose. The paper presents essential aspects of the data sharing concept, the IT architecture and the methods used. © 2018 European Federation for Medical Informatics (EFMI) and IOS Press.","3LGM2; Clinical research infrastructure; OAIS; Open data; Research data management","Algorithms; Humans; Research; Statistics as Topic; Access control; Clinical research; Information management; Medical informatics; Open Data; 3LGM2; IT architecture; Medical research; OAIS; Research data managements; Research infrastructure; Technical aspects; Web-based portal; clinical research; conference paper; information processing; publication; algorithm; human; research; statistics; Data Sharing","","","","","","","Homepage, (2017); Wilkinson M.D., Dumontier M., Aalbersberg I.J.J., Appleton G., Axton M., Baak A., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Space Data and Information Transfer Systems -- Open Archival Information System (OAIS) -- Reference Model, (2012); Leipziger Forschungszentrum Für Zivilisationserkrankungen (LIFE), (2017); Meineke F.A., Staubert S., Applying the open archival information system (OAIS) to medical data integration centers, HEC 2016: Health - Exploring Complexity 2016: Joint Conference of GMDS, DGEpi, (2016); Wendt T., Haber A., Brigl B., Winter A., Modeling hospital information systems (part 2): Using the 3LGM2 tool for modeling patient record management, Methods Inf. Med., 43, 3, pp. 256-267, (2004); Winter A., Brigl B., Wendt T., Modeling hospital information systems (Part 1): The revised three-layer graph-based meta model 3LGM2, Methods Inf. Med., 42, 5, pp. 544-551, (2003); Uciteli A., Beger C., Rillich K., Meineke F.A., Loffler M., Herre H., Ontology-based modelling of web content: Example leipzig health atlas, Semantic Applications: Methodology, Technology, Corporate Use, (2018); Beger C., Uciteli A., Herre H., Light-weighted automatic import of standardized ontologies into the content management system drupal, Stud. Health. Technol. Inform., 243, pp. 170-174, (2017); Murphy S.N., Weber G., Mendis M., Gainer V., Chueh H.C., Churchill S., Et al., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), Journal of The American Medical Informatics Association JAMIA, 17, 2, pp. 124-130, (2010); Scheufele E., Aronzon D., Coopersmith R., McDuffie M.T., Kapoor M., Et al., TranSMART: An open source knowledge management and high content data analytics platform, AMIA Joint Summits on Translational Science Proceedings AMIA Summit on Translational Science, 2014, pp. 96-101, (2014); Shiny, (2017); ODM-XML A Vendor-Neutral, Platform-Independent Format for Exchanging and Archiving Clinical and Translational Research Data, (2017); Meineke F.A., Staubert S., Lobe M., Winter A., A comprehensive clinical research database based on CDISC ODM and i2b2, E-Health - For Continuity of Care, pp. 1115-1119, (2014); Git - Distributed Version Control System, (2017); Lobe M., Ganslandt T., Lotzmann L., Mate S., Et al., Simplified deployment of health informatics applications by providing docker images, EXPLORING COMPLEXITY IN HEALTH: An Interdisciplinary Systems Approach, pp. 643-647, (2016); (2017); Drupal(TM), (2017); Nelson B., Data sharing: Empty archives, Nature, 461, 7261, pp. 160-163, (2009); The Comprehensive R Archive Network (CRAN), (2017); Gene Expression Omnibus (GEO): A Public Functional Genomics Data Repository, (2017); Loffler M., Scherag A., Marx G., Smart Medical Information Technology for Healthcare (SMITH), (2017)","F.A. Meineke; Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig, Leipzig, Härtelstr. 16-18, 04107, Germany; email: frank.meineke@imise.uni-leipzig.de","Ugon A.; Karlsson D.; Klein G.O.; Moen A.","IOS Press BV","","40th Medical Informatics in Europe Conference, MIE 2018","24 April 2018 through 26 April 2018","Gothenburg","136052","09269630","978-161499851-8","","29677996","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-85046552089"
"Jarolímková A.; Drobíková B.; Souček M.","Jarolímková, Adéla (27567887500); Drobíková, Barbora (56278562500); Souček, Martin (56114541200)","27567887500; 56278562500; 56114541200","Attitudes of Charles University academic staff to data sharing","2018","Grey Journal","14","Special Winter Issue","","37","43","6","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040722122&partnerID=40&md5=114f0d28c084396619b00ff84a68c1df","Institute of Information Studies and Librarianship, Faculty of Arts, Charles University, Czech Republic","Jarolímková A., Institute of Information Studies and Librarianship, Faculty of Arts, Charles University, Czech Republic; Drobíková B., Institute of Information Studies and Librarianship, Faculty of Arts, Charles University, Czech Republic; Souček M., Institute of Information Studies and Librarianship, Faculty of Arts, Charles University, Czech Republic","Data management and sharing are an integral part of contemporary research work. At Charles University, we carried out a survey of selected aspects of current data management practices and researchers' attitudes to data management and sharing. In our paper we present a part of its results focused on academic staff and comparison of their answers with the answers of doctoral students, interdisciplinary comparisons, selected comments and recommendations based on survey results. © 2018 TextRelease.","Academic staff; Open access; Research data management; Research data sharing; Researchers","","","","","","","","Calzada Prado J., Miguel Angel M., Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents, Libri, 63, 2, pp. 123-134, (2013); Carlson J., Fosmire M., Miller C.C., Sapp Nelson M., Determining Data Information Literacy Needs: A Study of Students and Research Faculty, Libraries and the Academy, 11, 2, pp. 629-657, (2011); Drobikova B., Jarolimkova A., Soucek M., (2017); Boustanygrassian E., Mizrachi D., Roy L., Kos D., The Fifth European Conferece on Information Literacy (ECIL): Abstracts, Saint-Malo: Information Literacy Association; Enwald H., Korteleinen T., Huotari M.-L., Research data management: Experiences of scholars in Finland, The Fifth European Conferece on Information Literacy (ECIL): Abstracts. Saint-Malo: Information Literacy Association; Haendel M.A., Vasilevsky N.A., Wurz J.A., Dealing with Data: A Case Study on Information and Data Management Literacy, Plos Biology, 10, 5, (2012); Chowdhury G., Walton G., Kurbanoglu S., Unal Y., Boustany J., Information Practices for Sustainability: Information, Data and Environmental Literacy, The Fourth European Conference on Information Literacy (ECIL), (2016); Jarolimkova A., Drobikova B., Soucek M., Výzkumná Data Na Univerzitě Karlově, (2017); Pavlaskova E., (2016); Sapp Nelson M.R., A Pilot Competency Matrix for Data Management Skills: A Step toward the Development of Systematic Data Information Literacy Programs, Journal of Escience Librarianship, 6, 1, (2017); Tenopir C., Dalton E.D., Allard S., Frame M., Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide, Plos One, (2015)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Article","Final","","Scopus","2-s2.0-85040722122"
"Fernández-del-Pino Torres B.; Malo-de-Molina y Martín-Montalvo T.","Fernández-del-Pino Torres, Belén (57203975819); Malo-de-Molina y Martín-Montalvo, Teresa (57203969206)","57203975819; 57203969206","Fair play at carlos III university of madrid library","2018","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11057 LNCS","","","373","376","3","0","10.1007/978-3-030-00066-0_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053857592&doi=10.1007%2f978-3-030-00066-0_43&partnerID=40&md5=a0bd49a9f43beba8886ec34baa9f7802","Universidad Carlos III de Madrid, Leganés, Spain","Fernández-del-Pino Torres B., Universidad Carlos III de Madrid, Leganés, Spain; Malo-de-Molina y Martín-Montalvo T., Universidad Carlos III de Madrid, Leganés, Spain","Our purpose is to show projects held at Carlos III University Library related to FAIR principles. As time passes the Library evolves from a traditional library to “as open as possible, as closed as necessary” Digital Library. © 2018, Springer Nature Switzerland AG.","Author disambiguation; Digital libraries; Geolocation; Identifiers; Library research services; Linked data; Metadata; Open data; Open science; Open source; ORCID; Repositories; Research data management; University libraries","Information management; Linked data; Metadata; Author disambiguation; Geolocations; Identifiers; Library research; Open datum; Open science; Open sources; ORCID; Repositories; Research data managements; University libraries; Digital libraries","","","","","","","Wilkinson M., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, pp. 160018-160027, (2016); Sipos G., What is FAIR? EGI Newsletter Inspired, (2017); Kraft A., The FAIR Data Principles for Research Data, (2017); Openaire Compatible Data Providers: E-Archivo; RECOLECTA [Open Science Harvester] is a Platform that Gathers All the Spanish Scientific Repositories Together in One Place and Provides Services to Repository Managers, Researchers and Decision-Makers; Rasero V., Poveda A., Integración Del Sistema De Gestión De La Investigación Y El Repositorio Institucional. E-Archivo, (2012); UC3M Research Portal; Federico G., Tena Junguito A., Federico-Tena World Trade Historical Database: World Trade, (2018); Principe P., Infrastructures for open science in Europe: The power of repositories. In: Ponencia presentada en el Congreso Ecosistemas del Conocimiento Abierto (ECA 2017), Salamanca, (2017)","B. Fernández-del-Pino Torres; Universidad Carlos III de Madrid, Leganés, Spain; email: belen.fernandez-delpino@uc3m.es","Mendez E.; Ribeiro C.; David G.; Lopes J.C.; Crestani F.","Springer Verlag","","22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018","10 September 2018 through 13 September 2018","Porto","218159","03029743","978-303000065-3","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85053857592"
"Faniel I.M.; Connaway L.S.","Faniel, Ixchel M. (18436473200); Connaway, Lynn Silipigni (6603106537)","18436473200; 6603106537","Librarians’ perspectives on the factors influencing research data management programs","2018","College and Research Libraries","79","1","","100","119","19","28","10.5860/crl.79.1.100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040745866&doi=10.5860%2fcrl.79.1.100&partnerID=40&md5=ea19ee969ed9d6c74072046b58c875a3","OCLC Research, United States","Faniel I.M., OCLC Research, United States; Connaway L.S., OCLC Research, United States","This qualitative research study examines librarians’ research data management (RDM) experiences, specifically the factors that influence their ability to support researchers’ needs. Findings from interviews with 36 academic library professionals in the United States identify 5 factors of influence: 1) technical resources; 2) human resources; 3) researchers’ perceptions about the library; 4) leadership support; and 5) communication, coordination, and collaboration. Findings show different aspects of these factors facilitate or constrain RDM activity. The implications of these factors on librarians’ continued work in RDM are considered. © 2018 OCLC.","","","","","","","RDM","Researchers’ perceptions of the library constrained RDM programs. Findings indicated perceptions were based on long-held assumptions about librarians and library services and the library’s slow response to researchers’ RDM needs. Moreover, less than a quarter of the librarians mentioned communicating, coordinating, and collaborating with researchers to facilitate RDM support. Reports on collaboration from prior research vary. In the Tenopir, Birch, and Allard study, librarians report academic departments as common collaborators; but, in the Pinfield et al. study, librarians report a lack of engagement from researchers.46Although liaison librarians have been integral in outreach and education efforts to faculty and students, liaisons are not the only answer. More and different connections between librarians and researchers need to be explored to not only build awareness of researchers’ needs and enhance RDM services, but also to build researchers’ awareness of librarians’ support and to enhance their perception of librarians’ abilities to perform RDM services. Findings from this study indicate that engagement with researchers can come in many forms and via different librarian roles. Librarians would do well to consider their various opportunities to engage with faculty and students throughout the research lifecycle from research proposal to publications. For instance, librarians serving on internal grant proposal committees to award institutional funds or partnering with sponsored research offices and graduate schools to lead RDM workshops to present ways to plan, manage, and share data in the early stages of faculty and student research. Such early exposure provides an opportunity for librarians to keep abreast of the challenges researchers face and to proactively plan and partner with researchers to address their RDM needs.","Luce R.E., A New Value Equation Challenge: The Emergence of eResearch and Roles for Research Libraries, No Brief Candle: Reconceiving Research Libraries for the 21St Century, (2016); Gabridge T., The Last Mile: Liaison Roles in Curating Science and Engineering Research Data, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 265, (2016); Tyler O., Walters and Katherine Skinner, New Roles for New Times: Digital Curation for Preservation, (2011); Cox A.M., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities, Journal of Librarianship and Information Science, 46, 4, (2014); Corrall S., Kennan M.A., Afzal W., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research, Library Trends, 61, 3, pp. 636-674, (2013); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2016); Antell K., Foote J.B., Turner J., Shults B., Dealing with Data: Science Librarians Participation in Data Management at Association of Research Libraries Institutions,”, College & Research Libraries, 75, 4, pp. 557-574, (2013); Kotarski R., Reilly S., Smit E., Walshe K., Reports on Best Practices for Citability of Data and on Evolving Roles in Scholarly Communication (Opportunities for Data Exchange, 2012), (2016); Pinfield S., Cox R.M., Smith J., Launois P., Research Data Management and Libraries: Relationships, Activities, Drivers and Influences, Plos ONE, 9, 12, (2014); Erway R., Starting the Conversation: University-Wide Research Data Management Policy, (2013); Witt M., Institutional Repositories and Research Data Curation in a Distributed Environment, Library Trends, 57, 2, pp. 191-201, (2008); Jones S., Ball A., Ekmekcioglu C., The Data Audit Framework: A First Step in the Data Management Challenge, International Journal of Digital Curation, 3, 2, pp. 112-120, (2008); Witt M., Co-Designing, Co-Developing, and Co-Implementing an Institutional Data Repository Service, Journal of Library Administration, 52, 2, pp. 172-188, (2012); Newton M.P., Miller C.C., Bracke M.S., Librarian Roles in Institutional Repository Data Set Collecting: Outcomes of a Research Library Task Force, Collection Management, 36, 1, pp. 53-67, (2010); Delserone L.M., At the Watershed: Preparing for Research Data Management and Stewardship at the University of Minnesota Libraries, Library Trends, 57, 2, pp. 202-210, (2008); Thomas J., Future-proofing: The Academic Librarys Role in E-Research Support,”, Library Management 32, No, 1, 2, pp. 37-47, (2011); Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Simons N., Richardson J., New Roles, New Responsibilities: Examining Training Needs of Repository Staff, Journal of Librarianship and Scholarly Communication, 1, 2, (2012); Cassella M., Morando M., Fostering New Roles for Librarians: Skills Set for Repository Managers—Results of a Survey in Italy, Liber Quarterly, 21-4, 3, pp. 407-428, (2012); Xia J., Wang M., Competencies and Responsibilities of Social Science Data Librarians: An Analysis of Job Descriptions, College & Research Libraries, 75, 3, pp. 362-388, (2014); Connaway L.S., Radford M.L., Research Methods for Library and Information Science, pp. 249-250, (2016); Kansa E., Kansa S.W., Toward A Do-It-Yourself Cyberinfrastructure: Open Data, Incentives, and Reducing Costs and Complexities of Data Sharing, Archaeology 2.0: New Approaches to Communication & Collaboration, Cotsen Digital Archaeology Series, pp. 57-91, (2011); Faniel I.M., Yakel E., Practices Do Not Make Perfect: Disciplinary Data Sharing and Reuse Practices and Their Implications for Repository Data Curation, Curating Research Data, 1, pp. 103-125, (2017); Kafel D., Creamer R., Martin E., Building the New England Collaborative Data Management Curriculum, Journal of Escience Librarianship, 3, 1, (2014); Fry J., Leahey A., Metadata for Social Science Data: Collaborative Best Practices, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 269-282, (2016); Mohr A.H., Johnston L.R., Lindsay T.A., The Data Management Village: Collaboration among Research Support Providers in the Large Academic Environment, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 51-66, (2016); Clement R., The Data Librarian in the Liberal Arts College, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 67-79, (2016)","","","Association of College and Research Libraries","","","","","","00100870","","","","English","Coll. Res. Libr.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85040745866"
"Nelson M.S.; Pouchard L.","Nelson, Megan Sapp (16402823900); Pouchard, Line (8709358200)","16402823900; 8709358200","A pilot ""Big Data"" education modular curriculum for engineering graduate education: Development and implementation","2017","Proceedings - Frontiers in Education Conference, FIE","2017-October","","","1","5","4","2","10.1109/FIE.2017.8190688","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043258780&doi=10.1109%2fFIE.2017.8190688&partnerID=40&md5=b9051f16e60d27dd2f94ebdd32f48994","Purdue University Libraries, Purdue University, West Lafayette, IN, United States; Computational Science Initiative Dir., Center for Data Driven Discovery, Brookhaven National Laboratory, Upton, NY, United States","Nelson M.S., Purdue University Libraries, Purdue University, West Lafayette, IN, United States; Pouchard L., Computational Science Initiative Dir., Center for Data Driven Discovery, Brookhaven National Laboratory, Upton, NY, United States","Engineering higher education increasingly produces data in the volume, variety, velocity, and need for veracity such that the output of the research is considered ""Big Data"". While engineering faculty members do conceive of and direct the research producing this data, there may be gaps in faculty members' knowledge in training graduate and undergraduate research assistants in the management of Big Data. The project described herein details the development of a Big Data education module for a group of graduate researchers and undergraduate research assistants in Electrical and Computer Engineering. This project has the following objectives: to document and describe current data management practices; to identify gaps in knowledge that need to be addressed in order for research assistants to successfully manage Big Data; and to create curricular interventions to address these gaps. This paper details the motivation, relevant literature, research methodology, curricular intervention, and pilot presentation of the module. Results indicate that, generally, students involved in Big Data projects need comprehensive introduction to the topic, which will be most effective when contextualized to the work that they are performing in the research or classroom environment. © 2017 IEEE.","Big Data; Curriculum development; Graduate education; Research data management; Undergraduate education","Curricula; Engineering research; Information management; Personnel training; Research and development management; Students; Teaching; Curriculum development; Electrical and computer engineering; Engineering faculty members; Graduate education; Research data managements; Research methodologies; Undergraduate education; Undergraduate research; Big data","","","","","U.S. Department of Energy, USDOE, (DESC0012704)","This manuscript has been authored in part by employees of Brookhaven Science Associates, LLC under Contract No. DESC0012704 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.","Pouchard L.C., Nelson M.S., Yung-Hsiang L., Data sharing and re-use policies for webcam video feeds from international sources, IASSIST Quarterly, 39, pp. 14-23, (2015); Sapp Nelson M., Learning Objectives to [excised] Lab, (2014); De Mauro A., Greco M., Grimaldi M., Giannakopoulos G., Sakas D.P., Kyriaki-Manessi D., What is big data? A consensual definition and a review of key research topics, AIP Conference Proceedings, pp. 97-104, (2015); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, pp. 629-657, (2011); Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, (2014); Keil D.E., Research data needs from academic libraries: The perspective of a faculty researcher, Journal of Library Administration, 54, pp. 233-240, (2014); Diekema A.R., Wesolek A., Walters C.D., The NSF/NIH effect: Surveying the effect of data management requirements on faculty, sponsored programs, and institutional repositories, Journal of Academic Librarianship, 40, pp. 322-331, (2014); Haendel M.A., Vasilevsky N.A., Wirz J.A., Dealing with data: A case study on information and data management literacy, PLoS Biol, 10, (2012); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, pp. crl-255, (2011); Mervis J., Agencies rally to tackle big data, Science, 336, (2012); Sapp Nelson M., A pilot competency matrix for data management skills: A step toward the development of systematic data information literacy programs, Journal of EScience Librarianship, 6, (2017); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, pp. 5-26, (2013); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, pp. 211-219, (2014); Samuel S.M., Grochowski P.F., Lalwani L.N., Carlson J., Analyzing data management plans: Where librarians can make a difference, ASEE Annual Conference and Exposition, (2015); Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, pp. 171-191, (2014); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, (2014); Lyon L., Librarians in the lab: Toward radically re-engineering data curation services at the research coalface, New Review of Academic Librarianship, 22, pp. 391-409, (2016); Poole A.H., How has your science data grown? Digital curation and the human factor: A critical literature review, Archival Science, 15, pp. 101-139, (2015); Calzada Prado J., Marzal Miguel A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, (2013); Qin J., D'Ignazio J., The central role of metadata in a science data literacy course, Journal of Library Metadata, 10, pp. 188-204, (2010); Wanner A., Data Literacy Instruction in Academic Libraries: Best Practices for Librarians, 1, (2015); Carlson J., Stowell Bracke M., Planting the seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, pp. 95-110, (2015); Saltz J., Heckman R., Big data science education: A case study of a project-focused introductory course, Themes in Science and Technology Education, 8, pp. 85-94, (2015); O'Neil M., As data proliferate, so do data-related graduate programs, The Chronicle of Higher Education, (2014); Horton N.J., Baumer B.S., Wickham H., Setting the Stage for Data Science: Integration of Data Management Skills in Introductory and Second Courses in Statistics, (2015); Carlson J., Sapp Nelson M.R., Bracke M.S., Wright S.J., The Data Information Literacy Toolkit, (2015); Sapp Nelson M.R., Pouchard L.C., A pilot ""big data"" education module curriculum for engineering graduate education: Development and implementation, Libraries Faculty and Staff Scholarship and Research, (2017); Diebold F.X., On the origin(s) and development of the term ""Big data"", (2012); Pouchard L.C., Revisiting the data life cycle with big data curation, International Journal of Digital Curation, 10, 2, pp. 176-192, (2016); Sapp Nelson M., Pouchard L., Applied Big Data Workshop, (2016); Sapp Nelson M., Pouchard L., Applied Big Data Workshop: Resources, (2016)","M.S. Nelson; Purdue University Libraries, Purdue University, West Lafayette, United States; email: msn@purdue.edu","","Institute of Electrical and Electronics Engineers Inc.","American Society for Engineering Education (ASEE), Educational Research Methods (ERM) Division; IEEE Computer Society; IEEE Education Society; Institute of Electrical and Electronics Engineers (IEEE)","47th IEEE Frontiers in Education Conference, FIE 2017","18 October 2017 through 21 October 2017","Indianapolis","133822","15394565","978-150905919-5","PFECD","","English","Proc. Front. Educ. Conf. FIE","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85043258780"
"Morgan A.; Duffield N.; Hall L.W.","Morgan, Ann (57199871093); Duffield, Nel (57201998880); Hall, Liz Walkley (55849707700)","57199871093; 57201998880; 55849707700","Research data management support: Sharing our experiences","2017","Journal of the Australian Library and Information Association","66","3","","299","305","6","14","10.1080/24750158.2017.1371911","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057218257&doi=10.1080%2f24750158.2017.1371911&partnerID=40&md5=0761be07ebf300a39aff088190898f95","Sir Eric Neal Library, University of South Australia, Adelaide, Australia; Barr Smith Library, University of Adelaide, Adelaide, Australia; Central Library, Flinders University, Adelaide, Australia","Morgan A., Sir Eric Neal Library, University of South Australia, Adelaide, Australia; Duffield N., Barr Smith Library, University of Adelaide, Adelaide, Australia; Hall L.W., Central Library, Flinders University, Adelaide, Australia","The role of librarians in supporting researchers is ever changing and expanding. One of the most significant trends is for libraries to work in conjunction with other units in their institutions, for example information technology units and research offices, to support research data management (RDM). This is because RDM is a complex area involving work roles and expertise that librarians have not traditionally engaged with, and which in the past were largely left in the hands of researchers to manage themselves. Examples include data description and storage, data curation, data preservation, licensing and open access. This paper outlines how three South Australian academic institutions (the University of South Australia, University of Adelaide and Flinders University) have responded to this change. We describe how these libraries provide support (workshops, web pages, library guides and appointments); what tools and software packages are used; what additional skills library staff have had to acquire to provide support; and, what the outcomes were, what worked, and what did not work, and future plans. © 2017 Australian Library & Information Association.","Academic libraries; EResearch; Research data; Research data management; Research education; Research support","","","","","","Flinders Academic Commons; Australian Research Council, ARC; National Health and Medical Research Council, NHMRC","Liz Walkley Hall manages the open access requirements for the University’s NHMRC and ARC funded research, as well as managing the University’s open access institutional repository, the Flinders Academic Commons. Liz also contributes to research data management planning for the University, coordinates the Library’s contributions to the RHD Professional Development Program, and is Chair of the Library’s Research Working Group.","Akers K., Sferdean F., Nicholls N., Green J., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, pp. 171-191, (2014); What is Research Data?, (2017); Australian National Data Service. (N.D.-A); 23 (Research Data) Things; Corrall S., Kennan M., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, pp. 636-674, (2013); Groenewegen D., Treloar A., Adding value by taking a national and institutional approach to research data: The ANDS experience, International Journal of Digital Curation, 8, pp. 89-98, (2013); McAlpine K., McIntosh L., Honing the edge: An integrated model for supporting eResearch, ALIA Information Online 2015: At the Edge, pp. 1-13, (2015); McBain I., Culshaw H., Walkley Hall L., Establishing a culture of research practice in an academic library: An Australian case study, Library Management, 34, pp. 448-461, (2013); Nixon A.L., Hall E., McBain I., Constantine R., Carati C., We built it and they are coming: The development of eResearch@Flinders, VALA 2014: Streaming with Possibilities, (2014); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, Plos ONE, 9, 12, (2014); Public Library of Science ONE. (N.D.). Data Availability; Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, pp. 84-90, (2014); Thomas J., Future-proofing: The academic library’s role in e-research support, Library Management, 32, pp. 37-47, (2011); Data Access Portal","A. Morgan; Sir Eric Neal Library, University of South Australia, Adelaide, Australia; email: ann.morgan@unisa.edu.au","","Australian Library and Information Association","","","","","","24750158","","","","English","J. Aust. Libr. Inf. Assoc.","Article","Final","","Scopus","2-s2.0-85057218257"
"Matusiak K.K.; Sposito F.A.","Matusiak, Krystyna K. (14626810000); Sposito, Frank A. (57189973650)","14626810000; 57189973650","Types of research data management services: An international perspective","2017","Proceedings of the Association for Information Science and Technology","54","1","","754","756","2","6","10.1002/pra2.2017.14505401144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040778570&doi=10.1002%2fpra2.2017.14505401144&partnerID=40&md5=ca0a91283a6d72a212d466b4281d1431","Department of Research Methods & Information Science, University of Denver, United States; The Sié Chéou-Kang Center for International Security and Diplomacy, University of Denver, United States","Matusiak K.K., Department of Research Methods & Information Science, University of Denver, United States; Sposito F.A., The Sié Chéou-Kang Center for International Security and Diplomacy, University of Denver, United States","Research data management (RDM) services are increasingly offered by university libraries and data centers worldwide to support researchers in meeting funders' requirements for data management planning and to promote access to open research data. RDM remains an emergent service area and, as it continues to evolve, a dizzying array of program designs and strategies have been explored and refined to meet new technological challenges and user needs. This poster presents findings from an international study that investigated various organizational models for RDM services delivered at selected academic libraries and research centers in Australia, Europe and North America. While most RDM services examined in the study were provided by academic libraries, the research also revealed examples of embedded curation services and identified new organizational strategies, including distributed networks of RDM expertise and multi-purpose research data centers. Copyright © 2017 by Association for Information Science and Technology","academic libraries; embedded services; research data management; research data service centers","Libraries; Academic libraries; Data management services; Data services; Datacenter; Embedded service; International perspective; Research data; Research data managements; Research data service center; Service center; Information management","","","","","","","Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Antell K., Bales Foote J., Turner J., Shults B., Dealing with data: Science librarians' participation in data management at Association of Research Libraries institutions, College & Research Libraries, 75, 4, pp. 557-574, (2014); Open data; Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship & Information Science, 46, 4, pp. 299-316, (2014); Guidelines on the implementation of Open Access to scientific publications and research data in projects supported by the European Research Council under Horizon 2020, (2017); Guss S., A studio model for academic data services, Databrarianship: The academic data librarian in theory and practice, pp. 9-24, (2016); Hudson-Vitale C., Embedded options: A common framework, Databrarianship: The academic data librarian in theory and practice, (2016); Mohr A.H., Johnston L.R., Lindsay T.A., The data management village: Collaboration among research support providers in the large academic environment, Databrarianship: The academic data librarian in theory and practice, pp. 51-66, (2016); Dissemination and sharing of research results; Guidance on best practice in the management of research data, (2015); Tammaro A.M., Matusiak K.K., Sposito F.A., Pervan A., Casarosa V., Understanding roles and responsibilities of data curators: An international perspective, Libellarium, 9, 2, pp. 39-47, (2016); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, pp. 1-21, (2015); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, 1, pp. 23-44, (2017)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85040778570"
"Ayer V.; Pietsch C.; Vompras J.; Schirrwagen J.; Wiljes C.; Jahn N.; Cimiano P.","Ayer, Vidya (57193209865); Pietsch, Christian (56548514200); Vompras, Johanna (23390824000); Schirrwagen, Jochen (55485372400); Wiljes, Cord (55532719500); Jahn, Najko (55923122000); Cimiano, Philipp (15838793700)","57193209865; 56548514200; 23390824000; 55485372400; 55532719500; 55923122000; 15838793700","Conquaire: Towards an architecture supporting continuous quality control to ensure reproducibility of research","2017","D-Lib Magazine","23","1-2","","","","1","1","10.1045/january2017-ayer","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011708796&doi=10.1045%2fjanuary2017-ayer&partnerID=40&md5=efdd6764f0bc38f364310cd1578de917","CITEC, Bielefeld University, Germany; Bielefeld University Library, Germany","Ayer V., CITEC, Bielefeld University, Germany; Pietsch C., Bielefeld University Library, Germany; Vompras J., Bielefeld University Library, Germany; Schirrwagen J., Bielefeld University Library, Germany; Wiljes C., CITEC, Bielefeld University, Germany; Jahn N., Bielefeld University Library, Germany; Cimiano P., CITEC, Bielefeld University, Germany","Analytical reproducibility in scientific research has become a keenly discussed topic within scientific research organizations and acknowledged as an important and fundamental goal to strive for. Recently published scientific studies have found that irreproducibility is widely prevalent within the research community, even after releasing data openly. At Bielefeld University, nine research project groups from varied disciplines have embarked on a ""reproducibility"" journey by collaborating on the Conquaire project as case study partners. This paper introduces the Conquaire project. In particular, we describe the goals and objectives of the project as well as the underlying system architecture which relies on a DCVS system for storing data, and on continuous integration principles to foster data quality. We describe a first prototype implementation of the system and discuss a running example which illustrates the functionality and behaviour of the system. © 2017 Vidya Ayer, Christian Pietsch, Johanna Vompras, Jochen Schirrwagen, Cord Wiljes, Najko Jahn and Philipp Cimiano.","Analytical reproducibility; Computational science; Conquaire; Data science; DVCS; Infrastructure architecture; Quality control; Reproducible computational research; Research data management system","","","","","","","","Nosek B., Et al., Open Science Collaboration: Estimating the reproducibility of psychological science, Science, (2015); Prinz F., Schlange T., Asadullah K., Believe it or not: How much can we rely on published data on potential drug targets?, Nature, (2011); Baker M., Is there a reproducibility crisis?, Nature, (2016); Cimiano P., McCrae J., Jahn N., Pietsch C., Schirrwagen J., Vompras J., Wiljes C., CONQUAIRE-Continuous quality control for research data to ensure reproducibility: An institutional approach, Project proposal; Fenner M.; Wilson G., Bryan J., Cranston K., Kitzes J., Nederbragt L., Teal T., Good Enough Practices in Scientific Computing; Wilson G., Software Carpentry, (2016)","V. Ayer; CITEC, Bielefeld University, Germany; email: vayer@techfak.uni-bielefeld.de","","Corporation for National Research Initiatives","","","","","","10829873","","","","English","D-Lib Mag.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85011708796"
"Hickson S.; Poulton K.A.; Connor M.; Richardson J.; Wolski M.","Hickson, Susan (57192253358); Poulton, Kylie Ann (57192235694); Connor, Maria (57192253069); Richardson, Joanna (55463114800); Wolski, Malcolm (25961220900)","57192253358; 57192235694; 57192253069; 55463114800; 25961220900","Modifying researchers’ data management practices: A behavioural framework for library practitioners","2016","IFLA Journal","42","4","","253","265","12","16","10.1177/0340035216673856","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002156884&doi=10.1177%2f0340035216673856&partnerID=40&md5=bab4ca0fda638cae47e4514b1cbd6960","Griffith University, Gold Coast Campus, Australia; Griffith University, Nathan Campus, Australia","Hickson S., Griffith University, Gold Coast Campus, Australia; Poulton K.A., Griffith University, Nathan Campus, Australia; Connor M., Griffith University, Gold Coast Campus, Australia; Richardson J., Griffith University, Nathan Campus, Australia; Wolski M., Griffith University, Nathan Campus, Australia","Data is the new buzzword in academic libraries, as policy increasingly mandates that data must be open and accessible, funders require formal data management plans, and institutions are implementing guidelines around best practice. Given concerns about the current data management practices of researchers, this paper reports on the initial findings from a project being undertaken at Griffith University to apply a conceptual (A-COM-B) framework to understanding researchers’ behaviour. The objective of the project is to encourage the use of institutionally endorsed solutions for research data management. Based on interviews conducted by a team of librarians in a small, social science research centre, preliminary results indicate that attitude is the key element which will need to be addressed in designing intervention strategies to modify behaviour. The paper concludes with a discussion of the next stages in the project, which involve further data collection and analysis, the implementation of targeted strategies, and a follow-up activity to assess the extent of modifications to current undesirable practices. © 2016, © The Author(s) 2016.","Attitude; behaviour; behavioural framework; capability; libraries; motivation; opportunity; research data management","","","","","","","","Capability Maturity, (2016); Creating a Data Management Framework, (2016); Funding Rules for Schemes under the Discovery Program for the Years 2015 and 2016 – Australian Laureate Fellowships, Discovery Projects, Discovery Early Career Researcher Award and Discovery Indigenous, (2014); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, Australian Library Journal, 64, 3, pp. 224-234, (2015); Guidelines on Data Management in Horizon, (2016); Fear K., ‘You made it, you take care of it’: Data management as personal information management, International Journal of Digital Curation, 6, 2, pp. 53-77, (2011); Federer L.M., Lu Y.L., Joubert D.J., Et al., Biomedical data sharing and reuse: Attitudes and practices of clinical and scientific research staff, PLoS ONE, 10, 6, (2015); Henty M., Weaver B., Bradbury S.J., Et al., Investigating Data Management Practices in Australian Universities, (2008); Jahnke L., Asher A., Keralis S.D., The Problem of Data. Council on Library and Information Resources (CLIR) Report, pub. #154, (2012); Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Martinez-Uribe L., Macdonald S., User engagement in research data curation, pp. 309-314, (2009); Michie S., van Stralen M.M., West R., The behaviour change wheel: A new method for characterising and designing behaviour change interventions, Implementation Science, 6, 42, pp. 1-12, (2011); NHMRC’s Policy on the Dissemination of Research Findings, (2014); Australian Code for the Responsible Conduct of Research, (2007); National Science Foundation History, (2016); Dissemination and Sharing of Research Results, (2016); O'Reilly K., Johnson J., Sanborn G., Improving university research value: A case study, SAGE Open, 2, 3, pp. 1-13, (2012); Peters C., Dryden A.R., Assessing the academic library’s role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Piderit S.K., Rethinking resistance and recognizing ambivalence: A multidimensional view of attitudes toward an organizational change, Academy of Management Review, 25, 4, pp. 783-794, (2000); QS top 50 under 50 2015, (2015); Research Outcomes Overview, (2014); About researchfish, (2014); RCUK Common Principles on Data Policy, (2015); Schumacher J., VandeCreek D., Intellectual capital at risk: Data management practices and data loss by faculty members at five American universities, International Journal of Digital Curation, 10, 2, pp. 96-109, (2015); Searle S., Wolski M., Simons N., Et al., Librarians as partners in research data service development at Griffith University, Program: Electronic Library & Information Systems, 49, pp. 440-460, (2015); Si L., Xing W., Zhuang X., Et al., Investigation and analysis of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); Stanford Prize for Innovation in Research Libraries (SPIRL), (2013); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Sandusky R.J., Allard S., Et al., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Wang M., Fong B.L., Embedded data librarianship: A case study of providing data management support for a science department, Science & Technology Libraries, 34, 3, pp. 228-240, (2015); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Westra B., Developing data management services for researchers at the University of Oregon, Research Data Management: Practical Strategies for Information Professionals, pp. 375-391, (2014); Wolff C., Rod A.B., Schonfeld R.C., Ithaka S+R US Faculty Survey 2015, (2016); Wolski M., Richardson J., Improving data management practices of researchers by using a behavioural framework, (2015); Theories of Behavior Change. Communication for Governance and Accountability Program (CommGAP), (2010)","S. Hickson; Griffith University, Southport, Gold Coast Campus, University Drive, 4222, Australia; email: s.hickson@griffith.edu.au","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85002156884"
"Wehrle D.; Wiebelt B.; Von Suchodoletz D.","Wehrle, Dennis (53867392900); Wiebelt, Bernd (57192238628); Von Suchodoletz, Dirk (36195233800)","53867392900; 57192238628; 36195233800","Design of a Research Data Management capable storage system; [Design eines FDM-fähigen Speichersystems]","2017","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","271","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029679086&partnerID=40&md5=e078dac9ff7855efc0500f9064d0edf8","Universität Freiburg, Professur für Kommunikationssysteme, Hermann-Herder-Str. 10, Freiburg, 79104, Germany; Universität Freiburg, Rechenzentrum, Hermann-Herder-Str. 10, Freiburg, 79104, Germany","Wehrle D., Universität Freiburg, Professur für Kommunikationssysteme, Hermann-Herder-Str. 10, Freiburg, 79104, Germany; Wiebelt B., Universität Freiburg, Rechenzentrum, Hermann-Herder-Str. 10, Freiburg, 79104, Germany; Von Suchodoletz D., Universität Freiburg, Rechenzentrum, Hermann-Herder-Str. 10, Freiburg, 79104, Germany","[No abstract available]","Data life cycle; Forschungsdatenmanagement; Governance; Speichersystem; Storage","","","","","","","","Ball A., Review of Data Management Lifecycle Models, (2012); Ball A., Duke M., How to Cite Datasets and Link to Publications, (2011); Buttner S., Hobohm H.-C., Muller L., Handbuch Forschungsdatenmanagement, (2011); Eifert T., Muckel S., Schmitz D., Introducing research data management as a service suite at RWTH Aachen, Ges. für Informatik EV (GI); Erway R., Starting the conversation: University-wide research data management policy, ERIC, (2013); (2017); Klar J., Enke H., Report ""Organisation und Struktur"", (2013); Neuroth H., Strathmann S., Osswald A., Klump J., Ludwig J., Langzeitarchivierung von Forschungsdaten, (2012); Empfehlungen Zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland, (2016); Treloar A., Groenewegen D., Harboe-Ree C., The data curation continuum: Managing data objects in institutional repositories, D-Lib Magazine, 13, 9, (2007); Tristram F., Bamberger P., Cayoglu U., Hertzer J., Knopp J., Kratzke J., Rex J., Schwabe F., Shcherbakov D., Svoboda D.-F., Wehrle D., öffentlicher Abschlussbericht von BwFDM-Communities, (2015); Von Suchodoletz D., Schulz J., Leendertse J., Hotzel H., Wimmer M., Kooperation von Rechenzentren Governance und Steuerung - Organisation, Rechtsgrundlagen, (2016)","","Raiser H.; Muller P.; Rodosek G.D.; Neumair B.","Gesellschaft fur Informatik (GI)","","10. DFN-Forum Kommunikationstechnologien - 10th DFN Forum on Communication Technologies","30 May 2017 through 31 May 2017","Berlin","129893","16175468","978-388579665-7","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-85029679086"
"","","","21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10450 LNCS","","","1","655","654","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029578705&partnerID=40&md5=23ff600d9702a3a8933f89c9c80e8c30","","","The proceedings contain 63 papers. The special focus in this conference is on Theory and Practice of Digital Libraries. The topics include: Exploiting interlinked research metadata; preserving bibliographic relationships in mappings from FRBR to BIBFRAME 2.0; exploring ontology-enhanced bibliography databases using faceted search; taxonomic corpus-based concept summary generation for document annotation; a german corpus for a historical epidemic with temporal annotation; facet embeddings for explorative analytics in digital libraries; automatic hierarchical categorization of research expertise using minimum information; extracting event-centric document collections from large-scale web archives; information governance maturity model final development iteration; challenges of research data management for high performance computing; how linked data can aid machine learning-based tasks; classifying document types to enhance search and recommendations in digital libraries; understanding the influence of hyperparameters on text embeddings for text classification tasks; what users search for and why; on the uses of word sense change for research in the digital humanities; multi-aspect entity-centric analysis of big social media archives; a comparative study of language modeling to instance-based methods, and feature combinations for authorship attribution; semantic author name disambiguation with word embeddings; towards a knowledge graph representing research findings by semantifying survey articles; integration of scholarly communication metadata using knowledge graphs; analysing scholarly communication metadata of computer science events; high-pass text filtering for citation matching and sentiment classification over opinionated data streams through informed model adaptation.","","","","","","","","","","","Manolopoulos Y.; Kamps J.; Tsakonas G.; Iliadis L.; Karydis I.","Springer Verlag","The Coalition for Networked Information (CNI)","21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017","18 September 2017 through 21 September 2017","Thessaloniki","197829","03029743","978-331967007-2","","","English","Lect. Notes Comput. Sci.","Conference review","Final","","Scopus","2-s2.0-85029578705"
"De Waard A.","De Waard, Anita (8717707600)","8717707600","Research data management at Elsevier: Supporting networks of data and workflows","2016","Information Services and Use","36","1-2","","49","55","6","7","10.3233/ISU-160805","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996899165&doi=10.3233%2fISU-160805&partnerID=40&md5=64692b6b039936936d68721be9ca9b83","Elsevier Research Data Management, 71 Hanley Lane, Jericho, VT, United States","De Waard A., Elsevier Research Data Management, 71 Hanley Lane, Jericho, VT, United States","Sharing research data has the potential to make research more reproducible and efficient. Scientific research is a complex process and it is crucial that at the different stages of this process, researchers handle data in a way that will allow sharing and reuse. In this paper, we present a framework for the different steps involved in managing research data: a hierarchy of research data needs, and describe some of our own ongoing efforts to support these needs. Creating a good data ecosystem that supports each of these data needs requires collaboration between all parties that are involved in the generation, storage, retrieval and use of data: researchers, librarians, institutions, government offices, funders, and also publishers. We are actively collaborating with many other participants in the research data field, to develop a data ecosystem that enables data to be more useful, and reusable, throughout science and the humanities. © 2016 IOS Press and the authors.","data reuse; data sharing; open data; reproducibility; Research data; research data management; research integrity; scholarly publishing; transparency","Ecosystems; Information management; Publishing; Transparency; Data reuse; Data Sharing; Open datum; Reproducibilities; Research data; Research data managements; Research integrities; Scholarly publishing; Digital storage","","","","","","","A framework for interpreting genome-wide association studies of psychiatric disorders, Molecular Psychiatry, 14, 1, pp. 10-17, (2009); Ball A., How to License Research Data; Bandrowski A., Brush M., Grethe J.S., Haendel M.A., Kennedy D.N., Hill S., Hof P.R., Martone M.E., Pols M., Tan S.C., Washington N., Zudilova-Seinstra E., Vasilevsky N., RINL resource identification initiative, the resource identification initiative: A cultural shift in publishing, Brain and Behavior, 6, 1, (2016); Bechhofer S., Buchan I., De Roure D., Missier P., Ainsworth J., Bhagat J., Couch P., Cruickshank D., Delderfield M., Dunlop I., Gamble M., Michaelides D., Owen S., Newman D., Sufi S., Goble C., Why linked data is not enough for scientists, Future Generation Computer Systems, 29, 2, pp. 599-611, (2013); Burton A., Koers H., Manghi P., La Bruzzo S., Aryani A., Diepenbroek M., Schindler U., Metadata and Semantics Research, pp. 324-335, (2015); Cousijn H., Haak W., Koers H., Finding Better Ways to Connect Research Data with Scientific Literature: The Scholix Initiative is Building An Interoperability Framework That Will Make It Easier to Share, Exchange and Aggregate Data; Custovic A., Ainsworth J., Arshad H., Bishop C., Buchan I., Cullinan P., Devereux G., Henderson J., Holloway J., Roberts G., Turner S., Woodcock A., Simpson A., The study team for early life asthma research (STELAR) consortium 'Asthma e-lab': Team science bringing data, methods and investigators together, Thorax, 70, 8, pp. 799-801, (2015); De Waard A., Ten habits of highly effective data, discovery informatics, Papers from the AAAI-14 Workshop; De Waard A., Cousijn H., Aalbersberg I.J., 10 aspects of highly effective research data: Good research data management makes data reusable, Elsevier Connect, (2015); Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020; Hall J., 1,500 scientists speak out on Science's reproducibility crisis, Extreme Tech Blog, (2016); Koers H., How do we make it easy and rewarding for researchers to share their data? A Publisher's perspective, Journal of Clinical Epidemiology, 70, pp. 261-263, (2016); Koers H., Gabriel A., Capone R., Executable papers in computer science go live on science direct, Elsevier Connect, (2013); Lovegrove D., Lehnert K., The 2015 International Data Rescue Award in the Geosciences, Elsevier; Martone M., Data Citation Synthesis Group: Joint Declaration of Data Citation Principles, (2014); Maslow A.H., A theory of human motivation, Psychological Review, 50, 4, pp. 370-396, (1943); Mesirov J.P., Accessible reproducible research, Science, 327, 5964, pp. 415-416, (2010); Narock T., Arko R., Carbotte S., Krisnadhi A., Hitzler P., Cheatham M., Shepherd A., Chandler C., Raymond L., Wiebe P., Finin T., The ocean link project, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, pp. 14-21, (2015); National Institutes of Health Plan for Increasing Access to Scientific Publications and Digital Scientific Data from NIH Funded Scientific Research, (2015); Nature, (2014); NWO, Start Pilot Data Management, Netherlands Organisation for Scientific Research, (2015); Policy on Data Management and Sharing, Wellcome Trust, (2010); Access Vs. Importance: A Global Study Assessing the Importance of and Ease of Access to Professional and Academic Information, (2010); Reilly S., Schallier W., Schrimpf S., Smit E., Wilkinson M., Report on Integration of Data and Publications, (2011); Sadler J.M., Ames D.P., Livingston S.J., Extending hydro share to enable hydrologic time series data as social media, Journal of Hydroinformatics, 18, 2, pp. 198-209, (2016); Singleton A.D., Spielman S., Brunsdon C., Establishing a framework for open geographic information science, International Journal of Geographical Information Science, 30, 8, pp. 1507-1521, (2016); St Clair G., Ryan D., Olive: A Digital Archive for Executable Content, Coalition for Networked Information, (2011); Stvilia B., Hinnant C.C., Wu S., Worrall A., Lee D.J., Burnett K., Burnett G., Kazmer M.M., Marty P.F., Research project tasks, data, and perceptions of data quality in a condensed matter physics community, Journal of the Association for Information Science and Technology, 66, 2, pp. 246-263, (2015); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, (2011); Tripathy S.J., Alder J., Burton S.D., Harviston M., Marques D., Urban N.N., De Waard A., The urban legend project: A system for cellular neurophysiology data management and exploration, Frontiers in Neuroinformatics, Conference Abstract: Neuroinformatics 2014; Van Noorden R., Sluggish data sharing hampers reproducibility effort, Nature News, (2015); Vasilevsky N.A., Brush M.H., Paddock H., Ponting L., Tripathy S.J., Larocca G.M., Et al., On the reproducibility of science: Unique identification of research resources in the biomedical literature, PeerJ, 1, (2013); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data, 3, (2016)","A. De Waard; Elsevier Research Data Management, Jericho, 71 Hanley Lane, United States; email: a.dewaard@elsevier.com","","IOS Press","","","","","","01675265","","ISUSD","","English","Inf Serv Use","Conference paper","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-84996899165"
"Hadziselimovic E.; Fatema K.; Pandit H.; Lewis D.","Hadziselimovic, Ensar (57196035638); Fatema, Kaniz (57213696905); Pandit, Harshvardhan (57190943010); Lewis, David (57035492800)","57196035638; 57213696905; 57190943010; 57035492800","Linked data contracts to support data protection and data ethics in the sharing of scientific data","2017","CEUR Workshop Proceedings","1931","","","55","62","7","6","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031101711&partnerID=40&md5=80b26b7db1b4e79edc44799ac4bd8cbf","Adapt Centre, Trinity College Dublin, Ireland","Hadziselimovic E., Adapt Centre, Trinity College Dublin, Ireland; Fatema K., Adapt Centre, Trinity College Dublin, Ireland; Pandit H., Adapt Centre, Trinity College Dublin, Ireland; Lewis D., Adapt Centre, Trinity College Dublin, Ireland","In light of the new EU General Data Protection Regulation (GDPR) [1] there are certain challenges in relation to the sharing of scientific data. For a data controller in a research institute, the requirement to monitor, implement and demonstrate conformance to the provisions of data subjects' rights represents a major upshift in the complexity of research data management. We are trying to address the issues by analysing the details of data subject rights in GDPR followed by extending existing linked open data vocabularies. We are proposing a concept of machine- readable data protection rights contract through introducing Data Protection Rights Language (DPRL).","Data protection; DataID; GDPR; ODRL; Open scientific data","Data privacy; DataID; GDPR; General data protection regulations; Linked open datum; ODRL; Research data managements; Research institutes; Scientific data; Information management","","","","","ADAPT Centre for Digital Content Technology; Science Foundation Ireland, SFI, (13/RC/2106)","Supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under theuEropeaneRgional Development Fund.","Regulation (EU) 2016/679 of the European Parliament and of the Council (GDPR), Official Journal of The European Union, 119, 1, pp. 1-88, (2016); Godecharle S., Nemery B., Dierick K., Guidance on Research Integrity: No Union in Europe, The Lancet, 381, 9872, pp. p1097-p1098, (2013); Thompson B., Research and The General Data Protection Regulation, (2016); Roadmap for Research Data, (2013); Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2016); Hovy D., Spruit S.L., The Social Impact of Natural Language Processing, ACL, (2016); Montjoye Y.-A., Hidalgo C.A., Verleysen M., Blondel V.D., Unique in the Crowd: The privacy bounds of human mobility, Scientific Reports, 3, (2013); Gymrek M., McGuire A.L., Golan D., Halperin E., Erlich Y., Identifying Personal Genomes by Surname Inference, 339, 6117, pp. 321-324, (2013); Rights of Data Subjects Under The GDPR; Iannella R., Villata S., W3C: ODRL Information Model. 21 July 2016, W3C Working Draft, (2016); Freudenberg M., Brummer M.,  Dataid Core Ontology, W3C Member Submission; Maali F., Erickson J., Archer P., Data Catalog Vocabulary (DCAT), W3C Recommendation, (2014); Lebo T., Sahoo S., McGuinness D., W3C: PROV-O: The PROV Ontology, W3C Recommendation, (2013); Loscio B.F., Stephan E., Purohit S., W3C: Data on the Web Best Practices: Dataset Usage Vocabulary, W3C Note, (2016); CC Profile; ODRL Linked Data Profile; Profile or Recommendation; Rodriguez-Doncel V., Legal aspects of linked data – The European framework, Computer Law & Security Review, (2016); OEG: Linked Data Rights; Manghi P., An Infrastructure for Managing EC Funded Research Output - The Open-AIRE Project, The Grey Journal: An International Journal on Grey Literature, 6, (2010)","","Garijo D.; van Hage W.R.; Kauppinen T.; Zhao J.; Kuhn T.","CEUR-WS","","1st Workshop on Enabling Open Semantic Science, SemSci 2017","21 October 2017","Vienna","130673","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85031101711"
"Grant R.","Grant, Rebecca (56288648800)","56288648800","Recordkeeping and research data management: a review of perspectives","2017","Records Management Journal","27","2","","159","174","15","5","10.1108/RMJ-10-2016-0036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025112650&doi=10.1108%2fRMJ-10-2016-0036&partnerID=40&md5=e8f3a8e90a5656c6fedfba9928048131","Department of Digital Collections, National Library of Ireland, Dublin, Ireland; Department of Archivistics, University College Dublin, Dublin, Ireland","Grant R., Department of Digital Collections, National Library of Ireland, Dublin, Ireland, Department of Archivistics, University College Dublin, Dublin, Ireland","Purpose: The purpose of this paper is to explore a range of perspectives on the relationship between research data and records and between recordkeeping and research data management. Design/methodology/approach: This paper discusses literature in the field of research data management as part of preliminary work for the author’s doctoral research on the topic. The literature included in the review reflects contemporary and historical perspectives on the management and preservation of research data. Findings: Preliminary findings indicate that records professionals have been involved in the management and preservation of research data since the early twentieth century. In the literature, research data is described as comparable to records, and records professionals are widely acknowledged to have skills and expertise which are applicable to research data management. Records professionals are one of a number of professions addressing research data management. However, they are not currently considered to be leaders in research data management practice. Originality/value: Research data management is an emerging challenge as stakeholders in the research lifecycle increasingly mandate the publication of open, transparent research. Recent developments such as the publication of the OCLC report “The Archival Advantage: Integrating Archival Expertise into Management of Born-digital Library Materials”, and the creation of the Research Data Alliance Interest Group Archives and Records Professionals for Research Data indicates that research data is, or can be, within the remit of records professionals. This paper represents a snapshot of contemporary and historical attitudes towards research data and recordkeeping and thus contributes to this emerging area of discussion. © 2017, © Emerald Publishing Limited.","Competences; Data handling; Research data; Research data management; Skills","","","","","","","","Akmon D., Zimmerman A., Daniels M., Hedstrom M., The application of archival concepts to a data-intensive environment: working with scientists to understand data management and preservation needs, Archival Science, 11, 3, pp. 329-348, (2011); Anderson B.G., The structure of scientific archives: digital research data and the nature of scientific documentation, (2014); Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Beagrie N., Digital curation for science, digital libraries, and individuals, International Journal of Digital Curation, 1, pp. 3-16, (2006); Borglund E., Engvall T., Open data: data, information, document or record?, Records Management Journal, 24, 2, pp. 163-180, (2014); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1-40, (2012); Brichford M.J., Scientific and Technological Documentation Archival Evaluation and Processing of University Records Relating to Science and Technology, (1969); Brown T.E., Myth or reality: is there a generation gap among electronic records archivists?, Archivaria, 41, pp. 234-243, (1996); Buckland M., Information as thing, Journal of the American Society for Information Science, 42, 5, pp. 351-360, (1991); Bunn J., Higgins S., Mainstreaming digital curation: an overview of activity in the UK archives and records management profession, Proceedings of the Framing the Digital Curation Curriculum Conference, (2013); Research Data Management Briefing Paper, (2014); Childs S., McLeod J., Sharing research records and research data: findings from a research project in higher education, New Review of Information Networking, 10, 2, pp. 131-145, (2004); Childs S., McLeod J., Lomas E., Cook G., Opening research data: issues and opportunities, Records Management Journal, 24, 2, pp. 142-162, (2014); Conway E., Giaretta D., Lambert S., Matthews B., Curating scientific research data for the long term: a preservation analysis method in context, The International Journal of Digital Curation, 6, 2, pp. 38-52, (2011); Dooley J., The Archival Advantage: Integrating Archival Expertise into Management of Born-digital Library Materials, (2015); Doorn P., Tjalsma H., Introduction: archiving research data, Archival Science, 7, 1, pp. 1-20, (2007); Elliott C.A., Experimental data as a source for the history of science, The American Archivist, 37, 1, pp. 27-35, (1974); Evans J., Reed B., Linger H., Goss S., Holmes D., Drobik J., Woodyat B., Henbest S., Winds of change: a recordkeeping informatics approach to information management needs in data-driven research environments, Records Management Journal, 24, 3, pp. 205-233, (2014); Garrod P., Use of the UNESCO thesaurus for archival subject indexing at UK NDAD, Journal of the Society of Archivists, 21, 1, pp. 37-54, (2000); Grover W.C., The role of the archivist in the preservation of scientific records, Isis, 53, 1, pp. 55-62, (1962); Gruman G.J., Preserving the stuff of history, Science, 127, 3313, (1958); Harper P., Preserving Scientific Archives: The Work of the National Cataloguing Unit for the Archives of Contemporary Scientists, (2005); ISAD(G): General International Standard Archival Description, (2000); Jones M., ‘Encountering the stranger’: working digitally to connect records and data for communities, (2012); McCarthy G., Sherratt T., Mapping scientific memory: understanding the role of recordkeeping in scientific practice, Archives and Manuscripts, 24, 1, pp. 78-85, (1996); McDonald J., Records management and data management: closing the gap, Records Management Journal, 20, 1, pp. 53-60, (2010); McLeod J., Childs S., Managing Primary Research Data and Records for Research in HE Institutions: Final Project Report, (2003); Maday C., Moysan M., Records management for scientific data, Archives and Manuscripts, 24, 2, pp. 190-192, (2014); Miller W.E., The less obvious functions of archiving survey research data, The American Behavioural Scientist, 19, 4, (1976); National principles for open access policy statement, (2013); Nielsen P., Merging cultures: Danish integration of academic data service into traditional archive system, IASSIST Quarterly, 19, 2, pp. 27-35, (1995); Noonan D., Chute T., Data curation and the university archives, The American Archivist, 77, 1, pp. 201-240, (2014); O'Neill Adams M., Analyzing archives and finding facts: use and users of digital data records, Archival Science, 7, 1, pp. 21-36, (2007); Palmer C.L., Weber N.M., Munoz T., Renear A.H., Foundations of data curation: the pedagogy and practice of ‘purposeful work’ with research data, Archive Journal, 3, (2013); Poole A.M., How has your science data grown? Digital curation and the human factor: a critical literature review, Archival Science, 15, pp. 113-119, (2015); Pryor G., Donnelly M., Skilling up to do data: whose role, whose responsibility, whose career?, International Journal of Digital Curation, 4, 2, (2009); Reed B., Records, Archives: Recordkeeping in Society, Centre for Information Studies, pp. 101-130, (2005); Reingold N., The national archives and the history of science in America, Isis, 46, 1, pp. 22-28, (1955); Shankar K., Towards a framework for managing electronic records in scientific research, Archival Issues, 24, 1, pp. 21-36, (1999); Sleeman P., It’s public knowledge: the national digital archive of datasets, Archivaria, 58, pp. 173-200, (2004); Thorpe K., Gardiner G., Creating, preserving and connecting data in the digital domain, (2012); Wallis J.C., Borgman C.L., Mayernik M.S., Pepe A., Moving archival practices upstream: an exploration of the life cycle of ecological sensing data in collaborative field research, The International Journal of Digital Curation, 3, 1, pp. 114-126, (2008); Wilson J.A.J., Fraser M.A., Martinez Uribe L., Patrick M., Akram A., Mansoori T., Developing Infrastructure for research data management at the University of Oxford, Ariadne, 65, (2010); Yarmey K., Yarmey L., All in the family: a dinner table conversation about libraries, archives, data, and science, Archives Journal, (2013); Yorke S., Management of petroleum data records in the custody of Australian archives, Archives and Manuscripts, 25, 1, pp. 62-73, (1997); Multilingual Archival Terminology: Archivist, (2015); Multilingual Archival Terminology: Record, (2015); Multilingual Archival Terminology: Records Manager, (2015); ISO 15489-1:2016 Information and Documentation – Records Management – Part 1: Concepts and Principles, (2016); Knight G., Pennock M., Data without meaning: establishing the significant properties of digital research, The International Journal of Digital Curation, 1, 4, pp. 159-172, (2009); McLeod J., Hare C., Development of RMJ, Records Management Journal, 20, 1, pp. 9-40, (2010); Information and documentation, records management: part 1: general, (2004)","R. Grant; Department of Digital Collections, National Library of Ireland, Dublin, Ireland; email: beck.grant@gmail.com","","Emerald Group Publishing Ltd.","","","","","","09565698","","","","English","Rec. Manage. J.","Review","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85025112650"
"Ulrich H.; Kock A.-K.; Duhm-Harbeck P.; Habermann J.K.; Ingenerf J.","Ulrich, Hannes (56347799300); Kock, Ann-Kristin (57194129811); Duhm-Harbeck, Petra (57194134090); Habermann, Jens K. (7004162394); Ingenerf, Josef (6603276740)","56347799300; 57194129811; 57194134090; 7004162394; 6603276740","Metadata repository for improved data sharing and reuse based on HL7 FHIR","2017","Studies in Health Technology and Informatics","228","","","162","166","4","27","10.3233/978-1-61499-678-1-162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020374693&doi=10.3233%2f978-1-61499-678-1-162&partnerID=40&md5=e1099269953a18b4fa53b9ec5d94134b","Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany; IT for Clinical Research, University of Lübeck, Lübeck, Germany; Interdisciplinary Center for Biobanking-Lübeck, University of Lübeck, Germany; Department of Surgery, University of Lübeck and University Clinical Center Schleswig-Holstein, Campus Lübeck, Germany","Ulrich H., Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany; Kock A.-K., IT for Clinical Research, University of Lübeck, Lübeck, Germany; Duhm-Harbeck P., IT for Clinical Research, University of Lübeck, Lübeck, Germany; Habermann J.K., Interdisciplinary Center for Biobanking-Lübeck, University of Lübeck, Germany, Department of Surgery, University of Lübeck and University Clinical Center Schleswig-Holstein, Campus Lübeck, Germany; Ingenerf J., Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany, IT for Clinical Research, University of Lübeck, Lübeck, Germany","Unreconciled data structures and formats are a common obstacle to the urgently required sharing and reuse of data within healthcare and medical research. Within the North German Tumor Bank of Colorectal Cancer, clinical and sample data, based on a harmonized data set, is collected and can be pooled by using a hospital-integrated Research Data Management System supporting biobank and study management. Adding further partners who are not using the core data set requires manual adaptations and mapping of data elements. Facing this manual intervention and focusing the reuse of heterogeneous healthcare instance data (value level) and data elements (metadata level), a metadata repository has been developed. The metadata repository is an ISO 11179-3 conformant server application built for annotating and mediating data elements. The implemented architecture includes the translation of metadata information about data elements into the FHIR standard using the FHIR Data Element resource with the ISO 11179 Data Element Extensions. The FHIR-based processing allows exchange of data elements with clinical and research IT systems as well as with other metadata systems. With increasingly annotated and harmonized data elements, data quality and integration can be improved for successfully enabling data analytics and decision support. © 2016 European Federation for Medical Informatics (EFMI) and IOS Press.","Data curation; Hl7 FHIR; MDR; RDMS","Biomedical Research; Colorectal Neoplasms; Database Management Systems; Humans; Metadata; Tissue Banks; Clinical research; Data Analytics; Data curation; Decision support systems; Diseases; Health care; Medical informatics; Metadata; Hl7 FHIR; Implemented architectures; Integrated research; Manual intervention; Metadata information; Metadata repositories; RDMS; Server applications; colorectal tumor; database management system; human; medical research; metadata; organization and management; pathology; standards; tissue bank; Information management","","","","","","","Oberlander M., Linnebacher M., Habermann J.K., Consortium C., The ""north German tumor bank of colorectal cancer"": Status report after the first 2 years of support by the German cancer aid foundation, Langenbecks Archives of Surgery, 398, 2, pp. 251-258, (2013); Smits M., Kramer E., Harthoorn M., Cornet R., A comparison of two detailed clinical model representations: FHIR and CDA, European Journal for Biomedical Informatics, 11, 2, pp. en7-en17, (2015); Ingenerf J., Kock A.-K., Poelker M., Seidl K., Zeplin G., Mersmann S., Et al., Standardizing intensive care device data to enable secondary usages, Stud Health Technol Inform, 180, pp. 619-623, (2012); Choquet R., Maaroufi M., De Carrara A., Messiaen C., Luigi E., Landais P., A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research, J Am Med Inform Assoc, 22, 1, pp. 76-85, (2014); Park Y.R., Yoon Y.J., Kim H.H., Kim J.H., Establishing semantic interoperability of biomedical metadata registries using extended semantic relationships, Stud Health Technol Inform, 192, pp. 618-621, (2013); Dugas M., Metadata Repository for Medical Forms, (2016); Kopcke F., Kraus S., Scholler A., Nau C., Schuttler J., Prokosch H.U., Et al., Secondary use of routinely collected patient data in a clinical trial: An evaluation of the effects on patient recruitment and data acquisition, Int J Med Inform, 82, 3, pp. 185-192, (2013)","H. Ulrich; Institute of Medical Informatics, University of Lübeck, Lübeck, Ratzeburger Allee 160, 23562, Germany; email: hannes.ulrich@student.uni-luebeck.de","Hoerbst A.; Hackl W.O.; de Keizer N.; Prokosch H.-H.; Hercigonja-Szekeres M.; de Lusignan S.","IOS Press","","Medical Informatics Europe Conference, MIE 2016 at the Health - Exploring Complexity: An Interdisciplinary Systems Approach, HEC 2016","28 August 2016 through 2 September 2016","Munich","131592","09269630","","","27577363","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-85020374693"
"Simons E.; Jetten M.; Messelink M.; Van Berchum M.; Schoonbrood H.; Wittenberg M.","Simons, Ed (57194549505); Jetten, Mijke (57194546253); Messelink, Maaike (35491347800); Van Berchum, Marnix (56175640800); Schoonbrood, Hans (57194544480); Wittenberg, Marion (57194548933)","57194549505; 57194546253; 35491347800; 56175640800; 57194544480; 57194548933","The Important Role of CRIS's for Registering and Archiving Research Data. the RDS-project at Radboud University (the Netherlands) in Cooperation with Data-archive dans","2017","Procedia Computer Science","106","","","321","328","7","7","10.1016/j.procs.2017.03.031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020719905&doi=10.1016%2fj.procs.2017.03.031&partnerID=40&md5=d229a06894fbafb32973fb55eb6f9996","Radboud University, Comeniuslaan 4, HP Nijmegen, 6525, Netherlands; Data Archiving and Networked Services (DANS), Anna van Saksenlaan 51, HW Den Haag, 2593, Netherlands","Simons E., Radboud University, Comeniuslaan 4, HP Nijmegen, 6525, Netherlands; Jetten M., Radboud University, Comeniuslaan 4, HP Nijmegen, 6525, Netherlands; Messelink M., Radboud University, Comeniuslaan 4, HP Nijmegen, 6525, Netherlands; Van Berchum M., Data Archiving and Networked Services (DANS), Anna van Saksenlaan 51, HW Den Haag, 2593, Netherlands; Schoonbrood H., Radboud University, Comeniuslaan 4, HP Nijmegen, 6525, Netherlands; Wittenberg M., Data Archiving and Networked Services (DANS), Anna van Saksenlaan 51, HW Den Haag, 2593, Netherlands","Optimal research data management and archiving is a key condition for progress in modern science and of vital importance from both the point of view of research as such as well as research policy and management. More specifically, it is a conditio sine qua non for the realization of Open Science and at the same time it is indispensable for the monitoring and assessment of the quality and integrity of research. Various aspects play a role here: optimal infrastructures and tools for the actual handling of data during the research lifecycle, appropriate metadata to describe the datasets, and - last but not least - an adequate organizational framework to curate and archive the datasets professionally and provide optimal support and services to the researchers. The paper presents the Research Data Services (RDS) project of Radboud University (the Netherlands) in cooperation with one of the Dutch national research data archives: DANS (Data Archiving and Networked Services). In this project, a model is worked out for the archiving of research datasets via the CRIS (Current Research Information System) of the university, including both the registration of the metadata as well as the actual upload of the data files to the DANS archive. It is argued that an optimal solution is not only a technical matter, but also requires the definition and organization of appropriate support, management structures and workflows, involving both local and national partners. In this respect, attention is paid to the explanation of the frontoffice-backoffice model(FoBo) that is being defined and implemented as part of the project and which forms the organizational backbone of the solution worked out. The paper starts by arguing that aCRIS-oriented approach in research data archiving holds substantial added value, and it ends with an overview of lessons learned and a peek into the future of the RDS-project. © 2017 The Authors.","Current Research Information System (CRIS); data archiving; Data Archiving and Networked Services (DANS); data curation; data registration; frontoffice-backoffice model; Radboud University; research data management (RDM); research information services (RIS)","Data handling; Information services; Information systems; Metadata; Sounding apparatus; Data archiving; Data curation; Data registration; Networked services; Radboud University; Research data managements; Research information systems; Information management","","","","","","","Wilkinson M.D., Dumontier M., Et al., The Fair Guiding Principles for scientific data management and stewardship, Nature - Scientific Data, 3","","Clements A.; Sicilia M.-A.; Simons E.; de Castro P.","Elsevier B.V.","","13th International Conference on Current Research Information Systems, CRIS 2016","9 June 2016 through 11 June 2016","Scotland","135998","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85020719905"
"Tripathi M.; Shukla A.; Sonker S.K.","Tripathi, Manorama (7007046122); Shukla, Archana (56074392600); Sonker, Sharad Kumar (56904054700)","7007046122; 56074392600; 56904054700","Research data management practices in university libraries: A study","2017","DESIDOC Journal of Library and Information Technology","37","6","","417","424","7","28","10.14429/djlit.37.6.11336","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033566319&doi=10.14429%2fdjlit.37.6.11336&partnerID=40&md5=4be86ecbd1a2abd93e33010216fc684c","Jawaharlal Nehru University, New Delhi, India; Department of Library and Information Science, Indira Gandhi National Open University, New Delhi, India; Department of Library and Information Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India","Tripathi M., Jawaharlal Nehru University, New Delhi, India, Department of Library and Information Science, Indira Gandhi National Open University, New Delhi, India, Department of Library and Information Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India; Shukla A., Jawaharlal Nehru University, New Delhi, India, Department of Library and Information Science, Indira Gandhi National Open University, New Delhi, India, Department of Library and Information Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India; Sonker S.K., Jawaharlal Nehru University, New Delhi, India, Department of Library and Information Science, Indira Gandhi National Open University, New Delhi, India, Department of Library and Information Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India","Research data is the data which is generated when the researchers undertake or execute any research activity or project. The data may be textual, quantitative, qualitative, images, recordings, musical compositions, verbal communication, experimental readings, simulations, codes and so on. It needs to be preserved for future use.In this context, the paper has studied the research data management (RDM) services implemented by different university libraries for managing, organising, curating and preserving research data generated at their universities’ departments and laboratories, for reusing and sharing. It has surveyed the central university libraries and the best 20 university libraries of the world to highlight how RDM is extended to the researchers. Further, it has suggested a model for the university libraries in the country to follow for actually deploying RDM services. © 2017, DESIDOC.","Data curation; Data preservation; Data repositories; Research data; Research data management","","","","","","National Science Foundation, NSF","The National Science Foundation (NSF) mandates that","Prado C., Javier A., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Chabot L., Bivens-Tatum W., Coates H., Kern M., 2016 top trends in academic libraries A review of the trends and issues affecting academic libraries in higher education, College Res. Libr. News, 77, 6, pp. 274-281, (2016); Chao T.C., Cragin M.H., Palmer C.L., Data Practices and Curation Vocabulary (DPCVocab): An empirically derived framework of scientific data practices and curatorial processes, J. Assoc. Info. Sci. Technol., 66, 3, pp. 616-633, (2015); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Curdt C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DBproject database, Program: Electronic Lib. Info. Syst, 49, 4, pp. 494-512, (2015); Cushing A.L., Dumbleton O., We have to make an effort with it’ Exploring the use of stages to help understand the personal information management needs of humanities and social science doctoral students managing dissertation information, IFLA Journal, 43, 1, pp. 40-50, (2017); Faniel I.M., Kriesberg A., Yakel E., Social scientists’ satisfaction with data reuse, J. Assoc. Info. Sci. Technol., 67, 6, pp. 1404-1416, (2016); Hickson S., Et al., Modifying researchers’ data management practices: A behavioural framework for library practitioners, IFLA Journal, 42, 4, pp. 253-265, (2016); Jackson A.S., Wheeler J., Quinn T., Data Curation and the Arts: How Do Musicians Curate Their Data?, Music Ref. Serv. Q, 19, pp. 191-207, (2016); Karcher S., Kirilova D., Weber N., Beyond the matrix: Repository services for qualitative data, IFLA Journal, 42, 4, pp. 292-302, (2016); Koltay T., Data governance, data literacy and the management of data quality, IFLA Journal, 42, 4, pp. 303-312, (2016); Lassi M., Johnsson M., Golub K., Research data services: An exploration of requirements at two Swedish universities, IFLA Journal, 42, 4, pp. 266-277, (2016); Lyon L., Librarians in the Lab: Toward radically re-engineering data curation services at the research coalface, New Rev. Acad. Librarianship, 22, pp. 391-409, (2016); Macy K.V., Coates H.L., Data information literacy instruction in Business and Public Health: Comparative case studies, IFLA Journal, 42, 4, pp. 313-327, (2016); Maybee C., Zilinski L., Data informed learning: A next phase data literacy framework for higher education, Proceedings Assoc. Info. Sci. Technol, 52, 1, pp. 1-4, (2015); Mayernik M.S., Research data and metadata curation as institutional issues, J. Assoc. Inf. Sci. Technol, 67, pp. 973-993, (2016); Ou S., Zhou Y., Current Status of Scientific Data Curation Research and Practices in Mainland China, Libr. Libr. Inf. Sci, 26, 1, pp. 73-88, (2016); Poole A.H., How has your science data grown? Digital curation and the human factor: A critical literature review, Archival Science, 15, 2, pp. 101-139, (2015); Renwick S., Winter M., Gill M., Managing research data at an academic library in a developing country, IFLA Journal, 43, 1, pp. 51-64, (2017); Tenopir C., Et al., Research data management services in academic research libraries and perceptions of librarians, Lib. Info. Sci. Res., 36, 2, pp. 84-90, (2014); Tripathi M., Et al., A brief assessment of researchers’ perceptions towards research data in India, IFLA Journal, 43, 1, pp. 22-39, (2017); Loon V., James E., Et al., Quality evaluation of data management plans at a research university, IFLA Journal, 43, 1, pp. 98-104, (2017); Witt M., Horstmann W., International approaches to research data services in libraries, IFLA Journal, 42, 4, pp. 251-252, (2016); Weber N.M., Palmer C.L., Chao T.C., Current trends and future directions in data curation research and education, J. Web Librarianship, 6, 4, pp. 305-320, (2012)","","","Defence Scientific Information and Documentation Centre","","","","","","09740643","","","","English","DESIDOC J. Libr. Inf. Technol.","Article","Final","","Scopus","2-s2.0-85033566319"
"Liu Q.","Liu, Qian (56941048200)","56941048200","Research data management and academic library service","2016","Conference Proceedings of the 4th International Symposium on Project Management, ISPM 2016","","","","559","563","4","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988001306&partnerID=40&md5=48660e9c645e9b26f4304ff9b09e172b","Library, Central China Normal University, Wuhan, Hubei Province, China","Liu Q., Library, Central China Normal University, Wuhan, Hubei Province, China","In data intensive era, research data is generated at an amazing speed. Research data management will promote scientific research, it has become a great challenge to manage scientific data and it is argued that research data management is rapidly becoming the focus of attention in the field of science and however the existing research also lacks details of the study. This paper is based on the recognize the importance of providing services to organize and reuse research data, focus on researchers requirement of research data management and data types, access control etc. The paper explored the research data management architecture and aims to be a guide for future work in academic library.","Academic library; Data curation; Data sharing; E-science era; Research data management","Access control; Libraries; Project management; Academic libraries; Data curation; Data Sharing; E-sciences; Research data managements; Information management","","","","","","","Taylor J., Defining E-science[EB/OL], (2016); Ralph S., E-Sciences as research technologies: Reconfiguring disciplines, globalizing knowledge[J], Social Science Information, 47, 2, pp. 131-157, (2008); XiaoFeng P., XiaoLin Z., The Fouth Paradigm: Data-Intensive Scientific Discovery[M], (2012); (2016); Brandt D.S., Librarians As Partners in E-research-Purdue University Libraries Promote Collaboration [EB/OL], (2016); Luce R., A New Value Equation Challenge: The Emergence of E-Research and Roles for Research Libraries [EB/OL.], (2016); Gray J., Scientific Data Management in the Coming Decade [EB/OL.], (2016); Peng Q., An analysis of the duality of information lifecycle management: A case study of scientific data management[J], Information Studies: Theory & Application, 3, pp. 11-14, (2013); Daqing C., Study on data management service framework in foreign academic universities[J], Journal of Academic Libraries, 6, pp. 10-17, (2013); Ying X., Jianfei L., Ning D., Practice and exploration of scientific data management service in University library - A case study of the social research data management in Wuhan university[J], Information Studies: Theory & Application., 12, pp. 89-93, (2013); XinNian W., On the Scientific data management services of Academic libraries[J], Information and Documentation Services, 5, pp. 74-78, (2014); (2014); (2016); Witt M., Carlson D., Brandt D.S., Et al., Constructing data curation profiles[J], International Journal of Digital Curation, 4, 3, pp. 93-103, (2009); Huang H., Stvilia B., Roles and perceived priorities for data quality dimensions and skills in genome curation work[C], Proceedings of ASIST, pp. 26-30, (2012)","Q. Liu; Library, Central China Normal University, Wuhan, Hubei Province, China; email: 190350454@qq.com","Cao X.S.; Zhang H.; Cheng C.B.","Aussino Academic Publishing House","Hubei Zhongke Institute of Geology and Environment Technology","4th International Symposium on Project Management, ISPM 2016","9 July 2016 through 10 July 2016","Wuhan","123412","","978-192171248-7","","","English","Conf. Proc. Int. Symp. Proj. Manag., ISPM","Conference paper","Final","","Scopus","2-s2.0-84988001306"
"Chard K.; Tuecke S.; Foster I.","Chard, Kyle (9132950200); Tuecke, Steven (6602740450); Foster, Ian (35572232000)","9132950200; 6602740450; 35572232000","Globus: Recent enhancements and future plans","2016","ACM International Conference Proceeding Series","17-21-July-2016","","a27","","","","11","10.1145/2949550.2949554","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989181278&doi=10.1145%2f2949550.2949554&partnerID=40&md5=323c5238068032df7ce8d7eaa64fb168","Computation Institute, University of Chicago, Argonne National Laboratory, United States","Chard K., Computation Institute, University of Chicago, Argonne National Laboratory, United States; Tuecke S., Computation Institute, University of Chicago, Argonne National Laboratory, United States; Foster I., Computation Institute, University of Chicago, Argonne National Laboratory, United States","Globus offers a broad suite of research data management ca-pabilities to the research community as web-accessible ser-vices. The initial service, launched in 2010, focused on re-liable, high-performance, secure data transfer; since that time, Globus capabilities have been progressively enhanced in response to user demand. In 2015, secure data sharing and publication services were introduced. Other recent en-hancements include support for secure HTTP data access, new storage system types (e.g., Amazon S3, HDFS, Ceph), endpoint search, and administrator management. A power-ful new authentication and authorization platform service, Globus Auth, addresses identity, credential, and delegation management needs encountered in research environments. New REST APIs allow external and third-party services to leverage Globus data management, authentication, and authorization capabilities as a platform, for example when building research data portals. We describe these and other recent enhancements to Globus, review adoption trends (to date, 40,000 registered users have operated on more than 150PB and 25B files), and present future plans. © 2016 ACM.","Globus; Research data management; Science as a service","Authentication; Big data; Data transfer; Digital storage; HTTP; Search engines; World Wide Web; Authentication and authorization; Globus; Research communities; Research data managements; Research environment; Science as a service; Secure data transfer; Third party services; Information management","","","","","","","(2016); Dataverse, (2016); (2016); PURR: Purdue University Research Repository, (2016); (2016); Allen B., Bresnahan J., Childers L., Foster I., Kandaswamy G., Kettimuthu R., Kordas J., Link M., Martin S., Pickett K., Tuecke S., Software as a service for data scientists, Communications of the ACM, 55, 2, pp. 81-88, (2012); Ananthakrishnan R., Chard K., Foster I., Tuecke S., Globus platform-as-a-service for collaborative science applications, Concurrency-Practice and Experience, 27, pp. 290-305, (2014); Ardestani S.B., Hakansson C.J., Laure E., Livenson I., Stranak P., Dima E., Blommesteijn D., Van De Sanden M., B2share: An open escience data sharing platform, Proceedings of the 11th IEEE International Conference on E-Science, pp. 448-453, (2015); Basney J., Fleury T., Gaynor J., Cilogon: A federated x.509 certification authority for cyberinfrastructure logon, Concurrency and Computation: Practice and Experience, 26, 13, pp. 2225-2239, (2014); Blaiszik B., Chard K., Ananthakrishnan R., Tuecke S., Foster I., The materials data facility: Data services to advance materials science research, Journal of the Minerals, Metals & Materials Society, (2016); Chard K., Lidman M., McCollam B., Bryan J., Ananthakrishnan R., Tuecke S., Foster I., Globus nexus: A platform-as-a-service provider of research identity, profile, and group management, Future Generation Computer Systems, 56, pp. 571-583, (2016); Chard K., Pruyne J., Blaiszik B., Ananthakrishnan R., Tuecke S., Foster I., Globus data publication as a service: Lowering barriers to reproducible science, Proceedings of the 11th IEEE International Conference on E-Science, pp. 401-410, (2015); Chard K., Tuecke S., Foster I., Efficient and secure transfer, synchronization, and sharing of big data, IEEE Cloud Computing, 1, 3, pp. 46-55, (2014); Dooley R., Vaughn M., Stanzione D., Skidmore E., Software-as-a-service: The iplant foundation api, 5th IEEE Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS), (2012); Foster I., Globus online: Accelerating and democratizing science through cloud-based services, Internet Computing, IEEE, 15, 3, pp. 70-73, (2011); Hanushevsky A., Trunov A., Cottrell L., Peer-to-peer computing for secure high performance data copying, Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, (2001); Hardt D., The OAuth 2.0 Authorization Framework, (2012); Kosar T., Livny M., A framework for reliable and efficient data placement in distributed computing systems, Journal of Parallel and Distributed Computing, 65, 10, pp. 1146-1157, (2005); Mailman M., Feolo M., Jin Y., Kimura M., Tryka K., Bagoutdinov R., Hao L., Kiang A., Paschall J., Phan L., Popova N., Pretel S., Ziyabari L., Lee M., Shao Y., Wang Z., Sirotkin K., Ward M., Kholodov M., Zbicz K., Beck J., Kimelman M., Shevelev S., Preuss D., Yaschenko E., Graeff A., Ostell J., Sherry S., The ncbi dbgap database of genotypes and phenotypes, Nature Genetics, 39, 10, pp. 1181-1186, (2007); Pierce M., Marru S., Gunathilake L., Kanewala T., Singh R., Wijeratne S., Wimalasena C., Herath C., Chinthaka E., Mattmann C., Slominski A., Tangchaisin P., Apache airavata: Design and directions of a science gateway framework, Proceedings of the 6th International Workshop on Science Gateways (IWSG), pp. 48-54, (2014); Rajasekar A., Moore R., Hou C.-Y., Lee C.A., Marciano R., De Torcy A., Wan M., Schroeder W., Chen S.-Y., Gilbert L., Tooby P., Zhu B., IRODS Primer: Integrated Rule-Oriented Data System, (2010); Sakimura N., Bradley J., Jones M., De Medeiros B., Mortimore C., OpenID Connect Core 1.0, (2014); Smith M., Barton M., Bass M., Branschofsky M., McClellan G., Stuve D., Tansley R., Walker J.H., Dspace: An open source dynamic digital repository, D-Lib Magazine, 9, 1, (2003); Towns J., Cockerill T., Dahan M., Foster I., Gaither K., Grimshaw A., Hazlewood V., Lathrop S., Lifka D., Peterson G.D., Roskies R., Scott J.R., Wilkins-Diehr N., Xsede: Accelerating scientific discovery, Computing in Science Engineering, 16, 5, pp. 62-74, (2014)","","","Association for Computing Machinery","Cray; DataDirect Networks; Dell; et al.; Hewlett Packard Enterprise; Intel","Conference on Diversity, Big Data, and Science at Scale, XSEDE 2016","17 July 2016 through 21 July 2016","Miami","123713","","978-145034755-6","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-84989181278"
"Linne M.; Zenk-Möltgen W.","Linne, Monika (57189801860); Zenk-Möltgen, Wolfgang (55781093800)","57189801860; 55781093800","Strengthening institutional data management and promoting data sharing in the social and economic sciences","2017","LIBER Quarterly","27","1","","58","72","14","1","10.18352/lq.10195","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046339558&doi=10.18352%2flq.10195&partnerID=40&md5=574c4b934a59ebfd8702e90b2d9dcbaa","GESIS Leibniz Institute for the Social Sciences, Köln, Germany","Linne M., GESIS Leibniz Institute for the Social Sciences, Köln, Germany; Zenk-Möltgen W., GESIS Leibniz Institute for the Social Sciences, Köln, Germany","In the German social and economic sciences there is a growing awareness of flexible data distribution and research data reuse, especially as increasing numbers of research funders recommend publishing research data as the basis for scientific insight. However, a data-sharing mentality has not yet been established in Germany attributable to researchers’ strong reservations about publishing their data. This attitude is exacerbated by the fact that, at present, there is no trusted national data sharing repository that covers the particular requirements of institutions regarding research data. This article discusses how this objective can be achieved with the project initiative SowiDataNet. The development of a community-driven data repository is a logically consistent and important step towards an attitude shift concerning data sharing in the social and economic sciences. © 2017, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Data archiving; Data curation; Data sharing; Digital preservation; Research data management; Research data repository","","","","","","German Institute for Economic Research; German National Library of Economics; Social Science Centre Berlin; Utrecht University Library Open Access Journals; WZB; Association of College and Research Libraries; Leibniz-Gemeinschaft","Funding text 1: data, making it both visible and available to the scholarly community. Nevertheless, to counteract growing disparities in the social and economic scientific data landscape, additional development work focusing on integrating research data from different sources was needed. In this context integrating data from scientific institutions is of special interest. Only a small number of research institutes are able to assemble and continually operate research data infrastructures using their own resources. For this reason the project SowiDataNet was initiated. GESIS, in collaboration with the Social Science Centre Berlin (WZB), the German Institute for Economic Research (DIW), and the German National Library of Economics (ZBW), started SowiDataNet’s development and is funded by the Leibniz Association.; Funding text 2: E-ISSN: 2213-056X Published by LIBER, the Association of European Research Libraries | Supported by Utrecht University Library Open Access Journals; Funding text 3: The project SowiDataNet is funded by the Leibniz Association.","Dross P.J., Kurzstudie: Anforderungen an Die Archivierung Sozial- Und Wirtschaftswissenschaftlicher Forschungsdaten, (2015); Dross P., Linne M., Sicheres und einfaches Data Sharing mit SowiDataNet: Dokumentieren - veröffentlichen - nachnutzen, Bibliotheksdienst, 50, 7, pp. 649-660, (2016); Riding the Wave. How Europe Can Gain from the Rising Tide of Scientific Data, (2010); Fienberg S.E., Martin M.E., Straf M.L., Sharing Research Data: Report of the Committee on National Statistics, (1985); Safeguarding Good Scientific Practice, (1998); Leitlinien Zum Umgang Mit Forschungsdaten, (2015); Gherghina S., Katsanidou A., Data availability in political science journals, European Political Science, 12, 3, pp. 333-349, (2013); Hausstein B., Zenk-Moltgen W., Da|ra - Ein Service der GESIS für die Zitation sozialwissenschaftlicher Daten, Digitale Wissenschaft: Stand Und Entwicklung Digital Vernetzter Forschung in Deutschland, pp. 139-147, (2011); Jasny B.R., Chin G., Chong L., Vignieri S., Again, and Again, and again… Science, 334, 6060, (2011); Kuhne M., Meusel D., Data Sharing. Arbeitspapier TU Dresden; Philosophische Fakultät, (2007); Kvalheim V., Kvamme T., Policies for Sharing Research Data in Social Sciences and Humanities, (2014); Linne M., Sustainable data preservation using datorium: Facilitating the scientific ideal of data sharing in the social sciences, Proceedings of the 10Th International Conference on Preservation of Digital Objects, pp. 150-155, (2013); Nair V.K., Chandra V., Informatics, (2014); Nelson B., Data sharing: Empty archives, Nature, 461, pp. 160-163, (2009); What is the Open Research Data Pilot?, (2016); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, Plos One, 2, 3, (2007); To Share Or Not to Share: Publication and Quality Assurance of Research Data Outputs, (2008); Vlaeminck S., Siegert O., Welche Rolle spielen Forschungsdaten eigentlich für Fachzeitschriften?, Eine Analyse Mit Fokus Auf Die Wirtschaftswissenschaften, (2012); Weichselgartner E., Gunther A., Dehnhard I., Archivierung von Forschungsdaten, Handbuch Forschungsdatenmanagement, pp. 191-202, (2011); Wissenschaftsrat, Empfehlungen Zur Weiterentwicklung Der Wissenschaftlichen Informationsinfrastrukturen in Deutschland Bis 2020, (2012); Zenk-Moltgen W., Linne M., Datorium - ein neuer Service für Archivierung und Zugang zu sozialwissenschaftlichen Forschungsdaten, Beiträge Des Workshops „Digitale Langzeitarchivierung“auf Der Informatik 2013, 1, pp. 14-22, (2013); Zenk-Moltgen W., Lepthien G., Data sharing in sociology journals, Online Information Review, 38, 6, pp. 709-722, (2014)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85046339558"
"Arend D.; Junker A.; Scholz U.; Schüler D.; Wylie J.; Lange M.","Arend, Daniel (55531371500); Junker, Astrid (35792080900); Scholz, Uwe (56124842400); Schüler, Danuta (56289878700); Wylie, Juliane (57189043864); Lange, Matthias (36028279400)","55531371500; 35792080900; 56124842400; 56289878700; 57189043864; 36028279400","PGP repository: A Plant phenomics and genomics data publication infrastructure","2016","Database","2016","","baw033","","","","70","10.1093/database/baw033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964822503&doi=10.1093%2fdatabase%2fbaw033&partnerID=40&md5=d4dc7bcdcdbf82b3050e786d1ba9b22c","Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany","Arend D., Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany; Junker A., Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany; Scholz U., Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany; Schüler D., Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany; Wylie J., Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany; Lange M., Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, Gatersleben, 06466, Germany","Plant genomics and phenomics represents the most promising tools for accelerating yield gains and overcoming emerging crop productivity bottlenecks. However, accessing this wealth of plant diversity requires the characterization of this material using state-ofthe-art genomic, phenomic and molecular technologies and the release of subsequent research data via a long-term stable, open-access portal. Although several international consortia and public resource centres offer services for plant research data management, valuable digital assets remains unpublished and thus inaccessible to the scientific community. Recently, the Leibniz Institute of Plant Genetics and Crop Plant Research and the German Plant Phenotyping Network have jointly initiated the Plant Genomics and Phenomics Research Data Repository (PGP) as infrastructure to comprehensively publish plant research data. This covers in particular cross-domain datasets that are not being published in central repositories because of its volume or unsupported data scope, like image collections from plant phenotyping and microscopy, unfinished genomes, genotyping data, visualizations of morphological plant models, data from mass spectrometry as well as software and documents. The repository is hosted at Leibniz Institute of Plant Genetics and Crop Plant Research using e!DAL as software infrastructure and a Hierarchical Storage Management System as data archival backend. A novel developed data submission tool was made available for the consortium that features a high level of automation to lower the barriers of data publication. After an internal review process, data are published as citable digital object identifiers and a core set of technical metadata is registered at DataCite. The used e!DAL-embedded Web frontend generates for each dataset a landing page and supports an interactive exploration. PGP is registered as research data repository at BioSharing.org, re3data.org and OpenAIRE as valid EU Horizon 2020 open data archive. Above features, the programmatic interface and the support of standard metadata formats, enable PGP to fulfil the FAIR data principles-findable, accessible, interoperable, reusable. © The Author(s) 2016. Published by Oxford University Press.","","Database Management Systems; Databases, Factual; Genome, Plant; Genomics; Internet; Plant Physiological Phenomena; Plants; Publications; database management system; factual database; genetics; genomics; Internet; plant; plant genome; plant physiology; procedures; publication","","","","","","","Brooksbank C., Et al., The European Bioinformatics Institute's data resources 2014, Nucleic Acids Res., 42, pp. D18-D25, (2014); Craddock T., Et al., E-Science: Relieving bottlenecks in large-scale genome analyses, Nat. Rev. Microbiol., 6, pp. 948-954, (2008); Clarke L., Et al., The 1000 Genomes Project: Data management and community access, Nat. Methods, 9, pp. 459-462, (2012); Tellam R., Et al., The primary reasons behind data sharing, its wider benefits and how to cope with the realities of commercial data, BMC Genom., 16, pp. 1-4, (2015); Chavan V., Penev L., The data paper: A mechanism to incentivize data publishing in biodiversity science, BMC Bioinform., 12, pp. S1-S12, (2011); Kodama Y., Et al., The Sequence Read Archive: Explosive growth of sequencing data, Nucleic Acids Res., 40, pp. D54-D56, (2012); Singh J., FigShare, J. Pharmacol. Pharmacother., 2, pp. 138-139, (2011); Fernandez-Suarez X.M., Rigden D.J., Galperin M.Y., The 2014 nucleic acids research database issue and an updated NAR online molecular biology database collection, Nucleic Acids Res., 42, pp. D1-D6, (2014); Monaco M.K., Et al., Gramene 2013: Comparative plant genomics resources, Nucleic Acids Res., 42, pp. D1193-D1199, (2014); Vines T.H., Et al., The availability of research data declines rapidly with article age, Curr. Biol., 24, pp. 94-97, (2014); Data's shameful neglect, Nature, 461, (2009); Gibney E., Van Noorden R., Scientists losing data at a rapid rate, Nature, (2013); Piwowar H.A., Who shares? Who doesn't? Factors associated with openly archiving raw research data, PLoS One, 6, pp. e186571-e1865713, (2011); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, pp. e3081-e3085, (2007); Crosswell L.C., Thornton J.M., ELIXIR: A distributed infrastructure for European biological data, Trends Biotechnol., 30, pp. 241-242, (2012); Lagoze C.H., Van De Sompel, The Open Archives Initiative: Building a low-barrier interoperability framework, Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries, 2001, (2001); Neumann J., Brase J., DataCite and DOI names for research data, J. Comput. Aided Mol. Des., 28, pp. 1035-1041, (2014); Krajewski P., Et al., Towards recommendations for metadata and data handling in plant phenotyping, J. Exp. Bot., 66, pp. 5417-5427, (2015); Arend D., Et al., E!DAL-A framework to store, share and publish research data, BMC Bioinform., 15, pp. 1-13, (2014); Pampel H., Et al., Making research data repositories visible: The re3data.org registry, PLoS One, 8, pp. e780801-e7808010, (2013); Field D., Et al., Omics data sharing, Science, 326, pp. 234-236, (2009); Sompel H.V., Et al., Resource harvesting within the OAI-PMH framework, D-Lib Mag., (2004); Peng R.D., Reproducible research in computational science, Science, 334, pp. 1226-1227, (2011); Weibel S., The Dublin core: A simple content description model for electronic resources, Bull. Am. Soc. Inform. Sci. Technol., 24, pp. 9-11, (1997); Rocca-Serra P., Et al., ISA software suite: Supporting standards-compliant experimental annotation and enabling curation at the community level, Bioinformatics, 26, pp. 2354-2356, (2010); Neuroth H., Et al., Nestor Handbuch: Eine kleine Enzyklopädie der digitalen Langzeitarchivierung v2.3, Niedersächsische Staats- und Universitätsbibliothek Göttingen, Platz der Gö Ttinger Sieben, 1, (2010); Field D., Et al., Meeting report: BioSharing at ISMB 2010, Stand. Genom. Sci., 3, pp. 254-258, (2010); Force M.M., Robinson N.J., Encouraging data citation and discovery with the Data Citation Index, J. Comput. Aided Mol. Des., 28, pp. 1043-1048, (2014); Garfield E., Citation indexes for science. A new dimension in documentation through association of ideas. 1955, Int. J. Epidemiol., 35, pp. 1123-1127, (2006); Schmutzer T., Et al., Species-wide genome sequence and nucleotide polymorphisms from the model allopolyploid plant Brassica napus, Sci. Data, (2015)","D. Arend; Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Stadt Seeland, Gatersleben, Corrensstraße 3, 06466, Germany; email: arendd@ipk-gatersleben.de","","Oxford University Press","","","","","","17580463","","","27087305","English","Database","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84964822503"
"Canals A.; López-Borrull A.","Canals, Agustí (7005977499); López-Borrull, Alexandre (52563880600)","7005977499; 52563880600","Big data for scientific knowledge","2017","Proceedings of the European Conference on Knowledge Management, ECKM","1","","","197","205","8","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035313382&partnerID=40&md5=25f3ccd629a8a79ffe31d9012d075216","Universitat Oberta de Catalunya, Spain","Canals A., Universitat Oberta de Catalunya, Spain; López-Borrull A., Universitat Oberta de Catalunya, Spain","The recent big data hype has emphasized data analysis as one of the main sources of knowledge generation. Of course, the idea of data as a fundamental element for the existence of knowledge is not new. But the possibility of acquiring and analyzing large amounts of data from varied sources at high speed (the so-called 3 V's of big data) has become possible only lately in many areas. Therefore, many companies are starting to rely on machine learning algorithms applied to huge databases to know more about their customers, their competitors, or their environment. In other fields, though, this trend is not new at all. In many fields of science like high energy physics, genomics or astrophysics big data have been there for a long time. For instance, the famous High Energy Physics experiments that have taken place at CERN in the last decades would have been impossible without the capacity to gather, combine and analyze the large streams of data churned out by their huge particle detectors. Similar cases are those of the Human Genome Project or the large telescopes. In this paper we will examine how the process of extracting knowledge from data is performed in one of the most complex experiments ever: the ATLAS experiment. ATLAS is one of the main experiments of the Large Hadron Collider at CERN, where the Higgs' boson was detected. Relying on a knowledge management approach, our research combines archival research on the ATLAS experiment documentation with in-depth interviews with several physicists and engineers conducted at CERN in the last years. We look at different aspects of the generation of scientific knowledge from data like the research data management, data infrastructures, collaboration in data analysis, the role of simulations or the validity of scientific knowledge derived from data. We also examine the ATLAS policy on research data management and their bet for open science through CERN's Open Data Portal. Some of the insights we extract from this research can be useful not only to other fields of scientific research now getting into big data like some areas of social science (i.e., computational social science) but also to companies and institutions testing the waters of the big data business.","Big data; Knowledge generation; Scientific research","Astrophysics; Behavioral research; Data handling; Data mining; High energy physics; Information analysis; Information management; Knowledge management; Learning algorithms; Learning systems; Social sciences; Computational social science; High energy physics experiments; Human Genome Project; Knowledge generations; Large amounts of data; Large Hadron Collider; Research data managements; Scientific researches; Big data","","","","","KIBIS, (CSO2012-33959, CSO2015-71867-REDT); CERN; Generalitat de Catalunya, (2014-SGR-1486); Ministerio de Economía y Competitividad, MINECO, (CSO2009-09194); Institut de Física d'Altes Energies, IFAE","The authors would like to thank the participation and help in the present research of the ATLAS Collaboration, of CERN and of IFAE (Institut de Física d'Altes Energies) in Barcelona. We would also thank the support from the Generalitat de Catalunya to the KIMO research group (2014-SGR-1486) and from MINECO through the funding of the projects KESIR (CSO2009-09194), KIBIS (CSO2012-33959) and the MAREDATA Network (CSO2015-71867-REDT).","Aad G., Et al., The ATLAS Simulation Infrastructure, Eur. Phys. J. C, 70, pp. 823-874, (2010); Expected Performance of the ATLAS Experiment-Detector, (2009); Beech M., The Large Hadron Collider, (2010); Boisot M.H., Information space: A framework for learning in organizations, (1995); Boisot M.H., Knowledge assets: securing competitive advantage in the information economy, (1998); Boisot M.H., Canals A., Data, information and knowledge: have we got it right?, Journal of Evolutionary Economics, 14, pp. 43-67, (2004); Boisot M.H., Et al., Collisions and collaboration: the organization of learning in the ATLAS experiment at the LHC, (2011); Brun R., Et al., From the Web to the Grid and Beyond: Computing Paradigms Driven by High-Energy Physics, (2012); Canals A., Knowledge in Big Science, Knowledge and the Study of Organization and Management: Building on the Work of Max Boisot, (2013); Canals A., Et al., Redes de colaboración en big science: el experimento ATLAS en el CERN, La colaboración científica: una aproximación multidisciplinar, pp. 237-251; Canals A., Et al., Collaboration networks in big science: The ATLAS experiment at CERN, El Profesional de la Información, (2017); Chretien J.-P., Et al., Make Data Sharing Routine to Prepare for Public Health Emergencies, PLoS Med, 13, (2016); Christensen C., The innovator's dilemma: when new technologies cause great firms to fail, (2013); Creus A., Canals A., Desarrollo profesional e intercambio de conocimiento en los grandes experimentos científicos, Revista Española de Documentación Científica, 37, (2014); Diebold F.X., A Personal Perspective on the Origin (s) and Development of'Big Data': The Phenomenon, (2012); Estalella A., Ardevol E., e-research: Desafíos y oportunidades para las ciencias sociales, Convergencia, 18, pp. 87-111, (2011); Eynon R., The rise of Big Data: what does it mean for education, (2013); Galison P., How experiments end, (1987); Galison P., Image and logic, (1997); Garcia-Garcia A., Et al., Data journals: eclosión de nuevas revistas especializadas en datos, El Profesional de la Información, 24, pp. 845-854, (2015); Groves P., Et al., The 'big data' revolution in healthcare, (2013); Hey T., Et al., Jim Gray on eScience: a transformed scientific method, The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Huang Y., Et al., How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China, PloS one, 11, (2016); Ihrig M., Et al., Mapping critical knowledge assets in the ATLAS Collaboration at CERN: An I-Space approach, OLKC 2012-International Conference on Organizational Learning, (2012); Ivezic Z., Et al., SDSS data management and photometric quality assessment, Astronomische Nachrichten, 325, pp. 583-589, (2004); Jagadish H., Big data and science: myths and reality, Big Data Research, 2, pp. 49-52, (2015); Jenni P., Et al., What is ATLAS?, Collisions and collaboration: The organization of learning in the ATLAS experiment at the LHC, (2011); Jin X., Et al., Significance and challenges of big data research, Big Data Research, 2, pp. 59-64, (2015); Jordan M., Mitchell T., Machine learning: Trends, perspectives, and prospects, Science, 349, pp. 255-260, (2015); Kitchin R., Big Data, new epistemologies and paradigm shifts, Big Data & Society, 1, (2014); Kitchin R., The data revolution: Big data, open data, data infrastructures & their consequences, (2014); Kordas K., Et al., The ATLAS Data Acquisition and Trigger: concept, design and status, Nuclear Physics B, pp. 178-182, (2007); Laney D., 3D data management: Controlling data volume, velocity and variety, (2001); Lopez-Borrull A., Prom-Open: Promoting open science in an on-line e-learning environment, Edulearn 2014 6th International Conference on Education and New Learning Technologies, (2014); Lopez-Borrull A., Canals A., La colaboración científica en el marco de nuevas propuestas científicas: Open Science, e-Science y Big Data, La colaboración científica: una aproximación multidisciplinar; Mayer-Schonberger V., Cukier K., Big data: A revolution that will transform how we live, (2013); Mcelheny V.K., Drawing the map of life: Inside the Human Genome Project, (2010); Metzler K., Et al., Who Is Doing Computational Social Science? Trends in Big Data Research, (2016); Ortoll E., Et al., Principales parámetros para el estudio de la colaboración científica en ""big science, Revista Española de Documentación Científica, 37, (2014); Pampel H., Dallmeier-Tiessen S., Open research data: From vision to practice, (2014); Panger G., Reassessing the Facebook experiment: critical thinking about the validity of Big Data research, Information, Communication & Society, 19, pp. 1108-1126, (2016); Popper K.R., The Logic of Scientific Discovery, (1959); Schroeder R., Taylor L., Big data and Wikipedia research: social science knowledge across disciplinary divides, Information, Communication & Society, 18, pp. 1039-1056, (2015); Shroff G., The Intelligent Web: Search, (2013); Sonnenwald D.H., Scientific Collaboration, Annual Review of Information, Science and Technology, 41, pp. 643-680, (2007); Zikopoulos P., Et al., Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, (2011)","","Bastida Vialcanet R.; Marimon F.; Berbegal-Mirabent J.; Bastida Vialcanet R.; Mas-Machuca M.; Berbegal-Mirabent J.","Academic Conferences Limited","","18th European Conference on Knowledge Management, ECKM 2017","7 September 2017 through 8 September 2017","Barcelona","131537","20488963","978-191121848-7","","","English","Proc. Eur. Conf. Knowl. Manage., ECKM","Conference paper","Final","","Scopus","2-s2.0-85035313382"
"McKinney B.; Meyer P.A.; Crosas M.; Sliz P.","McKinney, Bill (57192274447); Meyer, Peter A. (13907805800); Crosas, Mercè (6602344670); Sliz, Piotr (6507858144)","57192274447; 13907805800; 6602344670; 6507858144","Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets","2017","Annals of the New York Academy of Sciences","1387","1","","95","104","9","4","10.1111/nyas.13272","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002807312&doi=10.1111%2fnyas.13272&partnerID=40&md5=ab1e79536ffbcf5a35c55aa2c67968fb","Department of Biochemistry and Molecular Pharmacology and SBGrid Initiative, Harvard Medical School, Boston, MA, United States; Dataverse Project, Harvard University, Cambridge, MA, United States","McKinney B., Department of Biochemistry and Molecular Pharmacology and SBGrid Initiative, Harvard Medical School, Boston, MA, United States, Dataverse Project, Harvard University, Cambridge, MA, United States; Meyer P.A., Department of Biochemistry and Molecular Pharmacology and SBGrid Initiative, Harvard Medical School, Boston, MA, United States, Dataverse Project, Harvard University, Cambridge, MA, United States; Crosas M., Department of Biochemistry and Molecular Pharmacology and SBGrid Initiative, Harvard Medical School, Boston, MA, United States, Dataverse Project, Harvard University, Cambridge, MA, United States; Sliz P., Department of Biochemistry and Molecular Pharmacology and SBGrid Initiative, Harvard Medical School, Boston, MA, United States, Dataverse Project, Harvard University, Cambridge, MA, United States","Access to experimental X-ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof-of-concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open-source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension—functionality supporting preservation of file system structure within Dataverse—which is essential for both in-place computation and supporting non-HTTP data transfers. © 2016 New York Academy of Sciences.","Data Access Alliance; Dataverse; RDMS; research data management system; SBGrid; X-ray diffraction","Access to Information; Animals; Biomedical Research; Computational Biology; Computer Communication Networks; Crystallography, X-Ray; Data Mining; Database Management Systems; Databases, Protein; Humans; Image Interpretation, Computer-Assisted; Internet; Periodicals as Topic; Protein Conformation; Software; biology; human; information processing; publication; quantitative study; sociology; university; X ray diffraction; access to information; animal; computer assisted diagnosis; computer network; data mining; database management system; devices; Internet; medical research; procedures; protein conformation; protein database; software; trends; X ray crystallography","","","","","SBGrid Consortium; National Cancer Institute, NCI, (R01CA163647); National Institute of General Medical Sciences, NIGMS, (P41GM103403); National Institute of Biomedical Imaging and Bioengineering, NIBIB, (U54EB020406); National Center for Research Resources, NCRR, (S10RR028832); Leona M. and Harry B. Helmsley Charitable Trust","Development of the SBDG is funded by the SBGrid Consortium and the Leona M. and Harry B. Helmsley Charitable Trust 2016PG-BRI002 to P.S. and M.C. The development of citation workflows is supported by the National Science Foundation 1448069 to P.S. The DAA was developed with additional funds to support storage and technology development, including from National Institutes of Health (NIH) P41 GM103403 (NE-CAT) and 1S10RR028832 (HMS) and DOE DE-AC02-06CH11357; NIH 1U54EB020406-01, Big Data for Discovery Science Center; and NIST 60NANB15D077 (Globus Project). Collections of pilot datasets were supported by grants from the NIH, NIH Intramural Program, the Howard Hughes Medical Institute, EU Infrastructure grant, the Swiss National Science Foundation, National Science and Engineering Research Council of Canada, McKnight Scholar Award, Wellcome Trust, Canadian Institutes of Health, ANRS/Fondation de France, Fundação para a Ciência e a Tecnologia, Portugal, Welch Foundation, Edward Mallinckrodt Jr. Foundation, and Cancer Prevention and Research Institute of Texas.","Berman H., Henrick K., Nakamura H., Announcing the worldwide protein data bank, Nat. Struct. Biol., 10, (2003); Berman H., Kleywegt G., Nakamura H., Et al., The protein data bank archive as an open data resource, J. Comput. Aided Mol. Des., 28, pp. 1009-1014, (2014); Bilderback D.H., Elleaume P., Weckert E., Review of third and next generation synchrotron light sources, J. Phys. B At. Mol. Opt. Phys., 38, pp. S773-S797, (2005); Winn M.D., Ballard C.C., Cowtan K.D., Et al., Overview of the CCP4 suite and current developments, Acta Crystallogr. D Biol. Crystallogr., 67, pp. 235-242, (2011); Adams P.D., Afonine P.V., Bunkoczi G., Et al., PHENIX: a comprehensive python-based system for macromolecular structure solution, Acta Crystallogr. D Biol. Crystallogr., 66, pp. 213-221, (2010); Meyer P.A., Socias S., Key J., Et al., Data publication with the structural biology data grid supports live analysis, Nat. Commun., 7, (2016); King G., An introduction to the Dataverse Network as an infrastructure for data sharing, Sociol. Methods Res., 36, pp. 173-199, (2007); Crosas M., The Dataverse Network: an open-source application for sharing, discovering and preserving data, D-Lib Mag, 17, (2011); Crosas M., A data sharing story, J. eScience Librariansh., 1, (2013); Martone M., Joint Declaration of Data Citation Principles, (2014); Nannenga B.L., Shi D., Leslie A.G.W., Et al., High-resolution structure determination by continuous-rotation data collection in MicroED, Nat. Methods, 11, pp. 927-930, (2014); Rodriguez J.A., Ivanova M.I., Sawaya M.R., Et al., Structure of the toxic core of α-synuclein from invisible crystals, Nature, 525, pp. 486-490, (2015); Nannenga B.L., Shi D., Hattne J., Et al., Structure of catalase determined by MicroED, Elife, 3, (2014); Vreven T., Moal I.H., Vangone A., Et al., Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2, J. Mol. Biol., 427, pp. 3031-3041, (2015); Lazarus M.B., Nam Y., Jiang J., Et al., Structure of human O-GlcNAc transferase and its complex with a peptide substrate, Nature, 469, pp. 564-567, (2011); Foster I., Globus online: accelerating and democratizing science through cloud-based services, IEEE Internet Comput, 15, pp. 70-73, (2011); Bourne P.E., Clark T., Dale R., Et al., Improving the future of research communication and e-scholarship, Dagstuhl Manifestos, 1, pp. 41-60, (2012); Altman M., King G., A proposed standard for the scholarly citation of quantitative data, D-Lib Mag, 13, (2007); Altman M., Borgman C., Crosas M., Et al., An introduction to the joint principles for data citation, Bull. Am. Soc. Inf. Sci. Technol., 41, pp. 43-45, (2015); Socias S.M., Morin A., Timony M.A., Et al., AppCiter: a web application for increasing rates and accuracy of scientific software citation, Structure, 23, pp. 807-808, (2015); Winter G., Lobley C.M.C., Prince S.M., Decision making in xia2, Acta Crystallogr. D Biol. Crystallogr., 69, pp. 1260-1273, (2013); Evans P.R., Murshudov G.N., How good are my data and what is the resolution, Acta Crystallogr. D Biol. Crystallogr., 69, pp. 1204-1214, (2013); Evans P., Scaling and assessment of data quality, Acta Crystallogr. D Biol. Crystallogr., 62, pp. 72-82, (2006); Kabsch W., XDS, Acta Crystallogr. D Biol. Crystallogr., 66, pp. 125-132, (2010); Winn M.D., Et al., Overview of the CCP4 suite and current developments, Acta Crystallogr. D Biol. Crystallogr., 67, pp. 235-242, (2011); Leslie A.G.W., Integration of macromolecular diffraction data, Acta Crystallogr. D Biol. Crystallogr., 55, pp. 1696-1702, (1999); Waterman D.G., Et al., The DIALS framework for integration software, CCP4 Newslett. Protein Crystallogr., 49, pp. 13-15, (2013); Morin A., Eisenbraun B., Key J., Et al., Collaboration gets the most out of software, Elife, 2, (2013); Crosas M., King G., Honaker J., Et al., Automating open science for Big data, Ann. Am. Acad. Pol. Soc. Sci., 659, pp. 260-273, (2015); Terwilliger T.C., Bricogne G., Continuous mutual improvement of macromolecular structure models in the PDB and of X-ray crystallographic software: the dual role of deposited experimental data, Acta Crystallogr. D Biol. Crystallogr., 70, pp. 2533-2543, (2014); Terwilliger T.C., Archiving raw crystallographic data, Acta Crystallogr. D Biol. Crystallogr., 70, pp. 2500-2501, (2014); Cheney J., Chong S., Foster N., Et al., Provenance: a future history, Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, (2009)","P. Sliz; Department of Biochemistry and Molecular Pharmacology and SBGrid Initiative, Harvard Medical School, Boston, United States; email: sliz@hkl.hms.harvard.edu","","Blackwell Publishing Inc.","","","","","","00778923","","ANYAA","27862010","English","Ann. New York Acad. Sci.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85002807312"
"Grunzke R.; Neumann M.; Ilsche T.; Hartmann V.; Jejkal T.; Stotzka R.; Knüpfer A.; Nagel W.E.","Grunzke, Richard (37096970200); Neumann, Maximilian (57195349505); Ilsche, Thomas (36662073600); Hartmann, Volker (7005982861); Jejkal, Thomas (24478712700); Stotzka, Rainer (6602188741); Knüpfer, Andreas (8950527300); Nagel, Wolfgang E. (9435404200)","37096970200; 57195349505; 36662073600; 7005982861; 24478712700; 6602188741; 8950527300; 9435404200","Design Evaluation of a Performance Analysis Trace Repository","2017","Procedia Computer Science","108","","","2190","2199","9","1","10.1016/j.procs.2017.05.190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027305000&doi=10.1016%2fj.procs.2017.05.190&partnerID=40&md5=bab09e285e7570b8ca4e125839a4e589","Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany","Grunzke R., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Neumann M., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Ilsche T., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Hartmann V., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Jejkal T., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Stotzka R., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Knüpfer A., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Nagel W.E., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany","Parallel and high performance computing experts are obsessed with performance and scalability. Performance analysis and tuning are important and complex but there are a number of software tools to support this. One methodology is the detailed recording of parallel runtime behavior in event traces and their subsequent analysis. This regularly produces very large data sets with their own challenges for handling and data management. This paper evaluates the utilization of the MASi research data management service as a trace repository to store, manage, and find traces in an efficient and usable way. First, we give an introduction to trace technologies in general, metadata in OTF2 traces specifically, and the MASi research data management service. Then, the trace repository is described with its potential for both performance analysts and parallel tool developers, followed with how we implemented it using existing metadata and how it can utilized. Finally, we give an outlook on how we plan to put the repository into productive use for the benefit of researchers using traces. © 2017 The Authors. Published by Elsevier B.V.","Data Repository; Metadata Extraction; Performance Analysis","Information management; Metadata; Data repositories; Design evaluation; High performance computing; Meta-data extractions; Performance analysis; Performance and scalabilities; Research data managements; Runtime behaviors; Data handling","","","","","","","KIT Data Manager, (2016); Taurus Supercomputer at ZIH, (2017); Eschweiler D., Wagner M., Geimer M., Knupfer A., Nagel W.E., Wolf F., Open Trace Format 2: The Next Generation of Scalable Trace Formats and Support Libraries, Applications, Tools and Techniques on the Road to Exascale Computing, Volume 22 of Advances in Parallel Computing, pp. 481-490, (2012); Geimer M., Wolf F., Wylie BrianJ. N., Abraham E., Becker D., Mohr B., The Scalasca Performance Toolset Architecture, Concurrency and Computation: Practice and Experience, 22, 6, (2010); Grunzke R., Hartmann V., Jejkal T., Prabhune A., Herres-Pawlis S., Hoffmann A., Deicke A., Schrade T., Herold H., Meinel G., Stotzka R., Nagel W.E., Towards a Metadata-driven Multi-community Research Data Management Service, 2016 8th International Workshop on Science Gateways (IWSG), (2016); Huck K., Malony A., Bell R., Morris A., Design and implementation of a parallel performance data management framework, Parallel Processing, 2005. ICPP 2005. International Conference on, pp. 473-482, (2005); Ilsche T., Schuchart J., Schone R., Hackenberg D., Combining Instrumentation and Sampling for Trace-Based Application Performance Analysis, Tools for High Performance Computing 2014, pp. 123-136, (2015); Jejkal T., Vondrous A., Kopmann A., Stotzka R., Hartmann V., KIT Data Manager: The Repository Architecture Enabling Cross-Disciplinary Research, Large-Scale Data Management and Analysis (LSDMA) - Big Data in Science, pp. 9-11, (2014); Karavanic K., May J., Mohror K., Miller B., Huck K., Knapp R., Pugh B., Integrating Database Technology with comparison-based parallel Performance Diagnosis: The Perftrack Performance Experiment Management Tool, Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, (2005); Knupfer A., Brendel R., Brunst H., Mix H., Nagel W.E., Introducing the Open Trace Format (OTF), 6th International Conference on Computational Science (ICCS), 2, pp. 526-533, (2006); Knupfer A., Brunst H., Doleschal J., Jurenz M., Lieber M., Mick-Ler H., Muller M., Nagel W., The Vampir Performance Analysis Tool-set, Tools for High Performance Computing, pp. 139-155, (2008); Knupfer A., Rossel C., Mey D., Biersdorff S., Diethelm K., Es-Chweiler D., Geimer M., Gerndt M., Lorenz D., Malony A., Nagel WolfgangE., Oleynik Y., Philippen P., Saviankou P., Schmidl D., Shende S., Tschuter R., Wagner M., Wesarg B., Wolf F., Score P., A Joint Performance Measurement RunTime Infrastructure for Periscope, Scalasca, TAU, and Vampir, Tools for High Performance Computing 2011, pp. 79-91, (2012); Muller M.S., Knupfer A., Jurenz M., Lieber M., Brunst H., Mix H., Nagel E.W., Developing Scalable Applications with Vampir, VampirServer and VampirTrace, Parallel Computing: Architectures, Algorithms and Applications, Volume 15 of Advances in Parallel Computing, pp. 637-644, (2008); Shende SameerS., Malony AllenD., The Tau Parallel Performance System, The International Journal of High Performance Computing Applications, 20, 2, pp. 287-311, (2006); Tallent N., Mellor-Crummey J., Franco M., Landrum R., Adhi-Anto L., Scalable Fine-grained Call Path Tracing. in Proceedings of the International Conference on Supercomputing, (2011); Sompel H., Nelson M., Lagoze C., Warner S., Resource Harvesting within the OAI-PMH Framework, D-lib Magazine, 10, 12, pp. 1082-9873, (2004); Weber M., Brendel R., Brunst H., Trace File Comparison with a Hierarchical Sequence Alignment Algorithm, Parallel and Distributed Processing with Applications (ISPA), 2012 IEEE 10th International Symposium on, pp. 247-254, (2012)","","Lees M.; Sloot P.; Krzhizhanovskaya V.; Dongarra J.; Koumoutsakos P.","Elsevier B.V.","","International Conference on Computational Science ICCS 2017","12 June 2017 through 14 June 2017","Zurich","136818","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85027305000"
"Cox A.M.; Pinfield S.; Smith J.","Cox, Andrew M. (7402563906); Pinfield, Stephen (6602090850); Smith, Jennifer (57213084572)","7402563906; 6602090850; 57213084572","Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem","2016","Journal of Librarianship and Information Science","48","1","","3","17","14","33","10.1177/0961000614533717","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957878844&doi=10.1177%2f0961000614533717&partnerID=40&md5=fddfd0fadf8e603d24cf4019b8ac1642","University of Sheffield, United Kingdom","Cox A.M., University of Sheffield, United Kingdom; Pinfield S., University of Sheffield, United Kingdom; Smith J., University of Sheffield, United Kingdom","The purpose of this paper is to explore the value to librarians of seeing research data management as a ‘wicked’ problem. Wicked problems are unique, complex problems which are defined differently by different stakeholders making them particularly intractable. Data from 26 semi-structured in-depth telephone interviews with librarians was analysed to see how far their perceptions of research data management aligned with the 16 features of a wicked problem identified from the literature. To a large extent research data management is perceived to be wicked, though over time good practices may emerge to help to ‘tame’ the problem. How interviewees thought research data management should be approached reflected this realisation. The generic value of the concept of wicked problems is considered and some first thoughts about how the curriculum for new entrants to the profession can prepare them for such problems are presented. © 2014, © The Author(s) 2014.","Academic libraries; data curation; library roles; research data management; research support; United Kingdom; wicked problems","","","","","","EU/FP7; Sheffield Hallam University","Jennifer Smith is a Researcher on the AHRC-funded Reading Digital Fiction project at Sheffield Hallam University and also works for the Open Access Team at The University of Sheffield Library. Her PhD applied a Bourdieusian framework to a field study of artefact books. She has previously worked on PATHS (Personalised Access to Cultural Heritage Spaces), an EU/FP7- funded project to create a new user interface for Europeana; and RDMRose, a JISC-funded project to develop training materials on research data management for librarians.","Alvaro E., Brooks H., Ham M., Et al., E-science librarianship: Field undefined, Issues in Science and Technology Librarianship, 66, (2011); Bell S.J., Design Thinking & User Experience, (2007); Bell S.J., Design thinking, American Libraries, 39, 1, pp. 44-49, (2008); Bell S.J., ‘Design thinking’ and Higher Education, (2010); Bell S.J., Shank J.D., Academic Librarianship by Design: A Blended Librarian’s Guide to the Tools and Techniques, (2007); Benbya H., McKelvey B., Toward a complexity theory of information systems development, Information Technology & People, 19, 1, pp. 12-34, (2006); Bauer R.M., Eagen W.M., Design thinking: Epistemic plurality in management and organization, Aesthesis, 2, 3, pp. 64-74, (2008); Borgman C., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2008); Brown T., Design thinking, Harvard Business Review, 86, 6, (2008); Childs S., McLeod J., Tackling the wicked problem of ERM: Using the Cynefin framework as a lens, Records Management Journal, 23, 3, pp. 191-227, (2013); Conklin E.J., Weil W., Wicked Problems: Naming the Pain in Organizations, (1997); Conklin J., Wicked Problems & Social Complexity, (2005); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing Research Data, pp. 105-133, (2012); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management: Emerging trends in library research support services, Library Trends, 61, 3, pp. 636-674, (2013); Corti L., van den Eynden V., Bishop L., Et al., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Library and Information Science; 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McLeod J., Childs S., A strategic approach to making sense of the wicked problem of ERM, Records Management Journal, 23, 2, pp. 104-135, (2013); Mittleton-Kelly E., The information systems professional as a hermit, Innovation, 17, 4, pp. 289-323, (2004); Monastersky R., Publishing frontiers: The library reboot, Nature, 495, 7442, pp. 430-432, (2013); Ney S.M., Verweij M., Messy Institutions for Wicked Problems: How to Generate Clumsy Solutions, (2014); Pryor G., Managing Research Data, (2012); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services, (2014); Razzouk R., Shute V., What is design thinking and why is it important?, Review of Educational Research, 82, 3, pp. 330-348, (2012); Common Principles on Data Policy, (2011); Patterns of Information Use and Exchange: Case Studies of Researchers in the Life Sciences, (2009); Rittel H.W., Webber M.M., Dilemmas in a general theory of planning, Policy Sciences, 4, 2, pp. 155-169, (1973); Rylander A., Design thinking as knowledge work: Epistemological foundations and practical implications, Design Management Journal, 4, 1, pp. 7-19, (2009); Snowden D., The origins of Cynefin, (2010); Snowden D.J., Boone M.E., A leader’s framework for decision making, Harvard Business Review, 85, 11, (2007); Snyder J., Heckman R., Scialdone M.J., Information studios: Integrating arts-based learning into the education of information professionals, Journal of the American Society for Information Science and Technology, 60, 9, pp. 1923-1932, (2009); Bootcamp Bootleg, 2, (2010); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services, (2012); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and Third Space: Theorising professional service responses to Research Data Management, Journal of Academic Librarianship; Verweij M., Douglas M., Ellis R., Et al., Clumsy solutions for a complex world: The case of climate change, Public Administration, 84, 4, pp. 817-843, (2006); Verweij M., Douglas M., Ellis R., Et al., The case for clumsiness, Clumsy Solutions for a Complex World: Governance, Politics and Plural Perceptions, pp. 1-27, (2011); Watson D., Managing in higher education: The ‘wicked issues’, Higher Education Quarterly, 54, 1, pp. 5-21, (2000); Zenke P.F., Higher education leaders as designers, Design in Educational Technology: Design Thinking, Design Process, and the Design Studio, pp. 249-259, (2014); Zimmerman J., Forlizzi J., Evenson S., Research through design as a method for interaction design research in HCI, pp. 493-502, (2007)","A.M. Cox; University of Sheffield, Sheffield, Regent Court, Portobello, S1 4DP, United Kingdom; email: a.m.cox@sheffield.ac.uk","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84957878844"
"Koltay T.; Špiranec S.","Koltay, Tibor (6505905944); Špiranec, Sonja (35312018400)","6505905944; 35312018400","Libraries meet research 2.0: Literacies and services","2016","Research 2.0 and the Impact of Digital Technologies on Scholarly Inquiry","","","","32","52","20","2","10.4018/978-1-5225-0830-4.ch003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014789316&doi=10.4018%2f978-1-5225-0830-4.ch003&partnerID=40&md5=c8e56af82dd3c5d1d280ffe99e6919d9","Department of Information and Library Studies, Faculty of Applied and Professional Arts in Jászberény, Szent István University, Hungary; Department of Information and Communication Sciences, University of Zagreb, Croatia","Koltay T., Department of Information and Library Studies, Faculty of Applied and Professional Arts in Jászberény, Szent István University, Hungary; Špiranec S., Department of Information and Communication Sciences, University of Zagreb, Croatia","This chapter is intended mainly for the researcher. Its main goal is to identify what services are already provided or could be planned by academic libraries, identified as important stakeholders in facilitating Research 2.0. Indicating the changing contexts of literacies, the focus is on research-related literacies, such as information literacy, academic literacy and data literacy, which pertain to the advisory and educational roles of the academic library. The ways of counterbalancing information overload, partially by personal information management are also described. After outlining the importance of data-intensive research, services facilitating research data management, (including the preparation of data-management plans) are portrayed. Issues of data curation, data quality and data citation, as well as the ways to identify professionals, who provide services to researchers, are outlined. © 2017 by IGI Global. All rights reserved.","","","","","","","","","Acord S.K., Harley D., Credit, time, and personality: The human challenges to sharing scholarly work using Web 2.0, New Media & Society, 5, 3, pp. 379-397, (2013); Information Literacy Competency Standards for Higher Education, (2000); Framework for Information Literacy for Higher Education, (2015); Final Report, American Library Association Presidential Commission on Information Literacy, (1989); Antonijevic S., Cahoy E.S., Personal Library Curation: An Ethnographic Study of Scholars' Information Practices. portal, Libraries and the Academy, 14, 2, pp. 287-306, (2014); Attfield S., Blandford A., Dowell J., Information seeking in the context of writing: A design psychology interpretation of the 'problematic situation, The Journal of Documentation, 59, 4, pp. 430-453, (2003); Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Ball A., Duke M., How to cite datasets and link to publications?, (2015); Bawden D., Information and digital literacies: A review of concepts, The Journal of Documentation, 57, 2, pp. 218-259, (2001); Bawden D., Robinson L., The dark side of information: Overload, anxiety and other paradoxes and pathologies, Journal of Information Science, 35, 2, pp. 180-191, (2009); Blummer B., Kenton J.M., Reducing patron information overload in academic libraries, College & Undergraduate Libraries, 21, 2, pp. 115-135, (2014); Boekhorst A., Becoming information literate in the Netherlands, Library Review, 52, 7, pp. 298-309, (2003); Bornmann L., Do altmetrics point to the broader impact of research? 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A Study of Faculty Perceptions of Information Literacy. portal, Libraries and the Academy, 14, 4, pp. 475-501, (2014); Cordes S., Broad Horizons: The Role of Multimodal Literacy in 21st Century Library Instruction, (2009); Dashkin M., Electronic writing: Defining the core competency, Information Outlook, 7, 9, pp. 34-37, (2003); All best practices, (2016); Davis N., Information Overload, Reloaded, Bulletin of the American Society for Information Science and Technology, 37, 5, pp. 45-49, (2011); Doty J., Herndon J., Lyle J., Stephenson L., Learning to curate, Bulletin of the American Society for Information Science and Technology, 40, 6, pp. 31-34, (2014); Eaker C., Educating researchers for effective data management, Bulletin of the American Society for Information Science and Technology, 40, 3, pp. 45-46, (2014); Elmborg J., Critical Information Literacy: Implications for Instructional Practice, Journal of Academic Librarianship, 32, 2, pp. 192-199, (2006); Eshet-Alkalai Y., Digital literacy: A conceptual framework for survival skills in the digital era, Journal of Educational Multimedia and Hypermedia, 13, 1, pp. 93-107, (2004); Exner N., Research Information Literacy: Addressing Original Researchers Needs, Journal of Academic Librarianship, 40, 5, pp. 460-466, (2014); Federer L., The librarian as research informationist: A case study, Journal of the Medical Library Association: JMLA, 101, 4, pp. 298-302, (2013); Foster N.F., Information literacy and research practices, (2014); Franganillo J., Gestión de información personal: elementos, actividades e integración, El Profesional de la Información, 18, 4, (2009); Fruin C., Scholarly Communication in the Field. Assessing the Scholarly Communication Needs of Cooperative Extension Faculty and Staff, Common Ground at the Nexus of Information Literacy and Scholarly Communication, pp. 249-267, (2013); Geisler C., Bazerman C., Doheny-Farina S., Gurak L., Haas C., Johnson-Eilola J., Yates J., Et al., IText future directions for research on the relationship between information technology and writing, Journal of Business and Technical Communication, 15, 3, pp. 269-308, (2001); Giarlo M., Academic Libraries as Quality Hubs, Journal of Librarianship and Scholarly Communication, 1, 3, pp. 1-10, (2013); Gilster P., Digital literacy, (1997); Goodfellow R., The literacies of digital scholarship-truth and use values, Literacy in the Digital University: Learning as social practice in a digital world: Critical perspectives on learning, scholarship and technology, pp. 67-78, (2013); Goodman A., Pepe A., Blocker A.W., Borgman C.L., Cranmer K., Crosas M., Ten simple rules for the care and feeding of scientific data, PLoS Computational Biology, 10, 4, (2014); Henry C., Introduction, The problem of data, pp. 1-2, (2012); Huang H., Jorgensen C., Stvilia B., Genomics data curation roles, skills and perception of data quality, Library & Information Science Research, 37, 1, pp. 10-20, (2015); Quick guide to data citation, (2012); Moscow Declaration on Media and Information Literacy, (2012); Ipri T., Introducing transliteracy. What does it mean to academic libraries?, College & Research Libraries News, 71, 10, pp. 532-567, (2010); Jakobs E.M., Reproductive writing - writing from sources, Journal of Pragmatics, 35, 5, pp. 893-906, (2003); Jamali H.R., Russell B., Nicholas D., Watkinson A., Do Online Communities Support Research Collaboration?, Aslib Journal of Information Management, 66, 6, pp. 603-622, (2014); Johnson C.A., The information diet: A case for conscious consumption, (2012); Jones D., Critical Thinking in an Online World, (1996); Karvalics Z.L., From Scientific Literacy to Lifelong Research: A Social Innovation Approach, Worldwide Commonalities and Challenges in Information Literacy Research and Practice, pp. 126-133, (2013); Koltay T., Data literacy: In search of a name and identity, The Journal of Documentation, 71, 2, pp. 401-415, (2015); Kowalczyk S., Shankar K., Data sharing in the sciences, Annual Review of Information Science & Technology, 45, 1, pp. 247-294, (2011); Kroll S., Forsman R., A Slice of Research Life: Information Support for Research in the United States, (2010); Lankshear C., Knobel M., New Literacies: Research and Social Practice, (2004); LERU Roadmap for Research Data, (2013); Li S., Xiaozhe Z., Wenming X., Weining G., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Livingstone S., van Couvering E.J., Thumin N., Converging traditions of research on media and information literacies: Disciplinary and methodological issues, Handbook of Research on New Literacies, pp. 103-132, (2008); Luke A., Freebody P., A map of possible practices: Further notes on the four resources model, Practically Primary, 2, 4, pp. 5-8, (1999); Lynch C., Information literacy and information technology literacy: new components in the curriculum for a digital culture, (1998); Mackey T., Jacobson T., Reframing Information Literacy as a Metaliteracy, College & Research Libraries, 72, 1, pp. 62-78, (2011); Manguel A., The Library at Night, (2008); Martin A., Literacies for the digital age, Digital Literacies for Learning, pp. 3-25, (2006); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data Curation: A Study of Researcher Practices and Needs, Libraries and the Academy, 14, 2, pp. 139-164, (2014); Miller H., The multiple dimensions of information quality, Information Systems Management, 13, 2, pp. 79-82, (1996); Molloy L., Snow K., The data management skills support initiative: Synthesising postgraduate training in research data management, International Journal of Digital Curation, 7, 2, pp. 101-109, (2012); Mooney H., Newton M.P., The anatomy of a data citation: Discovery, reuse, and credit, Journal of Librarianship and Scholarly Communication, 1, 1, pp. 1-14, (2012); Morville P., Ambient Findability, (2005); Nersessian N.J., How Do Engineering Scientists Think? Model-Based Simulation in Biomedical Engineering Research Laboratories, Topics in Cognitive Science, 1, 4, pp. 1756-1765, (2009); Neylon C., It's not filter failure, it's a discovery deficit, Serials, 24, 1, pp. 21-25, (2011); Nicholas D., Herman E., Jamali H., Rodriguez-Bravo B., Boukacem-Zeghmouri C., Dobrowolski T., Pouchot S., New ways of building, showcasing, and measuring scholarly reputation, Learned Publishing, 28, 3, pp. 169-183, (2015); Nicholas D., Watkinson A., Jamali H.R., Herman E., Tenopir C., Volentine R., Levine K., Et al., Peer review: Still king in the digital age, Learned Publishing, 28, 1, pp. 15-21, (2015); Pfaff-Rudiger S., Riesmeyer C., Moved into action. Media literacy as social process, Journal of Children and Media, 10, 2, pp. 164-172, (2016); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); Ramirez M.L., Opinion: Whose role is it anyway? A library practitioner's appraisal of the digital data deluge, Bulletin of the American Society for Information Science and Technology, 37, 5, pp. 21-23, (2011); Policy recommendations for open access to research data, (2014); Rieh S.Y., Judgment of Information Quality and Cognitive Authority in the Web, Journal of the American Society for Information Science and Technology, 53, 2, pp. 145-161, (2002); The SCONUL Seven Pillars of Information Literacy Core Model for Higher Education, (2011); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Spiranec S., Banek Zorica M., Information Literacy 2.0: Hype or discourse refinement?, The Journal of Documentation, 66, 1, pp. 140-153, (2010); Stordy P.H., Taxonomy of literacies, The Journal of Documentation, 71, 3, pp. 456-476, (2015); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Et al., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Dorsett K., Et al., Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide, PLoS ONE, 10, 8, (2015); Tenopir C., Levine K., Allard S., Christian L., Volentine R., Boehm R., Watkinson A., Trustworthiness and authority of scholarly information in a digital age: Results of an international questionnaire, Journal of the Association for Information Science and Technology, (2015); Thompson C.A., Robertson W.D., Greenberg J., Where have all the scientific data gone? LIS perspective on the data-at-risk predicament, College & Research Libraries, 75, 6, pp. 842-861, (2013); Why is data management important?, (2016); Varvel V.E., Shen Y., Data management consulting at the Johns Hopkins University, New Review of Academic Librarianship, 19, 3, pp. 224-245, (2013); Researcher Development Framework, (2011); Wang M., Supporting the research process through expanded library data services, Program, 47, 3, pp. 282-303, (2013); Whitworth A., Radical information literacy: Reclaiming the political heart of the IL movement, (2014); Wiley C., Metadata use in research data management, Bulletin of the American Society for Information Science and Technology, 40, 6, pp. 38-40, (2014); Williams P., Leighton John J., Rowland I., The personal curation of digital objects: A lifecycle approach, Aslib Proceedings, 61, 4, pp. 340-363, (2009); Xia J., Wang M., Competencies and Responsibilities of Social Science Data Librarians: An Analysis of Job Descriptions, College & Research Libraries, 75, 3, pp. 362-388, (2014)","","","IGI Global","","","","","","","978-152250830-4; 1522508309; 978-152250830-4","","","English","Res. 2.0 and the Impact of Digit. Technol. on Sch. Inq.","Book chapter","Final","","Scopus","2-s2.0-85014789316"
"Wilms K.; Meske C.; Stieglitz S.; Rudolph D.; Vogl R.","Wilms, Konstantin (57190278061); Meske, Christian (55806873000); Stieglitz, Stefan (8982225800); Rudolph, Dominik (56244944800); Vogl, Raimund (6701668973)","57190278061; 55806873000; 8982225800; 56244944800; 6701668973","How to improve research data management: The case of sciebo (science box)","2016","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","9735","","","434","442","8","8","10.1007/978-3-319-40397-7_41","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978863702&doi=10.1007%2f978-3-319-40397-7_41&partnerID=40&md5=3e8d95202ee1e7d3361c167d36fddd3e","Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Essen, Germany; ZIV-Centre for Applied Information Technology, University of Muenster, Münster, Germany","Wilms K., Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Essen, Germany; Meske C., Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Essen, Germany; Stieglitz S., Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Essen, Germany; Rudolph D., ZIV-Centre for Applied Information Technology, University of Muenster, Münster, Germany; Vogl R., ZIV-Centre for Applied Information Technology, University of Muenster, Münster, Germany","The digitalization of research processes has led to a vast amount of data. Since third-party funding institutions progressively set standards and requirements regarding the handling of such data, research data management has become important in the context of international research collaboration projects. Simultaneously, adequate collaboration systems are needed to support scientists in this context. In this paper we discuss existing standards for research data management in the context of third-party funding and how cloud technology could support the fulfillment of existing provisions. © Springer International Publishing Switzerland 2016.","Cloud computing; Research data management; Technology adoption; Usability","Cloud computing; Human computer interaction; Information management; Cloud technologies; Collaboration systems; International researches; Research data managements; Research process; Standards and requirements; Technology adoption; Usability; Data handling","","","","","National Science Foundation, NSF; Australian Research Council, ARC; Deutsche Forschungsgemeinschaft, DFG","In our paper, we focus on existing standards for RDM in the context of third-party funding and how cloud technology could support the fulfillment of existing provisions. We compare three major research funding institutions from North America (USA), Australia and Europe (Germany) in terms of requirements regarding RDM. In this first investigation we analyzed documents published by the National Science Foundation (NSF), Australian Research Council (ARC) and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) with regard to requirements for research proposals for funding. Furthermore, we take the users perspective into account and focus on factors and barriers diminishing the acceptance of such systems. Additionally, we analyze, if and how sciebo (“science box”), an on-premise cloud service hosted by universities and used by over 5,000 researchers in Germany, can support scientists to meet existing RDM requirements. We especially focus on the following research questions: Which claims result from the guidelines of third-party funding institutions and from the needs of researchers for dealing with research data? How could an infrastructure like sciebo be implemented to deal with these requirements? ","Amorim R.C., Castro J.A., de Silva J.R., Ribeiro C., A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential, New Contributions in Information Systems and Technologies. AISC, 353, pp. 101-111, (2015); Bauer B., Ferus A., Gorraiz J., Grundhammer V., Gumpenberger C., Maly N., Muhlegger J.M., Preza J.L., Sanchez Solis B., Schmidt N., Steineder C.; Ball R., Wiederkehr S., (2015); Benson D.A., Karsch-Mizrachi I., Lipman D.J., Ostell J., Wheeler D.L., Genbank. Nucleic Acids Res, 36, 1, pp. D25-D30, (2008); Bukavova H., Supporting the initiation of research collaborations, Jena Research Papers in Business and Economics, 64, (2009); Commission E., (2013); Corti L., Van Den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Da Silva J.R., Barbosa J.P., Gouveia M., Ribeiro C., Lopes J.C., UPBox and DataNotes: A collaborative data management environment for the long tail of research data, Ipress2013: Proceedings of the 10Th International Conference on Preservation of Digital Objects, (2013); Easterbrook P.J., Et al., Publication bias in clinical research, Lancet, 337, 8746, pp. 867-872, (1991); Feijen M., What Researchers Want, (2011); Hager R., Hildmann T., Bittner P., Ein Jahr Mit Owncloud – Von Der Planung Bis Zur Neustrukturierung, (2014); Hilber M., Reintzsch D., Cloud Computing und Open Source-Wie groß ist die Gefahr des Copyleft bei SaaS? Comput. Und Recht: Forum für die Praxis des Rechts der Datenverargeitung, Inf. Und Autom, 30, 11, pp. 697-702, (2014); Hildmann T., Kao O., Deploying and extending on-premise cloud storage based on ownCloud, 2014 IEEE 34Th International Conference on Distributed Computing Systems Workshops. IEEE, (2014); (2009); Joshi M., Krag S.S., Issues in data management, Science and Engineering Ethics, 16, 4, pp. 743-748, (2010); 57, 2, pp. 191-201, (2008); Lyon L., Dealing with Data: Roles, (2007); Meske C., Brockmann T., Wilms K., Stieglitz S., Gamify employee collaboration – a critical review of gamification elements in social software, ACIS, (2015); (2009); (2010); Palmer C.L., Cragin M.H., Heidorn P.B., Smith L.C., Data curation for the long tail of science: The case of environmental sciences, Third International Digital Curation Conference, (2007); Peterson M., Zasman G., Mojica P., Porter J., 100 year archive requirements survey, SNIA’s Data Management Forum, (2007); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, Plos ONE, 2, 3, (2007); Savage C.J., Vickers A.J., Empirical study of data sharing by authors publishing in PloS journals, Plos ONE, 4, 9, (2009); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Gov. Inf. Q, 30, pp. 19-31, (2013); Schlitter N., Yasnogorbw A., Sync&Share: A Cloud Solution for Academia in the State of Baden-Württemberg, (2014); (2009); Stieglitz S., Meske C., Vogl R., Rudolph D., Demand for cloud services as an infrastructure in higher education, Icis, (2014); Suresh S., Biography, (2016); Vogl R., Angenent H., Bockholt R., Rudolph D., Stieglitz S., Meske C., Designing a large scale cooperative sync&share cloud storage plattform for the academic community in Northrhine-Westfalia, Proceedings of EUNIS 2013 Congress, 1, 1, (2013); Vogl R., Rudolph D., Thoring A., Angenent H., Stieglitz S., Meske C., How to build a cloud storage service for half a million users in higher education: Challenges met and solutions found, HICCS 2016 Proceedings of the 49Th Hawaii International Conference on System Sciences, pp. 5328-5337, (2016); Voorbrood C.M., Voer Voor Psychologen, (2010); Whyte A., Tedd J., Making the Case for Research Data Management, (2011); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008)","K. Wilms; Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Essen, Germany; email: konstantin.wilms@uni-due.de","Yamamoto S.","Springer Verlag","","18th International Conference on Human-Computer  Interaction, HCI International 2016","17 July 2016 through 22 July 2016","Toronto","178039","03029743","978-331940396-0","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-84978863702"
"Kraft A.; Razum M.; Potthoff J.; Porzel A.; Engel T.; Lange F.; Van Den Broek K.; Furtado F.","Kraft, Angelina (57188695916); Razum, Matthias (25825498000); Potthoff, Jan (55846710000); Porzel, Andrea (56186420200); Engel, Thomas (57190035381); Lange, Frank (57531844800); Van Den Broek, Karina (57217778234); Furtado, Filipe (57516950900)","57188695916; 25825498000; 55846710000; 56186420200; 57190035381; 57531844800; 57217778234; 57516950900","The RADAR project-a service for research data archival and publication","2016","ISPRS International Journal of Geo-Information","5","3","28","","","","6","10.3390/ijgi5030028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962339647&doi=10.3390%2fijgi5030028&partnerID=40&md5=43eeb40da6eb6190e5fcec135bb4f68a","German National Library of Science and Technology (TIB), Welfengarten 1B, Hannover, D-30167, Germany; FIZ Karlsruhe-Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany; Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany; Leibniz Institute of Plant Biochemistry (IPB), Weinberg 3, Halle (Saale), D-06120, Germany; Chemistry Department, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, Munich, D-81377, Germany","Kraft A., German National Library of Science and Technology (TIB), Welfengarten 1B, Hannover, D-30167, Germany; Razum M., FIZ Karlsruhe-Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany; Potthoff J., Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, D-76344, Germany; Porzel A., Leibniz Institute of Plant Biochemistry (IPB), Weinberg 3, Halle (Saale), D-06120, Germany; Engel T., Chemistry Department, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, Munich, D-81377, Germany; Lange F., Leibniz Institute of Plant Biochemistry (IPB), Weinberg 3, Halle (Saale), D-06120, Germany; Van Den Broek K., Chemistry Department, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, Munich, D-81377, Germany; Furtado F., Leibniz Institute of Plant Biochemistry (IPB), Weinberg 3, Halle (Saale), D-06120, Germany","The aim of the RADAR (Research Data Repository) project is to set up and establish an infrastructure that facilitates research data management: the infrastructure will allow researchers to store, manage, annotate, cite, curate, search and find scientific data in a digital platform available at any time that can be used by multiple (specialized) disciplines. While appropriate and innovative preservation strategies and systems are in place for the big data communities (e.g., environmental sciences, space, and climate), the stewardship for many other disciplines, often called the ""long tail research domains"", is uncertain. Funded by the German Research Foundation (DFG), the RADAR collaboration project develops a service oriented infrastructure for the preservation, publication and traceability of (independent) research data. The key aspect of RADAR is the implementation of a two-stage business model for data preservation and publication: clients may preserve research results for up to 15 years and assign well-graded access rights, or to publish data with a DOI assignment for an unlimited period of time. Potential clients include libraries, research institutions, publishers and open platforms that desire an adaptable digital infrastructure to archive and publish data according to their institutional requirements and workflows. © 2016 by the authors.","Data publication; Data storage; Information infrastructure; Preservation; Repository; Research data management","","","","","","","","Whyte J., Stasis A., Lindkvist C., Managing change in the delivery of complex projects: Configuration management, asset information and ""big data, Int. J. Proj. Manag., 34, pp. 339-351, (2016); Challenges and opportunities, Science, 331, pp. 692-693, (2011); Harris S.J., Long-distance corporations, big sciences and the geography of knowledge, The Postcolonial Science and Technology Studies Reader, pp. 61-83, (2011); Thessen A.E., Patterson D.J., Data issues in the life sciences, Zookeys, 150, pp. 15-51, (2011); Proposal Preparation Instructions; Safeguarding good scientific practice, Recommendations of the Commission on Professional Self Regulation in Science, pp. 74-76, (2013); Treloar A., Harboe-Ree C., Data management and the curation continuum: How the Monash experience is informing repository relationships, Proceedings of the 14th Victorian Association for Library Automation Conference and Exhibition; Klump J., Managing the Data Continuum; Neuroth H., Strathmann S., Osswald A., Scheffel R., Klump J., Ludwig J., Langzeitarchivierung von Forschungsdaten-Eine Bestandsaufnahme; Razum M., Repository Platforms; Reference Model for An Open Archival Information System (OAIS); DataCite Metadata Schema for the Publication and Citation of Research Data; Kaur K., Herterich P., Dallmeier-Tiessen S., Schmitt K., Schrimpf S., Tjalsma H., Lambert S., McMeekin S., D32.1 Report on Cost Parameters for Digital Repositories; 4C Project Collaboration to Clarify the Costs of Curation: D4.5 from Costs to Business Models; Palaiologk A.S., Economides A.A., Tjalsma H.D., Sesink L.B., An activity-based costing model for long-term preservation and dissemination of digital research data: The case of dans, Int. J. Digit. Libr., 12, pp. 195-214, (2012); DP4lib Project, Kostenmodell für Einen LZA-Dienst","A. Kraft; German National Library of Science and Technology (TIB), Hannover, Welfengarten 1B, D-30167, Germany; email: angelina.kraft@tib.eu","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84962339647"
"Khokhar M.; Schwamm H.; Krug J.; Albin-Clark A.","Khokhar, Masud (57194543857); Schwamm, Hardy (56719720600); Krug, John (57194547707); Albin-Clark, Adrian (19638715500)","57194543857; 56719720600; 57194547707; 19638715500","Data Management Administration Online (DMAOnline)","2017","Procedia Computer Science","106","","","291","298","7","2","10.1016/j.procs.2017.03.028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020744539&doi=10.1016%2fj.procs.2017.03.028&partnerID=40&md5=258b35d5a5b57b1d443adffd714ccedb","Library, Lancaster University, Lancaster, LA1 4YH, United Kingdom","Khokhar M., Library, Lancaster University, Lancaster, LA1 4YH, United Kingdom; Schwamm H., Library, Lancaster University, Lancaster, LA1 4YH, United Kingdom; Krug J., Library, Lancaster University, Lancaster, LA1 4YH, United Kingdom; Albin-Clark A., Library, Lancaster University, Lancaster, LA1 4YH, United Kingdom","In the uncertain Higher Education environment today, where value for money and financial rigour is more important than ever before, it is vital that institutions create and sustain services that exhibit evidence of impact and provide value for money. In the last two years, external pressures from UK funding councils on complying with their Research Data Management (RDM) policies has caused institutions to develop services and support models urgently. These services are usually created for a fixed period, often with a short term investment in staff and/or infrastructure, and primarily because of the lack of clarity in the resultant value for money at an early stage. Monitoring compliance with funding council requirements is complex. Many institutions use Current Research Information Systems (CRIS) to handle their publication and research data catalogues. However, these systems provide only a basic level of functionality for RDM (e.g. submission of datasets information and linking it with project and publications information). Compliance reporting is not provided out of the box and essential information is usually kept in additional systems or spreadsheets by institutions (e.g. whether a data access statement exists or not). This makes the whole process of RDM compliance monitoring cumbersome and time consuming. We introduce Data Management Administration Online (DMAOnline)1, a Jisc Research Data Spring2 project, which facilitates a novel metric based analysis of an institution's compliance with RDM mandates. DMAOnline brings together key RDM information from a variety of sources and provides a normalised structure for the underlying data. This enables ingest of data from a variety of sources e.g. CRIS, Institutional Repositories or Excel sheets. Currently, DMAOnline has the capability to harvest its information from Elsevier's Pure CRIS and Excel files. It also allows users to add in additional information not available from these sources. A powerful dashboard is created for the user that provides information on compliance with RDM policies, data storage usage, data management plans, DOIs minted, datasets preserved, and basic costing. Other systems that DMAOnline already does or intends to harvest information from include DMPOnline3, Archivematica4, DataCite5, and IRUS-data UK6. © 2017 The Authors.","Analytics; Compliance; CRIS; Jisc; Metrics; RDM; Research Data Management","Digital storage; Finance; Information services; Information systems; Investments; Societies and institutions; Sounding apparatus; Analytics; Compliance; CRIS; Jisc; Metrics; Research data managements; Information management","","","","","","","Graham P., Sarah J., Angus W., The Facet Scholarly Communication Collection: Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Louise C., Managing and Sharing Research Data, (2014); Clarifications of EPSRC Expectations on Research Data Management, (2014); Masud K., Hardy S., John K., Adrian A., Data Management Administration Online (DMAOnline) Phase i Report. Version 1.2. Figshare, (2015); Masud K., Hardy S., John K., DMAOnline - Phase Two Report. Figshare, (2015); John K., Adrian A., Technical Choices for DMAOnline, (2015); Jenny M., Chris A., Julie A., Richard G., Simon W., Filling the Digital Preservation Gap. A Jisc Research Data Spring Project. Phase One Report, (2015); Masud K., Lancaster University Research Data Management Architecture. Figshare, (2016); Chris A., Jim B., Brian C., Janette C., Andrew C., Nick D., Paul D., Yvonne F., Martin G., Kerry G., Anita G., Juliet H., Masud K., Dawn L., Ronan O., Rachel P., Hardy S., Andrew S., Eddy V., Liz W., Laurian W., Martin W., Matthew Z., Research Data Management as a wicked problem, Library Review, 64, 4-5, pp. 356-371, (2015)","M. Khokhar; Library, Lancaster University, Lancaster, LA1 4YH, United Kingdom; email: masud.khokhar@lancaster.ac.uk","Clements A.; Sicilia M.-A.; Simons E.; de Castro P.","Elsevier B.V.","","13th International Conference on Current Research Information Systems, CRIS 2016","9 June 2016 through 11 June 2016","Scotland","135998","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85020744539"
"Koopman M.M.; de Jager K.","Koopman, Margaret M. (57479023400); de Jager, Karin (6603555997)","57479023400; 6603555997","Archiving South African digital research data: How ready are we?","2016","South African Journal of Science","112","7-8","2015-0316","","","","8","10.17159/SAJS.2016/20150316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058617565&doi=10.17159%2fSAJS.2016%2f20150316&partnerID=40&md5=7ecd15148f75283086b44149b61b09f6","South African Environmental Observation Network – Fynbos Node, Cape Town, South Africa; Library and Information Studies Centre, University of Cape Town, Cape Town, South Africa","Koopman M.M., South African Environmental Observation Network – Fynbos Node, Cape Town, South Africa; de Jager K., Library and Information Studies Centre, University of Cape Town, Cape Town, South Africa","Digital data archiving and research data management have become increasingly important for institutions in South Africa, particularly after the announcement by the National Research Foundation, one of the principal South African academic research funders, recommending these actions for the research that they fund. A case study undertaken during the latter half of 2014, among the biological sciences researchers at a South African university, explored the state of data management and archiving at this institution and the readiness of researchers to engage with sharing their digital research data through repositories. It was found that while some researchers were already engaged with digital data archiving in repositories, neither researchers nor the university had implemented systematic research data management. © 2016. The Author(s).","Data preservation; Data repositories; Data sharing; Long-term ecological data; Research data management","","","","","","","","Grundlingh ML, Von St Ange UB, Bolton JJ, Bursey M, Compagno L, Cooper R, Et al., AfrOBIS: A marine biogeographic information system for sub-saharan Africa, S Afr J Sci, 103, pp. 91-93, (2007); Van Jaarsveld AS, Pauw JC, Mundree S, Mecenero S, Coetzee BWT, Alard GF., South African Environmental Observation Network: Vision, design and status, S Afr J Sci, 103, pp. 289-294, (2007); Henschel J, Pauw J, Banyikwa F, Brito R, Chabwela H, Palmer T, Et al., Developing the Environmental Long-Term Observatories Network of Southern Africa (ELTOSA), S Afr J Sci, 99, pp. 100-108, (2003); Disciplinary metadata [homepage on the Internet]; Kenall A, Edmonds S, Goodman L, Bal L, Flintoft L, Shanahan DR, Et al., Better reporting for better research: A checklist for reproducibility, BMC Neurosci, 16, (2015); Dasgupta S., Arpanet, Encyclopedia of virtual communities and technologies, (2006); Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Et al., Ecological responses to recent climate change, Nature, 416, pp. 389-395, (2002); Editorial: Dr No Money: The broken science funding system, Sci Am, (2011); Doorn P, Dillo I, Van Horik R., Lies, damned lies and research data: Can data sharing prevent data fraud?, Int J Digit Curation, 8, pp. 229-243, (2013); Digital Dark Age: Revolution preview [video on the Internet]; Diekmann F., Data practices of agricultural scientists: Results from an exploratory study, J Agr Food Inform, 13, pp. 14-34, (2012); Elliot G., Otago biodiversity data management project report, (2008); Scaramozzino JM, Ramirez ML, McGaughey KJ., A study of faculty data curation behaviors and attitudes at a teaching-centered university, Coll Res Libr, 75, pp. 349-365, (2012); Costello ML., Motivating online publication of data, BioScience, 59, pp. 418-427, (2009); Huang X, Hawkins BA, Qiao G., Biodiversity data sharing: Will peer-reviewed data papers work?, BioScience, 63, 1, pp. 5-6, (2013); Fry J, Lockyer S, Oppenheim C., Identifying benefits arising from the duration and open sharing of research data produced by UK higher education and research institutes [programme/project deposit]; Borgman CL., The conundrum of sharing research data, J Am Soc Inform Sci Technol, 63, pp. 1059-1078, (2012); Piwowar HA., Who shares? Who doesn’t? Factors associated with openly archiving raw research data, PLoS ONE, 6, 7, (2011); Molloy JC., The Open Knowledge Foundation: Open data means better science, PLoS Biol, 9, 12, (2011); Piwowar HA, Day RS, Fridsma DB, Ionnidis J., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, 3, (2007); Van Noorden R., Confusion over open-data rules, Nature, 515, (2014); Hufton A., Data matters: Interview with Gavin Simpson, Scientific Data Updates, (2014); Vines TH, Andrew RL, Bock DG, Franklin MT, Gilbert KJ, Kane NC, Et al., Mandated data archiving greatly improves access to research data, FASEB J, 27, pp. 1304-1308, (2013); All that glitters, Nature, 520, (2015); Data Archiving and Networked Services (DANS) [homepage on the Internet]; GenBank overview [homepage on the Internet]; Data Seal of Approval [homepage on the Internet]; Babbie E, Mouton J., The practice of social research, (2001); Research data management [homepage on the Internet]","M.M. Koopman; SAEON – Fynbos Node, Claremont, Private Bag X07, 7735, South Africa; email: margaret@saeon.ac.za","","Academy of Science of South Africa","","","","","","19967489","","SAJSA","","English","S. Afr. J. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85058617565"
"Van Den Berghe S.; Van Gaeveren K.","Van Den Berghe, Steven (57225741907); Van Gaeveren, Kyle (57194549842)","57225741907; 57194549842","Data Quality Assessment and Improvement: A Vrije Universiteit Brussel Case Study","2017","Procedia Computer Science","106","","","32","38","6","11","10.1016/j.procs.2017.03.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020717357&doi=10.1016%2fj.procs.2017.03.006&partnerID=40&md5=2bf805894110078930a6d131f99760aa","Vrije Universiteit Brussel, R and D Department, Pleinlaan 2, Brussel, 1050, Belgium","Van Den Berghe S., Vrije Universiteit Brussel, R and D Department, Pleinlaan 2, Brussel, 1050, Belgium; Van Gaeveren K., Vrije Universiteit Brussel, R and D Department, Pleinlaan 2, Brussel, 1050, Belgium","As the Vrije Universiteit Brussel switched from an in-house built CRIS to Pure, a large number of data quality issues were discovered. In order to solve these, a large-scale data quality assessment and improvement program was started. The assessment sought to find data quality issues and prioritize cleaning tasks along different dimensions, such as reusability and complexity, while taking into account compliance and stakeholder happiness. Moreover, in doing these assessments, an attempt was made to isolate relatively easy to clean parts of the data in order to make them more feasible for people with less domain-knowledge. Finally, some of these data quality improvement operations turned out to be straightforward enough to fully automate them. © 2017 The Authors.","Data Cleaning; Data Quality Assessment; Data Quality Improvement; Research Data Management","Information management; Information systems; Reusability; Sounding apparatus; Data cleaning; Data quality; Data quality assessment; Domain knowledge; Large scale data; Number of datum; Research data managements; Vrije universiteit brussel; Data reduction","","","","","","","Batini C., Cappiello C., Francalanci C., Maurino A., Methodologies for data quality assessment and improvement, ACM Comput. Surv., 41, 3, (2009); Mosley M., Brackett M., The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide), (2010); Kovalenko D., Selenium Design Patterns and Best Practices, (2014)","S. Van Den Berghe; Vrije Universiteit Brussel, R and D Department, Brussel, Pleinlaan 2, 1050, Belgium; email: svdbergh@vub.ac.be","Clements A.; Sicilia M.-A.; Simons E.; de Castro P.","Elsevier B.V.","","13th International Conference on Current Research Information Systems, CRIS 2016","9 June 2016 through 11 June 2016","Scotland","135998","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85020717357"
"Sesartíc A.; Fischlin A.; Töwe M.","Sesartíc, Ana (35280798500); Fischlin, Andreas (6603609828); Töwe, Matthias (57192257570)","35280798500; 6603609828; 57192257570","Towards narrowing the curation gap-theoretical considerations and lessons learned from decades of practice","2016","ISPRS International Journal of Geo-Information","5","6","91","","","","5","10.3390/ijgi5060091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009100588&doi=10.3390%2fijgi5060091&partnerID=40&md5=48f1df50582b6c33ba32b45330d7d027","Digital Curation, ETH-Bibliothek, ETH Zurich, Rämistrasse 101, Zurich, 8092, Switzerland; Terrestrial Systems Ecology, ETH Zurich, Universitätstrasse 16, Zurich, 8092, Switzerland","Sesartíc A., Digital Curation, ETH-Bibliothek, ETH Zurich, Rämistrasse 101, Zurich, 8092, Switzerland; Fischlin A., Terrestrial Systems Ecology, ETH Zurich, Universitätstrasse 16, Zurich, 8092, Switzerland; Töwe M., Digital Curation, ETH-Bibliothek, ETH Zurich, Rämistrasse 101, Zurich, 8092, Switzerland","Research as a digital enterprise has created new, often poorly addressed challenges for the management and curation of research to ensure continuity, transparency, and accountability. There is a common misunderstanding that curation can be considered at a later point in the research cycle or delegated or that it is too burdensome or too expensive due to a lack of efficient tools. This creates a curation gap between research practice and curation needs. We argue that this gap can be narrowed if curators provide attractive support that befits research needs and if researchers consistently manage their work according to generic concepts consistently from the beginning. A rather uniquely long-Term case study demonstrates how such concepts have helped to pragmatically implement a research practice intentionally using only minimalist tools for sustained, self-contained archiving since 1989. The paper sketches the concepts underlying three core research activities. (i) handling of research data, (ii) reference management as part of scholarly publishing, and (iii) advancing theories through modelling and simulation. These concepts represent a universally transferable best research practice, while technical details are obviously prone to continuous change. We hope it stimulates researchers to manage research similarly and that curators gain a better understanding of the curation challenges research practice actually faces. © 2016 by the authors; licensee MDPI, Basel, Switzerland.","Archiving; Best practice; Curation gap; Data lifecycle management; Data preservation; Digital data curation; Research data management; Theory lifecycle management","","","","","","","","Bunge M., Scientific Research I: The Search for System; Studies in the Foundations Methodology and Philosophy of Science, (1967); Bunge M., Scientific Research II: The Search for Truth; Studies in the Foundations Methodology and Philosophy of Science, (1967); Popper K.R., Conjectures and Refutations: The Growth of Scientific Knowledge, (1963); Popper K., Objective Knowledge: An Evolutionary Approach, (1972); Pearce-Moses R., A glossary of archival and records terminology, The Society of American Archivists (SAA), (2005); Whyte A., Job D., Giles S., Lawrie S., Meeting curation challenges in a neuroimaging group, Int. J. Digit. Curation, 3, pp. 171-181, (2008); Baltensweiler W., Fischlin A., The larch bud moth in the Alps, Dynamics of Forest Insect Populations, 1, pp. 331-351, (1988); Baltensweiler W., Fischlin A., On methods of analyzing ecosystems: Lessons from the analysis of forest-insect systems, Ecol. Stud, 61, pp. 401-415, (1987); Fischlin A., Baltensweiler W., Systems analysis of the larch bud moth system. Part I: The larch-larch bud moth relationship, Mitt. Schweiz. Ent. Ges, 52, pp. 273-289, (1979); Ruchti J., Fischlin A., Ddldml S.E., Hilfsprogramm för PASCAL Programmierer Zur Definition und Verwaltung von INFOSYS-Datenfiles (MANUAL), (1978); Consultative Committee for Space Data Systems and Secretariat (CCSDS), (2012); Andre P.Q.C., Besser H., Elkington N., Garrett J., Gladney H., Hedstrom M., Hirtle P.B., Hunter K., Kelly R., Kresh D., Et al., Preserving Digital Information: Report of the Task Force on Archiving of Digital Information; the Commission on Preservation and Access and the Research Libraries Group (RLG), (1996); Knight G., Pennock M., Data without meaning: Establishing the significant properties of digital research, Int. J. Digit. Curation, 4, pp. 159-174, (2009); Hsu L., Martin R.L., McElroy B., Litwin-Miller K., Kim W., Data management, sharing, and reuse in experimental geomorphology: Challenges, strategies, and scientific opportunities, Geomorphology, 244, pp. 180-189, (2015); Stafford N., Science in the digital age, Nature, 467, pp. S19-S21, (2010); Beagrie N., Lavoie B., Woollard M., Keeping Research Data SAFE 2, (2010); Beagrie N., Chruszcz J., Lavoie B., Keeping Research Data Safe: A Cost Model and Guidance for UK Universities, (2008); Deridder J.L., Benign neglect: Developing life rafts for digital content, Inf. Technol. Libr, 30, pp. 71-74, (2011); Kuipers T., Van Der Hoeven J., Insights into Digital Preservation of Research Output in Europe: PARSE-Insight Survey Report D3.6, (2010); Waller M., Sharpe R., Mind the Gap-Assessing Digital Preservation Needs in the UK, (2006); Nelson B., Data sharing: Empty archives, Nature, 461, pp. 160-163, (2009); Pryor G., Managing Research Data, (2012); Ayris P., Davies R., McLeod R., Miao R., Shenton H., Wheatley P., The LIFE2 Final Project Report; Final Report (22/08/08), (2008); Burton A., Groenewegen D., Love C., Treloar A., Wilkinson R., Making research data available in Australia, IEEE Intell. Syst, 27, pp. 40-43, (2012); Davidson J., Jones S., Molloy L., Kejser U.B., Emerging good practice in managing research data and research information within UK universities, Proecedia Comput. Sci, 33, pp. 215-222, (2014); Kuipers T., Van Der Hoeven J., Insights into Digital Preservation of Research Output in Europe: PARSE-Insight Survey Report; Insight Report D3.4, (2009); Neuroth H., Strathmann S., Osswald A., Scheffel R., Klump J., Ludwig J., Digital Curation of Research Data-Experiences of A Baseline Study in Germany, (2013); OpenAIRE Horizon2020 Factsheets: Open Research Data Pilot in Horizon 2020 2015, (2016); Pryor G., Jones S., Collins E., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2014); Tenopir C., Birch B., Allard S., Academic libraries and research data services, Current Practices and Plans for the Future, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Libr. Inf. Sci. Res, 36, pp. 84-90, (2014); Palaiologk A.S., Economides A.A., Tjalsma H.D., Sesink L.B., An activity-based costing model for long-Term preservation and dissemination of digital research data: The case of dans, Int. J. Digit. Libr, 12, pp. 195-214, (2012); Kejser U.B., Nielsen A.B., Thirifays A., Cost model for digital preservation: Cost of digital migration, Int. J. Digit. Curation, 6, pp. 255-267, (2011); Carlson D., A lesson in sharing, Nature, 469, (2011); Bjork B.C., Hedlund T., A formalised model of the scientific publication process, Online Inf. Rev, 28, pp. 8-21, (2004); Thomas A., Campbell L.M., Barker P., Hawksey M., Into the Wild: Technology for Open Educational Resources, (2012); Van Den E.V., Bishop L., Sowing the Seed: Incentives and Motivations for Sharing Research Data, A Researcher's Perspective, (2014); Chu H., Research methods in library and information science: A content analysis, Libr. Inf. Sci. Res, 37, pp. 36-41, (2015); Tammaro A.M., Casarosa V., Research data management in the curriculum: An interdisciplinary approach, Proecedia Comput. Sci, 38, pp. 138-142, (2014); Jones S., How to Develop A Data Management and Sharing Plan, (2011); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, J. Acad. Libr, 40, pp. 211-219, (2014); Goodman A., Pepe A., Blocker A.W., Borgman C.L., Cranmer K., Crosas M., Di Stefano R., Gil Y., Groth P., Hedstrom M., Et al., Ten simple rules for the care and feeding of scientific data, PLoS Comput. Biol, 10, (2014); Foster N.F., Gibbons S., Understanding faculty to improve content recruitment for institutional repositories, D-Lib Mag, 11, pp. 1-12, (2005); Tenopir C., King D.W., The use and value of scientific journals: Past, present and future, Serials, 14, pp. 113-120, (2001); Tenopir C., King D.W., Spencer J., Wu L., Variations in article seeking and reading patterns of academics: What makes a difference?, Libr. Inf. Sci. Res, 31, pp. 139-148, (2009); Morrow T., Beagrie N., Jones M., Chruszcz J., A Comparative Study of E-Journal Archiving Solutions; Joint Information Systems Committee (JISC), (2008); Sutter R.D., Wainscott S.B., Boetsch J.R., Palmer C.J., Rugg D.J., Practical guidance for integrating data management into long-Term ecological monitoring projects, Wildl. Soc. Bull, 39, pp. 451-463, (2015); Waldrop M.M., The origins of personal computing, Sci. Am, 285, pp. 84-91, (2001); Treloar A., Harboe-Ree C., Data management and the curation continuum: How the Monash experience is informing repository relationships, Proceedings of 14th Victorian Association for Library Automation, (2008); Treloar A., Private Research and Shared Research, (2016); Space Data and Information Transfer Systems-Open Archival Information System (OAIS)-Reference Model, (2012); Bongulielmi A.P., Cellier F.E., On the usefulness of deterministic grammars for simulation languages, ACM SIGSIM Simul. Digest, 15, pp. 14-36, (1984); Zumstein P., Stohr M., Zur Nachnutzung von bibliographischen Katalog-und Normdaten för die persönliche Literaturverwaltung undWissensorganisation, ABI Tech, 35, pp. 210-221, (2015); Zeigler B.P., The five elements, Theory of Modelling and Simulation, pp. 27-49, (1976); Zeigler B.P., Multilevel multiformalism modeling: An ecosystem example, Theoretical Systems Ecology, pp. 17-54, (1979); Zeigler B.P., Theory of Modelling and Simulation, (1976); Wymore A.W., Theory of systems, Handbook of Software Engineering, pp. 119-133, (1984); Fischlin A., Interactive modeling and simulation of environmental systems on workstations, Analysis of Dynamic Systems in Medicine, Biology, and Ecology, 275, pp. 131-145, (1991); Nemecek T., The Role of Aphid Behavior in the Epidemiology of Potato Virus Y: A Simulation Study; Diss. ETH 10086, (1993); Androulakis S., Buckle A.M., Atkinson I., Groenewegen D., Nicholas N., Treloar A., Beitz A., ARCHER-E-Research tools for research data management, Int. J. Digit. Curation, 4, pp. 22-33, (2009); Kunkel R., Sorg J., Kolditz O., Rink K., Klump J., Gasche R., Neidl F., TEODOOR-A spatial data infrastructure for terrestrial observation data, Proceedings of the 2013 IEEE 10th International Conference on Networking, Sensing and Control (ICNSC), pp. 242-245, (2013); Steffen W.L., Walker B.H., Ingram J.S.I., Global Change and Terrestrial Ecosystems: The Operational Plan; Global Change Report No. 21; International Geosphere-Biosphere Program (IGBP), (1992); GCTE-Global Change and Terrestrial Ecosystems, (2016); Seitzinger S.P., Gaffney O., Brasseur G., Broadgate W., Ciais P., Claussen M., Erisman J.W., Kiefer T., Lancelot C., Monks P.S., Et al., International geosphere-biosphere programme and earth system science: Three decades of co-evolution, Anthropocene, 12, pp. 3-16, (2015)","A. Sesartíc; Digital Curation, ETH-Bibliothek, ETH Zurich, Zurich, Rämistrasse 101, 8092, Switzerland; email: ana.sesartic@library.ethz.ch","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85009100588"
"Siart C.; Kopp S.; Apel J.","Siart, Christoph (24479210100); Kopp, Simon (57188964602); Apel, Jochen (26666489200)","24479210100; 57188964602; 26666489200","The Interface between Data Science, Research Assessment and Science Support - Highlights from the German Perspective and Examples from Heidelberg University","2016","Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015","","","7373955","472","476","4","1","10.1109/IIAI-AAI.2015.177","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964331026&doi=10.1109%2fIIAI-AAI.2015.177&partnerID=40&md5=227fe5cf8e96ea6638c24436c33667be","Research Division, Heidelberg University, Heidelberg, Germany; Heidelberg University Library, Heidelberg University, Heidelberg, Germany","Siart C., Research Division, Heidelberg University, Heidelberg, Germany; Kopp S., Research Division, Heidelberg University, Heidelberg, Germany; Apel J., Heidelberg University Library, Heidelberg University, Heidelberg, Germany","Assessment of research and acquisition of research information has become an integral part of university business. The motivation for obtaining such data can be ascribed to both internal and external demands. However, this raises questions about common standards and efforts for data capture, storage and provision. This paper aims at giving insight into some general framework conditions of data science and research assessment, along with the transfer of outcomes into the daily business of science support. Examples from Heidelberg University are presented. © 2015 IEEE.","information systems; open access; research assessment; research data management; research database; research information; science support","Digital storage; Information science; Information systems; Common standards; Framework conditions; Heidelberg; Integral part; Open Access; Research data managements; Research database; Information management","","","","","","","Guidelines on Data Management in Horizon 2020, (2013); Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2013); Philips M., Research universities and research assessment, LERU Position Paper, (2012); Empfehlungen zu Einem Kerndatensatz Forschung, (2013); Bittner S., Hornbostel S., Scholze F., Forschungsinformation in Deutschland: Anforderungen Stand und Nutzen existierender Forschungsinformationssysteme, Workshop Forschungsin for Mationssysteme, (2011); CRIS Concept and CRIS Benefits; CERIF 1.3 Full Data Model; Higher Education Act; Heidelberg University Bibliography (HeiBIB); Integrated Authority File (GND) of the German National Library; Open Access Policy of Heidelberg University; Open Access Services of Heidelberg University Library; DFG Funding Programme 'Open Access Publishing.; Competence Centre for Research Data at Heidelberg University; Research Data Policy of Heidelberg University; HeiDATA Research Data Repoitory; Science As An Open Enterprise, (2012)","","Hirokawa S.; Hashimoto K.; Matsuo T.; Mine T.","Institute of Electrical and Electronics Engineers Inc.","International Institute of Applied Informatics (IIAI)","4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015","12 July 2015 through 16 July 2015","Okayama","119024","","978-147999958-3","","","English","Proc. - IIAI Int. Congr. Adv. Appl. Inform., IIAI-AAI","Conference paper","Final","","Scopus","2-s2.0-84964331026"
"Thakkar J.; Barry T.; Thiagalingam A.; Redfern J.; McEwan A.L.; Rodgers A.; Chow C.K.","Thakkar, Jay (56042254900); Barry, Tony (56924742100); Thiagalingam, Aravinda (57203029022); Redfern, Julie (14013351600); McEwan, Alistair L. (14016086300); Rodgers, Anthony (55585900000); Chow, Clara K. (8871779800)","56042254900; 56924742100; 57203029022; 14013351600; 14016086300; 55585900000; 8871779800","Design considerations in development of a mobile health intervention program: The text me and textmeds experience","2016","JMIR mHealth and uHealth","4","4","e127","","","","11","10.2196/mhealth.5996","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048874607&doi=10.2196%2fmhealth.5996&partnerID=40&md5=d9813320555f0f17a73f0233e6ecd8e9","The George Institute for Global Health, Camperdown, Australia; Sydney Medical School, The University of Sydney, Sydney, Australia; Westmead Hospital, Sydney, Australia; The University of Sydney, Sydney, Australia","Thakkar J., The George Institute for Global Health, Camperdown, Australia, Sydney Medical School, The University of Sydney, Sydney, Australia, Westmead Hospital, Sydney, Australia; Barry T., Westmead Hospital, Sydney, Australia, The University of Sydney, Sydney, Australia; Thiagalingam A., Sydney Medical School, The University of Sydney, Sydney, Australia, Westmead Hospital, Sydney, Australia; Redfern J., The George Institute for Global Health, Camperdown, Australia, Sydney Medical School, The University of Sydney, Sydney, Australia; McEwan A.L., The University of Sydney, Sydney, Australia; Rodgers A., The George Institute for Global Health, Camperdown, Australia, Sydney Medical School, The University of Sydney, Sydney, Australia; Chow C.K., The George Institute for Global Health, Camperdown, Australia, Sydney Medical School, The University of Sydney, Sydney, Australia, Westmead Hospital, Sydney, Australia","Background: Mobile health (mHealth) has huge potential to deliver preventative health services. However, there is paucity of literature on theoretical constructs, technical, practical, and regulatory considerations that enable delivery of such services. Objectives: The objective of this study was to outline the key considerations in the development of a text message-based mHealth program; thus providing broad recommendations and guidance to future researchers designing similar programs. Methods: We describe the key considerations in designing the intervention with respect to functionality, technical infrastructure, data management, software components, regulatory requirements, and operationalization. We also illustrate some of the potential issues and decision points utilizing our experience of developing text message (short message service, SMS) management systems to support 2 large randomized controlled trials: TEXT messages to improve MEDication adherence & Secondary prevention (TEXTMEDS) and Tobacco, EXercise and dieT MEssages (TEXT ME). Results: The steps identified in the development process were: (1) background research and development of the text message bank based on scientific evidence and disease-specific guidelines, (2) pilot testing with target audience and incorporating feedback, (3) software-hardware customization to enable delivery of complex personalized programs using prespecified algorithms, and (4) legal and regulatory considerations. Additional considerations in developing text message management systems include: balancing the use of customized versus preexisting software systems, the level of automation versus need for human inputs, monitoring, ensuring data security, interface flexibility, and the ability for upscaling. Conclusions: A merging of expertise in clinical and behavioral sciences, health and research data management systems, software engineering, and mobile phone regulatory requirements is essential to develop a platform to deliver and manage support programs to hundreds of participants simultaneously as in TEXT ME and TEXTMEDS trials. This research provides broad principles that may assist other researchers in developing mHealth programs. ©Jay Thakkar, Tony Barry, Aravinda Thiagalingam, Julie Redfern, Alistair L McEwan, Anthony Rodgers, Clara K Chow.","Coronary artery disease; MHealth; Mobile phone; Text message","","","","","","National Health and Medical Research Council, NHMRC, (1033478, 1042290, 1061793)","","Mhealth-New Horizons for Health through Mobile Technologies, (2011); Measuring the Information Society Report, (2015); Koivusilta L.K., Lintonen T.P., Rimpela A.H., Orientations in adolescent use of information and communication technology: A digital divide by sociodemographic background, educational career, and health, Scand J Public Health, 35, 1, pp. 95-103, (2007); Atun R., Sittampalan S., The Role of Mobile Phone in Increasing Accessibility and Efficiency in Health Care; Goggin G., Cell Phone Culture: Mobile Technology in Everyday Life, (2006); Shaw R.J., Bosworth H.B., Hess J.C., Silva S.G., Lipkus I.M., Davis L.L., Et al., Development of a theoretically driven mHealth text messaging application for sustaining recent weight loss, JMIR Mhealth Uhealth, 1, 1, (2013); Cole-Lewis H., Kershaw T., Text messaging as a tool for behavior change in disease prevention and management, Epidemiol Rev, 32, pp. 56-69, (2010); Kannisto K.A., Koivunen M.H., Valimaki M.A., Use of mobile phone text message reminders in health care services: A narrative literature review, J Med Internet Res, 16, 10, (2014); Wei J., Hollin I., Kachnowski S., A review of the use of mobile phone text messaging in clinical and healthy behaviour interventions, J Telemed Telecare, 17, 1, pp. 41-48, (2011); Thakkar J., Kurup R., Laba T.L., Santo K., Thiagalingam A., Rodgers A., Et al., Mobile telephone text messaging for medication adherence in chronic disease: A meta-analysis, JAMA Intern Med, 176, 3, pp. 340-349, (2016); Chow C.K., Redfern J., Thiagalingam A., Jan S., Whittaker R., Hackett M., Et al., Design and rationale of the tobacco, exercise and diet messages (TEXT ME) trial of a text message-based intervention for ongoing prevention of cardiovascular disease in people with coronary disease: A randomised controlled trial protocol, BMJ Open, 2, 1, (2012); Chow C.K., Redfern J., Hillis G.S., Thakkar J., Santo K., Hackett M.L., Et al., Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial, JAMA, 314, 12, pp. 1255-1263, (2015); ANZ Clinical Trials Register, (2013); Redfern J., Thiagalingam A., Jan S., Whittaker R., Hackett M.L., Mooney J., Et al., Development of a set of mobile phone text messages designed for prevention of recurrent cardiovascular events, Eur J Prev Cardiol, 21, 4, pp. 492-499, (2014); Safeer R.S., Keenan J., Health literacy: The gap between physicians and patients, Am Fam Physician, 72, 3, pp. 463-468, (2005); van der Ploeg H.P., Chey T., Korda R.J., Banks E., Bauman A., Sitting time and all-cause mortality risk in 222 497 Australian adults, Arch Intern Med, 172, 6, pp. 494-500, (2012); Wald D.S., Butt S., Bestwick J.P., One-way versus two-way text messaging on improving medication adherence: Meta-analysis of randomized trials, Am J Med, 128, 10, pp. e1-e1139, (2015); 2013, (2013); Adriancs H.T., Salted Password Hashing-Doing It Right. Codeproject, (2016); Health Insurance Portablity and Accountablity Act Of, (1996); Karasz H.N., Eiden A., Bogan S., Text messaging to communicate with public health audiences: How the HIPAA Security Rule affects practice, Am J Public Health, 103, 4, pp. 617-622, (2013); Federal Register of Legislation. Spam Act, 2003, (2014); Federal Communications Commission. Unwanted Commercial Electronic Mail; The Telecom Commercial Communications Customer Preference Regulations, (2010); Karasz H., Bogan S., Investing in Text Messaging System: A Comparision of Three Solutions., (2012); Text messaging in health care: Research toolkit, CRISP, University of Colorado School of Medicine, (2013); Thakkar J., Karthikeyan G., Purohit G., Thakkar S., Sharma J., Verma S., Et al., Development of macaronic Hindi-English 'Hinglish' text message content for a coronary heart disease secondary prevention programme, Heart Asia, 8, 2, pp. 32-38, (2016)","C.K. Chow; The George Institute for Global Health, Camperdown, Level 10, King George V Building Missenden Road, 2050, Australia; email: cchow@georgeinstitute.org.au","","JMIR Publications Inc.","","","","","","22915222","","","","English","JMIR mHealth uHealth","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85048874607"
"Ahmadi N.A.; Jano Z.; Khamis N.","Ahmadi, Nurul Aqilah (57191348700); Jano, Zanariah (55655673100); Khamis, Noorli (56259099400)","57191348700; 55655673100; 56259099400","Analyzing crucial elements of research data management policy","2016","International Business Management","10","17","","3847","3852","5","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988967313&partnerID=40&md5=3465431d097f6d18e34b910daf86971f","Centre for Languages and Human Development, Universiti Teknikal Malaysia, Melaka Hang Tuah Jaya, Durian Tunggal, Melaka, 761 00, Malaysia","Ahmadi N.A., Centre for Languages and Human Development, Universiti Teknikal Malaysia, Melaka Hang Tuah Jaya, Durian Tunggal, Melaka, 761 00, Malaysia; Jano Z., Centre for Languages and Human Development, Universiti Teknikal Malaysia, Melaka Hang Tuah Jaya, Durian Tunggal, Melaka, 761 00, Malaysia; Khamis N., Centre for Languages and Human Development, Universiti Teknikal Malaysia, Melaka Hang Tuah Jaya, Durian Tunggal, Melaka, 761 00, Malaysia","Research data management is essential for an institution. Before planning a research data management, an institution needs to build a research data management policy. Each university needs the policy as a guideline for the implementation of research data management. Therefore, this study aimed to investigate essential elements of research data management policy. A qualitative content analysis was used to gain insights into best practices of data management policy in selected universities. The findings yielded eight standard items of research data management policy utilized by most top universities globally. The findings are useful toward devising a research data management policy for the Malaysian higher institutions to support research data life cycle and to provide improvements in research efficiency. Future studies should focus on implementing the framework and measuring the effectiveness. © Medwell Journals, 2016.","Efficiency; Policy; Research data and record; Research data management element","","","","","","","","Bohemier K.A., Atwood T., Kuehn A., Qin J., A content analysis of institutional data policies, Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 409-410, (2011); Fitzgerald A.M., Hashim M., Nor H., Enabling access to research data in developing countries: Designing a policy and practice framework for Malaysias public research universities, Proceedings of the IASC 1st Thematic Conference on the Knowledge Commons, pp. 1-14, (2012); Johare R., Yunus A.M., Kadir I.K.A., Mohamed H., Managing primary research data and records for research in research institution and related organizations: Examples from the TEAM Malaysia case studies, Proceedings of the Interpares 3rd International Symposium, pp. 1-9, (2009); Mazlan K.S., Sui L.K.M., Jano Z., Designing an eportfolio conceptual framework to enhance written communication skills among undergraduate students, Asian Soc. Sci, 11, pp. 35-47, (2015); Wolski M., Richardson J., A framework for university research data management, Proceedings of the Conference on CCA EDUCAUSE Australasia, pp. 1-9, (2011)","","","Medwell Journals","","","","","","19935250","","","","English","Int. Bus. Manage.","Article","Final","","Scopus","2-s2.0-84988967313"
"Smith P.L., II; McIntyre L.","Smith, Plato L. (24475610400); McIntyre, Lauren (7101992431)","24475610400; 7101992431","Developing, linking, and providing access to supplemental genetics dataset vcf files","2017","GL-Conference Series: Conference Proceedings","Part F126833","","","91","95","4","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020435670&partnerID=40&md5=db2d3ddaca830e6200f62bcb0fdb6806","University of Florida (Libraries), United States; University of Florida (Microbiology), United States","Smith P.L., II, University of Florida (Libraries), United States; McIntyre L., University of Florida (Microbiology), United States","This conference proceeding paper is the written version component of the data panel discussion on developing a dataset collection using Zenodo for a professor in the Department of Molecular Genetics & Microbiology at the University of Florida. An internal University of Florida George A. Smathers Libraries Strategic Opportunities Program (SOP) grant award provided support for the creation and development of an initial supplemental datasets digital collection of large, static variant call format (vcf) in zenodo. The ""Documenting a Genomics Variant Files Data Management: Developing Research Data management (RDM) workflows and providing research data access via HPC"" project inspired this paper. The large vcf datasets used for this project ranged from 34 megabytes to 43 gigabytes. The researcher needed to (1) develop a data repository for supplemental datasets vcf files too large for attachment as supplemental data files for journal submissions, (2) provide digital object identifiers (DOIs) for all vcf dataset files, and (3) link the supplemental vcf dataset files to the journal article via the vcf doi. These three outcomes were accomplished during phase 1 (June 2016 - December 2016) of this project and presented at the GL18. Phase 2 (January 2017 - June 2017) of this project includes performing (1) a dataset reproducibility interview, (2) an open archival initiative protocol for metadata harvest (OAI-PMH) from Zenodo to the University of Florida institutional repository (IR@UF), and (3) developing a similar use case project for researchers in UF/IFAS Nature Coast Biological Station (NCBS).","","Biology; Chromosomes; Information management; Information services; Molecular biology; Digital collections; Institutional repositories; Molecular genetics; Panel discussions; Reproducibilities; Research data managements; Strategic opportunity; University of Florida; Digital libraries","","","","","","","Reference Model for An Open Archival Information System (OAIS), (2012); IGSR: The International Genome Sample Resource, (2016); Kurmangaliyev Y.Z., Favorov A.V., Osman N.M., Lehmann K., Campo D., Salomon M.P., Tower J., Gelfand M.S., Nuzhdin S.V., Natural variation of gene models in Drosophilia melanogaster, BMC Genomics, (2015); National Institutes of Health Plan for Increasing Access to Scientific Publications and Digital Scientific Data from NIH Funded Scientific Research, (2015); Today's Data, Tomorrow's Discoveries: Increasing Access to the Results of Research Funded by the National Science Foundation, (2015); SRA, Sequence Read Archive, (2016)","","","TextRelease","EBSCO; et al.; Korea Institute of Science and Technology Information (KISTI); Nuclear Information Section - International Atomic Energy Agency (NIS-IAEA); Slovak Centre of Scientific and Technical Information (CVTISR); The New York Academy of Medicine","18th International Conference on Grey Literature: Leveraging Diversity in Grey Literature, GL 2016","28 November 2016 through 29 November 2016","New York","126833","13862316","978-907748430-2","","","English","GL-Conf. Series: Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85020435670"
"Eifert T.; Muckel S.; Schmitz D.","Eifert, Thomas (6508209571); Muckel, Stephan (57191109021); Schmitz, Dominik (57213912824)","6508209571; 57191109021; 57213912824","Introducing research data management as a service suite at RWTH Aachen","2016","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-257","","","55","64","9","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986910133&partnerID=40&md5=9500728632b364de8bc3f9fa3e4c9c8b","RWTH Aachen University, IT Center, Aachen, 52056, Germany; RWTH Aachen University, Central Administration, Aachen, 52056, Germany; RWTH Aachen University, University Library, Aachen, 52056, Germany","Eifert T., RWTH Aachen University, IT Center, Aachen, 52056, Germany; Muckel S., RWTH Aachen University, Central Administration, Aachen, 52056, Germany; Schmitz D., RWTH Aachen University, University Library, Aachen, 52056, Germany","Research Data Management (RDM) receives more and more attention as a core component of scientific work. This importance equally stems from the scientific work with everincreasing amounts of data, the value of this data for subsequent use, and the formal requirements of funding agencies. While these requirements are widely accepted among the researchers, the individual acceptance depends on many factors. In this paper we describe the initial steps at RWTH Aachen University to implement RDM as a widely accepted service suite. Here, service means appropriate IT solutions as well as a comprehensive training and support. The steps include the preliminary work to get the top management's awareness and the dialog with the scientists to learn the practical requirements. We present the initial solutions and solution concepts derived so far. We explain how our local approach relates to broader external initiatives. It is especially important that all these concepts together encompass a comprehensive suite of support offerings, guidelines, and various IT service modules built or planned on the underlying IT infrastructure.","Research Data Management; Service introduction","Individual acceptance; Initial solution; IT infrastructures; Local approaches; Practical requirements; Research data managements; Service introduction; Solution concepts; Information management","","","","","","","How Europe Can Gain from the Rising Tide of Scientific Data, (2010); Ho Chschulrektorenkonferenz: Management of Research Data-A Key Strategic Challenge for University Management, (2014); Orientation Paths, Options for Action and Scenarios, (2015); Klar J., Enke H., Rahmenbedingungen Einer Disziplinübergreifenden Forschungsdateninfrastruktur, (2013); Kraft A., Et al., The rada r project-A service for research data archival and publication, ISPRS Int. J. Geo-Inf, 5, (2016); Pampel H., Et al., Stand und perspektive des globalen verzeichnisses von forschungsdaten-repositorien re3data.org, 8. DFN-Forum Kommunikationstechnologien: Beiträge der Fachtagung 08, pp. 13-22, (2015); Amtliche Bekanntmachung Vom 11.01, (2011); Rektoratsbeschluss Vom 08.03, (2016); Simukovic E., Et al., Was Sind Ihre Forschungsdaten? Interviews mit Wissenschaftlern der Humboldt-Universität zu Berlin, (2014); Tristram F., Et al., Offentlicher Abschlussbericht von BwFDM Communities-Wissenschaftliches Datenmanagement An Den Universitäten Baden Württembergs, (2016); Eifert T., Bunsen G., Grundlagen und Entwicklung von Identity Management an der RWTH Aachen, PIK-Praxis der Informationsverarbeitung und Kommunikation. Band 36, pp. 109-116; Wittenburg P., Data foundation & terminology wg. Data fabric ig, Presentation at the RDA-DE-DINI Workshop "" Aktuelle Resultate der Research Data Alliance (RDA) und Deren Zukünftige Bedeutung "", (2015)","","Rodosek G.D.; Universitat der Bundeswehr Munchen, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Munchen; Reiser H.; LRZ, Munchen; Muller P.; Technische Universitat Kaiserslautern, Postfach 3049, Kaiserslautern; Neumair B.; Karlsruher Institut fur Technologie (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen","Gesellschaft fur Informatik (GI)","","9. DFN-Forum Kommunikationstechnologien - 9th DFN-Forum on Communication Technologies","31 May 2016 through 1 June 2016","Rostock","123337","16175468","978-388579651-0","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-84986910133"
"Moulaison Sandy H.; Corrado E.M.; Ivester B.B.","Moulaison Sandy, Heather (58024933100); Corrado, Edward M. (14833992500); Ivester, Brandi B. (57193647234)","58024933100; 14833992500; 57193647234","Personal digital archiving: an analysis of URLs in the.edu domain","2017","Library Hi Tech","35","1","","40","52","12","7","10.1108/LHT-11-2016-0120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015395707&doi=10.1108%2fLHT-11-2016-0120&partnerID=40&md5=7088e3381c04f7658bd08558961bf171","School of Information Science and Learning Technologies, University of Missouri, Columbia, MO, United States; University of Alabama, Tuscaloosa, AL, United States","Moulaison Sandy H., School of Information Science and Learning Technologies, University of Missouri, Columbia, MO, United States; Corrado E.M., University of Alabama, Tuscaloosa, AL, United States; Ivester B.B., School of Information Science and Learning Technologies, University of Missouri, Columbia, MO, United States","Purpose: The purpose of this paper is to consider personal digital archiving (PDA) from an academic perspective. Although elements of research data management and personal information management are relevant, it is unclear what is available on university websites supporting PDA. The following question guided the research: where is “PDA” content housed in the top-level.edu domain and what is the format and nature of the content made available? Design/methodology/approach: This descriptive study analyzed Google hits yielded by searching “PDA” within the.edu domain. Results were analyzed to determine where content was housed and its format and nature. Placement in the domain, delivery methods, topics, and the nature of the most highly ranking Uniform Resource Locators (URLs) were analyzed. Findings: In the academy, PDA is not exclusively of interest in libraries; not quite half of the.edu URLs (45 percent) pointed to a library site. Scholarly papers were the most returned content, followed by blogs and conferences information. Closer analysis of the top 20 URLs showed that libraries are popular and papers, and blogs continue to be dominant. Research limitations/implications: The results suggest good PDA practices and recommendations are evolving. Academic librarians should examine these practices, refine them, and make them available and discoverable on the web. Originality/value: This is the first paper, to the knowledge, to consider PDA content from the perspective of universities and university libraries. © 2017, © Emerald Publishing Limited.","Academic libraries; Personal digital archiving; Personal information management; Research data management; Universities; Websites","","","","","","","","About us, (2016); Brown N., Helping members of the community manage their digital lives: developing a personal digital archiving workshop, D-Lib Magazine, 21, 5-6, (2015); Cabanac G., Hartley J., Issues of work-life balance among JASIST authors and editors, Journal of the American Society for Information Science & Technology, 64, 10, pp. 2182-2186, (2013); Chiware E.R.T., Mathe Z., Academic libraries’ role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2016); Copeland A., Public library: a place for the digital community archive, Preservation, Digital Technology & Culture, 44, 1, pp. 12-21, (2015); Copeland A.J., Analysis of public library users’ digital preservation practices, Journal of the American Society for Information Science and Technology, 62, 7, pp. 1288-1300, (2011); Copeland A.J., Barreau D., Helping people to manage and share their digital information: a role for public libraries, Library Trends, 59, 4, pp. 637-649, (2011); Corrado E.C., Moulaison Sandy H., Digital Preservation for Libraries, Archives, and Museums, (2017); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cushing A.L., Highlighting the archives perspective in the personal digital archiving discussion, Library Hi Tech, 28, 2, pp. 301-312, (2010); Cushing A.L., If it computes, patrons have brought it in: personal information management and personal technology assistance in public libraries, Library & Information Science Research, 38, 1, pp. 81-88, (2016); Den-Nagy I., A double-edged sword? A critical evaluation of the mobile phone in creating work-life balance, New Technology, Work & Employment, 29, 2, pp. 193-211, (2014); Jacques J., Fastrez P., Personal information management competences: a case study of future college students, International Conference on Human Interface and the Management of Information, pp. 320-331, (2014); Jones W., Keeping Found Things Found: The Study and Practice of Personal Information Management, (2010); Marshall C.C., Rethinking personal digital archiving part 1: four challenges from the field, D-Lib Magazine, 14, 3-4, (2008); Peer L., Wykstra S., New curation software: step-by-step preparation of social science data and code for publication and preservation, IASSIST Quarterly, 39, 4, pp. 6-13, (2015); Redwine G., Personal digital archiving, (2015); Well F.A.Q., (2016); Word of the week: personal digital archiving, (2016); Strasser C., Research Data Management: A Primer Publication of the National Information Standards Organization, (2015); Su A.J., Hu Y.C., Kuzmanovic A., Koh C.K., How to improve your search engine ranking: myths and reality, ACM Transactions on the Web, 8, 2, pp. 8:1-8:25, (2014); Suiter A.M., Moulaison H.L., Supporting scholars: an analysis of academic library websites’ documentation on metrics and impact, Journal of Academic Librarianship, 41, 6, pp. 814-820, (2015); Sullivan D., Dear Bing, We have 10,000 ranking signals to your 1,000. Love, Google, (2010); Waller A.D., Ragsdell G., The impact of e-mail on work-life balance, Aslib Proceedings, 64, 2, pp. 154-177, (2012); Xie X., Sonnenwald D., Fulton C., The role of memory in document re-finding, Library Hi Tech, 33, 1, pp. 83-102, (2015)","H. Moulaison Sandy; School of Information Science and Learning Technologies, University of Missouri, Columbia, United States; email: moulaisonhe@missouri.edu","","Emerald Group Publishing Ltd.","","","","","","07378831","","","","English","Libr. Hi Tech","Article","Final","","Scopus","2-s2.0-85015395707"
"Cox A.M.; Verbaan E.","Cox, Andrew M. (7402563906); Verbaan, Eddy (24470394900)","7402563906; 24470394900","How academic librarians, IT staff, and research administrators perceive and relate to research","2016","Library and Information Science Research","38","4","","319","326","7","21","10.1016/j.lisr.2016.11.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004010208&doi=10.1016%2fj.lisr.2016.11.004&partnerID=40&md5=1ef69e474ce80f9518e2c431addd503a","Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom","Cox A.M., Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom; Verbaan E., Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom","Academic libraries are changing how they support research. For example, their involvement in research data management (RDM) implies a much deeper relationship with researchers throughout the research lifecycle. Perhaps we are witnessing a shift from support to partnership. This study examines how librarians, IT staff, and research administrators see research and their own relation to it. Within an interpretative methodology, 20 semi-structured interviews with librarians, IT staff, and research administrators were analyzed thematically. Librarians often talked about research via the discourse of research-led teaching. They also conceived of it via notions of collection and to a lesser extent through reference work or copyright expertise. They saw some of their own continuing professional development or service development work as akin to the work of university researchers, but at the other end of a spectrum. Some saw a categorical difference and considered that research was only conducted by people who had a job title of researcher. IT managers tended to see research via infrastructure or specialist expertise. However, at least one IT staff member saw himself as both partly a researcher and a bridge between research and support. Research administrators tended to see research through the roles of administrative support and policy influence. In summary, seven broad narratives about research were identified: influencing researchers to align with policy; being a researcher; being a bridge with research; offering expertise; providing infrastructure; supporting a research/teaching nexus; and relieving researchers of administrative burdens. As institutions develop research partnerships, e.g., around RDM, training and curricula will need to expand existing conceptions and build deeper empathetic relationships with research. © 2016 Elsevier Inc.","","","","","","","","","Abbott A., The system of professions, (1988); Akerlind G.S., An academic perspective on research and being a researcher: An integration of the literature, Studies in Higher Education, 33, 1, pp. 17-31, (2008); Alvaro E., Brooks H., Ham M., Poegel S., Rosencrans S., E-science librarianship: Field undefined, Issues in Science and Technology Librarianship, 66, (2011); Becher T., Trowler P., Academic tribes and territories: Intellectual enquiry and the culture of disciplines, (2001); Borgman C., Big data, little data, no data: Scholarship in the networked world, (2015); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2008); Brew A., Conceptions of research: A phenomenographic study, Studies in Higher Education, 26, 3, pp. 271-285, (2001); Brew A., Lucas L., Academic research and researchers, (2009); Carlson J., Kneale R., Embedded librarianship in the research context navigating new waters, College & Research Libraries News, 72, 3, pp. 167-170, (2011); Collinson J.A., Just “non-academics”? Research administrators and contested occupational identity, Work, Employment and Society, 20, 2, pp. 267-288, (2006); Collinson J.A., ‘Get yourself some nice, neat, matching box files!’ Research administrators and occupational identity work, Studies in Higher Education, 32, 3, pp. 295-309, (2007); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing research data, pp. 105-133, (2012); Corrall S., Designing libraries for research collaboration in the network world: An exploratory study, Liber Quarterly, 24, 1, pp. 17-48, (2014); Corrall S., Lester R., The researcher's view: Context is critical, Better library and learning spaces: Projects, trends and ideas, pp. 183-192, (2013); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management: Emerging trends in library research support services, Library Trends, 61, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A.M., Verbaan E., Sen B., A spider, an octopus, or an animal just coming into existence? Designing a curriculum for librarians to support research data management, Journal of eScience Librarianship, 3, 1, (2014); Cox A.M., Verbaan E., Sen B.A., Upskilling liaison librarians for research data management, Ariadne, 70, (2012); Delaney G., Bates J., Envisioning the academic library: A reflection on roles, relevancy and relationships, New Review of Academic Librarianship, 21, 1, pp. 30-51, (2015); Dempsey L., Malpas C., Lavoie B., Collection directions: The evolution of library collections and collecting. portal, Libraries and the Academy, 14, 3, pp. 393-423, (2014); Falciani-White N., Understanding the “complexity of experience”: Modeling faculty research practices, The Journal of Academic Librarianship, 42, 2, pp. 118-126, (2016); Fanghanel J., Being an academic, (2012); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the research data management landscape, The process of discovery: The CLIR Postdoctoral Fellowship Program and the future of the academy, pp. 82-102, (2015); Gabridge T., The last mile: Liaison roles in curating science and engineering research data, Research Library Issues, 265, pp. 15-21, (2009); Green J., Langley D., Professionalising research management, (2009); Hoffman S., Dynamic research support for academic libraries, (2016); Huutoniemi K., Klein J.T., Bruun H., Hukkinen J., Analyzing interdisciplinarity: Typology and indicators, Research Policy, 39, 1, pp. 79-88, (2010); Jaguszewski J.M., Williams K., New roles for new times: Transforming liaison roles in research libraries, (2013); Klein J.T., Crossing boundaries: Knowledge, disciplinarities, and interdisciplinarities, (1996); Lewis M., Libraries and the management of research data, Envisioning future academic library services: Initiatives, ideas and challenges, pp. 145-168, (2010); Lyon L., The informatics transform: Re-engineering libraries of the data decade, The International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Macfarlane B., The morphing of academic practice: Unbundling and the rise of the para-academic, Higher Education Quarterly, 65, 1, pp. 59-73, (2011); McNicol S., Nankivell C., The LIS research landscape: A review and prognosis, (2003); Monroe-Gulick A., O'Brien M.S., White G.W., Librarians as partners: Moving from research supporters to research partners, Paper presented at the Association of College and Research Libraries Conference, Indianapolis, Indiana, (2013); O'Brien L., Richardson J., Supporting research through partnership, Partnerships and new roles in the 21st-century academic library: Collaborating, embedding, and cross-training for the future, pp. 191-212, (2015); Powell R.R., Baker L.M., Mika J.J., Library and information science practitioners and research, Library and Information Science Research, 24, pp. 49-72, (2002); Schmidt M.G., Reznik-Zellen R., Science boot camp for librarians: CPD on a shoestring, Paper presented at IFLA Conference, Costa Rica, (2011); Scott P., Foreword, Academic research and researchers, pp. xiii-xviii, (2009); Tenopir C., Birch B., Allard S., Academic libraries and research data services [white paper], (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, pp. 84-90, (2014); Thornton M., Academic un-freedom in the new knowledge economy, Academic research and researchers, pp. 19-34, (2009); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, 3, pp. 211-219, (2014); Wang M., Fong B.L., Embedded data librarianship: A case study of providing data management support for a science department, Science & Technology Libraries, 34, pp. 228-240, (2015); Whitchurch C., Reconstructing identities in higher education: The rise of “third space” professionals, (2012); Williamson L., Roles, responsibilities and skills matrix for research data management (RDM) support. (version 3.0), (2013); Woods H.B., Booth A., What is the current state of practitioner research? The 2013 LIRG research scan, Library and Information Research, 37, 116, pp. 2-22, (2013)","A.M. Cox; Information School, University of Sheffield, Sheffield, Regent Court, 211 Portobello, S1 4DP, United Kingdom; email: a.m.cox@sheffield.ac.uk","","Elsevier Ltd","","","","","","07408188","","LISRD","","English","Libr. Inf. Sci. Res.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85004010208"
"Verbakel E.; Grootveld M.","Verbakel, Ellen (56505319300); Grootveld, Marjan (24484362000)","56505319300; 24484362000","‘Essentials 4 Data Support’: Five years’ experience with data management training","2016","IFLA Journal","42","4","","278","283","5","5","10.1177/0340035216674027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85001958582&doi=10.1177%2f0340035216674027&partnerID=40&md5=2ac5aab26a4584fb5c012ffd31688f5c","TU Delft, Netherlands; Data Archiving and Networked Services (DANS), Netherlands","Verbakel E., TU Delft, Netherlands; Grootveld M., Data Archiving and Networked Services (DANS), Netherlands","This article describes a research data management course for support staff such as librarians and IT staff. The authors, who coach the participants, introduce the three course formats and describe the training in more detail. In the last years over 170 persons have participated in this training. It combines a wealth of online information with face-to-face meetings. The aim of the course is to support the participants in strengthening various skills and acquiring knowledge so they feel confident to support, advise and train researchers. Interaction among the students is embedded in the structure of the training, because we regard it as a valuable instrument to develop a professional network. Recently the course has taken on a new challenge: in addition to the regular courses a couple of in house trainings have been delivered on request. The paper ends with a description of the key group assignments for such compact trainings. © 2016, © The Author(s) 2016.","Blended learning; data education; data literacy; data support; information skills; training library staff","","","","","","","","De Smaele M., Et al., Data intelligence training for library staff, International Journal of Digital Curation, 8, 1, pp. 218-228, (2013); A Curriculum Framework for Digital Curation, (2013); Dillo I., Doorn P., The front office-back office model: Supporting research data management in the Netherlands, International Journal of Digital Curation, 9, 2, pp. 39-46, (2014); EDISON: Building the Data Science Profession, (2015); Essentials 4 Data Support Course, (2014); Goldstein S., Training for Research Data Management: Comparative European Approaches, (2016); Grootveld M., Verbakel E., Essentials for data support: Training the front office, International Journal of Digital Curation, 10, 1, pp. 240-248, (2015); Interest Group: Education and Training on Handling of Research Data, (2013)","E. Verbakel; TU Delft Library, MG Delft, PO Box 98, NL-2600, Netherlands; email: p.m.verbakel@tudelft.nl","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85001958582"
"Myneni S.; Patel V.L.; Bova G.S.; Wang J.; Ackerman C.F.; Berlinicke C.A.; Chen S.H.; Lindvall M.; Zack D.J.","Myneni, Sahiti (35097608700); Patel, Vimla L. (35600762400); Bova, G. Steven (7005052044); Wang, Jian (55444775900); Ackerman, Christopher F. (56992814900); Berlinicke, Cynthia A. (26535546900); Chen, Steve H. (55443555600); Lindvall, Mikael (7005523126); Zack, Donald J. (35375821300)","35097608700; 35600762400; 7005052044; 55444775900; 56992814900; 26535546900; 55443555600; 7005523126; 35375821300","Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration","2016","Computer Methods and Programs in Biomedicine","126","","","160","170","10","4","10.1016/j.cmpb.2015.11.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960130810&doi=10.1016%2fj.cmpb.2015.11.001&partnerID=40&md5=321279da177ab174d989f77b30bb8c3f","School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States; New York Academy of Medicine, New York, NY, United States; Department of Biomedical Informatics, Arizona State University, United States; Departments of Pathology, Genetic Medicine, Health Sciences Informatics, Oncology and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; BioFortis Inc., Columbia, MD, United States; Fraunhofer Institute for Experimental Software Engineering, College Park, MD, United States; Wilmer Eye Institute, United States; Institute of Genetic Medicine Johns Hopkins University School of Medicine, Baltimore, MD, United States","Myneni S., School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States; Patel V.L., New York Academy of Medicine, New York, NY, United States, Department of Biomedical Informatics, Arizona State University, United States; Bova G.S., Departments of Pathology, Genetic Medicine, Health Sciences Informatics, Oncology and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Wang J., BioFortis Inc., Columbia, MD, United States; Ackerman C.F., Fraunhofer Institute for Experimental Software Engineering, College Park, MD, United States; Berlinicke C.A., Wilmer Eye Institute, United States; Chen S.H., BioFortis Inc., Columbia, MD, United States; Lindvall M., Fraunhofer Institute for Experimental Software Engineering, College Park, MD, United States; Zack D.J., Departments of Pathology, Genetic Medicine, Health Sciences Informatics, Oncology and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, Wilmer Eye Institute, United States, Institute of Genetic Medicine Johns Hopkins University School of Medicine, Baltimore, MD, United States","This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity. © 2015 Elsevier Ireland Ltd.","Biomedical laboratory; Cognition; Industry-academia collaboration; Informatics implementation; Information management; Virtual research environment","Anthropology, Cultural; Biomedical Research; Cognition; Community-Institutional Relations; Cooperative Behavior; Humans; Information Management; Information Systems; Laboratories; Reproducibility of Results; Software; Technology; Universities; Cognitive systems; Distributed computer systems; Information science; Information use; Interoperability; Knowledge based systems; Laboratories; Personnel; Technology transfer; Biomedical laboratories; Cognition; Ethnographic observations; Informatics; Information management systems; Research data managements; Semi structured interviews; Virtual research environment; data analysis; human; human experiment; information processing; information science; investment; learning; productivity; qualitative analysis; qualitative research; scientist; semi structured interview; technology; cognition; cooperation; cultural anthropology; information system; laboratory; medical research; organization and management; procedures; public relations; reproducibility; software; university; Information management","","","","","National Institute of Health/National Cancer Institute; National Institutes of Health, NIH; National Eye Institute, NEI, (R01EY024249); National Cancer Institute, NCI, (R42CA105217)","Funding text 1: The study was supported in part by a grant (1R41CA105217-01A1-STTR) from National Institute of Health/National Cancer Institute (NIH/NCI). Support was also provided by generous gifts from the Guerrieri Family Foundation and from Mr. and Mrs. Clarice Smith. We thank all study participants for their valuable time and contributions.; Funding text 2: The study was supported in part by a grant ( 1R41CA105217-01A1-STTR ) from National Institute of Health/National Cancer Institute (NIH/NCI) . Support was also provided by generous gifts from the Guerrieri Family Foundation and from Mr. and Mrs. Clarice Smith. We thank all study participants for their valuable time and contributions.","The human genome, Nature, pp. 860-921, (2001); Sansone S.A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hofmann O., Hide W., Toward interoperable bioscience data, Nat. Genet., 44, 2, pp. 121-126, (2012); Louie B., Mork P., Sanchez F.M., Halevy A., Hornoch P.T., Data integration and genomic medicine, J. Biomed. Inform., 40, pp. 5-16, (2007); Dennis C., Biology databases: information overload, Nature, 14, MAY, (2002); Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Et al., Issues in biomedical research data management and analysis: needs and barriers, J. Am. Med. Inform. Assoc., 14, pp. 478-488, (2007); Zhang Z., Townsend J.P., Yu J., Cheung K.H., Bajic V.B., Data Integration in Bioinformatics: Current Efforts and Challenges, (2011); Anderson N.R., Ash J.S., Hornoch P.T., A qualitative study of the implementation of a bioinformatics tool in a biological research laboratory, Int. J. Med. Inform., 76, pp. 821-828, (2007); Sujansky W., Heterogeneous database integration in biomedicine, J. Biomed. Inform., 34, pp. 285-298, (2001); Tenenbaum J.D., Sansone S.A., Haendel M., A sea of standards for omics data: sink or swim?, J. Am. Med. Inform. 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Assoc., 2, 6, (1995); Anderson N.R., Ash J.S., Tarczy-Hornoch P., A qualitative study of the implementation of a bioinformatics tool in a biological research laboratory, Int. J. Med. Inform., 76, 11, pp. 821-828, (2007); Li H., Gennari J.H., Brinkley J.F., Model driven laboratory information management systems, AMIA Annual Symposium Proceedings, vol. 2006, (2006); Jakobovits R., Soderland S.G., Taira R.K., Brinkley J.F., Requirements of a web-based experiment management system, Proceedings of the AMIA Symposium, (2000); Fong C., Brinkley J.F., Customizable Electronic Laboratory Online (CELO): a web-based data management system builder for biomedical research laboratories, AMIA Annual Symposium Proceedings, vol. 2006, (2006); Afsarmanesh H., Belleman R.G., Belloum A.S.Z., Benabdelkader A., van den Brand J.F.J., Eijkel G.B., Et al., VLAM-G: a grid-based virtual laboratory, Sci. Program., 10, 2, pp. 173-181, (2002); Suh K.S., Remache Y.K., Patel J.S., Chen S.H., Shaikh A.M., Wang J., Et al., Translational bioinformatics-guided workflow for procurement of patient samples, Paper Presented at the American Medical Informatics Association Translational Bioinformatics Summit, (2008); Gopalan B., Orloff M., Stanuch K., Waite K., Platzer P., Eng C., Integration of clinical, molecular and cellular phenotypes to unravel the mechanisms of complex diseases, Paper Presented at the American Medical Informatics Association Translational Bioinformatics Summit, (2008); Comley J., High content screening, Drug Discov., (2005); Berlinicke C.A., Ackermann C.F., Chen S.H., Schulze C., Shafranovich Y., Myneni S., Et al., High-content screening data management for drug discovery in a small-to medium-size laboratory results of a collaborative pilot study focused on user expectations as indicators of effectiveness, J. Lab. Autom., 17, 4, pp. 255-265, (2012); Van Maanen J., Ethnography, The Social Science Encyclopedia, pp. 263-265, (1996); Bernard R., Research Methods in Anthropology: Qualitative and Quantitative Methods, (2002); Crabtree B., Miller W., Doing Qualitative Research, (1992); Boyatzis R.E., Transforming Qualitative Information: Thematic Analysis and Code Development, (1998); Patel V.L., Kaufman D.R., Allen V.G., Shortliffe E.H., Cimino J.J., Greenes R.A., Toward a framework for computer-mediated collaborative design in medical informatics, Methods Inform. Med., 38, pp. 158-176, (1999); Peristeras V., Tarabanis K., The connection, communication, consolidation, collaboration interoperability framework (C<sup>4</sup>IF) for information systems interoperability, IBIS, 1, pp. 61-72, (2006); Myneni S., Patel V.L., Organization of biomedical data for collaborative scientific research: a research information management system, Int. J. Inform. 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Assoc., 15, 3, pp. 283-289, (2008); Boonstra A., Broekhuis M., Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions, BMC Health Serv. Res., 10, 1, (2010); Kushniruk A., Evaluation in the design of health information systems: application of approaches emerging from usability engineering, Comput. Biol. Med., 32, 3, pp. 141-149, (2002); Nielsen J., Usability inspection methods, Conference Companion on Human Factors in Computing Systems, pp. 413-414, (1994); Preece J., Rogers Y., Sharp H., Interaction Design: Beyond Human-Computer Interaction, (2002); Jeffries R., Miller J.R., Wharton C., Uyeda K., User interface evaluation in the real world: a comparison of four techniques, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 119-124, (1991); Nagarajan K., Ahmed R.M., Phatak A., Database challenges in the integration of biomedical data sets, Proceedings of the 30th VLDB Conference, pp. 1202-1213, (2004); Li A., Facing the challenges of data integration in biosciences, Eng. Lett., 13, 3, pp. 5-8, (2006)","S. Myneni; Houston, 7000 Fannin, Suite 600, 77030, United States; email: Sahiti.Myneni@uth.tmc.edu","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","26652980","English","Comput. Methods Programs Biomed.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84960130810"
"da Silva J.R.; Ribeiro C.; Lopes J.C.","da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594); Lopes, João Correia (36791598000)","55496903800; 7201734594; 36791598000","Usage-driven dublin core descriptor selection: A case study using the dendro platform for research dataset description","2016","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","9819 LNCS","","","27","38","11","0","10.1007/978-3-319-43997-6_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984817812&doi=10.1007%2f978-3-319-43997-6_3&partnerID=40&md5=8c3ebe80b51abd841e4ef40f4497b543","Faculdade de Engenharia da Universidade do Porto/INESC TEC, Porto, Portugal; DEI—Faculdade de Engenharia da Universidade do Porto/INESC TEC, Porto, Portugal","da Silva J.R., Faculdade de Engenharia da Universidade do Porto/INESC TEC, Porto, Portugal; Ribeiro C., DEI—Faculdade de Engenharia da Universidade do Porto/INESC TEC, Porto, Portugal; Lopes J.C., DEI—Faculdade de Engenharia da Universidade do Porto/INESC TEC, Porto, Portugal","Dublin Core schemas are the core metadata models of most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the needs of different communities with the so-called Dublin Core Application Profiles. DCAPs rely on the agreement within user communities, in a process mainly driven by their evolving needs. In this paper, we propose a complementary automated process, designed to help curators and users discover the descriptors that better suit the needs of a specific research group. We target the description of datasets, and test our approach using Dendro, a prototype research data management platform, where an experimental method is used to rank and present DC Terms descriptors to the users based on their usage patterns. In a controlled experiment, we gathered the interactions of two groups as they used Dendro to describe datasets from selected sources. One of the groups had descriptor ranking on, while the other had the same list of descriptors throughout the whole experiment. Preliminary results show that 1. some DC Terms are filled in more often than others, with different distribution in the two groups, 2. selected descriptors were increasingly accepted by users in detriment of manual selection and 3. users were satisfied with the performance of the platform, as demonstrated by a post-study survey. © Springer International Publishing Switzerland 2016.","Linked data; Ontologies; Ranking; Research data management; User feedback","Automation; Information management; Metadata; Ontology; Automated process; Controlled experiment; Different distributions; Experimental methods; Linked datum; Ranking; Research data managements; User feedback; Digital libraries","","","","","Fundação para a Ciência e a Tecnologia, FCT, (POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-016736. ","Piwowar H., Vision T., Data reuse and the open data citation advantage, Peerj, 1, (2013); Piwowar H., Day R., Fridsma D., Sharing detailed research data is associated with increased citation rate, Plos ONE, 2, 3, (2007); Dealing with Data. Challenges and Opportunities. Introduction. Science 331, pp. 692-693, (2011); Jahnke L., Asher A., Keralis S.D.C., The Problem of Data. Council on Library and Information Resources, (2012); Rocha J., Ribeiro C., Correia Lopes J., Managing research data at U.Porto: Requirements, technologies and services, Innovations in XML Applications and Metadata Management: Advancing Technologies. IGI Global, (2012); Martinez-Uribe L., Using the Data Audit Framework: An Oxford Case Study, (2009); Borgman C., The conundrum of sharing research data, J. Am.Soc. Inf. Sci. Technol, 63, 6, pp. 1059-1078, (2012); Lord P., Macdonald A., Data Curation for E-Science in the UK: An Audit to Establish Requirements for Future Curation and Provision, (2003); Lyon L., Dealing with Data: Roles, (2007); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Leonelli S., Spichtinger D., Prainsack B., Sticks and carrots: Encouraging open science at its source, Geo: Geogr. Environ, 2, 1, pp. 12-16, (2015); (2012); (2012); Heery R., Patel M., Application Profiles: Mixing and Matching Metadata Schemas, (2000); Malta M.C., Baptista A.A., State of the art on methodologies for the development of a metadata application profile, MTSR 2012. CCIS, 343, pp. 61-73, (2012); Malta M., Baptista A., A panoramic view on metadata application profiles of the last decade, Int. J. Metadata Semant. Ontol, 9, 1, pp. 58-73, (2014); Krause E.M., Clary E., Greenberg J., Ogletree A., Evolution of an application profile: Advancing metadata best practices through the dryad data repository, Proceedings of the International Conference on Dublin Core and Metadata Applications 2015, pp. 63-75, (2015); Martinez-Uribe L., Macdonald S., User engagement in research data curation, ECDL 2009. LNCS, 5714, pp. 309-314, (2009); Eynden V.V.D., Corti L., Bishop L., Horton L., Managing and Sharing Data: A Guide to Good Practice, (2011); Ball A., Scientific Data Application Profile Scoping Study Report. Technical Report, UKOLN, (2009); Hodson S., ADMIRAL: A Data Management Infrastructure for Research Activities in the Life Sciences, (2011); Hanahoe H., Baxter R., Carter A., Reetz J., Riedel M., Ritz R., Van De Sanden M., Wittenberg P., (2014); Rocha J., Castro J., Ribeiro C., Correia Lopes J., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Ipres 2014 Conference Proceedings, (2014); Rocha J., Castro J., Ribeiro C., Correia Lopes J., Dendro: Collaborative research data management built on linked open data, Proceedings of the 11Th European Semantic Web Conference, (2014); Rocha J., Ribeiro C., Correia Lopes J., Ontology-based multi-domain metadata for research data management using triple stores, Proceedings of the 18Th International Database Engineering and Applications Symposium, (2014); Amorim R., Castro J., Rocha J., Ribeiro C., A comparative study of platforms for research data management: Interoperability, metadata capabilities, New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, 353, pp. 101-111, (2015); Berners-Lee T., Linked Data—Design Issues, (2006); Sinha R., Swearingen K., The role of transparency in recommender systems, Extended Abstracts on Human Factors in Computing Systems (CHI 2002), (2002); Swearingen K., Sinha R., Beyond algorithms: An HCI perspective on recommender systems, ACM SIGIR 2001Workshop on Recommender Systems, pp. 1-11, (2001); Joachims T., Granka L., Pan B., Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28Th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154-161, (2005); Strickroth S., Pinkwart N., High quality recommendations for small communities: The case of a regional parent network, Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 107-114, (2012); Goy A., Magro D., Petrone G., Picardi C., Segnan M., Ontology-driven collaborative annotation in shared workspaces, Future Gener. Comput. Syst, 54, pp. 435-449, (2015); Greenberg J., Swauger S., Feinstein E.M., Metadata capital in a data repository, Proceedings of the International Conference on Dublin Core and Metadata Applications 2013, Lisbon, pp. 140-150, (2013)","J.R. da Silva; Faculdade de Engenharia da Universidade do Porto/INESC TEC, Porto, Portugal; email: joaorosilva@gmail.com","Kovács L.; Fuhr N.; Risse T.; Nejdl W.","Springer Verlag","","20th International Conference on Theory and Practice of Digital Libraries, TPDL 2016","5 September 2016 through 9 September 2016","Hannover","179729","03029743","978-331943996-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-84984817812"
"Pereira N.; da Silva J.R.; Ribeiro C.","Pereira, Nelson (57195715570); da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","57195715570; 55496903800; 7201734594","Social dendro: Social network techniques applied to research data description","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10450 LNCS","","","566","571","5","2","10.1007/978-3-319-67008-9_47","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029573652&doi=10.1007%2f978-3-319-67008-9_47&partnerID=40&md5=2478d6515fa761295666bb7c4ae67bcb","INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Pereira N., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; da Silva J.R., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Research data management has become an integral part of the research workflow. Currently, concern with data appears mainly at the very last stages of projects, rather than being present from the moment of data creation. The goal of this work is to make data easier to find, share and reuse through early metadata production and in-group review. The approach proposed in this paper, Social Dendro, introduces social network concepts such as posts, shares and comments, in Dendro, our research data management platform. The implementation follows the ontology-based architecture of the platform. Results of a preliminary user test have provided insights for future improvements. © Springer International Publishing AG 2017.","Data curation; Data repository; Ontologies; Research data management; Social networks; User interfaces","Digital libraries; Ontology; Social networking (online); User interfaces; Data creation; Data curation; Data repositories; Future improvements; Integral part; Network techniques; Ontology-based; Research data managements; Information management","","","","","ERDF-European Regional Development Fund; Fundação para a Ciência e a Tecnologia, FCT; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN, (POCI-01-0145-FEDER-016736)","Acknowledgements. This work is financed by the ERDF-European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸cão para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-016736.","Assante M., Et al., Science 2.0 repositories: Time for a change in scholarly communication, D-Lib Magazine, (2015); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol., 63, 6, pp. 1059-1078, (2012); Dillo I., Doorn P.K., The Dutch data landscape in 32 interviews and a survey. Reporting year: 2011, Data Archiving and Networked Services (DANS), (2011); Guidelines on Open Access to Scientific Publications and Research Data in Horizon, 2020, (2013); Grants.Gov Application Guide a Guide for Preparation and Submission of NSF Applications via Grants.Gov, (2011); Rocha J., Ribeiro C., Lopes J.C., Ontology-based multi-domain metadata for research data management using triple stores, Proceedings of the 18Th International Database Engineering & Applications Symposium, (2014); Rocha J., Et al., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Ipres Conference Proceedings, (2014); Yang S.J.H., Chen I.Y.L., A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network, Int. J. Hum. Comput. Stud., 66, 1, (2008)","N. Pereira; INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: nelsonpereira1991@gmail.com","Manolopoulos Y.; Kamps J.; Tsakonas G.; Iliadis L.; Karydis I.","Springer Verlag","The Coalition for Networked Information (CNI)","21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017","18 September 2017 through 21 September 2017","Thessaloniki","197829","03029743","978-331967007-2","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85029573652"
"Grunzke R.; Nagel W.E.; Hartmann V.; Jejkal T.; Prabhune A.; Stotzka R.; Hoffmann A.; Herres-Pawlis S.; Deicke A.; Schrade T.; Herold H.; Meinel G.","Grunzke, Richard (37096970200); Nagel, Wolfgang E. (9435404200); Hartmann, Volker (7005982861); Jejkal, Thomas (24478712700); Prabhune, Ajinkya (56534978900); Stotzka, Rainer (6602188741); Hoffmann, Alexander (55706038800); Herres-Pawlis, Sonja (9277407800); Deicke, Aline (57190859263); Schrade, Torsten (57190125722); Herold, Hendrik (35077070400); Meinel, Gotthard (6506545059)","37096970200; 9435404200; 7005982861; 24478712700; 56534978900; 6602188741; 55706038800; 9277407800; 57190859263; 57190125722; 35077070400; 6506545059","Towards a metadata-driven multi-community research data management service","2016","CEUR Workshop Proceedings","1871","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025162118&partnerID=40&md5=fa93f4ac8961d2a2f51a1d138635fb22","Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Institut für Anorganische Chemie, Rheinisch-Westfälische Technische, Hochschule Aachen, Aachen, Germany; Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany; Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany","Grunzke R., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Nagel W.E., Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany; Hartmann V., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Jejkal T., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Prabhune A., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Stotzka R., Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Hoffmann A., Institut für Anorganische Chemie, Rheinisch-Westfälische Technische, Hochschule Aachen, Aachen, Germany; Herres-Pawlis S., Institut für Anorganische Chemie, Rheinisch-Westfälische Technische, Hochschule Aachen, Aachen, Germany; Deicke A., Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany; Schrade T., Digitale Akademie, Akademie der Wissenschaften und Literatur Mainz, Mainz, Germany; Herold H., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany; Meinel G., Monitoring of Settlement and Open Space Development, Institute of Ecological and Regional Development, Dresden, Germany","Nowadays, the daily work of many research communities is characterized by an increasing amount and complexity of data. This makes it increasingly difficult to manage, access and utilize to ultimately gain scientific insights based on it. At the same time, domain scientists want to focus on their science instead of IT. The solution is research data management in order to store data in a structured way to enable easy discovery for future reference. An integral part is the use of metadata. With it, data becomes accessible by its content instead of only its name and location. The use of metadata shall be as automatic and seamless as possible in order to foster a high usability. Here we present the architecture and initial steps of the MASi project with its aim to build a comprehensive research data management service. First, it extends the existing KIT Data Manager framework by a generic programming interface and by a generic graphical web interface. Advanced additional features includes the integration of provenance metadata and persistent identifiers. The MASi service aims at being easily adaptable for arbitrary communities with limited effort. The requirements for the initial use cases within geography, chemistry and digital humanities are elucidated. The MASi research data management service is currently being built up to satisfy these complex and varying requirements in an efficient way.","Communities; Metadata; Research data management","Ecosystems; Metadata; Community researches; Comprehensive research; Digital humanities; Generic programming; Integral part; Metadata driven; Research communities; Research data managements; Information management","","","","","","","Metadata Management for Applied Sciences, (2016); Rajasekar A., Moore R., Hou C.-Y., Lee C.A., Marciano R., De Torcy A., Wan M., Schroeder W., Chen S.-Y., Gilbert L., Et al., IRODS primer: Integrated rule-oriented data system, Synthesis Lectures on Information Concepts, Retrieval, and Services, 2, 1, pp. 1-143, (2010); Grunzke R., Kruger J., Gesing S., Herres-Pawlis S., Hoffmann A., Aguilera A., Nagel W.E., Managing complexity in distributed data life cycles enhancing scientific discovery, IEEE 11th International Conference on E-science, August 2015, pp. 371-380; Jejkal T., Vondrous A., Kopmann A., Stotzka R., Hartmann V., KIT data manager: The repository architecture enabling cross-disciplinary research, Large-scale Data Management and Analysis - Big Data in Science, (2014); Flannery D., Matthews B., Griffin T., Bicarregui J., Gleaves M., Lerusse L., Downing R., Ashton A., Sufi S., Drinkwater G., Et al., ICAT: Integrating data infrastructure for facilities based science, E-science, 2009. E-Science'09. Fifth IEEE International Conference on, pp. 201-207, (2009); Grunzke R., Hesser J., Starek J., Kepper N., Gesing S., Hardt M., Hartmann V., Kindermann S., Potthoff J., Hausmann M., Muller-Pfefferkorn R., Jakel R., Device-driven metadata management solutions for scientific big data use cases, 22nd Euromicro International Conference on Parallel, Distributed, and Network-based Processing (PDP 2014), (2014); Smith M., Barton M., Bass M., Branschofsky M., McClellan G., Stuve D., Tansley R., Walker J.H., DSpace: An Open Source Dynamic Digital Repository, (2003); Fedora Commons Repository Software, (2016); Lecarpentier D., Wittenburg P., Elbers W., Michelini A., Kanso R., Coveney P., Baxter R., EUDAT: A new cross-disciplinary data infrastructure for science, International Journal of Digital Curation, 8, 1, pp. 279-287, (2013); Eudat Semantics Working Group, (2015); McDonough J.P., METS: Standardized encoding for digital library objects, International Journal on Digital Libraries, 6, 2, pp. 148-158, (2006); Moreau L., Freire J., Futrelle J., McGrath R.E., Myers J., Paulson P., The open provenance model: An overview, Provenance and Annotation of Data and Processes, pp. 323-326, (2008); Cuevas-Vicenttin V., Ludascher B., Missier P., Belhajjame K., Chirigati F., Wei Y., Dey S., Kianmajd P., Koop D., Bowers S., Altintas I., ProvONE: A PROV extension data model for scientific workflow provenance, DataONE Provenance Working Group, (2014); Enterprise Open Source Portal and Collaboration Software, (2016); EduGAIN - Interconnecting Federations to Link Services and Users Worldwide, (2015); Herold H., Meinel G., Hecht R., Csaplovics E., A GEOBIA approach to map interpretation-multitemporal building footprint retrieval for high resolution monitoring of spatial urban dynamics, International Conference on Geographic Object-based Image Analysis, pp. 252-256, (2012); Herres-Pawlis S., Hoffmann A., Rosener T., Kraoeger J., Grunzke R., Gesing S., Multi-layer meta-metaworkflows for the evaluation of solvent and dispersion effects in transition metal systems using the mosgrid science gateways, Science Gateways (IWSG), 2015 7th International Workshop on, pp. 47-52, (2015); Corpus Vitrearum Deutschland, (2016); Benedyczak K., Schuller B., Petrova M., Rybicki J., Grunzke R., UNICORE 7 - Middleware services for distributed and federated computing, International Conference on High Performance Computing Simulation (HPCS), (2016); Kruger J., Grunzke R., Gesing S., Breuers S., Brinkmann A., De La Garza L., Kohlbacher O., Kruse M., Nagel W.E., Packschies L., Muller-Pfefferkorn R., Schafer P., Scharfe C., Steinke T., Schlemmer T., Warzecha K.D., Zink A., Herres-Pawlis S., The mosgrid science gateway - A complete solution for molecular simulations, Journal of Chemical Theory and Computation, 10, 6, pp. 2232-2245, (2014)","","Gesing S.; Kruger J.","CEUR-WS","","8th International Workshop on Science Gateways, IWSG 2016","8 June 2016 through 10 June 2016","Rome","128453","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85025162118"
"Bauer V.B.; Budroni P.; Ferus A.; Ganguly R.; Ramminger E.; Solís B.S.","Bauer, Von Bruno (57200436821); Budroni, Paolo (56624471600); Ferus, Andreas (37009618700); Ganguly, Raman (56702193000); Ramminger, Eva (55376789000); Solís, Barbara Sánchez (56702943200)","57200436821; 56624471600; 37009618700; 56702193000; 55376789000; 56702943200","E-infrastructures Austria 2015: Report about the second year of the higher education area structural funding project for the coordinated establishment and coordinated development of repository infrastructures; [E-infrastructures Austria 2015: Bericht über das zweite jahr des hochschulraumstrukturmittelprojekts für den koordinierten aufbau und die kooperative weiterentwicklung von repositorieninfrastrukturen]","2016","VOEB-Mitteilungen","69","1","","9","40","31","2","10.31263/voebm.v69i1.1394","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974705050&doi=10.31263%2fvoebm.v69i1.1394&partnerID=40&md5=5ac3001d4c232a17caf60ac0b4fcf078","Universitätsbibliothek der Medizinischen Universität Wien, Austria; Bibliotheks- und Archivwesen der Universität Wien, Austria; Universitätsbibliothek und -archiv der Akademie der bildenden Künste Wien, Austria; Zentraler Informatikdienst der Universität Wien, Austria; Universitäts- und Landesbibliothek Tirol, Austria","Bauer V.B., Universitätsbibliothek der Medizinischen Universität Wien, Austria; Budroni P., Bibliotheks- und Archivwesen der Universität Wien, Austria; Ferus A., Universitätsbibliothek und -archiv der Akademie der bildenden Künste Wien, Austria; Ganguly R., Zentraler Informatikdienst der Universität Wien, Austria; Ramminger E., Universitäts- und Landesbibliothek Tirol, Austria; Solís B.S., Bibliotheks- und Archivwesen der Universität Wien, Austria","In the second year, the HRSM project entitled e-Infrastructures Austria has not only established a widely accepted platform for networking forums, meetings and training units, but the first deliverables are also available, which have been instrumental in the overall objective of the development of sustainable archive infrastructure and the consolidation of knowledge in the field of security and provision of digital data. In early 2015, a survey was carried out on the project, which encompassed the scientific and artistic academic staff from the 20 public universities and three non-university research institutions in Austria. The aim was to boost the practical application of research data and coordinate the services offered in this area for existing needs. The survey results show a significant need to catch up in the field of data management. The construction of the technical infrastructure in the area of an electronic document repository (project column A) for all partners is well advanced, therefore, the concentration in the last year of the project will mainly be on column B (research data). The research data final report forms the basis for a group of experts, which deals with the question of strategy for handling research data and research data management in Austria. Project column C (Knowledge Network) is currently supported in the last year of the project by a centrally organized, four-day training event „Training Seminar for Research Data and e-Infrastructures“ for all project partners. © 2016, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Archiving; Austria; Digital resources; Document server; e-Accessibility; Infrastructure; Network; Open Access; Open Data; Open Data; Open Science; Open Universities; Policies; Repository; Research data; Research data management","","","","","","","","Bauer B., Budroni P., Ferus A., Ganguly R., Ramminger E., Solis B.S., e-Infrastructures Austria 2014: Bericht über das erste Jahr des Hochschulraumstrukturmittelprojekts, Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen & Bibliothekare, 68, 1, pp. 91-118, (2015); Bauer B., Et al., Empfehlungen für die Umsetzung von Open Access in Österreich, (2015)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84974705050"
"Aydinoglu A.U.; Dogan G.; Taskin Z.","Aydinoglu, Arsev Umur (36701092900); Dogan, Guleda (56289638800); Taskin, Zehra (55865568297)","36701092900; 56289638800; 55865568297","Research data management in Turkey: perceptions and practices","2017","Library Hi Tech","35","2","","271","289","18","25","10.1108/LHT-11-2016-0134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019712996&doi=10.1108%2fLHT-11-2016-0134&partnerID=40&md5=7fd27ecf4293a771d34866e5f4ae74cb","Middle East Technical University, Ankara, Turkey; Hacettepe University, Ankara, Turkey","Aydinoglu A.U., Middle East Technical University, Ankara, Turkey; Dogan G., Hacettepe University, Ankara, Turkey; Taskin Z., Hacettepe University, Ankara, Turkey","Purpose: The massive increase in research data being produced nowadays has highlighted the importance of research data management (RDM) to science. Research data not only have to be cost effective but also reliable, discoverable, accessible, and reusable. In this regard, the purpose of this paper is to investigate the perceptions and practices of Turkish researchers on the subject of RDM. Design/methodology/approach: An online survey was distributed to the academicians in 25 universities in Turkey, and 532 responses were gathered. Findings: Results indicate that although Turkish researchers are aware of the benefits of data management, are willing to share their research data with certain groups, and have decent preservation habits, they express that they lack the technical skills and knowledge needed for RDM. In addition, no institutionalized support (staff, training, software, and hardware) is provided to researchers. Research limitations/implications: A well-structured data strategy or policy that includes resource allocation (awareness, training, software/hardware) and is supported by Turkish research agencies is required for better data management practices among researchers in Turkey. Originality/value: This is the first study that investigates the data practices of Turkish academics who produce around 30,000 scientific articles annually that are indexed by Web of Science. It contributes to the growing literature on RDM. © 2017, © Emerald Publishing Limited.","Data preservation; Data repository; Data sharing; Data storage; Research data; Research data management","","","","","","","","Allard S., Aydinoglu A.U., Environmental researchers’ data practices: an exploratory study in Turkey, E-Science and Information Management, IMCW 2012, Communications in Computer and Information Science, 317, pp. 13-24, (2012); Al-Omar M., Cox A.M., Scholars’ research-related personal information collections a study of education and health researchers in a Kuwaiti University, Aslib Journal of Information Management, 68, 2, pp. 155-173, (2016); Aydinoglu A.U., Suomela T., Malone J., Data management in astrobiology: challenges and opportunities for an interdisciplinary community, Astrobiology, 14, 6, pp. 451-461, (2014); Birnholtz J.P., Bietz M.J., Data at work: supporting sharing in science and engineering, pp. 339-348, (2003); Borgman C.L., Golshan M.S., Sands A.E., Wallis J.C., Cummings R.L., Darch P.T., Randles B.M., Data management in the long tail: science, software, and service, International Journal of Digital Curation, 11, 1, pp. 128-149, (2016); Calvert P., Should all lab books be treated as vital records? An investigation into the use of lab books by research scientists, Australian Academic and Research Libraries, 46, 4, pp. 289-303, (2015); Chen C.L.P., Zhang C.Y., Data-intensive applications, challenges, techniques and technologies: a survey on big data, Information Sciences, 275, pp. 314-347, (2014); Cochran W.G., Sampling Techniques, (1963); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2016); Douglass K., Allard S., Tenopir C., Wu L., Frame M., Managing scientific data as public assets: data sharing practices and policies among full-time government employees, Journal of the Association for Information Science & Technology, 65, 2, pp. 251-262, (2014); Faniel I., Kansa E., Kansa S.W., Barrera-Gomez J., Yakel E., The challenges of digging data: a study of context in archaeological data reuse, pp. 295-304, (2013); Faniel I.M., Jacobsen T.E., Reusing scientific data: how earthquake engineering researchers assess the reusability of colleagues’ data, Computer Supported Cooperative Work, 19, 3-4, pp. 355-375, (2010); Gurdal G., Bitri E., Araştırma verisi yönetimi, açık veri ve Avrupa Birliği Bilimsel Veri Altyapısı: OpenAIRE2020 (Research data management, open data and the European Scholarly Communication Data Infrastructure: OpenAIRE2020), (2015); Hey T., Tansley S., Tole K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Guidelines on data management in Horizon 2020: version 1.0, (2013); Research data management: getting your organization started, (2014); Knyazkov K.V., Kovalchuk S.V., Tchurov T.N., Maryin S.V., Boukhanovsky A.V., CLAVIRE: e-Science infrastructure for data-driven computing, Journal of Computational Science, 3, 6, pp. 504-510, (2012); Lee D.J., Research data curation practices in institutional repositories and data identifiers, (2015); Malkoc B., Research data alliance ve DataCite, (2015); Open gov plan 2016 outline, (2016); Final NIH statement on sharing research data, (2003); NSF 07-28, cyberinfrastructure vision for 21st century discovery, (2007); Data management for NSF SBE directorate proposals and awards, (2010); OECD principles and guidelines for access to research data from public funding, (2007); Onder A., Büyük veri (Big data), (2013); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, (2013); Powell K., Young, talented and fed-up: scientists tell their stories, Nature News, pp. 446-449, (2016); Big data, for better or worse: 90% of world’s data generated over last two years, (2013); Steiner K., Research data management and information literacy – new developments at New Zealand University libraries, Information-Wissenschaft und Praxis, 66, 4, pp. 230-236, (2015); Surkis A., Read K., Research data management, Journal of the Medical Library Association, 103, 3, pp. 154-156, (2015); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Tenopir C., Hughes D., Allard S., Frame M., Birch W.B., Baird L., Sandusky R., Langseth M., Lundeen A., Research data services in academic libraries: data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tonta Y., Açık erişim, kurumsal arşivler ve MedOANet Projesi (Open access, institutional repositories and MedOANet Project), (2012); Tonta Y., Açık erişimin geleceği ve araştırma verilerine açık erişim (The future of open access and open access for research data), (2013); Tonta Y., Al U., Araştırma verilerinin yönetimi (research data management), Türk Kütüphaneciliği [Turkish Librarianship], 29, 1, pp. 36-45, (2015); Türkiye üniversitelerinin bilimsel yayın performansı: 2004-2014 (Scholarly production performance of Turkish universities: 2004-2014), (2016); Vines T.H., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Vogeli C., Yucel R., Bendavid E., Jones L.M., Anderson M.S., Louis K.S., Campbell E.G., Data withholding and the next generation of scientists: results of a national survey, Academic Medicine, 81, 2, pp. 128-136, (2006); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); (2016); Bringing big data to the enterprise, (2016)","A.U. Aydinoglu; Middle East Technical University, Ankara, Turkey; email: arsevu@gmail.com","","Emerald Group Publishing Ltd.","","","","","","07378831","","","","English","Libr. Hi Tech","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85019712996"
"Dierkes J.; Wuttke U.","Dierkes, Jens (56727998600); Wuttke, Ulrike (26037703100)","56727998600; 26037703100","The Göttingen eResearch alliance: A case study of developing and establishing institutional support for research data management","2016","ISPRS International Journal of Geo-Information","5","8","133","","","","6","10.3390/ijgi5080133","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994323889&doi=10.3390%2fijgi5080133&partnerID=40&md5=d496d3b3ed9a3ee622ca97e16acc996d","Niedersächsische Staats- und Universitätsbibliothek Göttingen, Göttingen, 37073, Germany; Gesellschaft für Wissenschaftliche Datenverarbeitung MbH Göttingen, Göttingen, 37077, Germany; Union der Deutschen Akademien DerWissenschaften, Berlin, 10117, Germany; Akademie DerWissenschaften zu Göttingen, Göttingen, 37073, Germany","Dierkes J., Niedersächsische Staats- und Universitätsbibliothek Göttingen, Göttingen, 37073, Germany, Gesellschaft für Wissenschaftliche Datenverarbeitung MbH Göttingen, Göttingen, 37077, Germany; Wuttke U., Niedersächsische Staats- und Universitätsbibliothek Göttingen, Göttingen, 37073, Germany, Union der Deutschen Akademien DerWissenschaften, Berlin, 10117, Germany, Akademie DerWissenschaften zu Göttingen, Göttingen, 37073, Germany","The Göttingen eResearch Alliance is presented as a case study for establishing institutional support for research data management within the context of the Göttingen Campus, a particular alliance of several research institutes at Göttingen. The cross-cutting, ""horizontal"" approach of the Göttingen eResearch Alliance, established by two research-oriented infrastructure providers, a research library and a computing and IT competence center, aims to coordinate Campus-led activities to establish sustainable and innovative services to support all phases of the research data life cycle. In this article, the core activities of the first phase aimed at developing a modular approach to provide support for research data management to researchers will be described. It closes with lessons learned and an outlook on future activities.","Computing centers; E-research; Institutional support; Research data management; Research libraries","","","","","","","","Meyer E.T., Schroeder R., The world wide web of research and access to knowledge, J. Knowl. Manag. Res. Pract., 7, pp. 218-233, (2009); Management von Forschungsdaten - Eine Zentrale Strategische Herausforderung Für Hochschulleitungen, Empfehlung der 16, (2016); Wie Hochschulleitungen Die Entwicklung des Forschungsdatenmanagements Steuern Können. Orientierungspfade, Handlungsoptionen, Szenarien, Empfehlung der 19., (2016); Sicherung Guter Wissenschaftlicher Praxis (Safeguarding Good Scientific Practice), Denkschrift, Ergänzte Auflage., (2016); Empfehlungen Zur Gesicherten Aufbewahrung und Bereitstellung Digitaler Forschungsdaten, (2009); Leitlinien Zum Umgang mit Forschungsdaten, (2015); Forschungsdaten.org, (2016); Forschungsdaten.org/Data Policies, (2016); Kindling M., Schirmbacher P., Simukovic E., Forschungsdatenmanagement an Hochschulen: Das Beispiel der Humboldt-Universität zu Berlin, LIBREAS, 23, pp. 43-63, (2013); Meyer-Doerpinghaus U., Troger B., Forschungsdatenmanagement als Herausforderung für Hochschulen und Hochschulbibliotheken, O-bib, 4, pp. 64-72, (2015); Göttingen EResearch Alliance Web Page, (2016); Research Data Policy of the Georg-August University Goettingen (Incl. UMG)., (2014); The Göttingen Campus., (2016); Gesellschaft Für Wissenschaftliche Datenverarbeitung MBH., (2016); Niedersächsische Staats- Und Universitätsbibliothek, (2016); Ordnung der Georg-August-Universität Göttingen Zur Sicherung Guter Wissenschaftlicher Praxis., (2012); Die Open Access Politik der Universität Göttingen, (2016); Schmidt B., Dierkes J., New alliances for research and teaching support: Establishing the Göttingen eResearch Alliance, Program, 49, pp. 461-474, (2015); Luce R.E., A new value equation challenge: The emergence of eResearch and roles for research libraries, No Brief Candle, Reconceiving Research Libraries for the 21st Century: Council on Library and Information Resources, pp. 42-50, (2008); Im Qualitätssicherungsprozess Betrachtete Aspekte zu Forschungsdatenmanagement und Publikationsstrategie., (2016); Digital Curation Center (DDC), (2016); Ludwig J., Enke H., Leitfaden Zum Forschungsdaten-Management: Handreichungen Aus Dem WissGrid-Projekt, Glückstadt, Germany, (2013); Engelhardt C., Forschungsdatenmanagement in DFG-Sonderforschungsbereichen: Teilprojekte Informationsinfrastruktur (INF-Projekte), LIBREAS, 23, pp. 106-130, (2013); Cremer F., Engelhardt C., Neuroth H., Embedded data manager-Integriertes Forschungsdatenmanagement: Praxis, Perspektiven und Potentiale, Bibl. Forsch. Prax., 1, pp. 13-31, (2015); Schmidt B., Ludwig J., LIBER Case Study: Piloting Research Data Support at the University of Goettingen., (2014); Informationskompetenz-Menü Für Arbeitsgruppen: Angebote der SUB Göttingen Für Die Fakultäten Chemie und Physik., (2016); Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, Int. J. Digit. Curation, 9, pp. 171-191, (2014); Registry of Research Data Repositories (re3data.org)., (2016); Tristram F., Bamberger P., Cayoglu U., Hertzer J., Knopp J., Kratzke J., Rex J., Schwabe F., Shcherbakov D., Svoboda D.-F., Et al., Öffentlicher Abschlussbericht von BwFDM-Communities, (2016); Bauer B., Ferus A., Gorraiz J., Grundhammer V., Gumpenberger C., Maly N., Muhlegger J., Preza J., Sanchez Solis B., Schmidt N., Et al., Forschende und Ihre Daten. Ergebnisse Einer Österreichweiten Befragung, (2015); DataCite/Members., (2016)","J. Dierkes; Niedersächsische Staats- und Universitätsbibliothek Göttingen, Göttingen, 37073, Germany; email: dierkes@sub.uni-goettingen.de","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84994323889"
"Xie Z.; Speer J.; Chen Y.; Jiang T.; Brittle C.; Mather P.","Xie, Zhiwu (7402267368); Speer, Julie (56414366100); Chen, Yinlin (35182700600); Jiang, Tingting (36930272800); Brittle, Collin (57192381915); Mather, Paul (36795653600)","7402267368; 56414366100; 35182700600; 36930272800; 57192381915; 36795653600","Developing institutional research data repository: A case study","2016","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10075 LNCS","","","51","56","5","1","10.1007/978-3-319-49304-6_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006043950&doi=10.1007%2f978-3-319-49304-6_7&partnerID=40&md5=0a666eb9e3527ba1d936f5de0727ee23","University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States","Xie Z., University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States; Speer J., University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States; Chen Y., University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States; Jiang T., University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States; Brittle C., University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States; Mather P., University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States","We introduce VTechData, a Sufia/Fedora based institutional repository specifically implemented to meet the needs of research data management at Virginia Tech. Despite the rapid maturity of Hydra and Fedora code bases, the gaps between the released packages and a launched production-level service are still many and far from trivial. In this practitioner paper we describe the strategy and efforts through which these gaps were filled and lessons learned in the process of creating our first Hydra/Sufia-based repository. © Springer International Publishing AG 2016.","Digital library; Institutional repository; Research data management","Information management; Information services; Societies and institutions; Institutional repositories; Production level; Research data; Research data managements; Virginia Tech; Digital libraries","","","","","","","Betz S., Hall R., Self-archiving with ease in an institutional repository: Microinteractions and the user experience, Inf. Technol. Libr, 34, 3, pp. 43-58, (2015)","Z. Xie; University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, United States; email: zhiwuxie@vt.edu","Morishima A.; Rauber A.; li Liew C.","Springer Verlag","","18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016","7 December 2016 through 9 December 2016","Tsukuba","187139","03029743","978-331949303-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85006043950"
"Liu X.; Ding N.","Liu, Xia (24381716800); Ding, Ning (57191370618)","24381716800; 57191370618","Research data management in universities of central China: Practices at Wuhan University Library","2016","Electronic Library","34","5","","808","822","14","13","10.1108/EL-04-2015-0063","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989233468&doi=10.1108%2fEL-04-2015-0063&partnerID=40&md5=bdec8e488357175e74ad717813cd712f","Wuhan University, Wuhan, China","Liu X., Wuhan University, Wuhan, China; Ding N., Wuhan University, Wuhan, China","Purpose - Revealing research data's production and use, the status of research data management (RDM) and researchers' service requirements in universities of Central China; this study aims to investigate the feasibility of university libraries in providing RDM services without any supporting policies from governments or funding agencies. Design/methodology/approach - Using a stratified sampling method, faculties and graduate students from 11 universities were investigated. Four pilot subjects at Wuhan University (WHU) were chosen for whom a pilot RDM platform was to be constructed. Findings - Research data at Chinese universities are small, sporadic and discontinuous. Such data are intensively or dispersedly under researcher's management, with some unresolved problems regarding data security, data sharing and utilisation efficiency. Researchers' needs for data services are strong. University libraries in China can develop RDM systems and provide related services. To realise this, more work should be done on service mechanism, service promotion, software development and staff training. Research limitations/implications - The user survey covered 11 universities in central China, which may not reveal the real RDM status of researcher in different areas of China. Practical implications - The practice at WHU could provide reference to other university libraries in China or other developing countries. Social implications - The practice at WHU could provide reference to other university libraries in China or other developing countries. Originality/value - The user survey is designed to be as comprehensive as possible and cover 902 researchers from 11 different types of Chinese universities. The practice at WHU is one of the first RDM initiatives led by university library in China. © 2016 Emerald Group Publishing Limited.","China; Data management; Pilot study; Research data; University library; User investigation","case report; China; computer security; developing country; feasibility study; funding; government; graduate student; human; library; pilot study; scientist; software; staff training; stratified sample","","","","","","","Beagrie N., Beagrie R., Rowlands I., Research data preservation and access: The views of researchers, Ariadne, 60, (2009); Brown D., Scientific Communication and the Dematerialization of Scholarship, (2007); Center for Social Survey (CSS), Center for Social Survey, Sun Yat-sen University, (2015); DataConservancy (DC), Data Conservancy, (2015); Fudan Institute of Social Research (FISR), Fudan University Dataverse Network, (2015); Hong C., Qian P., Investigation and analysis on the scientific data needs and utilization behaviors of graduate students: Taking Southeast University for example, Journal of the National Library of China, 23, 1, pp. 16-21, (2014); Institute of Social Survey (ISSS), Institute of Social Survey of Peking University, (2015); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services - A Guide for HEIs, (2013); Khan H., Caruso B., Corson-Rikert J., Dietrich D., Lowe B., Steinhart G., DataStaR: Using the Semantic Web approach for data curation, The International Journal of Digital Curation, 6, 2, pp. 209-221, (2011); Lawrence S., Free online availability substantially increases a paper's impact, Nature, 411, 6837, (2001); Li X., Research data management and service pattern in libraries, Journal of Library Science in China, 37, 5, pp. 46-52, (2011); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data curation: A study of researcher practices and needs, Libraries and the Academy, 14, 2, pp. 139-164, (2014); Ministry of Education (MOE), The Twelfth Five-year Plan for Science and Technology of HEI, (2015); Monash University Library (MUL), Research Data Skills Development, Monash University Library, (2014); National Survey Research Center (NSRC), Chinese Social Survey Open Database, (2015); Peer L., Green A., Building an open data repository for a specialized research community: Process, challenges and lessons, The International Journal of Digital Curation, 7, 1, pp. 151-162, (2012); Penn State University (PSU), Scholar Sphere, (2015); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, College & Research Libraries, 73, 4, pp. 349-365, (2012); Si L., Xing W., Zhuang X., Hua X., Zhou L., Investigation and of research data services in university libraries, The Electronic Library, 33, 3, pp. 417-449, (2015); University College London (UCL), DSpace@Cambridge, (2015); Ward C., Freiman L., Making sense: Talking data management with researchers, The International Journal of Digital Curation, 6, 2, pp. 265-273, (2011); Wu M., Digital curation: An emerging research field of library and information science, Library Journal, 31, 3, pp. 8-12, (2012); Wuhan University Institute of Quality Development Strategy (WHUIQDS), China Quality Survey, (2015); Wuhan University (WHU), Wuhan University Home Page, (2015); Zhang J., Intention to acquire scientific data of college scientific researchers, Journal of Intelligence, 32, 6, pp. 70-75, (2013); Zhang J., Intention to share scientific data of college scientific researchers, Information Studies: Theory & Application, 36, 10, pp. 25-30, (2013); Zhang Q., Research into scientific data curation of colleges and universities, Information Science, 31, 5, pp. 42-45, (2013)","X. Liu; Wuhan University, Wuhan, China; email: liuxia@lib.whu.edu.cn","","Emerald Group Publishing Ltd.","","","","","","02640473","","ELLID","","English","Electron. Libr.","Article","Final","","Scopus","2-s2.0-84989233468"
"Eifert T.; Schilling U.; Bauer H.-J.; Krämer F.; Lopez A.","Eifert, Thomas (6508209571); Schilling, Ulrich (57510922500); Bauer, Hans-Jörg (57195073874); Krämer, Florian (57195074250); Lopez, Ania (55949158100)","6508209571; 57510922500; 57195073874; 57195074250; 55949158100","Infrastructure for research data management as a cross-university project","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10274 LNCS","","","493","502","9","4","10.1007/978-3-319-58524-6_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025173765&doi=10.1007%2f978-3-319-58524-6_39&partnerID=40&md5=92c39b675861c5a046b09c636037d175","IT Center, RWTH Aachen University, Aachen, Germany; Centre for Information and Media Services, University of Duisburg-Essen, Essen, Germany; Regional Computing Centre, University of Cologne, Cologne, Germany; University Library, University of Duisburg-Essen, Essen, Germany","Eifert T., IT Center, RWTH Aachen University, Aachen, Germany; Schilling U., Centre for Information and Media Services, University of Duisburg-Essen, Essen, Germany; Bauer H.-J., Regional Computing Centre, University of Cologne, Cologne, Germany; Krämer F., IT Center, RWTH Aachen University, Aachen, Germany; Lopez A., University Library, University of Duisburg-Essen, Essen, Germany","Research Data Management (RDM) receives more and more attention as a core component of scientific work. This importance equally stems from the scientific work with ever-increasing amounts of data, the value of this data for subsequent use, and the formal requirements of funding agencies. While these requirements are widely accepted among the research communities in general, the individual acceptance depends on many factors. In particular, we found that the ratio between the benefits achieved by RDM and the burdens imposed is not equal among the different roles that participate in the scientific process. In consequence, we analyse how we can optimize this ratio by different factors. Despite these different factors, common to all solutions is the demand for accessible and persistent storage that suits the particular needs imposed by RDM. At the Universities of Aachen, Bochum, Dortmund, Duisburg-Essen, and Cologne, we started a joint project to build up a distributed storage infrastructure dedicated to the needs of RDM and to address some of the acceptance factors. © Springer International Publishing AG 2017.","Collaboration; Extended domain model; Research data management","Digital storage; Human computer interaction; Information management; Collaboration; Distributed storage; Domain model; Funding agencies; Individual acceptance; Persistent storage; Research communities; Research data managements; Decision making","","","","","European Union program Horizon 2020, the German Research Foundation; HRK; Bundesministerium für Bildung und Forschung, BMBF","Research data (RD) is the outcome as well as the foundation of scientific work. Researchers need an environment that enables them to work efficiently and securely with their research data (cf. [, ]. National and international research funding institutions, such as the European Union program Horizon 2020, the German Research Foundation, the HRK, the German university rectors’ conference [, ] and the Federal Ministry of Education and Research as well as various publishers (e.g. NATURE), are increasingly requiring scientists and scholars to plan and execute good data management practices. An obligation to archive produced data already exists by carrying out “good scientific practice” [], some even ask for the publication of primary data, e.g. the Open Data Pilot of the EU. ","How Europe Can Gain from the Rising Tide of Scientific Data, (2010); Management of Research Data – a Key Strategic Challenge for University Management, (2014); How University Management Can Guide the Development of Research Data Management. Orientation Paths, Options for Action and Scenarios, (2015); Eifert T., Muckel S., Schmitz D., Introducing Research Data Management as a Service Suite at RWTH Aachen University, GI Lecture Notes in Informatics – Proceedings, P257, pp. 55-64; Grundsätze Zur Sicherung Guter Wissenschaftlicher Praxis Der Rheinisch-Westfälischen Technischen Hochschule Aachen, (2011); Leitlinien Zum Forschungsdatenmanagement für Die RWTH Aachen, (2016); Eifert T., Bunsen G., Grundlagen und Entwicklung von Identity Management an der RWTH Aachen, PIK - Praxis Der Informationsverarbeitung Und Kommunikation, 36, 2, pp. 109-116, (2013); Klar J., Enke H., Rahmenbedingungen einer disziplinübergreifenden Forschungs-dateninfrastruktur, Report Organisation Und Struktur, (2013)","T. Eifert; IT Center, RWTH Aachen University, Aachen, Germany; email: eifert@itc.rwth-aachen.de","Yamamoto S.","Springer Verlag","","Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017","9 July 2017 through 14 July 2017","Vancouver","194159","03029743","978-331958523-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85025173765"
"Wu M.; Chen X.","Wu, Ming (57193621897); Chen, Xiujuan (57193630334)","57193621897; 57193630334","Library service design based on the needs of chemistry research data management and sharing survey","2016","Proceedings of the Association for Information Science and Technology","53","1","","1","4","3","2","10.1002/pra2.2016.14505301137","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015280486&doi=10.1002%2fpra2.2016.14505301137&partnerID=40&md5=f7a4eafaccb41abdc86b20bbfbbb55e8","National Science Library, Chinese Academy of Sciences, No.33 Beisihuan Xilu, Haidian District, Beijing, 100190, China","Wu M., National Science Library, Chinese Academy of Sciences, No.33 Beisihuan Xilu, Haidian District, Beijing, 100190, China; Chen X., National Science Library, Chinese Academy of Sciences, No.33 Beisihuan Xilu, Haidian District, Beijing, 100190, China","This paper aims to survey five perspectives of research data in chemistry research process, which including data generation and collection, data recording and processing, data preservation and backup, data management and sharing, needs for data sharing services. By means of questionnaires survey, 119 researchers and graduate students of Chinese Academy of Science in chemistry disciplines provided us with insights in research data management and sharing. The outputs of statistical analytical results help us have a better understanding on their currency attitudes and needs of researchers about their data management and sharing in chemistry disciplines. Although the survey in chemistry discipline, it could be provide us some insights for designing a range of library service, particularly in promotion, consulting and training of research data management and sharing. Copyright © 2016 by Association for Information Science and Technology","Chemistry discipline; Data management and sharing; Library data service; Needs survey","Chemical analysis; Data acquisition; Data handling; Students; Surveys; Chemistry discipline; Chemistry research; Data services; Data Sharing; Library data; Library data service; Library services; Need survey; Research data managements; Services designs; Information management","","","","","","","Anderson N.R., Lee E.S., Brockenbrough J.S., Et al., Issues in biomedical research data management and analysis: needs and barriers, J Am Med Inform Assoc, 14, pp. 478-488, (2007); Bardyn T.P., Resnick T., Camina S.K., Translational Researchers' Perceptions of Data Management Practices and Data Curation Needs: Findings from a Focus Group in an Academic Health Sciences Library, Journal of Web Librarianship, 6, pp. 274-287, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, pp. 1059-1078, (2012); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, pp. 17-30, (2007); Carlson J., Demystifying the data interview, Reference Services Review, 40, pp. 7-23, (2012); Carlson J., Stowell-Bracke M., Data Management and Sharing from the Perspective of Graduate Students: An Examination of the Culture and Practice at the Water Quality Field Station, portal: Libraries and the Academy, 13, pp. 343-361, (2013); Gray J., Liu D.T., Nieto-Santisteban M., Et al., Scientific data management in the coming decade, SIGMOD Rec., 34, pp. 34-41, (2005); Hall N.F., Environmental studies faculty attitudes towards sharing of research data, Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pp. 383-384, (2013); Huang X., Hawkins B.A., Lei F., Et al., Willing or unwilling to share primary biodiversity data: results and implications of an international survey, Conservation Letters, 5, pp. 399-406, (2012); Kim J., A Study on the Perceptions of University Researchers on Data Management and Sharing, Journal of the Korean Society for Library and Information Science, 49, pp. 413-436, (2015); Peters C., Dryden A.R., Assessing the Academic Library's Role in Campus-Wide Research Data Management: A First Step at the University of Houston, Science & Technology Libraries, 30, pp. 387-403, (2011); Tenopir C., Allard S., Douglass K., Et al., Data sharing by scientists: practices and perceptions, PLoS One, 6, (2011); Williams S.C., Using a Bibliographic Study to Identify Faculty Candidates for Data Services, Science & Technology Libraries, 32, pp. 202-209, (2013)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85015280486"
"","","","CEUR Workshop Proceedings","2016","CEUR Workshop Proceedings","1871","","","","","86","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025123907&partnerID=40&md5=2f5d3c6909a9ee547120d4a68dff25fc","","","The proceedings contain 12 papers. The topics discussed include: a model for information and action flows connecting science gateways to distributed computing infrastructures; virtual research environments as-a-service by gCube; Rosemary: a flexible programming framework to build science gateways; an innovative workspace for the Cherenkov telescope array; a science gateway for biodiversity and climate change research; Endofday: a container workflow engine for scalable, reproducible computation; MiCADO - towards a microservice-based cloud application-level dynamic orchestrator; a microservice-based portal for x-ray transient and variable sources; fast access to remote objects 2.0 - a renewed gateway to ENEAGRID distributed computing resources; from the desktop to the grid and cloud: conversion of KNIME workflows to WS-PGRADE; milky way analysis through a science gateway: workflows and resource monitoring; and towards a metadata-driven multi-community research data management service.","","","","","","","","","","","Gesing S.; Kruger J.","CEUR-WS","","8th International Workshop on Science Gateways, IWSG 2016","8 June 2016 through 10 June 2016","Rome","128453","16130073","","","","English","CEUR Workshop Proc.","Conference review","Final","","Scopus","2-s2.0-85025123907"
"Lassi M.; Johnsson M.; Golub K.","Lassi, Monica (33368126000); Johnsson, Maria (57192251854); Golub, Koraljka (6506428393)","33368126000; 57192251854; 6506428393","Research data services: An exploration of requirements at two Swedish universities","2016","IFLA Journal","42","4","","266","277","11","5","10.1177/0340035216671963","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002328305&doi=10.1177%2f0340035216671963&partnerID=40&md5=5c81e9ad6e31832f47e953a0dffd7f25","Lund University, Sweden; Linnaeus University, Sweden","Lassi M., Lund University, Sweden; Johnsson M., Lund University, Sweden; Golub K., Linnaeus University, Sweden","The paper reports on an exploratory study of researchers’ needs for effective research data management at two Swedish universities, conducted in order to inform the ongoing development of research data services. Twelve researchers from diverse fields have been interviewed, including biology, cultural studies, economics, environmental studies, geography, history, linguistics, media and psychology. The interviews were structured, guided by the Data Curation Profiles Toolkit developed at Purdue University, with added questions regarding subject metadata. The preliminary analysis indicates that the research data management practices vary greatly among the respondents, and therefore so do the implications for research data services. The added questions on subject metadata indicate needs of services guiding researchers in describing their datasets with adequate metadata. © 2016, © The Author(s) 2016.","Academic libraries; data services; metadata and semantic web; organization of information; services to user populations; types of libraries and information providers","","","","","","","","Andersson U., Alfredsson A., Arvidsson S., Et al., Projektet Forskningsdata inom humaniora och konstnärliga vetenskaper - Open access? Projektrapport till Kungl. biblioteket, Programmet för OpenAccess.se, (2011); Bjorklund C., Eriksson J., Forskningsdata i öppna arkiv och universitetsarkiv: en förstudie vid Göteborgs universitet, Lunds universitet och Sveriges Lantbruksuniversitet.Projektrapport till Kungliga biblioteket, Programmet för OpenAccess.se, (2007); Bohlin A., Offentlighet & sekretess i myndighets forskningsverksamhet, (1997); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Bracke M., Emerging data curation roles for librarians: A case study of agricultural data, Journal of Agricultural and Food Information, 12, 1, pp. 65-74, (2011); Brandt S.D., Kim E., Data curation profiles as a means to explore managing, sharing, disseminating or preserving digital outcomes, International Journal of Performance Arts and Digital Media, 10, 1, pp. 21-34, (2014); Carlhed C., Alfredsson I., Swedish National Data Service’s strategy for sharing and mediating data: Practices of open access to and reuse of research data - the state of the art in Sweden 2009, (2009); Carlson J., Bracke M.S., Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the Water Quality Field Station, portal: Libraries and the Academy, 13, 4, pp. 343-361, (2013); Carlson J., Brandt S.D., Data Curation Profiles, (2014); Carlson J., Brandt S.D., Data Curation Profiles Directory, (2015); Disciplinary Metadata, (2016); ICOS Carbon Portal, (2016); Johnsson M., Lassi M., Hantering av forskningsdata vid fakulteterna inom Lunds universitet – en lägesbeskrivning hösten 2015: Rapport av ett projekt utfört vid Universitetsbiblioteket, (2016); Lancaster F.W., Indexing and Abstracting in Theory and Practice, (2003); Digital Humanities, (2016); Organisation, (2016); McLure M., Level A.V., Cranston C.L., Et al., Data curation: A study of researcher practices and needs, Libraries and the Academy, 14, 2, pp. 139-164, (2014); God forskningssed, (2011); Förslag till nationella riktlinjer för öppen tillgång till vetenskaplig information, (2015); Witt M., Carlson J.D., Brandt S.D., Et al., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009); Wright S.J., Kozlowski W.A., Dietrich D., Et al., Using data curation profiles to design the datastar dataset registry, D-Lib Magazine, 19, 7-8, pp. 37-49, (2013); Ahlfeldt J., Johnsson M., Research Libraries and Research Data Management within the Humanities and Social Sciences: Project Report, (2015)","M. Lassi; Department of Scholarly Communication at University Library, Lund University, Lund, PO Box 3, 22100, Sweden; email: monica.lassi@ub.lu.se","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","","Scopus","2-s2.0-85002328305"
"Mayernik M.S.","Mayernik, Matthew S. (23009234400)","23009234400","Research data and metadata curation as institutional issues","2016","Journal of the Association for Information Science and Technology","67","4","","973","993","20","32","10.1002/asi.23425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961836692&doi=10.1002%2fasi.23425&partnerID=40&md5=78dc9141badaf0c2a91c5cdde41c4770","NCAR/UCAR Library, National Center for Atmospheric Research (NCAR), University Corporation for Atmospheric Research (UCAR), P.O. Box 3000, Boulder, 80307-3000, CO, United States","Mayernik M.S., NCAR/UCAR Library, National Center for Atmospheric Research (NCAR), University Corporation for Atmospheric Research (UCAR), P.O. Box 3000, Boulder, 80307-3000, CO, United States","Research data curation initiatives must support heterogeneous kinds of projects, data, and metadata. This article examines variability in data and metadata practices using ""institutions"" as the key theoretical concept. Institutions, in the sense used here, are stable patterns of human behavior that structure, legitimize, or delegitimize actions, relationships, and understandings within particular situations. Based on prior conceptualizations of institutions, a theoretical framework is presented that outlines 5 categories of ""institutional carriers"" for data practices: (a) norms and symbols, (b) intermediaries, (c) routines, (d) standards, and (e) material objects. These institutional carriers are central to understanding how scientific data and metadata practices originate, stabilize, evolve, and transfer. This institutional framework is applied to 3 case studies: the Center for Embedded Networked Sensing (CENS), the Long Term Ecological Research (LTER) network, and the University Corporation for Atmospheric Research (UCAR). These cases are used to illustrate how institutional support for data and metadata management are not uniform within a single organization or academic discipline. Instead, broad spectra of institutional configurations for managing data and metadata exist within and across disciplines and organizations. © 2015 ASIS&T.","data; information infrastructure; organizational environment","Behavioral research; C (programming language); Metadata; Atmospheric research; data; Information infrastructures; Institutional framework; Institutional support; Long term ecological research networks; organizational environment; Theoretical framework; behavior; conceptual framework; ecology; human; human experiment; information processing; organization; theoretical model; university; Societies and institutions","","","","","","","Abbott A., An old institutionalist reads the new institutionalism, Contemporary Sociology, 21, 6, pp. 754-756, (1992); Agre P.E., Institutional circuitry: Thinking about the forms and uses of information, Information Technology and Libraries, 14, 4, pp. 225-230, (1995); Agre P.E., Computation and Human Experience, (1997); Agre P.E., Information and institutional change: The case of digital libraries, Digital Library Use: Social Practice in Design and Evaluation, pp. 219-240, (2003); Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Aronova E., Baker K.S., Oreskes N., Big science and big data in biology: From the International Geophysical Year through the International Biological Program to the Long Term Ecological Research (LTER) Network, 1957-present, Historical Studies in the Natural Sciences, 40, 2, pp. 183-224, (2010); Baker K.S., Benson B.J., Henshaw D.L., Blodgett D., Porter J.H., Stafford S.G., Evolution of a multisite network information system: The LTER information management paradigm, Bioscience, 50, 11, pp. 963-978, (2000); Barley S.R., Tolbert P.S., Institutionalization and structuration: Studying the links between action and institution, Organization Studies, 18, 1, pp. 93-117, (1997); Batcheller A.L., Requirements Engineering in Building Climate Science Software, (2011); Berente N., Yoo Y., Institutional contradictions and loose coupling: Postimplementation of NASA's enterprise information system, Information Systems Research, 23, 2, pp. 376-396, (2012); Blanchette J.F., A material history of bits, Journal of the American Society for Information Science and Technology, 62, 6, pp. 1042-1057, (2011); Blanchette J.F., Burdens of Proof: Cryptographic Culture and Evidence Law in the Age of Electronic Documents, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, 1, pp. 17-30, (2007); Borgman C.L., Wallis J.C., Mayernik M.S., Who's got the data? 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Pinch T., Technology and institutions: Living in a material world, Theory and Society, 37, pp. 461-483, (2008); Porter J.H., A brief history of data sharing in the US Long Term Ecological Research network, Bulletin of the Ecological Society of America, 91, 1, pp. 14-20, (2010); To Share or Not to Share: Publication and Quality Assurance of Research Data Outputs, Main Report, (2008); Rew R., Davis G., NetCDF: An interface for scientific data access, IEEE Computer Graphics and Applications, 10, 4, pp. 76-82, (1990); Ribes D., Jackson S.J., Data bite man: The work of sustaining a long-term study, ""Raw Data"" Is An Oxymoron, pp. 147-166, (2013); Rood R.B., Edwards P.N., Climate Informatics: Human Experts and the End-to-end System, (2014); San Gil I., Baker K., Campbell J., Denny E.G., Vanderbilt K., Riordan B., Brunt J., The long-term ecological research community metadata standardisation project: A progress report, International Journal of Metadata, Semantics and Ontologies, 4, 3, pp. 141-153, (2009); Savolainen R., Everyday Information Practices: A Social Phenomenological Perspective, (2008); Schatzki T.R., Introduction: Practice theory, The Practice Turn in Contemporary Theory, pp. 1-14, (2001); Schluter A., Theesfeld I., The grammar of institutions: The challenge of distinguishing between strategies, norms, and rules, Rationality and Society, 22, 4, pp. 445-475, (2010); Schneiberg M., Clemens E.S., The typical tools for the job: Research strategies in institutional analysis, Sociological Theory, 24, 3, pp. 195-227, (2006); Scott W.R., Institutions and Organizations: Ideas and Interests, (2008); Scott W.R., Lords of the dance: Professionals as institutional agents, Organization Studies, 29, 2, pp. 219-238, (2008); Selznick P., Institutionalism ""old"" and ""new, Administrative Science Quarterly, 41, 2, pp. 270-277, (1996); Servilla M., Brunt J., San Gil I., Costa D., PASTA: A network-level architecture design for generating synthetic data products in the LTER Network, LTER DataBits, (2006); Stichweh R., Scientific disciplines, history of, International Encyclopedia of the Social & Behavioral Sciences, pp. 13727-13731, (2001); Strand G., Community Earth System Model data management: Policies and challenges, Procedia Computer Science, pp. 558-566, (2011); Talja S., Analyzing qualitative interview data: The discourse analytic method, Library & Information Science Research, 21, 4, pp. 459-477, (1999); Tolbert P., Zucker L.G., The institutionalization of institutional theory, Handbook of Organization Studies, pp. 175-190, (1996); Preliminary Plans for a National Institute for Atmospheric Research Prepared for the National Science Foundation, (1959); Policies & Procedures: 3-5 Publication and Information Dissemination, (2009); Weber N., Thomer A., Strand G., Complications in climate data classification: The political and cultural production of variable names, Advances in Classification Research Online, 23, 1, pp. 57-58, (2013); Williams D.N., Drach R., Ananthakrishnan R., Foster I.T., Fraser D., Siebenlist F., Sim A., The Earth System Grid: Enabling access to multimodel climate simulation data, Bulletin of the American Meteorological Society, 90, 2, pp. 195-205, (2009); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, Journal of the American Society for Information Science and Technology, 63, 8, pp. 1505-1520, (2012); Witt M., Co-designing, co-developing, and co-implementing an institutional data repository service, Journal of Library Administration, 52, 2, pp. 172-188, (2012); Wynholds L., Fearon D.S., Borgman C.B., Traweek S., When use cases are not useful: Data practices, astronomy, and digital libraries, Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 383-386, (2011); Yan E., Sugimoto C.R., Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks, Journal of the American Society for Information Science and Technology, 62, 8, pp. 1498-1514, (2011); Yarmey L., Baker K.S., Towards standardization: A participatory framework for scientific standard-making, International Journal of Digital Curation, 8, 1, pp. 157-172, (2013)","M.S. Mayernik; NCAR/UCAR Library, National Center for Atmospheric Research (NCAR), University Corporation for Atmospheric Research (UCAR), Boulder, P.O. Box 3000, 80307-3000, United States; email: mayernik@ucar.edu","","John Wiley and Sons Inc.","","","","","","23301635","","","","English","J. Assoc. Soc. Inf. Sci. Technol.","Article","Final","","Scopus","2-s2.0-84961836692"
"Jubb M.","Jubb, Michael (36962500800)","36962500800","Libraries and the support of university research","2016","Quality and the Academic Library: Reviewing, Assessing and Enhancing Service Provision","","","","143","156","13","3","10.1016/B978-0-12-802105-7.00014-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84967436531&doi=10.1016%2fB978-0-12-802105-7.00014-2&partnerID=40&md5=afae1e39d4f774ffaf23ee79b29995f3","Research Information Network, London, United Kingdom","Jubb M., Research Information Network, London, United Kingdom","Universities are increasingly interested in supporting the activities of researchers and assessing the quality of what they produce, since research performance is a key determinant of each university's reputation and financial success. In this context, many university libraries are seeking to enhance their support for research. Hence they are rethinking the roles of their collection-based services; changing the roles of liaison librarians; and developing new services for researchers including advice on scholarly communications and open access, bibliometric services, research data management and library-led publishing services. The challenges for libraries in seeking to develop their services in these ways are considered. © 2016 Jeremy Atkinson Published by Elsevier Ltd.","Academic libraries; Bibliometrics; Collections; Liaison; Open access; Publishing; Research; Research data management; Research support services; Universities","","","","","","","","Anderson R., The crisis in research librarianship, Journal of Academic Librarianship, 37, 4, pp. 289-290, (2011); Brown L., Griffiths R., Rascoff M., University publishing in a digital age, (2007); Green J., Langley D., Professionalising research management, (2009); Ivins O., Luther J., Publishing support for small print-based publishers: Options for ARL libraries, (2011); Jaguszewski J., Williams K., New roles for new times: Transforming liaison roles in research libraries, (2013); King D.W., Tenopir C., Using and reading scholarly literature, Annual review of information science and technology, 34, pp. 423-477, (2001); Kroll S., Forsman R., A slice of research life: Information support for research in the United States. A report commissioned by OCLC Research in support of the RLG partnership, (2010); ARL statistics 2012-2013, (2014); Library publishing directory 2015, (2014); The data harvest: How sharing research data can yield knowledge, jobs and growth, (2014); E-journals: Their use, value and impact: Final report, (2011); The value of libraries for research and researchers, (2011); Supporting researchers, (2014); Tenopir C., Mays R., Wu L., Journal article growth and reading patterns, New Review of Information Networking, 16, 1, pp. 4-22, (2011); Tenopir C., Volentine R., UK scholarly reading and the value of library resources: Summary results of the study conducted Spring 2011, (2012); Van den Eynden V., Bishop L., Sowing the seed: Incentives and motivations for sharing research data, a researcher's perspective, (2014); Whitley R., Glaser J., Engwall L., Reconfiguring knowledge production: Changing authority relationships in the sciences and their consequences for intellectual innovation, (2010)","","","Elsevier Inc.","","","","","","","978-012802105-7","","","English","Qual. and the Acad. Libr.: Rev., Assess. and Enhanc. Serv. Provis.","Book chapter","Final","","Scopus","2-s2.0-84967436531"
"Schembera B.; Bönisch T.","Schembera, Björn (56829559000); Bönisch, Thomas (12646414000)","56829559000; 12646414000","Challenges of research data management for high performance computing","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10450 LNCS","","","140","151","11","7","10.1007/978-3-319-67008-9_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029576681&doi=10.1007%2f978-3-319-67008-9_12&partnerID=40&md5=324a1b7e82a081a2669f345f590e84a2","High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany","Schembera B., High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany; Bönisch T., High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Nobelstr. 19, Stuttgart, 70569, Germany","This paper targets the challenges of research data management with a focus on High Performance Computing (HPC) and simulation data. Main challenges are discussed: The Big Data qualities of HPC research data, technical data management, organizational and administrative challenges. Emerging from these challenges, requirements for a feasible HPC research data management are derived and an alternative data life cycle is proposed. The requirement analysis includes recommendations which are based on a modified OAIS architecture: To meet the HPC requirements of a scalable system, metadata and data must not be stored together. Metadata keys are defined and organizational actions are recommended. Moreover, this paper contributes by introducing the role of a Scientific Data Manager, who is responsible for the institution’s data management and taking stewardship of the data. © Springer International Publishing AG 2017.","Archive; Big data; Data life cycle; HPC; Metadata; OAIS; Research data management; Simulation","Computation theory; Digital libraries; Information management; Life cycle; Metadata; Archive; Data life cycle; OAIS; Research data managements; Simulation; Big data","","","","","Deutsche Forschungsgemeinschaft; Deutsche Forschungsgemeinschaft; National Science Foundation; European Geosciences Union","Funding text 1: Open Access is the paradigm to make research data publicly available and is another challenge in HPC context. Open Access is still not commonly accepted - this is also a problem of regulations and conventions by institutions as well as a legal challenge. However, Open Access will become a top priority in the future [3]. A European Union report argues that in the future, all generated research data has to be made available to the public [7]. In addition, the German Research Foundation (DFG) sets as a vision that publicly funded research data should be publicly available for a long-term period [5]. This prevents duplicate work and saves resources.; Funding text 2: Data Management Plans (DMP) are formal documents that specify plans and numbers on data management and fulfill the requirement of organizational security [14]. This documentation has to name how research data is handled during and after a research project. Research proposals already require management plans, for example those of the European Union [6] or of the National Science Foundation [20]. A DMP has therefore to be mandatory for all HPC research data management efforts and must be raised as a general requirement when handling research data. All persons involved have to negotiate on a DMP4.","Arora R., Data management: State-of-the-practice at open-science data centers, Handbook on Data Centers, pp. 1095-1108, (2015); Askhoj J., Sugimoto S., Nagamori M., Preserving records in the cloud, Rec. Manage. J., 21, 3, pp. 175-187, (2011); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J. Librarian. Inf. Sci., 46, 4, pp. 299-316, (2014); (2016); (2013); (2016); European Cloud Initiative - Building a Competitive Data and Knowledge Economy in Europe, (2016); Faulhaber P., Investing in the Future of Tape Technology, (2015); Gray J., Liu D.T., Nieto-Santisteban M., Szalay A., Dewitt D.J., Heber G., Scientific data management in the coming decade, SIGMOD Rec, 34, 4, pp. 34-41, (2005); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Helly J., Staudigel H., Koppers A., Scalable models of data sharing in earth sciences, Geochem. Geophy. Geosyst., 4, 1, (2003); Hick J., HPSS in the Extreme Scale Era: Report to DOE Office of Science on HPSS in 2018–2022, (2010); Hick J., The Fifth Workshop on HPC best practices: File systems and archives. Lawrence Berkeley National Laboratory, LBNL Paper LBNL-5262E, (2013); Jensen U., Datenmanagementpläne, Handbuch Forschungsdatenmanagement, (2011); Jones S.N., Strong C.R., Parker-Wood A., Holloway A., Long D.D.E., Easing the burdens of HPC file management, Proceedings of the Sixth Workshop on Parallel Data Storage, PDSW 2011, NY, USA, pp. 25-30, (2011); Lautenschlager M., Toussaint F., Thiemann H., Reinke M., The CERA-2 Data Model, (1998); Liang S., Holmes V., Antoniou G., Higgins J., ICurate: A research data management system, MIWAI 2015. LNCS, 9426, pp. 39-47, (2015); Malik T., Geobase: Indexing NetCDF files for large-scale data analysis, Big Data Management, Technologies, and Applications, pp. 295-313, (2014); Mattmann C.A., Computing: A vision for data science, Nature, 493, 7433, pp. 473-475, (2013); Grant Proposal Guide Chapter Ii.C.2.J, (2014); Reference model for an Open Archival Information System. Technical report, CCSDS 650.0-M-2 (Magenta Book)Issue, 2, (2012); Parker-Wood A., Long D.D.E., Madden B.A., Adams I.F., McThrow M., Wildani A., Examining extended and scientific metadata for scalable index designs, Proceedings of the 6Th International Systems and Storage Conference, SYSTOR 2013, pp. 1-4, (2013); Potthoff J., Van Wezel J., Razum M., Walk M., Anforderungen eines nach-haltigen, disziplinübergreifenden Forschungsdaten-Repositoriums, Dfn-Forum Kommunikationstechnologien, pp. 11-20, (2014)","B. Schembera; High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Nobelstr. 19, 70569, Germany; email: schembera@hlrs.de","Manolopoulos Y.; Kamps J.; Tsakonas G.; Iliadis L.; Karydis I.","Springer Verlag","The Coalition for Networked Information (CNI)","21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017","18 September 2017 through 21 September 2017","Thessaloniki","197829","03029743","978-331967007-2","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85029576681"
"Zhang B.; Pouchard L.C.; Smith P.M.; Gasc A.; Pijanowski B.C.","Zhang, Boyu (57192587413); Pouchard, Line C. (8709358200); Smith, Preston M. (24766713600); Gasc, Amandine (36548102000); Pijanowski, Bryan C. (6602167326)","57192587413; 8709358200; 24766713600; 36548102000; 6602167326","Data storage and sharing for the long tail of science","2016","2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings","","","7747811","","","","7","10.1109/NYSDS.2016.7747811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006880723&doi=10.1109%2fNYSDS.2016.7747811&partnerID=40&md5=03d25cae2f5f26c8040f39b7c3c60c20","Information Technology at Purdue, Purdue University, United States; Libraries, Purdue University, United States; Forestry and Natural Resources Department, Purdue University, United States","Zhang B., Information Technology at Purdue, Purdue University, United States; Pouchard L.C., Libraries, Purdue University, United States; Smith P.M., Information Technology at Purdue, Purdue University, United States; Gasc A., Forestry and Natural Resources Department, Purdue University, United States; Pijanowski B.C., Forestry and Natural Resources Department, Purdue University, United States","Research data infrastructure such as storage must now accommodate new requirements resulting from trends in research data management that require researchers to store their data for the long term and make it available to other researchers. We propose Data Depot, a system and service that provides capabilities for shared space within a group, shared applications, flexible access patterns and ease of transfer at Purdue University. We evaluate Depot as a solution for storing and sharing multi-terabytes of data produced in the long tail of science with a use case in soundscape ecology studies from the Human-Environment Modeling and Analysis Laboratory. We observe that with the capabilities enabled by Data Depot, researchers can easily deploy fine-grained data access control, manage data transfer and sharing, as well as integrate their workflows into a High Performance Computing environment. © 2016 IEEE.","Data Depot; data sharing; data storage; long tail of science; Soundscapes","Access control; Data storage equipment; Data transfer; Digital storage; Data Depot; Data Sharing; Data storage; Long tail; Soundscapes; Information management","","","","","","","Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, pp. 280-299, (2008); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them data sharing and reuse in the long tail of science and technology, PloS One, 8, (2013); Shreeves S.L., Cragin M.H., Introduction: Institutional repositories: Current state and future, Library Trends, 57, pp. 89-97, (2008); Lynch C., Big data: How do your data grow, Nature, 455, pp. 28-29, (2008); Pouchard L., Revisiting the data lifecycle with big data curation, International Journal of Digital Curation, 10, pp. 176-192, (2016); Moore R.L., Baru C., Baxter D., Fox G.C., Majumdar A., Papadopoulos P., Pfeiffer W., Sinkovits R.S., Strande S., Tatineni M., Wagner R.P., Wilkins-Diehr N., Norman M.L., Gateways to discovery: Cyberinfrastructure for the long tail of science, Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, pp. 391-398, (2014); Parsons M.A., Godoy O., Ledrew E., De Bruin T.F., Danis B., Tomlinson S., Carlson D., A conceptual framework for managing very diverse data for complex, interdisciplinary science, Journal of Information Science, 37, pp. 555-569, (2011); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, pp. 1-21, (2011); Carlyle A.G., Harrell S.L., Smith P.M., Cost-effective hpc: The community or the cloud, Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), pp. 169-176, (2010); Chapin F.S., Zavaleta E.S., Eviner V., Naylor R.L., Vitousek P.M., Reynolds H.L., Hooper D.U., Lavorel S., Sala O.E., Hobbie S.E., Mack M.C., Diaz S., Consequences of changing biodiversity, Nature, 405, pp. 234-242, (2000); Balmford A., Crane P., Dobson A., Green R.E., Mace G.M., The 2010 challenge: Data availability, information needs and extraterrestrials insights, Philosophical Transaction of the Royal Society, 360, pp. 221-228, (2005); Pijanowski B.C., Villanueva-Rivera L.J., Dumyahn S.L., Farina A., Krause B.L., Napoletano B.M., Pieretti N., Soundscape ecology: The science of sound in the landscape, BioScience, 61, 3, pp. 203-216, (2011); Farina A., Soundscape Ecology Principles, Patterns, Methods and Applications, (2014); Sueur J., Pavoine S., Hamerlynck O., Duvail S., Rapid acoustic survey for biodiversity appraisal, PloS One, 3, 12, (2008); Acevedo M.A., Villanueva-Rivera L.J., Using automated digital recording systems as effective tools for the monitoring of birds and amphibians, Wildlife Society Bulletin, 34, 1, pp. 211-214, (2006); Brandes T.S., Automated sound recording and analysis techniques for bird surveys and conservation, Bird Conservation International, 18, pp. S163-S173, (2008); Villanueva-Rivera L.J., Pijanowski B.C., Pumilio: A web-based management system for ecological recordings, The Bulletin of the Ecological Society of America, 93, pp. 71-81, (2012); Zhang J., Huang K., Cottman-Fields M., Truskinger A., Roe P., Duan S., Dong X., Towsey M., Wimmer J., Managing and analysing big audio data for environmental monitoring, Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering, pp. 997-1004, (2013); Baker E., Price B.W., Rycroft S.D., Hill J., Smith V.S., BioAcoustica: A free and open repository and analysis platform for bioacoustics, Database, 2015, (2015); Chard K., Tuecke S., Foster I., Efficient and secure transfer, synchronization, and sharing of big data, IEEE Cloud Computing, 1, pp. 46-55, (2014); Foster I., Globus toolkit version 4: Software for service-oriented systems, Proceedings of TheIFIP International Conference on Network and Parallel Computing, pp. 2-13, (2006); Colby K.D., Dietz D.T., Smith P.M., Cumberland D.D., Selfservice queue and user management in shared clusters, Proceedings of the First International Workshop on HPC User Support Tools, pp. 22-31, (2014); Estrada T., Zhang B., Cicotti P., Armen R.S., Taufer M., Reengineering high-throughput molecular datasets for scalable clustering using MapReduce, Proceedings of the 14th IEEE International Conference on High Performance Computing and Communications, pp. 351-359, (2012); Zhang B., Estarda T., Cicotti P., Taufer M., Enabling in-situ data analysis for large protein folding trajectory datasets, Proceedings of 28th IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 221-230, (2014); Foster I., Globus online: Accelerating and democratizing science through cloud-based services, IEEE Internet Computing, 15, (2011); Witt M., Co-designing, co-developing, and co-implementing an institutional data repository service, Journal of Library Administration, 52, pp. 172-188, (2012); Dearborn C.C., Barton A.J., Harmeyer N.A., The Purdue University Research Repository: HUBzero customization for dataset publication and digital preservation, OCLC Systems & Services, 30, pp. 15-27, (2014); McLennan M., Kennell R., HUBzero: A platform for dissemination and collaboration in computational science and engineering, Computing in Science & Engineering, 12, 2, pp. 48-53, (2010); Brase, DataCite-a global registration agency for research data, Proceedings of the Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology, pp. 257-261, (2009); Skinner K., Halbert M., The MetaArchive cooperative: A collaborative approach to distributed digital preservation, Library Trends, 57, pp. 371-392, (2009); Skinner K., Halbert M., MetaArchive: A cooperative approach to distributed digital preservation, Against the Grain, 21, (2013)","","","Institute of Electrical and Electronics Engineers Inc.","","2016 New York Scientific Data Summit, NYSDS 2016","14 August 2016 through 17 August 2016","New York","124920","","978-146739051-4","","","English","New York Sci. Data Summit, NYSDS - Proc.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85006880723"
"Dobreva M.","Dobreva, Milena (23396436300)","23396436300","Collective knowledge and creativity: The future of citizen science in the humanities","2016","Advances in Intelligent Systems and Computing","416","","","565","573","8","3","10.1007/978-3-319-27478-2_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975745484&doi=10.1007%2f978-3-319-27478-2_44&partnerID=40&md5=e3a1843d4b7aaa9e08c2fa410896e55c","Library Information and Archive Sciences Department, University of Malta, Msida, 2280, Malta","Dobreva M., Library Information and Archive Sciences Department, University of Malta, Msida, 2280, Malta","Citizen science is a contemporary reinvention of some research practices of the past when ‘unprofessional’ researchers contributed to scientific projects led by academics; a worth-noting peak of research undertaken in this paradigm had been observed in the 19th century. In the 21st century, citizen science mostly resides in digital environments and depends upon eInfrastructures which not only provide citizens with access to research data management, but also play the role of novel scientific communication tools aiming to engage and support citizens in their research contributions. This paper’s main purpose is to introduce the concept focusing on citizen science within the Humanities where its use is still limited compared to other research domains, as well as frequently confused with crowd-sourcing. We also present some initial outcomes of the user studies undertaken within the EC-funded Civic Epistemologies project featuring a set of three international focus groups and a web questionnaire; these help to understand better the current attitudes and challenges in this area. Finally the paper delves into some possible reasons for the slower uptake of citizen science in both the humanities domain and digital cultural heritage and explores to what extent such projects contribute to ‘collective knowledge’ as well as to creativity. © Springer International Publishing Switzerland 2016.","Activities; Citizen science models; Crowdsourcing; EInfrastructures; Motivation","Computer programming; Computer science; Crowdsourcing; Motivation; Access to research; Activities; Citizen science; Digital cultural heritages; Digital environment; E-infrastructures; Scientific communication; Scientific projects; Information management","","","","","EC-funded projects Civic Epistemologies; Seventh Framework Programme, FP7, (316087, 632694)","This research is supported by the EC-funded projects Civic Epistemologies funded under FP7 grant agreement 632694 and AComIn “Advanced Computing for Innovation”, Grant 316087, funded by the FP7 (Research Potential of Convergence Regions).","Bonne R., Et al., Public Participation in Scientific Research: Defining the Field and Assessing Its Potential for Informal Science Education, (2009); Christian C., Lintott C., Smith A., Fortson L., Bamford S., Citizen Science: Contributions to Astronomy Research, (2012); Coughlan S., Dictionary Reaches Final Definition after Century, (2014); (2013); Dafis L.L., Hughes L., What’s the Welsh for “Crowdsourcing”? Citizen science and community engagement at the National Library of Wales, Crowdsourcing Our Cultural Heritage, (2014); Dobreva M., Azzopardi D., Citizen science in the humanities: A promise for creativity, Proceedings of the 9Th International Conference KICSS, pp. 446-451, (2014); Ellis S., A history of collaboration, a future in crowdsourcing: Positive impacts of cooperation on British librarianship, Libri, 64, pp. 1-10, (2014); Franzoni C., Sauermann H., Crowd science: The organization of scientific research in open collaborative projects, Res. Policy, 43, 1, pp. 1-20, (2014); (2013); Hecker A., Knowledge beyond the individual? Making sense of a notion of collective knowledge in organization theory, Organ. Stud, 33, pp. 423-445, (2012); Hughes L., Ell P., Dobreva M., Knight G., Assessing and measuring impact of a digital collection in the humanities: LLC. J. Digit, Scholarsh. Humanities, (2013); Law E., Dalton C., Merrill N., Young A., Gajos K., Curio: A platform for supporting mixed-expertise crowdsourcing. Human computation and crowdsourcing, Works in Progress and Demonstration Abstracts. AAAI Technical Report CR-13-01. An Adjunct to the Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing, pp. 99-100, (2013); Noordegraaf J., Bartholomew A., Eveleigh A., Modeling crowdsourcing for cultural heritage. Museums and the Web 2014, Museums and the Web, (2014); Oomen J., Aroyo L., Crowdsourcing in the cultural heritage domain: Opportunities and challenges, Proceedings of the 5Th International Conference on Communities and Technologies (C&T’11), pp. 138-149, (2011); Rotman D., Et al., Dynamic changes in motivation in collaborative citizen-science projects, Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCW’12), pp. 217-226, (2012); Smith A.M., Lynn S., Lintott C.J., An introduction to the Zooniverse., Crowdsourcing: Works in Progress and Demonstration Abstracts, (2013); Wiggins A., Crowston K., Describing Public Participation in Scientific Research, (2012)","M. Dobreva; Library Information and Archive Sciences Department, University of Malta, Msida, 2280, Malta; email: milena.dobreva@um.edu.mt","Papadopoulos G.A.; Skulimowski A.M.J.; Kacprzyk J.; Kunifuji S.","Springer Verlag","Austrian Airlines; Cyprus Tourism Organisation; Etal; JAIST; JCS; Springer","9th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2014","6 November 2014 through 8 November 2014","Limassol","164129","21945357","978-331927477-5","","","English","Adv. Intell. Sys. Comput.","Conference paper","Final","","Scopus","2-s2.0-84975745484"
"Bruder I.; Heuer A.; Schick S.; Spors S.","Bruder, Ilvio (6602607489); Heuer, Andreas (9533312500); Schick, Sebastian (54279308700); Spors, Sascha (18038954600)","6602607489; 9533312500; 54279308700; 18038954600","Concepts for the management of research data at the university of rostock (Extended abstract); [Konzepte für das Forschungsdatenmanagement an der Universität Rostock { Extended abstract]","2017","CEUR Workshop Proceedings","1917","","","165","175","10","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030104387&partnerID=40&md5=b8ef397c642f161f5503d28ac5fb587f","Universität Rostock, Germany","Bruder I., Universität Rostock, Germany; Heuer A., Universität Rostock, Germany; Schick S., Universität Rostock, Germany; Spors S., Universität Rostock, Germany","Research Data Management aims at gathering, capturing, storing, tracking, and archiving all the data being produced in scientific projects and experiments. Besides these data, all the processing steps on these data - eventually resulting in scientific publications - have to be stored as well. Research Data Management is not only a scientific discipline in Computer Science. Universities and Research Institutes have to provide organizational structures and processes and pragmatic solutions (hardware and software resources) to implement first, simple tasks of Research Data Management. In this paper, we sketch the organizational, pragmatic, and research aspects of Research Data Management from a local (University of Rostock) point of view. At the University of Rostock, we have wider experiences with research data management in marine biology and medical research. The research aspects are part of modern database research topics such as temporal databases, data integration, schema evolution, and provenance management. © 2017 by The Paper's Authors.","","Biology; Data integration; Education; Marine biology; Extended abstracts; Hardware and software; Organizational structures; Research data managements; Research institutes; Scientific discipline; Scientific projects; Scientific publications; Information management","","","","","","","Bruder I., Ignatova T., Milewski L., Knowledge-based scribe recognition in historical music archives, Research and Advanced Technology for Digital Libraries, 8th European Conference, ECDL'04, (2004); Bruder I., Klettke M., Moller M.L., Meyer F., Heuer A., Jurgensmann S., Feistel S., Daten wie sand am meer - datenerhebung, -strukturierung, -management und data provenance fur die ostseeforschung, Datenbank-Spektrum, 17, 2, pp. 183-196, (2017); Buttner S., Hobohm H.-C., Muller L., Handbuch Forschungsdatenmanagement, (2011); Cheney J., Chiticariu L., Tan W.C., Provenance in databases: Why, how, and where, Foundations and Trends in Databases, 1, 4, pp. 379-474, (2009); Dittrich J., Bender P., Janiform intra-document analytics for reproducible research, PVLDB, 8, 12, pp. 1972-1975, (2015); Fagin R., Kolaitis P.G., Popa L., Tan W.C., Schema mapping evolution through composition and inversion, Schema Matching and Mapping, Data-Centric Systems and Applications, pp. 191-222, (2011); Glavic B., Alonso G., The PERM provenance management system in action, Proc. SIGMOD'09, (2009); Heuer A., METIS in Paradise: Provenance management bei der auswertung von sensordatenmengen fur die entwicklung von assistenzsystemen, Datenbanksysteme Fur Business, Technologie und Web (BTW 2015), pp. 131-136, (2015); Heuer A., Meyer H., Bruder I., Nachhaltigkeit von digitalen dokumenten { das rostocker modell, Steinbeis Transfermagazin, (2014); Information Technology - Database Languages - SQL-Part 2: Foundation (SQL/Foundation), (2011); Kandogan E., Roth M., Schwarz P.M., Hui J., Terrizzano I.G., Christodoulakis C., Miller R.J., Labbook: Metadata-driven social collaborative data analysis, Proc. International Conference on Big Data, pp. 431-440, (2015); Max-Planck-Gesellschaft, Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, (2003); McPhilips T., Bowers S., Ludascher B., Collection-oriented scientific workows for integrating and analyzing biological data, Proceedings of the DILS Workshop, (2006); Meyer F., Temporale Aspekte und Provenance-Anfragen Im Umfeld des Forschungsdatenmanagements, (2016); Meyer H., Schering A.-C., Heuer A., The hydra.Power graph system - building digital archives with directed and typed hypergraphs, Datenbank-Spektrum, 17, 2, pp. 113-129, (2017); Moreau L., Groth P.T., Provenance: An Introduction to PROV, (2013); Oeltjen W., Virtuelle Bibliotheken exibel gestalten. In elibrary { den wandel gestalten, Proceedings of the WissKom'10, pp. 259-266, (2010); Spors S., Geier M., Wierstorf H., Towards open science in acoustics: Foundations and best practices, Tagungsband Der DAGA'17, pp. 218-221, (2017); Straube G., Bruder I., Loper D., Heuer A., Data integration in a clinical environment using the global-As-local-view-extension technique, Health Information Science - Third International Conference, pp. 148-159, (2014); Wilkinson M.D., Et al., The FAIR guiding principles for scientific data management and stewardship, Scientific Data, 3, (2016); Witten I., Bainbridge D., Nichols D., How to Build a Digital Library, (2010)","","Leyer M.","CEUR-WS","Data Group; futureTV; Wegtam","Lernen, Wissen, Daten, Analyse - 2017 Learning. Knowledge. Data. Analytics, LWDA 2017","11 September 2017 through 13 September 2017","Rostock","130403","16130073","","","","German","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-85030104387"
"Sesartic A.; Töwe M.","Sesartic, Ana (35280798500); Töwe, Matthias (57192257570)","35280798500; 57192257570","Research Data Services at ETH-Bibliothek","2016","IFLA Journal","42","4","","284","291","7","7","10.1177/0340035216674971","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002045815&doi=10.1177%2f0340035216674971&partnerID=40&md5=192f56a5018d17a758341368c996717b","ETH-Bibliothek, ETH Zürich, Switzerland","Sesartic A., ETH-Bibliothek, ETH Zürich, Switzerland; Töwe M., ETH-Bibliothek, ETH Zürich, Switzerland","The management of research data throughout its life-cycle is both a key prerequisite for effective data sharing and efficient long-term preservation of data. This article summarizes the data services and the overall approach to data management as currently practised at ETH-Bibliothek, the main library of ETH Zürich, the largest technical university in Switzerland. The services offered by service providers within ETH Zürich cover the entirety of the data life-cycle. The library provides support regarding conceptual questions, offers training and services concerning data publication and long-term preservation. As research data management continues to play a steadily more prominent part in both the requirements of researchers and funders as well as curricula and good scientific practice, ETH-Bibliothek is establishing close collaborations with researchers, in order to promote a mutual learning process and tackle new challenges. © 2016, © The Author(s) 2016.","Data life-cycle; data management plan; libraries; preservation; research data; research data management","","","","","","","","Blumer E., Burgi P.-Y., Data Life-Cycle Management Project : SUC P2 2015-2018, Revue électronique suisse de science de l’information, 16, pp. 1-17, (2015); Corti L., Van den Eynden V., Bishop L., Et al., Managing and Sharing Research Data, (2014); DataCite, (2016); Data Management Checklist, (2016); docuteam packer, (2016); Guidelines for Research Integrity, (2011); ETH Archives for Contemporary History, (2016); ETH Zürich, (2016); ETH-Bibliothek, (2016); OpenBIS, (2016); Critical Thinking Initiative, (2016); Digital Curation Office, (2016); E-Rara, (2016); ETH Zürich University Archives, (2016); File Formats for Archiving, (2016); Intended Purpose of Docuteam Packer, (2016); Data Management Checklist, (2016); Guidelines on Data Management in Horizon 2020, (2013); Primo Central Index, (2016); Rosetta, (2016); Goodman A., Pepe A., Blocker A.W., Et al., Ten simple rules for the care and feeding of scientific data, PLoS Computational Biology, 10, 4, (2014); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, 3, (2007); Sesartic A., Fischlin A., Towe M., Towards narrowing the curation gap: Theoretical considerations and lessons learned from decades of practice, ISPRS International Journal of Geo-Information, 5, 6, (2016); Rectors’ Conference of Swiss Higher Education Institutions, (2016); Data Citation Index, Web of Science, (2016); Treloar A., Private Research, Shared Research, Publication, and the Boundary Transitions, (2012)","A. Sesartic; ETH-Bibliothek, ETH Zürich, Rӓmistrasse 101, CH-8092, Switzerland; email: ana.sesartic@library.ethz.ch","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85002045815"
"AL-Omar M.; Cox A.M.","AL-Omar, Mashael (55807557800); Cox, Andrew Martin (7402563906)","55807557800; 7402563906","Scholars’ research-related personal information collections: A study of education and health researchers in a Kuwaiti University","2016","Aslib Journal of Information Management","68","2","","155","173","18","13","10.1108/AJIM-04-2015-0069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959511707&doi=10.1108%2fAJIM-04-2015-0069&partnerID=40&md5=dd8e7066b87cd1f06fb1e903ef2859c2","The Public Authority for Applied Education and Training (PAAET), Kuwait City, Kuwait; University of Sheffield, Sheffield, United Kingdom","AL-Omar M., The Public Authority for Applied Education and Training (PAAET), Kuwait City, Kuwait; Cox A.M., University of Sheffield, Sheffield, United Kingdom","Purpose – The purpose of the paper is to explore the character of scholars’ research-related personal information collections (PICs). Design/methodology/approach – The study was based on in-depth interviews and office tours of 17 scholars in Education and Health Sciences in a Kuwaiti Higher Education Institution. Findings – Scholars’ research-related PICs were added to throughout the research life-cycle. They were huge, diverse, hybrid and fragmented. Key factors shaping the collections were the pressure to do research, time pressure in general, quality of space available, technology opportunity, lack of support from central services, the need to collect Arabic material, self-presentation and self-management. Older scholars and non-Kuwaiti nationals experienced the pressures slightly differently. Research limitations/implications – The study was limited to scholars in two disciplines, in one institution in a developing world context. However the models produced are suggestive of factors involved in shaping of the research-related PICs of scholars in general. Practical implications – Failures in personal information management are a cause for concern in terms of data integrity and validity of research. Interventions could include training of early career researchers for a life time of collecting. Originality/value – This is the first study to examine the contents of scholars’ research-related PICs and to provide a model of factors shaping them. © 2016, © Emerald Group Publishing Limited.","Personal information collections; Personal information management; Research data management; Researchers; Scholarly information practices; Scholars","Developing countries; Life cycle; Information practices; Personal information; Personal information management; Research data managements; Researchers; Scholars; Information management","","","","","","","Baldry C., Barnes A., The open-plan academy: space, control and the undermining of professional identity, Work Employment Society, 26, 2, pp. 228-245, (2012); Ball A., Review of Data Management Lifecycle Models, (2012); Barreau D., Nardi B.A., Finding and reminding: file organization from the desktop, SIGCHI Bulletin, 27, 3, pp. 39-43, (1995); Becher T., Trowler P., Academic Tribes and Territories: Intellectual Enquiry and the Culture of Disciplines, (2001); Belk R.W., Watson J.C., Material culture and the extended or unextended self in our university offices, Advances in Consumer Research, 25, 1, pp. 305-310, (1998); Bondarenko O., Janssen R., Documents at hand: learning from paper to improve digital technologies, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 121-130, (2005); Borgman C., Scholarship in the Digital Age: Information, Infrastructure and the Internet, (2007); Borgman C., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2006); Bruce H., Personal, anticipated information need, Information Research, 10, 3, (2005); Bussert K., Chiang K., Tancheva K., Personal management of scholarly information, Scholarly Practice, Participatory Design and the Extensible Catalog, pp. 123-151, (2011); Case D.O., Collection and organization of written information by social scientists and humanists: a review and exploratory study, Journal of Information Science, 12, 3, pp. 97-104, (1986); Case D.O., Looking for Information: A Survey of Research on Information Seeking, Needs, and Behavior, (2012); Corrall S., Lester R., The researcher’s view: context is critical, Better Library and Learning Spaces: Projects, Trends and Ideas, pp. 183-192, (2013); Fanghanel J., Being an Academic, (2012); Garrett L., Gramstadt M., Burgess R., Murtagh J., Spalding M., Nadim T., (2012); Harrison A., Hutton L., Design for the Changing Educational Landscape, (2014); Hartel J., Thomson L., Visual approaches and photography for the study of immediate information space, Journal of the American Society for Information Science and Technology, 62, 11, pp. 2214-2224, (2011); Henderson S., (2009); Higgins S., The lifecycle of data management, Managing Research Data, pp. 17-45, (2012); Huvila I., Eriksen J., Hausner E.M., Jansson I.M., Continuum thinking and the contexts of personal information management, Information Research, 19, 1, (2014); Jarvis D.S.L., Regulating higher education: quality assurance and neo-liberal managerialism in higher education – a critical introduction, Policy and Society, 33, 3, pp. 155-166, (2014); Jervis M., Masoodian M., How do people attempt to integrate the management of their paper and electronic documents?, Aslib Journal of Information Management, 66, 2, pp. 134-155, (2014); Jones W., Personal information management, Annual Review of Information Science and Technology, 41, 1, pp. 453-504, (2008); Jones W., Teevan J., Personal Information Management, (2007); Kaye J.J., Vertesi J., Avery S., Dafoe A., David S., Onaga L., Rosero I., Pinch T., To have and to hold: exploring the personal archive, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 275-284, (2006); Kuntz A.M., Reconsidering the workplace: faculty perceptions of their work and working environments, Studies in Higher Education, 37, 7, pp. 769-782, (2012); Massey C., Lennig T., Whittaker S., Cloudy forecast: an exploration of the factors underlying shared repository use, pp. 2461-2470, (2014); Miller A., Sharp J., Strong J., What is Research-Led Teaching? Multi-Disciplinary Perspectives, (2012); Palmer C., Teffeau L., Pirmann C., Scholarly Information Practices in the Online Environment Themes from the Literature and Implications for Library Service Development, (2009); Pikas C.K., Personal information management strategies and tactics used by senior engineers, Proceedings of the American Society for Information Science and Technology, 44, 1, pp. 1-21, (2007); Pinder J., Parkin J., Austin S., Duggan F., Lansdale M., Demian P., Baguley T., Allenby S., The Case for New Academic Workspace, (2009); Pryor G., Managing Research Data, (2012); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services, (2014); Scott P., Foreword, Academic Research and Researchers, (2009); Teevan J., Jones W., Bederson B.B., Personal information management, Communications of the ACM, 49, 1, pp. 40-43, (2006); Thomson L., Situating information spaces: the ‘office continuum, (2013); Tian K., Belk R.W., Extended self and possessions in the workplace, Journal of Consumer Research, 32, 2, pp. 297-310, (2005); UNEVOC Network, (2012); Whittaker S., Personal information management: from information consumption to curation, Annual Review of Information Science and Technology, 45, pp. 3-62, (2011); Whittaker S., Hirschberg J., The character, value, and management of personal paper archives, ACM Transactions on Computer-Human Interaction (TOCHI), 8, 82, pp. 150-170, (2001)","A.M. Cox; University of Sheffield, Sheffield, United Kingdom; email: a.m.cox@sheffield.ac.uk","","Emerald Group Publishing Ltd.","","","","","","20503806","","","","English","Aslib J. Inf. Manage.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84959511707"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Research 2.0 and Research Data Services in academic and research libraries: priority issues","2017","Library Management","38","6-7","","345","353","8","9","10.1108/LM-11-2016-0082","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028560603&doi=10.1108%2fLM-11-2016-0082&partnerID=40&md5=b67a80e752d8f39f7b72423633cb779b","Institute of Learning Technologies, Eszterházy Károly University, Jászberény, Hungary","Koltay T., Institute of Learning Technologies, Eszterházy Károly University, Jászberény, Hungary","Purpose: The purpose of this paper is to examine the role of Research Data Services (RDSs), consisting of research data management, data curation and data stewardship, and data literacy education in supporting Research 2.0. Besides this, theory and principles, as well as selected examples of best practices in the relevant fields are presented. Design/methodology/approach: A literature-based overview of actual insights on tasks and roles that academic and research libraries have to fulfil in order to react to the developments generated by the appearance and growing importance of Research 2.0 is provided. Taking the wide spectre of related issues into account, the discussion is limited to RDSs. Findings: Even though Research 2.0 is evolving in different countries and some local environments in dissimilar ways, its data-intensive nature requires the helping presence of academic libraries and librarians. Being an emerging phenomenon, it will undoubtedly take several different shapes as it works itself out in time, but librarians should try to discover service niches, which may not be covered by other academic organisations, or their coverage is only partial or even unsatisfactory. Research limitations/implications: Taking the wide spectre of issues into account, the review of literature is limited to the period between 2014 and 2016. Originality/value: The paper intends to add to the body of knowledge about the relationship between RDSs and Research 2.0, as well as about the association between the components of the former. © 2017, © Emerald Publishing Limited.","Academic libraries; Data literacy; Research 2.0; Research data management; Research Data Services; Research libraries","","","","","","","","2012 top ten trends in academic libraries: a review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 73, 6, pp. 311-320, (2012); Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 75, 6, pp. 294-302, (2014); 2016 top trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 77, 6, pp. 274-281, (2016); Chiware E., Mathe Z., Academic libraries’ role in research data management services: a South African perspective, South African Journal of Libraries and Information Science, 81, 2, pp. 1-10, (2015); Clement R., Blau A., Abbaspour P., Gandour-Rood E., Team-based data management instruction at small liberal arts colleges, IFLA Journal, 43, 1, pp. 105-118, (2017); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, 48, 1, pp. 3-17, (2014); Delaney G., Bates J., Envisioning the academic library: a reflection on roles, relevancy and relationships, New Review of Academic Librarianship, 21, 1, pp. 30-51, (2015); Definitions of data governance, (2015); Drachen T., Ellegaard O., Larsen A., Dorch S., Sharing data increases citations, LIBER Quarterly, 26, 2, pp. 67-82, (2016); The compelling case for data governance, (2015); Erway R., Rinehart A., If You Build it, will they Fund? Making Research Data Management Sustainable, (2016); Erway R., Horton L., Nurnberger A., Otsuji R., Rushing A., Building Blocks: Laying the Foundation for a Research Data Management Program, (2015); Faniel I.M., Kriesberg A., Yakel E., Social scientists’ satisfaction with data reuse, Journal of the Association for Information Science and Technology, 67, 6, pp. 1404-1416, (2016); Federer L.M., Lu Y.L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Flores J.R., Brodeur J.J., Daniels M.G., Nicholls N., Turnator E., Libraries and the research data management landscape, The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy, pp. 82-102, (2015); Hey T., Hey J., e-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Higman R., Pinfield S., Research data management and openness: the role of data sharing in developing institutional policies and practices, Program, 49, 4, pp. 364-381, (2015); Higman R., Teperek M., Kingsley D., Creating a community of data champions, (2017); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the University of Bristol: charting a course from project to service, Program, 49, 4, pp. 475-493, (2015); Johnston L., Jeffryes J., Steal this idea, College and Research Libraries News, 75, 8, pp. 431-434, (2014); Kafel D., Creamer A.T., Martin E.R., Building the new England collaborative data management curriculum, Journal of eScience Librarianship, 3, 1, (2014); Kennan M.A., Data Management: Knowledge and Skills Required in Research, Scientific and Technical Organisations, (2016); Kennan M.A., Markauskaite L., Research data management practices: a snapshot in time, International Journal of Digital Curation, 10, 2, pp. 69-95, (2015); Kim J., Competency-based curriculum: an effective approach to digital curation education, Journal of Education for Library and Information Science, 56, 4, pp. 283-297, (2015); Kim Y., Stanton J.M., Institutional and individual factors affecting scientists’ data-sharing behaviors: a multilevel analysis, Journal of the Association for Information Science and Technology, 67, 4, pp. 776-799, (2016); Knight G., Building a research data management service for the London School of Hygiene and Tropical Medicine, Program, 49, 4, pp. 424-439, (2015); Koltay T., Data literacy: in search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, 49, 1, pp. 3-14, (2015); Koltay T., Data governance, data literacy and the management of data quality, IFLA Journal, 42, 4, pp. 303-312, (2016); Kouper I., Professional participation in digital curation, Library and Information Science Research, 38, 3, pp. 212-223, (2016); Marcum D., Educating the Research Librarian: Are We Falling Short?, (2015); Data Management, (2016); The Five Most Common Big Data Integration Mistakes to Avoid, (2015); Partlo K., Symons D., Carlson J.D., Revolutionary or evolutionary? 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Professional development for librarians needed, College and Research Libraries News, 76, 9, pp. 504-506, (2015); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program, 49, 4, pp. 440-460, (2015); Sesartic A., Fischlin A., Towe M., Towards narrowing the curation gap – theoretical considerations and lessons learned from decades of practice, ISPRS International Journal of Geo-Information, 5, 6, (2016); Stamatoplos A., Neville T., Henry D., Analyzing the data management environment in a master’s-level institution, The Journal of Academic Librarianship, 42, 2, pp. 154-160, (2016); Sturges P., Bamkin M., Anders J.H., Hubbard B., Hussain A., Heeley M., Research data sharing: developing a stakeholder-driven model for journal policies, Journal of the Association for Information Science and Technology, 66, 12, pp. 2445-2455, (2015); Sula C.A., Research ethics in an age of big data, Bulletin of the Association for Information Science and Technology, 42, 2, pp. 17-21, (2016); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R.J., Langseth M., Lundeen A., Research data services in academic libraries: data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Why is data management important?, (2015); van Deventer M., Pienaar H., Research data management in a developing country: a personal journey, International Journal of Digital Curation, 10, 2, pp. 33-47, (2015); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: implications for data services development from a faculty survey, Program, 49, 4, pp. 382-407, (2015); Wittenberg J., Elings M., Building a research data management service at the University of California, Berkeley: a tale of collaboration, IFLA Journal, 43, 1, pp. 89-97, (2017); York J., Gutmann M., Berman F., What do We Know about the Stewardship Gap?, (2016); Zilinski L.D., Nelson M.S., Thinking critically about data consumption: creating the data credibility checklist, Proceedings of the American Society for Information Science and Technology, 51, 1, pp. 1-4, (2014); MacMillan D., Developing data literacy competencies to enhance faculty collaborations, Liber Quarterly, 24, 3, pp. 140-160, (2015)","T. Koltay; Institute of Learning Technologies, Eszterházy Károly University, Jászberény, Hungary; email: koltay.tibor@uni-eszterhazy.hu","","Emerald Group Publishing Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Review","Final","","Scopus","2-s2.0-85028560603"
"Patel D.","Patel, Dimple (57210521351)","57210521351","Research data management: a conceptual framework","2016","Library Review","65","4-5","","226","241","15","33","10.1108/LR-01-2016-0001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978878282&doi=10.1108%2fLR-01-2016-0001&partnerID=40&md5=7432057159b96b34a440c1ad05bf954f","Library and Information Science, Central University of Himachal Pradesh, Dharamsala, India","Patel D., Library and Information Science, Central University of Himachal Pradesh, Dharamsala, India","Purpose: Research data management (RDM) is gaining a lot of momentum in the present day and rightly so. Research data are the core of any research study. The findings and conclusions of a study are entirely dependent on the research data. Traditional publishing did not focus on the presentation of data, along with the publications such as research monographs and especially journal articles, probablyprobably because of the difficulties involved in managing the research data sets. The current day technology, however, has helped in making this task easier. The purpose of this paper is to present a conceptual framework for managing research data at the institutional level. Design/methodology/approach: This paper discusses the significance and advantages of sharing research data. In the spirit of open access to publications, freeing research data and making it available openly, with minimal restrictions, will help in not only furthering research and development but also avoiding duplication of efforts. The issues and challenges involved in RDM at the institutional level are discussed. Findings: A conceptual framework for RDM at the institutional level is presented. A model for a National Repository of Open Research Data (NRORD) is also proposed, and the workflow of the functioning of NRORD is also presented. Originality/value: The framework clearly presents the workflow of the data life-cycle in its various phases right from its creation, storage, organization and sharing. It also attempts to address crucial issues in RDM such as data privacy, data security, copyright and licensing. The framework may help the institutions in managing the research data life-cycle in a more efficient and effective manner. © 2016, © Emerald Group Publishing Limited.","Conceptual framework; Data anonymization; Data privacy; Data repositories; Open data policy; Research data management","","","","","","","","Albright J.J., Privacy protection in social science research: possibilities and impossibilities, PS: Political Science and Politics, 44, 4, pp. 777-782, (2011); Berkhout S.G., Private talk: testimony, evidence, and the practice of anonymization in research, International Journal of Feminist Approaches to Bioethics, 6, 1, pp. 19-45, (2013); Bloom T., Data access for the open access literature: PloS’s data policy, (2013); Guidelines for data classification-computing services ISO – CARNEGIE Mellon university, (2016); Heffetz O., Ligett K., Privacy and data-based research, The Journal of Economic Perspectives, 28, 2, pp. 75-98, (2014); (1957); Loukides G., Gkoulalas-Divanis A., Malin B., Fienberg S.E., Anonymization of electronic medical records for validating genome-wide association studies, Proceedings of the National Academy of Sciences of the United States of America, 107, 17, pp. 7898-7903, (2010); Sweeney L., Datafly: a system for providing anonymity in medical data, in Proceedings of the IFIP TC11 WG11.3 11th International Conference on Database Security XI: Status and Prospects, (1998); Sweeney L., K-anonymity: a model for protecting privacy, International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 5, pp. 557-570, (2002); Sweeney L., Abu A., Winn J., Identifying participants in the personal genome project by name, White Paper 1021-1, Harvard university, Data Privacy Lab, (2013)","D. Patel; Library and Information Science, Central University of Himachal Pradesh, Dharamsala, India; email: dimplerp@gmail.com","","Emerald Group Publishing Ltd.","","","","","","00242535","","","","English","Libr. Rev.","Article","Final","","Scopus","2-s2.0-84978878282"
"Felmeister A.S.; Rivera T.J.; Masino A.J.; Resnick A.C.; Pennington J.W.","Felmeister, Alex S. (57188700522); Rivera, Tyler J. (57188708359); Masino, Aaron J. (56276469700); Resnick, Adam C. (7004870502); Pennington, Jeffrey W. (56037362600)","57188700522; 57188708359; 56276469700; 7004870502; 56037362600","Scalable biobanking: A modular electronic Honest Broker and Biorepository for integrated clinical, specimen and genomic research","2015","Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015","","","7359732","484","490","6","3","10.1109/BIBM.2015.7359732","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962425331&doi=10.1109%2fBIBM.2015.7359732&partnerID=40&md5=54f722989a9436d245dfae95acca3f20","Dept. of Biomedical and Health Informatics, Children's Hospital of Phila., Philadelphia, United States; College of Computing and Informatics, Drexel University, Philadelphia, United States; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States","Felmeister A.S., Dept. of Biomedical and Health Informatics, Children's Hospital of Phila., Philadelphia, United States, College of Computing and Informatics, Drexel University, Philadelphia, United States; Rivera T.J., Dept. of Biomedical and Health Informatics, Children's Hospital of Phila., Philadelphia, United States; Masino A.J., Dept. of Biomedical and Health Informatics, Children's Hospital of Phila., Philadelphia, United States; Resnick A.C., Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States; Pennington J.W., Dept. of Biomedical and Health Informatics, Children's Hospital of Phila., Philadelphia, United States","Biorepository research has introduced significant challenges to biomedical informatics systems design and implementation. We take a best-of-breed system integrative approach to finding solutions to administer a biobank operationally, use a biobank scientifically and integrate clinical, specimen and genomic data. We introduce an electronic Honest Broker (eHB) and Biorepository Portal (BRP) open source project that, in tandem, allow for integration of data on the input and output while protecting subject privacy, a primary issue in research and healthcare informatics. Research data management systems like REDCap and our proprietary laboratory information management system (LIMS) are used as best-of-breed client systems and integrated with downstream genomic analyses. Taking this honest brokered system-client based, modular approach to biorepository research, we allow for data and specimens to be associated with a biorepository subject at any time point asynchronously, lowering the bar to develop new research projects based on scientific merit and having a future proofed specimen set for collaborative advanced genomic and tissue research yet to be established. © 2015 IEEE.","Biomedical Research; Biorepository Research; Cancer Genomics; Computer Security; Data Integration; Data Representation; Health Informatics; Honest Broker; Information Systems; Open Source; Patient Health Information Protection; Patient Privacy; Precision Medicine; Translational Bioinformatics","Bioinformatics; Computer privacy; Data integration; Data privacy; Genes; Information science; Information systems; Medical computing; Open systems; Security of data; Biomedical research; Cancer genomics; Data representations; Health informatics; Honest Broker; Open sources; Patient health; Patient privacies; Information management","","","","","","","Benz E., Genomics and the Future of Cancer Treatment.; Hirtzlin I., Dubreuil C., Preaubert N., Duchier J., Jansen B., Simon J., DeFaria P.L., Perez-Lezaun A., Visser B., Williams G.D., Cambon-Thomsen A., An empirical survey on biobanking of human genetic material and data in six EU countries, Eur. J. Hum. Genet, 11, 6, pp. 475-488, (2003); Vegvari A., Welinder C., Lindberg H., Fehniger T.E., Marko-Varga G., Biobank resources for future patient care: Developments, principles and concepts, J. Clin. Biolnforma, 1, 1, (2011); Vaught J., Rogers J., Carolin T., Compton C., Biobankonomics: Developing a sustainable business model approach for the formation of a human tissue biobank, J. Natl. Cancer Inst. Monogr, 2011, 42, pp. 24-31, (2011); Kamm L., Bogdanov D., Laur S., Vilo J., A new way to protect privacy in large-scale genome-wide association studies, BioInformatics, 29, 7, pp. 886-893, (2013); Swanson D., Aamodt R., Becich M.J., A Multi-disciplinary Approach to Honest Broker Services for Tissue Banks and Clinical Data: A Pragmatic and Practical Model, 113, 7, pp. 1705-1715, (2009); Health Insurance Portability and Accountability Act of 1996, (1996); The Children's Brain Tumor Tissue Consortium; Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G., Research electronic data capture (REDCap) - A metadatadriven methodology and workflow process for providing translational research informatics support, J. Biomed. Inform, 42, 2, pp. 377-381, (2009); Chen P.P.-S., The entity-relationship model-toward a unified view of data, ACM Trans. Database Syst, 1, 1, pp. 9-36, (1976); Pennington J.W., Ruth B., Italia M.J., Miller J., Wrazien S., Loutrel J.G., Crenshaw E.B., White P.S., Harvest: An open platform for developing web-based biomedical data discovery and reporting applications, J. Am. Med. Inform. Assoc, 21, 2, pp. 379-383, (2013); Cerami E., Gao J., Dogrusoz U., Gross B.E., Sumer S.O., Aksoy B.A., Jacobsen A., Byrne C.J., Heuer M.L., Larsson E., Antipin Y., Reva B., Goldberg A.P., Sander C., Schultz N., The cBio Cancer Genomics Portal: An open platform for exploring multidimensional cancer genomics data, Cancer Discov, 2, 5, pp. 401-404, (2012); Dhir R., Patel A.A., Winters S., Bisceglia M., Swanson D., Aamodt R., Becich M.J., A multidisciplinary approach to honest broker services for tissue banks and clinical data: A pragmatic and practical model, Cancer, 113, 7, pp. 1705-1715, (2008); Hutton J.J., Pediatric Biomedical Informatics, 2, (2012); National Cancer Institute, NCI Best Practices for Biospecimen Resources, (2010); Boyd A.D., Saxman P.R., Hunscher D.A., Smith K.A., Morris T.D., Kaston M., Bayoff F., Rogers B., Hayes P., Rajeev N., Kline-Rogers E., Eagle K., Clauw D., Greden J.F., Green L.A., Athey B.D., The university of Michigan honest broker: A web-based service for clinical and translational research and practice, J. Am. Med. Inform. Assoc, 16, 6, pp. 784-791; Boyd A.D., Hunscher D.A., Kramer A.J., Hosner C., Saxman P., Athey B.D., Greden J.F., Clauw D.C., The 'Honest Broker' method of integrating interdisciplinary research data, AMIA Annu. Symp. Proc, (2005); Blobel B., Nordberg R., Davis J.M., Pharow P., Modelling privilege management and access control, Int. J. Med. Inform, 75, pp. 597-623, (2006); Morris R., Thompson K., Password security: A case history, Commun ACM, 22, 11, pp. 594-597, (1979); The Office for Civil Rights (OCR), Malin B., Guidance regarding methods for de-identification of protected health information in accordance with the health insurance portability and accountability act (hipaa) privacy rule, Heal. Inf. Priv, pp. 1-32, (2012); Brastianos P.K., Taylor-Weiner A., Manley P.E., Jones R.T., Dias-Santagata D., Thorner A.R., Lawrence M.S., Rodriguez F.J., Bernardo L.A., Schubert L., Sunkavalli A., Shillingford N., Calicchio M.L., Lidov H.G.W., Taha H., Martinez-Lage M., Santi M., Storm P.B., Lee J.Y.K., Palmer J.N., Adappa N.D., Scott R.M., Dunn I.F., Laws E.R., Stewart C., Ligon K.L., Hoang M.P., Van Hummelen P., Hahn W.C., Louis D.N., Resnick A.C., Kieran M.W., Getz G., Santagata S., Exome sequencing identifies BRAF mutations in papillary craniopharyngiomas, Nat. Genet., 46, 2, pp. 161-165, (2014); Parsons D.W., Li M., Zhang X., Jones S., Leary R.J., Lin J.C.-H., Boca S.M., Carter H., Samayoa J., Bettegowda C., Gallia G.L., Jallo G.I., Binder Z.A., Nikolsky Y., Hartigan J., Smith D.R., Gerhard D.S., Fults D.W., VandenBerg S., Berger M.S., Marie S.K.N., Shinjo S.M.O., Clara C., Phillips P.C., Minturn J.E., Biegel J.A., Judkins A.R., Resnick A.C., Storm P.B., Curran T., He Y., Rasheed B.A., Friedman H.S., Keir S.T., McLendon R., Northcott P.A., Taylor M.D., Burger P.C., Riggins G.J., Karchin R., Parmigiani G., Bigner D.D., Yan H., Papadopoulos N., Vogelstein B., Kinzler K.W., Velculescu V.E., The genetic landscape of the childhood cancer medulloblastoma, Science, 331, pp. 435-439, (2011)","","Schapranow I.M.; Zhou J.; Hu X.T.; Ma B.; Rajasekaran S.; Miyano S.; Yoo I.; Pierce B.; Shehu A.; Gombar V.K.; Chen B.; Pai V.; Huan J.","Institute of Electrical and Electronics Engineers Inc.","IEEE Computer Society; National Science Foundation (NSF)","IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015","9 November 2015 through 12 November 2015","Washington","118735","","978-146736798-1","","","English","Proc. - 2015 IEEE Int. Conf. Bioinform. Biomed., BIBM","Conference paper","Final","","Scopus","2-s2.0-84962425331"
"Rebouillat V.","Rebouillat, Violaine (57194552464)","57194552464","Inventory of research data management services in France","2017","Expanding Perspectives on Open Science: Communities, Cultures and Diversity in Concepts and Practices - Proceedings of the 21st International Conference on Electronic Publishing","","","","174","181","7","4","10.3233/978-1-61499-769-6-174","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020745451&doi=10.3233%2f978-1-61499-769-6-174&partnerID=40&md5=7f59cfcc16365d18b486554395ef512e","Dicen-IDF, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, Paris, 75003, France","Rebouillat V., Dicen-IDF, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, Paris, 75003, France","Data has become more and more ubiquitous in the research context. As a result, a growing number of services are created to analyze, store and share research data. This has induced the Research Data Working Group of the Digital Scientific Library (BSN10) to launch an inventory of French research data management services, funded by the Ministry of Higher Education and Research. The inventory covers all services that are managed by French institutions and infrastructures and dedicated to public research teams from all fields. Sixty services, provided by forty-five structures, have already been identified and analyzed. The paper describes the methodology used to carry out the inventory and analyzes these first results by service type, scope and research field. It also emphasizes the heterogeneous and emergent nature of the inventoried services. © 2017 The authors and IOS Press.","Data analysis; Data archiving; Data discovery; Data sharing; Research data","Data reduction; Electronic publishing; Information management; Data archiving; Data discovery; Data Sharing; Higher education and researches; Number of services; Research data; Research data managements; Research fields; Digital libraries","","","","","","","Science as an Open Enterprise, (2012); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2012); Tenopir C., Sandusky R., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Schmidt B., Baird L., Sandusky R., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, (2017); Delay-Artous C., Où sont les données de la recherche? Essai de cartographie, Systèmes d'Organisation des Connaissances et Humanités Numériques, (2017); Registry of Research Data Repositories; Kindling M., Pampel H., Sandt S., Rucknagel J., Vierkant P., Kloska G., Witt M., Schirmbacher P., Bertelmann R., Scholze F., The landscape of research data repositories in 2015: A re3data analysis, D-Lib Magazine, 23, 3-4, (2017); Leiden Research Data Information Sheets; ECOSCOPE Metadata Portal of Biodiversity Research Observatories; CINES Archiving Platform; Plateforme Universitaire de Données de Caen (PUDC)","V. Rebouillat; Dicen-IDF, Conservatoire National des Arts et Métiers, Paris, 292 Rue Saint-Martin, 75003, France; email: violaine.rebouillat.auditeur@lecnam.net","Chan L.; Loizides F.","IOS Press BV","Association Computing Machinery (ACM); ELSEVIER; Emerald Publishing; et al.; Frontiers; IEEE","21st International Conference on Electronic Publishing, ELPUB 2017","6 June 2017 through 8 June 2017","Limassol","128010","","978-161499768-9","","","English","Expand. Perspect. Open Sci.: Communities, Cult. Divers. Concepts Pract. - Proc. Int. Conf. Electron. Publ.","Conference paper","Final","","Scopus","2-s2.0-85020745451"
"Federer L.","Federer, Lisa (55918619800)","55918619800","Research data management in the age of big data: Roles and opportunities for librarians","2016","Information Services and Use","36","1-2","","35","43","8","35","10.3233/ISU-160797","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996841792&doi=10.3233%2fISU-160797&partnerID=40&md5=0de8782f69afa4c5ffc9d2e03819dcf3","National Institute of Health Library, Division of Library Services, Office of Research Services, National Institutes of Health, 10 Center Drive, Bethesda, 20892, MD, United States","Federer L., National Institute of Health Library, Division of Library Services, Office of Research Services, National Institutes of Health, 10 Center Drive, Bethesda, 20892, MD, United States","In the age of ""big data,"" scientific researchers are increasingly struggling with how to manage, organize, and make sense of the vast amount of data that often characterizes scientific research in the 21st century. With their expertise in knowledge management, information professionals can be valuable collaborators for research teams facing these challenges. This article discusses just a few of the myriad opportunities for librarians and other information professionals to become involved with research teams and provide valuable support for the management, analysis, and preservation of research data. © 2016 IOS Press and the authors.","big data; data reuse; data sharing; Research data management","Human resource management; Information management; Information services; Knowledge management; Libraries; Data reuse; Data Sharing; Information professionals; Research data; Research data managements; Research teams; Scientific researches; Big data","","","","","","","Alvaro N., Conway M., Doan S., Lofi C., Overington J., Collier N., Crowdsourcing twitter annotations to identify first-hand experiences of prescription drug use, Journal of Biomedical Information, 58, pp. 280-287, (2015); Science: Editorial Policies [Internet], (2016); Beagrie N., Houghton J., The Value and Impact of Data Sharing and Curation [Internet], (2014); Carbonell P., Mayer M.A., Bravo A., Exploring brand-name drug mentions on twitter for pharmacovigilance, Studies in Health Technology and Informatics, 210, pp. 55-59, (2015); Charles-Smith L.E., Reynolds T.L., Cameron M.A., Conway M., Lau E.H., Olsen J.M., Pavlin J.A., Shigematsu M., Streichert L.C., Suda K.J., Corley C.D., Using social media for actionable disease surveillance and outbreak management: A systematic literature review, PloS One, 10, 10, (2015); Check H.E., Technology: The $1,000 genome, Nature, (2014); Coorevits P., Sundgren M., Klein G.O., Bahr A., Claerhout B., Daniel C., Dugas M., Dupont D., Schmidt A., Singleton P., De Moor G., Kalra D., Electronic health records: New opportunities for clinical research, Journal of Internal Medicine, 274, 6, pp. 547-560, (2013); DCC Curation Lifecycle Model [Internet], (2016); Data ONE: Data Observation Network for Earth [Internet], (2016); Dobre C., Xhafa F., Parallel programming paradigms and frameworks in big data era, International Journal of Parallel Programming, 42, 5, pp. 710-738; Dryad, Dryad Digital Repository, (2016); Federer L.M., Lu Y.-L., Joubert D.J., Data literacy training needs of biomedical researchers, Journal of the Medical Library Association, 104, 1, pp. 52-57, (2016); Federer L.M., Lu Y.-L., Joubert D.J., Welsh J., Brandys B., Biomedical data sharing and reuse: Attitudes and practices of clinical and scientific research staff, PloS One, 10, 6, (2015); Figshare, Figshare, (2016); Finley K., Google Just Made Near-infinite Storage Cheap and Easy, (2015); Gesualdo F., Stilo G., D'Ambrosio A., Carloni E., Pandolfi E., Velardi P., Fiocchi A., Tozzi A.E., Can twitter be a source of information on allergy? Correlation of pollen counts with tweets reporting symptoms of allergic rhinoconjunctivitis and names of antihistamine drugs, PloS One, 10, 7, (2015); Holdren J.P., Increasing Access to the Results of Federally Funded Scientific Research [Internet], (2013); IBM Big Data & Analytics Hub, the Four v'S of Big Data [Internet], (2016); Bringing Big Data to the Enterprise [Internet], (2016); Institute of Museum and Library Services, Talking Points: Museums, Libraries, and Makerspaces, (2014); Longo D.L., Drazen J.M., Data sharing, New England Journal of Medicine, 374, 3, pp. 276-277, (2016); Maldarelli C., We can now sequence a whole human genome in 26 hours, Popular Science, (2015); Morris Z.S., Wooding S., Grant J., The answer is 17 years, what is the question: Understanding time lags in translational research, Journal of the Royal Society of Medicine, 104, 12, pp. 510-520, (2011); Formatting your submission, The Gen-Bank Submissions Handbook, (2014); National Human Genome Research Institute, The Human Genome Project Completion: Frequently Asked Questions, (2010); National Institutes of Health, National Institutes of Health Plan for Increasing Access to Scientific Publications and Digital Scientific Data from NIH Funded Scientific, Research 2015, (2016); National Institutes of Health Data and Informatics Working Group, Draft Report to the Advisory Committee to the Director, (2012); National Institutes of Health Library, NIH Library: Data Services, (2016); Finance and Award Management, Dissemination and Sharing of Research Results, (2011); Policies: Availability of Data, Material and Methods, (2016); Piwowar H.A., Who shares? Who doesn't? Factors associated with openly archiving raw research data, PloS One, 6, 7, (2011); Registry of Research Data Repositories, (2016); Silva L., EveryONE: PLoS One Community Blog, (2014); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Mouse Genome Informatics (MGI), (2016); Research Data Lifecycle, (2016); DMPTool, (2016); Data Repository for the U of M (DRUM), (2016); Steps in the Data Life Cycle, (2016); Wetterstrand K.A., DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP), (2016)","L. Federer; National Institute of Health Library, Division of Library Services, Office of Research Services, National Institutes of Health, Bethesda, 10 Center Drive, 20892, United States; email: lisa.federer@nih.gov","","IOS Press","","","","","","01675265","","ISUSD","","English","Inf Serv Use","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-84996841792"
"Von Schwerin J.; Lyons M.; Loos L.; Billen N.; Auer M.; Zipf A.","Von Schwerin, Jennifer (54882346400); Lyons, Mike (57191927572); Loos, Lukas (55944804000); Billen, Nicolas (55955588100); Auer, Michael (56956504900); Zipf, Alexander (7004007248)","54882346400; 57191927572; 55944804000; 55955588100; 56956504900; 7004007248","Show me the data!: Structuring archaeological data to deliver interactive, transparent 3D reconstructions in a 3D WebGIS","2016","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10025 LNCS","","","198","230","32","6","10.1007/978-3-319-47647-6_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994844580&doi=10.1007%2f978-3-319-47647-6_10&partnerID=40&md5=0a864512c133653f80d27ca24b8defb6","Commission for the Archaeology of Non-European Cultures (KAAK), German Archaeological Institute, Bonn, Germany; Institute of Geography, Heidelberg University, Heidelberg, Germany","Von Schwerin J., Commission for the Archaeology of Non-European Cultures (KAAK), German Archaeological Institute, Bonn, Germany; Lyons M., Commission for the Archaeology of Non-European Cultures (KAAK), German Archaeological Institute, Bonn, Germany; Loos L., Institute of Geography, Heidelberg University, Heidelberg, Germany; Billen N., Institute of Geography, Heidelberg University, Heidelberg, Germany; Auer M., Institute of Geography, Heidelberg University, Heidelberg, Germany; Zipf A., Institute of Geography, Heidelberg University, Heidelberg, Germany","Creating 3D reconstructions is a common approach today in archaeology and cultural heritage. The problem is that 3D models in online virtual research environments may tempt users to believe them as historical truth. What must be done to enable the public to view a 3D reconstruction as a hypothesis and have access to the supporting data? This paper explains – via use-case examples from the ancient Maya city of Copan, Honduras – a procedure for structuring heterogeneous data to enable interactive, web-based access to 3D reconstructions of cultural heritage. A prototype 3D WebGIS system was built that can store, manage, and visualize 3D models and integrates these with georeferenced archaeological data. An ontology was created, a segmentation pipeline was developed, and databases and services were designed to structure and integrate the data in the 3D WebGIS. Results include two interactive 3D reconstructions: a city model and a temple model – these demonstrate how proper data structuring can deliver transparent models for archaeological argumentation. © Springer International Publishing AG 2016.","3D digital reconstructions; 3D webgis; Copan; Maya archaeology; Maya architecture; Metadata; Ontologies; Paradata; Research data management; Semantic data structuring; Virtual research environments","History; Image reconstruction; Information management; Metadata; Ontology; Repair; Semantics; Copan; Digital reconstruction; Maya archaeology; Paradata; Research data managements; Semantic data; Virtual research environment; Web-GIS; Three dimensional computer graphics","","","","","Universität Heidelberg; Bundesministerium für Bildung und Forschung, BMBF","This research has been funded by the German Federal Ministry of Education and Research (BMBF) in the funding program e-Humanities in a grant to the German Archaeological Institute and the University of Heidelberg from 2012–2015. Project coordinator, Markus Reindel, provided valuable guidance and collaboration. Special thanks go to the Honduran Institute of Anthropology and History for permission to work at Copan. Thanks to Reinhard Förtsch and Philipp Gerth for collaborative work and support with iDAI.field and other modules of iDAI.world. Heather Richards-Rissetto, Department of Anthropology and Center for Research in the Digital Humanities (CDRH) at the University of Nebraska-Lincoln kindly shared her Copan city model for the project and was involved with early discussions about ontologies and the importance of transparency and uncertainty. Fabio Remondino and Belen Jimenez Fernandez of the 3D Optical Metrology Unit, Bruno Kessler Foundation collected and processed much of the 3D data for the project, including the data for Structure 10L-18. Barbara Fash of Harvard University, and Karina Garci, and Reyna Flores in Copan kindly helped with access to the archival materials about Structure 10L-18 excavations. Elisabeth Wagner of the University of Bonn generously shared her theories on the original appearance of the structure. Martin Doerr and George Bruseker of FORTH, and Nicola Carboni of CNRS worked to map our database into CIDOC-CRM. Rossella Suma, University of Warwick assisted with post-processing. ","Frischer B., Dakouri-Hild A., Beyond illustration: 2D and 3D digital technologies as tools for discovery in archaeology, BAR International Series, 1805, (1999); Bentkowska-Kafel A., Denard H., Baker D., Paradata and Transparency in Virtual Heritage, (2012); Von Schwerin J., Richards-Rissetto H., Remondino F., Agugiaro G., Girardi G., The MayaArch3D project: A 3D WebGIS for analyzing ancient architecture and landscapes. Literary and Linguistic Computing, Spec. Iss. Digital Humanit. 2012: Digital Divers. Cult. Lang. Methods, 28, 4, pp. 736-753, (2013); Ahlfeldt J., On Reconstructing and Performing Ancient Maya Architecture: Structure 22. Copan Honduras (AD 715) UMI Microform 3128915, (2004); Pillsbury J., Past Presented at the History of Archaeological Illustration, (2012); Proskouriakoff T., An Album of Maya Architecture, (1946); Miller P., Richards J., The good, the bad and the downright misleading: Archaeological adoption of computer visualization, CAA94 Proceedings of the 22Nd CAA Conference, (1994); Frankland T., Graeme E., Authority and authenticity in future archaeological visualisation, Proceedings of Ads-Vis2011: Making Visible the Invisible: Art, Design and Science in Data Visualisation, pp. 62-68, (2011); Lengyel D., Schock-Werner B., Toulouse C., Die Bauphasen Des Kölner Domes Und Seiner Vorgängerbauten, Cologne Cathedral and Preceding Buildings, (2011); Jansen P., Paap I., Dzehkabtún 3D-leventamiento topográfico y reconstrucción virtual de un sitio maya, Los Investigadores De La Cultura Maya, (2014); Forte M., Pescarin S., Pietroni E., The Appia antica project, The Reconstruction of Archaeological Landscapes through Digital Technologies. In: Proceedings of the 2Nd Italy-United States Workshop, 1379, pp. 79-91, (2005); Georgiou R., Hermon S., A London Charter’s visualization: The ancient hellenistic-roman theatre in Paphos, VAST: International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage-Short and Project Papers, pp. 53-56, (2011); Manferdini A., Remondino F., Baldissini S., Gaiani M., Benedetti, B.: 3D modeling and semantic classification of archaeological finds for management and visualization in 3D archaeological databases, Proceedings of 14Th VSMM Conference, Limassol, Cyprus, (2008); Manferdini A.M., Remondino F., A review of reality-based 3D model generation, segmentation and web-based visualization methods, Int. J. Heritage Digital Era, 1, 1, pp. 103-123, (2012); Agugiaro G., Remondino F., Girardi G., Von Schwerin J., Richards-Rissetto H., De Amicis R., QueryArch3D: Querying and visualising three-dimensional archaeological models in a web-based interface, Geoinformatics; De Luca L., 3D modelling and semantic enrichment in cultural heritage, Photogrammetric Week 2013, pp. 323-333, (2013); Kuroczynski P., Poster: Digital 3D Reconstructions in Virtual Research Environments. the Portal: Palaces and Parks in Former East Prussia; De Kleijn M., De Hond R., Martinez-Rubi O., Mapping the Via Appia. Poster, Computer Applications in Archaeology Meetings, Siena; Auer M., Agugiaro G., Billen N., Loos L., Zipf A., Web-based visualization and query of semantically segmented multiresolution 3D models in the field of cultural heritage. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-5, pp. 33-39, (2014); Billen N., Auer M., Loos L., Agugiaro G., Zipf A., MayaArch3D: An integrative analytical platform for 3D archaeological data, Proceedings of the 4Th Conference on Scientific Computing and Cultural Heritage (SCCH 2013), (2013); Loos L., Auer M., Billen N., Zipf A., MayaArch3D –a 3D webgis for archaeological research, Proceedings of Digital Geoarchaeology 2013, (2013); Reindel M., Isla J., Otten H., Gorbahn H., Von Schwerin J., Archäologische Forschungen in Peru und Honduras im Jahr 2013, Zeitschrift für Archäologie Außereuropäischer Kulturen, 6, pp. 289-308, (2014); Reindel M., Isla J., Otten H., Gorbahn H., Von Schwerin J., Archäologische Forschungen in Peru und Honduras, Zeitschrift für Archäologie Außereuropäischer Kulturen, 5, pp. 297-313, (2013); Von Schwerin J., Reindel M., Auer M., Billen N., Loos L., Zipf A., Ein webbasiertes 3D-GIS zur Analyse der Archäologie von Copan, Honduras, Proceedings of the Digital Humanities Summit 2015; Von Schwerin J., Richards-Rissetto H., Remondino F., Grazia Spera M., Auer M., Billen N., Loos L., Stelson L., Reindel M., Airborne LiDAR acquisition, post-processing and accuracy-checking for a 3D WebGIS of Copan, Honduras, J. Archaeol. Res. Rep, 5, pp. 85-104, (2016); Fash W., Scribes, Warriors, and Kings: The City of Copan and The Ancient Maya, (2001); Webster D., Freter A., Gonlin N., Copan: The Rise and Fall of an Ancient Maya Center, (2002); Baudez C.-F., Introducción a La Arqueología De Copán, Honduras. Proyecto Arqueológico Copán, Secretaria De Estado En El Despacho De Cultura Y Turismo, Tegucigalpa, DC, pp. 108-134, (1983); Richards-Rissetto H., From mounds to maps to models: Visualizing ancient architecture across landscapes, Proceedings of Digital Heritage International Congress, (2013); Becker M.J., Cheek C.D., La Estructura 10L-18, Proyecto Arqueológico Copán, pp. 382-500, (1983); Hohmann H., Vogrin A., The Architecture of Copan (Honduras), Measurements, Plans, Investigation of Structural Elements and Spatial Concepts (In German), (1982); Remondino F., Gruen A., Von Schwerin J., Eisenbeiss H., Rizzi A., Girardi S., Sauerbier M., Richards H., Multi-Sensor 3D Documentation of the Maya Site of Copan. XXII CIPA Symposium, (2009); Gerth P., Idai.Field-Archaeological Field Research Database: A Modular Documentation System for Field Projects, (2015); Loten S.H., Pendergast D.M., A Lexicon for Maya Architecture, (1984); Schilling A., Kolbe T.H., (2010); Stelson L., Bruseker G., Von Schwerin J., A public database and digital research tool for maya iconography, European Association of Mayanists, (2015)","J. Von Schwerin; Commission for the Archaeology of Non-European Cultures (KAAK), German Archaeological Institute, Bonn, Germany; email: jennifer.vonschwerin@dainst.de","Pfarr-Harfst M.; Munster S.; Kuroczynski P.; Ioannides M.","Springer Verlag","","5th International Euro-Mediterranean Conference on Cultural Heritage, EuroMed 2014","3 November 2014 through 8 November 2014","Limassol","185989","03029743","978-331947646-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-84994844580"
"Fransson J.; Lagunas P.T.; Kjellberg S.; Toit M.D.","Fransson, Jonas (57193623205); Lagunas, Pablo Tapia (55589475500); Kjellberg, Sara (35096401100); Toit, Madeleine du (57193622180)","57193623205; 55589475500; 35096401100; 57193622180","Developing integrated research data management support in close relation to doctoral students' research practices","2016","Proceedings of the Association for Information Science and Technology","53","1","","1","4","3","3","10.1002/pra2.2016.14505301094","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015311318&doi=10.1002%2fpra2.2016.14505301094&partnerID=40&md5=9fa8f47b59b7ebe33b6a454add5a98fb","Malmö University Library, Sweden","Fransson J., Malmö University Library, Sweden; Lagunas P.T., Malmö University Library, Sweden; Kjellberg S., Malmö University Library, Sweden; Toit M.D., Malmö University Library, Sweden","The quest for open research data is the driving force behind the development of the whole area of research data management practices. We, as a university library, offer and develop support to researchers and doctoral students. Based on the result of a web survey submitted to all researchers at Malmö University, and the knowledge that doctoral students are on their way of forming their individual research practices, we have made doctoral students our first target group for specific seminars and workshops promoting conscious research data management practices. We will organise these seminars and workshops, which both take into account the general aspects of research data management and the discipline specific practices, so as to develop integrated research data management support in close relation to doctoral students' research practices. Copyright © 2016 by Association for Information Science and Technology","doctoral students; RDM-support; Research data management; research practices; training","Information management; Research and development management; Doctoral student; Driving forces; Integrated research; Management practises; Management support; RDM-support; Research data; Research data managements; Research practice; Student research; Students","","","","","","","Amsen E., Guide to open science publishing, F1000Research; Borgman C.L., Big data, little data, no data: scholarship in the networked world, (2015); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: a guide to good practice, (2014); Guidelines on Data Management in Horizon 2020, (2015); Gullbekk E., Rullestad T., Calvo M.C.T., PhD candidates and the research process. The library's contribution, (2013); Jones W.P., Keeping found things found: the study and practice of personal information management, (2008); Michener W.K., Ten simple rules for creating a good data management plan, Plos Computional Biology, 11, 10, (2015); Nielsen H.J., Hjorland B., Curating research data: the potential roles of libraries and information professionals, Journal of Documentation, 70, 2, pp. 221-240, (2014); Principles and Guidelines for Access to Research Data from Public Funding, (2007); Peixoto A., De mest lämpade: en studie av doktoranders habituering på det vetenskapliga fältet.[Survival of the fittest – A study of doctoral students' habituation on the scientific field], (2014); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library & Information Science Research, 36, 2, pp. 84-90, (2014); MANTRA Research Data Management Training, (2014); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, Journal of Academic Librarianship, 40, 3-4, pp. 211-219, (2014); Förslag till Nationella riktlinjer för öppen tillgång till vetenskaplig information, (2015)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85015311318"
"Lipton V.","Lipton, Vera (57194140779)","57194140779","Open data for open science: Aspirations, realities, challenges and opportunities","2016","Open Innovation: A Multifaceted Perspective","1","","","33","65","32","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018825473&partnerID=40&md5=cf8c0b4b210c7402840b88b526f555fb","Thomas More Law School, Australian Catholic University, Australia","Lipton V., Thomas More Law School, Australian Catholic University, Australia","This chapter examines the emergent approaches to online sharing of the data underpinning scientifi c publications. Such data is referred to as open scientifi c data. Four key drivers are identifi ed: (i) Changing societal approaches and increasing expectations that the outcomes of publicly-funded research be freely available, (ii) the emergence of data journals; (iii) changing policies of research funders and publishers; and (iv) the requirements for research organisations and individual researchers to comply with these policies and introduce research data management plans. In this chapter, the European Organisation for Nuclear Research (CERN) is used as a case study to show how open scientifi c data can be successfully managed and used, in innovative ways, to deliver open education in particle physics. Finally, this chapter identifi es three challenges that require attention of research funders and scientifi c organisations. First, the challenge to devise a minimum standard for open scientifi c data. Second, the challenge to develop incentives for researchers and librarians, and third, the challenge to reconceptualise the ownership of open scientifi c data. This chapter argues that addressing these challenges can advance the preservation and sharing of open scientifi c data for the global public good. © 2016 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.","Digital science; E-science; Open access to knowledge; Open data; Open research; Open science; Research data management; The european organisation for nuclear research","","","","","","","","Rogers J., Genome Sequencing: Wellcome News? Frontiers 03: New Writing on Cutting-Edge Science by Leading Scientists, (2003); Lessig L., Code 2.0, (2006); Gasser U., Faris R., Heacock R., Internet Monitor 2013: Reflections on the Digital World, Berman Center for Internet and Society Research Publication No. 27, (2013); Cribb J., The Case for Open Science, Broadcast for ABC Radio National Ockham's Razor, (2010); Meho L., Yang K., Impact of Data Source on Citation Counts and Rankings of LIS Faculty: Web of Science Versus Scopus and Google Scholar, Journal of the American Society for Information Science and Technology, 58, 13, pp. 2015-2125, (2007); Lemley M.A., Shapiro C., Probabilistic patents, Journal of Economic Perspectives, 19, 2, pp. 75-98, (2005); Cribb J., The Case for Open Science, Broadcast for ABC Radio National Ockham's Razor, (2010); Piwowar H.A., Day R.S., Frisma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, 3, (2007); Roy A.S.A., Stifling New Cures: The True Cost of Lengthy Clinical Drug Trials, FDA Report, (2012); Oldenburg H., Philosophical Transactions of the Royal Society, 1, 1, (1665); Davidson J., Mastering Digital Librarianship, pp. 82-102, (2014); Lassila-Perini K.G., Alverson I., Cabrillo A., Calderon D., Colling M., Hildreth A., Huffman T., Lampen P., Lukka J., Marco T., McCauley T., Sonnenschein L., Implementing the data preservation and open access policy at CMS., 20th International Conference on Computing in High Energy and Nuclear Physics, IOP Publishing Journal of Physics: Conference Series, 513, (2014); Stodden V., Reproducible Research in Computational Science: Problems and Solutions For Data and Code Sharing, (2010); Piwowar H.A., Altmetrics: Value all research products, Nature, 493, (2013); Research Performance of University Patenting in Australia, Report Commissioned by the Department of Industry, (2013); Piwowar H.A., Altmetrics, (2013); Piwowar H.A., Day R.S., Frisma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, 3, (2007); Levine M., Research Data Management: Practical Strategies for Information Professionals, pp. 120-148, (2014); Wilbanks J., Privacy, Big Data and the Public Good, (2014); Bambauer J.R., Tragedy of the data commons, Harvard Journal of Law and Technology, 25, pp. 1-67, (2011)","V. Lipton; Thomas More Law School, Australian Catholic University, Australia; email: vera.lipton@bigpond.com","","World Scientific Publishing Co. Pte. Ltd.","","","","","","","978-981471918-6; 978-981471917-9","","","English","Open Innov.: A Multifaceted Perspect.","Book chapter","Final","","Scopus","2-s2.0-85018825473"
"Curdt C.","Curdt, Constanze (36681871300)","36681871300","Metadata management in an interdisciplinary, project-specific data repository: A case study from earth sciences","2016","Communications in Computer and Information Science","672","","","357","368","11","2","10.1007/978-3-319-49157-8_31","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85000730512&doi=10.1007%2f978-3-319-49157-8_31&partnerID=40&md5=737ef79d76eb87f0e85f8b1e7ff3c85a","University of Cologne, Cologne, Germany","Curdt C., University of Cologne, Cologne, Germany","This paper presents an approach to manage metadata of (research) data from the interdisciplinary, long-term, DFG-funded, collaborative research project ‘Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation’. In this framework, a data repository, the so-called TR32DB project database, was established in 2008 with the aim to manage the resulting data of the involved scientists. The data documentation with accurate, extensive metadata has been a key task. Consequently, a standardized, interoperable, multi-level metadata schema has been designed and implemented to ensure a proper documentation and publication of all project data (e.g. data, publication, reports), as well as to facilitate data search, exchange and re-use. A user-friendly web-interface was designed for a simple metadata input and search. © Springer International Publishing AG 2016.","Data repository; Interdisciplinary project; Metadata; Metadata schema; Research data management","Metadata; Semantics; Collaborative research projects; Data documentation; Data repositories; Interdisciplinary project; Metadata management; Metadata schema; Research data managements; Soil vegetation atmospheres; Information management","","","","","Deutsche Forschungsgemeinschaft, DFG","The presented study is conducted for the Collaborative Research Centre/Transregio (CRC/TR) 32 ‘Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation’ ( www.tr32.de ) funded by the German Research Foundation (DFG) since 2007. The CRC/TR32 is an interdisciplinary, long-term research project in the area of earth sciences with several research groups from the German Universities of Aachen, Bonn and Cologne, and from the Research Centre Jülich. The involved scientists focus their work on exchange processes between the soil, vegetation and atmospheric (SVA) boundary layer. Their aim is to yield improved numerical SVA models to predict water, energy and CO transfer by calculating patterns at various spatial and temporal scales []. To achieve this goal, scientists from the field of soil- and plant sciences, hydrology, geography, geophysics, meteorology, remote sensing and mathematics are involved. The scientists conduct their research within the river Rur catchment, mainly situated in western Germany. 2 ","Corti L., Van Den Eynden V., Bissell A., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Ma J., Managing metadata for digital projects, Libr. Collect. Acquisitions Techn. Serv, 30, pp. 3-17, (2006); Greenberg J., Swauger S., Feinstein E., Metadata capital in a data repository, International Conference on Dublin Core and Metadata Applications, (2013); Karasti H., Baker K.S., Millerand F., Infrastructure time: Long-term matters in collaborative development, Comput. Support. Coop. Work, 19, pp. 377-415, (2010); Berman F., Got data? A guide to data preservation in the information age, Commun. ACM, 51, pp. 50-56, (2008); Mayernik M.S., Metadata realities for cyberinfrastructure: Data authors as metadata creators, Iconference 2010, (2010); Miller S.J., Metadata for Digital Collections: A How-To-Do-It Manual, (2011); Qin J., Ball A., Greenberg J., Functional and architectural requirements for metadata: Supporting discovery and management of scientific data, The Kuching Proceedings of DCMI International Conference on Dublin Core and Metadata Applications DC-2012, pp. 62-71, (2012); Farnel S., Shiri A., Metadata for research data: Current practices and trends, DCMI International Conference on Dublin Core and Metadata Applications DC-2014–The Austin Proceedings, pp. 74-82, (2014); Krause E.M., Clary E., Ogletree A., Greenberg J., Evolution of an application profile: Advancing metadata best practices through the dryad data repository, DCMI International Conference on Dublin Core and Metadata Applications DC-2015–The São Paulo Proceedings, pp. 63-75, (2015); Wira-Alam A., Dimitrov D., Zenk-Moltgen W., Extending basic dublin core elements for an open research data archive, DCMI International Conference on Dublin Core and Metadata Applications DC-2012–The Kuching Proceedings, pp. 56-61, (2012); Klump J., Ulbricht D., Conze R., Curating the web’s deep past – migration strategies for the German Continental Deep Drilling Program web content, Georesj, 6, pp. 98-105, (2015); Gerstner E.-M., Bachmann Y., Hahn K., Lykke A.M., Schmidt M., The west african data and metadata repository: A long-term data archive for ecological datasets from west africa, Flora Et Vegetatio Sudano-Sambesica, 18, pp. 3-10, (2015); Cordero-Llana L., Ramage K., Law K.S., Keckhut P., LABEX L-IPSL arctic metadata portal, Data Sci. J., 15, pp. 1-11, (2016); Takeda K., Brown M., Coles S., Carr L., Earl G., Frey J., Hancock P., White W., Nichols F., Whitton M., Gibbs H., Fowler C., Wake P., Patterson S., Data management for all: The institutional data management blueprint project, 6Th International Digital Curation Conference, (2010); Jensen U., Katsanidou A., Zenk-Moltgen W., Metadaten und standards, Handbuch Forschungsdatenmanagement, pp. 83-100, (2011); Simmer C., Thiele-Eich I., Masbou M., Amelung W., Bogena H., Crewell S., Diekkruger B., Ewert F., Franssen H.-J.H., Huisman J.A., Kemna A., Klitzsch N., Kollet S., Langensiepen M., Lohnert U., Rahman A.S.M.M., Rascher U., Schneider K., Schween J., Shao Y., Shrestha P., Stiebler M., Sulis M., Vanderborght J., Vereecken H., Kruk J.V.D., Waldhoff G., Zerenner T., Monitoring and modeling the terrestrial system from pores to catchments: The transregional collaborative research center on patterns in the soil–vegetation– atmosphere system, Bull. Am. Meteorol. Soc, 96, pp. 1765-1787, (2015); Curdt C., Hoffmeister D., Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DB project database, Program, 49, pp. 494-512, (2015); Curdt C., TR32DB Metadata Schema for the Description of Research Data in the TR32DB, (2014); Backes M., Dorschlag D., Plumer L., Landwirtschaftliche Geodaten - Nachhaltige Datenhaltung Und -Nutzung Durch ISO Standards, pp. 18-23, (2005); Gottlicher D., Bendix J., Eine Modulare Multi-User Datenbank für Eine ökologische Forschergruppe Mit Heterogenem Datenbestand, pp. 95-103, (2004); Shumilov S., Rogmann A., Laubach J., GLOWA Volta GeoPortal: An interactive geodata repository and communication system, Digital Earth Summit on Geoinformatics 2008: Tools for Global Change Research, pp. 363-368, (2008); Duval E., Hodgins W., Sutton S., Weibel S.L., Metadata principles and practicalities, Dlib Mag, 8, (2002); Datacite Metadata Schema for the Publication and Citation of Research Data, (2011); Commission E., Commission regulation (EC) No 1205/2008 of 3 December 2008 implementing directive 2007/2/EC of the European Parliament and of the Council as regards metadata, Official J. Eur. Union L, 326, pp. 1-19, (2008); Foulonneau M., Riley J., Metadata for Digital Resources: Implementation, Systems Design and Interoperability, (2008)","C. Curdt; University of Cologne, Cologne, Germany; email: c.curdt@uni-koeln.de","Garoufallou E.; Coll I.S.; Stellato A.; Greenberg J.","Springer Verlag","","10th International Conference on Metadata and Semantics Research, MTSR 2016","22 November 2016 through 25 November 2016","Gottingen","186869","18650929","978-331949156-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85000730512"
"Kvale L.; Stangeland E.","Kvale, Live (57195713528); Stangeland, Elin (57200317077)","57195713528; 57200317077","Skills for research data management – developing RDM courses at the university of Oslo","2017","Proceedings of the Association for Information Science and Technology","54","1","","728","730","2","2","10.1002/pra2.2017.14505401134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040769550&doi=10.1002%2fpra2.2017.14505401134&partnerID=40&md5=3751acbe358b466ec46f60c769d4c130","University of Oslo, Norway; Oslo and Akershus University College of Applied Sciences, Norway","Kvale L., University of Oslo, Norway; Stangeland E., Oslo and Akershus University College of Applied Sciences, Norway","How can the library contribute to developing research data services by focusing on training? This poster presents experiences and best practices with organizing research data management (RDM) training through the University of Oslo Library. The work is based on recommendations from the report “The Data Explosion –A Great Challenge and a Big Opportunity” that mapped existing data practices at the University of Oslo (UiO) (Working group storing and sharing of research data UiO, 2015). Based on these recommendations, a division of labor among the research support services at the university was decided upon and the university library (UL) was tasked with developing courses and training for better data practices. By establishing contact with existing initiatives, inviting in experts and holding meetings for library staff to create ownership and interest in the subject the library now has a portfolio of courses in data practices and data management that is constantly expanding. Copyright © 2017 by Association for Information Science and Technology","digital skills; RDM skills; research data management","Curricula; E-learning; Human resource management; Personnel training; Data practices; Data services; Digital skills; Management course; Management skills; Research data; Research data management skill; Research data managements; University of Oslo; Information management","","","","","","","H2020 Programme - Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2016); Tilgjengeliggjôring av forskningsdata - policy for Norges Forskningsråd, (2014); Hey T., Tansley S., Tolle K., The fourth paradigm: Data-intensive scientific discovery, x0026;, (Eds.), (2009); Wilson G., Software carpentry: Lessons learned [version 1; referees: 3 approved], F1000Research, 3, (2014); The data explosion – a major challenge, and a great opportunity! Oslo: University of Oslo, (2015)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","","Scopus","2-s2.0-85040769550"
"Lyon L.","Lyon, Liz (56835287100)","56835287100","Librarians in the Lab: Toward Radically Re-Engineering Data Curation Services at the Research Coalface","2016","New Review of Academic Librarianship","22","4","","391","409","18","12","10.1080/13614533.2016.1159969","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962383284&doi=10.1080%2f13614533.2016.1159969&partnerID=40&md5=5b5166bfab8185c8d78768883b03cb3d","School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States","Lyon L., School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States","This article presents a case study where students aspiring to professional library roles who need to understand diverse disciplinary research data practices are placed in a laboratory with domain researchers during an immersive module within graduate MLIS programs at the School of Information Sciences (iSchool), University of Pittsburgh. A qualitative analysis of evaluation commentary from faculty researchers and MLIS students demonstrates their positive bilateral learning experiences. The potential extension of the immersive model for the delivery of research data services directly to researchers at their point of need is explored and a connection made with the established concept of an informationist, as a medical library specialist working in a clinical setting. The re-engineering challenges for academic libraries in operationalizing the immersive model for research data services are articulated, together with the challenges for iSchools in building workforce capacity and capability for immersive team science. © 2016, © Published with license by Taylor & Francis Group, LLC.","academic library service models; data curation education; immersive team science; research data management","","","","","","","","Akers K.G., Doty J., Disciplinary differences in research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Alexogiannopoulos E., McKenney S., Pickton M., Research Data Management project: A DAF investigation of research management practices at the University of Northampton, (2010); Auckland M., Reskilling for research. RLUK Report, (2012); Averkamp S., Gu X., Rogers B., Data management at the University of Iowa: A University Libraries Report on Campus Research Data Needs, (2014); Borgman C.L., Big data, little data, no data, (2015); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, Australian Library Journal, 64, 3, pp. 224-234, (2015); Carlson J., Kneale R., Embedded librarianship in the research context, C&RL News, pp. 167-170, (2011); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Davidoff F., Florance V., The Informationist: A new health profession?, Annals Internal Medicine, 132, 12, pp. 996-998, (2000); Davis H.M., Cross W.M., Using a data management plan review service as a training ground for librarians, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Detlefsen E.G., The education of informationists, from the perspective of a library and information sciences educator, Journal of the Medical Library Association, 90, 1, pp. 59-67, (2002); Embed; Ferguson J., Lurking in the lab: Analysis of data from molecular biology laboratory instruments, Journal of eScience Librarianship 1, pp. 148-158, (2012); Florance V., Informationist careers for librarians–A brief history of NLM's involvement, Journal of eScience Librarianship, 2, 1, pp. 3-5, (2013); Guise N.B., Advancing the practice of clinical medical librarianship (Editorial), Bulletin of the Medical Library Association, 85, 4, pp. 437-438, (1997); Guise N.B., Kafantaris S.R., Miller M.D., Wilder K.S., Martin S.L., Sathe N.A., Campbell J.D., Clinical medical librarianship: The Vanderbilt experience, Bulletin of the Medical Library Association, 86, 3, pp. 412-416, (1998); Hiom D., Fripp D., Gray S., Snow K., Steer D., Research data management at the University of Bristol: charting a course from project to service, Program, 49, 4, pp. 475-493, (2015); Knight G., Research Data Management at LSHTM: Web Survey Report, (2012); Larsen R., What can we learn from the data?, (2014); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, pp. 126-138, (2012); Lyon L., Reflections & challenges. Preparing the workforce for digital curation: the iSchool Perspective, (2014); Lyon L., Brenner A., Bridging the data Talent Gap–positioning the iSchool as an Agent for Change, International Journal of Digital Curation, 10, 1, pp. 111-122, (2015); Lyon L., Mattern E., Acker A., Langmead A., Applying translational principles to data science curriculum development, (2015); Lyon L., Webster K., Embedding immersive informatics research data management within the iSchool curriculum: A laboratory-based action research case study (Abstract), Library Research Seminar VI, (2014); Marchionini G., Research Data Stewardship at UNC, (2012); Martin E.R., Highlighting the informationist as a data librarian embedded in a research team (Editorial), Journal of eScience Librarianship, 2, 1, pp. 1-2, (2013); Mayernik M., Thompson C.A., Williams V., Allard S., Palmer C.L., Tenopir C., Enriching education with exemplars in practice: Iterative development of data curation internships, International Journal of Digital Curation, 10, 1, pp. 123-134, (2015); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, 12, (2014); Plutchak T.S., Informationists and librarians (Editorial), Bulletin of the Medical Library Association, 88, 4, pp. 391-392, (2000);  2015, (2015); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program, 49, 4, pp. 440-460, (2015); Shadbolt A., Konstantelos L., Lyon L., Guy M., Delivering innovative RDM training: the immersiveInformatics Pilot Programme, International Journal of Digital Curation, 9, 1, pp. 313-323, (2014); Shumaker D., The embedded librarian. Innovative strategies for taking knowledge where it's needed, (2012); Shumaker D., Talley M., Models of embedded librarianship final report, (2009); Simons N., Searle S., Redefining “the librarian” in the context of emerging eResearch services, (2014); Stanton J., Palmer C.L., Blake C., Allard S., Interdisciplinary data science education, (2012); Tenopir C., Birch B., Allard S., Academic libraries and research data services. Current practices and plans for the future, ACRL White Paper, (2012); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Pollock D., Dorsett K., Changes in data sharing and data reuse practices and perceptions amongst scientists worldwide, PLoS ONE, 10, 8, (2015); Webster K., The evolving role of libraries in the scholarly ecosystem, Academic and Professional Publishing, pp. 315-335, (2012); Whyte A., A pathway to sustainable research data services: From scoping to sustainability, Delivering research data management services, pp. 59-88, (2014)","L. Lyon; School of Information Sciences, University of Pittsburgh, Pittsburgh, 135 North Bellefield Avenue, 15260, United States; email: elyon@pitt.edu","","Routledge","","","","","","13614533","","","","English","New Rev. Acad. Librariansh.","Article","Final","","Scopus","2-s2.0-84962383284"
"Pinnick J.","Pinnick, Jaana (57195055519)","57195055519","Exploring digital preservation requirements: A case study from the National Geoscience Data Centre (NGDC)","2017","Records Management Journal","27","2","","175","191","16","5","10.1108/RMJ-04-2017-0009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025069356&doi=10.1108%2fRMJ-04-2017-0009&partnerID=40&md5=a6725d240886def7370b1738b32233be","Department of Informatics Directorate, British Geological Survey, Keyworth, United Kingdom","Pinnick J., Department of Informatics Directorate, British Geological Survey, Keyworth, United Kingdom","Purpose: The aim of this paper was to explore digital preservation requirements within the wider National Geoscience Data Centre (NGDC) organisational framework in preparation for developing a preservation policy and integrating associated preservation workflows throughout the existing research data management processes. This case study is based on an MSc dissertation research undertaken at Northumbria University. Design/methodology/approach: This mixed methods case study used quantitative and qualitative data to explore the preservation requirements and triangulation to strengthen the design validity. Corporate and the wider scientific priorities were identified through literature and a stakeholder survey. Organisational preparedness was investigated through staff interviews. Findings: Stakeholders expect data to be reliable, reusable and available in preferred formats. To ensure digital continuity, the creation of high-quality metadata is critical, and data depositors need data management training to achieve this. Recommendations include completing a risk assessment, creating a digital asset register and a technology watch to mitigate against risks. Research limitations/implications: The main constraint in this study is the lack of generalisability of results. As the NGDC is a unique organisation, it may not be possible to generalise the organisational findings, although those relating to research data management may be transferrable. Originality/value: This research examines the specific nature of geoscience data retention requirements and looks at existing NGDC procedures in terms of enhancing digital continuity, providing new knowledge on the preservation requirements for a number of national datasets. © 2017, © Emerald Publishing Limited.","Data centre; Digital continuity; Digital preservation; Digital repository; Geoscience data management","","","","","","Natural Environment Research Council, NERC, (bgs05014)","","Baker G., Giles J., BGS geoscience integrated database system: a repository for corporate data, Earthwise, 16, pp. 12-13, (2000); Ball A., Preservation and Curation in Institutional Repositories, (2010); Beagrie N., Lavoie B., Woollard M., Keeping Research Data Safe 2, (2010); Becker C., Kulovits H., Guttenbrunner M., Strodl S., Rauber A., Hofman H., Systematic planning for digital preservation: evaluating potential strategies and building preservation plans, International Journal of Digital Libraries, 10, pp. 133-157, (2009); Bowie R., BGS Geological Survey – The Legislative Framework, (2010); (2003); (2014); OGC Catalogue Service for the Web (CSW, (2016); Summary of BGS highlights for November to January 2016, (2016); BS ISO 15836 Information and Documentation – The Dublin Core Metadata Element Set, (2009); BSI ISO 14721:2012 Space Data and Information Transfer Systems. Open Archival Information System (OAIS) Reference Model, (2012); BS ISO 16363:2012, Space Data and Information Transfer Systems, Audit and Certification of Trustworthy Digital Repositories, (2012); Brown A., Practical Digital Preservation: A How-to Guide for Organisations of Any Size, (2013); Campbell J., Access to scientific data in the 21st century: rationale and illustrative usage rights review, Data Science Journal, 13, pp. 203-230, (2015); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Daniels M., Faniel I., Fear K., Yakel E., Managing fixity and fluidity in data repositories, pp. 279-286, (2012); Denscombe M., Communities of practice: a research paradigm for the mixed methods approach, Journal of Mixed Methods Research, 2, 3, pp. 270-283, (2008); Denzin N.K., Triangulation 2.0, Journal of Mixed Methods Research, 6, 2, pp. 80-88, (2012); (2016); Digital Repository Audit Method Based on Risk Assessment (DRAMBORA, (2015); (2016); Erwin T., Sweet-Kinder J., Larsgaard M.L., The national geospatial digital archives – collection development: lessons learned, Library Trends, 57, 3, pp. 490-515, (2009); (2012); Science Data Infrastructure for Preservation – Earth Science (SCIDIP-ES, (2014); (2016); Farquhar A., Hockx-Yu H., Planets: integrated services for digital preservation, International Journal of Digital Curation, 2, 2, pp. 88-99, (2007); Garrett J., Waters D., Preserving digital information: report of the task force on archiving of digital information, (1996); Hockx-Yu H., Digital preservation in the context of institutional repositories, Program, 40, 3, pp. 232-243, (2006); Hoebelheinrich N., Banning J., An Investigation Into Metadata for Long-Lived Geospatial Data Formats, (2008); (2016); Jones M., The digital preservation coalition, The Serials Librarian, 49, 3, pp. 95-104, (2006); Jones S., The range and components of RDM infrastructure and services, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 89-114, (2014); Kilbride W., Getting started in digital preservation: what do I need to know? [PowerPoint presentation], (2013); Lavoie B., The Open Archival Information System (OAIS) Reference Model: Introductory Guide, (2014); Lavoie B., Gartner R., Preservation Metadata, (2013); (2014); PREMIS Data Dictionary for Preservation Metadata, Version 3.0, (2016); (2016); McGarva G., Morris S., Janee G., Preserving geospatial data, (2009); McGovern N.Y., McKay A.C., Leveraging short-term opportunities to address long-term obligations: a perspective on institutional repositories and digital preservation programs, Library Trends, 57, 2, pp. 262-279, (2008); Morris S., Issues in the appraisal and selection of geospatial data, (2013); Geoscience Data and Collections: National Resources in Peril, (2002); (2010); A Guide to the Role of Standards in Geospatial Information Management, (2015); O'Reilly K., Ethnographic Methods, (2012); Park J.-R., Metadata quality in digital repositories: a survey of the current state of the art, Cataloging & Classification Quarterly, 47, 3-4, pp. 213-228, (2009); Park J.-R., Brenza A., Evaluation of semi-automatic metadata generation tools, Information Technology and Libraries, 34, 3, pp. 22-42, (2015); Pickard A.J., Research Methods in Information, (2013); Pryor G., Options and approaches to RDM service provision, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 21-40, (2014); Guidance on Best Practice in the Management of Research Data, (2015); RCUK Common Principles on Data Policy, (2015); DSA–WDS Partnership Working Group Catalogue of Common Requirements, (2016); Ross S., Digital preservation, archival science and methodological foundations for digital libraries, New Review of Information Networking, 17, 1, pp. 43-68, (2012); Ruusalepp R., Dobreva M., Digital Preservation Services: State of the Art Analysis, (2012); Smith I., The digital geoscience spatial model: the shape of the BGS of the future, Earthwise, 15, (2000); Sweetkind-Singer J., Laarsgaard M.L., Erwin T., Digital preservation of geospatial data, Library Trends, 55, 2, pp. 214-304, (2006); Risk Assessment Handbook, (2011); USGS Guidelines for the Preservation of Digital Scientific Data, (2014); Van den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data: Best Practice for Researchers, (2011); Whyte A., Where to Keep Research Data: DCC Checklist for Evaluating Data Repositories, (2015); Woollard M., Corti L., Case study 4: a national solution – the UK Data Service, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 191-204, (2014); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2014)","J. Pinnick; Department of Informatics Directorate, British Geological Survey, Keyworth, United Kingdom; email: jpak@bgs.ac.uk","","Emerald Group Publishing Ltd.","","","","","","09565698","","","","English","Rec. Manage. J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85025069356"
"Van Zeeland H.; Ringersma J.","Van Zeeland, Hilde (55625497700); Ringersma, Jacquelijn (6503879741)","55625497700; 6503879741","The development of a research data policy at wageningen university & research: Best practices as a framework","2017","LIBER Quarterly","27","1","","153","170","17","5","10.18352/lq.10215","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029707178&doi=10.18352%2flq.10215&partnerID=40&md5=54185e47936579a7399ce6a16a845d78","Wageningen University and Research, Netherlands","Van Zeeland H., Wageningen University and Research, Netherlands; Ringersma J., Wageningen University and Research, Netherlands","The current case study describes the development of a Research Data Management policy at Wageningen University & Research, the Netherlands. To develop this policy, an analysis was carried out of existing frameworks and principles on data management (such as the FAIR principles), as well as of the data management practices in the organisation. These practices were defined through interviews with research groups. Using criteria drawn from the existing frameworks and principles, certain research groups were identified as ‘best-practices’: cases where data management was meeting the most important data management criteria. These best-practices were then used to inform the RDM policy. This approach shows how engagement with researchers can not only provide insight into their data management practices and needs, but directly inform new policy guidelines. © 2017, Igitur, Utrecht Publishing and Archiving Services. All rights reserved.","Data archiving; Data storage; Policy; Research data; Research data management","","","","","","Digital Curation Centre; National Coordination Point Research Data Management; Australian National Data Service","To ensure that they meet the above-discussed requirements, and that their research data assets are safely guarded and fully exploited, more and more universities are implementing research data management policies. While data policies have been most prolific in the UK and Australia (Shearer, 2015), they have also been found to exist at 44% and 41% of North American and European universities, respectively (Briney, Goben, & Zilinski, 2015; Tenopir et al., 2017).1 Where policies are not yet in place, they are often planned or in the process of being established. Overviews of implemented data policies are provided and maintained by the Digital Curation Centre (DCC) for the UK (Horton & DCC, 2016), the Australian National Data Service (ANDS) for Australia (ANDS, 2017a), and the National Coordination Point Research Data Management (LCRDM) for the Netherlands (LCRDM, 2017).","Outline of a research data management policy for Australian Universities/ Institutions, (2010); ANDS project registry., (2017); Creating a data management framework, (2017); van B.M., Grootveld M.J., Het beheren van onderzoeksdata, Handboek Informatiewetenschap, (2016); Briney K., Goben A., Zilinski L., Do you have an institutional data policy? A review of the current landscape of library data services and institutional data policies, Journal of Librarianship and Scholarly Communication, 3, 2, pp. 1-25, (2015); Briney K., Goben A., Zilinski L., Institutional, funder, and journal data policies, Curating research data: Practical strategies for your digital repository, pp. 61-78, (2017); Budroni P., Sanchez Solis B., Traub I.D., Development of a model policy for RDM at Austrian research institutions, LEARN toolkit of best practice for research data management, pp. 14-18, (2017); Data seal of approval., (2017); Erway R., Starting the conversation: University-wide research data management policy, (2013); Commission E., H2020 Programme: Guidelines on FAIR data management in Horizon 2020., (2016); Hall N., Corey B., Mann W., Wilson T., Model language for research data management policies.; Hoetink P., Broekhoven M., Van Den Hoogen H., Working towards incentives., (2016); Horton L., Overview of UK institution RDM policies, (2016); Jones S., Research data policy briefing., (2011); Jones S., Bringing it all together: A case study on the improvement of research data management at Monash University., (2013); RDM bij universiteiten., (2017); SURVEY: Is your institution ready for managing research data?, (2017); Data availability, (2017); Rans J., Jones S., RDM strategy: Moving from plans to action, (2013); Shearer K., Comprehensive brief on research data management policies, (2015); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, LIBER Quarterly, 27, 1, pp. 23-44, (2017); Verhaar P., Schoots F., Sesink L., Frederiks F., Fostering effective data management practices at Leiden university, LIBER Quarterly, 27, 1, pp. 1-22, (2017); The Netherlands code of conduct for scientific practice, (2014); Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Mons B., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, (2016)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85029707178"
"Smith P.L., II; McIntyre L.","Smith, Plato L. (24475610400); McIntyre, Lauren (7101992431)","24475610400; 7101992431","Developing, linking, and providing access to supplemental genetics dataset vcf files","2017","Grey Journal","13","1","","37","40","3","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041043677&partnerID=40&md5=6ba76e8f485d490ca6d47684e6e26eff","University of Florida, United States; United States","Smith P.L., II, University of Florida, United States; McIntyre L., United States","This conference proceeding paper is the written version component of the data panel discussion on developing a dataset collection using Zenodo for a professor in the Department of Molecular Genetics & Microbiology at the University of Florida. An internal University of Florida George A. Smathers Libraries Strategic Opportunities Program (SOP) grant award provided support for the creation and development of an initial supplemental datasets digital collection of large, static variant call format (vcf) in zenodo. The “Documenting a Genomics Variant Files Data Management: Developing Research Data management (RDM) workflows and providing research data access via HPC” project inspired this paper. The large vcf datasets used for this project ranged from 34 megabytes to 43 gigabytes. The researcher needed to (1) develop a data repository for supplemental datasets vcf files too large for attachment as supplemental data files for journal submissions, (2) provide digital object identifiers (DOIs) for all vcf dataset files, and (3) link the supplemental vcf dataset files to the journal article via the vcf doi. These three outcomes were accomplished during phase 1 (June 2016 – December 2016) of this project and presented at the GL18. Phase 2 (January 2017 – June 2017) of this project includes performing (1) a dataset reproducibility interview, (2) an open archival initiative protocol for metadata harvest (OAI-PMH) from Zenodo to the University of Florida institutional repository (IR@UF), and (3) developing a similar use case project for researchers in UF/IFAS Nature Coast Biological Station (NCBS). © 2017, GreyNet. All Rights Reserved.","","","","","","","SOP; University of Florida","This conference proceeding paper is the written version component of the data panel discussion on developing a dataset collection using Zenodo for a professor in the Department of Molecular Genetics & Microbiology at the University of Florida. An internal University of Florida George A. Smathers Libraries Strategic Opportunities Program (SOP) grant award provided support for the creation and development of an initial supplemental datasets digital collection of large, static variant call format (vcf) in zenodo. The “Documenting a Genomics Variant Files Data Management: Developing Research Data management (RDM) workflows and providing research data access via HPC” project inspired this paper. The large vcf datasets used for this project ranged from 34 megabytes to 43 gigabytes. The researcher needed to (1) develop a data repository for supplemental datasets vcf files too large for attachment as supplemental data files for journal submissions, (2) provide digital object identifiers (DOIs) for all vcf dataset files, and (3) link the supplemental vcf dataset files to the journal article via the vcf doi. These three outcomes were accomplished during phase 1 (June 2016 – December 2016) of this project and presented at the GL18. Phase 2 (January 2017 – June 2017) of this project includes performing (1) a dataset reproducibility interview, (2) an open archival initiative protocol for metadata harvest (OAI-PMH) from Zenodo to the University of Florida institutional repository (IR@UF), and (3) developing a similar use case project for researchers in UF/IFAS Nature Coast Biological Station (NCBS).","(2012); (2016); Kurmangaliyev Y.Z., Favorov A.V., Osman N.M., Lehmann K., Campo D., Salomon M.P., Tower J., Gelfand M.S., Nuzhdin S.V., Natural variation of gene models in Drosophilia melanogaster, BMC Genomics, (2015); (2015); (2015); (2016)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Article","Final","","Scopus","2-s2.0-85041043677"
"Stamnas E.; Lammert A.; Winkelmann V.; Lang U.","Stamnas, Erasmia (57192976480); Lammert, Andrea (8699594600); Winkelmann, Volker (57210032641); Lang, Ulrich (7006119097)","57192976480; 8699594600; 57210032641; 7006119097","The HD(CP)2 data archive for atmospheric measurement data","2016","ISPRS International Journal of Geo-Information","5","7","ijgi5070124","","","","10","10.3390/ijgi5070124","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984823117&doi=10.3390%2fijgi5070124&partnerID=40&md5=bc2d0f77c2fc1823dfb5ddbe77cf24eb","Regional Computing Centre (RRZK), University of Cologne, Weyertal 121, Köln, 50931, Germany; Meteorological Institute, University of Hamburg, Hamburg, 20146, Germany","Stamnas E., Regional Computing Centre (RRZK), University of Cologne, Weyertal 121, Köln, 50931, Germany; Lammert A., Meteorological Institute, University of Hamburg, Hamburg, 20146, Germany; Winkelmann V., Regional Computing Centre (RRZK), University of Cologne, Weyertal 121, Köln, 50931, Germany; Lang U., Regional Computing Centre (RRZK), University of Cologne, Weyertal 121, Köln, 50931, Germany","The archiving of scientific data is a sophisticated mission in nearly all research projects. In this paper, we introduce a new online archive of atmospheric measurement data from the ""High definition clouds and precipitation for advancing climate prediction"" (HD(CP)2) research initiative. The project data archive is quality managed, easy to use, and is now open for other atmospheric research data. The archive's creation was already taken into account during the HD(CP)2 project planning phase and the necessary resources were granted. The funding enabled the HD(CP)2 project to build a sound archive structure, which guarantees that the collected data are accessible for all researchers in the project and beyond. © 2016 by the authors; licensee MDPI, Basel, Switzerland.","Atmospheric physics; Clouds; Data archive; Data quality management; Data standard; Interdisciplinary; Metadata; Meteorology; Research data management","","","","","","","","Overpeck J.T., Meehl G.A., Bony S., Easterling D.R., Climate data challenges in the 21st century, Science, 331, pp. 700-702, (2011); Bony S., Stevens B., Frierson D.M.W., Jakob C., Kageyama M., Pincus R., Shepherd T.G., Sherwood S.C., Siebesma A.P., Sobel A.H., Et al., Clouds, circulation and climate sensitivity, Nat. Geosci., 8, (2015); Procter R., Halfpenny P., Voss A., Research data management: Opportunities and challenges for HEIs, Managing Research Data, pp. 135-150, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, (2011); Muckschel C., Nieschulze J., Weist C., Sloboda B., Kohler W., Challenges, Problems and Solutions in Data Management of Collaborative Research Centers, (2007); Buttner S., Hobohm H.-S., Muller L., Handbuch Forschungsdatenmanagement, (2011); Kuiper T., Van Der Hoeven J., Insight into digital preservation of research output in Europe, PARSE Insight Survey Report, 2009; Data Publisher for Earth & Environmental Science: PANGAEA; Climate and Environmental Retrieval and Archive (CERA); HD(CP) 2 Observation Data Product Standard (HOPS); Steinke S., Eikenberg S., Lohnert U., Dick G., Klocke D., Di Girolamo P., Crewell S., Assessment of small-scale integrated water vapor variability during HOPE, Atmos. Chem. Phys., 15, pp. 2675-2692, (2015); Greenberg J., Swauger S., Feinstein E.M., Metadata capital in a data repository, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 140-150, (2013); DataCite Metadata Schema Version 3.1, June 2015; Data Server T.; Domenico B., Caron J., Davis E., Kambic R., Nativi S., Thematic real-time environmental distributed data services (THREDDS): Incorporating interactive analysis tools into NSDL, J. Digital Inform., 2, (2006); Furber C., Data Quality Management with Semantic Technologies, (2016); The Integrated Climate Data Center (ICDC); The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH); Fritsch B., Klimaforschung, Langzeitarchivierung von Forschungsdaten, pp. 195-212, (2012)","E. Stamnas; Regional Computing Centre (RRZK), University of Cologne, Köln, Weyertal 121, 50931, Germany; email: estamnas@uni-koeln.de","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84984823117"
"Chowdhury G.; Boustany J.; Kurbanoğlu S.; Ünal Y.; Walton G.","Chowdhury, Gobinda (7006058701); Boustany, Joumana (36055814600); Kurbanoğlu, Serap (15056480700); Ünal, Yurdagül (6603278482); Walton, Geoff (37116110400)","7006058701; 36055814600; 15056480700; 6603278482; 37116110400","Preparedness for research data sharing: A study of university researchers in three European countries","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10647 LNCS","","","104","116","12","2","10.1007/978-3-319-70232-2_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034073121&doi=10.1007%2f978-3-319-70232-2_9&partnerID=40&md5=b3fe98d07fbe823983da33704145fc4e","iSchool, Northumbria University, Newcastle upon Tyne, United Kingdom; DICEN-IdF EA 7339, UNIVERSITE PARIS-EST MARNE-LA-VALLEE (UPEM), Champs-sur-Marne, France; Department of Information Management, Hacettepe University, Ankara, Turkey; Department of Languages, Information and Communications, Manchester Metropolitan University, Manchester, United Kingdom","Chowdhury G., iSchool, Northumbria University, Newcastle upon Tyne, United Kingdom; Boustany J., DICEN-IdF EA 7339, UNIVERSITE PARIS-EST MARNE-LA-VALLEE (UPEM), Champs-sur-Marne, France; Kurbanoğlu S., Department of Information Management, Hacettepe University, Ankara, Turkey; Ünal Y., Department of Information Management, Hacettepe University, Ankara, Turkey; Walton G., Department of Languages, Information and Communications, Manchester Metropolitan University, Manchester, United Kingdom","Many government and funding bodies around the world have been advocating open access to research data, arguing that such open access can bring a significant degree of economic and social benefit. However, the question remains, do researchers themselves want to share their research data, and even if they do how far they are prepared to make this happen? In this paper we report on an international survey involving university researchers in three countries, viz. UK, France and Turkey. We found that researchers have a number of concerns for data sharing, and in general there is a lack of understanding of the requirements for making data publicly available and accessible. We note that significant training and advocacy will be required to make the vision of data sharing a reality. © 2017, Springer International Publishing AG.","Data sharing; Ethics; Metadata; Research data management; User education","Information management; Metadata; Data Sharing; Economic and social benefits; Ethics; European Countries; International survey; Research data managements; University researchers; User education; Digital libraries","","","","","","","Borgman C.L., Wallis J.C., Mayernik M.S., Who’s got the data? Interdependencies in science and technology collaborations, Comput. Support. Coop. Work, 21, 6, pp. 485-523, (2012); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol., 63, 6, pp. 1059-1078, (2012); Beagrie N., Houghton J., The Value and Impact of Data Sharing and Curation: A Synthesis of Three Recent Studies of UK Research Data Centres, JISC, (2013); Data Archive U.K., Create & manage data. Research data lifecycle, Concordat on Open Access Data Version, 10 July, (2015); How Sharing Research Data Can Yield Knowledge, Jobs and Growth, (2014); Faniel I.M., Kriesberg A., Yakel E., Data reuse and sensemaking among novice social scientists, Proc. Am. Soc. Inf. Sci. Technol, 49, 1, pp. 1-10, (2012); Faniel I., Kansa E., Kansa S.W., Barrera-Gomez J., Yakel E., The challenges of digging data: A study of context in archaeological data reuse, JCDL 2013 Proceedings of the 13Th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 295-304, (2013); Yakel E., Faniel I., Virtuous circles: Circulating old data through new collaborations, 17Th ACM Conference on Computer Supported Cooperative Work and Social Computing Workshop: Sharing, Re-Use and Circulation of Resources in Cooperative Scientific Work. Baltimore, MD, 15, February, (2014); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, Plos ONE, 8, 7, (2013); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty. Portal, Libr. Acad, 11, 2, pp. 629-657, (2011); Mayernik M.S., Wallis J.C., Borgman C.L., Unearthing the infrastructure: Humans and sensors in field-based scientific research, Comput. Support. Coop. Work, 22, 1, pp. 65-101, (2013); Koltay T., Data literacy: In search of a name and identity, J. Documentation, 71, 2, pp. 401-415, (2015); Macmillan D., Data sharing and discovery: What librarians need to know, J. Acad. Librarianship, 40, 5, pp. 541-549, (2014); Verbaan E., Cox A.M., Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management, J. Acad. Librarianship, 40, 34, pp. 211-219, (2014); Frank E.P., Pharo N., Academic librarians in data information literacy instruction: A case study in meteorology, Coll. Res. Libr., 77, 4, pp. 536-552, (2016); Federer L.M., Lu Y.L., Joubert D.J., Data literacy training needs of biomedical researchers, J. Med. Libr. Assoc., 104, 1, pp. 52-57, (2016); Fane B., Treadway J., Gallagher A., Penny D., Hahnel M., Open season for open data: A survey of researchers, Digital Science Report, the State of Open Data: A Selection of Analyses and Articles about Open Data, Curated by Figshare, Digital Science, pp. 12-19, (2016); Prado J.C., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Common Principles on data Policy, Research Councils UK; Tonta Y., Açık erişimin geleceği Ve araştırma Verilerine açık erişim, (Future of Open Access and Open Access to Research Data).; Aydinoglu A.U., Araştırma Verileri yönetimi: Türkiye, (Reserach Data Management: Turkey), (2016); Frascati Manual: Proposed Standard Practice for Surveys on Research and Experimental Development","G. Chowdhury; iSchool, Northumbria University, Newcastle upon Tyne, United Kingdom; email: gobinda.chowdhury@northumbria.ac.uk","Cunningham S.J.; Choemprayong S.; Crestani F.","Springer Verlag","","19th International Conference on Asia-Pacific Digital Libraries, ICADL 2017","13 November 2017 through 15 November 2017","Bangkok","203799","03029743","978-331970231-5","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85034073121"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Data governance, data literacy and the management of data quality","2016","IFLA Journal","42","4","","303","312","9","53","10.1177/0340035216672238","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002412700&doi=10.1177%2f0340035216672238&partnerID=40&md5=1c5478d9c4a2b8d9f7ed4d2bc7b9b1f3","Eszterházy Károly University, Hungary","Koltay T., Eszterházy Károly University, Hungary","Data governance and data literacy are two important building blocks in the knowledge base of information professionals involved in supporting data-intensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data. © 2016, © The Author(s) 2016.","Data governance; data librarian; data literacy; data-intensive research; research data services","","","","","","","","Information Literacy Competency Standards for Higher Education, (2000); Intersections of Scholarly Communication and Information Literacy: Creating Strategic Collaborations for a Changing Academic Environment, (2013); ACRL Research Planning and Review Committee. Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College & Research Libraries News, 75, 6, pp. 294-302, (2014); Framework for Information Literacy for Higher Education, (2015); Final Report, American Library Association Presidential Commission on Information Literacy, (1989); Andretta S., Pope A., Walton G., Information Literacy Education in the UK, Communications in Information Literacy, 2, 1, pp. 36-51, (2008); Badke W., Information overload? Maybe not, Online, 34, 5, pp. 52-54, (2010); Bailey C.W., Research Data Curation Bibliography, Version 5, (2015); Bawden D., Robinson L., ‘An intensity around information’: The changing face of chemical information literacy, Journal of Information Science, (2015); Boyd D., Crawford K., Critical questions for big data, Information, Communication and Society, 15, 5, pp. 662-669, (2012); Briney K., Data Management for Researchers: Organize, Maintain and Share your Data for Research Success, (2015); Bundy A., Australian and New Zealand Information Literacy Framework, (2004); Calzada Prado J.C., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Carlson J., Johnston L.R., Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, (2015); Carlson J., Stowell Bracke M.S., Planting seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, 1, pp. 95-110, (2015); Carlson J., Fosmire M., Miller C., Et al., Determining data information literacy needs: A study of students and research faculty, portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Definitions of Data Governance, (2015); Data Governance: The Basic Information, (2015); Dong X.L., Srivastava D., Big data integration, Data Engineering (ICDE), 2013 IEEE 29th international conference, pp. 1245-1248, (2013); DosSantos J., What librarians can teach us about managing Big Data, InFocus, (2015); Duncan J., Clement K., Rozum B., Teaching our faculty. developing copyright and scholarly communication outreach programs, Common Ground at the Nexus of Information Literacy and Scholarly Communication, pp. 269-286, (2013); The Compelling Case for Data Governance, (2015); Exner N., Research information literacy: Addressing original researchers’ needs, Journal of Academic Librarianship, 40, 5, pp. 460-466, (2014); Farrell R., Badke W., Situating information literacy in the disciplines, Reference Services Review, 43, 2, pp. 319-340, (2015); Fosmire M., Miller C., Creating a culture of data integration and interoperability: Librarians and Earth Science Faculty collaborate on a geoinformatics course, Proceedings of the IATUL conferences, (2008); Giarlo M., Academic libraries as quality hubs, Journal of Librarianship and Scholarly Communication, 1, 3, pp. 1-10, (2013); Hartter J., Ryan S.J., MacKenzie C.A., Et al., Spatially explicit data: Stewardship and ethical challenges in science, PLoS Biology, 11, 9, (2013); Hunt K., The challenges of integrating data literacy into the curriculum in an undergraduate institution, IASSIST Quarterly, 28, 2, pp. 12-15, (2004); Successful Information Governance through High-Quality Data, (2012); What is Data Integration?, (2016); Breaking Big: When Big Data Goes Bad: The Importance of Data Quality Management in Big Data Environments, (2014); Jahnke L., Asher A., Keralis S.D., The Problem of Data, (2012); Johnson C.A., The Information Diet: A Case for Conscious Consumption, (2012); Khatri V., Brown C.V., Designing data governance, Communications of the ACM, 53, 1, pp. 148-152, (2010); Koltay T., Data literacy: In search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Data literacy for researchers and data librarians, Journal of Librarianship and Information Science, (2015); Krier L., Strasser C.A., Data Management for Libraries, (2014); Lenzerini M., Data integration: A theoretical perspective, pp. 233-246, (2002); MacMillan D., Data sharing and discovery: What librarians need to know, Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); Madrid M.M., A study of digital curator competences: A survey of experts, International Information and Library Review, 45, 3-4, pp. 149-156, (2013); Mandinach E., Gummer E., A systemic view of implementing data literacy in educator preparation, Educational Researcher, 42, 1, pp. 30-37, (2013); Martell C., sAccess: The social dimension of a new paradigm for academic librarianship, Journal of Academic Librarianship, 35, 3, pp. 205-206, (2009); Maybee C., Zilinski L., Data informed learning: A next phase data literacy framework for higher education, pp. 108-111, (2015); Nicholas D., Watkinson A., Volentine R., Et al., Trust and authority in scholarly communications in the light of the digital transition, Learned Publishing, 27, 2, pp. 121-134, (2014); NMC Horizon Report: 2014 Library Edition, (2014); The Five Most Common Big Data Integration Mistakes to Avoid, (2015); Pinto M., Pulgarin A., Escalona M., Viewing information literacy concepts: A comparison of two branches of knowledge, Scientometrics, 98, 3, pp. 231-232, (2014); Qin J., D'Ignazio J., Lessons learned from a two-year experience in science data literacy education, 31st annual IATUL conference, (2010); Ramirez M.L., Whose role is it anyway? A library practitioner’s appraisal of the digital data deluge, Bulletin of the American Society for Information Science and Technology, 37, 5, pp. 21-23, (2011); Ridsdale C., Rothwell J., Smit M., Et al., Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report, (2015); Riley A.C., Data management and curation: Professional development for librarians needed, College & Research Libraries News, 76, 9, pp. 504-506, (2015); The Role of Research Supervisors in Information Literacy, (2011); Rosenbaum S., Data governance and stewardship: Designing data stewardship entities and advancing data access, Health Services Research, 45, 5, pp. 1442-1455, (2010); Sarsfield S., The Data Governance Imperative: A Business Strategy for Corporate Data, (2009); Schneider R., Research data literacy, Worldwide Commonalities and Challenges in Information Literacy Research and Practice, pp. 134-140, (2013); The SCONUL Seven Pillars of Information Literacy. Core Model for Higher Education, (2011); Searle S., Wolski M., Simons N., Et al., Librarians as partners in research data service development at Griffith University, Program, 49, 4, pp. 440-460, (2015); Seiner R.S., Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success, (2014); Si L., Zhuang X., Xing W., Et al., The cultivation of scientific data specialists, Library Hi Tech, 31, 4, pp. 700-724, (2013); Smith A.M., Data governance best practices: The beginning, EIMInsight, 1, (2007); Soares S., Big Data Governance: An Emerging Imperative, (2012); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services. Current Practices and Plans for the Future, (2012); Tenopir C., Hughes D., Allard S., Et al., Research data services in academic libraries: Data intensive roles for the future?, Journal of eScience Librarianship, 4, 2, (2015); Tenopir C., Sanduski R.J., Allard S., Et al., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Researcher Development Framework, (2011); Wang M., Supporting the research process through expanded library data services, Program, 47, 3, pp. 282-303, (2013); Weber N.M., Palmer C.L., Chao T.C., Current trends and future directions in data curation Research and education, Journal of Web Librarianship, 6, 4, pp. 305-320, (2012); Weill P., Ross J.W., IT Governance: How Top Performers Manage IT Decision Rights for Superior Results, (2004); Zilinski L.D., Nelson M.S., Thinking critically about data consumption: Creating the data credibility checklist, Proceedings of the American Society for Information Science and Technology, 51, 1, pp. 1-4, (2014)","T. Koltay; Eszterházy Károly University, Jászberény, Rákóczi út 53, 5100, Hungary; email: koltay.tibor@uni-eszterhazy.hu","","SAGE Publications Ltd","","","","","","03400352","","","","English","IFLA J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85002412700"
"Helbig K.","Helbig, Kerstin (57188698727)","57188698727","Research data management training for geographers: First impressions","2016","ISPRS International Journal of Geo-Information","5","4","40","","","","5","10.3390/ijgi5040040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962436414&doi=10.3390%2fijgi5040040&partnerID=40&md5=f3af9b1d4cd39e7a14daa27c1bcaaf93","Computer and Media Service, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany","Helbig K., Computer and Media Service, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany","Sharing and secondary analysis of data have become increasingly important for research. Especially in geography, the collection of digital data has grown due to technological changes. Responsible handling and proper documentation of research data have therefore become essential for funders, publishers and higher education institutions. To achieve this goal, universities offer support and training in research data management. This article presents the experiences of a pilot workshop in research data management, especially for geographers. A discipline-specific approach to research data management training is recommended. The focus of this approach increases researchers' interest and allows for more specific guidance. The instructors identified problems and challenges of research data management for geographers. In regards to training, the communication of benefits and reaching the target groups seem to be the biggest challenges. Consequently, better incentive structures as well as communication channels have to be established. © 2016 by the authors.","Geography; Information literacy; Research data management; Training","","","","","","","","Miller H.J., Han J., Geographic data mining and knowledge discovery: An overview, Geographic Data Mining and Knowledge Discovery, pp. 1-26, (2009); PANGAEA Data Publisher for Earth & Environmental Science; Crystallography Open Database (COD); Mooney H., Newton M.P., The Anatomy of A Data Citation: Discovery Reuse and Credit; Research Data Lifecycle; Fecher B., Friesike S., Hebing M., Linek S., Sauermann A., A Reputation Economy: Results from An Empirical Survey on Academic Data Sharing; Sallans A., Lake S., Data management assessment and planning tools, Research Data Management: Practical Strategies for Information Professionals, pp. 87-107, (2014); Simukovic E., (2014); Schenk U., Kindling M., Simukovic E., Zielke D., Humboldt-Universitätzu Berlin, (2015); Humboldt-Universität zu Berlin Research Data Management Policy; Simukovic E., Kindling M., Schirmbacher P., Umfrage Zum Umgang Mit Digitalen Forschungsdaten An der Humboldt-Universität zu Berlin; Simukovic E., Thiele R., Struck A., Kindling M., Schirmbacher P., Was sind ihre forschungsdaten?, Interviews Mit Wissenschaftlern der Humboldt-Universität zu Berlin; Burger M., Kindling M., Liebenau L., Lienhard C., Lilienthal S., Plewka P., Pohlkamp S., Reinhardt K., Rugenhagen M., Schulz K., Et al., Forschungsdatenmanagement An Hochschulen-Internationaler Überblick und Aspekte Eines Konzepts für Die Humboldt-Universität zu Berlin; Kennan M.A., Markauskaite L., Research data management practices: A snapshot in time, Int. J. Digit. Curation, 10, pp. 69-95, (2015); Simons N., Visser K., Searle S., Growing institutional support for data citation: Results of a partnership between Griffith University and the Australian National Data Service, D-Lib Mag., 19, (2013); Corti L., Eynden V.V.D., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); Piorun M.A., Kafel D., Leger-Hornby T., Najafi S., Martin E.R., Colombo P., LaPelle N.R., Teaching research data management: An undergraduate/graduate curriculum, J. ESci. Librariansh., 1, pp. 46-50, (2012); Sesartic A., Digitales Datenmanagement seit 1988 Überführung eines digitalen Forschungsarchivs aus dem Bereich Systemökologie in das ETH Data Archive, Proceedings of DINI/nestor-Workshop Langzeitarchivierung von Forschungsdaten; Towe M., Scheid S., User expectations in archived research data, Eidgenössische Technische Hochschule Zürich, (2011); Bertelmann R., Gebauer P., Hasler T., Kirchner I., Peters-Kottig W., Razum M., Recker A., Ulbricht D., Van Gasselt S., Einstieg Ins Forschungsdatenmanagement in Den Geowissenschaften; Pink C., Cope J., Managing Your Research Data; Neumann J., Ziedorn F., Forschungsdatenmanagement-Erstellen Bearbeiten Archivieren; Bertelmann R., Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences Potsdam Germany, (2015); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, Peer J, 1, (2013); Kindling M., Schirmbacher P., Die digitale forschungswelt als gegenstand Der forschung, Inf. Wiss. Prax., 64, pp. 127-136, (2013); A Supplement to the Humboldt-Universität zu Berlin Research Data Management Policy; Guidelines on Data Management in Horizon 2020; Darwin Core; ISO 19115-1:2014 Geographic Information-Metadata-Part 1: Fundamentals; ISO 19115-2:2009 Geographic Information-Metadata-Part 2: Extensions for Imagery and Gridded Data; Disciplinary Metadata; DMPonline; ZENODO; Joint Declaration of Data Citation Principles, (2014); FORCE11 Data Citation Example, (2015); Carlson J., Johnston L.R., Data Information Literacy: Librarians, Data, and the Education of A New Generation of Researchers, (2015); Treloar A., Groenewegen D., Harboe-Ree C., The data curation continuum-Managing data objects in institutional repositories, D-Lib Mag., 13, (2007); Pennock M., Web-Archiving, (2013); WebCite; Hook L.A., Vannan S.K.S., Beaty T.W., Cook R.B., Wilson B.E., Best Practices for Preparing Environmental Data Sets to Share and Archive, (2010); Klump J., Huber R., Diepenbroek M., DOI for geoscience data-How early practices shape present perceptions, Earth Sci. Inform., 9, pp. 123-136, (2016)","K. Helbig; Computer and Media Service, Humboldt-Universität zu Berlin, Berlin, Unter den Linden 6, 10099, Germany; email: kerstin.helbig@cms.hu-berlin.de","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84962436414"
"Budroni P.; Ganguly R.; Miksa T.; Rauber A.; Solís B.S.","Budroni, Paolo (56624471600); Ganguly, Raman (56702193000); Miksa, Tomasz (55260160000); Rauber, Andreas (57074846700); Solís, Barbara Sánchez (56702943200)","56624471600; 56702193000; 55260160000; 57074846700; 56702943200","RDA Europe workshop - From planning to action. Towards the establishment of an Austrian research infrastructure (Vienna, november 23, 2017)","2017","VOEB-Mitteilungen","70","3-4","","382","389","7","0","10.31263/voebm.v70i3.1962","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041318791&doi=10.31263%2fvoebm.v70i3.1962&partnerID=40&md5=d27da7c8b3007140e25b38cc3cfd37da","University of Vienna, Austria; University of Vienna, Computer Center, Austria; Vienna University of Technology, SBA Research, Austria; University of Vienna Library and Archives, AUSSDA, Austria","Budroni P., University of Vienna, Austria; Ganguly R., University of Vienna, Computer Center, Austria; Miksa T., Vienna University of Technology, SBA Research, Austria; Rauber A., Vienna University of Technology, SBA Research, Austria; Solís B.S., University of Vienna Library and Archives, AUSSDA, Austria","The RDA Europe Workshop “From Planning to Action. Towards the Establishment of an Austrian Research Infrastructure” was organized in Vienna in November 2017 with the support of RDA Europe, the Vienna University of Technology, and the University of Vienna. The workshop was the first RDA national event in Austria in a series of RDA Europe events dedicated to practical issues surrounding the adoption of RDA recommendations, the implementation of European Open Science Cloud and other key European data initiatives in order to support EU researchers and data centres. The different sections focused on transversal and domain-specific projects and infrastructures in Austria, such as data centres and data repositories that have emerged as a result of initiatives developed at the national level. The presentations also reflected the European perspective on research data management, focusing on the political and scientific work of international organisations. Furthermore there was an analysis of various RDA Europe working group recommendations and their potential for adoption in local contexts. The event also included the announcement of a new dedicated RDA group for Austria. This node will formally represent the Austrian data management community within the new RDA administrative structure as soon as formal procedures are completed. © 2017, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Data centre; EOSC; European open science cloud; Open science; RDA; Repositories; Research data; Research data alliance; Research data management; Research infrastructure","","","","","","RDA Europe; Universität Wien; Technische Universität Wien","The event, entitled „Data Stewardship Realized: From Planning to Action. Towards the Establishment of an Austrian Research Infrastructure“ was organized with the support of RDA Europe, Vienna University of Technology, and the University of Vienna.","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85041318791"
"Jeng W.; He D.; Chi Y.","Jeng, Wei (37023332900); He, Daqing (15044318000); Chi, Yu (57191041389)","37023332900; 15044318000; 57191041389","Social science data repositories in data deluge A case study of ICPSR's workflow and practices","2017","Electronic Library","35","4","","626","649","23","7","10.1108/EL-11-2016-0243","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031314074&doi=10.1108%2fEL-11-2016-0243&partnerID=40&md5=c1c878465b0e046e7aa71aad030cbe37","School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States","Jeng W., School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; He D., School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States; Chi Y., School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States","Purpose - Owing to the recent surge of interest in the age of the data deluge, the importance of researching data infrastructures is increasing. The open archival information system (OAIS) model has been widely adopted as a framework for creating and maintaining digital repositories. Considering that OAIS is a reference model that requires customization for actual practice, this paper aims to examine how the current practices in a data repository map to the OAIS environment and functional components. Design/methodology/approach - The authors conducted two focus-group sessions and one individual interview with eight employees at the world's largest social science data repository, the Interuniversity Consortium for Political and Social Research (ICPSR). By examining their current actions (activities regarding their work responsibilities) and IT practices, they studied the barriers and challenges of archiving and curating qualitative data at ICPSR. Findings - The authors observed that the OAIS model is robust and reliable in actual service processes for data curation and data archives. In addition, a data repository's workflow resembles digital archives or even digital libraries. On the other hand, they find that the cost of preventing disclosure risk and a lack of agreement on the standards of text data files are the most apparent obstacles for data curation professionals to handle qualitative data; the maturation of data metrics seems to be a promising solution to several challenges in social science data sharing. Originality/value - The authors evaluated the gap between a research data repository's current practices and the adoption of the OAIS model. They also identified answers to questions such as how current technological infrastructure in a leading data repository such as ICPSR supports their daily operations, what the ideal technologies in those data repositories would be and the associated challenges that accompany these ideal technologies. Most importantly, they helped to prioritize challenges and barriers from the data curator's perspective and to contribute implications of data sharing and reuse in social sciences. © Emerald Publishing Limited.","Data sharing; Digital repositories; Open archival information system (OAIS); Research data curation","adoption; employee; financial management; human; information center; information processing; interview; library; maturation; responsibility; sociology; workflow","","","","","National Science Foundation, NSF; Defense Advanced Research Projects Agency, DARPA; Andrew W. Mellon Foundation, AWMF; Council on Library and Information Resources, CLIR; University of Pittsburgh, Pitt; National Natural Science Foundation of China, NSFC, (71420107026); titled Research on Knowledge Organization and Service Innovation","Funding text 1: Daqing He is an Associate Professor at the School of Computing and Information and the Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. He earned his PhD in Artificial Intelligence from the University of Edinburgh, Scotland. Prior to joining the University of Pittsburgh in 2004, he served on the research faculties of the Robert Gordon University, Scotland, and the University of Maryland at College Park. His main research interests cover information retrieval (monolingual and multilingual), information access on the social Web, adaptive Web systems and user modelling, interactive retrieval interface design, Web log mining and analysis and research data management. Dr He has been the principal investigator (PI) and co-PI for more than ten research projects, funded by the National Science Foundation (NSF), United States Defense Advanced Research Projects Agency (DARPA), ALISE/OCLC, University of Pittsburgh and other agencies. He has published more than 150 articles in internationally recognized journals and conferences in these areas, which include Journal of Association for Information Science and Technology, Information Processing and Management, ACM Transactions on Information Systems, Journal of Information Science, Association for Computing Machinery’s Special Interest Group on Information Retrieval (ACM SIGIR), Conference on Information and Knowledge Management (CIKM), World Wide Web Conference (WWW), ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) and so on. Dr He has served as a member on the programme committees for more than 40 major international conferences in the area of information retrieval and Web technologies, and has been called upon to be a reviewer for many top-ranked international journals in the same areas. He serves on the editorial board of the SCI/SSCI indexed journals Information Processing and Management, Internet Research, and Aslib Journal of Information Management. Daqing He is the corresponding author and can be contacted at: dah44@pitt.edu Yu Chi is a PhD student at the School of Computing and Information at the University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Her research relates to information behaviour and seeking. Her current research project investigates how laypeople obtain information and improve knowledge through online health-related information seeking.; Funding text 2: Purpose – Owing to the recent surge of interest in the age of the data deluge, the importance of researching data infrastructures is increasing. The open archival information system (OAIS) model has been widely adopted as a framework for creating and maintaining digital repositories. Considering that OAIS is a reference model that requires customization for actual practice, this paper aims to examine how the current practices in a data repository map to the OAIS environment and functional components. Design/methodology/approach – The authors conducted two focus-group sessions and one individual interview with eight employees at the world’s largest social science data repository, the Interuniversity Consortium for Political and Social Research (ICPSR). By examining their current actions (activities regarding their work responsibilities) and IT practices, they studied the barriers and challenges of archiving and curating qualitative data at ICPSR. Findings – The authors observed that the OAIS model is robust and reliable in actual service processes for data curation and data archives. In addition, a data repository’s workflow resembles digital archives or even digital libraries. On the other hand, they find that the cost of preventing disclosure risk and a lack of agreement on the standards of text data files are the most apparent obstacles for data curation professionals to handle qualitative data; the maturation of data metrics seems to be a promising solution to several challenges in social science data sharing. Originality/value – The authors evaluated the gap between a research data repository’s current practices and the adoption of the OAIS model. They also identified answers to questions such as how current technological infrastructure in a leading data repository such as ICPSR supports their daily operations, what the ideal technologies in those data repositories would be and the associated challenges that accompany these ideal technologies. Most importantly, they helped to prioritize challenges and barriers from the data curator’s perspective and to contribute implications of data sharing and reuse in social sciences. Keywords Data sharing, Digital repositories, Open archival information system (OAIS), Research data curation Paper type Research paper The authors thank the iFellowship, guided by the Committee on Coherence at Scale (CoC) for Higher Education, sponsored by the Council on Library and Information Resources (CLIR) and Andrew W. Mellon Foundations, as well as Beta-Phi-Mu Honor Society, which provided research funding for this project. This study is also partially supported by the project titled Research on Knowledge Organization and Service Innovation in the Big Data Environments funded by the National Natural Science Foundation of China (No. 71420107026). The authors also thank Drs Nora Mattern, Liz Lyon, Sheila Corrall, Jian Qin, Jung Sun Oh and Stephen Griffin for their invaluable comments and suggestions on this research project. Last but not least, the authors thank all participants and people who helped facilitate the field study at ICPSR for their valuable input and assistance.","Beecher B., The ICPSR Pipeline Process, (2009); Beedham H., Missen J., Palmer M., Ruusalepp R., Assessment of UKDA and TNA compliance with OAIS and METS standards, Joint Information Systems Committee (JISC), (2005); Bingham J.L., Information technology and the conduct of research, Bulletin of the Medical Library Association, 78, 3, (1990); Bishoff C., Johnston L., Approaches to data sharing: An analysis of NSF data management plans from a large research university, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Borgman C.L., Wallis J.C., Mayernik M.S., Pepe A., Drowning in data: Digital library architecture to support scientific use of embedded sensor networks, Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 269-277, (2007); Bowler L., Knobel C., Mattern E., From cyberbullying to well-being: A narrative-based participatory approach to values-oriented design for social media, Journal of the Association for Information Science and Technology, 66, 6, pp. 1274-1293, (2015); Curty R.G., Actors influencing research data reuse in the social sciences: An exploratory study, International Journal of Digital Curation, 11, 1, pp. 96-117, (2016); Elman C., Kapiszewski D., A Guide to Sharing Qualitative Data, (2013); Fecher B., Friesike S., Hebing M., What drives academic data sharing?, PLOS ONE, 10, 2, (2015); Griffin S., Libraries in the digital age: Technologies, innovation, shared resources and new responsibilities, Communication and Technology, Volume 5 (Handbook of Communication Science Series), (2015); Guest G., Namey E.E., Mitchell M.L., Collecting Qualitative Data:AField Manual for Applied Research, (2012); Gutmann M.P., Evans B., Mitchell D., Schurer K., The data archive technologies alliance: Looking towards a common future, IASSIST Conference, Tampere, (2009); Hey T., Trefethen A., The data deluge: An e-science perspective, Grid Computing: Making the Global Infrastructure A Reality, (2003); Hey T., Tansley S., Tolle K., The Fourth Paradigm; Data-Intensive Scientific Discovery, (2009); Size of ICPSR's Holdings, (2016); ICPSR: A Case Study in Repository Management, (2016); Jeng W., Lyon L., A report of data-intensive capability, institutional support, and data management practices in social sciences, International Journal of Digital Curation, 11, 1, pp. 156-171, (2016); Jeng W., He D., Oh J., Toward a conceptual framework for data sharing practices in social sciences: A profile approach, Proceedings of the ASIS&T 2016 Annual Meeting, (2016); Krauwer S., Hinrichs E., The CLARIN research infrastructure: Resources and tools for e-humanities ""scholars, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014) European Language Resources Association (ELRA), pp. 1525-1531, (2014); Lavoie B.F., The open archival information system reference model: Introductory guide, Microform & Imaging Review, 33, 2, pp. 68-81, (2004); Lazer D., Pentland A.S., Adamic L., Aral S., Barabasi A.L., Brewer D., Jebara T., King G., Macy M., Roy D., Van Alstyne M., Life in the network: The coming age of computational social science, Science, 323, 5915, (2009); Lyon L., Jeng W., Mattern E., Research transparency: A preliminary study of disciplinary conceptualisation, drivers, tools and support services, Proceedings of the 12th International Digital Curation Conference (IDCC), (2017); Mattern E., Jeng W., He D., Lyon L., Brenner A., Using participatory design and visual narrative inquiry to investigate researchers' data challenges and recommendations for library research data services, Program: Electronic Library and Information Systems, 49, 4, pp. 408-423, (2015); Mischo W.H., Schlembach M.C., O'Donnell M.N., An analysis of data management plans in University of Illinois National Science Foundation grant proposals, Journal of EScience Librarianship, 3, 1, (2014); Myers J., Hedstrom M., Akmon D., Payette S., Plale B.A., Kouper I., Kumar P., Elag M., Lee J., Kooper R., Marini L., Towards sustainable curation and preservation: The SEAD project's data services approach, IEEE 11th International Conference on E-Science, pp. 485-494, (2015); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Vardigan M., Whiteman C., ICPSR meets OAIS: Applying the OAIS reference model to the social science archive context, Archival Science, 7, 1, pp. 73-87, (2007); Yoon A., Data reusers' trust development, Journal of the Association for Information Science and Technology, 68, 4, (2016); Yoon A., Tibbo H., Examination of data deposit practices in repositories with the OAIS model, IASSIST Quarterly, 35, 4, (2011); Bohemier K.A., Atwood T., Kuehn A., Qin J., A content analysis of institutional data policies, Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 409-410, (2011); Kim Y., Institutional and Individual Influences on Scientists' Data Sharing Behaviors, (2013); National Science Foundation's Merit Review Criteria: Review and Revisions, (2013); Trusted Digital Libraries: Attributes and Responsibilities, (2002)","D. He; School of Computing and Information, University of Pittsburgh, Pittsburgh, United States; email: dah44@pitt.edu","","Emerald Group Publishing Ltd.","","","","","","02640473","","ELLID","","English","Electron. Libr.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85031314074"
"Grguric E.; Davis H.; Davidson B.","Grguric, Ekatarina (57188630767); Davis, Hilary (16021152100); Davidson, Bret (57193623832)","57188630767; 16021152100; 57193623832","Supporting the modern research workflow","2016","Proceedings of the Association for Information Science and Technology","53","1","","1","4","3","1","10.1002/pra2.2016.14505301136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015309200&doi=10.1002%2fpra2.2016.14505301136&partnerID=40&md5=fe883db547616e4582d1241a87159ad3","North Carolina State University Libraries, 2 Broughton Dr. Campus Box 7111, Raleigh, 27695-7111, NC, United States","Grguric E., North Carolina State University Libraries, 2 Broughton Dr. Campus Box 7111, Raleigh, 27695-7111, NC, United States; Davis H., North Carolina State University Libraries, 2 Broughton Dr. Campus Box 7111, Raleigh, 27695-7111, NC, United States; Davidson B., North Carolina State University Libraries, 2 Broughton Dr. Campus Box 7111, Raleigh, 27695-7111, NC, United States","Libraries are uniquely positioned to support the quickly shifting landscape of modern research practice. The NCSU Libraries has paired user research approaches with iterative design practice to determine contextually relevant services to support researchers. This poster will showcase two case studies as examples of successful avenues of support through library services, the user research and application of frameworks that supported the development of these services, and the project management methodology that applied research to practice. Each case study will outline strategies used to develop services that support modern research practice through the investigation of a complex problem space. Copyright © 2016 by Association for Information Science and Technology","digital literacy; Open science; public access compliance support; research data management; science and technology support; user research; user-centered design","Information management; Iterative methods; Project management; Research and development management; Case-studies; Digital literacies; Open science; Public Access; Public access compliance support; Research data managements; Science and Technology; Science and technology support; Technology support; User research; User centered design","","","","","","","Bartling S.F.S., Friesike S., Opening Science, (2014); Davis H.M., Cross W.M., Using a Data Management Plan Review Service as a Training Ground for Librarians, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); D4.2 Toolkit for Training Sessions, Facilitate open science Training for European Research, (2015); Librarians' Competencies Profile for Research Data Management, (2014); Making open science a Reality, OECD Science, Technology and Industry Policy Papers, No. 25, (2015)","","","John Wiley and Sons Inc","","","","","","23739231","","","","English","Proceedings of the Association for Information Science and Technology","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85015309200"
"Karimova Y.; Castro J.A.; Da Silva J.R.; Pereira N.; Ribeiro C.","Karimova, Yulia (57195369729); Castro, João Aguiar (55977255100); Da Silva, João Rocha (55496903800); Pereira, Nelson (57195715570); Ribeiro, Cristina (7201734594)","57195369729; 55977255100; 55496903800; 57195715570; 7201734594","Promoting semantic annotation of research data by their creators: A use case with B2NOTE at the end of the RDM workflow","2017","Communications in Computer and Information Science","755","","","112","122","10","6","10.1007/978-3-319-70863-8_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036611239&doi=10.1007%2f978-3-319-70863-8_11&partnerID=40&md5=a44892a0aeda9357924cfa51624f5c38","INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Karimova Y., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Castro J.A., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Da Silva J.R., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Pereira N., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal; Ribeiro C., INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal","Research data management is promoted at different levels with awareness actions carried out to encourage cooperation between researchers. However, data management requires tools to set the scene for researchers and institutions to disseminate the research data they produce. In this context good quality metadata play an important role by enabling data reuse. EUDAT is an European common data infrastructure, with integrated services for data preservation and dissemination. The TAIL project, at the University of Porto, proposes workflows based on Dendro, a collaborative environment that helps researchers prepare well described datasets and deposit them in a data repository. We propose a data deposit workflow use case for a small research project with emphasis in data annotation. Data is organized and described in Dendro; deposited in B2SHARE; and semantic annotation is performed with the new B2NOTE service from EUDAT. © Springer International Publishing AG 2017.","B2NOTE; Dendro; Research data management; Semantic annotation","Deposits; Information management; Metadata; B2NOTE; Collaborative environments; Data preservations; Data repositories; Dendro; Integrated service; Research data managements; Semantic annotations; Semantics","","","","","Funda¸cão para a Ciência e a Tec-nologia; Operational Programme for Competitiveness and Internationalisation; Fundação para a Ciência e a Tecnologia, FCT; Federación Española de Enfermedades Raras, FEDER; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN, (PD/BD/114143/2015, POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","Funding text 1: Acknowledgements. This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸cão para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT - Funda¸cão para a Ciência e a Tec-nologia. We thank Yann Le Frank and the B2NOTE team for the availability of the beta version of B2NOTE and the helpful remarks.; Funding text 2: This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project TAIL, POCI-01-0145-FEDER-016736. João Aguiar Castro is supported by research grant PD/BD/114143/2015, provided by the FCT - Fundação para a Ciência e a Tecnologia. We thank Yann Le Frank and the B2NOTE team for the availability of the beta version of B2NOTE and the helpful remarks.","Addis M., RDM Workflows and Integrations for Higher Education Institutions Using Hosted Services, (2015); Assante M., Et al., Are scientific data repositories coping with research data publishing, Data Sci. J, 15, 6, pp. 1-24, (2016); Castro J.A., Da Silva J.R., Ribeiro C., Creating lightweight ontologies for dataset description. Practical applications in a cross-domain research data management workflow, IEEE/ACM Joint Conference on Digital Libraries (JCDL), (2014); Castro J.A., Et al., Involving Data Creators in an Ontology-Based Design Process for Metadata Models, pp. 181-214, (2017); EUDAT, (2017); EUDAT, (2017); Van Den Eynden V., Et al., Managing and sharing data -best practice for researchers, UK Data Archive, pp. 1-40, (2011); Hedden H., Taxonomies and controlled vocabularies best practices for metadata, J. Digit. Asset Manag, 6, 5, pp. 279-284, (2010); Huang S.-L., Lin S.-C., Chan Y.-C., Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing, Inf. Process. Manage, 48, 4, pp. 599-617, (2012); Karimova Y., Castro J.A., Vocabulários controlados na descrição de dados de investigação no Dendro, Cadernos BAD N.2, pp. 241-255, (2016); Latif A., (2017); Le Franc Y., Organise, Retrieve and Aggregate Data Using Annotations with B2NOTE, (2017); Mayernik M.S., Metadata Realities for Cyberinfrastructure: Data Authors as Metadata Creators, (2011); Pereira N., Da Silva J.R., Ribeiro C., Social Dendro: Social network techniques applied to research data description, TPDL 2017. LNCS, 10450, pp. 566-571, (2017); Pires A., Named Entity Recognition on Portuguese Web Text, (2017); Ribeiro C., Et al., Projeto TAIL -Gestão de dados de investigação da produção ao depósito e à partilha (Resultados preliminares), Cadernos BAD N.2, pp. 256-264, (2016); Shearer K., Furtado F., COAR survey of research data management: Results, Confederation of Openaccess Repositories, (2017); Silva F., Amorim R.C., Castro J.A., Da Silva J.R., Ribeiro C., End-to-end research data management workflows: A case study with Dendro and EUDAT, MTSR 2016. CCIS, 672, pp. 369-375, (2016); Da Silva J.R., Et al., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Ipres 2014 Conference Proceedings, (2014); Tenopir C., Et al., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, Plos One, 10, 8, (2015); Vines T.H., Et al., The availability of research data declines rapidly with article age, Curr. Biol, 24, 1, pp. 94-97, (2014); White H.C., Descriptive metadata for scientific data repositories: A comparison of information scientist and scientist organizing behaviors, J. Libr. Metadata, 14, 1, pp. 24-51, (2014); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, J. Assoc. Inf. Sci. Technol, 63, 8, pp. 1505-1520, (2012); Zervas P., Sampson D.G., The effect of users’ tagging motivation on the enlargement of digital educational resources metadata, Comput. Hum. Behav, 32, pp. 292-300, (2014); Zhang Y., Et al., Controlled vocabularies for scientific data: Users and desired functionalities, Proc. Assoc. Inf. Sci. Technol, 52, 1, pp. 1-8, (2015)","Y. Karimova; INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, Rua Dr. Roberto Frias, 4200-465, Portugal; email: ylaleo@gmail.com","Koutsomiha D.; Garoufallou E.; Siatri R.; Virkus S.","Springer Verlag","","11th International Conference on Metadata and Semantic Research, MTSR 2017","28 November 2017 through 1 December 2017","Tallinn","207139","18650929","978-331970862-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85036611239"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Are you ready? Tasks and roles for academic libraries in supporting Research 2.0","2016","New Library World","117","1-2","","94","104","10","18","10.1108/NLW-09-2015-0062","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953776303&doi=10.1108%2fNLW-09-2015-0062&partnerID=40&md5=e7c6f81235cb70a3996fe23dbf289860","Szent István University, Jászberény, Hungary","Koltay T., Szent István University, Jászberény, Hungary","Purpose – The purpose of this paper is to identify tasks and roles that academic libraries have to fulfil to react to the developments brought in by the appearance of Research 2.0. Design/methodology/approach – A review of current literature about the topic was performed. Findings – Literature used reveals that currently, there is a need for providing information literacy (IL) education (mainly in the form of data literacy), providing research data services (RDSs) (addressing data quality and data citation), raising awareness of faculty members on different issues and providing individual support to them. Originality/value – The paper intends to be an add-on to the body of knowledge about academic library support to researchers. © 2016, © Emerald Group Publishing Limited.","Academic libraries; Data literacy; Information literacy; Research 2.0; Research data management; Research data services","","","","","","","","Acord S.K., Harley D., Credit, time, and personality: the human challenges to sharing scholarly work using Web 2.0, New Media & Society, 5, 3, pp. 379-397, (2013); Information Literacy Competency Standards for Higher Education, (2000); Intersections of Scholarly Communication and Information Literacy: Creating Strategic Collaborations for a Changing Academic Environment, (2013); ACRL Research Planning and Review Committee. Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 75, 6, pp. 294-302, (2014); Framework for Information Literacy for Higher Education, (2015); Auckland M., Re-skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Ball A., Duke M., How to Cite Datasets and Link to Publications, (2015); Berger M., Cirasella J., Beyond Beall’s list: better understanding predatory publishers, College & Research Libraries News, 76, 3, pp. 132-135, (2015); Borgman C., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Brydges B., Clarke K., Is it time to re-envision the role of academic librarians in faculty research?, Library Connect, 13, 7, (2015); Calzada Prado J., Marzal M.A., Incorporating data literacy into information literacy programs: core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Candela L., Castelli D., Manghi P., Tani A., Data journals: a survey, Journal of the Association for Information Science and Technology, 66, 9, pp. 1747-1762, (2015); Christensen-Dalsgaard B., Ten Recommendations for Libraries to Get Started with Research Data Management, (2012); (2010); Collins E., Shorley D., Jubb M., Social media and scholarly communications: the more they change, the more they stay the same?, The Future of Scholarly Communication, pp. 89-102, (2013); Corrall S., Educating the academic librarian as a blended professional: a review and case study, Library Management, 31, 8-9, pp. 567-593, (2010); Davis-Kahl S., Hensley M.K., Common Ground at the Nexus of Information Literacy and Scholarly Communication, (2013); What is Digital Curation?, (2015); English R., The ACRL scholarly communications initiative: a progress report, College and Research Libraries News, 65, 8, pp. 450-453, (2004); Federer L., Emerging Trends in Librarianship: Exploring New Roles for Librarians: The Research Informationist, (2014); Genoni P., Merrick H., Willson M.A., Scholarly communities, e-research literacy and the academic librarian, The Electronic Library, 24, 6, pp. 734-746, (2006); Giarlo M., Academic libraries as quality hubs, Journal of Librarianship and Scholarly Communication, 1, 3, pp. 1-10, (2013); Gwyer R., Identifying and exploring future trends impacting on academic libraries: a mixed methodology using journal content analysis, focus groups, and trend reports, New Review of Academic Librarianship, 21, 3, pp. 269-285, (2015); Hswe P., Holt A., A New Leadership Role for Libraries, (2012); Quick guide to data citation, International Association for Social Science Information Services and Technology, Special Interest Group on Data Citation, (2012); Jahnke L., Asher A., Keralis S.D., The Problem of Data, (2012); Koltay T., Data literacy: in search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Spiranec S., Karvalics L.Z., The shift of information literacy towards Research 2.0, Journal of Academic Librarianship, 41, 1, pp. 87-93, (2015); LERU Roadmap for Research Data, (2013); Lyon L., Patel M., Takeda K., Assessing requirements for research data management support in academic libraries: introducing a new multi-faceted capability tool, Libraries in the Digital Age (LIDA) Proceedings, 13, pp. 131-134, (2014); McCluskey C., Being an embedded research librarian: supporting research by being a researcher, Journal of Information Literacy, 7, 2, pp. 4-14, (2013); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data curation: a study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); MacMillan D., Developing data literacy competencies to enhance faculty collaborations, LIBER Quarterly, 24, 3, pp. 140-160, (2015); Maatta S.L., Placements  &  salaries 2013: the emerging databrarian, Library Journal, 138, 17, pp. 26-33, (2013); Marcum D., Educating the Research Librarian: Are We Falling Short?, (2015); Molloy L., Snow K., The data management skills support initiative: synthesising postgraduate training in research data management, International Journal of Digital Curation, 7, 2, pp. 101-109, (2012); Nicholas D., Watkinson A., Jamali H., Herman E., Tenopir C., Volente R., Allard S., Levine K., Peer review: still king in the digital age, Learned Publishing, 28, 1, pp. 15-21, (2015); Nicholas D., Watkinson A., Volente R., Allard S., Levine K., Tenopir C., Herman E., Trust and authority in scholarly communications in the light of the digital transition, Learned Publishing, 27, 2, pp. 121-134, (2014); NMC Horizon Report: 2014 Library Edition, (2014); Ramirez M.L., Opinion: whose role is it anyway? A library practitioner’s appraisal of the digital data deluge, Bulletin of the American Society for Information Science and Technology, 37, 5, pp. 21-23, (2011); RECODE Policy recommendations for open access to research data, (2015); Research Data Netherlands R.D.N., Data prize, (2015); The SCONUL Seven Pillars of Information Literacy, (2011); Sharma S., Qin J., Data management: Graduate student’s awareness of practices and policies, 51, 1, pp. 1-3, (2014); Si L., Zhuang X., Xing W., Guo W., The cultivation of scientific data specialists: development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Starr J., Gastl A., isCitedBy: a metadata scheme for DataCite, D-LIB Magazine, 17, 1, (2011); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); The Royal Society T.R.S., Science as an Open Enterprise, (2012); Why is data management important?, (2015); Vitae V., Research development framework, (2011); Xia J., Li Y., Changed responsibilities in scholarly communication services: an analysis of job descriptions, Serials Review, 41, 1, pp. 15-22, (2015); Xia J., Wang M., Competencies and responsibilities of social science data librarians: an analysis of job descriptions, College and Research Libraries, 75, 3, pp. 362-388, (2014)","T. Koltay; Szent István University, Jászberény, Hungary; email: koltay.tibor@abpk.szie.hu","","Emerald Group Publishing Ltd.","","","","","","03074803","","","","English","New Libr. World","Article","Final","","Scopus","2-s2.0-84953776303"
"von Blumesberger S.","von Blumesberger, Susanne (56803873200)","56803873200","“Challenges for repositories!?„ Conference on the occasion of the tenth anniversary of the launch of phaidra at the university of Vienna (Vienna, october 24, 2017); [“Herausforderungen für repositorien!?„ Tagung anlässlich 10 jahre phaidra an der universität Wien (Wien, 24. oktober 2017)]","2017","VOEB-Mitteilungen","70","3-4","","372","376","4","0","10.31263/voebm.v70i3.1960","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041334487&doi=10.31263%2fvoebm.v70i3.1960&partnerID=40&md5=d9c7b7d56ea6c6d14c231e460f9f333e","Universität Wien, Bibliotheks- und Archivwesen, Austria","von Blumesberger S., Universität Wien, Bibliotheks- und Archivwesen, Austria","Ten years of experience with the operation of a repository shows that needs and issues are constantly changing. For example, it is necessary to consider which technologies and requirements of different stakeholder groups currently give rise to new or different thinking on repositories, and how current developments in this area can be conceived and implemented. Recent tasks, such as research data management and user habits, are contributing to a changing image of repositories. At this event, experts from various disciplines discussed how the future of repositories might look like, what considerations and resources are needed and necessary. © 2017, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Repositories; Research data management; Research support","","","","","","","","Schmale W., Strategische Optionen für institutionengebundene Repositorien in den Digital Humanities; Die Folien sind unter","S. von Blumesberger; Universität Wien, Bibliotheks- und Archivwesen, Austria; email: susanne.blumesberger@univie.ac.at","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85041334487"
"Fina F.; Proven J.","Fina, Federica (56045385200); Proven, Jackie (16231396100)","56045385200; 16231396100","Using a CRIS to Support Communication of Research: Mapping the Publication Cycle to Deposit Workflows for Data and Publications","2017","Procedia Computer Science","106","","","232","238","6","5","10.1016/j.procs.2017.03.020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020751028&doi=10.1016%2fj.procs.2017.03.020&partnerID=40&md5=f931d92f9c0f0439cff468fb9db4c905","University Library, University of St.Andrews, St.Andrews, KY16 9TR, United Kingdom","Fina F., University Library, University of St.Andrews, St.Andrews, KY16 9TR, United Kingdom; Proven J., University Library, University of St.Andrews, St.Andrews, KY16 9TR, United Kingdom","This paper describes a case study to explore how we continue to develop our CRIS and support the University's research needs and how it has become an embedded tool for researchers to manage their research outputs and to enable Open Access and Open Data. The paper will show how we used researchers' feedback and comments to develop a simple and easy to remember workflow mapped against existing and familiar research lifecycles. We examine some of the technical, practical and cultural issues we have encountered in implementing these workflows, and show how the CRIS as a single portal has streamlined tasks and reduced duplication of effort. © 2017 The Authors.","CRIS; Open Access; Pure; Research Data Management; workflows","Information management; Information systems; CRIS; Open Access; Pure; Research data managements; Work-flows; Sounding apparatus","","","","","","","Wellcome Trust Open Access Policy; Wellcome Trust Policy on Data Management and Sharing; EC Open Access; RCUK Policy on Open Access; RCUK Common Principles on Data Policy - RCUK Common Principles on Data Policy; Clarifications of EPSRC Expectations on Research Data Management; Clements A., McCutcheon V., Research data meets research information management: Two casestudies using (a) Pure CERIF-CRIS and (b) EPrints repositoryplatform with CERIF extensions, Procedia Computer Science, 33, pp. 199-206, (2014); Pure; DSpace Repository Software; Proven J., Aucock J., Increasing uptake at St.Andrews: Strategies for developing the research repository, ALISS Quarterly, 6, 3, pp. 6-9, (2011); OPEN REPOSITORIES 2008 from St.Andrews University LIS: Project to Integrate Research Publications Deposit Through A Centralised Deposit Workflow: Optimising the Relationship and Functionality of the St.Andrews Research Expertise Database and the St.Andrews Digital Research Repository; Open Access and Research Assessment Policy; LOCH Project; DataCite; St.Andrews Research Portal","F. Fina; University Library, University of St.Andrews, St.Andrews, KY16 9TR, United Kingdom; email: ff23@st-andrews.ac.uk","Clements A.; Sicilia M.-A.; Simons E.; de Castro P.","Elsevier B.V.","","13th International Conference on Current Research Information Systems, CRIS 2016","9 June 2016 through 11 June 2016","Scotland","135998","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85020751028"
"Schöpfel J.; Južnič P.; Prost H.; Malleret C.; Češarek A.; Koler-Povh T.","Schöpfel, Joachim (14619562900); Južnič, Primož (26432691900); Prost, Hélène (15069878000); Malleret, Cécile (57077016400); Češarek, Ana (57193317211); Koler-Povh, Teja (55803556500)","14619562900; 26432691900; 15069878000; 57077016400; 57193317211; 55803556500","Dissertations and data","2016","GL-Conference Series: Conference Proceedings","0","","","15","38","23","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012899521&partnerID=40&md5=9b537b7319d43899c7df60ab4c9bd1d9","GERiiCO Laboratory, University of Lille 3, France; Department of LIS, University of Ljubljana, Slovenia; CNRS, GERiiCO Laboratory, France; Academic Library, University of Lille 3, France; Academic Library, University of Ljubljana, Faculty of Civil and Geodetic Engineering, Slovenia","Schöpfel J., GERiiCO Laboratory, University of Lille 3, France; Južnič P., Department of LIS, University of Ljubljana, Slovenia; Prost H., CNRS, GERiiCO Laboratory, France; Malleret C., Academic Library, University of Lille 3, France; Češarek A., Department of LIS, University of Ljubljana, Slovenia; Koler-Povh T., Academic Library, University of Ljubljana, Faculty of Civil and Geodetic Engineering, Slovenia","The keynote provides an overview on the field of research data produced by PhD students, in the context of open science, open access to research results, e-Science and the handling of electronic theses and dissertations. The keynote includes recent empirical results and recommendations for good practice and further research. In particular, the paper is based on an assessment of 864 print and electronic dissertations in sciences, social sciences and humanities from the Universities of Lille (France) and Ljubljana (Slovenia), submitted between 1987 and 2015, and on a survey on data management with 270 scientists in social sciences and humanities of the University of Lille 3. The keynote starts with an introduction into data-driven science, data life cycle and data publishing. It then moves on to research data management by PhD students, their practice, their needs and their willingness to disseminate and share their data. After this qualitative analysis of information behaviour, we present the results of a quantitative assessment of research data produced and submitted with dissertations Special attention is paid to the size of the research data in appendices, to their presentation and link to the text, to their sources and typology, and to their potential for further research. The discussion puts the focus on legal aspects (database protection, intellectual property, privacy, third-party rights) and other barriers to data sharing, reuse and dissemination through open access. Another part adds insight into the potential handling of these data, in the framework of the French and Slovenian dissertation infrastructures. What could be done to valorise these data in a centralized system for electronic theses and dissertations (ETDs)? The topics are formats, metadata (including attribution of unique identifiers), submission/deposit, long-term preservation and dissemination. This part will also draw on experiences from other campuses and make use of results from surveys on data management at the Universities of Berlin and Lille. The conclusion provides some recommendations for the assistance and advice to PhD students in managing and depositing their research data, and also for further research. Our study will be helpful for academic libraries to develop assistance and advice for PhD students in managing their research data, in collaboration with the research structures and the graduate schools. Moreover, it should be helpful to prepare and select research data for long-term preservation, curate research data in open repositories and design data repositories. The French part of paper is part of an ongoing research project at the University of Lille 3 (France) in the field of digital humanities and research data, conducted with scientists and academic librarians. Its preliminary results have been presented at a conference on research data in February 2015 at Lille, at the 8th Conference on Grey Literature and Repositories at Prague in October 2015 and published in the Journal of Librarianship and Scholarly Communication. The Slovenian research results have not been published before.","Data repository; Electronic theses and dissertations; Institutional repository; Open access; Open data; Open science; Research data; Research data management","Behavioral research; Information management; Information services; Libraries; Life cycle; Students; Surveys; Data repositories; Electronic theses and dissertations; Institutional repositories; Open Access; Open datum; Open science; Research data; Research data managements; Data handling","","","","","","","Abbott D., Digital curation and doctoral research, International Journal of Digital Curation, 10, 1, pp. 1-17, (2015); Blake J.A., Bult C.J., Beyond the data deluge: Data integration and bio-ontologies, Journal of Biomedical Informatics, 39, pp. 314-320, (2006); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, 1-2, pp. 17-30, (2007); Brown S., Bruce R., Kernohan D., Directions for Research Data Management in UK Universities, (2015); Bult C.J., Data integration standards in model organisms: From genotype to phenotype in the laboratory mouse, Targets, 1, 5, pp. 163-168, (2002); Burnham A., An Introduction to Managing Research Data for Researchers and Students, (2013); Carr L., White W., Miles S., Mortimer B., Institutional repository checklist for serving institutional management, Third International Conference on Open Repositories 2008, (2008); Carroll M.W., Sharing research data and intellectual property law: A primer, PLoS Biol, 13, 8, (2015); Cassella M., Calvi L., New journal models and publishing perspectives in the evolving digital environment, IFLA Journal, 36, 1, pp. 7-15, (2010); Chaudiron S., Maignant C., Schopfel J., Westeel I., Livre Blanc sur les Données de la Recherche dans les Thèses de Doctorat, (2015); Costello M.J., Motivating online publication of data, BioScience, 59, 5, pp. 418-427, (2009); Cox A., Verbaan E., Sen B., A spider, an octopus, or an animal just coming into existence? Designing a curriculum for librarians to support research data management, Journal of EScience Librarianship, 3, 1, (2014); Doty J., Kowalski M.T., Nash B.C., O'Riordan S., Making student research data discoverable: A pilot program using dataverse, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Riding the Wave. How Europe can Gain from the Rising Tide of Scientific Data, (2010); Halipre A., Malleret C., Prost H., Les données de la recherche dans les thèses en SHS de l'Université de Lille 3 (poster), Journées ABES, (2015); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Hey T., Trefethen A.E., Cyberinfrastructure for e-science, Science, 308, 5723, pp. 817-821, (2005); Hey T., Hey J., E-science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Hey T., Tansley S., Tolle K., The Fourth Paradigm. Data-intensive Scientific Discovery, (2009); Higgins S., Draft DCC curation lifecycle model, International Journal of Digital Curation, 2, 2, pp. 82-87, (2008); Juznic P., Grey literature produced and published by universities: A case for ETDs, Grey Literature in Library and Information Studies, pp. 39-51, (2010); Kindling M., Doctoral theses' research data and metadata documentation, ETD 2013 Hong Kong 16th International Symposium on Electronic Theses and Dissertations 25 September 2013, (2013); Koler-Povh T., Lisec A., Geodetski vestnik and its path to better international recognition, Geodetski Vestnik, 59, 2, pp. 289-319, (2015); Koler-Povh T., Mikos M., Turk G., Institutional repository as an important part of scholarly communication, Library Hi Tech, 32, 3, pp. 423-434, (2014); Koler-Povh T., Turk G., Instructions for Theses Designing and Citing on UL FGG = Navodila za Oblikovanje Zaključnih Izdelkov Študijev na FGG in Navajanje Virov, (2011); Kowalczyk S., Shankar K., Data sharing in the sciences, Annual Review of Information Science and Technology, 45, 1, pp. 247-294, (2011); Laney D., 3D data management: Controlling data volume, velocity and variety, Tech. Rep., Gartner META Group, (2001); Ten Recommendations for Libraries to Get Started with Research Data Management, (2012); Lynch C., Jim gray's fourth paradigm and the construction of the scientific record, The Fourth Paradigm. Data-intensive Scientific Discovery, pp. 177-183, (2009); Lynch C., The need for research data inventories and the vision for SHARE, Information Standards Quarterly, 26, 2, (2014); Malleret C., Prost H., Les données de la recherche dans les thèses en SHS de l'Université de Lille 3, Séminaire DRTD-SHS ""Les Données de la Recherche dans les Humanités Numériques"", (2015); McDowell C.S., Evaluating institutional repository deployment in American academe since early 2005, D-Lib Magazine, 13, 9-10, (2007); McMahon B., Interactive publications and the record of science, Information Services and Use, 30, 1, pp. 1-16, (2010); Morris R.W., Bean C.A., Farber G.K., Gallahan D., Jakobsson E., Liu Y., Lyster P.M., Peng G.C.Y., Roberts F.S., Twery M., Whitmarsh J., Skinner K., Digital biology: An emerging and promising discipline, TRENDS in Biotechnology, 23, 3, pp. 113-117, (2005); Murray-Rust P., The power of the electronic scientific thesis, ETD 2007 10th International Symposium on Electronic Theses and Dissertations, (2007); Murray-Rust P., Open data in science, Serials Review, 34, 1, pp. 52-64, (2008); Neuroth H., Strathmann S., Osswald A., Ludwig J., Digital curation of research data, Experiences of a Baseline Study in Germany, (2013); Ojstersek M., Brezovnik J., Kotar M., Ferme M., Hrovat G., Bregant A., Borovic M., Establishing of a Slovenian open access infrastructure: A technical point of view, Program, 48, 4, pp. 394-412, (2014); Pampel H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., Goebelbecker H.-J., Gundlach J., Schirmbacher P., Dierolf U., Making research data repositories visible: The re3data.org registry, PLoS ONE, 8, 11, (2013); Prost H., Malleret C., Schopfel J., Hidden treasures. Opening data in PhD dissertations in social sciences and humanities, Journal of Librarianship and Scholarly Communication, 3, 2, (2015); Prost H., Schopfel J., Les Données de la Recherche en SHS, (2015); Reilly S., Schallier W., Schrimpf S., Smit E., Wilkinson M., Report on Integration of Data and Publications, (2011); Savage C.J., Vickers A.J., Empirical study of data sharing by authors publishing in PLoS journals, PLoS ONE, 4, 9, (2009); Savic D., INIS: Nuclear grey literature repository, 8th Conference on Grey Literature and Repositories, (2015); Schopfel J., Chaudiron S., Jacquemin B., Prost H., Severo M., Thiault F., Open access to research data in electronic theses and dissertations: An overview, Library Hi Tech, 32, 4, pp. 612-627, (2014); Schopfel J., Farace D.J., Grey literature, Encyclopedia of Library and Information Sciences, pp. 2029-2039, (2010); Schopfel J., Lipinski T.A., Legal aspects of grey literature, The Grey Journal, 8, 3, pp. 137-153, (2012); Schopfel J., Prost H., Degrees of secrecy in an open environment. The case of electronic theses and dissertations, ESSACHESS - Journal for Communication Studies, 6, 2, (2013); Schopfel J., Prost H., Malleret C., Making data in PhD dissertations reusable for research, 8th Conference on Grey Literature and Repositories, (2015); Schopfel J., Prost H., Piotrowski M., Hilf E.R., Severiens T., Grabbe P., A French-German survey of electronic theses and dissertations: Access and restrictions, D-Lib Magazine, 21, 3-4, (2015); Schultz M., Krabbenhoeft N., Skinner K., Guidance Documents for Lifecycle Management of ETDs, (2014); Sengupta S.S., E-thesis Repositories in the World: A Critical Analysis, (2014); Shotton D., The five stars of online journal articles - A framework for article evaluation, D-Lib Magazine, 18, 1-2, (2012); Siegel E.R., Lindberg D.A.B., Campbell G.P., Harless W.G., Goodwin C.R., Defining the next generation journal: The NLM-Elsevier interactive publications experiment, Information Services and Use, 30, 1, pp. 17-30, (2010); Simpson P., Hey J., Repositories for research: Southampton's evolving role in the knowledge cycle, Program: Electronic Library and Information Systems, 40, 3, pp. 224-231, (2006); Simukovic E., Kindling M., Schirmbacher P., Unveiling research data stocks: A case of Humboldt-Universität zu Berlin, IConference, pp. 742-748, (2014); Suber P., Open Access, (2012); Sun S., Chen J., Li W., Altintas I., Lin A., Peltier S., Stocks K., Allen E.E., Ellisman M., Grethe J., Wooley J., Community cyberinfrastructure for advanced microbial ecology research and analysis: The CAMERA resource, Nucleic Acids Research, 39, pp. D546-D551, (2011); Vompras J., Schirrwagen J., Repository workflow for interlinking research data with grey literature, 8th Conference on Grey Literature and Repositories, (2015); Walker E.P., What we can learn from ETDs: Using ProQuest dissertations & theses as a dataset, USETDA 2011: The Magic of ETDs⋯Where Creative Minds Meet, (2011); Wang S., Liu Y., TeraGrid GIScience gateway: Bridging cyberinfrastructure and GIScience, International Journal of Geographical Information Science, 23, 5, pp. 631-656, (2009)","","","TextRelease","Data Archiving and Networked Services (DANS), Royal Netherlands Academy of Arts and Sciences; EBSCO; et al.; Korea Institute of Science and Technology Information (KISTI); Nuclear Information Section - International Atomic Energy Agency (NIS-IAEA); Slovak Centre of Scientific and Technical Information (CVTISR)","17th International Conference on Grey Literature: A New Wave of Textual and Non-Textual Grey Literature, GL 2016","1 December 2015 through 2 December 2015","Amsterdam","125826","13862316","978-907748427-2","","","English","GL-Conf. Series: Conf. Proc.","Conference paper","Final","","Scopus","2-s2.0-85012899521"
"Traub I.D.; Solís B.S.; Budroni P.","Traub, von Imola Dora (57189700692); Solís, Barbara Sánchez (56702943200); Budroni, Paolo (56624471600)","57189700692; 56702943200; 56624471600","Forschungsdaten und zeitgemäße aufarbeitung durch policies – 2. internationaler learn workshop zum thema ‚Forschungsdatenmanagement‘ (Wien, 6. April 2016)","2016","VOEB-Mitteilungen","69","1","","142","150","8","1","10.31263/voebm.v69i1.1406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974712843&doi=10.31263%2fvoebm.v69i1.1406&partnerID=40&md5=2e049a3bb41cfb2b160e2cf32c024589","Bibliotheks- und Archivwesen der Universität Wien, Austria","Traub I.D., Bibliotheks- und Archivwesen der Universität Wien, Austria; Solís B.S., Bibliotheks- und Archivwesen der Universität Wien, Austria; Budroni P., Bibliotheks- und Archivwesen der Universität Wien, Austria","The 2nd Workshop of the H2020 project LEARN was held at the University of Vienna on April 6th 2016. The Workshop was focused on Research Data Management towards Open Science and the development of policies and was organised together with the partners from University College London (UCL), University of Barcelona, LIBER and the United Nations Economic Commission for Latin America and the Caribbean (ECLAC). The Workshop was designed to encourage all stakeholders - researchers, research funders, research organisations and senior decision makers - to explore what their roles and responsibilities are in the fast-changing environment of infrastructure development and research data management. The topics were driven by four keynotes in the morning session which addressed policy development and alignment. In the afternoon session, representatives from eight European countries shared their experiences in three parallel Round Tables. © 2016, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Open science; Policies; Research data; Research data management; Research funders; Research institutions","","","","","","","","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84974712843"
"Budroni P.; Flicker K.; Solís B.S.","Budroni, Paolo (56624471600); Flicker, Katharina (57193201294); Solís, Barbara Sánchez (56702943200)","56624471600; 57193201294; 56702943200","E-infrastructures Austria – Training seminar for research data stewardship and e-infrastructures (Vienna, June 6–9, 2016); [E-infrastructures Austria – Fortbildungsseminar für forschungsdaten und e-infrastrukturen (Wien, 6.–9. Juni 2016)]","2016","VOEB-Mitteilungen","69","3-4","","492","500","8","0","10.31263/voebm.v69i3.1738","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011599375&doi=10.31263%2fvoebm.v69i3.1738&partnerID=40&md5=6a6287e4e5ec1b343d8c897876b009a5","E-Infrastructures Austria, Archivwesen der Universität Wien, Austria","Budroni P., E-Infrastructures Austria, Archivwesen der Universität Wien, Austria; Flicker K., E-Infrastructures Austria, Archivwesen der Universität Wien, Austria; Solís B.S., E-Infrastructures Austria, Archivwesen der Universität Wien, Austria","e-infrastructures Austria hosted a four-day training seminar for research data stewardship and e-infrastructures in order to aid representatives from libraries, research services and IT services in the establishment of institutional repositories and research support services. The lectures offered covered technical, organizational and legal themes. They acted as a supplement to existing educational opportunities and encouraged the exchange of knowledge in the areas of research data management, data stewardship and workflows of research processes and digital archiving. © 2016, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All Rights Reserved.","E-infrastructures Austria; Education and training; Electronic infrastructures; Research data management","","","","","","","","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85011599375"
"Bauer B.; Budroni P.; Ferus A.; Ganguly R.; Ramminger E.; Solís B.S.","Bauer, Bruno (57200436821); Budroni, Paolo (56624471600); Ferus, Andreas (37009618700); Ganguly, Raman (56702193000); Ramminger, Eva (55376789000); Solís, Barbara Sánchez (56702943200)","57200436821; 56624471600; 37009618700; 56702193000; 55376789000; 56702943200","E-infrastructures Austria 2016: Report about the third year of the higher education area structural funding project for the coordinated establishment and cooperative development of repository infrastructures; [E-infrastructures Austria 2016: Bericht über das dritte jahr des hochschulraumstrukturmittelprojekts für den koordinierten aufbau und die kooperative weiterentwicklung von repositorieninfrastrukturen]","2017","VOEB-Mitteilungen","70","1","","66","93","27","2","10.31263/voebm.v70i1.1834","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019251708&doi=10.31263%2fvoebm.v70i1.1834&partnerID=40&md5=c357d6939ef6c83be272ab8c07b3cbb4","Universitätsbibliothek der Medizinischen Universität Wien, Austria; Bibliotheks- und Archivwesen der Universität Wien, Austria; Universitätsbibliothek der Akademie der bildenden Künste Wien, Austria; Zentraler Informatikdienst der Universität Wien, Austria; Universitäts-und Landesbibliothek Tirol, Austria","Bauer B., Universitätsbibliothek der Medizinischen Universität Wien, Austria; Budroni P., Bibliotheks- und Archivwesen der Universität Wien, Austria; Ferus A., Universitätsbibliothek der Akademie der bildenden Künste Wien, Austria; Ganguly R., Zentraler Informatikdienst der Universität Wien, Austria; Ramminger E., Universitäts-und Landesbibliothek Tirol, Austria; Solís B.S., Bibliotheks- und Archivwesen der Universität Wien, Austria","In the final project year of e-Infrastructures Austria, a program for the coordinated expansion and continued development of repositories across Austria, funded by the Austrian Ministry of Science, Research and Commerce (BMWFW), the objectives of the three defined subprojects were successfully met. While at the beginning of the project only 3 institutions in Austria had repositories in place, after three years, 5 institutions had data repositories and 17 institutions had publication servers in use, five more were in development and 3 being planned. In 2016, a „task force dedicated to finding strategies for the management of research data in Austria“ created a Model Policy for Research Data Management at Austrian research institutions. At some partner institutions the topic of data management plans was introduced. The exchange of experiences on technical, organisational, legal, and content related issues could be achieved through Workshops on metadata and long term archiving and particularly through a four-day training seminar for research data stewardship and e-infrastructures. Topics related to research data management will fortunately be continued. Apart from the follow-up project e-Infrastructures Austria Plus, there are currently several national publicly funded cooperative projects in place supporting the Amsterdam Call for Action on Open Science and complying with the requirements of the European Open Science Cloud (EOSC). © 2017, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Archiving; Austria; Digital assets; Document server; E-accessibility; Infrastructure; Network; Open access; Open data; Open science; Open universities; Policies; Repository; Research data; Research data management","","","","","","","","Bauer B., Budroni P., Ferus R., Ganguly R., Ramminger E., Barbara Sánchez Solís: E-Infrastructures Austria 2014: Bericht über das erste Jahr des Hochschulraumstrukturmittelprojekts zur Förderung für den koordinierten Aufbau und die kooperative Weiterentwicklung von Repositorieninfrastrukturen, Mitteilungen Der Vereinigung Österreichischer Bibliothekarinnen Und Bibliothekare, 68, 1, pp. 91-118, (2015); Bauer B., Budroni P., Ferus R., Ganguly R., Ramminger E., Barbara Sánchez Solís: E-Infrastructures Austria 2015: Bericht über das zweite Jahr des Hochschulraumstrukturmittelprojekts für den koordinierten Aufbau und die kooperative Weiterentwicklung von Repositorieninfrastrukturen, Mitteilungen Der Vereinigung Österreichischer Bibliothekarinnen Und Bibliothekare, 69, 1, pp. 9-40, (2016)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85019251708"
"Koltay T.","Koltay, Tibor (6505905944)","6505905944","Facing the challenge of data-intensive research: Research data services and data literacy in academic libraries","2016","Advances in Library Administration and Organization","35","","","45","61","16","5","10.1108/S0732-067120160000035008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006298289&doi=10.1108%2fS0732-067120160000035008&partnerID=40&md5=08746b06870140e6c2da17babeb10509","","","Purpose-This chapter describes challenges that academic libraries face in the era of data-intensive research. Methodology/approach-A review of current literature about the topic was performed. The main features of the data-intensive paradigm of research are outlined and new tasks to be performed by academic libraries are explored. Findings-To fulfil their mission in this environment, academic libraries have to be equipped with tools that can be epitomised as research data services and include research data-management and digital data curation. Issues of data quality, data citation and data literacy are also of prime importance for related academic library services that also need to employ new' librarians, that is professionals, armed with novel and adequate skills. Originality/value-The chapter outlines both background and practice, associated with data-related opportunities and responsibilities. © 2017 by Emerald Group Publishing Limited All rights of reproduction in any form reserved.","Data citation; Data curation; Data literacy; Data quality; Research data management; Research data services","","","","","","","","Intersections of Scholarly Communication and Information Literacy: Creating Strategic Collaborations for A Changing Academic Environment, (2013); ACRL research planning and review committee. Top ten trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 75, 6, pp. 294-302, (2014); ACRL research planning and review committee. 2016 top trends in academic libraries. A review of the trends and issues affecting academic libraries in higher education, College and Research Libraries News, 77, 6, pp. 274-281, (2016); Altman M., Crosas M., The evolution of data citation: From principles to implementation, IASSIST Quarterly, 37, pp. 62-70, (2013); Auckland M., Re-skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Bonn M., Tooling up. Scholarly communication education and training, College and Research Libraries News, 75, 3, pp. 132-135, (2014); Buckland M., Data management as bibliography, Bulletin of the American Society for Information Science and Technology, 37, 6, pp. 34-37, (2011); Calzada Prado J., Marzal M.A., Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63, 2, pp. 123-134, (2013); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty, Portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Johnston L., Westra B., Nichols M., Developing an approach for data management education: A report from the data information literacy project, International Journal of Digital Curation, 8, 1, pp. 204-217, (2013); Carlson J., Stowell Bracke M., Planting the seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, 1, pp. 95-110, (2015); Christensen-Dalsgaard B., Van Den Berg M., Grim R., Horstmann W., Jansen D., Pollard T., Roos A., Ten Recommendations for Libraries to Get Started with Research Data Management, (2012); Data Citation Standards and Practices, (2010); Corrall S., Library service capital: The case for measuring and managing intangible assets, Libraries in the Digital Age (LIDA) Proceedings, 13, pp. 21-32, (2014); Cox A.M., Corrall S., Evolving academic library specialties, Journal of the American Society for Information Science and Technology, 64, 8, pp. 1526-1542, (2013); Dalbello M., A genealogy of digital humanities, Journal of Documentation, 67, 3, pp. 480-506, (2011); What Is Digital Curation? Digital Curation Centre, (2015); Davis-Kahl S., Hensley M.K., Common Ground at the Nexus of Information Literacy and Scholarly Communication, (2014); Erway R., Starting the Conversation: University-wide Research Data Management Policy. Educause Review Online, (2013); Federer L., The librarian as research informationist: A case study, Journal of the Medical Library Association, 101, 4, pp. 298-302, (2013); Giarlo M., Academic libraries as quality hubs, Journal of Librarianship and Scholarly Communication, 1, 3, pp. 1-10, (2013); Haendel M.A., Vasilevsky N.A., Wirz J.A., Dealing with data: A case study on information and data management literacy, PLoS Biology, 10, 5, (2012); Hunt K., The challenges of integrating data literacy into the curriculum in an undergraduate institution, IASSIST Quarterly, 28, 2, pp. 12-15, (2004); Quick Guide to Data Citation. International Association for Social Science Information Services and Technology, (2012); Jahnke L., Asher A., Keralis S.D., The Problem of Data, (2012); Johnson C.A., The Information Diet: A Case for Conscious Consumption, (2012); Johnston L., Lafferty M., Petsan B., Training researchers on data management: A scalable, cross-disciplinary approach, Journal of EScience Librarianship, 1, 2, pp. 79-87, (2012); Koltay T., Data literacy: Search of a name and identity, Journal of Documentation, 71, 2, pp. 401-415, (2015); Koltay T., Spiranec S., Karvalics Z.L., The shift of information literacy towards research 2.0, Journal of Academic Librarianship, 41, 1, pp. 87-93, (2015); Kowalczyk S., Shankar K., Data sharing in the sciences, Annual Review of Information Science and Technology, 45, 1, pp. 247-294, (2011); Lagoze C., Edwards P., Sandvig C., Plantin J.C., Should i stay or should i go? Alternative infrastructures in scholarly publishing, International Journal of Communication, 9, 20, pp. 1052-1071, (2015); Li S., Xiaozhe Z., Wenming X., Weining G., The cultivation of scientific data specialists: Development of LIS education oriented to e-science service requirements, Library Hi Tech, 31, 4, pp. 700-724, (2013); Little G., We are all digital humanists now, The Journal of Academic Librarianship, 37, 4, pp. 352-354, (2011); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Lyon L., Patel M., Takeda K., Assessing requirements for research data management support in academic libraries: Introducing a new multi-faceted capability tool, Libraries in the Digital Age (LIDA) Proceedings, 13, pp. 131-134, (2014); Maatta S.L., Placements and salaries (2013). The emerging databrarian, Library Journal, 138, 17, pp. 26-33, (2013); MacMillan D., Data sharing and discovery: What librarians need to know, Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); MacMillan D., Developing data literacy competencies to enhance faculty collaborations, LIBER Quarterly, 24, 3, pp. 140-160, (2015); Mandinach E.B., Gummer E.S., A systemic view of implementing data literacy in educator preparation, Educational Researcher, 42, 1, pp. 30-37, (2013); About MANTRA. University of Edinburgh, (2014); McCluskey C., Being an embedded research librarian: Supporting research by being a researcher, Journal of Information Literacy, 7, 2, pp. 4-14, (2013); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data cura-tion: A study of researcher practices and needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014); Library+, Public Services Quarterly, 7, 3-4, pp. 144-148, (2011); Molloy L., Snow K., The data management skills support initiative: Synthesising postgraduate training in research data management, International Journal of Digital Curation, 7, 2, pp. 101-109, (2012); Mooney H., A practical approach to data citation: The special interest group on data citation and development of the quick guide to data citation, IASSIST Quarterly, 37, pp. 71-77, (2013); Mooney H., Newton M.P., The anatomy of a data citation: Discovery, reuse, and credit, Journal of Librarianship and Scholarly Communication, 1, 1, pp. 1-14, (2012); Moretti F., Graphs, Maps, Trees: Abstract Models for Literary Theory, (2005); Nielsen H.J., Hjorland B., Curating research data: The potential roles of libraries and information professionals, Journal of Documentation, 70, 2, pp. 221-240, (2014); NMC Horizon Report: 2014 Library Edition, (2014); Qin J., D'Ignazio J., Lessons Learned from a Two-year Experience in Science data Literacy education, 2, (2010); Ramirez M.L., Opinion: Whose role is it anyway? A library practitioners appraisal of the digital data deluge, Bulletin of the American Society for Information Science and Technology, 37, 5, pp. 21-23, (2011); RECODE Policy Recommendations for Open Access to Research Data, (2015); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Government Information Quarterly, 30, pp. S19-S31, (2013); Schneider R., Research data literacy, Worldwide Commonalities and Challenges in Information Literacy Research and Practice, pp. 134-140, (2013); Sharma S., Qin J., Data management: Graduate student's awareness of practices and policies, Proceedings of the American Society for Information Science and Technology, 51, 1, pp. 1-3, (2014); Shen Y., Varvel V.E., Developing data management services at the Johns Hopkins University, The Journal of Academic Librarianship, 39, 6, pp. 552-557, (2013); Shen Y., Varvel V.E., Data management consulting at the Johns Hopkins University, New Review of Academic Librarianship, 19, 3, pp. 224-245, (2013); Shorish Y., Data information literacy and undergraduates: A critical competency, College and Undergraduate Libraries, 22, 1, pp. 97-106, (2015); Smith M., Communicating with data: New roles for scientists, publishers and librarians, Learned Publishing, 24, 3, pp. 203-205, (2011); Soehner C., Steeves C., Ward J., E-science and Data Support Services: A Study of ARL Member Institutions, (2010); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Thomas W.J., The structure of scholarly communications within academic libraries, Serials Review, 39, 3, pp. 167-171, (2013); Torres-Salinas D., Martin-Martin A., Fuente-Gutierrez E., Analysis of the coverage of the data citation index-Thomson Reuters: Disciplines, document types and repositories, Revista Espanola de Documentacion Científica, 37, 1, (2014); Why Is Data Management Important?, (2015); Wang M., Supporting the research process through expanded library data services, Program: Electronic Library and Information Systems, 47, 3, pp. 282-303, (2013); Witt M., Research data services for compliance, collaboration and scholarship, Library Connect, 13, 5, (2015); Xia J., Li Y., Changed responsibilities in scholarly communication services: An analysis of job descriptions, Serials Review, 41, 1, pp. 15-22, (2015); Xia J., Wang M., Competencies and responsibilities of social science data librarians: An analysis of job descriptions, College and Research Libraries, 75, 3, pp. 362-388, (2014); Zikopoulos P., DeRoos D., Bienko C.H., Buglio R., Andrews M., Big Data beyond the Hype. A Guide to Conversations for Today's Data Center, (2015)","","","Emerald Group Publishing Ltd.","","","","","","07320671","","","","English","Adv. Libr. Adm. Organ.","Review","Final","","Scopus","2-s2.0-85006298289"
"Raju R.; Raju J.; Johnson G.","Raju, Reggie (36162221500); Raju, Jaya (7006701909); Johnson, Glynnis (57189274124)","36162221500; 7006701909; 57189274124","Research support services in south african academic libraries","2016","Quality and the Academic Library: Reviewing, Assessing and Enhancing Service Provision","","","","167","177","10","4","10.1016/B978-0-12-802105-7.00016-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84967604765&doi=10.1016%2fB978-0-12-802105-7.00016-6&partnerID=40&md5=81895f3dab6055a7c68c96c343b42c58","University of Cape Town Libraries, Cape Town, South Africa; Library and Information Studies Centre, University of Cape Town, Cape Town, South Africa","Raju R., University of Cape Town Libraries, Cape Town, South Africa; Raju J., Library and Information Studies Centre, University of Cape Town, Cape Town, South Africa; Johnson G., University of Cape Town Libraries, Cape Town, South Africa","Changing pedagogy and rapid growth of commensurate technologies have triggered demand from academic libraries for new research support services such as bibliometrics, data management, digital preservation and curation, open access (OA) and open journal publishing. South African academic libraries have responded, to an extent, to the changing research support needs of their research communities. Some of the mainstream research support services provided by South African academic libraries include bibliometrics, open scholarship services and research data management. There are also some non-typical research support services and/or activities that are provided - these include research landscape analysis, research week and research engagement provision. © 2016 Jeremy Atkinson Published by Elsevier Ltd.","Bibliometrics; Open access; Research data management; Research support services; Scholarly communication; South Africa","","","","","","","","Astrom F., Hansson J., Olsson M., Bibliometrics and the changing role of the university libraries, (2011); Auckland M., Re-skilling for research: An investigation into the roles and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Jantz R.C., Innovation in academic libraries: An analysis of university librarians' perspectives, Library and Information Science Research, 34, 1, pp. 3-12, (2012); Kennan M.A., Corrall S., Afzal W., Making space' in practice and education: Research support services in academic libraries, Library Management, 35, 8-9, pp. 666-683, (2013); Martell C., The disembodied librarian in the digital age, College & Research Libraries, 61, 1, pp. 10-28, (2000); Growth of the OpenDOAR database - South Africa, (2015); Raju R., Smith I., Gibson H., Talliard P., Open access: Are we there yet? - The case of Stellenbosch University, South Africa, South African Journal of Libraries and Information Science, (2012); Schoombee L., Du Plessis P., Making the link: The library's role in facilitating research collaboration, (2013); Schrader A.M., Teaching bibliometrics, Library Trends, 30, 1, pp. 151-172, (1981); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Library and Information Science Research, 36, 2, pp. 84-90, (2014); Zhao D., Bibliometrics and LIS education: How do they fit together?, Proceedings of the American Society for Information Science and Technology, 48, 1, pp. 1-4, (2011)","","","Elsevier Inc.","","","","","","","978-012802105-7","","","English","Qual. and the Acad. Libr.: Rev., Assess. and Enhanc. Serv. Provis.","Book chapter","Final","","Scopus","2-s2.0-84967604765"
"Ganguly R.; Budroni P.; Solís B.S.","Ganguly, Raman (56702193000); Budroni, Paolo (56624471600); Solís, Barbara Sánchez (56702943200)","56702193000; 56624471600; 56702943200","Living digital ecosystems for data Preservation: An Austrian Use Case Towards the European Open Science Cloud","2017","Expanding Perspectives on Open Science: Communities, Cultures and Diversity in Concepts and Practices - Proceedings of the 21st International Conference on Electronic Publishing","","","","203","210","7","0","10.3233/978-1-61499-769-6-203","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020735631&doi=10.3233%2f978-1-61499-769-6-203&partnerID=40&md5=7a57892d41a3dfbfbf5c28af561b58d9","University of Vienna, University Computer Center, Universitaetsstraße 7 (NIG), Vienna, 1010, Austria; University of Vienna, University Library and Archive Services, Austria","Ganguly R., University of Vienna, University Computer Center, Universitaetsstraße 7 (NIG), Vienna, 1010, Austria; Budroni P., University of Vienna, University Computer Center, Universitaetsstraße 7 (NIG), Vienna, 1010, Austria; Solís B.S., University of Vienna, University Library and Archive Services, Austria","This paper will address issues concerning the handling of complex data such as research data, multimedia content, e-learning content, and the use of repositories infrastructures. At the University of Vienna, an ecosystem for digital data preservation and research data management has already been established and will be subsequently be enlarged according to future needs and requirements. in the future. This living digital ecosystem is the foundation for research data management and was implemented from the beginning as a central service according to the FAIR principles as stated in the first HLEG-EOSC [1] report. With the help of ten years of professional experience, a model for digital data preservation was established to address the complexity of heterogeneous data. This was necessary because of different use cases assigned to the interdisciplinary data management team based at the Computer Centre and the Library. The source for the use cases are research projects, their different approach to research and their multifaceted requirements regarding the efficient re-use of data. The usage of this model might be considered as the foundation on which an ecosystem for digital data preservation can be built. © 2017 The authors and IOS Press.","Data life cycle; Digital workflow; Repositories infrastructure; Research data management; Visualization of data","Data visualization; Ecology; Ecosystems; Electronic publishing; Human resource management; Information management; Life cycle; Data life cycle; Digital workflows; E-learning contents; Multimedia contents; Professional experiences; Repositories infrastructure; Research data managements; University of Vienna; Data handling","","","","","","","Realising the European Open Science Cloud. First Report and Recommendations of the Commission High Level Expert Group on the European Open Science Cloud; PARSE. Insight Report from the FP7-2007-223758; Reilly S., Schallier W., Schrimpf S., Smit E., Wilkinson M., Report on Integration of Data and Publications. Opportunities for Data Exchange (ODE); Reference Model for an Open Archival Information System (OAIS), (2012); Wilkinson M.D., Dumontier M., Mons B., The FAIR guiding principles for scientific data management and stewardship, Nature; Burgelman J.C., European Open Science Agenda, (2016); E-infrstructures Austria. Researchers and their Data. Results of an Austrian Survey, (2015); SKOS Simple Knowledge Organization System; Miksa T., Information integration through actionable data management plans,  Plenary","R. Ganguly; University of Vienna, University Computer Center, Vienna, Universitaetsstraße 7 (NIG), 1010, Austria; email: raman.ganguly@univie.ac.at","Chan L.; Loizides F.","IOS Press BV","Association Computing Machinery (ACM); ELSEVIER; Emerald Publishing; et al.; Frontiers; IEEE","21st International Conference on Electronic Publishing, ELPUB 2017","6 June 2017 through 8 June 2017","Limassol","128010","","978-161499768-9","","","English","Expand. Perspect. Open Sci.: Communities, Cult. Divers. Concepts Pract. - Proc. Int. Conf. Electron. Publ.","Conference paper","Final","","Scopus","2-s2.0-85020735631"
"Weber A.; Piesche C.","Weber, Andreas (36984357000); Piesche, Claudia (54929877200)","36984357000; 54929877200","Requirements on long-Term accessibility and preservation of research results with particular regard to their provenance","2016","ISPRS International Journal of Geo-Information","5","4","49","","","","3","10.3390/ijgi5040049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008885233&doi=10.3390%2fijgi5040049&partnerID=40&md5=22255cf79acee3a0bf6aa614d7c0a7ef","IT Service Centre, University of Bayreuth, Bayreuth, 95447, Germany","Weber A., IT Service Centre, University of Bayreuth, Bayreuth, 95447, Germany; Piesche C., IT Service Centre, University of Bayreuth, Bayreuth, 95447, Germany","Since important national and international funders of research projects require statements on the long-Term accessibility of research results, many new solutions appeared to fulfil these demands. The solutions are implemented on various scopes, starting from specific solutions for one research group up to solutions with a national focus (i.e., the RADAR project). While portals for globally standardized research data (e.g., climate data) are available, there is currently no provision for the large amount of data resulting from specialized research in individual research foci, the so called long-Tail of sciences. In this article we describe the considerations regarding the implementation of a local research data repository for the Collaborative Research Centre (CRC) 840. The main focus will be on the examination of requirements for, and an agenda of, a possible technical implementation. Requirements were derived from a more theoretical examination of similar projects and relevant literature, diverse discussions with researchers and project leaders, by analysis of existing publication data, and finally the prototypical implementation with refining iterations. Notably, the discussions with the researchers lead to new features going beyond the challenges of the mere long-Term preservation of research data. Besides the need for an infrastructure that permits long-Term preservation and retrieval of research data, our system will allow the reconstruction of the complete provenance of published research results. This requirement is a serious diversification of the problem, because it creates the need to qualify additional transformation data, describing the transformation process from primary research data to research results. © 2016 by the authors; licensee MDPI, Basel, Switzerland.","Long-Term preservation; Metadata; Publications; Research Data Management","","","","","","Deutsche Forschungsgemeinschaft, DFG","This research was supported by the Deutsche Forschungsgemeinschaft (DFG) in the scope of the sub-project ""INF Z2"" of the Collaborative Research Centre (CRC) 840.","OECD: OECD Principles and Guidelines for Access to Research Data from Public Funding; Effertz E., The funder's perspective: Data management in coordinated programs of the German Research Foundation (DFG), Proceedings of the Data Management Workshop, pp. 35-38; HGP: The Human Genome Project; The Human Genome Project (Archived Information); GRBIO: The Global Registry of Biorepositories; Kommel C., Nach den Sondersammelgebieten: Fachinformationen als forschungsnaher Service, Z. Bibl. Bibliogr, 60, pp. 5-15, (2013); Mittler E., Nachhaltige Infrastruktur för die Literatur-und Informationsversorgung: Im digitalen Zeitalter ein öberholtes Paradigma-Oder so wichtig wie noch nie? Bibl, Forsch. Praxis, 3, pp. 344-364, (2014); Scientific Library Services & Information Systems-Funding Priorities Through 2015. Available Online; Ludwig J., Enke H., Leitfaden Zum Forschungsdaten-Management-Handreichungen Aus Dem WissGrid-Projekt, (2013); Treloar A., Harboe-Ree C., Data Management and the Curation Continuum: How the Monash Experience Is Informing Repository Relationships; Hunter J., Scientific publication packages-A selective approach to the communication and archival of scientific output, Int. J. Digit. Curation, 1, pp. 33-52, (2006); RADAR-Research Data Repository; RADAR-Research Data Repositorium. DFG-Antrag; Razum M., Neumann J., Das RADAR Projekt: Datenarchivierung Und-publikation Als Dienstleistung-disziplinöbergreifend, Nachhaltig, Kostendeckend, 1, pp. 30-44, (2014); Potthoff J., Van Wezel J., Razum M., Walk M., Anforderungen Eines Nachhaltigen Disziplinöbergreifenden Forschungsdaten-Repositoriums; Kraft A., RADAR-A Repository for Long Tail Data; Woutersen-Windhouwer S., Brandsma R., Enhanced Publications State of the Art Enhanced Publications-Linking Publications and Research Data in Digital Repositories, pp. 19-91, (2009); Linking Compounds and Concepts in Articles; Bottner S., Hobohm H.-C., Moller L., Research Data Management Handbuch Forschungsdatenmanagement, pp. 13-25, (2011); Collaborative Research Centre 840: ""from Particulate Nanosystems to Mesotechnology"": Focus and Approach of the Collaborative Research Center SFB 840; API Documentation","A. Weber; IT Service Centre, University of Bayreuth, Bayreuth, 95447, Germany; email: andreas.weber@uni-bayreuth.de","","MDPI AG","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85008885233"
"Willmes C.; Becker D.; Verheul J.; Yener Y.; Zickel M.; Bolten A.; Bubenzer O.; Bareth G.","Willmes, C. (55878198600); Becker, D. (57162021100); Verheul, J. (57193456455); Yener, Y. (57202620459); Zickel, M. (57202621329); Bolten, A. (15749839500); Bubenzer, O. (6506755668); Bareth, G. (6505760587)","55878198600; 57162021100; 57193456455; 57202620459; 57202621329; 15749839500; 6506755668; 6505760587","AN OPEN SCIENCE APPROACH to GIS-BASED PALEOENVIRONMENT DATA","2016","ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","3","","","159","164","5","2","10.5194/isprs-annals-III-2-159-2016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048921797&doi=10.5194%2fisprs-annals-III-2-159-2016&partnerID=40&md5=36cad3cf47db2c5af49fee1e362e549b","Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany","Willmes C., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Becker D., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Verheul J., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Yener Y., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Zickel M., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Bolten A., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Bubenzer O., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Bareth G., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany","Paleoenvironmental studies and according information (data) are abundantly published and available in the scientific record. However, GIS-based paleoenvironmental information and datasets are comparably rare. Here, we present an Open Science approach for creating GIS-based data and maps of paleoenvironments, and Open Access publishing them in a web based Spatial Data Infrastructure (SDI), for access by the archaeology and paleoenvironment communities. We introduce an approach to gather and create GIS datasets from published non-GIS based facts and information (data), such as analogous maps, textual information or figures in scientific publications. These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web based Open Access Research Data Management Infrastructure. The geo-datasets are additionally published in an Open Geospatial Consortium (OGC) standards compliant SDI, and available for GIS integration via OGC Open Web Services (OWS).","Data integration; Data management; FOSS4G; GIS; Open Access; Open Data; Open Science; Paleoenvironment; SDI; Spatio-Temporal","","","","","","","","Becker D., De Andres-Herrero M., Willmes C., Bareth G., Weniger G.-C., Investigating the influence of different DEMs and environmental data on GIS-based cost distance modelling for a case study of prehistoric sites in Andalusia, ISPRS International Journal of Geo-Information, (2016); Becker D., Verheul J., Zickel M., Willmes C., LGM paleoenvironment of Europe-Map, CRC806-Database, (2015); Becker D., Willmes C., Bareth G., Weniger G.-C., A plugin to interface openModeller from QGIS for species' potential distribution modelling, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, (2016); OpenGeo Suite Plugin for QGIS, (2014); Braconnot P., Otto-Bliesner B., Harrison S., Joussaume S., Peterschmitt J.-Y., Abe-Ouchi A., Crucifix M., Driesschaert E., Fichefet T., Hewitt C.D., Kageyama M., Kitoh A., Laine A., Loutre M.-F., Marti O., Merkel U., Ramstein G., Valdes P., Weber S.L., Yu Y., Zhao Y., Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum-Part 1: Experiments and large-scale features, Climate of the Past, 3, 2, pp. 261-277, (2007); Busby J., Bioclim- A bioclimate analysis and prediction system, Plant Protection Quarterly (Australia), (1991); Edwards M.E., Anderson P.M., Brubaker L.B., Ager T.A., Andreev A.A., Bigelow N.H., Cwynar L.C., Eisner W.R., Harrison S.P., Hu F.-S., Jolly D., Lozhkin A.V., MacDonald G.M., Mock C.J., Ritchie J.C., Sher A.V., Spear R.W., Williams J.W., Yu G., Pollen-based biomes for Beringia 18, 000, 6000 and 0 14C yr BP, Journal of Biogeography, 27, 3, pp. 521-554, (2000); Ehlers J., Gibbard P.L., Hughes P.D., Quaternary glaciations-extent and chronology a closer look, Developments in Quaternary Sciences, 15, (2011); Farr T.G., Rosen P.A., Caro E., Crippen R., Duren R., Hensley S., Kobrick M., Paller M., Rodriguez E., Roth L., Seal D., Shaffer S., Shimada J., Umland J., Werner M., Oskin M., Burbank D., Alsdorf D., The shuttle radar topography mission, Reviews of Geophysics, 45, 2, (2007); Friesike S., Creative commons licences, Opening Science - The Evolving Guide on How the Internet is Changing Research, pp. 287-289, (2014); GEBCO 2014 Grid-Gridded Bathymetry Data, (2014); Geonode-open Source Geospatial Content Management System, (2015); Geoserver-open Source Server for Sharing Geospatial Data, (2015); Hijmans R., Cameron S., Parra J., Jones P., Jarvis A., Very high resolution interpolated climate surfaces for global land areas, International Journal of Climatology, 25, pp. 1965-1978, (2005); Jarvis A., Reuter A.H.I., Nelson E.G., Hole-filled SRTM for the Globe Version 4, (2008); Krotzsch M., Vrandecic D., Volkel M., Semantic mediawiki, The Semantic Web-ISWC 2006, Lecture Notes in Computer Science, 4273, pp. 935-942, (2006); Z39. 84-2005 (r2010) Syntax for the Digital Object Identifier, (2010); Open Definition 2. 1., (2016); Openlayers- A Highperformance, Feature-packed Library for All Your Mapping Needs, (2015); Pachur H.-J., Altmann N., Die Ostsahara im Spätquartär: Ö Kosystemwandel im GrÖßten Hyperariden Raum der Erde, (2007); QGIS Geographic Information System, (2015); Richter J., Melles M., Schabitz F., Temporal and spatial corridors of homo sapiens sapiens population dynamicsduring the late pleistocene and early holocene, Quaternary International, 274, pp. 1-4, (2012); Sitek D., Bertelmann R., Open access: A state of the art, Opening Science - The Evolving Guide on How the Internet is Changing Research, (2014); Verheul J., Zickel M., Becker D., Willmes C., LGM major inland waters of Europe-GIS dataset, CRC806-Database, (2015); Willmes C., Bareth G., A data integration concept for an interdisciplinary research database, Proceedings of the Young Researchers Forum on Geographic Information Science-GI Zeitgeist, pp. 67-72, (2012); Willmes C., Becker D., Brocks S., Hutt C., Bareth G., High resolution Köppen-Geiger classifications of paleoclimate simulations, Transactions in GIS., (2016); Willmes C., Brocks S., Hoffmeister D., Hutt C., Kurner D., Volland K., Bareth G., Facilitating integrated spatio-temporal visualization and analysis of heterogeneous archaeological and palaeoenvironmental research data, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences I-2, pp. 223-228, (2012); Willmes C., Kurner D., Bareth G., Building research data management infrastructure using open source software, Transactions in GIS, 18, pp. 496-509, (2014)","C. Willmes; Institute of Geography, University of Cologne, Cologne, Albertus-Magnus-Platz, 50923, Germany; email: c.willmes@uni-koeln.de","Brazdil K.; Shi W.; Liu Y.; Stein A.; Tong X.; Safar V.; Kawashima H.; Li S.; Ryerson University, ISPRS Technical Commission II, 350 Victoria St., Toronto, ON; Li Q.-Q.; Sester M.; Madden M.; Coltekin A.; Rapant P.; Brovelli M.A.; HaeKyong K.; Halounova L.; Anton F.; Pettit C.; Mostafavi M.A.; Tomkova M.; Cheng T.","Copernicus GmbH","","23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016","12 July 2016 through 19 July 2016","Prague","129643","21949042","","","","English","ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85048921797"
"Blümm M.; Schmunk S.","Blümm, Mirjam (56958388300); Schmunk, Stefan (57191918423)","56958388300; 57191918423","Digital research infrastructures: DARIAH","2016","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10025 LNCS","","","62","73","11","4","10.1007/978-3-319-47647-6_4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994797867&doi=10.1007%2f978-3-319-47647-6_4&partnerID=40&md5=adcf46ed93e0a822e9aa78c1e4368a0b","Research and Development Department, Göttingen State and University Library, Papendiek 14, Göttingen, 37073, Germany","Blümm M., Research and Development Department, Göttingen State and University Library, Papendiek 14, Göttingen, 37073, Germany; Schmunk S., Research and Development Department, Göttingen State and University Library, Papendiek 14, Göttingen, 37073, Germany","DARIAH-DE, the German national contribution to DARIAH-EU – a European initiative, initiated by the European Strategy Forum on Research Infrastructures (ESFRI), which aims to enhance and support digitally-enabled research and teaching across the arts and humanities – develops and maintains a digital research infrastructure for the Arts and Humanities. This research infrastructure consists of four components: teaching, research, research data and technical modules. DARIAH-DE addresses current research questions and methods, integrates them into the digital research infrastructure, and is in particular research driven. The topic of “digital reconstruction” will be one of the most important new topics of DARIAH-DE and it will be one of the challenges to integrate tools and cover research-lifecycles of these specific communitiy. © Springer International Publishing AG 2016.","Digital humanities; E-humanities; Research data management; Research infrastructure","Artificial intelligence; Computer science; Computers; Digital humanities; Digital reconstruction; Digital researches; European initiatives; nocv1; Research data managements; Research infrastructure; Research questions; Research-driven; Information management","","","","","","","Klimpel P., Weitzmann J.H., Forschen in Der Digitalen Welt. Juristische Handreichung für Die Geisteswissenschaften","M. Blümm; Research and Development Department, Göttingen State and University Library, Göttingen, Papendiek 14, 37073, Germany; email: bluemm@sub.uni-goettingen.de","Pfarr-Harfst M.; Munster S.; Kuroczynski P.; Ioannides M.","Springer Verlag","","5th International Euro-Mediterranean Conference on Cultural Heritage, EuroMed 2014","3 November 2014 through 8 November 2014","Limassol","185989","03029743","978-331947646-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-84994797867"
"Bugaje M.; Chowdhury G.","Bugaje, Maryam (57197719494); Chowdhury, Gobinda (7006058701)","57197719494; 7006058701","Is data retrieval different from text retrieval? An exploratory study","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10647 LNCS","","","97","103","6","6","10.1007/978-3-319-70232-2_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034071645&doi=10.1007%2f978-3-319-70232-2_8&partnerID=40&md5=b2bad46c9a3eba27df2861ce4371804e","iSchool, Faculty of Engineering and Environment, Northumbria University, Newcastle, United Kingdom","Bugaje M., iSchool, Faculty of Engineering and Environment, Northumbria University, Newcastle, United Kingdom; Chowdhury G., iSchool, Faculty of Engineering and Environment, Northumbria University, Newcastle, United Kingdom","The fundamental characteristics of and form of user interaction with research datasets differ considerably from those of research publications. Notwithstanding these differences, however, the majority of currently available research data repositories use the same retrieval engines for research data (datasets) as for publications (text), which retrieval engines, inevitably, are ill-suited as long-term solutions for sustainable data retrieval and use. This paper, through a systematic experiment, demonstrates the fundamental and deep-rooted differences between retrieval of research publications (predominantly text) and research data (i.e. datasets), and justifies the need for more research to build more efficient and effective data retrieval systems. © 2017, Springer International Publishing AG.","Data retrieval; Research data management; Text retrieval","Digital libraries; Engines; Information management; Information retrieval; Data retrieval; Exploratory studies; Fundamental characteristics; Long-term solutions; Research data managements; Retrieval engines; Systematic experiment; Text retrieval; Search engines","","","","","","","The Economist, 6-12 May, (2017); Borgman C.L., The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol., 63, 6, pp. 1059-1078, (2012); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Borgman C.L., Wallis J.C., Mayernik M.S., Who’s got the data? Interdependencies in science and technology collaborations, Comput. Support. Coop. Work, 21, 6, pp. 485-523, (2012); How Sharing Research Data Can Yield Knowledge, Jobs and Growth, (2014); Macmillan D., Data sharing and discovery: What librarians need to know, J. Acad. Librarianship, 40, 5, pp. 541-549, (2014); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, Plos ONE, 8, 7, (2013); Jansen B.J., Spink A., How are we searching the world wide web? A comparison of nine search engine transaction logs, Inf. Process. Manag., 42, 1, pp. 248-263, (2006); Spink A., Wolfram D., Jansen B.J., Saracevik T., Searching the web: The public and their queries, J. Am. Soc. Inf. Sci, 53, 2, pp. 226-234, (2001); Richardson M., Dominowska E., Ragno R., Predicting clicks: Estimating the click-through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, WWW, 2007, pp. 521-530, (2007); Maley C., Baum N., Getting to the top of google: Search engine optimization, J. Med. Pract. Manag. MPM, 25, 5, pp. 301-303, (2010); Wu M., Marian A., A framework for corroborating answers from multiple web sources, Inf. Syst., 36, 2, pp. 431-449, (2011); Chowdhury G.G., Sustainability of Scholarly Information, (2014); Chowdhury G.G., How to improve the sustainability of digital libraries and information services?, J. Assoc. Inf. Sci. Technol., 67, 10, pp. 2379-2391, (2016); Boru D., Kliazovich D., Granelli F., Bouvry P., Zomaya A.Y., Energy-efficient data replication in cloud computing datacenters, Cluster Comput, 18, 1, pp. 385-402, (2015)","G. Chowdhury; iSchool, Faculty of Engineering and Environment, Northumbria University, Newcastle, United Kingdom; email: gobinda.chowdhury@northumbria.ac.uk","Cunningham S.J.; Choemprayong S.; Crestani F.","Springer Verlag","","19th International Conference on Asia-Pacific Digital Libraries, ICADL 2017","13 November 2017 through 15 November 2017","Bangkok","203799","03029743","978-331970231-5","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85034071645"
"Kvale L.","Kvale, Live (57195713528)","57195713528","Research data in Norway: How do expectations, demands and solutions correspond in the knowledge infrastructure for research data?","2017","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","10450 LNCS","","","628","631","3","0","10.1007/978-3-319-67008-9_58","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029596838&doi=10.1007%2f978-3-319-67008-9_58&partnerID=40&md5=d0793177ebc4fc2f7ee1a55c6f973fbf","Oslo and Akershus University College of Applied Sciences, Oslo, Norway","Kvale L., Oslo and Akershus University College of Applied Sciences, Oslo, Norway","Amounts of digital data combined with incentives for open research challenges researchers to share their research data (RD). Researchers are meeting requirements for data management plans (DMPs), and in Norway the Research Council has made it a priority to develop an infrastructure for making RD available. This project investigates how these constraints influence the researchers’ choices and how the solutions created fit with the expectations from other stakeholders. Is there a satisfying dialog between the research environments and the service providers? Do the researchers experience that their voices are heard by the infrastructure providers and the research funders? The work aims to strengthen the dialog between the stakeholders in the knowledge infrastructure (KI) for RD and contribute to an improvement of this. © Springer International Publishing AG 2017.","Knowledge infrastructure; Research data management","Information management; Digital datas; Infrastructure providers; Knowledge infrastructure; Management plans; Research challenges; Research data managements; Research environment; Service provider; Digital libraries","","","","","","","Edwards P., A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, (2010); Borgman C.L., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); H2020 Programme - Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2016); Latour B., Science in Action: How to Follow Scientists and Engineers through Society; Expectation - Merriam-Webster Dictionary","L. Kvale; Oslo and Akershus University College of Applied Sciences, Oslo, Norway; email: live.kvale@hioa.no","Manolopoulos Y.; Kamps J.; Tsakonas G.; Iliadis L.; Karydis I.","Springer Verlag","The Coalition for Networked Information (CNI)","21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017","18 September 2017 through 21 September 2017","Thessaloniki","197829","03029743","978-331967007-2","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-85029596838"
"Onyancha O.B.","Onyancha, Omwoyo Bosire (23397584400)","23397584400","Open Research Data in Sub-Saharan Africa: A Bibliometric Study Using the Data Citation Index","2016","Publishing Research Quarterly","32","3","","227","246","19","17","10.1007/s12109-016-9463-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980371740&doi=10.1007%2fs12109-016-9463-6&partnerID=40&md5=a83bd965df176ca5842114f43845c194","Department of Information Science, University of South Africa, Post Office Box 392, Unisa, Pretoria, 0003, South Africa","Onyancha O.B., Department of Information Science, University of South Africa, Post Office Box 392, Unisa, Pretoria, 0003, South Africa","The purpose of the study was to explore the status of research data sharing among researchers in sub-Saharan Africa (SSA) and internationally. Relevant data was extracted from the Data Citation Index (DCI) using an advanced search strategy, which was limited to the publication years between 2009 and 2014. Data was analysed to obtain the number of data records by country, institution, subject category, year of publication, and document type as well as the number of citations. A Spearman’s correlation analysis was conducted to gauge the relationship between the data records and research articles. Findings indicate that only 20 (out of 50) countries in sub-Saharan Africa produced at least one data record in the DCI, with South Africa leading the pack with 539 (61.39 %) records followed by Kenya, Cameroon and Ghana. SSA contributes a mere 0.03 % of the world’s research data as compared to 1.4 % of the world’s research articles. Research institutions and universities are the major contributors of research data, which largely focuses on Genetics and Heredity (61.3 %), Biochemistry and Molecular Biology (61.3 %), Agriculture (29.2 %) and Forestry (27.3 %). Citation-wise, the research data has attracted fewer average citations than the articles. A correlational analysis of the data reveals that there is a significant correlation between the publication of data and research articles. © 2016, Springer Science+Business Media New York.","Electronic publishing; Open Access; Research Data; Research Data Management; Scholarly publishing; Sub-Saharan Africa","","","","","","","","CDC scientist admits they destroyed data that showed vaccines caused autism in children, (2015); Data journals, (2013); Open data, (2013); Global-level data sets may be more highly cited than most journal articles, (2014); Data sharing may lead to some embarrassment but will ultimately improve scientific transparency and accuracy, (2014); Blom A., Lan G., Adil M., Sub-Saharan African science, technology, engineering, and mathematics research: a decade of development, (2016); Bornmann L., Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics, J Inform, 8, 4, pp. 895-903, (2014); Read the Budapest Open Access Initiative, (2002); Paper presented at the 15th Department of Information Studies Conference, University of Zululand, South Africa, 3rd–5th September, (2014); Cohen L., Manion L., Morrison K., Research methods in education, (2013); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: a guide to good practice, (2014); Crane D., Invisible colleges: diffusion of knowledge in scientific communities, (1972); Diodato V.P., Dictionary of Bibliometrics, (1994); Guston D.H., Encyclopedia of nanoscience and society, (2010); Citation analysis across disciplines: the impact of different data sources and citation metrics, (2010); Growth J.D., A bibliometric study, Bothma TJD, Kaniki, A. 2004. ProLISSA 2004. Proceedings of the 3rd biennialDISSAnet Conference, Pretoria, 28–29 October 2004, pp. 211-220; Krier L., Strasser C.A., Data management for libraries: a LITA guide, (2014); The citation revolution will not be televised: the end of papers and the rise of data, (2014); Luwel M., Is the science citation index US-biased?, Scientometrics, 46, 3, pp. 549-562, (1999); Narvaez-Berthelemot N., Russell J.M., Arvanitis R., Waast R., Gaillard J., Science in Africa: an overview of mainstream scientific output, Scientometrics, 54, 2, pp. 229-241, (2002); Statement on open access to research publications from National Research Foundation (NRF)-funded research, (2015); Nelson B., Data sharing: empty archives, Nature, 461, pp. 160-163, (2009); Neuroth H., Strathmann S., Oswald A., Ludwig J., Digital curation of research data: experiences of a baseline study in Germany, (2013); Nwagwu W.E., Cyberneting the academe: centralized scholarly ranking and visibility of scholars in the developing world, J Inf Sci, 36, 2, pp. 228-241, (2010); Circular A-110: Uniform administrative requirements for grants and agreements with institutions of higher education, (2013); Onyancha O.B., Authorship patterns of the literature on HIV/AIDS in Eastern and Southern Africa: an exposition of the responsible authors, institutions and countries, 1980–2005, S Afr J Libr Inf Sci, 74, 1, pp. 9-22, (2008); Onyancha O.B., Ngoepe M., Maluleka J.R., Trends, patterns, challenges and types of archival research in sub-Saharan Africa, Afr J Arch Libr Inf Sci, 25, 2, pp. 145-159, (2015); Onyancha O.B., Ocholla D.N., Country-wise collaborations in HIV/AIDS research in Kenya and South Africa, 1980–2005, LIBRI, 57, 4, pp. 239-254, (2007); Proportion of repositories by continent—worldwide, (2015); OECD principles and guidelines for access to research data from public funding, Massachusetts: OECD, (2007); Piwowar H., Value all research products, Nature, 493, (2013); Pouris A., An assessment of the impact and visibility of South African journals, Scientometrics, 62, 2, pp. 213-222, (2005); Pouris A., Richter L., Investigation into state-funded research journals in South Africa, S Afr J Sci, 96, pp. 98-104, (2000); The data citation index and datacite, (2014); Contestation of Impact Factor as a measure of journal quality, (2012); Sooryamoorthy R., Collaboration and publication: how collaborative are scientists in South Africa?, Scientometrics, 80, 2, pp. 419-439, (2009); Timeline of the opne access movement, (2009); Swoger B., Thomson Reuters data citation index, Libr J, 137, 20, (2012); Torres-Salinas D., Martin-Martin A., Fuente-Gutierrez E., Analysis of the coverage of the Data Citation Index—Thomson Reuters, Disciplines, document types, and repositories. Revista Española de DocumentaciónCientífica, 37, 1, pp. 1-6, (2014); UNESCO Institute for Statistics, What do bibliometric indicators tell us about world scientific output?, UIS Bull Sci Technol Stat, 2, pp. 1-6, (2005); Vetterli M., Open access, open data, open science, (2014); Melbourne, Research data management: data deposit requirements of selected science journals, (2015); Opportunities for ‘data intensive’ social research are growing but funding for data management remains a challenge, (2014); Wicherts J., Data sharing not only helps facilitate the process of psychology research, it is also a reflection of rigour, (2013)","O.B. Onyancha; Department of Information Science, University of South Africa, Pretoria, Post Office Box 392, Unisa, 0003, South Africa; email: onyanob@unisa.ac.za","","Springer New York LLC","","","","","","10538801","","","","English","Publ. Res. Q.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84980371740"
"Yu F.; Deuble R.; Morgan H.","Yu, Fei (57471514900); Deuble, Rebecca (56724179800); Morgan, Helen (57202509674)","57471514900; 56724179800; 57202509674","Designing research data management services based on the research lifecycle – a consultative leadership approach","2017","Journal of the Australian Library and Information Association","66","3","","287","298","11","16","10.1080/24750158.2017.1364835","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044588164&doi=10.1080%2f24750158.2017.1364835&partnerID=40&md5=8e8ccc873036e1de62375e8a80cbbddd","University of Queensland Library, University of Queensland, St Lucia, Australia","Yu F., University of Queensland Library, University of Queensland, St Lucia, Australia; Deuble R., University of Queensland Library, University of Queensland, St Lucia, Australia; Morgan H., University of Queensland Library, University of Queensland, St Lucia, Australia","Research data are a primary research output. Research Data Management (RDM) involves a complex and varying set of processes at each stage of research. Integrated RDM support services can assist researchers to manage their data proactively, and thereby, comply with data-sharing requirements from funding agencies and journal publishers. The strategic long-term benefits of supporting RDM practice by researchers include, increasing individual and institutional research reputations as trusted providers of data. Establishing RDM support services across large academic institutions requires input from multiple organisational units. The library is well positioned to lead this effort, having reach and influence across the institution. The RDM team is able to maximise the benefits for researchers by leading support services in consultation with multiple units across the university. This paper gives an overview of University of Queensland (UQ) Library RDM services provided in the planning and preparing, conducting, archiving, publishing and disseminating stages, using a research lifecycle model. It details the strategies in designing and delivering RDM services, which include preparing guides, designing training programmes for faculty librarians and researchers and engaging stakeholders within and external to the university. We present the impact of these services to-date as an informal self-assessment. © 2017 Australian Library & Information Association.","Academic libraries; Research data; Research data management; Research lifecycle; Universities","","","","","","Australian Research Council, ARC","Another ANDS supported project was to digitise the indigenous languages as part of the Queensland Speech Survey conducted in the 1960s (Yu & Morgan, 2016). The UQ Library has established a digitisation programme to enable these heritage collections to be digitised for use in research projects. This project was highly valuable because it ensured the long-term preservation of some of these languages that are no longer spoken in public. The RDM team collaborated with the Office of the Pro-Vice Chancellor (Indigenous Education), academic staff, aboriginal communities, ITS and research administrators to ensure the successful delivery of the project. The project was finished on time, and the interactive search interface was launched nationally as well as in the local community. The partnerships formed during this project continued, with academics obtaining an Australian Research Council grant to work on another indigenous language research project.","Atkinson J., Chapter 13 – Academic libraries and research support: An overview, Quality and the Academic Library, pp. 135-141, (2016); Research Data Management in Practice, (2013); What We Do, (2017); DOI Service (Cite My Data, (2017); Brown R.A., Wolski M., Richardson J., Developing new skills for research support librarians, The Australian Library Journal, 64, pp. 224-234, (2015); About the Centre, (2017); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, pp. 299-316, (2014); Keller A., Research support in Australian University Libraries: An outsider view, Australian Academic and Research Libraries, pp. 73-85, (2015); Koltay T., Facing the challenge of data-intensive research: Research data services and data literacy in academic libraries, Advances in Library Administration and Organization, 35, pp. 45-61, (2016); NHMRC Statement on Data Sharing, (2016); Porter S., Shadholt A., Creating a university research data registry: Enabling compliance, and raising the profile of research data at the University of Melbourne, Paper Presented at the 31St Annual IATUL Conference, (2010); Pryor G., Managing Research Data, (2012); Queensland Cyber Infrastructure Foundation, (2017); Rambo N., Research Data Management: Roles for Libraries, (2015); (2017); Searle S., Wolski M., Simons N., Richardson J., Librarians as partners in research data service development at Griffith University, Program-Electronic Library and Information Systems, 49, pp. 440-460, (2015); Tenopir C., Talja S., Horstmann W., Late E., Hughes D., Pollock D., Allard S., Research data services in European academic research libraries, Liber Quarterly, 27, pp. 23-44, (2017); Vaughan K.T.L., Hayes B.E., Lerner R.C., McElfresh K.R., Pavlech L., Romito D., Morris E.N., Development of the research lifecycle model for library services, Journal of the Medical Library Association, 101, pp. 310-314, (2013); Yu F., Morgan H., Engaging Stakeholders: The Key to Successful Research Data Management Library Services, (2016)","F. Yu; University of Queensland Library, University of Queensland, St Lucia, Australia; email: f.yu@library.uq.edu.au","","Australian Library and Information Association","","","","","","24750158","","","","English","J. Aust. Libr. Inf. Assoc.","Article","Final","","Scopus","2-s2.0-85044588164"
"Pouchard L.; Bracke M.S.","Pouchard, Line (8709358200); Bracke, Marianne Stowell (16644670800)","8709358200; 16644670800","An analysis of selected data practices: A case study of the purdue college of agriculture","2016","Issues in Science and Technology Librarianship","2016","85","","","","","6","10.5062/F4057CX4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007106688&doi=10.5062%2fF4057CX4&partnerID=40&md5=7362cc0cf711cfd5e8917d4bd8191d0d","Purdue University Libraries, West Lafayette, IN, United States","Pouchard L., Purdue University Libraries, West Lafayette, IN, United States; Bracke M.S., Purdue University Libraries, West Lafayette, IN, United States","This paper describes a survey of data practices given to the Purdue College of Agriculture. Data practices are a concern for many researchers with new governmental funding mandates that require data management plans, and for the institution providing resources to comply with these mandates. The survey attempted to answer these questions: What are the characteristics of the data held by respondents? What tools do the respondents use in managing, analyzing, or manipulating their data? Where do students primarily learn research data management skills? The survey documents that there is a statistically significant difference in data holding sizes between faculty and graduate students, and that MS-Excel is still the analysis tool of choice. Results also showed that many researchers in the College were not thinking of the Libraries as a resource for data management practices, preservation, or data literacy instruction for graduate students. The survey results may inform the Libraries in developing new data services and instruction, while also highlighting the need for additional research into data practices for specific disciplinary areas or types of researchers. © 2016, Association of College and Research Libraries. All rights reserved.","","","","","","","","","Borgman C.L., Darch P.T., Sands A.E., Pasquetto I.V., Golshan M.S., Wallis J.C., Traweek S., Knowledge infrastructures in science: Data, diversity, and digital libraries, International Journal on Digital Libraries, 16, 3, pp. 207-227, (2015); Bracke M.S., Fosmire M., Teaching data information literacy skills in a library workshop setting: A case study in agricultural and biological engineering, Data information literacy: Librarians, data, and the education of a new generation of researchers, pp. 129-148, (2015); Carlson J., Bracke M., Planting the seeds for data literacy: Lessons learned from a student-centered education program, International Journal of Digital Curation, 10, 1, pp. 95-110, (2015); Carlson J., Fosmire M., Miller C.C., Nelson M.S., Determining data information literacy needs: A study of students and research faculty, portal: Libraries and the Academy, 11, 2, pp. 629-657, (2011); Carlson J., Nelson M.S., Johnston L.R., Koshoffer A., Developing data literacy programs: Working with faculty, graduate students and undergraduates, Bulletin of the American Society for Information Science and Technology, 41, 6, pp. 14-17, (2015); Chen M., Mao S., Zhang Y., Leung V.C., Big Data Analysis, Big Data, pp. 51-58, (2014); Davis M.L.E.S., Tenopir C., Allard S., Frame M.T., Facilitating access to biodiversity information: A survey of users’ needs and practices, Environmental management, 53, 3, pp. 690-701, (2014); Diekema A.R., Wesolek A., Walters C.D., The NSF/NIH effect: Surveying the effect of data management requirements on faculty, sponsored programs, and institutional repositories, The Journal of Academic Librarianship, 40, 3, pp. 322-331, (2014); Diekmann F., Data practices of agricultural scientists: Results from an exploratory study, Journal of Agricultural & Food Information, 13, 1, pp. 14-34, (2012); Fernandez P., Eaker C., Swauger S., Davis M.S., Public progress, data management, and the land grant mission: A survey of agriculture researchers’ practices and attitudes at two land-grant institutions, Issues in Science and Technology Librarianship, (2016); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, 7-8, pp. 662-672, (2011); Holdren J.P., Memorandum for the Heads of Executive Departments and Agencies, (2010); KDNuggets, (2016); Lynch C., Big data: How do your data grow?, Nature, 455, 7209, pp. 28-29, (2008); Pouchard L.C., Revisiting the data life cycle with big data curation, International Journal of Digital Curation, 10, 2, pp. 176-192, (2016); Pouchard L.C., Bracke M.S., Nelson M.S., Data storage options at Purdue libguide, (2016); Sandelowski M., Sample size in qualitative research, Research in nursing & Health, 18, 2, pp. 179-183, (1995); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, Journal of eScience Librarianship, 1, 2, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PloS one, 6, 6, (2011); Implementation Plan to Increase Public Access to Results of USDA-funded Scientific Research, (2014); Williams S.C., Data practices in the crop sciences: A review of selected faculty publications, Journal of Agricultural & Food Information, 13, 4, pp. 308-325, (2012)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","","Scopus","2-s2.0-85007106688"
"Thelwall M.; Kousha K.","Thelwall, Mike (57527841900); Kousha, Kayvan (55933111000)","57527841900; 55933111000","Do journal data sharing mandates work? Life sciences evidence from Dryad","2017","Aslib Journal of Information Management","69","1","","36","45","9","12","10.1108/AJIM-09-2016-0159","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010686463&doi=10.1108%2fAJIM-09-2016-0159&partnerID=40&md5=0ac0ed2e62a80f0ba4cb0664c077eed1","School of Mathematics and Computing, University of Wolverhampton, Wolverhampton, United Kingdom; Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, United Kingdom","Thelwall M., School of Mathematics and Computing, University of Wolverhampton, Wolverhampton, United Kingdom; Kousha K., Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, United Kingdom","Purpose: Data sharing is widely thought to help research quality and efficiency. Data sharing mandates are increasingly being adopted by journals and the purpose of this paper is to assess whether they work. Design/methodology/approach: This study examines two evolutionary biology journals, Evolution and Heredity, that have data sharing mandates and make extensive use of Dryad. It uses a quantitative analysis of presence in Dryad, downloads and citations. Findings: Within both journals, data sharing seems to be complete, showing that the mandates work on a technical level. Low correlations (0.15-0.18) between data downloads and article citation counts for articles published in 2012 within these journals indicate a weak relationship between data sharing and research impact. An average of 40-55 data downloads per article after a few years suggests that some use is found for shared life sciences data. Research limitations/implications: The value of shared data uses is unclear. Practical implications: Data sharing mandates should be encouraged as an effective strategy. Originality/value: This is the first analysis of the effectiveness of data sharing mandates. © 2017, © Emerald Publishing Limited.","Citation analysis; Data sharing; Digital archive; Digital repository; Dryad; Research data management","Biology; Information management; Citation analysis; Data Sharing; Digital archives; Digital repository; Dryad; Research data managements; Information dissemination","","","","","Japan International Science and Technology Exchange Center JISTEC; U.S. Department of Energy, USDOE","This work was supported by Science and Technology Award (STA) from Japan International Science and Technology Exchange Center JISTEC (Japan) and by the U.S. Department of Energy under Contract No. DE-ACO3-76SF00098. ZLW thanks Professor J. Washburn for fruitful discussion and Dr. J. Ager for editing this paper.","Anagnostou P., Capocasa M., Milia N., Bisol G.D., Research data sharing: lessons from forensic genetics, Forensic Science International: Genetics, 7, 6, pp. e117-e119, (2013); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Borgman C.L., If data sharing is the answer, what is the question?, 100, pp. 15-16, (2015); Caetano D.S., Aisenberg A., Forgotten treasures: the fate of data in animal behaviour studies, Animal Behaviour, 98, 1, pp. 1-5, (2014); Curty R.G., Qin J., Towards a model for research data reuse behavior, Proceedings of the American Society for Information Science and Technology, 51, pp. 1-4, (2014); Dorch S.B.F., On the citation advantage of linking to data: astrophysics, (2012); Fairclough R., Thelwall M., More precise methods for national research citation impact comparisons, Journal of Informetrics, 9, 4, pp. 895-906, (2015); Gleditsch N.P., Metelits C., Strand H., Posting your data: will you be scooped or will you be famous, International Studies Perspectives, 4, 1, pp. 89-97, (2003); Greenberg J., Theoretical considerations of lifecycle modeling: an analysis of the Dryad repository demonstrating automatic metadata propagation, inheritance, and value system adoption, Cataloging & Classification Quarterly, 47, 3-4, pp. 380-402, (2009); He L., Nahar V., Reuse of scientific data in academic publications: an investigation of Dryad digital repository, Aslib Journal of Information Management, 68, 4, pp. 478-494, (2016); Henneken E.A., Accomazzi A., Linking to data-effect on citation rates in astronomy, (2011); Hrynaszkiewicz I., Norton M.L., Vickers A.J., Altman D.G., Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers, Trials, 11, 9, (2010); Hsu L., Lehnert K.A., Goodwillie A., Delano J.W., Gill J.B., Tivey M.A., Arko R.A., Rescue of long-tail data from the ocean bottom to the Moon: IEDA data rescue mini-awards, GeoResJ, 6, 1, pp. 108-114, (2015); Huang X., Hawkins B.A., Lei F., Miller G.L., Favret C., Zhang R., Qiao G., Willing or unwilling to share primary biodiversity data: results and implications of an international survey, Conservation Letters, 5, 5, pp. 399-406, (2012); Ingwersen P., Chavan V., Indicators for the data usage index (DUI): an incentive for publishing primary biodiversity data through global information infrastructure, BMC Bioinformatics, 12, S15, (2011); Ioannidis J.P., Allison D.B., Ball C.A., Coulibaly I., Cui X., Culhane A.C., Mangion J., Repeatability of published microarray gene expression analyses, Nature Genetics, 41, 2, pp. 149-155, (2009); Kenall A., Harold S., Foote C., An open future for ecological and evolutionary data?, BMC Ecology, 14, 1, (2014); Krause E.M., Clary E., Ogletree A., Greenberg J., Evolution of an application profile: advancing metadata best practices through the Dryad data repository, pp. 63-75, (2015); Kroon-Batenburg L.M., Helliwell J.R., Experiences with making diffraction image data available: what metadata do we need to archive?, Acta Crystallographica Section D: Biological Crystallography, 70, 10, pp. 2502-2509, (2014); MacMillan D., Data sharing and discovery: what librarians need to know, The Journal of Academic Librarianship, 40, 5, pp. 541-549, (2014); Mennes M., Biswal B.B., Castellanos F.X., Milham M.P., Making data sharing work: the FCP/INDI experience, Neuroimage, 82, pp. 683-691, (2013); Miller G.W., Making data accessible: the Dryad experience, Toxicological Sciences, 149, 1, pp. 2-3, (2016); Pienta A.M., Alter G.C., Lyle J.A., The enduring value of social science research: the use and reuse of primary research data, (2010); Piwowar H.A., Chapman W., A review of journal policies for sharing research data, (2008); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, 3, (2007); Robinson-Garcia N., Jimenez-Contreras E., Torres-Salinas D., Analyzing data citation practices using the Data Citation Index, Journal of the Association for Information Science and Technology, 67, 12, pp. 2964-2975, (2016); Sandve G.K., Nekrutenko A., Taylor J., Hovig E., Ten simple rules for reproducible computational research, PLoS Computational Biology, 9, 10, (2013); Sears J.R., Data sharing effect on article citation rate in paleoceanography, (2011); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Frame M., Data sharing by scientists: practices and perceptions, PLoS One, 6, 6, (2011); Tenopir C., Dalton E.D., Allard S., Frame M., Pjesivac I., Birch B., Dorsett K., Changes in data sharing and data reuse practices and perceptions among scientists worldwide, PLoS One, 10, 8, (2015); Thelwall M., Kousha K., Figshare: a universal repository for academic resource sharing?, Online Information Review, 40, 3, pp. 333-346, (2016); Turney P.D., Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, pp. 417-424, (2002); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS One, 8, 7, (2013); Zitt M., The journal impact factor: angel, devil, or scapegoat? A comment on JK Vanclay’s article 2011, Scientometrics, 92, 2, pp. 485-503, (2012)","M. Thelwall; School of Mathematics and Computing, University of Wolverhampton, Wolverhampton, United Kingdom; email: M.Thelwall@wlv.ac.uk","","Emerald Group Publishing Ltd.","","","","","","20503806","","","","English","Aslib J. Inf. Manage.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85010686463"
"Yu H.H.","Yu, Holly H. (8253955600)","8253955600","The role of academic libraries in research data service (RDS) provision Opportunities and challenges","2017","Electronic Library","35","4","","783","797","14","23","10.1108/EL-10-2016-0233","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031305471&doi=10.1108%2fEL-10-2016-0233&partnerID=40&md5=fd142613656ca1e72a8d807c204adfc4","California State University, Los Angeles, CA, United States","Yu H.H., California State University, Los Angeles, CA, United States","Purpose - Propelled by fast-evolving computational technology and cloud-based data storage, the increasing ease in research data collection is outstripping the capacity in research data service (RDS) in academic institutions. To illustrate the challenges and opportunities in providing RDS, the author provides a systematic review of the RDS offered in academic institutions and libraries by combining existing literature and survey data collected from the Association of Research Libraries (ARL) and the Association of College and Research Libraries (ACRL). In addition, the RDS websites of 2013 ARL survey-participating institutions are also examined. The aim of the paper is to provide an environmental scan of the current state of RDS provision in academic institutions, to add to the body of knowledge of RDS development, and to inform and enable academic libraries to make strategic RDS plans. Design/methodology/approach - The paper analyzes the strategies used and levels of RDS provided by reviewing recent literature, exploiting existing survey data from ARL and ACRL, and examining RDS websites of the 2013 ARL survey-participating institutions, in areas that reflect the life cycle of RDS provision including research data management planning, metadata consultation and tool provision, data archiving, institutional repository provision and data sharing and access. Findings - The overall offerings of the library-led research data services in ARL research-intensive institutions have shown signs of increasing. Increased engagement and expanded scope and level of services are two noticeable trends in academic library RDS provision. Academic libraries are taking advantage of open access repositories by advising researchers to use the available resources alongside their local repositories for data safe-keeping and sharing. Discussions on RDS policy and infrastructure development are inadequate or largely non-existent. Originality/value - Through systematically reviewing current literature, drawing on the results of available surveys on RDS offerings by academic libraries conducted between 2009 and 2014 and examining and further reviewing the websites of these 2013 ARL survey-participating institutions, the author presents the current state of academic library activities in RDS provision, and provides a critical evaluation of the scope and level of services currently being offered in academic libraries, and the opportunities in RDS development, to add to the body of knowledge of RDS provision by academic institutions. c Emerald Publishing Limited.","Academic libraries; Data collection; Data management; Data management plan; Digital repositories; Research data; Research data service","college; consultation; drawing; human; information processing; library; life cycle; metadata; publishing; scientist; systematic review","","","","","National Science Foundation, NSF; Research Down Syndrome, RDS","Funding text 1: Literature searches were conducted using the Summon Discovery platform and EBSCO databases, such as Academic Search Complete, Thomson Reuters’ Web of Science and scholarly journal packages, including Elsevier ScienceDirect and Sage Premier Collection, to exclusively focus on identifying literature to answer these critical questions with broad search terms, such as “libraries and research data services or RDS” and “open access repositories and research data service”, with a date limit between 2008 and 2016. In addition to conducting literature searches using academic databases, scholarly journal packages and the Discovery platform, the author also conducted searches using Google Scholar and Google to find surveys or studies on RDS provision that may or may not be covered by academic databases and the Discovery platform. The literature included are primarily articles discussing library-led research data services. The chosen articles focused on the role of libraries in RDS following the National Science Foundation (NSF) mandate on dissemination and sharing of research results in 2011, evolution of library services covering data support as part of reference service extension and libraries playing a leadership role in providing RDS. Geographically, articles focusing on the provisions of RDS in academic libraries in the USA are selected. Articles providing quality, systematic and extensive literature review are also included.; Funding text 2: While there was no follow-up on the abovementioned approaches, two new services are documented in their 2013 survey, which focus on the process of research data management at the grant proposal stage and on data archiving, preservation and dissemination and discovery at the end of the project (Fearon et al., 2013). These two trends have now become part of the RDS process and services for the life cycle of research data, including data collection, metadata consultation, data analysis, preservation and data sharing. These new services are a clear response to the mandate by the NSF and other funding agencies on research data preservation and sharing.","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Coates H., Building data services from the ground up: Strategies and resources, Journal of EScience Librarianship, 3, 1, pp. 52-59, (2014); Cox A., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Fearon D., Gunia B., Pralle B., Lake S., Sallans A., Research Data Management Services, SPEC Kit, 334, (2013); Gibbs C., Hernandez M., Sennyey P., Adopting a distributed model for data services, Code4Lib Journal, 35, (2017); Goldberg D., Olivares M., Li Z., Klein A.G., Maps&GIS data libraries in the era of big data and cloud computing, Journal of Map & Geography Libraries, 10, 1, pp. 100-122, (2014); Guss S., A studio model for academic data services, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 9-24, (2016); Data Management Services, (2016); Koltay T., Are you ready? Tasks and roles for academic libraries in supporting research 2.0, New Library World, 117, 1, pp. 94-104, (2016); Kutay S., Advancing digital repository services for faculty primary research assets: An exploratory study, Journal of Academic Librarianship, 40, 6, pp. 642-649, (2014); MacMillan D., Developing data literacy competencies to enhance faculty collaborations, LIBER Quarterly, 24, 3, pp. 140-160, (2015); Mattern E., Jeng W., He D., Lyon L., Brenner A., Using participatory design and visual narrative inquiry to investigate researchers' data challenges and recommendations for library research data services, Program: Electronic Library and Information Systems, 49, 4, pp. 408-423, (2015); Mohr A.H., Johnston L.R., Lindsay T.A., The data management village: Collaboration among research support providers in the large academic environment, Databrarianship: The Academic Data Librarian in Theory and Practice, pp. 51-66, (2016); Palumbo L.B., Jantz R., Lin Y., Morgan A., Wang M., White K., Zhu Y., Preparing to accept research data: Creating guidelines for librarians, Journal of EScience Librarianship, 4, 2, (2015); Pryor G., Options and approaches to RDM service provision, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 21-40, (2014); Shen Y., Strategic planning for a data-driven, shared-access research enterprise: Virginia tech research data assessment and landscape study, College & Research Libraries, 77, 4, pp. 500-519, (2016); Soehner C., Steeves C., Ward J., E-science and Data Support Services: A Study of ARL Member Institutions, (2010); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, An ACRL White Paper, (2012); Tenopir C., Hughes D., Allard S., Frame M., Birch B., Baird L., Sandusky R., Langseth M., Lundeen A., Research data services in academic libraries: Data intensive roles for the future?, Journal of EScience Librarianship, 4, 2, (2015); Selecting A Data Sharing Repository, (2016); Whitmire A.L., Boock M., Sutton S.C., Variability in academic research data management practices: Implications for data services development from a faculty survey, Program: Electronic Library and Information Systems, 49, 4, pp. 382-407, (2015); Research Data Support, (2016); Akers K.G., Sferdean F.C., Nicholls N.H., Green J.A., Building support for research data management: Biographies of eight research universities, International Journal of Digital Curation, 9, 2, pp. 171-191, (2014); Pinfield S., Salter J., Bath P., Hubbard B., Millington P., Anders J., Hussain A., Open access repositories worldwide, 2005-2012: Past growth, current characteristics, and future possibilities, Journal of the Association for Information Science and Technology, 46, 12, pp. 2404-2421, (2014)","H.H. Yu; California State University, Los Angeles, United States; email: hyu3@calstatela.edu","","Emerald Group Publishing Ltd.","","","","","","02640473","","ELLID","","English","Electron. Libr.","Article","Final","","Scopus","2-s2.0-85031305471"
"","","","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","2016","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-257","","","","","134","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986921983&partnerID=40&md5=7f1921b51d9dc53d063e2d317664a6fa","","","The proceedings contain 12 papers. The topics discussed include: improving storage performance with bcache in a virtualization scenario; introducing research data management as a service suite at RWTH Aachen University; improving the scalability of identity federations through level of assurance management automation; and privacy-aware intrusion detection in high-speed backbone networks - design and prototypical implementation of a multi-layered NIDS.","","","","","","","","","","","Rodosek G.D.; Universitat der Bundeswehr Munchen, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Munchen; Reiser H.; LRZ, Munchen; Muller P.; Technische Universitat Kaiserslautern, Postfach 3049, Kaiserslautern; Neumair B.; Karlsruher Institut fur Technologie (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen","Gesellschaft fur Informatik (GI)","","9. DFN-Forum Kommunikationstechnologien - 9th DFN-Forum on Communication Technologies","31 May 2016 through 1 June 2016","Rostock","123337","16175468","978-388579651-0","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference review","Final","","Scopus","2-s2.0-84986921983"
"Silva F.; Amorim R.C.; Castro J.A.; da Silva J.R.; Ribeiro C.","Silva, Fábio (57198845450); Amorim, Ricardo Carvalho (56442184300); Castro, João Aguiar (55977255100); da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","57198845450; 56442184300; 55977255100; 55496903800; 7201734594","End-to-end research data management workflows: A case study with Dendro and EUDAT","2016","Communications in Computer and Information Science","672","","","369","375","6","2","10.1007/978-3-319-49157-8_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85000819170&doi=10.1007%2f978-3-319-49157-8_32&partnerID=40&md5=edf7cbcda9a7e404d0ec9e3ee4a353b9","INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Silva F., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Amorim R.C., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Castro J.A., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; da Silva J.R., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; Ribeiro C., INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal","Depositing and sharing research data is at the core of open science practices. However, institutions in the long tail of science are struggling to properly manage large amounts of data. Support for research data management is still fragile, and most existing solutions adopt generic metadata schemas for data description. These might be unable to capture the production contexts of many datasets, making them harder to interpret. EUDAT is a large ongoing EU-funded project that aims to provide a platform to help researchers manage their datasets and share them when they are ready to be published. Data- Publication@U.Porto is an EUDAT Data Pilot proposing the integration between Dendro, a prototype research data management platform, and the EUDAT B2Share module. The goal is to offer researchers a streamlined workflow: they organize and describe their data in Dendro as soon as they are available, and decide when to deposit in a data repository. Dendro integrates with the API of B2Share, automatically filling the standard metadata descriptors and complementing the data package with additional files for domain-specific descriptors. Our integration offers researchers a simple but complete workflow, from data preparation and description to data deposit. © Springer International Publishing AG 2016.","","Data integration; Deposits; Metadata; Semantics; Data packages; Data preparation; Data repositories; Domain specific; Large amounts of data; Open science; Research data; Research data managements; Information management","","","","","Fundação para a Ciência e a Tecnologia, FCT, (POCI-01-0145-FEDER-016736); European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-016736. ","Amorim R.C., Castro J.A., Da Silva J.R., Ribeiro C., LabTablet: Semantic metadata collection on a multi-domain laboratory notebook, MTSR 2014. CCIS, 478, pp. 193-205, (2014); Amorim R.C., Castro J.A., Da Silva J.R., Ribeiro C., A comparison of research data management platforms: Architecture, flexible metadata and interoperability, Univ. Access Inf. Soc, pp. 1-12, (2016); Assante M., Candela L., Castelli D., Tani A., Are scientific data repositories coping with research data publishing, Data Sci. J, 15, (2016); Castro J.A., Da Silva J.R., Ribeiro C., Creating lightweight ontologies for dataset description: Practical applications in a cross-domain research data management workflow, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, (2014); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, Int. J. Digit. Curation, 6, 2, pp. 232-244, (2011); Da Silva J.R., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Ipres Conference Proceedings, (2014)","C. Ribeiro; INESC TEC, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal; email: mcr@fe.up.pt","Garoufallou E.; Coll I.S.; Stellato A.; Greenberg J.","Springer Verlag","","10th International Conference on Metadata and Semantics Research, MTSR 2016","22 November 2016 through 25 November 2016","Gottingen","186869","18650929","978-331949156-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-85000819170"
"Bauer C.R.; Umbach N.; Baum B.; Buckow K.; Franke T.; Grütz R.; Gusky L.; Nussbeck S.Y.; Quade M.; Rey S.; Rottmann T.; Rienhoff O.; Sax U.","Bauer, Christian R. (57200871605); Umbach, Nadine (55891434100); Baum, Benjamin (57188586539); Buckow, Karoline (55539072400); Franke, Thomas (56043721700); Grütz, Romanus (54983345400); Gusky, Linda (55953478600); Nussbeck, Sara Yasemin (55815970400); Quade, Matthias (36989687600); Rey, Sabine (23475090000); Rottmann, Thorsten (57023951400); Rienhoff, Otto (55915353100); Sax, Ulrich (8956991900)","57200871605; 55891434100; 57188586539; 55539072400; 56043721700; 54983345400; 55953478600; 55815970400; 36989687600; 23475090000; 57023951400; 55915353100; 8956991900","Architecture of a biomedical informatics research data management pipeline","2017","Studies in Health Technology and Informatics","228","","","262","266","4","9","10.3233/978-1-61499-678-1-262","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992019496&doi=10.3233%2f978-1-61499-678-1-262&partnerID=40&md5=9c535b3f586ce14d263fc08daed247e0","Department of Medical Informatics, University Medical Center Göttingen, Germany; University Medical Center Göttingen, UMG Biobank, Germany","Bauer C.R., Department of Medical Informatics, University Medical Center Göttingen, Germany; Umbach N., Department of Medical Informatics, University Medical Center Göttingen, Germany; Baum B., Department of Medical Informatics, University Medical Center Göttingen, Germany; Buckow K., Department of Medical Informatics, University Medical Center Göttingen, Germany; Franke T., Department of Medical Informatics, University Medical Center Göttingen, Germany; Grütz R., Department of Medical Informatics, University Medical Center Göttingen, Germany; Gusky L., Department of Medical Informatics, University Medical Center Göttingen, Germany; Nussbeck S.Y., University Medical Center Göttingen, UMG Biobank, Germany; Quade M., Department of Medical Informatics, University Medical Center Göttingen, Germany; Rey S., Department of Medical Informatics, University Medical Center Göttingen, Germany; Rottmann T., Department of Medical Informatics, University Medical Center Göttingen, Germany; Rienhoff O., Department of Medical Informatics, University Medical Center Göttingen, Germany; Sax U., Department of Medical Informatics, University Medical Center Göttingen, Germany","In University Medical Centers, heterogeneous data are generated that cannot always be clearly attributed to patient care or biomedical research. Each data set has to adhere to distinct intrinsic and operational quality standards. However, only if high-quality data, tools to work with the data, and most importantly guidelines and rules of how to work with the data are addressed adequately, an infrastructure can be sustainable. Here, we present the IT Research Architecture of the University Medical Center Göttingen and describe our ten years experience and lessons learned with infrastructures in networked medical research. © 2016 European Federation for Medical Informatics (EFMI) and IOS Press.","Computer security; Data collection; Data curation; Information storage and retrieval; Medical informatics application; Policies; Use and access","Academic Medical Centers; Biomedical Research; Health Information Exchange; Humans; Medical Informatics; Data curation; Digital storage; Hospitals; Informatics; Medical informatics; Public policy; Security of data; Biomedical informatics; Biomedical research; Data collection; Heterogeneous data; Information storage and retrieval; Medical informatics applications; Operational quality; Use and access; computer security; conference paper; human; information retrieval; medical informatics; medical research; pipeline; university hospital; medical information system; medical research; organization and management; Information management","","","","","","","Buneman P., Et al., Data Provenance: Some Basic Issues, (2000); Demiroglu S.Y., Et al., Managing sensitive phenotypic data and biomaterial in large-scale collaborative psychiatric genetic research projects: Practical considerations, Molecular Psychiatry, 17, (2012); Bauer C.R., Et al., Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases, Brief Bioinform, (2016); Buckow K., Et al., Changing requirements and resulting needs for IT-infrastructure for longitudinal research in the neurosciences, Neurosci. Res., 102, pp. 22-28, (2016); Umbach N., Et al., Managing OMICS-Data: Considerations for the design of a clinical research itinfrastructure, Stud Health Technol Inform, 216, pp. 668-671, (2015); Bellazzi R., Big data and biomedical informatics: A challenging opportunity, IMIA Yearbook, (2014); Weber G.M., Et al., Finding the missing link for big biomedical data, JAMA, 311, pp. 2479-2480, (2014); Fujita K.A., Et al., Integrating pathways of Parkinson's disease in a molecular interaction map, Mol Neurobiol, 49, pp. 88-102, (2014); Zhang W., Et al., Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium, Journal of Genetics, 94, pp. 731-740, (2015); Bauer C.R., Et al., Integrated Data Repository Toolkit (IDRT). A suite of programs to facilitate health analytics on heterogeneous medical data, Methods Inf Med, (2015); Murphy S., Et al., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), J Am Med Inform Assoc, 17, pp. 124-130, (2010); Scheufele E., Et al., A study of the age attribute in a query tool for a clinical data warehouse, AMIA Annu Symp Proc, (2008); McNutt M., #IAmAResearchParasite, Science, 351, (2016)","U. Sax; Department of Medical Informatics, University Medical Center Göttingen, Germany; email: ulrich.sax@med.uni-goettingen.de","Hoerbst A.; Hackl W.O.; de Keizer N.; Prokosch H.-H.; Hercigonja-Szekeres M.; de Lusignan S.","IOS Press","","Medical Informatics Europe Conference, MIE 2016 at the Health - Exploring Complexity: An Interdisciplinary Systems Approach, HEC 2016","28 August 2016 through 2 September 2016","Munich","131592","09269630","","","27577384","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-84992019496"
"McKee A.E.; Stamison C.M.; Bahnmaier S.","McKee, Anne E. (36712087600); Stamison, Christine M. (6505728515); Bahnmaier, Sara (55641225500)","36712087600; 6505728515; 55641225500","Creation, Transformation, Dissemination, and Preservation: Advocating for Scholarly Communication","2014","Serials Librarian","66","1-4","","189","195","6","1","10.1080/0361526X.2014.877298","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901390533&doi=10.1080%2f0361526X.2014.877298&partnerID=40&md5=d304b8e0dc832d9a826b90d3147fea73","Program Office for Sharing Resources, Greater Western Library Alliance (GWLA), United States; Swets Information Services, New Jersey, United States; University of Michigan, Ann Arbor, United States","McKee A.E., Program Office for Sharing Resources, Greater Western Library Alliance (GWLA), United States; Stamison C.M., Swets Information Services, New Jersey, United States; Bahnmaier S., University of Michigan, Ann Arbor, United States","Scholarly communication is often thought of as the preservation of knowledge. In fact, it also influences the creation, transformation, and dissemination of knowledge. The new norms of scholarly communication are multiple authorships, inter-institutional and international collaboration, and use of social media. The evolving norms for the librarians and consortial groups are supporting research data management, aiding discovery of collaborators, and dissemination and preservation of results, especially in digital formats. Librarians are viewed as experts in scholarly communication on many campuses but their leadership is not always recognized. Published with license by Taylor & Francis.","data repositories; EndNote; Mendeley; role of library consortia; scholarly communication; Zotero","","","","","","GWLA; Alfred P. Sloan Foundation","Funding text 1: Technical Reports Archives and Image Library (TRAIL) is a digitization initiative for technical reports and images issued primarily before 1976.17 It was supported by a grant from GWLA, and then was transferred to the Center for Research Libraries about a year and a half ago. Maliaca Oxnam won the 2010 Congressional Information Services/Government Document Round Table-American Library Association (CIS/GODORT/ALA) “Documents to the People Award” for directing the TRAIL project.18; Funding text 2: The virtual community named Future of Research Communications and e-Scholarship, better known as FORCE11, wrote a manifesto in 2011 and started efforts to improve the creation and dissemination of scholarly knowledge.13 FORCE11 is supported by a grant from the Alfred P. Sloan Foundation.","Gazni A., Didegah F., Investigating Different Types of Research Collaboration and Citation Impact: A Case Study of Harvard University's Publications, Scientometrics, 87, pp. 251-265, (2011); Knowledge, Networks and Nations: Global Scientific Collaboration in the 21st Century; Ball R., The Scholarly Communication of the Future: From Book Information to Problem Solving, Publishing Research Quarterly, 27, (2011); Reuters T., The Scholarly Communication of the Future: From Book Information to Problem Solving, Publishing Research Quarterly, 27, (2011); Zotero; Blixrud J., Principles for Emerging Systems of Scholarly Publishing (a.k.a. the Tempe Principles); Great Plains Network; Sponsoring Consortium for Open Access Publishing in Particle Physics (SCOAP3); Science Europe; Booker E., E-Textbook Pilot puts College Books in Cloud, Information Week, (2013); The Future of Research Communications and e-Scholarship (FORCE11), Force 11 Manifesto; HathiTrust Digital Library; WEST: Western Regional Storage Trust; BioOne; TRAIL Technical Report Archive & Image Library; CIS/ALA/GODORT 'Documents to the People' Award; Western Waters Digital Library (WWDL)-GWLA member projects; Occam's Reader; Open Access Research & Scholarships (OARS) Fund; Web of Science RSS Feed-OSU Authors; Elementa: Science of the Anthropocene; The Open/Alternative Textbook Initiative; Journals@UIC; First Monday: Peer-Reviewed Journal on the Internet; Coalition of Open Access Policy Institutions (COAPI); SPEC Kit 332: Organization of Scholarly Communication Services (November 2012)","","","Routledge","","","","","","0361526X","","","","English","Ser. Libr.","Article","Final","","Scopus","2-s2.0-84901390533"
"Tammaro A.M.; Casarosa V.","Tammaro, Anna Maria (8554921900); Casarosa, Vittore (54939304100)","8554921900; 54939304100","Research Data Management in the curriculum: An interdisciplinary approach","2014","Procedia Computer Science","38","C","","138","142","4","10","10.1016/j.procs.2014.10.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923311694&doi=10.1016%2fj.procs.2014.10.023&partnerID=40&md5=48335196e3e37f6ca54dff4e0e26f334","University of Parma, Viale Usberti 181a, Parma, 43125, Italy; ISTI-CNR, Via Moruzzi 1, Pisa, 56124, Italy","Tammaro A.M., University of Parma, Viale Usberti 181a, Parma, 43125, Italy; Casarosa V., ISTI-CNR, Via Moruzzi 1, Pisa, 56124, Italy","Research Data Management is broadly understood as collecting, analyzing, publishing, reanalyzing, critiquing, and reusing data. The increase of digital content in the broad areas of Institutional and domain specific Repositories, Libraries, Archives and Museums and the increased interest in the sharing and preservation of ""research data"" have triggered the emergence of new roles such as Data Curator. The paper refers about the on-going investigation of current data curator education and training programs with regard to the role of information professionals and/or data scientists in the research lifecycle. The investigation has been based on a series of workshops and events discussing the concerns of researchers and teachers about digital library and digital curation. A first list of competencies and skills at technical and operational level that professionals should have, has been evidenced. The theoretical framework and structure of educational programmes should have sufficient flexibility to accommodate the needs of various groups of specialists. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 10.1016/j.procs.2014.10.023.","Data Curator; Digital library education; Research Data Management","","","","","","","","Borgman C., Why Data Matters to Librarians - and How to Educate the Next Generation. The Changing Role of Libraries in Support of Research Data Activities: A Public Symposium, (2010); Borgman C., IS289 ""Data, Data Practices, and Data Curation"", (2012); Bergamin G., Magazzini digitali = the digital stacks, Int. Conference Trusted Digital Repositories & Trusted Professionals, (2012); Boschini M., Cortese C., The road (and the roadmap) for building Trusted Digital Repositories within an Interuniversitary Consortium, Int. Conference Trusted Digital Repositories & Trusted Professionals, (2012); Casarosa V., Cousins J., Tammaro A.M., Ioannidis I., The web versus digital libraries: Time to revisit this once hot topic, Research and Advanced Technology for Digital Libraries, 12th European Conference, ECDL 2008, Aarhus, Denmark, September 14-19, 2008. Proceedings. Volume 5173 of Lecture Notes in Computer Science, pp. 383-384, (2008); Casarosa V., Castelli D., Tammaro A.M., Report on the Workshop ""Linking Research and Education in Digital Libraries, D-lib Magazine, 17, 11-12, (2011); DataCur2013, Workshop ""Moving Beyond Technology: ISchools and Education in Data Curation. Is Data Curator a New Role?"", (2013); DCERC, Data Curation Education in Research Centers, (2013); DigiCurV, Digital Curator Vocational Education Europe, (2010); DL.org and DILL, Workshop Research and Education for Digital Libraries, (2010); Guercio M., The Italian case: Legal framework and good practices for digital preservation, Int. Conference Trusted Digital Repositories & Trusted Professionals, (2012); IMLS, Institute of Museum and Library Services, (2013); Lee C.A., Tibbo H., Where's the archivist in digital curation? Exploring the possibilities through a matrix of knowledge and skills, Archivaria, 72, pp. 123-168, (2011); Madrid M., A Study of Digital Curator Competences: A Survey of Experts, (2011); Tammaro A.M., IT profiles and curricula for digital libraries in Europe, Int. Conference Libraries in the Digital Age (LIDA 2006), (2006); Tammaro A.M., Madrid M., Casarosa V., Digital curators' education: Professional identity vs. Convergence of LAM (Libraries, Archives, Museums), Communications in Computer and Information Science, 354, pp. 184-194, (2012); Tammaro A.M., Casarosa V., Borgman C., Silipigni Connaway L., Castelli D., Radford M., Can research help education in digital libraries?, Int. Conference Libraries in the Digital Age (LIDA 2012), (2012); Tammaro A.M., Casarosa V., Castelli D., Closing the Gap: Interdisciplinary Perspectives on Research and Education for Digital Libraries, (2013); Tammaro A.M., Casarosa V., Ross S., Moulaison H., Weech T., Lugya F., ISchools building on the strengths found in the convergence of librarianship, archival, and museum studies to improve the education of managing digital collections, IConference 2013 Proceedings, pp. 1024-1025, (2013); Tammaro A.M., Integrating digital curation in a digital library curriculum: The international master DILL case study, Final DigCurV Conference Framing the Digital Curation Curriculum, (2013); Vivarelli M., Cassella M., Valacchi F., The digital curator between continuity and change: Developing a training course at the University of Turin, Final DigCurV Conference Framing the Digital Curation Curriculum, (2013)","A.M. Tammaro; University of Parma, Parma, Viale Usberti 181a, 43125, Italy; email: annamaria.tammaro@unipr.it","Catarci T.; Agosti M.; Esposito F.","Elsevier B.V.","CULTURA; Department of Information Engineering of the University of Padua","Italian Research Conference on Digital Libraries, IRCDL 2014","30 January 2014 through 31 January 2014","Padova","114921","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-84923311694"
"Herrick R.; McKay M.; Olsen T.; Horton W.; Florida M.; Moore C.J.; Marcus D.S.","Herrick, Rick (55770505200); McKay, Michael (56053267400); Olsen, Timothy (16231128400); Horton, William (7103132912); Florida, Mark (56246471700); Moore, Charles J. (56247306800); Marcus, Daniel S. (9941107300)","55770505200; 56053267400; 16231128400; 7103132912; 56246471700; 56247306800; 9941107300","Data dictionary services in XNAT and the Human Connectome Project","2014","Frontiers in Neuroinformatics","8","JULY","65","","","","12","10.3389/fninf.2014.00065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903759708&doi=10.3389%2ffninf.2014.00065&partnerID=40&md5=1c871c6f58e8f3e1e1e1c74cf83bab96","Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States","Herrick R., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States; McKay M., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States; Olsen T., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States; Horton W., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States; Florida M., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States; Moore C.J., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States; Marcus D.S., Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States","The XNAT informatics platform is an open source data management tool used by biomedical imaging researchers around the world. An important feature of XNAT is its highly extensible architecture: users of XNAT can add new data types to the system to capture the imaging and phenotypic data generated in their studies. Until recently, XNAT has had limited capacity to broadcast the meaning of these data extensions to users, other XNAT installations, and other software. We have implemented a data dictionary service for XNAT, which is currently being used on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website. The data dictionary service provides a framework to define key relationships between data elements and structures across the XNAT installation. This includes not just core data representing medical imaging data or subject or patient evaluations, but also taxonomical structures, security relationships, subject groups, and research protocols. The data dictionary allows users to define metadata for data structures and their properties, such as value types (e.g., textual, integers, floats) and valid value templates, ranges, or field lists. The service provides compatibility and integration with other research data management services by enabling easy migration of XNAT data to standards-based formats such as the Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML). It also facilitates the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures. © 2014 Herrick, McKay, Olsen, Horton, Florida, Moore and Marcus.","Human computer interaction; Human connectome; Ontologies; Publishing; Translations; XNAT","article; clinical assessment; computer program; conceptual framework; connectome; human computer interaction; JavaScript Object Notation; markup language; medical informatics; phenotype; Resource Description Framework; validation process; XNAT informatics","","","","","","","Marcus D.S., Harms M.P., Snyder A.Z., Jenkinson M., Wilson J.A., Glasser M.F., Et al., Human Connectome Project informatics: Quality control, database services, and data visualization, Neuroimage, 80, pp. 202-219, (2013); Marcus D.S., Olsen T.R., Ramaratnam M., Buckner R.L., The extensible neuroimaging archive toolkit: An informatics platform for managing, exploring, and sharing neuroimaging data, Neuroinformatics, 5, pp. 11-34, (2007); Poline J.B., Breeze J.L., Ghosh S., Gorgolewski K., Halchenko Y.O., Hanke M., Et al., Data sharing in neuroimaging research, Front. Neuroinform., 6, (2012); Van Essen D.C., Ugurbil K., Auerbach E., Barch D., Behrens T.E.J., Bucholz R., Et al., The Human Connectome Project: A data acquisition perspective, Neuroimage, 62, pp. 2222-2231, (2012)","R. Herrick; Neuroinformatics Research Group, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63119-2451, Campus Box 8225, 4525 Scott Avenue, United States; email: rick.herrick@wustl.edu","","Frontiers Research Foundation","","","","","","16625196","","","","English","Front. Neuroinformatics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84903759708"
"Chad K.; Enright S.","Chad, Ken (9240372900); Enright, Suzanne (55454504100)","9240372900; 55454504100","The research cycle and research data management (RDM): Innovating approaches at the University of Westminster","2014","Insights: the UKSG Journal","27","2","","147","153","6","3","10.1629/2048-7754.152","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904207277&doi=10.1629%2f2048-7754.152&partnerID=40&md5=c170913b8b2014350faaea2be3791774","Ken Chad Consulting Ltd (KCC), United Kingdom; Dept. of Information Services, University of Westminster, United Kingdom","Chad K., Ken Chad Consulting Ltd (KCC), United Kingdom; Enright S., Dept. of Information Services, University of Westminster, United Kingdom","This article presents a case study based on experience of delivering a more joined-up approach to supporting institutional research activity and processes, research data management (RDM) and open access (OA). The result of this small study, undertaken at the University of Westminster in 2013, indicates that a more holistic approach should be adopted, embedding RDM more fully into the wider research management landscape and taking researchers' priorities into consideration. Rapid development of an innovative pilot system followed closely on from a positive engagement with researchers, and today a purpose-built, integrated and fully working set of tools are functioning within the virtual research environment (VRE). This provides a coherent 'thread' to support researchers, doctoral students and professional support staff throughout the research cycle. The article describes the work entailed in more detail, together with the impact achieved so far and what future work is planned.","","","","","","","","","Information Support Systems for the Research Pathway: Setting Priorities, (2012)","","","United Kingdom Serials Group","","","","","","20487754","","","","English","Insights","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84904207277"
"Wainscott S.B.","Wainscott, Susan Beth (56730958400)","56730958400","A biologist adapts to librarianship","2015","Skills to Make a Librarian: Transferable Skills Inside and Outside the Library","","","","71","80","9","1","10.1016/B978-0-08-100063-2.00007-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941622986&doi=10.1016%2fB978-0-08-100063-2.00007-7&partnerID=40&md5=4bd461a9969b31280cad82880adaf711","University of Nevada, Las Vegas, United States","Wainscott S.B., University of Nevada, Las Vegas, United States","A former conservation biologist who worked in the nonprofit, and government arenas-describes the key transferable skills, and knowledge that are brought to bear in her new role as a science librarian at an advanced degree granting public university. Having worked for nearly two decades to manage natural resources and rare species: using an adaptive management, or active learning approach, assessment, and iterative improvements to policy and practice, are standard operating procedure for this professional. Prior experience in evaluating and writing grant proposals, defining research data management, and metadata guidelines, writing technical reports, and serving as an editor for a scholarly journal, enhances the science content knowledge of this librarian, and builds credibility with both faculty and graduate students. The public speaking experience gained during this professional's time with a government agency has enhanced her ability to provide instruction to students, and faculty in a wide variety of settings, and to groups large and small. Additionally, experience with meeting facilitation, negotiation, and project management has proven invaluable for developing partnerships with faculty and administrators, coordinating collaborative projects, and managing multiple assignments. Last, but certainly not least, familiarity with government budget cycles, purchasing rules, and policy-making allows effective navigation of the complex landscape of purchase options, and funds available for collection development. © 2015 Elsevier Ltd. All rights reserved.","Active learning-based decision-making; Adaptive management; Applied conservation biology; Conservation biologist; Grant proposals; Science librarianship; Transferable skills","","","","","","","","Fisher R., Ury B., Patton W., Getting to yes: negotiating agreement without giving in, (2011); Hunter D., Bailey A., Taylor B., The art of facilitation: how to create group synergy, (1995); Lee K.N., Compass and gyroscope: integrating science and politics for the environment, (1993); Facilitator's tool kit, (2000); Portny S.E., Project management for dummies, (2013); Schwarz R., The skilled facilitator: a comprehensive resource for consultants, facilitators, managers, trainers and coaches, (2002)","","","Elsevier Inc.","","","","","","","978-008100065-6; 978-008100063-2","","","English","Skills to Make a Libr.: Transferable Skills Inside and Outside the Libr.","Book chapter","Final","","Scopus","2-s2.0-84941622986"
"Keller A.","Keller, Alice (56519079900)","56519079900","Research Support in Australian University Libraries: An Outsider View","2015","Australian Academic and Research Libraries","46","2","","73","85","12","22","10.1080/00048623.2015.1009528","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930042580&doi=10.1080%2f00048623.2015.1009528&partnerID=40&md5=8c890d29dc087a1420a11d659a841e6e","Zentralbibliothek Zurich, Zurich, Switzerland","Keller A., Zentralbibliothek Zurich, Zurich, Switzerland","This study examines the ongoing changes within Australian university libraries to support research. After establishing the reasons for focusing so strongly on research support, this study gives an overview of the adjustments made to libraries' service portfolios and the changes in the roles and responsibilities of subject or liaison librarians. Throughout the study, in-depth comparisons are drawn to the developments in Europe, in particular to the situation in the UK, Switzerland and Germany. This study identifies and discusses five research support services: institutional repositories, open access, bibliometrics and enhancement of research impact, support for research students and research data management. It then examines how these services are resourced and embedded in the library. The study reveals three measures or approaches that were taken by senior management to build up and sustain efficient and effective research support services: (1) rationalisation of student services, (2) focusing activities of liaison librarians on research support and creation of subject-specific teams to achieve better effectivity and efficiency gains, and (3) definition of new positions responsible for research support. In the ‘Conclusion’ section, the author asks to what extent Australian libraries are influenced by government and university policies, or whether Australian librarians are free to set their own priorities. © 2015 Australian Library & Information Association.","academic libraries; bibliometrics; institutional repositories; job profiles; liaison librarians; open access; research support","","","","","","","","Auckland M., Re-Skilling for Research: An Investigation into the Roles and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Bradley L., ANU Library's Support for Research Data Management, Incite, 34, 4, (2013); Burrows T., Croker K., Supporting Research in an Era of Data Deluge: Developing a New Service Portfolio Within Information Services at the University of Western Australia, (2012); Corrall S., Kennan M.A., Afzal W., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research, Library Trends, 61, 3, pp. 636-674, (2013); Daly R., McIntosh L.M., Heresy or Innovation? Transforming Culture and Service for Impact, (2013); Drummond R., Wartho R., RIMS: The Research Impact Measurement Service at the University of New South Wales, Australian Academic & Research Libraries, 40, 2, pp. 76-87, (2009); Du J.T., Evans N., Academic Library Services Support for Research Information Seeking, Australian Academic & Research Libraries, 42, 2, pp. 103-120, (2011); Genoni P., Merrick H., Willson M.A., Scholarly Communities, e-Research Literacy and the Academic Librarian, The Electronic Library, 24, 6, pp. 734-746, (2006); Glaser J., Lange S., Laudel G., Auswirkungen der evaluationsbasierten Forschungsfinanzierung an Universitäten auf die Inhalte der Forschung, Australien und Deutschland im Vergleich. Wissenschaftsrecht, 42, 4, pp. 329-352, (2009); Hare J., Reforms Will Not Boost Rankings, (2014); Kaube J., Forschung in Deutschland und Australien: Der Utilitarist und der Umweg-Utilitarist, (2013); Keller A., Forschungsunterstützung an australischen Universitätsbibliotheken, Bibliothek Forschung Und Praxis, 38, 3, pp. 478-491, (2014); Mamtora J., Transforming Library Research Services: Towards a Collaborative Partnership, Library Management, 34, 4-5, pp. 352-371, (2013); Norman B., Stanton K.V., From Project to Strategic Vision: Taking the Lead in Research Data Management Support at the University of Sydney Library, International Journal of Digital Curation, 9, 1, pp. 253-262, (2014); Organ M.K., Leveraging Research Quality Assessment Exercises to Increase Repository Content: An Australian Case Study, (2010); Parker R., What the Library Did Next: Strengthening Our Visibility in Research Support, (2012); Richardson J., Nolan-Brown T., Loria P., Bradbury S., Library Research Support in Queensland: A Survey, Australian Academic & Research Libraries, 43, 4, pp. 258-277, (2012); Sparks J., O'Brien L., Richardson J., Wolski M., Tadic S., Morris J., Embedding Innovation for Scholarly Information and Research, Library Management, 34, 1-2, pp. 128-140, (2013); Steiner A., Thomas J.A., Thompson E.E., Supporting Research at QUT: A Tale of Three Librarians and a Creative Industries Super-Faculty, (2012); Zhao L., Riding the Wave of Open Access: Providing Library Research Support for Scholarly Publishing Literacy, Australian Academic & Research Libraries, 45, 1, pp. 3-18, (2014)","A. Keller; Zentralbibliothek Zurich, Zurich, Switzerland; email: alice.keller@zb.uzh.ch","","Australian Library and Information Association","","","","","","00048623","","","","English","Aust. Acad. Res. Libr.","Article","Final","","Scopus","2-s2.0-84930042580"
"Goben A.; Raszewski R.","Goben, Abigail (55849675300); Raszewski, Rebecca (53878345400)","55849675300; 53878345400","Policies and background literature for self-education on research data management: An annotated bibliography","2015","Issues in Science and Technology Librarianship","2015","82","","","","","0","10.5062/F4GB222C","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950107505&doi=10.5062%2fF4GB222C&partnerID=40&md5=bdaa55897259a986d305023d0f3156b5","University of Illinois at Chicago, Chicago, IL, United States","Goben A., University of Illinois at Chicago, Chicago, IL, United States; Raszewski R., University of Illinois at Chicago, Chicago, IL, United States","[No abstract available]","","","","","","","","","Bailey C., Research data curation bibliography, (2015)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","","Scopus","2-s2.0-84950107505"
"Childs S.; McLeod J.; Lomas E.; Cook G.","Childs, Sue (7005762192); McLeod, Julie (56309856800); Lomas, Elizabeth (22951098400); Cook, Glenda (7401954651)","7005762192; 56309856800; 22951098400; 7401954651","Opening research data: Issues and opportunities","2014","Records Management Journal","24","2","","142","162","20","22","10.1108/RMJ-01-2014-0005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927609078&doi=10.1108%2fRMJ-01-2014-0005&partnerID=40&md5=15f5efe7aed2b906ccdc28921e7f6f8f","Department of Information Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom; Northumbria University, Newcastle Upon Tyne, United Kingdom","Childs S., Department of Information Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom; McLeod J., Department of Information Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom; Lomas E., Department of Information Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom; Cook G., Northumbria University, Newcastle Upon Tyne, United Kingdom","Purpose – This paper aims to explore the issues, the role of research data management (RDM) as a mechanism for implementing open research data and the role and opportunities for records managers. The open data agenda is premised on making as much data as possible open and available. However, in the context of open research data there are methodological, ethical and practical issues with this premise. Design/methodology/approach – Two collaborative research projects focusing on qualitative health data were conducted.“DATUMfor Health” designed and delivered a tailoredRDMskills training programme for postgraduate research students in health studies. “DATUM in Action” was an action research project between researchers from information sciences, health, mathematics and computing, looking at planning and implementing RDM. Findings – Three key issues emerged about what research data is appropriate to make open/ accessible for sharing and reuse: re-using qualitative data conflicts with some of the epistemological and methodological principles of qualitative research; there are ethical concerns about making data obtained from human participants open, which are not completely addressed by consent and anonymisation; many research projects are small scale and the costs of preparing and curating data for open access can outweigh its value. In exploring these issues, the authors advocate the need for effective appraisal skills and researcher-focused RDM with records managers playing a useful role. Research limitations/implications – The findings come from two small-scale qualitative projects in health studies. Further exploration of these issues is required. Practical implications – Records managers have new crucial opportunities in the open data and RDM contexts, bringing their expertise and experience in managing a wider range of data and information. They can help realise the benefits of multiple perspectives (researcher, data manager, records manager and archivist) on open research data. Social implications – Researcher-focused RDM offers a mechanism for implementing open research data. © Emerald Group Publishing Limited.","Data reuse; Open data; Qualitative data; Research data management; Research ethics; Research methodology","","","","","","","","Bailey S., The 10 (published) principles of records management 2.0, posted on 18 June 2008, (2008); The Belfast project, Boston college, and a sealed subpoena, (2014); Caldicott F., Information: To share or not to share? The information governance review, (2013); Carusi A., Jirotka M., From data archive to ethical labyrinth, Qualitative Research, 9, 3, pp. 285-298, (2009); de Montjoye Y.-A., Hidalgo C.A., Verleysen M., Blondel V.D., Unique in the crowd: The privacy bounds of human mobility, Scientific Reports, 3, (2013); Data management plans, (2014); ESRC research data policy, (2010); Engineering and Physical Sciences Research Council, EPSRC policy framework on research data, (2011); Commission proposes a comprehensive reform of the data protection rules, Justice Newsroom. Data Protection News, (2012); Faniel I.M., Jacobsen T.E., Reusing scientific data: How earthquake engineering researchers assess the reusability of colleagues' data, Computer Supported Cooperative Work, 19, 3-4, pp. 355-375, (2010); Fry J., Lockyer S., Oppenheim C., Houghton J., Rasmussen B., Identifying benefits arising from the curation and open sharing of research data produced by UK higher education and research institutes, Japanese Industrial Standards Committee, (2008); Godlee F., Wager E., Research misconduct in the UK. Time to act. [Editorial], British Medical Journal, 344, 8357, (2012); Goldacre B., What the tamiflu saga tells us about drug trials and big pharma, Guardian, (2014); Great Britain, Freedom of Information Act 2000: Elizabeth II, (2000); Great Britain, Cabinet Office, Open data white paper unleashing the potential, Cm. 8353, (2012); Response to the Nuffield council on bioethics open consultation on biological and health data: The collection, linking, use and exploitation of biological and health data: Ethical issues, National Health Service, (2014); Heaton J., Secondary analysis of qualitative data: An overview, Historical Social Research, 33, 3, pp. 33-45, (2008); Freedom of information act 2000 (Section 50) environmental information regulations 2004, Decision notice, (2011); Anonymisation: Managing data protection risk code of practice, Information Commissioner's Office, (2012); Irwin S., Qualitative secondary data analysis: Ethics, epistemology and context, Progress in Development Studies, 13, 4, pp. 295-306, (2013); Irwin S., Winterton M., Debates in qualitative secondary analysis: Critical reflections, A Timescapes Working Paper Series, 4, (2011); Jefferson T., Jones M., Doshi P., Spencer E.A., Onakpoya I., Heneghan C.J., Oseltamivir for influenza in adults and children: Systematic review of clinical study reports and summary of regulatory comments, British Medical Journal, 348, (2014); Jenkinson H., Manual of Archive Administration, (1966); Freedom of information and research data: Questions and answers, (2010); Lomas E., An auto ethnography exploring the engagement of records management (rm) through a computer mediated communication co-operative inquiry, (2013); McLeod J., DATUM for Health, research data management training for health studies, Japanese Industrial Standards Committee Final Report, (2011); McLeod J., Thoughts on the opportunities for records professionals of the open access, open data agenda, Records Management Journal, 22, 2, pp. 92-97, (2012); McLeod J., Childs S., DATUM in Action, supporting researchers to plan and manage their research data, Japanese Industrial Standards Committee Final Report, (2012); Mason J., Re-using' qualitative data: On the merits of an investigative epistemology, Sociological Research Online, 12, 3, (2007); Mayer-Schonberger V., Delete: The Virtue of Forgetting in the Digital Age, (2009); MRC policy on research data-sharing, (2014); Data management plans, (2014); Open Data Institute, Guides: What is open data?, (2014); Pearce N., Smith A.H., Data sharing: Not as simple as it seems, Environmental Health, 10, 107, pp. 1-7, (2011); Perspectives on working with archived textual and visual material in social research, International Journal of Social Research Methodology, 15, 4, (2012); Research Councils UK, RCUK common principles on data policy, (2014); Scottish Information Commissioner, Decision 142/2011. Philip Morris international and the university of Stirling, whether a request was vexatious, (2011); Smith A., Unpublished' research to be exempt from FOI requests, Research Professional, (2012); Todd R., Care data delayed, EHealth Insider, (2014); Tschan R., A comparison of Jenkinson and Schellenberg on appraisal, American Archivist, 65, 2, pp. 176-195, (2002); Collections development policy, Version 02. 00,UKData Service, Essex, (2013); Manual. Records Management. Number 251.8M, (2006); Parliamentary briefing. Intellectual Property Bill: 2nd Reading (House of Lords). Clause 19: Freedom of information: Exemption for research. Clause 4: Exception to Design Rights for Purpose of Teaching, (2013); Verfaellie M., McGwin J., The case of Diederik Stapel, Psychological Science Agenda, (2011); Ward J.S., Barker A., Undefined by data: A survey of big data definitions, (2013); Weber N., IDCC 2013 Poster: The tail and the telling - distributions of NSF funding and long-tail data, figshare, (2013); Policy on data management and sharing, (2010); Whyte A., Wilson A., How to appraise and select research data for curation, DCC How-to Guides, (2010)","S. Childs; Department of Information Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom; email: sue.childs@northumbria.ac.uk","","Emerald Group Holdings Ltd.","","","","","","09565698","","","","English","Rec. Manage. J.","Article","Final","","Scopus","2-s2.0-84927609078"
"Liang S.; Holmes V.; Antoniou G.; Higgins J.","Liang, Shuo (55561701400); Holmes, Violeta (35292226300); Antoniou, Grigoris (7005674407); Higgins, Joshua (57026971700)","55561701400; 35292226300; 7005674407; 57026971700","ICurate: A research data management system","2015","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","9426","","","39","47","8","3","10.1007/978-3-319-26181-2_4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952323240&doi=10.1007%2f978-3-319-26181-2_4&partnerID=40&md5=8059d6cca3b353d72a69ca8d88310396","University of Huddersfield, Huddersfield, United Kingdom","Liang S., University of Huddersfield, Huddersfield, United Kingdom; Holmes V., University of Huddersfield, Huddersfield, United Kingdom; Antoniou G., University of Huddersfield, Huddersfield, United Kingdom; Higgins J., University of Huddersfield, Huddersfield, United Kingdom","Scientific research activities generate a large amount of data, which varies in format, volume, structure and ownership. Although there are revision control systems and databases developed for data archiving, the traditional data management methods are not suitable for High-Performance Computing (HPC) systems. The files in such systems do not have semantic annotations and cannot be archived and managed for public dissemination. We have proposed and developed a Research Data Management (RDM) system, ‘iCurate’, which provides easy-to-use RDM facilities with semantic annotations. The system incorporates Metadata Retrieval, Departmental Archiving,Workflow Management System, Meta data Validation and Self Inferencing. The ‘i’ emphasises the user-oriented design. iCurate will support researchers by annotating their data in a clearer and machine readable way from its production to publication for the future reuse. © Springer International Publishing Switzerland 2015.","Big data; Data curation; Humancomputer interaction; Linked Open Data; Research Data Management; Research support; Semantic web","Artificial intelligence; Big data; Human computer interaction; Metadata; Search engines; Semantic Web; Work simplification; World Wide Web; Data curation; High performance computing systems; Linked open datum; Research data managements; Research support; Revision control systems; Scientific researches; Workflow management systems; Information management","","","","","","","(2015); (2013); (2012); Jones S., (2013); Allinson J., Francois S., Lewis S., SWORD: Simple Web-Service Offering Repository Deposit, (2008); (2014); The Regents of the University of California: Dataup: Describe","S. Liang; University of Huddersfield, Huddersfield, United Kingdom; email: shuo.liang@hud.ac.uk","Zheng X.; Bikakis A.","Springer Verlag","","9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015","13 November 2015 through 15 November 2015","Fuzhou","158349","03029743","978-331926180-5","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84952323240"
"Hosking R.; Gahegan M.; Dobbie G.","Hosking, Richard (57211622994); Gahegan, Mark (55970397200); Dobbie, Gillian (6602511428)","57211622994; 55970397200; 6602511428","An eScience tool for understanding copyright in data driven sciences","2014","Proceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014","1","","6972259","145","152","7","4","10.1109/eScience.2014.37","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919497063&doi=10.1109%2feScience.2014.37&partnerID=40&md5=3a66c633c3d0952eaf170f1627d35ea2","Dept of Computer Science, University of Auckland, Auckland, New Zealand; Centre for EResearch, University of Auckland, Auckland, New Zealand","Hosking R., Dept of Computer Science, University of Auckland, Auckland, New Zealand, Centre for EResearch, University of Auckland, Auckland, New Zealand; Gahegan M., Dept of Computer Science, University of Auckland, Auckland, New Zealand, Centre for EResearch, University of Auckland, Auckland, New Zealand; Dobbie G., Dept of Computer Science, University of Auckland, Auckland, New Zealand","Understanding the impacts of copyright is a challenge for the sharing and reuse of our research data. There is growing recognition of the problem, but the legal knowledge required to navigate through the minefield of restrictions and risks is often too difficult to uncover and understand. As of yet there are no appropriate tools to aid researchers, librarians and research policy makers. To address this gap we present Camden, an automated copyright reasoning tool designed to integrate into existing research workflows. At its core, Camden uses dynamically generated defeasible rules to reason over the legality of a situation of using, combining and publishing data, while additionally suggesting potential licenses by which to safely share derived research outputs. This functionality has been wrapped up into an embedded software library and offered as a web application. In this paper we introduce Camden, describe its model of computational reasoning and discuss how it can be included into existing and future eResearch tools and services. © 2014 IEEE.","Camden; Copyright; Data Science; eScience; Licensing; Research Data Management","Application programs; Copyrights; Data Science; Information management; Nuclear reactor licensing; Camden; Defeasible rules; Legal knowledge; Research data; Research data managements; Research outputs; Research policies; WEB application; e-Science","","","","","","","Wilbanks J., I Have Seen the Paradigm Shift, and It is Us, The Fourth Paradigm: Data-Intensive Scientific Discovery; Hosking R., Gahegan M., The Effects of Licensing on Open Data: Computing a Measure of Health for Our Scholarly Record, Link. Springer. Com, 8219, 28, pp. 432-439, (2013); Gordon T.F., Walton D., Legal reasoning with argumentation schemes, 12th International Conference, (2009); Gordon T., Walton D., Proof burdens and standards, Argumentation in Artificial Intelligence, 12, pp. 239-258, (2009); Gordon T.F., Prakken H., Walton D., The Carneades model of argument and burden of proof, Argumentation in Artificial Intelligence, 171, 10, pp. 875-896, (2007); Gordon T.F., Analyzing open source license compatibility issues with Carneades, Presented at the the 13th International Conference, pp. 51-55, (2011); Nute D., Defeasible logic, Handbook of Logic in Artificial Intelligence and Logic, (1994); Governatori G., On the relationship between Carneades and Defeasible Logic, Presented at the the 13th International Conference, pp. 31-40, (2011); Dung P.M., Kowalski R.A., Toni F., Dialectic proof procedures for assumption-based, admissible argumentation, Argumentation in Artificial Intelligence, 170, 2, pp. 114-159; Statute of Anne, (1710); Patterson L.R., Copyright in Historical Perspective, (1968); Kaplan B., An unhurried view of copyright: Proposals and prospects, Columbia Law Review, 66, 5, pp. 831-854, (1966); Deazley R., Kretschmer M., Bently L., Privilege and Property: Essays on the History of Copyright Law, (2010); Harvey D., Copyright the IT Countrey Justice; Eisenstein E.L., The Printing Press As An Agent of Change, (2009); Eisenstein E.L., The Printing Revolution in Early Modern Europe, (2009); Hamilton M.A., Copyright duration extension and the dark heart of copyright, Cardozo Arts & Ent. LJ, 14, (1996); Hargreaves I., Digital Opportunity, (2011); Tehranian J., Infringement Nation: Copyright 2. 0 and You, (2011); David P.A., The Economic Logic of Open Science and the Balance between Private Property Rights and the Public Domain in Scientific Data and Information: A Primer, pp. 19-34, (2003); Rife M.C., The fair use doctrine: History, application, and implications for (new media) writing teachers, Computers and Composition, 24, 2, pp. 154-178, (2007); Hugenholtz P.B., Dommering E.J., The Future of Copyright in A Digital Environment: Proceedings of the Royal Academy Colloquium Organized by the Royal Netherlands Academy of Sciences (KNAW) and the Institute for Information Law, 4, (1996); Wilbanks J., Another reason for opening access to research, BMJ, 333, 7582, pp. 1306-1308, (2006); Wilbanks J., Openness as infrastructure, J Cheminf, 3, 1, (2011); Wilbanks J., We need a Web for data, Learn. Pub., 23, 4, pp. 333-335, (2010); Korn N., Oppenheim C., Duncan C., IPR and Licensing issues in Derived Data, Report Submitted to the JISC, (2007); Hagedorn G., Mietchen D., Morris R.A., Agosti D., Penev L., Berendsohn W.G., Hobern D., Creative Commons licenses and the noncommercial condition: Implications for the re-use of biodiversity information, ZooKeys, 150, (2011); Hilty R., Copyright law and scientific research, Copyright Law, 13, (2009); Hilty R.M., Five lessons about copyright in the information society: Reaction of the scientific community to over-protection and what policy makers should learn, J Copyright Soc'y USA, (2005); Reichman J.H., Uhlir P.F., A Contractually Reconstructed Research Commons for Scientific Data in a Highly Protectionist Intellectual Property Environment, Law and Contemporary Problems, 66, 1, pp. 315-462, (2003); Reichman J.H., Okediji R., When Copyright Law and Science Collide: Empowering Digitally Integrated Research Methods on a Global Scale, Minnesota Law Review, 96, 4, (2013); Stodden V., Enabling reproducible research: Licensing for scientific innovation, Int'l J. Comm. L. & Pol'y, 13, (2009); Stodden V.C., Open science: Policy implications for the evolving phenomenon of user-led scientific innovation, Journal of Science Communication, 9, 1; Wilbanks J., Licence restrictions: A fool's errand, Nature, 495, 7442, pp. 440-441, (2013); Wilbanks J., Public domain, copyright licenses and the freedom to integrate science, JCOM, 7, 2, (2008); Wilbanks J.T., Wilbanks T.J., Science, open communication and sustainable development, Sustainability, 2, 4, pp. 993-1015, (2010); Stallman R.M., Gay J., Free software, free society: Selected essays of richard m stallman, CreateSpace, (2009); Mathews D.J.H., Graff G.D., Saha K., Winickoff D.E., Access to Stem Cells and Data: Persons, Property Rights, and Scientific Progress, Science, 331, 6018, pp. 725-727, (2011); Williams A.J., Wilbanks J., Ekins S., Why open drug discovery needs four simple rules for licensing data and models, PLoS Comput Biol, 8, 9, (2012); Reese R.A., Reflections on the intellectual commons: Two perspectives on copyright duration and reversion, Stanford Law Review, 47, 4, pp. 707-747, (1995); Dublin Core; Data Catalog Vocabulary (DCAT); Description of A Project (DOAP); MyExperiment Base Ontology; Open Digital Rights Language (ODRL); METSRights Loc. Gov; Abelson H., Adida B., Linksvayer M., Yergler N., CcREL: The Creative Commons Rights Expression Language, (2008); Villata S., Gandon F., Licenses Compatibility and Composition in the Web of Data, (2012); Garcia R., Gil R., Delgado J., A web ontologies framework for digital rights management, Artif Intell Law, 15, 2, pp. 137-154, (2007); Athan T., Boley H., Governatori G., Palmirani M., Paschke A., Wyner A., OASIS legalruleml, ICAIL '13: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law, (2013); Gordon T.F., Introducing the Carneades web application, Fourteenth International Conference, (2013); Barlas C., Digital Rights Expression Languages (DRELs), JISC Technology and Standards Watch, (2006); Coyle K., Rights expression languages, A Report for the Library of Congress, (2004); Garcia R., Gil R., Gallego I., Delgado J., Formalising ODRL semantics using web ontologies, Proc. 2nd Intl. ODRL Workshop, (2005); Speiser S., Studer R., A self-policing policy language, Link. Springer. Com, 6496, 46, pp. 730-746, (2010); Governatori G., Rotolo A., Villata S., Gandon F., One license to compose them all-A deontic logic approach to data licensing on the web of data, ISWC-12th International, (2013); Bench-Capon T.J.M., Dunne P.E., Argumentation in artificial intelligence, Argumentation in Artificial Intelligence, 171, 10, pp. 619-641, (2007)","","","Institute of Electrical and Electronics Engineers Inc.","Brazilian Computer Society (SBC); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq); et al.; FAPESP; Google; Microsoft Research","10th IEEE International Conference on eScience, eScience 2014","20 October 2014 through 24 October 2014","Guaruja","109575","","978-147994288-6","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","","Scopus","2-s2.0-84919497063"
"Borgman C.L.; Darch P.T.; Sands A.E.; Wallis J.C.; Traweek S.","Borgman, Christine L. (7006568281); Darch, Peter T. (35766212500); Sands, Ashley E. (55206994400); Wallis, Jillian C. (13006877800); Traweek, Sharon (55229814800)","7006568281; 35766212500; 55206994400; 13006877800; 55229814800","The ups and downs of knowledge infrastructures in science: Implications for data management","2014","Proceedings of the ACM/IEEE Joint Conference on Digital Libraries","","","6970177","257","266","9","16","10.1109/JCDL.2014.6970177","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919335145&doi=10.1109%2fJCDL.2014.6970177&partnerID=40&md5=0a3e1ebf5185f204ea6b916fc30acb64","Knowledge Infrastructures Project, Department of Information Studies, University of California, Los Angeles, Box 951520, Los Angeles, 90095-1520, CA, United States","Borgman C.L., Knowledge Infrastructures Project, Department of Information Studies, University of California, Los Angeles, Box 951520, Los Angeles, 90095-1520, CA, United States; Darch P.T., Knowledge Infrastructures Project, Department of Information Studies, University of California, Los Angeles, Box 951520, Los Angeles, 90095-1520, CA, United States; Sands A.E., Knowledge Infrastructures Project, Department of Information Studies, University of California, Los Angeles, Box 951520, Los Angeles, 90095-1520, CA, United States; Wallis J.C., Knowledge Infrastructures Project, Department of Information Studies, University of California, Los Angeles, Box 951520, Los Angeles, 90095-1520, CA, United States; Traweek S., Knowledge Infrastructures Project, Department of Information Studies, University of California, Los Angeles, Box 951520, Los Angeles, 90095-1520, CA, United States","The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowledge infrastructures that are not yet in place. Scientific data management requires expertise in the scientific domain and in organizing and retrieving complex research objects. The Knowledge Infrastructures project compares data management activities of four large, distributed, multidisciplinary scientific endeavors as they ramp their activities up or down; two are big science and two are small science. Research questions address digital library solutions, knowledge infrastructure concerns, issues specific to individual domains, and common problems across domains. Findings are based on interviews (n=113 to date), ethnography, and other analyses of these four cases, studied since 2002. Based on initial comparisons, we conclude that the roles of digital libraries in scientific data management often depend upon the scale of data, the scientific goals, and the temporal scale of the research projects being supported. Digital libraries serve immediate data management purposes in some projects and long-term stewardship in others. In small science projects, data management tools are selected, designed, and used by the same individuals. In the multi-decade time scale of some big science research, data management technologies, policies, and practices are designed for anticipated future uses and users. The need for library, archival, and digital library expertise is apparent throughout all four of these cases. Managing research data is a knowledge infrastructure problem beyond the scope of individual researchers or projects. The real challenges lie in designing digital libraries to assist in the capture, management, interpretation, use, reuse, and stewardship of research data. © 2014 IEEE.","astronomy; Big data; big science; biology; data management; digital libraries; knowledge infrastructures; little science; sensor networks; small science","Astronomy; Big data; Biology; Information management; Sensor networks; big science; Data management tools; knowledge infrastructures; little science; Long term stewardship; Management technologies; Scientific data management; small science; Digital libraries","","","","","","","Blocker A.W., Meng X.-L., The potential and perils of preprocessing: Building new foundations, Bernoulli, 19, 4, pp. 1176-1211, (2013); Borgman C.L., Big Data, Little Data, Data: Scholarship in the Networked World; Borgman C.L., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Edwards P.N., Jackson S.J., Chalmers M.K., Bowker G.C., Borgman C.L., Ribes D., Burton M., Calvert S., Knowledge Infrastructures: Intellectual Frameworks and Research Challenges, (2013); Borgman C.L., Wallis J.C., Enyedy N.D., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, Int J Digit Libr, 7, 1-2, pp. 17-30, (2007); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368, pp. 4023-4038, (2010); Taper M.L., Lele S.R., Models of Scientific Inquiry and Statistical Practice: Implications for the structure of scientific knowledge, The Nature of Scientific Evidence: Statistical, Philosophical, and Empirical Considerations, pp. 17-50, (2004); Edwards P.N., Vast A., Machine: Computer Models, Climate Data, and the Politics of Global Warming, (2010); Berman F., Cerf V.G., Who will pay for public access to research data?, Science, 341, 6146, pp. 616-617, (2013); Bell G., Hey T., Szalay A., Beyond the Data Deluge (Computer Science), Science, 323, 5919, pp. 1297-1298, (2009); Out of Cite, out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data, Data Science Journal, 12, pp. 1-75, (2013); Renear A.H., Sacchi S., Wickett K.M., Definitions of dataset in the scientific and technical literature, Proc. Am. Soc. Info. Sci. Tech., 47, 1, pp. 1-4, (2010); Faniel I.M., Jacobsen T.E., Reusing scientific data: How earthquake engineering researchers assess the reusability of colleagues' data, Comput Supported Coop Work, 19, 3-4, pp. 355-375, (2010); Karasti H., Baker K.S., Millerand F., Infrastructure Time: Long-term Matters in Collaborative Development, Computer Supported Cooperative Work (CSCW), 19, 3-4, pp. 377-415, (2010); Knorr-Cetina K., Epistemic Cultures: How the Sciences Make Knowledge, (1999); Latour B., Woolgar S., Laboratory Life: The Construction of Scientific Facts, (1986); Traweek S., Beamtimes and Lifetimes: The World of High Energy Physicists, (1988); Wallis J.C., Borgman C.L., Mayernik M.S., Pepe A., Moving archival practices upstream: An exploration of the life cycle of ecological sensing data in collaborative field research, IJDC, 3, 1, pp. 114-126, (2008); Borgman C.L., Bates M., Cloonan M., Efthimiadis E., Gilliland-Swetland A., Kafai Y.B., Leazer G.H., Maddox A.B., Social aspects of digital libraries. Final report to the national science foundation, Background Paper for UCLA-National Science Foundation Workshop, (1996); Parsons M.A., Fox P.A., Is data publication the right metaphor?, Data Science Journal, 12, pp. WDS32-WDS46, (2013); Borgman C.L., Bowker G.C., Finholt T.A., Wallis J.C., Towards a Virtual Organization for Data Cyberinfrastructure, Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 353-356, (2009); Borgman C.L., Wallis J.C., Enyedy N.D., Building digital libraries for scientific data: An exploratory study of data practices in habitat ecology, Proceedings of the 10th European Conference on Research and Advanced Technology for Digital Libraries, 4172, pp. 170-183, (2006); Borgman C.L., Wallis J.C., Mayernik M.S., Who's got the data? Interdependencies in science and technology collaborations, Computer Supported Cooperative Work, 21, 6, pp. 485-523, (2012); Wallis J.C., Borgman C.L., Mayernik M.S., Pepe A., Ramanathan N., Hansen M.A., Know thy sensor: Trust, data quality, and data integrity in scientific digital libraries, Proceedings of the 11th European Conference on Research and Advanced Technology for Digital Libraries, 4675, pp. 380-391, (2007); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PLoS ONE, 8, 7, (2013); Fearon D.S., Borgman C.L., Traweek S., Wynholds L.A., Curators to the Stars (Poster), Proceedings of the American Society for Information Science and Technology, 47, (2010); Wynholds L.A., Fearon D.S., Borgman C.L., Traweek S., When use cases are not useful: Data practices, astronomy, and digital libraries, Proceedings of the 11th Annual Joint Conference on Digital Libraries, pp. 383-386, (2011); Wynholds L.A., Wallis J.C., Borgman C.L., Sands A.E., Traweek S., Data, data use, and scientific inquiry: Two case studies of data practices, Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 19-22, (2012); Glaser B.G., Strauss A.L., The Discovery of Grounded Theory: Strategies for Qualitative Research, (1967); Borgman C.L., Wallis J.C., Mayernik M.S., Pepe A., Drowning in Data: Digital library architecture to support scientific use of embedded sensor networks, Joint Conference on Digital Libraries, pp. 269-277, (2007); Mayernik M.S., Metadata Realities for Cyberinfrastructure: Data Authors As Metadata Creators, (2011); Wallis J.C., The Distribution of Data Management Responsibility Within Scientific Research Groups, (2012); Pepe A., Goodman A.A., Muench A., Crosas M., Erdmann C., Sharing, archiving, and citing data in astronomy, PLoS ONE / Authorea, Forthcoming; Ahn C.P., Alexandroff R., Prieto C.A., Anderson S.F., Anderton T., Andrews B.H., Aubourg E., Bailey S., Balbinot E., Barnes R., Bautista J., Beers T.C., Beifiori A., Berlind A.A., Bhardwaj V., Bizyaev D., Blake C.H., Blanton M.R., Blomqvist M., Bochanski J.J., Bolton A.S., Borde A., Bovy J., Brandt W.N., Brinkmann J., Brown P.J., Brownstein J.R., Bundy K., Busca N.G., Carithers W., Carnero A.R., Carr M.A., Casetti-Dinescu D.I., Chen Y., Chiappini C., Comparat J., Connolly N., Crepp J.R., Cristiani S., Croft R.A.C., Cuesta A.J., Da Costa L.N., Davenport J.R.A., Dawson K.S., De Putter R., Lee N.D., Delubac T., Dhital S., Ealet A., Ebelke G.L., Edmondson E.M., Eisenstein D.J., Escoffier S., Esposito M., Evans M.L., Fan X., Castella B.F., Alvar E.F., Ferreira L.D., Ak N.F., Finley H., Fleming S.W., Font-Ribera A., Frinchaboy P.M., Garcia-Hernandez D.A., Perez A.E.G., Ge J., Genova-Santos R., Gillespie B.A., Girardi L., Hernandez J.I.G., Grebel E.K., Gunn J.E., Guo H., Haggard D., Hamilton J.-C., Harris D.W., Hawley S.L., Hearty F.R., Ho S., Hogg D.W., Holtzman J.A., Honscheid K., Huehnerhoff J., Ivans I.I., Ivezi Z., Jacobson H.R., Jiang L., Johansson J., Johnson J.A., Kauffmann G., Kirkby D., Kirkpatrick J.A., Klaene M.A., Knapp G.R., Kneib J.-P., Goff J.-M.L., Leauthaud A., Lee K.-G., Et al., The ninth data release of the sloan digital sky survey: First spectroscopic data from the sdss-iii baryon oscillation spectroscopic survey, ApJS, 203, 2, (2012); Sloan Digital Sky Survey (SDSS): Home, (2014); Finkbeiner A.K., A Grand and Bold Thing: The Extraordinary New Map of the Universe Ushering in A New Era of Discovery, (2010); Gray J., Liu D.T., Nieto-Santisteban M., Szalay A., Dewitt D.J., Heber G., Scientific data management in the coming decade, SIGMOD Rec., 34, 4, pp. 34-41, (2005); Sands A.E., Borgman C.L., Wynholds L.A., Traweek S., We're working on it:' transferring the sloan digital sky survey from laboratory to library, Presented at the 9th International Digital Curation Conference; Center for Dark Energy Biosphere Investigations, (2014); International Ocean Discovery Program, (2014); Darch P.T., Borgman C.L., Ship Space to Database: Scientific and Social Motivations for a Database to Support Deep Subseafloor Biosphere Research, Proceedings of the 77th Annual Meeting of the Association for Information Science and Technology, (2014); Large synoptic survey telescope: Timeline, Large Synoptic Survey Telescope, (2013); Astronomy and Astrophysics in the New Millennium, (2001); Ivezic Z., Tyson J.A., Acosta E., Allsman R., Anderson S.F., Andrew J., Angel R., Axelrod T., Barr J.D., Becker A.C., Becla J., Beldica C., Blandford R.D., Bloom J.S., Borne K., Brandt W.N., Brown M.E., Bullock J.S., Burke D.L., Chandrasekharan S., Chesley S., Claver C.F., Connolly A., Cook K.H., Cooray A., Covey K.R., Cribbs C., Cutri R., Daues G., Delgado F., Ferguson H., Gawiser E., Geary J.C., Gee P., Geha M., Gibson R.R., Gilmore D.K., Gressler W.J., Hogan C., Huffer M.E., Jacoby S.H., Jain B., Jernigan J.G., Jones R.L., Juric M., Kahn S.M., Kalirai J.S., Kantor J.P., Kessler R., Kirkby D., Knox L., Krabbendam V.L., Krughoff S., Kulkarni S., Lambert R., Levine D., Liang M., Lim K.-T., Lupton R.H., Marshall P., Marshall S., May M., Miller M., Mills D.J., Monet D.G., Neill D.R., Nordby M., O'connor P., Oliver J., Olivier S.S., Olsen K., Owen R.E., Peterson J.R., Petry C.E., Pierfederici F., Pietrowicz S., Pike R., Pinto P.A., Plante R., Radeka V., Rasmussen A., Ridgway S.T., Rosing W., Saha A., Schalk T.L., Schindler R.H., Schneider D.P., Schumacher G., Sebag J., Seppala L.G., Shipsey I., Silvestri N., Smith J.A., Smith R.C., Strauss M.A., Stubbs C.W., Sweeney D., Szalay A., Thaler J.J., Et al., (2011); Abell P.A., Allison J., Anderson S.F., Andrew J.R., Angel J.R.P., Armus L., Arnett D., Asztalos S.J., Axelrod T.S., Bailey S., Ballantyne D.R., Bankert J.R., Barkhouse W.A., Barr J.D., Barrientos L.F., Barth A.J., Bartlett J.G., Becker A.C., Becla J., Beers T.C., Bernstein J.P., Biswas R., Blanton M.R., Bloom J.S., Bochanski J.J., Boeshaar P., Borne K.D., Bradac M., Brandt W.N., Bridge C.R., Brown M.E., Brunner R.J., Bullock J.S., Burgasser A.J., Burge J.H., Burke D.L., Cargile P.A., Chandrasekharan S., Chartas G., Chesley S.R., Chu Y.-H., Cinabro D., Claire M.W., Claver C.F., Clowe D., Connolly A.J., Cook K.H., Cooke J., Cooray A., Covey K.R., Culliton C.S., De Jong R., De Vries W.H., Debattista V.P., Delgado F., Dell'antonio I.P., Dhital S., Di Stefano R., Dickinson M., Dilday B., Djorgovski S.G., Dobler G., Donalek C., Dubois-Felsmann G., Durech J., Eliasdottir A., Eracleous M., Eyer L., Falco E.E., Fan X., Fassnacht C.D., Ferguson H.C., Fernandez Y.R., Fields B.D., Finkbeiner D., Figueroa E.E., Fox D.B., Francke H., Frank J.S., Frieman J., Fromenteau S., Furqan M., Galaz G., Gal-Yam A., Garnavich P., Gawiser E., Geary J., Gee P., Gibson R.R., Gilmore K., Grace E.A., Green R.F., Gressler W.J., Grillmair C.J., Habib S., Haggerty J.S., Hamuy M., Harris A.W., Et al., LSST Science Book, Version 2. 0, (2009); Mayernik M.S., Wallis J.C., Borgman C.L., Unearthing the infrastructure: Humans and sensors in field-based research, Computer Supported Cooperative Work, 22, 1, pp. 65-101, (2013); Wallis J.C., Mayernik M.S., Borgman C.L., Pepe A., Digital libraries for scientific data discovery and reuse, Proceedings of the 10th Annual Joint Conference on Digital Libraries-JCDL '10, (2010); Monge P.R., Contractor N.S., Theories of Communication Networks, (2003)","","","Institute of Electrical and Electronics Engineers Inc.","","2014 14th IEEE/ACM Joint Conference on Digital Libraries, JCDL 2014","8 September 2014 through 12 September 2014","London","109584","15525996","978-147995569-5","","","English","Proc. ACM IEEE Joint Conf. Digit. Libr.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84919335145"
"Knight G.","Knight, Gareth (55329653900)","55329653900","Building a research data management service for the London school of hygiene  &  tropical medicine","2015","Program","49","4","","424","439","15","10","10.1108/PROG-01-2015-0011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941343439&doi=10.1108%2fPROG-01-2015-0011&partnerID=40&md5=8fdcd19944316c3f7eb1e7ef1a14ecdd","London School of Hygiene & Tropical Medicine, London, United Kingdom","Knight G., London School of Hygiene & Tropical Medicine, London, United Kingdom","Purpose – The purpose of this paper is to present a case study of work performed at the London School of Hygiene and Tropical Medicine to set-up a Research Data Management Service and tailor it to the needs of health researchers. Design/methodology/approach – The paper describes the motivations for establishing the RDM Service and outlines the three objectives that were set to improve data management practice within the institution. Each of the objectives are explored in turn, stating how they were addressed. Findings – A university with limited resources can operate a RDM Service that pro-actively supports researchers wishing to manage research data by monitoring evolving support needs, identifying common trends and developing resources that will reduce the time investment needed. The institution-wide survey identified a need for guidance on developing data documentation and archiving research data following project completion. Analysis of ongoing support requests identifies a need for guidance on data management plans and complying with journal sharing requirements. Research limitations/implications – The paper provides a case study of a single institution. The results may not be generally applicable to universities that support other disciplines. Practical implications – The case study may be helpful in helping other universities to establish an RDM Service using limited resources. Originality/value – The paper outlines how the evolving data management needs of public health researchers can be identified and a strategy that can be adopted by an RDM Service to efficiently address these requirements. © 2015, Emerald Group Publishing Limited.","Archives; Digital curation; Digital repository; Public health; RDM service; Research data management","","","","","","Wellcome Trust","","Alexogiannopoulos E., McKenney S., Pickton M., Research Data Management Project: A DAF Investigation of Research Data Management Practices at The University of Northampton, (2010); Ayris P., Davies R., McLeod R., Miao R., Shenton H., Wheatley P., (2008); Beagrie N., Lavoie B., Woollard M., (2010); Beard L., (2014); Cope J., (2013); Cranna V., (2003); Ekmekcioglu C., Rice R., (2009); Fitt A., Rouse R., Taylor S., RDM: an approach from a modern university with a growing research portfolio, International Journal of Digital Curation, 10, 1, pp. 154-162, (2015); Gibbs H., (2009); Harrison S., Research data management: analysis of research data management survey, Royal Veterinary College, pp. 1-19, (2013); Horton L., (2014); Jones S., Pryor G., Whyte A., How to develop RDM services – a guide for HEI, Digital Curation Centre, pp. 1-24, (2013); Jones S., Ross S., Ruusalepp R., (2009); Jump P., (2014); Knight G., Funder Requirements for Data Management and Sharing, (2012); Knight G., (2012); Knight G., (2013); Knight G., LSHTM Research Data Management Policy, (2014); Knight G., (2014); Knight G., (2014); (2011); McHugh A., (2007); Martinez-Uribe L., (2008); Parsons T., Grimshaw S., Williamson L., (2013); White W., Coles S., (2014)","G. Knight; London School of Hygiene & Tropical Medicine, London, United Kingdom; email: gareth.knight@lshtm.ac.uk","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84941343439"
"Evans J.; Reed B.; Linger H.; Goss S.; Holmes D.; Drobik J.; Woodyat B.; Henbest S.","Evans, Joanne (37070179100); Reed, Barbara (24382148900); Linger, Henry (6506098517); Goss, Simon (7004957955); Holmes, David (56591447000); Drobik, Jan (6508159517); Woodyat, Bruce (56593239500); Henbest, Simon (6603241675)","37070179100; 24382148900; 6506098517; 7004957955; 56591447000; 6508159517; 56593239500; 6603241675","Winds of change:A recordkeeping informatics approach to information management needs in data-driven research environments","2014","Records Management Journal","24","3","","205","223","18","4","10.1108/RMJ-01-2014-0006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927605278&doi=10.1108%2fRMJ-01-2014-0006&partnerID=40&md5=b0e83e3301cf566065a668c7dcb20255","Centre for Organisational and Social Informatics, Monash University, Melbourne, Australia; Recordkeeping Innovation Pty Ltd, Sydney, Australia; Centre for Organisational and Social Informatics, Monash University, Melbourne, VIC, Australia; Aerospace Division, Defence Science and Technology Organisation, Melbourne, Australia","Evans J., Centre for Organisational and Social Informatics, Monash University, Melbourne, Australia; Reed B., Recordkeeping Innovation Pty Ltd, Sydney, Australia; Linger H., Centre for Organisational and Social Informatics, Monash University, Melbourne, VIC, Australia; Goss S., Aerospace Division, Defence Science and Technology Organisation, Melbourne, Australia; Holmes D., Aerospace Division, Defence Science and Technology Organisation, Melbourne, Australia; Drobik J., Aerospace Division, Defence Science and Technology Organisation, Melbourne, Australia; Woodyat B., Aerospace Division, Defence Science and Technology Organisation, Melbourne, Australia; Henbest S., Aerospace Division, Defence Science and Technology Organisation, Melbourne, Australia","Purpose – This paper aims to examine the role a recordkeeping informatics approach can play in understanding and addressing these challenges. In 2011, the Wind Tunnel located at the Defence Science Technology Organisation (DTSO)’s Fisherman’s Bend site in Melbourne and managed by the Flight Systems Branch (FSB) celebrated its 70th anniversary. While cause for celebration, it also raised concerns for DSTO aeronautical scientists and engineers as to capacities to effectively and efficiently manage the data legacy of such an important research facility for the next 70 years, given increased technological, organisational and collaboration complexities. Design/methodology/approach – This paper will detail how, through a collaborative action research project, the twin pillars of continuum thinking and recordkeeping metadata and the three facets of organisational culture, business process analysis and archival access, were used to examine the data, information, records and knowledge management challenges in this research data context. It will discuss how this perspective, was presented, engaged with and evolved into a set of strategies for the sustained development of FSB’s data, information and records management infrastructure, along with what is learnt about the approach through the action research process. Findings – The project found that stressing the underlying principles of recordkeeping, applied to information resources of all kinds, resonated with the scientific community of FSB. It identified appropriate strategic, policy and process frameworks to better govern information management activities. Research limitations/implications – The utility of a recordkeeping informatics approach to unpack, explore and develop strategies in technically and organisationally complex recordkeeping environment is demonstrated, along with the kinds of professional collaboration required to tackle research data challenges. Practical implications – In embracing technical and organisational complexity, the project has provided FSB with a strategic framework for the development of their information architecture so that it is both responsive to local needs, and consistent with broader DSTO requirements. Originality/value – This paper further develops recordkeeping informatics as an emerging approach for tackling the recordkeeping challenges of our era in relation to maintaining and sustaining the evidential authenticity, integrity and reliability of big complex research data sets. © Emerald Group Publishing Limited.","Business analysis; Recordkeeping informatics; Recordkeeping metadata; Records continuum; Research data management","","","","","","","","Towards the Australian data commons: A proposal for an Australian National Data Service, (2007); What is research data? (ANDS Guides), (2011); Data curation continuum, (2011); Our approach, (2011); Metadata stores solutions, (2012); Baskerville R.L., Investigating information systems with action research, Communications of the Association for Information Systems, 2, 19, (1999); Baskerville R.L., Wood-Harper A.T., A critical perspective on action research as a method for information systems research, Journal of Information Technology, 11, 3, pp. 235-246, (1996); Belanger F., Theorizing in information systems research using focus groups, Australasian Journal of Information Systems, 17, 2, (2012); Borgman C., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Botticelli P., Records appraisal in network organisations, Archivaria, 2000, 49, pp. 161-191, (2000); Coiera E., Guide to Health Informatics, (2003); Cumming K., Purposeful data: The roles and purposes of recordkeeping metadata, Records Management Journal, 17, 3, pp. 186-200, (2007); Defence information management strategic framework: First steps, (2010); Evans J., Linger H., Reed B., DSTO flight systems research consultancy sole supplier information, (2012); Evans J., Reed B., McKemmish S., Interoperable data: Sustainable frameworks for creating and managing recordkeeping metadata, Records Management Journal, 18, 2, pp. 115-129, (2008); Gilliland A.J., McKemmish S., Recordkeeping metadata, the archival multiverse, and societal grand challenges, Proceedings of the International Conference on Dublin Core and Metadata Applications 2012, (2012); Groenewegen D., Treloar A., Adding value by taking a national and institutional approach to research data: The ANDS experience, International Journal of Digital Curation, 8, 2, pp. 89-98, (2013); Halbert M., The problematic future of research data management: Challenges, opportunities and emerging patterns identified by the datares project, International Journal of Digital Curation, 8, 2, pp. 111-122, (2013); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); ISO 15489-1:2001 Information and Documentation - Records Management - Part 1: General, (2001); Ketelaar E., Archivistics research saving the profession, American Archivist, 63, 2, pp. 322-340, (2000); Koshy V., Action Research for Improving Practice, (2005); Linger H., Evans J., Reed B., Information Management in Aeronautic Research: Managing Knowledge, Records and Data Report to Flight Systems Branch, AVD, DSTO, (2013); McKay J., Marshall P., Quality and rigour of action research in information systems, ECIS 2000 Proceedings, (2000); McKay J., Marshall P., The dual imperatives of action research, Information Technology & People, 14, 1, pp. 46-59, (2001); McKemmish S., Acland G., Ward N., Reed B., Describing records in context in the continuum: The Australian Recordkeeping Metadata Schema, Archivaria, 48, 1, pp. 3-43, (1999); McKemmish S., Upward F., Reed B., Records Continuum Model, Encyclopedia of Library and Information Sciences, pp. 4447-4459, (2010); McNiff J., Action Research: Principles and Practice, (2003); Research data management in practice, (2013); Research data management policy, (2010); Cyberinfrastructure vision for 21st century discovery (No. NSF -07-28), (2007); Nelson B., Data sharing: Empty archives, Nature, 461, 7261, pp. 160-163, (2009); OECD Principles and Guidelines for Access to Research Data From Public Funding, (2007); Declaration on access to research data from public funding, (2004); Oliver G., Information culture: Exploration of differing values and attitudes to information in organisations, Journal of Documentation, 64, 3, pp. 363-385, (2008); Oliver G., Foscarini F., Records Management and Information Culture: Tackling the People Problem, (2014); Oliver G., Evans J., Reed B., Upward F., Achieving the right balance: Recordkeeping informatics, part 1, IQ, 25, 4, pp. 18-21, (2009); Oliver G., Evans J., Reed B., Upward F., Achieving the right balance: Recordkeeping informatics, part 2, IQ, 26, 1, (2010); Orlikowski W.J., Material knowing: The scaffolding of human knowledgeability, European Journal of Information Systems, 15, 5, pp. 460-466, (2006); Pink C., Meeting the data management compliance challenge: Funder expectations and institutional reality, International Journal of Digital Curation, 8, 2, pp. 157-171, (2013); Reed B., Service-oriented architectures and recordkeeping, Records Management Journal, 18, 1, pp. 7-20, (2008); Reed B., Raising standards for recordkeeping, Image and Data Manager, (2010); Sobreperez P., Using plenary focus groups in information systems research: More than a collection of interviews, Electronic Journal of Business Research Methods, 6, 2, pp. 181-188, (2008); Treloar A., Groenewegen D., Harboe-Ree C., The data curation continuum: Managing data objects in institutional repositories, D-Lib Magazine, 13, 9-10, (2007); Upward F., Structuring the records continuum: Part one: Postcustodial principles and properties, Archives and Manuscripts, 24, 2, pp. 268-285, (1996); Upward F., Structuring the records continuum: Part two: Structuration theory and recordkeeping, Archives and Manuscripts, 25, 1, pp. 10-35, (1997); Upward F., McKemmish S., Reed B., Archivists and changing social and information spaces: A continuum approach to recordkeeping and archiving in online cultures, Archivaria, 72, 2011, pp. 197-237, (2011); Upward F., Reed B., Oliver G., Evans J., Recordkeeping informatics: Re-figuring a discipline in crisis with a single minded approach, Records Management Journal, 23, 1, pp. 37-50, (2013); Circular A-110 revised 11/19/93 as further amended 9/30/99, (1999)","J. Evans; Centre for Organisational and Social Informatics, Monash University, Melbourne, Australia; email: joanne.evans@monash.edu","","Emerald Group Holdings Ltd.","","","","","","09565698","","","","English","Rec. Manage. J.","Article","Final","","Scopus","2-s2.0-84927605278"
"Ardestani S.B.; Hakansson C.J.; Laure E.; Livenson I.; Stranak P.; Dima E.; Blommesteijn D.; Van De Sanden M.","Ardestani, Sarah Berenji (57136569800); Hakansson, Carl Johan (57136549000); Laure, Erwin (6602503944); Livenson, Ilja (42761791800); Stranak, Pavel (15043417100); Dima, Emanuel (35185906100); Blommesteijn, Dennis (57136841500); Van De Sanden, Mark (55587882100)","57136569800; 57136549000; 6602503944; 42761791800; 15043417100; 35185906100; 57136841500; 55587882100","B2SHARE: An open eScience data sharing platform","2015","Proceedings - 11th IEEE International Conference on eScience, eScience 2015","","","7304328","448","453","5","13","10.1109/eScience.2015.44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959053352&doi=10.1109%2feScience.2015.44&partnerID=40&md5=bc0c2b3d32eda8269f137e4c656e05e1","PDC Center for High Performance Computing, KTH Royal Institute of Technology, Stockholm, Sweden; NICPB, Estonia; Institute of Formal and Applied Linguistics, Charlel University, Prague, Czech Republic; Department of Linguistics, University of Tubingen, Germany; SURFsara, Amsterdam, Netherlands","Ardestani S.B., PDC Center for High Performance Computing, KTH Royal Institute of Technology, Stockholm, Sweden; Hakansson C.J., PDC Center for High Performance Computing, KTH Royal Institute of Technology, Stockholm, Sweden; Laure E., PDC Center for High Performance Computing, KTH Royal Institute of Technology, Stockholm, Sweden; Livenson I., NICPB, Estonia; Stranak P., Institute of Formal and Applied Linguistics, Charlel University, Prague, Czech Republic; Dima E., Department of Linguistics, University of Tubingen, Germany; Blommesteijn D., SURFsara, Amsterdam, Netherlands; Van De Sanden M., SURFsara, Amsterdam, Netherlands","Scientific data sharing is becoming an essential service for data driven science and can significantly improve the scientific process by making reliable, and trustworthy data available. Thereby reducing redundant work, and providing insights on related research and recent advancements. For data sharing services to be useful in the scientific process, they need to fulfill a number of requirements that cover not only discovery, and access to data. But to ensure the integrity, and reliability of published data as well. B2SHARE, developed by the EUDAT project, provides such a data sharing service to scientific communities. For communities that wish to download, install and maintain their own service, it is also available as software. B2SHARE is developed with a focus on user-friendliness, reliability, and trustworthiness, and can be customized for different organizations and use-cases. In this paper we discuss the design, architecture, and implementation of B2SHARE. We show its usefulness in the scientific process with some case studies in the biodiversity field. © 2015 IEEE.","Data repository; Data sharing; European data infrastructure; Research data management","Biodiversity; Data infrastructure; Data repositories; Data Sharing; Data-sharing platforms; Essential services; Research data managements; Scientific community; Scientific data sharing; Information management","","","","","","","EUDAT; Zenodo; CKAN; Figshare; DataCite; Common Language and Resource Technology Infrastructure; Dropbox; Box; Google Drive; Doorn P., Tjalsma H., Introduction: Archiving research data, Archival Science, 7, 1, pp. 1-20, (2007); Lewis J.A., Research Data Management Technical Infrastructure: A Review of Options for Development at the University of Sheffield, (2014); Garrett L., Silva C., Gramstadt M.-T., KAPTUR: Technical Analysis Report, (2012); Pyrounakis G., Nikolaidou M., Hatzopoulos M., Building digital collections using open source digital repository software: A comparative study, International Journal of Digital Library Systems (IJDLS), 4, 1, pp. 10-24, (2014); Gentzsch W., Lecarpentier D., Wittenburg P., Big data in science and the EUDAT project, Global Conference (SRII), 2014 Annual SRII, pp. 191-194, (2014); PIDs in EUDAT; Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH); Lindat License Selector; Sanden De M.Van, Baxter R.B., Cacciari C., Fiameni G., Laure E., Broeder D., Thiemann H., Iozzi F., Jensen J., D5. 2. 2 eudat early candidate services, Tech. Rep, (2013); Invenio; SQLAlchemy, A Python SQL Toolkit and Object Relational Mapper; MARC 21 Format for Authority Data; Integrated Rule-Oriented Data System (IRODS); Zenodo FAQ; CKAN Extension for Assigning A DOI to Datasets; Winn J., Et al., Open Data and the Academy: An Evaluation of CKAN for Research Data Management, (2013); Figshare Licensing; Figshare API; Docker","","","Institute of Electrical and Electronics Engineers Inc.","eScience Steering Committee; Ludwig-Maximilians-Universitat Munchen","11th IEEE International Conference on eScience, eScience 2015","31 August 2015 through 4 September 2015","Munich","117074","","978-146739325-6","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84959053352"
"Ananthakrishnan R.; Chard K.; Foster I.; Tuecke S.","Ananthakrishnan, Rachana (14051682800); Chard, Kyle (9132950200); Foster, Ian (35572232000); Tuecke, Steven (6602740450)","14051682800; 9132950200; 35572232000; 6602740450","Globus platform-as-a-service for collaborative science applications","2015","Concurrency and Computation: Practice and Experience","27","2","","290","305","15","36","10.1002/cpe.3262","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922953650&doi=10.1002%2fcpe.3262&partnerID=40&md5=a6e4ae9b8bd5b6b19b0b9ddadd3cf46f","Computation Institute, Argonne National Laboratory, University of Chicago, Chicago, 60637, IL, United States","Ananthakrishnan R., Computation Institute, Argonne National Laboratory, University of Chicago, Chicago, 60637, IL, United States; Chard K., Computation Institute, Argonne National Laboratory, University of Chicago, Chicago, 60637, IL, United States; Foster I., Computation Institute, Argonne National Laboratory, University of Chicago, Chicago, 60637, IL, United States; Tuecke S., Computation Institute, Argonne National Laboratory, University of Chicago, Chicago, 60637, IL, United States","Summary Globus, developed as software-as-a-service for research data management, also provides APIs that constitute a flexible and powerful platform-as-a-service to which developers can outsource data management activities such as transfer and sharing, as well as identity, profile, and group management. By providing these frequently important but always challenging capabilities as a service, accessible over the network, Globus platform-as-a-service streamlines Web application development and makes it easy for individuals, teams, and institutions to create collaborative applications such as science gateways for science communities. We introduce the capabilities of this platform and review representative applications. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.","authentication; authorization; cloud; collaboration; group; identity; platform-as-a-service; profile; science gateways; sharing; transfer","Authentication; Clouds; Information management; Software as a service (SaaS); Web services; authorization; collaboration; group; identity; profile; Science gateway; sharing; transfer; Platform as a Service (PaaS)","","","","","","","Wilkins-Diehr N., Science gateways - Common community interfaces to Grid resources, Concurrency and Computation: Practice and Experience, 19, 6, pp. 743-749, (2007); Foster I., Globus online: Accelerating and democratizing science through cloud-based services, IEEE Internet Computing, pp. 70-73, (2011); Ananthakrishnan R., Et al., Globus Nexus: An identity, profile, and group management platform for science gateways and other collaborative science applications, Science Gateway Institute Workshop, Co-located with IEEE Cluster, (2013); Allen B., Et al., Software as a service for data scientists, Communications of the ACM, 55, 2, pp. 81-88, (2012); Cinquini L., Et al., E Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, Cience (E-Science), 2012 IEEE 8th International Conference on, (2012); Smith M., Et al., DSpace: An open source dynamic digital repository, D-Lib Magazine, 9, 1, (2003); Bo L., Et al., Deploying bioinformatics workflows on clouds with galaxy and globus provision, High Performance Computing, Networking, Storage and Analysis (SCC), (2012); Helmer K.G., Et al., Enabling collaborative research using the Biomedical Informatics Research Network (BIRN), Journal of the American Medical Informatics Association, (2011); Barnett W., Et al., A Roadmap for Using NSF Cyberinfrastructure with InCommon, (2011); Anderson K.M., Integrating open hypermedia systems with the World Wide Web, Eighth ACM Conference on Hypertext, (1997); Allcock W., Et al., The Globus striped GridFTP framework and server, Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, (2005); Foster I., Vasilliadis V., Tuecke S., Software As A Service As A Path to Software Sustainability, Figshare, (2013); Dooley R., Et al., Software-as-a-service: The iPlant Foundation API, 5th IEEE Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS), (2012); Goff S.A., Et al., The iPlant collaborative: Cyberinfrastructure for plant biology, Frontiers in Plant Science, 2, (2011); Bajaj C., Cutchin S., Web based collabortive visualization of distributed and parallel simulation, IEEE Parallel Symposium on Visualization, (1999); Anderson K.M., Taylor R.N., Whitehead Jr.E.J., Chimera: Hypermedia for heterogeneous software environments, ACM Transactions on Information Systems, pp. 211-245, (2000); Alfieri R., Et al., From gridmap-file to voms: Managing authorization in a Grid environment, Future Generation Computer Systems, 21, 4, pp. 549-558, (2005); Anderson K.M., Supporting industrial hyperwebs: Lessons in scalability, 21st International Conference on Software Engineering, (1999); Hanushevsky A., Trunov A., Cottrell L., Peer-to-peer computing for secure high performance data copying, 2001 International Conference on Computing in High Energy and Nuclear Physics, (2001); Thain D., Et al., The Kangaroo approach to data movement on the Grid. in High Performance Distributed Computing, 2001, Proceedings. 10th IEEE International Symposium on, (2001); Monti H.M., Butt A.R., Vazhkudai S.S., CATCH: A cloud-based adaptive data transfer service for HPC, Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, pp. 1242-1253, (2011); Kosar T., Livny M., A framework for reliable and efficient data placement in distributed computing systems, Journal of Parallel and Distributed Computing, 65, 10, pp. 1146-1157, (2005)","","","John Wiley and Sons Ltd","","","","","","15320626","","CCPEB","","English","Concurr. Comput. Pract. Exper.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84922953650"
"Kálmán T.; Tonne D.; Schmitt O.","Kálmán, Tibor (34872395200); Tonne, Danah (55248295800); Schmitt, Oliver (57169933500)","34872395200; 55248295800; 57169933500","Sustainable preservation for the arts and humanities","2015","New Review of Information Networking","20","1","","123","136","13","0","10.1080/13614576.2015.1114831","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983379138&doi=10.1080%2f13614576.2015.1114831&partnerID=40&md5=97fdd6bd359b2a7a62de6d42e432f594","Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Goettingen, Germany; Karlsruhe Institute of Technology, Karlsruhe, Germany","Kálmán T., Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Goettingen, Germany; Tonne D., Karlsruhe Institute of Technology, Karlsruhe, Germany; Schmitt O., Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Goettingen, Germany","DARIAH (Digital Research Infrastructure for the Arts and Humanities) aims to support digitally-enabled research across the arts and humanities. The activities and service portfolios are centered around communities to enable transnational, interdisciplinary research. One of the most important goals of DARIAH is the sustainable research data management. Although widely-acknowledged standards and best practices are utilized for essential long-term storage components, offering an interoperable technological solution is challenging due to the heterogeneity of the tools and data. In this article, we analyze these problems, discuss a general concept for long-term storage in DARIAH, and present two implementations of the corresponding preservation services. © Tibor Kálmán, Danah Tonne, and Oliver Schmitt.","Arts; CDSTAR; DARIAH; Humanities; IRods; Preservation; REST","Information management; Wood preservation; Arts; CDSTAR; DARIAH; Humanities; IRods; REST; Digital storage","","","","","","","(2006); Bazar S., Et al., Enterprise Compatible Cloud Object Storage and Synchronization Service, Cloud Computing in Emerging Markets (CCEM), IEEE International Conference, pp. 1-6, (2012); Bazzanella B., Tzitzikas Y., Interoperability Objectives and Approaches: Results from the APARSEN NoE, International Conference on Digital Preservation, pp. 53-62; Bergmeyer W., The Keep Emulation Framework, Proceedings of the 1St International Workshop on Semantic Digital Archives, pp. 8-22, (2011); Cantor S., Et al., Assertions and Protocol for the OASIS Security Assertion Markup Language (SAML) V2.0, OASIS Standard, (2015); Reference Model for an Open Archival Information System (OAIS), (2012); Introduction-Definitions and Concepts, Digital Preservation Handbook, (2015); Ernst M., Et al., Dcache, a Distributed Data Storage Caching System. Computing in High Energy and Nuclear Physics, pp. 241-244; (2014); Funk S.E., Et al., DARIAH Storage Application Programming Interface, (2015); Giaretta D., The CASPAR Approach to Digital Preservation, International Journal of Digital Curation 2.1, pp. 112-121, (2008); Gietz P., Et al., Auf dem Wege zur DFN-AAI: Identity Management, DFN Mitteilungen, 71, pp. 12-15, (2006); Harmsen H., Kalman T., Wandl-Vogt E., DARIAH Meets EGI, (2015); Innocenti P., Et al., Assessing Digital Preservation Frameworks: The Approach of the SHAMAN Project, Proceedings of the International Conference on Management of Emergent Digital Ecosystems, (2009); Kahn R., Wilensky R., A Framework for Distributed Digital Object Services, International Journal on Digital Libraries 6.2, pp. 115-123, (2006); Kalman T., Kurzawe D., Schwardmann U., European Persistent Identifier Consortium—PIDs für Die Wissenschaft, Langzeitarchivierung Von Forschungsdaten—Standards Und Disziplinspezifische Lösungen, pp. 151-168, (2012); Kalman T., Wandl-Vogt E., DARIAH-ERIC: Towards a Sustainable, Social and Technical European E-Research Infrastructure for the Arts and Humanities [Abstract], (2015); Moore R., Rajasekar A., Irods: Integrated Rule-Oriented Data System, (2008); Morgan R.L., Et al., Federated Security: The Shibboleth Approach, Educause Quarterly 27.4, pp. 12-17, (2004); Practical Policy Working Group. Outcomes Policy Templates., (2014); Schmidt R., “An Architectural Overview of the Scape Preservation Platform, 9Th International Conference on Preservation of Digital Objects (Ipres 2012), (2012); Schmidt R., Et al., The Planets IF: A Framework for Integrated Access to Preservation Tools, Proceedings of the 1St International Digital Preservation Interoperability Framework Symposium, (2010); Schmitt O., Et al., GWDG Object Storage and Search Solution for Research-Common Data Storage Architecture (CDSTAR), (2015); Advancing Storage and Information Technology. Cloud Data Management Interface (CDMI) V1.1.1; Sun S., Lannom L., Boesch B., Handle System Overview; Tonne D., Et al., Access to the DARIAH Bit Preservation Service for Humanities Research Data, Parallel, Distributed and Network-Based Processing (PDP), 21St Euromicro International Conference. 2013. 9-15. Print, (2013); (2013)","T. Kálmán; Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Goettingen, Am Fassberg 11, 37077, Germany; email: tibor.kalman@gwdg.de","","Routledge","","","","","","13614576","","NRINF","","English","New Rev Inf Networking","Article","Final","","Scopus","2-s2.0-84983379138"
"Moles N.; Ross S.","Moles, Nathan (56505291600); Ross, Seamus (7401610385)","56505291600; 7401610385","Report on the context of the DigCurV curriculum framework","2015","CEUR Workshop Proceedings","1016","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922324319&partnerID=40&md5=f411363fc78e1394dd38672812346fe5","Faculty of Information, University of Toronto, Toronto, Canada","Moles N., Faculty of Information, University of Toronto, Toronto, Canada; Ross S., Faculty of Information, University of Toronto, Toronto, Canada","This paper presents an overview of current or recently completed initiatives to create, structure, or help foster curricula for the on-going vocational training of information professionals with the aim of informing the implementation of DigCurV's curriculum framework. The initiatives examined include the Digital Curation Centre, DaMSSI (Research Data Management Skills Support Initiative), DigCCurr (Carolina Digital Curation Curriculum Project), Closing the Digital Curation Gap, Digital Curation Exchange, International Digital Curation Education Action (IDEA) Working Group, Digital Preservation Coalition, Digital Preservation Training Programme, the Library of Congress' Digital Preservation Outreach and Education, the Society of American Archivists' Digital Archives Specialist (DAS) Curriculum and Certification and nestor, the German competence network.","DigCurV; Digital Curation curriculum","Curricula; Digital image storage; Digital libraries; Digital storage; Information management; Information services; Competence networks; DigCurV; Digital curation; Digital Curation Centre; Digital preservation; Information professionals; Research data managements; Training programmes; E-learning","","","","","","","Molloy L.M., Konstantelos Ann Gow L., Wilson D., Ross S., Moles N., D4.1 initial curriculum for digital curators, DigCurV, (2013); Molloy L.M., Konstantelos Ann Gow L., Wilson D., A curriculum framework for digital curation, DigCurV, (2013); Gow A., Karvelyte V., Klingaite N., Kupriene J., Molloy L., Snow K., Report on Baseline Survey and Evaluation Framework, (2012); Gow A., Karvelyte V., Klingaite N., Kupriene J., Molloy L., Snow K., Report on Baseline Survey and Evaluation Framework, (2012); Engelhardt C., Strathmann S., McCadden K., D3.1 report on survey of sector training needs, DigCurV, (2012); Ross S., ERPANET, a European platform for enabling digital preservation, Vine: The Journal of Information and Knowledge Management, 135, pp. 77-83, (2004); DELOS Preservation Summer Schools, (2005); Costello K.L., Brown M.E., Preliminary report on the 2010-2011 digccurr professional institute: Curation practices for the digital object lifecycle, D-Lib Magazine, 16, 11-12, (2010); Hank C., Davidson J., International data curation education action (idea) working group, D-Lib Magazine, 15, 3-4, (2009); Kilbride W., Cirinna C., McMeekin S., Training in digital preservation: What we've learned and what we're going to do about it, Fondazione Rinascimento Digitale; Neuroth H., Osswald A., Strathmann S., Qualification & education in digital curation: The nestor experience in germany, Proceedings of DigCurr 2009, (2009); Cirinna C., Fernie K., Lunghi Round Table M., Creating a common vision for digital curation education: Building alliances, DigCurV, 2013","","Fernie K.; Casarosa V.; Lunghi M.; Cirinna C.","CEUR-WS","","Framing the Digital Curation Curriculum Conference, DigCurV 2013","6 May 2013 through 7 May 2013","Florence","110432","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84922324319"
"Chard K.; Pruyne J.; Blaiszik B.; Ananthakrishnan R.; Tuecke S.; Foster I.","Chard, Kyle (9132950200); Pruyne, Jim (6602252957); Blaiszik, Ben (15043808100); Ananthakrishnan, Rachana (14051682800); Tuecke, Steven (6602740450); Foster, Ian (35572232000)","9132950200; 6602252957; 15043808100; 14051682800; 6602740450; 35572232000","Globus data publication as a service: Lowering barriers to reproducible science","2015","Proceedings - 11th IEEE International Conference on eScience, eScience 2015","","","7304323","401","410","9","35","10.1109/eScience.2015.68","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959060310&doi=10.1109%2feScience.2015.68&partnerID=40&md5=d175b1f7cfa2516cddc8010071c14e7e","Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States","Chard K., Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States; Pruyne J., Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States; Blaiszik B., Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States; Ananthakrishnan R., Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States; Tuecke S., Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States; Foster I., Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, IL, United States","Broad access to the data on which scientific results are based is essential for verification, reproducibility, and extension. Scholarly publication has long been the means to this end. But as data volumes grow, new methods beyond traditional publications are needed for communicating, discovering, and accessing scientific data. We describe data publication capabilities within the Globus research data management service, which supports publication of large datasets, with customizable policies for different institutions and researchers, the ability to publish data directly from both locally owned storage and cloud storage, extensible metadata that can be customized to describe specific attributes of different research domains, flexible publication and curation workflows that can be easily tailored to meet institutional requirements, and public and restricted collections that give complete control over who may access published data. We describe the architecture and implementation of these new capabilities and review early results from pilot projects involving nine research communities that span a range of data sizes, data types, disciplines, and publication policies. © 2015 IEEE.","Data publication; Globus; Research data management","Digital storage; Publishing; Complete control; Data publications; Globus; Reproducibilities; Research communities; Research data managements; Scholarly publication; Scientific results; Information management","","","","","National Institutes of Health, NIH, (1U54EB020406-01); U.S. Department of Energy, USDOE, (DE-AC02-06CH11357); National Institute of Standards and Technology, NIST, (60NANB15D077)","We thank the Globus team for implementing and operating Globus services, and the participants in our publication pilots for their invaluable contributions. This research was supported in part by DOE contract DE-AC02-06CH11357; NIH contract 1U54EB020406-01, Big Data for Discovery Science Center; and NIST contract 60NANB15D077.","Vines T.H., Albert A., Andrew R., Debarre F., Bock D., Franklin M., Gilbert K., Moore J.-S., Renaut S., Rennison D., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 94-97, (2014); Alsheikh-Ali A.A., Quresh W., Al-Mallah M.H., Ioannidis J.P., Public availability of published research data in high-impact journals, PLoS ONE, 6, 9, (2011); Waters B., Software as a service: A look at the customer benefits, J Digit Asset Manag, 1, 1, pp. 32-39, (2005); Foster I., Globus Online: Accelerating and democratizing science through cloud-based services, Internet Computing, IEEE, 15, 3, pp. 70-73, (2011); Chard K., Tuecke S., Foster I., Efficient and secure transfer, synchronization, and sharing of big data, Cloud Computing, IEEE, 1, 3, pp. 46-55, (2014); Costello M.J., Motivating online publication of data, BioScience, 59, 5, pp. 418-427, (2009); Kratz J., Strasser C., Data publication consensus and controversies, F1000Research, 3, 94, (2014); Candela L., Castelli D., Manghi P., Tani A., Data journals: A survey, Journal of the Association for Information Science and Technology, (2015); Nardone J., Computerized material property data information system, Plastics Technical Evaluation Center (PLASTEC), Tech. Rep, 31, (1976); Mailman M., Feolo M., Jin Y., Kimura M., Tryka K., Bagoutdinov R., Hao L., Kiang A., Paschall J., Phan L., Popova N., Pretel S., Ziyabari L., Lee M., Shao Y., Wang Z., Sirotkin K., Ward M., Kholodov M., Zbicz K., Beck J., Kimelman M., Shevelev S., Preuss D., Yaschenko E., Graeff A., Ostell J., Sherry S., The NCBI dbGaP database of genotypes and phenotypes, Nature Genetics, 39, 10, pp. 1181-1186, (2007); Consortium U., Uniprot: A hub for protein information, Nucleic Acids Research, 43, D1, pp. D204-D212, (2015); SIMBAD Astronomical Database; National Database for Autism Research (NDAR); Federal Interagency Traumatic Brain Injury Research (FITBIR); National Climatic Data Center (NCDC); Chen M., Stott A.C., Li S., Dixon D.A., Construction of a robust, large-scale, collaborative database for raw data in computational chemistry: The Collaborative Chemistry Database Tool (CCDBT), Journal of Molecular Graphics and Modelling, 34, pp. 67-75, (2012); Materials Atlas; PURR: Purdue University Research Repository; Data Repository for the University of Minnesota (DRUM); ScholarSphere; Dataverse; Figshare; Zenodo; Dryad; Data Citation Standards and Practices, (2010); Renear A.H., Sacchi S., Wickett K.M., Definitions of dataset in the scientific and technical literature, Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in An Information Ecosystem-Volume 47, Ser. ASIS&T '10, pp. 811-814, (2010); DSpace; Smith M., Barton M., Bass M., Branschofsky M., McClellan G., Stuve D., Tansley R., Walker J.H., DSpace: An open source dynamic digital repository, D-Lib Magazine, 9, 1, (2003); Chard K., Lidman M., Bryan J., Howe T., McCollam B., Ananthakrishnan R., Tuecke S., Foster I., Globus Nexus: Research identity, profile, and group management as a service, IEEE 10th International Conference on E-Science, 1, pp. 31-38, (2014); Barnett W., Welch V., Walsh A., Stewart C.A., A Roadmap for Using NSF Cyberinfrastructure with InCommon, (2011); Towns J., Cockerill T., Dahan M., Foster I., Gaither K., Grimshaw A., Hazlewood V., Lathrop S., Lifka D., Peterson G.D., Roskies R., Scott J.R., Wilkins-Diehr N., XSEDE: Accelerating scientific discovery, Computing in Science and Engineering, 16, 5, pp. 62-74, (2014); Allen B., Bresnahan J., Childers L., Foster I., Kandaswamy G., Kettimuthu R., Kordas J., Link M., Martin S., Pickett K., Tuecke S., Software as a service for data scientists, Communications of the ACM, 55, 2, pp. 81-88, (2012); Sun S., Lannom L., Boesch B., Handle system overview, Internet Engineering Task Force, Network Working Group, RFC, 3650, (2003); Digital Object Identifier System; EZID; Bitly; Weibel S., Kunze J., Lagoze C., Wolf M., Dublin core metadata for resource discovery, Internet Engineering Task Force RFC, 2413, 222, (1998); DataCite; Apache Solr; Malik T., Chard K., Foster I., Benchmarking cloud-based tagging services, IEEE 30th International Conference on Data Engineering Workshops (ICDEW), pp. 231-238, (2014); Winslow R.L., Saltz J., Foster I., Carr J.J., Ge Y., Miller M.I., Younes L., Geman D., Graniote S., Kurc T., Madduri R., Ratnanather T., Larkin J., Ardekani S., Brown T., Kolasny A., Reynolds K., Shipway M., Toerper M., The cardiovascular research grid project, Proceedings of the AMIA Summit on Translational Bioinformatics, pp. 77-81, (2011); Domenico B., Caron J., Davis E., Kambic R., Nativi S., Thematic real-time environmental distributed data services (thredds): Incorporating interactive analysis tools into nsdl, Journal of Digital Information, 2, 4, (2002); NetCDF: An Access Interface for Self-describing Portable Data, (2002); Eaton B., Gregory J., Drach B., Taylor K., Hankin S., Caron J., Signell R., Bentley P., Rappa G., Hck H., Pamment A., Juckes M., NetCDF Climate and Forecast (CF) metadata conventions, Version, 1, 5, (2010); Allcock W., Bresnahan J., Kettimuthu R., Link M., Dumitrescu C., Raicu I., Foster I., The Globus striped GridFTP framework and server, SC'2005, (2005); Elliott J., Muller C., Deryng D., Chryssanthacopoulos J., Boote K., Buchner M., Foster I., Glotter M., Heinke J., Iizumi T., Et al., The global gridded crop model intercomparison: Data and modeling protocols for phase 1 (v1. 0), Geoscientific Model Development, 8, 2, pp. 261-277, (2015); MINiML (MIAME Notation in Markup Language)","","","Institute of Electrical and Electronics Engineers Inc.","eScience Steering Committee; Ludwig-Maximilians-Universitat Munchen","11th IEEE International Conference on eScience, eScience 2015","31 August 2015 through 4 September 2015","Munich","117074","","978-146739325-6","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","","Scopus","2-s2.0-84959060310"
"Shahi A.; Haas C.T.; West J.S.; Akinci B.","Shahi, Arash (22981436200); Haas, Carl T. (7202620442); West, Jeffrey S. (7402746437); Akinci, Burcu (6603543201)","22981436200; 7202620442; 7402746437; 6603543201","Workflow-based construction research data management and dissemination","2014","Journal of Computing in Civil Engineering","28","2","","244","252","8","4","10.1061/(ASCE)CP.1943-5487.0000251","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894488849&doi=10.1061%2f%28ASCE%29CP.1943-5487.0000251&partnerID=40&md5=56f3ef3af3fb5b67d5c78a6dd281f7aa","Dept. of Civil and Environmental Engineering, Univ. of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., Pittsburgh, PA 15213-3890, United States","Shahi A., Dept. of Civil and Environmental Engineering, Univ. of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Haas C.T., Dept. of Civil and Environmental Engineering, Univ. of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; West J.S., Dept. of Civil and Environmental Engineering, Univ. of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Akinci B., Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., Pittsburgh, PA 15213-3890, United States","Sharing research data is necessary for collaboration within a research network and is required by funding agencies, such as the National Science Foundation (NSF), that enforce the scientific method and ethics associated with data management and sharing. However, methods and infrastructure for supporting construction research data management are currently underdeveloped; emphasizing the need for developing effective and efficient means for managing and sharing research data. A review of existing data management models reveals that there is currently no effective universal system for sharing the data obtained from construction research endeavours. This paper presents electronic product and process management systems (EPPMS) as a construction research data management and sharing approach. The developed EPPMS is a web-based system that utilizes workflows that can automate the collection, authorization, and dissemination of construction research data. A comparative analysis of the developed system to the existing web-based cloud and web-based share point systems indicates that an EPPMS offers a more fitting solution for construction research data management. © 2012 American Society of Civil Engineers.","Construction; Data dissemination; Data management; Electronic product and process management system (EPPMS); Research data; Web-based; Workflow driven design","","","","","","","","Axelsson A., Schroeder R., Making it open and keeping it safe: E-enabled data-sharing in Sweden, Acta Sociologica, 52, 3, pp. 213-226, (2009); Ceci S.J., Scientists' attitudes toward data sharing, Sci. Technol. Human Values, 13, 12, pp. 45-46, (1988); 2009 Strategic Plan, (2009); Membership, (2010); CII Benchmarking Code of Conduct, (2011); Fischer B.A., Zigmond M.J., The essential nature of sharing in science, Sci. Eng. Ethics, 16, 4, pp. 783-799, (2010); Giffels J., Vollmer S.H., Bird S.J., Editors' overview: Topics in the responsible management of research data, Sci. Eng. Ethics, 16, 4, pp. 631-637, (2010); Lee S., Thomas S., Tucker R., Web-based benchmarking system for the construction industry, J. Constr. Eng. Manage., pp. 790-798, (2005); Applying the the Data Resources Program Solicitation: Funding for the Analysis of Existing Data, (2010); NIH Data Sharing Policy and Implementation Guidance, (2003); Chapter VI - Other Post Award Requirements and Considerations, (2011); Tseng S., Huang J., The correlation between wikipedia and knowledge sharing on job performance, Expert Syst. Appl., 38, 5, pp. 6118-6124, (2011); University of Pittsburgh Guidelines on Research Data Management, (2009); Weil V., Hollander R., Sharing scientific data II: Normative issues, Rev. Human Subj. Res., 12, 2, pp. 7-8, (1990); Yang H., Lai C., Motivations of Wikipedia content contributors, Comput. Human Behav., 26, 6, pp. 1377-1383, (2010); Yang H., Lai C., Understanding knowledge-sharing behaviour in Wikipedia, Behav. Inf. Technol., 30, 1, pp. 131-142, (2011)","A. Shahi; Dept. of Civil and Environmental Engineering, Univ. of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; email: arash.shahi@uwaterloo.ca","","","","","","","","08873801","","JCCEE","","English","J. Comput. Civ. Eng.","Article","Final","","Scopus","2-s2.0-84894488849"
"Arend D.; Lange M.; Chen J.; Colmsee C.; Flemming S.; Hecht D.; Scholz U.","Arend, Daniel (55531371500); Lange, Matthias (36028279400); Chen, Jinbo (55561593200); Colmsee, Christian (35104579700); Flemming, Steffen (36943802800); Hecht, Denny (56244885300); Scholz, Uwe (56124842400)","55531371500; 36028279400; 55561593200; 35104579700; 36943802800; 56244885300; 56124842400","E!DAL - a framework to store, share and publish research data","2014","BMC Bioinformatics","15","1","214","","","","64","10.1186/1471-2105-15-214","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903700852&doi=10.1186%2f1471-2105-15-214&partnerID=40&md5=4495ab919eea238f0d43ccd4d2d72092","Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany","Arend D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; Lange M., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; Chen J., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; Colmsee C., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; Flemming S., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; Hecht D., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; Scholz U., Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany","Background: The life-science community faces a major challenge in handling "" big data"" , highlighting the need for high quality infrastructures capable of sharing and publishing research data. Data preservation, analysis, and publication are the three pillars in the "" big data life cycle"" . The infrastructures currently available for managing and publishing data are often designed to meet domain-specific or project-specific requirements, resulting in the repeated development of proprietary solutions and lower quality data publication and preservation overall.Results: e!DAL is a lightweight software framework for publishing and sharing research data. Its main features are version tracking, metadata management, information retrieval, registration of persistent identifiers (DOI), an embedded HTTP(S) server for public data access, access as a network file system, and a scalable storage backend. e!DAL is available as an API for local non-shared storage and as a remote API featuring distributed applications. It can be deployed "" out-of-the-box"" as an on-site repository.Conclusions: e!DAL was developed based on experiences coming from decades of research data management at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). Initially developed as a data publication and documentation infrastructure for the IPK's role as a data center in the DataCite consortium, e!DAL has grown towards being a general data archiving and publication infrastructure. The e!DAL software has been deployed into the Maven Central Repository. Documentation and Software are also available at: http://edal.ipk-gatersleben.de. © 2014 Arend et al.; licensee BioMed Central Ltd.","Data publication; JAVA API; Metadata annotation; Persistent identifier; Research data management; Shared repositories","Databases, Factual; Information Dissemination; Software; Computer programming; Digital storage; Information management; Metadata; Publishing; Research; Data publications; Java API; Metadata annotations; Persistent identifier; Research data managements; Shared repositories; article; computer program; factual database; information dissemination; Big data","","","","","Seventh Framework Programme, FP7, (283496); European Commission, EC; Leibniz-Gemeinschaft, LG; Bundesministerium für Bildung und Forschung, BMBF, (031A053)","Funding text 1: We thank Joscha Joel Benz for the initial WebDAV code and Thomas Münch, Heiko Miehe as administrator of the project website, code, and artifact repositories. This work was supported by the European Commission within its 7th Framework Program, under the thematic area “Infrastructures”, contract number 283496, by the Leibniz Association in the framework “Pakt für Forschung”. Part of this work was performed within the; Funding text 2: German-Plant-Phenotyping Network, which is funded by the German Federal Ministry of Education and Research (project identification number: 031A053).","Craddock T., Harwood C.R., Hallinan J., Wipat A., E-Science: relieving bottlenecks in large-scale genome analyses, Nat Rev Microbiol, 6, 12, pp. 248-954, (2008); Brooksbank C., Bergman M.T., Apweiler R., Birney E., Thornton J., The european bioinformatics institute's data resources 2014, Nucleic Acids Res, 42, (2013); Roos D.S., Computational biology: bioinformatics-trying to swim in a sea of data, Science, 291, 5507, pp. 1260-1261, (2001); Fernandez-Suarez X.M., Galperin M.Y., The 2013 nucleic acids research database issue and the online molecular biology database collection, Nucleic Acids Res, 41, D1, pp. 1-7, (2013); Kodama Y., Shumway M., Leinonen R., International nucleotide sequence database collaboration: the sequence read archive: explosive growth of sequencing data, Nucleic Acids Res, 40, DATABASE ISSUE, (2012); Lu Z., PubMed and beyond: a survey of web tools for searching biomedical literature, Database, (2011); Smith B., Ashburner M., Rosse C., Bard J., Bug W., Ceusters W., Goldberg L.J., Eilbeck K., Ireland A., Mungall C.J., Leontis N., Rocca-Serra P., Ruttenberg A., Sansone S.-A.A., Scheuermann R.H., Shah N., Whetzel P.L., Lewis S., The OBO foundry: coordinated evolution of ontologies to support biomedical data integration, Nat Biotechnol, 25, 11, pp. 1251-1255, (2007); Sansone S.-A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hofmann O., Fang H., Neumann S., Tong W., Amaral-Zettler L., Begley K., Booth T., Bougueleret L., Burns G., Chapman B., Clark T., Coleman L.-A., Copeland J., Das S., de Daruvar A., de Matos P., Dix I., Edmunds S., Evelo C.T., Forster M.J., Gaudet P., Gilbert J., Goble C., Griffin J.L., Jacob D., Et al., Toward interoperable bioscience data, Nat Genet, 44, 2, pp. 121-126, (2012); Zhang J., Haider S., Baran J., Cros A., Guberman J.M., Hsu J., Liang Y., Yao L., Kasprzyk A., BioMart: a data federation framework for large collaborative projects, Database, 2011, 0, (2011); Gray J., Jim Gray on eScience: a Transformed Scientific Method; Smith V.S., Data publication: towards a database of everything, BMC Res Notes, 2, (2009); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? 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The DOI System; Open Archives Initiative; Pan-European Research Infrastructure for Biological Information; JAVA Authentiction and Authorisation Service; Java Remote Method Invocation; Hibernate Search; Kiczales G., Hilsdale E., Hugunin J., Kersten M., Palm J., Griswold W.G., Proceedings of the 15th European Conference on Object-Oriented Programming, (2001); Janusz M., A toolbox for developing bioinformatics software, Brief Bioinform, 13, 2, pp. 244-257, (2012); Kane D., Hohman M., Cerami E., McCormick M., Kuhlmman K., Byrd J., Agile methods in biomedical software development: a multi-site experience report, BMC Bioinformatics, 7, 1, (2006); Le Novere N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M., BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems, Nucleic Acids Res, 34, SUPPL. 1, pp. 689-691, (2006); The COMBINE Archive; Pico A.R., Kelder T., van Iersel M.P., Hanspers K., Conklin B.R., Evelo C., WikiPathways: pathway Editing for the People, PLoS Biol, 6, 7, (2008); Hucka M., Finney A., Sauro H.M., Bolouri H., Doyle J.C., Kitano H., Arkin A.P., Bornstein B.J., Bray D., Cornish-Bowden A., Cuellar A.A., Dronov S., Gilles E.D., Ginkel M., Gor V., Goryanin I.I., Hedley W.J., Hodgman T.C., Hofmeyr J.-H., Hunter P.J., Juty N.S., Kasberger J.L., Kremling A., Kummer U., Le Novere N., Loew L.M., Lucio D., Mendes P., Minch E., Mjolsness E.D., Et al., The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models, Bioinformatics, 19, 4, pp. 524-531, (2003); Rohn H., Junker A., Hartmann A., Grafahrend-Belau E., Treutler H., Klapperstuck M., Czauderna T., Klukas C., Schreiber F., VANTED v2: a framework for systems biology applications, BMC Syst Biol, 6, 1, (2012); Apache Hadoop Distributed File System; Apache Commons Virtual File System; Rocca-Serra P., Brandizi M., Maguire E., Sklyar N., Taylor C., Begley K., Field D., Harris S., Hide W., Hofmann O., Neumann S., Sterk P., Tong W., Sansone S.-A., ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level, Bioinformatics, 26, 18, pp. 2354-2356, (2010)","D. Arend; Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Stadt Seeland, Corrensstr. 3, Germany; email: arendd@ipk-gatersleben.de","","BioMed Central Ltd.","","","","","","14712105","","BBMIC","24958009","English","BMC Bioinform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84903700852"
"Koltay T.; Špiranec S.; Karvalics L.","Koltay, Tibor (6505905944); Špiranec, Sonja (35312018400); Karvalics, László Z. (6508131056)","6505905944; 35312018400; 6508131056","Research 2.0 and the Future of Information Literacy","2015","Research 2.0 and the Future of Information Literacy","","","","1","180","179","20","10.1016/C2014-0-01027-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960303022&doi=10.1016%2fC2014-0-01027-1&partnerID=40&md5=9c832227915dc78422323276d98a2da8","Department of Information and Library Studies, Szent István University, Hungary; Department of Information and Communication Sciences, University of Zagreb, Croatia; Department of Cultural Heritage and Human Information Science, University of Szeged, Hungary","Koltay T., Department of Information and Library Studies, Szent István University, Hungary; Špiranec S., Department of Information and Communication Sciences, University of Zagreb, Croatia; Karvalics L., Department of Cultural Heritage and Human Information Science, University of Szeged, Hungary","Research 2.0 and the Future of Information Literacy examines possible congruencies between information literacy and Research 2.0, because the work of today's researcher mobilizes a number of literacies. From among the various types of relevant literacies, at least three types of literacies can be mentioned in this relation: information literacy, scientific literacy and academic literacy. This book addresses these literacies in the light of the changing research landscape. Broad contexts of the researcher's abilities, as adaptive and innovative thinking, problem solving skills, self-management and design mindset are also examined. Computational thinking and the computational paradigm in a number of fields of research are taken into consideration, as well. Researchers differ to non-researchers when populating social media, which means that these two different groups require different literacies. The relationship between information literacy and information is approached in a new way. Among the multitude of issues, we introduce a new interface between information literacy and Research 2.0. It encompasses the issues of research data management and data literacy, which represent also a challenge both for the academic library and for the communities of researchers. Similarly, the questions of new metrics of scientific output are addressed in the book. • Summarizes the most important and up-to date approaches towards Research 2.0, including researchers' skills and abilities, the data-intensive paradigm of scientific research, open science, not forgetting about factors that inhibit a wider uptake of Research 2.0. • Discusses the nature of information literacy in the light of its definitions, declarations and related frameworks and by outlining the new literacies context, reading and writing, the cultural context, and the turns of library and information science. • Numerous literacies, other than information literacy, its relationship to information overload and personal information management are also subject of the book. • Theoretical and practical perspectives are given to enable the understanding of the transformations of information literacy and its relationship to Research 2.0. © 2016 Elsevier Ltd. 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A critical investigation of Web 2.0, First Monday, 13, 3, (2008); Ji Q., Ha L., Sypher U., The role of news media use and demographic characteristics in the possibility of information overload prediction, International Journal of Communication, 8, 16, pp. 699-714, (2014); Johnson C.A., The information diet: A case for conscious consumption, (2011); Johnson A.M., Sproles C., Detmering R., English J., Library instruction and information literacy 2011, Reference Services Review, 40, 4, pp. 601-703, (2012); Johnston B., Webber S., Information literacy in higher education: A review and case study, Studies in Higher Education, 28, 3, pp. 335-352, (2003); Johnston B., Webber S., As we may think: Information literacy as a discipline for the information age, Research Strategies, 20, 3, pp. 108-121, (2005); Jones D., Critical thinking in an online world, (1996); Jones W., Keeping found things found: The study and practice of personal information management (interactive technologies), (2007); Jones W., Maier D., Report from the session on personal information management, Workshop of the information and data management program, (2003); Jones W., Marchionini G., Personal information management (synthesis lectures on information concepts, retrieval, and services), (2011); Jubb M., Communication or competition: What motivates researchers to write articles for journals?, Learned Publishing, 27, 4, pp. 251-252, (2014); Julien H., Barker S., How high-school students find and evaluate scientific information: A basis for information literacy skills development, Library and Information Science Research, 31, 1, pp. 12-17, (2009); Julien H., Williamson C., Discourse and practice in information literacy and information seeking: gaps and opportunities, Information Research, 16, 1, pp. 1-10, (2011); Juznic P., Vilar P., Bartol T., What do researchers think about altmetrics and are they familiar with their abilities?, Libraries in the Digital Age (LIDA) Proceedings, 13, (2014); Kapitzke C., Information literacy: The changing library, Journal of Adolescent and Adult Literacy, 44, 5, pp. 450-456, (2001); Kapitzke C., Information literacy: A positivist epistemology and a politics of outformation, Educational Theory, 53, 1, pp. 37-53, (2003); Kaplan A.M., Haenlein M., Users of the world, unite! The challenges and opportunities of social media, Business Horizons, 53, 1, pp. 59-68, (2010); Kari J., Conceptualizing the personal outcomes of information, Information Research, 12, 2, (2007); Karvalics L.Z., From scientific literacy to lifelong research: A social innovation approach, Worldwide commonalities and challenges in information literacy research and practice, pp. 126-133, (2013); Keen A., The cult of the amateur, (2007); Kelly A.R., Autry M.K., Access, accommodation, and science: Knowledge in an ""open"" world, First Monday, 18, 6, (2013); Khodiyar V.K., Rowlett K.A., Lawrence R.N., Altmetrics as a means of assessing scholarly output, Learned Publishing, 27, 5, pp. 25-32, (2014); King D., Greaves F., Exeter C., Darzi A., Gamification: Influencing health behaviours with games, Journal of the Royal Society of Medicine, 106, 3, pp. 76-78, (2013); Knoth P., Herrmannova D., Towards semantometrics: A new semantic similarity based measure for assessing a research publication's contribution, D-Lib Magazine, 20, 11-12, (2014); Koltay T., Abstracting: Information literacy on a professional level, Journal of Documentation, 65, 5, pp. 841-855, (2009); Koltay T., The media and the literacies: Media literacy, information literacy, digital literacy, Media, Culture & Society, 33, 2, pp. 211-221, (2011)","","","Elsevier Ltd","","","","","","","978-008100089-2; 978-008100075-5","","","English","Res. 2.0 and the Future of Inf. Lit.","Book","Final","","Scopus","2-s2.0-84960303022"
"","","","CEUR Workshop Proceedings","2015","CEUR Workshop Proceedings","1529","","","","","81","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962545949&partnerID=40&md5=f98fa00501cf0d2be3b522e7d0296cc7","","","The proceedings contain 7 papers. The topics discussed include: learning from human memory: managed forgetting and contextualized remembering for digital memories; integrating research data management into geographical information systems; semantic-based expert search in textbook research archives; an automated annotation process for the SciDocAnnot scientific document model; world views a digital archive infrastructure for the Georg Eckert Institute for international textbook research; temporal anchor text as proxy for real user queries; and temporal anchor text as proxy for real user queries.","","","","","","","","","","","Nurnberger A.; Risse T.; Predoiu L.; Ross S.","CEUR-WS","","5th International Workshop on Semantic Digital Archives, SDA 2015","18 September 2015","Poznan","118194","16130073","","","","English","CEUR Workshop Proc.","Conference review","Final","","Scopus","2-s2.0-84962545949"
"Wehrle D.; Van Ekeris W.; Hahn U.","Wehrle, Dennis (53867392900); Van Ekeris, Wiebke (56512449100); Hahn, Uli (57200387752)","53867392900; 56512449100; 57200387752","Looking beyond data through the ages","2014","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-232","","","1675","1685","10","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922572025&partnerID=40&md5=50c3e6027f00a3e091183f30c103120b","Albert-Ludwigs University Freiburg, Communication Systems, Germany; Albert-Ludwigs University Freiburg, University Library Freiburg, Germany; Ulm University, Communication-and Information Center, Germany","Wehrle D., Albert-Ludwigs University Freiburg, Communication Systems, Germany; Van Ekeris W., Albert-Ludwigs University Freiburg, University Library Freiburg, Germany; Hahn U., Ulm University, Communication-and Information Center, Germany","Accessing data has always been crucial in order to get access to (cultural) knowledge. But in the new ages different challenges arise to keep complex data accessible. Concerning these challenges a linguistic use case, which has been set up in 1993, serves as an example to show that it might be hard to re-enact an old system in order to reuse its research data and processes. Re-enacting the old system showed that other crucial points emerge in the context of research data management that need attention. In order to increase the probability of gaining access to research data in the future Data Management Plans can be identified as central tool. Although preservation of and creating access to data is a global task, the scientific management of data is still strongly shaped through national regulations and practices and hence delivers a quite fragmented picture.","","Information management; Accessing data; Complex data; Gaining access; Management plans; Research data; Research data managements; Scientific management; Use-case; Big data","","","","","","","Buttner S., Hobohm H., Muller L., Handbuch Forschungsdatenmanagement, (2011); Brunger-Weilandt S., Gesamtkonzept für die informationsinfrastruktur in deutschland: empfehlungen der kommission zukunft der informationsinfrastruktur im auftrag der gemeinsamen wissenschaftskonferenz des bundes und der länder, Wissenschaftsgemeinschaft Gottfried Wilhelm Leibniz e, 5, (2011); European Commission, Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, (2013); European Commission, Multi-beneficiary General Model Grant Agreement, (2013); Deutsche Forschungsgemeinschaft, Proposals for Safeguarding Good Scientific Practice, (2013); Lorblanchet M., Bosinski G., Höhlenmalerei. Thorbecke, (2000); Ludwig J., Enke H., Leitfaden Zum Forschungsdaten-Management: Handreichungen Aus Dem WissGrid-Projekt, (2013); Macleod R., The library of alexandria: Centre of learning in the ancient world, IB, (2000); Neuroth H., Osswald A., Scheffel R., Strathmann S., Huth K., Handbuch N., Eine Kleine Enzyklopädie der Digitalen Langzeitarchivierung, (2010); Plassmann E., Rosch H., Seefeldt J., Umlauf K., Bibliotheken und Informationsgesellschaft in Deutschland: Eine Einführung, (2011); Rechert K., Valizada I., Von Suchodoletz D., Latocha J., BwFLA-A functional approach to digital preservation, PIK-Praxis der Informationsverarbeitung und Kommunikation, 35, 4, pp. 259-267, (2012); Niklas Rehfeld I., Cochrane E., Von Suchodoletz D., A practical approach to system preservation workflows, PIK-Praxis der Informationsverarbeitung und Kommunikation, 35, 4, pp. 269-280, (2012)","","Plodereder E.; Universitat Stuttgart, Institut fur Softwaretechnologie, Universitatsstr. 38, Stuttgart; Grunske L.; Universitat Stuttgart, Institut fur Softwaretechnologie, Universitatsstr. 38, Stuttgart; Ull D.; Universitat Stuttgart, Institut fur Technische Informatik, Pfaffenwaldring 47, Stuttgart; Schneider E.; Universitat Stuttgart, Institut fur Technische Informatik, Pfaffenwaldring 47, Stuttgart","Gesellschaft fur Informatik (GI)","","44. Jahrestagung der Gesellschaft fur Informatik INFORMATIK 2014 - Big Data - Komplexitat meistern - Big Data - Mastering Complexity: 44th Annual Meeting of the Society for Computer Science, INFORMATICS 2014","22 September 2014 through 26 September 2014","Stuttgart","110425","16175468","978-388579626-8","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-84922572025"
"Meyer-Doerpinghaus U.; Neuroth H.","Meyer-Doerpinghaus, Ulrich (56695268600); Neuroth, Heike (6508082130)","56695268600; 6508082130","Die Stärkung von Informationskompetenz im Kontext des Forschungsdatenmanagements: eine Herausforderung für Hochschulen und Politik","2015","Zeitschrift fur Bibliothekswesen und Bibliographie","62","2","","80","84","4","0","10.3196/186429501562237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84932163364&doi=10.3196%2f186429501562237&partnerID=40&md5=d65f83fe4bc0d62dee1bc552cf6e042f","Hochschulrektoren-konferenz (HRK), Ahrstraße 39, Bonn, 53175, Germany; Fachbereich Informationswissenschaften, Fachhochschule Potsdam, Fried-rich-Ebert-Str.4, Potsdam, 14467, Germany","Meyer-Doerpinghaus U., Hochschulrektoren-konferenz (HRK), Ahrstraße 39, Bonn, 53175, Germany; Neuroth H., Fachbereich Informationswissenschaften, Fachhochschule Potsdam, Fried-rich-Ebert-Str.4, Potsdam, 14467, Germany","In order to implement research data management successfully at universities it is imperative that information literacy be strengthened appropriately. Students, teaching staff and researchers need to adapt to this new challenge, but so, too, do university service facilities. The universities are called upon to offer new courses and to create new professional careers such as data steward, data librarian or data scientist. The support of the university administration is crucial here. A number of good examples regarding the establishment both of research data management and also corresponding information literacy are already to be found in German universities. Coordinated action is also called for here to ensure that existing bottom-up initiatives can develop into nationally and internationally harmonised research data management.This is primarily the responsibility of the political institutions. It should be actively involved in the national coordination of research data management but also make the necessary funding available.","","","","","","","","","Siehe Z.B., Die Stellungnahme des Netzwerks Informationskompetenz Baden-Württemberg (NIK-BW) Vom, (2013); DFG:Merkblatt Sonderforschungsbereiche, DFG-Vordruck 50.06-01/14, s-2-Verfügbar Unter; Cremer F., Claudia E., Neuroth H., Bibliothek Forschung und Praxis; MALIS 2, Semester im Modul Informationstechnologie 2","","","Vittorio Klostermann","","","","","","00442380","","","","German","Z. Bibliothekswes. Bibliogr.","Article","Final","","Scopus","2-s2.0-84932163364"
"O'Brien K.K.; Solomon P.; Worthington C.; Ibáñez-Carrasco F.; Baxter L.; Nixon S.A.; Baltzer-Turje R.; Robinson G.; Zack E.","O'Brien, Kelly K. (8899883900); Solomon, Patricia (7201453109); Worthington, Catherine (7005608194); Ibáñez-Carrasco, Francisco (36671383800); Baxter, Larry (37025480000); Nixon, Stephanie A (7103408355); Baltzer-Turje, Rosalind (56094916600); Robinson, Greg (7402255412); Zack, Elisse (25951663300)","8899883900; 7201453109; 7005608194; 36671383800; 37025480000; 7103408355; 56094916600; 7402255412; 25951663300","Considerations for conducting web-based survey research with people living with human immunodeficiency virus using a community-based participatory approach","2014","Journal of Medical Internet Research","16","3","","e81","","","7","10.2196/jmir.3064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897433580&doi=10.2196%2fjmir.3064&partnerID=40&md5=34faf794c21874ec4bf5bac754b3584e","Department of Physical Therapy, Faculty of Medicine, University of Toronto, Toronto, ON, 160-500 University Avenue, Canada; School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Canadian Working Group on HIV and Rehabilitation, Toronto, ON, Canada; School of Public Health and Social Policy, University of Victoria, Victoria, BC, Canada; Ontario HIV Treatment Network, Toronto, ON, Canada; AIDS Foundation, Vancouver, BC, Canada","O'Brien K.K., Department of Physical Therapy, Faculty of Medicine, University of Toronto, Toronto, ON, 160-500 University Avenue, Canada, School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada, Canadian Working Group on HIV and Rehabilitation, Toronto, ON, Canada; Solomon P., School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Worthington C., School of Public Health and Social Policy, University of Victoria, Victoria, BC, Canada; Ibáñez-Carrasco F., Ontario HIV Treatment Network, Toronto, ON, Canada; Baxter L., Canadian Working Group on HIV and Rehabilitation, Toronto, ON, Canada; Nixon S.A., Department of Physical Therapy, Faculty of Medicine, University of Toronto, Toronto, ON, 160-500 University Avenue, Canada; Baltzer-Turje R., AIDS Foundation, Vancouver, BC, Canada; Robinson G., Canadian Working Group on HIV and Rehabilitation, Toronto, ON, Canada; Zack E., Canadian Working Group on HIV and Rehabilitation, Toronto, ON, Canada","Background: Web or Internet-based surveys are increasingly popular in health survey research. However, the strengths and challenges of Web-based surveys with people living with human immunodeficiency virus (HIV) are unclear. Objective: The aim of this article is to describe our experience piloting a cross-sectional, Web-based, self-administered survey with adults living with HIV using a community-based participatory research approach. Methods: We piloted a Web-based survey that investigated disability and rehabilitation services use with a sample of adults living with HIV in Canada. Community organizations in five provinces emailed invitations to clients, followed by a thank you/reminder one week later. We obtained survey feedback in a structured phone interview with respondents. Participant responses were transcribed verbatim and analyzed using directed content analysis. Results: Of 30 people living with HIV who accessed the survey link, 24/30 (80%) initiated and 16/30 (53%) completed the survey instrument. A total of 17 respondents participated in post-survey interviews. Participants described the survey instrument as comprehensive, suggesting content validity. The majority (13/17, 76%) felt instruction and item wording were clear and easy to understand, and found the software easy to navigate. Participants felt having a pop-up reminder directing them to missed items would be useful. Conclusions: Strengths of implementing the Web-based survey included: our community-based participatory approach, ease of software use, ability for respondents to complete the questionnaire on one's own time at one's own pace, opportunity to obtain geographic variation, and potential for respondent anonymity. Considerations for future survey implementation included: respondent burden and fatigue, the potentially sensitive nature of HIV Web-based research, data management and storage, challenges verifying informed consent, varying computer skills among respondents, and the burden on community organizations. Overall, results provide considerations for researchers conducting community-based participatory Web-based survey research with people living with HIV. © Holly O Witteman.","Community-based participatory research; Health surveys; HIV infections; Internet; Questionnaires; Self-report","Adult; Canada; Community-Based Participatory Research; Cross-Sectional Studies; Female; Health Surveys; HIV Infections; Humans; Internet; Male; Middle Aged; Pilot Projects; Questionnaires; Rehabilitation Centers; Self Report; Software; adult; Canada; complication; computer program; cross-sectional study; female; health survey; HIV Infections; human; Internet; male; middle aged; participatory research; pilot study; procedures; questionnaire; rehabilitation center; self report; utilization","","","","","Canadian Institutes of Health Research, (CDE 108127)","","Buchanan E.A., Hvizdak E.E., Online survey tools: Ethical and methodological concerns of human research ethics committees, J Empir Res Hum Res Ethics, 4, 2, pp. 37-48, (2009); Zuidgeest M., Hendriks M., Koopman L., Spreeuwenberg P., Rademakers J., A comparison of a postal survey and mixed-mode survey using a questionnaire on patients' experiences with breast care, J Med Internet Res, 13, 3, (2011); Cook C., Heath F., Thompson R.L., A meta-analysis of response rates in web-or internet-based surveys, Educational and Psychological Measurement, 1, 606, pp. 821-836, (2000); Rhodes S.D., Bowie D.A., Hergenrather K.C., Collecting behavioural data using the world wide web: Considerations for researchers, J Epidemiol Community Health, 57, 1, pp. 68-73, (2003); Whitehead L.C., Methodological and ethical issues in internet-mediated research in the field of health: An integrated review of the literature, Soc Sci Med, 65, 4, pp. 782-791, (2007); Holmes S., Methodological and ethical considerations in designing an internet study of quality of life: A discussion paper, Int J Nurs Stud, 46, 3, pp. 394-405, (2009); Kongsved S.M., Basnov M., Holm-Christensen K., Hjollund N.H., Response rate and completeness of questionnaires: A randomized study of internet versus paper-and-pencil versions, J Med Internet Res, 9, 3, (2007); Im E.O., Chee W., Issues in internet survey research among cancer patients, Cancer Nurs, 27, 1, pp. 34-42, (2004); Lear S.A., Araki Y., Maric B., Kaan A., Horvat D., British columbia alliance on telehealth policyresearch. Prevalence and characteristics of home internet access in patients with cardiovascular disease from diverse geographical locations, Can J Cardiol, 25, 10, pp. 589-593, (2009); Jackson M.L., Bex P.J., Ellison J.M., Wicks P., Wallis J., Feasibility of a web-based survey of hallucinations and assessment of visual function in patients with parkinson's disease, Interact J Med Res, 3, 1, (2014); Schiotz M., Bogelund M., Willaing I., Challenges using online surveys in a danish population of people with type 2 diabetes, Chronic Illn, 8, 1, pp. 56-63, (2012); Chiasson M.A., Parsons J.T., Tesoriero J.M., Carballo-Dieguez A., Hirshfield S., Remien R.H., Hiv behavioral research online, J Urban Health, 83, 1, pp. 73-85, (2006); Bauermeister J., Pingel E., Zimmerman M., Couper M., Carballo-Dieguez A., Strecher V.J., Data quality in web-based hivaids research: Handling invalid and suspicious data, Field Methods, 24, 3, pp. 272-291, (2012); Pequegnat W., Rosser B.R., Bowen A.M., Bull S.S., DiClemente R.J., Bockting W.O., Et al., Conducting internet-based hivstd prevention survey research: Considerations in design and evaluation, AIDS Behav, 11, 4, pp. 505-521, (2007); Theriault N., Bi P., Hiller J.E., Nor M., Use of web 2.0 to recruit australian gay men to an online hiv/aids survey, J Med Internet Res, 14, 6, (2012); Hou S.I., Wisenbaker J., Using a web-based survey to assess correlates of intention towards hiv testing among never-been-tested but sexually experienced college students, AIDS Care, 17, 3, pp. 329-334, (2005); Dillman D.A., Bowker D.K., The web questionnaire challenge to survey methodologists, Dimensions of Internet Science, pp. 159-178, (2001); About the HIV/AIDS Community-Based Research Program URL, (2013); Sadler L.S., Larson J., Bouregy S., Lapaglia D., Bridger L., McCaslin C., Et al., Community-university partnerships in community-based research, Prog Community Health Partnersh, 6, 4, pp. 463-469, (2012); Israel B.A., Schulz A.J., Parker E.A., Becker A.B., Review of community-based research: Assessing partnership approaches to improve public health, Annu Rev Public Health, 19, pp. 173-202, (1998); Garcia C.M., Gilchrist L., Campesino C., Raymond N., Naughton S., De Patino J.G., Using community-based participatory research to develop a bilingual mental health survey for latinos, Prog Community Health Partnersh, 2, 2, pp. 105-120, (2008); Fawcett S.B., Schultz J.A., Carson V.L., Renault V.A., Francisco V.T., Chapter 8. Using Internet Based Tools To Build Capacity For Community Based Participatory Research And Other Efforts To Promote Community Health Development, (2003); Flicker S., Wilson M., Travers R., Bereket T., McKay C., Van Der Meulen A., Et al., Community-based research in aids-service organizations: What helps and what doesn't?, AIDS Care, 21, 1, pp. 94-102, (2009); Travers R., Wilson M.G., Flicker S., Guta A., Bereket T., McKay C., Et al., The greater involvement of people living with aids principle: Theory versus practice in ontario's hiv/aids community-based research sector, AIDS Care, 20, 6, pp. 615-624, (2008); Weiss J.J., Osorio G., Ryan E., Marcus S.M., Fishbein D.A., Prevalence and patient awareness of medical comorbidities in an urban aids clinic, AIDS Patient Care STDS, 24, 1, pp. 39-48, (2010); Willard S., Holzemer W.L., Wantland D.J., Cuca Y.P., Kirksey K.M., Portillo C.J., Et al., Does ""asymptomatic"" mean without symptoms for those living with hiv infection?, AIDS Care, 21, 3, pp. 322-328, (2009); Vance D.E., Mugavero M., Willig J., Raper J.L., Saag M.S., Aging with hiv: A cross-sectional study of comorbidity prevalence and clinical characteristics across decades of life, J Assoc Nurses AIDS Care, 22, 1, pp. 17-25, (2011); Goulet J.L., Fultz S.L., Rimland D., Butt A., Gibert C., Rodriguez-Barradas M., Et al., Aging and infectious diseases: Do patterns of comorbidity vary by hiv status age, and HIV severity?, Clin Infect Dis, 15, 4512, pp. 1593-1601, (2007); O'Brien K.K., Bayoumi A.M., Strike C., Young N.L., Davis A.M., Exploring disability from the perspective of adults living with hiv/aids: Development of a conceptual framework, Health Qual Life Outcomes, 6, (2008); O'Brien K.K., Davis A.M., Strike C., Young N.L., Bayoumi A.M., Putting episodic disability into context: A qualitative study exploring factors that influence disability experienced by adults living with hiv/aids, J Int AIDS Soc, 12, (2009); Worthington C., Myers T., O'Brien K., Nixon S., Cockerill R., Rehabilitation in hiv/aids: Development of an expanded conceptual framework, AIDS Patient Care STDS, 19, 4, pp. 258-271, (2005); Nixon S., Cott C., Shifting perspectives: Reconceptualizing hiv disease within a rehabilitation framework, Physiother Can, 52, pp. 189-207, (2000); Worthington C., Myers T., O'Brien K., Nixon S., Cockerill R., Bereket T., Rehabilitation professionals and human immunodeficiency virus care: Results of a national canadian survey, Arch Phys Med Rehabil, 89, 1, pp. 105-113, (2008); Worthington C., O'Brien K., Myers T., Nixon S., Cockerill R., Expanding the lens of hiv services provision in canada: Results of a national survey of hiv health professionals, AIDS Care, 21, 11, pp. 1371-1380, (2009); Rusch M., Nixon S., Schilder A., Braitstein P., Chan K., Hogg R.S., Impairments, activity limitations and participation restrictions: Prevalence and associations among persons living with hiv/aids in british columbia, Health Qual Life Outcomes, 2, (2004); Schmitz C., LimeSurvey Software Package, (2003); Dillman D., Chapter 11: Internet and interactive voice response surveys, Mail And Internet Surveys: The Tailored Design Method, pp. 352-412, (2007); Hsieh H.F., Shannon S.E., Three approaches to qualitative content analysis, Qual Health Res, 15, 9, pp. 1277-1288, (2005); Lancaster G.A., Dodd S., Williamson P.R., Design and analysis of pilot studies: Recommendations for good practice, J Eval Clin Pract, 10, 2, pp. 307-312, (2004); Ritter P., Lorig K., Laurent D., Matthews K., Internet versus mailed questionnaires: A randomized comparison, J Med Internet Res, 15, 63, (2004); Heberlein T.A., Baumgartner R., Factors affecting response rates to mailed questionnaires: A quantitative analysis of the published literature, American Sociological Review, 43, pp. 447-462, (1978); Fox J., Murray C., Warm A., Conducting research using web-based questionnaires: Practical, methodological, and ethical considerations, International Journal of Social Research Methodology, 6, 2, pp. 167-180, (2003); Eysenbach G., Wyatt J., Using the internet for surveys and health research, J Med Internet Res, 4, 2, (2002); Flicker S., Haans D., Skinner H., Ethical dilemmas in research on internet communities, Qual Health Res, 14, 1, pp. 124-134, (2004); Leece P., Bhandari M., Sprague S., Swiontkowski M.F., Schemitsch E.H., Tornetta P., Et al., Internet versus mailed questionnaires: A controlled comparison ( 2), J Med Internet Res, 64, (2004); (2012); Schonlau M., Will web surveys ever become part of mainstream research?, J Med Internet Res, 23, 6, (2004); Eysenbach G., Improving the quality of web surveys: The checklist for reporting results of internet e-surveys (cherries), J Med Internet Res, 29, 63, (2004)","K.K. O'Brien; Department of Physical Therapy, Faculty of Medicine, University of Toronto, Toronto, ON, 160-500 University Avenue, Canada; email: kelly.obrien@utoronto.ca","","JMIR Publications Inc.","","","","","","14388871","","","24642066","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84897433580"
"Deng S.; Hu X.","Deng, Sai (23979305900); Hu, Xiao (55496358400)","23979305900; 55496358400","Creating a Knowledge Map for the research lifecycle","2014","CEUR Workshop Proceedings","1311","","","40","46","6","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919398977&partnerID=40&md5=0e17f0934934f37df524e531423fb889","University of Central, Florida Libraries, Orlando, FL, United States; University of Hong Kong, Hong Kong, Hong Kong","Deng S., University of Central, Florida Libraries, Orlando, FL, United States; Hu X., University of Hong Kong, Hong Kong, Hong Kong","In this study, a Knowledge Map (KM) was created based on the Research Lifecycle at the University of Central Florida to provide campus-wide services and resources to researchers. The KM aims to meet the needs of researchers and delivers guided searching and assistance in all aspects of research, including literature review, citation management, research data management, grant management, research work publication and dissemination. It elaborates the research processes and their associated services as presented in the Research Lifecycle, and links these points to various campus resources including those provided by the University Libraries, the Office of Research and Commercialization, the Institute for Simulation and Training and the Faculty Center for Teaching and Learning. It gives unified support to the researchers during their entire research lifecycle and it will keep evolving and developing.","Information needs; Information seeking; Knowledge map; Research resources; Research services; Unified research support","Information management; Knowledge engineering; Life cycle; Teaching; Information needs; Information seeking; Knowledge map; Research data managements; Research support; Simulation and training; Teaching and learning; University of Central Florida; Digital libraries","","","","","","","Cyberinfrastructure Vision for 21st Century Discovery, (2007); Borgman C.L., Bowker G.C., Finholt T.A., Wallis J.C., Towards a virtual organization for data cyberinfrastructure, Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 353-356, (2009); OpenWetWare: The Research Lifecycle; Making Data Management Easier: Research Life Cycle; Research 360 Institutional Research Lifecycle; Lifecycle Model; Dursteler J.C., KartOO, (2002); Smarty A., SEJ: Search Engine Journal, (2009); University of Central Florida Libraries Research Lifecycle Committee, The Research Lifecycle at UCF, (2012); Beile P., UCF Research Data Management Survey: A Report of Faculty Practices and Needs, (2014); Chung G.K.W.K., Cheak A.M., Lee J.J., Baker E.L., Development Model for Knowledge Maps, (2012)","","Mayr P.; Scharnhorst A.; Mutschke P.","CEUR-WS","","KMIR 2014 - 1st Workshop on Knowledge Maps and Information Retrieval,  co-located with International Conference on Digital Libraries, DL 2014 - ACM/IEEE Joint Conference on Digital Libraries, JCDL 2014 and International Conference on Theory and Practice of Digital Libraries, TPDL 2014","11 September 2014 through 11 September 2014","London","109557","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84919398977"
"Makani J.","Makani, Joyline (26867884000)","26867884000","Knowledge management, research data management, and university scholarship: Towards an integrated institutional research data management support-system framework","2015","VINE","45","3","","344","359","15","9","10.1108/VINE-07-2014-0047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938386332&doi=10.1108%2fVINE-07-2014-0047&partnerID=40&md5=01f7d70cff868231f6dd712508fd95aa","Killam Library, Dalhousie University, Halifax, Canada","Makani J., Killam Library, Dalhousie University, Halifax, Canada","Purpose – The purpose of this paper is to synthesize existing research on research data management (RDM), academic scholarship and knowledge management and provide a conceptual framework for an institutional research data management support-system (RDMSS) for systems development, managerial and academic use. Design/methodology/approach – Viewing RDMSS from multiple theoretical perspectives, including data management, knowledge management, academic scholarship and the practice-based perspectives of knowledge and knowing, this paper conceptually explores the systems’ elements needed in the development of an institutional RDM service by considering the underlying data discovery and application issues, as well as the nature of academic scholarship and knowledge creation, discovery, application and sharing motivations in a university environment. Findings – The paper provides general criteria for an institutional RDMSS framework. It suggests that RDM in universities is at the very heart of the knowledge life cycle and is a central ingredient to the academic scholarships of discovery, integration, teaching, engagement and application. Research limitations/implications – This is a conceptual exploration and as a result, the research findings may lack generalisability. Researchers are therefore encouraged to further empirically examine the proposed propositions. Originality/value – The broad RDMSS framework presented in this paper can be compared with the actual situation at universities and eventually guide recommendations for adaptations and (re)design of the institutional RDM infrastructure and knowledge discovery services environment. Moreover, this paper will help to address some of the identified underlying scholarship and RDM disciplinary divides and confusion constraining the effective functioning of the modern day university’s RDM and data discovery environment. © Emerald Group Publishing Limited.","Data management systems; Knowledge integration; Knowledge management; Knowledge management success factors; Knowledge management systems; Research data management","Knowledge based systems; Knowledge management; Life cycle; Data management system; Knowledge integration; Knowledge management success; Knowledge management system; Research data managements; Research and development management","","","","","","","Alavi M., Leidner D.E., Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues, MIS Quarterly, 25, 1, pp. 107-136, (2001); Barker D., The scholarship of engagement: A taxonomy of five emerging practices, Journal of Higher Education Outreach and Engagement, 9, 2, pp. 123-137, (2004); Becher T., The counter-culture of specialisation, European Journal of Education, 25, 3, pp. 333-346, (1990); Belter C.W., Measuring the value of research data: A citation analysis of oceanographic data sets, PLoS One, 9, 3, (2014); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Boyer E.L., Scholarship Reconsidered: Priorities of the Professoriate, (1990); Boyer E.L., Scholarship reconsidered: Priorities of the professoriate, Issues in Accounting Education, 7, 1, pp. 87-91, (1992); Boyer E.L., The scholarship of engagement, Bulletin of the American Academy of Arts and Sciences, 49, 7, pp. 18-33, (1996); Brown J.S., Duguid P., Organizing knowledge, California Management Review, 40, 3, (1998); Carlisle J.P., A look into the relationship between knowledge management and the knowledge hierarchies, 40th Hawaii International Conference on System Sciences (HICSS'07), pp. 183a-193a, (2007); Davenport T.H., Thinking for a Living: How to Get Better Performance and Results from Knowledge Workers, (2005); Davenport T.H., Prusak L., Working Knowledge: How Organizations Manage What they Know, (1998); Gazit T., Apostle R., Branton R., Deployment, tracking, and data management: Technology and science for a global ocean tracking network, Journal of International Wildlife Law & Policy, 16, 2-3, pp. 112-127, (2013); Government of Canada, Capitalizing on big data: Toward a policy framework for advancing digital scholarship in Canada, (2013); Holcombe R.G., A theory of the theory of public goods, The Review of Austrian Economics, 10, 1, pp. 1-22, (1997); Holdren J., Increasing access to the results of federally funded research (Memorandum), (2013); Jashapara A., Knowledge Management an Integrated Approach, (2011); Klein J.T., Interdisciplinary needs: The current context, Library Trends, 45, 2, pp. 134-154, (1996); Klein J.T., Crossing Boundaries: Knowledge, Disciplinarities, and Interdisciplinarities, (1996); Musimwa-Makani J., Knowledge management in knowledge-intensive organizations: An investigation of factors influencing choices of knowledge management systems, (2012); National Data Service, The national data service, (2014); Nonaka I., The Knowledge-Creating Company, (2008); OECD, Principles and Guidelines for Access to Research Data from Public Funding, (2007); O'Reilly K., Johnson J., Sanborn G., Improving university research value: A case study, SAGE Open, 2, 3, (2012); Orlikowski W.J., Knowing in practice: Enacting a collective capability in distributed organizing, Organization Science, 13, 3, pp. 249-273, (2002); Patnayakuni R., Rai A., Tiwana A., Systems development process improvement: A knowledge integration perspective, IEEE Transactions on Engineering Management, 54, 2, pp. 286-300, (2007); Patrick M., Wilson J.A.J., Getting Data Creators On Board with the Digital Curation Agenda: Lessons Learned in Developing Training for Researchers, (2013); Pfeffer J., Sutton R.I., The Knowing-Doing Gap: How Smart Companies Turn Knowledge into Action, (2000); Rowley J., Where is the wisdom that we have lost in knowledge?, Journal of Documentation, 62, 2, pp. 251-270, (2006); Schon D.A., The new scholarship requires a new epistemology, Change, 27, 6, pp. 27-34, (1995); Spender J.-C., Pluralist epistemology and the knowledge-based theory of the firm, Organization, 5, 2, pp. 233-256, (1998); Stenmark D., Information vs. knowledge: The role of intranets in knowledge management, Proceedings of the 35th Annual Hawaii International Conference on System Sciences, pp. 928-937, (2002); Steup M., Sosa E., Contemporary Debates in Epistemology, (2005); Tam W., Fry J., Probets S., The disciplinary shaping of research data management practices, IConference 2014 Proceedings, pp. 721-728, (2014); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Policies and Guidelines: Open Access, (2014); Vines T., Albert A.K., Andrew R., Debarre F., Bock D., Franklin M., Gilbert K., Moore J., Renaut S., Rennison D., The availability of research data declines rapidly with article age, Current Biology, 24, 1, pp. 1-4, (2014); Wilson J.A., Jeffreys P., Towards a unified university infrastructure: The data management roll-out at the University of Oxford, International Journal of Digital Curation, 8, 2, pp. 235-246, (2013); Senge P., Learning organizations, Knowledge Management in Education: Enhancing Learning and Education, pp. 77-98, (2013)","J. Makani; Killam Library, Dalhousie University, Halifax, Canada; email: makani@dal.ca","","Emerald Group Holdings Ltd.","","","","","","03055728","","","","English","VINE","Article","Final","","Scopus","2-s2.0-84938386332"
"Awre C.; Baxter J.; Clifford B.; Colclough J.; Cox A.; Dods N.; Drummond P.; Fox Y.; Gill M.; Gregory K.; Gurney A.; Harland J.; Khokhar M.; Lowe D.; O’Beirne R.; Proudfoot R.; Schwamm H.; Smith A.; Verbaan E.; Waller L.; Williamson L.; Wolf M.; Zawadzki M.","Awre, Chris (8912791600); Baxter, Jim (57197080831); Clifford, Brian (56719370400); Colclough, Janette (57190491502); Cox, Andrew (7402563906); Dods, Nick (56719724300); Drummond, Paul (56719241900); Fox, Yvonne (56719588700); Gill, Martin (56719102100); Gregory, Kerry (56719207200); Gurney, Anita (7004912028); Harland, Juliet (19933716100); Khokhar, Masud (15064403300); Lowe, Dawn (56719109100); O’Beirne, Ronan (56719740800); Proudfoot, Rachel (56719839500); Schwamm, Hardy (56719720600); Smith, Andrew (57301784900); Verbaan, Eddy (24470394900); Waller, Liz (56166792800); Williamson, Laurian (56719253800); Wolf, Martin (56719024600); Zawadzki, Matthew (56719311900)","8912791600; 57197080831; 56719370400; 57190491502; 7402563906; 56719724300; 56719241900; 56719588700; 56719102100; 56719207200; 7004912028; 19933716100; 15064403300; 56719109100; 56719740800; 56719839500; 56719720600; 57301784900; 24470394900; 56166792800; 56719253800; 56719024600; 56719311900","Research data management as a “wicked problem”","2015","Library Review","64","4-5","","356","371","15","21","10.1108/LR-04-2015-0043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936935880&doi=10.1108%2fLR-04-2015-0043&partnerID=40&md5=874899ada046ad97779412faf05563f6","University of Hull, Hull, United Kingdom; University of Leeds, Leeds, United Kingdom; University of York, York, United Kingdom; University of Sheffield, Sheffield, United Kingdom; Liverpool University, Liverpool, United Kingdom; University of Durham, Durham, United Kingdom; Lancaster University, Lancaster, United Kingdom; Huddersfield University, Huddersfield, United Kingdom; Sheffield Hallam University, Sheffield, United Kingdom; Bradford College, Bradford, United Kingdom","Awre C., University of Hull, Hull, United Kingdom; Baxter J., University of Leeds, Leeds, United Kingdom; Clifford B., University of Leeds, Leeds, United Kingdom; Colclough J., University of York, York, United Kingdom; Cox A., University of Sheffield, Sheffield, United Kingdom; Dods N., Liverpool University, Liverpool, United Kingdom; Drummond P., University of Durham, Durham, United Kingdom; Fox Y., Lancaster University, Lancaster, United Kingdom; Gill M., Huddersfield University, Huddersfield, United Kingdom; Gregory K., University of Sheffield, Sheffield, United Kingdom; Gurney A., Sheffield Hallam University, Sheffield, United Kingdom; Harland J., Sheffield Hallam University, Sheffield, United Kingdom; Khokhar M., Lancaster University, Lancaster, United Kingdom; Lowe D., University of Hull, Hull, United Kingdom; O’Beirne R., Bradford College, Bradford, United Kingdom; Proudfoot R., University of Leeds, Leeds, United Kingdom; Schwamm H., Lancaster University, Lancaster, United Kingdom; Smith A., University of York, York, United Kingdom; Verbaan E., University of Sheffield, Sheffield, United Kingdom; Waller L., University of York, York, United Kingdom; Williamson L., University of Sheffield, Sheffield, United Kingdom; Wolf M., Liverpool University, Liverpool, United Kingdom; Zawadzki M., University of Sheffield, Sheffield, United Kingdom","Purpose – The purpose of this paper is to explore the usefulness of the concept to thinking about Research Data Management (RDM). The concept of “wicked problems” seeks to differentiate very complex, intractable challenges from tamer issues where approaches to problem solving are well-understood. Design/methodology/approach – The paper is based on and co-authored by a collaboration of practitioners from libraries, information technology and research administration, with facilitators from the Sheffield Information School. Participants worked together in two-day-long workshops to understand the wicked problem concept and advice on leadership in wicked problem contexts. Findings – Participants concurred that RDM had many features of a wicked problem and most of Grint’s advice on leadership for wicked problems also resonated. Some elements of the issue were simple; participants were optimistic about improving the situation over time. Participants were resistant to the more negative or fatalistic connotations of the phrase “wicked problem”. Viewing RDM as a wicked problem is an interesting way of looking at it as a challenge for support professionals. Practical implications – The notion of a wicked problem is a generative concept that can be usefully added to professional vocabulary. Originality/value – The paper captures an in-depth response from practitioners to the notion of wicked problems as a lens for examining RDM. © 2015, Emerald Group Publishing Limited.","Academic libraries; Data curation; Research; Research data management","","","","","","","","Borgman C., Big Data, Little Data, No Data: Scholarship in the Networked World, (2015); Brown T., Design thinking, Harvard Business Review, 86, 6, pp. 84-95, (2008); Systems Thinking, Systems Practice, (1981); Conklin E.J., Weil W., Wicked Problems Naming the Pain in Organizations, (1997); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management: Emerging trends in library research support services, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked problem”, Journal of Librarianship and Information Science, (2014); Policy Framework on Research Data, (2011); Grint K., Wicked Problems and the Role of Leadership, (2009); Grint K., Wicked Problems and Clumsy Solutions: The Role of Leadership, (2010); Hodson S., Molloy L., Case study 5: Development of institutional RDM services by projects in the Jisc managing research data programme, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 205-238, (2014); Horn R.E., Weber R.P., New Tools for Resolving Wicked Problems: Mess Mapping and Resolution Mapping Processes, Strategy Kinetics LLC, (2007); Johnson L., Adams Becker S., Estrada V., Freeman A., NMC Horizon Report: 2014 Library Edition, (2014); Jones S., Developments in research funder data policy, International Journal of Digital Curation, 7, 1, pp. 114-125, (2012); McLeod J., Childs S., A strategic approach to making sense of the wicked problem of ERM, Records Management Journal, 23, 2, pp. 104-135, (2013); Ney S.M., Verweij M., Messy institutions for wicked problems: How to generate clumsy solutions, Social Science Research Network, (2014); Principles and Guidelines for Access to Research Data from Public Funding, (2007); Pryor G., A patchwork of change, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 1-20, (2014); Common Principles on Data Policy, (2011); Rittel H.W., Webber M.M., Dilemmas in a general theory of planning, Policy Sciences, 4, 2, pp. 155-169, (1973); The origins of Cynefin (Pt. 1-7), (2010); Qualitative Inquiry, (2010)","A. Cox; University of Sheffield, Sheffield, United Kingdom; email: a.m.cox@sheffield.ac.uk","","Emerald Group Holdings Ltd.","","","","","","00242535","","","","English","Libr. Rev.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84936935880"
"Martínez-Uribe L.; Fernández P.","Martínez-Uribe, Luis (24344774800); Fernández, Paz (56770662800)","24344774800; 56770662800","Data services: A strategic function of 21st century libraries; [Servicios de datos: Función estratégica de las bibliotecas del siglo XXI]","2015","Profesional de la Informacion","24","2","","193","199","6","2","10.3145/epi.2015.mar.13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938925148&doi=10.3145%2fepi.2015.mar.13&partnerID=40&md5=93b0ddf5839584d35ba09b3c46ef389c","Fundación Juan March, Biblioteca, Castelló, 77, Madrid, 28006, Spain","Martínez-Uribe L., Fundación Juan March, Biblioteca, Castelló, 77, Madrid, 28006, Spain; Fernández P., Fundación Juan March, Biblioteca, Castelló, 77, Madrid, 28006, Spain","This article highlights the opportunity that libraries have to transform and develop their services towards the management and exploitation of data. After setting the scene in the introduction, the article provides a definition of library data services and explains the meaning of term ""data"" in multiple settings. In order to prove that data services within libraries are not uncommon, the evolution of these types of services is analysed: from the initial social science data libraries to the current research data management services and data curation. This leads to expanding the role of data services as a strategic area for the analysis of the organizational knowledge, always led by libraries. The article finishes by looking at the new professionals, their skills and the background required to provide this new library data services. © 2015, El Profesional de la Informacion. All rights reserved.","Data librarians; Data libraries; Data services; Digital humanities; Research data; Scientific data","","","","","","DGES, (ESP98-1351E, PB96-0883); National Science Foundation, NSF, (AST 99-80846); National Aeronautics and Space Administration, NASA; European Space Agency, ESA; Institute of Space and Astronautical Science, ISAS","Funding text 1: We thank Spanish DGES for this research under grants PB96-0883 and ESP98-1351E. We thank E. González-Alfonso for useful comments. The CSO is supported by NSF grant AST 99-80846.; Funding text 2: 1 Based on observations with the Infrared Space Observatory, an ESA project with instruments funded by ESA member states (especially the PI countries: France, Germany, the Netherlands, and the United Kingdom) and with the participation of ISAS and NASA.","Bisco R.L., Social science data archives: A review of developments, The American Political Science Review, 6, 1, pp. 93-109, (1966); Borgman C.L., What are digital libraries? Competing visions, Information Processing and Management, 35, 3, pp. 227-243, (1999); Granell-Canut C., Aguilar-Moreno E., Se busca geobibliotecario: Los datos geográficos entran en la biblioteca, El Profesional de la Información, 22, 6, pp. 569-575, (2013); Granville V., Data science programs and training currently available, Data Science Central. The Online Resource for Big Data Practitioners, (2013); Xia J., Wang M., Competencies and responsibilities of social science data librarians: An analysis of job descriptions, College & Research Libraries, 75, pp. 362-388, (2014); Leek J., The key word in 'data science' is not data, it is science, Simplystats, (2013); Lynch C., Digital collections, digital libraries and the digitization of cultural heritage information, First Monday, 7, 5, (2002); Macdonald S., Martinez-Uribe L., Collaboration to data curation: Harnessing institutional expertise, New Review of Academic Librarianship, 16, 1, pp. 4-16, (2010); Markham A., Can we go beyond 'data'? Questioning the dominance of a core term in scientific enquiry, Digital HSS 2014. Digital Scholarship Day of Ideas: Data, (2014); Martinez-Uribe L., Chronology of data libraries and data centres, iBlog. Iasssist: International Association for Social Science Information Services & Technology, (2014); Martinez-Uribe L., Macdonald S., Un nuevo cometido para los bibliotecarios académicos: Data curation, El Profesional de la Información, 17, 3, pp. 273-280, (2008); Monash University, Research Data Management Policy, (2014); OECD Principles and Guidelines for Access to Research Data from Public Funding, (2007); O'Neil C., Schutt R., Doing Data Science, (2013); Partlo K., The pedagogical data reference interview, Iassist Quarterly, 34-35, 1, pp. 6-10, (2010); Posner M., No half measures: Overcoming common challenges to doing digital humanities in libraries, Journal of Library Administration, 53, 1, pp. 43-52, (2013); Stroeker N., Vogels R., Survey Report on Digitisation in European Cultural Heritage Institutions 2012, (2012); University of Edinburgh, Research Data Management Policy, (2015)","","","El Profesional de la Informacion","","","","","","13866710","","","","Spanish","Prof. Inf.","Article","Final","","Scopus","2-s2.0-84938925148"
"Castro J.A.; Perrotta D.; Amorim R.C.; da Silva J.R.; Ribeiro C.","Castro, João Aguiar (55977255100); Perrotta, Deborah (55677390000); Amorim, Ricardo Carvalho (56442184300); da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","55977255100; 55677390000; 56442184300; 55496903800; 7201734594","Ontologies for research data description: A design process applied to vehicle simulation","2015","Communications in Computer and Information Science","544","","","348","354","6","1","10.1007/978-3-319-24129-6_30","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945898003&doi=10.1007%2f978-3-319-24129-6_30&partnerID=40&md5=bf0126d6247de8fc7f4f7773da5c715e","Universidade do Porto/INESC TEC, Porto, Portugal; Universidade do Porto/LIACC, Porto, Portugal; DEI, Universidade do Porto/INESC TEC, Porto, Portugal","Castro J.A., Universidade do Porto/INESC TEC, Porto, Portugal; Perrotta D., Universidade do Porto/LIACC, Porto, Portugal; Amorim R.C., Universidade do Porto/INESC TEC, Porto, Portugal; da Silva J.R., Universidade do Porto/INESC TEC, Porto, Portugal; Ribeiro C., DEI, Universidade do Porto/INESC TEC, Porto, Portugal","Data description is an essential part of research data management, and it is easy to argue for the importance of describing data early in the research workflow. Specific metadata schemas are often proposed to support description. Given the diversity of research domains, such schemas are often missing, and when available they may be too generic, too complex or hard to incorporate in a description platform. In this paper we present a method used to design metadata models for research data description as ontologies. Ontologies are gaining acceptance as knowledge representation structures, and we use them here in the scope of the Dendro platform. The ontology design process is illustrated with a case study from Vehicle Simulation. According to the design process, the resulting model was validated by a domain specialist. © Springer International Publishing Switzerland 2015.","Metadata models; Ontologies; Research data management; Vehicle simulation","Data description; Design; Information management; Knowledge representation; Ontology; Semantics; Vehicles; Design process; Metadata model; Ontology design; Research data; Research data managements; Research domains; Vehicle simulation; Metadata","","","","","","","Amorim R.C., Castro J.A., da Silva J.R., Ribeiro C., A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential, New Contributions in Information Systems and Technologies. AISC, 353, pp. 101-111, (2015); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, (2012); Castro J.A., Ribeiro C., da Silva J.R., Creating lightweight ontologies for dataset description. Practical applications in a cross-domain research data management workflow, IEEE/ACM Joint Conference on Digital Libraries (JCDL), pp. 0-3, (2014); Heidorn P.B., Shedding Light on the Dark Data in the Long Tail of Science, Library Trends, 57, 2, pp. 280-299, (2008); Weiss M.A., Heywood J.B., Reas Schafer E.M.D., Au Yeung F.F., On the Road in 2020 - a Life-Cycle Analysis of New Automobile Technologies, (2000); Martinez-Uribe L., Macdonald S., User engagement in research data curation, ECDL 2009. LNCS, 5714, pp. 309-314, (2009); Perrotta D., Macedo J.L., Rossetti R.J., Sousa J., Kokkinogenis Z., Ribeiro B., Afonso J., Route Planning for Electric Buses: A Case Study in Oporto, Procedia - Social and Behavioral Sciences, 111, pp. 1004-1014, (2014); Qin J., Li K., How portable are the metadata standards for scientific data? A proposal for a metadata infrastructure, Proceedings of the Internacional Conference on Dublin Core and Metadata Applications, pp. 5-34, (2013); Da Silva J.R., Castro J.A., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Proceedings of the Ipres 2014 Conference, (2014); Smit E., van der Hoeven J., Giaretta D., Avoiding a Digital Dark Age for Data: Why Publishers Should Care about Digital Preservation, 24, 1, pp. 35-49, (2011); Treloar A., Wilkinson R., Rethinking metadata creation and management in a data-driven research world, 2008 IEEE Fourth International Conference on Escience, pp. 782-789, (2008)","J.A. Castro; Universidade do Porto/INESC TEC, Porto, Portugal; email: joaoaguiarcastro@gmail.com","Garoufallou E.; Hartley R.J.; Gaitanou P.","Springer Verlag","","9th Metadata and Semantics Research Conference, MTSR 2015","9 September 2015 through 11 September 2015","Manchester","140829","18650929","978-331924128-9","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-84945898003"
"Kahn M.; Higgs R.; Davidson J.; Jones S.","Kahn, Michelle (56435386000); Higgs, Richard (56435735800); Davidson, Joy (7403932714); Jones, Sarah (57203292365)","56435386000; 56435735800; 7403932714; 57203292365","Research Data Management in South Africa: How We Shape Up","2014","Australian Academic and Research Libraries","45","4","","296","308","12","17","10.1080/00048623.2014.951910","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914142770&doi=10.1080%2f00048623.2014.951910&partnerID=40&md5=ed6eddaed128a0e7544bbefdf7d93471","Library and Information Science Centre (LISC), University of Cape Town, Cape Town, South Africa; Humanities Advanced Technology and Information Institute, Digital Curation Centre (DCC), University of Glasgow, Glasgow, United Kingdom","Kahn M., Library and Information Science Centre (LISC), University of Cape Town, Cape Town, South Africa; Higgs R., Library and Information Science Centre (LISC), University of Cape Town, Cape Town, South Africa; Davidson J., Humanities Advanced Technology and Information Institute, Digital Curation Centre (DCC), University of Glasgow, Glasgow, United Kingdom; Jones S., Humanities Advanced Technology and Information Institute, Digital Curation Centre (DCC), University of Glasgow, Glasgow, United Kingdom","This paper will explore some of the views that were expressed during the Library and Information Association of South Africa (LIASA) workshop held in cooperation with the UK's Digital Curation Centre (DCC) in March 2014. The event provided an ideal opportunity to assess librarians' views on the changing research data management landscape and to consider how these changes might affect the role of academic librarians in South Africa. The paper compares these views with experiences garnered through the DCC's work to support universities in the UK. © 2014, Australian Library & Information Association.","digital curation; digital preservation; research data management","","","","","","","","Adeleke O., Otoo E.J., An Integrated Metadata Access Interface for a Network of Federated Curated Data Repositories, Paper presented at the 5th African conference for Digital Scholarship and Curation, Durban, South Africa, (2013); ARC and Research Data, (2014); Aukland M., Re-Skilling for Research, (2013); Welcome to the 4C Project, (2014); Corrall S., Roles and Responsibilities: Libraries, Librarians and Data, Managing Research Data, pp. 105-133, (2012); Davidson J., Corrall S., Coulbourne G., Rauber A., Education Alignment, Aligning National Approaches to Digital Preservation, pp. 269-308, (2012); RDMF Special Event: Funding Research Data Management, (2013); Institutional Engagements, (2014); LIASA Research Data Management Workshop, 27 March 2014, (2014); A Curriculum Framework for Digital Curation, (2013); Factsheet, (2014); Hugo W., SAEON Assists in Building Data Intensive Research Infrastructure for SA, (2012); Incremental Project Blog, (2011); Jahnke L., Asher A., The Problem of Data: Data Management and Curation Practices Among University Researchers, The Problem of Data.(CLIR Publication No. 154), (2012); Jisc, Research Data Management Training Materials (RDMTrain), (2014); Jones S., Research Data Policies: Principles, Requirements and Trends, Managing Research Data, pp. 47-66, (2012); Jorum, Research Data Management Collection, (2014); Keralis S.D.C., Data Curation Education: A Snapshot, The Problem of Data,(CLIR Publication No. 154), (2012); New MPhil (Specialisation in Digital Curation), (2014); Lucia L., Reflections on the RDM Position in South Africa, Paper presented at the LIASA Research Data Management Workshop, Cape Town, South Africa, (2014); Njenga J.K., Fourie L.C.H., The Myths About E-Learning in Higher Education, British Journal of Educational Technology, 41, 2, pp. 199-212, (2010); Databases, (2014); Pienaar H., Survey of Research Data Management Practices at the University of Pretoria, South Africa: October 2009–March 2010, Paper presented at the 22nd International CODATA Conference, Cape Town, South Africa, (2010); Ryan B., Engineering and Physical Sciences Research Council: Research Data Management, Presentation at the assessing institutional awareness & readiness for compliance with EPSRC Policy, Glasgow, (2014); Introduction, (2014); South African National Park Data Repository, (2014); National Health Information Repository and Data Warehouse, (2014); Woolfrey L., UCT Research Data Management Policy Project: Report, (2014)","","","Australian Library and Information Association","","","","","","00048623","","","","English","Aust. Acad. Res. Libr.","Article","Final","","Scopus","2-s2.0-84914142770"
"Patrick M.; Wilson J.A.J.","Patrick, Meriel (35488801500); Wilson, James A. J. (57198215862)","35488801500; 57198215862","Getting data creators on board with the digital curation agenda. Lessons learned in developing training for researchers","2015","CEUR Workshop Proceedings","1016","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922287238&partnerID=40&md5=5e844cbabbfc017dcd0dd94f63334318","DaMaRO Project, University of Oxford, Oxford, United Kingdom","Patrick M., DaMaRO Project, University of Oxford, Oxford, United Kingdom; Wilson J.A.J., DaMaRO Project, University of Oxford, Oxford, United Kingdom","University research projects are a key source of digital information with potential long-term value. Researchers rarely need to be persuaded that preserving the fruits of their work is in principle a good thing, but may often lack knowledge of the best way to go about doing this. Additionally, time pressures on academics are such that curation can frequently end up being pushed down the priority list. It is therefore important that information professionals working alongside researchers are able to offer appropriate training and advice on both the practicalities of and the rationale for digital curation. The DaMaRO Project is one of a series of research data management projects based at the University of Oxford. The project's remit includes developing training for researchers (intended to encourage them to consider data sharing and preservation issues at an early stage in their research), plus the development of an institutional data archive (DataBank) and catalogue of datasets (DataFinder). This paper offers some reflections on our experiences thus far, and in particular looks at the question of how researchers and others who are involved in the creation of digital data may most effectively be engaged in planning for and facilitating its long-term preservation.","Data creators; Digital curation; HEIs; Research data; Research data management; Researchers; Training; Universities","Information management; Information services; Personnel training; Data creators; Digital curation; HEIs; Research data; Research data managements; Researchers; Universities; Curricula","","","","","Joint Information Systems Committee","","Wilson J.A.J., Patrick M., Sudamih Researcher Requirements Report, (2010); Patrick M., VIDaaS Researcher Requirements Report, (2011); Patrick M., Wilson J.A.J., Jeffreys P., DaMaRO Project Survey on Research Data Management Training for Scientists - Results, (2012); Wilson J.A.J., Jeffreys P., Patrick M., Rumsey S., Jefferies N., Results of the 2012 University of Oxford Research Data Management Survey, (2013); Common Principles on Data Policy; Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data, pp. 22-27, (2011); Sending Your Research Material into the Future, (2012)","","Fernie K.; Casarosa V.; Lunghi M.; Cirinna C.","CEUR-WS","","Framing the Digital Curation Curriculum Conference, DigCurV 2013","6 May 2013 through 7 May 2013","Florence","110432","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84922287238"
"Sharma V.; Eckels J.; Taylor G.K.; Shulman N.J.; Stergachis A.B.; Joyner S.A.; Yan P.; Whiteaker J.R.; Halusa G.N.; Schilling B.; Gibson B.W.; Colangelo C.M.; Paulovich A.G.; Carr S.A.; Jaffe J.D.; Maccoss M.J.; Maclean B.","Sharma, Vagisha (7404567972); Eckels, Josh (37053561400); Taylor, Greg K. (7404203498); Shulman, Nicholas J. (36015685100); Stergachis, Andrew B. (57190803838); Joyner, Shannon A. (36765033100); Yan, Ping (45261702300); Whiteaker, Jeffrey R. (12799209700); Halusa, Goran N. (55958075300); Schilling, Birgit (7006516334); Gibson, Bradford W. (7202975072); Colangelo, Christopher M. (6603524394); Paulovich, Amanda G. (6603609510); Carr, Steven A. (7202363695); Jaffe, Jacob D. (7201608537); Maccoss, Michael J. (7006289326); Maclean, Brendan (11438903400)","7404567972; 37053561400; 7404203498; 36015685100; 57190803838; 36765033100; 45261702300; 12799209700; 55958075300; 7006516334; 7202975072; 6603524394; 6603609510; 7202363695; 7201608537; 7006289326; 11438903400","Panorama: A targeted proteomics knowledge base","2014","Journal of Proteome Research","13","9","","4205","4210","5","151","10.1021/pr5006636","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907865588&doi=10.1021%2fpr5006636&partnerID=40&md5=df5f13d39fb32171aa5abe1147de761f","University of Washington, Seattle, 98195, WA, United States; LabKey Software, San Diego, 92101, CA, United States; Carnegie Melon University, Pittsburgh, 15213, PA, United States; Fred Hutchinson Cancer Research Center, Seattle, 98109, WA, United States; Leidos Biomedical Research Inc., Frederick, 21702, MD, United States; Buck Institute for Research on Aging, Novato, 94945, CA, United States; Yale University, New Haven, 06520, CT, United States; Broad Institute, Cambridge, 02142, MA, United States","Sharma V., University of Washington, Seattle, 98195, WA, United States; Eckels J., LabKey Software, San Diego, 92101, CA, United States; Taylor G.K., LabKey Software, San Diego, 92101, CA, United States; Shulman N.J., University of Washington, Seattle, 98195, WA, United States; Stergachis A.B., University of Washington, Seattle, 98195, WA, United States; Joyner S.A., Carnegie Melon University, Pittsburgh, 15213, PA, United States; Yan P., Fred Hutchinson Cancer Research Center, Seattle, 98109, WA, United States; Whiteaker J.R., Fred Hutchinson Cancer Research Center, Seattle, 98109, WA, United States; Halusa G.N., Leidos Biomedical Research Inc., Frederick, 21702, MD, United States; Schilling B., Buck Institute for Research on Aging, Novato, 94945, CA, United States; Gibson B.W., Buck Institute for Research on Aging, Novato, 94945, CA, United States; Colangelo C.M., Yale University, New Haven, 06520, CT, United States; Paulovich A.G., Fred Hutchinson Cancer Research Center, Seattle, 98109, WA, United States; Carr S.A., Broad Institute, Cambridge, 02142, MA, United States; Jaffe J.D., Broad Institute, Cambridge, 02142, MA, United States; Maccoss M.J., University of Washington, Seattle, 98195, WA, United States; Maclean B., University of Washington, Seattle, 98195, WA, United States","Panorama is a web application for storing, sharing, analyzing, and reusing targeted assays created and refined with Skyline,1 an increasingly popular Windows client software tool for targeted proteomics experiments. Panorama allows laboratories to store and organize curated results contained in Skyline documents with fine-grained permissions, which facilitates distributed collaboration and secure sharing of published and unpublished data via a web-browser interface. It is fully integrated with the Skyline workflow and supports publishing a document directly to a Panorama server from the Skyline user interface. Panorama captures the complete Skyline document information content in a relational database schema. Curated results published to Panorama can be aggregated and exported as chromatogram libraries. These libraries can be used in Skyline to pick optimal targets in new experiments and to validate peak identification of target peptides. Panorama is open-source and freely available. It is distributed as part of LabKey Server,2 an open source biomedical research data management system. Laboratories and organizations can set up Panorama locally by downloading and installing the software on their own servers. They can also request freely hosted projects on https://panoramaweb.org, a Panorama server maintained by the Department of Genome Sciences at the University of Washington. © 2014 American Chemical Society.","chromatogram libraries; knowledge base; mass spectrometry; MRM; skyline; Software; SRM; targeted proteomics","Databases, Protein; Internet; Knowledge Bases; Mass Spectrometry; Proteomics; Software; Article; chromatography; computer program; medical research; nuclear magnetic resonance scanner; proteomics; publishing; web browser; Internet; knowledge base; mass spectrometry; procedures; protein database; proteomics; software","","","","","National Institutes of Health, NIH; National Institute on Drug Abuse, NIDA, (P30DA018343); National Heart, Lung, and Blood Institute, NHLBI, (R01HL096738); National Cancer Institute, NCI, (U01CA164186, U24CA160034); National Institute of General Medical Sciences, NIGMS, (R01GM103551, T32GM007266); National Center for Advancing Translational Sciences, NCATS, (UL1TR000142)","","Maclean B., Tomazela D.M., Shulman N., Chambers M., Finney G.L., Frewen B., Kern R., Tabb D.L., Liebler D.C., Maccoss M.J., Skyline: An open source document editor for creating and analyzing targeted proteomics experiments, Bioinformatics, 26, pp. 966-968, (2010); Nelson E.K., Piehler B., Eckels J., Rauch A., Bellew M., Hussey P., Ramsay S., Nathe C., Lum K., Krouse K., LabKey Server: An open source platform for scientific data integration, analysis and collaboration, BMC Bioinf., 12, (2011); Marx V., Targeted proteomics, Nat. Methods, 10, pp. 19-22, (2013); Picotti P., Bodenmiller B., Aebersold R., Proteomics meets the scientific method, Nat. Methods, 10, pp. 24-27, (2013); Gillette M.A., Carr S.A., Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry, Nat. Methods, 10, pp. 28-34, (2013); Schilling B., Rardin M.J., Maclean B.X., Zawadzka A.M., Frewen B.E., Cusack M.P., Sorensen D.J., Bereman M.S., Jing E., Wu C.C., Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: Application to protein acetylation and phosphorylation, Mol. Cell. Proteomics, 11, pp. 202-214, (2012); Sherrod S.D., Myers M.V., Li M., Myers J.S., Carpenter K.L., Maclean B., Maccoss M.J., Liebler D.C., Ham A.-J.L., Label-free quantitation of protein modifications by pseudo selected reaction monitoring with internal reference peptides, J. Proteome Res., 11, pp. 3467-3479, (2012); Peterson A.C., Russell J.D., Bailey D.J., Westphall M.S., Coon J.J., Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics, Mol. Cell. Proteomics, 11, pp. 1475-1488, (2012); Venable J.D., Dong M.-Q., Wohlschlegel J., Dillin A., Yates J.R., Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra, Nat. Methods, 1, pp. 39-45, (2004); Gillet L.C., Navarro P., Tate S., Rost H., Selevsek N., Reiter L., Bonner R., Aebersold R., Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis, Mol. Cell. Proteomics, 11, (2012); Farrah T., Deutsch E.W., Kreisberg R., Sun Z., Campbell D.S., Mendoza L., Kusebauch U., Brusniak M.-Y., Huttenhain R., Schiess R., PASSEL: The PeptideAtlas SRM experiment library, Proteomics, 12, pp. 1170-1175, (2012); Cham J.A., Bianco L., Barton C., Bessant C., MRMaid-DB: A repository of published SRM transitions, J. Proteome Res., 9, pp. 620-625, (2010); Prakash A., Tomazela D.M., Frewen B., Maclean B., Merrihew G., Peterman S., Maccoss M.J., Expediting the development of targeted SRM assays: Using data from shotgun proteomics to automate method development, J. Proteome Res., 8, pp. 2733-2739, (2009); Mallick P., Schirle M., Chen S.S., Flory M.R., Lee H., Martin D., Ranish J., Raught B., Schmitt R., Werner T., Computational prediction of proteotypic peptides for quantitative proteomics, Nat. Biotechnol., 25, pp. 125-131, (2007); Bereman M.S., Maclean B., Tomazela D.M., Liebler D.C., Maccoss M.J., The development of selected reaction monitoring methods for targeted proteomics via empirical refinement, Proteomics, 12, pp. 1134-1141, (2012); Stergachis A.B., Maclean B., Lee K., Stamatoyannopoulos J.A., Maccoss M.J., Rapid empirical discovery of optimal peptides for targeted proteomics, Nat. Methods, 8, pp. 1041-1043, (2011); Abbatiello S.E., Mani D.R., Keshishian H., Carr S.A., Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry, Clin. Chem., 56, pp. 291-305, (2010); Escher C., Reiter L., Maclean B., Ossola R., Herzog F., Chilton J., Maccoss M.J., Rinner O., Using iRT, a normalized retention time for more targeted measurement of peptides, Proteomics, 12, pp. 1111-1121, (2012); Maclean B., Tomazela D.M., Abbatiello S.E., Zhang S., Whiteaker J.R., Paulovich A.G., Carr S.A., Maccoss M.J., Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry, Anal. Chem., 82, pp. 10116-10124, (2010); Whiteaker J.R., Halusa G.N., Hoofnagle A.N., Sharma V., Maclean B., Yan P., Wrobel J.A., Kennedy J., Mani D.R., Zimmerman L.J., CPTAC Assay Portal: A repository of targeted proteomic assays, Nat. Methods, 11, pp. 703-704, (2014)","","","American Chemical Society","","","","","","15353893","","JPROB","25102069","English","J. Proteome Res.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84907865588"
"Tristram F.; Wehrle D.; Çayoʇlu U.; Rex J.; Von Suchodoletz D.","Tristram, Frank (37762363400); Wehrle, Dennis (53867392900); Çayoʇlu, Uʇur (56512317900); Rex, Jessica (56511478700); Von Suchodoletz, Dirk (36195233800)","37762363400; 53867392900; 56512317900; 56511478700; 36195233800","Status report of bwFDM-communities-A state wide research data management initiative","2014","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-232","","","1669","1673","4","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922552063&partnerID=40&md5=9c3521cc1387853f1adc7c96af863a60","Karlsruhe Institute of Technology, Steinbuch Centre for Computing (SCC), Germany; Albert-Ludwigs University Freiburg, Communication Systems, Germany; University of Constance, Communication-, Information,-Mediacenter, Germany","Tristram F., Karlsruhe Institute of Technology, Steinbuch Centre for Computing (SCC), Germany; Wehrle D., Albert-Ludwigs University Freiburg, Communication Systems, Germany; Çayoʇlu U., Karlsruhe Institute of Technology, Steinbuch Centre for Computing (SCC), Germany; Rex J., University of Constance, Communication-, Information,-Mediacenter, Germany; Von Suchodoletz D., Albert-Ludwigs University Freiburg, Communication Systems, Germany","Research data are valuable goods that are often only reproducible with significant effort or, in the case of unique observations, not at all. Scientists focus on data analysis and its results. By now, data exploration is accepted as a fourth scientific pillar (next to experiments, theory, and simulation). A main prerequisite for easy data exploration is successful data management. A holistic approach includes all phases of a data lifecycle: data generation, data analysis, data ingest, data preservation, data access, reusage and long term preservation. Tackling the challenge of increasing complexity in managing research data, the objective of bwFDM-Communities is to expose problems of research communities.1 To achieve this goal, the project's key account managers enter into a dialogue with all relevant research groups at each university in Baden-Württemberg. Next to the identification of best practices, possible developments will be determined together with the scientists.","","Data handling; Information analysis; Information management; Data exploration; Data generation; Data lifecycle; Data preservations; Holistic approach; Long-term preservation; Research data managements; Research groups; Big data","","","","","","","Hartenstein H., Walter T., Castellaz P., Aktuelle umsetzungskonzepte der universitäten des landes baden-württemberg für hochleistungsrechnen und datenintensive dienste, PIK-Praxis der Informationsverarbeitung und Kommunikation, 36, 2, pp. 99-108, (2013); Simukovic E., Kindling M., Schirmbacher P., Umfrage Zum Umgang Mit Digitalen Forschungsdaten An der Humboldt-Universität zu Berlin, (2013)","","Plodereder E.; Universitat Stuttgart, Institut fur Softwaretechnologie, Universitatsstr. 38, Stuttgart; Grunske L.; Universitat Stuttgart, Institut fur Softwaretechnologie, Universitatsstr. 38, Stuttgart; Ull D.; Universitat Stuttgart, Institut fur Technische Informatik, Pfaffenwaldring 47, Stuttgart; Schneider E.; Universitat Stuttgart, Institut fur Technische Informatik, Pfaffenwaldring 47, Stuttgart","Gesellschaft fur Informatik (GI)","","44. Jahrestagung der Gesellschaft fur Informatik INFORMATIK 2014 - Big Data - Komplexitat meistern - Big Data - Mastering Complexity: 44th Annual Meeting of the Society for Computer Science, INFORMATICS 2014","22 September 2014 through 26 September 2014","Stuttgart","110425","16175468","978-388579626-8","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-84922552063"
"Willmes C.; Kürner D.; Bareth G.","Willmes, Christian (55878198600); Kürner, Daniel (55877561700); Bareth, Georg (6505760587)","55878198600; 55877561700; 6505760587","Building research data management infrastructure using open source software","2014","Transactions in GIS","18","4","","496","509","13","13","10.1111/tgis.12060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905576879&doi=10.1111%2ftgis.12060&partnerID=40&md5=cbc221ce78d8fbe7b61de8a4e231ebf1","Institute of Geography, University of Cologne, Germany","Willmes C., Institute of Geography, University of Cologne, Germany; Kürner D., Institute of Geography, University of Cologne, Germany; Bareth G., Institute of Geography, University of Cologne, Germany","The implementation of a research data management infrastructure for a large interdisciplinary research project is presented here, based on well-established Free and Open Source Software for Geospatial (FOSS4G) products such as MapServer, MapProxy, GeoExt and pyCSW, as well as the (not primarily geospatial) open source technologies Typo3 and CKAN. The presented implementation depends primarily on the demands for research data management infrastructure by the funding research agency. It also aligns to theory and practice in Research Data Management (RDM) and e-Science. After the research project and related work in the field of RDM are introduced, a detailed description of the architecture and its implementation is given. The article discusses why Open Source and open standards are chosen to implement the infrastructure and provides some suggestions and examples on how to make it easier and more attractive for researchers to upload and publish their primary research data. © 2013 John Wiley & Sons Ltd.","","e-Science; Information management; Open systems; Free and open source softwares; Fundings; Geo-spatial; Geospatial open source technologies; Management infrastructure; Map servers; Open-source softwares; Research agency; Research data managements; Theory and practice; data management; GIS; software; spatial analysis; Open source software","","","","","Deutsche Forschungsgemeinschaft; Horizon 2020 Framework Programme, H2020, (754549)","","Aalst W., van Hee K., Workflow Management: Models, Methods, and Systems, (2004); Anderson G., Moreno-Sanchez R., Building web-based spatial information solutions around open specifications and open source software, Transactions in GIS, 7, pp. 447-466, (2003); Bareth G., GIS- and RS-based spatial decision support: structure of a spatial environmental information system (SEIS), International Journal of Digital Earth, 2, pp. 134-154, (2009); Bareth G., Doluschitz R., Spatial data handling and management, Precision Crop Protection: The Challenge and Use of Heterogeneity, pp. 205-232, (2010); Berners-Lee T., Hendler J., Lassila O., The Semantic Web, Scientific American, 284, 5, pp. 34-43, (2001); Brunt J.W., Data management principles, implementation, and administration, Ecological Data: Design, Management and Processing, pp. 25-42, (2009); Libraries of the World, (2011); Christl A., Free software and open source business models, Open Source Approaches to Spatial Data Handling, pp. 21-48, (2008); Christl A., Free and open source software, FLOSS in Cadastre and Land Registration: Opportunities and Risks, pp. 7-14, (2010); Proceedings of the Data Management Workshop, 90, (2010); Curdt C., Hoffmeister D., Waldhoff G., Jekel C., Bareth G., Scientific research data management for soil-vegetation-atmosphere data: The TR32DB, International Journal of Digital Curation, 7, 2, pp. 68-80, (2012); (1998); Effertz E., The funders perspective: Data management in coordinated programmes of the German Research Foundation (DFG), Proceedings of the Data Management Workshop, pp. 35-38, (2010); Goodchild M.F., Beyond metadata: Towards user-centric description of data quality, (2007); Hey T., Tansley S., Tolle K., (2009); Janowicz K., Schade S., Broring A., Kessler C., Maue P., Stasch C., Semantic enablement for spatial data infrastructures, Transactions in GIS, 14, pp. 111-129, (2010); Klump J., Digitale forschungsdaten, nestor-Handbuch: Eine kleine Enzyklopädie der digitalen Langzeitarchivierung (Version 2.3), pp. 104-115, (2010); Klump J., Bertelmann R., Brase J., Diepenbroek M., Grobe H., Hock H., Lautenschlager M., Schindler U., Sens I., Wachter J., Data publication in the Open Access Initiative, Data Science Journal, 5, pp. 79-83, (2006); Kralidis A.T., Geospatial open source and open standards convergences, Open Source Approaches in Spatial Data Handling, pp. 1-20, (2008); Kurner D., (2013); Ludascher B., Altinas I., Bowers S., Cummings J., Critchlow T., Deelman E., Roure D.D., Freire J., Goble C., Jones M., Klasky S., McPhillips T., Podhorszki N., Silva C., Taylor I., Vouk M., Scientific process automation and workflow management, Scientific Data Management: Challenges, Technology and Deployment, pp. 467-508, (2010); Maue P., (2009); Moreno-Sanchez R., Free and Open Source Software for Geospatial Applications (FOSS4G): A mature alternative in the geospatial technologies arena, Transactions in GIS, 16, pp. 81-88, (2012); Nelson B., Data sharing: Empty archives, Nature, 461, pp. 160-163, (2009); Nogueras-Iso J., Muro-Medrano P., Zarazaga-Soria F., Geographic Information Metadata for Spatial Data Infrastructures: Resources, Interoperability and Information Retrieval, (2005); (2012); Richter J., Melles M., Schabitz F., Temporal and spatial corridors of homo sapiens sapiens population dynamics during the late Pleistocene and early Holocene, Quaternary International, 274, pp. 1-4, (2012); Taylor I., Deelman E., Gannon D.B., Shields M., Workflows for E-Science: Scientific Workflows for Grids, (2007); Taylor J., (1999); Willmes C., Bareth G., A data integration concept for an interdisciplinary research database, Proceedings of the Young Researchers Forum on Geographic Information Science - GI Zeitgeist, 44, pp. 67-72, (2012); Willmes C., Brocks S., Hoffmeister D., Hutt C., Kurner D., Volland K., Bareth G., Facilitating integrated spatio-temporal visualization and analysis of heterogeneous archaeological and paleoenvironmental research data, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1-2, pp. 223-228, (2012)","C. Willmes; GIS and RS Working Group, Institute of Geography, University of Cologne, Cologne, Albertus-Magnus-Platz, Germany; email: c.willmes@uni-koeln.de","","","","","","","","13611682","","TRGIF","","English","Trans. GIS","Article","Final","","Scopus","2-s2.0-84905576879"
"Rousidis D.; Garoufallou E.; Balatsoukas P.; Sicilia M.-A.","Rousidis, Dimitris (55496129100); Garoufallou, Emmanouel (23666959600); Balatsoukas, Panos (25633936000); Sicilia, Miguel-Angel (8266687800)","55496129100; 23666959600; 25633936000; 8266687800","Evaluation of metadata in research data repositories: The case of the DC.Subject element","2015","Communications in Computer and Information Science","544","","","203","213","10","3","10.1007/978-3-319-24129-6_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945917177&doi=10.1007%2f978-3-319-24129-6_18&partnerID=40&md5=c93653521e48ac9eb4f627e1d4d41db4","University of Alcala, Madrid, Spain; Alexander Technological Educational Institute of Thessaloniki, Kentriki Makedonia, Greece; University of Manchester, Manchester, United Kingdom","Rousidis D., University of Alcala, Madrid, Spain, Alexander Technological Educational Institute of Thessaloniki, Kentriki Makedonia, Greece; Garoufallou E., University of Alcala, Madrid, Spain, Alexander Technological Educational Institute of Thessaloniki, Kentriki Makedonia, Greece; Balatsoukas P., University of Manchester, Manchester, United Kingdom; Sicilia M.-A., University of Alcala, Madrid, Spain","Research Data repositories are growing in terms of volume rapidly and exponentially. Their main goal is to provide scientists the essential mechanism to store, share, and re-use datasets generated at various stages of the research process. Despite the fact that metadata play an important role for research data management in the context of these repositories, several factors - such as the big volume of data and its complex lifecycles, as well as operational constraints related to financial resources and human factors - may impede the effectiveness of several metadata elements. The aim of the research reported in this paper was to perform a descriptive analysis of the DC.Subject metadata element and to identify its data quality problems in the context of the Dryad research data repository. In order to address this aim a total of 4.557 packages and 13.638 data files were analysed following a data-preprocessing method. The findings showed emerging trends about the subject coverage of the repository (e.g. the most popular subjects and the authors that contributed the most for these subjects). Also, quality problems related to the lack of controlled vocabulary and standardisation were very common. This study has implications for the evaluation of metadata and the improvement of the quality of the research data annotation process. © Springer International Publishing Switzerland 2015.","Big data; Data quality; DC. subject; Descriptive analysis; Metadata; Open access repositories","Big data; Information management; Metadata; Semantics; Data preprocessing; Data quality; DC. subject; Descriptive analysis; Financial resources; Open Access; Operational constraints; Research data managements; Quality control","","","","","","","Gargouri Y., Hajjem C., Lariviere V., Gingras Y., Brody T., Carr L., Harnad S., Self- Selected or Mandated, Open Access Increases Citation Impact for Higher Quality Research, PLOS ONE, 5, 10, (2010); Mabe M., Amin M., Growth dynamics of scholarly and scientific journals, Scientometrics, 51, pp. 147-162, (2001); Hess C., Ostrom E., A Framework for Analyzing the Knowledge Commons: A Chapter From Understanding Knowledge as A Commons: From Theory to Practice, (2005); Garoufallou E., Papatheodorou C., A critical introduction to metadata for e science and e-research, special issue on metadata for e-science and e-research, International Journal of Metadata Semantics and Ontologies (IJMSO), 9, 1, pp. 1-4, (2014); Currier S., Barton J., O'Beirne R., Ryan B., Quality assurance for digital learning object repositories: Issues for the metadata creation process, ALT-J, Research in Learning Technology, 12, 1, pp. 5-20, (2004); Heery R., Erson S., Digital repositories review, Other. Joint Information Systems Committee, (2005); Greenberg J., Vision T., The Dryad Repository: A New Path for Data Publication in Scholarly Communication, (2011); Greenberg J., Swauger S., Feinstein E.M., Metadata capital in a data repository, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 140-150, (2013); Beagrie N., Eakin-Richards L., Vision T., Business Models and Cost Estimation: Dryad Repository Case Study, Ipres2010 Vienna, (2010); Palavitsinis N., Manouselis N., Sanchez-Alonso S., Metadata quality in digital repositories: Empirical results from the cross-domain transfer of a quality assurance process, Journal of the Association of Information Science and Technology, 65, 6, pp. 1202-1216, (2014); Rousidis D., Garoufallou E., Balatsoukas P., Sicilia M.A., Data Quality Issues and Content Analysis for Research Data Repositories: The Case of Dryad, ELPUB2014. Let’s put data to use: Digital scholarship for the next generation, 18Th International Conference on Electronic Publishing, (2014); White H., Carrier S., Thompson A., Greenberg J., Scherle R., The Dryad data repository: A Singapore framework metadata architecture in a DSpace environment, The 2008 International Conference on Dublin Core and Metadata Applications, (2008); Greenberg J., White H.C., Carrier S., Scherle R., A metadata best practice for a scientific data repository, Journal of Library Metadata, 9, 3, pp. 194-212, (2009); Greenberg J., Theoretical considerations of lifecycle modeling: An analysis of the Dryad repository demonstrating automatic metadata propagation, inheritance, and value system adoption, Cataloguing & Classification Quarterly, 47, 3-4, pp. 380-402, (2009); Peer L., The Role of Data Repositories in Reproducible Research, (2013); Greenberg J., Linking and Hiving Data in the Dryad Repository. The Semantic Web: Fact or Myth, CENDI, FLICC, and NFAIS Workshop. National Archives, (2009); Sokvitne L., An Evaluation of the Effectiveness of current Dublin Core Metadata for Retrieval, Proceedings of VALA 2000. Victorian Association for Library Automation: Melbourne, (2000); Beagrie N., Eakin-Richards L., Vision T., Business Models and Cost Estimation: Dryad Repository Case Study, Ipres2010 Vienna, (2010); Cataloging Guidelines, (2009); Greenberg J., Garoufallou E., Change and a future for metadata, MTSR 2013. CCIS, 390, pp. 1-5, (2013); (2015); Rousidis D., Garoufallou E., Balatsoukas P., Sicilia M.A., Metadata for big data: A preliminary investigation of metadata quality issues in research data repositories, Information Services and Use, 34, 3, pp. 279-286, (2014)","D. Rousidis; University of Alcala, Madrid, Spain; email: drousid@gmail.com","Garoufallou E.; Hartley R.J.; Gaitanou P.","Springer Verlag","","9th Metadata and Semantics Research Conference, MTSR 2015","9 September 2015 through 11 September 2015","Manchester","140829","18650929","978-331924128-9","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-84945917177"
"Jacobs C.T.; Avdis A.; Mouradian S.L.; Piggott M.D.","Jacobs, Christian T. (56498551100); Avdis, Alexandros (26537012900); Mouradian, Simon L. (57188732210); Piggott, Matthew D. (7102871137)","56498551100; 26537012900; 57188732210; 7102871137","Integrating research data management into geographical information systems","2015","CEUR Workshop Proceedings","1529","","","7","17","10","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962591198&partnerID=40&md5=9d214ef57cabbb1b3d60b7685a5f4d12","Department of Earth Science and Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, United Kingdom","Jacobs C.T., Department of Earth Science and Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, United Kingdom; Avdis A., Department of Earth Science and Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, United Kingdom; Mouradian S.L., Department of Earth Science and Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, United Kingdom; Piggott M.D., Department of Earth Science and Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, United Kingdom","Ocean modelling requires the production of high-fidelity com-putational meshes upon which to solve the equations of motion. The production of such meshes by hand is often infeasible, considering the complexity of the bathymetry and coastlines. The use of Geographical Information Systems (GIS) is therefore a key component to discretising the region of interest and producing a mesh appropriate to resolve the dynamics. However, all data abociated with the production of a mesh must be provided in order to contribute to the overall recomputability of the subsequent simulation. This work presents the integration of re-search data management in QMesh, a tool for generating meshes using GIS. The tool uses the PyRDM library to provide a quick and easy way for scientists to publish meshes, and all data required to regenerate them, to persistent online repositories. These repositories are abigned unique identifiers to enable proper citation of the meshes in journal articles.","Digital Curation; Digital Object Identifier; Geographical Information Systems; Online Repositories; Reproducibility; Research Data Man-agement","Data integration; Digital libraries; Equations of motion; Geographic information systems; Image segmentation; Information systems; Mesh generation; Online systems; Semantics; Digital curation; Digital Objects; Online repositories; Reproducibilities; Research data; Information management","","","","","","","Alsheikh-Ali A.A., Qureshi W., Al-Mallah M.H., Ioannidis J.P.A., Public availability of published research data in high-impact journals, PLoS ONE, 6, 9, (2011); Avdis A., Hill J., Jacobs C.T., Kramer S.C., Candy A.S., Gorman G.J., Pig-Gott M.D., Efficient unstructured mesh generation for renewable tidal energy using, Geographical Information Systems; Avdis A., Jacobs C.T., Hill J., Piggott M.D., Gorman G.J., Shoreline and bathymetry approximation in mesh generation for tidal renewable simulations, Proceedings of the 11th European Wave and Tidal Energy Conference; Boettiger C., Chamberlain S., Ram K., Hart E., Rfigshare: An R Interface to Figshare.com, (2014); Buckheit J.B., Donoho D.L., Wavelab and reproducible research, Wavelets and Statistics, Lecture Notes in Statistics, 103, pp. 55-81, (1995); Choi Y., Kida S., Takahashi K., The impact of oceanic circulation and phase transfer on the dispersion of radionuclides released from the Fukushima Dai-ichi Nuclear Power Plant, Biogeosciences, 10, pp. 4911-4925, (2013); Davidson L.A., Douglas K., Digital object identifiers: Promise and problems for scholarly publishing, Journal of Electronic Publishing, 4, 2, (1998); De Leeuw J., Reproducible Research: The Bottom Line, (2001); Figshare Blog, (2013); Figshare Blog, (2014); Geuzaine C., Remacle J.F., Gmsh: A 3-D finite element mesh generator with built-in pre-and post-procebing facilities, International Journal for Numerical Methods in Engineering, 79, 11, pp. 1309-1331, (2009); Gorman G.J., Piggott M.D., Wells M.R., Pain C.C., Allison P.A., A systematic approach to unstructured mesh generation for ocean modelling using GMT and Terreno, Computers & Geosciences, 34, 12, pp. 1721-1731, (2008); Hill J., Collins G.S., Avdis A., Kramer S.C., Piggott M.D., How does multiscale modelling and inclusion of realistic palaeobathymetry affect numerical simulation of the Storegga Slide tsunami, Ocean Modelling, 83, pp. 11-25, (2014); Jacobs C.T., Avdis A., Gorman G.J., Piggott M.D., PyRDM: A Python-based library for automating the management and online publication of scientific software and data, Journal of Open Research Software, 2, 1, (2014); Jacobs C.T., Avdis A., Gorman G.J., Piggott M.D., RDM Green Shoots Project Report: Research Data Management: Where Software Meets Data, (2014); Jacobs C.T., Avdis A., Gorman G.J., Piggott M.D., PyRDM: A library to facilitate the automated publication of software and data in computational science, Poster Presentation at the 10th International Digital Curation Conference, (2015); Leeper T.J., Archiving reproducible research with r and dataverse, The R Journal, 6, (2014); LeVeque R.J., Mitchell I.M., Stodden V., Reproducible research for scientific computing: Tools and strategies for changing the culture, Computing in Science & Engineering, 14, 4, pp. 13-17, (2012); Li R., Data Models for Marine and Coastal Geographic Information Systems, (2000); Martin-Short R., Hill J., Kramer S.C., Avdis A., Allison P.A., Piggott M.D., Tidal resource extraction in the Pentland Firth, UK: Potential impacts on flow regime and sediment transport in the Inner Sound of Stroma, Renewable Energy, 76, pp. 596-607, (2015); QGIS Geographic Information System, (2009); Ram K., Git can facilitate greater reproducibility and increased transparency in science, Source Code for Biology and Medicine, 8, (2013); Rew R.K., Davis G.P., NetCDF: An interface for scientific data acceb, IEEE Computer Graphics and Applications, 10, 4, pp. 76-82, (1990); Smith A., Fidgit-DOIs for Code. Figshare, (2013); Stodden V., Bailey D., Borwein J., LeVeque R.J., Rider W., Stein W., Setting the Default to Reproducible: Reproducibility in Computational and Experimental Mathematics, (2013); Vines T.H., Andrew R.L., Bock D.G., Franklin M.T., Gilbert K.J., Kane N.C., Moore J.S., Moyers B.T., Renaut S., Rennison D.J., Veen T., Yeaman S., Man-dated data archiving greatly improves acceb to research data, The FASEB Journal, 27, 4, pp. 1304-1308, (2013); Whitlock M.C., McPeek M.A., Rausher M.D., Rieseberg L., Moore A.J., Data archiving, The American Naturalist, 175, 2, pp. 145-146, (2010)","","Nurnberger A.; Risse T.; Predoiu L.; Ross S.","CEUR-WS","","5th International Workshop on Semantic Digital Archives, SDA 2015","18 September 2015","Poznan","118194","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84962591198"
"Weller T.; Monroe-Gulick A.","Weller, Travis (56365653800); Monroe-Gulick, Amalia (50161959800)","56365653800; 50161959800","Understanding methodological and disciplinary differences in the data practices of academic researchers","2014","Library Hi Tech","32","3","","467","482","15","29","10.1108/LHT-02-2014-0021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907232170&doi=10.1108%2fLHT-02-2014-0021&partnerID=40&md5=9d36527cc66f87e7ca67534128577a51","Institute for Policy and Social Research, University of Kansas, Lawrence, KS, United States; University of Kansas Libraries, University of Kansas, Lawrence, KS, United States","Weller T., Institute for Policy and Social Research, University of Kansas, Lawrence, KS, United States; Monroe-Gulick A., University of Kansas Libraries, University of Kansas, Lawrence, KS, United States","Design/methodology/approach: This paper is based on the results of a pre-tested, web-based survey of University of Kansas faculty, staff, researchers and graduate students.; Findings: Influences on data practices and data needs vary with the research methodology and academic discipline of the researcher.; Practical implications: Academic libraries may need to adjust the services they offer to meet the varying needs of researchers in differing disciplines using differing methodologies.; Originality/value: This study adds to the developing literature describing research data management.; Purpose: The purpose of this paper is to better understand the data practices, influences and needs of researchers at a major public research institution. © Emerald Group Publishing Limited.","Academic libraries; Assessment; Data management; Research; University libraries","","","","","","","","Akers K., Doty J., Differences among faculty ranks in views on research data management, IASSIST Quarterly Summer, 2012, pp. 16-20, (2012); Akers K., Doty J., Disciplinary differences in faculty research data management practices and perspectives, The International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Bresnahan M., Johnson A., Assessing scholarly communication and research data training needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Connaway L., Dickey T., Towards a profile of the researcher of today: What can we learn from JISC projects?, (2009); Cox A., Corrall S., Evolving academic library specialties, Journal of the American Society for Information Science and Technology, 64, 8, pp. 1526-1542, (2013); Deards K., Why, how, and where we're going next: A multi-institution look at data management service, (2013); Gu X., Averkamp S., Report on the university of Iowa libraries' data management needs survey, (2012); Hey T., Hey J., e-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Jahnke L., Asher A., The problem of data, (2012); Jones E., E-science talking points for ARL deans and directors, (2008); Luce R., A new value equation challenge: The emergence of eResearch and role for research libraries, (2008); Marcus C., Ball S., Delserone L., Hribar A., Loftus W., Understanding research behaviors, information resources, and services needs of scientists and graduate students: A study by the University of Minnesota Libraries, (2007); Parsons T., Grimshaw S., Williams L., Research data management survey, (2013); Peters C., Dryden A., Assessing the academic library's role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Patterns of information use and exchange: Case studies of researchers in the life sciences, (2009); Reinventing research? Information practices in the humanities, (2011); Soehner C., Steeves C., Ward J., E-science and data support services: A study of ARL member institutions, (2010); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future, (2012); Tenopir C., Sandusky R., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Wells Parham S., Bodnar J., Fuchs S., Supporting tomorrow's research: Assessing faculty data curation needs at Georgia Tech, C&RL News, 73, 1, pp. 10-13, (2012); Wilson J., University of Oxford research data management survey 2012: The results, (2013); Witt M., Carlson J., Brandt D., Cragin M., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009)","","","Emerald Group Holdings Ltd.","","","","","","07378831","","","","English","Libr. Hi Tech","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84907232170"
"Harvey M.J.; Mason N.J.; McLean A.; Murray-Rust P.; Rzepa H.S.; Stewart J.J.P.","Harvey, Matthew J. (8855686600); Mason, Nicholas J. (56110019700); McLean, Andrew (56765222800); Murray-Rust, Peter (6701575246); Rzepa, Henry S. (7005542267); Stewart, James J. P. (55637425900)","8855686600; 56110019700; 56765222800; 6701575246; 7005542267; 55637425900","Standards-based curation of a decade-old digital repository dataset of molecular information","2015","Journal of Cheminformatics","7","1","43","","","","3","10.1186/s13321-015-0093-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940045796&doi=10.1186%2fs13321-015-0093-3&partnerID=40&md5=8a437c8e19c6121e9b4047cf4e7708c1","High Performance Computing Service, Imperial College London, London, SW7 2AZ, United Kingdom; Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; Department of Chemistry, Centre for Molecular Informatics, Lensfield Road, Cambridge, CB2 1EW, United Kingdom; Stewart Computational Chemistry, 15210 Paddington Circle, Colorado Springs, 80921, CO, United States","Harvey M.J., High Performance Computing Service, Imperial College London, London, SW7 2AZ, United Kingdom; Mason N.J., Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; McLean A., High Performance Computing Service, Imperial College London, London, SW7 2AZ, United Kingdom; Murray-Rust P., Department of Chemistry, Centre for Molecular Informatics, Lensfield Road, Cambridge, CB2 1EW, United Kingdom; Rzepa H.S., Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; Stewart J.J.P., Stewart Computational Chemistry, 15210 Paddington Circle, Colorado Springs, 80921, CO, United States","Background: The desirable curation of 158,122 molecular geometries derived from the NCI set of reference molecules together with associated properties computed using the MOPAC semi-empirical quantum mechanical method and originally deposited in 2005 into the Cambridge DSpace repository as a data collection is reported. Results: The procedures involved in the curation included annotation of the original data using new MOPAC methods, updating the syntax of the CML documents used to express the data to ensure schema conformance and adding new metadata describing the entries together with a XML schema transformation to map the metadata schema to that used by the DataCite organisation. We have adopted a granularity model in which a DataCite persistent identifier (DOI) is created for each individual molecule to enable data discovery and data metrics at this level using DataCite tools. Conclusions: We recommend that the future research data management (RDM) of the scientific and chemical data components associated with journal articles (the ""supporting information"") should be conducted in a manner that facilitates automatic periodic curation. © 2015 Harvey et al.","Curation; Digital repositories; Metadata standards","","","","","","National Institute of General Medical Sciences; National Institutes of Health; National Institutes of Health, NIH, (R44GM108085); National Institute of General Medical Sciences, NIGMS","One of us (JJPS) thanks the National Institute of General Medical Sciences of the National Institutes of Health (Award Number R44GM108085) for funding. The project was funded by an Imperial College “Green shoots”RDM grant.","Smith M., Barton M., Bass M., Branschofsky M., McClellan G., Stuve D., Et al., DSpace: An Open Source Dynamic Digital Repository, D-lib Magazine, 9, (2003); Downing J., Murray-Rust P., Tonge A.P., Morgan P., Rzepa H.S., Cotterill F., Et al., SPECTRa: The deposition and validation of primary chemistry research data in digital repositories, J Chem Inf Mod, 48, pp. 1571-1581, (2008); Rzepa H.S., Chemical datuments as scientific enablers, J Cheminform, 5, (2013); EPSRC Policy Framework on Research Data; Frey J.G., Bird C.L., Scientific and technical data sharing: A trading perspective, J Comput Aided Mol Des, 28, pp. 989-996, (2014); Badiola K.A., Bird C., Brocklesby W.S., Casson J., Chapman R.T., Coles S.J., Et al., Experiences with a researcher-centric ELN, Chem Sci, 6, pp. 1614-1629, (2015); Murray-Rust P., Rzepa H.S., Stewart J.J.P., Zhang Y., A global resource for computational chemistry, J Mol Model, 11, pp. 532-541, (2005); Stewart J.J.P., MOPAC: A semiempirical molecular orbital program, J Comput Aided Mol Des, 4, pp. 1-103, (1990); The WorldWideMolecularMatrix; Stewart J.J.P., Optimization of parameters for semiempirical methods VI: More modifications to the NDDO approximations and reoptimization of parameters, J Mol Model, 19, pp. 1-32, (2013); Bera P.P., Sattelmeyer K.W., Saunders M., Schaefer H.F., Schleyer P.V.R., Mindless Chemistry, J Phys Chem a, 110, pp. 4287-4290, (2006); Ramakrishnan R., Dral P.O., Rupp M., Von Lilienfeld O.A., Quantum chemistry structures and properties of 134 kilo molecules, Sci Data, 1, (2014); Open Archives Initiative Object Reuse and Exchange; Murray-Rust P., Rzepa H.S., Chemical Markup Language and XML Part I. Basic principles, J Chem Inf Comp Sci, 39, (1999); Heller S., McNaught A., Stein S., Tchekhovskoi D., Pletnev I., InChI - The worldwide chemical structure identifier standard, J Cheminform, 5, (2013); CML Schema Version 2.4; O'Boyle N.M., Banck M., James C.A., Morley C., Vandermeersch T., Hutchison G.R., OpenBabel: An open chemical toolbox, J Cheminform, 3, (2011); Jenkins S., Liu Z., Kirk S.R., A bond, ring and cage resolved Poincaré-Hopf relationship for isomerisation reaction pathways, Mol Phys, 111, pp. 3104-3116, (2013); Rzepa H.S., The importance of being bonded, Nat Chem, 1, pp. 510-512, (2009); Downloadable Structure Files of NCI Open Database Compounds; Alinson J., Francois S., Lewis S., SWORD: Simple Web-Service Offering Repository Deposit Ariadne, 54, (2008); Lewis S., SWORD: Facilitating eposit Scenarios, D-Lib Magazine, 18, (2012); Metadata Encoding and Transmission Standard (METS); Haak L.L., Fenner M., Paglione L., Pentz E., Ratner H., ORCID: A system to uniquely identify researchers, Learn Publish, 25, pp. 259-264, (2012); Zang T., Rzepa H.S., Murray-Rust P., Harvey M.J., Mason N.J., McLean A., Revised Cambridge NCI Database, (2015); Datacite Metadata Search Interface; DOI Name Values; Handle REST API; 3 Resolution; Rzepa H.S., Murray-Rust P., Whitaker B.J., The application of chemical multipurpose internet mail extensions (Chemical MIME) internet standards to electronic mail and world-wide web information exchange, J Chem Inf Comput Sci, 38, pp. 976-982, (1998); Harvey M.J., Mason N.J., Rzepa H.S., Digital data repositories in chemistry and their integration with journals and electronic laboratory notebooks, J Chem Inf Mod, 54, pp. 2627-2635, (2014); Harvey M.J., McLlean A., Mason N.J., Rzepa H.S., Standards-based metadata procedures for retrieving data for display or mining utilizing Persistent (data-DOI) Identifiers, J Cheminform, (2015); FORCE2015 Conference, Oxford, England, January 12-13, 2015; DataCite's Metadata; Harvey M.J., Mason N., McLean A., Rzepa H.S., The JavaScripts Are Archived Figshare, (2015); Datecite Statistics Search Interface; Zittrain J., Albert K., Lessig L., Perma, Scoping and Addressing the Problem of Link and Reference Rot in Legal Citations, Harvard Public Law Working Paper No. 13-42, (2015); PREMIS (Preservation Metadata: Implementation Strategies); (2015); Programmatic Access to Data Files; Raghunathan R., Dral P.O., Rupp M., Von Lilienfeld O.A., Quantum chemistry structures and properties of 134 kilo molecules, Figshare, (2014); Hachmann J., Olivares-Amaya R., Atahan-Evrenk S., Amador-Bedolla C., Sanchez-Carrera R.S., Gold-Parker A., Et al., The harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid, J Phys Chem Lett, 2, pp. 2241-2251, (2011); The CERN OpenData Portal; A Typical CERN OpenData Collection; A Software Object in the CERN OpenData Collection; Hanson R.M., Prilusky J., Zhou R., Nakane T., Sussman J.L., JSmol and the next-generation web-based representation of 3D molecular structure as applied to proteopedia, Israel J Chem, 53, pp. 207-216, (2013); Hanwell M.D., Curtis D.E., Lonie D.C., Vandermeersch T., Zurek E., Hutchison G.R., Avogadro: An advanced semantic chemical editor, visualization and analysis platform, J. Cheminform, 4, (2012); Rzepa H.S., Harvey M.J., Mason N.J., Mclean A., Murray-Rust P., Stewart J.J.P., Standards-based curation of a decade-old digital repository dataset of molecular information, Figshare, (2015)","H.S. Rzepa; Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; email: rzepa@imperial.ac.uk","","BioMed Central Ltd.","","","","","","17582946","","","","English","J. Cheminformatics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84940045796"
"Macdonald S.; Rice R.","Macdonald, Stuart (23667859400); Rice, Robin (7403332002)","23667859400; 7403332002","'DIY' research data management training kit for librarians","2015","CEUR Workshop Proceedings","1016","","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922311468&partnerID=40&md5=089dd13395095ab7a142dc6ebb4cf5ae","EDINA, Data Library, University of Edinburgh, Edinburgh, United Kingdom","Macdonald S., EDINA, Data Library, University of Edinburgh, Edinburgh, United Kingdom; Rice R., EDINA, Data Library, University of Edinburgh, Edinburgh, United Kingdom","This paper discusses extended professional development training in research data management for librarians piloted at the University of Edinburgh. This is framed by the evolving research data management Roadmap at the University, national and international initiatives in managing research data by bodies such as Jisc and LIBER, and the subsequent need to 'up skill' information professionals in the emerging area of academic research data management. This knowledge-transfer exercise includes independent study based on the research data MANTRA course and reflective writing, face to face sessions with different speakers giving short presentations followed by discussion, and group exercises. The resultant training 'kit' was released in Spring 2013 with an open licence for other institutions, particularly those without local research data management expertise, to utilise for 'DIY' RDM training.","Librarians; Research data management; Training","Curricula; Information management; Information services; Knowledge management; Libraries; Personnel training; Societies and institutions; Academic research; Information professionals; Knowledge transfer; Librarians; Professional development training; Reflective writing; Research data managements; University of Edinburgh; Research and development management","","","","","","","Gold A., Cyberinfrastructure data, and libraries, part 2: Libraries and the data challenge: Roles and actions for libraries, Dlib Magazine, (2007); Ten Recommendations for Libraries to Get Started with Research Data Management the Hague, (2012); Auckland M., Re-skilling for Research London: Research Libraries UK (RLUK), (2012)","","Fernie K.; Casarosa V.; Lunghi M.; Cirinna C.","CEUR-WS","","Framing the Digital Curation Curriculum Conference, DigCurV 2013","6 May 2013 through 7 May 2013","Florence","110432","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84922311468"
"Silva J.R.D.; Ribeiro C.; Lopes J.C.","Silva, João Rocha Da (55496903800); Ribeiro, Cristina (7201734594); Lopes, João Correia (36791598000)","55496903800; 7201734594; 36791598000","Ontology-Based Multi-Domain metadata for research data management using triple stores","2014","ACM International Conference Proceeding Series","","","","105","114","9","4","10.1145/2628194.2628234","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906808885&doi=10.1145%2f2628194.2628234&partnerID=40&md5=c01944d44e9c0c6d08160466324124fe","Faculdade de Engenharia da, Universidade do Porto / INESC TEC, Portugal; DEI-Faculdade de, Engenharia da Universidade do Porto / INESC TEC, Portugal","Silva J.R.D., Faculdade de Engenharia da, Universidade do Porto / INESC TEC, Portugal; Ribeiro C., DEI-Faculdade de, Engenharia da Universidade do Porto / INESC TEC, Portugal; Lopes J.C., DEI-Faculdade de, Engenharia da Universidade do Porto / INESC TEC, Portugal","Most current research data management solutions rely on a fixed set of descriptors (e.g. Dublin Core Terms) for the description of the resources that they manage. These are easy to understand and use, but their semantics are limited to general concepts, leaving out domain-specific metadata. The textual values for descriptors are easily indexed through free-text indexes, but faceted search and dataset interlinking becomes limited. From the point of view of the relational database schema modeler, designing a more flexible metadata model represents a non-trivial challenge because it means representing entities with attributes unknown at the time of modeling and that can change in time. Those traits, combined with the presence of hierarchies among the entities, can make the relational schema quite complex. This work demonstrates the approaches followed by current opensource platforms and proposes a graph-based model for achieving modular, ontology-based metadata for interlinked data assets in the Semantic Web. The proposed model was implemented in a collaborative research data management platform currently under development at the University of Porto. © 2014 ACM.","Database Models; Metadata; Research Data Management; Triple Stores","Information management; Ontology; Collaborative research; Database models; Domain-specific metadata; Graph-based modeling; Open-source platforms; Relational database schemata; Research data managements; Triple store; Metadata","","","","","","","Alhajj R., Documenting legacy relational databases, Lecture Notes in Computer Science, 1727, pp. 161-172, (1999); Baca M., Practical issues in applying metadata schemas and controlled vocabularies to cultural heritage, Cataloging & Classification Quarterly, pp. 37-41, (2003); Ball A., Scientific Data Application Profile Scoping Study Report, (2009); Bontas E.P., Mochol M., Tolksdorf R., Case studies on ontology reuse, Proceedings of the 5th International Conference on Knowledge Management IKNOW05, (2005); Borgman C., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, (2012); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, 1-2, pp. 17-30, (2007); Castro J., Ribeiro C., Rocha J., Designing an application profile using qualified dublin core: A case study with fracture mechanics datasets, Proceedings of the DC-2013 Conference, pp. 47-52, (2013); Corrado E., The importance of open access, open source, and open standards for libraries, Issues in Science and Technology Librarianship, pp. 1-7, (2005); Haase K., Context for semantic metadata, Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 204-211, (2004); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, Ariadne, 25, (2000); Heidom P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Herzig D., Ell B., Semantic mediawiki in operation: Experiences with building a semantic portal, Lecture Notes in Computer Science, 6497, pp. 114-128, (2010); Hodson S., ADMIRAL: A Data Management Infrastructure for Research Activities in the Life Sciences. Technical Report, (2011); Horridge M., A Practical Guide to Building OWL Ontologies Using Protégé 4 and CO-ODE Tools Edition 1.2., (2009); Jahnke L., Asher A., Keralis S.D.C., The problem of data, Council on Library and Information Resources, pp. 1-43, (2012); Li Y.-F., Kennedy G., Davies F., Hunter J., PODD-An Ontology-Centric Data Management System for Scientific Research, (2010); Li Y.-F., Kennedy G., Ngoran F., Wu P., Hunter J., An ontology-centric architecture for extensible scientific data management systems, Future Generation Computer Systems, 29, 2, pp. 1-38, (2013); Pipitone A., Pirrone R., A framework for automatic semantic annotation of wikipedia articles, 6th Workshop on Semantic Web Applications And, Perspectives, (2010); Piwowar H.A., Day R.B., Fridsma D.S., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, 3, (2007); Rocha J., Barbosa J., Gouveia M., Ribeiro C., Correia Lopes J., UPBox and DataNotes: A Collaborative Data Management Environment for the Long Tail of Research Data, (2013); Rocha J., Ribeiro C., Correia Lopes J., Managing multidisciplinary research data: Extending DSpace to enable long-term preservation of tabular datasets, IPres 2012 Conference, pp. 105-108, (2012); Rocha J., Ribeiro C., Correia Lopes J., Managing research data at U. Porto: Requirements, technologies and services, Innovations in XML Applications And, Metadata Management: Advancing Technologies, (2012); Rocha J., Ribeiro C., Correia Lopes J., Semi-automated application profile generation for research data assets, Metadata And, Semantics Research, Conference, (2012); Shotton D., The JISC UMF dataflow project: Introduction to datastage, Technical Report, (2012); Volkel M., Krotzsch M., Vrandecic D., Semantic wikipedia, Demos And, Posters of the 3rd European Semantic Web Conference (ESWC 2006), (2006)","","","Association for Computing Machinery","BytePress; Concordia University; ISEP","18th International Database Engineering and Applications Symposium, IDEAS 2014","7 July 2014 through 9 July 2014","Porto","107181","","978-145032627-8","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-84906808885"
"von Budroni P.; Ganguly R.","von Budroni, Paolo (56624471600); Ganguly, Raman (56702193000)","56624471600; 56702193000","E-infrastructures Austria: A reference architecture for the permanent provision of research data as a task for research libraries; [E-Infrastructures Austria: Eine Referenzarchitektur zur dauerhaften Bereitstellung von Daten aus der Forschung als Aufgabe für wissenschaftliche Bibliotheken]","2015","VOEB-Mitteilungen","68","2","","202","216","14","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940200192&partnerID=40&md5=b94d28a63ac287b91effa911d17051d6","Universitätsbibliothek der Universität Wien e-Infrastructures Austria, Projektleitung, Austria; Zentraler Informatikdienst der Universität Wien e-Infrastructures Austria, Technische Projektleitung, Austria","von Budroni P., Universitätsbibliothek der Universität Wien e-Infrastructures Austria, Projektleitung, Austria; Ganguly R., Zentraler Informatikdienst der Universität Wien e-Infrastructures Austria, Technische Projektleitung, Austria","In January 2014, the national three-year HRSM project e-Infrastructures Austria was initiated. The overall objective is the coordinated design and development of repository infrastructures for research and teaching in Austria, as well as efficient and sustainable research data management at all participating 20 universities and five extramural research institutions. The project is divided into three sub-projects that thematically overlap each other and/or build on each other. Coordination is carried out by the University of Vienna. By the end of 2016, three objectives are to be realized:-Sub-project A (objective 1): Implementation of repositories at the local level at the partner universities. Purpose: establishment of institutional repositories at all participating institutions (all Austrian universities with the exception of the Medical University of Innsbruck)-Sub-project B (objective 2): Developing a strategic approach for future research data management in Austria-Sub-project C (objective 3): Establishing a knowledge infrastructure for all 25 project partners. In the current phase of the e-Infrastructures Austria project, the focus is on building repositories, including the category of so-called document servers (which can include the different institutional repositories of Austrian institutions). More recently, certain data requires even different solutions. These solutions can also have forms that are not common to the repository world. This is where the term „information infrastructure“(e-Infrastructures) becomes significant. © 2015, Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Cultural heritage; Cultural policy; Dissemination of information; Information storage; Open access; Open data; Open university; Research data management (RDM)","","","","","","","","Certifi-Cation as a Means of Providing Trust. Florence, Fondazione Rinascimen-To Digitale, (2012); Annual Meeting, Data Management Plans-How to Treat Digital Resources, (2012); Dazu V., Tomas Miksa, Stephan Strodl, Andreas Rauber: Process Ma-nagement Plans, International Journal of Digital Curation, 9, 1, pp. 83-97, (2014); Miksa S.T., Rauber R., Increasing preservability of research by process management plans, Proceedings of the 1St International Workshop on Digital Preservation of Research Methods and Artefacts (DPRMA'13)","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","","Scopus","2-s2.0-84940200192"
"Swijghuisen Reigersberg M.","Swijghuisen Reigersberg, Muriel (56736907500)","56736907500","Problematizing Digital Research Evaluation using DOIs in Practice-Based Arts, Humanities and Social Science Research","2015","F1000Research","4","","193","","","","3","10.12688/f1000research.6506.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937935188&doi=10.12688%2ff1000research.6506.1&partnerID=40&md5=c0057aa3a8d380d3a20c4b4312cfb3b1","Research Office, Goldsmiths', University of London, London, SE4 6NW, United Kingdom","Swijghuisen Reigersberg M., Research Office, Goldsmiths', University of London, London, SE4 6NW, United Kingdom","This paper explores emerging practices in research data management in the arts, humanities and social sciences (AHSS). It will do so vis-à-vis current citation conventions and impact measurement for research in AHSS. Case study findings on research data inventoried at Goldsmiths', University of London will be presented. Goldsmiths is a UK research-intensive higher education institution which specialises in arts, humanities and social science research. The paper's aim is to raise awareness of the subject-specific needs of AHSS scholars to help inform the design of future digital tools for impact analysis in AHSS. Firstly, I shall explore the definition of research data and how it is currently understood by AHSS researchers. I will show why many researchers choose not to engage with digital dissemination techniques and ORCID. This discussion must necessarily include the idea that practice-based and applied AHSS research are processes which are not easily captured in numerical 'sets' and cannot be labelled electronically without giving careful consideration to what a group or data item 'represents' as part of the academic enquiry, and therefore how it should be cited and analysed as part of any impact assessment. Then, the paper will explore: the role of the monograph and arts catalogue in AHSS scholarship; how citation practices and digital impact measurement in AHSS currently operate in relation to authorship and how digital identifiers may hypothetically impact on metrics, intellectual property (IP), copyright and research integrity issues in AHSS. I will also show that, if we are to be truly interdisciplinary, as research funders and strategic thinkers say we should, it is necessary to revise the way we think about digital research dissemination. This will involve breaking down the boundaries between AHSS and other types of research. © 2015 Swijghuisen Reigersberg M.","","access to information; Article; arts humanities and social science research; awareness; book; digital object identifier; information dissemination; information processing; interdisciplinary research; patent; publishing; research; research ethics; scientific literature; sociology","","","","","","","Latour B., Woolgar S., Laboratory Life: The Construction of Scientific Facts, (1986)","","","Faculty of 1000 Ltd","","","","","","20461402","","","","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84937935188"
"Castro J.A.; Da Silva J.R.; Ribeiro C.","Castro, João Aguiar (55977255100); Da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","55977255100; 55496903800; 7201734594","Creating lightweight ontologies for dataset description practical applications in a cross-domain research data management workflow","2014","Proceedings of the ACM/IEEE Joint Conference on Digital Libraries","","","6970185","313","316","3","8","10.1109/JCDL.2014.6970185","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919359437&doi=10.1109%2fJCDL.2014.6970185&partnerID=40&md5=b9611a6f49c10fe68f4a6867ba86594e","Faculdade de Engenharia, Universidade Do Porto, INESC TEC, Portugal; DEI, Faculdade de Engenharia, Universidade Do Porto / INESC TEC, Portugal","Castro J.A., Faculdade de Engenharia, Universidade Do Porto, INESC TEC, Portugal; Da Silva J.R., Faculdade de Engenharia, Universidade Do Porto, INESC TEC, Portugal; Ribeiro C., DEI, Faculdade de Engenharia, Universidade Do Porto / INESC TEC, Portugal","The description of data is a central task in research data management. Describing datasets requires deep knowledge of both the data and the data creation process to ensure adequate capture of their meaning and context. Metadata schemas are usually followed in resource description to enforce comprehensiveness and interoperability, but they can be hard to understand and adopt by researchers. We propose to address data description using ontologies, which can evolve easily, express semantics at different granularity levels and be directly used in system development. Considering that existing ontologies are often hard to use in a crossdomain research data management environment, we present an approach for creating lightweight ontologies to describe research data. We illustrate our process with two ontologies, and then use them as configuration parameters for Dendro, a software platform for research data management currently being developed at the University of Porto. © 2014 IEEE.","lightweight ontology; research data description; Research data management","Data description; Digital libraries; Information management; Semantics; Configuration parameters; Different granularities; Lightweight ontology; Research data; Research data managements; Resource description; Software platforms; System development; Ontology","","","","","","","Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, pp. 17-30, (2007); Martinez-Uribe L., Macdonald S., User engagement in research data curation, Proceedings of the 13th European Conference on Research and Advanced Technology for Digital Libraries, 5714, pp. 309-314, (2009); Qin J., Li K., How Portable Are the Metadata Standards for Scientific Data? A Proposal for a Metadata Infrastructure, Proceedings of the DC-2013 Conference, pp. 25-34, (2013); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, Ariadne, 25, (2000); Duval E., Hodgins W., Sutton S., Metadata principles and practicalities, D-lib Magazine, 8, 4, pp. 1-10, (2002); Madin J., Bowers S., Schildhauer M., Krivov S., Pennington D., Villa F., An ontology for describing and synthesizing ecological observation data, Ecological Informatics, 2, pp. 279-296, (2007); Soldatova L.N., King R.D., An ontology of scientific experiments, Journal of the Royal Society, Interface / the Royal Society, 3, 11, pp. 795-803, (2006); Pouchard L., Cinquini L., Strand G., The earth system Grid discovery and semantic web technologies, Workshop for Semantic Web Technologies for Searching and Retrieving Scientific Data-2nd International Semantic Web Conference, (2003); Jorg B., CERIF: The common European research information format model, Data Science Journal, 9, pp. 24-31, (2010); Lassila O., McGuinness D., The role of frame-based representation on the semantic web, Linkoping Electronic Articles in Computer and Information Science, 6, 5, (2001); Corcho O., Ontology based document annotation: Trends and open research problems, International Journal of Metadata, Semantics and Ontologies, 1, 1, pp. 47-57, (2006); Rocha J., Barbosa J., Gouveia M., Ribeiro C., Correia Lopes J., UPBox and DataNotes: A collaborative data management environment for the long tail of research data, IPres 2013 Conference Proceedings, (2013); Li Y.-F., Kennedy G., Ngoran F., Wu P., An ontology-centric architecture for extensible scientific data management systems, Future Generation Computer Systems, 29, 2, pp. 1-38, (2013); Castro J.A., Da Silva A.R.J., Ribeiro C., Designing an Application Profile Using Qualiéd Dublin Core: A Case Study with Fracture Mechanics Datasets, Proc. of the International Conference on Dublin Core and Metadata Applications, pp. 47-52, (2013); Greenberg J., Hill C., Functional and architectural requirements for metadata: Supporting discovery and management of scientific data, Proceedings of the DC-2012 Conference, pp. 62-71, (2012); Alexander K., Hausenblas M., Describing linked datasets-on the design and usage of void, the vocabulary of interlinked datasets, Linked Data on the Web Workshop (LDOW 09), (2009)","","","Institute of Electrical and Electronics Engineers Inc.","","2014 14th IEEE/ACM Joint Conference on Digital Libraries, JCDL 2014","8 September 2014 through 12 September 2014","London","109584","15525996","978-147995569-5","","","English","Proc. ACM IEEE Joint Conf. Digit. Libr.","Conference paper","Final","","Scopus","2-s2.0-84919359437"
"Cox A.M.; Pinfield S.","Cox, Andrew M. (7402563906); Pinfield, Stephen (6602090850)","7402563906; 6602090850","Research data management and libraries: Current activities and future priorities","2014","Journal of Librarianship and Information Science","46","4","","299","316","17","136","10.1177/0961000613492542","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893825528&doi=10.1177%2f0961000613492542&partnerID=40&md5=031762f8abc4f3005317723baf68ec75","University of Sheffield, United Kingdom","Cox A.M., University of Sheffield, United Kingdom; Pinfield S., University of Sheffield, United Kingdom","This paper reports research carried out at the end of 2012 to survey UK universities to understand in detail the ways in which libraries are currently involved in research data management and the extent to which the development of research data management services is a strategic priority for them. The research shows that libraries were offering limited research data management services, with highest levels of activity in large research-intensive institutions. There were major challenges associated with skills gaps, resourcing and cultural change. However, libraries are currently involved in developing new institutional research data management policies and services, and see this as an important part of their future role. Priorities such as provision of research data management advisory and training services are emerging. A systematic comparison between these results and other recent studies is made in order to create a full picture of activities and trends. An innovation hype-cycle framework is deployed to understand possible futures and Abbott’s theory of professions is used to gain an insight into how libraries are competing to extend their jurisdiction whilst at the same time working collaboratively with other stakeholders. © The Author(s) 2013.","Abbott’s system of professions; academic libraries; data curation; hype cycle; library roles; research data management; research support; United Kingdom","","","","","","","","Abbott A., The System of Professions, (1988); Alvaro E., Brooks H., Ham M., E-science librarianship: Field undefined, Issues in Science and Technology Librarianship66, (2011); Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, London: Research Libraries UK, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brewerton A., ‘… and any other duties deemed necessary:’ An analysis of subject librarian job descriptions, SCONUL Focus, 51, pp. 60-67, (2011); Carlson J.R., Garritano J.R., E-science, Cyberinfrastructure and the Changing Face of Scholarship: Organizing for New Models of Research Support at the Purdue University Libraries. Libraries Research Publications. Paper 137, (2010); Corrall S., Managing Research Data, pp. 105-133, (2012); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management: Emerging trends in library research support services, Library Trends, 61, 3, pp. 620-658, (2013); Cox A.M., Corrall S., Evolving academic library specialties, Journal of the American Society of Information Science and Technology, (2013); Cox A.M., Verbaan E., Sen B., Upskilling liaison librarians for research data management. Ariadne70, (2012); Data management in perspective: The career profile of data managers, Edinburgh: Digital Curation Centre, (2013); Fenn J., Raskino M., Mastering the Hype Cycle: How to Choose the Right Innovation at the Right Time, (2008); Fry J., Lockyer S., Oppenheim C., Identifying Benefits Arising from the Curation and Open Sharing of Research Data Produced by UK Higher Education and Research Institutes, Project Report. JISC, (2009); Gabridge T., The last mile: Liaison roles in curating science and engineering research data, Research Library Issues, 265, pp. 15-21, (2009); Garritano J.R., Carlson J.R., A subject librarian’s guide to collaborating on e-Science projects, Issues in Science and Technology Librarianship57, (2009); Gladwell M., The Tipping Point, (2002); Henty M., Developing the capability and skills to support eResearch. Ariadne55, (2008); Henty M., Dreaming of data: The library’s role in supporting e-research and data management, In: Australian Library and Information Association biennial conference, Alice Springs, Australia, 2–5September2008, (2008); Higgins S., The DCC curation lifecycle model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Hyams E., Data librarianship: A gap in the market: Stuart Macdonald and Luis Martinez-Uribe, Update Magazine, (2008); Managing Research Data Management, (2013); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services - A Guide for HEIs. DCC How-to Guides, Edinburgh: Digital Curation Centre, (2013); Klein J.T., Crossing Boundaries: Knowledge, Disciplinarities, and Interdisciplinarities, (1996); Lewis M., Envisioning Future Academic Library Services: Initiatives, Ideas and Challenges, pp. 145-168, (2010); Lyon L., The informatics transform: Re-engineering libraries of the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Monastersky R., Publishing frontiers: The library reboot, Nature, 495, 7442, pp. 430-432, (2013); Nalebuff B.J., Brandenburger A.M., Co-opetition: Competitive and cooperative business strategies for the digital economy, Strategy and Leadership, 25, 6, pp. 28-35, (1997); Pryor G., Managing Research Data, (2012); Pryor G., Donnelly M., Skilling up to do data: Whose role, whose responsibility, whose career?, International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Common Principles on Data Policy, (2013); Science as an Open Enterprise, (2012); Simmonds P., Stroyan J., Brown N., Data Centres: Their Use, Value and Impact, A Research Information Network Report, (2011); Swan A., Brown S., The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs, Bristol: JISC, (2008); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services, Association of College & Research Libraries, (2012); Whyte A., Tedds J., Making the Case for Research Data Management. DCC Briefing Papers, Edinburgh: Digital Curation Centre, (2011)","","","SAGE Publications Ltd","","","","","","09610006","","","","English","J. Librariansh. Inf. Sci.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84893825528"
"Searle S.","Searle, Sam (7005900451)","7005900451","Using scenarios in introductory research data management workshops for library staff","2015","D-Lib Magazine","21","11-12","","1","1","0","4","10.1045/november2015-searle","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957048605&doi=10.1045%2fnovember2015-searle&partnerID=40&md5=1b1b0e8c9b1cd0f7e69ac135e72abe26","Griffith University, Brisbane, QLD, Australia","Searle S., Griffith University, Brisbane, QLD, Australia","This case study describes the inclusion of a scenario-based group learning activity in introductory research data management workshops for librarians at two Australian universities in 2012-2013. The positive response from attendees at these workshops, and the successful re-use of the scenarios at several other Australian universities in 2014-2015, prompted further investigation into scenario-based learning (SBL) and reflection on how this approach could be better applied in future as part of in-house professional development programs for librarians. © 2015 Sam Searle.","","","","","","","","","Adam R., 'Schooling for Hard Knocks': Using Scenario-Based Learning (SBL) for Behaviour Management Skills in Pre-Service Teacher Education., Preparing Graduates for the Professions Using Scenario-Based Learning, pp. 97-109; Brown C.T., My Data Management Plan-a Satire., Living in an Ivory Basement, (2010); Brown R.A., Wolski M., Richardson J., Developing New Skills for Research Support Librarians., The Australian Library Journal, 64, 3, pp. 224-234, (2015); Chen K.-N., Lin P.-C., Chang S.-S., Sun H.-C., Library Use by Medical Students: A Comparison of Two Curricula., Journal of Librarianship and Information Science, 43, 3, pp. 176-184, (2011); Clark R.C., Mayer R.E., Scenario-Based E-Learning: Evidence-Based Guidelines for Online Workforce Learning, (2012); Cook P., Walsh M., Collaboration and Problem-Based Learning., Communications in Information Literacy, 6, 1, pp. 59-72, (2012); Corrall S., Kennan M.A., Afzal W., Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research., Library Trends, 61, 3, pp. 636-674, (2013); Corti L., Van Den Eyden V., Bishop L., Morgan-Brett B., ""Managing and Sharing Data: Training Resources."", (2011); Cox A., ""Why Librarians Should Be Clumsy with Research Data."", (2014); Cox A.M., Pinfield S., Research Data Management and Libraries: Current Activities and Future Priorities., Journal of Librarianship and Information Science, 46, 4, pp. 299-316, (2014); Cox A., Verbaan E., Sen B., Upskilling Liaison Librarians for Research Data Management., Ariadne, 70, (2012); Errington E.P., Preparing Graduates for the Professions Using Scenario-Based Learning, (2010); Errington, ""What Is Scenario-Based Learning?"", (2014); ""Scenarios Used in Introductory Research Data Management Workshops for Library Staffat Griffith University, 2013."", (2013); Henderson J., ""Problem-Based Scenarios for a Professional Future."", pp. 65-74; Hines S.S., Hines E., Faculty and Librarian Collaboration on Problem-Based Learning., Journal of Library Innovation, 3, 2, pp. 18-32, (2012); (2013); Lage K., Losoff B., Maness J., Receptivity to Library Involvement in Scientific Data Curation: A Case Study at the University of Colorado Boulder., Portal: Libraries and the Academy, 11, 4, pp. 915-937, (2011); Lombardi M.M., ""Authentic Learning for the 21st Century: An Overview."", (2007); Maness J.M., Miaskiewicz T., Sumner T., Using Personas to Understand the Needs and Goals of Institutional Repositories., D-Lib Magazine, 14, 9-10, (2008); Mi M., Renewed Roles for Librarians in Problem-Based Learning in the Medical Curriculum., Medical Reference Services Quarterly, 30, 3, pp. 269-282, (2011); (2014); Naidu S., ""Using Scenario-Based Learning to Promote Situated Learning and Develop Professional Knowledge."", pp. 39-49, (2010); Searle S., ""Case Studies."", (2012); Searle S., (2014); Searle S., Torres L., (2011); Shadbolt A., Konstantelos L., Lyon L., Guy M., Delivering Innovative RDM Training: The immersiveInformatics Pilot Programme., International Journal of Digital Curation, 9, 1, pp. 313-323, (2014); Simons N., Searle S., Redefining 'the Librarian' in the Context of Emerging eResearch Services., VALA2014 Proceedings, (2014); Todd P., Waylen J., Murphy K., (2011); (2015); Wenger K., Problem-Based Learning and Information Literacy: A Natural Partnership., Pennsylvania Libraries, 2, 2, pp. 142-154, (2014); Wright S.J., Kozlowski W.A., Dietrich D., Khan H.J., Steinhart G.S., McIntosh L., Using Data Curation Profiles to Design the Datastar Dataset Registry., D-Lib Magazine, 19, 7-8, (2013)","S. Searle; Griffith University, Brisbane, Australia; email: samantha.searle@griffith.edu.au","","Corporation for National Research Initiatives","","","","","","10829873","","","","English","D-Lib Mag.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84957048605"
"van Gaans D.; D'Onise K.; Cardone T.; McDermott R.","van Gaans, Deborah (55014275200); D'Onise, Katina (14420735800); Cardone, Tony (56638557300); McDermott, Robyn (55443425900)","55014275200; 14420735800; 56638557300; 55443425900","The development of the Public Health Research Data Management System","2015","Electronic Journal of Health Informatics","9","1","e10","","","","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929091546&partnerID=40&md5=1fed111b41fd30230cfb0820c11cccbf","CRE in the Prevention of Chronic Conditions in Rural and Remote Populations, School of Population Health, University of South Australia, South Australian Health and Medical Research Institute (SAMHRI), Level 8, North Terrace, Adelaide, 5001, Australia; Dept. of Geography, Environment and Population, The University of Adelaide, North Terrace, Adelaide, 5005, SA, Australia; Business Intelligence and Planning, University of South Australia, City West Campus, North Terrace, Adelaide, 5001, Australia; College of Public Health, Medical and Veterinary Sciences, James Cook University, PO Box 6811, Cairns, 4870, QLD, Australia","van Gaans D., CRE in the Prevention of Chronic Conditions in Rural and Remote Populations, School of Population Health, University of South Australia, South Australian Health and Medical Research Institute (SAMHRI), Level 8, North Terrace, Adelaide, 5001, Australia, Dept. of Geography, Environment and Population, The University of Adelaide, North Terrace, Adelaide, 5005, SA, Australia; D'Onise K., CRE in the Prevention of Chronic Conditions in Rural and Remote Populations, School of Population Health, University of South Australia, South Australian Health and Medical Research Institute (SAMHRI), Level 8, North Terrace, Adelaide, 5001, Australia; Cardone T., Business Intelligence and Planning, University of South Australia, City West Campus, North Terrace, Adelaide, 5001, Australia; McDermott R., College of Public Health, Medical and Veterinary Sciences, James Cook University, PO Box 6811, Cairns, 4870, QLD, Australia","The design and development of the Public Health Research Data Management System highlights how it is possible to construct an information system, which allows greater access to well, preserved public health research data to enable it to be reused and shared. The Public Health Research Data Management System (PHRDMS) manages clinical, health service, community and survey research data within a secure web environment. The conceptual model under pinning the PHRDMS is based on three main entities: participant, community and health service. The PHRDMS was designed to provide data management to allow for data sharing and reuse. The system has been designed to enable rigorous research and ensure that: data that are unmanaged be managed, data that are disconnected be connected, data that are invisible be findable, data that are single use be reusable, within a structured collection. The PHRDMS is currently used by researchers to answer a broad range of policy relevant questions, including monitoring incidence of renal disease, cardiovascular disease, diabetes and mental health problems in different risk groups. © by the authors.","Database management systems; Modelling; Public health; Secondary use","","","","","","","","Thompson S.C., Woods J.A., Katzenellenbogen J.M., The quality of indigenous identification in administrative health data in Australia: insights from studies using data linakge, BMC Medical Informatics and Decision Making, 12, (2012); Pisani E., AbouZahr C., Sharing Health Data: Good Intentions are Not Enough, Bull World Health Organ, 88, pp. 466-467, (2010); Lynch C., How Do Your Data Grow?, Nature, 455, pp. 28-29, (2008); Ackerman I.N., Osborne R.H., Integrating Data to Facilitate Clinical Research: A Case Study, Informatics in Primary Care, 13, pp. 263-270, (2005); Carvalho E.C., Batilana A.P., Simkins J., Martins H., Shah J., Rajgor D., Shah A., Rockart S., Pietrobon R., Application Description and Policy Model in Collaborative Environment for Sharing of Information on Epidemiological and Clinical Research Data Sets, PLoS ONE, 5, 2, (2010); Law M., Reduce, Reuse, Recycle: Issues in the Secondary Use of Research Data, IASSIST Quarterly, pp. 5-10, (2005); Elger B.S., Iavindrasana J., Iacono L.L., Muller H., Roduit N., Summers P., Wright J., Strategies for Health Data Exchange for Secondary, Crossinstitutional Clinical Research, Computer Methods and Programs in Biomedicine, 99, pp. 230-251, (2010); Piwowar H.A., Becich M.J., Bilofsky H., Crowley R.S., Towards a data sharing culture:Recommendations for leadership from academic health centers, PLoS Med, 5, 9, (2008); Soloff C., Sanson A., Wake M., Harrison L., Enhancing Longitudinal Studies by Linkage to National Databases: Growing Up in Australia, the Longitudinal Study of Australian Children, Int. J. Social Research Methodology, 10, 5, pp. 349-363, (2007); Reeder B., Hills R.A., Demiris G., Revere D., Pina J., Reusable Design: A Proposed Approach to Public Health Informatics System Design, BMC Public Health, 11, (2011); UniSA Framework for the Responsible Conduct of Research, (2012); Code of Conduct, (2012); Australian Code for the Responsible Conduct of Research, Australian Government, (2007)","","","Health Informatics Society Australia (HISA)","","","","","","14464381","","","","English","Electron. J. Health Inf.","Article","Final","","Scopus","2-s2.0-84929091546"
"Rautenberg P.L.; Kumaraswamy A.; Tejero-Cantero A.; Doblander C.; Norouzian M.R.; Kai K.; Jacobsen H.-A.; Ai H.; Wachtler T.; Ikeno H.","Rautenberg, Philipp L. (54953861600); Kumaraswamy, Ajayrama (56208578500); Tejero-Cantero, Alvaro (53882114300); Doblander, Christoph (55814595400); Norouzian, Mohammad R. (37119325700); Kai, Kazuki (56209129100); Jacobsen, Hans-Arno (7103073434); Ai, Hiroyuki (7005353366); Wachtler, Thomas (6602841638); Ikeno, Hidetoshi (7003566847)","54953861600; 56208578500; 53882114300; 55814595400; 37119325700; 56209129100; 7103073434; 7005353366; 6602841638; 7003566847","Neurondepot: Keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications","2014","Frontiers in Neuroinformatics","8","JUNE","55","","","","4","10.3389/fninf.2014.00055","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902590974&doi=10.3389%2ffninf.2014.00055&partnerID=40&md5=7a7985f46d5c416f810cf117cb86ac43","Department of Biology II, G-Node, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; Department for Innovations, Max Planck Digital Library, München, Germany; MRC ANU, Department of Pharmacology, University of Oxford, Oxford, United Kingdom; Department of Informatics, Technische Universität München, München, Germany; Department of Earth System Science, Fukuoka University, Fukuoka, Japan; School of Human Science and Environment, University of Hyogo, Hyogo, Japan","Rautenberg P.L., Department of Biology II, G-Node, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany, Department for Innovations, Max Planck Digital Library, München, Germany; Kumaraswamy A., Department of Biology II, G-Node, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; Tejero-Cantero A., MRC ANU, Department of Pharmacology, University of Oxford, Oxford, United Kingdom; Doblander C., Department of Informatics, Technische Universität München, München, Germany; Norouzian M.R., Department of Informatics, Technische Universität München, München, Germany; Kai K., Department of Earth System Science, Fukuoka University, Fukuoka, Japan; Jacobsen H.-A., Department of Informatics, Technische Universität München, München, Germany; Ai H., Department of Earth System Science, Fukuoka University, Fukuoka, Japan; Wachtler T., Department of Biology II, G-Node, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; Ikeno H., School of Human Science and Environment, University of Hyogo, Hyogo, Japan","Neuroscience today deals with a ""data deluge"" derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing-thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations. © 2014 Rautenberg, Kumaraswamy, Tejero-Cantero, Doblander, Norouzian, Kai, Jacobsen, Ai, WachtlerandIkeno.","Cloud services; Data management; Electrophysiology; Imaging; Morphology; Neuroinformatics; Research data management","article; backup; computer interface; computer security; confocal microscopy; data flow; data integration; data mangement; dendrite; electrophysiology; imaging; information processing; local file system; medical informatics; morphology; neuroanatomy; registration; simulation; storage service; Web application; work station","","","","","Japan Society for the Promotion of Science, JSPS, (22570079, 24657019, 25330342)","","Ai H., Vibration-processing interneurons in the honeybee brain, Front. Syst. Neurosci, 3, (2010); Ai H., Hagio H., Morphological analysis of the primary center receiving spatial information transferred by the waggle dance of honeybees, J. Comp. Neurol, 521, pp. 2570-2584, (2013); Ai H., Itoh T., The auditory system of the honeybee, Honeybee Neurobiology and Behaviors, pp. 269-284, (2012); Ai H., Nishino H., Itoh T., Topographic organization of sensory affer-ents of Johnston's organ in the honeybee brain, J. Comp. Neurol, 502, pp. 1030-1046, (2007); Ai H., Rybak J., Menzel R., Itoh T., Response characteristics of vibration-sensitive interneurons related to Johnston's organ in the honeybee, Apis mellifera, J. Comp. Neurol, 515, pp. 145-160, (2009); Ascoli G.A., Donohue D.E., Halavi M., NeuroMorpho.Org: A central resource for neuronal morphologies, J. Neurosci, 27, pp. 9247-9251, (2007); Austin J., Jackson T., Fletcher M., Jessop M., Liang B., Weeks M., Et al., CARMEN: Code analysis, repository and modeling for e-neuroscience, Proc. Comput. Sci, 4, pp. 768-777, (2011); Bennett K., Layzell P., Budgen D., Brereton P., Macaulay L., Munro M., Service-based software: The future for flexible software, Seventh Asia-Pacific Software Engineering Conference (APSEC 2000), pp. 214-221, (2000); Brandt R., Rohlfing T., Rybak J., Krofczik S., Maye A., Westerhoff M., Et al., Three-dimensional average-shape atlas of the honeybee brain and its applications, J. Comp. Neurol, 492, pp. 1-19, (2005); Bullinger H.-J., Fahnrich K.-P., Meiren T., Service engineering-methodical development of new service products, Int. J. Prod. Econ, 85, pp. 275-287, (2003); Cummings J.N., Kiesler S., Coordination costs and project outcomes in multi-university collaborations, Res. Pol, 36, pp. 1620-1634, (2007); Evers J., Schmitt S., Sibila M., Duch C., Progress in functional neuroanatomy: Precise automatic geometric reconstruction of neu-ronal morphology from confocal image stacks, J. Neurophysiol, 93, (2005); Frisch K., The tail-wagging dance as a means of communication when food sources are distant, The Dance Language and Orientation of Bees, pp. 57-235, (1967); Grewe J., Wachtler T., Benda J., A bottom-up approach to data annotation in neurophysiology, Front. Neuroinform, 5, (2011); Kai K., Ikeno H., Haupt S.S., Rautenberg P.L., Wachtler T., Ai H., Response properties of auditory interneurons in the honeybee brain, Bernstein Conference 2013, (2013); Kazawa T., Ikeno H., Kanzaki R., Development and application of a neuroinformatics environment for neuroscience and neuroethology, Neural Netw, 21, pp. 1047-1055, (2008); Koster J., Rahmann S., Snakemake-a scalable bioinformatics workflow engine, Bioinformatics, 28, pp. 2520-2522, (2012); Ludascher B., Altintas I., Bowers S., Cummings J., Critchlow T., Deelman E., Et al., Scientific process automation and workflow management, Scientific Data Management, (2009); Marcotte E., Responsive web design, A List Apart, (2010); Minemoto T., Saitoh A., Ikeno H., Isokawa T., Kamiura N., Matsui N., Et al., SIGEN: System for reconstructing three-dimensional structure of insect neurons, Proceedings of the Asia Simulation Conference, pp. 1-6, (2009); Moore G.E., Cramming More Components Onto Integrated Circuits, (1965); Shepherd G.M., Mirsky J.S., Healy M.D., Singer M.S., Skoufos E., Hines M.S., Et al., The human brain project: Neuroinformat-ics tools for integrating, searching and modeling multidisciplinary neu-roscience data, Trends Neurosci, 21, pp. 460-468, (1998); Sobolev A., Stoewer A., Leonhardt A.P., Rautenberg P.L., Kellner C.J., Garbers C., Et al., Integrated platform and API for elec-trophysiological data, Front. Neuroinform, 8, (2014); Sobolev A., Stoewer A., Pereira M., Kellner C.J., Garbers C., Rautenberg P.L., Et al., Data management routines for reproducible research using the G-Node Python Client library, Front. Neuroinform, 8, (2014); Truex D.P., Baskerville R., Klein H., Growing systems in emergent organizations, Commun. ACM, 42, pp. 117-123, (1999); Yamasaki T., Isokawa T., Matsui N., Ikeno H., Kanzaki R., Reconstruction andsimulation for three-dimensional morphological structure of insect neurons, Neurocomputing, 69, pp. 1043-1047, (2006)","P. L. Rautenberg; Department for Innovations, Max Planck Digital Library, 80799 München, Amalienstraße 33, Germany; email: rautenberg@mpdl.mpg.de","","Frontiers Research Foundation","","","","","","16625196","","","","English","Front. Neuroinformatics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84902590974"
"Vlaeminck S.; Wagner G.G.","Vlaeminck, Sven (25633256500); Wagner, Gert G. (56428054900)","25633256500; 56428054900","On the role of research data centres in the management of publication-related research data","2014","LIBER Quarterly","23","4","","336","357","21","5","10.18352/lq.9356","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940241157&doi=10.18352%2flq.9356&partnerID=40&md5=ccc88065f5e7c18a067c0e7b32f25776","German National Library of Economics, Leibniz Information Centre for Economics (ZBW), Hamburg, Germany; German Data Forum (RatSWD), German Institute for Economic Research (DIW Berlin), Max Planck Institute for Human Development and Berlin University of Technology (TUB), Berlin, Germany","Vlaeminck S., German National Library of Economics, Leibniz Information Centre for Economics (ZBW), Hamburg, Germany; Wagner G.G., German Data Forum (RatSWD), German Institute for Economic Research (DIW Berlin), Max Planck Institute for Human Development and Berlin University of Technology (TUB), Berlin, Germany","This paper summarizes the findings of an analysis of scientific infrastructure service providers (mainly from Germany but also from other European countries). These service providers are evaluated with regard to their potential services for the management of publication-related research data in the field of social sciences, especially economics. For this purpose we conducted both desk research and an online survey of 46 research data centres (RDCs), library networks and public archives; almost 48% responded to our survey. We find that almost three-quarters of all respondents generally store externally generated research data - which also applies to publication-related data. Almost 75% of all respondents also store and host the code of computation or the syntax of statistical analyses. If self-compiled software components are used to generate research outputs, only 40% of all respondents accept these software components for storing and hosting. Eight out of ten institutions also take specific action to ensure long-term data preservation. With regard to the documentation of stored and hosted research data, almost 70% of respondents claim to use the metadata schema of the Data Documentation Initiative (DDI); Dublin Core is used by 30% (multiple answers were permitted). Almost two-thirds also use persistent identifiers to facilitate citation of these datasets. Three in four also support researchers in creating metadata for their data. Application programming interfaces (APIs) for uploading or searching datasets currently are not yet implemented by any of the respondents. Least common is the use of semantic technologies like RDF. Concluding, the paper discusses the outcome of our survey in relation to Research Data Centres (RDCs) and the roles and responsibilities of publication- related data archives for journals in the fields of social sciences.","Journals; Libraries economics; Research data centres; Research data management","","","","","","","","Anderson R., Greene W.H., McCullough B.D., Vinod H.D., The role of data/code archives in the future of economic research, Journal of Economic Methodology, 15, 1, pp. 99-119, (2008); Baykoucheva S., What do libraries have to do with e-Science? An interview with James L. Mullins, Dean of Purdue University Libraries, Chemical Information Bulletin, 63, 1, pp. 45-49, (2011); Christensen-Dalsgaard B., Ten recommendations for libraries to get started with research data management., (2012); Costas R., Meijer I., Zahedi Z., Wouters P., The value of research data-Metrics for datasets from a cultural and technical point of view., (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, (2013); De Cock Bruning M., van Dither B., de Boer Jeppersen C.G., Ringnalda A., The legal status of research data in the Knowledge Exchange partner countries, (2011); De Waard A., Linking data to publications: Towards the execution of papers, For attribution-Developing data attribution and citation practices and standards: Summary of an International Workshop, pp. 157-159, (2012); Fecher B., Data sharing angst-An insight to an ongoing research on data sharing in academia, (2014); Empfehlungen zur Weiterentwicklung der wissenschaftlichen Informationsinfrastrukturen in Deutschland bis 2020, (2012); Guibault L., Wiebe A., Safe to be open. Study on the protection of research data and recommendations for access and usage, (2013); Hader M., Der Datenschutz in den Sozialwissenschaften. Anmerkungen zur Praxis sozialwissenschaftlicher Erhebungen und Datenverarbeitung in Deutschland, RatSWD Working Paper Series, (2009); Harvey D.R., Preserving digital materials, (2012); Hillegeist T., Rechtliche Probleme der elektronischen Langzeitarchivierung wissenschaftlicher Primärdaten, Göttinger Schriften zur Internetforschung, 8, (2012); King G., Replication, replication, PS: Political Science and Politics, 28, pp. 443-499, (1995); Kroes N., Commission recommendation of 17.7.2012 on access to and preservation of scientific information, (2012); Lyon L., Dealing with data-Roles, rights, responsibilities and relationships, (2007); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, 1, (2012); McCullough B.D., Open Access economics journals and the market for reproducible economic research, Economic Analysis and Policy, 39, 1, pp. 117-126, (2009); Piwowar H.A., Day R.S., Fridsma D.B., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 2, 3, (2007); Polhout M., Deposit instructions for social and behavioural sciences, (2012); Pullinger J., Wagner G., On the respective roles of national libraries, national archives and research data centers in the preservation of and access to research data, (2010); Reilly S., Schallier W., Schrimpf S., Smit E., Wilkinson M., Report on integration of data and publications., (2011); Data centres: Their use, value and impact. A Research Information Network report, (2011); Databases, data sets, and data accessibility-views and practices of scholarly publishers, (2006); Vlaeminck S., Data management in scholarly journals and possible roles for libraries-Some insights from EDaWaX, LIBER Quarterly, 23, 1, pp. 48-79, (2013)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84940241157"
"Lütjohann D.S.; Jung N.; Bräse S.","Lütjohann, Dominic S. (38761674900); Jung, Nicole (15064507300); Bräse, Stefan (7005396290)","38761674900; 15064507300; 7005396290","Open source life science automation: Design of experiments and data acquisition via ""dial-a-device""","2015","Chemometrics and Intelligent Laboratory Systems","144","","","100","107","7","14","10.1016/j.chemolab.2015.04.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928347643&doi=10.1016%2fj.chemolab.2015.04.002&partnerID=40&md5=a51c6e6373fbf3ef09253c471cfa4305","Karlsruhe Institute of Technology, Campus North, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany; Karlsruhe Institute of Technology, Campus South, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany","Lütjohann D.S., Karlsruhe Institute of Technology, Campus North, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Karlsruhe Institute of Technology, Campus South, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany; Jung N., Karlsruhe Institute of Technology, Campus North, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Karlsruhe Institute of Technology, Campus South, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany; Bräse S., Karlsruhe Institute of Technology, Campus North, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344, Germany, Karlsruhe Institute of Technology, Campus South, Fritz-Haber-Weg 6, Karlsruhe, 76131, Germany","In the field of natural sciences and especially life sciences, the quality of data acquisition and the availability of and the accessibility to research data are critical factors in the overall scientific method which have an impact on the quality of the published results. We present a novel software infrastructure for academic chemistry institutions that allows the management of modern laboratory environments. The fundamental parts of the scientific process, namely the design of experiments, the acquisition of data, and their management, have been considered and implemented in this new software platform "". dial-a-device"". This software platform is available in an open source and modular format that guarantees affordability and high flexibility. © 2015.","Chemistry research data; Electronic Laboratory Notebook (ELN); Laboratory Information Management System (LIMS); Life science informatics; Research data management","Article; chemical database; computer program; cost; dial a device; experimental design; information processing; information system; laboratory automation; laboratory device; laboratory information management system; mass communication; priority journal; remote sensing; web browser","","","","","","","Frey J.G., Dark lab or smart lab: the challenges for 21st century laboratory software, Org. Process Res. Dev., 8, pp. 1024-1035, (2004); Wild D.J., Grand challenges for cheminformatics, J. Cheminformatics, 1, pp. 1-2, (2009); Frey J.G., Bird C.L., Cheminformatics and the Semantic Web: adding value with linked data and enhanced provenance, Wiley Interdiscip. Rev. Comput. Mol. Sci., 3, pp. 465-481, (2013); Coles S.J., Frey J.G., Bird C.L., Whitby R.J., Day A.E., First steps towards semantic descriptions of electronic laboratory notebook records, J. Cheminformatics, 5, pp. 52-61, (2013); Bird C.L., Frey J.G., Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences, Chem. Soc. Rev., 42, pp. 6754-6776, (2013); Kihlen M., Waligorski M., Electronic lab notebooks-a crossroads is passed, Drug Discov. Today, 8, pp. 1007-1009, (2003); Quinnell R., Introducing an electronic laboratory notebook to PhD students undertaking chemistry research at a research intensive university; Rudolphi F., Goossen L.J., Electronic laboratory notebook: the academic point of view, J. Chem. Inf. Model., 52, pp. 293-301, (2012); Clarity: an open-source manager for laboratory automation, J. Lab. Autom., 18, pp. 171-177, (2013); Frey J.G., Milsted A., Michaelides D., Roure D.D., MyExperimentalScience, extending the 'workflow, Concurr. Comput., 25, pp. 481-496, (2013); Machina H.K., Wild D.J., Laboratory informatics tools integration strategies for drug discovery: integration of LIMS, ELN, CDS, and SDMS, J. Lab. Autom., 18, pp. 126-136, (2013); Open Source laboratory information management systems; OpenWetWare; Milsted A.J., Hale J.R., Frey J.G., Neylon C., LabTrove: a lightweight, Web based, laboratory ""blog"" as a route towards a marked up record of work in a bioscience research laboratory, PLoS ONE, 8, (2013); Coles S.J., Frey J.G., Hursthouse M.B., Light M.E., Milsted A.J., Carr L.A., DeRoure D., Gutteridge C.J., Mills H.R., Meacham K.E., Surridge M., Lyon E., Heery R., Duke M., Day M., An E-science environment for service crystallography-from submission to dissemination, J. Chem. Inf. Model., 46, pp. 1006-1016, (2006); Static, dynamic and pre-emptive scheduler; Agilent software & informatics; PerkinElmer informatics-E-notebook for chemistry; Van Eikeren P., Org. Process Res. Dev., 8, pp. 1015-1023, (2004); Vaquero L.M., Rodero-Merino L., Caceres J., Lindner M., SIGCOMM Comput. Commun. Rev., 39, pp. 50-55, (2008); Build software better, together; Ruby on rails; Thomson G., Netw. Secur., 2012, pp. 5-8, (2012); Lutjohann D.S., Open source code repositories on GitHub; Heroku | Cloud Application Platform; Smith R., Williamson R., Ventura D., Prince J.T., Rubabel: wrapping open Babel with Ruby, J. Cheminformatics, 5, pp. 35-44, (2013); Haider N., Functionality pattern matching as an efficient complementary structure/reaction search tool: an open-source approach, Molecules, 15, pp. 5079-5092, (2010); CanvasMol; GGA Software Services LLC. Ketcher; ChemDoodle Web Components | HTML5 Chemistry.; BeagleBone; Shown with Kern ABJ-220-4NM; Kern analytical balances; Shown with KNF SC-920; KNF vacuum pumps; Shown with Hei-VAP precision ML/G3B; Heidolph rotary evaporators","","","Elsevier","","","","","","01697439","","CILSE","","English","Chemometr. Intelligent Lab. Syst.","Article","Final","","Scopus","2-s2.0-84928347643"
"Herbst K.; Juvekar S.; Bhattacharjee T.; Bangha M.; Patharia N.; Tei T.; Gilbert B.; Sankoh O.","Herbst, Kobus (16645460600); Juvekar, Sanjay (7801632079); Bhattacharjee, Tathagata (55599815300); Bangha, Martin (26423490700); Patharia, Nidhi (57073831600); Tei, Titus (57073158900); Gilbert, Brendan (57072678500); Sankoh, Osman (6602427333)","16645460600; 7801632079; 55599815300; 26423490700; 57073831600; 57073158900; 57072678500; 6602427333","The INDEPTH Data Repository: An International Resource for Longitudinal Population and Health Data from Health and Demographic Surveillance Systems","2015","Journal of Empirical Research on Human Research Ethics","10","3","","324","333","9","21","10.1177/1556264615594600","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955303369&doi=10.1177%2f1556264615594600&partnerID=40&md5=9460b9fadaec4c53b40339add7b4eeed","INDEPTH Network, Accra, Ghana; Africa Centre for Health and Population Studies, UKZN, Durban, South Africa; KEM Hospital Research Centre, Pune, India; University of the Witwatersrand, Johannesburg, South Africa; Hanoi Medical University, Viet Nam","Herbst K., INDEPTH Network, Accra, Ghana, Africa Centre for Health and Population Studies, UKZN, Durban, South Africa; Juvekar S., INDEPTH Network, Accra, Ghana, KEM Hospital Research Centre, Pune, India; Bhattacharjee T., INDEPTH Network, Accra, Ghana, KEM Hospital Research Centre, Pune, India; Bangha M., INDEPTH Network, Accra, Ghana; Patharia N., INDEPTH Network, Accra, Ghana, KEM Hospital Research Centre, Pune, India; Tei T., INDEPTH Network, Accra, Ghana; Gilbert B., INDEPTH Network, Accra, Ghana, Africa Centre for Health and Population Studies, UKZN, Durban, South Africa; Sankoh O., INDEPTH Network, Accra, Ghana, University of the Witwatersrand, Johannesburg, South Africa, Hanoi Medical University, Viet Nam","The International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) is a global network of research centers that conduct longitudinal health and demographic evaluation of populations in low- and middle-income countries (LMICs) currently in 52 health and demographic surveillance system (HDSS) field sites situated in sub-Saharan Africa (14 countries), Asia (India, Bangladesh, Thailand, Vietnam, and Indonesia), and Oceania (Papua New Guinea). Through this network of HDSS field sites, INDEPTH is capable of producing reliable longitudinal data about the lives of people in the research communities as well as how development policies and programs affect those lives. The aim of the INDEPTH Data Repository is to enable INDEPTH member centers and associated researchers to contribute and share fully documented, high-quality datasets with the scientific community and health policy makers. © The Author(s) 2015.","data repository; data sharing; health and demographic surveillance; metadata; research data management","Academies and Institutes; Africa; Asia; Cooperative Behavior; Datasets as Topic; Developing Countries; Economic Development; Health Policy; Humans; Income; Information Dissemination; Information Services; Longitudinal Studies; Papua New Guinea; Public Health; Public Health Surveillance; Research; Research Personnel; Residence Characteristics; Africa; Asia; cooperation; demography; developing country; economic development; health care policy; health survey; human; income; information dissemination; information processing; information service; longitudinal study; organization; Papua New Guinea; personnel; public health; research","","","","","Alfred Manyeh; INDEPTH, (Tanzania, Thailand, Vietnam); Sida/Research Cooperation and the Hewlett Foundation; Wellcome Trust, WT, (097318/C/11/Z)","The authors acknowledge the following data managers and scientists from the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) Network who participated in the data management workshops and made their data available for inclusion in the INDEPTH Data Repository: Agincourt HDSS (South Africa): Sulaimon Afolabi and Paul Mee Chililab HDSS (Vietnam): Pham Viet Cuong and Tran Huu Bich Dabat HDSS (Ethiopia): Tesfahun Melese and Yigzaw Kebede Dikgale HDSS (South Africa): Timotheus Darikwa and Ian Cook Filabavi HDSS (Vietnam): Tran Thanh Do and Nguyen Thi Kim Chuc Gilge Gibe HDSS (Ethiopia): Muluemebet Abera and Fasil Tessema Ifakara and Rifiji HDSSs (Tanzania): Advocatus Kakorozya and Eveline Geubbels Kanchanaburi HDSS (Thailand): Jongiit Rittirong and Sureeporn Punpuin Karonga HDSS (Malawi): Keith Branson and Menard Chihana Kaya HDSS (Burkina Faso): Maurice Yameogo and Simon Tiendrebeogo Kilifi HDSS (Kenya): David Amadi and Marianne Munene Kilite Awlaelo HDSS (Ethiopia): Fisaha Haile and Gebrehiwot Weldu Kisumu HDSS (Kenya): David Obor and Christine Khaggayi Magu HDSS (Tanzania): Baltazar Mtenga Mbita HDSS (Kenya): Morris Mwangangi and Mohamed Karama Nairobi HDSS (Kenya): Patricia Elungata and Boniface Nganyi Nanoro HDSS (Burkina Faso): Adama Kazienga and Karim Derra Ouagadougou HDSS (Burkina Faso): Bruno Lankoande and Abdramane Soura Taabo HDSS (Cote d''Ivoire): Laubet Martial and Kone Siaka Wosera HDSS (Papua New Guinea): Lorna Samol, Stanley Aisi, and Suparat Phuanukoonnon Kersa HDSS (Ethiopia): Mahlet Mekonnen Gebeyehu and Nega Kassa Kombewa HDSS (Kenya): Mary Oyugi and Peter Sifuna Rakai HDSS (Uganda): Joseph Sekasanvu and Tom Lutalo Iganga/Mayuge HDSS (Uganda): Noah Kasunumba Niakhar HDSS (Senegal): Djibril Dione and Valerie Delaunay Mlomp HDSS (Senegal): Ousmane Ndiaye and Valerie Delaunay Dodowa HDSS (Ghana): Alfred Manyeh and Sheila Addei The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Wellcome Trust (Grant 097318/C/11/Z), with contributions from Sida/Research Cooperation and the Hewlett Foundation especially for the participation of Osman Sankoh, Martin Bangha, and Titus Tei.","Altman M., King G., A proposed standard for the scholarly citation of quantitative data, D-lib Magazine, 13, 34, (2007); DataCite, (2014); Gerritsen A., Bocquier P., White M., Mbacke C., Alam N., Beguy D., Collinson M.A., Health and demographic surveillance systems: Contributing to an understanding of the dynamics in migration and health, Global Health Action, 6, (2013); Nesstar Publisher, (2013); Paskin N., Digital object identifier (DOI) system, Encyclopedia of Library and Information Sciences, 3, pp. 1586-1592, (2008); Pentaho Community Edition, (2014); Rivest R., The MD5 Message-digest Algorithm, (1992); Sankoh O., Byass P., The INDEPTH Network: Filling vital gaps in global epidemiology, International Journal of Epidemiology, 41, pp. 579-588, (2012); Streatfield P.K., Khan W.A., Bhuiya A., Alam N., Sie A., Soura A.B., Byass P., Cause-specific mortality in Africa and Asia: Evidence from INDEPTH health and demographic surveillance system sites, Global Health Action, 7, (2014); Vardigan M., Heus P., Thomas W., Data documentation initiative: Toward a standard for the social sciences, International Journal of Digital Curation, 3, 1, pp. 107-113, (2008); National Data Archive (NADA), (2013); Zentyal S.L., Zentyal Server, (2013)","K. Herbst; Mtubatuba, P.O. Box 198, 3935, South Africa; email: kherbst@africacentre.ac.za","","SAGE Publications Inc.","","","","","","15562646","","","26297754","English","J. Empir. Res. Hum. Res. Ethics","Review","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84955303369"
"Demchenko Y.; Gruengard E.; Klous S.","Demchenko, Yuri (8904483500); Gruengard, Emanuel (56736907800); Klous, Sander (6603503897)","8904483500; 56736907800; 6603503897","Instructional model for building effective big data curricula for online and campus education","2015","Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom","2015-February","February","7037787","935","941","6","16","10.1109/CloudCom.2014.162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937864989&doi=10.1109%2fCloudCom.2014.162&partnerID=40&md5=5a7a5122218f9b1de217d611129236d3","University of Amsterdam, System and Network Engineering Group, Amsterdam, Netherlands; Laureate Online Education, United Kingdom; KPMG, Amsterdam, Netherlands","Demchenko Y., University of Amsterdam, System and Network Engineering Group, Amsterdam, Netherlands; Gruengard E., Laureate Online Education, United Kingdom; Klous S., University of Amsterdam, System and Network Engineering Group, Amsterdam, Netherlands, KPMG, Amsterdam, Netherlands","This paper presents current results and ongoing work to develop effective educational courses on the Big Data (BD) and Data Intensive Science and Technologies (DIST) that is been done at the University of Amsterdam in cooperation with KPMG and by the Laureate Online Education (online partner of the University of Liverpool). The paper introduces the main Big Data concepts: multicomponent Big Data definition and Big Data Architecture Framework that provide the basis for defining the course structure and Common Body of Knowledge for Data Science and Big Data technology domains. The paper presents details on approach, learning model, and course content for two courses at the Laureate Online Education/University of Liverpool and at the University of Amsterdam. The paper also provides background information about existing initiatives and activities related to information exchange and coordination on developing educational materials and programs on Big Data, Data Science, and Research Data Management. © 2014 IEEE.","Andragogy; Big data architecture framework; Bloom's taxonomy; Common body of knowledge; Education and training on big data technologies; Instructional methodology; Online education","Big data; Cloud computing; Curricula; Data Science; Information management; Andragogy; Bloom's taxonomy; Body of knowledge; Data architectures; Data technologies; Instructional methodology; On-line education; E-learning","","","","","","","(2010); (2009); Demchenko Y., Bernstein D., Belloum A., Oprescu A., Tomasz W., Wlodarczyk, cees de laat, new instructional models for building effective curricula on cloud computing technologies and engineering, Proc. The 5th IEEE International Conference and Workshops on Cloud Computing Technology and Science (CloudCom2013), (2013); Harris, Murphy, Vaisman, Analysing the Analysers, (2013); Demchenko Y., Zhao Z., Grosso P., Wibisono A., De Laat C., Addressing big data challenges for scientific data infrastructure, The 4th IEEE Conf. on Cloud Computing Technologies and Science (CloudCom2012), (2012); Demchenko Y., Membrey P., Grosso P., De Laat C., Addressing big data issues in scientific data infrastructure, First International Symposium on Big Data and Data Analytics in Collaboration (BDDAC 2013). Part of the 2013 International Conference on Collaboration Technologies and Systems (CTS 2013), (2013); Demchenko Y., Membrey P., De Laat C., Defining architecture components of the big data ecosystem, Second International Symposium on Big Data and Data Analytics in Collaboration (BDDAC 2014). Part of the 2014 International Conference on Collaboration Technologies and Systems (CTS 2014), (2014); 1-7; Anderson L.W., Krathwohl D.R., Airasian P.W., Cruikshank K.A., Mayer R.E., Pintrich P.R., Raths J., Wittrock M.C., A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives (Complete Edition), (2001); Knowles M.S., The Modern Practice of Adult Education: From Pedagogy to Andragogy, (1988); Merriam S.B., Andragogy and Self-Directed Learning: Pillars of Adult Learning Theory; Computer Science: Web Services and Cloud-Based Systems","","","IEEE Computer Society","et al.; IEEE; IEEE Cloud Computing Initiative (CCI); IEEE Computer Society; IEEE Computer Society Cloud Computing Special Technical Committee (CS CC STC); IEEE Computer Society Technical Committee on Scalable Computing (TCSC)","2014 6th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2014","15 December 2014 through 18 December 2014","Singapore","112984","23302194","978-147994093-6","","","English","Proc. Int. Conf. Cloud Comput. Technol. Sci., CloudCom","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84937864989"
"Pouchard L.; Barton A.; Zilinski L.","Pouchard, Line (8709358200); Barton, Amy (7102156562); Zilinski, Lisa (55755405700)","8709358200; 7102156562; 55755405700","Data narratives: Increasing scholarly value","2014","Proceedings of the ASIST Annual Meeting","51","1","","","","","3","10.1002/meet.2014.14505101088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961960735&doi=10.1002%2fmeet.2014.14505101088&partnerID=40&md5=8f618af6cdd5815b1d1ef86d0318607f","Purdue University, West Lafayette, IN, United States","Pouchard L., Purdue University, West Lafayette, IN, United States; Barton A., Purdue University, West Lafayette, IN, United States; Zilinski L., Purdue University, West Lafayette, IN, United States","Data narratives or data stories have emerged as a new form of the scholarly communication focused on data. In this paper, we explore the potential value of data narratives and the requirements for data stories to enhance scholarly communication. We examine three types of data stories that form a continuum from the less to the more structured: the DataONE data stories, the Data Curation Profiles, and the Data Descriptors from the journal Scientific Data. We take the position that these data stories will increase the value of scholarly communication if they are linked to the datasets and to the publications that describe results, and have instructional value.","Data narratives; Data stories; Datasets; Research data management; Scholarly communication","","","","","","","","Bleakley A., Stories as data, data as stories: Making sense of narrative inquiry in clinical education, Medical Education, 4, 3, pp. 543-640, (2005); Data Curation Profiles Toolkit, (2014); El Howayek A., Bobet A., Dawood S., Ferdon A., Santagata M., Siddiki N.Z., Project implementation: Classification of organic soils and classification of marls - Training of INDOT personnel?, Publication FHWA/IN/JTRP-2012/22. Joint Transportation Research Program, (2012); Hohmann T., #iatul2014 Tenopir Order to Convince Re. The Library's Value You Need Data and Stories [Tweet], (2014); Kenyon J., Sprague N.R., Trends in the use of supplementary materials in environmental science, Issues in Science and Technology Librarianship, Winter, 75, (2014); Menz S., Tallying Every Bug and Byte, (2014); Pfannkuch M., Regan M., Wild C., Horton N., Telling data stories: Essential dialogues for comparative reasoning, Journal of Statistics Education, 18, 1, pp. 1-38, (2010); Rebich Hespanha S., Metadata? i Thought You Were in Charge of That, (2013); Rebich Hespanha S., Menz S., Bragg J., Their own words: Researchers? Stories of challenges and triumphs in data management and sharing, Fall Meeting of the American Geophysical Union, (2013); Scherer D., We Need More Narrative Driven Messages That Can Also Compliment Data, (2014); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, pp. 93-103, (2009); Zilinski L., Scherer D., Bullock D., Horton D., Matthews C., Evolution of data creation, management, publication, and curation in the research process, Transportation Research Record: Journal of the Transportation Research Board","","","John Wiley and Sons Inc.","","","","","","15508390","","","","English","Proc. ASIST Ann. Meet.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84961960735"
"Linde P.; Norling E.; Pettersson A.; Petersson L.; Pettersson K.; Stockmann A.; Swartz S.","Linde, Peter (56381647900); Norling, Eva (57191921361); Pettersson, Anette (56156305800); Petersson, Lena (57191919452); Pettersson, Kent (57191921460); Stockmann, Anna (57191917886); Swartz, Sofia (57191925675)","56381647900; 57191921361; 56156305800; 57191919452; 57191921460; 57191917886; 57191925675","Researchers and open data - Attitudes and culture at blekinge institute of technology","2015","New Avenues for Electronic Publishing in the Age of Infinite Collections and Citizen Science: Scale, Openness and Trust - Proceedings of the 19th International Conference on Electronic Publishing, Elpub 2015","","","","173","177","4","0","10.3233/978-1-61499-562-3-173","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994845443&doi=10.3233%2f978-1-61499-562-3-173&partnerID=40&md5=bb6580f7c67c99fdb0648f1919fdfc6a","Blekinge Institute of Technology, Library, Sweden","Linde P., Blekinge Institute of Technology, Library, Sweden; Norling E., Blekinge Institute of Technology, Library, Sweden; Pettersson A., Blekinge Institute of Technology, Library, Sweden; Petersson L., Blekinge Institute of Technology, Library, Sweden; Pettersson K., Blekinge Institute of Technology, Library, Sweden; Stockmann A., Blekinge Institute of Technology, Library, Sweden; Swartz S., Blekinge Institute of Technology, Library, Sweden","During March 2015, the Blekinge Institute of Technology library carried out an interview survey comprising around 36 senior researchers and postdocs mainly in engineering sciences, with the objective to get a picture of how research data is managed at BTH and to find out what the researcher attitudes are to sharing data. The survey showed that most researchers in the study were positive to sharing research data but lacked any experience of making data management plans and had little or no knowledge of data preservation or of sharing open data. Uncertainties about data ownership are also an issue. © 2015 The authors and IOS Press.","Attitude; Data Management Plan; DMP; Open Access; Open Research Data; RDM; Research Data Management; Survey","Information management; Surveying; Surveys; Attitude; Data preservations; Engineering science; Interview survey; Management plans; Open Access; Research data; Research data managements; Electronic publishing","","","","","","","Vetenskapsrådet, Förslag Till Nationella Riktlinjer för Öppen Tillgång Till Vetenskaplig Information [Proposal for National Guidelines for Open Access to Scientific Information], (2015); Commission Recommendation of 17.7.2012 on Access to and Preservation of Scientific Information, (2012); Swedish Government, Freedom of the Press Act (SFS 1949:105); Bohlin A., Offentlighet och sekretess i myndighets forskningsverksamhet [Public disclosure and secrecy in government research], Rapport Riksarkivet, 2, (1997)","P. Linde; Blekinge Institute of Technology, Library, Sweden; email: peter.linde@bth.se","Schmidt B.; Dobreva M.","IOS Press BV","Copernicus Publications; Emerald; ProQuest; Springer","19th International Conference on Electronic Publishing, Elpub 2015","1 September 2015 through 3 September 2015","Malta","124225","","978-161499561-6","","","English","New Ave. Electron. Publ. Age Infin. Collect. Citiz. Sci.: Scale, Openness Trust - Proc. Int. Conf. Electron. Publ., Elpub","Conference paper","Final","","Scopus","2-s2.0-84994845443"
"Davidson J.; Jones S.; Molloy L.; Kejser U.B.","Davidson, Joy (7403932714); Jones, Sarah (57203292365); Molloy, Laura (55979673100); Kejser, Ulla Bøgvad (6507349181)","7403932714; 57203292365; 55979673100; 6507349181","Emerging good practice in managing research data and research information within UK Universities","2014","Procedia Computer Science","33","","","215","222","7","16","10.1016/j.procs.2014.06.035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904505045&doi=10.1016%2fj.procs.2014.06.035&partnerID=40&md5=143a9f9261652ea3bbdc6c53e45be3b3","Humanities Advanced Tehcnology and Information Institute (HATII), Glasgow G12 8QJ, United Kingdom; Royal Library of Denmark, Copenhagen DK-1016, Denmark","Davidson J., Humanities Advanced Tehcnology and Information Institute (HATII), Glasgow G12 8QJ, United Kingdom; Jones S., Humanities Advanced Tehcnology and Information Institute (HATII), Glasgow G12 8QJ, United Kingdom; Molloy L., Humanities Advanced Tehcnology and Information Institute (HATII), Glasgow G12 8QJ, United Kingdom; Kejser U.B., Royal Library of Denmark, Copenhagen DK-1016, Denmark","Sound data intensive science depends upon effective research data and information management. Efficient and interoperable research information systems will be crucial for enabling and exploiting data intensive research however it is equally important that a research ecosystem is cultivated within research-intensive institutions that foster sustainable communication, cooperation and support of a diverse range of research-related staff. Researchers, librarians, administrators, ethics advisors, and IT professionals all have a vital contribution to make in ensuring that research data and related information is available, visible, understandable and usable over the mid to long term. This paper will provide a summary of several ongoing initiatives that the Jisc-funded Digital Curation Centre (DCC) are currently involved with in the UK and internationally to help staff within higher education institutions prepare to meet funding body mandates relating to research data management and sharing and to engage fully in the digital agenda. © 2014 Published by Elsevier B.V.","Collaboration on clarifying the costs of curation; Digital Curation Centre; Faciliate open sceince training for European research; Jisc; Research data management","Human resource management; Information management; Information systems; Information use; Sounding apparatus; Curation; Digital Curation Centre; European research; Jisc; Research data managements; Interoperability","","","","","","","OECD Principles and Guidelines for Access to Research Data from Public Funding; Panton Principles; EPSRC Policy Framework on Research Data Expectations; DCC Overview of Funders' Data Policies Table; Data Asset Framework; Data Curation Profiles Toolkit; UK Data Service; NERC Data Centres; Jisc Research Data Registry and Discovery Service; Australian National Data Service; Research Data Australia; ORCA Registry Software Described in the ANDS Context at; Collaboration Clarify the Costs of Curation (4C)","","","Elsevier B.V.","CINECA; Elsevier; Epistemio; Thomson Reuters","12th International Conference on Current Research Information Systems, CRIS 2014","13 May 2014 through 15 May 2014","Rome","106373","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84904505045"
"Shaon A.; Smallwood E.; Frances M.; Cox S.; Betbeder-Matibet L.","Shaon, Arif (36091877800); Smallwood, Eamon (56455288500); Frances, Maude (56455046200); Cox, Shane (56454771600); Betbeder-Matibet, Luc (6507801274)","36091877800; 56455288500; 56455046200; 56454771600; 6507801274","Sustainable services for managing and disseminating UNSW Australia research data","2014","Proceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014","1","","6972258","137","144","7","1","10.1109/eScience.2014.24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919486134&doi=10.1109%2feScience.2014.24&partnerID=40&md5=6bfa9ef0de5522d740ef171f9fb811e3","UNSW Australia Library, Sydney, Australia; UNSW Australia IT, Sydney, Australia","Shaon A., UNSW Australia Library, Sydney, Australia; Smallwood E., UNSW Australia Library, Sydney, Australia; Frances M., UNSW Australia Library, Sydney, Australia; Cox S., UNSW Australia IT, Sydney, Australia; Betbeder-Matibet L., UNSW Australia IT, Sydney, Australia","The incorporation or lack thereof, of sound research data management practices can spell the difference between a successful and unsuccessful research project. This is typically reflected by the quality of the final project outputs. Establishment and sustainability of good institutional research data management practices require an institutional framework that includes policies, infrastructure and support services. UNSW Australia is developing a research data management infrastructure to support institutional research, including provision of tools for effectively managing and publishing research data. This endeavour has been an institution-wide collaboration involving a number of key stakeholders including UNSW Library, UNSW IT and the Division of Research. In this paper, we present UNSW Library's key contributions to development of this infrastructure that includes a curation-focussed information repository based on Fedora Commons software. In particular, we highlight the important relationships between the key stakeholders that the infrastructure embodies, and discuss the foundation for developing a sustainable information ecosystem for managing and disseminating UNSW research data. © 2014 IEEE.","digital repository infrastructure; institutional collaboration; research data management; sustainability","Information management; Sustainable development; Digital repository; Information ecosystems; Information repositories; Institutional collaboration; Institutional framework; Research data managements; Support services; Sustainable services; e-Science","","","","","","","Shaon A., Callaghan S., Lawrence B., Matthews B., Woolf A., Osborn T., Harpham C., A linked data approach to publishing complex scientific workflows, Proc. 7th IEEE International Conference on E-Science (eScience2011), (2011); Australian Code for the Responsible Conduct of Research, (2007); Discovery Projects: Instructions to Applicants for Funding Commencing in 2015, (2014); Amos H., Frances M., Ruthven T., Rsquared: Researching the researchers. A study into how researchers at the University of New South Wales use and share research data, Paper Presented at 31st Annual IATUL Conference, (2010); Wilson J., Jeffreys P., Towards a unified university infrastructure: The data management roll-out at the University of Oxford, The International Journal of Digital Curation, 2, 8, pp. 235-246, (2013); Reznik-Zellen R., Adamick J., McGinty S., Tiers of research data support services, Journal of EScience Librarianship, 1, 3, pp. 27-35, (2013); UNSWorks Digital Preservation Policy, (2013); Simmons N., Implementing dois for research data, D-Lib Magazine, 18, 5-6, (2012)","","","Institute of Electrical and Electronics Engineers Inc.","Brazilian Computer Society (SBC); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq); et al.; FAPESP; Google; Microsoft Research","10th IEEE International Conference on eScience, eScience 2014","20 October 2014 through 24 October 2014","Guaruja","109575","","978-147994288-6","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","","Scopus","2-s2.0-84919486134"
"Henderson M.E.; Knott T.L.","Henderson, Margaret E. (36194795100); Knott, Teresa L. (57196958504)","36194795100; 57196958504","Starting a Research Data Management Program Based in a University Library","2015","Medical Reference Services Quarterly","34","1","","47","59","12","24","10.1080/02763869.2015.986783","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921749116&doi=10.1080%2f02763869.2015.986783&partnerID=40&md5=d2ffddae7b4d6c49d1971b426dbab52e","VCU Libraries, Virginia Commonwealth University, Richmond, VA, United States","Henderson M.E., VCU Libraries, Virginia Commonwealth University, Richmond, VA, United States; Knott T.L., VCU Libraries, Virginia Commonwealth University, Richmond, VA, United States","As the need for research data management grows, many libraries are considering adding data services to help with the research mission of their institution. The Virginia Commonwealth University (VCU) Libraries created a position and hired a director of research data management in September 2013. The position was new to the libraries and the university. With the backing of the library administration, a plan for building relationships with VCU faculty, researchers, students, service and resource providers, including grant administrators, was developed to educate and engage the community in data management plan writing and research data management training. © , Published with license by Taylor & Francis.","Data management plans; data services; research data management","Information Management; Library Services; Organizational Case Studies; Program Development; Universities; health services research; information system; library; organization and management; procedures; program development; university","","","","","National Institutes of Health, NIH; National Institute on Aging, NIA, (R01AG041823)",". The VCU Center for Clinical and Translational Research (CCTR), funded by a Clinical and Translational Science Award, can support most clinical and biomedical research with special regulations or federal mandates. The DRDM refers researchers to the CCTR when needed and assists with NIH Data Sharing Plans.","Gold A., Cyberinfrastructure, Data, and Libraries, Part 1, D-Lib Magazine, 13, 9, (2007); (2006); Borgman C.L., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Fearon D.B., Gunia, Lake S., Pralle B.E., Sallans A.L., SPEC Kit 334: Research Data Management Services; Shen Y., Varvel V.E., Developing Data Management Services at the Johns Hopkins University, Journal of Academic Librarianship, 39, 6, pp. 552-557, (2013); Gabridge T., The Last Mile: Liaison Roles in Curating Science and Engineering Research Data, Research Library Issues, 265, pp. 15-21, (2009); Gold A., Cyberinfrastructure, Data, and Libraries, Part 2. Libraries and the Data Challenge: Roles and Activities for Libraries, D-Lib Magazine, 13, 9-10, (2007); Stebbins M., Expanding Public Access to the Results of Federally Funded Research, Office of Science and Technology Policy, (2013); Sinai N., Zarek C., OSTP's Own Open Government Plan, Open Government Initiative, (2014); Hoppe H.C., Second-Mover Advantages in the Strategic Adoption of New Technology Under Uncertainty, International Journal of Industrial Organization, 18, 2, pp. 315-338, (2000); Westra B., Data Services for the Sciences: A Needs Assessment, Ariadne: A Web & Print Magazine of Internet Issues for Librarians & Information Specialists, 30, 64, (2010); Johnson L.M., Butler J.T., Johnston L.R., Developing E-Science and Research Services and Support at the University of Minnesota Health Sciences Libraries, Journal of Library Administration, 52, 8, pp. 754-769, (2012); Parsons T., Creating a Research Data Management Service, International Journal of Digital Curation, 8, 2, pp. 146-156, (2013); Akers K.G., Doty J., Disciplinary Differences in Faculty Research Data Management Practices and Perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Marshall B., O'Bryan K., Qin N., Vernon R., Organizing, Contextualizing, and Storing Legacy Research Data: A Case Study of Data Management for Librarians, Issues in Science & Technology Librarianship, (2013); Case D.O., Looking for Information: A Survey of Research on Information Seeking, Needs, and Behavior, (2007); Raboin R., Reznik-Zellen R.C., Salo D., Forging New Service Paths: Institutional Approaches to Providing Research Data Management Services, Journal of eScience Librarianship, 1, 3, (2012); Christensen-Dalsgaard B., Ten Recommendations for Libraries to Get Started with Research Data Management: Final Report of the LIBER Working Group on E-Science / Research Data Management, (2012); Bresnahan M.M., Johnson A.M., Assessing Scholarly Communication and Research Data Training Needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Carlson J.R., The Use of Life Cycle Models in Developing and Supporting Data Services, Research Data Management: Practical Strategies for Information Professionals, pp. 63-86, (2014); The iSchool at Illinois. “Specialization in Data Curation.” University of Illinois at Urbana-Champaign, Accessed October, (2014); Goben A., Salo D., Stewart C., Federal Research, College & Research Libraries News, 74, 8, pp. 421-425, (2013); (2012); McLure M., Level A.V., Cranston C.L., Oehlerts B., Culbertson M., Data Curation: A Study of Researcher Practices and Needs, Portal: Libraries and the Academy, 14, 2, pp. 139-164, (2014)","","","Routledge","","","","","","02763869","","MRSQD","25611440","English","Med. Ref. Serv. Q.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84921749116"
"Luesebrink M.; Huang H.; Bogdan K.; Salo D.; Thomas C.; West J.; Wilson L.","Luesebrink, Michael (57216465795); Huang, Hong (55738237000); Bogdan, Kristin (56217925600); Salo, Dorothea (24470615000); Thomas, Charles (56606579400); West, Jevin (36922389600); Wilson, Lizabeth (15049957000)","57216465795; 55738237000; 56217925600; 24470615000; 56606579400; 36922389600; 15049957000","Curation and policy issues in collaborative research data management communities: Perspectives from key stakeholders","2014","Proceedings of the ASIST Annual Meeting","51","1","","","","","3","10.1002/meet.2014.14505101024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961911831&doi=10.1002%2fmeet.2014.14505101024&partnerID=40&md5=48a2eee2341cd4961989a5d7bca8c435","Monographs Acquisitions Librarian University Libraries, Florida State University, 711 W. Madison St, Tallahassee, 32306, FL, United States; School of Information, University of South Florida, Tampa, 33620, FL, United States; Center for Science and Social Science Information, Yale University, 219 Prospect St., New Haven, 06520-8111, CT, United States; Faculty Assoc., School of Library and Information Studies, University of Wisconsin, Madison, 4261 Helen C. White Hall, 600 N. Park St, Madison, 53706, WI, United States; University System of Maryland, Affiliated Institutions Library Consortium, 3300 Metzerott Road, Adelphi, 20783-1690, MD, United States; Information School, University of Washington, Box 352840, Seattle, 98195, WA, United States; Vice Provost of Digital Initiatives and Dean of the Libraries, Box 352900, Seattle, 98195-2900, WA, United States","Luesebrink M., Monographs Acquisitions Librarian University Libraries, Florida State University, 711 W. Madison St, Tallahassee, 32306, FL, United States; Huang H., School of Information, University of South Florida, Tampa, 33620, FL, United States; Bogdan K., Center for Science and Social Science Information, Yale University, 219 Prospect St., New Haven, 06520-8111, CT, United States; Salo D., Faculty Assoc., School of Library and Information Studies, University of Wisconsin, Madison, 4261 Helen C. White Hall, 600 N. Park St, Madison, 53706, WI, United States; Thomas C., University System of Maryland, Affiliated Institutions Library Consortium, 3300 Metzerott Road, Adelphi, 20783-1690, MD, United States; West J., Information School, University of Washington, Box 352840, Seattle, 98195, WA, United States; Wilson L., Vice Provost of Digital Initiatives and Dean of the Libraries, Box 352900, Seattle, 98195-2900, WA, United States","The explosion of scientific data in recent years has resulted in the need to compile, store and globally share this research information with the multi-disciplinary communities of science. This has resulted in research data management communities that are responsible for curating data within institutional repositories. This panel is sponsored by the SIG Scientific and Technical Information to initiate a discussion addressing data curation issues as they pertain to collaborative research data management communities within the current institutional research environment from the perspective of key stakeholders. The panel objective encompasses an examination of contemporary curation practices and data management policy issues that impact the initiation and implementation of research data management plans for scientific researchers. In addition it will explore the role that institutional repositories play in the extraction, preservation, and storage of research data while providing access to data sharing in the global scientific research communities. The panel will be comprised of key institutional stakeholders, which include university administrators, information professionals, and research scientists.","Data curation; Data policy; Key stakeholders; Repositories; Research data management community","","","","","","","","","","","John Wiley and Sons Inc.","","","","","","15508390","","","","English","Proc. ASIST Ann. Meet.","Conference paper","Final","","Scopus","2-s2.0-84961911831"
"","","","9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015","2015","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","9426","","","1","457","456","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952326426&partnerID=40&md5=9e0ee67335361457bf5731144ea7bdec","","","The proceedings contain 42 papers. The special focus in this conference is on Knowledge Representation, Reasoning, Management, Multi-agent Systems, Data Mining and Machine Learning. The topics include: Representing time for the semantic web; construction of p-minimal models using paraconsistent relational model; a research data management system; adaptive model of multi-objective agent behavior in real-time systems; nested Monte-Carlo search of multi-agent coalitions mechanism with constraints; robust feature extraction based on Teager-entropy and half power spectrum estimation for speech recognition; on-line monitoring and fault diagnosis of PV array based on BP neural network optimized by genetic algorithm; elitist quantum-inspired differential evolution based wrapper for feature subset selection; towards efficiently mining frequent interval-based sequential patterns in time series databases; optimizing ontology learning systems that use heterogeneous sources of evidence; imbalanced extreme learning machine based on probability density estimation; integrating simplified inverse representation and CRC for face recognition; segmentation of motion capture data based on measured MDS and improved oblique space distance; single-sample face recognition based on WSSRC and expanding sample; scanned document images skew correction based on shearlet transform; classification of German scripts by adjacent local binary pattern analysis of the coded text; motion detection system based on improved LBP operator; online detection of moving object in video; the design and experiment of the leg model based on galvanic coupling intra-body communication and a method of motif mining based on backtracking and dynamic programming.","","","","","","","","","","","Zheng X.; Bikakis A.","Springer Verlag","","9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015","13 November 2015 through 15 November 2015","Fuzhou","158349","03029743","978-331926180-5","","","English","Lect. Notes Comput. Sci.","Conference review","Final","","Scopus","2-s2.0-84952326426"
"Hoy M.B.","Hoy, Matthew B. (35799981300)","35799981300","Big Data: An Introduction for Librarians","2014","Medical Reference Services Quarterly","33","3","","320","326","6","19","10.1080/02763869.2014.925709","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904315026&doi=10.1080%2f02763869.2014.925709&partnerID=40&md5=6ffeb8fd03e7a3009fe3696b023230a3","Mayo Clinic Health System-Eau Claire, Eau Claire, WI, United States","Hoy M.B., Mayo Clinic Health System-Eau Claire, Eau Claire, WI, United States","Modern life produces data at an astounding rate and shows no signs of slowing. This has lead to new advances in data storage and analysis and the concept of ""big data,"" that is, massive data sets that can yield surprising insights when analyzed. This column will briefly describe what big data is and why it is important. It will also briefly explore the possibilities and problems of big data and the implications it has for librarians. A list of big data projects and resources is also included. © 2014 Copyright Matthew B. Hoy.","Big data; Internet; research data management","Access to Information; Humans; Information Dissemination; Information Storage and Retrieval; Internet; Librarians; access to information; human; information dissemination; information retrieval; Internet; librarian; trends","","","","","","","Anderson C., The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, Wired, (2008); The Big Data Conundrum: How to Define It?, MIT Technology Review, (2013); Dumbill E., Making Sense of Big Data, Big Data, 1, 1, pp. 1-2, (2013); Laney D., 3D Data Management: Controlling Data Volume, Velocity and Variety, META Group Research Note, 6; McAfee A., Brynjolfsson E., Big Data: The Management Revolution, Harvard Business Review, 90, 10, pp. 60-66, (2012); Mayer-Schonberger V., Cukier K., Big Data: A Revolution That Will Transform How We Live, Work, and Think, (2013); Winslow R., Big Data' for Cancer Care, Wall Street Journal, (2013); Swan M., The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery, Big Data, 1, 2, pp. 85-99, (2013); Duhigg C., How Companies Learn Your Secrets, The New York Times, (2012); Hill K., You Can Hide Your Pregnancy Online, But You'll Feel Like a Criminal, Forbes, (2014); Ohm P., Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization; Boyd D., Crawford K., Critical Questions for Big Data, Information, Communication & Society, 15, 5, pp. 662-679, (2012); Lazer D., Kennedy R., King G., Vespignani A., Big Data. The Parable of Google Flu: Traps in Big Data Analysis, Science, 343, 6176, pp. 1203-1205, (2014); Clark J., IT Now 10 Percent of World's Electricity Consumption, Report Finds, The Register, (2013); Blum A., Tubes: A Journey to the Center of the Internet, (2012); Huwe T.K., Big Data and the Library: A Natural Fit, Computers in Libraries, 34, 2, pp. 17-18, (2014); Schwartz M., What Governmental Big Data May Mean For Libraries, Library Journal, (2013); Creamer A.T., Martin E.R., Kafel D., Research Data Management and the Health Sciences Librarian, Health Sciences Librarianship, pp. 252-274, (2014); Bell S., Promise and Problems of Big Data {pipe} From the Bell Tower, Library Journal, (2013)","M. B. Hoy; Mayo Clinic Health System-Eau Claire, Eau Claire, WI 54701, 1221 Whipple Street, United States; email: hoy.matt@mayo.edu","","Routledge","","","","","","02763869","","MRSQD","25023020","English","Med. Ref. Serv. Q.","Article","Final","","Scopus","2-s2.0-84904315026"
"Kruse F.; Thestrup J.B.","Kruse, Filip (9234035200); Thestrup, Jesper Boserup (22982034000)","9234035200; 22982034000","Research libraries' new role in research data management, current trends and visions in Denmark","2014","LIBER Quarterly","23","4","","310","335","25","14","10.18352/lq.9173","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928950689&doi=10.18352%2flq.9173&partnerID=40&md5=ddb8f6931aa7f7a39a9305fd53964e96","The State and University Library, University Library Division, Aarhus, Denmark","Kruse F., The State and University Library, University Library Division, Aarhus, Denmark; Thestrup J.B., The State and University Library, University Library Division, Aarhus, Denmark","The amount of research data is growing constantly, due to new technology with new potentials for collecting and analysing both digital data and research objects. This growth creates a demand for a coherent IT-infrastructure. Such an infrastructure must be able to provide facilities for storage, preservation and a more open access to data in order to fulfil the demands from the researchers themselves, the research councils and research foundations. This paper presents the findings of a research project carried out under the auspices of DEFF (Danmarks Elektroniske Fag- og Forskningsbibliotek - Denmark's Electronic Research Library)1 to analyse how the Danish universities store, preserve and provide access to research data. It shows that they do not have a common IT-infrastructure for research data management. This paper describes the various paths chosen by individual universities and research institutions, and the background for their strategies of research data management. Among the main reasons for the uneven practices are the lack of a national policy in this field, the different scientific traditions and cultures and the differences in the use and organization of IT-services. This development contains several perspectives that are of particular relevance to research libraries. As they already curate digital collections and are active in establishing web archives, the research libraries become involved in research and dissemination of knowledge in new ways. This paper gives examples of how The State and University Library's services facilitate research data management with special regard to digitization of research objects, storage, preservation and sharing of research data. This paper concludes that the experience and skills of research libraries make the libraries important partners in a research data management infrastructure.","National cultural heritage; Research data management; University library","","","","","","","","Andersen B., Being a national library in a research infrastructure landscape, Microform & Digitization Review, 41, 3-4, pp. 175-179, (2012); Andersen B., Statistik_bja_2012, (2012); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, (2012); Christensen-Dalsgaard B., FIF-fælles infrastruktur for forskningsdata., (2013); Del C., Strategic goal: Coordinate solutions concerning data management and large data amounts., (2012); Del C., Diskussionsoplæg-National strategi for datamanagement., (2013); Donelly M., Data management plans and planning, Managing research data, pp. 83-104, (2012); Doorn P., Tjalsma H., Introduction: Archiving research data, Archival Science, 7, 1, pp. 1-20, (2007); Elstrom G.V., Jensen T.S., Planning for mass digitisation of newspapers: A castle, a shed or something in between?, Microform & Digitization Review, 41, 3-4, pp. 129-139, (2012); Hey A.J.G., Tansley S., Tolle K.M., The fourth paradigm: Dataintensive scientific discovery, (2009); Higgins S., The lifecycle of data management, Managing research data, pp. 17-46, (2012); Hox J.J., Boeije H.R., Data collection, primary vs. secondary, Encyclopedia of social measurement, pp. 593-599, (2005); Hyman H.H., Secondary analysis of sample surveys: Principles, procedures, and potentialities, (1972); Kirring E., Andersen B., Dansk radio og TV på statsbiblioteket, DF Revy, 31, 3, pp. 4-7, (2008); Lauersen D., Christiansen K.F., Olsen L.L., Management of metadata for digital heritage collections, Microform & Digitization Review, 41, 3-4, pp. 151-158, (2012); Nelson B., Data sharing: Empty archives, Nature-LA English, 461, 7261, (2009); Nordicom-Information, Aktuella forskningsprojekt, Nordicom-Information, 34, 3-4, pp. 105-136, (2012); Pattenden-Fail J., Sorensen A.B., Kruse F., Thogersen J., Molloy L., Ballaux B., Report on academic research practices, (2010); Schostag S., Fonss-Jorgensen E., Webarchiving: Legal deposit of internet in Denmark-A curatorial perspective, Microform & Digitization Review, 41, 3-4, pp. 110-120, (2012); Sorensen A.B., Kruse F., Thogersen J., Molloy L., Pattenden-Fail J., Ballaux B., Report based on DT/7 questionnaire., (2009); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Et al., Data sharing by scientists: Practices and perceptions, PloS One, 6, 6, (2011); Thestrup J.B., Kruse F., Nondal L., Dorch B.F., Andersen M., Blaabjerg N.J., Et al., Forvaltning af forskningsdata i danmark, (2013); Williams K., Mediestream: The trials, tribulations and triumphs of making a digital collection available online, Microform & Digitization Review, 41, 3-4, pp. 171-174, (2012)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84928950689"
"Johnston P.; Hume M.","Johnston, Paul (55818716400); Hume, Margee (13404993400)","55818716400; 13404993400","Exploring one accord for the business of aged care industry: The CEO's perspective","2015","Electronic Journal of Health Informatics","9","1","e2","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929092509&partnerID=40&md5=24054adc1ad94269ce9e200869d4b8d7","Care Systems Head Office, Unit 1, Morrow Place 42 Morrow Street, Taringa, 4068, QLD, Australia; School of Management and Enterprise, Faculty of Business, Education, Law and Arts, University of Southern Queensland, PO Box 4196, Springfield Central, 4300, QLD, Australia","Johnston P., Care Systems Head Office, Unit 1, Morrow Place 42 Morrow Street, Taringa, 4068, QLD, Australia; Hume M., School of Management and Enterprise, Faculty of Business, Education, Law and Arts, University of Southern Queensland, PO Box 4196, Springfield Central, 4300, QLD, Australia","Three million Australians are aged over 65 years with a third requiring some level of assistance with their everyday activities Australian Government expenditure will need to increase from 0.8% of GDP in 2010 to 1.8% of GDP by 2050 as ABS projected growth of 65 years or over to increases from 15 per cent to 21 per cent by 2026 and 28 per cent by 2056 (AIHW 2012, ABS 2011). With the aged care industry growing faster than any other industry research, benefit realization in leadership, efficiency and productivity will impact to a great extent across the industry. Using a convenience sample, the purpose of this paper was to develop a better understanding of trends, issues, challenges and opinions of 24 Chief Executive Officers (CEO) and sector leaders in aged care toward government reforms and current aged care policy in Australia, in 2013. The in-depth interviews advanced the topics of aged care work force, work conditions, attractiveness of the industry to investment, funding models, government relationships, research data management and the future. The paper culminates in a research and practice agenda for the future and accentuates the areas that our sector leaders believe need focus. © by the authors.","Accreditation; Aged care; Community directed care (CDC); Health informatics; Living; Living better (LLLB,) reforms; Longer","","","","","","","","(2013); Barrientos J., Soar J., Su Y., Impact analysis of assessment, consultation and education services to support the adoption of smart home technologies, innovations for chronic disease prevention and solutions for independent living, 10th International Conference on Smart Homes and Health Telematics: Impact Analysis of Solutions for Chronic Disease Prevention and Management, (2012); Connelly L.B., Doessel D.P., Medical expenditures and health status in Australia: a story of increasing or decreasing returns?, Australian Economic Review, 37, 1, pp. 12-30, (2004); D'Andrea A., Ferri F., Grifoni P., Guzzo T., Multimodal social networking for healthcare providers, 2010 Workshop on Database and Expert Systems Applications (DEXA), (2010); Darkins A., Ryan P., Kobb R., Foster L., Edmonson E., Wakefield B., Lancaster A., Care Coordination/ Home Telehealth: the systematic implementation of health informatics, home Telehealth, and disease management to support the care of veteran patients with chronic conditions, Telemedicine and e-Health, 14, 10, pp. 1118-1126, (2008); The viability of residential aged care providers And the potential impact from Productivity Commission recommendations on changes to the aged care system ACAA and ACSA, (2011); Discussion Paper: Development of the Aged Care Workforce Compact), (2012); (2013); Eisenhardt K.M., Building theories from case study research, Academy of management review, 14, 4, pp. 532-550, (1989); Gramstad A., Storli S.L., Hamran T., Do I need it? Do I really need it? Elderly peoples experiences of unmet assistive technology device needs, Disability and Rehabilitation: Assistive Technology, 8, 4, pp. 287-293, (2013); Greenhalgh T., Robert G., Macfarlane F., Bate P., Kyriakidou O., Diffusion of innovations in service organizations: systematic review and recommendations, Milbank Quarterly, 82, 4, pp. 581-629, (2004); Hubbert A.R., Sehorn A., Brown S.W., Service expectations: the consumer versus the provider, International Journal of Service Industry Management, 6, 1, pp. 6-21, (1995); Johnston R., Service transaction analysis:assessing and improving the customer's experience, Managing Service Quality, 9, 2, pp. 102-109, (1999); Johnston R., Towards a better understanding of service excellence, Managing Service Quality, 14, 2-3, pp. 129-133, (2004); Kaur J., Comparative Study of Capability Maturity Model, International Journal of Advanced Research in Computer Science & Technology, 2, 1, pp. 50-55, (2014); Matthews R.A., Woll B., Clarke M., Researching the acceptability of using Communication platform to provide speech and language therapy, International Journal of Integrated Care, 12, 1, pp. 1-11, (2012); McKenna B., Rooney D., (2005); McManus P., Walmsley J., Argent N., Baum S., Bourke L., Martin J., Pritchard B., Sorensen T., Rural Community and Rural Resilience:What is important to farmers in keeping their country towns alive?, Journal of Rural Studies, 28, 1, pp. 20-29, (2012); Montealegre R., A process model of capability development: Lessons from the electronic commerce strategy at Bolsa de Valores de Guayaquil, Organizational Science, 13, 5, pp. 514-531, (2002); O'Reilly M., Courtney M., Edwards H., Hassall S., Clinical outcomes in residential care: Setting benchmarks for quality, Australasian Journal on Ageing, 30, pp. 63-69, (2011); Price L., Arnould E.J., Commercial Friendships:Service Provider-Client Relationships, Context Journal Of Marketing, 63, pp. 38-56, (1999); (2012); Soar J., Assistive technology: ready, steady, go, 2, 1, pp. 22-33, (2013); Soar J., Aging issues and policies in Australia, Global aging issues and policies: understanding the importance of comprehending and studying the aging process, pp. 295-311, (2013); Smith A.E., Humphreys M., Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping, Behaviour Research Methods, 38, 2, pp. 262-279, (2006); Stubbs M., Text and Corpus Analysis:Computer-Assisted Studies of Language and Culture, (1996); von Hippel E., Lead Users: A Source of Novel Product Concepts, Management Science, 32, 7, pp. 791-805, (1986); Williams T., Maya C., Mair F., Mort M., Gask L., Normative models of health technology assessment and the social production of evidence about Telehealth care, Health Policy, 64, 1, pp. 39-45, (2003); Whyte W.F.E., Participatory action research, (1991); Zomerdijk L.G., Voss C., Service design for experience-centric services, Journal of Service Research, 13, 1, pp. 67-82, (2010)","","","Health Informatics Society Australia (HISA)","","","","","","14464381","","","","English","Electron. J. Health Inf.","Article","Final","","Scopus","2-s2.0-84929092509"
"Von Bauer B.; Budroni P.; Ferus A.; Ganguly R.; Ramminger E.; Solís B.S.","Von Bauer, Bruno (57200436821); Budroni, Paolo (56624471600); Ferus, Andreas (37009618700); Ganguly, Raman (56702193000); Ramminger, Eva (55376789000); Solís, Barbara Sánchez (56702943200)","57200436821; 56624471600; 37009618700; 56702193000; 55376789000; 56702943200","E-infrastructures Austria 2014: Report about the first year of the higher education area structural funding project for the coordinated establishment and coordinated development of repository infrastructures; [E-infrastructures Austria 2014: Bericht Über das erste jahr des hochschulraumstrukturmittelprojekts fÜr den koordinierten aufbau und die kooperative weiterentwicklung von repositorieninfrastrukturen]","2015","VOEB-Mitteilungen","68","1","","91","118","27","5","10.31263/voebm.v68i1.1000","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933501864&doi=10.31263%2fvoebm.v68i1.1000&partnerID=40&md5=1e56fa4227ffe6b03f86ea0cd381ffb2","Generalversammlung von e-Infrastructures Austria, Universitätsbibliothek der Medizinischen Universität Wien, Austria; Projektleiter von e-Infrastructures Austria, Bibliotheks- und Archivwesen der Universität Wien, Austria; Synergies Teams von e-Infrastructures Austria, Universitätsbibliothek und des-archivs der Akademie der bildenden Künste Wien, Austria; Zentraler Informatikdienst der Universität Wien, Austria; Vorsitzende der Generalversammlung von e-Infrastructures Austria, Universitätsbibliothek der Technischen Universität Wien, Austria; Bibliotheks- und Archivwesen der Universität Wien, Austria","Von Bauer B., Generalversammlung von e-Infrastructures Austria, Universitätsbibliothek der Medizinischen Universität Wien, Austria; Budroni P., Projektleiter von e-Infrastructures Austria, Bibliotheks- und Archivwesen der Universität Wien, Austria; Ferus A., Synergies Teams von e-Infrastructures Austria, Universitätsbibliothek und des-archivs der Akademie der bildenden Künste Wien, Austria; Ganguly R., Zentraler Informatikdienst der Universität Wien, Austria; Ramminger E., Vorsitzende der Generalversammlung von e-Infrastructures Austria, Universitätsbibliothek der Technischen Universität Wien, Austria; Solís B.S., Bibliotheks- und Archivwesen der Universität Wien, Austria","In January 2014, e-Infrastructures Austria, a three-year project, funded by the Federal Ministry for Science, Research and Economy was launched. The project pursues the coordinated development and advancement of repository infrastructures, a strategic approach for future research data management in Austria, as well as the development of a knowledge network for secure archiving and dissemination of electronic publications, multimedia objects, and other digital data from research and education. Its main purposes are both for the construction of technical infrastructures as well as the creation of a knowledge base for future service offerings in this area. The basis for the successful completion of the project was the rapid development of an Austria-wide network of 25 institutions, including 20 universities. The cooperation is achieved through instruments that can be extended as required: five committees and governance that regulates roles and responsibilities. The identified themes are treated in interdisciplinary „work package clusters“ that are applied over time. The project provides a platform for numerous networking forums, meetings and training units that involves all provinces and invites librarians as well as other relevant stakeholders such as representatives from the local IT facilities, research services and legal departments and scientists to participate. Thus, the project reaches way beyond simply addressing thematically and organizationally specific library perspectives. The report provides information about the project objectives of e-Infrastructures Austria, the successful establishment of the network that took place in 2014 and the different governing bodies. The report also includes the results achieved in the first year of the project in the clusters and the work to be performed in 2015. © 2015 Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare. All rights reserved.","Archiving; Austria; Digital resources; Document server; Infrastructure; Network; Open Access; Policies; Repository; Research data; Research data management","","","","","","","","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84933501864"
"Cimino J.J.; Ayres E.J.; Remennik L.; Rath S.; Freedman R.; Beri A.; Chen Y.; Huser V.","Cimino, James J. (7006337145); Ayres, Elaine J. (6603949450); Remennik, Lyubov (55939944900); Rath, Sachi (55940485900); Freedman, Robert (55940592800); Beri, Andrea (56153631200); Chen, Yang (55940773600); Huser, Vojtech (57192265539)","7006337145; 6603949450; 55939944900; 55940485900; 55940592800; 56153631200; 55940773600; 57192265539","The National Institutes of Health's Biomedical Translational Research Information System (BTRIS): Design, contents, functionality and experience to date","2014","Journal of Biomedical Informatics","52","","","11","27","16","25","10.1016/j.jbi.2013.11.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919676461&doi=10.1016%2fj.jbi.2013.11.004&partnerID=40&md5=bcb444f333610c26ddc4d714a252e307","Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD, United States; Computer Sciences Corporation, Falls Church, VA, United States","Cimino J.J., Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD, United States; Ayres E.J., Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD, United States; Remennik L., Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD, United States; Rath S., Computer Sciences Corporation, Falls Church, VA, United States; Freedman R., Computer Sciences Corporation, Falls Church, VA, United States; Beri A., Computer Sciences Corporation, Falls Church, VA, United States; Chen Y., Computer Sciences Corporation, Falls Church, VA, United States; Huser V., Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD, United States","The US National Institutes of Health (NIH) has developed the Biomedical Translational Research Information System (BTRIS) to support researchers' access to translational and clinical data. BTRIS includes a data repository, a set of programs for loading data from NIH electronic health records and research data management systems, an ontology for coding the disparate data with a single terminology, and a set of user interface tools that provide access to identified data from individual research studies and data across all studies from which individually identifiable data have been removed. This paper reports on unique design elements of the system, progress to date and user experience after five years of development and operation. © 2013.","Clinical research; Ontology; Research data repository; Research data warehouse; Translational research","Biological Ontologies; Biomedical Research; Database Management Systems; Electronic Health Records; Humans; National Institutes of Health (U.S.); Translational Medical Research; United States; Clinical research; Data warehouses; Electronic document exchange; Information systems; Information use; Management information systems; Ontology; User experience; User interfaces; Data repositories; Development and operations; Electronic health record; National Institutes of Health; Research data; Research data managements; Research studies; Translational Research; Article; biomedical translational research information system; clinical data repository; clinical research; clinical study; electronic medical record; human; information processing; information system; national health organization; ontology; policy; translational research; biological ontology; data base; medical research; procedures; United States; Information management","","","","","National Institutes of Health, NIH, (ZIACL000904, ZIACL000907); U.S. National Library of Medicine, NLM; NIH Clinical Center","The authors thank NIH Clinical Center Director John Gallin and former NIH Director Elias Zerhouni for the vision to launch the BTRIS project. We have had additional invaluable guidance from Dr. Richard Cannon and Dr. Richard Davey on many design and policy decisions. We also thank all the BTRIS staff, past and present, for their contributions to the project, especially Mark Budd, James Rohrbaugh and Donald Griffin. We are grateful to the National Cancer Institute’s Center for Biomedical Informatics and Information Technology (CBIIT) for sharing their ontology development tools and experience. Drs. Cimino and Huser are supported by research funding from the NIH Clinical Center and the National Library of Medicine.","Bernstam E.V., Hersh W.R., Johnson S.B., Et al., CTSA Biomedical Informatics Key Function Committee. Synergies and distinctions between computational disciplines in biomedical research: perspective from the Clinical and Translational Science Award programs, Acad Med, 84, 7, pp. 964-970, (2009); Cimino J.J., Ayres E.J., The clinical research data repository of the US National Institutes of Health, Stud Health Technol Inform, 160, pp. 1299-1303, (2010); Cimino J.J., Ayres E.J., Beri A., Freedman R., Oberholtzer E., Rath S., Developing a self-service query interface for re-using de-identified electronic health record data, Stud Health Technol Inform, 192, pp. 632-636, (2013); Cimino J.J., Munson P.J., Lewis T., Rodbard D., Graphical display of patient data using a desk-top computer, pp. 1085-1088, (1981); (2008); Warner H.R., Olmsted C.M., Rutherford B.D., HELP - a program for medical decision-making, Comput Biomed Res, 5, 1, pp. 65-74, (1972); Johnson S.B., Generic data modeling for clinical repositories, J Am Med Inform Assoc, 3, pp. 328-339, (1996); Cimino J.J., Socratous S.A., Clayton P.D., Internet as clinical information system: application development using the World Wide Web, J Am Med Inform Assoc, 2, 5, pp. 273-284, (1995); Hripcsak G., Cimino J.J., Sengupta S., WebCIS: large scale deployment of a Web-based clinical information system, Proc AMIA symp, pp. 804-808, (1999); Wilcox A.B., Vawdrey D.K., Chen Y.H., Forman B., Hripcsak G., The evolving use of a clinical data repository: facilitating data access within an electronic medical record, AMIA Annu Symp Proc, 2009, pp. 701-705, (2009); Nadkarni P.M., QAV: querying entity-attribute-value metadata in a biomedical database, Comput Methods Programs Biomed, 53, 2, pp. 93-103, (1997); Cimino J.J., Clayton P.D., Hripcsak G., Johnson S.B., Knowledge-based approaches to the maintenance of a large controlled medical terminology, J Am Med Inform Assoc, 1, 1, pp. 35-50, (1994); Cimino J.J., From data to knowledge through concept-oriented terminologies: experience with the Medical Entities Dictionary, J Am Med Inform Assoc, 7, 3, pp. 288-297, (2000); Cimino J.J., Desiderata for controlled medical vocabularies in the Twenty-First Century, Methods Inf Med, 37, 4-5, pp. 394-403, (1998); Cimino J.J., In defense of the desiderata, J Biomed Inform, 39, 3, pp. 299-306, (2006); Cimino J.J., Johnson S.B., Hripcsak G., Hill C.L., Clayton P.D., Managing vocabulary for a centralized clinical system, Proceedings of the world congress on medical informatics - Medinfo '95; Vancouver, Canada; Healthcare Computing and Communications Canada, Edmonton, Alberta, pp. 117-120, (1995); Kush R., Alschuler L., Ruggeri R., Cassells S., Gupta N., Bain L., Et al., Implementing Single Source: the STARBRITE proof-of-concept study, J Am Med Inform Assoc, 14, 5, pp. 662-673, (2007); Fridsma D.B., Evans J., Hastak S., Mead C.N., The BRIDG project: a technical report, J Am Med Inform Assoc, 15, 2, pp. 130-137, (2008); Murphy S.N., Mendis M., Hackett K., Kuttan R., Pan W., Phillips L.C., Et al., Architecture of the open-source clinical research chart from Informatics for Integrating Biology and the Bedside, AMIA Annu Symp Proc, 11, OCTOBER, pp. 548-552, (2007); (2008); Huser V., Cimino J.J., Desiderata for healthcare integrated data repositories based on architectural comparison of three public repositories, Proceedings of 2013 AMIA fall symposium, pp. 648-656, (2013); (2009); Noy N.F., de Coronado S., Solbrig H., Fragoso G., Hartel F.W., Musen M.A., Representing the NCI Thesaurus in OWL DL: modeling tools help modeling languages, Appl Ontol, 3, 3, pp. 173-190, (2008); Wang T.D., Wongsuphasawat K., Plaisant C., Shneiderman B., Extracting insights from electronic health records: case studies, a visual analytics process model, and design recommendations, J Med Syst, 35, 5, pp. 1135-1152, (2011); Zarin D.A., Tse T., Williams R.J., Califf R.M., Ide N., The ClinicalTrials.gov results database - update and key issues, N Engl J Med, 364, 9, pp. 852-860, (2011); Cimino J.J., Ayres E., Rath S., Freedman R., Automated submission of clinical trials results to ClinicalTrials.gov (Poster). In: Bernstam E, editor. San Francisco, CA., AMIA clinical research informatics summit, (2013); Cimino J.J., Ayres E.J., Beri A., Freedman R., Oberholtzer E., Rath S., Developing a self-service query interface for re-using de-identified electronic health record data, Proceedings of Medinfo 2013, pp. 632-636, (2013); (2013); Weber G.M., Murphy S.N., McMurry A.J., Macfadden D., Nigrin D.J., Churchill S., Et al., The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories, J Am Med Inform Assoc, 16, 5, pp. 624-630, (2009); Huff S.M., Rocha R.A., Bray B.E., Warner H.R., Haug P.J., An event model of medical information representation, J Am Med Inform Assoc, 2, pp. 116-134, (1995); Chute C.G., Beck S.A., Fisk T.B., Mohr D.N., The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data, J Am Med Inform Assoc, 17, pp. 131-135, (2010); Lowe H.J., Ferris T.A., Hernandez P.M., Weber S.C., STRIDE - an integrated standards-based translational research informatics platform, AMIA Annu Symp Proc, 2009, pp. 391-395, (2009); Horvath M.M., Winfield S., Evans S., Slopek S., Shang H., Ferranti J., The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement, J Biomed Inform, 44, pp. 266-276, (2011); Kohane I.S., Churchill S.E., Murphy S.N., A translational engine at the national scale: informatics for integrating biology and the bedside, J Am Med Inform Assoc, 19, 2, pp. 181-185, (2012); Hornbrook M.C., Hart G., Ellis J.L., Bachman D.J., Ansell G., Greene S.M., Et al., Building a virtual cancer research organization, J Natl Cancer Inst Monogr, pp. 12-25, (2005); (2012); Pace W.D., Cifuentes M., Valuck R.J., Staton E.W., Brandt E.C., West D.R., An electronic practice-based network for observational comparative effectiveness research, Ann Intern Med, 151, pp. 338-340, (2009); Stang P.E., Ryan P.B., Racoosin J.A., Overhage J.M., Hartzema A.G., Reich C., Et al., Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership, Ann Intern Med, 153, pp. 600-606, (2010); Smith B., From concepts to clinical reality: an essay on the benchmarking of biomedical terminologies, J Biomed Inform, 39, 3, pp. 288-298, (2006); Mailman M.D., Feolo M., Jin Y., Kimura M., Tryka K., Bagoutdinov R., Et al., The NCBI dbGaP database of genotypes and phenotypes, Nat Genet, 39, 10, pp. 1181-1186, (2007); Cimino J.J., The false security of blind dates: chrononymization's lack of impact on data privacy of laboratory data, Appl Clin Inform, 3, 4, pp. 392-403, (2012); Walker L., Starks H., West K.M., Fullerton S.M., DbGaP data access requests: a call for greater transparency, Sci Transl Med, 3, 113, (2011); Fung K.W., Jao C.S., Demner-Fushman D., Extracting drug indication information from structured product labels using natural language processing, J Am Med Inform Assoc, 20, 3, pp. 482-488, (2013)","","","Academic Press Inc.","","","","","","15320464","","JBIOB","24262893","English","J. Biomed. Informatics","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84919676461"
"Tenopir C.; Sandusky R.J.; Allard S.; Birch B.","Tenopir, Carol (7005106498); Sandusky, Robert J. (16029249400); Allard, Suzie (7006450046); Birch, Ben (57225693289)","7005106498; 16029249400; 7006450046; 57225693289","Research data management services in academic research libraries and perceptions of librarians","2014","Library and Information Science Research","36","2","","84","90","6","156","10.1016/j.lisr.2013.11.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903693418&doi=10.1016%2fj.lisr.2013.11.003&partnerID=40&md5=c2fe2bf6f48ecd039ae567141750daf2","School of Information Sciences, University of Tennessee, 451 Communications Bldg., Knoxville, TN 37996-0341, 1345 Circle Park Drive, United States; Richard J. Daley Library, MC-234, Chicago, IL 60607, 801 S. Morgan St., United States","Tenopir C., School of Information Sciences, University of Tennessee, 451 Communications Bldg., Knoxville, TN 37996-0341, 1345 Circle Park Drive, United States; Sandusky R.J., Richard J. Daley Library, MC-234, Chicago, IL 60607, 801 S. Morgan St., United States; Allard S., School of Information Sciences, University of Tennessee, 451 Communications Bldg., Knoxville, TN 37996-0341, 1345 Circle Park Drive, United States; Birch B., School of Information Sciences, University of Tennessee, 451 Communications Bldg., Knoxville, TN 37996-0341, 1345 Circle Park Drive, United States","The emergence of data intensive science and the establishment of data management mandates have motivated academic libraries to develop research data services (RDS) for their faculty and students. Here the results of two studies are reported: librarians' RDS practices in U.S. and Canadian academic research libraries, and the RDS-related library policies in those or similar libraries. Results show that RDS are currently not frequently employed in libraries, but many services are in the planning stages. Technical RDS are less common than informational RDS, RDS are performed more often for faculty than for students, and more library directors believe they offer opportunities for staff to develop RDS-related skills than the percentage of librarians who perceive such opportunities to be available. Librarians need opportunities to learn more about these services either on campus or through attendance at workshops and professional conferences. © 2014 The Authors.","","","","","","","National Science Foundation, NSF, (0830944, 0830955)","This work was supported by Data Observation Network for Earth (DataONE), National Science Foundation award # 0830955 under a Cooperative Agreement. We would like to thank Graduate Research Assistant Madison Langseth for her careful work on helping us respond to the peer reviewers' suggested revisions. ","To stand the test of time: Long-term stewardship of digital data sets in science and engineering, (2006); E-science and data support services: A study of ARL member institutions, (2010); Bach K., Schafer D., Enke N., Seeger B., Gemeinholzer B., Bendix J., A comparative evaluation of technical solutions for long-term data repositories in integrative biodiversity research, Ecological Informatics, 11, pp. 16-24, (2012); Brown E., I know what you researched last summer: How academic librarians are supporting researchers in the management of data curation, The New Zealand Library & Information Management Journal, 52, 1, pp. 55-69, (2010); Brown S., Swan A., Researchers' use of academic libraries and their services: A report commissioned by the Research Information Network and the Consortium of Research Libraries, (2007); Carlson's J., Demystifying the data interview: Developing a foundation for reference librarians to talk with researchers about their data, Reference Services Review, 40, 1, pp. 7-23, (2012); Cheek F.M., Bradigan P.S., Academic health sciences library research support, Journal of the Medical Library Association, 98, 2, pp. 167-171, (2010); Corrall S., Keenan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); No brief candle: Reconceiving research libraries for the 21st century, (2008); Creamer A., Morales M.E., Crespo J., Kafel D., Martin E.R., An assessment of needed competencies to promote the data curation and management librarianship of health sciences and science and technology librarians in New England, Journal of eScience Librarianship, 1, 1, pp. 18-26, (2012); Gabridge T., The last mile: The liaison role in curating science and engineering research data, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 265, pp. 15-21, (2009); Gold A., Cyberinfrastructure, data, and libraries, part 2: Libraries and the data challenge: Roles and actions for libraries, D-Lib Magazine, 13, 9-10, (2007); Tri-agency open access policy, (2013); Hey T., Hey J., E-science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Hey T., Tansley S., Tolle K., The fourth paradigm: Data-intensive scientific discovery, (2009); Hey T., Tansley S., Tolle K., Jim Gray on eScience: A transformed scientific method, The fourth paradigm: Data-intensive scientific discovery, pp. 19-33, (2009); Jones E., Reinventing science librarianship: Themes from the ARL-CNI forum, Research library issues: A bimonthly report from ARL, CNI, and SPARC, 262, pp. 12-17, (2009); Kuipers T., Van der Hoeven J., Insight into digital preservation of research output in Europe: Survey report (D3.4), (2009); MacColl J., Library roles in university research assessment, LIBER Quarterly, 20, 2, pp. 152-168, (2010); Markauskaite L., Kennan M.A., Richardson J., Aditomo A., Hellmers L., Investigating eResearch: Collaboration practices and future challenges, Collaborative and distributed e-research: Innovations in technologies, strategies and applications, pp. 1-33, (2012); Newton M.P., Miller C.C., Bracke M.S., Librarian roles in institutional repository data set collecting: Outcomes of a research library task force, Collection Management, 36, 1, pp. 53-67, (2010); Peters C., Dryden A.R., Assessing the academic library's role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, 4, pp. 387-403, (2011); Potter W.G., Cook C., Kyrillidou M., ARL profiles: Research libraries 2010, (2011); Steinhart G., Saylor J., Albert P., Alpi K., Baxter P., Brown E., Et al., Digital research data curation: Overview of issues, current activities, and opportunities for the Cornell University Library, A report of the Cornell University Library Data Working Group, (2008); Tenopir C., Birch B., Allard S., Academic libraries and research data services: Current practices and plans for the future, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA Journal, 39, 1, pp. 70-78, (2013); Funding agency and data management guidelines, (2011)","B. Birch; School of Information Sciences, University of Tennessee, 451 Communications Bldg., Knoxville, TN 37996-0341, 1345 Circle Park Drive, United States; email: wbirch@utk.edu","","Elsevier Ltd","","","","","","07408188","","LISRD","","English","Libr. Inf. Sci. Res.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-84903693418"
"Verbaan E.; Cox A.M.","Verbaan, E. (24470394900); Cox, A.M. (7402563906)","24470394900; 7402563906","Occupational Sub-Cultures, Jurisdictional Struggle and Third Space: Theorising Professional Service Responses to Research Data Management","2014","Journal of Academic Librarianship","40","3-4","","211","219","8","37","10.1016/j.acalib.2014.02.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905106005&doi=10.1016%2fj.acalib.2014.02.008&partnerID=40&md5=646da93b0138ea082f4999adb95e1e0a","The Information School, University of Sheffield, United Kingdom","Verbaan E., The Information School, University of Sheffield, United Kingdom; Cox A.M., The Information School, University of Sheffield, United Kingdom","Effective Research Data Management (RDM) is becoming an increasing concern in UK universities as a result of mandates from research funders. The study explored the usefulness of theories of occupational sub-culture, jurisdictional struggle and Third Space to understand how librarians, IT staff and research administrators view developing services to support RDM. Data were collected through 20 semi-structured interviews with staff in the Library, IT services and Research Office of a research intensive university in Northern England.The notion of occupational sub-culture directs attention to the different ways professional services view RDM. Broadly speaking, IT Services focussed on short term data storage Research Office on compliance and research quality; librarians on preservation and advocacy. In terms of Abbott's theories, the Library was the only department claiming a new jurisdiction in RDM. This could be seen as an extension of its existing jurisdiction in Open Access and Information Literacy. The other departments claimed to be short of resources to take on such a complex project. Some interviewees feared RDM might be risky and demand lots of resources. Third Space theory is a powerful way to think about roles that might emerge in a new intra-professional space as RDM services become a reality. © 2014 Elsevier Inc.","Academic libraries; IT services; Occupational sub-culture; Research administration; Research data management; Third Space","","","","","","","","Abbott A., The system of professions: An essay on the division of expert labor, (1988); Abbott A., Professionalism and the future of librarianship, Library Trends, 46, 3, pp. 430-443, (1998); Alvaro E., Brooks H., Ham M., Poegel S., Rosencrans S., E-science librarianship: Field undefined, Issues in Science and Technology Librarianship, (2011); About ARMA's members, (2013); Auckland M., Re-skilling for research: An investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, (2012); Bhabha H.K., The location of culture, (1994); Borgman C., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2008); Bryman A., Social research methods, (2012); Buche M.W., Influence of gender on IT professional work identity: Outcomes from a PLS study, Proceedings of the 2008 ACM SIGMIS CPR Conference, pp. 134-140, (2008); Cain M., The two cultures? Librarians and technologists, The Journal of Academic Librarianship, 29, 3, pp. 177-181, (2003); Collinson J.A., Just ""non-academics""? Research administrators and contested occupational identity, Work, Employment and Society, 20, 2, pp. 267-288, (2006); Collinson J.A., Get yourself some nice, neat, matching box files!' Research administrators and occupational identity work, Studies in Higher Education, 32, 3, pp. 295-309, (2007); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing research data, pp. 105-133, (2012); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management: Emerging trends in library research support services, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Corrall S., Evolving academic library specialities, Journal of the American Society for Information Science and Technology, 64, 8, pp. 1526-1542, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, (2013); Cox A.M., Verbaan E., Sen B., Upskilling liaison librarians for research data management, Ariadne, 70, (2012); Creth S.D., Creating a virtual information organization: Collaborative relationships between libraries and computing centers, Journal of Library Administration, 19, 3-4, pp. 111-132, (1993); Danner R.A., Redefining a profession, Law Library Journal, 90, 3, pp. 315-356, (1998); Favini R., The Library and academic computing center: Cultural perspectives and recommendations for improved interaction (ACRL whitepaper); Ferguson C., Metz T., Finding the third space: On leadership issues related to the integration of library and computing, Leadership, higher education, and the information age: A new era for information technology and libraries, pp. 95-112, (2003); Gabridge T., The last mile: Liaison roles in curating science and engineering research data, Research Library Issues, 265, pp. 15-21, (2009); Green J., Langley D., Professionalising Research Management, (2009); Guzman I.R., Stam K.R., Stanton J.M., The occupational culture of IS/IT personnel within organizations, Data Base for Advances in Information Systems, 39, 1, pp. 33-50, (2008); Guzman I.R., Stanton J.M., IT occupational culture: The cultural fit and commitment of new information technologists, Information Technology & People, 22, 2, pp. 157-187, (2009); Hockey J., Collinson J.A., Occupational knowledge and practice amongst UK university research administrators, Higher Education Quarterly, 63, 2, pp. 141-159, (2009); Hodson S., Jones S., Seven rules of successful research data management in universities. The Guardian Higher Education Network; Hofstede G., Cultures and organizations: Software of the mind, (1991); Hofstede G., Identifying organizational subcultures: An empirical approach, Journal of Management Studies, 35, 1, pp. 1-12, (1998); Hwang C.J., Why us? 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Cornell studies in industrial and labor relations 26, (1993); Van House N., Sutton S.A., The panda syndrome: An ecology of LIS education, Journal of Education for Library and Information Science, 37, 2, pp. 131-147, (1996); Whitchurch C., Shifting identities and blurring boundaries: The emergence of third space professionals in UK higher education, Higher Education Quarterly, 62, 4, pp. 377-396, (2008); Whitchurch C., Reconstructing identities in higher education: The rise of third space professionals, (2012); Whyte A., Tedds J., Making the case for research data management (a digital curation centre briefing paper), (2011); Wilson K.M., Halpin E., Convergence and professional identity in the academic library, Journal of Librarianship and Information Science, 38, 2, pp. 79-91, (2006)","A.M. Cox; The Information School, University of Sheffield, United Kingdom; email: a.m.cox@sheffield.ac.uk","","Elsevier Ltd","","","","","","00991333","","","","English","J. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84905106005"
"Curdt C.; Hoffmeister D.","Curdt, Constanze (36681871300); Hoffmeister, Dirk (15764969300)","36681871300; 15764969300","Research data management services for a multidisciplinary, collaborative research project: Design and implementation of the TR32DB project database","2015","Program","49","4","","494","512","18","12","10.1108/PROG-02-2015-0016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941335736&doi=10.1108%2fPROG-02-2015-0016&partnerID=40&md5=2bb9708874b5e61cb1ccbd77b6a534a5","Institute of Geography, University of Cologne, Cologne, Germany","Curdt C., Institute of Geography, University of Cologne, Cologne, Germany; Hoffmeister D., Institute of Geography, University of Cologne, Cologne, Germany","Purpose – Research data management (RDM) comprises all processes, which ensure that research data are well-organized, documented, stored, backed up, accessible, and reusable. RDM systems form the technical framework. The purpose of this paper is to present the design and implementation of a RDM system for an interdisciplinary, collaborative, long-term research project with focus on Soil-Vegetation-Atmosphere data. Design/methodology/approach – The presented RDM system is based on a three-tier (client-server) architecture. This includes a file-based data storage, a database-based metadata storage, and a self-designed user-friendly web-interface. The system is designed in cooperation with the local computing centre, where it is also hosted. A self-designed interoperable, project-specific metadata schema ensures the accurate documentation of all data. Findings – A RDM system has to be designed and implemented according to requirements of the project participants. General challenges and problems of RDM should be considered. Thus, a close cooperation with the scientists obtains the acceptance and usage of the system. Originality/value – This paper provides evidence that the implementation of a RDM system in the provided and maintained infrastructure of a computing centre offers many advantages. Consequently, the designed system is independent of the project funding. In addition, access and re-use of all involved project data is ensured. A transferability of the presented approach to another interdisciplinary research project was already successful. Furthermore, the designed metadata schema can be expanded according to changing project requirements. © 2015, Emerald Group Publishing Limited.","Data repository; File system; Metadata schema; Research data management; Services; System architecture","","","","","","Courant Forschungszentrum Geobiologie, Georg-August-Universität Göttingen; Deutsche Forschungsgemeinschaft","","Brown M.L., White W., A partnership approach to research data management, Delivering Research Data Management Services: Fundamentals of Good Practice, pp. 135-161, (2013); Carlson J., Demystifying the data interview, Reference Services Review, 40, 1, pp. 7-23, (2012); Corti L., Van den Eynden V., Bissell A., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, (2014); (2011); Curdt C., (2014); Curdt C., Hoffmeister D., Waldhoff G., Jekel C., Bareth G., Scientific Research Data Management for Soil-Vegetation-Atmosphere Data-The TR32DB, International Journal of Digital Curation, 7, 2, pp. 68-80, (2012); Proposals for Safeguarding Good Scientific Practice. 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Photogramm. Remote Sens. Spatial Inf. Sci., 1-7, pp. 177-182, (2012); Willmes C., Kurner D., Bareth G., Building research data management infrastructure using open source software, Transactions in GIS, 18, 4, pp. 496-509, (2014); Wilson A., Jeffreys P., Towards a unified university infrastructure: the data management roll-out at the University of Oxford, International Journal of Digital Curation, 8, 2, pp. 235-246, (2013); Winkler-Nees S., Status of discussion and current activities: national developments, Digital Curation of Research Data – Experiences of a Baseline Study in Germany, pp. 18-36, (2013); Zander F., Kralisch S., Busch C., Flugel W.-A., Data and information management for integrated research – requirements, experiences and solutions, pp. 2201-2206, (2013)","C. Curdt; Institute of Geography, University of Cologne, Cologne, Germany; email: c.curdt@uni-koeln.de","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","","Scopus","2-s2.0-84941335736"
"Jiang G.; Evans J.; Oniki T.A.; Coyle J.F.; Bain L.; Huff S.M.; Kush R.D.; Chute C.G.","Jiang, G. (55486706700); Evans, J. (37070183300); Oniki, T.A. (7004907721); Coyle, J.F. (57213801303); Bain, L. (21233497200); Huff, S.M. (7005508146); Kush, R.D. (6507111391); Chute, C.G. (7006581202)","55486706700; 37070183300; 7004907721; 57213801303; 21233497200; 7005508146; 6507111391; 7006581202","Harmonization of detailed clinical models with clinical study data standards","2015","Methods of Information in Medicine","54","1","","65","74","9","17","10.3414/ME13-02-0019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921262409&doi=10.3414%2fME13-02-0019&partnerID=40&md5=e03f5e46afdd0a201d24c06a833236ba","Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States; Clinical Data Interchange Standards Consortium (CDISC), Austin, TX, United States; Intermountain Medical Center, Intermountain Healthcare, Murray, UT, United States","Jiang G., Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States; Evans J., Clinical Data Interchange Standards Consortium (CDISC), Austin, TX, United States; Oniki T.A., Intermountain Medical Center, Intermountain Healthcare, Murray, UT, United States; Coyle J.F., Intermountain Medical Center, Intermountain Healthcare, Murray, UT, United States; Bain L., Clinical Data Interchange Standards Consortium (CDISC), Austin, TX, United States; Huff S.M., Intermountain Medical Center, Intermountain Healthcare, Murray, UT, United States; Kush R.D., Clinical Data Interchange Standards Consortium (CDISC), Austin, TX, United States; Chute C.G., Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States","Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.; Background: Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR).; Objective: The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards.; Methods: We were focused on three generic domains: demographics, lab tests, and medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs. Results: We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues.; Conclusions: We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical study and broad secondary use domains. © Schattauer 2015.","Clinical Data Interchange Standards Consortium (CDISC); Clinical Information Modeling Initiative (CIMI); Clinical study; Data stand - Ards; Detailed Clinical Model (DCM); Electronic health records (EHR)","Biomedical Research; Electronic Health Records; Health Level Seven; Information Storage and Retrieval; Programming Languages; Semantics; computer language; electronic health record; health level 7; information retrieval; medical research; semantics; standards","","","","","","","El Fadly A., Daniel C., Bousquet C., Dart T., Lastic P.Y., Degoulet P. Electronic Healthcare Record and clinical research in cardiovascular radiology, HL7 CDA and CDISC ODM interoperability. AMIA Annu Symp Proc, pp. 216-220, (2007); Hammond W.E., Jaffe C., Kush R.D., Healthcare standards development. The value of nurturing collaboration, J AHIMA, 80, 7, pp. 44-50, (2009); (2012); (2012); (2013); Coyle J.F., Mori A.R., Huff S.M., Standards for detailed clinical models as the basis for medical data exchange and decision support, Int J Med Inform, 69, 2-3, pp. 157-174, (2003); (2012); Goossen W., Goossen-Baremans A., Van Der Zel M., Detailed clinical models: A review, Healthc Inform Res, 16, 4, pp. 201-214, (2010); Beale T., Archetypes: Constraint-based domain models for future-proof information systems, Eleventh OOPSLA Workshop on Behavioral Semantics: Serving the Customer, pp. 16-32, (2002); Beale T., Archetypes and the EHR, Stud Health Technol Inform, 96, pp. 238-244, (2003); Huff S.M., Rocha R.A., Coyle J.F., Narus S.P., Integrating detailed clinical models into application development tools, Stud Health Technol Inform, 107, pp. 1058-1062, (2004); (2013); (2014); (2012); (2012); Chute C.G., Pathak J., Savova G.K., Et al., The SHARPn project on secondary use of Electronic Medical Record data: Progress, plans, and possibilities, AMIA Annu Symp Proc, pp. 248-256, (2011); (2012); Jiang G., Solbrig H.R., Iberson-Hurst D., Kush R.D., Chute C.G., A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki, AMIA Summits Transl Sci Proc, pp. 11-15, (2010); (2012); (2012); Rea S., Pathak J., Savova G., Et al., Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project, J Biomed Inform, (2012); (2013); Huff S.M., Rocha R.A., Solbrig H.R., Barnes M.W., Schrank S.P., Smith M., Linking a medical vocabulary to a clinical data model using Abstract Syntax Notation 1, Methods Inf Med, 37, 4-5, pp. 440-452, (1998); Richesson R.L., Krischer J., Data standards in clinical research: Gaps, overlaps, challenges and future directions, J Am Med Inform Assoc, 14, 6, pp. 687-696, (2007); Rector A., Rossi A., Consorti M.F., Zanstra P., Prac-tical development of re-usable terminologies: GALEN-IN-USE and the GALEN Organisation, Int J Med Inform, 48, 1-3, pp. 71-84, (1998); Rogers J.E., Price C., Rector A.L., Solomon W.D., Smejko N., Validating clinical terminology structures: Integration and cross-validation of Read Thesaurus and GALEN, Proceedings/AMIA Annual Symposium, pp. 845-849, (1998); (2012); Jiang G., Solbrig H.R., Chute C.G., Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups, J Am Med Inform Assoc, 19, e1, pp. e129-136, (2012); (2012); Jiang G., Solbrig H.R., Evans J., Et al., OpenCEM Wiki: A Semantic-Web-based Repository for Supporting Harmonization of Clinical Study Data Standards and Clinical Element Models, (2012); (2014); Jiang G., Evans J., Endle C.M., Sh R., Chute C.G., Using Semantic Web Technologies for the Generation of Domain Templates to Support Clinical Study Meta-Data Standards, Proceedings of the 6th International Workshop on Semantic Web Applications and Tools for Life Sciences, (2013)","","","Schattauer GmbH","","","","","","00261270","","MIMCA","25426730","English","Methods Inf. Med.","Article","Final","","Scopus","2-s2.0-84921262409"
"Whitmire A.L.; Boock M.; Sutton S.C.","Whitmire, Amanda L. (24463842400); Boock, Michael (16199480900); Sutton, Shan C. (13608323000)","24463842400; 16199480900; 13608323000","Variability in academic research data management practices: Implications for data services development from a faculty survey","2015","Program","49","4","","382","407","25","43","10.1108/PROG-02-2015-0017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941369608&doi=10.1108%2fPROG-02-2015-0017&partnerID=40&md5=08eaa8100ef361bab34ba7706c449557","Oregon State University, Corvallis, OR, United States; University of Arizona, Tuscon, AZ, United States","Whitmire A.L., Oregon State University, Corvallis, OR, United States; Boock M., Oregon State University, Corvallis, OR, United States; Sutton S.C., University of Arizona, Tuscon, AZ, United States","Purpose – The purpose of this paper is to demonstrate how knowledge of local research data management (RDM) practices critically informs the progressive development of research data services (RDS) after basic services have already been established. Design/methodology/approach – An online survey was distributed via e-mail to all university faculty in the fall of 2013, and was left open for just over one month. The authors sent two reminder e-mails before closing the survey. Survey data were downloaded from Qualtrics survey software and analyzed in R. Findings – In this paper, the authors reviewed a subset of survey findings that included data types, volume, and storage locations, RDM roles and responsibilities, and metadata practices. The authors found that Oregon State University (OSU) researchers are generating a wide variety of data types, and that practices vary between colleges. The authors discovered that faculty are not utilizing campus-wide storage infrastructure, and are maintaining their own storage servers in surprising numbers. Faculty-level research assistants perform the majority of data-related tasks at OSU, with the exception of data sharing, which is primarily handled by the professorial ranks. The authors found that many faculty on campus are creating metadata, but that there is a need to provide support in how to discover and create standardized metadata. Originality/value – This paper presents a novel example of how to efficiently move from establishing basic RDM services to providing more focussed services that meet specific local needs. It provides an approach for others to follow when tackling the difficult question of, “What next?” with regard to providing academic RDS. © 2015, Emerald Group Publishing Limited.","Academic libraries; Data management; Data sharing; Metadata; Research data services; Survey","","","","","","","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, Int. J. Digit. Curation, 8, 2, pp. 5-26, (2013); Averkamp S., Gu X., Rogers B., Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs, (2014); Avery B.E., Chau M., Vondracek R., Wirth A.A., OSU libraries and research dataset curation: a beginning, (2010); Boock M., Chadwell F.A., (2011); Carlson J., Opportunities and barriers for librarians in exploring data: observations from the data curation profile workshops, J. EScience Librariansh, 2, 2, (2013); Cornell University Library R.D., (2000); E-Science Institute E.-S.I., (2012); Holdren J.P., (2013); Jisc J., (2015); Keon D., Pancake C., Wright D., Virtual oregon: seamless access to distributed environmental information, Proceedings of the 2Nd ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL ’02, (2002); Marchionini G., Research Data Stewardship at UNC: Recommendations for Scholarly Practice and Leadership, (2012); Rolando L., Doty C., Hagenmaier W., Valk A., Parham S.W., (2013); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A study of faculty data curation behaviors and attitudes at a teaching-centered university, Coll. Res. Libr, 73, 4, pp. 349-365, (2012); Steinhart G., Chen E., Arguillas F., Dietrich D., Kramer S., Prepared to plan? A snapshot of researcher readiness to address data management planning requirements, J. EScience Librariansh, 1, 2, (2012); Sutton S., Barber D., Whitmire A.L., Oregon State University Libraries and Press Strategic Agenda for Research Data Services, (2013); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: practices and perceptions, PLoS ONE, 6, (2011); Whitmire A.L., (2015); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing data curation profiles, Int. J. Digit. Curation, 4, 3, pp. 93-103, (2009); Zgorski L.-J., (2012)","A.L. Whitmire; Oregon State University, Corvallis, United States; email: amanda.whitmire@oregonstate.edu","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","","Scopus","2-s2.0-84941369608"
"Goben A.; Raszewski R.","Goben, Abigail (55849675300); Raszewski, Rebecca (53878345400)","55849675300; 53878345400","Research data management self-education for librarians: A webliography","2015","Issues in Science and Technology Librarianship","2015","82","","","","","3","10.5062/F4348HCK","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950148764&doi=10.5062%2fF4348HCK&partnerID=40&md5=758cd6c03ba977066a4c56aa21b65e5e","University of Illinois at Chicago, Chicago, IL, United States","Goben A., University of Illinois at Chicago, Chicago, IL, United States; Raszewski R., University of Illinois at Chicago, Chicago, IL, United States","[No abstract available]","","","","","","","","","Bailey C., Research Data Curation Bibliography, (2015); Fearon D.J., Gunia B., Lake S., Pralle B.E., Sallans A.L., Research Data Management Services, SPEC Kit 334 (July 2013), (2013); National Science Foundation, Dissemination and Sharing of Research Results, (2010); Westra B., Developing Data Management Services for Researchers at the University of Oregon, Research Data Management: Practical Strategies for Information Professionals, pp. 375-391, (2014); Whitmire A., Briney K., Nurnberger A., Henderson M., Atwood T., Janz M., Kozlowski W., Lake S., Vandegrift M., Zilinski L., A table summarizing the Federal public access policies resulting from the US Office of Science and Technology Policy memorandum of February 2013, (2015)","","","Association of College and Research Libraries","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","","Scopus","2-s2.0-84950148764"
"Robinson J.D.","Robinson, John D. (56230889000)","56230889000","The dogs bark and the circus moves on","2015","Bottom Line","28","1-2","","7","18","11","2","10.1108/BL-01-2015-0002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931079497&doi=10.1108%2fBL-01-2015-0002&partnerID=40&md5=fb8a945a90a0097fda03450e31423ff2","School of Oriental and African Studies, University of London, London, United Kingdom","Robinson J.D., School of Oriental and African Studies, University of London, London, United Kingdom","Purpose – The paper aims to set out challenges that libraries face while developing their Digital Library capabilities and capacity and propose an approach to estimating the costs for these functions. There is a skills challenge as well as an organisational challenge. The opportunities to build new teams or re-train existing staff are discussed. Design/methodology/approach – The approach builds on a 2008 paper about Digital Library economics and discusses the changes in the environment since then. A model is described in which a library takes on the full responsibility for building and operating a Digital Library function in-house. This is used to benchmark other options such as managed services, outsourced infrastructure and “cloud” services. Findings – The Open Access Publication and Research Data Management mandates present challenges to all libraries based in academic institutions in the UK. New working methods and new costs are unavoidable. There are a number of ways to deal with this depending upon the institutional circumstance. The bottom line can be increases in revenue budgets of around 10 per cent with variable requirements for capital investment. Originality/value – Libraries and librarians have different experiences in closely working with colleagues in information technology (IT). A number of propositions are presented about the value of cooperation and collaboration between library and IT and also with external partners and service providers. © Emerald Group Publishing Limited.","Cloud provision; Cost models; Digital library; In-house versus outsourced; Service models","","","","","","","","Robinson J.D., Spinning the disks-lessons from the circus, Digital Library Economics, Chandos Publishing, Oxford, pp. 227-246, (2009)","J.D. Robinson; School of Oriental and African Studies, University of London, London, United Kingdom; email: jdr@johnrobinson.org.uk","","Emerald Group Holdings Ltd.","","","","","","0888045X","","","","English","Bottom line","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84931079497"
"Higman R.; Pinfield S.","Higman, Rosie (56835600200); Pinfield, Stephen (6602090850)","56835600200; 6602090850","Research data management and openness: The role of data sharing in developing institutional policies and practices","2015","Program","49","4","","364","381","17","33","10.1108/PROG-01-2015-0005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941338286&doi=10.1108%2fPROG-01-2015-0005&partnerID=40&md5=4c33ef67c7bf0dec3b3ee12e1a980ee5","University of Reading, Reading, United Kingdom; University of Sheffield, Sheffield, United Kingdom","Higman R., University of Reading, Reading, United Kingdom; Pinfield S., University of Sheffield, Sheffield, United Kingdom","Purpose – The purpose of this paper is to investigate the relationship between research data management (RDM) and data sharing in the formulation of RDM policies and development of practices in Higher Education Institutions (HEIs). Design/methodology/approach – Two strands of work were undertaken sequentially: first, content analysis of 37 RDM policies from UK HEIs; and second, two detailed case studies of institutions with different approaches to RDM based on semi-structured interviews with staff involved in the development of RDM policy and services. The data are interpreted using insights from Actor Network Theory. Findings – RDM policy formation and service development has created a complex set of networks within and beyond institutions involving different professional groups with widely varying priorities shaping activities. Data sharing is considered an important activity in the policies and services of HEIs studied, but its prominence can in most cases be attributed to the positions adopted by large research funders. Research limitations/implications – The case studies, as research based on qualitative data, cannot be assumed to be universally applicable but do illustrate a variety of issues and challenges experienced more generally, particularly in the UK. Practical implications – The research may help to inform development of policy and practice in RDM in HEIs and funder organisations. Originality/value – This paper makes an early contribution to the RDM literature on the specific topic of the relationship between RDM policy and services, and openness – a topic which to date has received limited attention. © 2015, Emerald Group Publishing Limited.","Actor network theory; Data sharing; Open data; Openness; Research data management; Research data services","","","","","","","","Arzberger P., Schroeder P., Beaulieu A., Bowker G., Casey K., Laaksonen L., Moorman D., Promoting access to public research data for scientific, economic, and social development, Data Science Journal, 3, November, pp. 135-152, (2004); Berman F., Cerf V., Who will pay for public access?, Science, 341, 6146, pp. 616-617, (2013); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Brase J., Making data citeable: datacite, Opening Science: The Evolving Guide on how the Internet is Changing Research, Collaboration and Scholarly Publishing, Vol. 445, pp. 327-329, (2014); Braun V., Clarke V., Using thematic analysis in psychology, Qualitative Research in Psychology, 3, 2, pp. 77-101, (2006); Brown M.L., White W., Case study 2: university of southampton – a partnership approach to research data management, Delivering Research Data Management Services, pp. 135-162, (2014); Bryman A., Social Research Methods, (2012); Callon M., Actor-network theory – the market test, The Sociological Review, 47, 1, pp. 181-195, (1999); Carlson J., Garritano J., E-science, cyberinfrastructure and the changing face of scholarship: organizing for new models of research support at the Purdue university libraries, The Expert Library: Staffing, Sustaining, and Advancing the Academic Library in the 21st Century, pp. 234-269, (2010); Cecez-Kecmanovic D., Kennan M.A., The methodological landscape: information systems and knowledge management, Research Methods: Information, Systems, and Contexts, pp. 113-138, (2013); Cox A.M., Pinfield S., Research data management and libraries: current activities and future priorities, Journal of Librarianship and Information Science, 46, 4, pp. 1-18, (2013); EPSRC Policy Framework on Research Data, (2011); Finch J., Bell S., Bellingan L., Campbell R., Donnelly P., Gardner R., Hall M., Hall S., Kiley R., van der Stelt W., Sweeney D., Sykes P., Tickell A., Wissenburg A., Egginton R., Accessibility, Sustainability, Excellence: How to Expand Access to Research Publications, (2012); Data Protection Act 1998, (1998); Freedom of Information Act 2000, (2000); Hickman L., (2012); Higgins S., The lifecycle of data management, Managing Research Data, pp. 17-46, (2012); Hine C., Databases as scientific instruments and their role in the ordering of scientific work, Social Studies of Science, 36, 2, pp. 269-298, (2006); Jones S., Research data policies: principles, requiremets and trends, Managing Research Data, pp. 4-66, (2012); Jones S., The range and components of RDM infrastructure and services, Delivering Research Data Management Services, pp. 89-114, (2014); Kennan M., Cecez-Kecmanovic D., Reassembling scholarly publishing: institutional repositories, open access, and the process of change, (2007); Kennan M.A., Learning to share: mandates and open access, Library Management, 32, 4-5, pp. 302-318, (2011); Latour B., On actor-network theory: a few clarifications, Soziale Welt, 47, 4, pp. 369-381, (1996); Latour B., On recalling ANT, Actor Network Theory and After, pp. 15-25, (1999); Latour B., Reassembling the Social: An Introduction to Actor-Network Theory, (2005); Latour B., Woolgar S., Laboratory Life: The Construction on Scientific Facts, (1986); Lavoie B.F., Sustainable research data, Managing Research Data, pp. 67-82, (2012); Law J., Notes on the theory of the actor-network: ordering, strategy, and heterogeneity, Systems Practice, 5, 4, pp. 379-393, (1992); Law J., After ANT: complexity, naming and topology, Actor Network Theory and After, pp. 1-14, (1999); Lewis M., Libraries and the management of research data, Envisioning Future Academic Library Servcies: Initiatives, Ideas and Challenges, pp. 145-168, (2010); Lynch C., Big data: how do your data grow?, Nature, 455, 7209, pp. 28-29, (2008); Murray-Rust P., Open data in science, Serials Review, 34, 1, pp. 52-64, (2008); National Research Council N.R.C., A Question of Balance: Private Rights and the Public Interest in Scientific and Technical Databases, (1999); Pampel H., Dallmeier-Tiessen S., Open research data: from vision to practice, Opening Science: The Evolving Guide on how the Internet is Changing Research, Collaboration and Scholarly Publishing, pp. 213-224, (2014); Pinfield S., Cox A.M., Smith J., Research data management and libraries: relationships, activities, drivers and influences, PLoS ONE, 9, 12, (2014); Pinfield S., Salter J., Bath P.A., Hubbard B., Millington P., Anders J.H.S., Hussain A., Open-access repositories worldwide, 2005-2012: past growth, current characteristics, and future possibilities, Journal of the Association for Information Science and Technology, 65, 12, pp. 2404-2421, (2014); Piwowar H.A., Vision T.J., Data reuse and the open data citation advantage, PeerJ, 1, (2013); Pryor G., Why manage research data?, Managing Research Data, pp. 1-16, (2012); Pryor G., Options and approaches to RDM service provision, Delivering Research Data Management Services, pp. 21-40, (2014); Pryor G., A patchwork of change, Delivering Research Data Management Services, pp. 1-19, (2014); RCUK common principles on data policy – research councils UK, Research Councils UK, (2011); Richards L., Handling Qualitative Data: A Practical Guide, (2009); Rogers E.M., Diffusion of Innovations, (1962); Rogers E.M., Diffusion of Innovations, (2003); Royal Society R.S., (2012); Silvis E., Alexander P.M., A study using a graphical syntax for actor-network theory, Information Technology and People, 27, 2, pp. 110-128, (2014); Suber P., Open Access, (2012); Thomas G., How to do your Case Study: A Guide for Students and Researchers, (2011); Research Data Management Policy v7, (2012); Research Data Management Policy for UEL, (2012); Open Access Research and Research Data Management Policy, (2013); Section IV: Research Data Management Policy, (2012); Vision T.J., Open data and the social contract of scientific publishing, Bioscience, 60, 5, pp. 330-331, (2010); Wallis J.C., Rolando E., Borgman C.L., If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology, PloS ONE, 8, 7, (2013); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Willinsky J., The Access Principle: The Case for Open Access to Research and Scholarship, (2006)","S. Pinfield; University of Sheffield, Sheffield, United Kingdom; email: s.pinfield@sheffield.ac.uk","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84941338286"
"da Silva J.R.; Castro J.A.; Ribeiro C.; Lopes J.C.","da Silva, João Rocha (55496903800); Castro, João Aguiar (55977255100); Ribeiro, Cristina (7201734594); Lopes, João Correia (36791598000)","55496903800; 55977255100; 7201734594; 36791598000","Dendro: Collaborative research data management built on linked open data","2014","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","8798","","","483","487","4","8","10.1007/978-3-319-11955-7_71","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908695620&doi=10.1007%2f978-3-319-11955-7_71&partnerID=40&md5=f56a27d6209f49962640bc26b48f2e5e","Universidade do Porto/INESC TEC, Porto, Portugal","da Silva J.R., Universidade do Porto/INESC TEC, Porto, Portugal; Castro J.A., Universidade do Porto/INESC TEC, Porto, Portugal; Ribeiro C., Universidade do Porto/INESC TEC, Porto, Portugal; Lopes J.C., Universidade do Porto/INESC TEC, Porto, Portugal","Research datasets in the so-called “long-tail of science” are easily lost after their primary use. Support for preservation, if available, is hard to fit in the research agenda. Our previous work has provided evidence that dataset creators are motivated to spend time on data description, especially if this also facilitates data exchange within a group or a project. This activity should take place early in the data generation process, when it can be regarded as an actual part of data creation. We present the first prototype of the Dendro platform, designed to help researchers use concepts from domain-specific ontologies to collaboratively describe and share datasets within their groups. Unlike existing solutions, ontologies are used at the core of the data storage and querying layer, enabling users to establish meaningful domain-specific links between data, for any domain. The platform is currently being tested with research groups from the University of Porto. © Springer International Publishing Switzerland 2014.","","Digital storage; Electronic data interchange; Information management; Linked data; Ontology; Semantic Web; Collaborative research; Data creation; Data generation; Domain specific; Domain-specific ontologies; Linked open datum; Research agenda; Research groups; Open Data","","","","","National Strategic Reference Framework; Fundação para a Ciência e a Tecnologia, FCT, (SFRH/BD/77092/2011); European Regional Development Fund, ERDF","This work is supported by project NORTE-07-0124-FEDER-000059, financed by the North Portugal Regional Operational Programme (ON.2–O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciêancia e a Tecnologia (FCT). João Rocha da Silva is also supported by research grant SFRH/BD/77092/2011, provided by the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT). ","Castro J., Ribeiro C., Rocha J., Designing an application profile using qualified dublin core: A case study with fracture mechanics datasets, Proceedings of the DC-2013 Conference, pp. 47-52, (2013); Chan L., Metadata interoperability and standardization-a study of methodology Part I, D-Lib Mag, 12, pp. 1-34, (2006); Heery R., Patel M., Application profiles: Mixing and matching metadata schemas, (2000); Heidorn P.B., Shedding light on the dark data in the long tail of science, Libr. Trends, 57, 2, pp. 280-299, (2008); Hodson S., ADMIRAL: A Data Management Infrastructure for Research Activities in the Life sciences, (2011); Li Y.-F., Kennedy G., Ngoran F., Wu P., An ontology-centric architecture for extensible scientific data management systems, Future Gener. Comput. Syst, 29, 2, pp. 1-38, (2013); Rocha J., Barbosa J., Gouveia M., Ribeiro C., Correia Lopes J., UPBox and DataNotes: A collaborative data management environment for the long tail of research data, iPres 2013 Conference Proceedings, (2013); Treloar A., Wilkinson R., Rethinking metadata creation and management in a data-driven research world, 2008 IEEE Fourth International Conference on eScience, pp. 782-789","","Presutti V.; Blomqvist E.; Troncy R.; Sack H.; Papadakis I.; Tordai A.","Springer Verlag","Annomarket; Big Data Public-Private Forum; fluid Operations; Forging Online Education through FIRE (FORGE); iSOCO; LDBC: Linked Data Benchmark Council; LinkedTV; LinkedUp; MediaMixer; PlanetData; Prelida: Preserving Linked Data; VideoLectures.NET; XLike; Yahoo! Labs","11th European Semantic Web Symposium on Satellite Events, ESWC 2014","20 October 2014 through 22 October 2014","Ouro Preto","109369","03029743","978-331911954-0","","","English","Lect. Notes Comput. Sci.","Article","Final","","Scopus","2-s2.0-84908695620"
"Amorim R.C.; Castro J.A.; Dasilva J.R.; Ribeiro C.","Amorim, Ricardo Carvalho (56442184300); Castro, João Aguiar (55977255100); Dasilva, João Rocha (56442622200); Ribeiro, Cristina (7201734594)","56442184300; 55977255100; 56442622200; 7201734594","Engaging researchers in data management with labtablet, an electronic laboratory notebook","2015","Communications in Computer and Information Science","563","","","216","223","7","3","10.1007/978-3-319-27653-3_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952690627&doi=10.1007%2f978-3-319-27653-3_21&partnerID=40&md5=3ff258648e0f37057179efeea262c74c","INESC TEC, Universidade do Porto, Porto, Portugal","Amorim R.C., INESC TEC, Universidade do Porto, Porto, Portugal; Castro J.A., INESC TEC, Universidade do Porto, Porto, Portugal; Dasilva J.R., INESC TEC, Universidade do Porto, Porto, Portugal; Ribeiro C., INESC TEC, Universidade do Porto, Porto, Portugal","Dealing with research data management can be a complex task, and recent guidelines prompt researchers to actively participate in this activity. Emergent research data platforms are proposing workflows to motivate researchers to take an active role in the management of their data. Other tools, such as electronic laboratory notebooks, can be embedded in the laboratory environment to ease the collection of valuable data and metadata as soon as it is available. This paper reports an extension of the previously developed LabTablet application to gather data and metadata for different research domains. Along with this extension, we present a case study from the social sciences, concerning the identification of the data description requirements for one of its domains. We argue that the LabTablet can be crucial to engage researchers in data organization and description. After starting the process, researchers can then manage their data in Dendro, a staging platform with stronger, collaborative management capabilities, which allows them to export their annotated datasets to selected research data repositories. © Springer International Publishing Switzerland 2015.","","Computational linguistics; Metadata; Slate; Annotated datasets; Collaborative management; Data and metadata; Data organization; Electronic laboratory notebook; Laboratory environment; Research data managements; Research domains; Information management","","","","","National Strategic Reference Framework; Fundação para a Ciência e a Tecnologia, FCT, (SFRH/BD/77092/2011); European Regional Development Fund, ERDF","Project SIBILA-Towards Smart Interacting Blocks that Improve Learned Advice, reference NORTE-07-0124-FEDER000059, funded by the North Portugal Regional Operational Programme (ON.2-O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT). João Rocha da Silva is also supported by research grant SFRH/BD/77092/2011, provided by the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT). ","Amorim R.C., Castro J.A., da Silva J.R., Ribeiro C., LabTablet: Semantic metadata collection on a multi-domain laboratory notebook, MTSR 2014, 478, pp. 193-205, (2014); Amorim R.C., Castro J.A., Silva J.R., Ribeiro C., A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential, New Contributions in Information Systems and Technologies, 353, pp. 101-111, (2015); Borgman C.L., Advances in information science: The conundrum of sharing research data, J. Am. Soc. Inf. Sci. Technol, 63, 6, pp. 1059-1078, (2011); Castro J.A., da Silva J.R., Ribeiro C., Creating lightweight ontologies for dataset description. Practical applications in a cross-domain research data management workflow, IEEE/ACM Joint Conference on Digital Libraries (JCDL), pp. 313-316, (2014); da Silva J.R., Castro J.A., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Proceedings of the iPres 2014 Conference, (2014); Lynch C.A., Institutional repositories: Essential infrastructure for scholarship in the digital age, (2003); Lyon L., Dealing with data: Roles, rights, responsibilities and relationships, pp. 1-65, (2007); Jason T., Nickla and Matthew B Boehm, Proper laboratory notebook practices: Protecting your intellectual property. J. Neuroimmune Pharmacol, 6, 1, pp. 4-9, (2011); Rice R., ApplyingDC to institutional data repositories, Proceedings of the International Conference on Dublin Core and Metadata Applications, (2008); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011); Treloar A., Wilkinson R., Rethinking metadata creation and management in a data-driven research world, IEEE Fourth International Conference on eScience, pp. 782-789, (2008); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals, J. Am. Soc. Inf. Sci. Technol, 63, 8, pp. 1505-1520, (2012)","R.C. Amorim; INESC TEC, Universidade do Porto, Porto, Portugal; email: ricardo.amorim3@gmail.com","Sierra-Rodríguez J.-L.; Leal J.P.; Simões A.","Springer Verlag","","4th International Symposium on Languages, Applications and Technologies, SLATE 2015","18 June 2015 through 19 June 2015","Madrid","159539","18650929","978-331927652-6","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-84952690627"
"Steiner K.","Steiner, Katrin (56808895600)","56808895600","Research Data Management and Information Literacy-New Developments at New Zealand University Libraries; [Forschungsdatenmanagement und Informationskompetenz-Neue Entwicklungen an Hochschulbibliotheken Neuseelands]","2015","Information-Wissenschaft und Praxis","66","4","","230","236","6","2","10.1515/iwp-2015-0040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940369379&doi=10.1515%2fiwp-2015-0040&partnerID=40&md5=e208b50c54353a582891827f5f26a63f","LOTSE-Geschäftsstelle, ULB Informationskompetenz, Universitäts-und Landesbibliothek Münster, Krummer Timpen 3, Münster, Telefon, 48143, Germany","Steiner K., LOTSE-Geschäftsstelle, ULB Informationskompetenz, Universitäts-und Landesbibliothek Münster, Krummer Timpen 3, Münster, Telefon, 48143, Germany","Dealing with research data as the basis of academic research is a new field for libraries. Their role and scope in this new field is still being discussed and gains importance within the changing reality of the digital academic research setting. In Anglo-American countries as well as in Germany, the concept of information literacy has been broadened and now includes the whole academic research process as well as dealing with research data. After a short glance at the broadened concept of information literacy, the author analyses which services and structures are being developed and applied in New Zea-land in the field of research data management and reflects on the problems which have to be considered when setting up these new services. New Zealand with its well-developed tertiary education system can serve as a model here to see which problems have to be taken into account when striving for the same aim in Germany. Structurally and conceptually, the holistic Research Content Ecology model of Lincoln University can serve as an example for the German context on how to establish a university-wide service for the support of research activities and the enhancement of researchers' information literacy. © 2015 by Walter de Gruyter Berlin Boston.","Data; Information literacy; Library; New Zealand; Research; Service","","","","","","","","Buttner S., Hobohm H.-C., Muller L., Handbuch For-schungsdatenmanagement. Bad Honnef: Bock + Herchen, 2011; Bundy A., Australian and New Zealand Information Literacy Framework. 2. Auflage, (2004); Carlson J., Et al., Developing an approach for data management education: A report from the data information literacy project, International Journal of Digital Curation, 1, pp. 204-217, (2013); Carlson J., Johnston L., Huang Y., Data Information Literacy. Librarians, Data and the Education of A New Generation of Researchers. Purdue Information Literacy Handbooks, (2015); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing Research Data, pp. 105-133, (2012); Cox M., Verbaan E., Sen B., A Spider, an Octopus, or an Animal Just Coming into Existence? Designing a Curriculum for Librarians to Support Research Data Management, Journal of EScience Librarianship, 3, 1, (2014); Dawson R., Making Visible the Experience and Activity in the Research Ecology at Lincoln University. An Invited Address to Colleagues at the Holmes Bay Retreat, Banks Peninsula, 12 November 2012, (2012); Hochschule im Digitalen Zeit-alter: Informationskompetenz Neu Begreifen-Prozesse Anders Steuern, (2012); Nardi B., O'Day V., Information Ecologies, (1999); Pryor G., Managing Research Data, (2012); The SCONUL Seven Pillars of Information Literacy. A Research Lens for Higher Education, (2011); Steiner K., Forschungsdatenmanagement und Informationskompetenz-Neue Entwicklungen in Hochschulbibliotheken Neuseelands, (2013); Tappenbeck I., Fachreferat 2020: From Collections to Connections, Bibliotheksdienst, 49, 1, pp. 37-48, (2015); Information Literacy Lens on the Vitae Researcher Development Framework Using the SCONUL Seven Pillars of Information Literacy, (2012)","K. Steiner; LOTSE-Geschäftsstelle, ULB Informationskompetenz, Universitäts-und Landesbibliothek Münster, Münster, Telefon, Krummer Timpen 3, 48143, Germany; email: steinerk@uni-muenster.de","","Deutsche Gesellschaft fur Dokumentation E.V","","","","","","14344653","","","","German","Inf.-Wiss. Prax.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84940369379"
"Lee D.J.; Stvilia B.","Lee, Dong Joon (56133692500); Stvilia, Besiki (22836964300)","56133692500; 22836964300","Developing a Data Identifier Taxonomy","2014","Cataloging and Classification Quarterly","52","3","","303","336","33","8","10.1080/01639374.2014.880166","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897394505&doi=10.1080%2f01639374.2014.880166&partnerID=40&md5=2d38b2667a6a863703262b375c015e30","College of Communication and Information, Florida State University, Tallahassee, FL, United States","Lee D.J., College of Communication and Information, Florida State University, Tallahassee, FL, United States; Stvilia B., College of Communication and Information, Florida State University, Tallahassee, FL, United States","As the amount of research data management is growing, the use of identity metadata for discovering, linking, and citing research data is growing too. To support the awareness of different identifier systems and the comparison and selection of an identifier for a particular data management environment, there is need for a knowledge base. This article contributes to that goal and analyzes the data management and related literatures to develop a data identifier taxonomy. The taxonomy includes four categories (domain, entity types, activities, and quality dimensions). In addition, the article describes 14 identifiers referenced in the literature and analyzes them along the taxonomy. © 2014 © Dong Joon Lee and Besiki Stvilia.","identifier; quality requirements; research data","","","","","","National Science Foundation, NSF, (10-077); National Institutes of Health, NIH, (03-05-2003)","Funding text 1: Major funding agencies, such as the National Science Foundation (NSF) and National Institutes of Health (NIH), now require applicants to submit plans for managing and providing access to research data.97 This pressure from funding agencies and their user communities encourages libraries and data centers to establish projects like DataCite to help researchers find, access, and reuse data. DataCite also provides services and tools for data publishers to generate associated metadata. DataCite uses DOIs as its only value for identifiers.98; Funding text 2: 83. International Federation of Library Associations and Institutions, Functional Requirements for Bibliographic Records. 84. “PROV Model Primer.” 85. Google Developers, “Google Schema,” 2012, https://developers.google.com/public-data/ docs/schema/dspl9 86. International Federation of Library Associations and Institutions, Functional Requirements for Bibliographic Records. 87. Ibid., 79. 88. NISO/NFAIS, Recommended Practices for Online Supplemental Journal Article Materials. 89. Jian Qin, Alex Ball, and Jane Greenberg, “Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data,” in Proceedings of International Conference on Dublin Core and Metadata Applications (Kuching, Sarawak, Malaysis, 2012). 90. DataUp, “DataUp,” n.d., http://dataup.cdlib.org/ 91. Toby Green, “We Need Publishing Standards for Datasets and Data Tables” (OECD Publishing, 2009), http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.40/2010/wp.8.e.pdf 92. Altman and King, “A Proposed Standard for the Scholarly Citation of Quantitative Data.” 93. Duerr et al., “On the Utility of Identification Schemes for Digital Earth Science Data.” 94. Altman and King, “A Proposed Standard for the Scholarly Citation of Quantitative Data.” 95. Lee and Stvilia, “Identifier Schemas and Research Data.” 96. Besiki Stvilia et al., “Composition of Scientific Teams and Publication Productivity at a National Science Lab,” Journal of the American Society for Information Science and Technology 62, no. 2 (February 2011): 270–283, doi:10.1002/asi.21464; Charles C. Hinnant et al., “Author-Team Diversity and the Impact of Scientific Publications: Evidence from Physics Research at a National Science Lab,” Library & Information Science Research 34, no. 4 (October 2012): 249–257, doi:10.1016/j.lisr.2012.03.001 97. National Institutes of Health, “NIH Data Sharing Policy and Implementation Guidance (NIH Publication No. 03-05-2003)”; National Science Foundation, “Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans (NSF 10-077).” 98. DataCite, “DataCite,” accessed November 6, 2012, http://datacite.org/ 99. Mercè Crosas, “The Dataverse Network©R: An Open-Source Application for Sharing, Discovering and Preserving Data,” D-Lib Magazine 17, no. 1/2 (January 2011), doi:10.1045/january2011-crosas 100. William Michener et al., “DataONE: Data Observation Network for Earth—Preserving Data and Enabling Innovation in the Biological and Environmental Sciences,” D-Lib Magazine 17, no. 1/2 (January 2011), doi:10.1045/january2011-michener 101. T Heath, “Linked Data,” n.d., http://linkeddata.org/home 102. Christian Bizer, Tom Heath, and Tim Berners-Lee, “Linked Data—The Story so Far,” International Journal on Semantic Web and Information Systems 5, no. 3 (2009): 1–22, doi:10.4018/jswis.2009081901 103. Tim Berners-Lee, “Linked Data” (W3C, 2006), http://www.w3.org/DesignIssues/ LinkedData.html 104. Ibid. 105. World Wide Web Consortium (W3C), “Resource Description Framework (RDF): Concepts and Abstract Syntax,” 2004, http://www.w3.org/TR/rdf-concepts/ 106. Chris Bizer, Richard Cyganiak, and Tom Heath, “How to Publish Linked Data on the Web,” 2007, http://www4.wiwiss.fu-berlin.de/bizer/pub/linkeddatatutorial/ 107. Stvilia et al., “Studying the Data Practices of a Scientific Community.” 108. Aalbersberg and Kähler, “Supporting Science through the Interoperability of Data and Articles.” 109. D Abbott, “Annotation,” DCC Briefing Papers: Introduction to Curation (2008), http:// www.dcc.ac.uk/resources/briefing-papers/introduction-curation/annotation 110. Wu, Stvilia, and Lee, “Authority Control for Scientific Data.” 111. K. D. Pruitt et al., “NCBI Reference Sequences: Current Status, Policy and New Initiatives,” Nucleic Acids Research 37, no. Database (January 1, 2009): D32–D36, doi:10.1093/nar/gkn721 112. W3C, “Open Annotation Data Model,” 2013, http://www.w3.org/ns/oa 113. Akhondi, Kors, and Muresan, “Consistency of Systematic Chemical Identifiers Within and Between Small-molecule Databases”; Altman and King, “A Proposed Standard for the Scholarly Citation of Quantitative Data”; Berners-Lee, “Cool URIs Don’t Change”; Amy Brand, Frank Daly, and Barbara Meyers, Metadata Demystified (Sheridan and NISO Press, 2003), http://www.niso.org/standards/resources/Metadata_Demystified.pdf; Sarah Callaghan et al., “Making Data a First Class Scientific Output: Data Citation and Publication by NERC’s Environmental Data Centres,” International Journal of Digital Curation 7, no. 1 (October 3, 2012): 107–113, doi:10.2218/ijdc.v7i1.218; Andrew Clark, “Anonymising Research Data” (ESRC National Centre for Research Methods, 2006), http://eprints.ncrm.ac.uk/480/1/0706_anonymising_research_data.pdf; Clark, Martin, and Liefeld, “Globally Distributed Object Identification for Biological Knowledgebases”; Crosas, “The Dataverse Network©R ”; Duerr et al., “On the Utility of Identification Schemes for Digital Earth Science Data”; N. Juty, N. Le Novère, and C. Laibe, “Identifiers.org and MIRIAM Registry: Community Resources to Provide Persistent Identification,” Nucleic Acids Research 40, no. D1 (December 2, 2011): D580–D586, doi:10.1093/nar/gkr1097; Lee and Stvilia, “Identifier Schemas and Research Data”; Michener et al., “DataONE”; NISO/NFAIS, Recommended Practices for Online Supplemental Journal Article Materials; Carl Lagoze et al., “Specification and XML Schema for the OAI Identifier Format,” March 9, 2006, http://www.openarchives.org/ OAI/2.0/guidelines-oai-identifier.htm; Paskin, “Digital Object Identifier (DOI©R) System”; Pepler and O’Neil, Preservation Intent and Collection Identifiers; Tonkin, “Persistent Identifiers: Considering the Options”; Vitiello, “Identifiers and Identification Systems”; Toblas Weigel et al., “A Framework for Extended Persistent Identification of Scientific Assets,” Data Science Journal 12 (2013): 10–22.","NIH Data Sharing Policy and Implementation Guidance (NIH Publication No. 03-05-2003), (2010); Grant Proposal Guide (gpg 11001), (2010); Aalbersberg I.J., Kahler O., Supporting Science through the Interoperability of Data and Articles, D-Lib Magazine, 17, 1-2, (2011); 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CAS Registry and CAS Registry Number FAQs, (2012); CAS Registry-The Gold Standard for Chemical Substance Information, (2012); Clark T., Martin S., Liefeld T., Globally Distributed Object Identification for Biological Knowledgebases, Briefings in Bioinformatics, 5, 1, pp. 59-70, (2004); ISNI, (2012); VIAF; What Is ORCID?; CrossRef & ORCID, (2011); What Is ORCID?; What Is researcherID?; Reuters T., Thomson Reuters Unveils Data Citation Index for Discovering Global Data Sets; Warner S., Author Identifiers in Scholarly Repositories, (2010); OpenID, (2013); About GeoNames; Uniform Administrative Requirements for Grants and Agreements With Institutions of Higher Education, Hospitals, and Other Non-Profit Organizations (OMB Circular 110), (1999); Huang H., Et al., Prioritization of Data Quality Dimensions and Skills Requirements in Genome Annotation Work, Journal of the American Society for Information Science and Technology, 63, 1, pp. 195-207, (2012); Erway R., Lasting Impact: Sustainability of Diciplinary Repositories, (2012); Tonkin E., Persistent Identifiers: Considering the Options, ARIADNE, 56, (2008); Wu, Stvilia, Lee, Authority Control for Scientific Data; Stvilia B., Et al., Studying the Data Practices of a Scientific Community, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '13, pp. 425-426, (2013); Workshop on FRBR and Identifiers, (2010); LeBoeuf P., Identifying 'Textual Works': ISTC: Controversy and Potential, FRBR in 21st Century Catalogues: An Invitational Workshop, (2005); Baker T., Dekkers M., Identifying Metadata Elements with URIs, D-Lib Magazine, 9, 7-8, (2003); Wynholds L., Linking to Scientific Data: Identity Problems of Unruly and Poorly Bounded Digital Objects, International Journal of Digital Curation, 6, 1, (2011); PREMIS Data Dictionary for Preservation Metadata, Version 2.2, (2012); Definition of the CIDOC Conceptual Reference Model, (2012); Functional Requirements for Bibliographic Records, (2009); Premis Implementation Registry, (2011); Functional Requirements for Bibliographic Records; Bibliographic Framework as a Web of Data: Linked Data Model and Supporting Services, (2012); Definition of the CIDOC Conceptual Reference Model; PREMIS Data Dictionary for Preservation Metadata, Version 2.2; PREMIS Data Dictionary for Preservation Metadata, Version 2.2; Carlyle A., FRBR and the Bibliographic Universe, or, How to Read FRBR as a Model, ALA Annual Conference, (2004); Renear A., Et al., An XML Document Corresponds to Which FRBR Group 1 Entity?, Proceedings of Extreme Markup Languages 2003, (2003); Buckland M., What Is a Digital Document?, Document Numerique (Paris), 2, 2, pp. 221-230, (1998); Floyd I.R., Renear A.H., What Exactly Is an Item in the Digital World?, Proceedings of the American Society for Information Science and Technology, 44, 1, pp. 1-7, (2007); Halpin H., The Principle of Self-description: Identity through Linking, Proceedings of the 1st IRSW 2008, (2008); Caplan, Understanding PREMIS; Vitiello G., Identifiers and Identification Systems, D-Lib Magazine, 10, 1, (2004); PREMIS Data Dictionary for Preservation Metadata, Version 2.2; Caplan, Understanding PREMIS; PREMIS Data Dictionary for Preservation Metadata, Version 2.2; Functional Requirements for Bibliographic Records; Definition of the CIDOC Conceptual Reference Model; Wynholds, Linking to Scientific Data; Pollard T., Wilkinson J., Making Datasets Visible and Accessible: DataCite's First Summer Meeting, Ariadne, (2010); Simmhan, Plale, Gannon, A Survey of Data Provenance in E-science; Stvilia B., Et al., A Framework for Information Quality Assessment, Journal of the American Society for Information Science and Technology, 58, 12, pp. 1720-1733, (2007); PROV Model Primer, (2013); Functional Requirements for Bibliographic Records; Definition of the CIDOC Conceptual Reference Model; Information and Documentation-International Standard Name Identifier (ISNI), (2012); GIS Across the Disciplines, (2013); Functional Requirements for Bibliographic Records; Definition of the CIDOC Conceptual Reference Model; About GeoNames; Definition of the CIDOC Conceptual Reference Model; PREMIS Data Dictionary for Preservation Metadata, Version 2.2; Definition of the CIDOC Conceptual Reference Model; Functional Requirements for Bibliographic Records; PROV Model Primer; Google Developers, Google Schema, (2012); Functional Requirements for Bibliographic Records; Functional Requirements for Bibliographic Records; Recommended Practices for Online Supplemental Journal Article Materials; Qin J., Ball A., Greenberg J., Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data, Proceedings of International Conference on Dublin Core and Metadata Applications, (2012); DataUp; Green T., We Need Publishing Standards for Datasets and Data Tables, (2009); Altman, King, A Proposed Standard for the Scholarly Citation of Quantitative Data; Duerr, Et al., On the Utility of Identification Schemes for Digital Earth Science Data; Altman, King, A Proposed Standard for the Scholarly Citation of Quantitative Data; Lee, Stvilia, Identifier Schemas and Research Data; Stvilia B., Et al., Composition of Scientific Teams and Publication Productivity at a National Science Lab, Journal of the American Society for Information Science and Technology, 62, 2, pp. 270-283, (2011); Hinnant C.C., Et al., Author-Team Diversity and the Impact of Scientific Publications: Evidence from Physics Research at a National Science Lab, Library & Information Science Research, 34, 4, pp. 249-257, (2012); NIH Data Sharing Policy and Implementation Guidance (NIH Publication No. 03-05-2003); Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans (NSF 10-077); DataCite, (2012); Crosas M., The Dataverse Network®: An Open-Source Application for Sharing, Discovering and Preserving Data, D-Lib Magazine, 17, 1-2, (2011); Michener W., Et al., DataONE: Data Observation Network for Earth-Preserving Data and Enabling Innovation in the Biological and Environmental Sciences, D-Lib Magazine, 17, 1-2, (2011); Heath T., Linked Data; Bizer C., Heath T., Berners-Lee T., Linked Data-The Story so Far, International Journal on Semantic Web and Information Systems, 5, 3, pp. 1-22, (2009); Berners-Lee T., Linked Data, (2006); Berners-Lee T., Linked Data, (2006); Resource Description Framework (RDF): Concepts and Abstract Syntax, (2004); Bizer C., Cyganiak R., Heath T., How to Publish Linked Data on the Web, (2007); Stvilia, Et al., Studying the Data Practices of a Scientific Community; Aalbersberg, Kahler, Supporting Science through the Interoperability of Data and Articles; Abbott D., Annotation, DCC Briefing Papers: Introduction to Curation, (2008); Wu, Stvilia, Lee, Authority Control for Scientific Data; Pruitt K.D., Et al., NCBI Reference Sequences: Current Status, Policy and New Initiatives, Nucleic Acids Research, 37, DATABASE, (2009); Open Annotation Data Model, (2013); Akhondi, Kors, Muresan, Consistency of Systematic Chemical Identifiers Within and Between Small-molecule Databases; Altman, King, A Proposed Standard for the Scholarly Citation of Quantitative Data; Berners-Lee, Cool URIs Don't Change; Brand A., Daly F., Meyers B., Metadata Demystified, (2003); Callaghan S., Et al., Making Data a First Class Scientific Output: Data Citation and Publication by NERC's Environmental Data Centres, International Journal of Digital Curation, 7, 1, pp. 107-113, (2012); Clark A., Anonymising Research Data, (2006); Clark, Martin, Liefeld, Globally Distributed Object Identification for Biological Knowledgebases; Crosas, The Dataverse Network®; Duerr, Et al., On the Utility of Identification Schemes for Digital Earth Science Data; Juty N., Le Novere N., Laibe C., Identifiers. org and MIRIAM Registry: Community Resources to Provide Persistent Identification, Nucleic Acids Research, 40, D1, (2011); Lee, Stvilia, Identifier Schemas and Research Data; Michener, Et al., DataONE; Recommended Practices for Online Supplemental Journal Article Materials; Lagoze C., Et al., Specification and XML Schema for the OAI Identifier Format, (2006); Paskin, Digital Object Identifier (DOI®) System; Pepler, O'Neil, Preservation Intent and Collection Identifiers; Tonkin, Persistent Identifiers: Considering the Options; Vitiello, Identifiers and Identification Systems; Weigel T., Et al., A Framework for Extended Persistent Identification of Scientific Assets, Data Science Journal, 12, pp. 10-22, (2013); Wang R., Strong D., Beyond Accuracy: What Data Quality Means to Data Consumers, Journal of Management Information Systems, 12, 4, pp. 5-35, (1996); Eppler M., Managing Information Quality: Increasing the Value of Information in Knowledgeintensive Products and Processes, (2003); Stvilia, Et al., A Framework for Information Quality Assessment; Berners-Lee, Cool URIs Don't Change; Recommended Practices for Online Supplemental Journal Article Materials; Tonkin, Persistent Identifiers: Considering the Options; Erway, Lasting Impact; Clark, Martin, Liefeld, Globally Distributed Object Identification for Biological Knowledgebases; Berners-Lee T., Message on Www-tag@w3.org List, (2003); Berners-Lee T., Message to Www-tag@w3.org List, (2003); Halpin H., Sense and Reference on the Web, Minds and Machines, 21, 2, pp. 153-178, (2011); Hayes P., RDF Semantics, (2004); Abbott D., Interoperability, DCC Briefing Papers: Introduction to Curation, (2009); Paskin, Identifier Interoperability; Dunsire G., Distinguishing Content from Carrier, D-Lib Magazine, 13, 1-2, (2007); Paskin, Identifier Interoperability; Pabon G., Et al., Linked Open Data Technologies for Publication of Census Microdata, Journal of the American Society for Information Science and Technology, 64, 9, (2013); Paskin, Digital Object Identifier (DOI®) System; Lee, Stvilia, Identifier Schemas and Research Data; Michener, Et al., DataONE; Lee, Stvilia, Identifier Schemas and Research Data; Buckland, What Is a Digital Document?; Carlyle, FRBR and the Bibliographic Universe, or, How to Read FRBR as a Model; Floyd, Renear, What Exactly Is an Item in the Digital World?; Renear, Et al., An XML Document Corresponds to Which FRBR Group 1 Entity?; Workshop on FRBR and Identifiers; Halpin, The Principle of Self-description: Identity through Linking; Vitiello, Identifiers and Identification Systems; Clark, Martin, Liefeld, Globally Distributed Object Identification for Biological Knowledgebases; Altman, King, A Proposed Standard for the Scholarly Citation of Quantitative Data; Michener, Et al., DataONE; Publication Manual of the APA, (2010); CAS Information Use Policies, (2012); Clark, Martin, Liefeld, Globally Distributed Object Identification for Biological Knowledgebases; About GeoNames; Paskin N., Digital Object Identifiers for Scientific Data, Data Science Journal, (2005); Pruitt, Et al., NCBI Reference Sequences; Paskin, Identifier Interoperability; Altman, King, A Proposed Standard for the Scholarly Citation of Quantitative Data; Michener, Et al., DataONE; Duerr, Et al., On the Utility of Identification Schemes for Digital Earth Science Data; Altman, King, A Proposed Standard for the Scholarly Citation of Quantitative Data; Vitiello, Identifiers and Identification Systems; LeBoeuf, Identifying 'Textual Works': ISTC: Controversy and Potential; Workshop on FRBR and Identifiers; Simmhan, Plale, Gannon, PROV Model Primer, A Survey of Data Provenance in E-science; Stvilia, Et al., Studying the Data Practices of a Scientific Community","D. J. Lee; College of Communication and Information, Florida State University, Tallahassee, FL 32306-2100, 266 Louis Shores Bldg., 142 Col. Loop, P.O. Box 3062100, United States; email: dl10e@my.fsu.edu","","Routledge","","","","","","01639374","","","","English","Cat. Classif. Q.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84897394505"
"Mattern E.; Jeng W.; He D.; Lyon L.; Brenner A.","Mattern, Eleanor (51161516100); Jeng, Wei (37023332900); He, Daqing (15044318000); Lyon, Liz (56835287100); Brenner, Aaron (55909476000)","51161516100; 37023332900; 15044318000; 56835287100; 55909476000","Using participatory design and visual narrative inquiry to investigate researchers’ data challenges and recommendations for library research data services","2015","Program","49","4","","408","423","15","19","10.1108/PROG-01-2015-0012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941355311&doi=10.1108%2fPROG-01-2015-0012&partnerID=40&md5=3f90d35140496aa2afdcb1d6af660007","School of Information Sciences and University Library System, University of Pittsburgh, Pittsburgh, PA, United States; School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; University Library System, University of Pittsburgh, Pittsburgh, PA, United States","Mattern E., School of Information Sciences and University Library System, University of Pittsburgh, Pittsburgh, PA, United States; Jeng W., School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; He D., School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; Lyon L., School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; Brenner A., University Library System, University of Pittsburgh, Pittsburgh, PA, United States","Purpose – The purpose of this paper is to report on an information gathering study on users’ research data-related challenges and proposals for library research data services (RDS). This study probes how early career researchers visually conceptualize the research process in their disciplines, their self-reported research data challenges, and their recommendations for library RDS. Design/methodology/approach – Two focus group sessions were undertaken with a total of eight early career researchers. Adopting the visual narrative inquiry method, the participants were asked to sketch the general research process in their domain. The individuals’ illustrations of the research process were then used as the basis for reflecting on their data-related needs and potential RDS that would assist them during the research process. Findings – Participants presented a research process that was more personal and, in most cases, more imperfect than the research lifecycle models that academic libraries are increasingly using for RDS development and communication. The authors present their data-related challenges, which included data access barriers, low knowledge of best practices for research data management, the need for a deeper understanding of post-publication impact, and inconsistent awareness of existing library and institution RDS. The authors outline RDS recommendations that participants proposed, which included a web-based tools, customized training sessions, and “distilled” guides to research data best practices. Practical implications – The study flagged users’ gaps in understandings of existing library and institutional RDS, suggesting that there may be an opportunity to engage users in the design of communications plans for services. The findings from this user study will inform the development of RDS at the institution. Originality/value – This paper puts forth a methodological approach that academic libraries can adapt for understanding users’ needs and user-generated design solutions. © 2015, Emerald Group Publishing Limited.","Library service development; Participatory design; Research data services; Visual narrative inquiry","","","","","","University of Pittsburgh","","Bach H., Visual narrative inquiry, The Sage Encyclopedia of Qualitative Research Methods, pp. 939-941, (2008); Bowler L., Knobel C., Mattern E., From cyberbullying to well-being: a narrative-based participatory approach to values-oriented design for social media, Journal of the Association for Information Science and Technology, 66, 6, pp. 1274-1293, (2015); Bowler L., Mattern E., Knobel C., (2014); Bresnahan M.M., Johnson A.M., Assessing scholarly communication and research data training needs, Reference Services Review, 41, 3, pp. 413-433, (2013); Carlson J., (2010); Carlson J., The use of life cycle models in developing and supporting data services, Research Data Management: Practical Strategies for Information Professionals, pp. 63-86, (2014); Connelly F.M., Clandinin D.J., Stories of experience and narrative inquiry, Educational Researcher, 19, 5, pp. 2-14, (1990); Fischer G., (2011); Foster N.F., Participatory design in academic libraries: the second CLIR seminar, Participatory Design in Academic Libraries: New Reports and Findings, pp. 1-6, (2014); Graham S., (2011); Hittinger F., (2014); Marcus C., Ball S., Delserone L., Hribar A., Loftus W., (2007); Posner M., (2011); (2009); Schuler D., Namioka A., Participatory Design: Principles and Practices, (1993); Spinuzzi C., The methodology of participatory design, Technical Communication, 52, 2, pp. 163-174, (2005); Stanfill J.P., (2013); (2012); Weller T., Monroe-Gulick A., Understanding methodological and disciplinary differences in the data practices of academic researchers, Library Hi Tech, 32, 3, pp. 467-482, (2014); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing data curation profiles, The International Journal of Digital Curation, 3, 4, pp. 93-103, (2009); Carlson J., Opportunities and barriers for librarians in exploring data: observations from the data curation profile workshops, Journal of eScience Librarianship, 2, 2, pp. 17-33, (2013); Carlson J., Brandt D.S., (2013)","E. Mattern; School of Information Sciences and University Library System, University of Pittsburgh, Pittsburgh, United States; email: emm100@pitt.edu","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84941355311"
"Walsh P.; Carroll J.","Walsh, Paul (55842012800); Carroll, John (57192310068)","55842012800; 57192310068","Simplicity - Enabling a rapid route to publication","2014","Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014","","","6999386","1","8","7","0","10.1109/BIBM.2014.6999386","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922784231&doi=10.1109%2fBIBM.2014.6999386&partnerID=40&md5=87e55e2526a55838dcb608f109d14b48","Nsilico Life Science Ltd., Melbourn Building, CIT Campus, Bishopstown, Cork, Ireland","Walsh P., Nsilico Life Science Ltd., Melbourn Building, CIT Campus, Bishopstown, Cork, Ireland; Carroll J., Nsilico Life Science Ltd., Melbourn Building, CIT Campus, Bishopstown, Cork, Ireland","Recent years have witnessed extraordinary advancement in the data generation capacity of high throughput sequencing technology. This has led to an enormous increase in the amount of data that needs to be managed, stored, visualised, integrated and shared. Moreover there is a widely recognised need for integration of high throughput sequencing data with heterogeneous data sources in both clinical and translational research. Additionally, clinical data sets have stringent privacy and security requirements that must be adhered to when deploying data handling systems. Also, many researchers who need to access these data sets are non-specialists in the IT domain so need systems that are easy to use. Herein, we present an overview of a novel cloud-based research management software system, which has simplicity, scalability, security (including traceability), speed, reproducibility and integration at its core. Indeed, its goal is to democratise research data management-enabling researchers who do not have specialist IT administration, software coding or data management training to handle large sets of integrated cloud-based software tools that automate many complex research data workflows without the need for extensive customisation or manual intervention. Herein, we describe the sequence information management platform (SimplicityTM), a workflow based bioinformatics management tool, which allows life science researchers to rapidly annotate large amounts of DNA and protein sequence data, and receive a detailed, editable and customisable generated report. The efficacy of the SimplicityTM is demonstrated by showing how the system enables rapid publication of life science discoveries. We present results of a workflow run by SimplicityTM for the Marie Cure funded project ClouDx-i. © 2014 IEEE.","cloud architecture; data provenance; high throughput sequencing; publishable report; traceability","","","","","","","","Schadt E.E., Linderman M.D., Sorenson J., Lee L., Nolan G.P., Computational solutions to large-scale data management and analysis, Nat Rev Genet, 11, 9, pp. 647-657, (2010); Liu C.M., Wong T., Wu E., Luo R., Yiu S.M., Li Y., Wang B., Yu C., Chu X., Zhao K., Li R., Lam T.W., SOAP3: Ultra-fast GPUbased parallel alignment tool for short reads, Bioinformatics, 28, 6, pp. 878-789, (2011); Grossman R., Managing and Analysing 1,000,000 Genomes, (2012); Foster I., Accelerating and democratizing science through cloud-based services, IEEE Internet Computing, (2011); Reed D., Democratizing Research: How 'Client Plus Cloud' Computing Can Amplify What's Possible for Scientists, (2010); Mell P., Grance T., The NIST definition of cloud computing, National Institute of Standards and Technology, (2011); Hyek P., Cloud computing issues and impacts, Global Technology Industry Discussion, (2011); Shvachko K., The hadoop distributed file system, Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium, Mass Storage Systems and Tech., IEEE, (2010); Hull D., Wolstencroft K., Stevens R., Goble C., Pocock M.R., Li P., Oinn T., Taverna: A tool for building and running workflows of services, Nucleic Acids Research.,(Web Server Issue), 34, pp. 729-732, (2006); Brooksbank C., Cameron G., Thornton J., The european bioinformatics institute's data resources, Nucleic Acids Research, Advance Access, (2009); Luscombe N.M., Greenbaum D., Gerstein M., What is bioinformatics? a proposed definition and overview of the field, Methods of Information in Medicine, 4, (2001); Brazas M.D., Yamada J.T., Ouellette B.F., Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory, Nucleic Acids Research, 37, pp. 3-5, (2009); Dudley J.T., Butte A.J., A quick guide for developing effective bioinformatics skills, PLoS Computational Biology, 5, 12, (2009); Papazoglou M.P., Service-oriented computing: State of the art and research challenges, Computer, Volume:40 Issue:11, IEEE Computer Society; Armbrust M., Fox A., Griffith R., Joseph A.D., Katz R., Konwinski A., Lee G., Patterson D., Rabkin A., Stoica I., Zaharia M., A view of cloud computing, Commun. ACM, 53, 4, pp. 50-58; Lu W., Jackson J., Barga R., AzureBlast: A case study of developing science applications on the cloud, Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC '10), pp. 413-420, (2010); Cockburn A., Agile Software Development, (2002); Shachak A., Shuval K., Fine S., Barriers and enablers to the acceptance of bioinformatics tools: A qualitative study, Journal of the Medical Library Association, 95, 4, (2007); Stajich J., Lapp H., Open source tools and toolkits for bioinformatics: Significance, and where are we?, Briefings in Bioinformatics, 7, 3, pp. 287-296, (2006); Greene S., Jones L., Matchen P., Thomas J., Iterative development in the field, IBM Systems Journal, 42, 4, (2003); Seemann T., Prokka: Rapid prokaryotic genome annotation, Bioinformatics, 30, pp. 2068-2069, (2014); Wu C.H., Apweiler R., Bairoch A., Natale D.A., Barker W.C., Boeckmann B., Ferro S., Gasteiger E., Huang H., Lopez R., Et al., The universal protein resource (UniProt): An expanding universe of protein information, Nucleic Acids Res., 34, pp. D187-D191, (2006); Zhengping Q., Yong H., Chunzhi S., Zhuojie W., Hongyu Z., Taizhi Z., Lidong A., Yuan Y., Zhang Z., TimeStream: Reliable stream computation in the cloud, Proceedings of the 8th ACM European Conference on Computer Systems (EuroSys '13), pp. 1-14, (2013); Dikaiakos M.D., Katsaros D., Mehra P., Pallis G., Vakali A., Cloud computing: Distributed internet computing for IT and scientific research, IEEE Internet Comput, 13, pp. 10-13, (2009); Regola N., Chawla N.V., Storing and using health data in a virtual private cloud, Journal of Medical Internet Research, 15, 3, (2013); Kahn S.D., On the future of genomic data, Science, 331, 6018, pp. 728-729, (2011); Foster I., Globus Online: Accelerating and democratizing science through cloud-based services, Internet Computing, IEEE, (2011); Nekrutenko A., Taylor J., Next-generation sequencing data interpretation: Enhancing reproducibility and accessibility, Nature Reviews Genetics, 13, 9, pp. 667-672, (2012); Evans J.A., Foster J.G., Metaknowledge, Science, 331, 6018, pp. 721-725, (2011)","","Zheng H.; Hu X.T.; Berrar D.; Wang Y.; Dubitzky W.; Hao J.-K.; Cho K.-H.; Gilbert D.","Institute of Electrical and Electronics Engineers Inc.","BioBusiness; et al.; IEEE; National Science Foundation (NSF); Nsilico-Simplicity; University of Ulster","2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014","2 November 2014 through 5 November 2014","Belfast","110082","","978-147995669-2","","","English","Proc. - IEEE Int. Conf. Bioinform. Biomed., IEEE BIBM","Conference paper","Final","","Scopus","2-s2.0-84922784231"
"","","","World Conference on Information Systems and Technologies, WorldCIST 2015","2015","Advances in Intelligent Systems and Computing","353","","","1","1249","1248","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927764160&partnerID=40&md5=c84add5641b14d999a7651e6b901525f","","","The proceedings contain 133 papers. The special focus in this conference is on Information and Knowledge Management, Organizational Models, Information Systems, Decision Support Systems, Big Data Analytics and Applications. The topics include: Projects as knowledge swirls in the technological innovation Romania’s situation; concurrency detection on finish-to-start activity precedence networks; refactoring rules for graph databases; knowledge asset management pertinent to information systems outsourcing; meta-model of information visualization based on treemap; a structural prototype for planning and controlling a manufacturing system; towards a multidimensional information retrieval; a comparative study of platforms for research data management; automatic upload of professional profiles directly from sources; prototype of knowledge management in social networks; strength Pareto fitness assignment for generating expansion features; big data, internet of things and cloud convergence for E-health applications; user-driven methodology for data quality assessment in the context of robbery events; a guideline for using knowledge management in telemedicine systems dedicated for diabetes patients in Saudi Arabia; commercial business intelligence suites comparison; a multi-agent framework for web information foraging; the length of hospital stay in acute myocardial infarction; managing academic profiles on scientific social networks; the influence of the use of mobile devices and the cloud computing in organizations; an integrated information architecture for the quality management system of hospitals; types of linkages between business processes and regulations; evolution of methodological proposals for the development of enterprise architecture and an integrated service solution for digital discount coupons processing.","","","","","","","","","","","Rocha A.; Rocha A.; LIACC - Laboratório de Inteligência Artificial e Ciência de Computadores, Rua Dr. Roberto Frias, s/n, Porto, 4200-465; Correia A.M.; Instituto Superior de Estatística e Gestão de Informação, ISEGI, University Nova de Lisboa, Campus de Campolide, Lisboa; Costanzo S.; Reis L.P.","Springer Verlag","aisti; Asociación de Técnicos de Informática; Camoes; Global Institute for IT Management; LIACC; Universidade dos Açores","World Conference on Information Systems and Technologies, WorldCIST 2015","1 April 2015 through 3 April 2015","Ponta Delgada","115919","21945357","978-331916485-4","","","English","Adv. Intell. Sys. Comput.","Conference review","Final","","Scopus","2-s2.0-84927764160"
"Corti L.; Van den Eynden V.","Corti, Louise (8914152700); Van den Eynden, Veerle (6507769964)","8914152700; 6507769964","Learning to manage and share data: Jump-starting the research methods curriculum","2015","International Journal of Social Research Methodology","18","5","","545","559","14","16","10.1080/13645579.2015.1062627","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941803388&doi=10.1080%2f13645579.2015.1062627&partnerID=40&md5=7879c99672c610c8ec156add978b4630","UK Data Archive, University of Essex, Colchester, Essex CO43SQ, United Kingdom","Corti L., UK Data Archive, University of Essex, Colchester, Essex CO43SQ, United Kingdom; Van den Eynden V., UK Data Archive, University of Essex, Colchester, Essex CO43SQ, United Kingdom","Researchers’ responsibilities towards their research data are changing across all domains of social scientific endeavour. Government, funders and publishers expect greater transparency of, open access to, and re-use of research data, and fears over data loss call for more robust information security practices. Researchers must develop, enhance and professionalise their research data management skills to meet these challenges and to deal with a rapidly changing data sharing environment. This paper sets out how we have contributed to jump-starting the research methods training curriculum in this field by translating high-level needs into practical guidance and training activities. Our pedagogical approach involves applicable, easy-to-digest, modules based on best practice guidance for managing and sharing research data. In line with recent findings on successful practices in methods teaching, we work on the principle of embedding grounded learning activities within existing narratives of research design and implementation. © 2015 The Author(s). Published by Taylor & Francis.","data management; data sharing; research methods curriculum; teaching approaches; transferrable skills","","","","","","Economic and Social Research Council, ESRC, (ES/H023445/1, ES/J023477/1)","","Bishop L., Using archived qualitative data for teaching: Practical and ethical considerations, International Journal of Social Research Methodology, 15, pp. 341-350, (2012); Blomfield M., Ethics in economics: Lessons from human subjects research, Erasmus Journal for Philosophy and Economics, 5, pp. 22-44, (2012); A position statement – Society counts, quantitative skills of social sciences and humanities, (2012); High level strategy group for quantitative skills, (2014); Buckley J., Brown M., Thomson S., Olsen W., Carter J., Embedding quantitative skills into the social science curriculum: Case studies from Manchester, International Journal of Social Research Methodology, 18, 5, pp. 495-510, (2015); Learning environment for multilevel methodology and applications, (2014); Corti L., Van den Eynden V., Bishop L., Woollard M., Managing and sharing research data: A guide to good practice, (2014); Corti L., Watkins W., Corti L., Watkins W., IASSIST quarterly[Special edition on statistical literacy], (2004); Corti L., Witzel A., Bishop L., Secondary analysis of qualitative data, Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 6, (2005); Crow G., Edwards R., Perspectives on working with archived textual and visual material in social research, International Journal of Social Research Methodology, 15, (2012); Digital preservation training programme, (2014); Digital curation training for all, (2013); EPSRC policy framework on research data, (2011); Secondary data analysis initiative, (2012); Big data network, (2013); International benchmarking review of UK sociology, (2010); Green A., Gutman M., Building partnerships among social science researchers, institution-based repositories and domain specific data archives, OCLC Systems and Services: International Digital Library Perspectives, 23, pp. 35-53, (2006); Hamilton P., Gossman P., Southern K., Developing innovative support structures for students undertaking small-scale research projects in work settings, (2014); Haynes J., Jones D., A tale of two analyses: The use of archived qualitative data, Sociological Research Online, 17, 2, (2012); Age and attitudes about the rights of homosexuals: A data-driven learning guide, (2009); Kelly A., Introducing sociology students to quantitative research methods, (2012); Kilburn D., Nind M., Wiles R., Learning as researchers and teachers: The development of a pedagogical culture for social science research methods?, British Journal of Educational Studies, 62, pp. 191-207, (2014); Kilburn D., Nind M., Wiles R., Short courses in advanced research methods: Challenges and opportunities for teaching and learning, (2014); Kirton A., Campbell P., Hardwick L., Developing applied research skills through collaboration in extra-academic contexts, (2014); Kottmann A., Reform of doctoral training in Europe: A silent revolution?, Reform of higher education in Europe, pp. 29-43, (2011); Kynaston D., The uses of sociology for real-time history, Forum: Qualitative Social Research, 6, 1, (2005); Leston-Bandera C., Ten tips to develop engaging undergraduate research methods teaching. Blog post, Political Insight, (2013); Digital preservation outreach and education, (2013); MacInnes J., Quantitative methods teaching in UK higher education: The state of the field and how it might be improved, HEA Social Sciences teaching and learning summit: Teaching research methods, (2012); Current research programme, (2014); Programme backgrounds – Promoting a step change in the quantitative skills of social science undergraduates, (2012); Why Q-step? Hopes for wider change, (2013); Common principles on data policy, (2011); Research councils UK policy on open access, (2012); Getstats – Statistical literacy for all, (2014); Shared and collaborative services strategy group, (2014); Silver C., Woolf N., From guided-instruction to facilitation of learning: The development of five-level QDA as a CAQDAS pedagogy that explicates the practices of expert users, International Journal of Social Research Methodology, 18, pp. 527-543, (2015); Sloan L., Innovation in the assessment of social science research methods, social sciences blog, (2013); Smith E., Using secondary data in educational and social research, (2008); Strayhorn T., The (in)effectiveness of various approaches to teaching research methods, Teaching research methods in the social sciences, pp. 119-130, (2009); Trzesniewski K., Donnellamn B., Lucas R., Secondary data analysis: An introduction for psychologists, (2011); Turton J., Getting sociology students into archived qualitative data, (2012); Case studies of re-use, (2014); Prepare and manage data, (2014); Events at the UK Data Service, (2014); Data.bris, (2014); Research data management guidance, (2014); Social network analysis course, (2014); Web science: How the web is changing the world course, (2014); Overview, (2014); Vartanian T., Secondary data analysis, (2010); Transforming professional development for researchers: Vitae achievements and impact 2008–2012, (2013); Williams M., Payne G., Hodgkinson L., Poole D., Does British sociology count? Sociology students’ attitudes toward quantitative methods, Sociology, 42, pp. 1003-1021, (2008)","L. Corti; UK Data Archive, University of Essex, Colchester, Essex CO43SQ, United Kingdom; email: corti@essex.ac.uk","","Routledge","","","","","","13645579","","","","English","Int. J. Soc. Res. Methodol.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84941803388"
"Pinfield S.; Cox A.M.; Smith J.","Pinfield, Stephen (6602090850); Cox, Andrew M. (7402563906); Smith, Jen (57213084572)","6602090850; 7402563906; 57213084572","Research data management and libraries: Relationships, activities, drivers and influences","2014","PLoS ONE","9","12","e114734","","","","94","10.1371/journal.pone.0114734","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915749468&doi=10.1371%2fjournal.pone.0114734&partnerID=40&md5=b1b5b4c94ca751d3ddea5d4f7abd91eb","Information School, University of Sheffield, Sheffield, United Kingdom","Pinfield S., Information School, University of Sheffield, Sheffield, United Kingdom; Cox A.M., Information School, University of Sheffield, Sheffield, United Kingdom; Smith J., Information School, University of Sheffield, Sheffield, United Kingdom","The management of research data is now a major challenge for research organisations. Vast quantities of born-digital data are being produced in a wide variety of forms at a rapid rate in universities. This paper analyses the contribution of academic libraries to research data management (RDM) in the wider institutional context. In particular it: examines the roles and relationships involved in RDM, identifies the main components of an RDM programme, evaluates the major drivers for RDM activities, and analyses the key factors influencing the shape of RDM developments. The study is written from the perspective of library professionals, analysing data from 26 semi-structured interviews of library staff from different UK institutions. This is an early qualitative contribution to the topic complementing existing quantitative and case study approaches. Results show that although libraries are playing a significant role in RDM, there is uncertainty and variation in the relationship with other stakeholders such as ITservices and research support offices. Current emphases in RDM programmes are on developments of policies and guidelines, with some early work on technology infrastructures and support services. Drivers for developments include storage, security, quality, compliance, preservation, and sharing with libraries associated most closely with the last three. The paper also highlights a 'jurisdictional' driver in which libraries are claiming a role in this space. A wide range of factors, including governance, resourcing and skills, are identified as influencing ongoing developments. From the analysis, a model is constructed designed to capture the main aspects of an institutional RDM programme. This model helps to clarify the different issues involved in RDM, identifying layers of activity, multiple stakeholders and drivers, and a large number of factors influencing the implementation of any initiative. Institutions may usefully benchmark their activities against the data and model in order to inform ongoing RDM activity. © 2014 Pinfield et al.","","Academies and Institutes; Biomedical Research; Data Collection; Databases, Bibliographic; Humans; Library Administration; Article; conceptual framework; consultation; data base; decision making; good clinical practice; human; information processing; leadership; library; life cycle; methodology; policy; process development; program development; qualitative analysis; quantitative analysis; research data management; research ethics; resource allocation; responsibility; scientist; semi structured interview; telephone interview; United Kingdom; university; bibliographic database; information processing; library science; medical research; organization; organization and management; procedures; standards","","","","","","","McAfee A., Brynjolfsson E., Big data: The management revolution, Harv Bus Rev, 90, pp. 60-68, (2012); Laney D., 3D Data Management: Controlling Data Volume, Velocity and Variety, (2001); Borgman C.L., The conundrum of sharing research data, J Am Soc Inf Sci Technol, 63, pp. 1059-1078, (2012); Science As An Open Enterprise: Final Report, (2012); EPSRC, EPSRC Policy Framework on Research Data, (2011); NIH, Final NIH Statement on Sharing Research Data, (2003); Lewis M.J., Libraries and the management of research data, Envisioning Future Academic Library Services, (2010); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, J Librariansh Inf Sci, (2014); Corrall S., Kennan M., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Libr Trends, 61, pp. 636-674, (2013); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future: An ACRL White Paper, (2012); Pryor G., Jones S., Whyte A., Delivering Research Data Management Services: Fundamentals of Good Practice, (2013); Pryor G., Managing Research Data, (2012); Auckland M., Re-skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Nielsen H.J., Hjorland B., Curating research data: The potential roles of libraries and information professionals, J Doc, 70, pp. 221-240, (2014); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Delserone L.M., At the watershed: Preparing for research data management and stewardship at the University of Minnesota libraries, Libr Trends, 57, pp. 202-210, (2008); Henty M., Dreaming of data: The library's role in supporting e-research and data management, Australian Library and Information Association Biennial Conference, Alice Springs, (2008); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing Research Data, pp. 105-133, (2012); Cox A.M., Verbaan E., Sen B.A., Upskilling liaison librarians for research data management, Ariadne, 70, (2013); Lyon L., The informatics transform: Re-engineering libraries for the data decade, Int J Digit Curation, 7, pp. 126-138, (2012); Procter R., Halfpenny P., Voss A., Research data management: Opportunities and challenges for HEIs, Managing Research Data, pp. 135-150, (2012); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA J, 39, pp. 70-78, (2013); Tenopir C., Sandusky R.J., Allard S., Birch B., Research data management services in academic research libraries and perceptions of librarians, Libr Inf Sci Res, 36, pp. 84-90, (2014); Cox A.M., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ""wicked"" problem, J Librariansh Inf Sci, (2014); Jones S., Pryor G., Whyte A., How to Develop RDM Services: A Guide for HEIs, (2013); Jones S., The range and components of RDM infrastructure and services, Delivering Research Data Management Services, pp. 89-114, (2014); Whyte A., A pathway to sustainable research data services: From scoping to sustainability, Delivering Research Data Management Services, pp. 59-88, (2014); Mayernik M., Choudhury G., DiLauro T., Metsger E., Pralle B., Et al., The data conservancy instance: Infrastructure and organizational services for research data curation, D-Lib Mag, 18, (2012); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, Int J Digit Curation, 6, pp. 232-244, (2011); Wilson J.A.J., Martinez-Uribe L., Fraser M.A., Jeffreys P., An institutional approach to developing research data management infrastructure, Int J Digit Curation, 6, pp. 274-287, (2011); Carlson J., Garritano J., E-science, Cyberinfrastructure and the Changing Face of Scholarship: Organizing for New Models of Research Support at the Purdue University Libraries, The Expert Library: Staffing, Sustaining, and Advancing the Academic Library in the 21st Century, pp. 234-269, (2010); Shen Y., Varvel V.E., Developing data management services at the Johns Hopkins University, J Acad Librariansh, 39, pp. 552-557, (2013); Treloar A., Choudhury G.S., Michener W., Contrasting national research data strategies: Australia and the USA, Managing Research Data, pp. 173-204, (2012); Braun V., Clarke V., Using thematic analysis in psychology, Qual Res Psychol, 3, pp. 77-101, (2006); Kennan M.A., Learning to share: Mandates and open access, Libr Manag, 32, pp. 302-318, (2011); Abbott A., The System of Professions, (1988)","","","Public Library of Science","","","","","","19326203","","POLNC","25485539","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84915749468"
"Clements A.; McCutcheon V.","Clements, Anna (56115261300); McCutcheon, Valerie (55830464400)","56115261300; 55830464400","Research data meets research information management: Two case studies using (a) pure CERIF-CRIS and (b) EPrints repository platform with CERIF extensions","2014","Procedia Computer Science","33","","","199","206","7","9","10.1016/j.procs.2014.06.033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904467416&doi=10.1016%2fj.procs.2014.06.033&partnerID=40&md5=74301e6f3cd5e3eadeff5093516879b0","University of St Andrews, United Kingdom; University of Glasgow, United Kingdom","Clements A., University of St Andrews, United Kingdom; McCutcheon V., University of Glasgow, United Kingdom","This paper will describe how two research-intensive universities in the UK, St Andrews and Glasgow, have worked together over several years and projects to develop their institutional research management systems to deliver services to support the rapidly evolving needs of funders, institutional policy makers and management, and, importantly, the researchers themselves. This challenge is particularly acute at the moment with 'Open Science' one of the hottest topics around with organisations and funders from the G81 downwards stressing the importance of open data in driving everything from global innovation through to more accountable governance; not to mention the more direct possibility that non-compliance could result in research grant income drying up. There is a need to work with those researchers that need support to develop research data management processes and infrastructures that complement their ways of working and not just impose box- Ticking exercises. We will explain the strategies, systems developed, and concerns arising to date at our two Universities to help support researchers and managers in this (r)evolution. © 2014 Published by Elsevier B.V.","CERIF; CRIS-IR; Institutional repository; Research data management; Research information management","Binary alloys; Information services; Information systems; Information use; Management information systems; Potassium alloys; Sounding apparatus; Uranium alloys; CERIF; Global innovation; Institutional policies; Institutional repositories; Research data managements; Research grants; Research intensive universities; Research management systems; Information management","","","","","","","G8 Open Data Charter and Technical Annex; Common European Research Information Format (CERIF); The Consortia Advancing Standards in Research Administration Information (CASRAI); Elsevier Scopus; Thomson Reuteurs Web of Science; Pure CRIS; DSpace Repository Software; Higher Education Funding Council England, Higher Education Funding Council Wales, Scottish Funding Council, Department for Employment and Learning Northern Ireland, (2014); JISC Research Information Management Programme, (2010); Integrated Research Input and Output System, (2011); JISC RIM Programme, (2011); Cerif for Datasets Project, JISC RIM Programme 2011-2013; EU Engage Open Data Project; Houssos N., Jorg B., Matthews B., A multi-level metadata approach for a public sector information data infrastructure, CRIS2012, (2012); Clements A., Et al., First Workshop on Linking and Contextualising Publications and Datasets, (2013); Joerg B., Datasets in CERIF Blog Post; Eprints ReCollect Plugin; Vocabularies for Open Access; Jones S., Pryor G., Whyte A., How to develop research data management services - A guide for HEIs, DCC How- To Guides, (2013); Datacite; CERIF for Datasets Project Team","","","Elsevier B.V.","CINECA; Elsevier; Epistemio; Thomson Reuters","12th International Conference on Current Research Information Systems, CRIS 2014","13 May 2014 through 15 May 2014","Rome","106373","18770509","","","","English","Procedia Comput. Sci.","Conference paper","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84904467416"
"Maita E.","Maita, Eiichi (23035516900)","23035516900","Professions and facilities for the research data publication - The potential of research libraries","2014","Japanese Journal of Ecology","64","1","","81","86","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906895622&partnerID=40&md5=8e7bdf290d862fc399b0c40f74f71dbb","Center for Global Environmental Research, National Institute for Environmental Studies, Japan","Maita E., Center for Global Environmental Research, National Institute for Environmental Studies, Japan","[No abstract available]","Data scientist; Research data management; Research data publication; Subject librarian","","","","","","","","Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management: Emerging trends in library research support services, Library Trends, 61, pp. 636-674, (2013); Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, (2013); Monastersky R., The Library Reboot, Nature, 495, pp. 430-432, (2013); Nelson B., Empty Archives. Nature, 461, pp. 160-163, (2009); Pryor G., Ch.1 Why manage research data?, Managing Research Data, pp. 1-16, (2012); Sayogo D.S., Pardo T.A., Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data, Goverment Information Quarterly, 30, pp. 519-531, (2013); Tanopir C., Allard S., Douglass K., Andinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, (2011)","E. Maita; Center for Global Environmental Research, National Institute for Environmental Studies, Japan; email: maita.eiichi@nies.go.jp","","Tohoku University","","","","","","00215007","","","","English","Jpn. J. Ecol.","Article","Final","","Scopus","2-s2.0-84906895622"
"Calvert P.","Calvert, Philip (7102368185)","7102368185","Should All Lab Books Be Treated as Vital Records? An Investigation into the Use of Lab Books by Research Scientists","2015","Australian Academic and Research Libraries","46","4","","291","304","13","3","10.1080/00048623.2015.1108897","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954381244&doi=10.1080%2f00048623.2015.1108897&partnerID=40&md5=851af72cfc2a4c7b0b9b81ce63e348c6","School of Information Management, Victoria University of Wellington, Wellington, New Zealand","Calvert P., School of Information Management, Victoria University of Wellington, Wellington, New Zealand","Scientists use lab books to record information about experiments. They are used to review work done in the lab, to replicate experiments, and are critical to intellectual property (IP) claims. Organisations must keep the lab books as records, but the question is asked if lab books are vital records. Because the practice of using lab books varies widely amongst scientists, a qualitative method was used to discover practices and opinions from individual scientists. Two research institutions in New Zealand were used for data collection, with nine scientists, two records managers and two IP managers being interviewed. The conclusion is that not all lab books are vital records but most of them are until they are more than 10 years old, and even then they might be necessary to support IP claims. The storage of lab books in some organisations does not match the status of lab books as vital records and needs improving, perhaps by the use of fire-resistant safes. Data saved on computers is often hard to match with experiments recorded in lab books, so the filenames and metadata links used by scientists need more standardisation. Organisations could occasionally audit the location of lab books. © 2015 Australian Library & Information Association.","Lab books; Records management; Research data management; Scientific research; Vital records","","","","","","","","Andolsen A.A., The pillars of vital records protection, Information Management Journal, 42, pp. 28-32, (2008); Australian Code for the Responsible Conduct of Research, (2007); Bird C.L., Willoughby C., Frey J.G., Laboratory notebooks in the digital era: The role of ELNs in record keeping for chemistry and other sciences, Chemical Society Reviews, 42, pp. 8157-8175, (2013); Borman S., Electronic laboratory notebooks may revolutionize research record-keeping, Chemical & Engineering News, 72, pp. 10-20, (1994); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, pp. 636-674, (2013); Downing J., Murray-Rust P., Tonge A.P., Morgan P., Rzepa H.S., Cotterill F., Day N., Harvey M.J., SPECTRa: The deposition and validation of primary chemistry research data in digital repositories, Journal of Chemical Information and Modelling, 48, pp. 1571-1581, (2008); Franks P.C., Records and information management, (2013); Kenny A., Establishing a vital records programme, Records Management Journal, 1, pp. 54-60, (1989); Macdonald S., Macneil R., Service integration to enhance research data management: RSpace electronic laboratory notebook case study, International Journal of Digital Curation, 10, pp. 163-172, (2015); Milsted A.J., Hale J.R., Frey J.G., Neylon C., LabTrove: A lightweight, web based, laboratory “Blog” as a route towards a marked up record of work in a bioscience research laboratory, PLoS One, 8, (2013); Morris S.E., Cracking the code: Assessing institutional compliance with the Australian code for the responsible conduct of research, Australian Universities’ Review, 52, pp. 18-26, (2010); Records management standard for the New Zealand public sector, (2014); Crown research institutes, (2014); Pickard A.J., Research methods in information, (2013); Pinfield S., Cox A.M., Smith J., Research data management and libraries: Relationships, activities, drivers and influences, PLoS One, 9, (2014); Rowley J., Conducting research interviews, Management Research Review, 35, pp. 260-271, (2012); Saffady W., Managing electronic records, (2009); Shankar K., Ambiguity and legitimate peripheral participation in the creation of scientific documents, Journal of Documentation, 65, pp. 151-165, (2009); Vellucci S., Research data and lined data: A new future for technical services, Rethinking technical services: Redefining our profession for the future, pp. 85-122, (2015); Yorke S., Coping with disaster: Strategies for the records manager, Informaa Quarterly, 13, pp. 16-21, (1997)","P. Calvert; School of Information Management, Victoria University of Wellington, Wellington, New Zealand; email: philip.calvert@vuw.ac.nz","","Australian Library and Information Association","","","","","","00048623","","","","English","Aust. Acad. Res. Libr.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-84954381244"
"Ribeiro C.; da Silva J.R.; Castro J.A.; Amorim R.C.; Fortuna P.","Ribeiro, Cristina (7201734594); da Silva, João Rocha (55496903800); Castro, João Aguiar (55977255100); Amorim, Ricardo Carvalho (56442184300); Fortuna, Paula (56505263900)","7201734594; 55496903800; 55977255100; 56442184300; 56505263900","Motivators and deterrents for data description and publication: Preliminary results","2015","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","9416","","","512","516","4","0","10.1007/978-3-319-26138-6_55","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951037347&doi=10.1007%2f978-3-319-26138-6_55&partnerID=40&md5=b0fca1342b722da03eaa2b614613d864","INESC TEC, Universidade do Porto, Porto, Portugal","Ribeiro C., INESC TEC, Universidade do Porto, Porto, Portugal; da Silva J.R., INESC TEC, Universidade do Porto, Porto, Portugal; Castro J.A., INESC TEC, Universidade do Porto, Porto, Portugal; Amorim R.C., INESC TEC, Universidade do Porto, Porto, Portugal; Fortuna P., INESC TEC, Universidade do Porto, Porto, Portugal","In the recent trend of data-intensive science, data publication is essential and institutions have to promote it with the researchers. For the past decade, institutional repositories have been widely established for publications, and the motivations for deposit are well established. The situation is quite different for data, as we argue on the basis of a 5-year experience with research data management at the University of Porto. We address research data management from a disciplined yet flexible point of view, focusing on domain-specific metadata models embedded in intuitive tools, to make it easier for researchers to publish their datasets. We use preliminary data from a recent experiment in data publishing to identify motivators and deterrents for data publishing. © Springer International Publishing Switzerland 2015.","","Information management; Information services; Internet; Data intensive science; Data publications; Data publishing; Domain-specific metadata; Institutional repositories; Recent trends; Research data managements; Publishing","","","","","Fundação para a Ciência e a Tecnologia, (SFRH/BD/77092/2011)","","Akers K.G., Doty J., Disciplinary differences in faculty research data management practices and perspectives, International Journal of Digital Curation, 8, 2, pp. 5-26, (2013); Amorim R.C., Castro J.A., Silva J.R., Ribeiro C., A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential, New Contributions in Information Systems and Technologies. AISC, 353, pp. 101-111, (2015); Borgman C.L., The conundrum of sharing research data, Journal of the Association for Information Science and Technology, 63, 6, pp. 1059-1078, (2012); Candela L., Castelli D., Manghi P., Tani A., Data journals: A survey, Journal of the Association for Information Science and Technology, 66, 9, pp. 1747-1762, (2015); Castro J.A., Silva J.R., Ribeiro C., Creating lightweight ontologies for dataset description: Practical applications in a cross-domain research data management workflow, ACM/IEEE Joint Conference on Digital Libraries, pp. 313-316, (2014); Crystal A., Greenberg J., Usability of a metadata creation application for resource authors, Library & Information Science Research, 27, 2, pp. 177-189, (2005); Silva J.R., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Proceedings of the Ipres 2014 Conference, (2014)","C. Ribeiro; INESC TEC, Universidade do Porto, Porto, Portugal; email: mcr@fe.up.pt","Bollen P.; Debruyne C.; Ciuciu I.; Fensel A.; Ferri F.; Panetto H.; Aubry A.; Debruyne C.; Ciuciu I.; Mishra A.; Fensel A.; Panetto H.; Aubry A.; Bollen P.; Valencia-Garcia R.; Valencia-Garcia R.; Mishra A.; Ferri F.","Springer Verlag","","International Workshops on the Move to Meaningful Internet Systems, OTM 2015","26 October 2015 through 30 October 2015","Rhodes","153259","03029743","978-331926137-9; 978-331926137-9","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-84951037347"
"Amorim R.C.; Castro J.A.; da Silva J.R.; Ribeiro C.","Amorim, Ricardo Carvalho (56442184300); Castro, João Aguiar (55977255100); da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594)","56442184300; 55977255100; 55496903800; 7201734594","A comparative study of platforms for research data management: Interoperability, metadata capabilities and integration potential","2015","Advances in Intelligent Systems and Computing","353","","","101","111","10","18","10.1007/978-3-319-16486-1_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926333988&doi=10.1007%2f978-3-319-16486-1_10&partnerID=40&md5=237bcde03ad5ee092639ce19d6decf34","INESC TEC, Portugal; DEI, INESC TEC, Portugal","Amorim R.C., INESC TEC, Portugal; Castro J.A., INESC TEC, Portugal; da Silva J.R., INESC TEC, Portugal; Ribeiro C., DEI, INESC TEC, Portugal","Research data management is acknowledged as an important concern for institutions and several platforms to support data deposits have emerged. In this paper we start by overviewing the current practices in the data management workflow and identifying the stakeholders in this process. We then compare four recently proposed data repository platforms—DSpace, CKAN, Zenodo and Figshare—considering their architecture, support for metadata, API completeness, as well as their search mechanisms and community acceptance. To evaluate these features, we take into consideration the identified stakeholders’ requirements. In the end, we argue that, depending on local requirements, different data repositories can meet some of the stakeholders requirements. Nevertheless, there is still room for improvements, mainly regarding the compatibility with the description of data from different research domains, to further improve data reuse. © Springer International Publishing Switzerland 2015.","","Information systems; Metadata; Comparative studies; Current practices; Data management workflow; Data repositories; Data reuse; Research data managements; Research domains; Search mechanism; Information management","","","","","","","Lynch C.A., Institutional Repositories: Essential Infrastructure for Scholarship in the Digital Age, (2003); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, 2, pp. 280-299, (2008); Burns C.S., Lana A., Budd J., Institutional repositories: Exploration of costs and value, D-Lib Magazine, (2013); Coles S.J., Frey J.G., Bird C.L., First steps towards semantic descriptions of electronic laboratory notebook records, Journal of Cheminformatics, 2013, pp. 1-10, (2013); Commission E., Guidelines on Open Access to Scientific Publications and Research Data in Horizon, (2013); Fay E., Repository software comparison: Building digital library infrastructure at LSE, Ariadne, pp. 1-11, (2010); Armbruster C., Romary L., Comparing repository types: Challenges and barriers for subject-based repositories, research repositories, national repository systems and institutional repositories in, International Journal of Digital Library Systems, (2009); Bankier J.G., Institutional Repository Software Comparison, UNESCO Communication and Information, (2014); Piwowar H.A., Day R.B., Fridsma D.S., Sharing detailed research data is associated with increased citation rate, PLOS ONE, 2, 3, (2007); Lyon L., Dealing with Data: Roles, Rights, Responsibilities and Relationships, (2007); Silva J., Ribeiro C., Lopes J.C., Ontology-based multi-domain metadata for research data management using triple stores, Proceedings of the 18Th International Database Engineering & Applications Symposium, (2014); Pampel H., Vierkant P., Scholze F., Making research data repositories visible: The re3data. org registry, PLOS ONE, 8, 11, pp. 1-18, (2013); Ribeiro C., Barbosa J., Gouveia M., Lopes J., Silva J., UPBox and DataNotes: A collaborative data management environment for the long tail of research data, Ipres, (2013); Borgman C.L., The conundrum of sharing research data, Journal of the American Society for Information Science and Technology, 63, 6, (2012); Green A., Macdonald S., Rice R., Policy-making for Research Data in Repositories: A Guide. DISC-UK, Edinburgh, (2009); Foundation N.S., Grants.Gov Application Guide a Guide for Preparation and Submission of NSF Applications via Grants.Gov, (2011); Swan A., Brown S., The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs, Report to the JISC, (2008); Lagoze C., Sompel H., Nelson M., Warner S., The Open Archives Initiative Protocol for Metadata Harvesting, Proceedings of the First ACM/IEEE-CS Joint Conference on Digital Libraries, (2001); Ramalho J.C., Ferreira M., Faria L., Castro R., RODA and CRiB a serviceoriented digital repository, Proceedings of the 5Th International Conference on Preservation of Digital Objects, Ipres, (2008); Corti L., Eynden V.D., Bishop L., Woollard M., Managing and Sharing Research Data: A Guide to Good Practice, SAGE Publications, (2014); Silva J., Ribeiro C., Correia Lopes J., Managing multidisciplinary research data: Extending DSpace to enable long-term preservation of tabular datasets, Ipres 2012 Conference, pp. 105-108, (2012); Willis C., Greenberg J., White H., Analysis and Synthesis of Metadata Goals, Journal of the Association for Information Science and Technology, 63, 8, pp. 1505-1520, (2012); Breu F.X., Guggenbichler S., Wollmann J.C., Research and Advanced Technology for Digital Libraries, Vasa, (2008); Devarakonda R., Palanisamy G., Data sharing and retrieval using OAI-PMH, Earth Science Informatics, 4, 1, pp. 1-5, (2011); Silva J.R., Dendro: Collaborative research data management built on linked open data, ESWC, 8798, pp. 3-13, (2014); Silva J., Ribeiro C., Lopes J.C., The Dendro research data management platform: Applying ontologies to long-term preservation in a collaborative environment, Ipres 2014 Conference Proceedings, (2014); Amorim R.C., Castro J.A., Da Silva J.R., Ribeiro C., LabTablet: Semantic metadata collection on a multi-domain laboratory notebook, MTSR 2014. Communications in Computer and Information Science, 478, pp. 193-205, (2014)","","Rocha A.; Rocha A.; LIACC - Laboratório de Inteligência Artificial e Ciência de Computadores, Rua Dr. Roberto Frias, s/n, Porto, 4200-465; Correia A.M.; Instituto Superior de Estatística e Gestão de Informação, ISEGI, University Nova de Lisboa, Campus de Campolide, Lisboa; Costanzo S.; Reis L.P.","Springer Verlag","aisti; Asociación de Técnicos de Informática; Camoes; Global Institute for IT Management; LIACC; Universidade dos Açores","World Conference on Information Systems and Technologies, WorldCIST 2015","1 April 2015 through 3 April 2015","Ponta Delgada","115919","21945357","978-331916485-4","","","English","Adv. Intell. Sys. Comput.","Conference paper","Final","","Scopus","2-s2.0-84926333988"
"Oostdijk N.; Van Den Heuvel H.","Oostdijk, Nelleke (23012608600); Van Den Heuvel, Henk (7003805856)","23012608600; 7003805856","The evolving infrastructure for language resources and the role for data scientists","2014","Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014","","","","608","612","4","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929212434&partnerID=40&md5=e427e6a0255bbd62f9d3632cd5ffe8cd","CLS / Centre for Language and Speech Technology, Radboud University, Nijmegen, Netherlands","Oostdijk N., CLS / Centre for Language and Speech Technology, Radboud University, Nijmegen, Netherlands; Van Den Heuvel H., CLS / Centre for Language and Speech Technology, Radboud University, Nijmegen, Netherlands","In the context of ongoing developments as regards the creation of a sustainable, interoperable language resource infrastructure and spreading ideas of the need for open access, not only of research publications but also of the underlying data, various issues present themselves which require that different stakeholders reconsider their positions. In the present paper we relate the experiences from the CLARIN-NL data curation service (DCS) over the two years that it has been operational, and the future role we envisage for expertise centres like the DCS in the evolving infrastructure.","Data curation; Language resources; Research data management; Sustainable infrastructure","Data curation; Language resources; Open Access; Research data managements; Sustainable infrastructure; Information management","","","","","","","Calzolari N., Quochi V., Soria C., The Strategic Language Resource Agenda; Donnelly M., Jones S., Template for a Data Management Plan, (2009); The META-NET Strategic Research Agenda for Multilingual Europe, (2012); Odijk J., The CLARIN-NL project, Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC-2010, pp. 48-53, (2010); Oostdijk N., Van Den Heuvel H., Introducing the CLARIN-NL data curation service, Proceedings of the Workshop Challenges in the Management of Large Corpora. LREC2012, (2012); Oostdijk N., Van Den Heuvel H., Treurniet M., The CLARIN-NL data curation service: Bringing data to the foreground, The International Journal of Digital Curation, 8, 2, pp. 134-145, (2013); Sanders E., Van De Craats I., De Lint V., The Dutch LESLLA corpus, Proceedings of the Ninth International Conference on Language Resources and Evaluation, LREC-2014, (2014); Spyns P., Odijk J., Essential Speech and Language Technology for Dutch. Results by the STEVIN Programme, (2013); Van Den Heuvel H., Sanders E., Rutten R., Scagliola S., An oral history annotation tool for INTER-VIEWs, Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC-2012, (2012)","","Calzolari N.; Choukri K.; Goggi S.; Declerck T.; Mariani J.; Maegaard B.; Moreno A.; Odijk J.; Mazo H.; Piperidis S.; Loftsson H.","European Language Resources Association (ELRA)","European Media Laboratory GmbH (EML); Holmes Semantic Solutions; IMMI; KDictionaries; VoiceBox Technologies","9th International Conference on Language Resources and Evaluation, LREC 2014","26 May 2014 through 31 May 2014","Reykjavik","131726","","978-295174088-4","","","English","Int. Conf. Lang. Resourc. and Eval. - LREC","Conference paper","Final","","Scopus","2-s2.0-84929212434"
"Brown R.A.; Wolski M.; Richardson J.","Brown, Rebecca A. (56172143700); Wolski, Malcolm (25961220900); Richardson, Joanna (55463114800)","56172143700; 25961220900; 55463114800","Developing new skills for research support librarians","2015","Australian Library Journal","64","3","","224","234","10","47","10.1080/00049670.2015.1041215","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937525534&doi=10.1080%2f00049670.2015.1041215&partnerID=40&md5=0d3f81d99a6f6bb73ee3d327d0069441","Division of Information Services, Griffith University, Brisbane, Australia","Brown R.A., Division of Information Services, Griffith University, Brisbane, Australia; Wolski M., Division of Information Services, Griffith University, Brisbane, Australia; Richardson J., Division of Information Services, Griffith University, Brisbane, Australia","In recent years, there has been considerable discussion about the key role which university libraries can play by engaging with their research community. As a result libraries are scoping, developing and implementing new roles and service models, especially in the relatively new area of research data. This article explores the specific challenges experienced by a traditional academic librarian at Griffith University as she moved into a new role as a data librarian. It was found that this transition needed to be underpinned by a skills development programme, a mentor/coach and a support network of specialists. The authors then outline some strategies to facilitate this type of role transition, which include investing in a range of training and staff development activities, leveraging existing core librarian capabilities and understanding the researcher perspective. The article concludes with a suggestion that several national organisations will continue to have an important role in supporting librarians as they develop new skills. © 2015 Australian Library & Information Association.","data librarian; library roles; research data management; research data services; research libraries","","","","","","","","Andreoli-Versbach P., Mueller-Langer F., Open access to data: An ideal professed but not practiced, Research Policy, 43, pp. 1621-1633, (2014); ARL Strategic Thinking & Design: A Framework for the Organization Going Forward, (2014); Auckland M., Re-skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Research Data Infrastructure: 2009-2018: The ANDS Project Response to the DIISR Research Infrastructure Roadmap Discussion Paper, (2011); ANDS Audio & Video, (2014); Ball A., Les Métiers Liés Aux Données de la Recherche: Data Librarian [Professions Related to Research Data: Data Librarian], (2013); Brown R., Reflections on the Path to Data Librarianship [Web Blog Post], (2014); Carlson . J R., Opportunities and barriers for librarians in exploring data: Observations from the Data Curation Profile workshops, Journal of EScience Librarianship, 2, pp. 17-33, (2013); Christensen-Dalsgaard B., Van De Berg M., Grim R., Horstmann W., Jansen D., Pollard T., Roos A., Ten Recommendations for Libraries to Get Started with Research Data Management, (2012); Corrall S., Designing libraries for research collaboration in the networked world, LIBER 42nd Annual Conference, (2013); Healey A., Morgan A., Stringer G., Do Researchers Dream of Data Management?, (2014); Huggard S., Sullivan K., Nichols-Boyd M., Opening Up the Dialogue on Research Data: How Librarians at la Trobe University Are Enabling the Process, (2014); Jaguszewski J.M., Williams K., New Roles for New Times: Transforming Liaison Roles in Research Libraries, (2013); Lewis M.J., Libraries and the management of research data, Envisioning Future Academic Library Services, pp. 145-168, (2010); MacColl J., Library roles in university research assessment, Liber Quarterly, 20, pp. 152-168, (2010); Malenfant K.J., Leading change in the system of scholarly communication: A case study of engaging liaison librarians for outreach to faculty, College & Research Libraries, 71, pp. 63-76, (2010); Mertens M., Silk K., Herndon J., Beyond Data Management Plans, Creative Data Services in Libraries, (2014); Monastersky R., Publishing frontiers: The library reboot, Nature, 495, pp. 430-432, (2013); O'Brien L., Richardson J., Supporting research through partnership, Creating the 21st-century Academic Library, 5; O'Reilly K., Johnson J., Sanborn G., Improving university research value: A case study, SAGE Open, 2, pp. 1-13, (2012); RDA Europe Data Practice Analysis, (2014); Searle S., Using Scenarios in Introductory Research Data Management Workshops for Library Staff, (2014); Research Commons: Research Lifecycle for Graduate Researchers, (2012); Simons N., Searle S., Redefining 'the Librarian' in the Context of Emerging EResearch Services, (2014); Stuart D., Libraries could play key role in managing research data, Research Information, 7, pp. 16-17, (2014); Tanner L., Declaration of Open Government, (2010); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future; An ACRL White Paper, (2012); Walters T., Skinner K., New Roles for New Times: Digital Curation for Preservation, (2011); Wolski M., Richardson J., Terra Nova: A New Land for Librarians?, (2014); Wolski M., Richardson J., Improving Data Management Practices of Researchers Using Behavioural Models, (2015); Yanosky R., Institutional Data Management in Higher Education, (2009)","R.A. Brown; Division of Information Services, Griffith University, Brisbane, Australia; email: r.brown@griffith.edu.au","","Australian Library and Information Association","","","","","","00049670","","","","English","Aust. Libr. J.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84937525534"
"von Bauer B.; Ferus A.; Gorraiz J.; Gründhammer V.; Gumpenberger C.; Maly N.; MüHlegger J.M.; Preza J.L.; Sánchez-Solís B.; Schmidt N.; Steineder C.","von Bauer, Bruno (57200436821); Ferus, Andreas (37009618700); Gorraiz, Juan (36980578600); Gründhammer, Veronika (55882157800); Gumpenberger, Christian (36473425800); Maly, Nikolaus (57074067600); MüHlegger, Johannes Michael (57074168200); Preza, José Luis (57073895600); Sánchez-Solís, Barbara (56624461200); Schmidt, Nora (56109269100); Steineder, Christian (13103392400)","57200436821; 37009618700; 36980578600; 55882157800; 36473425800; 57074067600; 57074168200; 57073895600; 56624461200; 56109269100; 13103392400","Researchers and their data. Results of an Austrian survey - report 2015. Executive summary and recommendations; [Forschende und ihre daten: Ergebnisse einerosterreichweiten befragung. Report 2015 - executive summary und empfehlungen]","2015","VOEB-Mitteilungen","68","3","","566","579","13","1","10.31263/voebm.v68i3.1298","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955258375&doi=10.31263%2fvoebm.v68i3.1298&partnerID=40&md5=d3f84b3ba0f16dbfcdfd398b23688c1b","Medizinische Universität Wien, Austria; Akademie der bildenden Künste Wien, Austria; Universität Wien, Austria; Universität Wien, Austria; FH Campus Wien, Austria; Universität Salzburg, Austria; Universität Wien, Austria; Universität Wien (aktuell: Universität Lund), Austria; Technische Universität Wien, Austria","von Bauer B., Medizinische Universität Wien, Austria; Ferus A., Akademie der bildenden Künste Wien, Austria; Gorraiz J., Universität Wien, Austria; Gründhammer V.; Gumpenberger C., Universität Wien, Austria; Maly N., FH Campus Wien, Austria; MüHlegger J.M., Universität Salzburg, Austria; Preza J.L., Universität Wien, Austria; Sánchez-Solís B., Universität Wien, Austria; Schmidt N., Universität Wien (aktuell: Universität Lund), Austria; Steineder C., Technische Universität Wien, Austria","This paper provides executive summary and recommendations of “Researchers and their data. Results of an Austrian survey – Report 2015”. This report provides an overview of the nation-wide survey on research data, which was carried out within the project e-Infrastructures Austria in 2015. This was directed at the arts, humanities and sciences staff of all 21 public universities and three extramural research institutions in Austria. The participants were asked about the following to-pics: data types and formats; data archiving, backup and loss; ethical and legal aspects; accessibility and re-use; and infrastructure and services. The first survey conducted at the national level in this context was used for the practical handling of research data in Austria, and is therefore the basis for a consecutive optimization of relevant infrastructure, an adaptation of the services provided, as well as a reorientation in identifying resources in this strategic area which correspond to the expressed needs of people in the research process. © by Clive Tooth, 2006.","Austria; E-Infrastructures Austria; Extramural research institution; Public university; Report; Research data; Research data management (rdm); Researcher; Survey","","","","","","","","","","","Vereinigung Osterreichischer Bibliothekarinnen und Bibliothekare","","","","","","10222588","","","","German","VOEB-Mitteilungen","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84955258375"
"Surkis A.; Read K.","Surkis, Alisa (57190153933); Read, Kevin (57205931894)","57190153933; 57205931894","Research data management","2015","Journal of the Medical Library Association","103","3","","154","156","2","40","10.3163/1536-5050.103.3.011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939181953&doi=10.3163%2f1536-5050.103.3.011&partnerID=40&md5=ae1e3ad4bdabaea7926241fdaa62f82a","NYU Health Sciences Library, NYU Langone Medical Center, 577 First Avenue, New York, 10016, NY, United States","Surkis A., NYU Health Sciences Library, NYU Langone Medical Center, 577 First Avenue, New York, 10016, NY, United States; Read K., NYU Health Sciences Library, NYU Langone Medical Center, 577 First Avenue, New York, 10016, NY, United States","[No abstract available]","","Biomedical Research; Database Management Systems; Humans; Information Storage and Retrieval; Knowledge Management; Librarians; Libraries; database management system; human; information retrieval; knowledge management; librarian; library; medical research; organization and management; procedures; statistics and numerical data","","","","","","","Data [Internet], (2014); University of Edinburgh Information Services, Research data management programme: Research data management home [Internet]; West L., MRI [Internet]; Burton N., Brain tumor MRI scans [Internet], (2012); UK Data Archive, Create and manage data: Research data lifecycle [Internet]; National Human Genome Research Institute, All about the Human Genome Project [Internet], (2014); University of Edinburgh, MANTRA: Research data management training [Internet]; Coursera, Data management for clinical research [Internet]; Read K.B., Surkis A., Larson C., McCrillis A., Graff A., Nicholson J., Xu J., Starting the data conversation: Informing data services at an academic health sciences library, J Med Lib Assoc, 103, 3, (2015)","","","Medical Library Association","","","","","","15365050","","JMLAC","26213510","English","J. Med. Libr. Assoc.","Note","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84939181953"
"Skripcak T.; Belka C.; Bosch W.; Brink C.; Brunner T.; Budach V.; Büttner D.; Debus J.; Dekker A.; Grau C.; Gulliford S.; Hurkmans C.; Just U.; Krause M.; Lambin P.; Langendijk J.A.; Lewensohn R.; Lühr A.; Maingon P.; Masucci M.; Niyazi M.; Poortmans P.; Simon M.; Schmidberger H.; Spezi E.; Stuschke M.; Valentini V.; Verheij M.; Whitfield G.; Zackrisson B.; Zips D.; Baumann M.","Skripcak, Tomas (38562369100); Belka, Claus (7005062271); Bosch, Walter (55172856000); Brink, Carsten (15841284000); Brunner, Thomas (57201016930); Budach, Volker (7006247333); Büttner, Daniel (56441133700); Debus, Jürgen (7102072188); Dekker, Andre (8876190500); Grau, Cai (7005938346); Gulliford, Sarah (19034231400); Hurkmans, Coen (6602511769); Just, Uwe (7005152354); Krause, Mechthild (7202278334); Lambin, Philippe (35242663400); Langendijk, Johannes A. (6701731681); Lewensohn, Rolf (56238017500); Lühr, Armin (24332332800); Maingon, Philippe (55322335100); Masucci, Michele (56402834400); Niyazi, Maximilian (16307758800); Poortmans, Philip (6701731185); Simon, Monique (56402365700); Schmidberger, Heinz (7006459591); Spezi, Emiliano (7801406840); Stuschke, Martin (55188240900); Valentini, Vincenzo (7006177042); Verheij, Marcel (7003902760); Whitfield, Gillian (26022719700); Zackrisson, Björn (7003393913); Zips, Daniel (6602109384); Baumann, Michael (7202494765)","38562369100; 7005062271; 55172856000; 15841284000; 57201016930; 7006247333; 56441133700; 7102072188; 8876190500; 7005938346; 19034231400; 6602511769; 7005152354; 7202278334; 35242663400; 6701731681; 56238017500; 24332332800; 55322335100; 56402834400; 16307758800; 6701731185; 56402365700; 7006459591; 7801406840; 55188240900; 7006177042; 7003902760; 26022719700; 7003393913; 6602109384; 7202494765","Creating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets","2014","Radiotherapy and Oncology","113","3","","303","309","6","70","10.1016/j.radonc.2014.10.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84916203454&doi=10.1016%2fj.radonc.2014.10.001&partnerID=40&md5=a64f7550b31404c7696202f909efb02a","German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany; Dept. of Radiation Oncology, Washington University, St. Louis, MO, United States; Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; German Cancer Consortium (DKTK), Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany; German Cancer Consortium (DKTK), Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany; Dept. of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands; CIRRO Department of Oncology, Aarhus University Hospital, Aarhus, Denmark; Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom; Dept. of Radiation Oncology, Catharina Hospital, Eindhoven, Netherlands; EORTC-Radiation Oncology Group, Brussels, Belgium; EORTC-Global Clinical Trial QART Harmonisation Group, Brussels, Belgium; Dept. of Radiation Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz-Zentrum Dresden-Rossendorf, Germany; Institute of Radiooncology, Helmholtz-Zentrum Dresden-Rossendorf, Germany; University Medical Center Groningen, University of Groningen, Groningen, Netherlands; Department of Radiation Oncology, Centre Georges - François Leclerc, Dijon Cedex, France; EurocanPlatform and Karolinska Institutet Stockholm, Sweden; Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, Netherlands; ARO-Speaker, University Medical Center Mainz, Germany; Dept. of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom; German Cancer Consortium (DKTK), Essen/Düsseldorf and German Cancer Research Center (DKFZ), Heidelberg, Germany; Radiation Oncology Department GEMELLI-ART, Universita Cattolica S. Cuore, Rome, Italy; Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands; Christie NHS Foundation Trust, University of Manchester, Manchester, United Kingdom; Umea University, Department of Radiation Sciences, Oncology, Umea, Sweden; German Cancer Consortium (DKTK), Tübingen and German Cancer Research Center (DKFZ), Heidelberg, Germany; Radiation Oncology, University Hospital Tübingen, Eberhard Karls University Tübingen, Germany; Department Clinical Committee, ESTRO, Netherlands; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark","Skripcak T., German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany; Belka C., German Cancer Consortium (DKTK), Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany; Bosch W., Dept. of Radiation Oncology, Washington University, St. Louis, MO, United States; Brink C., Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Brunner T., German Cancer Consortium (DKTK), Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany; Budach V., German Cancer Consortium (DKTK), Germany; Büttner D., German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany; Debus J., German Cancer Consortium (DKTK), Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany; Dekker A., Dept. of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands; Grau C., CIRRO Department of Oncology, Aarhus University Hospital, Aarhus, Denmark; Gulliford S., Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom; Hurkmans C., Dept. of Radiation Oncology, Catharina Hospital, Eindhoven, Netherlands, EORTC-Radiation Oncology Group, Brussels, Belgium, EORTC-Global Clinical Trial QART Harmonisation Group, Brussels, Belgium; Just U., Dept. of Radiation Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Krause M., German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany, Dept. of Radiation Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany, OncoRay - National Center for Radiation Research in Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz-Zentrum Dresden-Rossendorf, Germany, Institute of Radiooncology, Helmholtz-Zentrum Dresden-Rossendorf, Germany; Lambin P., Dept. of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands; Langendijk J.A., University Medical Center Groningen, University of Groningen, Groningen, Netherlands; Lewensohn R., Department of Radiation Oncology, Centre Georges - François Leclerc, Dijon Cedex, France; Lühr A., German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany, OncoRay - National Center for Radiation Research in Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz-Zentrum Dresden-Rossendorf, Germany; Maingon P., EORTC-Radiation Oncology Group, Brussels, Belgium, Department of Radiation Oncology, Centre Georges - François Leclerc, Dijon Cedex, France; Masucci M., EurocanPlatform and Karolinska Institutet Stockholm, Sweden; Niyazi M., German Cancer Consortium (DKTK), Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany; Poortmans P., Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, Netherlands; Simon M., German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany; Schmidberger H., ARO-Speaker, University Medical Center Mainz, Germany; Spezi E., Dept. of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom; Stuschke M., German Cancer Consortium (DKTK), Essen/Düsseldorf and German Cancer Research Center (DKFZ), Heidelberg, Germany; Valentini V., Radiation Oncology Department GEMELLI-ART, Universita Cattolica S. Cuore, Rome, Italy; Verheij M., Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands; Whitfield G., Christie NHS Foundation Trust, University of Manchester, Manchester, United Kingdom; Zackrisson B., Umea University, Department of Radiation Sciences, Oncology, Umea, Sweden; Zips D., German Cancer Consortium (DKTK), Tübingen and German Cancer Research Center (DKFZ), Heidelberg, Germany, Radiation Oncology, University Hospital Tübingen, Eberhard Karls University Tübingen, Germany, Department Clinical Committee, ESTRO, Netherlands; Baumann M., German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany, Dept. of Radiation Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany, OncoRay - National Center for Radiation Research in Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz-Zentrum Dresden-Rossendorf, Germany, Institute of Radiooncology, Helmholtz-Zentrum Dresden-Rossendorf, Germany","Disconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within ""Big Data"". Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology. © 2014 Elsevier Ireland Ltd..","Data exchange; Data pooling; Interoperability; Large scale studies; Public data; Radiotherapy","Biomedical Research; Data Collection; Data Mining; Databases, Factual; Electronic Health Records; Humans; Information Dissemination; International Cooperation; Neoplasms; Research Design; cancer radiotherapy; cancer research; case report form; data base; data processing; electronic medical record; human; medical record; Review; data mining; factual database; information dissemination; information processing; international cooperation; medical research; methodology; Neoplasms; organization and management; procedures","","","","","National Institutes of Health, NIH; National Cancer Institute, NCI, (U24CA081647, U24CA180803)","","Roelofs E., Persoon L., Nijsten S., Wiessler W., Dekker A., Lambin P., Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial, Radiother Oncol, 108, pp. 174-179, (2013); Lambin P., Van Stiphout R.G.P.M., Starmans M.H.W., Rios-Velazquez E., Nalbantov G., Aerts H.J.W.L., Predicting outcomes in radiation oncology - Multifactorial decision support systems, Nat Rev Clin Oncol, 10, pp. 27-40, (2012); Aerts H.J.W.L., Velazquez E.R., Leijenaar R.T.H., Parmar C., Grossmann P., Cavalho S., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat Commun, 5, (2014); Reymen B., Van Baardwijk A., Wanders R., Borger J., Dingemans A.-M.C., Bootsma G., Long-term survival of stage T4N0-1 and single station IIIA-N2 NSCLC patients treated with definitive chemo-radiotherapy using individualised isotoxic accelerated radiotherapy (INDAR), Radiother Oncol, 110, pp. 482-487, (2014); Stacey D., Legare F., Col N.F., Bennett C.L., Barry M.J., Eden K.B., Decision aids for people facing health treatment or screening decisions, Cochrane Database Syst Rev, 1, (2014); Roelofs E., Engelsman M., Rasch C., Persoon L., Qamhiyeh S., De Ruysscher D., Results of a multicentric in silico clinical trial (ROCOCO): Comparing radiotherapy with photons and protons for non-small cell lung cancer, J Thorac Oncol, 7, pp. 165-176, (2012); Roelofs E., Persoon L., Qamhiyeh S., Verhaegen F., De Ruysscher D., Scholz M., Design of and technical challenges involved in a framework for multicentric radiotherapy treatment planning studies, Radiother Oncol, 97, pp. 567-571, (2010); Langendijk J.A., Lambin P., De Ruysscher D., Widder J., Bos M., Verheij M., Selection of patients for radiotherapy with protons aiming at reduction of side effects: The model-based approach, Radiother Oncol, 107, pp. 267-273, (2013); (2014); Roelofs E., Dekker A., Meldolesi E., Van Stiphout R.G.P.M., Valentini V., Lambin P., International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining, Radiother Oncol, 110, pp. 370-374, (2014); Integrating the Healthcare Enterprise, (2014); Lambin P., Roelofs E., Reymen B., Velazquez E.R., Buijsen J., Zegers C.M.L., Rapid learning health care in oncology - An approach towards decision support systems enabling customised radiotherapy, Radiother Oncol, 109, pp. 159-164, (2013); Biomedical Research Integrated Domain Group., (2014); De Montjoie J., Introducing the CDISC Standards: New Efficiencies for Medical Research, (2009); Health Level Seven International., (2014); Martin J., Frantzis J., Chung P., Langah I., Crain M., Cornes D., Prostate radiotherapy clinical trial quality assurance: How real should real time review be? (A TROG-OCOG Intergroup Project), Radiother Oncol, 107, pp. 333-338, (2013); Data Protection Legislation., (2014); Protecting Health and Scientific Research in the Data Protection Regulation (2012/0011(COD)): Position of Non-commercial Research Organisations and Academics., (2014); Weber R., The Right to Be Forgotten More Than A Pandora's Box?; Casali P.G., Risks of the new EU Data protection regulation: An ESMO position paper endorsed by the European oncology community, Ann Oncol, 25, pp. 1458-1461, (2014); Breil B., Kenneweg J., Fritz F., Bruland P., Doods D., Trinczek B., Multilingual medical data models in ODM format: A novel form-based approach to semantic interoperability between routine healthcare and clinical research, Appl Clin Inform, 3, pp. 276-289, (2012); Marcus D.S., Olsen T.R., Ramaratnam M., Buckner R.L., The Extensible Neuroimaging Archive Toolkit: An informatics platform for managing, exploring, and sharing neuroimaging data, Neuroinformatics, 5, pp. 11-34, (2007); Westberg J., Krogh S., Brink C., Vogelius I.R., A DICOM based radiotherapy plan database for research collaboration and reporting, J Phys: Conf ser, 489, (2014); Baumann M., Holscher T., Begg A.C., Towards genetic prediction of radiation responses: ESTRO's GENEPI project, Radiother Oncol, 69, pp. 121-125, (2003); De Ruysscher D., Severin D., Barnes E., Baumann M., Bristow R., Gregoire V., First report on the patient database for the identification of the genetic pathways involved in patients over-reacting to radiotherapy: GENEPI-II, Radiother Oncol, 97, pp. 36-39, (2010); Melidis C., Bosch W.R., Izewska J., Fidarova E., Zubizarreta E., Ishikura S., Radiation therapy quality assurance in clinical trials - Global Harmonisation Group, Radiother Oncol, 111, pp. 327-329, (2014); Efstathiou J.A., Nassif D.S., McNutt T.R., Bogardus C.B., Bosch W., Carlin J., Practice-based evidence to evidence-based practice: Building the National Radiation Oncology Registry, J Oncol Pract, 9, pp. 90-e95, (2013); Meldolesi E., Van Soest J., Dinapoli N., Dekker A., Damiani A., Gambacorta M.A., An umbrella protocol for standardized data collection (SDC) in rectal cancer: A prospective uniform naming and procedure convention to support personalized medicine, Radiother Oncol, 5, (2014); CRF Harmonization and Standardization., (2014)","","","Elsevier Ireland Ltd","","","","","","01678140","","RAOND","25458128","English","Radiother. Oncol.","Review","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-84916203454"
"McEwen L.; Li Y.","McEwen, Leah (56270423600); Li, Ye (39961765500)","56270423600; 39961765500","Academic librarians at play in the field of cheminformatics: Building the case for chemistry research data management","2014","Journal of Computer-Aided Molecular Design","28","10","","975","988","13","2","10.1007/s10822-014-9777-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84932080815&doi=10.1007%2fs10822-014-9777-4&partnerID=40&md5=201fa3a50536fdba89167d7399b33f41","Physical Sciences Library, Cornell University, 283 Clark Hall, Ithaca, 14853, NY, United States; Shapiro Science Library, University of Michigan, 919 South University Avenue, Ann Arbor, 48109, MI, United States","McEwen L., Physical Sciences Library, Cornell University, 283 Clark Hall, Ithaca, 14853, NY, United States; Li Y., Shapiro Science Library, University of Michigan, 919 South University Avenue, Ann Arbor, 48109, MI, United States","There are compelling needs from a variety of camps for more chemistry data to be available. While there are funder and government mandates for depositing research data in the United States and Europe, this does not mean it will be done well or expediently. Chemists themselves do not appear overly engaged at this stage and chemistry librarians who work directly with chemists and their local information environments are interested in helping with this challenge. Our unique understanding of organizing data and information enables us to contribute to building necessary infrastructure and establishing standards and best practices across the full research data cycle. As not many support structures focused on chemistry currently exist, we are initiating explorations through a few case studies and focused pilot projects presented here, with an aim of identifying opportunities for increased collaboration among chemists, chemistry librarians, cheminformaticians and other chemistry professionals. © 2014 Springer International Publishing Switzerland.","Chemical health and safety; Chemistry librarians; Chemistry metadata; Research data management","Academies and Institutes; Chemistry; Crystallography; Databases, Chemical; Librarians; Magnetic Resonance Spectroscopy; Publications; Research; Statistics as Topic; Libraries; Chemical health and safety; Cheminformatics; Chemistry librarian; Chemistry metadatum; Chemistry research; Health and safety; Information environment; Local information; Research data; Research data managements; chemical database; chemistry; crystallography; librarian; nuclear magnetic resonance spectroscopy; organization; organization and management; publication; research; statistics; Information management","","","","","","","Carol T., Suzie A., Kimberly D., Aydinoglu A.U., Wu L., Read E., Manoff M., Mike F., Data sharing by scientists: Practices and perceptions, PLoS One, 6, 6, (2011); Li Y., Tschirhart L., Preparing to support research data sharing, Special Issues in Data Management, 1110, pp. 145-162, (2012); Velden T., Lagoze C., Communicating chemistry, Nat Chem, 1, 9, pp. 673-678, (2009); Weisgerber D.W., Chemical abstracts service chemical registry system: History, scope, and impacts, J Am Soc Inf Sci, 48, 4, pp. 349-360, (1997); Ridley D.D., Front matter, Information Retrieval: SciFinder®, pp. i-xii, (2009); Meehan P., Schofield H., CrossFire: A structural revolution for chemists, Online Inf Rev, 25, 4, pp. 241-249, (2001); Royal Society of Chemistry; Open PHACTS: Open Pharmaceutical Space Open PHACTS Consortium; Data Management and Sharing Frequently Asked Questions (FAQs); Tenopir C., Sandusky R.J., Allard S., Birch B., Academic librarians and research data services: Preparation and attitudes, IFLA J, 39, 1, pp. 70-78, (2013); Environmental Scan 2013, (2013); Willett P., From chemical documentation to chemoinformatics: 50 years of chemical information science, J Inf Sci, 34, 4, pp. 477-499, (2008); Warr W., Some trends in chem(o)informatics, Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology, pp. 1-37, (2011); William K.M., Suzie A., Amber E.B., Robert C., Kimberly D., Mike F., Steve K., Rebecca J.K., Carol T., David A.V., Participatory design of DataONE - Enabling cyberinfrastructure for the biological and environmental sciences, Ecol Inform, 11, pp. 5-15, (2012); Erway R., Starting the Conversation: University-wide Research Data Management Policy, (2013); Currano J.N., Roth D., Chemical Information for Chemists: A Primer, (2014); Data Curation Profile Toolkit; Data Curation Profiles Directory; Townsend J.A., Adams S.E., Waudby C.A., De Souza V.K., Goodman J.M., Murray-Rust P., Chemical documents: Machine understanding and automated information extraction, Org Biomol Chem, 2, 22, pp. 3294-3300, (2004); Gurulingappa H., Mudi A., Toldo L., Hofmann-Apitius M., Bhate J., Challenges in mining the literature for chemical information, RSC Adv, (2013); Li Y., Profiling common types of research data produced by chemists at the University of Michigan, 247th ACS National Meeting and Exposition, (2014); Batchelor C., Chem Methods Ontol, (2014); Chemical Methods Ontology (CMO), R Soc Chem.; The Database and Ontology of Chemical Entities of Biological Interest; Park J., Rosania G.R., Saitou K., Tunable machine vision-based strategy for automated annotation of chemical databases, J Chem Inf Model, 49, 8, pp. 1993-2001, (2009); Globally Harmonized System of Classification and Labelling of Chemicals (GHS) (Rev. 5), (2013); Borkum M., Machine-processable representation and application of the Globally Harmonized System, 247th ACS National Meeting and Exposition; Kemsley J., Chemistry professors promote lab safety, Chem Eng News, 92, 23, pp. 30-31, (2014); U.S. Chemical Safety Board; American Chemical Society Committee on Chemical Safety, (2013); Hill R.H., Finster D.C., Laboratory Safety for Chemistry Students, (2010); Busacca C.A., Eriksson M.C., Haddad N., Han Z.S., Lorenz J.C., Qu B., Zeng X., Senanayake C.H., Practical synthesis of di-tert-butyl-phosphinoferrocene, Org Synth, 90, pp. 316-326, (2013); Urben P.G., Bretherick's Handbook of Reactive Chemical Hazards, (2007); Kemsley J., Explosion injures university of minnesota graduate student, (2014); Gonzalez-Bobes F., Kopp N., Li L., Deerberg J., Sharma P., Leung S., Davies M., Bush J., Hamm J., Hrytsak M., Scale-up of azide chemistry: A case study, Org Process Res Dev, 16, 12, pp. 2051-2057, (2012); Information Literacy Competency Standards for Higher Education; Safety in Research Laboratories-UC CLS Workshop 2014 UC Center for Laboratory Safety; Wrublewski D., Lab Safety - Chemistry - Libguides at Caltech, (2014); Baysinger G., Lab Safety - Guides - Stanford University Libraries, (2014); Conneting Chemistry and Safety; Lab and Research Safety; Stuart R., Toreki R., Learning opportunities in three years of hazmat headlines, J Chem Health Saf, 21, 2, pp. 2-8, (2014); Bassan E., Ruck R.T., Dienemann E., Emerson K.M., Humphrey G.R., Raheem I.T., Tschaen D.M., Vickery T.P., Wood H.B., Yasuda N., Merck's reaction review policy: An exercise in process safety, Org Process Res Dev, 17, 12, pp. 1611-1616, (2013); UC Center for Laboratory Safety; Prudent Practices in the Laboratory: Handling and Management of Chemical Hazards, Updated Version, (2011); Brecher J., Graphical representation standards for chemical structure diagrams (IUPAC recommendations 2008), Pure Appl Chem, 80, 2, pp. 277-410, (2008); NIST Standard Reference Data National Institute of Standards and Technology; Cambridge Crystallographic Data Center; David P.M., Supplemental journal article materials, Special Issues in Data Management, 1110, pp. 31-45, (2012); Bird C.L., Frey J.G., Chemical information matters: An e-research perspective on information and data sharing in the chemical sciences, Chem Soc Rev, 42, 16, pp. 6754-6776, (2013); International Union of Pure and Applied Chemistry; National Institute of Standards and Technology; Ray J.M., Research Data Management: Practical Strategies for Information Professionals, (2014); Vaughan K.T.L., Hayes B.E., Lerner R.C., McElfresh K.R., Pavlech L., Romito D., Reeves L.H., Morris E.N., Development of the research lifecycle model for library services, J Med Libr Assoc, 101, 4, pp. 310-314, (2013); Coles S.J., Frey J.G., Bird C.L., Whitby R.J., Day A.E., First steps towards semantic descriptions of electronic laboratory notebook records, J Cheminform, 5, (2013); Cuevas-Vicenttin V., Dey S., Kohler S., Riddle S., Ludascher B., Scientific workflows and provenance: Introduction and research opportunities, Datenbank-Spektrum, 12, 3, pp. 193-203, (2012); Roadmap for Synthesis in the 2st Century Engineering and Physical Sciences Research Council","","","Kluwer Academic Publishers","","","","","","0920654X","","JCADE","25038898","English","J. Comp.-Aided Mol. Des.","Article","Final","","Scopus","2-s2.0-84932080815"
"Sánchez Solís B.; Budroni P.","Sánchez Solís, Barbara (56624461200); Budroni, Paolo (56624471600)","56624461200; 56624471600","E-Infrastructures Austria-Ein nationales Projekt für die Aufbereitung, dauerhafte Bereitstellung und Nachnutzung von Daten an wissenschaftlichen Einrichtungen e-Infrastructures Austria-A national project for the preparation, sustainable provision and re-use of data at scientific institutions e-Infrastructures Autriche-Un projet pour la fourniture durable et la réutilisation des données dans les établissements universitaires","2015","Information-Wissenschaft und Praxis","66","2-3","","129","136","7","1","10.1515/iwp-2015-0023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928949506&doi=10.1515%2fiwp-2015-0023&partnerID=40&md5=797861b94a8e93a8f070fe229b9521a5","Universität Wien, Bibliotheks-und Archivwesen der Universität Wien, Universitätsring 1, 1010 Wien, Osterreich, Austria","Sánchez Solís B., Universität Wien, Bibliotheks-und Archivwesen der Universität Wien, Universitätsring 1, 1010 Wien, Osterreich, Austria; Budroni P., Universität Wien, Bibliotheks-und Archivwesen der Universität Wien, Universitätsring 1, 1010 Wien, Osterreich, Austria","In January 2014, the three-year collaborative project e-Infrastructures Austria was initiated in Austria, a project which was funded by the Federal Ministry of Science, Research and Economy for the coordinated establishment and further development of repository infrastructures for research and teaching as well as to provide an efficient and sustainable research data management system at all 20 participating universities and the other five extra-university institutions involved. The project is based on a broad concept of research data and focuses on the opportunities and challenges arising from new applications and innovative forms of the re-use of data. In this context, the purpose of such a project is not only to provide the technical infrastructure needed, but also to create the appropriate organizational and legal framework. To ensure that data remains secure and accessible over the long term, as well as understandable and re-usable, it is a basic requirement of the system development team to promote the interaction of different groups of people, strategies and techniques. Therefore in the project, there are different important stakeholders: Scientific libraries, IT service facilities, research funders, researchers and publishers. It is necessary to clearly define areas of responsibility with associated roles and responsibilities and to set these roles in the related institutions. By introducing data management plans, policies and technical systems with clear terms of use and regulated accessibility, institutions can significantly benefit the creation of digital content in the long term, making such data accessible and re-usable while maintaining legal security for all parties involved. © 2015 by Walter de Gruyter Berlin Boston 2015.","Archivage; Autriche; Coopération; Données de recherche; Infrastructure; Usage","","","","","","","","Dillo I., Data Archiving Networked Services (DANS, (2012); Miksa T., Strodl S., Rauber A., Process management plans, International Journal of Digital Curation, 9, 1, pp. 83-97, (2014)","","","Deutsche Gesellschaft fur Dokumentation E.V","","","","","","14344653","","","","German","Inf.-Wiss. Prax.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-84928949506"
"Hoorn E.; Domingus M.","Hoorn, Esther (12753034800); Domingus, Marlon (57191927051)","12753034800; 57191927051","Finding the law for sharing data in academia","2015","New Avenues for Electronic Publishing in the Age of Infinite Collections and Citizen Science: Scale, Openness and Trust - Proceedings of the 19th International Conference on Electronic Publishing, Elpub 2015","","","","131","139","8","0","10.3233/978-1-61499-562-3-131","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994887764&doi=10.3233%2f978-1-61499-562-3-131&partnerID=40&md5=6370fcff7a82cef2cabf3533e652ea12","Rijksuniversiteit Groningen, Netherlands; Erasmus University Rotterdam, Netherlands","Hoorn E., Rijksuniversiteit Groningen, Netherlands; Domingus M., Erasmus University Rotterdam, Netherlands","How can universities provide good advice about the legal aspects of research data management? At the same time, how can universities prevent that perceived legal risks become barriers to: conducting research, sharing research data, valorisation of research data, and control mechanisms for the purpose of scientific integrity? A Dutch expert group developed a creative approach based on some core ideas3 about regulation in the field of academic research. © 2015 The authors and IOS Press.","Code of conduct; Guidelines; Hard law; Model contracts; Research; Soft law; Wiki","Information management; Research; Code of conduct; Guidelines; Hard law; Soft law; Wiki; Electronic publishing","","","","","","","Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020; Owen R., Responsible research and innovation: From science in society to science for society, With Society, Science and Public Policy, 39, 6, pp. 751-760, (2012); The European Code of Conduct for Research Integrity, (2011); Kleppner D., Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, (2010); Open Data. An Engine for Innovation, Growth and Transparent Governance, (2011); Online Source: Artikel 1.7, Wet Op Het Hoger Onderwijs en Wetenschappelijk Onderzoek; Van De Wijngaart A., Mapping Ownership in the Data Landscape","E. Hoorn; Rijksuniversiteit Groningen, Netherlands; email: e.hoorn@rug.nl","Schmidt B.; Dobreva M.","IOS Press BV","Copernicus Publications; Emerald; ProQuest; Springer","19th International Conference on Electronic Publishing, Elpub 2015","1 September 2015 through 3 September 2015","Malta","124225","","978-161499561-6","","","English","New Ave. Electron. Publ. Age Infin. Collect. Citiz. Sci.: Scale, Openness Trust - Proc. Int. Conf. Electron. Publ., Elpub","Conference paper","Final","","Scopus","2-s2.0-84994887764"
"Görögh E.; Kędzierska E.; Kavalchuk N.; Stepniak J.; Dzivak J.; Pejšová P.; Vyčítalová H.","Görögh, Edith (57194540116); Kędzierska, Edyta (57110231400); Kavalchuk, Natalia (57110378200); Stepniak, Jolanta (57110302300); Dzivak, Jozef (57204222421); Pejšová, Petra (36621032700); Vyčítalová, Hana (56538740500)","57194540116; 57110231400; 57110378200; 57110302300; 57204222421; 36621032700; 56538740500","Enhanced publications in v4 countries","2015","Grey Journal","11","Specialwinterissue","","6","17","11","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957799732&partnerID=40&md5=3e6dfebe3ddd5024c52e72a7d7147983","University of Debrecen, Hungary; Warsaw University of Technology, Poland; Slovak University of Technology, Chemical Library, Slovakia; National Library of Technology, Czech Republic","Görögh E., University of Debrecen, Hungary; Kędzierska E., Warsaw University of Technology, Poland; Kavalchuk N., Warsaw University of Technology, Poland; Stepniak J., Warsaw University of Technology, Poland; Dzivak J., Slovak University of Technology, Chemical Library, Slovakia; Pejšová P., National Library of Technology, Czech Republic; Vyčítalová H., National Library of Technology, Czech Republic","The article describes the project Enhancing scholarly communication: National initiatives to manage research data in V4 countries. The main goal of the project is a survey about state of research data management, repository contents, services and archiving policies in colleges, universities and research institutions on national levels. The results of the survey will be presented by representatives from Hungary, Czech Republic, Slovak Republic and Poland. This text is a collaborative work by authors participating in the “Enhancing scholarly communication: national initiatives to manage research data in the V4 countries” project. This paper contains jive parts: • An introduction to Open Access in scholarly communication, research data and projects. • The report from the survey of Hungarian universities. • The report from the Survey of Polish Scientific and Research-Development Units. • The report from the survey of Slovak universities and scholarly institutions. • The report from the survey of Czech research institutions and universities. © 2015 GreyNet. All rights reserved.","Digital repositories; Enhanced publications; Open access; Research; Research data; Scholarly communication; Visegrad funds","","","","","","","","Ayris P., Open Research Data: Statement, (2012); Farace D., Repurposing Grey Literature - Linking Research Data to Full-Text Publications: Some Preliminary Results, Seminar on Providing Access to Grey Literature 2011:4Th Year of the Seminar Focused on Storage and Making the Grey Literature Accessible, 6, (2011); Farace D., Frantzen J., Stock C., Laurents S., Rabina D., Linking full-text grey literature to underlying research and post publication data: An Enhanced Publications Project, Thirteenth International Conference on Grey Literature: The Grey Circuit, from Social Networking to Wealth Creation, (2012); Harnad S., Maximizing University Research Impact through Self-Archiving, (2003); Hoorn E., Maurits V., Copyright Issues in Open Access Research Journals: The Authors' Perspective, 12, 2, (2006); Trends in Scholarly Communication, (2014)","","","GreyNet","","","","","","15741796","","","","English","Grey J.","Article","Final","","Scopus","2-s2.0-84957799732"
"Diepenbroek M.; Glöckner F.O.; Grobe P.; Güntsch A.; Huber R.; König-Ries B.; Kostadinov I.; Nieschulze J.; Seeger B.; Tolksdorf R.; Triebel D.","Diepenbroek, Michael (6508139114); Glöckner, Frank Oliver (7003727520); Grobe, Peter (12042261000); Güntsch, Anton (48461209500); Huber, Robert (56511063900); König-Ries, Birgitta (55864942100); Kostadinov, Ivaylo (24169064900); Nieschulze, Jens (35198774300); Seeger, Bernhard (7004609788); Tolksdorf, Robert (6701669777); Triebel, Dagmar (6602510653)","6508139114; 7003727520; 12042261000; 48461209500; 56511063900; 55864942100; 24169064900; 35198774300; 7004609788; 6701669777; 6602510653","Towards an integrated biodiversity and ecological research data management and archiving platform: The German federation for the curation of biological data (GFBio)","2014","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-232","","","1711","1721","10","87","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922496341&partnerID=40&md5=1c95a977eb654a02512cf3d4b225c51b","MARUM, Universität Bremen, Germany; Jacobs University, MPI für Marine Mikrobiologie, Germany; Zoologisches Forschungsmuseum König, Bonn, Germany; Botanischer Garten und Botanisches Museum Berlin-Dahlem, Germany; Universität Jena, IDiv, Germany; Universität Göttingen, Germany; Universität Marburg, Germany; Freie Universität Berlin, Germany; Staatl. Naturwissenschaftl. Sammlungen Bayerns, Germany","Diepenbroek M., MARUM, Universität Bremen, Germany; Glöckner F.O., Jacobs University, MPI für Marine Mikrobiologie, Germany; Grobe P., Zoologisches Forschungsmuseum König, Bonn, Germany; Güntsch A., Botanischer Garten und Botanisches Museum Berlin-Dahlem, Germany; Huber R., MARUM, Universität Bremen, Germany; König-Ries B., Universität Jena, IDiv, Germany; Kostadinov I., Jacobs University, MPI für Marine Mikrobiologie, Germany; Nieschulze J., Universität Göttingen, Germany; Seeger B., Universität Marburg, Germany; Tolksdorf R., Freie Universität Berlin, Germany; Triebel D., Staatl. Naturwissenschaftl. Sammlungen Bayerns, Germany","Biodiversity research brings together the many facets of biological environmental research. Its data management is characterized by integration and is particularly challenging due to the large volume and tremendous heterogeneity of the data. At the same time, it is particularly important: A lot of the data is not reproducible. Once it is gone, potential knowledge that could have been gained from it is irrevocably lost. In this paper, we describe challenges to biodiversity data management along the data life cycle and sketch the solution that is currently being developed within the GFBio project, a collaborative effort of nineteen German research institutions ranging from museums and archives to biodiversity researchers and computer scientists.","","Biodiversity; Distributed computer systems; Information management; Biodiversity datum; Biological data; Computer scientists; Data life cycle; Environmental researches; Large volumes; Research data managements; Research institutions; Big data","","","","","Deutsche Forschungsgemeinschaft","","Bach K., Schafer D., Enke N., Seeger B., Gemeinholzer B., Bendix J., A comparative evaluation of technical solutions for long-term data repositories in integrative biodiversity research, Ecological Informatics, 11, pp. 16-24, (2012); Costello M.J., Michener W.K., Gahegan M., Zhang Z., Bourne P.E., Biodiversity data should be published, cited, and peer reviewed, Trends in Ecology & Evolution, 28, 8, pp. 454-461, (2013); Enke N., Thessen A., Bach K., Bendix J., Seeger B., Gemeinholzer B., The user's view on biodiversity data sharing-Investigating facts of acceptance and requirements to realize a sustainable use of research data, Ecological Informatics, 11, pp. 25-33, (2012); Ingwersen P., Chavan V., Indicators for the data usage index (dui): An incentive for publishing primary biodiversity data through global information infrastructure, BMC Bioinformatics, 12, (2011); Jablonski S., Kehl A., Neubacher D., Poschlod P., Rambold G., Schneider T., Triebel D., Volz B., Weiss M., DiversityMobile-mobile data retrieval platform for biodiversity research projects, GI Jahrestagung, pp. 610-624, (2009); Kattge J., Diaz S., Lavorel S., Prentice I.C., Leadley P., Bonisch G., Garnier E., Reich B M.Peter, Wright I.J., Et al., TRY-A global database of plant traits, Global Change Biology, 17, 9, pp. 2905-2935, (2011); Lotz T., Nieschulze J., Bendix J., Dobbermann M., Konig-Ries B., Diverse or uniform?-Intercomparison of two major German project databases for interdisciplinary collaborative functional biodiversity research, Ecological Informatics, 8, pp. 10-19, (2012); Mora C., Tittensor D.P., Adl S., Simpson A.G.B., Worm B., How many species are there on Earth and in the ocean?, PLoS Biology, 9, 8, (2011); Nadrowski K., Ratcliffe S., Bonisch G., Bruelheide H., Kattge J., Liu X., Maicher L., Mi X., Prilop M., Seifarth D., Et al., Harmonizing, annotating and sharing data in biodiversity-ecosystem functioning research, Methods in Ecology and Evolution, 4, 2, pp. 201-205, (2013); Sears J.R., Data sharing effect on article citation rate in paleoceanography, AGU Fall Meeting Abstracts, 1, (2011); Triebel D., Hagedorn G., Rambold G., An appraisal of megascience platforms for biodiversity information, MycoKeys, 5, pp. 45-63, (2012); Tolle K.M., Tansley D., Hey A.J.G., The fourth paradigm: Data-intensive scientific discovery [point of view], Proceedings of the IEEE, 99, 8, pp. 1334-1337, (2011); Whitlock M.C., Data archiving in ecology and evolution: Best practices, Trends in Ecology & Evolution, 26, 2, pp. 61-65, (2011)","","Plodereder E.; Universitat Stuttgart, Institut fur Softwaretechnologie, Universitatsstr. 38, Stuttgart; Grunske L.; Universitat Stuttgart, Institut fur Softwaretechnologie, Universitatsstr. 38, Stuttgart; Ull D.; Universitat Stuttgart, Institut fur Technische Informatik, Pfaffenwaldring 47, Stuttgart; Schneider E.; Universitat Stuttgart, Institut fur Technische Informatik, Pfaffenwaldring 47, Stuttgart","Gesellschaft fur Informatik (GI)","","44. Jahrestagung der Gesellschaft fur Informatik INFORMATIK 2014 - Big Data - Komplexitat meistern - Big Data - Mastering Complexity: 44th Annual Meeting of the Society for Computer Science, INFORMATICS 2014","22 September 2014 through 26 September 2014","Stuttgart","110425","16175468","978-388579626-8","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-84922496341"
"Hiom D.; Fripp D.; Gray S.; Snow K.; Steer D.","Hiom, Debra (6508212991); Fripp, Dom (36633855300); Gray, Stephen (56835034300); Snow, Kellie (24382133200); Steer, Damian (56767810900)","6508212991; 36633855300; 56835034300; 24382133200; 56767810900","Research data management at the University of Bristol: Charting a course from project to service","2015","Program","49","4","","475","493","18","11","10.1108/PROG-02-2015-0019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941366435&doi=10.1108%2fPROG-02-2015-0019&partnerID=40&md5=cd96982d0dff858b8922ff91511afb14","Arts and Social Sciences Library, University of Bristol, Bristol, United Kingdom; Research IT, University of Bristol, Bristol, United Kingdom","Hiom D., Arts and Social Sciences Library, University of Bristol, Bristol, United Kingdom; Fripp D., Arts and Social Sciences Library, University of Bristol, Bristol, United Kingdom; Gray S., Arts and Social Sciences Library, University of Bristol, Bristol, United Kingdom; Snow K., Arts and Social Sciences Library, University of Bristol, Bristol, United Kingdom; Steer D., Research IT, University of Bristol, Bristol, United Kingdom","Purpose – The purpose of this paper is to chart the development of research data management services within the University of Bristol, from the initial Jisc-funded project, through to pilot service and planned core funding of the service. Design/methodology/approach – The paper provides a case study of the approach of the University of Bristol Library service to develop a sustainable Research Data Service. Findings – It outlines the services developed during the project and pilot phases of the service. In particular it focuses on the sustainability planning to ensure that research data management is embedded as a core university service. Originality/value – The case study provides practical advice and valuable insights into the issues and experiences of ensuring that research data management is properly valued and supported within universities. © 2015, Emerald Group Publishing Limited.","Data curation; Data publication; Research data management; Research support; Service development; Sustainability","","","","","","","","Auckland M., (2012); Cox A., Pinfield S., Smith J., Moving a brick building: UK libraries coping with research data management as a ‘wicked’ problem, Journal of Librarianship and Information Science, (2014); (2009); (2011); (2013); Donnelly M., ‘Five Steps to Developing a Research Data Policy’. DCC ‘Quickstart’ Leaflets, (2014); (2014); (2015); Evans C., Barker G., Heesom K.J., Fan J., Bessant C., Matthews D.A., De novo derivation of proteomes from transcriptomes for transcript and protein identification, Nature Methods, 9, pp. 1207-1211, (2012); Hodson S., Molloy L., Development of institutional RDM services by projects in the JISC managing research data programmes, Delivering Research Data Management Services, pp. 207-209, (2014); Jorum J., (2013); Knowledge Exchange K.E., (2014); (2011); (2010); Whyte A., (2014); Wiley W., (2014); (2006)","D. Hiom; Arts and Social Sciences Library, University of Bristol, Bristol, United Kingdom; email: d.hiom@bristol.ac.uk","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","","Scopus","2-s2.0-84941366435"
"Kennan M.A.; Corrall S.; Afzal W.","Kennan, Mary Anne (56001293900); Corrall, Sheila (16303268400); Afzal, Waseem (26325353700)","56001293900; 16303268400; 26325353700","“making space” in practice and education: Research support services in academic libraries","2014","Library Management","35","","","666","683","17","39","10.1108/LM-03-2014-0037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84913591350&doi=10.1108%2fLM-03-2014-0037&partnerID=40&md5=3af5b180cc817cb652a38faae81d4bd6","School of Information Studies, Charles Sturt University, Silverwater, Australia; School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; School of Information Studies, Charles Sturt University, Wagga Wagga, Australia","Kennan M.A., School of Information Studies, Charles Sturt University, Silverwater, Australia; Corrall S., School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; Afzal W., School of Information Studies, Charles Sturt University, Wagga Wagga, Australia","Design/methodology/approach – The present paper uses data from a recent survey of research support provision by academic libraries in Australia, New Zealand, the UK and Ireland, (authors 2013), and provides additional in depth analysis of the textual responses to extend the analysis in the light of forces for change in higher education. The original online questionnaire surveyed current and planned research support in academic libraries, and constraints or support needs related to service developments. It was distributed to 219 institutions in Australia, New Zealand, the UK, and Ireland, and obtained 140 valid responses (response rate of 63.9 percent). Results were analyzed using descriptive statistics with thematic categorization and coding for the textual responses.; Findings – Most academic libraries surveyed are already providing or planning services in the focal areas of bibliometrics and data management. There was also increasing demand for other research support services, not the focus of the study, such as eresearch support, journal publishing platforms, and grant writing support. The authors found that while many academic libraries perceive increasing research support services as a “huge opportunity” they were constrained by gaps in staff skills, knowledge, and confidence and resourcing issues.With regard to staff education and training, it was reported they require a broader understanding of the changing research and scholarly landscape, the research cultures of different disciplines, and technological change. There was a near-universal support for development of more comprehensive, specialized, LIS education to prepare professionals for broader research support roles.; Originality/value – This further analysis of the implications of our survey in relation to influences such as economics, academic culture, technology, raises questions for both educators and practitioners about the future direction of the profession and how the authors collectively “make space” as new potential services arise.; Purpose – How academic libraries support the research of their parent institutions has changed as a result of forces such as changing scholarly communication practices, technological developments, reduced purchasing power and changes in academic culture. The purpose of this paper is to examine the professional and educational implications of current and emerging research support environments for academic libraries, particularly with regard to research data management and bibliometrics and discuss how do professionals and educators “make space” as new service demands arise? © Emerald Group Publishing Limited.","Academic libraries; Bibliometrics; Research data management; Research support services","","","","","","","","Abbott A., The System of Professions, (1988); Abbott A., Professionalism and the future of librarianship, Library Trends, 46, 3, pp. 430-443, (1998); Adams J., Bibliometrics, assessment and UK research, Serials, 20, 3, pp. 188-191, (2007); Auckland M., Re-Skilling for Research: An Investigation into the Roles and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); ARC Open Access Policy, (2013); Ball R., Tunger D., Bibliometric analysis – a new business area for information professionals in libraries?, Scientometrics, 66, 3, pp. 561-577, (2006); Barr N.A., Higher education funding, Oxford Review of Economic Policy, 20, 2, pp. 264-283, (2004); Becher T., Trowler P.R., Academic Tribes and Territories: Intellectual Enquiry and the Culture of Disciplines, (2001); Borgman C., Scholarship in the Digital Age: Information, Infrastructure, and the Internet, (2007); Choi Y., Rasmussen E., What is needed to educate future digital librarians: A study of current practice and staffing patterns in academic and research libraries, D-Lib Magazine, 12, 9, (2006); Corrall S., Professional education for a digital world, University Libraries and Digital Learning Environments, pp. 49-67, (2010); Corrall S., Roles and Responsibilities: Libraries, Librarians and Data, pp. 105-133, (2012); Corrall S., Kennan M.A., Afzal W., Bibliometrics and research data management services: Emerging trends in library support for research, Library Trends, 61, 3, pp. 636-674, (2013); Cox A.M., Corrall S., Evolving academic library specialties, Journal of the American Society for Information Science and Technology, 64, 8, pp. 1526-1542, (2013); Cragin M.H., Palmer C.L., Carlson J.R., Witt M., Data sharing, small science and institutional repositories, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368, 1926, pp. 4023-4038, (2010); Crowe S., Jaguszewski J., Preparing our librarians for the future: Identifying and assessing core competencies at the University of Minnesota Libraries, The Expert Library: Staffing, Sustaining, and Advancing the Academic Library in the 21St Century, pp. 127-157, (2010); Davis M., Wilson C.S., Horn H., Informing decision-making in libraries: Informetric research as input to LIS education and practice, Australian Academic and Research Libraries, 36, 4, pp. 195-213, (2005); Dillon A., Norris A., Crying wolf: An examination and reconsideration of the perception of crisis in LIS education, Journal of Education for Library and Information Science, 46, 4, pp. 280-298, (2005); Drummond R., Wartho R., RIMS: The research impact measurement service at the university of new south Wales, Australian Academic &Amp; Research Libraries, 40, 2, pp. 76-87, (2009); Ferreira F., Santos J.N., Nascimento L., Rade R.S., Barros S., Borges J., Information professionals in Brazil: Core competencies and professional development, Information Research, 12, 2, (2007); Accessibility, Sustainability, Excellence: How to Expand Access to Research Publications, (2012); Fitzgerald T., White J., Gunter H., Hard Labour? Academic Work and the Changing Landscape of Higher Education, (2012); Gorman M., Whither library education?, New Library World, 105, 9-10, pp. 376-380, (2004); Greenwood E., Attributes of a profession, Social Work, 2, 3, pp. 45-55, (1957); Harris-Pierce R.L., Liu Y.Q., Is data curation education at library and information science schools in North America adequate?, New Library World, 113, 11-12, pp. 598-613, (2012); Henty M., Developing the Capability and Skills to Support E-Research, 55, (2008); Henty M., Weaver B., Bradbury S.J., Porter S., Investigating Data Management Practices in Australian Universities, (2008); Hjorland B., Library and information science: Practice, theory, and philosophical basis, Information Processing &Amp; Management, 36, 3, pp. 501-531, (2000); Hughes E.C., The professions, Daedalus, 92, 4, pp. 655-668, (1963); Kennan M.A., Cole F., Willard P., Wilson C., Marion L., Changing workplace demands: What job ads tell us, Aslib Proceedings, 58, 3, pp. 179-196, (2006); Maccoll J., Library Roles in University Research Assessment, 20, 2, pp. 152-168, (2010); Macdonald K.M., The Sociology of the Professions, Sage, (1995); Markauskaite L., Digital media, technologies and scholarship: Some shapes of eResearch in educational inquiry, Australian Educational Researcher, 37, 4, pp. 79-101, (2010); Markauskaite L., Kennan M.A., Richardson J., Aditomo A., Hellmers L., Investigating eResearch: Collaboration practices and future challenges, Collaborative and Distributed E-Research: Innovations in Technologies, Strategies and Applications, pp. 1-33, (2012); Murray-Rust P., Open data in science, Serials Review, 34, 1, pp. 52-64, (2008); Nicholas D., Rowlands I., Jubb M., Jamali H.R., The impact of the economic downturn on libraries: With special reference to university libraries, Journal of Academiclibrarianship, 36, 5, pp. 376-382, (2010); Partridge H.L., Hanisch J., Hughes H.E., Henninger M., Carroll M., Combes B., Genoni P., Reynolds S., Tanner K., Burford S., Ellis L., Hider P., Yates C., Re-Conceptualising and Re-Positioning Australian Library and Information Science Education for the 21St Century: Final Report 2011, (2011); Peacock J., Teaching skills for teaching librarians: Postcards from the edge of the educational paradigm, Australian Academic and Research Libraries, 32, 1, pp. 26-42, (2001); Pryor G., Managing Research Data, (2012); Pryor G., Donnelly M., Skilling up to do data: Whose role, whose responsibility, whose career?, International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Sabelli M., Library and information sciences in the information disciplines environment: Towards integrative models of disciplines, professional community and information and communication public policies, Information Research, 15, 4, (2010); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Steele C., Butler L., Kingsley D., The publishing imperative: The pervasive influence of publication metrics, Learned Publishing, 19, 4, pp. 277-290, (2006); Swan A., Brown S., The Skills, Role and Career Structure of Data Scientists and Curators: An Assessment of Current Practice and Future Needs: A Report to the Joint Information Systems Committee (JISC), (2008); Tenopir C., I never learned about that in library school: Curriculum changes in LIS, Online, 24, 2, pp. 42-46, (2000); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future: An ACRL White Paper, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, Plos ONE, 6, 6, (2011); Treloar A., Design and implementation of the Australian National Data Service, International Journal of Digital Curation, 4, 1, pp. 125-137, (2009); Van House N., Sutton S.A., The panda syndrome: An ecology of LIS education, Journal of Education for Library and Information Science, 37, 2, pp. 131-147, (1996); Varalakshmi R., Curriculum for digital libraries: An analytical study of Indian LIS curricula, D-Lib Magazine, 15, 9-10, (2009); Walter S., Instructional improvement: Building capacity for the professional development of librarians as teachers, Reference and User Services Quarterly, 45, 3, pp. 213-218, (2006); Williamson K., Given L., Scifleet P., Qualitative data analysis, Research Methods: Information, Systems, and Contexts, pp. 476-491, (2013); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008); Zhao D., Bibliometrics and LIS education: How do they fit together? (Panel proposal), Proceedings of the American Society for Information Science and Technology, 48, pp. 9-12, (2011)","","","Emerald Group Publishing Ltd.","","","","","","01435124","","","","English","Libr. Manage.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84913591350"
"Shreeves S.L.","Shreeves, Sarah L. (23490541500)","23490541500","The role of repositories in the future of the journal","2014","The Future of the Academic Journal: Second Edition","","","","299","315","16","5","10.1533/9781780634647.299","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017645783&doi=10.1533%2f9781780634647.299&partnerID=40&md5=81e5d9cc56b9cf915a8cb38e8fe328ad","Illinois Digital Environment for Access to Learning and Scholarship (IDEALS), University of Illinois at Urbana-Champaign, United States","Shreeves S.L., Illinois Digital Environment for Access to Learning and Scholarship (IDEALS), University of Illinois at Urbana-Champaign, United States","The report of the UK Working Group on Expanding Access to Published Research Findings, chaired by Dame Janet Finch, entitled Accessibility, Sustainability, Excellence: How to Expand Access to Research Publications, helped to crystallize a long-simmering debate within the open access (OA) community: should the focus for OA advocates be 'green' OA - that is, the use of repositories to make research published through traditional subscription-based venues openly available - or should it be 'gold' OA - that is, through publication within venues that are themselves open access? This chapter argues that this has never truly been an either/or proposition, and that this debate often ignores or minimizes the wide variety of roles - direct and indirect - that repositories play within the larger scholarly publishing ecosystem. Research data, funder and institutional mandates for open access to published research via repositories, and the growing role of library as publisher, are all evidence that the repository - whether institutional or disciplinary or format driven - will continue to play a role within the larger scholarly publishing environment. © 2014 The editors and contributors. All rights reserved.","Disciplinary repository; Funder mandates; Institutional repository; Library publishing; Open access; Research data management","","","","","","","","Alexander B., The New (In)Visible College: Emergent Scholarly Communication Environment and The Liberal Arts, (2011); Bjork B.-C., Welling P., Laakso M., Majlender P., Hedlund T., Open access to the scientific journal literature: situation 2009, PLOS ONE, 5, 6, (2010); Duranceau E., Kriegsman S., Implementing open access policies using institutional repositories, The Institutional Repository: Benefits and Challenge, pp. 75-97, (2013); Accessibility, Sustainability, Excellence: How to Expand Access to Research Publications, (2012); Hackman T., What's the opposite of a Pyrrhic victory: lessons learned from an open access defeat, College and Research Library News, 70, 9, pp. 518-521, (2009); Harnad S., Brody T., Vallieres F., Carr L., Hitchcock S., Et al., The access/impact problem and the green and gold roads to open access, Serials Review, 30, 4, pp. 310-314, (2004); Howard J., Wired campus: some associations, scholars protest bill that would curb public access to research, The Chronicle of Higher Education 25 January 2012, (2012); Howard J., Legislation to bar public-access requirement on federal research is dead, The Chronicle of Higher Education 27 February 2012, (2012); Koers H., Elsevier Data and Publications Linking, (2012); Li Y., Banach M., Institutional repositories and digital preservation: assessing current practices at research libraries, D-Lib Magazine, 17, 5-6, (2011); Journal Article Versions (JAV): Recommendations of the NISO/ALPSP JAV Technical Working Group, (2008); Radom R., Feltner-Reichert M., Stringer-Stanback K., SPEC Kit 332: Organization of Scholarly Communication Services, (2012); Survey of Academic Attitudes to Open Access and Institutional Repositories: An RSP and UKCoRR Initiative; Rieh S.Y., Jean B., Yakel E., Markey K., Kim J., Perceptions and experiences of staff in the planning and implementation of institutional repositories, Library Trends, 57, 2, pp. 168-190, (2008); Salo D., The innkeeper at the roach motel, Library Trends, 57, 2, pp. 98-123, (2008); Shreeves S.L., Cannot predict now': the role of repositories in the future of the journal, The Future of the Academic Journal, pp. 197-211, (2009); A thumbs up for open access, but an expensive way of getting there; Suber P., Open Access, (2012); Taylor M., Academic publishers have become the enemies of science; Government response to the Finch Group Report, Accessibility, Sustainability, Excellence: How to Expand Access to Research Publications, (2012); Ware M., Summary and Conclusions of the ALPSP Survey of Librarians on Factors in Journal Cancellations, (2006)","","","Elsevier Inc.","","","","","","","978-178063464-7; 978-184334783-5","","","English","The Future of the Acad. J.: Second Ed.","Book chapter","Final","","Scopus","2-s2.0-85017645783"
"Schmidt B.; Dierkes J.","Schmidt, Birgit (56905492900); Dierkes, Jens (56727998600)","56905492900; 56727998600","New alliances for research and teaching support: establishing the Göttingen eResearch Alliance","2015","Program","49","4","","461","474","13","7","10.1108/PROG-02-2015-0020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941363440&doi=10.1108%2fPROG-02-2015-0020&partnerID=40&md5=f2ffd42621734ae7c5c453c09f1a60cb","University of Göttingen, Göttingen, Germany","Schmidt B., University of Göttingen, Göttingen, Germany; Dierkes J., University of Göttingen, Göttingen, Germany","Purpose – The purpose of this paper is to describe the design and implementation of policies, digital infrastructures and hands-on support for eResearch at the University of Göttingen. Core elements of this activity are to provide support for research data management to researchers of all disciplines and to coordinate on-campus activities. These activities are actively aligned with disciplinary, national and international policies and e-infrastructures. Design/methodology/approach – The process of setting up and implementing an institutional data policy and its necessary communications and workflows are described and analysed. A first assessment of service development and uptake is provided in the area of embedded research data support. Findings – A coordination unit for eResearch brings together knowledge about methods and tools that are otherwise scattered across disciplinary units. This provides a framework for policy implementation and improves the quality of institutional research environments. Practical implications – The study provides information about an institutional implementation strategy for infrastructure and services related to research data. The lessons learned allow insights into current challenges and work ahead. Originality/value – With a cross-cutting, “horizontal” approach, in the Göttingen eResearch Alliance, two research-orientated infrastructure providers, a library and an IT service, combine their services and expertise to develop an eResearch service and support portfolio for the Göttingen Campus. © 2015, Authors.","e-Infrastructures; eResearch; Policies; Research data management","","","","","","","","Cremer F., Engelhardt C., Neuroth H., Embedded data manager – Integriertes Forschungsdatenmanagement: Praxis, Perspektiven und Potentiale, Bibliothek: Forschung und Praxis, 39, 1, pp. 13-31, (2015); Dierkes J., Gnadt T., Cremer F., Kiraly P., Menke C., Schmitt O., Steilen L., Wuttke U., Horstmann W., Yahyapour R., (2015); Engelhardt C., (2013); Georg-August-University Gottingen G.-A.-U.G., (2012); German Rectors' Conference G.R.C., (2014); German Research Foundation G.R.F., Recommendations for Secure Storage and Availability of Digital Primary Research Data, (2009); Proposals for Safeguarding Good Scientific Practice: Recommendations of the Commission on Professional Self Regulation in Science, (1998); (2014); Horstmann W., Schallier W., Siren J., Pires C.M., Libraries as e-infrastructure, ZfBB, 61, 4-5, pp. 215-219, (2014); Jones S., Research data policies: principles, requirements and trends, Managing Research Data, (2012); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services – A Guide for HEIs, DCC How-to Guides, (2013); Lossau N., An overview of research infrastructures in Europe – and recommendations to LIBER, LIBER Quarterly, 21, 3-4, pp. 313-329, (2012); Neuroth H., Strathmann S., Osswald A., Ludwig J., Digital Curation of Research Data – Experiences of a Baseline Study in Germany, (2013); (2015); (2014); Schmidt B., Ludwig J., (2014); The Royal Society T.R.S., (2012); Van den Eynden V., Bishop L., (2014); Whyte A., Pryor G., Open science in practice: researcher perspectives and participation, International Journal of Digital Curation, 6, 1, pp. 199-213, (2011); Georg-August-University Gottingen G.-A.-U.G., (2014)","B. Schmidt; University of Göttingen, Göttingen, Germany; email: bschmidt@sub.uni-goettingen.de","","Emerald Group Holdings Ltd.","","","","","","00330337","","","","English","Program","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84941363440"
"Zahoransky R.; Semaan S.; Rechert K.","Zahoransky, Richard (56367725800); Semaan, Saher (56118744600); Rechert, Klaus (13105127400)","56367725800; 56118744600; 13105127400","Identity and access management for complex research data workflows","2013","Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)","P-217","","","107","118","11","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898834534&partnerID=40&md5=1bb716acf3210990b48a562efa1e25b7","Department of Computer Science, University of Freiburg, 79104 Freiburg, Hermann-Herder-Str. 10, Germany","Zahoransky R., Department of Computer Science, University of Freiburg, 79104 Freiburg, Hermann-Herder-Str. 10, Germany; Semaan S., Department of Computer Science, University of Freiburg, 79104 Freiburg, Hermann-Herder-Str. 10, Germany; Rechert K., Department of Computer Science, University of Freiburg, 79104 Freiburg, Hermann-Herder-Str. 10, Germany","Identity and Access Management (IAM) infrastructures already provide a crucial and established technology, enabling researchers and students to access services like computing facilities and electronic resources. However, the rise of complex and fully digitalized scientific workflows, world-wide research co-operations, and the reliance on external services and data sources poses new challenges to IAM architectures and their federations. Due to the non-uniform structure of such services each service provider is implementing its own access- And security-policy. As a result of license restrictions or privacy concerns, a user has to be authenticated and authorized by different entities in different contexts and roles to access complex research data, i.e. requesting a digital object as well as appropriate processing tools and a rendering environment. In order to enable seamless scientific workflows, an efficient federated IAM architecture is required. In this paper we discuss the use-case of functional research data preservation and the requirements for a common authentication and authorization scheme. The goal is to develop a security architecture allowing the user to login only once, e.g. at his or her university library and the Identity Management (IdM) system should be able to delegate the user's request to the related service providers. All these entities need to interact with and on behalf of the user without the user having to enter his credentials at every point. The results of this work are particularly useful when facing upcoming challenges to securing and managing access to non-uniform and inhomogenous cloud services and external data sources as a basis for today's scientific workflows and electronic business processes.","","Network security; Authorization; Digital libraries; Information management; Authentication and authorization; Computing facilities; Electronic resources; External data sources; Identity and access managements; Scientific workflows; Security Architecture; University libraries; Data-source; Management infrastructure; Non-uniform; Research data; Service provider; Work-flows; Research; Authentication","","","","","","","Amberg M., Hirschmeier M., Schobert D., DART - ein ansatz zur analyse und evaluierung der benutzerakzeptanz, Wirtschaftinformatik Proceedings, (2003); Basney J., Gaynor J., An oauth service for issuing certificates to science gateways for teragrid users, Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, TG '11, pp. 321-326, (2011); Cantor S., SAML V2.0 Condition for Delegation Restriction Version 1.0. Technical Report, OASIS Security Services TC, (2009); Cantor S., Kemp J., Philpott R., Maler E., Assertions and Protocols for the OASIS Security Assertion Markup Language (SAML) V2.0. Technical Report, OASIS, (2005); Delve J., Anderson D., The Trustworthy Online Technical Environment Metadata Database - TOTEM, (2012); Freeman B., OpenID: One Key, Many Doors Technical Report, Yahoo, (2008); Gasmi T., Schneider G., Suchodoletz D.V., Von der accountverwaltung zum erweiterten identity management, GI Jahrestagung (2), INFORMATIK 2008, Beherrschbare Systeme - Dank Informatik, 2, pp. 589-595, (2008); Hamlen K., Liu P., Kantarcioglu M., Thuraisingham B., Yu T., Identity management for cloud computing: Developments and directions, Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research, CSIIRW '11, (2011); McKenzie R., Crompton M., Wallis C., Use cases for identity management in e-government, IEEE Security and Privacy, 6, 2, pp. 51-57, (2008); Rechert K., Von Suchodoletz D., Welte R., Emulation based services in digital preservation, Proceedings of the 10th Annual Joint Conference on Digital Libraries, JCDL '10, pp. 365-368, (2010); Rechert K., Valizada I., Suchodoletz D.V., Latocha J., Bwfla - A functional approach to digital preservation, PIK - Praxis Der Informationsverarbeitung und Kommunikation, 35, 4, pp. 259-267, (2012); Sun S.-T., Beznosov K., The devil is in the (implementation) details: An empirical analysis of oauth SSO systems, Proceedings of the 2012 ACMconference on Computer and Communications Security, CCS '12, pp. 378-390, (2012); Squicciarini A., Bhargav-Spantzel A., Czeskis A., Bertino E., Traceable and automatic compliance of privacy policies in federated digital identity management, Proceedings of the 6th International Conference on Privacy Enhancing Technologies, PET'06, pp. 78-98, (2006); Simon M., Waldvogel M., Schober S., Semaan S., Nussbaumer M., Bwidm: Foderieren auch nicht-webbasierter dienste auf basis von saml, DFN-Forum Kommunikationstechnologien, 5. DFN-Forum Kommunikationstechnologien: Verteilte Systeme Im Wissenschaftsbereich, pp. 119-128, (2012); Semaan S., Zahoransky R., BwiDM: Anbindung nicht-webbasierter itinfrastrukturen an eine SAML/shibboleth-foderation, 8th Joint BFG/bwGRiD Conference & Workshop, (2012); Verdegem R., Van Den Hoeven J., Emulation: To be or not to be, IS&T Conference on Archiving 2006, pp. 55-60, (2006)","","","Gesellschaft fur Informatik (GI)","","6. DFN-Forum Kommunikationstechnologien: Verteilte Systeme imWissenschaftsbereich - 6th DFN-Forum on Communication Technologies: Distributed Systems in Science","3 June 2013 through 4 June 2013","Erlangen","104297","16175468","978-388579611-4","","","English","Lect. Notes Informatics (LNI), Proc. - Series Ges. Inform. (GI)","Conference paper","Final","","Scopus","2-s2.0-84898834534"
"Engeström Y.; Kaatrakoski H.; Kaiponen P.; Lahikainen J.; Laitinen A.; Myllys H.; Rantavuori J.; Sinikara K.","Engeström, Yrjö (6601953253); Kaatrakoski, Heli (55193610500); Kaiponen, Pälvi (55193610800); Lahikainen, Johanna (55193610600); Laitinen, Anne (55193053700); Myllys, Heli (55193610700); Rantavuori, Juhana (55193610900); Sinikara, Kaisa (35976791800)","6601953253; 55193610500; 55193610800; 55193610600; 55193053700; 55193610700; 55193610900; 35976791800","Knotworking in academic libraries: Two case studies from the university of Helsinki","2012","LIBER Quarterly","21","3-4","","387","405","18","20","10.18352/lq.8032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859992231&doi=10.18352%2flq.8032&partnerID=40&md5=c6d4de3530d3929dd56de8a4618ed34a","University of Helsinki, Finland","Engeström Y., University of Helsinki, Finland; Kaatrakoski H., University of Helsinki, Finland; Kaiponen P., University of Helsinki, Finland; Lahikainen J., University of Helsinki, Finland; Laitinen A., University of Helsinki, Finland; Myllys H., University of Helsinki, Finland; Rantavuori J., University of Helsinki, Finland; Sinikara K., University of Helsinki, Finland","Librarians in academic libraries are facing major changes in their work due to, e.g., the internet, digitization, and increasing use of new channels for information retrieval by their most important clients, namely researchers. This creates challenges for librarians: both to deepen their own expertise and to develop innovative service models for their clients. In this paper we present a development project entitled 'Knotworking in the Library' from the Helsinki University Library. The project made use of the Change Laboratory method, which is an intensive developmental effort which facilitates improvements in the activities of organizations and changes in the organizational culture. The process started in Viikki Campus Library in 2009-2010 and continued in the City Centre Campus Library in 2010-2011. The aim was to create new kinds of partnership between libraries and research groups in the form of knotworking. By knotworking we mean a boundary-crossing, collective problem-solving way of organizing work. The knotworking model presented in this paper generated practical tools to assist selected research groups in dealing with data management related-issues.","Change Laboratory; Knotworking; Research data management; Service innovation","","","","","","","","Brophy P., Communicating the library: Librarians and faculty in dialogue, Library Management, 28, pp. 515-523, (2007); Earnshaw R.E., Vince J.A., Digital Convergence: Libraries of the Future, (2007); Engestrom Y., Learning by Expanding. An Activity Theoretical Approach to Developmental Research, (1987); Engestrom Y., Activity theory as a framework for analyzing and redesigning work, Ergonomics, 43, 7, pp. 960-974, (2000); Engestrom Y., Putting Vygotsky to work: The Change Laboratory as an application of double stimulation, The Cambridge companion to Vygotsky, (2007); Engestrom Y., From design experiments to formative interventions, Theory and Psychology, 21, 5, (2011); Engestrom Y., Sannino A., Volition and agency in organizations: An activity theoretical perspective; Engestrom Y., Engestrom R., Vahaaho T., When the center does not hold: The importance of knotworking, Activity Theory and Social Practice, (1999); Rader H., Managing academic and research libraries partnerships, Library Management, 23, pp. 187-191, (2002)","","","Igitur, Utrecht Publishing and Archiving Services","","","","","","14355205","","","","English","LIBER Q.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84859992231"
"Strand R.H.; Farrell M.P.; Goyert J.C.; Daniels K.L.","Strand, R.H. (7004390556); Farrell, M.P. (7202679387); Goyert, J.C. (6603247517); Daniels, K.L. (7101809119)","7004390556; 7202679387; 6603247517; 7101809119","Environmental assessments through research data management","1983","Journal of Environmental Management","16","3","","269","280","11","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020975334&partnerID=40&md5=e005b472f665b2acccbdb7ad235d970d","United States","Strand R.H., United States; Farrell M.P., United States; Goyert J.C., United States; Daniels K.L., United States","[No abstract available]","","assessments; data management; environmental management.; statistics; computer analysis; data base; environmental sanitation; methodology; nonhuman; statistics","","","","","","","","","","","","","","","","03014797","","JEVMA","","English","J. ENVIRON. MANAGE.","Article","Final","","Scopus","2-s2.0-0020975334"
"Kolb T.L.; Agnes Blukacz-Richards E.; Muir A.M.; Claramunt R.M.; Koops M.A.; Taylor W.W.; Sutton T.M.; Arts M.T.; Bissel E.","Kolb, Tracy L. (55889655700); Agnes Blukacz-Richards, E. (55532356200); Muir, Andrew M. (57213960202); Claramunt, Randall M. (8641713300); Koops, Marten A. (6603895391); Taylor, William W. (7402890793); Sutton, Trent M. (7102027517); Arts, Michael T. (7003941048); Bissel, Ed (55583131800)","55889655700; 55532356200; 57213960202; 8641713300; 6603895391; 7402890793; 7102027517; 7003941048; 55583131800","How to manage data to enhance their potential for synthesis, preservation, sharing, and reuse-a Great Lakes case study; [Cómo manejar datos para incrementar el potencial para su síntesis, preservación, intercambio y reutilización -los Grandes Lagos como caso de estudio]","2013","Fisheries","38","2","","52","64","12","19","10.1080/03632415.2013.757975","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873436075&doi=10.1080%2f03632415.2013.757975&partnerID=40&md5=7978f0fb09baa8c4d9849cbbea8f6e23","Michigan Department of Natural Resources, Charlevoix Research Station, Charlevoix, MI 49720, 96 Grant Street, United States; Great Lakes Laboratory for Fisheries and Aquatic Sciences, Fisheries and Oceans Canada, Burlington, ON L7R 4A6, 867 Lakeshore Rd, Canada; Great Lakes Fish Commission, Ann Arbor MI, 48105, 2100 Commonwealth Blvd,. Suite 100, United States; Michigan State University, East Lansing, MI 48824, 14 Natural Resources Building, United States; University of Alaska Fairbanks, School of Fisheries and Ocean Sciences, Fairbanks, AK 99775, 905 N. Koyukuk Dr, United States; Environment Canada, Burlington, ON L7R 4A6, 867 Lakeshore Rd, Canada","Kolb T.L., Michigan Department of Natural Resources, Charlevoix Research Station, Charlevoix, MI 49720, 96 Grant Street, United States; Agnes Blukacz-Richards E., Great Lakes Laboratory for Fisheries and Aquatic Sciences, Fisheries and Oceans Canada, Burlington, ON L7R 4A6, 867 Lakeshore Rd, Canada; Muir A.M., Great Lakes Fish Commission, Ann Arbor MI, 48105, 2100 Commonwealth Blvd,. Suite 100, United States, Michigan State University, East Lansing, MI 48824, 14 Natural Resources Building, United States; Claramunt R.M., Michigan Department of Natural Resources, Charlevoix Research Station, Charlevoix, MI 49720, 96 Grant Street, United States; Koops M.A., Great Lakes Laboratory for Fisheries and Aquatic Sciences, Fisheries and Oceans Canada, Burlington, ON L7R 4A6, 867 Lakeshore Rd, Canada; Taylor W.W., Michigan State University, East Lansing, MI 48824, 14 Natural Resources Building, United States; Sutton T.M., University of Alaska Fairbanks, School of Fisheries and Ocean Sciences, Fairbanks, AK 99775, 905 N. Koyukuk Dr, United States; Arts M.T., Environment Canada, Burlington, ON L7R 4A6, 867 Lakeshore Rd, Canada; Bissel E., Michigan State University, East Lansing, MI 48824, 14 Natural Resources Building, United States","Proper data management (applying coordinated standards and structures to data collection, maintenance, retrieval, and documentation) is essential for complex projects to ensure data accuracy and accessibility. In this article, we used a recent project evaluating changes in Lake Whitefish (Coregonus clupeaformis) growth, condition, and recruitment in the Great Lakes as a case study to illustrate how thoughtful data management approaches can enhance and improve research. Data management best practices described include dedicating personnel to data curation, setting data standards, building a relational database, managing data updates, checking for and trapping errors, extracting data, documenting data sets, and coordinating with project collaborators. The data management actions taken ultimately resulted in a rich body of scientific publication and a robust database available for future studies. Investing in data management allowed this project to serve as a model for taking the first steps toward a common goal of sharing, documenting, and preserving data that are collected and reported during the scientific research process.","","Coregonus clupeaformis","","","","","Great Lakes Fishery Trust, GLFT, (2004.570); Purdue University; Environment Canada","We thank E. Volkman, C. Benoit, A. Charlton, R. Cripe, G. Fodor, J. Hoffmeister, V. Lee, A. McAlexander, R. Mollen-hauer, S. Shaw, D. Rajchel, B. Williston, and W. Zak for their assistance in the field and the laboratory. Thanks also to M. Ebener and D. Tagerson for assistance with the Lake Superior samples, C. Krause on Lake Erie, and L. Barbeau, D. Frazier, D. Hickey, K. King, T. King, P. Jensen, P. Peeters, B. Peterson, and J. Peterson for Lake Whitefish collections in Lake Michigan. We thank D. Clapp, E. Grove, K. Porath, T. Pattison, D. Wiefreich, J. Witzig, and two anonymous reviewers for their thoughtful comments that helped improve this article. Funding for this project was provided by the Great Lakes Fishery Trust, project number 2004.570, Department of Forestry and Natural Resources at Purdue University, Department of Fisheries and Oceans (to M. Koops) and Environment Canada (to M. Arts).","Akmon D., Zimmerman A., Daniels M., Hedstrom M., The application of archival concepts to a data-intensive environment: Working with scientists to understand data management and preservation needs, Archival Science, 11, pp. 1-20, (2011); Baker K.S., Benson B.J., Henshaw D.L., Blodgett D., Porter J.H., Stafford S.G., Evolution of a multisite network information system: The LTER information management paradigm, Bioscience, 50, 11, pp. 963-978, (2000); Baker K.S., Bowker G.C., Information ecology: Open system environment for data, memories and knowing, Journal of Intelligent Information Systems, 29, pp. 127-144, (2007); Baker K.S., Jackson S.J., Wanetick J.R., Strategies supporting heterogeneous data and interdisciplinary collaboration: Towards an ocean informatics environment, Proceedings of the 38th Hawaii International Conference On System Sciences, pp. 1-10, (2005); Baker K.S., Stocks K.I., Building Environmental Information Systems: Myths and Interdisciplinary Lessons, (2007); Barnas K., Katz S.L., The challenges of tracking habitat at various spatial scales, Fisheries, 35, 5, pp. 232-241, (2010); Beard D.T., Austen D., Brady S.J., Costello M.E., Drewes H.G., Young-Dubovsky C.H., Flather C.H., Gengerke C.L., Loftus A.J., Mac M.J., The multi-state aquatic resources information system, Fisheries, 18, 2, pp. 14-18, (1998); Birnholtz J.P., Bietz M.J., Data at work: Supporting sharing in science and engineering. in Proceedings of the 2003 International ACM SIGGROUP Conference on Supporting Group Work, Sanibel Island, Florida, 9, 12, pp. 339-348, (2003); Blukacz E.A., Koops M.A., Sutton T.M., Arts M.T., Fitzsimmons J.D., Muir A.M., Claramunt R.M., Johnson T.B., Kinnunen R.E., Ebner M.P., Suski C., Burness G., Linking Lake Whitefish (Coregonus clupeaformis) condition with male gamete quality and quantity, Journal of Great Lakes Research, 36, 1, pp. 78-83, (2010); Blumenthal D., Campbell E.G., Anderson M.S., Causino N., Seashore K.S., Withholding research results in academic life science: Evidence from a national survey, Journal of American Medical Association, 277, 15, pp. 1224-1228, (1997); Blumenthal D., Campbell E.G., Gokhale M., Yucel R., Clarridge B., Hilgartner S., Holtzman N.A., Data withholding in genetics and other life sciences: Prevalences and predictors, Academic Medicine, 81, 2, pp. 137-145, (2006); Borer E.T., Seabloom E.W., Jones M.B., Schildhauer M., Some simple guidelines for effective data management, Bulletin of the Ecological Society of America, 90, 2, pp. 205-214, (2009); Borgman C.L., Research Data: Who Will Share What, With Whom, When, and Why?, (2010); Borgman C.L., Wallis J.C., Enyedy N., Building Digital Libraries For Scientific Data: An Exploratory Study of Data Practices In Habitat Ecology, (2006); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks and digital libraries, International Journal of Digital Libraries, 7, 1-2, pp. 17-30, (2007); Brenden T.O., Ebner M.P., Sutton T.M., Special Issue on Assessing the Health of Lake Whitefish Populations in the Laurentian Great Lakes, Journal of Great Lakes Research, 36, 1, pp. 1-142, (2010); Brunt J.W., Research data management in ecology: A practical approach for long-term projects, Seventh International Working Conference Scientific and Statistical Database Management, pp. 272-275, (1994); Brunt J.W., Michener W.K., The resource discovery initiative for field stations: Enhancing data management at North American biological field stations, Bioscience, 59, 6, pp. 482-487, (2009); Campbell E.G., Clarridge B.R., Gokhale M., Birenbaum L., Hilgartner S., Holtzman N.A., Blumenthal D., Data withholding in academic genetics: Evidence from a national survey, Journal of the American Medical Association, 287, 4, pp. 473-479, (2002); Carlson S., Anderson B., What are data? 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Professional norms, reference groups, and information withholding among life scientists, Organization Science, 21, 4, pp. 873-891, (2011); Haerder T., Reuter A., Principles of transaction-oriented database recovery, ACM Computing Surveys, 15, 4, pp. 287-317, (1983); Hale S.S., Miglarese A.H., Bradley M.P., Belton T.J., Cooper L.D., Frame M.T., Friel C.A., Harwell L.M., King R.E., Michner W.K., Nicolson D.T., Peterjohn B.G., Managing troubled data: Coastal data partnerships smooth data integration, Environmental Monitoring and Assessment, 81, pp. 133-148, (2003); Heidorn P.B., Shedding light on the dark data in the long tail of science, Library Trends, 57, pp. 280-299, (2008); Hernandez M.J., Database Design For Mere Mortals: A Hand's On Guide to Relational Database Design, (2003); Hook L.A., Vannan S.K.S., Beaty T.W., Cook R.B., Wilson B.E., Best Practices For Preparing Environmental Datasets to Share and Archive, (2010); Certification For High Standards of Quality Management, ISO 9001:2011, (2011); Katz S.L., Barnas K., Hicks R., Cowan J., Jenkinson R., Freshwater habitat restoration actions in the Pacific Northwest: A decade's investment in habitat improvement, Restoration Ecology, 15, 3, pp. 494-505, (2007); Kelling S., Hochachka W., Fink D., Riedewald M., Caruana R., Data-intensive science: A new paradigm for biodiversity studies, Bioscience, 59, 7, pp. 613-620, (2009); Lele S., Norgaard R.B., Practicing interdisciplinarity, Bioscience, 55, 11, pp. 967-975, (2005); Ling L.M., Ozsu T., Encyclopedia of Database Systems, (2009); Lord P., Macdonald A., Lyon L., Giarretta D., From data deluge to data curation, Proceedings of the UK E-Science All Hands Meeting. (MARIS) the Multistate Aquatic Resource Information System, (2004); McDade L.A., Maddison D.R., Guralick R., Piowar H.A., Jameson M.L., Helgen K.M., Herendeen P.S., Hill A., Vis M.L., Biology needs a modern assessment system for professional productivity, Bioscience, 61, pp. 619-625, (2011); McDonald L.L., Bilby R., Bisson P.A., Coutant C.C., Epifanio J.M., Goodman D., Hanna S., Huntly N., Merrill E., Riddell B., Liss W., Loudenslager E.J., Phillipp D.P., Smoker W., Whitney R.R., Williams R.N., Research, monitoring, and evaluation of fish and wildlife restoration projects in the Columbia River Basin, Fisheries, 32, 12, pp. 582-590, (2007); McLaughlin R.L., Carl L.M., Middel T., Ross M., Noakes D.L.G., Hayes D.B., Baylis J.R., Potentials and pitfalls of integrating data from diverse sources: Lessons from a historical database for Great Lakes stream fisheries, Fisheries, 26, 7, pp. 14-23, (2001); McLaughlin R.L., Jones M.L., Mandrak N.E., Stacey D., FishMAP Online: A Web Application Supporting Science-based Decisions Concerning Fish Movement and Passage, (2010); Michener W.K., Allard S., Budden A., Cook R.B., Douglass K., Frame M., Kelling S., Koskela R., Tenopir C., Vieglai D.A., Participatory design of DataONE-enabling cyberinfrastructure for the biological and environmental sciences, Ecological Informatics, 11, pp. 5-15, (2012); Michener W.K., Jones M.B., Ecoinformatics: Supporting ecology as a data intensive science, Trends In Ecology and Evolution, 27, 2, pp. 85-93, (2011); Muir A.M., Sutton T.M., Arts M.T., Claramunt R.M., Ebner M.P., Fitzsimons J.D., Johnson T.B., Kinnunen R.E., Koops M.A., Sepulveda M.M., Does condition of Lake Whitefish spawners affect physiological condition of juveniles?, Journal of Great Lakes Research, 36, 1, pp. 92-99, (2010); Data Sharing Policy and Implementation Guidance, (2003); Dissemination and Sharing of Research Results, (2010); Nelson B., Data sharing: Empty archives, Nature, 461, 7261, pp. 160-163, (2009); Newman H.B., Ellisman M.H., Orcutt J.A., Data intensive e-science frontier research, Communications of the ACM, 46, 11, pp. 68-77, (2003); (2010); Information and Data Administration Policy, (2011); Data Citation Guidelines, (2012); Parr C.S., Open sourcing ecological data, Bioscience, 57, 4, pp. 309-310, (2007); Porter J.H., Ramsey K.W., Integrating ecological data: Tools and techniques, Proceedings of the 6th World Multi-Conference On Systematics, pp. 396-401, (2002); Postel B.R., Shapiro L.A., Biesanz J.C., On having one's data shared, Journal of Cognitive Neuroscience, 14, 6, pp. 838-840, (2002); Pratt P.J., Adamski J.J., Concepts of Database Management, (2007); (2011); (2008); Sutton T.M., Koops M.A., Arts M.T., Muir A.M., Blukacz A.E., Claramunt R.M., Ebener M.P., Fitzsimons J.D., Johnson T.B., Kinnunen R.E., Project Completion Report: Does Adult Body Condition Affect Recruitment Potential In Lake Whitefish (Coregonus Clupeaformis)?, (2007); Tenopir C., Allard S., Douglas K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, 6, pp. 1-21, (2011); Uhlir P., Information gulags, intellectual straightjackets, and memory holes: Three principles to guide the preservation of scientific data, Data Science Journal, 7, (2010); (2006); Van den Eynden V., Corti L., Woollard M., Bishop L., Horton L., Managing and Sharing Data-best Practice For Researchers, (2011); Vogeli C., Recai Y., Bendavid E., Jones L.M., Anderson M.S., Louis K.S., Campbell E.G., Data withholding and the next generation of scientists: Results of a national survey, Academic Medicine, 81, 2, pp. 128-136, (2006); Wake M.H., Integrative biology: Science for the 21st century, Bioscience, 58, 4, pp. 224-232, (2008); Wallis J.C., Borgman C.L., Mayernik M., Pepe A., Moving archival practices upstream: An exploration of the life cycle of ecological sensing data in collaborative field research, International Journal of Digital Curation, 3, 1, pp. 114-126, (2008); Wang L., Infante D., Esselman P., Cooper A., Wu D., Taylor W.W., Beard D., Whelan G., Ostroff A., A hierarchical spatial framework and database for the National River Fish Habitat Condition Assessment, Fisheries, 36, 9, pp. 436-449, (2011); Watson R., Kura R.Y., Fishing gear associated with global marine catches: Database development, Fisheries Research, 79, pp. 97-102, (2006); Whiteed D.C., Kimball J.S., Lucotch J.A., Manmenee N.K., Wu H., Chilcote S.D., Stanford J.A., A riverscape analysis tool developed to assist wild salmon conservation across the North Pacific Rim, Fisheries, 37, 7, pp. 305-314, (2012); Whitlock M.C., Data archiving in ecology and evolution: Best practices, Trends In Ecology and Evolution, 26, 2, pp. 61-65, (2011); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, pp. 191-201, (2009); Witt M., Carlson J., Brandt S., Constructing data curation profiles, International Journal of Digital Curation, 3, 4, pp. 93-103, (2009)","T. L. Kolb; Michigan Department of Natural Resources, Charlevoix Research Station, Charlevoix, MI 49720, 96 Grant Street, United States; email: kolbt@michigan.gov","","","","","","","","03632415","","","","English","Fisheries","Article","Final","","Scopus","2-s2.0-84873436075"
"Sharma A.; Saghar Y.N.; Saltz J.H.","Sharma, Ashish (57201794355); Saghar, Yusuf N. (57215600911); Saltz, Joel H. (7004423034)","57201794355; 57215600911; 7004423034","Research Data Management, Integration, and Security","2013","Informatics in Radiation Oncology","","","","131","136","5","0","10.1201/b15508-20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135661086&doi=10.1201%2fb15508-20&partnerID=40&md5=09f0554e128eb65e6b5b5c616c7980bf","Department of Biomedical Informatics, Center for Comprehensive Informatics, Emory University, Atlanta, GA, United States","Sharma A., Department of Biomedical Informatics, Center for Comprehensive Informatics, Emory University, Atlanta, GA, United States; Saghar Y.N., Department of Biomedical Informatics, Center for Comprehensive Informatics, Emory University, Atlanta, GA, United States; Saltz J.H., Department of Biomedical Informatics, Center for Comprehensive Informatics, Emory University, Atlanta, GA, United States","Another difference between the access patterns of clinical data and research data lies in the inherent unpredictability of how data is discovered and used in research. Unlike a clinical operations, where access is very well defined, research data often have to be browsed in novel ways to devise a hypothesis. Such browsing of data requires the execution of queries that are novel and ideally require an on-demand integration of different data modalities. For example, a researcher, who is developing new algorithms to help craft tighter dose margins, will want to explore the planning computed tomography (CT), the radiotherapy (RT) dose and structures, patient diagnosis, and outcome data. To test their algorithms, the researcher may want to retrieve RT structures and possibly the associated planning CT, given a particular diagnosis, dose, and outcome. Most existing systems will have a hard time executing a query where some of the queryable attributes are in the electronic medical records (diagnosis and outcome) and a RT PACS (dose), whereas the objects of interest are in a RT PACS (structure) and radiology PACS (planning CT). From these examples, it becomes evident that existing clinical systems fall short when it comes to research use cases. Next, we explore some specific research data management systems, the benefits of a service-oriented architecture in facilitating data federation, and finally information security. © 2014 by Taylor and Francis Group, LLC.","","","","","","","","","(2012); Cantor S., Scavo T., Shibboleth architecture, (2005); Cantor S., Et al., Assertions and protocols for the oasis security assertion markup language, (2005); Curbera F., Et al., Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging and More, (2005); Hammer-Lahav E., The oauth 1.0 protocol, (2010); Jomier J., Et al., An open-source digital archiving system for medical and scientific research, (2010); Koutsonikola V., Vakali A., LDAP: Framework, prac-tices, and trends, IEEE Internet Computing, 8, 5, pp. 66-72, (2004); Marcus D.S., Et al., XNAT: A software framework for man-aging neuroimaging laboratory data, (2006); Morgan R.L., Et al., Federated Security: The Shibboleth Approach, Educause Quarterly, 27, 4, pp. 12-17, (2004); Moses T., Extensible Access Control Markup Language (XACML), (2005); Nadalin A., Et al., WS-Trust 1.3, OASIS Standard, 19, (2007); (2012); (2011); Single Sign-On, (2012); Oster S., Et al., caGrid 1.0: An enterprise grid infrastructure for biomedical research, Journal of the American Medical Informatics Association, 15, 2, pp. 138-149, (2008); Perry J., CTP-The RSNA Clinical Trial Processor, (2012); Recordon D., Reed D., OpenID 2.0: A platform for user-centric identity management, (2006); Rissanen E., eXtensible Access Control Markup Language (XACML), (2010)","","","CRC Press","","","","","","","978-143982583-9; 978-143982582-2","","","English","Informatics in Radiation Oncology","Book chapter","Final","","Scopus","2-s2.0-85135661086"
"Pryor G.","Pryor, Graham (16067337000)","16067337000","Diversions and distractions on the path to effective research data curation","2012","ACS Symposium Series","1110","","","1","17","16","0","10.1021/bk-2012-1110.ch001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905253793&doi=10.1021%2fbk-2012-1110.ch001&partnerID=40&md5=8cba8b076d3fa12eff32c33e12cc1ef2","Digital Curation Centre, Edinburgh EH8 9LE, United Kingdom","Pryor G., Digital Curation Centre, Edinburgh EH8 9LE, United Kingdom","The question we need to ask before driving everyone down the road to data curation nirvana is why do it at all and, in corollary, what are the consequences from not doing it? Those who will be most engaged in the doing of it, whether researchers, librarians, informaticians or other faculty support, already have to contend with a barrage of competing calls on their time and their budgets, so why should they welcome the additional burden implied by demands that they curate and share their data? This chapter starts by examining the key policy and business drivers for doing just that, identifying the synergies that management strategies may have with the cause of data curation and data sharing, and considering the extent to which they coincide with the increasingly prescriptive policies of the major research funders, principally in the UK. Setting these two perspectives in the context of the traditional research culture, I conclude by drawing some inferences concerning those actions essential to achieving any motivational impetus for adopting new practices in the curation and sharing of data across the global research community. © 2012 American Chemical Society.","","Information management; Business drivers; Data curation; Faculty support; Key policies; Management strategies; Research communities; Research culture; Research data; Budget control","","","","","","","Beagrie N., Lavoie B., Woollard M., Keeping Research Data Safe, 2, (2010); National Science Foundation; (2014); (2014); BBC News Channel; Beyond Impact; Patterns of Information Use and Exchange, A Report by the Research Information Network and the British Library;, (2009); Patterns of Information Use and Exchange, A Report by the Research Information Network and the British Library, (2009); Patterns of Information Use and Exchange, A Report by the Research Information Network and the British Library, (2009); Panton Principles; Open to All? Case Studies of Openness in Research, A Joint RIN/NESTA Report, (2010); Atlas Computing Technical Design Report, ATLAS TDR-017, (2005); South D.M., Data Preservation in High Energy Physics, (2010); Corrall S., Managing Research Data, (2012); Hey T., E-Science H.J., And its implications for the library community, Library Hi Tech, 24, 4, (2006); Patterns of Information Use and Exchange, A Report by the Research Information Network and the British Library, (2009); Freiman L., Incremental Scoping Study and Implementation Plan, (2010); Piwowar H., Tracking Data Reuse: Motivations, Methods, and Obstacles; The Value and Benefits of Text Mining, A Digital Infrastructure Directions Report by JISC, (2012); Whyte A., Tedds J., Making the Case for Research Data Management, (2011); Knowledge Networks and Nations: Global Scientific Collaboration in the 21st Century, (2011); Wood J., Riding the Wave: How Europe Can Gain from the Rising Tide of Scientific Data, (2010)","G. Pryor; Digital Curation Centre, Edinburgh EH8 9LE, United Kingdom; email: graham.pryor@ed.ac.uk","","American Chemical Society","","","","","","00976156","978-084122712-5","ACSMC","","English","ACS Symp. Ser.","Article","Final","","Scopus","2-s2.0-84905253793"
"Dragalin V.","Dragalin, Vladimir (55982445400)","55982445400","Designing, monitoring, and modifying an adaptive trial","2008","American Pharmaceutical Outsourcing","9","5","","","","","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-55549107541&partnerID=40&md5=a7b854639bcee79a74c450a5770f9b22","","","The process of implementing an adaptive design in a dose-ranging study will be reviewed with the emphasis on three major steps: planning, simulating, and executing. The first step involves the review of the clinical plan, identification of the protocol design requirements, and the review of potential designs that can best address these requirements. The simulating step consists of comparing the different design options on simulated data for a set of scenarios, reviewing their operating characteristics, and fine-tuning design parameters. As a result, the most appropriate design is selected for implementation. The execution step comprises the finalization of the protocol, Statistical Analysis Plan (SAP), Data Monitoring Committee (DMC) charter, and then conduct of the interim analyses with the preplanned adaptations. The US Food and Drug Administration has recently released ""The Critical Path Opportunities Report"" [1], emphasizing that, ""the two most important areas for improving medical product development are biomarker development (Topic 1) and streamlining clinical trials (Topic 2)."" Adaptive designs for clinical trials provide efficient tools to demonstrate the safety and effectiveness of new medical products in faster timeframes with more certainty, at lower costs, and with better information. With introduced flexibility within trial design, this approach saves resources by identifying failures early and increases efficiency by focusing precious patient resource on treatments that have a higher probability of success. While clearly advantageous to the drug development program, this is also ethically beneficial to the patients in the trial as it restricts patient exposure to ineffective treatments. By adaptive design, we mean a multi-stage study design that uses accumulating data to decide on how to modify aspects of the study without undermining the validity and integrity of the trial [2,3]. To maintain study validity means providing correct statistical inference (such as adjusted p-values, valid estimates and confidence intervals, etc.), assuring consistency between different stages of the study. To maintain study integrity means minimizing operational bias, providing convincing results to a broader scientific community, preplanning as much as possible, based on intended adaptations, and maintaining the blind of interim analysis results. An adaptive design requires the trial to be conducted in multiple stages with access to the accumulated data. At any stage, the data may be analyzed and subsequent stages can be redesigned taking into account all available data. Potential modifications include changing sample size to assure that study goals are achieved at minimal cost, changing how patients are allocated to treatment arms, early stopping a trial for efficacy or futility, focusing the study on a prospectively defined subpopulation, rolling a Phase II study into a confirmatory Phase III trial combining the data from both parts in the final analysis. However, successful implementation of adaptive designs in clinical trials requires integrating input from and a well-coordinated effort of a number of different line functions, including biostatistics, medical research, data management, drug supply, and clinical operations [4]. In this paper, I will describe the biostatistics' contribution to the process of implementing an adaptive design and I will focus my presentation to dose-ranging studies. This process consists of three major steps: planning, simulating, and executing. ©2008 Russell Publishing. All Rights Reserved.","","biostatistics; clinical protocol; drug research; drug safety; food and drug administration; methodology; planning; review; simulation","","","","","","","Critical Path Opportunity List, (2006); Gallo P., Chuang-Stein C., Dragalin V., Gaydos B., Krams M., Pinheiro J., Adaptive designs in clinical drug development - an executive summary of the PhRMA working group, Journal of Biopharmaceutical Statistics, 16, 3, pp. 275-283, (2006); Dragalin V., Adaptive Designs: Terminology and Classification, Drug Information Journal, 40, 4, pp. 425-436, (2006); Quinlan J., Krams M., Implementing Adaptive Designs: Logistical and Operational Considerations, Drug Information Journal","","","","","","","","","15296318","","APOMC","","English","Am. Pharm. Outsourcing","Review","Final","","Scopus","2-s2.0-55549107541"
"Jiang G.; Evans J.; Oniki T.; Coyle J.; Bain L.; Huff S.; Kush R.; Chute C.G.","Jiang, Guoqian (55486706700); Evans, Julie (37070183300); Oniki, Tom (7004907721); Coyle, Joey (57213801303); Bain, Landen (21233497200); Huff, Stan (7005508146); Kush, Rebecca (6507111391); Chute, Christopher G. (7006581202)","55486706700; 37070183300; 7004907721; 57213801303; 21233497200; 7005508146; 6507111391; 7006581202","Harmonization of detailed clinical models with clinical study data standards","2012","International Conference on Information and Knowledge Management, Proceedings","","","","23","30","7","1","10.1145/2389672.2389678","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870435811&doi=10.1145%2f2389672.2389678&partnerID=40&md5=f903e2e28b969c22007e141295ba7414","Mayo Clinic, Rochester, MN, United States; CDISC, Austin, TX, United States; Intermountain Healthcare, Murray, UT, United States","Jiang G., Mayo Clinic, Rochester, MN, United States; Evans J., CDISC, Austin, TX, United States; Oniki T., Intermountain Healthcare, Murray, UT, United States; Coyle J., Intermountain Healthcare, Murray, UT, United States; Bain L., CDISC, Austin, TX, United States; Huff S., Intermountain Healthcare, Murray, UT, United States; Kush R., CDISC, Austin, TX, United States; Chute C.G., Mayo Clinic, Rochester, MN, United States","Data sharing and integration between clinical research data management system (CDMS) and electronic health record (EHR) system remains a challenging issue. To deal with the challenge, there is emerging interest in utilizing the Detailed Clinical Modeling (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator (ONC) awarded SHARPn project for normalizing patient data from the electronic medical records (EMRs). The objective of the present study is to describe our preliminary efforts on harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards. We were focused on three generic domains: Demographics, Lab Tests and Medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs. We have identified a set of data elements that are common to the context of both clinical research and secondary use and discussed outstanding harmonization issues. We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical research and secondary use. Copyright © 2012 ACM.","Clinical research; Data standards; Detailed clinical models; Electronic medical records","Cobalt compounds; Health care; Hospital data processing; Knowledge management; Medical computing; Research; Semantics; Standards; Clinical data interchange standards consortiums; Clinical research; Clinical study; Data elements; Data Sharing; Data standards; Electronic health record systems; Electronic medical record; Patient data; Secondary use; Semantic interoperability; Interoperability","","","","","","","El F.A., Daniel C., Bousquet C., Dart T., Lastic P.Y., Degoulet P., Electronic healthcare record and clinical research in cardiovascular radiology, HL7 CDA and CDISC ODM Interoperability. AMIA Annu Symp Proc., pp. 216-220, (2007); Hammond W.E., Jaffe C., Kush R.D., Healthcare standards development. The value of nurturing collaboration, J AHIMA, 80, 7, pp. 44-50, (2009); CDISC Standards; Health Level Seven International (HL7); The Biomedical Research Integrated Domain Group (BRIDG) Model; Coyle J.F., Mori A.R., Huff S.M., Standards for detailed clinical models as the basis for medical data exchange and decision support, International Journal of Medical Informatics, 69, 2-3, pp. 157-174, (2003); HL7 Detailed Clinical Models; Goossen W., Goossen-Baremans A., Van Der Z.M., Detailed clinical models: A review, Healthc Inform Res., 16, 4, pp. 201-214, (2010); ISO/CEN 13606: Health Informatics-electronic Health Record Communication, (2010); Beale T., Archetypes and the EHR, Stud Health Technol Inform., 96, pp. 238-244, (2003); Huff S.M., Rocha R.A., Coyle J.F., Narus S.P., Integrating detailed clinical models into application development tools, Stud Health Technol Inform, 107, PART 2, pp. 1058-1062, (2004); The Clinical Information Modeling Initiative (CIMI); CIMI - Initial Public Statement; Chute C.G., Pathak J., Savova G.K., Et al., The SHARPn project on secondary use of electronic medical record data: Progress, plans, and possibilities, AMIA Annu Symp Proc. 2011, pp. 248-256, (2011); CDISC SHARE; Jiang G., Solbrig H.R., Iberson-Hurst D., Kush R.D., Chute C.G., A collaborative framework for representation and harmonization of clinical study data elements using semantic mediawiki, AMIA Summits Transl Sci Proc. 2010, pp. 11-15, (2010); Information Technology - Metadata Registries (MDR); Rea S., Pathak J., Savova G., Et al., Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project, J Biomed Inform, (2012); Health Informatics - Harmonized Data Types for Information Interchange, (2011); Jiang G., Solbrig H.R., Chute C.G., Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups, J Am Med Inform Assoc., (2012); The OMG Common Terminology Services 2 Standard, (2012); Jiang G., Solbrig H.R., Evans J., Et al., OpenCEM Wiki: A semantic-web-based repository for supporting harmonization of clinical study data standards and clinical element models, The SHARPn Summit 2012, (2012)","G. Jiang; Mayo Clinic, Rochester, MN, United States; email: jiang.guoqian@mayo.edu","","","ACM SIGIR; ACM SIGWEB","2nd International Workshop on Managing Interoperability and Complexity in Health Systems, MIX-HS 2012, Collocated with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012","29 October 2012 through 29 October 2012","Maui, HI","94127","","978-145031721-4","","","English","Int Conf Inf Knowledge Manage","Conference paper","Final","","Scopus","2-s2.0-84870435811"
"Brahaj A.; Razum M.; Schwichtenberg F.","Brahaj, Armand (54405395200); Razum, Matthias (25825498000); Schwichtenberg, Frank (36020819400)","54405395200; 25825498000; 36020819400","Ontological formalization of scientific experiments based on core scientific metadata model","2012","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","7489 LNCS","","","273","279","6","6","10.1007/978-3-642-33290-6_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867668552&doi=10.1007%2f978-3-642-33290-6_29&partnerID=40&md5=29f1b427e0fb14bb0e57587f7f0c6254","FIZ Karlsruhe, 76344 Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, Germany; Humboldt-Universität Zu Berlin, D-10099 Berlin, Unter den Linden 6, Germany","Brahaj A., FIZ Karlsruhe, 76344 Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, Germany, Humboldt-Universität Zu Berlin, D-10099 Berlin, Unter den Linden 6, Germany; Razum M., FIZ Karlsruhe, 76344 Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, Germany; Schwichtenberg F., FIZ Karlsruhe, 76344 Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, Germany","This paper describes an ontology for the representation of contextual information for laboratory-centered scientific experiments based on Core of Scientific Metadata Model. This information describes entities such as instruments, investigations, studies, researchers, and institutions that play a key role in the generation of research data, thus forming an important source for understanding the provenance of the data. Formalization of this information in the form of an ontology and reusing existing and well-established vocabularies foster the publication of research data and accompanying provenance metadata as Linked Open Data. Core Scientific Model Ontology (CSMO) is part of a larger effort, which includes data acquisition in the laboratory and semi-automated metadata generation. It is intended to support cataloging, data curation and data reuse. A formal definition of the RDF classes and properties introduced for CSMO is provided. We demonstrate the efficacy of this ontology by applying it to two different research domains. © 2012 Springer-Verlag.","contextual information; CSMD; CSMO; data cataloging; Ontologies; research data management; science study; scientific experiments","Digital libraries; Experiments; Information management; Ontology; Contextual information; CSMD; CSMO; data cataloging; Research data; science study; Metadata","","","","","","","Hey T., Tansley S., Tolle K., The Fourth Paradigm: Data-Intensive Scientific Discovery, (2009); Hey T., Trefethen A., The Data Deluge: An e-Science Perspective, Grid Computing - Making the Global Infrastructure A Reality, (2003); Matthews B., Sufi S., Flannery D., Lerusse L., Griffin T., Gleaves M., Kleese K., Using a Core Scientific Metadata Model in Large-Scale Facilities, The International Journal of Digital Curation, 5, 1, pp. 106-118, (2010); Razum M., Schwichtenberg F., Wagner S., Hoppe M., eSciDoc Infrastructure: A Fedora-Based e-Research Framework, LNCS, 5714, pp. 227-238, (2009); Qin J., D'Ignazio J., The Central Role of Metadata in a Science Data Literacy Course, Journal of Library Metadata, 10, pp. 188-204, (2010); Razum M., Schwichtenberg F., Metadatenkonzept für Dynamische Date - BW-eLabs Report, 2012; Benjamins V.R., Fensel D., Gomez-Perez A., Decker S., Erdmann M., Motta E., Musen M., The Knowledge Annotation Initiative of the Knowledge Acquisition Community (KA)2, Eleventh Workshop on Knowledge Acquisition, Modeling and Management, Voyager Inn, Alberta, Canada (1998); Soldatova L.N., King R.D., An ontology of scientific experiments, Journal of the Royal Society Interface, pp. 795-803, (2006); Hoxha J., Rula A., Ell B., Towards Green Linked Data, Proceedings of the Second International Workshop on Consuming Linked Data (CEUR 2011) CEUR Workshop Proceedings (Oktober 2011)","A. Brahaj; FIZ Karlsruhe, 76344 Eggenstein-Leopoldshafen, Hermann-von-Helmholtz-Platz 1, Germany; email: Armand.Brahaj@fiz-karlsruhe.de","","","","2nd International Conference on Theory and Practice of Digital Libraries, TPDL 2012","23 September 2012 through 27 September 2012","Paphos","93282","16113349","978-364233289-0","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","","Scopus","2-s2.0-84867668552"
"O'Connor M.J.; Shankar R.D.; Parrish D.B.; Das A.K.","O'Connor, Martin J. (7402686283); Shankar, Ravi D. (55806107400); Parrish, David B. (15136737900); Das, Amar K. (7403597070)","7402686283; 55806107400; 15136737900; 7403597070","Knowledge-level querying of temporal patterns in clinical research systems","2007","Studies in Health Technology and Informatics","129","","","311","315","4","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-38449090592&partnerID=40&md5=46314c900ed264468703e81075cce2f0","Stanford Medical Informatics, Stanford University, MSOB X275, Stanford, CA 94305, 251 Campus Drive, United States; Immune Tolerance Network, Pittsburgh, PA, United States","O'Connor M.J., Stanford Medical Informatics, Stanford University, MSOB X275, Stanford, CA 94305, 251 Campus Drive, United States; Shankar R.D., Stanford Medical Informatics, Stanford University, MSOB X275, Stanford, CA 94305, 251 Campus Drive, United States; Parrish D.B., Immune Tolerance Network, Pittsburgh, PA, United States; Das A.K., Stanford Medical Informatics, Stanford University, MSOB X275, Stanford, CA 94305, 251 Campus Drive, United States","Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing this domain knowledge is difficult in relational database systems. As a result, there is a need for principled methods to overcome the disconnect between the database representation of time-oriented research data and corresponding knowledge of domain-relevant concepts. In this paper, we present a set of methodologies for undertaking knowledge level querying of temporal patterns, and discuss its application to the verification of temporal constraints in clinical-trial applications. Our approach allows knowledge generated from query results to be tied to the data and, if necessary, used for further inference. We show how the Semantic Web ontology and rule languages, OWL and SWRL, respectively, can support the temporal knowledge model needed to integrate low-level representations of relational data with high-level domain concepts used in research data management. We present a scalable bridge-based software architecture that uses this knowledge model to enable dynamic querying of time-oriented research data. © 2007 The authors. All rights reserved.","clinical trials; knowledge-based systems; ontology; Semantic Web; temporal querying","Biomedical Research; Databases as Topic; Information Storage and Retrieval; Knowledge Bases; Semantics; Software; Time; Vocabulary, Controlled; Clinical research; High level languages; Information management; Knowledge based systems; Medical applications; Ontology; Query processing; Semantic Web; Clinical trial; Low level representation; Research activities; Research data managements; Semantic Web ontologies; Temporal constraints; Temporal knowledge; temporal querying; article; computer program; data base; information retrieval; knowledge base; linguistics; medical research; semantics; time; Relational database systems","","","","","National Institute of Allergy and Infectious Diseases, NIAID, (N01AI015416)","","Rotrosen D., Matthews J.B., Bluestone J.A., The immune tolerance network: A new paradigm for developing tolerance-inducing therapies, J Allergy Clin Immunol, 110, 1, pp. 17-23, (2002); Berners-Lee T., Hendler J., Lassila O., The semantic web, Scientific American, 35, pp. 43-52, (2001); Knublauch H., Fergerson R.W., Noy N.F., Musen M.A., The Protégé OWL Plugin: An open development environment for semantic web applications, Third ISWC (ISWC 2004), pp. 229-243, (2004); O'connor M.J., Knublauch H., Tu S.W., Grossof B., Dean M., Grosso W.E., Musen M.A., Supporting rule system interoperability on the Semantic Web with SWRL, Fourth International Semantic Web Conference (ISWC 2005), pp. 974-986, (2005); Snodgrass R.T., On the semantics of 'now' in databases, ACM Trans Database Systems, 22, 2, pp. 171-214, (1997); Snodgrass R.T., The TSQL2 Temporal Query Language, (1995); O'connor M.J., Tu S.W., Musen M.A., The Chronus II temporal database mediator, AMIA Annual Symposium, pp. 567-571, (2002); Shoham Y., Temporal logics in AI: Semantical and ontological considerations, Artif Intell, 33, 1, pp. 89-104, (1987); O'connor M.J., Shankar R.D., Das A.K., An ontologydriven mediator for querying time-oriented biomedical data, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS2006), pp. 264-269, (2006); Allen J.F., Maintaining knowledge about temporal intervals, Comm ACM, 26, 11, pp. 832-843, (1993); Shankar R., Martins S.B., O'connor M.J., Parrish D., Das A.K., Towards semantic interoperability in a clinical trials management system, Fifth International Semantic Web Conference (ISWC 2006), pp. 901-912, (2006); Das A.K., Musen M.A., Synchronus: A reusable software module for temporal integration, AMIA Annual Symposium, pp. 195-199, (2002); Narayanan P.S., O'connor M.J., Das A.K., Ontologydriven mapping of temporal data in biomedical databases, AMIA Annual Symposium, (2006); Weng C., Kahn M., Gennari J., Temporal knowledge representation for scheduling tasks in clinical trial protocols, AMIA Annual Symposium, pp. 879-883, (2002); Deshpande A.M., Brandt C., Nadkarni P.M., Temporal query of attribute-value patient data: Utilizing the constraints of clinical studies, Int J Med Inform, 70, 1, pp. 59-77, (2003)","M.J. O'Connor; Stanford Medical Informatics, Stanford University, MSOB X275, Stanford, CA 94305, 251 Campus Drive, United States; email: martin.oconnor@stanford.edu","","IOS Press","","12th World Congress on Medical Informatics, MEDINFO 2007","20 August 2007 through 24 August 2007","Brisbane, QLD","","09269630","978-158603774-1","","17911729","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-38449090592"
"Huggett M.; Rasmussen E.","Huggett, Michael (57207556099); Rasmussen, Edie (7102328873)","57207556099; 7102328873","Integrative online research-data management","2012","SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval","","","","1007","","","2","10.1145/2348283.2348432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866606770&doi=10.1145%2f2348283.2348432&partnerID=40&md5=b54b11537f53b4d2c9dfca8f8da3f497","University of British Columbia, 1961 East Mall, Vancouver, BC V6T 1Z1, Canada","Huggett M., University of British Columbia, 1961 East Mall, Vancouver, BC V6T 1Z1, Canada; Rasmussen E., University of British Columbia, 1961 East Mall, Vancouver, BC V6T 1Z1, Canada","In support of our research projects in information retrieval, we have developed an integrated multi-process software system that shepherds research data from induction through aggregation, analysis, and presentation. We combine public-domain code libraries with our own software to provide a flexible, easily- configured modular system that exposes data online for easier collaboration. The goal is to create a single online infrastructure that allows colleagues to submit, process, analyze and visualize data, and discuss and prioritize issues through a single integrated interface. We demonstrate our system within the context of the large data set provided by the Indexer's Legacy project [1]. © 2012 Authors.","Digital collections; user interfaces; visualization","Flow visualization; Information management; Information retrieval; User interfaces; Code libraries; Digital collections; Integrated interface; Large data; Modular system; Public domains; Research data; Software systems; Research","","","","","","","Huggett M., Rasmussen E., The Indexer's Legacy: Promoting Access to a Million Books, ICDL 2010; Kauppinen T., Espindola G.M.D., Linked Open Science-Communicating, Sharing and Evaluating Data, Methods and Results for Executable Papers, Procedia Computer Science, 4, (2011); Apache SOLR; The R Project for Statistical Computing","M. Huggett; University of British Columbia, 1961 East Mall, Vancouver, BC V6T 1Z1, Canada; email: m.huggett@ubc.ca","","","Assoc. Comput. Mach., Spec.; Interest Group Inf. Retr. (ACM SIGIR)","35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012","12 August 2012 through 16 August 2012","Portland, OR","92799","","978-145031658-3","","","English","SIGIR - Proc. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr.","Conference paper","Final","","Scopus","2-s2.0-84866606770"
"Delserone L.M.","Delserone, Leslie M. (6508336627)","6508336627","At the watershed : Preparing for research data management and stewardship at the University of minnesota libraries","2008","Library Trends","57","2","","202","210","8","30","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-68149092685&partnerID=40&md5=b8251371e68c7d2d1c75c47a72f2d80c","University of Minnesota (UMN), United States","Delserone L.M., University of Minnesota (UMN), United States","Like many research universities, the University of Minnesota (UMN), and its Libraries, attempts to understand the nature and intensity of data produced by its researchers, and address the management and stewardship of its institutional research output. The recent activities of the Libraries, in support of and in conjunction with campus-wide efforts, illustrate a set of approaches that show the Libraries to be at the watershed, integrated into and contributing leadership to the growing river of campus-wide exploration and planning of cyberinfrastructure needs. This brief article highlights the hiring of the ""science librarian cohort,"" the Libraries' study concerning the needs and behaviors of scientific researchers, the implementation of the University Digital Conservancy, the Libraries' involvement in the UMN's Research Cyberinfrastructure Alliance, and its initiation of the e-Science and Data Services Collaborative.","","","","","","","","","Hey T., Trefethen A., The data deluge: An e-science perspective, Grid computing: Making the global infrastructure a reality, pp. 809-824, (2003); Long-lived digital data collections: Enabling research and education in the 21st century, (2005); Academic Programs, Reinventing reference, (2008); Academic Programs, (2008); Academic Programs, Diversity Outreach Collaborative, (2008); E-Science and Data Services Collaborative, E-Science and Data Services Collaborative, (2008); Compact for the University Libraries, 2006-2007, (2006); Understanding research behaviors, information resources, and service needs of scientists and graduate students: A study by the University of Minnesota Libraries, (2007); Assessing research cyberinfrastructure needs at the University of Minnesota, (2008); Assessing research cyberinfrastructure needs at the University of Minnesota, (2008)","","","","","","","","","00242594","","","","English","Libr. Trends","Article","Final","","Scopus","2-s2.0-68149092685"
"McMillan A.C.; MacIver D.; Sukloff W.B.","McMillan, A.C. (7102843376); MacIver, D. (6701766088); Sukloff, W.B. (6506462126)","7102843376; 6701766088; 6506462126","Atmospheric environmental information - An overview with Canadian examples","2000","Environmental Modelling and Software","15","3","","245","248","3","6","10.1016/S1364-8152(00)00010-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034089573&doi=10.1016%2fS1364-8152%2800%2900010-4&partnerID=40&md5=77e5788d71e1883516f464d4016b56eb","Atmospheric Environment Service, North York, ON M3H 5T4, 4905 Dufferin Street, Canada","McMillan A.C., Atmospheric Environment Service, North York, ON M3H 5T4, 4905 Dufferin Street, Canada; MacIver D., Atmospheric Environment Service, North York, ON M3H 5T4, 4905 Dufferin Street, Canada; Sukloff W.B., Atmospheric Environment Service, North York, ON M3H 5T4, 4905 Dufferin Street, Canada","The study of meteorology has been revolutionized by modern computing. In this paper, an overview of the handling of meteorological, climatological and air quality data is provided. Some suggestions are given as to a framework for designing systems to handle atmospheric environmental data. Several examples of Canadian systems that are in use now are presented, some of which illustrate application of the framework.","Air quality data; CAPMoN; Data management; Meteorology; NAPs; NAtChem; Quality control; Research Data Management and Quality Control (RDMQ) System","atmosphere; canada; computer analysis; environment; meteorology; Canada; Air quality; Climatology; Data handling; Environmental engineering; Information management; Weather forecasting; Weather information services; air quality; atmospheric dynamics; data management; information system; Air quality data; Atmospheric environmental information; Data management; Research data management and quality control system; Management information systems","","","","","","","The Case for Canadian Contributions to the Global Climate Observing System (GCOS), (1995); AES Guidelines for Co-operative Climatological Autostations Guide 92-1, Version 2, (1992); Dansereau M., CMC infrastructure, The Canadian Meteorological Centre Review, 3, 3, (1996); Gagne A., Wide area network, The Canadian Meteorological Centre Review, 3, 3, (1996); Ro C., Vet R., Ord D., Holloway A., Yum S., Acid precipitation in eastern North America, National Atmospheric Chemistry Data Base (NAtChem) - 1992 Annual Report, (1995)","","","","","","","","","13648152","","","","English","Environ. Model. Softw.","Article","Final","","Scopus","2-s2.0-0034089573"
"Richardson J.; Nolan-Brown T.; Loria P.; Bradbury S.","Richardson, Joanna (55463114800); Nolan-Brown, Therese (55569765600); Loria, Pat (55570885200); Bradbury, Stephanie (55570119900)","55463114800; 55569765600; 55570885200; 55570119900","Library Research Support in Queensland: A Survey","2012","Australian Academic and Research Libraries","43","4","","258","277","19","20","10.1080/00048623.2012.10722287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872945212&doi=10.1080%2f00048623.2012.10722287&partnerID=40&md5=7c925b3ad70702829a1f6c9cfad3f443","Division of Information Services, Griffith University, Brisbane, 4111, Australia; Division of Information Services, Griffith University, Brisbane, 4111, Australia; Library Services Academic Services Division, University of Southern Queensland, Toowoomba, 4350, Australia; Library, Queensland University of Technology, Brisbane, 4000, Australia","Richardson J., Division of Information Services, Griffith University, Brisbane, 4111, Australia; Nolan-Brown T., Division of Information Services, Griffith University, Brisbane, 4111, Australia; Loria P., Library Services Academic Services Division, University of Southern Queensland, Toowoomba, 4350, Australia; Bradbury S., Library, Queensland University of Technology, Brisbane, 4000, Australia","University libraries worldwide are reconceptualising the ways in which they support the research agenda in their respective institutions. This paper is based on a survey completed by member libraries of the Queensland University Libraries Office of Cooperation (QUL OC), the findings of which may be informative for other university libraries. After briefly examining major emerging trends in research support, the paper discusses the results of the survey specifically focussing on support for researchers and the research agenda in their institutions. All responding libraries offer a high level of research support, however, eResearch support, in general, and research data management support, in particular, have the highest variance among the libraries, and signal possible areas for growth. Areas for follow-up, benchmarking and development are suggested. Copyright 2012 Australian Academic and Research Libraries.","","","","","","","","","2012 top ten trends in academic libraries, College & Research Libraries News, 73, pp. 311-320, (2012); Standards for Libraries in Higher Education, (2011); Borgman C.L., Research Data: Who Will Share What, with Whom, When, and Why?, (2010); Burrows T., Croker K., Supporting Research in an Era of Data Deluge: Developing a New Service Portfolio within Information Services at the University of Western Australia, pp. 6-9, (2012); Christensen-Dalsgaard B., Ten Recommendations for Libraries to Get Started with Research Data Management, (2012); Cook R., Research Services and Advanced IT – the Next Generation, (2010); Furlough M., What we talk about when we talk about repositories, Reference & User Services Quarterly, 18-23, 32, (2009); Gold A., Cyberinfrastructure, data and libraries, part 1, D-Lib Magazine, 13, 9-10, pp. 1-12, (2007); Hey T., Trefethen A.E., The uk e-science core programme and the grid, Future Generation Computer Systems, 18, pp. 1017-1031, (2002); Hswe P., Holt A., Guide for Research Libraries: The NSF Data Sharing Policy, (2010); Johnson B.L., Transforming roles for academic librarians: Leading and participating in new partnerships, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 272, pp. 7-15, (2010); Research Information Management Infokit, (2011); Krafft D.B., Cappadona N.A., Caruso B., Corson-Rikert J., Devare M., Lowe B.J., VIVO: Enabling National Networking of Scientists, (2010); Kroll S., Forsman R., A Slice of Research Life: Information Support for Research in the United States, (2010); Law D., Digital library economics: Aspects and prospects, David Baker and Wendy Evans, pp. 71-85, (2009); Lougee W., The diffuse library revisited: Aligning the library as strategic asset, Library Hi Tech, 27, 4, pp. 610-623, (2009); Lowry C.B., Adler P., Hahn K., Stuart C., Transformational Times: An Environmental Scan Prepared for the ARL Strategic Plan Review Task Force, (2009); Luce R.E., A new value equation challenge: The emergence of eresearch and roles for research libraries, No Brief Candle: Reconceiving Research Libraries for the 21St Century, pp. 42-50, (2008); Lynch C., The institutional challenges of cyberinfrastructure and e-research, EDUCAUSE Review, 43, 6, pp. 74-88, (2008); MacColl J., Library roles in university research assessment, Liber Quarterly, 20, pp. 152-168, (2010); Revised Policy on Dissemination of Research Findings, (2012); Dissemination and Sharing of Research Results, (2010); Oakleaf M., Value of Academic Libraries: A Comprehensive Research Review and Report, (2010); O'Brien L., “The Changing Scholarly Information Landscape: Reinventing Information Services to Increase Research Impact, (2010); O'Brien L., E-Research Partnerships Revisited, (2011); Parker R., What the Library Did Next: Strengthening Our Visibility in Research Support, (2012); Potter W.G., Cook C., Kyrillidou M., ARL Profiles: Research Libraries 2010; Puente M.A., Developing a vital research library workforce, Research Library Issues: A Bimonthly Report from ARL, CNI, and SPARC, 272, pp. 1-6, (2010); Research Support Services in UK Universities, (2010); Sennyey P., Ross L., Mills C., Exploring the future of academic libraries: A definitional approach, Journal of Academic Librarianship, 35, pp. 252-259, (2009); Research Commons: Research Lifecycle for Graduate Researchers, (2012); Simons N., Richardson J., “new roles, new responsibilities: Examining training needs of repository staff, Journal of Librarianship and Scholarly Communication, 1, (2012); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Sparks J., O'Brien L., Richardson J., Wolski M., Tadic S., Morris J., Embedding Innovation for Scholarly Information & Research for the New Generation, (2012); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services: Current Practices and Plans for the Future, (2012); Redefining the Academic Library: Managing the Migration to Digital Information Services, (2011); Vaill P.B., Managing as a Performing Art: New Ideas for a World of Chaotic Change, (1991); Walters T., Skinner K., New Roles for New Times: Digital Curation for Preservation, (2011); Walton G., Data curation and the academic library, New Review of Academic Librarianship, 16, pp. 1-3, (2010); Williams K., Jaguscewski J., New Roles for New Times: Transforming Liaison Roles; Williams R., Pryor G., Patterns of Information Use and Exchange: Case Studies of Researchers in the Life Sciences, (2009)","J. Richardson; Division of Information Services, Griffith University, Brisbane, 4111, Australia; email: j.richardson@griffith.edu.au","","","","","","","","00048623","","","","English","Aust. Acad. Res. Libr.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84872945212"
"Marinković J.; Babić D.; Maksimović R.; Stanisavljević D.","Marinković, J. (7004611210); Babić, D. (55057433600); Maksimović, R. (23985614900); Stanisavljević, D. (23566969700)","7004611210; 55057433600; 23985614900; 23566969700","Some aspects on medical research systems at the medical school in Belgrade; [Elementi racunarske podrske u naucnim istrazivanjima iz oblasti medicine.]","1995","Srpski arhiv za celokupno lekarstvo","123 Suppl 2","","","14","16","2","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-41149110565&partnerID=40&md5=f7ff87d3fbaaa5b21bd5ded1adf61829","","","The ongoing development of new technologies is rapidly expanding the number and/or quality of medical researches. In order to evaluate the situation in our country we analysed master - degree and doctoral thesis that have been done at the Belgrade Medical School in 1987., 1990. and 1994. year. These years have been chosen because of medical knowledge's known rate of exponential growth. According to the main tasks of medical research: data management system and data analyses, we classified medical researches in two major categories: prospective and retrospective. The crucial difference is whether or not the investigators collect new data for analyses. First has been discovered in 11 doctoral and 10 master theses out of 225 reviewed. Medical researchers use variety of different database management systems and statistical techniques to store, retrieve and analyse data. These were identified in respectively 23 percent and 24 percent. Beside these major research activities we were interested in usage of application programmes such as text processors or graphical packages that were found in 28 percent and 20 percent of analysed theses. In summary, technological advances in computer hardware and software have reduced the barriers to long - term storage and processing of rich biomedical data. The development of new techniques for analysing such a data also encourages the discovery and validation of new medical relationships.","","Biomedical Research; Dissertations, Academic as Topic; Medical Informatics Applications; Yugoslavia; article; medical informatics; medical research; scientific literature; Yugoslavia","","","","","","","","","","","","","","","","03708179","","","18193778","Croatian","Srp Arh Celok Lek","Article","Final","","Scopus","2-s2.0-41149110565"
"Kallesøe C.S.","Kallesøe, Claus Stie (55958828200)","55958828200","Building research data handling systems with open source tools","2012","Open Source Software in Life Science Research: Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond","","","","9","34","25","0","10.1016/B978-1-907568-97-8.50001-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904030694&doi=10.1016%2fB978-1-907568-97-8.50001-1&partnerID=40&md5=8f8fa285b731891565f58eb1167c4929","","","Pharmaceutical discovery and development requires handling of complex and varied data across the process pipeline. This covers chemical structures information, biological assay and structure versus activity data, as well as logistics for compounds, plates and animals. An enterprise research data handling system must meet the needs of industrial scientists and the demands of a regulatory environment, and be available to external partners. Within Lundbeck, we have adopted a strategy focused on agile and rapid internal development using existing open source software toolkits. Our small development team developed and integrated these tools to achieve these objectives, producing a data management environment called the Life Science Project (LSP). In this chapter, I describe the challenges, rationale and methods used to develop LSP. A glimpse into the future is given as we prepare to release an updated version of LSP, LSP4All, to the research community as an open source project. © 2012 Woodhead Publishing Limited. All rights reserved.","LSP; LSP4All; Lundbeck; Open source software; Pharmaceutical research; Research data management; Software development","","","","","","","","Agrafiotis D.K., Et al., Advanced Biological and Chemical Discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world, Journal of Chemical Information and Modeling, 47, pp. 1999-2014, (2007); Brafman O., Brafman R., Sway: The Irresistible Pull of Irrational Behavior, (2008); Kernighan B.W., Ritchie D.M., C Programming Language, (1988); Torvalds L., Diamond D., Just for Fun: The Story of an Accidental Revolutionary, (2001); Kelley T., Littman J., The Art of Innovation:Lessons in Creativity from IDEO, America's Leading Design Firm, (2001); Fried J., Hansson D.H., Rework, (2010); Bizer C., Heath T., Berners-Lee T., Linked Data - The Story So Far, International Journal on Semantic Web and Information Systems (IJSWIS), 5, 3, (2009); Samwald M., Jentzsch A., Bouton C., Et al., Linked open drug data for pharmaceutical research and development, Journal of Cheminformatics, 3, (2011); GGA Open-Source Initiative: Products; RDKit: Cheminformatics and Machine Learning Software","","","Elsevier Ltd","","","","","","","978-190756897-8","","","English","Open Source Softw. in Life Sci. Res.: Pract. Solut. to Common Chall. in the Pharma. Ind. and Beyond","Book chapter","Final","","Scopus","2-s2.0-84904030694"
"Stöckle G.","Stöckle, Gabriel (57202006171)","57202006171","A Checklist for Planning Research Data Management","2012","Springer Series in Astrostatistics","2","","","247","251","4","1","10.1007/978-1-4614-3323-1_26","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046751802&doi=10.1007%2f978-1-4614-3323-1_26&partnerID=40&md5=eab0201d81707779cd467f5f4f59c10d","Astronomisches Rechen-Institut am Zentrum für Astronomie der Universität Heidelberg, Mönchhostr. 12-14, Heidelberg, 69120, Germany","Stöckle G., Astronomisches Rechen-Institut am Zentrum für Astronomie der Universität Heidelberg, Mönchhostr. 12-14, Heidelberg, 69120, Germany","WissGrid’s objective is to establish long-term organizational and technical grid structures for the academic world. WissGrid is a joint project of the five scientific grid communities AstroGrid-D [1], C3Grid, HEP-Grid, Medigrid, and TextGrid. It combines the heterogeneous needs of a variety of scientific disciplines and develops concepts for the long-term and sustainable use of the organizational and technical grid infrastructure of D-Grid. Zentrum für Astronomie der Universität Heidelberg (ZAH) (Center for Astronomy at the University of Heidelberg) is building on the experience of the astrophysical community project AstroGrid-D in the development and transfer of applications to the grid and in setting up grid structures and services. Here we present a checklist as a tool for scientific project managers for planning their data management. Our goal is to ensure that data collected today can be mined by scientists and data mining experts in the future. © 2012, Springer Science+Business Media New York.","","","","","","","Bundesministerium für Bildung und Forschung, BMBF","Acknowledgements I wish to thank J. Ludwig (SUB, Göttingen), T. Rathmann (DKRZ, Hamburg), H. Enke (Leibniz Institute for Astrophysics, Potsdam), P. Heraudau (Argelander-Institute for Astronomy, Bonn), M. Demleitner, J. Fohlmeister (Astronomisches Rechen-Institut at the Centre for Astronomy of Heidelberg University, Heidelberg), and the WissGrid team for their expertise and support. The WissGrid project is funded by the German Federal Ministry of Education and Research (BMBF).","Enke H., Et al., AstroGrid-D: Grid technology for astronomical science, New Astron, 16, 2, pp. 79-93, (2011); Demleitner M., Et al., The German Astrophysical Virtual Observatory (GAVO): Archives and applications, status and services, Astronomische Nachrichten, 328, 7, (2007); (2011); Lyon L., Dealing with Data: Roles, Rights, Responsibilities and Relationships, (2007)","G. Stöckle; Astronomisches Rechen-Institut am Zentrum für Astronomie der Universität Heidelberg, Heidelberg, Mönchhostr. 12-14, 69120, Germany; email: gst@ari.uni-heidelberg.de","","Springer International Publishing","","","","","","21991030","","","","English","Springer Ser. Astrostat.","Book chapter","Final","","Scopus","2-s2.0-85046751802"
"Dilaura R.P.","Dilaura, Robert P. (16686471600)","16686471600","Clinical and translational science sustainability: Overcoming integration issues between electronic health records (EHR) and clinical research data management systems 'separate but equal'","2007","Studies in Health Technology and Informatics","129","","","137","141","4","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956311434&partnerID=40&md5=67680094930c122d8aaba3b4232b426f","Cleveland Clinic and Case Western Reserve University, Cleveland, OH, United States","Dilaura R.P., Cleveland Clinic and Case Western Reserve University, Cleveland, OH, United States","The use of health information technology (HIT) is growing rapidly for patient care systems required to test, diagnose and treat patients, and to bill for these services. Today's Electronic Health Record (EHR) systems are a response to this pressure, enabling feature rich computer-assisted decisions and communication. And even though EHR benefits dramatically outweigh the costs, required investments are nonetheless significant. Continuing to invest in HIT at a revolutionary rate is unsustainable given institutional financial constraints and continuing reimbursement cuts. Future improvements must come from new treatments, test methods, drugs and devices-from research. But data management information systems for clinical research receive less funding than patient care systems, and in less coherent ways. It is easy to imagine using the high cost, patient-based EHRs for clinical research data management, and thus accelerate the speed of translating new medical discoveries into standard practice. But taking this step requires thoughtful planning to overcome significant technology, legal/regulatory, policy, process, and administrative issues. © 2007 The authors. All rights reserved.","biomedical research; clinical research; computerized medical record systems; information systems; medical informatics","Clinical research; Information systems; Information use; Management information systems; Medical computing; Medical information systems; Records management; Testing; Biomedical research; Computerized medical records; Electronic health record; Electronic health record systems; Financial constraints; Health information technologies (HIT); Medical informatics; Research data managements; Information management","","","","","","","Health Informatics-Electronic Health Records-Definition, Scope, and Context; Key Capabilities of An Electronic Health Record System, (2003); The connecting for health common framework: Resources for implementing private and secure health information exchange, Markle Foundation, (2006); Detmer D., Steen E., Learning from Abroad: Lessons and Questions on Personal Health Records for National Policy, (2006); The future vision of electronic health records as eSource for clinical research, EClinical Forum, PhRMA EDC Task Group, (2006); Jha A., Ferris T., Donelan K., Desroches C., Shields A., Rosenbaum S., Blumenthal D., How common are electronic health record systems in the United States? A summary of the evidence, Health Aff, 25, 6, pp. 496-507, (2006); Girosi F., Meili R., Scoville R., Extrapolating evidence of health information technology savings and costs, Santa Monica: Rand, (2005); The Lewin Group, (2005); President's FY2007 Budget Proposal: Overview and Briefing Charts; Sung N., Crowley W., Genel M., Salber P., Sandy L., Sherwood L., Et al., Central challenges facing the national clinical research enterprise, JAMA, 289, pp. 1278-1287, (2003); US Food and Drug Administration (FDA), (2004); Milne C.P., US and European regulatory initiatives to improve R&D performance, Expert Opinion on Drug Discovery, 1, 1, pp. 11-14, (2006); American Academy of Neurology, Electronic Health Records Work Group, (2006); Turisco F., Keogh D., Stubbs C., Glaser J., Crowley W., Current status of integrating information technologies into the clinical research enterprise within US academic health centers: Strategic value and opportunities for investment, J Investigative Med, 53, 8, pp. 425-433, (2005); Moulton B., Two years later: The impact of the EU directive, Appl Clin Trials, (2006); General principles of software validation: Final guidance for industry and FDA staff, FDA Guidance, (2002); Flory J., Emanuel E., Interventions to improve research participants' understanding in informed consent for research: A systematic review, JAMA, 292, 13, pp. 1593-1601, (2004); Guidelines for Writing Informed Consent Documents; A Chronology of Data Breaches","R.P. Dilaura; Cleveland Clinic and Case Western Reserve University, Cleveland, OH, United States; email: dilaurr@ccf.org","","IOS Press","","12th World Congress on Medical Informatics, MEDINFO 2007","20 August 2007 through 24 August 2007","Brisbane, QLD","","09269630","978-158603774-1","","","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-79956311434"
"Mönch L.; Zimmermann J.","Mönch, L. (6602003174); Zimmermann, J. (12140043000)","6602003174; 12140043000","Providing production planning and control functionality by web services: State of the art and experiences with prototypes","2009","2009 IEEE International Conference on Automation Science and Engineering, CASE 2009","","","5234141","495","500","5","2","10.1109/COASE.2009.5234141","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449122719&doi=10.1109%2fCOASE.2009.5234141&partnerID=40&md5=103554714662f299b6fbaab331e42a2f","University of Hagen, Department of Mathematics and Computer Science, Department of Enterprisewide Software Systems, 58097 Hagen, Universitätstraße 1, Germany","Mönch L., University of Hagen, Department of Mathematics and Computer Science, Department of Enterprisewide Software Systems, 58097 Hagen, Universitätstraße 1, Germany; Zimmermann J., University of Hagen, Department of Mathematics and Computer Science, Department of Enterprisewide Software Systems, 58097 Hagen, Universitätstraße 1, Germany","Web services have received an increasing importance over the last years, especially in connection with service oriented architectures (SOA) for the automation of business processes. Some authors anticipate that traditional ERP systems will lose their importance in the near future. We report on the development of software prototypes that allow the experimental investigation of these expectations at the laboratory level. We find that production planning and control (PPC) functionality can be offered by web services, but a systematic identification of appropriate services that encapsulate PPC functionality is not straightforward and requires more research. Data management issues and transaction processing, that are an important part of integrated applications, have to be re-invented in service-based application systems. © 2009 IEEE.","","Automation; Information management; Information services; Photolithography; Planning; Production control; Production engineering; Project management; Service oriented architecture (SOA); Web services; Application systems; Business Process; Data management; ERP system; Experimental investigations; Integrated applications; Production planning and control; Service-based; Software prototypes; State of the art; Systematic identification; Transaction processing; Software prototyping","","","","","","","(2009); Brehm N., Gomez J.M., Web service-based specification and implementation of functional components in federated ERPsystems, Proceedings Business Information Systems (BIS) 2007, pp. 133-146, (2007); Brehm N., Gomez J.M., Rautenstrauch C., An ERP solution based on web services and peer-to-peer networks for small and medium enterprises, International Journal for Information Systems and Change Management, 1, 1, pp. 99-111, (2006); Eggert S., Korf R., Lammer A., Marktüberblick: Web Service orientierte ERP-Systeme, ERP Management, 1, pp. 56-65, (2006); Eisele M., Kolb R., Kraus E., Von Ehrenstein C., SAP Net Weaver: Slicing the fridge, Informatik Spektrum, 30, 6, pp. 407-412, (2007); Ferstl O., Sinz E.J., Grundlagen der Wirtschaftsinformatik, (2006); Habla C., Driessel R., Monch L., Ponsignon T., Ehm H., A short-term forecast method for sales quantities in semiconductor manufacturing, Proceedings IEEE Conference on Automation Science and Engineering, pp. 94-99, (2007); Hopp W., Spearman M., Factory Physics, (2001); Kambhampaty S., Service oriented analysis and design process for the Enterprise, Proceedings 7th WSEAS International Conference on Applied Computer Science, pp. 366-371, (2007); Klose K., Knackstedt R., Serviceidentifikation für die Produktionsplanung eines mittelständischen Auftragsfertigers, HMD-praxis der Wirtschaftsinformatik, 253, pp. 47-56, (2007); Kurbel K., Dabkowski A., Jankowska A.M., A multi-tier architecture for mobile enterprise resource planning, Wirtschaftsinformatik 2003/Band I-medien, Märkte, Mobilität;, pp. 75-93, (2003); Lange R., Monch L., Zimmermann J., Service oriented computing for the manufacturing domain: Experiences from the development of a system prototype, Proceedings of the 2007 International Conference on e-Learning, e-business, Enterprise Information Systems, and E-government, pp. 197-202, (2007); Mertens P., Lohmann M., Branche oder Betriebstyp als Klassifikationskriterien für die Standardsoftware der Zukunft? Erste Überlegungen, wie künftig betriebswirtschaftliche Standardsoftware entstehen könnte, Proceedings Verbundtagung Wirtschaftsinformatik, pp. 110-135, (2000); Pazoglou M.P., Traverso P., Dustar S., Leymann F., Serviceoriented computing: A research roadmap, International Journal of Cooperative Information Systems, 17, 2, pp. 223-255, (2008); Qui R.G., A service-oriented integration framework for semiconductor manufacturing systems, International Journal Manufacturing Technology and Management, 10, 2-3, pp. 177-191, (2007); Scheer A.-W., Business Process Engineering-reference Models for Industrial Enterprises, (1994); Siedersleben J., SOA revisited: Komponentenorientierung bei Systemlandschaften, WIRTSCHAFTSINFORMATIK, 49, pp. 110-117, (2007); Sinz E.J., SOA und die bewährten methodischen Grundlagen der Entwicklung betrieblicher IT-Systeme, WIRTSCHAFTSINFORMATIK, 1, pp. 70-72, (2008); Tarantilis C.D., Kiranoudis C.T., Theodoadoakopoulos N.D., A Web-based ERP system for business services and supply chain management: Application to real-world process scheduling, European Journal of Operational Research, 187, pp. 1310-1326, (2008); Winkler V., Buhl H.U., Identifikation und Gestaltung von Services: Vorgehen und beispielhafte Anwendung im Finanzdienstleistungsbereich, WIRTSCHAFTSINFORMATIK, 49, pp. 257-266, (2007)","L. Mönch; University of Hagen, Department of Mathematics and Computer Science, Department of Enterprisewide Software Systems, 58097 Hagen, Universitätstraße 1, Germany; email: Lars.Moench@fernuni-hagen.de","","","Qualtech Systems Inc., QSI; IEEE Bangalore Section; Spansion; Infosys; IEEE","2009 IEEE International Conference on Automation Science and Engineering, CASE 2009","22 August 2009 through 25 August 2009","Bangalore","78145","","978-142444578-3","","","English","IEEE Int. Conf. Autom. Sci. Eng., CASE","Conference paper","Final","","Scopus","2-s2.0-70449122719"
"Anderson N.R.; Lee E.S.; Brockenbrough J.S.; Minie M.E.; Fuller S.; Brinkley J.; Tarczy-Hornoch P.","Anderson, Nicholas R. (57194225921); Lee, E. Sally (7406969129); Brockenbrough, J. Scott (56950513800); Minie, Mark E. (56730820300); Fuller, Sherrilynne (7202323728); Brinkley, James (7006036480); Tarczy-Hornoch, Peter (6701576629)","57194225921; 7406969129; 56950513800; 56730820300; 7202323728; 7006036480; 6701576629","Issues in Biomedical Research Data Management and Analysis: Needs and Barriers","2007","Journal of the American Medical Informatics Association","14","4","","478","488","10","108","10.1197/jamia.M2114","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250696127&doi=10.1197%2fjamia.M2114&partnerID=40&md5=ae9ec99d29032f6361e3797ff8c95ba7","Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States; Department of Biological Structure, University of Washington, Seattle, WA, United States; Health Sciences Libraries and Information Center, University of Washington, Seattle, WA, United States; Department of Health Services, School of Public Health and Community Medicine, University of Washington, Seattle, WA, United States; Department of Pediatrics, University of Washington, Seattle, WA, United States; Department of Computer Science and Engineering, University of Washington, Seattle, WA, United States","Anderson N.R., Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States; Lee E.S., Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States; Brockenbrough J.S., Department of Biological Structure, University of Washington, Seattle, WA, United States; Minie M.E., Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States, Health Sciences Libraries and Information Center, University of Washington, Seattle, WA, United States; Fuller S., Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States, Health Sciences Libraries and Information Center, University of Washington, Seattle, WA, United States, Department of Health Services, School of Public Health and Community Medicine, University of Washington, Seattle, WA, United States; Brinkley J., Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States, Department of Biological Structure, University of Washington, Seattle, WA, United States, Department of Computer Science and Engineering, University of Washington, Seattle, WA, United States; Tarczy-Hornoch P., Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States, Department of Pediatrics, University of Washington, Seattle, WA, United States, Department of Computer Science and Engineering, University of Washington, Seattle, WA, United States","Objectives: A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs. Design: A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest. Measurements: The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews. Results: Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support. Conclusion: Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities. © 2007 J Am Med Inform Assoc.","","Biomedical Research; Data Collection; Information Management; Information Storage and Retrieval; Information Systems; Internet; Interviews; Needs Assessment; article; e-mail; health survey; information processing; medical research; model; nonhuman; online analysis; qualitative analysis; semi structured interview; standard","","","","","National Library of Medicine Training, (DC02310, P20-LM007714, R01-HG02288, T15LM07442); National Human Genome Research Institute, NHGRI, (R01HG002288); National Institute on Deafness and Other Communication Disorders, NIDCD, (R01DC002310); U.S. National Library of Medicine, NLM, (P20LM007714, T15LM007442)","The authors would like to thank and acknowledge National Library of Medicine Training grant (Biomedical Health Informatics training program) T15LM07442, the BioMediator grant R01-HG02288, BISTI planning grant P20-LM007714, and the Human Brain Project grant DC02310 for providing the funding to support parts of this work.  ","NIH Roadmap on Translational Research, (2005); NIH RFP for Institutional Clinical and Translational Science award, (2005); Biomedical Information Science and Technology Initiative (BISTI), (2001); Science and Engineering Information Integration and Informatics, (2004); NIH Announces Draft Statement on Sharing Research Data, (2002); Cadeg E., Louie B., Myler P., Tarczy-Hornoch P., BioMediator Data Integration and Inference for Function Annotation of Anonymous Sequences, Pacific Symposium on Biocomputing, (2007); Louie B., Mork P., Martin-Sanchez F., Halevy A., Tarczy-Hornoch P., Data integration and genomic medicine, J Biomed Inform, 40, pp. 5-16, (2007); Mei H., Tarczy-Hornoch P., Mork P., Rossini A.J., Shaker R., Donelson L., Expression array annotation using the BioMediator biological data integration system and the BioConductor analytic platform, AMIA Annu Symp Proc, pp. 445-449, (2003); Donelson L., Tarczy-Hornoch P., Mork P., Et al., The BioMediator system as a data integration tool to answer diverse biologic queries, Medinfo, 11, PART 2, pp. 768-772, (2004); Jakobovits R.M., Rosse C., Brinkley J.F., WIRM: an Open Source Toolkit for Building Biomedical Web Applications, J Am Med Inform Soc, 9, 6, pp. 557-570, (2002); Li H., Gennari J.H., Brinkley J.F., Model Driven Laboratory Information Management Systems, (2006); Fong C., Brinkley J., Customizable Electronic Laboratory Online (CELO): A web-based data management system builder for biomedical laboratories, (2006); Oinn T., Addis M., Ferris J., Et al., Taverna: a tool for the composition and enactment of bioinformatics workflows, Bioinformatics, 20, 17, pp. 3045-3054, (2004); Jeng S., Wang K., Barbero J., Brinkley J., Tarczy-Hornoch P., A Pilot Bridging Data Integration and Analytics: BioMediator and R, (2005); Jones C., Patterns of Software System Failure and Success, (1996); Brooks F.P., The Mythical Man-Month: Essays on Software Engineering, (1995); Seaman C., Communication and Organization in Software Development: An Empirical Study, IBM Systems Journal, 36, pp. 550-563, (1997); Seaman C., Qualitative Methods in Empirical Studies of Software Engineering, IEEE Transactions on Software Engineering, 25, 4, pp. 557-572, (1999); Gittens R., Hope S., Williams I., Qualitative Studies of XP in a Medium Sized Business, (2001); Lindgaard G., Dillon R., Trbovich P., Et al., User needs analysis and requirements engineering: theory and practice, Interact Comp, 18, 1, pp. 47-70, (2006); Bryman A., Integrating quantitative and qualitative research: how is it done?, Qual Res, 6, pp. 97-113, (2006); Boverhof D.R., Zacharewski T.R., Toxicogenomics in risk assessment: applications and needs, Toxicol Sci, 89, pp. 352-360, (2005); Korjonen-Close H., The information needs and behaviour of clinical researchers: a user-needs analysis, Health Info Libr J, 22, 2, pp. 96-106, (2005); Strasberg H.R., Tudiver F., Geiger G., Keshavjee K.K., Troyan S., Moving towards an electronic patient record: a survey to assess the needs of community family physicians, AMIA Annu Symp Proc, pp. 965-969, (1998); Tanner C., Eckstrom E., Desai S.S., Ririe M.R., Bowen J.L., Uncovering frustrations. a qualitative needs assessment of academic general internists as geriatric care providers and teachers, J Gen Intern Med, 21, 1, pp. 51-55, (2006); Rosenal T.W., Forsythe D.E., Musen M.A., Seiver A., Support for information management in critical care: a new approach to identify needs, Proc Annu Symp Comput Appl Med Care, pp. 2-6, (1995); Forsythe D.E., Using ethnography to investigate life scientists' information needs, Bull Med Libr Assoc, 86, 3, pp. 402-409, (1998); Ammenwerth E., Shaw N.T., Bad health informatics can kill--is evaluation the answer?, Methods Inf Med, 44, 1, pp. 1-3, (2005); Kaplan B., Shaw N.T., Future directions in evaluation research: people, organizational, and social issues, Methods Inf Med, 43, 3, pp. 215-231, (2004); Kaplan B., Evaluating informatics applications - some alternative approaches: theory, social interactionism, and call for methodological pluralism, Int J Med Inform, 64, pp. 39-56, (2001); Yarfitz S., Ketchell D.S., A library-based bioinformatics services program, Bull Med Libr Assoc, 88, 1, pp. 36-48, (2000); Tran D., Dubay C., Gorman P., Hersh W., Applying task analysis to describe and facilitate bioinformatics tasks, Medinfo, 11, PART 2, pp. 818-822, (2004); Anderson N., Ash J., Tarczy-Hornoch P., A qualitative study of the implementation of a bioinformatics tool in a biological research laboratory, Int J Biomed Inform, (2006); Bartlett J., Toms E., Developing a protocol for bioinformatics analysis: an integrated information behaviour and task analysis approach, J Am Soc Inform Sci Technol, 56, 5, pp. 469-482, (2005); Bartlett J., Toms E., Discovering and structuring information flow among bioinformatics resources, (2003); Ash J.S., Sittig D.F., Seshadri V., Dykstra R.H., Carpenter J.D., Stavri P.Z., Adding insight: a qualitative cross-site study of physician order entry, Int J Med Inform, 74, pp. 7-8, (2005); Ash J.S., Fournier L., Stavri P.Z., Dykstra R., Principles for a successful computerized physician order entry implementation, AMIA Annu Symp Proc, pp. 36-40, (2003); Crabtree B., Miller W., Doing Qualitative Research. 2nd ed., (1999); Fisher K., Erdelez S., McKenchie L., Theories of Information Behavior, (2005); Wolcott H.F., Writing Up Qualitative Research, (2001); Ash J.S., Stavri P.Z., Kuperman G.J., A consensus statement on considerations for a successful CPOE implementation, J Am Med Inform Assoc, 10, 3, pp. 229-234, (2003); Wooldridge A., The Brains Business, The Economist, (2005); Miles M., Huberman A.M., Qualitative Data Analysis: An Expanded Sourcebook, (1994); Jakobovits R., Soderland S.G., Taira R.K., Brinkley J.F., Requirements of a Web-based experiment management system, Proc AMIA Symp, pp. 374-378, (2000); Arnstein L., Grimm R., Hung C., Et al., Systems Support for Ubiquitous Computing: A Case Study of Two Implementations of LabScape. Proc First Intern Conf Perv Comp, (2002); Flowers S., Software Failure: Management Failure: Amazing Stories and Cautionary Tales, (1996); Forsythe D.E., Using ethnography to build a working system: rethinking basic design assumptions, Proc Annu Symp Comput Appl Med Care, pp. 505-509, (1992); Pittendrigh S., Jacobs G., NeuroSys: a semistructured laboratory database, Neuroinform, 1, 2, pp. 167-176, (2003); Marenco L., Tosches N., Crasto C., Shepherd G., Miller P.L., Nadkarni P.M., Achieving evolvable Web-database bioscience applications using the EAV/CR framework: recent advances, J Am Med Inform Assoc, 10, 5, pp. 444-453, (2003); Li H., Brinkley J., Gennari J., Semi-automatic Database Design for Neuroscience Experiment Management Systems, (2004); Gentleman R.C., Carey V.J., Bates D.M., Et al., Bioconductor: open software development for computational biology and bioinformatics, Genome Biol, 5, 10, (2004); Forsythe D.E., New bottles, old wine: hidden cultural assumptions in a computerized explanation system for migraine sufferers, Med Anthropol Q, 10, 4, pp. 551-574, (1996)","N.R. Anderson; Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, United States; email: nicka@u.washington.edu","","","","","","","","10675027","","JAMAF","17460139","English","J. Am. Med. Informatics Assoc.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-34250696127"
"Clements A.","Clements, Anna (56115261300)","56115261300","Research information meets research data management ... In the library?","2013","Insights: the UKSG Journal","26","3","","298","304","6","4","10.1629/2048-7754.99","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054545704&doi=10.1629%2f2048-7754.99&partnerID=40&md5=bae6c1151c2806fbc54b45b2c3dafd03","Enterprise Architect University, St.Andrews, United Kingdom","Clements A., Enterprise Architect University, St.Andrews, United Kingdom","Research data management (RDM) is a major priority for many institutions as they struggle to cope with the plethora of pronouncements including funder policies, a G8 statement, REF2020 consultations, all stressing the importance of open data in driving everything from global innovation through to more accountable governance; not to mention the more direct possibility that non-compliance could result in grant income drying up. So, at the coalface, how do we become part of this global movement? In this article the author explains the approach being taken at the University of St.Andrews, building on the research information management infrastructure (data, systems and people) that has evolved since 2006. Continuing to navigate through the rapidly evolving research policy and cultural landscape, they aim to establish services to support their research community as it moves to this 'open by default' requirement of funders and governments. © 2013 Anna Clements.","","","","","","","","","Scopus; Web of Science; Pure; Common European Research Information Format (CERIF; Research Excellence Framework (REF, (2014); Research Councils UK Research Outcomes System (ROS; Research Councils UK (RCUK) Policy on Open Access; Engineering and Physical Sciences Research Council (EPSRC) Policy Framework on Research Data; G8 Open Data Charter; Digital Curation Centre (DCC; UK Government Department for Business Innovation and Skills (BIS; Research Councils UK (RCUK) Block Grants for Universities to Aid Drives to Open Access to Research Outputs; Digital Curation Centre DCC Curation Lifecycle Model; EPSRC Policy Framework on Research Data; Jones S., Pryor G., Whyte A How to Develop Research Data Management Services-A Guide for HEIs, (2013); UKOLN Russell R Adoption of CERIF in Higher Education Institutions in the UK: A Landscape Study March 2012 London UKOLN; JISC Managing Research Data (JISCMRD) Programme; JISC JORUM Research Data Management; Australian National Data Service (ANDS; Data Centres; Economic and Social Research Council (ESRC) Economic, Social Data Service; Archaeology Data Services (ADS","A. Clements; Enterprise Architect University, St.Andrews, United Kingdom; email: akc@st-andrews.ac.uk","","Ubiquity Press Ltd","","","","","","20487754","","","","English","Insights UKSG J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85054545704"
"Abugessaisa I.; Gomez-Cabrero D.; Snir O.; Lindblad S.; Klareskog L.; Malmström V.; Tegnér J.","Abugessaisa, Imad (6506529160); Gomez-Cabrero, David (24779430500); Snir, Omri (22636232700); Lindblad, Staffan (7004052631); Klareskog, Lars (7101784599); Malmström, Vivianne (55930478200); Tegnér, Jesper (7004724921)","6506529160; 24779430500; 22636232700; 7004052631; 7101784599; 55930478200; 7004724921","Implementation of the CDC translational informatics platform - from genetic variants to the national Swedish Rheumatology Quality Register","2013","Journal of Translational Medicine","11","1","85","","","","8","10.1186/1479-5876-11-85","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875527384&doi=10.1186%2f1479-5876-11-85&partnerID=40&md5=897d5bd0a4bb22a808f46618dfe347f7","Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; Department of Medicine, Rheumatology Unit, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; Rheumatology clinic, Karolinska University Hospital, Solna, Sweden; Department of Immunology, Centre for Immune Regulation, Oslo University Hospital-Rikshospitalet, University of Oslo, Oslo, Norway","Abugessaisa I., Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; Gomez-Cabrero D., Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; Snir O., Department of Medicine, Rheumatology Unit, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden, Department of Immunology, Centre for Immune Regulation, Oslo University Hospital-Rikshospitalet, University of Oslo, Oslo, Norway; Lindblad S., Rheumatology clinic, Karolinska University Hospital, Solna, Sweden; Klareskog L., Department of Medicine, Rheumatology Unit, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; Malmström V., Department of Medicine, Rheumatology Unit, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; Tegnér J., Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden","Background: Sequencing of the human genome and the subsequent analyses have produced immense volumes of data. The technological advances have opened new windows into genomics beyond the DNA sequence. In parallel, clinical practice generate large amounts of data. This represents an underused data source that has much greater potential in translational research than is currently realized. This research aims at implementing a translational medicine informatics platform to integrate clinical data (disease diagnosis, diseases activity and treatment) of Rheumatoid Arthritis (RA) patients from Karolinska University Hospital and their research database (biobanks, genotype variants and serology) at the Center for Molecular Medicine, Karolinska Institutet.Methods: Requirements engineering methods were utilized to identify user requirements. Unified Modeling Language and data modeling methods were used to model the universe of discourse and data sources. Oracle11g were used as the database management system, and the clinical development center (CDC) was used as the application interface. Patient data were anonymized, and we employed authorization and security methods to protect the system.Results: We developed a user requirement matrix, which provided a framework for evaluating three translation informatics systems. The implementation of the CDC successfully integrated biological research database (15172 DNA, serum and synovial samples, 1436 cell samples and 65 SNPs per patient) and clinical database (5652 clinical visit) for the cohort of 379 patients presents three profiles. Basic functionalities provided by the translational medicine platform are research data management, development of bioinformatics workflow and analysis, sub-cohort selection, and re-use of clinical data in research settings. Finally, the system allowed researchers to extract subsets of attributes from cohorts according to specific biological, clinical, or statistical features.Conclusions: Research and clinical database integration is a real challenge and a road-block in translational research. Through this research we addressed the challenges and demonstrated the usefulness of CDC. We adhered to ethical regulations pertaining to patient data, and we determined that the existing software solutions cannot meet the translational research needs at hand. We used RA as a test case since we have ample data on active and longitudinal cohort. © 2013 Abugessaisa et al.; licensee BioMed Central Ltd.","Patient de-identification; Secondary use of clinical data; Swedish Rheumatology Quality Register (SRQ); Translational medicine platform","Arthritis, Rheumatoid; Centers for Disease Control and Prevention (U.S.); Cohort Studies; Computer Graphics; Genetic Variation; Genomics; Genotype; Humans; Medical Informatics; Polymorphism, Single Nucleotide; Registries; Rheumatology; Sequence Analysis, DNA; Software; Sweden; Translational Medical Research; United States; User-Computer Interface; article; computer interface; computer security; disease activity; gene frequency; gene locus; genetic database; genetic variability; health care planning; health care quality; human; information processing; medical informatics; medical information system; medical research; patient identification; register; rheumatoid arthritis; serology; single nucleotide polymorphism; Sweden; translational research; workflow","","","","","FP7 SYNERGY-COPD; National Institutes of Health, NIH; Stockholms Läns Landsting; Vetenskapsrådet, VR","Funding text 1: A number of technology platform solutions are available to manage biomedical data in translational research. Some of them, developed by research community are released as open-source under General Public License (GPL [9]), developed by research communities at universities and research institutes. One of the commonly used platforms is Informatics for Integrating Biology and the Bedside (i2b2) [10]. The i2b2 platform is funded by the National Institutes of Health (NIH). i2b2 uses The International Classification of Diseases (ICD) [11] as a taxonomic standard to classify diseases, and it enables the creation of formal ontologies to meet the specific requirements of different research studies.; Funding text 2: The authors would like to acknowledge and thank Leonid Padyukov and Gordon Ball for feedback and comments on the manuscript. Our research is supported by Swedish Research Council (Tegnér), Swedish Research Council, CERIC (Tegnér, Abugessaisa), Swedish Research Council, SerC (Tegnér, Abugessaisa), FP7 SYNERGY-COPD (Gomez-Cabrero, Tegnér), Stockholm County Council(Gomez-Cabrero ,Tegnér).","Kuehn B.M., 1000 Genomes Project promises closer look at variation in human genome, JAMA, 300, 23, (2008); Gerstein M., Genomics: ENCODE leads the way on big data, Nature, 489, 7415, (2012); Swedish Rheumatology Register; Szalma S., Effective knowledge management in translational medicine, J Transl Med, 8, (2010); Neovius M., Simard J.F., Askling J., Nationwide prevalence of rheumatoid arthritis and penetration of disease-modifying drugs in Sweden, Ann Rheum Dis, 70, 4, pp. 624-629, (2011); Neovius M., Sick leave and disability pension before and after initiation of antirheumatic therapies in clinical practice, Ann Rheum Dis, 70, 8, pp. 1407-1414, (2011); Stahl E.A., Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci, Nat Genet, 42, 6, pp. 508-514, (2010); Klareskog L., Catrina A.I., Paget S., Rheumatoid arthritis, Lancet, 373, 9664, pp. 659-672, (2009); GNU General Public License; Murphy S.N., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), J Am Med Inform Assoc: JAMIA, 17, 2, pp. 124-130, (2010); Slee V.N., The International Classification of Diseases: ninth revision (ICD-9), Ann Intern Med, 88, 3, pp. 424-426, (1978); Murphy S.N., Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2), J Am Med Inform Assoc, 17, 2, pp. 124-130, (2010); Lowe H.J., Ferris T.A., Hernandez P.M., Weber S.C., An Integrated Standards-Based Translational Research Informatics Platform. AMIA ... Annual Symposium proceedings / AMIA Symposium, AMIA Symposium, 2009, 2009, pp. 391-395, (2009); Van Hentenryck K., Health Level Seven. Shedding light on HL7's Version 2.3 Standard, Healthc Inform, 14, 3, (1997); Shahpori R., Doig C., Systematized Nomenclature of Medicine-Clinical Terms direction and its implications on critical care, J Crit Care, 25, 2, (2010); Elmasri R.A., Fundamentals of Database Systems, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, Volume 6, (2011); The Unified Modeling Language™ - UML; Trujillo J., An engineering process for developing Secure Data Warehouses, Inf Softw Technol, 51, 6, pp. 1033-1051, (2009); Cheng H.H., Trang D.T., Object-oriented interactive mechanism design and analysis, Eng Comput, 21, 3, pp. 237-246, (2006); Gruber T.R., A translation approach to portable ontology specifications, Knowl Acquis, 5, 2, pp. 199-220, (1993); Gomez-Perez A.B.R., Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web, pp. 1-4, (2002); Albert D., Steiner C.M., Representing domain knowledge by concept maps: How to validate them?, (2005); Horaitis O., A database of locus-specific databases, Nat Genet, 39, 4, (2007); Snir O., Antibodies to several citrullinated antigens are enriched in the joints of rheumatoid arthritis patients, Arthritis Rheum, 62, 1, pp. 44-52, (2010); Chacon-Cortes D., Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study, Mol Biol Rep, 39, 5, pp. 5961-5966, (2012); Askling J., Swedish registers to examine drug safety and clinical issues in RA, Ann Rheum Dis, 65, 6, pp. 707-712, (2006); Nadkarni P.M., Deshpande A.M., Brandt C., Temporal query of attribute-value patient data: utilizing the constraints of clinical studies, Int J Med Inform, 70, 1, pp. 59-77, (2003); van Vollenhoven R.F., Physician-defined remission in RA (""Disease activity: none"") in the Swedish RA registry: relationship with DAS28 and ACR core set variables, Arthritis Rheum, 58, 9, (2008); Szalay A., Thakar A.R., Gray J., The sqlLoader data-loading pipeline, Comput Sci Eng, 10, 1, pp. 38-48, (2008); Coombes K.R., Wang J., Baggerly K.A., Microarrays: retracing steps, Nat Med, 13, 11, pp. 1276-1277, (2007); Rosenfeld L., Information architecture: looking ahead, J Am Soc Inf Sci Technol, 53, 10, pp. 874-876, (2002); Robins D., User studies and information architecture. Asist 2002, Proc 65th Asist Annu Meet, 39, pp. 448-448, (2002); Tolle K.M., Tansley D.S.W., Hey A.J.G., The fourth paradigm: data-intensive scientific discovery, Proc IEEE, 99, 8, pp. 1334-1337, (2011); Collins J.P., The fourth paradigm data-intensive scientific discovery, Science, 327, 5972, pp. 1455-1456, (2010); Nielsen M., The fourth paradigm: data-intensive scientific discovery, Nature, 462, 7274, pp. 722-723, (2009); Stein L.D., Integrating biological databases, Nat Rev Genet, 4, 5, pp. 337-345, (2003); Van Hoyweghen I., Horstman K., European practices of genetic information and insurance: lessons for the Genetic Information Nondiscrimination Act, JAMA, 300, 3, pp. 326-327, (2008); Diergaarde B., Genetic information: special or not? Responses from focus groups with members of a health maintenance organization, Am J Med Genet A, 143, 6, pp. 564-569, (2007); Gilbar R., Patient autonomy and relatives' right to know genetic information, Med Law, 26, 4, pp. 677-697, (2007); Gilbar R., Communicating genetic information in the family: the familial relationship as the forgotten factor, Indian J Med Ethics, 33, 7, pp. 390-393, (2007); Knoppers B.M., The emergence of an ethical duty to disclose genetic research results: international perspectives, Eur J Hum Genet, 14, 11, pp. 1170-1178, (2006); Homer N., Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays, PLoS Genet, 4, 8, (2008)","J. Tegnér; Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden; email: jesper.tegner@ki.se","","","","","","","","14795876","","","23548156","English","J. Transl. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84875527384"
"Milner J.","Milner, John (35216310000)","35216310000","A UK Research Data Service (UKRDS): The way forward for research data management?","2009","Serials","22","1","","83","85","2","1","10.1629/2283","https://www.scopus.com/inward/record.uri?eid=2-s2.0-67649528099&doi=10.1629%2f2283&partnerID=40&md5=4fff0af5d14cba1d32606a9fa8889e6e","UKRDS, United Kingdom","Milner J., UKRDS, United Kingdom","[No abstract available]","","","","","","","","","","","","","","","","","","14753308","","","","English","Serials","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-67649528099"
"Fawcett J.; Buhle Jr. E.L.","Fawcett, J. (7202551603); Buhle Jr., E.L. (6701311177)","7202551603; 6701311177","Using the Internet for data collection. An innovative electronic strategy.","1995","Computers in nursing","13","6","","273","279","6","59","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029399546&partnerID=40&md5=a64ad73f6eaddacc2c5b958556cb8610","University of Pennsylvania School of Nursing, Philadelphia, United States","Fawcett J., University of Pennsylvania School of Nursing, Philadelphia, United States; Buhle Jr. E.L., University of Pennsylvania School of Nursing, Philadelphia, United States","Computer hardware and software have revolutionized research data management. Data collection has, however, remained a time-consuming and expensive component of most research projects. This article presents a description of an innovative strategy for data collection using computer network forums on the Internet (the ""information superhighway""). The success of the electronic data collection strategy is illustrated by a report of the results of a survey of the needs and coping mechanisms of cancer survivors.","","Adaptation, Psychological; Adult; Aged; Computer Communication Networks; Data Collection; Female; Humans; Male; Middle Aged; Neoplasms; Nursing Research; Survivors; adaptive behavior; adult; aged; article; computer network; female; human; information processing; male; methodology; middle aged; neoplasm; nursing research; psychological aspect; survivor","","","","","","","","","","","","","","","","07368593","","","8529140","English","Comput Nurs","Article","Final","","Scopus","2-s2.0-0029399546"
"Ashley K.","Ashley, Kevin (55761240700)","55761240700","Research data and libraries: Who does what","2012","Insights: the UKSG Journal","25","2","","155","157","2","2","10.1629/2048-7754.25.2.155","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878872308&doi=10.1629%2f2048-7754.25.2.155&partnerID=40&md5=e94ad010e2eae75fa4b9003878e2574c","Digital Curation Centre, United Kingdom","Ashley K., Digital Curation Centre, United Kingdom","A range of external pressures are causing research data management (RDM) to be an increasing concern at senior level in universities and other research institutions. But as well as external pressures, there are also good reasons for establishing effective research data management services within institutions which can bring benefits to researchers, their institutions and those who publish their research. In this article some of these motivating factors, both positive and negative, are described. Ways in which libraries can play a role - or even lead - in the development of RDM services that work within the institution and as part of a national and international research data infrastructure are also set out. © Kevin Ashley.","","","","","","","","","DCC Policy & Legal Resources Home; Crystal Structure Communications Online; Piwowar H., Day R., Fridsma D., Sharing detailed research data is associated with increased citation rate, PLoS ONE, 23, (2007); Pienta A.M., Alter G.C., Lyle J.A., The Enduring Value of Social Science Research: The use and Reuse of Primary Research Data","K. Ashley; Digital Curation Centre, United Kingdom; email: kevin.ashley@ed.ac.uk","","United Kingdom Serials Group","","","","","","20487754","","","","English","Insights","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84878872308"
"Bahls D.; Zapilko B.; Tochtermann K.","Bahls, Daniel (23007572200); Zapilko, Benjamin (49964797500); Tochtermann, Klaus (16053767400)","23007572200; 49964797500; 16053767400","A data restore model for reproducibility in computational statistics","2013","ACM International Conference Proceeding Series","","","","","","","0","10.1145/2494188.2494205","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888158276&doi=10.1145%2f2494188.2494205&partnerID=40&md5=c73527ca7088064f3a5781a5acd7dee0","Leibniz Information Centre for Economics (ZBW), 20354 Hamburg, Neuer Jungfernstieg 21, Germany; GESIS - Leibniz Institute for the Social Sciences, 50667 Cologne, Unter Sachsenhausen 6-8, Germany; Leibniz Information Centre for Economics (ZBW), 24105 Kiel, Düsternbrooker Weg 120, Germany","Bahls D., Leibniz Information Centre for Economics (ZBW), 20354 Hamburg, Neuer Jungfernstieg 21, Germany; Zapilko B., GESIS - Leibniz Institute for the Social Sciences, 50667 Cologne, Unter Sachsenhausen 6-8, Germany; Tochtermann K., Leibniz Information Centre for Economics (ZBW), 24105 Kiel, Düsternbrooker Weg 120, Germany","Researchers are more and more requested to publish their scientific data sets for purposes of transparency, re-use, and reproducibility. Particularly in economics and the social sciences, researchers often use sensitive statistical data that underlie protection policies which inhibit distribution to third party archives. In addition, a considerable quantity of data sets combines data from one or more external providers, which complicates the setting for curation-related activities. These circumstances give us reason to pursue a data restore model on the basis of fine-grained referencing that allows to trace data provenance to the original archive in charge of curation. One goal is to enable data publication in difficult cases, and another one is to show how the gaps between data citation and code integration can be closed in order to eliminate all manual efforts of arranging code and data files for reproduction attempts. On this basis we develop the requirements for a data restore model and elaborate a generic design in view of an overall data management infrastructure. We further explore an experimental implementation which we validate by taking the example of a real-world publication in economics. Eventually we close with the vision of a data and code ontology that carries statistical models from paper to a re-usable semantic level. © 2013 ACM.","Linked Data; Research Data Management; Semantic Digital Data Library; Statistics","Knowledge management; Research; Semantics; Statistics; Computational statistics; Linked datum; Management infrastructure; Protection policy; Reproducibilities; Research data managements; Semantic digital data libraries; Statistical datas; Restoration","","","","","","","Wavelab and reproducible research, Wavelets and Statistics, (1995); Arai M., Karlsson J., Lundholm M., On fragile grounds: A replication of ""are muslim immigrants different in terms of cultural integration?, Journal of the European Economic Association, 9, 5, pp. 1002-1011, (2011); Bahls D., Tochtermann K., Addressing the long tail in empirical research data management, Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies, I-KNOW '12, pp. 191-198, (2012); Baiocchi G., Reproducible research in computational economics: Guidelines, integrated approaches, and open source software, Computational Economics, 30, 1, pp. 19-40, (2007); Barnes N., The Science Code Manifesto, (2012); Bechhofer S., Ainsworth J., Bhagat J., Buchan I., Couch P., Cruickshank D., Roure D.D., Delderfeld M., Dunlop I., Gamble M., Et al., Why linked data is not enough for scientists, e-Science (e-Science), 2010 IEEE Sixth International Conference on, pp. 300-307, (2010); Berthoud R.E.A., Fourth National Survey of Ethnic Minorities, 1993-1994, (1997); Bisin A., Patacchini E., Verdier T., Zenou Y., Errata corrige:""Are muslim immigrants di erent in terms of cultural integration?, Journal of the European Economic Association, 9, 5, pp. 1012-1019, (2011); Brauer P.C., Hasselbring W., Pubfow: Provenance-aware workflows for research data publication, 5th USENIX Workshop on the Theory and Practice of Provenance (TaPP '13), (2013); Cassey P., Blackburn T.M., Reproducibility and repeatability in ecology, BioScience, 56, 12, pp. 958-959, (2006); De Leeuw J., Reproducible Research. The Bottom Line, (2001); Feijen M., What Researchers Want - A Literature Study of Researchers' Requirements with Respect to Storage and Access to Research Data, (2011); Gavish M., Donoho D., A universal identifier for computational results, Procedia Computer Science, 4, pp. 637-647, (2011); Gentle J.E., Hardle W.K., Mori Y., How Computational Statistics Became the Backbone of Modern Data Science, (2011); Gentleman R., Lang D.T., Statistical analyses and reproducible research, Journal of Computational and Graphical Statistics, 16, 1, pp. 1-23, (2007); Gonzalez-Barahona J.M., Robles G., On the reproducibility of empirical software engineering studies based on data retrieved from development repositories, Empirical Software Engineering, 17, 1-2, pp. 75-89, (2012); Halb W., Raimond Y., Hausenblas M., Building Linked Data For Both Humans and Machines, WWW 2008 Workshop: Linked Data on the Web (LDOW2008), Beijing, China, 2008; Hothorn T., Leisch F., Case studies in reproducibility, Briefings in Bioinformatics, 12, 3, pp. 288-300, (2011); Kauppinen T., Baglatzi A., Kessler C., Linked Science: Interconnecting Scientific Assets, Data Intensive Science, (2012); Koenker R., Zeileis A., On Reproducible Econometric Research, pp. 1-13, (2008); Leisch F., Sweave: Dynamic generation of statistical reports using literate data analysis, Compstat 2002 - Proceedings in Computational Statistics, pp. 575-580, (2002); Leisch F., Eugster M., Hothorn T., Executable papers for the r community: The r2 platform for reproducible research, Procedia Computer Science, 4, pp. 618-626, (2011); McCullough B.D., Got replicability? The journal of money, credit and banking archive, Econ Journal Watch, 4, 3, pp. 326-337, (2007); Nowakowski P., Ciepiela E., Harezlak D., Kocot J., Kasztelnik M., Bartynski T., Meizner J., Dyk G., Malawski M., The collage authoring environment, Procedia Computer Science, 4, pp. 608-617, (2011); Peng R.D., Reproducible research in computational science, Science, 334, 6060, pp. 1226-1227, (2011); Rahmandad H., Sterman J.D., Reporting guidelines for simulation-based research in social sciences, System Dynamics Review, 28, 4, pp. 396-411, (2012); Rauber A., Digital preservation in data-driven science: On the importance of process capture, preservation and validation, SDA, pp. 7-17, (2012); Rechert K., Von Suchodoletz D., Welte R., Emulation based services in digital preservation, Proceedings of the 10th Annual Joint Conference on Digital Libraries, JCDL '10, pp. 365-368, (2010); Rossini A., Leisch F., Literate Statistical Practice, (2003); Schwab M., Karrenbach M., Claerbout J., Making scientific computations reproducible, Computing in Science and Engg, 2, 6, pp. 61-67, (2000); Vandewalle P., Kovacevic J., Vetterli M., Reproducible research in signal processing, Signal Processing Magazine, IEEE, 26, 3, pp. 37-47, (2009); Wood J., Andersson T., Bachem A., Best C., Genova F., Lopez D.R., Los W., Marinucci M., Romary L., Van De Sompel H., Vigen J., Wittenburg P., Giaretta D., Riding the wave: How Europe can gain from the rising tide of scientific data, Final Report of the High Level Expert Group on Scientific Data: A Submission to the European Commission, (2010)","","","","Graz University of Technology","13th International Conference on Knowledge Management and Knowledge Technologies, i-KNOW 2013","4 September 2013 through 6 September 2013","Graz","100663","","978-145032300-0","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84888158276"
"Gwede C.; Daniels S.; Johnson D.","Gwede, Clement (6603343614); Daniels, Stephanie (57197355014); Johnson, Darlene (57197153803)","6603343614; 57197355014; 57197153803","Organization of clinical research services at investigative sites: Implications for workload measurement","2001","Therapeutic Innovation & Regulatory Science","35","3","","695","705","10","1","10.1177/009286150103500307","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84993682022&doi=10.1177%2f009286150103500307&partnerID=40&md5=fc208ad5783e30211254776dee7848de","Radiation Oncology Research Coordinator, Interdisciplinary Oncology Program, Clinical Research and Compliance Services, Tampa, FL 33612, 12902 Mangolia Drive, United States; ECOG Clinical Trials Coordinator, Clinical Research and Compliance Services, United States; Radiation Oncology Program, Moffitt Cancer Center, Research Institute, Tampa, Florida, United States","Gwede C., Radiation Oncology Research Coordinator, Interdisciplinary Oncology Program, Clinical Research and Compliance Services, Tampa, FL 33612, 12902 Mangolia Drive, United States; Daniels S., ECOG Clinical Trials Coordinator, Clinical Research and Compliance Services, United States; Johnson D., Radiation Oncology Program, Moffitt Cancer Center, Research Institute, Tampa, Florida, United States","The impact of the structure and organization of clinical research services (data management model) on the workload of clinical research coordinators (CRCs) at investigative sites is undocumented. This paper describes three types of data management models and their potential influence on the workload of CRCs. A 20-item survey, covering information about accrual to clinical trials, staffing levels, use of workload measurement tools, and the data management model, was e-mailed to nine CRCs working at selected cancer centers in the United States. Six CRCs representing four university-based institutions and two community hospitals responded. Staffing levels and number of patients placed on clinical trials varied by institution and data management model. One out of six centers used a workload formula based upon the time it takes to complete a task. The centralized clinical data management model and the modified/mixed models were common. Our findings suggest that it is important to understand the structure of the clinical data management model, among other factors, in evaluating the workload of CRCs. © 2001, Drug Information Association. All rights reserved.","Clinical research; Clinical research coordinators; Clinical research data management services; Data management models; Workload measurement","","","","","","","","Gwede C., Johnson D., Trotti A., Measuring the workload of clinical research coordinators, Part 1: Tools to study workload issues, Appl Clin Trials., pp. 40-44, (2000); Gwede C., Johnson D., Trotti A., Measuring the workload of clinical research coordinators, Part 2: Workload Implications for Sites, Appl Clin Trials., pp. 42-47, (2000); Hancock R.D., Wiland S., Brown N.A., Kerner-Slemons S., Brown P.B., Development and testing of a complexity rating scale for clinical trial protocol management, The Monitor., pp. 36-39, (1995); Storfjell J.L., Allen C.E., Easley C.E., Analysis and management of home health nursing caseloads and workloads, J Nursing Adm., 27, 9, pp. 24-33, (1997); Medvec B.R., Productivity and workload measurement in ambulatory oncology, Sem Oncology Nursing., 10, 4, pp. 288-1295, (1994); Meyer M.A., Manpower planning, one: An American approach, Nursing Times., pp. 52-54, (1984); Arthur T., James N., Determining nurse staffing levels: A critical review of the literature, J Advanced Nursing., 19, pp. 558-565, (1994)","","","","","","","","","21684790","","","","English","Ther. Innov. Regul. Sci.","Article","Final","","Scopus","2-s2.0-84993682022"
"Jayapandian C.P.; Zhao M.; Ewing R.M.; Zhang G.-Q.; Sahoo S.S.","Jayapandian, Catherine P. (55761667400); Zhao, Meng (57198160260); Ewing, Rob M. (7201658725); Zhang, Guo-Qiang (7405271191); Sahoo, Satya S. (9735293400)","55761667400; 57198160260; 7201658725; 7405271191; 9735293400","A semantic proteomics dashboard (SemPoD) for data management in translational research","2012","BMC Systems Biology","6","SUPPL3","S20","","","","5","10.1186/1752-0509-6-S3-S20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878836216&doi=10.1186%2f1752-0509-6-S3-S20&partnerID=40&md5=1a8a2f331a87e49456411fb30840a33b","Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; Center for Proteomics and Bioinformatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States","Jayapandian C.P., Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; Zhao M., Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; Ewing R.M., Center for Proteomics and Bioinformatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; Zhang G.-Q., Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; Sahoo S.S., Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States","Background: One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving ""What"", ""Where"", ""When"", ""Which"", ""Who"", ""How"", and ""Why"" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a ""provenance-aware"" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Semantic Proteomics Dashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research.Results: The SemPoD platform, currently in use at the Case Center for Proteomics and Bioinformatics (CPB), consists of three components: (a) Ontology-driven Visual Query Composer, (b) Result Explorer, and (c) Query Manager. Currently, SemPoD allows provenance-aware querying of 1153 mass-spectrometry experiments from 20 different projects. SemPod uses the systems molecular biology provenance ontology (SysPro) to support a dynamic query composition interface, which automatically updates the components of the query interface based on previous user selections and efficientlyprunes the result set usinga ""smart filtering"" approach. The SysPro ontology re-uses terms from the PROV-ontology (PROV-O) being developed by the World Wide Web Consortium (W3C) provenance working group, the minimum information required for reporting a molecular interaction experiment (MIMIx), and the minimum information about a proteomics experiment (MIAPE) guidelines. The SemPoD was evaluated both in terms of user feedback and as scalability of the system.Conclusions: SemPoD is an intuitive and powerful provenance ontology-driven data access and query platform that uses the MIAPE and MIMIx metadata guideline to create an integrated view over large-scale systems molecular biology datasets. SemPoD leverages the SysPro ontology to create an intuitive dashboard for biologists to compose queries, explore the results, and use a query manager for storing queries for later use. SemPoD can be deployed over many existing database applications storing 'omics data, including, as illustrated here, the LabKey data-management system. The initial user feedback evaluating the usability and functionality of SemPoD has been very positive and it is being considered for wider deployment beyond the proteomics domain, and in other 'omics' centers. © 2012 Jayapandian et al.; licensee BioMed Central Ltd.","","Algorithms; Animals; Computer Simulation; Database Management Systems; Databases, Factual; Disease; Disease Models, Animal; Humans; Mass Spectrometry; Mice; Polymorphism, Single Nucleotide; Proteins; Proteomics; Reproducibility of Results; Semantics; Signal Transduction; Software; Systems Biology; Translational Medical Research; User-Computer Interface; protein; algorithm; animal; article; chemistry; computer interface; computer program; computer simulation; data base; disease model; diseases; factual database; genetics; human; mass spectrometry; methodology; mouse; proteomics; reproducibility; semantics; signal transduction; single nucleotide polymorphism; systems biology; translational research","","protein, 67254-75-5; Proteins, ","","","Case Western Reserve University/Cleveland Clinic, (1 RR024989); National Science Foundation, NSF, (1141979); National Center for Research Resources, NCRR, (UL1RR024989); National Center for Advancing Translational Sciences, NCATS, (UL1TR000439)","This research was supported in part by the PhysioMIMI project (grant#NCRR-94681DBS78) and Case Western Reserve University/Cleveland Clinic & CTSA Grant (grant#UL1 RR024989.) We also thank members of the Center for Proteomics and Bioinformatics for their help in evaluating SemPoD prototypes. This article has been published as part of BMC Systems Biology Volume 6 Supplement 3, 2012: Proceedings of The International Conference on Intelligent Biology and Medicine (ICIBM) - Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/ bmcsystbiol/supplements/6/S3.","Challenges and Opportunities, Science, 331, 6018, pp. 692-692, (2011); Integrating with integrity, Nat Genet, 42, 1, (2010); Goble C., Position Statement: Musings on Provenance, Workflow and (Semantic Web) Annotations for Bioinformatics, Workshop on Data Derivation and Provenance: 2002; Chicago, (2002); Sahoo S.S., Nguyen V., Bodenreider O., Parikh P., Minning T., Sheth A.P., A unified framework for managing provenance information in translational research, BMC Bioinformatics, 12, (2011); Lee T., Bressan S., Multimodal Integration of Disparate Information Sources with Attribution, Entity Relationship Workshop on Information Retrieval and Conceptual Modeling, (1997); Buneman P., Khanna S., Tan W.C., Data Provenance: Some Basic Issues, Lecture Notes in Computer Science, 1974, pp. 87-93, (2000); Zhao J., Wroe C., Goble C., Stevens R., Quan D., Greenwood M., Using Semantic Web Technologies for Representing e-Science Provenance, 3rd International Semantic Web Conference ISWC2004: 2004; Hiroshima, Japan: Springer, (2004); Zhang G.Q., Siegler T., Saxman P., Sandberg N., Mueller R., Johnson N., Hunscher D., Arabandi S., VISAGE: A Query Interface for Clinical Research, AMIA Clinical Research Informatics Summit. San Francisco, pp. 76-80, (2010); Taylor C.F., Et al., Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project, Nat Biotechnol, 26, pp. 889-896, (2008); Orchard S., Et al., The minimum information required for reporting a molecular interaction experiment (MIMIx), Nature Biotechnology, 25, pp. 894-898, (2007); Taylor C.F., Et al., The minimum information about a proteomics experiment (MIAPE), Nat Biotechnol, 25, pp. 887-893, (2007); Lebo T., Sahoo S.S., McGuinness D., PROV-O: The PROV Ontology (Working Draft), W3C Provenance Working Group, (2012); Bodenreider O., Quality assurance in biomedical terminologies and ontologies, Technical report, (2010); Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M., Rubin G.M., Sherlock G., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat Genet, 25, 1, pp. 25-29, (2000); Natale D.A., Arighi C.N., Barker W.C., Blake J., Chang T.C., Hu Z., Liu H., Smith B., Wu C.H., Framework for a protein ontology, BMC Bioinformatics, 8, SUPPL. 9, (2007); Rauch A., Bellew M., Eng J., Fitzgibbon M., Holzman T., Hussey P., Igra M., Maclean B., Lin C.W., Detter A., Fang R., Faca V., Gafken P., Zhang H., Whitaker J., States D., Hanash S., Paulovich A., McIntosh M.W., Computational Proteomics Analysis System (CPAS): An Extensible, Open-Source Analytic System for Evaluating and Publishing Proteomic Data and High Throughput Biological Experiments, J Proteome Res, 5, pp. 112-121, (2006); The National Center for Biomedical Ontology; The Ontology for Biomedical Investigations; Malone J., Holloway E., Adamusiak T., Kapushesky M., Zheng J., Kolesnikov N., Zhukova A., Brazma A., Parkinson H., Modeling sample variables with an Experimental Factor Ontology, Bioinformatics, 26, 8, pp. 1112-1118, (2010)","S.S. Sahoo; Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; email: satya.sahoo@case.edu","","BioMed Central Ltd.","","","","","","17520509","","","23282161","English","BMC Syst. Biol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84878836216"
"Henderson F.; Vandebroek I.; Balick M.J.; Kennelly E.J.","Henderson, Flor (15759658400); Vandebroek, Ina (6507733866); Balick, Michael J. (7004083687); Kennelly, Edward J. (7004641877)","15759658400; 6507733866; 7004083687; 7004641877","Ethnobotanical research skills for students of underrepresented minorities in STEM disciplines","2012","Ethnobotany Research and Applications","10","","","389","402","13","4","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875468824&partnerID=40&md5=a6ac06443d50564d2b5c6e77b04532af","Natural Sciences Department, Hostos Community College, City University of New York, Bronx, NY 10451, 500 Grand Concourse, United States; Institute of Economic Botany, New York Botanical Garden, Bronx, NY 10458, 2900 Southern Boulevard, United States; Department of Biology, Lehman College, City University of New York, Bronx, NY 10468, 250 Bedford Park Blvd. West, United States","Henderson F., Natural Sciences Department, Hostos Community College, City University of New York, Bronx, NY 10451, 500 Grand Concourse, United States; Vandebroek I., Institute of Economic Botany, New York Botanical Garden, Bronx, NY 10458, 2900 Southern Boulevard, United States; Balick M.J., Institute of Economic Botany, New York Botanical Garden, Bronx, NY 10458, 2900 Southern Boulevard, United States; Kennelly E.J., Department of Biology, Lehman College, City University of New York, Bronx, NY 10468, 250 Bedford Park Blvd. West, United States","We developed a collaborative educational strategy to actively engage students from underrepresented minorities in Science, Technology, Engineering and Mathematics (STEM) in the study of ethnobotanical knowledge and practices of Dominicans in New York City and the Dominican Republic. Three Dominican students from Hostos Community College in New York City were taught basic botany in preparation for their training as research assistants in an ongoing Dominican ethnomedicine project at The New York Botanical Garden. The aim of this internship was to teach appropriate research skills while raising awareness and promoting cultural appreciation of Dominican and Latino health traditions. Students were selected based on their academic achievements and potential, their interest in learning more about Dominican culture and traditions, and their bilingual (English-Spanish) skills. At the end of the six month internship period the students were competent in basic botanical identification techniques, plant collection methodology, ethnobotanical interviewing, as well as research data management, and expressed increased awareness of the richness of Dominican medicinal plant knowledge. The inclusion of underrepresented minority students enrolled in community colleges in ethnobotanical research can contribute to safeguarding cultural traditions, especially in urban settings.","","","","","","","","","College Learning for the New Global Century, (2007); Balick M., Kronenberg F., Ososki A., Reiff M., Fugh-Berman A., O'Connor A., Roble M., Lohr P., Atha D., Medicinal plants used by Latino healers for women's health conditions in New York City, Economic Botany, 54, pp. 344-357, (2000); Bennett B., Ethnobotany education, opportunities, and needs in the U. S, Ethnobotany Research & Applications, 3, pp. 113-121, (2005); Benz B., Cevallos J., Santana F., Rosales J., Graf S., Losing knowledge about plant use in the Sierra de Manantlan Biosphere Reserve, Mexico, Economic Botany, 54, pp. 183-191, (2000); Brosi B., Balick M.J., Wolkow R., Lee R., Kostka M., Raynor W., Gallen R., Raynor A., Raynor P., Lee Ling D., Cultural erosion and biodiversity: Canoe-making knowledge in Pohnpei, Micronesia, Conservation Biology, 21, pp. 875-879, (2007); Expanding Underrepresented Minority Participation: America's science and technology talent at the crossroads, (2011); Drew C., Why science majors change their minds (It's Just So Darn Hard), (2011); Fishman S., Student writing in philosophy: A sketch of five techniques, New Directions for Teaching and Learning, 69, pp. 58-59, (1997); Gomez-Beloz A., Chavez N., The Botánicas as a culturally appropriate health care option for Latinos, (2001); Ethnic/racial Background Distribution of Students Graduating during the 2010-2011 Academic Year, (2011); General Education, (2012); Analyses & Reports., (2012); Human subjects IRB; Liogier H., Diccionario Botánico de nombres vulgares de La Española, (2000); McCarter J., Gavin M., Perceptions of the value of traditional ecological knowledge to formal school curricula: Opportunities and challenges from Malekula Island, Vanuatu, Journal of Ethnobiology and Ethnomedicine, 7, (2011); Nguyen L.T., Doherty K., Wieting J., Market survey research: A model for ethnobotanical education, Ethnobotany Research & Applications, 6, pp. 087-092, (2008); Oakes J., Opportunities, achievement and choice: Women and minority students in science and mathematics, Review of Research in Education, 16, pp. 153-222, (1990); O'Brien C., Do they really ""know nothing""? An inquiry into ethnobotanical knowledge of students in Arizona, USA, Ethnobotany Research & Applications, 8, pp. 035-047, (2010); Ososki A., Lohr P., Reiff M., Balick M.J., Kronenberg F., Fugh-Berman A., O'Connor B., Ethnobotanical literature survey of medicinal plants in the Dominican Republic used for women's health conditions, Journal of Ethnopharmacology, 79, pp. 285-298, (2002); Ramirez C., Ethnobotany and the loss of traditional knowledge in the 21st century, Ethnobotany Research & Applications, 5, pp. 245-247, (2007); Reyes-Garcia V., Valdes V., Byron E., Apaza L., Leonard W., Perez E., Wilkie D., Market economy and the loss of folk knowledge of plant uses: Estimates from the Tsimane' of the Bolivian Amazon, Current Anthropology, 46, pp. 651-656, (2005); Rochin R., Mello S., Latinos in science: Trends and opportunities, Journal of Hispanic Higher Education, 6, pp. 305-355, (2007); Srithi K., Balslev H., Wangpakapattanawong P., Srisanga P., Trisonthi C., Medicinal plant knowledge and its erosion among the Mien (Yao) in northern Thailand, Journal of Ethnophamacology, 123, pp. 335-342, (2009); Vandebroek I., Balick M.J., Globalization and loss of plant knowledge: Challenging the paradigm, PLoS ONE, 7, 5, (2012); Vandebroek I., Balick M.J., Yukes J., Duran L., Kronenberg F., Wade C., Ososki A., Cushman L., Lantigua R., Mejia M., Robineau L., Use of medicinal plants by Dominican immigrants in New York City for the treatment of common health problems. A comparative analysis with literature data from the Dominican Republic, Traveling Cultures and Plants. The ethnobiology and ethnopharmacy of human migrations, pp. 39-63, (2007); Vandebroek I., Balick M.J., Ososki A., Kronenberg F., Yukes J., Wade C., Jimenez F., Peguero B., Castillo D., The importance of botellas and other plant mixtures in Dominican traditional medicine, Journal of Ethnopharmacology, 128, pp. 20-41, (2010); Viladrich A., Botánicas in America's backyard: Uncovering the world of Latino healers' herb-healing practices in New York City, Human Organization, 65, pp. 407-419, (2006); Wagner G., Botanical Knowledge of a group of college students in South Carolina, Ethnobotany Research & Applications, 6, pp. 443-458, (2008)","","","Ilia State University, Institute of Botany, Department of Ethnobotany","","","","","","15473465","","","","English","Ethnobotany Res. Appl.","Article","Final","","Scopus","2-s2.0-84875468824"
"Bahls D.; Tochtermann K.","Bahls, Daniel (23007572200); Tochtermann, Klaus (16053767400)","23007572200; 16053767400","Addressing the long tail in empirical research data management","2012","ACM International Conference Proceeding Series","","","19","","","","1","10.1145/2362456.2362481","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867453352&doi=10.1145%2f2362456.2362481&partnerID=40&md5=3a676a03094e56728745d02ee6744320","Leibniz Information Centre for Economics (ZBW), 20354 Hamburg, Neuer Jungfernstieg 21, Germany; Leibniz Information Centre for Economics (ZBW), 24105 Kiel, Düsternbrooker Weg 120, Germany","Bahls D., Leibniz Information Centre for Economics (ZBW), 20354 Hamburg, Neuer Jungfernstieg 21, Germany; Tochtermann K., Leibniz Information Centre for Economics (ZBW), 24105 Kiel, Düsternbrooker Weg 120, Germany","At present, efforts are being made to pick up research data as bibliographic artifacts for re-use, transparency and citation. When approaching research data management solutions, it is imperative to consider carefully how filed data can be retrieved and accessed again on the user side. In the field of economics, a large amount of research is based on empirical data, which is often combined from several sources such as data centers, affiliated institutes or self-conducted surveys. Respecting this practice, we motivate and elaborate on techniques for fine-grained referencing of data fragments as to avoid multiple copies of same data archived over and over again, which may result in questionable transparency and difficult curation tasks. In addition, machines should have a deeper understanding of the given data, so that high-quality services can be installed. The paper first discusses the challenges of data management for the management of research data as used in empirical research. We conclude a comparison of referencing and copying strategies and reflect on their implications respectively. As a result from this argumentation, we elaborate on a data representation model, which we further examine in regard to considerable extensions. A Generating Model is subsequently introduced to enable citation, transparency and re-use. Eventually, we close with the demonstration of an explorative prototype for data access and investigate a distance metric for assisting in finding similar data sets and evaluating existing compositions. © 2012 ACM.","Linked Data; Research Data Management; Semantic Digital Data Library; Statistics","Knowledge management; Semantics; Statistics; Transparency; Curation; Data access; Data centers; Data fragments; Data representation models; Data sets; Digital datas; Distance metrics; Empirical data; Empirical research; High quality; Linked datum; Long tail; Research data; Research","","","","","","","Assem M.V., Rijgersberg H., Wigham M., Top J., Converting and annotating quantitative data tables, Proceedings of the 9th International Semantic Web Conferene ISWC 2010, pp. 1-16, (2010); Bizer C., Heath T., Berners-Lee T., Linked data - The story so far, International Journal on Semantic Web and Information Systems, 5, 3, pp. 1-22, (2009); Cyganiak R., Field S., Gregory A., Halb W., Tennison J., Semantic statistics: Bringing together sdmx and scovo, LDOW, Volume 628 of CEUR Workshop Proceedings, (2010); Dallmeier-Tiessen S., Positionspapier Forschungsdaten, (2009); Wissenschaftliche Literatur- Und Informationsversorgungssysteme. Schwerpunkte der FÃurderung Bis 2015; Feijen M., What Researchers Want - A Literature Study of Researchers' Requirements with Respect to Storage and Access to Research Data, (2011); Gonzalez H., Halevy A.Y., Jensen C.S., Langen A., Madhavan J., Shapley R., Shen W., Google fusion tables: Data management, integration and collaboration in the cloud, SoCC, pp. 175-180, (2010); Gottron T., Hachenberg C., Harth A., Zapilko B., Towards a semantic data library for the social sciences, SDA'11: Proceedings of the International Workshop on Semantic DigitalArchives, (2011); Gray J., Jim Gray on EScience: A Transformed Scientific Method, (2007); Hausenblas M., Halb W., Raimond Y., Feigenbaum L., Ayers D., Scovo: Using statistics on the web of data, ESWC, Volume 5554 of Lecture Notes in Computer Science, pp. 708-722, (2009); Hey T., Tansley S., Tolle K.M., Jim gray on escience: A transformed scientific method, The Fourth Paradigm. Microsoft Research, (2009); Hoeven J.V.D., Insight into digital preservation of research output in europe case studies report PARSE, Insight. PARSEInsight Deliverable D33 Case Studies Report, (2010); Lynn S., Embley D., Semantically Conceptualizing and Annotating Tables, pp. 345-359, (2008); Matthies K., Commodity prices remain high, Intereconomics, 42, 2, pp. 109-112, (2007); Montgomery M.R., Gragnolati M., Burke K.A., Paredes E., Measuring living standards with proxy variables, Demography, 37, 2, pp. 155-174, (2000); Oldakowski R., Bizer C., Berlin F.U., Produktion I., Or W., Berlin D., Semmf: A framework for calculating semantic similarity of objects represented as rdf graphs pid-58, World Wide Web Internet and Web Information Systems, pp. 2-4, (2005); Popp G., Konfigurationsmanagement Mit Subversion, Ant und Maven: Ein Praxishandbuch für Software-architekten und Entwickler, (2006); Siegert O., Speicherung und Publikation von Forschungsdaten. der Beitrag der Deutschen Zentralbibliothek für Wirtschaftswissenschaften, (2010); Starr J., Gastl A., Iscitedby: A metadata scheme for datacite, D-lib Magazine, 17, 1-2, (2011)","D. Bahls; Leibniz Information Centre for Economics (ZBW), 20354 Hamburg, Neuer Jungfernstieg 21, Germany; email: d.bahls@zbw.eu","","","","12th International Conference on Knowledge Management and Knowledge Technologies, i-KNOW 2012","5 September 2012 through 7 September 2012","Graz","93153","","978-145031242-4","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84867453352"
"Curcin V.; Soljak M.; Majeed A.","Curcin, Vasa (8510022100); Soljak, Michael (57202841728); Majeed, Azeem (7102027801)","8510022100; 57202841728; 7102027801","Managing and exploiting routinely collected NHS data for research","2012","Informatics in Primary Care","20","4","","225","231","6","7","10.14236/jhi.v20i4.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882997506&doi=10.14236%2fjhi.v20i4.1&partnerID=40&md5=53ef3a54d0b30bf1603b84da437762a9","Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom; Department of Primary Care and Public Health, Imperial College London, United Kingdom","Curcin V., Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom; Soljak M., Department of Primary Care and Public Health, Imperial College London, United Kingdom; Majeed A., Department of Primary Care and Public Health, Imperial College London, United Kingdom","Introduction Health research using routinely collected National Health Service (NHS) data derived from electronic health records (EHRs) and health service information systems has been growing in both importance and quantity. Wide population coverage and detailed patient-level information allow this data to be applied to a variety of research questions. However, the sensitivity, complexity and scale of such data also hamper researchers from fully exploiting this potential. Objective Here, we establish the current challenges preventing researchers from making optimal use of the data sets at their disposal, on both the legislative and practical levels, and give recommendations as to how these challenges can be overcome. Method A number of projects has recently been launched in the UK to address poor research data management practices. Rapid Organisation of Healthcare Research Data (ROHRD) at Imperial College, London produced a useful prototype that provides local researchers with a one-stop index of available data sets together with relevant metadata. Findings Increased transparency of data sets' availability and their provenance leads to better utilisation and facilitates compliance with regulatory requirements. Discussion Research data resulting from NHS data is often not utilised fully, or is handled in a haphazard manner that prevents full auditability of the research. Furthermore, lack of informatics and data management skills in research teams act as a barrier to implementing more advanced practices, such as provenance capture and detailed, regularly updated, data management strategies. Only by a concerted effort at the levels of research organisations, funding bodies and publishers, can we achieve full transparency and reproducibility of the research. © 2012 PHCSG, British Computer Society.","Data governance; Electronic health records; Open data; Provenance","Confidentiality; Data Collection; Electronic Health Records; Great Britain; Health Services Research; Humans; State Medicine; article; confidentiality; electronic medical record; health services research; human; information processing; legal aspect; methodology; national health service; organization and management; standard; United Kingdom","","","","","","","The Royal Society Science Policy Centre Report 02/12, (2012); Kush R.D., EHRs for clinical research, AMIA Standards Winter, 2, 2, (2011); Groves T., Godlee F., Open science and reproducible research, BMJ, 344, (2012); HES Protocol; Data Linkage Applications; Weng C., Appelbaum P., Hripcsak G., Et al., Using EHRs to integrate research with patient care: Promises and challenges, Journal of the American Medical Informatics Association, 19, pp. 684-687, (2012); Clark S., Weale A., Access to Person-Level Data in Health Care: Understanding Information Governance, (2011); Open Data in Science; Opening Up Government, (2012); Open Data Measures in the Autumn Statement, (2011); Prescribing by GP Practice, 09/2011 Ed; The Government Plan for A Secure Data Service: Strengthening the International Competitiveness of UK Life Sciences Research, (2011); Collecting patient data will help UK become world leader in research, says Cameron, BMJ, 345, (2012); General Practice Extraction Service; Rapid Organisation of Healthcare Research Data; SNOMED Clinical Terms. Copenghagen: International Health Terminology Standards Development Organisation, (2012); BRISSkit; Rohde H., Qin J., Cui Y., Et al., Open-source genomic analysis of shiga-toxin-producing E coli O104:H4, New England Journal of Medicine, 365, pp. 718-724, (2011); CDISC Analysis Data Model; TRANSFoRm: Translational Research and Patient Safety in Europe; EHR4CR: Electronic Health Records for Clinical Research; Moreau L., Freire J., Futrelle J., McGrath R., Myers J., Paulson P., The Open Provenance Model (Specification 1), (2007); W3C PROV Model Primer; Overview of Funders' Data Policies; Simmhan Y.L., Plale P., Gannon G., A framework for collecting provenance in data-centric scientific workflows, Proceedings of the 6th International Conference on Web Services, pp. 427-436, (2006); Schlauch T., Schreiber A., DataFinder - A scientific data management solution, Proceedings of PV, (2007)","V. Curcin; Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom; email: vasa.curcin@imperial.ac.uk","","British Computer Society","","","","","","14760320","","IPCNB","23890333","English","Informatics Prim. Care","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84882997506"
"Curdt C.; Hoffmeister D.; Waldhoff G.; Jekel C.; Bareth G.","Curdt, C. (36681871300); Hoffmeister, D. (15764969300); Waldhoff, G. (33768158800); Jekel, C. (49663510600); Bareth, G. (6505760587)","36681871300; 15764969300; 33768158800; 49663510600; 6505760587","DEVELOPMENT of A METADATA MANAGEMENT SYSTEM for AN INTERDISCIPLINARY RESEARCH PROJECT","2012","ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","1","","","7","12","5","3","10.5194/isprsannals-I-4-7-2012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048909649&doi=10.5194%2fisprsannals-I-4-7-2012&partnerID=40&md5=4d886350cd728583c46d5612cef58d11","Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany","Curdt C., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Hoffmeister D., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Waldhoff G., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Jekel C., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; Bareth G., Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany","In every interdisciplinary, long-term research project it is essential to manage and archive all heterogeneous research data, produced by the project participants during the project funding. This has to include sustainable storage, description with metadata, easy and secure provision, back up, and visualisation of all data. To ensure the accurate description of all project data with corresponding metadata, the design and implementation of a metadata management system is a significant duty. Thus, the sustainable use and search of all research results during and after the end of the project is particularly dependent on the implementation of a metadata management system. Therefore, this paper will describe the practical experiences gained during the development of a scientific research data management system (called the TR32DB) including the corresponding metadata management system for the multidisciplinary research project Transregional Collaborative Research Centre 32 (CRC/TR32) 'Patterns in Soil-Vegetation-Atmosphere Systems'. The entire system was developed according to the requirements of the funding agency, the user and project requirements, as well as according to recent standards and principles. The TR32DB is basically a combination of data storage, database, and web-interface. The metadata management system was designed, realized, and implemented to describe and access all project data via accurate metadata. Since the quantity and sort of descriptive metadata depends on the kind of data, a user-friendly multi-level approach was chosen to cover these requirements. Thus, the self-developed CRC/TR32 metadata framework is designed. It is a combination of general, CRC/TR32 specific, as well as data type specific properties.","Atmosphere; Database; Internet/Web; Management; Metadata; Research; Soil; Spatial Infrastructures; Vegetation","","","","","","","","Bolten A., Waldhoff G., Error Estimation of ASTER GDEM for regional applications-Comparison to ASTER DEM and ALS elevation models, Proceedings of the Digital Earth Summit, (2010); Borgman C.L., Research Data: Who will share what, with whom, when, and why, China-North America Library Conference, (2010); Brase J., Using digital library techniques-registration of scientific primary data, Lecture Notes in Computer Science, pp. 488-497, (2004); Brase J., Farquhar A., Access to research data, D-Lib Magazine, 17, (2011); Buttner S., Hobohm H.-C., Muller L., Research data management, Handbuch Forschungsdatenmanagement, pp. 13-24, (2011); CRC/TR32 Funding Proposal, 2nd Funding Phase, (2011); Curdt C., Hoffmeister D., Jekel C., Brocks S., Waldhoff G., Bareth G., TR32DB-Management and visualization of heterogeneous scientific data, Proceedings of the 19th International Conference on Geoinformatics, (2011); Curdt C., Hoffmeister D., Jekel C., Udelhofen K., Waldhoff G., Bareth G., Implementation of a centralized data management system for the CRC Transregio 32 'Patterns in soil-vegetation-atmosphere-systems, Proceedings of the Data Management Workshop, pp. 27-33, (2010); Damm T., Gotze H.-J., Modern geodata management: Application of interdisciplinary interpretation and visualization in central america, Intern. Journal of Geophysics 2009, (2009); Proposals for Safeguarding Good Scientific Practice. DFG, Bonn, (1998); Merkblatt-service-projekte zu informations-management und informationsinfrastruktur in sonderfor-schungsbereichen INF, DFG, Bonn, (2009); Recommendations for secure storage and availability of digital primary research data, DFG, Bonn, (2009); Downs R.R., Chen R.S., Self-assessment of a long-term archive for interdisciplinary scientific data as a trust-worthy digital repository, Journal of Digital Information, (2010); Gottlicher D., Dobbermann M., Nauss T., Bendix J., Central data services in multidisciplinary environmental research projects-the data-management of the DFG research unit, Proceedings of the Data Management Workshop. Kölner Geographische Arbeiten, 816, pp. 59-64, (2010); Heimann D., Nieschulze J., Konig-Ries B., A flexible statistics web processing service-Added value for information systems for experiment data, JIB, 7, 1, (2010); Hoffmeister D., Bolten A., Curdt C., Waldhoff G., Bareth G., High-resolution Crop Surface Models (CSM) and Crop Volume Models (CVM) on field level by terrestrial laser scanning, Proceedings SPIE, 7840, (2009); Jensen U., Katsanidou A., Zenk-Moltgen W., Metadaten und Standards, Handbuch Forschungsdatenmanagement, pp. 83-100, (2011); Korres W., Koyama C.N., Fiener P., Schneider K., Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions, Hydrol. Earth Syst. Sci., 14, pp. 751-764, (2009); Kralisch S., Zander F., Environmental Data Management with the River Basin Information System (RBIS), Proceedings of the Data Management Workshop, pp. 83-91, (2010); Michener W.K., Meta-information concepts for ecological data management, Ecological Informatics, 1, pp. 3-7, (2006); Muckschel C., Nieschulze J., Editorial zum schwerpunktthema dieser ausgabe: Datenmanagement in interdisziplinären umwelt-forschungsprojekten, Zeitschrift für Agrarinformatik, 4, (2004); Muckschel C., Nieschulze J., Weist C., Sloboda B., Kohler W., Herausforderungen, probleme und lösungs-ansätze im datenmanagement von sonderforschungsbereichen, EZAI, (2007); Nelson B., Empty archives, Nature, 461, pp. 160-163, (2009); Piwowar H.A., Who shares who doesn't factors associated with openly archiving raw research data, PLoS ONE, 6, (2011); Rajabifard A., Kalantari M., Binns A., SDI and metadata entry and updating tools, SDI Convergence, pp. 121-136, (2009); Shumilov S., Rogmann A., Laubach J., GLOWA Volta GeoPortal: An interactive geodata repository and communi-cation system, Digital Earth Summit on Geoinformatics: Tools for Global Change Research, (2008); Willmes C., Brocks S., Hoffmeister D., Hutt C., Kurner D., Volland K., Bareth G., Facilitating integrated spatio-temporal visualization and analysis of heterogeneous archaeological and palaeoenvironmental research data, Proceedings of the XXII Congress of the ISPRS, (2012); Waldhoff G., Land Use Classification of 2009 for the Rur Catchment, (2010); Waldhoff G., Bareth G., GIS-and RS-based land use and land cover analysis-case study Rur-Watershed, Germany, Proceedings SPIE, 7146, (2009)","C. Curdt; Institute of Geography, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany; email: c.curdt@uni-koeln.de","Shortis M.; Madden M.","Copernicus GmbH","ESRI; Hexagon","22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012","25 August 2012 through 1 September 2012","Melbourne","129545","21949042","","","","English","ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.","Conference paper","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85048909649"
"Bahls D.; Tochtermann K.","Bahls, Daniel (23007572200); Tochtermann, Klaus (16053767400)","23007572200; 16053767400","Semantic retrieval interface for statistical research data","2013","CEUR Workshop Proceedings","1091","","","93","103","10","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923683267&partnerID=40&md5=5eba28bb71eb4c0c0c3b1da2d36d1d94","Leibniz Information Centre for Economics (ZBW), Kiel, Germany","Bahls D., Leibniz Information Centre for Economics (ZBW), Kiel, Germany; Tochtermann K., Leibniz Information Centre for Economics (ZBW), Kiel, Germany","Statistical research data is the foundation for empirical studies. Researchers in economics or social sciences often obtain such data from external sources through specially designed retrieval interfaces from statistical offices, commercial data providers as well as from data agencies and other archives. With the advancements in data cataloguing and acquisition of long tail research data sets from individual scientists and institutes, the opportunity is there to install central services for a more holistic data search. In view of a rapid increase in amount of data available and by association an emerging retrieval problem, retrieval interfaces must make effective use of provided metadata in order to help Find relevant data sets efficiently. This paper presents a multi-step retrieval interface that aims to support the researchers' natural approach to data search and composition. Starting with an idea of the concepts that are to be compared, users kick off their search with thesauri terms and successively specify requirements according to their priorities until suitable data can be selected easily from a manageable number of matching data sets. The prototype presented in this paper also provides means for convenient data harmonization, which is an essential aspect especially when combining statistical data from different sources. © 2013 for the individual papers by the papers' authors.","Data Retrieval; Linked Data; Research Data Management; Semantic Digital Data Library; Statistics","Information management; Semantics; Statistics; Data retrieval; Linked datum; Natural approaches; Research data managements; Semantic digital data libraries; Semantic retrieval; Statistical Offices; Statistical research; Digital libraries","","","","","","","Gray J., Jim Gray on EScience: A Transformed Scientific Method, (2007); Treloar A., Harboe-Ree C., Data management and the curation continuum: How the Monash experience is informing repository relationships, Proceedings of VALA 2008, (2007); Rumpel S., Data Librarianship : Anforderungen An Bibliothekare im Forschungsdatenmanagement, (2010); Vlaeminck S., Siegert O., Welche rolle spielen forschungsdaten eigentlich für fachzeitschriften? eine analyse mit fokus auf die wirtschaftswissenschaften, Technical Report German Council for Social and Economic Data (RatSWD), (2012); Wood J., Andersson T., Bachem A., Best C., Genova F., Lopez D.R., Los W., Marinucci M., Romary L., Van De Sompel H., Vigen J., Wittenburg P., Giaretta D., Riding the Wave: How Europe Can Gain from the Rising Tide of Scientific Data European Union, (2010); Feijen M., What Researchers Want-A Literature Study of Researchers' Requirements with Respect to Storage and Access to Research Data, (2011); Kampgen B., Harth A., Transforming statistical linked data for use in olap systems, Proceedings of the 7th International Conference on Semantic Systems, ACM, pp. 33-40, (2011); Boland K., Ritze D., Eckert K., Mathiak B., Identifying references to datasets in publications, Theory and Practice of Digital Libraries. Volume 7489 of Lecture Notes in Computer Science, pp. 150-161, (2012); Bahls D., Tochtermann K., Addressing the long tail in empirical research data management, Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies, 19, pp. 1-19+8, (2012); Cyganiak R., Field S., Gregory A., Halb W., Tennison J., Semantic statistics: Bringing together sdmx and scovo, LDOW. Volume 628 of CEUR Workshop Proceedings, (2010)","","Risse T.; Ross S.; Predoiu L.; Mitschick A.; Nurnberger A.","CEUR-WS","","3rd International Workshop on Semantic Digital Archives, SDA 2013 - Co-located with 17th International Conference on Theory and Practice of Digital Libraries, TPDL 2013","26 September 2013","Valetta","110960","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84923683267"
"Peters C.; Dryden A.R.","Peters, Christie (55014387000); Dryden, Anita Riley (55014183000)","55014387000; 55014183000","Assessing the academic library's role in campus-wide research data management: A first step at the University of Houston","2011","Science and Technology Libraries","30","4","","387","403","16","48","10.1080/0194262X.2011.626340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857310291&doi=10.1080%2f0194262X.2011.626340&partnerID=40&md5=f93cfd9ee0071bae58d71d9dfbc61498","University of Houston Libraries, 114 University Libraries, Houston, TX 77204, United States","Peters C., University of Houston Libraries, 114 University Libraries, Houston, TX 77204, United States; Dryden A.R., University of Houston Libraries, 114 University Libraries, Houston, TX 77204, United States","In an effort to support the University of Houston's goal of becoming a Carnegie-designated Tier One research university, several science librarians within the Department of Liaison Services have undertaken a study to assess current data management practices on campus. The goal of this study was to determine if data management needs are being met on campus and how the library might help meet those needs. We found that rather than physical storage capacity, researchers need assistance with funding agencies' data management requirements, the grant proposal process, finding campus data-related services, publication support, and targeted research assistance attendant to data management. © 2011 Copyright Taylor and Francis Group, LLC.","data; data management; data-support services; dmp; interviews; NSF; University of Houston","Libraries; Research; data; data-support services; dmp; interviews; NSF; University of Houston; Information management","","","","","Texas Learning and Computation Center; National Science Foundation, NSF; National Institutes of Health, NIH; Texas Center for Superconductivity, University of Houston, TcSUH","Funding text 1: None of the researchers interviewed for this pilot study are working on the type of projects that Borgman and others (2007, 17) describe as “Big Science,” a term that indicates large-scale, networked projects. The projects taking place on campus that fit this discription do not appear to be funded by NSF or NIH, but are supported by units such as the Texas Learning and Computation Center (TLC2) and the Texas Center for Superconductivity (TCSuh), both of which are UH research centers. It turns out that UH research centers currently support a number of large-scale projects. A consequence of this is that the need for infrastructure support did not present itself over the course of this study. A more comprehensive study will help us determine if such a need does in fact exist on the UH campus. In addition to looking at research on campus irrespective of funding agency, the expanded study will include interviews with graduate students, post-doctorates, and lab technicians, since most of the day-to-day management of data, including both collection and analysis, falls to the these individuals.; Funding text 2: A project team consisting of two science liaisons and the library’s digital and web projects fellow worked with the Office of Contracts and Grants, located within the Division of Research, to obtain a list of all of the NSF and NIH grant-funded projects for fiscal year 2010. Projects were selected based on the department affiliation of the Principal Investigator (PI), the dollar amount of the grant, and whether the PI was the sole recipient of the grant or part of an interdisciplinary project team. Our goals were to interview PIs of significant grants, to assess individuals in as many science and engineering departments as possible, and to obtain information on data management practices from both individual and group-based projects. The results of this pilot study will inform the nature and scope of future assessments.","ARL workshop on new collaborative relationships: The role of academic libraries in the digital data universe, To Stand the Test of Time: Long-Term Stewardship of Digital Data Sets in Science and Engineering, (2006); Agenda for Developing E-Science in Research Libraries, (2007); Borgman C.L., Wallis J.C., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, 1-2, pp. 17-30, (2007); Hey T., Hey J., E-Science and its implications for the library community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Mullins J.L., Enabling international access to scientific data sets: Creation of the Distributed Data Curation Center (D2C2), Libraries Research Publications, (2007); Revolutionizing Science and Engineering through Cyberinfrastructure, (2003); Cyberinfrastructure Vision for 21st Century Discovery, (2007); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Steinhart G., Et al., Digital Research Data Curation: Overview of Issues, Current Activities, and Opportunities for the Cornell University Library, (2008); Plan to Achieve Recognition as a National Research University, (2010)","C. Peters; University of Houston Libraries, 114 University Libraries, Houston, TX 77204, United States; email: cpeters@uh.edu","","","","","","","","15411109","","STELD","","English","Sci Technol Libr","Article","Final","","Scopus","2-s2.0-84857310291"
"Westra B.; Ramirez M.; Parham S.W.; Scaramozzino J.M.","Westra, Brian (6701647865); Ramirez, Marisa (36107280600); Parham, Susan Wells (36700267200); Scaramozzino, Jeanine Marie (18233774800)","6701647865; 36107280600; 36700267200; 18233774800","Science and technology resources on the internet: Selected internet resources on digital research data curation","2010","Issues in Science and Technology Librarianship","63","","","","","","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650543746&partnerID=40&md5=f87e663fc5fc92666267d7a74b70ab05","Lorry I. Lokey Science Data Services Librarian, University of Oregon, Eugene, Oregon, United States; Digital Repository Librarian, California Polytechnic State University, San Luis Obispo, California, United States; Research Data Project Librarian, Georgia Tech Library and Information Center, Atlanta, Georgia, United States; College of Science and Mathematics Librarian, School of Education Librarian, California Polytechnic State University, San Luis Obispo, California, United States","Westra B., Lorry I. Lokey Science Data Services Librarian, University of Oregon, Eugene, Oregon, United States; Ramirez M., Digital Repository Librarian, California Polytechnic State University, San Luis Obispo, California, United States; Parham S.W., Research Data Project Librarian, Georgia Tech Library and Information Center, Atlanta, Georgia, United States; Scaramozzino J.M., College of Science and Mathematics Librarian, School of Education Librarian, California Polytechnic State University, San Luis Obispo, California, United States","[No abstract available]","","","","","","","","","Borgman C., Wallis J., Enyedy N., Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries, International Journal on Digital Libraries, 7, pp. 17-30, (2007); Choudhury S., Rethinking Scholarly Communication: Building Data Curation Infrastructure, (2009); Gold A., Data Curation and Libraries: Short-Term Developments, Long-Term Prospects, (2010); Rusbridge C., Burnhill P., Ross S., Buneman P., Giaretta D., Lyon L., Atkinson M., The digital curation centre: A vision for digital curation, Proceedings From Local to Global: Data Interoperability--Challenges and Technologies, pp. 1-11, (2005); Wright M., Sumner T., Moore R., Koch T., Connecting digital libraries to eScience: The future of scientific scholarship, International Journal of Digital Libraries, 7, pp. 1-4, (2007)","B. Westra; Lorry I. Lokey Science Data Services Librarian, University of Oregon, Eugene, Oregon, United States; email: bwestra@uoregon.edu","","","","","","","","10921206","","","","English","Issues Sci. Technol. Librariansh.","Article","Final","","Scopus","2-s2.0-78650543746"
"Berlinicke C.A.; Ackermann C.F.; Chen S.H.; Schulze C.; Shafranovich Y.; Myneni S.; Patel V.L.; Wang J.; Zack D.J.; Lindvall M.; Bova G.S.","Berlinicke, Cynthia A. (26535546900); Ackermann, Christopher F. (9434908300); Chen, Steve H. (55443555600); Schulze, Christoph (57207719196); Shafranovich, Yakov (55442987800); Myneni, Sahiti (35097608700); Patel, Vimla L. (35600762400); Wang, Jian (55444775900); Zack, Donald J. (35375821300); Lindvall, Mikael (7005523126); Bova, G. Steven (7005052044)","26535546900; 9434908300; 55443555600; 57207719196; 55442987800; 35097608700; 35600762400; 55444775900; 35375821300; 7005523126; 7005052044","High-content screening data management for drug discovery in a small- to medium- size laboratory: Results of a collaborative pilot study focused on user expectations as indicators of effectiveness","2012","Journal of Laboratory Automation","17","4","","255","265","10","1","10.1177/2211068211431207","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868231968&doi=10.1177%2f2211068211431207&partnerID=40&md5=dfd8d61b041fd754c7b3f5aeb61f7404","Wilmer Eye Institute, Baltimore, MD, United States; Fraunhofer Center for Experimental Software Engineering, College Park, MD, United States; BioFortis,Inc., Columbia, MD, United States; Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States; Departments of Molecular Biology and Genetics and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Institut de la vision, UPMC, Paris, France; Departments of Pathology and Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD, United States","Berlinicke C.A., Wilmer Eye Institute, Baltimore, MD, United States; Ackermann C.F., Fraunhofer Center for Experimental Software Engineering, College Park, MD, United States; Chen S.H., BioFortis,Inc., Columbia, MD, United States; Schulze C., Fraunhofer Center for Experimental Software Engineering, College Park, MD, United States; Shafranovich Y., BioFortis,Inc., Columbia, MD, United States; Myneni S., Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States; Patel V.L., Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States; Wang J., BioFortis,Inc., Columbia, MD, United States; Zack D.J., Wilmer Eye Institute, Baltimore, MD, United States, Departments of Molecular Biology and Genetics and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States, Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States, Institut de la vision, UPMC, Paris, France; Lindvall M., Fraunhofer Center for Experimental Software Engineering, College Park, MD, United States; Bova G.S., Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States, Departments of Pathology and Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD, United States","High-content screening (HCS) technology provides a powerful vantage point to approach biological problems; it allows analysis of cell parameters, including changes in cell or protein movement, shape, or texture. As part of a collaborative pilot research project to improve bioscience research data integration, we identified HCS data management as an area ripe for advancement. A primary goal was to develop an integrated data management and analysis system suitable for small- to medium-size HCS programs that would improve research productivity and increase work satisfaction. A system was developed that uses Labmatrix, a Webbased research data management platform, to integrate and query data derived from a Cellomics STORE database. Focusing on user expectations, several barriers to HCS productivity were identified and reduced or eliminated. The impact of the project on HCS research productivity was tested through a series of 18 lab-requested integrated data queries, 7 of which were fully enabled, 7 partially enabled, and 4 enabled through data export to standalone data analysis tools. The results are limited to one laboratory, but this pilot suggests that through an ""implementation research"" approach, a network of small- to medium-size laboratories involved in HCS projects could achieve greater productivity and satisfaction in drug discovery research. © 2012 Society for Laboratory.","Bioscience laboratory data management research; Drug discovery; High-content screening; Implementation research; Vision research","Animals; Automatic Data Processing; Drug Discovery; Humans; Integrated Advanced Information Management Systems; Mass Screening; Pilot Projects; Data integration; Laboratories; Productivity; Query processing; Search engines; Textures; Drug discovery; High-content screening; Implementation researches; Management research; Vision research; article; data analysis; data analysis software; data base; data integration; data processing; drug research; expectation; high content screening; information processing; job satisfaction; laboratory; laboratory productivity; pilot study; productivity; screening; small to medium size laboratory; animal; drug development; human; information system; instrumentation; mass screening; methodology; organization and management; Information management","","","Labmatrix, Biofortis, United States","Biofortis, United States","Guerrieri Family Foundation; National Institutes of Health, NIH, (R41CA105217); National Eye Institute, NEI, (P30EY001765, R01EY019305, R21EY019737)","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The BRISP project was supported by NIH NCI STTR R41 CA105217. The described laboratory work was funded in part by NIH 5R21 EY019737, 5R01EY019305, 5P30EY001765, by generous support from the Guerrieri Family Foundation, and by a research grant from the Investigator Initiated Studies Program of Merck Sharp & Dohme Corp. The opinions expressed in this paper are those of the authors and do not necessarily represent those of Merck Sharp & Dohme Corp. This manuscript is dedicated in memory of Mrs. Patti Guerrieri.","Taylor D.L., A Personal Perspective on High-Content Screening (HCS): From the Beginning, J. Biomol. Screen., 15, pp. 720-725, (2010); Jackson D., Lenard M., Zelensky A., Shaikh M., Scharpf J.V., Shaginaw R., Nawade M., Agler M., Cloutier N.J., Fennell M., Et al., HCS Road: An Enterprise System for Integrated HCS Data Management and Analysis, J. Biomol. Screen., 15, pp. 882-891, (2010); Loo L.H., Lin H.J., Steininger III R.J., Wang Y., Wu L.F., Altschuler S.J., An Approach for Extensibly Profiling the Molecular States of Cellular Subpopulations, Nat. Methods, 6, pp. 759-765, (2009); Perlman Z.E., Mitchison T.J., Mayer T.U., High-Content Screening and Profiling of Drug Activity in an Automated Centrosome-Duplication Assay, Chembiochem, 6, pp. 145-151, (2005); Trask O.J., Nickischer D., Burton A., Williams R.G., Kandasamy R.A., Johnston P.A., High-Throughput Automated Confocal Microscopy Imaging Screen of a Kinase-Focused Library to Identify p38 Mitogen-Activated Protein Kinase Inhibitors Using the GE InCell 3000 Analyzer, Methods Mol. Biol., 565, pp. 159-186, (2009); Swedlow J.R., Goldberg I.G., Eliceiri K.W., Bioimage Informatics for Experimental Biology, Annu. Rev. Biophys., 38, pp. 327-346, (2009); Kozak K., Bakos G., Hoff A., Bennett E., Dunican D., Davies A., Kelleher D., Long A., Csucs G., Workflow-Based Software Environment for Large-Scale Biological Experiments, J. Biomol. Screen., 15, pp. 892-899, (2010); Kerrison J.B., Zack D.J., Neurite Outgrowth in Retinal Ganglion Cell Culture, Methods Mol. Biol., 356, pp. 427-434, (2007); Myneni S., Patel V.L., Assessment of Collaboration and Interoperability in an Information Management System to Support Bioscience Research, AMIA Annu. Symp. Proc., 2009, pp. 463-467, (2009); Myneni S., Patel V.L., Organization of Biomedical Data for Collaborative Scientific Research: A Research Information Management System, Int. J. Inf. Manage., 30, pp. 256-264, (2010); Zhang J.H., Chung T.D., Oldenburg K.R., A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays, J. Biomol. Screen., 4, pp. 67-73, (1999); Anderson N.R., Ash J.S., Tarczy-Hornoch P., A Qualitative Study of the Implementation of a Bioinformatics Tool in a Biological Research Laboratory, Int. J. Med. Inform., 76, pp. 821-828, (2007); Anderson N.R., Lee E.S., Brockenbrough J.S., Minie M.E., Fuller S., Brinkley J., Tarczy-Hornoch P., Issues in Biomedical Research Data Management and Analysis: Needs and Barriers, J. Am. Med. Inform. Assoc., 14, pp. 478-488, (2007); Ash J.S., Anderson N.R., Tarczy-Hornoch P., People and Organizational Issues in Research Systems Implementation, J. Am. Med. Inform. Assoc., 15, pp. 283-289, (2008); Topaloglou T., Davidson S.B., Jagadish H.V., Markowitz V.M., Steeg E.W., Tyers M., Biological Data Management: Research, Practice and Opportunities, Proceedings of the VLDB Conference, (2004); Burgun A., Bodenreider O., Accessing and Integrating Data and Knowledge for Biomedical Research, Yearb. Med. Inform., pp. 91-101, (2008); Mirel B., Eichinger F., Nair V., Kretzler M., Integrating Automated Workflows, Human Intelligence and Collaboration, Summit Translat. Bioinforma., 2009, pp. 79-83, (2009); Cuatrecasas P., Drug Discovery in Jeopardy, J. Clin. Invest., 116, pp. 2837-2842, (2006); Wadman M., NIH Director Wins Bid for Translational Medicine Centre.","G. S. Bova; Institute of Biomedical Technology, Molecular Biology of Prostate cancer Group, University of Tampere, FI-33520 Tampere, Biokatu 6, Finland; email: gsb@telamon1.us","","SAGE Publications Inc.","","","","","","22110682","","","22357564","English","J. Lab. Autom.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-84868231968"
"Rempusheski V.F.","Rempusheski, Veronica F. (6604017352)","6604017352","Research data management: Piles into files-Locked and secured","1991","Applied Nursing Research","4","3","","147","149","2","2","10.1016/S0897-1897(05)80073-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0026211711&doi=10.1016%2fS0897-1897%2805%2980073-4&partnerID=40&md5=35415d43633c6fd03fa860930bd102b9","","","[No abstract available]","","Data Collection; Database Management Systems; Human; Nursing Research; article; data base; human; information processing; methodology; nursing; organization and management; utilization review","","","","","","","Committee on the Responsible Conduct of Research, The Responsible Conduct of Research in the Health Sciences: Report of a study by a committee on the Responsible Conduct of Research, Institute of Medicine, Division of Health Sciences Policy, (1989); Guidelines for the Conduct of Research at the National Institutes of Health, (1990); Faculty, Harvard University, Faculty of Medicine, Guidelines for Investigators in Scientific Research, (1988); Dana-Farber Cancer Institute, Dana-Farber Cancer Institute Policy for Recording and Preserving Scientific Data, (1987)","","","","","","","","","08971897","","","1897925","English","Appl. Nurs. Res.","Article","Final","","Scopus","2-s2.0-0026211711"
"Ball A.; Darlington M.; Howard T.; McMahon C.; Culley S.","Ball, Alexander (55796629584); Darlington, Mansur (7004413270); Howard, Thomas (23492546400); McMahon, Chris (55887655100); Culley, Steve (35587180500)","55796629584; 7004413270; 23492546400; 55887655100; 35587180500","Visualizing research data records for their better management","2012","Journal of Digital Information","13","1","","","","","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864651624&partnerID=40&md5=ed60d7a8ac6a7c6228f3ccf63aee35c2","UKOLN, University of Bath, United Kingdom; IdMRC, University of Bath, United Kingdom; Department of Management Engineering, Technical University of Denmark, Denmark","Ball A., UKOLN, University of Bath, United Kingdom; Darlington M., IdMRC, University of Bath, United Kingdom; Howard T., Department of Management Engineering, Technical University of Denmark, Denmark; McMahon C., IdMRC, University of Bath, United Kingdom; Culley S., IdMRC, University of Bath, United Kingdom","As academia in general, and research funders in particular, place ever greater importance on data as an output of research, so the value of good research data management practices becomes ever more apparent. In response to this, the Innovative Design and Manufacturing Research Centre (IdMRC) at the University of Bath, UK, with funding from the JISC, ran a project to draw up a data management planning regime. In carrying out this task, the ERIM (Engineering Research Information Management) Project devised a visual method of mapping out the data records produced in the course of research, along with the associations between them. This method, called Research Activity Information Development (RAID) Modelling, is based on the Unified Modelling Language (UML) for portability. It is offered to the wider research community as an intuitive way for researchers both to keep track of their own data and to communicate this understanding to others who may wish to validate the findings or re-use the data.","","","","","","","","","Ball A., Review of the State of the Art of Digital Curation of Research Data, (2010); Ball A., Patel M., McMahon C., Culley S., Green S., Clarkson J., A Grand Challenge: Immortal Information and Through-Life Knowledge Management (KIM), International Journal of Digital Curation, 1, pp. 53-59, (2006); Blessing L., Chakrabarti A., DRM: A Design Research Methodology, (2009); Reference model for an Open Archival Information System (OAIS), (2002); Issues and Recommendations Associated with Distributed Computation and Data Management Systems for the Space Sciences, (1986); Darlington M., ERIM Terminology, (2011); Darlington M., RAID Associative Tool Requirements Specification, (2011); Darlington M., Ball A., Howard T., Culley S., McMahon C., The Draft IdMRC Projects Data Management Plan, (2010); Darlington M., Ball A., Howard T., McMahon C., Culley S., Principles for Engineering Research Data Management., (2010); Darlington M.J., Culley S.J., Zhao S., Austin S.A., Tang L.C.M., Defining a framework for the evaluation of information, International Journal of Information Quality, 2, 2, pp. 115-132, (2008); Donnelly M., Jones S., Template for a Data Management Plan, (2010); Down K.A., Managing Research Data (JISCMRD), (2010); Edwards A.M., Bountra C., Kerr D.J., Willson T.M., Open Access Chemical and Clinical Probes to Support Drug Discovery, Nature Chemical Biology, 5, pp. 436-440, (2009); Earth Observing System: Data and Information System, Technical Memorandum 87777, Earth Observing System Data Panel, (1986); EPSRC Policy Framework on Research Data, (2011); Evrard F., Virbel J., Realisation d'un prototype de station de lecture active et utilisation en milieu professionnel, (1996); Groth P., Gibson A., Velterop J., The Anatomy of a Non-Publication, Information Services and Use, 30, 1-2, pp. 51-56, (2010); Higgins S., The DCC Curation Lifecycle Model, International Journal of Digital Curation, 3, 1, pp. 134-140, (2008); Howard T., Darlington M., Ball A., Culley S., McMahon C., Understanding and Characterizing Engineering Research Data for its Better Management, (2010); Jones S., A Report on the Range of Policies Required for and Related to Digital Curation, (2009); Mayer R.J., Menzel C.P., Painter M.K., de Witte P.S., Blinn T., Perakath B., Information Integration for Concurrent Engineering (IICE) IDEF3 Process Description Capture Method Report, (1995); Final NIH Statement on Sharing Research Data, (2003); Grant Proposal Guide, (2011); OMG Unified Modelling Language, (2009); Patel M., Requirements Report, (2010); Pienta A.M., Alter G.C., Lyle J.A., The Enduring Value of Social Science Research: The Use and Reuse of Primary Research Data, (2010); Piwowar H.A., Day R.S., Fridsma D.B., Sharing Detailed Research Data is Associated with Increased Citation Rate, 2, 3, (2007); Open To All?: Case Studies of Openness in Research, (2010); Stodden V., Enabling Reproducible Research: Open Licensing for Scientific Innovation, International Journal of Communications Law and Policy, 13, pp. 1-25, (2009); Hubble Space Telescope Publication Statistics, (2011); Wilson J.A.J., Fraser M.A., Martinez-Uribe L., Jeffreys P., Patrick M., Akram A., Mansoori T., Developing Infrastructure for Research Data Management at the University of Oxford, (2010)","A. Ball; UKOLN, University of Bath, United Kingdom; email: a.ball@ukoln.ac.uk","","University of Southampton","","","","","","13687506","","","","English","J. Digit. Inf.","Article","Final","","Scopus","2-s2.0-84864651624"
"Zborowski M.","Zborowski, Mary (35424846400)","35424846400","Data management activities of Canada's national science library - 2010 Update and prospective","2011","Data Science Journal","9","","","100","106","6","0","10.2481/dsj.009-026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79551590054&doi=10.2481%2fdsj.009-026&partnerID=40&md5=1f4151babba77413353b8d8f109889fb","Strategy and Development Branch (NRC-SDB), National Research Council Canada, Ottawa, ON K1A 0R6, Canada","Zborowski M., Strategy and Development Branch (NRC-SDB), National Research Council Canada, Ottawa, ON K1A 0R6, Canada","NRC-CISTI serves Canada as its National Science Library (as mandated by Canada's Parliament in 1924) and also provides direct support to researchers of the National Research Council of Canada (NRC). By reason of its mandate, vision, and strategic positioning, NRC-CISTI has been rapidly and effectively mobilizing Canadian stakeholders and resources to become a lead player on both the Canadian national and international scenes in matters relating to the organization and management of scientific research data. In a previous communication (CODATA International Conference, 2008), the orientation of NRC-CISTI towards this objective and its shortand medium-term plans and strategies were presented. Since then, significant milestones have been achieved. This paper presents NRC-CISTI's most recent activities in these areas, which are progressing well alongside a strategic organizational redesign process that is realigning NRC-CISTI's structure, mission, and mandate to better serve its clients. Throughout this transformational phase, activities relating to data management remain vibrant.","Canada; National activities; National Research Council Canada; NRC-CISTI; Research data management; Scientific and technical research data","Computer applications; Computer science; Canada; National activities; National Research Council; NRC-CISTI; Research data managements; Technical research; Information management","","","","","","","Zborowski M., CISTI'S activities in support of scientific data management in Canada 2008-2010, Data Science Journal, 8, pp. 27-33, (2009)","M. Zborowski; Strategy and Development Branch (NRC-SDB), National Research Council Canada, Ottawa, ON K1A 0R6, Canada; email: mary.zborowski@nrc-cnrc.gc.ca","","Ubiquity Press Ltd","","","","","","16831470","","","","English","Data Sci. J.","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-79551590054"
"Qin J.; Solinger C.","Qin, Jian (16023002400); Solinger, Carrie (37023608600)","16023002400; 37023608600","Institutional policies on science research data: A pilot analysis","2011","ACM International Conference Proceeding Series","","","","761","762","1","0","10.1145/1940761.1940896","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952599140&doi=10.1145%2f1940761.1940896&partnerID=40&md5=705d24294ed5fd953a6e7700fe103101","School of Information Studies, Syracuse University, United States","Qin J., School of Information Studies, Syracuse University, United States; Solinger C., School of Information Studies, Syracuse University, United States","Institutions are increasingly feeling the pressure to develop strategies and policies to address these issues in science data management. Policies for data management, archiving, sharing, publishing, and use have sprouted on institutional and research centers' websites. The purpose of our pilot analysis was to collect policy examples for content analysis so that we have a better understanding of what types of policies exist and what issues they address. The poster will present an analysis of institutional policies on science research data management, archiving, sharing and publishing, and use. Copyright © 2011 ACM.","Data publishing; Data sharing; Institutional repository; Policy; Scientific data management","Information management; Information services; Research; Data publishing; Data sharing; Institutional repositories; Policy; Scientific data management; Societies and institutions","","","","","","","Anderson W., Some challenges and issues in managing, and preserving access to, long-lived collections for digital scientific and technical data, Data Science Journal, 3, pp. 191-202, (2004); Barton C., Smith R., Weaver R., Data practices, policy, and rewards in the information era demand a new paradigm, Data Science Journal, 9, (2010); Lambert S.C., E-infrastructure, science data and CRIS, Data Science Journal, 9, (2010); Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans, (2010); Sieber J.E., Ethics of sharing scientific and technological data: A heuristic for coping with complexity & uncertainty, Data Science Journal, 4, pp. 165-170, (2005)","J. Qin; School of Information Studies, Syracuse University, United States; email: jqin@syr.edu","","","","6th Annual Conference on 2011 iConference: Inspiration, Integrity, and Intrepidity, iConference 2011","8 February 2011 through 11 February 2011","Seattle, WA","84131","","978-145030121-3","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","","Scopus","2-s2.0-79952599140"
"Fearn P.A.; Regan K.; Sculli F.; Katz J.; Kattan M.W.","Fearn, Paul A. (6601991198); Regan, Kevin (7005006023); Sculli, Frank (6507147102); Katz, Jared (35588362600); Kattan, Michael W. (7102396973)","6601991198; 7005006023; 6507147102; 35588362600; 7102396973","A chronological database as backbone for clinical practice and research data management","2003","Proceedings of the IEEE Symposium on Computer-Based Medical Systems","","","","9","15","6","4","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0042622272&partnerID=40&md5=4e38cf25582df815214ab9e7e7897152","Mem. Sloan-Kettering Cancer Center, New York, NY, United States","Fearn P.A., Mem. Sloan-Kettering Cancer Center, New York, NY, United States; Regan K., Mem. Sloan-Kettering Cancer Center, New York, NY, United States; Sculli F., Mem. Sloan-Kettering Cancer Center, New York, NY, United States; Katz J., Mem. Sloan-Kettering Cancer Center, New York, NY, United States; Kattan M.W., Mem. Sloan-Kettering Cancer Center, New York, NY, United States","To be widely accepted by physicians, computerized support systems for medical decision-making must be seamlessly integrated into the clinical workflow. These systems must present accurate information in a useable format to the right person at the right time [1]. To build the large, clean, and reusable clinical databases needed for predictive modeling and for the coming wave of translational laboratory research, researchers and clinicians need more efficient medical database designs. We developed a database organized chronologically as date-variable-value entries. By collecting complete disease and treatment history for patients, the database could serve both clinical and research needs. A prostate cancer database was prototyped in Microsoft Access and subsequently upsized to a SQL Server and ColdFusion web application; the system, an open source application, is now in use at Memorial Sloan-Kettering Cancer Center and at several outside institutions. The system has reduced the difficulty of data entry, eased error checking, and improved the flexibility, efficiency, and reproducibility of creating research datasets from raw data. For clinicians, the system improves efficiency chiefly by displaying a single screen intuitive summary of patient data and by reducing the need for dictation. Currently, we are working to expand the system to other forms of cancer, and to implement a physician Tablet PC interface.","","Database systems; Decision making; Diseases; Information management; Patient monitoring; Research; Computer software reusability; Database systems; Decision making; Diseases; Efficiency; History; Hospital data processing; Information management; Laboratories; Medical computing; Patient treatment; Personal computers; Windows operating system; Cancer; Computerized support systems; Medical decision making; Medical treatment; Open source application; Predictive models; Research data managements; Spine; Clinical research; Health care; Open systems","","","","","","","James B.C., Making it easy to do it right, N Engl J Med, 345, 13, pp. 991-993, (2001); Jonietz E., Paperless medicine, Technology Review, pp. 59-64, (2003); Ensor D., Stevenson I., Oracle Design, (1997); Schneier B., Secrets and Lies: Digital Security in a Networked World, (2000); Kattan M.W., Fearn P.A., Leibel S., Potters L., The definition of biochemical failure in patients treated with definitive radiotherapy, Int J Rad Onc Biol Phys, 48, 5, pp. 1469-1474, (2000); Fearn P., Kattan M.W., Scardino P.T., Towards an efficient chronological database to support medical statistical prediction, Hawaii International Conference on Statistics, (2002); Potters L., Kattan M.W., Fearn P.A., A chronological database to support outcomes research in prostate cancer, Int J of Rad Onc Biol Phys","P.A. Fearn; Mem. Sloan-Kettering Cancer Center, New York, NY, United States; email: fearnp@mskcc.org","","","IEEE; Mount Sinai School of Medicine Department of Anesthology; Texas Tech University College of Engineering","Sixteenth IEEE Symposium on Computer Based Medical Systems","26 June 2003 through 27 June 2003","New York, NY","61315","10637125","","PSCSF","","English","Proc IEEE Symp Comput Based Med Syst","Conference paper","Final","","Scopus","2-s2.0-0042622272"
"Mayernik M.S.","Mayernik, Matthew S. (23009234400)","23009234400","Institutional structures for research data and metadata curation","2013","Proceedings of the ACM/IEEE Joint Conference on Digital Libraries","","","","401","402","1","0","10.1145/2467696.2467755","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882267476&doi=10.1145%2f2467696.2467755&partnerID=40&md5=4e4758e0e782907485dc66482015fcfd","National Center for Atmospheric Research (NCAR), Boulder, CO, P.O. Box 3000, United States","Mayernik M.S., National Center for Atmospheric Research (NCAR), Boulder, CO, P.O. Box 3000, United States","Institutions can be conceptualized as ""regulative, normative and cultural-cognitive elements that, together with associated activities and resources, provide stability and meaning to social life"" [14]. These regulative, normative, and cultural-cognitive elements provide the institutional structures in which organizations or individuals are embedded. By providing particular courses of action, institutions serve as patterns that constrain and empower individuals. Copyright © 2013 by the Association for Computing Machinery, Inc. (ACM).","Data curation; Institutions; Metadata; Research data management","Digital libraries; Information management; Metadata; Curation; Data curation; Institutional structure; Research data; Research data managements; Social life; Societies and institutions","","","","","","","Agre P.E., Computation and Human Experience, (1997); Agre P.E., Information and institutional change: The case of digital libraries, Digital Library Use: Social Practice in Design and Evaluation, pp. 219-240, (2003); Barley S.R., Tolbert P.S., Institutionalization and structuration: Studying the links between action and institution, Organization Studies, 18, 1, pp. 93-117, (1997); Blanchette J.F., Burdens of Proof: Cryptographic Culture and Evidence Law in the Age of Electronic Documents, (2012); Carpenter T., Standards and data citations, Rapporteur for Attribution - Developing Data Attribution and Citation Practices and Standards: Summary of An International Workshop, pp. 173-176, (2012); Fourie I., Librarians and the claiming of new roles: How can we try to make a difference?, Aslib Proceedings, 56, 1, pp. 62-74, (2004); Friedland R., Alford R.R., Bringing society back in: Symbols, practices, and institutional contradictions, The New Institutionalism in Organizational Analysis, pp. 232-263, (1991); Hawkins R., Mansell R., Steinmueller W.E., Toward digital intermediation in the information society, Journal of Economic Issues, 33, 2, pp. 383-391, (1999); Jepperson R.L., Institutions, institutional effects, and institutionalism, The New Institutionalism in Organizational Analysis, pp. 143-163, (1991); Lave J., Wenger E., Situated Learning: Legitimate Peripheral Participation, (1991); Marcial L.H., Hemminger B.M., Scientific data repositories on the web: An initial survey, Journal of the American Society for Information Science and Technology, 61, 10, pp. 2029-2048, (2010); Mayernik M.S., Batcheller A.L., Borgman C.L., How institutional factors influence the creation of scientific metadata, Proceedings of the 2011 IConference (IConference '11), pp. 417-425, (2011); Meyer J.W., World society, institutional theories, and the actor, Annual Review of Sociology, 36, pp. 1-20, (2010); Scott W.R., Institutions and Organizations: Ideas and Interests, (2008); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals for scientific data, Journal of the American Society for Information Science & Technology, 63, 8, pp. 1505-1520, (2012)","M.S. Mayernik; National Center for Atmospheric Research (NCAR), Boulder, CO, P.O. Box 3000, United States; email: mayernik@ucar.edu","","","Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB","13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2013","22 July 2013 through 26 July 2013","Indianapolis, IN","98495","15525996","978-145032076-4","","","English","Proc. ACM IEEE Joint Conf. Digit. Libr.","Conference paper","Final","","Scopus","2-s2.0-84882267476"
"Da Silva J.R.; Ribeiro C.; Lopes J.C.","Da Silva, João Rocha (55496903800); Ribeiro, Cristina (7201734594); Lopes, João Correia (36791598000)","55496903800; 7201734594; 36791598000","Semi-automated application profile generation for research data assets","2012","Communications in Computer and Information Science","343 CCIS","","","98","106","8","1","10.1007/978-3-642-35233-1_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869818863&doi=10.1007%2f978-3-642-35233-1_10&partnerID=40&md5=7dc427032f39303d4ed61e95b1e400e1","Faculdade de Engenharia, Universidade Do Porto/INESC TEC, Portugal; DEI, Faculdade de Engenharia, Universidade Do Porto / INESC TEC, Portugal","Da Silva J.R., Faculdade de Engenharia, Universidade Do Porto/INESC TEC, Portugal; Ribeiro C., DEI, Faculdade de Engenharia, Universidade Do Porto / INESC TEC, Portugal; Lopes J.C., DEI, Faculdade de Engenharia, Universidade Do Porto / INESC TEC, Portugal","Selecting the right set of descriptors for the annotation of a specific dataset can be a hard problem in research data management. Considering a dataset in an arbitrary domain, an application profile is complex to build because of the abundance of metadata standards, ontologies and other descriptor sources available for different domains. We propose to partially automate the process of data description by generating application profile recommendations based on a research data asset knowledge base. Our approach builds on existing technologies for exploring linked data and results in a process which can be tightly coupled with the research workflow, giving researchers more control over the description of their data. Preliminary experiments show that we can build on state-of-the-art technologies for search indexes, graph databases and triple stores to explore existing sources of linked data for our profile generation. © 2012 Springer-Verlag.","","Data handling; Information management; Knowledge based systems; Metadata; Semantics; Data sets; Descriptors; Different domains; Graph database; Hard problems; Knowledge base; Linked datum; Metadata Standards; Research data; Semi-automated; State-of-the-art technology; Tightly-coupled; Triple store; Research","","","","","Fundação para a Ciência e Tecnologia","Supported by Ph.D. grant SFRH/BD/77092/2011, provided by the FCT (Fundação para a Ciência e Tecnologia).","Al-Khalifa H.S., Davis H.C., The evolution of metadata from standards to semantics in E-learning applications, Proceedings of the Seventeenth Conference on Hypertext and Hypermedia - HYPERTEXT 2006, (2006); Bizer C., Lehmann J., Kobilarov G., Auer S., Becker C., Cyganiak R., Hellmann S., DBpedia - A crystallization point for the Web of Data, Web Semantics: Science, Services and Agents on the World Wide Web, 7, 3, pp. 154-165, (2009); Brickley D., Miller L., FOAF Vocabulary Specification 0.98, (2010); Calais E., Gravity and the Figure of the Earth, (2012); DCMI Metadata Terms, (2012); Fire M., Tenenboim L., Lesser O., Puzis R., Rokach L., Elovici Y., Link Prediction in Social Networks Using Computationally Efficient Topological Features, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, pp. 73-80, (2011); Freebase Documentation, (2012); Haase K., Context for semantic metadata, Proceedings of the 12th Annual ACM International, pp. 204-211, (2004); Hasan M.A., Chaoji V., Salem S., Link prediction using supervised learning, SDM 2006: Workshop on Link (2006); Huang Z., Link Prediction Based on Graph Topology: The Predictive Value of the Generalized Clustering Coefficient, (2006); Jones S., Ross S., Ruusalepp R., Data Audit Framework Methodology, (2009); Kleinberg J.M., Authoritative Sources in a Hyperlinked Environment, Journal of the ACM (JACM), 46, 5, pp. 604-632, (1999); LibenNowell D., The link prediction problem for social networks, CIKM 2003 Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 556-559, (2004); Lichtenwalter R.N., Dame N., Chawla N.V., Vertex Collocation Profiles: Subgraph Counting for Link Analysis and Prediction, 1019, pp. 1019-1028, (2012); Lyon L., Dealing with Data: Roles, Rights, Responsibilities and Relationships, (2007); Martinez-Uribe L., Macdonald S., User Engagement in Research Data Curation, LNCS, 5714, pp. 309-314, (2009); Digital preservation strategies, Workbook on Digital Private Papers, pp. 222-246, (2008); Measurement Units Ontology, (2008); Information Internet: Chemistry Gravimetry, (2012); Piwowar H.A., Day R.B., Fridsma D.S., Sharing detailed research data is associated with increased citation rate, PLoS One, 2, 3, (2007); Treloar A., Wilkinson R., Rethinking Metadata Creation and Management in a Data-Driven Research World, 2008 IEEE Fourth International Conference on EScience, pp. 782-789, (2008)","J.R. Da Silva; Faculdade de Engenharia, Universidade Do Porto/INESC TEC, Portugal; email: pro11004@fe.up.pt","","","Software Process Improvement Research Unit; Open Source and Free Knowledge Office of the University of Cadiz; Technology and Sustainability Research Institute in Spain; Agro-Know Technologies in Greece","6th Research Conference on Metadata and Semantics Research, MTSR 2012","28 November 2012 through 30 November 2012","Cadiz","94036","18650929","978-364235232-4","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-84869818863"
"Zhang J.-H.; Dai G.-H.; Shang H.-C.; Cao H.-B.; Ren M.; Xiang Y.-Z.; Gao X.-M.; Zhang B.-L.","Zhang, Jun-Hua (55720332300); Dai, Guo-Hua (14518900400); Shang, Hong-Cai (14519684100); Cao, Hong-Bo (12241389000); Ren, Ming (57215549906); Xiang, Yao-Zu (12040294100); Gao, Xiu-Mei (7403872165); Zhang, Bo-Li (9634766200)","55720332300; 14518900400; 14519684100; 12241389000; 57215549906; 12040294100; 7403872165; 9634766200","Data audit in large scale clinical trial of Traditional Chinese Medicine","2007","Chinese Journal of Evidence-Based Medicine","7","3","","230","232","2","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34047220035&partnerID=40&md5=9858d02ea4fa945244ded3625444e11d","Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China","Zhang J.-H., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Dai G.-H., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Shang H.-C., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Cao H.-B., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Ren M., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Xiang Y.-Z., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Gao X.-M., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Zhang B.-L., Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China","Based on evidence-based medicine (EBM) and from the viewpoint of providing scientific evidence for clinical application, we found that Traditional Chinese Medicine (TCM) was short of adequate evidence to support its therapeutic effects due to lack of high quality clinical research. Data management plays a very important role in clinical research. Lack of adequate data management may lead to low quality clinical research. Thus, it is of great importance to establish a set of standards for data management so as to improve the quality of clinical research. Based on the real practice in Myocardial Infarction Secondary Prevention Study in TCM (MISPS-TCM), this article introduces methods on data audit in clinical trials of TCM.","Clinical trial; Data audit; Data management","article; Chinese medicine; clinical research; evidence based practice; information processing; rating scale","","","","","","","He L.Y., Liu B.Y., Liang Z.W., Et al., Data administration and quality evaluation in clinical research, Chin J New Drugs Clin Rem, 24, 11, pp. 916-919, (2005); Sun Y.L., He J., Cao Y., Development of clinical data management system:current status home and abroad, Acad J Sec Mil Med Univ, 27, 7, pp. 721-725, (2006)","B.-L. Zhang; Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; email: tjzyxy@126.com","","","","","","","","16722531","","","","Chinese","Chin. J. Evid.-Based Med.","Article","Final","","Scopus","2-s2.0-34047220035"
"Mayernik M.S.; Choudhury G.S.; DiLauro T.; Metsger E.; Pralle B.; Rippin M.; Duerr R.","Mayernik, Matthew S. (23009234400); Choudhury, G. Sayeed (7004852001); DiLauro, Tim (6508091576); Metsger, Elliot (36195075500); Pralle, Barbara (55554458000); Rippin, Mike (55554148000); Duerr, Ruth (22233540500)","23009234400; 7004852001; 6508091576; 36195075500; 55554458000; 55554148000; 22233540500","The data conservancy instance: Infrastructure and organizational services for research data curation","2012","D-Lib Magazine","18","9-10","","","","","15","10.1045/september12/mayernik","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872258637&doi=10.1045%2fseptember12%2fmayernik&partnerID=40&md5=608efd4e8ca123406e35e10ba10dcadc","National Center for Atmospheric Research (NCAR), United States; Johns Hopkins University, United States; National Snow and Ice Data Center (NSIDC), United States","Mayernik M.S., National Center for Atmospheric Research (NCAR), United States; Choudhury G.S., Johns Hopkins University, United States; DiLauro T., Johns Hopkins University, United States; Metsger E., Johns Hopkins University, United States; Pralle B., Johns Hopkins University, United States; Rippin M., Johns Hopkins University, United States; Duerr R., National Snow and Ice Data Center (NSIDC), United States","Digital research data can only be managed and preserved over time through a sustained institutional commitment. Research data curation is a multi-faceted issue, requiring technologies, organizational structures, and human knowledge and skills to come together in complementary ways. This article provides a high-level description of the Data Conservancy Instance, an implementation of infrastructure and organizational services for data collection, storage, preservation, archiving, curation, and sharing. While comparable to institutional repository systems and disciplinary data repositories in some aspects, the DC Instance is distinguished by featuring a data-centric architecture, discipline-agnostic data model, and a data feature extraction framework that facilitates data integration and cross-disciplinary queries. The Data Conservancy Instance is intended to support, and be supported by, a skilled data curation staff, and to facilitate technical, financial, and human sustainability of organizational data curation services. The Johns Hopkins University Data Management Services (JHU DMS) are described as an example of how the Data Conservancy Instance can be deployed.© Matthew S. Mayernik, G. Sayeed Choudhury, Tim DiLauro, Elliot Metsger, Barbara Pralle, Mike Rippin, Ruth Duerr.","","","","","","","","","Agre P.E., Information and institutional change: The case of digital libraries, pp. 219-240, (2003); (2011); Reference Model for an Open Archival Information System (OAIS), (2012); Choudhury G.S., Data Conservancy Stack Model for Data Management, (2012); Delserone L.M., At the watershed: preparing for research data management and stewardship at the University of Minnesota Libraries, Library Trends, 57, 2, pp. 202-210, (2008); Fedora Repository Commons Software: Specsheet, (2012); Garritano J.R., Carlson J.R., A Subject Librarian's Guide to Collaborating on e-Science Projects, (2009); Lagoze C., Patzke K., A research agenda for data curation cyberinfrastructure, Proceeding of the 11th annual international ACM/IEEE joint conference on Digital libraries, pp. 373-382, (2011); Lavoie B.F., Sustainable research data, In G. Pryor (Ed. ) Managing Research Data, London: Facet Publishing., pp. 67-82, (2012); Li Y., Banach M., Institutional Repositories and Digital Preservation: Assessing Current Practices at Research Libraries, 17, 5-6, (2011); Long-Lived Digital Data Collections: Enabling Research and Education in the 21st, Century, (2005); Chapter II - proposal preparation instructions: special information and supplementary documentation, (2012); Award and Administration Guide, Chapter V - Allowability of Costs, (2012); Palmer C.L., Weber N.M., Cragin M.H., The Analytic Potential of Scientific Data: Understanding Re-use Value, Proceedings of the American Society for Information Science & Technology, 48, 1, pp. 1-10, (2011); Renear A.H., Sacchi S., Wickett K.M., Definitions of dataset in the scientific and technical literature, Proceedings of the American Society for Information Science and Technology, 47, 1, pp. 1-4, (2010); Salo D., Retooling Libraries for the Data Challenge, (2010); Treloar A., Choudhury G.S., Michener W., Contrasting national research data strategies: Australia and the USA, pp. 173-203, (2012); Blueprint for the Digital University: A Report of the UCSD Research Cyberinfrastructure Design Team, (2009); Research Data Stewardship at UNC: Recommendations for Scholarly Practice and Leadership, (2012); Varvel V.E., Palmer C.L., Chao T., Sacchi S., Report from the Research Data Workforce Summit: Sponsored by the Data Conservancy, (2011); Walters T., Skinner K., (2011); Witt M., Co-designing, Co-developing, and Co-implementing an Institutional Data Repository Service, Journal of Library Administration, 52, 2, (2012); Wynholds L., Fearon D., Borgman C.L., Traweek S., Awash in stardust: data practices in astronomy, Proceedings of the 2011 iConference, pp. 802-804, (2011)","M.S. Mayernik; National Center for Atmospheric Research (NCAR), United States; email: mayernik@ucar.edu","","","","","","","","10829873","","","","English","D-Lib Mag.","Article","Final","","Scopus","2-s2.0-84872258637"
"Accart J.","Accart, Jean-Philippe (6603091811)","6603091811","The Open Science: the OAI7 Workshop in Geneva (Switzerland) – June 2011","2011","Library Hi Tech News","28","7","","1","4","3","0","10.1108/07419051111184016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054035066&doi=10.1108%2f07419051111184016&partnerID=40&md5=bd0aa96c9bd8c4b4cbd5c0ea0bb6477c","","","The workshop is aimed at those involved in the development of open access (OA) repositories and who can influence the direction of developments either within their institution, their country or at an international level. The University of Geneva and CERN held the 7th Workshop on Innovations in Scholarly Communication (OAI7) from 22 June 2011 to 24 June 2011 in the beautiful city of Calvin and Rousseau, in the heart of the Alps and close to the worldwide famous Lake of Geneva. It has been several years that the University of Geneva and CERN co-organized the workshop, which became a “must to be” in the science profession. Some figures: several hundred of the participants coming mostly from Western countries, more than 30 papers and ten tutorials; and some famous sponsors such as UNESCO, SPARC Europe, ExLibris, Microsoft Research and Springer, etc. The OAI7 Workshop followed the successful format of previous workshops mixing practical tutorials, presentations from cutting-edge projects and research, discussion groups, posters, and an intense social programme to maximise interaction and communication. Previous workshops have built a strong community spirit and the event is a unique opportunity to exchange ideas and contact details with the wide range of people connected to the OA movement. The OAI series of workshops is one of the biggest international meetings in this field and takes place roughly every two years. Ownership, copyright, cost, new developments, OA publishing, e-research, data curation, research funding and institutional repositories can all be linked to OA. In this context it makes more sense, can play a bigger role, and eventually become a feature of local scholarship practice. The paper reports on the findings of the OA17 workshop. © 2011, Emerald Group Publishing Limited","Co-operation; Conferences; Education; Electronic publishing; Internet; Libraries","","","","","","","","","","","","","","","","","07419058","","","","English","Libr. Hi Tech News","Article","Final","","Scopus","2-s2.0-80054035066"
"Snyder D.C.; Epps S.; Beresford H.F.; Ennis C.; Levens J.S.; Woody S.K.; Tcheng J.E.; Stacy M.A.; Nahm M.","Snyder, Denise C. (55662980400); Epps, Shelly (56305898700); Beresford, Henry F. (8853132000); Ennis, Cory (55533581100); Levens, Justin S. (55222267900); Woody, Stephen K. (57218145505); Tcheng, James E. (7006868854); Stacy, Mark A. (7004093918); Nahm, Meredith (56744770800)","55662980400; 56305898700; 8853132000; 55533581100; 55222267900; 57218145505; 7006868854; 7004093918; 56744770800","Research Management Team (RMT): A Model for Research Support -Services at Duke University","2012","Clinical and Translational Science","5","6","","464","469","5","5","10.1111/cts.12010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871394912&doi=10.1111%2fcts.12010&partnerID=40&md5=688ad4c819e0ad4aac455eb21b3b4e82","Duke University, School of Nursing, Durham, NC, United States; Duke University, School of Medicine, Durham, NC, United States; Duke Clinical Research Institute, Durham, NC, United States; Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, United States; Division of Neurology, Department of Medicine, Duke University Medical Center, Durham, NC, United States; Duke Translational Medicine Institute, Durham, NC, United States","Snyder D.C., Duke University, School of Nursing, Durham, NC, United States, Duke University, School of Medicine, Durham, NC, United States; Epps S., Duke University, School of Nursing, Durham, NC, United States, Duke University, School of Medicine, Durham, NC, United States; Beresford H.F., Duke University, School of Nursing, Durham, NC, United States, Duke University, School of Medicine, Durham, NC, United States; Ennis C., Duke University, School of Medicine, Durham, NC, United States, Duke Clinical Research Institute, Durham, NC, United States; Levens J.S., Duke University, School of Nursing, Durham, NC, United States, Duke University, School of Medicine, Durham, NC, United States; Woody S.K., Duke Clinical Research Institute, Durham, NC, United States; Tcheng J.E., Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, United States; Stacy M.A., Duke University, School of Medicine, Durham, NC, United States, Division of Neurology, Department of Medicine, Duke University Medical Center, Durham, NC, United States; Nahm M., Duke Translational Medicine Institute, Durham, NC, United States","Collecting and managing data for clinical and translational research presents significant challenges for clinical and translational researchers, many of whom lack needed access to data management expertise, methods, and tools. At many institutions, funding constraints result in differential levels of research informatics support among investigators. In addition, the lack of widely shared models and ontologies for clinical research informatics and health information technology hampers the accurate assessment of investigators' needs and complicates the efficient allocation of crucial resources for research projects, ultimately affecting the quality and reliability of research. In this paper, we present a model for providing flexible, cost-efficient institutional support for clinical and translational research data management and informatics, the research management team, and describe our initial experiences with deploying this model at our institution. © 2012 Wiley Periodicals, Inc.","Biostatistics; Clinical trials; Computers; Translational research","Academies and Institutes; Database Management Systems; Medical Informatics; Models, Theoretical; North Carolina; Research Support as Topic; Translational Medical Research; Universities; access to information; article; clinical research; cost effectiveness analysis; data analysis; data collection method; funding; information processing; information system; medical information system; priority journal; reliability; research management team; resource allocation; statistical model; translational research","","","","","National Center for Research Resources, NCRR, (UL1RR024128); National Center for Advancing Translational Sciences, NCATS, (KL2TR000437, UL1TR000436)","","(2012); Building an infrastructure to support clinical trials, Envisioning a Transformed Clinical Trials Enterprise in the United States: Establishing an Agenda for 2020, (2012); Hersh W., A stimulus to define informatics and health information technology, BMC Med Inform Decis Mak, 9, (2009); Walden A., Eisenstein E.L., Nahm M., Barnett M.E., Conde J., Dent A., Fadiel A., Perry T., Tolk C., Tcheng J., (2011); Harris P.A., Swafford J.A., Edwards T.L., Zhang M., Nigavekar S.S., Yarbrough T.R., Lane L.D., Helmer T., Lebo L.A., Mayo G., Et al., StarBRITE: the Vanderbilt University Biomedical Research Integration, Translation and Education portal, J Biomed Inform, 44, pp. 655-662, (2011); (2012); Nahm M., Zhang J., Operationalization of the UFuRT methodology for usability analysis in the clinical research data management domain, J Biomed Inform, 42, pp. 327-333, (2009); Tenenbaum J., Nahm M., National Institutes of Health Clinical Center Natcher Conference Center, (2009); Nahm M., Shepherd J., Buzenberg A., Rostami R., Corcoran A., McCall J., Pietrobon R., Design and implementation of an institutional case report form library, Clin Trials, 8, pp. 94-102, (2011); Horvath M.M., Winfield S., Evans S., Slopek S., Shang H., Ferranti J., The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement, J Biomed Inform, 44, pp. 266-276, (2011); Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G., Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform, 42, pp. 377-381, (2009); (2012); (2011); (2011); (2011); Jeffery D.D., Tzeng J.P., Keefe F.J., Porter L.S., Hahn E.A., Flynn K.E., Reeve B.B., Weinfurt K.P., Initial report of the cancer Patient-Reported Outcomes Measurement Information System (PROMIS) sexual function committee: review of sexual function measures and domains used in oncology, Cancer, 115, pp. 1142-1153, (2009); Snyder D.C., Ennis C.L., Epps S.J., Woody S.K., Beresford H.F., Levens J.S., Tcheng J.E., National Institutes of Health Clinical Center Natcher Conference Center, (2011); (2011); Friedrich M.J., Ending extreme poverty, improving the human condition. Interview with Jeffrey Sachs, JAMA, 298, pp. 1849-1851, (2007); Nahm M., Data quality in clinical research, Clinical Research Informatics, pp. 175-202, (2012)","D.C. Snyder; Duke University, School of Nursing, Durham, NC, United States; email: denise.snyder@duke.edu","","","","","","","","17528062","","","23253668","English","Clin. Transl. Sci.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-84871394912"
"Steffen A.; Scherz T.; Olson M.; Gay D.; Blanchard P.","Steffen, Alexandra (7005074540); Scherz, Tina (55053372000); Olson, Mark (7402053012); Gay, David (36097494200); Blanchard, Pierrette (56260406200)","7005074540; 55053372000; 7402053012; 36097494200; 56260406200","A comparison of data quality control protocols for atmospheric mercury speciation measurements","2012","Journal of Environmental Monitoring","14","3","","752","765","13","60","10.1039/c2em10735j","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857779567&doi=10.1039%2fc2em10735j&partnerID=40&md5=1539b53b97397e8967d4f57a30fe645b","Environment Canada, Toronto, ON M3H5T4, 4905 Dufferin St., Canada; National Atmospheric Deposition Program, Illinois State Water Survey, University of Illinois, Champaign, IL 61820, 2204 Griffith Drive, United States","Steffen A., Environment Canada, Toronto, ON M3H5T4, 4905 Dufferin St., Canada; Scherz T., Environment Canada, Toronto, ON M3H5T4, 4905 Dufferin St., Canada; Olson M., National Atmospheric Deposition Program, Illinois State Water Survey, University of Illinois, Champaign, IL 61820, 2204 Griffith Drive, United States; Gay D., National Atmospheric Deposition Program, Illinois State Water Survey, University of Illinois, Champaign, IL 61820, 2204 Griffith Drive, United States; Blanchard P., Environment Canada, Toronto, ON M3H5T4, 4905 Dufferin St., Canada","Significant advances in the measurement of atmospheric mercury species have been made in the past 10 years yet limited protocols on quality control (QC) and assurance on this data have been published in the literature. Recently, considerable work has been done to develop quality control and assurance programs within North America. Environment Canada and the National Atmospheric Deposition Network (NADP) independently developed programs, RDMQ™ and AMQC, respectively, to QC atmospheric mercury speciation data (including gaseous elemental mercury (GEM), reactive gaseous mercury (RGM) and mercury associated to particles (PHg)). These 2 programs were assessed by the criteria on which the data is QCed and comparability of the final data products. Results show that the criteria used to flag data compare well within the 4 tested sites and that the number of flags for each criterion is generally comparable. The QC programs were applied to 2 distinct data sets and the final QCed data was compared. From a mid-latitude site, the final data sets compare very well and showed there to be a 0.3, 8.6 and 15% difference in the mean GEM, RGM and PHg concentrations post QC of each program. It is recommended that either the RDMQ or the AMQC programs be employed for a typical mid-latitude site. When the QC programs were applied to highly variable data, the data do not compare as well for RGM and PHg. Results showed a 2.7, 27 and 33% difference in the mean GEM, RGM and PHg concentrations, respectively, post QC of each program. It is recommended that RDMQ be used for data that is highly variable with high RGM/PHg concentrations as it allows for more manual correction over the QCed data. This investigation of 2 QC programs produced comparable data and that either of these programs can be used as standard methods for the quality control of atmospheric mercury speciation data. © 2012 The Royal Society of Chemistry.","","Air Pollutants; Air Pollution; Atmosphere; Environmental Monitoring; Mercury; Quality Control; mercury; air quality; AMNet Quality Control software; article; computer program; data analysis; priority journal; quality control; Research Data Management Quality software","","mercury, 14302-87-5, 7439-97-6; Air Pollutants, ; Mercury, 7439-97-6","","","","","Munthe J., Wangberg I., Pirrone N., Iverfeldt A., Ferrara R., Ebinghaus R., Feng X., Gardfeldt K., Keeler G., Lanzillotta E., Liundberg S.E., Lu J., Mamane Y., Prestbo E., Schmolke S., Schroeder W.H., Sommar J., Sprovieri F., Stevens R.K., Stratton W., Tuncel G., Urba A., Atmos. Environ., 35, pp. 3007-3017, (2001); Schroeder W.H., Ebinghaus R., Shoeib M., Timoschenko K., Barrie L.A., Water, Air, Soil Pollut., 80, pp. 1227-1236, (1995); Ebinghaus R., Jennings S.G., Schroeder W.H., Berg T., Donaghy T., Ferrara R., Guentzel J., Kenny D., Kock H.H., Kvietkus K., Landing W., Mazzolai B., Muhleck, Munthe J., Prestbo E.M., Schneeberger D., Sommar S.F.J., Urba A., Wallschlager D., Xiao Z., Atmos. Environ., 33, pp. 3063-3073, (1999); Landis M., Stevens R.K., Schaedlich F., Prestbo E.M., Environ. Sci. Technol., 36, pp. 3000-3009, (2002); Gustin M., Jaffe D., Environ. Sci. Technol., 44, pp. 2222-2227, (2010); Aspmo K., Gauchard P.-A., Steffen A., Temme C., Berg T., Bahlmann E., Banic C., Dommergue A., Ebinghaus R., Ferrari C., Pirrone N., Sprovieri F., Wibetoe G., Atmos. Environ., 39, pp. 7607-7619, (2005); Brown R.J.C., Pirrone N., Van Hock C., Sprovieri F., Fernandez R., Tote K., J. Environ. Monit., 12, pp. 689-695, (2010); Schroeder W.H., Keeler G., Kock H., Roussel P., Schneeberger D., Schaedlich F., Water, Air, Soil Pollut., 80, pp. 611-620, (1995); Lyman S.N., Jaffe D.A., Gustin M.S., Atmos. Chem. Phys., 10, pp. 8197-8204, (2010); Brown R.J.C., Brown A.S., Yardley R.E., Corns W.T., Stockwell P.B., Atmospheric Environment, (2008); Kellerhals M., Beauchamp S., Belzer W., Blanchard P., Froude F., Harvey B., McDonald K., Pilote M., Poissant L., Puckett K., Schroeder W.H., Steffen A., Tordon R., Atmos. Environ., 37, pp. 1003-1011, (2003); Temme C., Blanchard P., Steffen A., Beauchamp S.T., Poissant L., Tordon R.J., Weins B., Atmos. Environ., 41, pp. 5423-5441, (2007); Steffen A., Schroeder W., Standard Operating Procedures Manual for Total Gaseous Mercury Measurements, (1999); McMillan A.C., MacIver D., Sukloff W.B., Environ. Modell. Software, 15, pp. 245-248, (2000); Steffen A., Schroeder W.H., Bottenheim J., Narayan J., Fuentes J.D., Atmos. Environ., 36, pp. 2653-2661, (2002); Sukloff W.B., Vet R.J., Li S.-M., Atmos. Environ., 40, pp. 2783-2795, (2006); Steffen A., Schroeder W.H., MacDonald R., Poissant L., Konoplev A., Sci. Total Environ., 342, pp. 185-198, (2005); Steffen A., Douglas T., Amyot M., Ariya P., Aspmo K., Berg T., Bottenheim J., Brooks S., Cobbett F.D., Dastoor A., Dommergue A., Ebinghaus R., Ferrari C., Gardfeldt K., Goodsite M.E., Lean D., Poulain A.J., Scherz C., Skov H., Sommar J., Temme C., Atmos. Chem. Phys., 8, pp. 1445-1482, (2008); Temme C., Einax J.W., Ebinghaus R., Schroeder W.H., Environ. Sci. Technol., 37, pp. 22-31, (2003); Poissant L., Pilote M., Xu X., Zhang H., Beauvais C., J. Geophys. Res., 109, (2004)","","","","","","","","","14640333","","JEMOF","22318244","English","J. Environ. Monit.","Article","Final","","Scopus","2-s2.0-84857779567"
"Kim W.; Suh Y.; Whinston A.B.","Kim, Won (34770298700); Suh, Yongmoo (7202260358); Whinston, Andrew B (7005286020)","34770298700; 7202260358; 7005286020","An IBIS and object-oriented approach to scientific research data management","1993","The Journal of Systems and Software","23","2","","183","197","14","2","10.1016/0164-1212(93)90083-A","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0027697832&doi=10.1016%2f0164-1212%2893%2990083-A&partnerID=40&md5=6547ecc7727948628f7ba57a3649ca14","UniSQL, Inc., Austin, TexasU.S.A.; Samsung Data Systems, Seoul, South Korea; University of Texas at Austin, Austin, TexasU.S.A.","Kim W., UniSQL, Inc., Austin, TexasU.S.A.; Suh Y., Samsung Data Systems, Seoul, South Korea; Whinston A.B., University of Texas at Austin, Austin, TexasU.S.A.","One of the major challenges in information systems technology is to build a system of scientific data bases and associated tools to assist collaborative research among scientists worldwide across a broad range of scientific disciplines. Two of the many important issues that need to be addressed in meeting this challenge are the selection of a collaboration paradigm for the scientists and a technology for the management of a large-volume scientific data base. We propose the use of an issue-based collaborative exploration paradigm and an object-oriented data base technology for managing a large scientific data base. © 1993.","","Computer systems; Information management; Information retrieval systems; Object oriented programming; Collaboration paradigm; Information systems technology; Scientific databases; Scientific research data management; Scientists; Database systems","","","","","National Collaboratory Initiative; National Science Foundation, NSF","The National Science Foundation launched the National Collaboratory Initiative in 1989 [3]. The objective of the initiative is to fuse computer and electronic communication technology into support systems that will improve the pace and quality of discourse among people collectively engaged in scientific investigations. The ultimate goal of the initiative is to build a system of scientific data bases and associated tools to facilitate collaborative research among scientists worldwide across a broad range of scientific disciplines.","Pager, A Proposal for a Computer-Based Interactive Scientific Community, Communications of the ACM, 15, (1972); Schatz, Building an electronic scientific community, Proceedings of Hawaii International Conference on System Sciences, (1991); Lederberg, Uncapher, Towards a National Collaboratory, Report of an Invitational Workshop, (1989); Kornfeld, Hewitt, The Scientific Community Metaphor, IEEE Transactions on Systems, Man, and Cybernetics, 11 SMC, (1981); Popper, The Logic of Scientific Discovery, (1968); Simon, Models of Man, (1957); Rittel, Webber, Dilemmas in a General Theory of Planning, Policy Sciences, 4, (1973); Conklin, Begeman, gIBIS: A hypertext tool for exploratory/policy discussion, Computer Supported Cooperative Work '88, (1988); Fischer, McCall, Morch, Design environment for constructive and argumentative design, Proceedings of SIGCHI '89, (1989); Rein, Ellis, rIBIS: A Real-Time Group Hypertext System, Technical Report STP-095-90, (1990); Lee, SIBYL: A tool for managing group decision rationale, Proceedings of Computer Supported Cooperative Work '90, (1990); Yakemovic, Report on a development project use of an issue-based information system, Computer Supported Cooperative Work '90, (1990); Rittel, APIS: A Concept for An Argumentative Planning Information Systems, Working Paper No. 324, (1980); Rittel, STEIC: Systems Analysis of the Generation and Dissemination of Scientific and Technological Information in the European Community, Studiengruppe fuer Systemforshung, Internal Project Report No. 3, (1976); Pott, Bruns, Recording the Reasons for Design Decisions, Technical Report STP-304-87, (1987); McCall, PHIBIS: Procedurally hierarchical issuebased information systems, Proceedings of 1987 Conference on Planning and Design in Architecture, American Society of Mechanical Engineers, (1987); Smolensky, Fox, King, Lewis, Computer-aided reasoned discourse or, how to argue with a computer, Cognitive Science and Its Application for Human-Computer Interaction, (1988); Kim, Object-Oriented Databases: Definition and Research Directions, IEEE Trans. Knowl. Data Eng., 2, (1990); Lecluse, Richard, Velez, O2, an object-oriented data model, Proceedings of ACM-SIGMOD 1988 International Conference on Management of Data, (1988); Banerjee, Kim, Kim, Korth, Semantics and implementation of schema evolution in object-oriented databases, Proceedings of ACM-SIGMOD 1987 International Conference on Management of Data, (1987); Lorenzen, Constructive Philosophy, (1987); UniSQL/X User's Manual Release 1.0, (1992); Banerjee, Kim, Kim, Garza, Clustering a DAG for CAD Databases, IEEE Transactions on Software Engineering, 14, (1988); Conklin, Hypertext a Survey and Introduction, Computer, 20, (1987); Vorzimmer, Charles Darwin: The Years of Controversy, (1970)","","","","","","","","","01641212","","JSSOD","","English","J Syst Software","Article","Final","","Scopus","2-s2.0-0027697832"
"Nahm M.; Zhang J.","Nahm, Meredith (56744770800); Zhang, Jiajie (7601336181)","56744770800; 7601336181","Operationalization of the UFuRT methodology for usability analysis in the clinical research data management domain","2009","Journal of Biomedical Informatics","42","2","","327","333","6","11","10.1016/j.jbi.2008.10.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-62049085719&doi=10.1016%2fj.jbi.2008.10.004&partnerID=40&md5=192d72ac0311a99003ac9c136d2af5b8","Duke Translational Medicine Institute, Duke University Medical Center, Durham, NC 27705, 2424 Erwin Rd. Hock Plaza, Suite 500, United States; School of Health Information Sciences, University of Texas Health Science Center at Houston, Houston, TX, United States","Nahm M., Duke Translational Medicine Institute, Duke University Medical Center, Durham, NC 27705, 2424 Erwin Rd. Hock Plaza, Suite 500, United States, School of Health Information Sciences, University of Texas Health Science Center at Houston, Houston, TX, United States; Zhang J., School of Health Information Sciences, University of Texas Health Science Center at Houston, Houston, TX, United States","Data management software applications specifically designed for the clinical research environment are increasingly available from commercial vendors and open-source communities, however, general-purpose spreadsheets remain widely employed in clinical research data management (CRDM). The suitability of spreadsheets for this use is controversial, and no formal comparative usability evaluations have been performed. We report on an application of the UFuRT (user, function, representation, and task (analyses) methodology to create a domain-specific process for usability evaluation. We demonstrate this process in an evaluation of differences in usability between a spreadsheet program (Microsoft® Excel) and a commercially available clinical research data management system (Phase Forward Clintrial™). Through this domain-specific operationalization of UFuRT methodology, we successfully identified usability differences and quantified task and cost differences, while differentiating these from socio-technical aspects. UFuRT can be generalized to other domains. © 2008 Elsevier Inc. All rights reserved.","Clinical trials; Database management systems; HCI; Usability","Clinical Trials as Topic; Database Management Systems; Humans; Software; Database systems; Management information systems; Spreadsheets; Clinical researches; Clinical trials; Cost differences; Data-management softwares; Database management systems; Domain specifics; HCI; Microsoft; Open sources; Socio technicals; Spreadsheet programs; Usability; Usability analysis; Usability evaluations; article; clinical research; computer program; cost benefit analysis; data base; evaluation; information processing; nonhuman; priority journal; quantitative analysis; UFuRT methodology; Management","","","Microsoft Excel; Phase Forward Clintrial","","National Center for Research Resources, NCRR, (UL1RR024128, UL1RR024148); University of Texas Health Science Center at Houston, (1 UL1 RR 02414)","This work was supported by the Clinical and Translational Science Awards (CTSA) to Duke University (1UL1 RR 024128) and to the University of Texas Health Science at Houston (1 UL1 RR 02414). ","Zhang J., Butler K., UFuRT: A work-centered framework and process for design and evaluation of information systems, HCI International Proceedings, (2007); Butler K., Zhang J., Esposito C., Bahrami A., Hebron R., Kieras D., Work-centered design: A case study of a mixed initiative scheduler, Proceedings of CHI, (2007); Zhang J., Patel V.L., Johnson K.A., Malin J., Smith J.W., Designing human-centered distributed information systems, IEEE Intell Syst, 17, pp. 42-47, (2002); Schmier J.K., Kane D.W., Halpern M.T., Practical applications of usability theory to electronic data collection systems for clinical trials, Contemp Clin Trials, 26, pp. 376-385, (2005); Constantine L.L., Lockwood L.A.D., Software for use: a practical guide to the models and methods of usage-centered design, (1999); Litchfield J., Freeman J., Schou H., Elsley M., Fuller M., Chubb B., Is the future of clinical trials Internet-based? A cluster randomized trial, Clin Trials, 2, pp. 72-79, (2005); Weber B., Yarandi H., Rowe M., Weber J., A comparison study: paper-based versus web-based data collection and management, Appl Nurs Res, 18, pp. 182-185, (2005); Medical subject headings; Spear A., Ontology for the twenty-first century: an introduction with recommendations, (2006); Information technology vocabulary part 1: Fundamental terms,, (1993); Information technology vocabulary part 17: Databases,, (1996); Information technology metadata registries part 3: Registry metamodel and basic attributes,, (2003); Raymond S., Gawrylewski H., Ganter J., Gertel A., CDISC clinical research glossary, Applied clinical trials, (2006); Stevens S.S., On the theory of scales of measurement, Science, 103, pp. 677-680, (1946); Zeng Q., Cimino J.J., Zou K.H., Providing concept-oriented views for clinical data using a knowledge-based system: an evaluation, J Am Med Inform Assoc, 9, pp. 294-305, (2002); Tan J., Health management information systems: methods and practical applications. 2nd ed., (2001); Kieras D.E., Using the keystroke-level model to estimate execution times, (1993); Kieras D.E., A guide to GOMS task analysis (Technical report, (1994); Web site; Zhang J., Norman D.A., Representations in distributed cognitive tasks, Cogn Sci, 18, pp. 87-122, (1994); Zhang J., Patel V.L., Distributed cognition, representation, and affordance, Cogn Pragmatics, 14, pp. 333-341, (2006); Eisenstein E.L., Lemons II P.W., Tardiff B.E., Schulman K.A., Jolly M.K., Califf R.M., Reducing the costs of phase III cardiovascular clinical trials, Am Heart J, 149, pp. 482-488, (2005)","M. Nahm; Duke Translational Medicine Institute, Duke University Medical Center, Durham, NC 27705, 2424 Erwin Rd. Hock Plaza, Suite 500, United States; email: Meredith.nahm@duke.edu","","","","","","","","15320464","","JBIOB","19026765","English","J. Biomed. Informatics","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-62049085719"
"DiLaura R.P.","DiLaura, Robert P (16686471600)","16686471600","Clinical and translational science sustainability: overcoming integration issues between electronic health records (EHR) and clinical research data management systems ""separate but equal"".","2007","Medinfo. MEDINFO","12","Pt 1","","137","141","4","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-38449094735&partnerID=40&md5=35d2e62d46d272aa4efcc26133c40c7c","Cleveland Clinic and Case Western Reserve University, Cleveland Ohio, United States","DiLaura R.P., Cleveland Clinic and Case Western Reserve University, Cleveland Ohio, United States","The use of health information technology (HIT) is growing rapidly for patient care systems required to test, diagnose and treat patients, and to bill for these services. Today's Electronic Health Record (EHR) systems are a response to this pressure, enabling feature rich computer-assisted decisions and communication. And even though EHR benefits dramatically outweigh the costs, required investments are nonetheless significant. Continuing to invest in HIT at a revolutionary rate is unsustainable given institutional financial constraints and continuing reimbursement cuts. Future improvements must come from new treatments, test methods, drugs and devices - from research. But data management information systems for clinical research receive less funding than patient care systems, and in less coherent ways. It is easy to imagine using the high cost, patient-based EHRs for clinical research data management, and thus accelerate the speed of translating new medical discoveries into standard practice. But taking this step requires thoughtful planning to overcome significant technology, legal/regulatory, policy, process, and administrative issues.","","Biomedical Research; Database Management Systems; Great Britain; Humans; Medical Informatics; Medical Records Systems, Computerized; Systems Integration; United States; article; data base; economics; human; medical informatics; medical record; medical research; system analysis; United Kingdom; United States","","","","","","","","R.P. DiLaura; email: dilaurr@ccf.org","","","","","","","","","","","17911694","English","Medinfo","Article","Final","","Scopus","2-s2.0-38449094735"
"Ahmed K.","Ahmed, K. (18436115400)","18436115400","Research data management in health-related studies","1990","National Medical Journal of India","3","5","","241","248","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0025227994&partnerID=40&md5=32013dbea73de72a33c994c6dbd87fde","Canada","Ahmed K., Canada","[No abstract available]","","article; health statistics; information processing","","","","","","","","","","","","","","","","0970258X","","NMJIE","","English","NATL. MED. J. INDIA","Article","Final","","Scopus","2-s2.0-0025227994"
"Shahi A.; Carlson K.; Chettupuzha A.J.A.; Haas C.T.; West J.S.; Akinci B.","Shahi, Arash (22981436200); Carlson, Kaitlin (57548183700); Chettupuzha, A.J. Antony (55399750400); Haas, Carl T. (7202620442); West, Jeffrey S. (7402746437); Akinci, Burcu (6603543201)","22981436200; 57548183700; 55399750400; 7202620442; 7402746437; 6603543201","Construction research data management","2012","Construction Research Congress 2012: Construction Challenges in a Flat World, Proceedings of the 2012 Construction Research Congress","","","","678","687","9","0","10.1061/9780784412329.069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866246293&doi=10.1061%2f9780784412329.069&partnerID=40&md5=15f3537b548dbec8e56a84657ae6133e","Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213-3890, United States","Shahi A., Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Carlson K., Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Chettupuzha A.J.A., Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Haas C.T., Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; West J.S., Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; Akinci B., Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213-3890, United States","Sharing research data is necessary for collaboration within a research network and is required by funding agencies, such as the National Science Foundation (NSF), that are enforcing the scientific method and ethics. However, methods and infrastructure for supporting construction research data management are currently underdeveloped; emphasizing the need for developing effective and efficient means for managing and sharing research data. A review of existing data management models reveals that there is currently no effective universal system for sharing the data obtained from construction research endeavours. This paper presents electronic product and process management systems (EPPMS) as a construction research data management and sharing approach. The proposed EPPMS is a web-based system, which utilizes workflows that can automate the collection, authorization, and dissemination of construction research data. A comparative analysis of the proposed system to the existing web-based cloud and web-based share point systems indicates that an EPPMS offers a more fitting solution for construction research data management. © 2012 ASCE.","","Research; Websites; Comparative analysis; Construction research; Electronic product; Funding agencies; Management Model; National Science Foundations; Research data; Research networks; Scientific method; Web-based system; Work-flows; Information management","","","","","","","Axelsson A., Schroeder R., Making it open and keeping it safe: E-enabled data-dharing in sweden, Acta Sociologica, 52, 3, pp. 213-226, (2009); Ceci S.J., Scientists' attitudes toward data sharing, Science, Technology, & Human Values, 13, 1-2, (1988); 2009 Strategic Plan, (2009); Membership, (2010); CII Benchmarking Code of Conduct, (2011); Fischer B.A., Zigmond M.J., The essential nature of sharing in science, Science and Engineering Ethics, 16, 4, pp. 783-799, (2010); Giffels J., Vollmer S.H., Bird S.J., Editors' overview: Topics in the responsible management of research data, Science and Engineering Ethics, 16, 4, pp. 631-637, (2010); Lee S., Thomas S.R., Tucker R.L., Web-based benchmarking system for the construction industry, Journal of Construction Engineering and Management, 131, 7, pp. 790-798, (2005); Applying the the Data Resources Program Solicitation: Funding for the Analysis of Existing Data, (2010); NIH Data Sharing Policy and Implementation Guidance, (2003); Chapter VI - Other Post Award Requirements and Considerations, (2011); University of Pittsburgh Guidelines on Research Data Management, (2009); Yang H., Lai C., Understanding knowledge-sharing behaviour in wikipedia, Behaviour and Information Technology, 30, 1, pp. 131-142, (2011)","A. Shahi; Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, 200 University Ave. W., Canada; email: ashahi@engmail.uwaterloo.ca","","","Constr. Inst. (CI) Am. Soc. Civ. Eng. (ASCE); Div. Constr. Eng. Manage. Purdue Univ.","Construction Research Congress 2012: Construction Challenges in a Flat World","21 May 2012 through 23 May 2012","West Lafayette, IN","92566","","978-078441232-9","","","English","Constr. Res. Congr.: Constr. Challenges Flat World, Proc. Constr. Res. Congr.","Conference paper","Final","","Scopus","2-s2.0-84866246293"
"Brooks B.","Brooks, Brian (47560950400)","47560950400","Information Technology Systems for Sample Management","2012","Management of Chemical and Biological Samples for Screening Applications","","","","243","263","20","0","10.1002/9783527645251.ch13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885801653&doi=10.1002%2f9783527645251.ch13&partnerID=40&md5=b2d495ea8c159807eb4095371ddb2f92","Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, Lensfield Road, United Kingdom","Brooks B., Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, Lensfield Road, United Kingdom","[No abstract available]","Batch; Biological database; Biological results; Business rules; Combinatorial mixtures; Compound numbering; Compound numbers; Compound registration; Compound version; Database design; Electronic lab notebook; High-throughput screening; Intellectual property; Parent structure; Purity; Registrar; Research data management; Salt codes; Sample registration; Stereochemistry","","","","","","","","","","","Wiley-VCH","","","","","","","978-352732822-2","","","English","Manage. of Chemical and Biological Samples for Screening Appl.","Book chapter","Final","","Scopus","2-s2.0-84885801653"
"O'Reilly K.; Johnson J.; Sanborn G.","O'Reilly, Kelley (35077249200); Johnson, Jeffrey (57715411400); Sanborn, Georgiann (55840644600)","35077249200; 57715411400; 55840644600","Improving university research value: A case study","2012","SAGE Open","2","3","","1","13","12","6","10.1177/2158244012452576","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883207376&doi=10.1177%2f2158244012452576&partnerID=40&md5=a0101524129ab8e7ce74ec0073b3180d","Department of Marketing, Haworth College of Business, Western Michigan University, Kalamazoo, MI 49008-5430, 3141 Schneider Hall, United States; Utah State University, Logan, United States; Database Consultant, Kalamazoo, United States","O'Reilly K., Department of Marketing, Haworth College of Business, Western Michigan University, Kalamazoo, MI 49008-5430, 3141 Schneider Hall, United States; Johnson J., Utah State University, Logan, United States; Sanborn G., Database Consultant, Kalamazoo, United States","This article investigates the current data management practices of university researchers at an Intermountain West landgrant research university in the United States. Key findings suggest that researchers are primarily focused on the collection and housing of research data. However, additional research value exists within the other life cycle stages for research data- specifically in the stages of delivery and maintenance. These stages are where most new demands and requirements exist for data management plans and policies that are conditional for external grant funding; therefore, these findings expose a ""gap"" in current research practice. These findings should be of interest to academics and practitioners alike as findings highlight key management gaps in the life cycle of research data. This study also suggests a course of action for academic institutions to coalesce campus-wide assets to assist researchers in improving research value. © The Author(s) 2012.","Data science; Research data management; Research funding; University research","","","","","","","","Carlsen S., Lost in a sea of science data: Librarians are called in to archive huge amounts of information, but cultural and financial barriers stand in the way, Chronicle of Higher Education, 52, (2006); Carlson J., Ramsey A.E., Kotterman D.J., Using an institutional repository to address local-scale needs: A case study at Purdue University, Library Hi Tech, 28, 1, pp. 152-173, (2010); Deleserone L.M., At the watershed: Preparing for research data management and stewardship at the university of Minnesota libraries, Library Trends, 57, 2, pp. 202-210, (2008); Fear K., You made it, you take care of it. Data management as personal information management, International Journal of Digital Curation, 6, pp. 54-77, (2011); Glaser B.G., Strauss A.L., The Discovery of Grounded Theory, (1967); Glesne C., Becoming Qualitative Researchers, (2006); Hall B.H., University-industry Research Partnerships In the United States, (2004); Harman G., University-industry research partnerships in Australia: Extent, benefits and risks, Higher Education Research & Development, 20, pp. 243-264, (2001); Harman G., Australian university-industry research links: Researcher involvement, outputs, personal benefits and ""withholding"" behavior, Prometheus, 20, pp. 143-158, (2002); Harman G., Australian academics and prospective academics: Adjustment to a more commercial environment, Higher Education Management and Policy, 15, pp. 105-122, (2003); Henty M., Weaver B., Bradbury S., Porter S., Investigating Data Management Practices In Australian Universities. Australian Partnership For Sustainable Repositories (APSR), (2008); Hopwood P., Data Governance: One Size Does Not Fit All, (2008); Data Management Channel, (2011); US National Science Data Group Formed, (2007); Jones S., Developments in research funder data policy, International Journal of Digital Curation, 7, pp. 114-125, (2012); Jones S., Ball A., Ekmekcioglu C., The data audit framework: A first step in the data management challenge, International Journal of Digital Curation, 3, pp. 113-120, (2008); Lyon L., Dealing With Data: Roles, Rights, Responsibilities and Relationships, (2007); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, pp. 126-138, (2012); Ma Q., Pearson M.L., ISO 17799: Best practices in information security management?, Communication of the Association For Information Systems, 15, pp. 577-591, (2005); Mervis J., Grants management: NSF survey of applicants finds a system teetering on the brink, Science, 317, pp. 880-881, (2007); Miller H.G., Baldwin W.H., Research letters. A terse amendment produces board change in data access, American Journal of Public Health, 91, pp. 824-825, (2001); Mullins C.S., The Impact of Regulatory Compliance On Data Management, (2006); (2003); See National Science Foundation Web Site, Data Management & Sharing Frequently Asked Questions (FAQs), (2011); Nicholls M.G., Cargill B.J., Establishing best practice university research funding strategies using mixed-mode modeling, Omega, 39, pp. 214-225, (2011); Circular A-110, (1999); O'Reilly K., Paper D., Marx S., Demystifying grounded theory for business research, Organizational Research Methods, 15, pp. 247-262, (2012); Governance of Public Research: Steering and Funding of Research Institutions Country Report: United States, (2008); Stake R.E., Qualitative case studies, Handbook of Qualitative Research, pp. 443-466, (2005); Strauss A., Corbin J., Basics of Qualitative Research: Grounded Theory Procedures and Techniques, (1990); Swan A., Brown S., The Skills, Role and Career Structure of Data Scientists and Curators: An Assessment of Current Practice and Future Needs, (2008); (2009); Vincent-Lancrin S., What is changing in academic research? Trends and futures scenarios, European Journal of Education, 41, pp. 169-202, (2006)","K. O'Reilly; Department of Marketing, Haworth College of Business, Western Michigan University, Kalamazoo, MI 49008-5430, 3141 Schneider Hall, United States; email: kelley.oreilly@wmich.edu","","SAGE Publications Inc.","","","","","","21582440","","","","English","SAGE Open","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-84883207376"
"Atkinson I.; Buckle A.M.; Groenewegen D.; Nicholas N.; Treloar A.; Beitz A.","Atkinson, Ian (25941109900); Buckle, Ashley M. (35547911900); Groenewegen, David (23567996300); Nicholas, Nick (36708393300); Treloar, Andrew (22939397700); Beitz, Anthony (24342660100)","25941109900; 35547911900; 23567996300; 36708393300; 22939397700; 24342660100","ARCHER - An enabler of research data management","2008","Proceedings - 4th IEEE International Conference on eScience, eScience 2008","","","4736764","246","252","6","1","10.1109/eScience.2008.45","https://www.scopus.com/inward/record.uri?eid=2-s2.0-62749167943&doi=10.1109%2feScience.2008.45&partnerID=40&md5=5566f9f3f8326f444a7d0647f86fd63b","ANDS; ARCHER, Australia; ARROW; James Cook University, e-Research Centre, Australia; Department of Biochemistry and Molecular Biology, Monash University, Australia; Link Affiliates; Monash e-Research Centre, Australia","Atkinson I., ARCHER, Australia, James Cook University, e-Research Centre, Australia; Buckle A.M., Department of Biochemistry and Molecular Biology, Monash University, Australia; Groenewegen D., ANDS, ARCHER, Australia, ARROW; Nicholas N., ARCHER, Australia, Link Affiliates; Treloar A., ANDS; Beitz A., ARCHER, Australia, Monash e-Research Centre, Australia","With new scientific instruments growing exponentially in their capability to generate research data, new infrastructure needs to be developed and deployed to allow researchers to effectively and securely manage their research data from collection to publication. In particular, researchers need to be able to easily acquire data from instruments, store and manage potentially large quantities of data, easily process the data, share research resources and work spaces with colleagues both inside and outside of their institution, search and discover across their accessible collections, and easily publish datasets and related research artefacts. The ARCHER Project has developed production-ready generic e-Research infrastructure including: a Research Repository; Scientific Dataset Managers (both a web and desktop application); Distributed Integrated Multi-Sensor & Instrument Middleware; and a Collaborative Workspace Environment. Institutions can selectively deploy these components to greatly assist their researchers in managing their research data. © 2008 Crown Copyright.","","Middleware; Collaborative workspaces; Data sets; Desktop applications; E researches; Multi sensors; Research datum; Scientific instruments; Work spaces; Distributed computer systems","","","","","","","Faux N., Beitz A., Bate A., Amin A.A., Atkinson I., Enticott C., Mahmood K., Swift M., Treloar A., Abramson D., Whisstock J.C., Buckle A.M., eResearch Solutions for High Throughput Structural Biology, 3rd IEEE International Conference on e-Science and Grid Computing, (2007); Atkinson I.M., Beitz A., Buckle A., Treloar A., An X-Ray Crystallography Case Study: Using the DART Toolkit to enable more effective eResearch, APAC Conference Exhibition Australian Partnership for Advanced Computing, (2007); Treloar A., The Data Acquisition. Accessibility. Annotation and e-Research Technologies (DART) Project: Supporting the complete e-Research Lifecycle, Proceedings of UK e-Science Programme All Hands Meeting 2007 (AHM2007), (2007); Treloar A., DART: Building the new collaborative e-research infrastructure, Proceedings of Educause Australasia 2007, (2007); Treloar A., The Dataset Acquisition. Accessibility, and Annotation e-Research Technologies (DART) Project: Building the new collaborative e-research infrastructure, Proceedings of AusWeb06, the Twelfth Australian World Wide Web Conference, Southern Cross University Press, (2006); Atkinson I.M., Et al., Developing CIMA-Based Cyberinfrastructure for Remote Access to Scientific Instruments and Collaborative e-Research, Proceedings of 5th Australasian Symposium on Grid Computing and e-Research, Bendigo, (2007); Treloar A., Harboe-Ree C., Data management and the curation continuum: How the Monash experience is informing repository relationships, Proceedings of VALA 2008, (2008)","","","","","4th IEEE International Conference on eScience, eScience 2008","7 December 2008 through 12 December 2008","Indianapolis, IN","75550","","978-076953535-7","","","English","Proc. - IEEE Int. Conf. eScience, eScience","Conference paper","Final","","Scopus","2-s2.0-62749167943"
"Corrall S.; Kennan M.A.; Afzal W.","Corrall, Sheila (16303268400); Kennan, Mary Anne (56001293900); Afzal, Waseem (26325353700)","16303268400; 56001293900; 26325353700","Bibliometrics and research data management services: Emerging trends in library support for research","2013","Library Trends","61","3","","636","674","38","155","10.1353/lib.2013.0005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878452777&doi=10.1353%2flib.2013.0005&partnerID=40&md5=529014bd2446960216c7baacdeeee9ab","University of Pittsburgh, United States; Australian Graduate School of Management, University of New South Wales, University of Technology Sydney, Australia; Emporia State University, Emporia, KS, United States; University of Punjab, Pakistan","Corrall S., University of Pittsburgh, United States; Kennan M.A., Australian Graduate School of Management, University of New South Wales, University of Technology Sydney, Australia; Afzal W., Emporia State University, Emporia, KS, United States, University of Punjab, Pakistan","Developments in network technologies, scholarly communication, and national policy are challenging academic libraries to find new ways to engage with research communities in the economic downturn. Librarians are responding with service innovations in areas such as bibliometrics and research data management. Previous surveys have investigated research data support within North America and other research services globally with small samples. An online multiple-choice questionnaire was used to survey bibliometric and data support activities of 140 libraries in Australia, New Zealand, Ireland, and the United Kingdom, including current and planned services, target audiences, service constraints, and staff training needs. A majority of respondents offered or planned bibliometrics training, citation reports, and impact calculations but with significant differences between countries. Current levels of engagement in data management were lower than for bibliometrics, but a majority anticipated future involvement, especially in technology assistance, data deposit, and policy development. Initiatives were aimed at multiple constituencies, with university administrators being important clients and partners for bibliometric services. Gaps in knowledge, skills, and confidence were significant constraints, with near-universal support for including bibliometrics and particularly data management in professional education and continuing development programs. The study also found that librarians need a multilayered understanding of the research environment. © 2013 The Board of Trustees, University of Illinois.","","","","","","","","","Adams J., Bibliometrics, assessment and UK research, Serials, 20, pp. 188-191, (2007); Aldrich A.W., Following the phosphorous trail of research library mission statements into present and future harbors, Sailing Into the Future: Charting Our Destiny, ACRL 13th National Conference, pp. 304-316, (2007); Amos K., Mower A., James M.A., Weber A., Yaffe J., Youngkin M., Exploring publishing patterns at a large research university: Implications for library practice, Evidence Based Library and Information Practice, 7, 3, pp. 32-50, (2012); Citation Research: How to Find Citation Counts For Your Publications and How to Find Journal Rankings Such As Impact Factors, (2012); Auckland M., Re-skilling For Research: An Investigation Into the Roles and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Ball R., Tunger D., Bibliometric analysis - a new business area for information professionals in libraries? Support for scientific research by perception and trend analysis, Scientometrics, 66, pp. 561-577, (2006); Barrett J., UCD Data Management Checklist, (2012); Baughman J.C., Towards a structural approach to collection development, College & Research Libraries, 38, pp. 241-248, (1977); Beck S.E., Manuel K., Practical Research Methods For Librarians and Information Professionals, (2008); Bennett D.B., Leonard M., Wrublewski D., Comparing engineering departments across institutions: Gathering publication impact data in a short timeframe, Issues In Science and Technology Librarianship, 68, (2012); Bent M., Gannon-Leary P., Webb J., Information literacy in a researcher's learning life: Seven ages of research, New Review of Information Networking, 13, pp. 81-99, (2007); Bourg C., Coleman R., Erway R., Support For the Research Process: An Academic Library Manifesto, (2009); Broadus R.N., The applications of citation analyses to library collection building, Advances In Librarianship, 7, pp. 299-355, (1977); Brownlee R., Research data and repository metadata: Policy and technical issues at the University of Sydney Library, Cataloging & Classification Quarterly, 47, pp. 370-379, (2009); Burke L., Models of reference services in Australian academic libraries, Journal of Librarianship and Information Science, 40, pp. 269-286, (2008); Carlson J., Kneale R., Embedded librarianship in the research context: Navigating new waters, College & Research Libraries News, 72, pp. 167-170, (2011); Carlson J.R., Garritano J.R., E-science, cyberinfrastructure, and the changing face of scholarship: Organizing for new models of research support at the Purdue University Libraries, Staffing, Sustaining, and Advancing the Academic Library In the 21st Century, pp. 234-269, (2010); Case D., Looking For Information: A Survey of Research On Information Seeking, Needs and Behaviour, (2012); Choudhury G.S., Case study in data curation at Johns Hopkins University, Library Trends, 57, pp. 211-220, (2008); Connaway L.S., Powell R.R., Basic Research Methods For Librarians, (2010); Corrall S., Roles and responsibilities: Libraries, librarians and data, Managing Research Data, pp. 105-133, (2012); Cotta-Schonberg M., The changing role of the subject specialist, Liber Quarterly, 17, 3-4, (2007); Davis M., Wilson C.S., Horn H., Informing decision-making in libraries: Informetric research as input to LIS education and practice, Australian Academic and Research Libraries, 36, pp. 195-213, (2005); de Bellis N., Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics, (2009); Delasalle J., Research evaluation: Bibliometrics and the librarian, SCONUL Focus, 53, pp. 15-19, (2012); Delserone L.M., At the watershed: Preparing for research data management and stewardship at the University of Minnesota Libraries, Library Trends, 57, pp. 202-210, (2008); Drott M.C., Mancall J., Griffith B.C., Bradford's Law and libraries: Present applications-potential promise, ASLIB Proceedings, 31, pp. 296-304, (1979); Drummond R., Wartho R., RIMS: The Research Impact Measurement Service at the University of New South Wales, Australian Academic & Research Libraries, 40, pp. 76-87, (2009); Duranceau E.F., The ""Wealth of Networks"" and institutional repositories: MIT, DSpace, and the future of the scholarly commons, Library Trends, 57, pp. 244-261, (2008); Florance P., GIS collection development within an academic library, Library Trends, 55, pp. 222-234, (2006); Fonseca A.J., Viator V.P., Escaping the island of lost faculty: Collaboration as a means of visibility, Collaborative Librarianship, 1, pp. 81-90, (2009); Freiburger G., Kramer S., Embedded librarians: One library's model for decentralized service, Journal of the Medical Library Association, 97, pp. 139-142, (2009); Friedlander A., Adler P., To Stand the Test of Time: Long-term Stewardship of Digital Data Sets In Science and Engineering, a Report to the National Science Foundation From the ARL Workshop On New Collaborative Relationships: The Role of Academic Libraries In the Digital Data Universe, (2006); Fulton B., Botticelli P., Bradley J., DigIn: A hands-on approach to a digital curation curriculum for professional development, Journal of Education For Library and Information Science, 52, pp. 95-109, (2011); Gabridge T., The last mile: Liaison roles in curating science and engineering research data, Research Library Issues, 265, pp. 15-21, (2009); Garritano J.R., Carlson J.R., A subject librarian's guide to collaborating on e-science projects, Issues In Science and Technology Librarianship, 57, (2009); Gibbs C., Sergeant K., Opportunity, Not Hard Work: Scripted Solutions to Solving Our Bibliometric Nightmare, (2009); Gold A., Cyberinfrastructure, data, and libraries, part 2. Libraries and the data challenge: Roles and actions for libraries, D-Lib Magazine, 13, 9-10, (2007); Gumpenberger C., Wieland M., Gorraiz J., Bibliometric practices and activities at the University of Vienna, Library Management, 33, pp. 174-183, (2012); Harris-Pierce R.L., Liu Y.Q., Is data curation education at library and information science schools in North America adequate?, New Library World, 113, 11-12, pp. 598-613, (2012); Harvey R., Digital Curation: A How-to-do-It Manual, (2010); Heath F., Library assessment: The way we have grown, Library Quarterly, 81, pp. 7-25, (2011); Report On the Pilot Exercise to Develop Bibliometric Indicators For the Research Excellence Framework, (2009); Analysis of Data From the Pilot Exercise to Develop Bibliometric Indicators For the REF: The Effect of Using Normalised Citation Scores For Particular Staff Characteristics, (2011); Hendrix D., Tenure metrics: Bibliometric education and services for academic faculty, Medical Reference Services Quarterly, 29, pp. 183-189, (2010); Henty M., Developing the capability and skills to support e-research, Ariadne, 55, (2008); Herther N., Research evaluation and citation analysis: Key issues and implications, The Electronic Library, 27, pp. 361-375, (2009); Hey T., Hey J., E-science and its implications for the library community, Library Hi Tech, 24, pp. 515-528, (2006); High Level Expert Group on Scientific Data, Riding the Wave: How Europe Can Gain From the Rising Tide of Scientific Data, (2010); Holland M., Serving different constituencies: Researchers, Subject Librarians: Engaging With the Learning and Teaching Environment, pp. 131-147, (2006); Houser R., Building a library GIS service from the ground up, Library Trends, 55, pp. 315-326, (2006); Hswe P., Holt A., Joining in the enterprise of response in the wake of the NSF data management planning requirement, Research Library Issues, 274, pp. 11-17, (2011); Joint N., Bemused by bibliometrics: Using citation analysis to evaluate research quality, Library Review, 57, pp. 346-357, (2008); Jones-Evans A., Bangor university library service: An update, Journal of Librarianship and Information Science, 37, pp. 115-117, (2005); Kear R., Colbert-Lewis D., Citation searching and bibliometric measures, College & Research Libraries News, 72, pp. 470-474, (2011); Keralis S.D.C., Data curation education: A snapshot, The Problem of Data, pp. 32-43, (2012); Lage K., Losoff B., Maness J., Receptivity to library involvement in scientific data curation: A case study at the University of Colorado Boulder, Portal: Libraries and The Academy, 11, pp. 915-937, (2012); Lewis M., Libraries and the management of research data, Envisioning Future Academic Library Services: Initiatives, Ideas and Challenges, pp. 145-168, (2010); Line M.B., Rank lists based on citations and library use as indicators of journal usage in individual libraries, Collection Management, 2, pp. 313-316, (1978); Lyon L., Dealing With Data: Roles, Rights, Responsibilities and Relationships, (2007); Lyon L., The informatics transform: Re-engineering libraries for the data decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Maccoll J., Library roles in university research assessment, Liber Quarterly, 20, (2010); Maccoll J., Jubb M., Supporting Research: Environments, Administration and Libraries, (2011); Macdonald S., Martinez L., Supporting local data users in the UK academic community, Ariadne, 44, (2005); Macdonald S., Uribe L.M., Libraries in the converging world of open data, e-research, and Web 2.0, Online, 32, 2, pp. 36-40, (2008); Research Metrics: Research Metrics Allows You to Measure the Impact of Your Research and Publications, (2012); Marcum D.B., George G., The Data Deluge: Can Libraries Cope With E-science?, (2010); Mays R., Tenopir C., Kaufman P., Lib-Value: Measuring value and return on investment of academic libraries, Research Library Issues, 271, pp. 36-40, (2010); McNeill P., Chapman S., Research Methods, (2005); Nicholas D., Rowlands I., Jubb M., Jamali H.R., The impact of the economic downturn on libraries: With special reference to university libraries, Journal of Academic Librarianship, 36, pp. 376-382, (2010); Cyberinfrastructure Vision For 21st Century Discovery, (2007); Oakleaf M., The Value of Academic Libraries: A Comprehensive Research Review and Report, (2010); Pan R., Breen E., MyRI: An Open Access Bibliometrics Toolkit - Collaboration In Research Skills Support, (2011); Pendlebury D., Using Bibliometrics In Evaluating Research, (2008); Peters C., Dryden A.R., Assessing the academic library's role in campus-wide research data management: A first step at the University of Houston, Science & Technology Libraries, 30, pp. 387-403, (2011); Pickard A.J., Research Methods In Information, (2007); Pryor G., Managing Research Data, (2012); Pryor G., Donnelly M., Skilling up to do data: Whose role, whose responsibility, whose career?, International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Qin J., D'Ignazio J., Oakleaf M., Crowston K., Wiggins A., Kuehn A., Educating Escience Librarians, (2010); Rambo N., Changes in research libraries as a result of e-science initiatives: A snapshot, The Data Deluge: Can Libraries Cope With E-science?, pp. 43-60, (2010); Ray J., Sharks, digital curation, and the education of information professionals, Museum Management and Curatorship, 24, pp. 357-368, (2009); Renear A.H., Dolan M., Trainor K., Munox T., Extending An LIS Data Curation Curriculum to The Humanities: Selected Activities and Observations, (2010); Rice R., Haywood J., Research data management initiatives at University of Edinburgh, International Journal of Digital Curation, 6, 2, pp. 232-244, (2011); Research Information Network & Consortium of Research Libraries, Researchers' Use of Academic Libraries and Their Services: A Report Commissioned By the Research Information Network and The Consortium of Research Libraries, (2007); Research Information Network & Research Libraries UK, The Value of Libraries For Research and Researchers: A RIN and RLUK Report, (2011); Roemer R.C., Borchadt R., From bibliometrics to altmetrics: A changing scholarly landscape, College & Research Libraries News, 73, pp. 596-600, (2012); Schonfield R.C., Faculty survey 2009: Key strategic insights for libraries, publishers, and societies, (2010); Schottlaender B.E.C., An idiosyncratic perspective on the history and development at the University of California, San Diego, of support for Cyberinfrastructure-enabled e-science (99-112), The Data Deluge: Can Libraries Cope With E-science?, pp. 43-60, (2010); Schrader A.M., Teaching bibliometrics, Library Trends, 30, pp. 151-172, (1981); Shumaker D., The Embedded Librarian: Innovative Strategies For Taking Knowledge Where It's Needed, (2012); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Stanton J.M., Kim Y., Oakleaf M., Lankes R.D., Gandel P., Cogburn D., Liddy E.D., Education for eScience professionals: Job analysis, curriculum guidance, and program considerations, Journal of Education For Library and Information Science, 52, pp. 79-94, (2011); Steinhart G., Libraries as distributors of geospatial data: Data management policies as tools for managing partnerships, Library Trends, 55, pp. 264-284, (2006); Swan A., Institutional repositories - Now and next, University Libraries and Digital Learning Environments, pp. 119-133, (2011); Swan A., Brown S., The Skills, Role and Career Structure of Data Scientists and Curators: An Assessment of Current Practice and Future Needs, (2008); Thomas J., Future-proofing: The academic library's role in e-research support, Library Management, 32, pp. 37-47, (2011); Town J.S., Value, impact, and the transcendent library: Progress and pressures in performance measurement and evaluation, Library Quarterly, 81, pp. 111-125, (2011); Treloar A., Design and implementation of the Australian National Data Service, International Journal of Digital Curation, 4, 1, pp. 125-137, (2009); Citation Searching and Bibliometric Measures: A Discussion On Topics Such As the H-index, Eigenfactor, Impact Factor, Journal Citation Reports, and Other Tools, (2012); UNT Receives More Than $800,000 to Investigate Needs In Archiving Research Data, (2011); Varvel V.E., Bammerlin E., Palmer C.L., Education for data professionals: A study of current courses and programs, Poster Presented At the IConference 2012: Proceedings of the 2012 IConference, pp. 527-529, (2012); von Ungern-Sternberg S., Teaching bibliometrics, Journal of Education For Library and Information Science, 39, pp. 76-80, (1998); Wallace D.P., van Fleet C., Knowledge In Action: Research and Evaluation In Library and Information Science, (2012); Walters T.O., Data curation program development in U.S. universities: The Georgia Institute of Technology example, International Journal of Digital Curation, 3, 4, pp. 83-92, (2009); Warr R.B., Bibliometrics: A model for judging quality, Collection Building, 5, 2, pp. 29-34, (1983); Webb J., Gannon-Leary P., Bent M., Providing Effective Library Services For Research, (2007); Westra B., Data services for the sciences: A needs assessment, Ariadne, 64, (2010); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, pp. 191-201, (2008); Whitten J., Bentley L., Systems Analysis and Design Methods, (2007); Wildemuth B.M., Applications of Social Research Methods to Questions In Information and Library Science, (2009); Wong G.K.W., Exploring research data hosting at the HKUST institutional repository, Serials Review, 35, pp. 125-132, (2009); Wood E.J., Miller R., Knapp A., Beyond Survival: Managing Academic Libraries In Transition, (2007); Yakel E., Conway P., Hedstrom M., Wallace D., Digital curation for digital natives, Journal of Education For Library and Information Science, 52, pp. 23-31, (2011); Young H., Lund P., Reflections on a benchmarking survey of research support provided by 1994 Group libraries, SCONUL Focus, 43, pp. 51-56, (2008); Zhao D., Bibliometrics and LIS education: How do they fit together? [Panel proposal], Proceedings of the American Society For Information Science and Technology: ASIST, 48, pp. 1-4, (2011)","","","Johns Hopkins University Press","","","","","","00242594","","","","English","Libr. Trends","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84878452777"
"vo Schmidt N.","vo Schmidt, Nora (55873869600)","55873869600","Research data management and libraries. Localisation in cooperative networks; [Forschungsdatenmanagement und bibliotheken - Verortung in kooperationsnetzwerken]","2013","VOEB-Mitteilungen","66","2","","250","278","28","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885024273&partnerID=40&md5=a60018640313485430f991b30076e83e","Universitätsbibliothek Wien, Austria","vo Schmidt N., Universitätsbibliothek Wien, Austria","The funder Jisc supported 27 british universities to establish or improve their research data manangement (RDM) services in 2011-2013. The comprehensive materials and tools by the Digital Curation Center (DDC), which handle the entire life cycle of the data, offered orientation. The structure of this article also follows this cycle to describe the state-of-the-art RDM as it was realized by six Jisc projects that will be focussed on. The aim of this analysis was to elicit from these examples which tasks are undertaken by libraries. Primarily, this are the overall management of RDM Services and the implementation of training. Repositories and metadata are also mostly curated here, while these tasks are often located at the IT services, as well as the project management and requirements analysis. The policy development is one more important task of the library, frequently held in cooperation with the research services, which are almost always in charge of data management planning (DMP).","Academic library; Data librarian; New library services; RDM; Repository; Research data; Research data management; Research services; Research support","","","","","","","","Antell K., Foote J.B., Shults B., Dealing with Data: Science Librarians' Participation in Data Management at Association of Research Libraries Institutions, College & Research Libraries, (2013); Research Data e-Infrastructures: Framework for Action in H2020, (2013); Hucka M., Finney A., Sauro H.M., Bolouri H., Doyle J.C., Kitano H., Et al., The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models, Bioinformatics, 19, 4, pp. 524-531, (2003); Jones S., Pryor G., Whyte A., How to Develop Research Data Management Services-a guide for HEIs. DCC How-to Guides, (2013); Kometa S., Research data management requirements online survey report. iridium project output, (2012); Lautenschlager M., Sens I., Konzept zur Zitierfähigkeit Wissenschaftlicher Primärdaten, Information-Wissenschaft und Praxis, 54, 8, pp. 463-466, (2003); Lewis M., Libraries and the Management of Research Data, Envisioning Future Academic Library Services: Initiatives, ideas and challenges, (2010); Martinez L., Using the Data Audit Framework: An Oxford Case Study, (2009); Pampel H., Bertelmann R., Hobohm H.-C., Data Librarianship': Rollen, Aufgaben, Kompetenzen, Working Paper Series des Rates für Sozial-und Wirtschaftsdaten, (2010); Rice R., Haywood J., Research Data Management Initiatives at University of Edinburgh, The International Journal of Digital Curation, 6, 2, pp. 232-244, (2011); Starr J., Willett P., Federer L., Horning C., Bergstrom M.L., A Collaborative Framework for Data Management Services: The Experience of the University of California, Journal of eScience Librarianship, 1, 2, (2012); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data Sharing by Scientists: Practices and Perceptions, PLoS ONE, 6, 6, (2011); Insight into digital preservation of research output in Europe. Insight Report (D3.6, (2010); Wealth check: Financial data for UK higher education institutions, 2010-11, Times Higher Education, (2012); Whyte A., Wilson A., How to Appraise and Select Research Data for Curation. DCC How-to Guides, (2010); Williamson L., Parsons T., UK HEIs RDM service models and skills to support Research Data Management, (2013); Wilson J., University of Oxford Research Data Management Survey 2012. The Results, DaMaRO-Blog, (2013); Winn J., Open data and the academy: An evaluation of CKAN for research data management, IASSIST, (2013)","N. vo Schmidt; Universitätsbibliothek Wien, Austria; email: nora.schmidt@univie.ac.at","","","","","","","","10222588","","","","English","VOEB-Mitteilungen","Article","Final","","Scopus","2-s2.0-84885024273"
"Zhao Z.; Lu C.","Zhao, Zhimei (56166382900); Lu, Chaodong (35766687500)","56166382900; 35766687500","Des acclimatization development and mining of multimedia databases","2013","Lecture Notes in Electrical Engineering","220 LNEE","VOL. 5","","803","808","5","0","10.1007/978-1-4471-4844-9_106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900860346&doi=10.1007%2f978-1-4471-4844-9_106&partnerID=40&md5=bce85828d65f444788d2370e21b78c85","Henan Institute of Engineering, Zhengzhou 451191, Henan, China; Wuhan University of Technology, Wuhan 430070, Hubei, China","Zhao Z., Henan Institute of Engineering, Zhengzhou 451191, Henan, China; Lu C., Wuhan University of Technology, Wuhan 430070, Hubei, China","To solve the des acclimatization research data management problem, a form of a multimedia database, data management, indications are given off the plateau overall design of multimedia database and the database engine for its development, gives a plateau associated multimedia data off indications description method, proved that the design can be adapted to the development of multimedia database des acclimatization and general data mining requirements. © 2013 Springer-Verlag.","Data mining; Database; Des acclimatization; Multimedia","Data mining; Information management; Database engine; Des acclimatization; Description method; Multimedia; Multimedia data; Multimedia database; Overall design; Research data managements; Database systems","","","","","","","Oracle 9i Replication Management API Reference Releasel (9.0.1) Part Number A87502-01[EB/OL], (2002); Subrahmanian V.S., Principles of multimedia database system, Morgan Kaufmann, 3, 1, pp. 56-59, (1998); Chen W., Compulsion, Banyan Multimedia Database Engine Design and Implementation of Computer Application, 15, 11, pp. 104-109, (2003); Ning G., MEDB multimedia database management system design, Multi-Media World, 9, 7, pp. 36-41, (1994)","Z. Zhao; Henan Institute of Engineering, Zhengzhou 451191, Henan, China; email: zhaozhimei@hrsk.net","","Springer Verlag","National Science Foundation of China; Shanghai Jiao Tong University","2nd International Conference on Information Engineering and Applications, IEA 2012","26 October 2012 through 28 October 2012","Chongqing","99822","18761100","","","","English","Lect. Notes Electr. Eng.","Conference paper","Final","","Scopus","2-s2.0-84900860346"
"Bunakov V.; Matthews B.","Bunakov, Vasily (55918851000); Matthews, Brian (16643340700)","55918851000; 16643340700","Data curation framework for facilities science","2013","DATA 2013 - Proceedings of the 2nd International Conference on Data Technologies and Applications","","","","211","216","5","7","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887134321&partnerID=40&md5=1b681a9765b7380815c94d0fb2fe6b37","Scientific Computing Department, Science and Technology Facilities Council, Harwell OX11 0QX, United Kingdom","Bunakov V., Scientific Computing Department, Science and Technology Facilities Council, Harwell OX11 0QX, United Kingdom; Matthews B., Scientific Computing Department, Science and Technology Facilities Council, Harwell OX11 0QX, United Kingdom","The trend in research data management practice is that the role of large facilities represented by particle accelerators, neutron sources and other scientific instruments of scale extends beyond providing capabilities for the raw data collection and its initial processing. Managing data and publications catalogues, shared software repositories and sophisticated data archives have become common responsibilities of the research facilities. We suggest that facilities can further move from managing data to curating them which implies meaningful data enrichment, annotation and linkage according to the best practices which have emerged in the facilities science itself or have been borrowed elsewhere. We discuss the challenges and opportunities that are the drivers for this role transformation, and suggest a data curation framework harmonized with the research lifecycle in facilities science.","Big data; Data curation; Linked data; Research data; Research lifecycle","Information management; Life cycle; Metadata; Neutron sources; Research; Big datum; Data curation; Linked datum; Research data; Research data managements; Role transformation; Scientific instrument; Software repositories; Data handling","","","","","","","Bechhofer S., Et al., Why linked data is not enough for scientists, Future Generation Computer Systems, 2013, 29, 2, pp. 599-611, (2013); Coles S.J., Gale P.A., Changing and challenging times for service crystallography, Chemical Science, 3, 3, pp. 683-689, (2012); Dumontier M., Wild D., Linked data in drug discovery, IEEE Internet Computing, 16, 6, pp. 68-71, (2012); Matthews B., Et al., Model of the data continuum in photon and neutron facilities, PaNdata ODI, Deliverable D6.1, (2012); Marcos E., Et al., Author order: What science can learn from the arts, Communications of the ACM, 55, 9, pp. 39-41, (2012); Mesot J., A need to rethink the business model of user labs?, Neutron News, 23, 4, pp. 2-3, (2012); Reference model for an open archival information system, CCSDS 650.0-M-2 (Magenta Book), 2, (2012); Wilson M., Meeting a scientific facility provider's duty to maximise the value of data, DataCite Summer Meeting, Digital Research Data in Practice (DataCite2012), (2012)","","","","Institute for Systems and Technologies of; Information, Control and Communication (INSTICC)","2nd International Conference on Data Technologies and Applications, DATA 2013","29 July 2013 through 31 July 2013","Reykjavik","100614","","978-989856567-9","","","English","DATA - Proc. Int. Conf. Data Technol. Appl.","Conference paper","Final","","Scopus","2-s2.0-84887134321"
"Lowe H.J.; Ferris T.A.; Hernandez P.M.; Weber S.C.","Lowe, Henry J (7102871976); Ferris, Todd A (7006777623); Hernandez, Penni M (37077040500); Weber, Susan C (23973881900)","7102871976; 7006777623; 37077040500; 23973881900","STRIDE--An integrated standards-based translational research informatics platform.","2009","AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium","2009","","","391","395","4","315","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77958007735&partnerID=40&md5=286a6d820341155cf969450824ad906c","Stanford Center for Clinical Informatics, Stanford University, Stanford CA, United States","Lowe H.J., Stanford Center for Clinical Informatics, Stanford University, Stanford CA, United States; Ferris T.A.; Hernandez P.M.; Weber S.C.","STRIDE (Stanford Translational Research Integrated Database Environment) is a research and development project at Stanford University to create a standards-based informatics platform supporting clinical and translational research. STRIDE consists of three integrated components: a clinical data warehouse, based on the HL7 Reference Information Model (RIM), containing clinical information on over 1.3 million pediatric and adult patients cared for at Stanford University Medical Center since 1995; an application development framework for building research data management applications on the STRIDE platform and a biospecimen data management system. STRIDE's semantic model uses standardized terminologies, such as SNOMED, RxNorm, ICD and CPT, to represent important biomedical concepts and their relationships. The system is in daily use at Stanford and is an important component of Stanford University's CTSA (Clinical and Translational Science Award) Informatics Program.","","Academic Medical Centers; Biomedical Research; California; Computer Communication Networks; Database Management Systems; Humans; Medical Records Systems, Computerized; Programming Languages; Translational Research; article; computer language; computer network; data base; electronic medical record; human; medical research; organization and management; translational research; United States; university hospital","","","","","","","","","","","","","","","","1942597X","","","20351886","English","AMIA Annu Symp Proc","Article","Final","","Scopus","2-s2.0-77958007735"
"Dickmann F.; Mathieu N.; Grutz R.; Krawczak M.","Dickmann, F. (6507224680); Mathieu, N. (55614397700); Grutz, R. (54983345400); Krawczak, M. (7006351366)","6507224680; 55614397700; 54983345400; 7006351366","Management and preservation of genome research data; [Management und Langzeitarchivierung von Genomdaten aus der Forschung]","2013","Medizinische Genetik","25","1","","15","21","6","0","10.1007/s11825-013-0373-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874614976&doi=10.1007%2fs11825-013-0373-0&partnerID=40&md5=f52e92fa670df3f3c6be863cbe1c8809","Abteilung Medizinische Informatik, Universitatsmedizin Gottingen, 37075 Gottingen, Robert-Koch-Straße 40, Germany; Institut fur Medizinische Informatik und Statistik, Christian-Albrechts-Universitat zu Kiel, Germany","Dickmann F., Abteilung Medizinische Informatik, Universitatsmedizin Gottingen, 37075 Gottingen, Robert-Koch-Straße 40, Germany; Mathieu N., Abteilung Medizinische Informatik, Universitatsmedizin Gottingen, 37075 Gottingen, Robert-Koch-Straße 40, Germany; Grutz R., Abteilung Medizinische Informatik, Universitatsmedizin Gottingen, 37075 Gottingen, Robert-Koch-Straße 40, Germany; Krawczak M., Institut fur Medizinische Informatik und Statistik, Christian-Albrechts-Universitat zu Kiel, Germany","The ever increasing amount and complexity of newly generated molecular data requires the issue of reliable data management and digital preservation to be addressed in the context of genomic research as well. The named processes are highly relevant for ensuring good scientific practice. The present article introduces general aspects of research data management and digital preservation, and links them to genomic data as an example. In addition, the ensuing scientific, ethical and organizational challenges to genome research will be exposed. In conclusion, transparent governance rules turn out to be most vital for any management and preservation of genomic research to be both practical and sensible. © 2013 Springer-Verlag Berlin Heidelberg.","digital preservation; Genomic data; research data management","article; genetic database; genome; information processing; medical ethics; science","","","","","Europäische Kommission; Thema Management und Archivierung von Forschungsdaten; National Institutes of Health; Deutsche Forschungsgemeinschaft","In der modernen biomedizinischen Forschung werden Mess-und Analy- segeräte verwendet, die besonders große Mengen digitaler Daten produ- zieren, u.a. im Zuge des Next Genera tion Sequencing (NGS) [13]. Die daran beteiligten Forschungseinrichtungen stehen daher vor der Aufgabe, eine dramatisch wachsende Datenmen- ge verwalten und aufbewahren zu müssen [12]. So haben u.a. die Deut sche Forschungsgemeinschaft (DFG) [1], der Wissenschaftsrat [21,22], die Europäische Kommission [12] und die National Institutes of Health (NIH) [17] das Thema Management und Archivierung von Forschungsdaten eindringlich adressiert.","Empfehlungen Zur Gesicherten Aufbewahrung und Bereitstellung Digitaler Forschungsprimardaten. Deutsche Forschungsgemeinschaft (DFG), (2009); Bodenreider O., The Unified Medical Language System (UMLS): Integrating biomedical terminology, Nucleic Acids Research, 32, 1, (2004); Reference Model for An Open Archival Information System (OAIS), (2002); Pja C., Fields C.J., Goto N., Et al., The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants, Nucleic Acids Research, 38, 6, pp. 1767-1771, (2010); Darmoni S.J., Thirion B., Leroy J.P., Et al., The use of Dublin Core metadata in a structured health resource guide on the Internet, Bulletin of the Medical Library Association, 89, 3, pp. 297-301, (2001); Gute Wissenschaftliche Praxis, (1998); Vorschlage Zur Sicherung Guter Wissenschaftlicher Praxis: Empfehlungen der Kommission Selbstkontrolle in der Wissenschaft., (1998); Dickmann F., Grutz R., Rienhoff O., A meta- perspective on bit rot of biomedical research data, 24th European Medical Informatics Conference - MIE2012, pp. 260-264, (2012); Dickmann F., Rey S., Stakeholder Analysis for Digital Preservation in Biomedical Research, (2010); Field D., Garrity G., Gray T., Et al., The minimum information about a genome sequence (MIGS) specification, Nature Biotechnology, 26, 5, pp. 541-547, (2008); Frankel F., Reid R., Big data: Distilling meaning from data, Nature, 455, 7209, pp. 30-30, (2008); Riding the Wave - How Europe Can Gain from the Rising Tide of Scientific Data, (2010); Kahn S.D., On the future of genomic data, Science, 331, 6018, pp. 728-729, (2011); Kleppner D., Sharp P.A., Research data in the digital age, Science, 325, 5939, (2009); Krawczak M., Goebel J.W., Cooper D.N., Is the NIH policy for sharing GWAS data running the risk of being counterproductive?, Investigative Genetics, 1, (2010); Lynch C., Big data: How do your data grow?, Nature, 455, 7209, pp. 28-29, (2008); Policy for Sharing of Data Obtained in NIH Supported or Conducted Genome-Wide Association Studies (GWAS), (2007); Nelson B., Data sharing: Empty archives, Nature, 461, 7261, pp. 160-163, (2009); Pryor G., Donnelly M., Skilling up to do data: Whose role, whose responsibility, whose career?, The International Journal of Digital Curation, 4, 2, pp. 158-170, (2009); Scholz C., Aufbewahrungsfristen fur Arztliche Unterlagen. Deutsche Gesellschaft fur Humangenetik S 1, (2012); Ubergreifende Empfehlungen zu Informationsinfrastrukturen. Wissenschaftsrat, Berlin, Wissenschaftsrat, (2011); Empfehlungen zur Weiterentwickung der wissenschaftlichen Informationsinfrastrukturen in Deutschland bis 2020. Wissenschaftsrat, Berlin, Wissenschaftsrat, (2012)","F. Dickmann; Abteilung Medizinische Informatik, Universitatsmedizin Gottingen, 37075 Gottingen, Robert-Koch-Straße 40, Germany; email: fdickmann@med.uni-goettingen.de","","","","","","","","18635490","","MGENE","","German","Med. Genet.","Article","Final","","Scopus","2-s2.0-84874614976"
"El Fadly A.; Daniel C.; Bousquet C.; Dart T.; Lastic P.Y.; Degoulet P.","El Fadly, A. (25645754200); Daniel, C. (24334555100); Bousquet, C. (8609880700); Dart, T. (6506423980); Lastic, P-Y (25646440300); Degoulet, P. (7005820110)","25645754200; 24334555100; 8609880700; 6506423980; 25646440300; 7005820110","Electronic Healthcare Record and clinical research in cardiovascular radiology. HL7 CDA and CDISC ODM interoperability.","2007","AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium","","","","216","220","4","24","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-56149087491&partnerID=40&md5=becae754285854b71d21a3b5508b42e3","INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France","El Fadly A., INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France; Daniel C., INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France; Bousquet C., INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France; Dart T., INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France; Lastic P.Y., INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France; Degoulet P., INSERM, UMR_S 872, Eq. 20, F-75006 France., Paris, France","Integrating clinical research data entry with patient care data entry is a challenging issue. At the G.Pompidou European Hospital (HEGP), cardiovascular radiology reports are captured twice, first in the Electronic Health Record (EHR) and then in a national clinical research server. Informatics standards are different for EHR (HL7 CDA) and clinical research (CDISC ODM). The objective of this work is to feed both the EHR and a Clinical Research Data Management System (CDMS) from a single multipurpose form. We adopted and compared two approaches. First approach consists in implementing the single ""care-research"" form within the EHR and aligning XML structures of HL7 CDA document and CDISC ODM message to export relevant data from EHR to CDMS. Second approach consists in displaying a single ""care-research"" XForms form within the EHR and generating both HL7 CDA document and CDISC message to feed both EHR and CDMS. The solution based on XForms avoids overloading both EHR and CDMS with irrelevant information. Beyond syntactic interoperability, a perspective is to address the issue of semantic interoperability between both domains.","","Biomedical Research; Cardiovascular Diseases; Humans; Medical Record Linkage; Medical Records Systems, Computerized; Patient Care Management; Programming Languages; Radiology Information Systems; Systems Integration; article; cardiovascular disease; computer language; hospital information system; human; medical record; medical research; organization and management; patient care; radiography; standard; system analysis","","","","","","","","A. El Fadly; email: nelfadly@yahoo.fr","","","","","","","","15594076","","","18693829","English","AMIA Annu Symp Proc","Article","Final","","Scopus","2-s2.0-56149087491"
"Zborowski M.","Zborowski, Mary (35424846400)","35424846400","CISTI'S activities in support of scientific data management in Canada 2008-2010","2009","Data Science Journal","8","","","27","33","6","3","10.2481/dsj.8.27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-76449087536&doi=10.2481%2fdsj.8.27&partnerID=40&md5=9ce43a0c46194ec66355d3d42c7aa86b","National Research Council Canada (NRC), Strategy and Development Branch (SDB), Ottawa ON K1A 0R6, 1200 Montreal Road, Canada","Zborowski M., National Research Council Canada (NRC), Strategy and Development Branch (SDB), Ottawa ON K1A 0R6, 1200 Montreal Road, Canada","In the Canadian research environment, it is difficult for researchers to effectively discover, access, and use data sets, except for those that are the most well known. Several recent reports have discussed the issues around ""lost"" data sets: those which are intended to be shared but cannot be identified and utilized effectively because of insufficient associated metadata. Both problems are approaching critical levels in Canada and internationally, a situation that is unacceptable because these data sets are often generated as a result of public funding. Solutions may involve providing support and training for researchers on how they can best collect and manage their data sets or developing gateways to scientific data sets. NRC-CISTI is the largest comprehensive source of scientific, technical, and medical information in North America, with a mandate to serve as Canada's national science library. Through its publishing arm, NRC Research Press, it is also Canada's foremost scientific publisher. NRC-CISTI is an organization with demonstrated expertise in metadata management, which, until recently, focused primarily on library and publishing contexts. However in November 2007, it formally committed to expand its agenda to address the management of scientific research data and the related critical needs of the research community. This paper presents NRC-CISTI's activities in this area. NRC-CISTI has begun by hosting forums in which the critical players (including the granting agencies) mapped out targets and approaches. It has strengthened its own internal expertise regarding metadata and management of scientific data sets. Finally, NRC-CISTI is developing a gateway Web site which will provide access to Canadian scientific data sets and related metadata, tools, educational resources, and other informative and collaborative tools urgently needed by Canadian and international researchers. NRC-CISTI is the sponsoring body for the Canadian National Committee for CODATA and is committed to promoting and supporting CNC/CODATA's initiatives.","Canada; National activities; National Research Council Canada; NRC-CISTI; Research data management; Scientific and technical research data","Information management; Canada; National activities; National Research Council; NRC-CISTI; Research data managements; Technical research; Metadata","","","","","","","Brown J., The national science library: Information centre for industry, Industrial Canada, 65, 1, (1965); Brown J., The CAN/SDI project: The SDI program of Canada's national science library, Special Libraries, 60, 8, pp. 73-77, (1969); Campbell H.C., National planning for canadian science and social science information systems, Library Trends, 17, 3, pp. 280-288, (1975); Ember G., The health science resource centre: A new information service of the national science library of Canada, Proceedings of the 3rd International Congress on Medical Librarianship, pp. 383-389, (1969); Ember G., Dissemination of scientific and technological information in Canada, Journal of Chemical Documentation, 13, 1, pp. 4-7, (1973); Report on the Advancement of Research Using Social Statistics, (1998); Morton E., Cooperation in Canada, Library Trends, pp. 399-415, (1975); Perry C., Archiving of publicly funded research data: A survey of Canadian researchers, Government Information Quarterly, 25, 1, pp. 133-148, (2008); Phillipson D., The national research council of Canada: Its history, its chronology, its bibliography, Scientia Canadensis, 15, 2, pp. 177-200, (1991); Attributes and Responsibilities: An RLG/OCLC Report, (2002); Data Policy and Barriers to Data Access in Canada: Issues for Global Change Research, (1996); Sangster J., A databank of evaluated octanol-water partition coefficients, GDF Databanks Bulletin, 1, 1, (1997); Final Report, National Data Archive Consultation: Building Infrastructure for Access to and Preservation of Research Data, (2002); Strong D., Leach P., Final Report, National Consultation on Access to Scientific Research Data, (2005); Tyas J., Knowledge, the master resource: The future of scientific and technical information in Canada, Special Libraries, 61, 2, pp. 73-77, (1970); Witt M., Carlson J., Conducting a Data Interview, (2007)","M. Zborowski; National Research Council Canada (NRC), Strategy and Development Branch (SDB), Ottawa ON K1A 0R6, 1200 Montreal Road, Canada; email: mary.zborowski@nrc-cnrc.gc.ca","","Ubiquity Press Ltd","","","","","","16831470","","","","English","Data Sci. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-76449087536"
"O'Connor M.J.; Shankar R.D.; Parrish D.B.; Das A.K.","O'Connor, Martin J. (7402686283); Shankar, Ravi D. (55806107400); Parrish, David B. (15136737900); Das, Amar K. (7403597070)","7402686283; 55806107400; 15136737900; 7403597070","Knowledge-data integration for temporal reasoning in a clinical trial system","2009","International Journal of Medical Informatics","78","SUPPL. 1","","S77","S85","8","24","10.1016/j.ijmedinf.2008.07.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-62749121141&doi=10.1016%2fj.ijmedinf.2008.07.013&partnerID=40&md5=966241e644bee27c76f21fa8a43bd5c6","Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, 251 Campus Drive, MSOB X275, United States; The Immune Tolerance Network, Pittsburgh, PA, United States","O'Connor M.J., Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, 251 Campus Drive, MSOB X275, United States; Shankar R.D., Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, 251 Campus Drive, MSOB X275, United States; Parrish D.B., The Immune Tolerance Network, Pittsburgh, PA, United States; Das A.K., Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, 251 Campus Drive, MSOB X275, United States","Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing and integrating temporal data and domain knowledge is difficult with the database technologies used in most clinical research systems. There is often a disconnect between the database representation of research data and corresponding domain knowledge of clinical research concepts. In this paper, we present a set of methodologies for undertaking ontology-based specification of temporal information, and discuss their application to the verification of protocol-specific temporal constraints among clinical trial activities. Our approach allows knowledge-level temporal constraints to be evaluated against operational trial data stored in relational databases. We show how the Semantic Web ontology and rule languages OWL and SWRL, respectively, can support tools for research data management that automatically integrate low-level representations of relational data with high-level domain concepts used in study design. © 2008 Elsevier Ireland Ltd. All rights reserved.","Clinical trials; Knowledge-based systems; Ontology; OWL; Semantic Web; SWRL; Temporal constraints","Birds; Clinical research; Data integration; High level languages; Knowledge based systems; Medical applications; Ontology; Semantic Web; Clinical trial; Database technology; Low level representation; Research data managements; Semantic Web ontologies; SWRL; Temporal constraints; Temporal information; article; clinical assessment; clinical protocol; clinical research; clinical trial; computer program; data base; human; immunological tolerance; Internet; medical informatics; patient care; priority journal; Semantic Web Rule Language; semantics; time; Information management","","","","","Pharmaceutical Research and Manufacturers Association Foundation; National Institutes of Health, NIH; National Institute of Allergy and Infectious Diseases, NIAID, (N01AI015416); Immune Tolerance Network, ITN, (NO1-AI-15416)","This research was supported in part by the Immune Tolerance Network, funded by Grant NO1-AI-15416 from the National Institutes of Health (USA), and by a Pharmaceutical Research and Manufacturers Association Foundation Research Starter Grant. The authors thank Valerie Natale for her editorial comments.","Rotrosen D., Matthews J.B., Bluestone J.A., The immune tolerance network: a new paradigm for developing tolerance-inducing therapies, J. Allergy Clin. Immunol., 110, 1, pp. 17-23, (2002); Shankar R., Martins S.B., O'Connor M.J., Parrish D., Das A.K., Towards semantic interoperability in a clinical trials management system, Fifth International Semantic Web Conference, pp. 901-912, (2006); Snodgrass R.T., On the semantics of 'now' in databases, ACM Trans. Database Syst., 22, 2, pp. 171-214, (1997); The TSQL2 Temporal Query Language, (1995); O'Connor M.J., Tu S.W., Musen M.A., The Chronus II temporal database mediator, AMIA Annual Symposium, pp. 567-571, (2002); Das A.K., Musen M.A., Synchronus: a reusable software module for temporal integration, AMIA Annual Symposium, pp. 195-199, (2002); Shoham Y., Temporal logics in AI: semantical and ontological considerations, Artif. Intell., 33, 1, pp. 89-104, (1987); Shahar Y., Chen H., Stites D.P., Basso L., Kaizer H., Wilson D.M., Musen M.A., Semi-automated entry of clinical temporal-abstraction knowledge, J. Am. Med. Inform. Assoc., 6, 6, pp. 494-511, (1999); Berners-Lee T., Hendler J., Lassila O., The Semantic Web, Sci. Am., 35, May, pp. 43-52, (2001); OWL Web Ontology Language Reference, (2004); Horrocks I., Patel-Schneider P.F., Boley H., Tabet S., Grosof B., Dean M., A Semantic Web Rule Language Combining OWL and RuleML, (2004); Knublauch H., Fergerson R.W., Noy N.F., Musen M.A., The Protégé OWL Plug-in: an open development environment for Semantic Web applications, Third International Semantic Web Conference (ISWC 2004), pp. 229-243, (2004); O'Connor M.J., Knublauch H., Tu S.W., Grosof B., Dean M., Grosso W.E., Musen M.A., Supporting rule system interoperability on the Semantic Web with SWRL, Fourth International Semantic Web Conference (ISWC 2005), pp. 974-986, (2005); Allen J.F., Maintaining knowledge about temporal intervals, Commun. ACM, 26, 11, pp. 832-843, (1993); O'Connor M.J., Shankar R.D., Tu S.W., Nyulas C., Musen M.A., Das A.K., Using Semantic Web technologies for knowledge-driven queries in clinical trials, Proceedings of the 11th Conference on Artificial Intelligence in Medicine, (2007); Weng C., Kahn M., Gennari J., Temporal knowledge representation for scheduling tasks in clinical trial protocols, Proceedings of the AMIA Annual Symposium, pp. 879-883, (2002); Deshpande A.M., Brandt C., Nadkarni P.M., Temporal query of attribute-value patient data: utilizing the constraints of clinical studies, Int. J. Med. Inform., 70, 1, pp. 59-77, (2003); Terenziani P., Toward a unifying ontology dealing with both user-defined periodicity and temporal constraints about repeated events, Comput. Intell., 18, 3, pp. 336-385, (2002); Bettini C., Jajodia S., Wang X., Solving multi-granularity constraint networks, Artif. Intell., 140, 1-2, pp. 107-152, (2002); Musen M.A., Tu S.W., Das A.K., Shahar Y., EON: a component-based approach to automation of protocol-directed therapy, J. Am. Med. Inform. Assoc., 3, 6, pp. 367-388, (1996); Fox J., Johns N., Rahmanzadeh A., Thomson R., PROforma: a method and language for specifying clinical guidelines and protocols, Proceedings of Medical Informatics Europe, (1996); Boxwala A.A., Peleg M., Tu S.W., Ogunyemi O., Zeng Q.T., Wang D., Patel V.L., Greenes R.A., Shortliffe E.H., GLIF3: a representation format for sharable computer-interpretable clinical practice, J. Biomed. Inform., 37, 3, pp. 147-161, (2004); Weng C., Kahn M., Gennari J.H., Temporal knowledge representation for scheduling tasks in clinical trial protocols, Proceedings of the AMIA Annual Symposium, pp. 879-883, (2002); Sim I., Olasov B., Carini S., The Trial Bank system: capturing randomized trials for evidence-based medicine, Proceedings of the AMIA Annual Symposium, (2003); (2008); (2008); HL7, http, (2008); Shankar R., Martins S.B., O'Connor M.J., Parrish D., Das A.K., An ontological approach to representing and reasoning with temporal constraints in clinical trial protocols, Best Papers of Biomedical Engineering Systems and Technologies. Communications in Computer and Information Science Series, (2008); O'Connor M.J., Tu S.W., Nyulas C.I., Das A.K., Musen M.A., Querying the Semantic Web with SWRL, The International RuleML Symposium on Rule Interchange and Applications (RuleML2007), (2007)","M.J. O'Connor; Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, 251 Campus Drive, MSOB X275, United States; email: martin.oconnor@stanford.edu","","Elsevier Ireland Ltd","","","","","","13865056","","IJMIF","18789876","English","Int. J. Med. Informatics","Article","Final","","Scopus","2-s2.0-62749121141"
"Gruetz R.; Franke T.; Dickmann F.","Gruetz, Romanus (54983345400); Franke, Thomas (56043721700); Dickmann, Frank (6507224680)","54983345400; 56043721700; 6507224680","Concept for preservation and reuse of genome and biomedical imaging research data","2013","Studies in Health Technology and Informatics","192","1-2","","999","","","0","10.3233/978-1-61499-289-9-999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894380066&doi=10.3233%2f978-1-61499-289-9-999&partnerID=40&md5=201d9860d18f408d657d5cc765c6b2ca","Department of Medical Informatics, University Medical Center Goettingen, Germany","Gruetz R., Department of Medical Informatics, University Medical Center Goettingen, Germany; Franke T., Department of Medical Informatics, University Medical Center Goettingen, Germany; Dickmann F., Department of Medical Informatics, University Medical Center Goettingen, Germany","The German Research Foundation (DFG) recommends preserving research data for at least ten years. The DFG funded project LABIMI/F establishes an infrastructure for preservation, retrieval and reuse of biomedical research data based on grid/cloud computing technology for two applications a) genome and b) imaging data. The requirements for this infrastructure were determined during workshops with relevant stakeholders. Afterwards product evaluations were conducted and the relevant products were integrated into the infrastructure concept. In this paper, we address the suitability of our solution concerning the fulfillment of the requirements. It is shown that the solution satisfies five of the eight requirement categories completely and the other three categories partly. Furthermore, in order to prove the adherence to the widely accepted Open Archival Information System (OAIS) standard, we successfully performed a mapping of our technical components to the functional entities of the OAIS. © 2013 IMIA and IOS Press.","DSpace; OAIS; Research data management; XtreemFS","Data Curation; Database Management Systems; Databases, Genetic; Genomics; Germany; Guidelines as Topic; Information Storage and Retrieval; Medical Record Linkage; Information management; Medical imaging; Biomedical research; Computing technology; D-space; German research foundations; OAIS; Open archival information systems; Research data managements; XtreemFS; data base; genetic database; genomics; Germany; information processing; information retrieval; medical record; practice guideline; procedures; standards; Genes","","","","","","","Dickmann F., Rey S., Stakeholder analysis for digital preservation in biomedical research, Proceedings of the 13th World Congress on Medical Informatics, (2010); Gruetz R., Brodhun M., Loehnhardt B., Dickmann F., Evaluation of data management and transfer tools for the biomedical community, 2012 6th IEEE International Conference on Digital Ecosystems Technologies (DEST), pp. 1-6, (2012); Consultative Committee for Space Data Systems. Reference Model for An Open Archival Information System (OAIS), (2012)","R. Gruetz; Department of Medical Informatics, University Medical Center Goettingen, Germany; email: romanus.gruetz@med.uni-goettingen.de","","IOS Press","","14th World Congress on Medical and Health Informatics, MEDINFO 2013","20 August 2013 through 23 August 2013","Copenhagen","","09269630","978-161499288-2","","23920773","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-84894380066"
"Schneider R.","Schneider, René (16242554000)","16242554000","Research Data Literacy","2013","Communications in Computer and Information Science","397 CCIS","","","134","140","6","42","10.1007/978-3-319-03919-0_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904725955&doi=10.1007%2f978-3-319-03919-0_16&partnerID=40&md5=8f59f9d9da6d9787c513a289f812915e","Haute Ecole de Gestion, 1271 Carouge, 7, route de Drize, Switzerland","Schneider R., Haute Ecole de Gestion, 1271 Carouge, 7, route de Drize, Switzerland","This paper describes a pragmatic approach for the mediation and the teaching of research data literacy, i.e. those dimensions of information literacy that are dedicated to the creation, management, and reuse of research data. Based on prior work concerning the foundations of information literacy and curricula construction for data curation, the paper will begin with the definition of research data literacy, before describing an approach based on a fusion of core skills and a two dimensional matrix that reflects on the one hand the different student populations, and on the other hand a scale of various teaching modules. This matrix might serve as the basis for an operational implementation of different study programs. © Springer International Publishing Switzerland 2013.","data curation cycle; data scientists; research data literacy; Research data management","Curricula; Information management; Information science; Students; Data curation; data scientists; Information literacy; Research data; Research data managements; Student populations; Teaching module; Population statistics","","","","","","","Lewis M.J., Libraries and the Management of Research Data, Envisioning Future Academic Library Services, pp. 145-168, (2010); Higgins S., The DCC Curation Lifecycle Model, International Journal of Digital Curation, 3, 1, pp. 134-148, (2008); Treloar A., (2011); Shapiro J.J., Hughes S.K., Information Literacy as a Liberal Art, Educom Review, 31, 2, pp. 31-35, (2011); Eisenberg M., Information Literacy: Essential Skills for the Information Age, DESIDOC Journal of Library & Information Technology, 28, 2, pp. 39-47, (2008); The SCONUL Seven Pillars of Information Literacy: Core Model For Higher Education, (2008); Research Information Network: The Role of Research Supervisors In Information Literacy, (2011); Carlson J., Fosmire M., Miller C., Nelson S.M., Determining Data Information Literacy Needs: A study of Students and Research Faculty, Portal: Libraries and The Academy, 11, 2, pp. 629-657, (2008); Donnelly M., RDMF2: Core Skills Diagram, Research Data Management Forum, 17, (2008)","R. Schneider; Haute Ecole de Gestion, 1271 Carouge, 7, route de Drize, Switzerland; email: rene.schneider@hesge.ch","","Springer Verlag","Credo Reference; EBSCO Information Services; et al; Hacettepe University; Pandora Book Service; Wiley","1st European Conference on Information Literacy, ECIL 2013","22 October 2013 through 25 October 2013","Istanbul","106557","18650929","978-331903918-3","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-84904725955"
"Allen B.; Kettimuthu R.; Kordas J.; Link M.; Martin S.; Pickett K.; Tuecke S.; Bresnahan J.; Childers L.C.; Foster I.T.; Kandaswamy G.","Allen, Bryce (54981342100); Kettimuthu, Rajkumar (6507364595); Kordas, Jack (54974163400); Link, Michael (57197281592); Martin, Stuart (55450129900); Pickett, Karl (54980795400); Tuecke, Steven (6602740450); Bresnahan, John (35917624200); Childers, Lisa C. (36701009400); Foster, Ian T. (35572232000); Kandaswamy, Gopi (54971488100)","54981342100; 6507364595; 54974163400; 57197281592; 55450129900; 54980795400; 6602740450; 35917624200; 36701009400; 35572232000; 54971488100","Software as a service for data scientists","2012","Communications of the ACM","55","2","","81","88","7","157","10.1145/2076450.2076468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856912379&doi=10.1145%2f2076450.2076468&partnerID=40&md5=dab31ccb66dc1ca5c85c1305fe3783f4","Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Mathematics and Computer Science Division of Argonne National Laboratory, Argonne, IL, United States; Sciences Institute, Los Angeles, United States","Allen B., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Kettimuthu R., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Kordas J., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Link M., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Martin S., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Pickett K., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Tuecke S., Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; Bresnahan J., Mathematics and Computer Science Division of Argonne National Laboratory, Argonne, IL, United States; Childers L.C., Mathematics and Computer Science Division of Argonne National Laboratory, Argonne, IL, United States; Foster I.T., Mathematics and Computer Science Division of Argonne National Laboratory, Argonne, IL, United States; Kandaswamy G., Sciences Institute, Los Angeles, United States","Efforts are being made to deliver research data-management capabilities to users as a hosted 'software as a service' (SaaS) to address the challenges of computational crisis in many laboratories and a growing need for far more powerful data-management tools. SaaS is a software-delivery model in which software is hosted centrally and accessed by users using a thin client over the Internet. SaaS leverages intuitive Web 2.0 interfaces, deep domain knowledge, and economies of scale to deliver capabilities are easier to use, more capable, or more cost-effective than software accessed through other means as demonstrated in many business and consumer tools. The opportunity for continuous improvement via dynamic deployment of new features and bug fixes is also significant, along with the potential for expert operators to intervene and troubleshoot on the user's behalf.","","Computer applications; Computer science; Bug fixes; Continuous improvements; Domain knowledge; Dynamic deployment; Economies of scale; Software as a service; Thin clients; Troubleshoots; Web 2.0; Web services","","","","","","","Allcock B., Bresnahan J., Kettimuthu R., Link M., Dumitrescu C., Raicu I., Foster I., The Globus striped GridFTP framework and server, Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, (2005); Bell G., Hey T., Szalay A., Beyond the data deluge, Science, 323, 5919, pp. 1297-1298, (2009); Berriman G.B., Groom S., How will astronomy archives survive the data tsunami? Commun, ACM, 54, 12, pp. 52-56, (2011); Chervenak A., Schuler R., Kesselman C., Koranda S., Moe B., Wide-area data replication for scientific collaborations, Proceedings of the Sixth IEEE/ACM International Workshop on Grid Computing, (2005); Childers L., Liming L., Foster I., Perspectives on Distributed Computing: 30 People, Four User Types, and the Distributed Computing User Experience, (2008); Cho B., Gupta I., Budget-constrained bulk data transfer via Internet and shipping networks, Proceedings of the Eighth ACM International Conference on Autonomic Computing, pp. 71-80, (2011); Cholia S., Skinner D., Boverhof J., NEWT : A REST ful service for building high-performance computing Web applications, Proceedings of the 2010 Gateway Computing Environments Workshop, pp. 1-11, (2010); Cohen B., Incentives build robustness in BitTorrent, Proceedings of the First International Workshop on Economics of P2P Systems, (2003); Egeland R., Wildishb T., Huang C.-H., PhEDEx data service, Journal of Physics: Conference Series, 219, (2010); Erdos M., Cantor S., Internet 2, (2002); Gray J., Chong W., Barclay T., Szalay A., Vandenberg J., TeraScale SneakerNet: Using Inexpensive Disks for Backup, (2002); Hammer-Lahav E., Internet Engineering Task Force RFC 5849, (2010); Hanushevsky A., Trunov A., Cottrell L., Peer-topeer computing for secure high-performance data copying, Proceedings of the 2001 International Conference on Computing in High Energy and Nuclear Physics, (2001); Kosar T., Livny M., A framework for reliable and efficient data placement in distributed computing systems, Journal of Parallel and Distributed Computing, 65, 10, pp. 1146-1157, (2005); Monti H., Butt A.R., Vazhkudai S.S., CATCH : A cloud-based adaptive data-transfer service for HPC, Proceedings of the 25th IEEE International Parallel & Distributed Processing Symposium, pp. 1242-1253, (2011); Novotny J., Tuecke S., Welch V., An online credential repository for the grid: MyProxy, Proceedings of the 10th IEEE International Symposium on High-Performance Distributed Computing, pp. 104-111, (2001); Rajasekar A., Moore R., Hou C.-Y., Lee C.A., Marciano R., De Torcy A., Wan M., Schroeder W., Chen S.-Y., Gilbert L., Tooby P., Zhu B., IRODS Primer: Integrated Rule-Oriented Data System, (2010); Sun W., Zhang K., Chen S.-K., Zhang X., Liang H., Software as a service: An integration perspective, Proceedings of the Fifth International Conference on Service-Oriented Computing, pp. 558-569, (2007); Thain D., Basney J., Son S.-C., Livny M., The Kangaroo approach to data movement on the grid, IEEE International Symposium on High Performance Distributed Computing, Proceedings, pp. 325-333, (2001); Tridgell A., MacKerras P., The Rsync Algorithm TR-CS-96-05, (1994); Wang L., Park K.S., Pang R., Pai V., Peterson L., Reliability and security in the CoDeeN content distribution network, Proceedings of the USENIX Annual Technical Conference, pp. 171-184, (2004); Welch V., Foster I., Kesselman C., Mulmo O., Pearlman L., Tuecke S., Gawor J., Meder S., Siebenlist F., X.509 proxy certificates for dynamic delegation, Proceedings of the Third Annual Public Key Infrastructure R&D Workshop, (2004)","B. Allen; Computation Institute of the University of Chicago, Argonne National Laboratory, Argonne, IL, United States; email: ballen@ci.uchicago.edu","","","","","","","","15577317","","CACMA","","English","Commun ACM","Article","Final","","Scopus","2-s2.0-84856912379"
"Dickmann F.; Grütz R.; Rienhoff O.","Dickmann, Frank (6507224680); Grütz, Romanus (54983345400); Rienhoff, Otto (55915353100)","6507224680; 54983345400; 55915353100","A ""meta""-perspective on ""bit rot"" of biomedical research data","2012","Studies in Health Technology and Informatics","180","","","260","264","4","3","10.3233/978-1-61499-101-4-260","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872557846&doi=10.3233%2f978-1-61499-101-4-260&partnerID=40&md5=b543fc8c55105585c98701b98f53a161","Department of Medical Informatics, University of Göttingen, 37075 Göttingen, Robert-Koch-Straße 40, Germany","Dickmann F., Department of Medical Informatics, University of Göttingen, 37075 Göttingen, Robert-Koch-Straße 40, Germany; Grütz R., Department of Medical Informatics, University of Göttingen, 37075 Göttingen, Robert-Koch-Straße 40, Germany; Rienhoff O., Department of Medical Informatics, University of Göttingen, 37075 Göttingen, Robert-Koch-Straße 40, Germany","Research data management (RDM) is an important topic for biomedical research due to the issue of ""bit rot"". RDM aims to implement access to reliable digital data for local and distributed research groups. A key aspect for the understanding of data is the use of metadata. This understanding has been investigated on the basis of two use cases of the DFG project LABIMI/F: RDM for genome data and biomedical image data. The results show that metadata can improve research not only for others but also for the researcher himself. However, RDM is still far from integrating all biomedical data. In addition, RDM is not (yet) a valid approach for clinical trial data management. © 2012 European Federation for Medical Informatics and IOS Press. All rights reserved.","Digital preservation; Literature review; Metadata; Research data management","Algorithms; Biomedical Research; Computer Security; Database Management Systems; Databases, Factual; Information Storage and Retrieval; Libraries, Digital; Digital storage; Metadata; Biomedical data; Biomedical image data; Biomedical research; Clinical trial; Digital preservation; Literature reviews; Research data managements; Research groups; algorithm; article; computer security; data base; factual database; information retrieval; library; medical research; methodology; Information management","","","","","","","Robson B., Baek O.K., The Engines of Hippocrates: From the Dawn of Medicine to Medical and Pharmaceutical Informatics, (2009); Cerf V.G., Avoiding bit rot"": Long-term preservation of digital information, Proceedings of the IEEE, 99, 6, pp. 915-916, (2011); Haux R., Medical informatics: Past, present, future, Int J Med Inform, 79, 9, pp. 599-610, (2010); Dickmann F., Grutz R., LABIMI/F - Digital preservation of biomedical research data, Knowledge Exchange: Workshop Research Data Management - Activities and Challenges, (2011); Ayris P., Davies R., McLeod R., Miao R., Shenton H., Wheatley P., The LIFE2 Final Project Report, (2008); Schwartz C., Digital libraries: An overview, The Journal of Academic Librarianship, 26, 6, pp. 385-393, (2000); Hedstrom M., Digital preservation: A time bomb for digital libraries, Computers and the Humanities, 31, 3, pp. 189-202, (1998); Irshad T., Ure J., Clinical Data from Home to Health Centre: The Telehealth Curation Lifecycle, (2009); Albani L., Zg J.M.V., Giacomini M., Mediavilla C., Marchessoux C., Lampe J., Assessment of existing standards, (2011); Attwood T.K., Kell D.B., McDermott P., Marsh J., Pettifer S.R., Thorne D., Calling international rescue: Knowledge lost in literature and data landslide!, Biochemical Journal, 424, PART 3, pp. 317-333, (2009); Brase J., DataCite-A global registration agency for research data, Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology, 2009 COINFO 2009, (2009); Patterns of Information Use and Exchange: Case Studies of Researchers in the Life Sciences, (2009); Thorisson G.A., Accreditation and attribution in data sharing, Nature Biotechnology, 27, 11, pp. 984-985, (2009); Whyte A., Curating Brain Images in A Psychiatric Research Group, (2008); Reference Model for An Open Archival Information System (OAIS), (2002); Rubin D.L., Napel S., Imaging informatics: Toward capturing and processing semantic information in radiology images, IMIA Yearbook of Medical Informatics 2010, pp. 34-42, (2010); Wiley G., The prophet motive: How PACS was developed and sold, Imaging Economics, (2005); Adamson C.L., Wood A.G., DFBIdb: A software package for neuroimaging data management, Neuroinformatics, 8, 4, pp. 273-284, (2010); Mayer G., Data Management in Systems Biology i - Overview and Bibliography, (2009); Kim W., On metadata management technology: Status and issues, Journal of Object Technology, 4, 2, pp. 41-47, (2005); De Carvalho E.C.A., Batilana A.P., Simkins J., Martins H., Shah J., Rajgor D., Et al., Application description and policy model in collaborative environment for sharing of information on epidemiological and clinical research data sets, PLoS ONE, 5, 2, (2010); Witten I.H., Bainbridge D., Nichols D.M., How to Build A Digital Library, (2010); Ure J., Procter R., Lin Y.-W., Hartswood M., Anderson S., Lloyd S., Et al., The development of data infrastructures for ehealth: A socio-technical perspective, J Assoc Inf Syst, 10, 5, pp. 415-429, (2009); The Dublin Core Metadata Element Set, (2007); Darmoni S.J., Thirion B., Leroy J.P., Douyere M., Piot J., The use of Dublin Core metadata in a structured health resource guide on the Internet, Bulletin of the Medical Library Association, 89, 3, pp. 297-301, (2001); Jiang G., Solbrig H.R., Iberson-Hurst D., Kush R.D., Chute C.G., A collaborative framework for representation and harmonization of clinical study data elements using semantic mediawiki, AMIA Summits on Translational Science Proceedings 2010, pp. 11-15, (2010)","F. Dickmann; Department of Medical Informatics, University of Göttingen, 37075 Göttingen, Robert-Koch-Straße 40, Germany; email: fdickmann@med.uni-goettingen.de","","IOS Press","","24th Medical Informatics in Europe Conference, MIE 2012","26 August 2012 through 29 August 2012","Pisa","","09269630","978-161499100-7","","22874192","English","Stud. Health Technol. Informatics","Conference paper","Final","","Scopus","2-s2.0-84872557846"
"Bresnahan M.M.; Johnson A.M.","Bresnahan, Megan M. (55847496300); Johnson, Andrew M. (55848216000)","55847496300; 55848216000","Assessing scholarly communication and research data training needs","2013","Reference Services Review","41","3","","413","433","20","22","10.1108/RSR-01-2013-0003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883608423&doi=10.1108%2fRSR-01-2013-0003&partnerID=40&md5=3f60aea36544fb4b54abc7627fe86560","University Libraries, University of Colorado Boulder, Boulder, CO, United States","Bresnahan M.M., University Libraries, University of Colorado Boulder, Boulder, CO, United States; Johnson A.M., University Libraries, University of Colorado Boulder, Boulder, CO, United States","Purpose: This study aims to develop a systematic approach for assessing local training needs in order to reskill liaison librarians for new roles in scholarly communication and research data management. Design/methodology/approach: This study followed a training needs assessment approach to develop a survey instrument that was administered electronically to liaison librarians. Survey data were analysed to create an overall prioritization score used to rank local training topics in terms of need. Additional data will inform the design, including formats, of a training agenda to meet these needs. Findings: Survey results indicated that training for research data topics should be prioritized and addressed using hands-on methods that would allow liaison librarians to develop tangible skills directly applicable to individual outreach activities. Research limitations/implications: Training priorities often involve factors beyond the scope of this training needs assessment methodology. This methodology also presupposes a list of potential training topics. All training efforts resulting from this study will be assessed in order to determine the effectiveness of the initial interventions and inform the next steps in this iterative training agenda. Practical implications: Involving potential trainees in the prioritization and development of a training agenda provides valuable information and may lead to increased receptivity to training. Originality/value: This study provides a model for academic libraries to use to assess training needs in order to reskill current staff to adapt to a rapidly changing research and scholarly communication landscape. © Emerald Group Publishing Limited.","Data curation; Data management; Professional development; Research data management; Reskilling; Scholarly communication; Training; Training needs; Training needs assessment","","","","","","","","SPEC Kit 332: Organization of Scholarly Communication Services, (2012); Auckland M., Re-skilling for research, Research Libraries UK, (2012); Ball A., Review of Data Management Lifecycle Models, (2012); Sustainable Economics for a Digital Planet: Ensuring Long-Term Access to Digital Information, (2008); Bracke M.S., Emerging data curation roles for librarians: A case study of agricultural data, Journal of Agricultural & Food Information, 12, 1, pp. 65-74, (2011); Buehler M.A., Boateng A., The evolving impact of institutional repositories on reference librarians, Reference Services Review, 33, 3, pp. 291-300, (2005); Callahan D., Watson M., Care of the organization: Training and development strategies, The Journal of Academic Librarianship, 21, 5, pp. 376-381, (1995); Choi Y., Rasmussen E., What qualifications and skills are important for digital librarian positions in academic libraries? A job advertisement analysis, Journal of Academic Librarianship, 35, 5, pp. 457-467, (2009); Conroy B., Library Staff Development and Continuing Education, (1978); Creamer A., Morales M., Kafel D., Crespo J., Martin E., A sample of research data curation and management courses, Journal of EScience Librarianship, 1, 2, pp. 88-96, (2012); Creth S.D., Staff development and continuing education, Personnel Administration in Libraries, pp. 118-151, (1989); Dunn K., Crow S.J., van Moorsel T.G., Creazzo J., Tomasulo P., Markinson A., Mini-Medical School for Librarians': From needs assessment to educational outcomes, Journal of the Medical Library Association, 94, 2, pp. 166-173, (2006); Foster N.F., Gibbons S., Understanding faculty to improve content recruitment for institutional repositories, D-Lib Magazine, 11, 1, (2005); Goldstein I.L., Ford J.K., Training in Organizations: Needs Assessment, Development, and Evaluation, (2002); Horwood L., Sullivan S., Young E., Garner J., OAI compliant institutional repositories and the role of library staff, Library Management, 25, 4-5, pp. 170-176, (2004); Hswe P., Holt A., A New Leadership Role for Libraries, (2013); Jerabek J.A., McMain L.M., The answer you get depends on who (and what) you ask: Involving stakeholders in needs assessments, Journal of Library Administration, 37, 3-4, (2002); Mathews P.L., An investigation into internet training for academic library staff, New Library World, 98, 1134, pp. 84-97, (1997); Mercer H., Almost halfway there: An analysis of the open access behaviors of academic librarians, College & Research Libraries, 72, 5, pp. 443-453, (2011); Merrill A.N., Lindsay E.B., Growing your own: Building an internal leadership training program, Library Leadership and Management, 23, 2, pp. 85-87, (2009); Digital Research Data Sharing and Management, (2011); Oppenheim C., Electronic scholarly publishing and open access, Journal of Information Science, 34, 4, pp. 577-590, (2008); Parry J., Training needs assessment (TNA): Its place in an effective training programme, Learning Resources Journal, (1991); Pegrum M., Kiel R., Changing the way we talk': Developing librarians' competence in emerging technologies through a structured program, College & Research Libraries, 72, 6, pp. 583-598, (2011); Rossett A., Training Needs Assessment, (1987); Schrader A.M., Shiri A., Williamson V., Assessment of the research learning needs of University of Saskatchewan librarians: A case study, College & Research Libraries, 73, 2, pp. 147-163, (2012); Simons N., Richardson J., New roles, new responsibilities: Examining training needs of repository staff, Journal of Librarianship and Scholarly Communication, 1, 2, pp. 1-15, (2012); Simpson N., Learning and change: Continuing training for librarians, pp. 37-46, (1978); Spinuzzi C., The methodology of participatory design, Technical Communication, 52, 2, (2005); Tenopir C., Birch B., Allard S., Academic Libraries and Research Data Services, (2012); CU Boulder University Libraries 2010-2013 Strategic Plan, (2010); Urquhart C., Spink S., Thomas R., Durbin J., Systematic assessment of the training needs of health library staff, Library & Information Research, 29, 93, pp. 35-42, (2005); Research Data Management at the University of Colorado Boulder: Recommendations in Support of Fostering 21st Century Research Excellence, (2012); Ware S.A., IDNA for librarians: Assessing instructional development needs, Portal: Libraries & the Academy, 2, 3, (2002)","M. M. Bresnahan; University Libraries, University of Colorado Boulder, Boulder, CO, United States; email: megan.bresnahan@colorado.edu","","","","","","","","00907324","","","","English","Ref. Serv. Rev.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84883608423"
"Schlembach M.C.; Brach C.A.","Schlembach, Mary C. (6603468114); Brach, Carol A. (6603072341)","6603468114; 6603072341","Research data management and the role of libraries","2012","ACS Symposium Series","1110","","","129","144","15","2","10.1021/bk-2012-1110.ch008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905281529&doi=10.1021%2fbk-2012-1110.ch008&partnerID=40&md5=03dd1537631bdf17c86ac16d021dc293","Engineering, Physics, and Astronomy Librarian, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Engineering Librarian, University of Notre Dame, Notre Dame, IN 46556, United States","Schlembach M.C., Engineering, Physics, and Astronomy Librarian, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Brach C.A., Engineering Librarian, University of Notre Dame, Notre Dame, IN 46556, United States","Academic research libraries in the US and abroad are already playing roles as leaders in areas where libraries and librarians can bring significant value to data management efforts. With new data management stewardship mandates by national government agencies in place, libraries need to take advantage of new opportunities in data stewardship and curation. Focusing on e-science and the management of scientific data, this chapter highlights many of the data management programs developed at academic libraries. © 2012 American Chemical Society.","","Information management; Academic libraries; Academic research; Data management programs; Data stewardship; Management efforts; National governments; Research data managements; Scientific data; Libraries","","","","","","","Dissemination in Sharing of Research Results; Walters T., Skinner K., New roles for new times: Digital curation for preservation, Association of Research Libraries Report, (2011); Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, (2009); Heidorn P.B., The emerging role of libraries in data curation and e-science, Journal of Library Administration, 51, pp. 662-672, (2011); Newton M.R., Miller C.C., Bracke M.S., Librarian roles in institutional repository data set collecting: Outcomes of a research library task force, Collection Manage., 36, 1, pp. 53-67, (2011); Crow R., The case for institutional repositories: A SPARC position paper, ARL Bimonthly Report 223, (2002); Hswe P., Holt A., Joining in the enterprise of response in the wake of the NSF data management planning requirement research, Library Issues: A Bimonthly Report from ARL,CNI, and SPARC, pp. 11-17, (2011); Tenopir C., Allard S., Douglass K., Aydinoglu A.U., Wu L., Read E., Manoff M., Frame M., Data sharing by scientists: Practices and perceptions, PLoS ONE, 6, (2011); Research Information Network Research Information Network; Simmons College Open Access Directory at Simmons College; Johns Hopkins Library DMP Tools; Johns Hopkins Library Overview of Plans; MIT Library Data Management Checklist; Witt M., Carlson J.R., Cragin M.R., Brandt D.S., Constructing data curation profiles, Int. J. Digital Curation, 4, (2009); Witt M., Co-designing co-developing, and co-implementing an institutional data repository service, J. Libr. Admin., 52, pp. 172-188, (2012); Choudhury S., Integration of Digital Library Services","","","American Chemical Society","","","","","","00976156","978-084122712-5","ACSMC","","English","ACS Symp. Ser.","Article","Final","","Scopus","2-s2.0-84905281529"
"Ball J.","Ball, Joanna (56033800000)","56033800000","Research data management for libraries: Getting started","2013","Insights: the UKSG Journal","26","3","","256","260","4","6","10.1629/2048-7754.70","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893854964&doi=10.1629%2f2048-7754.70&partnerID=40&md5=2c0cc54898e35927ff39e6e6905f2d58","University of Sussex Library, Falmer, Brighton BN1 9QL, United Kingdom","Ball J., University of Sussex Library, Falmer, Brighton BN1 9QL, United Kingdom","Many libraries are keen to take on new roles in providing support for effective research data management (RDM), but lack the necessary skills and resources to do so. This article explores the approach used by the University of Sussex to engage with academic departments about their RDM practices and requirements in order to develop relevant library support services. It describes a project undertaken with three Academic Schools to inform a list of recommendations for senior management, to include areas which should be taken forward by the Library, IT and Research Office in order to create a sustainable RDM service. The article is unflinchingly honest in sharing the differing reactions to the project and the lessons learnt along the way.","","","","","","","","","RCUK Common Principles on Data Policy; EPSRC Policy Framework on Research Data; Cox A.M., Pinfield S., Research data management and libraries: Current activities and future priorities, Journal of Librarianship and Information Science, (2013); University of Sussex Code of Practice for Research; DCC's Data Asset Framework; University of Sussex Library's Web Support Guide to RDM","J. Ball; University of Sussex Library, Falmer, Brighton BN1 9QL, United Kingdom; email: j.e.ball@sussex.ac.uk","","United Kingdom Serials Group","","","","","","20487754","","","","English","Insights","Review","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84893854964"
"Sosnoski J.J.; Harvey K.Q.; Stalker J.; Monahan C.","Sosnoski, James J. (6504549369); Harvey, Kevin Q. (35147852900); Stalker, Jordan (36178957800); Monahan, Colleen (35148037500)","6504549369; 35147852900; 36178957800; 35148037500","Transpositions in configurable virtual storyworlds","2009","Cases on Collaboration in Virtual Learning Environments: Processes and Interactions","","","","72","94","22","0","10.4018/978-1-60566-878-9.ch005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901531363&doi=10.4018%2f978-1-60566-878-9.ch005&partnerID=40&md5=5c553e7f86d82dc69f4de2a299861113","University of Illinois at Chicago, United States; Center for the Advancement of Distance Education (CADE), School of Public Health, University of Illinois at Chicago (UIC), United States","Sosnoski J.J., University of Illinois at Chicago, United States; Harvey K.Q., Center for the Advancement of Distance Education (CADE), School of Public Health, University of Illinois at Chicago (UIC), United States; Stalker J., University of Illinois at Chicago, United States; Monahan C., Center for the Advancement of Distance Education (CADE), School of Public Health, University of Illinois at Chicago (UIC), United States","BACKGROUND: The Center for the Advancement of Distance Education (CADE) is a self-supporting unit within the School of Public Health at the University of Illinois at Chicago. The center's services range from online continuing education and professional training to multimedia Web-casting and research data management, analysis and presentation. TECHNOLOGY USED: In public health emergency response training, an isolation and quarantine situation is one of the most challenging. Second Life has the capability and potential to address many of the training and planning challenges associated with such a sensitive topic. It enables public health emergency responders to test and refine existing plans and procedures in a safe, controllable, immersive and repeatable environment. CASE STUDY: A quarantine scenario designed for emergency training. The authors designed ""The Canyon Crossroads"" as a key transit point between two quarantine areas and two uninfected areas. They placed a state border to divide the crossroads leaving quarantine zones in each jurisdiction. The local hospital was located in one of the quarantine zones and it is an official holding and treatment location for infected victims. The exercise involves transmitting persons in and out of the four areas. CHALLENGES: There are three challenges the authors are currently addressing: (a) how to increase the levels of engagement in the training process, (b) how to construct a virtual world that fosters collaboration, and (c) how to measure the levels of engagement in this collaborative environment. © 2010, IGI Global.","","","","","","","","","Barnett D.J., Everly Jr. G.S., Parker C.L., Links J.M., Applying educational gaming to public health workforce emergency preparedness, American Journal of Preventive Medicine, 28, pp. 490-495, (2005); Corti K., Games-based Learning: A Serious Business Application, (2006); Dickey M., Three-dimensional virtual worlds and distance learning: Two case studies of active worlds as a medium for distance education, British Journal of Educational Technology, 36, pp. 439-451, (2005); Dickey M., Brave new (interactive) worlds: A review of the design affordances and constraints of two 3d virtual worlds as interactive learning environments, Interactive Learning Environments, 13, pp. 121-137, (2005); Gibson J., The Ecological Approach to Visual Perception, (1979); Hawkridge D., Communication and education in open learning systems, Communication Research: A Half-century Appraisal, pp. 70-103, (1977); Green M., Brock T., In the mind's eye: Transportation-imagery model of narrative persuasion, Narrative Impact: Social and Cognitive Foundations, pp. 315-341, (2002); Herrington J., Reeves T., Oliver R., Immersive learning technologies: Realism and online authentic learning, Journal of Computing in Higher Education, 19, 1, pp. 65-84, (2007); Macmillan J., Paley M.J., Levchuk Y.N., Entin E.E., Serfaty D., Freeman J.T., Designing the best team for the task: Optimal organizational structures for military missions, New Trends in Cooperative Activities: System Dynamics in Complex Settings, (2002); Norman D., The Design of Everyday Things, (2002); Parker R., Commuters play large role in flu spread, FuturePundit.com, (2006); Shaffer D., Squire K., Halverson R., Gee J., Video games and the future of learning, Phi Delta Kappan, 87, 2, pp. 105-111, (2005); Sosnoski J., Harkin P., Carter B., Configuring History: Teaching the Harlem Renaissance Through Virtual Reality Cityscapes, (2006); Squire K., Jenkins H., Harnessing the power of games in education, Insight (American Society of Ophthalmic Registered Nurses), 3, 1, pp. 5-33, (2003); Swartout W., van Lent M., Making a game of system design, Communications of the ACM, 46, 7, pp. 32-39, (2003); Waugh Jr. W.L., Streib G., Collaboration and leadership for effective emergency management, Public Administration Review, 66, SUPPL. 1, pp. 131-140, (2006)","","","IGI Global","","","","","","","978-160566878-9","","","English","Cases on Collaboration in Virtual Lrng. Environments: Processes and Intrac.","Book chapter","Final","","Scopus","2-s2.0-84901531363"
"Brunt James W.","Brunt, James W. (7006565882)","7006565882","Research data management in ecology: a practical approach for long-term projects","1994","Scientific and Statistical Database Management - Proceedings of the International Working Conference","","","","272","275","3","6","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028599328&partnerID=40&md5=5f725091fba1075a84ce28ae96004dcf","Univ of New Mexico, Albuquerque, United States","Brunt James W., Univ of New Mexico, Albuquerque, United States","Effective management of ecological research data can insure the security and accessibility of data that cannot be collected again under the same conditions, and plays a key role in every aspect of the research project from experimental design to publication. Commercial relational database management software, developed primarily for business applications, does not provide an adequate solution for long-term scientific data management. An archive file format provides the standard around which a data file management system is implemented. The system works within the parameters of existing components of the operating system software. Data filters and data engines are used to communicate data to and from applications. This paper documents this working approach to research data management in use on the Sevilleta LTER project.","","Data structures; Ecology; File organization; Inference engines; Information management; Natural sciences computing; Project management; Relational database systems; Security of data; Systems analysis; Data engines; Data filters; Long term projects; Research data management; Data handling","","","","","","","","","","IEEE","CESDIS; NASA; NOAA","Proceedings of the 7th International Working Conference on Scientific and Statistical Database Management","28 September 1994 through 30 September 1994","Charlottesville, VA, USA","21524","","","00166","","English","Sci Stat Database Manage Proc Int Work Conf","Conference paper","Final","","Scopus","2-s2.0-0028599328"
"Witt M.","Witt, Michael (15119883100)","15119883100","Institutional repositories and research data curation in a distributed environment","2008","Library Trends","57","2","","191","201","10","64","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-68149117508&partnerID=40&md5=6ebc78ddf16ffe101036dac6c95d0512","Purdue University, United States","Witt M., Purdue University, United States","Broadly speaking, the lack of a framework for organizing, preserving, and making research data available for the long term has resulted in valuable datasets becoming lost or discarded. The approach of the Distributed Data Curation Center of the Purdue University Libraries has been to integrate librarians and the principles of library and archival sciences with domain sciences, computer and information sciences, and information technology to address the challenges of managing collections of research data and to learn how to better support interdisciplinary research through data curation. One piece of infrastructure that supports these activities is a ""distributed institutional repository"" that includes electronic documents, digitized archival collections, and research datasets housed in multiple systems that are connected together using Web Services and other middleware. Concurrently, roles for librarians and institutional repositories in data curation are being explored.","","","","","","","","","Baker R.A., In the research laboratory, Journal of Chemical Education, 10, 7, pp. 408-411, (1933); Barber D., Zauha J., Scientific data and social science data libraries, IASSIST Quarterly, 19, 4, pp. 5-6, (1995); Brandt D.S., Librarians as partners in e-research, College & Research Libraries News, 68, 6, (2007); Hey A.J.G., Trefethen A.E., The data deluge: An e-science perspective, Grid computing: Making the global infrastructure a reality, pp. 809-824, (2003); Mullins J., Enabling international access to scientific data sets: Creation of the Distributed Data Curation Center (D2C2), (2007); Revised policy on enhancing public access to archived publications resulting from NIH-funded research, (2008); Long-lived digital data collections: Enabling research and education in the 21st century, (2005); Cyberinfrastructure vision for 21st century discovery, (2007); Witt M., Providing an OAI-PMH interface to the Storage Resource Broker with OAISRB, International Journal on Digital Libraries, 7, 1, (2007)","","","","","","","","","00242594","","","","English","Libr. Trends","Article","Final","","Scopus","2-s2.0-68149117508"
"Abson C.; Allan A.","Abson, Claire (57195572344); Allan, Alastair (57531739200)","57195572344; 57531739200","Information and Library Services","2012","Research Methods for Postgraduates: Third Edition","","","","86","93","7","0","10.1002/9781118763025.ch10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028894125&doi=10.1002%2f9781118763025.ch10&partnerID=40&md5=8adda05f25e9bb306ec65075df79d288","Sheffield Hallam University, Sheffield, United Kingdom; University of Sheffield, Sheffield, United Kingdom","Abson C., Sheffield Hallam University, Sheffield, United Kingdom; Allan A., University of Sheffield, Sheffield, United Kingdom","This chapter points towards ways in which to locate, evaluate and make effective use of the information that is available to one, and how to get the most out of library, whatever subject and institution of study. Person should also be knowledgeable about information sources, and will probably have some responsibility for training students to develop effective information-seeking behaviours. Remote access to electronic resources is becoming increasingly common. There may be additional support available to you as a distance learner; for example, university library may offer to supply postal loans of print materials. In the United Kingdom, it is now a condition of many research grants that resulting articles should be published as open access (OA). Research data management is the technique whereby researchers ensure that their research data remain available to future researchers over a longer time period than their own project. © 2016 John Wiley & Sons, Ltd. All rights reserved.","Information-seeking behaviours; Library services; Open access; Remote access; Research data management","Information management; Information retrieval; Information seeking behaviours; Library services; Open Access; Remote access; Research data managements; Information use","","","","","","","Pryor G., Managing Research Data, (2012)","","","wiley","","","","","","","978-111876302-5; 978-111834146-9","","","English","Res. Methods for Postgrad.: Third Ed.","Book chapter","Final","","Scopus","2-s2.0-85028894125"
"Charbonneau D.H.","Charbonneau, Deborah H. (14065772500)","14065772500","Strategies for Data Management Engagement","2013","Medical Reference Services Quarterly","32","3","","365","374","9","10","10.1080/02763869.2013.807089","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880934702&doi=10.1080%2f02763869.2013.807089&partnerID=40&md5=bc5b252a66570a717ffb4a23989e8bbf","Wayne State University, Detroit, MI, United States","Charbonneau D.H., Wayne State University, Detroit, MI, United States","The research landscape is growing dramatically, and librarians are examining new roles, services, and types of collaborations to support data-intensive research. This column describes curricular enhancements at one School of Library and Information Science in the United States. Several key areas of data management in which health sciences librarians may wish to build or enhance their skills are outlined. Possible roles and opportunities for health sciences librarians to strategically engage in data management initiatives are also presented. © 2013 Copyright Deborah H. Charbonneau.","Curriculum; data management; health sciences librarianship; research; research data management","Curriculum; Health Information Management; Library Science; Michigan; Organizational Case Studies; Professional Competence; Quality Improvement; Research; article; curriculum; education; health services research; library science; medical information system; professional competence; research; total quality management; United States","","","","","National Science Foundation’s; National Institutes of Health","Understanding policies and data requirements researchers encounter with respect to funder compliance represents a key area for engagement. Dietrich et al. provide a nice overview of data policies from major funding agencies in the United States, including the Public Access Policy from the National Institutes of Health (NIH) and the National Science Foundation’s (NSF) data management plan requirement.17 In addition, investigators submitting a grant application to NIH “are expected to include a plan for sharing data or explain why sharing data is not possible.”18 As funding agency requirements continue to expand, it will become necessary for researchers to address their plans for effectively managing their research data, including the long-term storage, preservation, and access to their research data.17 To this end, librarians can play an important role in the education, awareness, and support of funder compliance.","Hey T., Hey J., E-Science and Its Implications for the Library Community, Library Hi Tech, 24, 4, pp. 515-528, (2006); Auckland M., Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers, (2012); Brandt D.S., Librarians as Partners in E-Research: Purdue University Libraries Promote Collaboration, College & Research Libraries News, 68, 6, pp. 365-396, (2007); Lyon L., The Informatics Transform: Re-engineering Libraries for the Data Decade, International Journal of Digital Curation, 7, 1, pp. 126-138, (2012); Ogburn J.L., The Imperative for Data Curation, Libraries and the Academy, 10, 2, pp. 241-246, (2010); Gore S.A., E-Science and Data Management Resources on the Web, Medical Reference Services Quarterly, 30, 2, pp. 167-177; Best Practices, (2013); Piorun M.E., Kafel D., Et al., Teaching Research Data Management: An Undergraduate/Graduate Curriculum, Journal of eScience Librarianship, 1, 1, (2012); Scaramozzino J.M., Ramirez M.L., McGaughey K.J., A Study of Faculty Data Curation Behaviors and Attitudes at a Teaching-Centered University, College & Research Libraries, 73, 4, pp. 349-365, (2012); Carlson J.R., Demystifying the Data Interview: Developing a Foundation for Reference Librarians to Talk with Researchers about their Data, Purdue University e-Pubs. Libraries Research Publications no. 153, (2011); Witt M., Carlson J.R., Conducting a Data Interview, Purdue University e-Pubs. Libraries Research Publications Paper no. 81, (2007); Main Page, (2013); Rambo N., E-Science and Biomedical Libraries, Journal of the Medical Library Association, 97, 3, pp. 159-161, (2009); E-Science and Data Support Services. A Study of ARL Member Institutions; Carlson J., Fosmire M., Miller C., Nelson M., Determining Data Information Literacy Needs: A Study of Students and Research Faculty, Libraries and the Academy, 11, 2, pp. 629-657, (2011); Shearer K., Argaez D., Addressing the Research Data Gap: A Review of Novel Services for Libraries, Canadian Association of Research Libraries, (2010); Dietrich D., Adamus T., Miner A., Steinhaart G., De-Mystifying the Data Management Requirements of Research Funders, Issues in Science and Technology Librarianship, (2012); National Institutes of Health Data Sharing Guidance, (2003); Data Citation, (2013); Joint Data Archiving Policy (JDAP), (2013); Lynch C., Carleton D., The Impact of Digital Scholarship on Research Libraries, The Journal of Library Administration, 49, 3, pp. 227-244, (2009); Agenda for Developing E-Science in Research Libraries, (2007); Gabridge T., The Last Mile: Liaison Roles in Curating Science and Engineering Research Data, (2009); Gold A., Cyberinfrastructure, Data and Libraries, Part 2: Libraries and the Data Challenge: Roles and Actions for Libraries, D-Lib Magazine, 13, 9-10; Qin J., D'Ignazio J., Lessons Learned from a Two-Year Experience in Science Data Literacy Education, Proceedings of the 31st Annual International Association of Scientific and Technology University Libraries Conference, (2010)","D. H. Charbonneau; School of Library and Information Science, Wayne State University, Detroit, MI 48202, United States; email: dcharbon@wayne.edu","","","","","","","","15409597","","MRSQD","23869641","English","Med. Ref. Serv. Q.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84880934702"
"Gastfriend D.R.","Gastfriend, D.R. (7004660249)","7004660249","Microcomputer database management systems concepts","1986","Psychopharmacology Bulletin","22","1","","281","286","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022592522&partnerID=40&md5=58b8acce6f62fab044b69c2bb6613328","United States","Gastfriend D.R., United States","With their increasing power and decreasing costs, microcomputers are proving a valuable tool for clinical research data management. A wide variety of database systems is commercially available, offering differing levels of ease of use and capabilities (Bond, 1984). In selecting the best software program, the researcher must consider what types of data elements will be stored and must make an approximate calculation of total data storage needs. The three basic forms of file organization are: 1) file management systems; 2) relational DBMSs; and 3) hierarchical DBMSs. File management systems are easiest to learn but are too limited for most studies. Relational DBMSs allow free rearrangement of complex relations between different files, but these relations can become very abstract and learning to use them can be as difficult as learning a new programming language. Hierarchical DBMSs are more powerful than file management systems and are more understandable than relational DBMSs. For novice or casual users, menu-driven programs are more easily learned and used, but those accessing computerized files on a daily basis will appreciate newer programs that allow switching from menu mode to command mode of operation. Finally, options such as data transfer into the database or out of it, integration of statistical or graphics functions designed for clinical research, and multi-user operation are becoming available in microcomputer DMBS software and enhanced productivity as well as accuracy.","","Computers; Information Systems; Microcomputers; Software; computer analysis; data base; management; microcomputer; nonhuman; organization and management; priority journal","","","","","","","","","","","","","","","","00485764","","PSYBB","3755247","English","PSYCHOPHARMACOL. BULL.","Article","Final","","Scopus","2-s2.0-0022592522"
"Wiljes C.; Cimiano P.","Wiljes, Cord (55532719500); Cimiano, Philipp (15838793700)","55532719500; 15838793700","Linked data for the natural sciences: Two use cases in chemistry and biology","2012","CEUR Workshop Proceedings","903","","","48","59","11","3","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892407559&partnerID=40&md5=6506d945db4c63c32eab693cedb088e5","Semantic Computing, CITEC, Bielefeld University, Germany","Wiljes C., Semantic Computing, CITEC, Bielefeld University, Germany; Cimiano P., Semantic Computing, CITEC, Bielefeld University, Germany","The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist.","E-science; Linked data; Methodology; Ontology; Research data management; Scientific publishing; Semantic web","Information management; Metadata; Ontology; Research; Semantic Web; e-Science; General methodologies; Linked datum; Methodology; Research data; Research data managements; Scientific data; Technological barriers; Data handling","","","","","","","Adams N., Cannon E., Murray-Rust P., Chemaxiom - An ontological framework for chemistry in science, Nature Precedings, (2009); Digital Information (June 11), (2008); Attwood T.K., Kell D.B., McDermott P., Marsh J., Pettifer S.R., Thorne D., Calling international rescue knowledge lost in literature and data landslide!, Biochemical Journal, 424, 3, pp. 317-333, (2009); Berners-Lee T., Fensel D., Hendler J.A., Lieberman H., Wahlster W., Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential, (2005); Buschges A., Akay T., Gabriel J.P., Schmidt J., Organizing network action for locomotion: Insights from studying insect walking, Brain Res. Rev., 57, 1, pp. 162-171, (2008); Corbett P., Murray-Rust P., High-throughput Identification of Chemistry in Life Science Texts, 4216, pp. 107-118, (2006); Cruse H., Durr V., Schilling M., Schmitz J., Principles of insect locomotion, Spatial Temporal Patterns for Action-oriented Perception in Roving Robots, pp. 43-96, (2009); De Floriani L., Hui A., Papaleo L., Huang M., Hendler J., A semantic web environment for digital shapes understanding, Proceedings of the Semantic and Digital Media Technologies 2nd International Conference on Semantic Multimedia. SAMT'07, pp. 226-239, (2007); Degtyarenko K., De Matos P., Ennis M., Hastings J., Zbinden M., McNaught A., Alcantara R., Darsow M., Guedj M., Ashburner M., Chebi: A database and ontology for chemical entities of biological interest, Nucleic Acids Research, 36, SUPPL. 1, (2008); Durr V., Schmitz J., Cruse H., Behaviour-based modelling of hexapod locomotion: Linking biology and technical application, Arthropod Struct Dev, 33, 3, pp. 237-250, (2004); Feijen M., What Researchers Want - A Literature Study of Researchers' Requirements with Respect to Storage and Access to Research Data, (2011); Groza T., Grimnes G., Handschuh S., Decker S., From raw publications to linked data, Knowledge and Information Systems, pp. 1-21; Hastings J., Chepelev L., Willighagen E., Adams N., Steinbeck C., Dumontier M., The chemical information ontology: Provenance and disambiguation for chemical data on the biological semantic web, PLoS ONE, 6, 10, (2011); Holsapple C., Handbook on Knowledge Management. International Handbooks on Information Systems, (2003); Koop T., Bookhold J., Shiraiwa M., Poschl U., Glass transition and phase state of organic compounds: Dependency on molecular properties and implications for secondary organic aerosols in the atmosphere, Phys. Chem. Chem. Phys., 13, pp. 19238-19255, (2011); Popper K.R., The Logic of Scientific Discovery, (1959); Sure Y., Staab S., Studer R., Ontology engineering methodology handbook on ontologies, Handbook on Ontologies. International Handbooks on Information Systems, pp. 135-152, (2009); Zobrist B., Marcolli C., Pedernera D.A., Koop T., Do atmospheric aerosols form glasses?, Atmos. Chem. Phys., 8, 17, pp. 5221-5244, (2008)","","","","","2nd Workshop on Semantic Publishing, SePublica 2012","28 May 2012 through 28 May 2012","Hersonissos, Crete","101972","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84892407559"
"Macdonald S.; Martinez-Uribe L.","Macdonald, Stuart (23667859400); Martinez-Uribe, Luis (24344774800)","23667859400; 24344774800","Collaboration to data curation: Harnessing institutional expertise","2010","New Review of Academic Librarianship","16","SUPPL. 1","","4","16","12","16","10.1080/13614533.2010.505823","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960567831&doi=10.1080%2f13614533.2010.505823&partnerID=40&md5=e1a5ef9c68b986422bb28739159f1d10","EDINA and Data Library, University of Edinburgh, Edinburgh, Scotland, EH9 1PR, Causewayside House, 160 Causewayside, United Kingdom; Oxford University Computing Services, University of Oxford, Oxford, United Kingdom","Macdonald S., EDINA and Data Library, University of Edinburgh, Edinburgh, Scotland, EH9 1PR, Causewayside House, 160 Causewayside, United Kingdom; Martinez-Uribe L., Oxford University Computing Services, University of Oxford, Oxford, United Kingdom","It can be argued that institutional repositories have not had the impact (Lynch 2003; Salo 2008), initially expected, on academic scholarly communications (the exception being in a few well-developed and successful instances). So why should data repositories expect to fare any better? First, data repositories can learn from publication repositories' experiences and their efforts to engage researchers to accept and use these new institutional services. Second, they provide a technical infrastructure for storing and sharing data with the potential for providing access to complimentary research support facilities. Finally, due to the interdisciplinary expertise required to develop and maintain such systems, stronger ties will be forged between libraries, information and computing services, and researchers. This will assist innovation and help to make them sustainable and embedded within academic institutional policy. This paper, while aware of the diverse nature of institutional and departmental practices, aims to highlight a number of initiatives in the Universities of Edinburgh and Oxford, showing how research data repository infrastructures can be effectively realized through collaboration and sharing of expertise. We argue that by employing agile community, strategic and policy judgment, a robust data repository infrastructure will be part of an integrated solution to effectively manage institutional research data assets. © 2010 Taylor & Francis Group, LLC.","Data curation; Data repositories; Knowledge transfer and exchange; Research data management","","","","","","","","Beagrie N., Lavoie B., Wollward M., Keeping Research Data Safe 2, (2010); Sustainable Economics For a Digital Planet: Ensuring Long-Term Access to Digital Information, (2010); A Vision For UK Research, (2010); Ekmekcioglu C., Rice R., Edinburgh Data Audit Implementation Project: Final Report, (2009); Heery R., Powell A., Digital Repositories Roadmap: Looking Forward, (2006); Jones S., A Report On the Range of Policies Required For and Related to Digital Curation, (2009); Lynch C.A., Institutional Repositories: Essential Infrastructure for Scholarship in the Digital Age, ARL, 226, pp. 1-7, (2003); Macdonald S., Martinez-Uribe L., User Engagement in Research Data Curation, Lecture Notes In Computer Science-Research In Advanced Technology For Digital Libraries, 57, 14, (2009); Martinez-Uribe L., Findings of the Scoping Study and Research Data Management Workshop, (2008); Rice R., DISC-UK DataShare Project: Final Report, (2009); Patterns of Information Use and Exchange: Case Studies of Researchers In the Life Sciences, (2009); Salo D., Innkeeper at the Roach Motel, Library Trends, 57, 2, (2008); Shotton D., Portwin K., Klyne G., Miles A., Adventures in Semantic Publishing: Exemplar Semantic Enhancement of a Research Article, PLoS Computational Biology, 5, 4, (2009); Szalay A.S., Blakeley J.A., Gray's Laws: Database-Centric Computing in Science, The Fourth Paradigm-Data Intensive Scientific Discovery, (2009)","S. Macdonald; EDINA and Data Library, University of Edinburgh, Edinburgh, Scotland, EH9 1PR, Causewayside House, 160 Causewayside, United Kingdom; email: stuart.macdonald@ed.ac.uk","","","","","","","","17407834","","","","English","New Rev. Acad. Librariansh.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-79960567831"
"Li Z.; Wen J.; Zhang X.; Wu C.; Li Z.; Liu L.","Li, Zuofeng (9274991000); Wen, Jingran (35793038600); Zhang, Xiaoyan (36669857500); Wu, Chunxiao (55806255200); Li, Zuogao (55806025500); Liu, Lei (57199085763)","9274991000; 35793038600; 36669857500; 55806255200; 55806025500; 57199085763","ClinData Express--a metadata driven clinical research data management system for secondary use of clinical data.","2012","AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium","2012","","","552","557","5","8","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880852522&partnerID=40&md5=a85df1a042e420fdcc3e83ef650e1c75","Shanghai Center for Bioinformation Technology, Shanghai, China","Li Z., Shanghai Center for Bioinformation Technology, Shanghai, China; Wen J.; Zhang X.; Wu C.; Li Z.; Liu L.","Aim to ease the secondary use of clinical data in clinical research, we introduce a metadata driven web-based clinical data management system named ClinData Express. ClinData Express is made up of two parts: 1) m-designer, a standalone software for metadata definition; 2) a web based data warehouse system for data management. With ClinData Express, what the researchers need to do is to define the metadata and data model in the m-designer. The web interface for data collection and specific database for data storage will be automatically generated. The standards used in the system and the data export modular make sure of the data reuse. The system has been tested on seven disease-data collection in Chinese and one form from dbGap. The flexibility of system makes its great potential usage in clinical research. The system is available at http://code.google.com/p/clindataexpress.","","Clinical Trials as Topic; Database Management Systems; Information Storage and Retrieval; Internet; Meta-Analysis as Topic; Software; article; clinical trial (topic); computer program; data base; information retrieval; Internet; meta analysis (topic)","","","","","","","","","","","","","","","","1942597X","","","23304327","English","AMIA Annu Symp Proc","Article","Final","","Scopus","2-s2.0-84880852522"
"Witt M.","Witt, Michael (15119883100)","15119883100","Co-designing, Co-developing, and Co-implementing an Institutional Data Repository Service","2012","Journal of Library Administration","52","2","","172","188","16","29","10.1080/01930826.2012.655607","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858183317&doi=10.1080%2f01930826.2012.655607&partnerID=40&md5=0f94dcb9a7faa722bbce1d0b574abba6","Purdue University, 1530 Stewart Center, West Lafayette, IN 47907, United States","Witt M., Purdue University, 1530 Stewart Center, West Lafayette, IN 47907, United States","In January of 2011, the National Science Foundation began requiring that all proposals for research funding include data management plans. At the time of the mandate, Purdue University's library and campus information technology units had been collaborating on enhancements to the HUBzero virtual research environment. These efforts were parlayed into the development of an institutional, digital data repository and service with the support of the campus research office. In the process, local library science practices have been extended to facilitate research data curation and cyberinfrastructure on campus. Librarians are consulting on data management plans, conducting data reference and instruction, advising on data organization and description, and stewarding collections of data within an evolving library service framework. © 2012 Taylor and Francis Group, LLC.","data management plans; digital curation; digital libraries; reference","","","","","","Clemson University, Purdue; National Science Foundation, NSF; Institute of Museum and Library Services, IMLS; National Park Service, NPS","Funding text 1: With subsequent funding from NSF, the nanoHUB was retooled into the “HUBzero Platform for Scientific Collaboration” and made available for implementation by other scientific communities. The non-profit HUBzero Consortium was established to guide and sustain the development and support of HUBzero, which was released as open source software in April 2010. Over 25 “hubs” have been launched and provide virtual research environments to a wide diversity of communities such as earthquake engineering, clinical and translational research in healthcare, manufacturing techniques, STEM education, assistive technology, National Parks rangers, and research ethics.; Funding text 2: Opportunities to pursue external grants motivated further collaboration. With support from a National Leadership Grant from the Institute of Museum and Libraries Services, Clemson University, Purdue, and the National Parks Service began development of the Open Parks Grid. A librarian from Purdue is a co-principal investigator on the grant with the HUBzero Project director from ITaP serving as an advisor. This work has resulted in the integration of HUBzero with the Semantic Web using Linked Data5 and vocabularies such as the Open Archives Initiative Object Reuse and Exchange (OAI-ORE).6","Anderson C., The end of theory: The data deluge makes the scientific method obsolete, Wired Magazine, 16, 7, (2008); To stand the test of time: Long-term stewardship of digital data sets in science and engineering, (2006); Brase J., DataCite-A global registration agency for research data, Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology, 2009, pp. 257-261, (2009); Carlson J., Yatcilla J.K., The intersection of virtual organizations and the library: A case study, The Journal of Academic Librarianship, 36, 3, pp. 192-201, (2010); Cukier K., Technology: The data deluge, The Economist, 394, 8671, (2010); Funders' data policies, (2011); Farquhar A., Fostering data citation and reuse: Shaping the DataCite roadmap, (2011); Gershon D., Dealing with the data deluge, Nature, 416, 6883, pp. 889-891, (2002); Hey A.J.G., Tansley S., Tolle K.M., The fourth paradigm: Data-intensive scientific discovery, (2009); Hey T., Trefethen A., The data deluge: An e-Science perspective, Grid Computing: Making the Global Infrastructure a Reality, pp. 809-824, (2003); Klimeck G., McLennan M., Brophy S.P., Adams G.B., Lundstrom M.S., nanoHUB, org: Advancing education and research in nanotechnology, Computing in Science & Engineering, 10, 5, pp. 17-23, (2008); Lord P., Macdonald A., Lyon L., Giaretta D., From data deluge to data curation, pp. 371-375, (2004); Mullins J.L., (2011); Usage: Overview, (2011); NIH data sharing policy and implementation guidance, (2003); Data archiving strategies for NIJ funding applicants, (2010); Long-lived digital data collections enabling research and education in the 21st century, (2005); Grant proposal guide, (2011); Trust worth repositories, Audit & certification: Criteria and checklist, (2007); Internal documentation, (2011); Purdue data digest: Extramural awards by sponsor, (2011); Data management plan self-assessment questionnaire, (2011); Strategic plan 2011-2016, (2011); Soehner C., Steeves C., Ward J., Association of Research Libraries, E-Science and data support services: A study of ARL member institutions, (2010); Witt M., Institutional repositories and research data curation in a distributed environment, Library Trends, 57, 2, pp. 191-201, (2008); Witt M., Carlson J., Brandt D.S., Cragin M.H., Constructing data curation profiles, International Journal of Digital Curation, 4, 3, (2009)","M. Witt; Purdue University, 1530 Stewart Center, West Lafayette, IN 47907, United States; email: mwitt@purdue.edu","","","","","","","","15403564","","","","English","J. Libr. Adm.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84858183317"
"Castro J.A.; Ribeiro C.; Da Silva J.R.","Castro, João Aguiar (55977255100); Ribeiro, Cristina (7201734594); Da Silva, João Rocha (55496903800)","55977255100; 7201734594; 55496903800","Designing an application profile using qualified Dublin core: A case study with fracture mechanics datasets","2013","Proceedings of the International Conference on Dublin Core and Metadata Applications","","","","47","52","5","6","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891273471&partnerID=40&md5=78072df346392a968ae1113c440d614a","FEUP, INESC TEC, Universidade do Porto, Portugal","Castro J.A., FEUP, INESC TEC, Universidade do Porto, Portugal; Ribeiro C., FEUP, INESC TEC, Universidade do Porto, Portugal; Da Silva J.R., FEUP, INESC TEC, Universidade do Porto, Portugal","Metadata production for research datasets is not a trivial problem. Standardized descriptors are convenient for interoperability, but each area requires specific descriptors in order to guarantee metadata comprehensiveness and accuracy. In this paper, we report on an ongoing research data management experience at the University of Porto (U. Porto), which led to the proposal of a domain-specific application profile. We presented two curation tools to a group of researchers from mechanical engineering, to help them manage and describe their datasets. After monitoring their interactions with the solutions and analyzing the needs of the group, we were able to select a subset of qualified Dublin Core (DC), as well as a set of complementary descriptors, to capture the main aspects of their experiments. The resulting application profile combines generic, standardized DC descriptors with descriptors from a different experimental standard, and introduces extra domain-specific ones. The profile has been validated by the researchers and is now being used in the description of their datasets. © DCMI 2013.","Application profile; Dublin core; Experimental data; Fracture mechanics; Research data management","Data processing; Fracture mechanics; Information management; Research; Curation; Descriptors; Domain specific; Domain-specific application; Dublin Core; Experimental data; Research data managements; Metadata","","","","","","","Akmon D., Zimmerman A., Daniels M., Hedstrom M., The application of archival concepts to a data-intensive environment: Working with scientists to understand data management and preservation needs, Archival Science, 11, 3, pp. 329-348, (2011); Barbosa J., UPBox: ArmazenamentonaNuvempara Dados de Investigação da U.Porto, (2013); Gouveia M., DataNotes - Um Sistemacolaborativoparaanotação de Estruturas de Directórios, (2013); Heery R., Patel M., Application Profiles: Mixing and Matching Metadata Schemas, (2000); Michener W.K., Meta-information concepts for ecological data management, Ecological Informatics, 1, 1, pp. 3-7, (2006); Ribeiro C., Eugenia M., Fernandes M., Data curation at u. Porto: Identifying current practices across disciplianarydomais, IASSIST Quarterly, pp. 14-17, (2011); Rice R., Applying dc to institutional data repositories, Proceedings of the International Conference on Dublin Core and Metadata Applications, (2008); Rocha Da Silva J., Ribeiro C., Lopes J., Managing multidisciplinary research data: Extending dspace to enable long-term preservation of tabular datasets, IPRES 2012 Conference, (2012); Tonge A., Morgen P., SPECTRa-T Final Report, (2008); Willis C., Greenberg J., White H., Analysis and synthesis of metadata goals, Journal of the American Society for Information Science and Technology, 63, 8, pp. 1505-2152, (2012); Wira-Alam A., Dimitardimitrov, Zenk-Moltgen W., Extending basic dublin core for an open research data archive, Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 56-61, (2012)","","","","","2013 International Conference on Dublin Core and Metadata Applications: Linking to the Future, DC 2013","2 September 2013 through 6 September 2013","Lisbon","101667","19391366","","","","English","Proc. Int. Conf. Dublin Core Metadata Appl.","Conference paper","Final","","Scopus","2-s2.0-84891273471"
"","","","Proceedings - IEEE Symposium on Computer-Based Medical Systems","2003","Proceedings - IEEE Symposium on Computer-Based Medical Systems","2003-January","","","","","371","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942769231&partnerID=40&md5=596921b2ae2f995b40dc113e41a2e397","","","The proceedings contain 64 papers. The topics discussed include: developing solution-focused technologies - New York City health and hospital corporation's electronic medical record; a chronological database as backbone for clinical practice and research data management; integration of biological data resources using image object keying; using a medical digital library for education purposes; just-in-time database-driven web applications; the associations between variables in large databases; planar arrangement of high-dimensional biomedical data sets by isomap coordinates; automatic locating the centromere on human chromosome pictures; a wavelet-based multi-spectral codec for efficient detection of cervical neoplasia from encoded cervical images; and an image processing system scheme in B mode ultrasonic ophthalmological scanner.","","","","","","","","","","","Krol M.; Mitra S.; Lee D.J.","Institute of Electrical and Electronics Engineers Inc.","IEEE Computer Society Technical Committee on Computational Medicine; Mount Sinai School of Medicine Department of Anesthesiology; Texas Tech University College of Engineering","16th IEEE Symposium on Computer-Based Medical Systems, CBMS 2003","26 June 2003 through 27 June 2003","New York","113980","10637125","0769519016","","","English","Proc. IEEE Symp. Comput.-Based Med. Syst.","Conference review","Final","","Scopus","2-s2.0-84942769231"
"Gwede C.; Daniels S.; Johnson D.","Gwede, C. (6603343614); Daniels, S. (57197355014); Johnson, D. (57197153803)","6603343614; 57197355014; 57197153803","Organization of clinical research services at investigative sites: Implications for workload measurement","2001","Drug Information Journal","35","3","","695","705","10","4","10.1177/009286150103500307","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034846758&doi=10.1177%2f009286150103500307&partnerID=40&md5=2b34ec88e9c25d2813cf90bbea346525","Moffitt Cancer Center and Res. Inst., Radiation Oncology Program, Tampa, FL 33612, 12902 Magnolia Drive, United States","Gwede C., Moffitt Cancer Center and Res. Inst., Radiation Oncology Program, Tampa, FL 33612, 12902 Magnolia Drive, United States; Daniels S., Moffitt Cancer Center and Res. Inst., Radiation Oncology Program, Tampa, FL 33612, 12902 Magnolia Drive, United States; Johnson D., Moffitt Cancer Center and Res. Inst., Radiation Oncology Program, Tampa, FL 33612, 12902 Magnolia Drive, United States","The impact of the structure and organization of clinical research services (data management model) on the workload of clinical research coordinators (CRCs) at investigative sites is undocumented. This paper describes three types of data management models and their potential influence on the workload of CRCs. A 20-item survey, covering information about accrual to clinical trials, staffing levels, use of workload measurement tools, and the data management model, was e-mailed to nine CRCs working at selected cancer centers in the United States. Six CRCs representing four university-based institutions and two community hospitals responded. Staffing levels and number of patients placed on clinical trials varied by institution and data management model. One out of six centers used a workload formula based upon the time it takes to complete a task. The centralized clinical data management model and the modified/mixed models were common. Our findings suggest that it is important to understand the structure of the clinical data management model, among other factors, in evaluating the workload of CRCs.","Clinical research; Clinical research coordinators; Clinical research data management services; Data management models; Workload measurement","article; cancer center; clinical research; drug industry; experimental model; information processing; priority journal; task performance; workload","","","","","","","Gwede C., Johnson D., Trotti A., Measuring the workload of clinical research coordinators, part 1: Tools to study workload issues, Appl Clin Trials, pp. 40-44, (2000); Gwede C., Johnson D., Trotti A., Measuring the workload of clinical research coordinators, part 2: Workload implications for sites, Appl Clin Trials, pp. 42-47, (2000); Hancock R.D., Wiland S., Brown N.A., Kerner-Slemons S., Brown P.B., Development and testing of a complexity rating scale for clinical trial protocol management, The Monitor, pp. 36-39, (1995); Storfjell J.L., Allen C.E., Easley C.E., Analysis and management of home health nursing caseloads and workloads, J Nursing Adm, 27, 9, pp. 24-33, (1997); Medvec B.R., Productivity and workload measurement in ambulatory oncology, Sem Oncology Nursing, 10, 4, pp. 288-1295, (1994); Meyer M.A., Manpower planning, one: An American approach, Nursing Times, pp. 52-54, (1984); Arthur T., James N., Determining nurse staffing levels: A critical review of the literature, J Advanced Nursing, 19, pp. 558-565, (1994)","C. Gwede; Moffitt Cancer Center and Res. Inst., Radiation Oncology Program, Tampa, FL 33612, 12902 Magnolia Drive, United States; email: gwede@moffitt.usf.edu","","Drug Information Association","","","","","","00928615","","DGIJB","","English","Drug Inf. J.","Article","Final","","Scopus","2-s2.0-0034846758"
"Grütz R.; Mathieu N.; Löhnhardt B.; Weil P.; Krawczak M.","Grütz, R. (54983345400); Mathieu, N. (55614397700); Löhnhardt, B. (34977093100); Weil, P. (55743041400); Krawczak, M. (7006351366)","54983345400; 55614397700; 34977093100; 55743041400; 7006351366","Archiving genome data; [Archivierung von Genomdaten]","2013","Medizinische Genetik","25","3","","388","394","6","1","10.1007/s11825-013-0403-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888326060&doi=10.1007%2fs11825-013-0403-y&partnerID=40&md5=9fa539c1d51ce88d0dc4b18a2df293a8","Institut für Medizinische Informatik, Universitätsmedizin Göttingen, 37075 Göttingen, Robert-Koch-Str. 40, Germany; DZHK (Deutsches Zentrum für Herz-Kreislauf-Forschung), Partnerstandort Göttingen, Göttingen, Germany; Geschäftsbereich Informationstechnologie, Universitätsmedizin Göttingen, Göttingen, Germany; Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Kiel, Germany","Grütz R., Institut für Medizinische Informatik, Universitätsmedizin Göttingen, 37075 Göttingen, Robert-Koch-Str. 40, Germany; Mathieu N., Institut für Medizinische Informatik, Universitätsmedizin Göttingen, 37075 Göttingen, Robert-Koch-Str. 40, Germany, DZHK (Deutsches Zentrum für Herz-Kreislauf-Forschung), Partnerstandort Göttingen, Göttingen, Germany; Löhnhardt B., Geschäftsbereich Informationstechnologie, Universitätsmedizin Göttingen, Göttingen, Germany; Weil P., Institut für Medizinische Informatik, Universitätsmedizin Göttingen, 37075 Göttingen, Robert-Koch-Str. 40, Germany; Krawczak M., Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Kiel, Germany","In view of the increasing amount of data arising from genome research, efficient research data management is becoming increasingly important in this domain. The third, and last, article of the series on ""Research data management for genome data"" describes the general lifecycle of research data-from their planning via the selection and inclusion into storage facilities to preservation measures and final user access. Archives play an important role in nearly all phases of this life cycle, which renders them an important component of genome data processing. Three exemplary public archives for genome data are introduced: the European Molecular Biology Laboratory (EMBL) databank, the Sequence Read Archive, and the Trace Archive. Owing to the high level of specialization of these institutions, however, additional archives are required that allow more generic data storage or, alternatively, easy extension to other genome data types. A generic concept for such archives will be described and recommendations given for their practical implementation. © 2013 Springer-Verlag Berlin Heidelberg.","Data lifecycle; Data management systems; Databases genetic; Genomics; Information storage and retrieval","article; data base; genomics; human; information center; information processing; information storage; medical research","","","","","","","Grundsätze Zum Umgang Mit Forschungsdaten, (2010); Benson D.A., Karsch-Mizrachi I., Lipman D.J., Ostell J., Wheeler D.L., GenBank, Nucleic Acids Research, 33, (2005); Cattell R., Scalable SQL and NoSQL data stores, Sigmod Rec, 39, pp. 12-27, (2011); Consortium T., A map of human genome variation from population-scale sequencing, Nature, 467, pp. 1061-1073, (2010); Consultative Committee for Space Data Systems Reference Model for An Open Archival Information System (OAIS), (2012); DDBJ Sequence Read Archive. DDBJ Sequence Read Archive-home, (2013); Deutsche Forschungsgemeinschaft Recommendations for Secure Storage and Availability of Digital Primary Research Data, (2009); Richtlinie 95/46/EG des Europäischen Parlaments und des Rates Vom 24, (1995); EMBL European Bioinformatics Institute, (2013); European Nucleotide Archive, (2013); ENA Data Formats. The European Nucleotide Archive, (2013); Gross M., Betriebssysteme-Der Verzeichnisdienst LDAP, (2011); Higgins S., The DCC curation lifecycle model, IJDC, 3, pp. 134-140, (2008); Hupfeld F., Cortes T., Kolbeck B., Et al., The XtreemFS architecture-A case for object-based file systems in Grids, Concurr Comput, 20, pp. 2049-2060, (2008); Brendel J., Speichertechnologien im Überblick, (2004); Karsch-Mizrachi I., Nakamura Y., Cochrane G., The international nucleotide sequence database collaboration, Nucleic Acids Res, 40, (2012); (2000); Referenzmodell Für Ein Offenes Archiv-Informations-System, (2012); Krawczak M., Goebel J.W., Cooper D.N., Is the NIH policy for sharing GWAS data running the risk of being counterproductive?, Investigative Genetics, 1, (2010); Lai E., Application of SNP technologies in medicine: Lessons learned and future challenges, Genome Research, 11, 6, pp. 927-929, (2001); Ludwig J., Enke H., Leitfaden Zum Forschungsdaten-Management: Handreichungen Aus Dem WissGrid-Projekt, (2013); Neuroth H., Langzeitarchivierung von Forschungsdaten: Eine Bestandsaufnahme, (2012); Sears R., Van Ingen C., Gray J., To BLOB or Not to BLOB: Large Object Storage in A Database or A Filesystem?, (2007); Smith M., Barton M., Branschofsky M., Et al., DSpace. D-Lib Magazine, (2003); Tateno Y., Imanishi T., Miyazaki S., Fukami-Kobayashi K., Saitou N., Sugawara H., Gojobori T., DNA Data Bank of Japan (DDBJ) for genome scale research in life science, Nucleic Acids Research, 30, 1, pp. 27-30, (2002); Whyte A., Wilson A., How to Appraise and Select Research Data for Curation, (2010)","R. Grütz; Institut für Medizinische Informatik, Universitätsmedizin Göttingen, 37075 Göttingen, Robert-Koch-Str. 40, Germany; email: romanus.gruetz@med.uni-goettingen.de","","","","","","","","18635490","","MGENE","","German","Med. Genet.","Article","Final","","Scopus","2-s2.0-84888326060"
"Luvisi A.; Panattoni A.; Triolo E.","Luvisi, Andrea (57193806900); Panattoni, Alessandra (6506612073); Triolo, Enrico (6506058152)","57193806900; 6506612073; 6506058152","Electronic identification-based Web 2.0 application for plant pathology purposes","2012","Computers and Electronics in Agriculture","84","","","7","15","8","14","10.1016/j.compag.2012.02.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858728657&doi=10.1016%2fj.compag.2012.02.008&partnerID=40&md5=d4a3b46932e35127bdd83e45bfebe42b","Department of Tree Science, Entomology, and Plant Pathology 'G. Scaramuzzi', University of Pisa, 56124 PISA, Via del Borghetto, 80, Italy","Luvisi A., Department of Tree Science, Entomology, and Plant Pathology 'G. Scaramuzzi', University of Pisa, 56124 PISA, Via del Borghetto, 80, Italy; Panattoni A., Department of Tree Science, Entomology, and Plant Pathology 'G. Scaramuzzi', University of Pisa, 56124 PISA, Via del Borghetto, 80, Italy; Triolo E., Department of Tree Science, Entomology, and Plant Pathology 'G. Scaramuzzi', University of Pisa, 56124 PISA, Via del Borghetto, 80, Italy","In order to integrate Web-based tools in plant pathology for storing, updating and sharing information, an electronic identification system based on radiofrequency technology was used for linking plants or samples to associated data. Radiofrequency identification microchips working at low or ultra high frequency were associated to different items such as organism, matrix or container commonly involved in a plant pathology test. Moreover, the microchips were subjected to various environmental conditions, such as thermal and chemical stress. A collaborative Web 2.0-based workspace was used to support research data management and interaction between users. Our findings demonstrate that the microchips maintained their reliability following environmental treatments, while the selected Web 2.0 collaborative workspace allowed useful data interchange and communications between labs during long-term trials as sanitary selection of grapevine. © 2012 Elsevier B.V.","Collaborative workspace; Microchip; Radiofrequency; Traceability","Vitis; Diseases; Information management; Microprocessor chips; World Wide Web; A plants; Chemical stress; Collaborative workspace; Data interchange; Environmental conditions; Environmental treatment; Microchip; Plant pathology; Radio frequencies; Radiofrequency technology; Research data; Sharing information; Traceability; Ultra-high frequency; Web 2.0; Web 2.0 applications; Web-based tools; agricultural technology; data management; data set; environmental conditions; identification method; pathology; World Wide Web; Pathology","","","","","","","Almeida N.F., Yan S., Cai R., Clarke C.R., Morris C.E., Schaad N.W., Schuenzel E.L., Lacy G.H., Sun X., Jones J.B., Castillo J.A., Bull C.T., Leman S., Guttman D.S., Setubal J.C., Vinatzer B.A., PAMDB, a multilocus sequence typing and analysis database and website for plant-associated microbes, Phytopathology, 100, pp. 208-215, (2010); Ampatzidis Y.G., Vougioukas S.G., Field experiments for evaluating the incorporation of RFID and barcode registration and digital weighing technologies in manual fruit harvesting, Comput. Electron. Agr., 66, pp. 166-172, (2009); (2007); Bandinelli R., Triolo E., Luvisi A., Pagano M., Gini B., Rinaldelli E., Employment of radiofrequency technology (RFID) in grapevine nursery traceability, Adv. Hort. Sci., 23, pp. 75-80, (2009); Bollen A.F., Riden C.P., Cox N.R., Agricultural supply system traceability, Part I: role of packing procedures and effects of fruit mixing, Biosyst. Eng., 98, pp. 391-400, (2007); Boulos M.N.K., Maramba I., Wheeler S., Wikis, blogs and podcasts: a new generation of web-based tools for virtual collaborative clinical practice and education, BMC Med. Ed., 6, (2006); Bowman K.D., Identification of woody plants with implanted microchips, Hort Technol., 15, pp. 352-354, (2005); Carrascal M.J., Pau L.F., Reiner L., Knowledge and information transfer in agriculture using hypermedia: a system review, Comput. Electron. Agr., 12, pp. 83-119, (1995); Chomczynski P., Sacchi N., The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on, Nat. Prot., 1, pp. 581-585, (2006); Clark M., Adams A., Characteristics of the microplate method of enzyme-linked immunosorbent assay for the detection of plant viruses, J. Gen. Virol., 34, pp. 475-483, (1977); Cunha C.R., Pere S., Morais R., Oliveira A.A., Matos S.G., Fernandes M.A., Ferreira P.J.S.G., Reis M.J.C.S., The use of mobile devices with multi-tag technologies for an overall contextualized vineyard management, Comput. Electron. Agr., 73, pp. 154-164, (2010); Eysenbach G., Medicine 2.0: social networking, collaboration, participation, apomediation and openness, J. Med. Internet Res., 10, (2008); Farcy C., de Terwangne B., Blerot P., A distributed information system for public forest and wildlife management in the Walloon Region (Belgium) using open GIS standards, Comput. Electron. Agr., 47, pp. 207-220, (2005); Giustini D.M., How Web 2.0 is changing medicine, Brit. Med. J., 33, pp. 1283-1284, (2006); Kinsey G.C., Paterson R.R., Kelley J., Methods for the determination of filamentous fungi in treated and untreated waters, J. Appl. Micriobiol., 85, pp. 214-224, (1999); Kumagai M.H., Miller P., Development of electronic barcodes for use in plant pathology and functional genomica, Plant Mol. Biol., 61, pp. 515-523, (2006); MacKenzie D.J., McLean M.A., Mukerji S., Green M., Improved RNA extraction from woody plants for the detection of viral pathogens by reverse transcription-polymerase chain reaction, Plant Dis., 81, pp. 222-226, (1997); McCall J.A., Richards P.K., Walters G.F., Factors in Software Quality, (1977); McLean R., Richards B.H., Wardman J.I., The effect of Web 2.0 on the future of medical practice and education: Darwikinina evolution or folksonomic revolution?, Med. J. Australia, 187, pp. 174-177, (2007); Morris T.J., Dodds J.A., Isolation and analysis of double-stranded RNA from virus infected plant and fungal tissue, Phytopathology, 69, pp. 854-858, (1979); Murashige T., Skoog F., A revised medium for rapid growth and bioassays with tobacco tissue cultures, Physiol. Plantarum, 15, pp. 473-497, (1962); Nakaune R., Nakano M., Efficient methods for sample processing and cDNA synthesis by RT-PCR for the detection of grapevine viruses and viroids, J. Virol. Methods, 134, pp. 244-249, (2006); Nielsen J., Usability Engineering, (1994); Nikkila R., Seilonen I., Koskinen K., Software next term architecture for farm management information systems in precision agriculture, Comput. Electron. Agr., 70, pp. 328-336, (2009); Ngai E.W.T., Moon K.K.L., Riggins F.J., Yi C.Y., RFID research: an academic literature review (1995-2005) and future research directions, Int. J. Prod. Econ., 112, pp. 510-520, (2008); Liscouski J., Integrating laboratory automation, Lab Manager, 4, pp. 42-44, (2009); Luchi N., Vannuccini M., Panzavolta T., Tiberi R., Feducci M., Salbitano F., Giachini M., Zocco Pisana L., Capretti P., Censimento e indicazioni gestionali contro le avversità delle alberature dell'Opera delle Mura di Lucca, Forest at, 5, pp. 253-261, (2008); Luvisi A., Triolo E., Rinaldelli E., Bandinelli R., Pagano M., Gini B., Radiofrequency applications in grapevine: from vineyard to web, Comput. Electron. Agr., 70, pp. 256-259, (2010); Luvisi A., Panattoni A., Bandinelli R., Rinaldelli E., Pagano M., Gini B., Triolo E., RFID microchip internal implants: effects on grapevine histology, Sci. Hortic., 124, pp. 349-353, (2010); Luvisi A., Pagano A., Bandinelli R., Rinadelli E., Gini B., Scarton M., Manzoni G., Triolo E., Virtual vineyard for grapevine management purposes: a RFID/GPS application, Comput. Electron. Agr., 75, pp. 368-371, (2011); Luvisi A., Panattoni A., Bandinelli R., Rinaldelli E., Pagano M., Triolo E., Implanting RFIDs into Prunus to facilitate electronic identification in support of sanitary certification, Biosyst. Eng., 109, pp. 167-173, (2011); O'Reilly T., Design patterns and business models for the next generation of software, (2005); Papavizas G.C., Davey C.B., Evaluation of various media and antimicrobial agents for isolation of soil fungi, Soil Sci., 88, pp. 112-117, (1959); Sandars J., Homer M., Pell G., Croker T., Web 2.0 and social software: the medical student way of e-learning, Med. Teach., 14, pp. 1-5, (2008); Schreiber W.E., Giustini D.M., Pathology in the Era of Web 2.0, Am. J. Clin. Pathol., 132, pp. 824-828, (2009); Schuck A., Andrienko G., Andrienko N., Folving S., Kohl M., Miina S., Paivinen R., Richards T., Voss H., The European Forest Information System - an Internet based interface between information providers and the user community, Comput. Electron. Agr., 47, pp. 185-206, (2005); Serodio C., Cunha J.B., Morais R., Couto C., Monteiro J., A networked platform for agricultural management systems, Comput. Electron. Agr., 31, pp. 75-90, (2001); Sorensen C.G., Fountas S., Nash E., Pesonen L., Bochtis D., Pedersen S.M., Basso B., Blackmore S.B., Conceptual model of a future farm management information system, Comput. Electron. Agr., 72, pp. 37-47, (2010); Sorensen C.G., Pesonen L., Bochtis D.D., Vougioukas S.G., Suomi P., Functional requirements for a future farm management information system, Comput. Electron. Agr., 76, pp. 266-276, (2011); Thrane C., Quality assurance in plant health diagnostics - the experience of the Danish Plant Directorate, Eur. J. Plant Pathol., 121, pp. 339-346, (2008); Vai N., Un sistema di mappatura dei platani colpiti dal cancro colorato, Alberi e territorio, 2, pp. 34-37, (2005); Virzi R.A., Refining the test phase of usability evaluation: how many subjects is enough?, Hum. Factors, 34, pp. 457-468, (1992); Voulodimos A.S., Patrikakis C.Z., Sideridis A.B., Ntafis V.A., Xylouri E.M., A complete farm management system based on animal identification using RFID technology, Comput. Electron. Agr., 70, pp. 380-388, (2010)","A. Luvisi; Department of Tree Science, Entomology, and Plant Pathology 'G. Scaramuzzi', University of Pisa, 56124 PISA, Via del Borghetto, 80, Italy; email: aluvisi@agr.unipi.it","","","","","","","","01681699","","CEAGE","","English","Comput. Electron. Agric.","Article","Final","","Scopus","2-s2.0-84858728657"
"Martinez-Uribe L.; MacDonald S.","Martinez-Uribe, Luis (24344774800); MacDonald, Stuart (23667859400)","24344774800; 23667859400","User engagement in research data curation","2009","Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","5714 LNCS","","","309","314","5","17","10.1007/978-3-642-04346-8_30","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952027075&doi=10.1007%2f978-3-642-04346-8_30&partnerID=40&md5=2a4889a2c58bb12bfab0cc3b34b81d8e","University of Oxford, e-Research Centre, Oxford OX1 3QG, 7 Keble Road, United Kingdom; University of Edinburgh, EDINA, Edinburgh, EH9 1PR, 160 Causewayside, United Kingdom","Martinez-Uribe L., University of Oxford, e-Research Centre, Oxford OX1 3QG, 7 Keble Road, United Kingdom; MacDonald S., University of Edinburgh, EDINA, Edinburgh, EH9 1PR, 160 Causewayside, United Kingdom","In recent years information systems such as digital repositories, built to support research practice, have struggled to encourage participation partly due to inadequate analysis of the requirements of the user communities. This paper argues that engagement of users in research data curation through an understanding of their processes, constraints and culture is a key component in the development of the data repositories that will ultimately serve them. In order to maximize the effectiveness of such technologies curation activities need to start early in the research lifecycle and therefore strong links with researchers are necessary. Moreover, this paper promotes the adoption of a pragmatic approach with the result that the use of open data as a mechanism to engage researchers may not be appropriate for all disciplinary research environments. © 2009 Springer.","Digital curation; Digital repository services; Open data; Research data management; User engagement","Research; Curation; Data repositories; Digital curation; Digital repository; Key component; Research data; Research environment; Strong link; User communities; User engagement; Digital libraries","","","","","","","Harnard S., For Whom the Gate Tolls? How and Why to Free the Referred Research Literature Online Through Author/institution Self-archiving Now, (2001); Raym C., The Case for Institutional Repositories: A SPARC Position Paper, (2002); Salo D., Innkeeper at the Roach Motel, Library Trends, 57, (2008); Murray-Rust P., Open Data in science, Nature Proceedings (2008); OECD: OECD Principles and guidelines for access to research data from public funding, Paris, (2007); Gibbs H., DISC-UK DataShare: State of the Art Review, (2007); Lyon L., Coles S., Duke M., Koch T., Scaling Up: Towards a Federation of Crystallography Data Repositories, (2008); Berman H.M., The Protein Data Bank: A historical perspective, Acta Crystallographica, pp. 88-95, (2008); Benton D., Recent changes in the GenBank online service, Nucleic. Acid. Research, 18, (1990); Steinhart G., DataStaR, a Data Staging Repository for Digital Research Data, (2008); Martinez-Uribe L., Findings of the Scoping Study and Research Data Management Workshop (2008); Ekmekcioglu C., Rice R., Edinburgh Data Audit Implementation Project: Final Report, (2009)","L. Martinez-Uribe; University of Oxford, e-Research Centre, Oxford OX1 3QG, 7 Keble Road, United Kingdom; email: luis.martinez-uribe@oerc.ox.ac.uk","","","Alpha Bank; European Res. Consort. Informatics Math. (ERCIM); SWETS Information Services; InterOptics; EBSCO","13th European Conference on Research and Advanced Technologies for Digital Libraries, ECDL 2009","27 September 2009 through 2 October 2009","Corfu","80163","16113349","3642043453; 978-364204345-1","","","English","Lect. Notes Comput. Sci.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-77952027075"
"Marciniak Thomas A.; Kuan Shi Hwa; Srivastava Sudhir","Marciniak, Thomas A. (57197973685); Kuan, Shi Hwa (7005350001); Srivastava, Sudhir (55735251500)","57197973685; 7005350001; 55735251500","Researcher: A user extensible statistical system","1988","Proceedings - Annual Symposium on Computer Applications in Medical Care","","","","595","598","3","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024111606&partnerID=40&md5=50b10072fe9bdc5911a850bf83dc194a","Natl Cancer Inst, Bethesda, MD, USA","Marciniak Thomas A., Natl Cancer Inst, Bethesda, MD, USA; Kuan Shi Hwa, Natl Cancer Inst, Bethesda, MD, USA; Srivastava Sudhir, Natl Cancer Inst, Bethesda, MD, USA","Researcher, an IBM PC-compatible software package for clinical research data management, statistical analysis, and graphics, is described. It is written in compiled BASIC, and all source programs are available in the public domain. Its features are described, emphasizing those that facilitate the incorporation of additional statistical analyses.","","Computer Graphics--Medical Applications; Computer Programming Languages--BASIC; Computers, Microcomputer; Statistical Methods--Medical Applications; Clinical Research Data Management; Microcomputer Software; Software Package Researcher; Statistical Analysis; Computer Software","","","","","","","","","","Publ by IEEE","Alliance for Continuing Medical Educuation; Alliance for Engineering in Medicine & Biology; American Acad of Family Physicians; American Acad of Physican Assistants; American Assoc for Medical Systems & Informatics; et al","Proceedings - Twelfth Annual Symposium on Computer Applications in Medical Care","6 November 1988 through 9 November 1988","Washington, DC, USA","","01954210","0818608811","PCMCD","","English","Proc Annu Symp Comput Appl Med Care","Conference paper","Final","","Scopus","2-s2.0-0024111606"
"Woodruff H.B.; Tway P.C.; Downing G.V.; Gilbert J.P.","Woodruff, Hugh B. (7003775658); Tway, Patricia C. (6603587379); Downing, George V. (7005209890); Gilbert, Jack P. (57121296600)","7003775658; 6603587379; 7005209890; 57121296600","Bulk pharmaceutical research data management","1982","Journal of Automatic Chemistry","4","4","","161","164","3","1","10.1155/S1463924682000406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958301222&doi=10.1155%2fS1463924682000406&partnerID=40&md5=e14a969ce9cf14e9a9b202f15b6e6d1f","Merck, Sharp & Dohme Research Laboratories, Rahway, New Jersey 07065, PO Box 2000, United States","Woodruff H.B., Merck, Sharp & Dohme Research Laboratories, Rahway, New Jersey 07065, PO Box 2000, United States; Tway P.C., Merck, Sharp & Dohme Research Laboratories, Rahway, New Jersey 07065, PO Box 2000, United States; Downing G.V., Merck, Sharp & Dohme Research Laboratories, Rahway, New Jersey 07065, PO Box 2000, United States; Gilbert J.P., Merck, Sharp & Dohme Research Laboratories, Rahway, New Jersey 07065, PO Box 2000, United States","[No abstract available]","","","","","","","","","Malmstadt H.V., Analyst, 28, (1980); Stockwell P.B., Journal of Automatic Chemistry, 1, (1978); Struthers S., Industrial Chemical News, 2, (1981); Fok J.S., Abrahamson E.A., American Laboratory, 7, (1975); Dessey R.E., Starling M.K., Analytical Chemistry, 51; Woodruff H.B., Caldwell W.B., Singleton B., Downing G.V., Rosenberg A.S., Baron S.F., Ocker W.A., Chemical, Biomedical and Environmental Instrumentation, 10; Binkley D.P., Major H.W., American Laboratory, 13, (1981); Grebel S.B., Sharrar C.E., American Laboratory, 13, (1981); Stockwell P.B., Journal of Automatic Chemistry, 1, (1979); Foreman J.K., Journal of Automatic Chemistry, 2, (1980); Ziegler E., Analytica Chimica Acta Computer Techniques and Optimization, 122, (1980)","","","","","","","","","01420453","","","","English","J. Autom. Chem.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-84958301222"
"Russell Channing H.","Russell, Channing H. (57204376430)","57204376430","ADVANCES IN SCIENTIFIC SOFTWARE PACKAGES.","1986","ACS Symposium Series","","","","23","30","7","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022902449&partnerID=40&md5=2d985edd66857fc140deff5044555b72","BBN Software Products Corp,, Cambridge, MA, USA, BBN Software Products Corp, Cambridge, MA, USA","Russell Channing H., BBN Software Products Corp,, Cambridge, MA, USA, BBN Software Products Corp, Cambridge, MA, USA","Early scientific software packages focused on compilers, individual applications, and specific aspects of computer support such as statistics. More recently, software packages provide a broad, integrated, easy to use, and extensible set of capabilities to support research data management. RS/1 (TM) is described as an example of modern scientific software.","","CHEMICAL LABORATORIES - Research; COMPUTER GRAPHICS; DATA MANAGEMENT; MODELING; RS/1 SOFTWARE; STATISTICS; COMPUTER SOFTWARE","","","","","","","","","","ACS","ACS, Div of Polymeric Materials Science & Engineering, Washin","Computer Applications in the Polymer Laboratory. Developed from a Symposium at the 189th Meeting of the American Chemical Society.","","Miami Beach, FL, USA","8860","00976156","0841209774","ACSMC","","English","ACS Symp Ser","Conference paper","Final","","Scopus","2-s2.0-0022902449"
"Li X.; Rastan R.; Shepherd J.; Paik H.Y.","Li, Xinyu (55937185100); Rastan, Roya (55937050700); Shepherd, John (7401742169); Paik, Hye Young (57213689865)","55937185100; 55937050700; 7401742169; 57213689865","Automatic affiliation extraction from calls-for-papers","2013","AKBC 2013 - Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, Co-located with CIKM 2013","","","","97","101","4","1","10.1145/2509558.2509575","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888160563&doi=10.1145%2f2509558.2509575&partnerID=40&md5=e7e47fd59ef6f698cc6964c8b6c51398","School of Comp. Sci. and Eng., UNSW, Sydney, NSW, Australia","Li X., School of Comp. Sci. and Eng., UNSW, Sydney, NSW, Australia; Rastan R., School of Comp. Sci. and Eng., UNSW, Sydney, NSW, Australia; Shepherd J., School of Comp. Sci. and Eng., UNSW, Sydney, NSW, Australia; Paik H.Y., School of Comp. Sci. and Eng., UNSW, Sydney, NSW, Australia","In this paper, we describe a system to collect information about academic affiliation (organisations where researchers work) from Calls-for-Papers for academic conferences. The system uses a range of heuristic approaches and open-source tools in order to extract and identify entities, and to incorporate the information into a pre-defined database schema. This forms part of a larger project to automatically populate and maintain a range of data related to academic research. The proposed system is currently being tested and some promising preliminary results are available. © 2013 ACM.","affiliation extraction; column-based structures; research data management","Heuristic methods; Information management; Knowledge based systems; Research; Academic conferences; Academic research; Database schemas; Heuristic approach; Open source tools; Research data managements; System use; Extraction","","","","","","","Bontcheva K., Cunningham H., Maynard D., Tablan V., Saggion H., Developing reusable and robust language processing components for information systems using gate, Database and Expert Systems Applications, 2002. Proceedings. 13th International Workshop on, pp. 223-227, (2002); Carpenter B., Baldwin B., Natural Language Processing with LingPipe 4, (2011); Correia F.L., Amaro R.F.S., Sarmento L., Rossetti R.J.F., Allcall: An automated call for paper information extractor, 5th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-4, (2010); Finkel J.R., Grenager T., Manning C., Incorporating non-local information into information extraction systems by gibbs sampling, Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp. 363-370, (2005); Forster K., Universities Worldwide; Ley M., DBLP: Computer Science Bibliography; Minton S.N., Knoblock C.A., Liuzzi R., Macskassy S.A., LaMonica P., See K., Monitoring entities in an uncertain world: Entity resolution and referential integrity, Twenty-Third IAAI Conference, 2011; Morton T., Kottmann J., Baldridge J., Bierner G., OpenNLP: A Java-based NLP Toolkit, (2005); Jeong S., Kim H.-G., SEDE: An ontology for scholarly event description, Journal of Information Science, 36, 2, pp. 209-227, (2010); Wick M., Boutreux C.; Yosef M.A., Hoffart J., Bordino I., Spaniol M., Weikum G., Aida: An online tool for accurate disambiguation of named entities in text and tables, Proceedings of the VLDB Endowment, 4, 12, (2011)","","","","Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB","2013 Workshop on Automated Knowledge Base Construction, AKBC 2013 - Co-located with CIKM 2013","27 October 2013 through 28 October 2013","San Francisco, CA","100911","","978-145032411-3","","","English","AKBC - Proc. Workshop Autom. Knowl. Base Constr., Co-located CIKM","Conference paper","Final","","Scopus","2-s2.0-84888160563"
"Bahls D.; Scherp G.; Tochtermann K.; Hasselbring W.","Bahls, Daniel (23007572200); Scherp, Guido (23393857900); Tochtermann, Klaus (16053767400); Hasselbring, Wilhelm (26643500000)","23007572200; 23393857900; 16053767400; 26643500000","Towards a recommender system for statistical research data","2012","CEUR Workshop Proceedings","912","","","61","72","11","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893212937&partnerID=40&md5=be4c4990432bba87a7601dd3bb2a321c","Leibniz Information Centre for Economics (ZBW), Kiel, Germany; Software Engineering Group, Kiel University, Germany","Bahls D., Leibniz Information Centre for Economics (ZBW), Kiel, Germany; Scherp G., Leibniz Information Centre for Economics (ZBW), Kiel, Germany, Software Engineering Group, Kiel University, Germany; Tochtermann K., Leibniz Information Centre for Economics (ZBW), Kiel, Germany; Hasselbring W., Software Engineering Group, Kiel University, Germany","To effectively promote the exchange of scientific data, retrieval services are required to suit the needs of the research community. A large amount of research in the field of economics is based on statistical data, which is often drawn from external sources like data agencies, statistical offices or affiliated institutes. Since producing such data for a particular research question is expensive in time and money-if possible at all-research activities are often influenced by the availability of suitable data. Researchers choose or adjust their questions, so that the empirical foundation to support their results is given. As a consequence, researchers look out and poll for newly available data in all sorts of directions due to a lacking information infrastructure for this domain. This circumstance and a recent report from the High Level Expert Group on Scientific Data motivate recommendation and notification services for research data sets. In this paper, we elaborate on a case-based recommender system for statistical data, which allows for precise query specification. We discuss required similarity measures on the basis of cross-domain code lists and propose a system architecture. To address the problem of continuous polling, we elaborate on a notification service to inform researchers on newly avaible data sets based on their personal request.","Case-based reasoning; Linked data; Recommender systems; Research data management; Semantic digital data library; Statistics","Information management; Research; Semantics; Statistics; Empirical foundations; Information infrastructures; Linked datum; Notification Service; Research communities; Research data managements; Semantic digital data libraries; Statistical research; Recommender systems","","","","","","","Feijen M., What Researchers Want - A Literature Study of Researchers' Requirements with Respect to Storage and Access to Research Data, (2011); Wood J., Andersson T., Bachem A., Best C., Genova F., Lopez D.R., Los W., Marinucci M., Romary L., Van De Sompel H., Vigen J., Wittenburg P., Giaretta D., Riding the wave: How europe can gain from the rising tide of scientific data, European Union (2010) Final Report of the High Level Expert Group on Scientific Data: A Submission to the European Commission; Gottron T., Hachenberg C., Harth A., Zapilko B., Towards a semantic data library for the social sciences, SDA'11: Proceedings of the International Workshop on Semantic DigitalArchives, (2011); Cyganiak R., Field S., Gregory A., Halb W., Tennison J., Semantic statistics: Bringing together sdmx and scovo, LDOW. Volume 628 of CEUR Workshop Proceedings, (2010); Miloevi U., Janev V., Spasi M., Milojkovi J., Vrane S., Publishing statistical data as linked open data, Proceedings of the 2nd International Conference on Information Society Technology, Information Society of the Republic of Serbia, (2012); Halb W., Raimond Y., Hausenblas M., Building linked data for both humans and machines, Www 2008 Workshop: Linked Data on the Web (LDOW2008), (2008); Bahls D., Tochtermann K., Addressing the long tail in empirical research data management, 12th International Conference on Knowledge Management (I-KNOW '12), (2012); Burke R., Recommender systems: An introduction, by dietmar jannach, markus zanker, alexander felfernig, and gerhard friedrich, International Journal of HumanComputer Interaction, 28, 1, pp. 72-73, (2012); Bergmann R., Richter M.M., Schmitt S., Stahl A., Vollrath I., Utility-oriented matching: A new research direction for case-based reasoning, Professionelles Wissensmanagement: Erfahrungen und Visionen. Proceedings of the 1st Conference on Professional Knowledge Management, pp. 264-274, (2001); Bergmann R., Kolodner J., Plaza E., Representation in case-based reasoning, Knowl. Eng. Rev., 20, 3, pp. 209-213, (2005); Bridge D., Goker M.H., McGinty L., Smyth B., Case-based recommender systems, Knowledge Engineering Review, 20, pp. 315-320, (2005); Richter M.M., Case based reasoning and the search for knowledge, Proceedings of the 7th Industrial Conference on Advances in Data Mining: Theoretical Aspects and Applications, pp. 1-14, (2007); Annex 1: Cross-domain concepts 2009, Area, pp. 1-47, (2009); Annex 2: Cross-domain code lists 2009, Area, (2009); Stahl A., Roth-Berghofer T., Rapid prototyping of cbr applications with the open source tool mycbr, ECCBR. Volume 5239 of Lecture Notes in Computer Science, pp. 615-629, (2008); Lenz M., Case Retrieval Nets as a Model for Building Flexible Information Systems, (1999); Roth-Berghofer T.R., Explanations and case-based reasoning: Foundational issues, Advances in Case-Based Reasoning. Volume 3155 of Lecture Notes in Computer Science, pp. 195-209, (2004); Bahls D., Roth-Berghofer T., Explanation support for the case-based reasoning tool mycbr, Proceedings of the TwentySecond AAAI Conference on Artificial Intelligence, pp. 1844-1845, (2007)","","","","","2nd International Workshop on Semantic Digital Archives, SDA 2012","27 September 2012 through 27 September 2012","Paphos","102269","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84893212937"
"Byrne A.","Byrne, Alex (7102233768)","7102233768","The importance of culture in digital ecosystems: Managing indigenous research data","2009","Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES '09","","","","1","7","6","3","10.1145/1643823.1643825","https://www.scopus.com/inward/record.uri?eid=2-s2.0-74549151640&doi=10.1145%2f1643823.1643825&partnerID=40&md5=1c69e059661a3a9a34b5050d881a56ce","University of Technology, Sydney, Sydney, NSW, Australia","Byrne A., University of Technology, Sydney, Sydney, NSW, Australia","The increasing emphasis on research data curation is raising new challenges for curators and the systems they employ to create sustainable data archives that will respond to the evolving research environment. The skills developed in a long history of managing 'monocultural' quantitative datasets must now be extended to the broader requirements entailed in the management of complex digital ecosystems whose elements range from structured qualitative datasets to collections of research materials in multifarious formats. As in natural ecosystems, all are highly contextualized and often extremely sensitive in regard to storage, access and potential reuse. The protocols that codify those requirements must be for mulated carefully and translated into systems management effectively. The curation of research data relating to Indigenous peoples starkly illustrates these challenges by presenting major cultural and ontological complexities. It suggests approaches for handling research data in other sensitive domains. Copyright 2009 ACM.","Culture; Data archive; Data curation; Indigenous; Ontology","Ecosystems; Ontology; Research; Culture; Curation; Data archives; Data sets; Digital ecosystem; Indigenous people; Natural ecosystem; Research data; Research environment; Systems management; Data handling","","","","","","","Aboriginal and Torres Strait Islander Protocols for Libraries; (2008); Nakata M., Gibson J., Nakata V., Byrne A., McKeough J., Indigenous digital collections: An early look at the organisation and culture interface, 39, 4, pp. 223-236, (2008); Nakata M., Nakata V., Anderson J., Hart V., Hunter J., Smallacombe S., Richmond C., Lloyd B., Maynard G., Evaluation of the Northern Territory Library's Libraries and Knowledge Centres Model, (2006); Policy on data management and sharing, (2007)","A. Byrne; University of Technology, Sydney, Sydney, NSW, Australia; email: alex.byrne@uts.edu.au","","","French Chapter of ACM Special Interest Group on Applied Computing","1st ACM International Conference on Management of Emergent Digital EcoSystems, MEDES '09","27 October 2009 through 30 October 2009","Lyon","78955","","978-160558829-2","","","English","Proc. Int. Conf. Manage. Emergent Digit. EcoSyst., MEDES","Conference paper","Final","","Scopus","2-s2.0-74549151640"
"Wiljes C.; Jahn N.; Lier F.; Paul-Stueve T.; Vompras J.; Pietsch C.; Cimiano P.","Wiljes, Cord (55532719500); Jahn, Najko (55923122000); Lier, Florian (55389377200); Paul-Stueve, Thilo (23991354200); Vompras, Johanna (23390824000); Pietsch, Christian (56548514200); Cimiano, Philipp (15838793700)","55532719500; 55923122000; 55389377200; 23991354200; 23390824000; 56548514200; 15838793700","Towards linked research data: An institutional approach","2013","CEUR Workshop Proceedings","994","","","27","38","11","5","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921652992&partnerID=40&md5=fae01ede6734dd3d54d2d5127720b8aa","CITEC, AG Semantic Computing, Bielefeld University, Germany; CITEC, Central Lab Facilities, Bielefeld University, Germany; Bielefeld University Library, Germany","Wiljes C., CITEC, AG Semantic Computing, Bielefeld University, Germany; Jahn N., Bielefeld University Library, Germany; Lier F., CITEC, Central Lab Facilities, Bielefeld University, Germany; Paul-Stueve T., CITEC, Central Lab Facilities, Bielefeld University, Germany; Vompras J., Bielefeld University Library, Germany; Pietsch C., Bielefeld University Library, Germany; Cimiano P., CITEC, AG Semantic Computing, Bielefeld University, Germany","For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and eficient way. Following a Linked Data approach we integrate this data into a unified linked data store and interlink it with additional data sources from inside the university and outside sources like DBpedia. Based on the existing platform, a concrete case study from the domain of biology is implemented that releases optical motion tracking data of stick insect locomotion. We investigate the cost and usefulness of such a detailed, domain-specific semantic enrichment in order to evaluate whether this approach might be considered for large-scale deployment.","E-science; Linked data; Ontology; Research data management; Scientific publishing; Semantic web","Data handling; Information management; Ontology; Social networking (online); Domain specific semantics; E-sciences; Institutional approaches; Large-scale deployment; Linked data approaches; Linked datum; Optical motion tracking; Research data managements; Semantic Web","","","","","","","Berlin 9 Open Access Conference: Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, (2003); Alliance of German Science Organisations: Priority Initiative ""Digital Information""; Feijen M., What Researchers Want - A Literature Study of Researchers' Requirements with Respect to Storage and Access to Research Data, (2011); Smith V.S., Data publication: Towards a database of everything, BMC Research Notes, 2, 113, (2009); Horstmann W., Jahn N., Persönliche publikationslisten als hochschulweiter dienst - eine bestandsaufnahme, BIBLIOTHEK Forschung Und Praxis, 34, 2, pp. 37-45, (2010); Lier F., Wrede S., Siepmann F., Lutkebohle I., Paul-Stueve T., Wachsmuth S., Facilitating research cooperation through linking and sharing of heterogenous research artefacts, Proceedings of the 8th International Conference on Semantic Systems, pp. 157-164, (2012); Wiljes C., Cimiano P., Linked data for the natural sciences: Two use cases in chemistry and biology, Proceedings of the Workshop on the Semantic Publishing (SePublica 2012), pp. 48-59, (2012); Durr V., Schmitz J., Cruse H., Behaviour-based modelling of hexapod locomotion: Linking biology and technical application, Arthropod.Struct.Dev., 33, 3, pp. 237-250, (2004); Sauermann L., Cyganiak R., Volkel M., Cool URIs for the Semantic Web, (2007); Conlon M., Corson-Rikert J., VIVO: A Semantic Approach to Scholarly Networking and Discovery (Synthesis Lectures on the Semantic Web), (2012); Bizer C., Lehmann J., Kobilarov G., Auer S., Becker C., Cyganiak R., Hellmann S., DBpedia - A crystallization point for the Web of Data, Web Semantics: Science, Services and Agents on the World Wide Web, 7, 3, pp. 154-165, (2009); Halpin H., Social Semantics: The Search for Meaning on the Web (Semantic Web and Beyond), (2012); Papaleo L., Albertoni R., Pitikakis M., Robbiano F., Vasilakis G., Hassner T., Moccozet L., Saleem W., Tal A., Veltkamp R., Ontology for Shape Acquisition and Processing 4th Version; Lohmann S., Heim P., Stegemann T., Ziegler J., The relfinder user interface: Interactive exploration of relationships between objects of interest, Proceedings of the 15th International Conference on Intelligent User Interfaces. IUI '10, pp. 421-422, (2010)","","Castro A.G.; Lord P.; Stevens R.; Lange C.","CEUR-WS","","3rd Workshop on Semantic Publishing, SePublica 2013 - 10th Extended Semantic Web Conference","26 May 2013","Montpellier","111477","16130073","","","","English","CEUR Workshop Proc.","Conference paper","Final","","Scopus","2-s2.0-84921652992"
"McKelvey K.; Menczer F.","McKelvey, Karissa (55613996900); Menczer, Filippo (6701785703)","55613996900; 6701785703","Truthy: Enabling the study of online social networks","2013","Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW","","","","23","25","2","25","10.1145/2441955.2441962","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874893586&doi=10.1145%2f2441955.2441962&partnerID=40&md5=5ff6594bf075b4c17788d27665a22d80","Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States","McKelvey K., Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States; Menczer F., Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States","The broad adoption of online social networking platforms has made it possible to study communication networks at an unprecedented scale. Digital trace data can be compiled into large data sets of online discourse. However, it is a challenge to collect, store, filter, and analyze large amounts of data, even by experts in the computational sciences. Here we describe our recent extensions to Truthy, a system that collects Twitter data to analyze discourse in near real-time. We introduce several interactive visualizations and analytical tools with the goal of enabling citizens, journalists, and researchers to understand and study online social networks at multiple scales. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).","Collective intelligence; Hcid; Online social networks; Research data management; Visualization","Computer supported cooperative work; Flow visualization; Information management; Interactive computer systems; Visualization; Collective intelligences; Computational science; Hcid; Interactive visualizations; Large amounts of data; On-line social networks; Online social networkings; Research data managements; Social networking (online)","","","","","Directorate for Computer and Information Science and Engineering, CISE, (1101743)","","Conover M.D., Ratkiewicz J., Goncalves B., Flammini A., Menczer F., Political polarization on twitter, International Conference on Weblogs and Social Media 2011, (2011); Goncalves B., Conover M., Menczer F., Abuse of social media and political manipulation, The Death of the Internet, (2012); Grier C., Thomas K., Paxson V., Zhang M., Spam : The underground on 140 characters or less categories and subject descriptors, Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27-37, (2010); Lazer D., Pentland A., Adamic L., Aral S., Barabasi A.-L., Brewer D., Christakis N., Contractor N., Fowler J., Gutmann M., Jebara T., King G., Macy M., Roy D., Van Alstyne M., Computational social science, Science, 323, 5915, pp. 721-723, (2009)","","","","ACM SIGCHI","2013 2nd ACM Conference on Computer Supported Cooperative Work Companion, CSCW 2013","23 February 2013 through 27 February 2013","San Antonio, TX","96005","","978-145031332-2","","","English","Proc. ACM Conf. Comput. Support. Coop. Work CSCW","Conference paper","Final","","Scopus","2-s2.0-84874893586"
"Hofstra Terrence D.; Sacklin John A.","Hofstra, Terrence D. (6602951922); Sacklin, John A. (6508341575)","6602951922; 6508341575","RESTORING THE REDWOOD CREEK ESTUARY.","1987","","1","","","812","825","13","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0023167906&partnerID=40&md5=9dc4d2be6dd516f1b4b645662b612405","Redwood Natl Park, Arcata, CA, USA, Redwood Natl Park, Arcata, CA, USA","Hofstra Terrence D., Redwood Natl Park, Arcata, CA, USA, Redwood Natl Park, Arcata, CA, USA; Sacklin John A., Redwood Natl Park, Arcata, CA, USA, Redwood Natl Park, Arcata, CA, USA","During the mid-1960's, construction of a flood control project drastically altered the lower 5. 1 kilometers of Redwood Creek, impairing the physical and biological functioning of the estuary. Data collected from research have shown that the estuary is critical to chinook salmon and steelhead trout in Redwood Creek and that natural estuarine function has been severely impacted. From research data, management techniques and restoration options were developed. Estuarine water levels are regulated by 'controlled breaching' of the berm to prevent flooding of private lands while protecting aquatic habitat. Redwood National Park has worked with other Federal and State agencies to implement an estuarine restoration project at the mouth of Redwood Creek.","","ECOSYSTEMS; ENVIRONMENTAL ENGINEERING; ENVIRONMENTAL PROTECTION; FLOOD CONTROL; AQUATIC HABITATS; REDWOOD CREEK ESTUARY; RESTORATION OPTIONS; SAND BERM BREACHING; RIVERS","","","","","","","","","","ASCE","American Fishing Tackle Manufacturers Assoc, Arlington Heights, I; API, Washington, DC, USA; American Shore & Beach Preservation Assoc, Berkeley, CA, USA; American Soc for Environmental Education, Durham, NH, USA; ASCE, New York, NY, USA; et al","Coastal Zone '87, Proceedings of the Fifth Symposium on Coastal and Ocean Management.","","Seattle, WA, USA","9992","","0872626024","","","English","","Conference paper","Final","","Scopus","2-s2.0-0023167906"
"Gruetz R.; Loehnhardt B.; Brodhun M.; Dickmann F.","Gruetz, Romannus (54983345400); Loehnhardt, Benjamin (34977093100); Brodhun, Maximilian (36619711000); Dickmann, Frank (6507224680)","54983345400; 34977093100; 36619711000; 6507224680","Evaluation of data management and transfer tools for the biomedical community","2012","IEEE International Conference on Digital Ecosystems and Technologies","","","6227930","","","","1","10.1109/DEST.2012.6227930","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864243784&doi=10.1109%2fDEST.2012.6227930&partnerID=40&md5=215ae7e8d86dd7bcb87d1081f35d1080","Department of Medical Informatics, University Medical Center, 37075 Goettingen, Germany","Gruetz R., Department of Medical Informatics, University Medical Center, 37075 Goettingen, Germany; Loehnhardt B., Department of Medical Informatics, University Medical Center, 37075 Goettingen, Germany; Brodhun M., Department of Medical Informatics, University Medical Center, 37075 Goettingen, Germany; Dickmann F., Department of Medical Informatics, University Medical Center, 37075 Goettingen, Germany","The biomedical community produces and uses a continuously growing amount of data while it lacks an inter-institutional research data management system. The LABIMI/F project founded by the German Research Foundation (DFG) implements an infrastructure to close this gap with an exemplary prototype for the use cases of medical image and genome research data. To determine suitable application(s) for this intention, several criteria for data management and data transfer, concerning their up-to-dateness, usability, metadata and payload management etc., are developed. These criteria are applied to three data management tools (DSpace, Fedora Commons and ISA-Tab tools), and four data transfer tools (PowerFolder, iRODS, CryptShare and Globus-Online), in an use-value analysis (UVA). The UVA reveals that no application meets all criteria; therefore other tools, e.g. eSciDoc, should be evaluated before making a final decision. Fedora Commons scores highly in the category metadata and payload management but has no sufficient user frontend. Therefore, further research is necessary in order to find an appropriate frontend for Fedora Commons. The data transfer tool with the highest total application score is PowerFolder, which provides easy synchronization of files and folders between the users and a central repository. © 2012 IEEE.","Archive; Data Management; Data Transfer; Grid; LABIMI/F; WissGrid","Data transfer; Ecosystems; Metadata; Research; Archive; Biomedical community; D-space; Data management tools; Final decision; Genome research; German research foundations; Grid; LABIMI/F; Medical images; Payload management; Research data; WissGrid; Information management","","","","","","","Helmer K.G., Et al., Enabling collaborative research using the Biomedical Informatics Research Network (BIRN), Journal of the American Medical Informatics Association, 18, pp. 416-422, (2011); Kahn S.D., On the future of genomic data, Science, 331, pp. 728-729, (2011); Dickmann F., Et al., Solutions for biomedical grid computing-Case studies from the D-Grid project Services@MediGRID, Journal of Computational Science, 6394, (2011); Dickmann F., Grutz R., LABIMI/F - Digital Preservation of Biomedical Research Data; Zangemeister C., Nutzwertanalyse in der Systemtechnik, (1976); Keeney R.L., Meyer R.F., Raiffa H., Decisions with Multiple Objectives. Preferences and Value Tradeoffs, (2003); Information Technology - Metadata Registries (MDR) - Part 1: Framework; Karran T., Pan-European grading scales: Lessons from national systems and the ECTS, Higher Education in Europe, pp. 5-22, (2005); Nielsen J., Usability Engineering Morgan Kaufmann, (1994); Vorschläge zur sicherung guter wissenschaftlicher praxis, Empfehlungen der Kommission ""selbstkontrolle in der Wissenschaft, (1998); Fedora Commons - Home; Cryptshare - Home; Hedges M., Blanke T., Hasan A., Rule-based curation and preservation of data: A data grid approach using iRODS, Future Generation Computer Systems, 25, pp. 446-452, (2009); Monson-Haefel R., Chappell D.A., Java Message Service, (2001); Henjes R., Et al., Throughput Performance of the ActiveMQ JMS Server Kommunikation in Verteilten Systemen KiVS, pp. 113-124, (2007); Welcome to Solr; Gellman R., Privacy in the Clouds - Risks to Privacy and Confidentiality from Cloud Computing; Globus Connect; McCarthy M.T., USA patriot act, Harv. J. on Legis., 39, (2002)","R. Gruetz; Department of Medical Informatics, University Medical Center, 37075 Goettingen, Germany; email: romanus.gruetz@med.uni-goettingen.de","","","Institute of Electrical and Electronic Engineers (IEEE); IEEE Industrial Electronics Society; International Federation for Information Processing (IFIP); Assoc. Ital. l'Inform. Calcolo Autom. (AICA)","2012 6th IEEE International Conference on Digital Ecosystems and Technologies: Complex Environment Engineering, DEST 2012","18 June 2012 through 20 June 2012","Campione d'Italia","91468","21504946","978-146731703-0","","","English","IEEE Int. Conf. Digit. Ecosyst. Technol.","Article","Final","","Scopus","2-s2.0-84864243784"
"Halbert M.; Moen W.; Keralis S.","Halbert, Martin (8972734400); Moen, William (6602503769); Keralis, Spencer (57871496000)","8972734400; 6602503769; 57871496000","The DataRes research project on data management","2012","ACM International Conference Proceeding Series","","","","589","591","2","1","10.1145/2132176.2132300","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857664221&doi=10.1145%2f2132176.2132300&partnerID=40&md5=6517be9b0ea4decdefcf17d7094fec26","University of North Texas, Denton, TX 76203, 1155 Union Circle #305190, United States; University of North Texas, Denton, TX 76203, 1155 Union Circle #311068, United States","Halbert M., University of North Texas, Denton, TX 76203, 1155 Union Circle #305190, United States; Moen W., University of North Texas, Denton, TX 76203, 1155 Union Circle #311068, United States; Keralis S., University of North Texas, Denton, TX 76203, 1155 Union Circle #305190, United States","The University of North Texas together with the Council on Library and Information Resources, have received $226,786 from the Institute of Museum and Library Services for a two year research project to investigate how the library and information science profession can best respond to emerging needs of research data management in universities. This project will address broad new issues concerning the emerging roles, expectations, and practices arising from requirements announced by NIH, NSF, IMLS and other funding agencies for data management plans as part of proposals. © 2012 Authors.","research data management and policy","Information management; Project management; Funding agencies; Information resource; Library and information science; Library services; Research data; University of North Texas; Research","","","","","","","Hswe P., Holt A., Guide for Research Libraries: The NSF Data Sharing Policy, (2010); Soehner C., Steeves C., Ward J., E-Science and Data Support Services: A Study of ARL Member Institutions, (2010); Agenda for Developing E-Science in Research Libraries: ARL Joint Task Force on Library Support for E-Science Final Report & Recommendations, (2007)","M. Halbert; University of North Texas, Denton, TX 76203, 1155 Union Circle #305190, United States; email: martin.halbert@unt.edu","","","","2012 iConference: Culture, Design, Society, iConference 2012","7 February 2012 through 10 February 2012","Toronto, ON","88819","","978-145030782-6","","","English","ACM Int. Conf. Proc. Ser.","Conference paper","Final","All Open Access; Green Open Access","Scopus","2-s2.0-84857664221"
"Farrell Michael P.; Strand Rodney H.; Goyert Jonathan C.; Daniels Karen L.; Holzworth Donald H.; O'Fallon Judith Rich; Magoun A.Dale","Farrell, Michael P. (7202679387); Strand, Rodney H. (7004390556); Goyert, Jonathan C. (6603247517); Daniels, Karen L. (7101809119); Holzworth, Donald H. (6507897479); O'Fallon, Judith Rich (57210202145); Magoun, A.Dale (6602603656)","7202679387; 7004390556; 6603247517; 7101809119; 6507897479; 57210202145; 6602603656","REVIEW OF RESEARCH DATA MANAGEMENT.","1982","Proceedings of the Hawaii International Conference on System Science","","","","457","467","10","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020246958&partnerID=40&md5=665a46ab9da612b0081fbdf75fc282b7","","","[No abstract available]","","CONSULTING FIRMS; DATA MANAGEMENT; GOVERNMENT AGENCIES; MEDICAL CENTERS; RESEARCH DATA; UNIVERSITES; DATA PROCESSING","","","","","","","","","","","Univ of Hawaii, Honolulu, USA; Univ of Southwestern Louisiana, Lafayette, La, USA; ACM, New York, NY, USA; IEEE Computer Soc Technical Committee on Computational Medicine,","Proceedings of the 15th Hawaii International Conference on System Sciences. Volume 2: Medical Information Processing. Proceedings of the Hawaii International Conference on System Science 15th, Distributed by Western Periodicals Co","","Honolulu, HI, USA","1686","00731129","","PHISD","","English","","Conference paper","Final","","Scopus","2-s2.0-0020246958"
"","","","PROCEEDINGS - ANNUAL SYMPOSIUM ON COMPUTER APPLICATIONS IN MEDICAL CARE, 4TH, INCLUDING PROCEEDINGS OF THE ANNUAL CONFERENCE OF THE SOCIETY FOR ADVANCED MEDICAL SYSTEMS, 12TH: HEALTH SYSTEMS, THE NEXT DECADE, 1980.","1945","Proceedings - Annual Symposium on Computer Applications in Medical Care","","","","","","1945","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069361169&partnerID=40&md5=8882c820c3472205d66e37aa6cdff4d0","","","This conference proceedings contains 316 papers of which 5 appear as abstracts only. 166 papers are indexed separately. Topics covered include: hospital information systems; therapeutic radiology; medical decision making; statistical methods for diagnosis and prediction; occupational health; laboratory computer systems; image analysis; selective scheduling and medical records applications; office practice; computer applications in mental health; drug studies; electrocardiology network; data processing in cardiology; research data management, managerial and administrative applications; distributed systems; programmerless systems development; and trends in laboratory and pathology computing.","","BIOMEDICAL ENGINEERING - Patient Treatment; Biomedical equipment; Data base systems; DATA PROCESSING - Medical Information; Nuclear medicine; Health care","","","","","","","","","O'Neill Joseph T.","IEEE","","Proc Annu Symp Comput Appl Med Care 4th, Proc of the Annu Conf of the Soc for Adv Med Syst, 12th","1 November 1980 through 5 November 1980","Washington, DC, USA","","","","PCMCD","","undefined","","Conference paper","Final","","Scopus","2-s2.0-85069361169"
"","","","Proceedings of the Joint Conference on Easier and More Productive Use of Computer Systems. (Part - I): Information Processing in the Social Sciences and Humanities - Volume 1981, CHI 1981","1981","Proceedings of the Joint Conference on Easier and More Productive Use of Computer Systems. (Part - I): Information Processing in the Social Sciences and Humanities - Volume 1981, CHI 1981","","","","","","57","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051320791&partnerID=40&md5=f7b51e492c9d05a40dbb08c07a1e0eac","","","The proceedings contain 9 papers. The topics discussed include: comparison of some available packages for use in research data management; the 1940 and 1950 public use sample project: data quality issues; the automation of data processing, analysis, and reporting in a large survey time-series database; a new process for documenting and checking archival data; an automated system for responding to data service requests; online searches of social science data sets: the RIQS system and ICPSR data; and developing an aggregated survey/macro-economic database for statistical and graphical social science applications.","","","","","","","","","","","Borman L.","Association for Computing Machinery, Inc","ACM Special Interest Group on Social and Behavioral Science Computing (SIGSOC)","1990 Conference on Computers and the Quality of Life, CQL 1990","20 May 1981 through 22 May 1981","Ann Arbor","130462","","0897910567; 978-089791056-9","","","English","Proc. Jt. Conf. Easier More Product. Use Comput. Syst. (Part): Inf. Process. Soc. Sci. Humanit. - Vol., CHI","Conference review","Final","","Scopus","2-s2.0-85051320791"
"Whitehead Susan F.; Bilofsky Howard S.","Whitehead, Susan F. (55228975000); Bilofsky, Howard S. (56613881300)","55228975000; 56613881300","CLINFO - A CLINICAL RESEARCH DATA MANAGEMENT AND ANALYSIS SYSTEM.","1980","Proceedings - Annual Symposium on Computer Applications in Medical Care","","","","1286","1291","5","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0019242386&partnerID=40&md5=e5803ddc593e18184c49951da8c5494f","","","CLINFO is a minicomputer-based information system specifically designed as a tool for clinical research. CLINFO was designed for researchers who have no prior computer experience and, therefore, requires neither programming knowledge nor extensive training. The CLINFO system has been designed with the clinician's day-to-day data analyses in mind; CLINFO allows users to perform a wide range of calculations and statistical procedures, and to easily explore the resulting data visually in forms such as scatter plots, histograms, and bar graphs.","","Data processing","","","","","","","","","","IEEE","","Proc Annu Symp Comput Appl Med Care 4th, Proc of the Annu Conf of the Soc for Adv Med Syst, 12th, vol 2","1 November 1980 through 5 November 1980","Washington, DC, USA","","","","PCMCD","","undefined","","Conference paper","Final","","Scopus","2-s2.0-0019242386"
"Anderson Gary D.","Anderson, Gary D. (7404222787)","7404222787","SIMPLE QUERY LANGUAGE REQUIREMENTS FOR RESEARCH DATA MANAGEMENT.","1981","","","","","247","251","4","0","10.1007/978-1-4613-9464-8_34","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0019668147&doi=10.1007%2f978-1-4613-9464-8_34&partnerID=40&md5=7c64755c605e282d03ecea4a0e385d24","","","[No abstract available]","","Data management; DISPLAY; QUERY LANGUAGE; REPORTING; RETRIEVAL; Mathematical statistics","","","","","","","","","","Springer-Verlag","Natl Inst of Health, Div of Comput Res and Technol, Bethesda, Md,; Natl Inst of Health, Div of Res Resour, Bethesda, Md, USA; Natl Inst of Health, Fogarty Int Cent, Bethesda, Md, USA; US Navy, Off of Nav Res, Washington, DC, USA","Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface.","","Pittsburgh, PA, USA","312","","0387906339; 978-038790633-1","","","English","","Conference paper","Final","","Scopus","2-s2.0-0019668147"
"Baker R.L.","Baker, R.L. (7404218399)","7404218399","An adaptable interactive system for medical and research data management","1974","Methods of Information in Medicine","13","4","","209","215","6","2","10.1055/s-0038-1636153","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0016275366&doi=10.1055%2fs-0038-1636153&partnerID=40&md5=391fff26bb97b7cbab323afd3fa56e95","United States","Baker R.L., United States","The design of interactive information systems that ease the implementation of new applications, and facilitate their use by persons (users and programmers) with varied levels of training remains a problem that has not been satisfactorily solved, although some progress is being made by many investigators. TIRRIS, an interactive data base management system in use at the Texas Institute for Rehabilitation and Research, includes features that permit it to adapt to a variety of applications, users, and terminals. Key features of the system, including capabilities, modes of operation, commands, and system tailoring options are described.","","computer analysis; diagnosis; digital computer; hospital management; information processing; therapy","","","","","","","","","","","","","","","","00261270","","MIMCA","4437395","English","METHODS INF. MED.","Article","Final","","Scopus","2-s2.0-0016275366"
"Stitt F.W.","Stitt, Frank W. (6603574068)","6603574068","clinical research data management and analysis—a physician's view","1978","Therapeutic Innovation & Regulatory Science","12","2","","98","110","12","0","10.1177/009286157801200204","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84972622976&doi=10.1177%2f009286157801200204&partnerID=40&md5=70d40fbf31724846fb1b8bfde5c0e519","Clinical Sciences, ALZA Research, Palo Alto, California, United States","Stitt F.W., Clinical Sciences, ALZA Research, Palo Alto, California, United States","[No abstract available]","","","","","","","","","","","","","","","","","","21684790","","","","English","Ther. Innov. Regul. Sci.","Article","Final","","Scopus","2-s2.0-84972622976"
"Green R.S.; Attkisson C.C.","Green, Rex S. (7403917557); Attkisson, C.Clifford (6701416595)","7403917557; 6701416595","A model system for psychotherapy research data management","1981","Behavior Research Methods & Instrumentation","13","4","","499","510","11","1","10.3758/BF03202059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864889161&doi=10.3758%2fBF03202059&partnerID=40&md5=ccadfd5f3709a1acb6cb198a575d0182","Systems Research Unit, Eastern Pennsylvania Psychiatric Institute, Philadelphia, 19129, Pennsylvania, United States; Department of Psychiatry, University of California, San Francisco, 94143, California, United States","Green R.S., Systems Research Unit, Eastern Pennsylvania Psychiatric Institute, Philadelphia, 19129, Pennsylvania, United States; Attkisson C.C., Department of Psychiatry, University of California, San Francisco, 94143, California, United States","An on-line data management system was developed to provide optimal access to research data. The specific application involved collecting data on four outcome measures from patients receiving brief psychotherapy to ameliorate a stress response syndrome. The on-line data management system also was designed to be transported to other research settings; a variety of other applications of this system are suggested. The need for such a system, the elements that it comprises, its operation, and its availability also are covered. © 1981 Psychonomic Society, Inc.","","","","","","","","","Evaluation of human service programs, (1978); Chapman R.L., The design of management information systems for mental health organizations: A primer, (1977); Davis G.B., Management information systems: Conceptual foundations, structure, and development, (1974); Derogatis L.R., SCL administration, scoring, and procedures manual-I, (1977); Enger N.L., Management standards for developing information systems, (1976); Resource materials for community mental health program evaluation, (1977); Horowitz M.J., Schaefer C., Hiroto D., Wilner N., Levin B., Life event questionnaires for measuring presumptive stress, Psychosomatic Medicine, 39, pp. 413-431, (1977); Horowitz M.J., Wilner N., Alvarez W., Impact of event scale: A measure of subjective distress, Psychosomatic Medicine, 41, pp. 209-218, (1979); IBM virtual machine facility/370: CMS user’s guide, (1976); OS/VS2 TSO terminal user’s guide, (1978); Johnson J.H., A practical guide to installing a computer system in a mental health setting, Technology in mental health care delivery systems, (1980); Krauss L.I., Computer-based management information systems, (1970); Lipman R.S., Rickles K., Covi L., Derogatis L.R., Uhlenhuth E.H., Factors of symptom distress, Archives of General Psychiatry, 21, pp. 328-338, (1969); Martin J., Principles of data-base management, (1976); Martin J., Computer data base organization, (1977); RAMIS users manual, (1977); Nie N.H., Hull C.H., Jenkins J.G., Steinbrenner K., Bent D.H., SPSS: Statistical package for the social sciences, (1975); Robinson B.N., Anderson G.D., Cohen E., Gazdzik W.F., Karpel L.C., Miller A.H., Stein J.R., SIR users manual: Version 2, (1980); Technology in mental health care delivery systems, (1980); Stiefel M.L., Surveying data base management systems, Minimicrosystems, 12, pp. 94-104, (1979); Psychotherapy change measures, (1974)","","","","","","","","","15543528","","","","English","Behav. Res. Methods","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-84864889161"
"Anderson G.D.; Cohen E.; Gazdzik W.; Robinson B.","Anderson, Gary D. (7404222739); Cohen, Eli (7403589394); Gazdzik, Wally (57196187007); Robinson, Barry (57214651332)","7404222739; 7403589394; 57196187007; 57214651332","Scientific information retrieval system: A new approach to research data management","1977","Proceedings ACM SIGUCCS User Services Conference","Part F130773","","","209","212","3","1","10.1145/800101.803299","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018830711&doi=10.1145%2f800101.803299&partnerID=40&md5=8904e3a151449e061265909953ad6e52","SIR, Inc., Box 1404, Evanston, 60204, IL, United States","Anderson G.D., SIR, Inc., Box 1404, Evanston, 60204, IL, United States; Cohen E., SIR, Inc., Box 1404, Evanston, 60204, IL, United States; Gazdzik W., SIR, Inc., Box 1404, Evanston, 60204, IL, United States; Robinson B., SIR, Inc., Box 1404, Evanston, 60204, IL, United States","This manuscript is addressed particularly to the researcher and data analyst faced with the need to manage and analyze data sets in a form of complex file. This complex file arises when data is collected from multiple sources over a period of time. In the health sciences, studies of this nature are quite common, examples being large multi-centered clinical trials, patient record studies, health surveys, patient monitoring studies, etc. Such studies generally contain variable amounts of data on each case as a result of missing examinations, a variable number of visits to the clinic, etc. The result of such situations is a case-oriented hierarchical file. Similarly, such a case can arise in the social sciences, as in surveying a family or a school population; or in economics, as in analyzing an industry or an entire country. No matter how the researcher delineates the case structure of a study, the data is usually voluminous, hierarchical, complex, and hard to manage. There are different types of data for each case, and varying amounts of each type of data. Inevitably, data is gathered over long periods of time, and requires correction and modification. Existing package systems for research statistical analysis of large data sets are mostly oriented toward a fixed-length record (ie. a rectangular data set). There is a very real need in the research area for a relatively general scientific information management system. Such a system, if it is to be successful, must have sufficient capability to handle the complex, time dependent, non-rectangular data files encountered. As well as capability, however, it must possess a language structure which is either an extension of that with which the researcher is already familiar, or sufficiently simple and direct that it can be learned without extensive study either by the researcher himself or by his supporting staff. SIR was designed specifically for the management of such complex hierarchical data. Also, SIR adopted the language of the most widely-used statistical system presently in existence, namely that of SPSS(l), which is easily learned by those who are not yet familiar with it.","","Patient monitoring; Search engines; Surveys; Case structures; Hierarchical data; Language structure; Multiple source; Research data managements; Scientific information; Statistical systems; Variable number; Information management","","","","","","","Nie H.N., Hull C.H., Jenkins G.J., Steinbrenner K., Bent H.D., SPSS-Statistical Package for the Social Sciences, (1970); BMDP-Biomedical Computer Programs, (1975)","","","Association for Computing Machinery","ACM Special Interest Group on University and College Computing Services (SIGUCCS); Association for Computing Machinery (ACM)","5th Annual ACM SIGUCCS Conference on User Services, SIGUCCS 1977","6 November 1977 through 9 November 1977","Kansas City","130773","","","","","English","Proc ACM SIGUCCS Serv Conf","Conference paper","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85018830711"
"Greenberg E.A.; Ivey Wm.M.; Lewis B.R.","Greenberg, Edward A. (55524986900); Ivey, Wm. Max (6505772214); Lewis, Bruce R. (57205872783)","55524986900; 6505772214; 57205872783","Comparison of some available packages for use in research data management","1981","Proceedings of the Joint Conference on Easier and More Productive Use of Computer Systems. (Part - I): Information Processing in the Social Sciences and Humanities - Volume 1981, CHI 1981","","","","1","8","7","0","10.1145/800275.810922","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051360517&doi=10.1145%2f800275.810922&partnerID=40&md5=effdb090d8bd6768b8d51c1bc4cb6c7f","Arizona State University, Computer Services, United States","Greenberg E.A., Arizona State University, Computer Services, United States; Ivey Wm.M., Arizona State University, Computer Services, United States; Lewis B.R., Arizona State University, Computer Services, United States","Data management features of SIR, SAS, and SPSS were applied to a sample hierarchical data base. For each package, the areas investigated included the logical definition of the data base, data entry, data retrieval, data integrity, security, reporting, and updating. 01981 ACM. Copyright © 1981 ACM, Inc.","Data base; Data management; Data retrieval; Information retrieval; Sas; Sir; Spss","Information retrieval; Network security; Data integrity; Data retrieval; Hierarchical data; Research data managements; Spss; Information management","","","","","","","","","Borman L.","Association for Computing Machinery, Inc","ACM Special Interest Group on Social and Behavioral Science Computing (SIGSOC)","1990 Conference on Computers and the Quality of Life, CQL 1990","20 May 1981 through 22 May 1981","Ann Arbor","130462","","0897910567; 978-089791056-9","","","English","Proc. Jt. Conf. Easier More Product. Use Comput. Syst. (Part): Inf. Process. Soc. Sci. Humanit. - Vol., CHI","Conference paper","Final","","Scopus","2-s2.0-85051360517"
"Willard Christopher G.; Gatewood Lael C.; Ellis Lynda B.M.","Willard, Christopher G. (7003776307); Gatewood, Lael C. (7003292674); Ellis, Lynda B.M. (7202635746)","7003776307; 7003292674; 7202635746","COMPUTER AIDS FOR CLINICAL RESEARCH MANAGEMENT AND CONTROL: GENERAL ANALYSIS AND DESIGN.","1979","Institution of Mechanical Engineers, Conference Publications","","","","91","97","6","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0018753463&partnerID=40&md5=2930fdc5a38c3df080d4e92c84a3dfa1","","","The authors have analyzed clinical research tasks involving data collection, communication, and management and have used this task analysis to develop a system model for clinical research data management and control. The model deals with data control, study monitoring, user interfaces, report and analysis libraries, and study subject and support data bases. It may be used as a basis for computerized data system study and development. The model also indicates that the role of computerized systems in clinical research could be expanded into such areas as automatic report generation and control, process control aids, quality assurance monitoring, and study documentation.","","BIOMEDICAL ENGINEERING - Research; Data processing","","","","","","","","","","IEEE","","Annu Symp on Comput Appl in Med Care, 3rd, Proc","14 October 1979 through 17 October 1979","Washington, DC, USA","","","","","","undefined","","Conference paper","Final","","Scopus","2-s2.0-0018753463"
"Thompson Howard K.; Baker William R.; Christopher T.Graham; Lacy William; Groner Gabriel","Thompson, Howard K. (7401992040); Baker, William R. (7202916241); Christopher, T.Graham (57198293405); Lacy, William (7006402169); Groner, Gabriel (6603419381)","7401992040; 7202916241; 57198293405; 7006402169; 6603419381","CLINFO, A RESEARCH DATA MANAGEMENT AND ANALYSIS SYSTEM ACCEPTABLE TO PHYSICIAN USERS.","1977","","","","","140","142","2","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0017631192&partnerID=40&md5=4a5e178879177a4b9e4685f348d41870","","","Since 1972 a national consortium has studied the needs of clinical research investigators for automation support. After determining that the most pressing need was to provide a data storage retrieval, and analysis capability which the physician investigator himself would find convenient, easy to use, and ″friendly″ , a prototype minicomputer-based system was designed and implemented. The system (CLINFO) has been extensively tried out by numerous clinical investigators at three sites and found to be very much to the liking of the investigators. It greatly accelerates the time between the posing of a research question and an answer to that question. With the current availability of the CLINFO system and as automated medical recordkeeping systems become available, it is reasonable to anticipate that it will soon be feasible for practicing physicians to carry out modest clinical research efforts on panels of their own patients, a valuable source of information which is rarely tapped for research purposes.","","INFORMATION RETRIEVAL SYSTEMS - Computer Applications; MEDICAL INFORMATION ONCOLOGY; YSTEMS; Data processing","","","","","","","","","","IEEE","","Proc &dollar;&mdash;&dollar; Annu Symp on Comput Appl in Med Care, 1st","3 October 1977 through 5 October 1977","Washington, DC, USA","","","","","","undefined","","Conference paper","Final","","Scopus","2-s2.0-0017631192"
"Van Hoose M.C.; Leaders F.E., Jr.","Van Hoose, Madeline C. (7801560938); Leaders, Floyd E. (57205042444)","7801560938; 57205042444","A time and cost effective data management system for clinical research data to support clinical monitoring guidelines","1980","Therapeutic Innovation & Regulatory Science","14","1","","10","14","4","1","10.1177/009286158001400102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973186196&doi=10.1177%2f009286158001400102&partnerID=40&md5=e823f5291022c7338bfbc4dfb77594f6","","","A versatile, efficient Clinical Research Data Management System based on a unique organizational structure is described. Time and cost effective results are obtained by separation of the routine Long Term Data Management function from the varied Short Term Data Management activities. Matching of personnel personality types with the group activities is done in staffing these groups. Advantages and disadvantages of an in-house Short Term Data Management group vs. an outside group are explored, since this function can effectively be handled either way. © 1980, Drug Information Association. All rights reserved.","","","","","","","","","","","","","","","","","","21684790","","","","English","Ther. Innov. Regul. Sci.","Article","Final","","Scopus","2-s2.0-84973186196"
"Marciniak Thomas A.","Marciniak, Thomas A. (57197973685)","57197973685","CLINFO - AN INDEPENDENT USER'S EVALUATION.","1980","Proceedings - Annual Symposium on Computer Applications in Medical Care","","","","1267","1270","3","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0019241611&partnerID=40&md5=6f4c8584e6110ad9fcb0ed1f4fce1645","","","CLINFO is a clinical research data management and analysis computer system developed for the NIH General Clinical Research Centers (GCRC) program. The prototype version of CLINFO was implemented at three GCRC's in 1976 and 1977, and a production version is now being extended to additional GCRC's. These systems have been implemented as total packages, including dedicated hardware, software, and a full-time systems manager. In addition, a CLINFO terminal has been operational on a trial basis at Walter Reed Army Medical Center during 1980. Results are presented of the latter experience and an on-going effort to adapt the prototype version of CLINFO to a time-sharing environment is described.","","HEALTH CARE - Computer Applications; Data processing","","","","","","","","","","IEEE","","Proc Annu Symp Comput Appl Med Care 4th, Proc of the Annu Conf of the Soc for Adv Med Syst, 12th, vol 2","1 November 1980 through 5 November 1980","Washington, DC, USA","","","","PCMCD","","undefined","","Conference paper","Final","","Scopus","2-s2.0-0019241611"