Citationsort descending Purpose Method Study Type
Akers, K., & Doty J. (2013).  Disciplinary differences in faculty research data management practices and perspectives. International Journal of Digital Curation. 8(2), 5 - 26. Investigated disciplinary differences in research data management needs at Emory University Sent email invitation to participate in online survey to all employees at Emory University with faculty status. 456 out of 5,590 (8%) initiated survey. 330 responded that conduct research that generates some kind of data and filled out one question Measurement, Wider
Akmon, D. (2014).  The Role of Conceptions of Value in Data Practices: A Multi-Case Study of Three Small Teams of Ecological Scientists. Investigated how scientists conceive of the value of their data, and how they enact conceptions of value in their data practices Conducted interviews and engaged in participant observation of three teams of scientists performing ecological research at a U.S. university-sponsored field station Measurement, Targeted
Alexogiannopoulos, E., McKenney S., & Pickton M. (2010).  Research Data Management Project: a DAF investigation of research data management practices at The University of Northampton. Investigated research data management practices at the University of Northampton, specifically the types of data held by researchers throughout the university, researchers‟ existing data management practices, and the risks associated with these practices. Used the Digital Asset Framework methodology Measurement, Wider
Atkins, D. E., Droegemeier K. K., Feldman S. I., Garcia-molina H., Klein M. L., Messerschmitt D. G., et al. (2003).  Revolutionizing Science and Engineering Through Cyberinfrastructure : Report of the National Science Foundation Blue-Ribbon Advisory Panel on. Evaluated major investments in cyberinfrastructure; recommended new areas of emphasis relevant to cyberinfrastructure; proposed an implementation plan for pursuing these new areas of emphasis Conducted 62 presentations at invitational public testimony sessions and a community-wide survey receiving 700 responses; reviewed prior relevant reports; received written critique from 60 reviewers of the draft report; attended conferences and workshops; conducted numerous unsolicited conversations by email and phone and extensive deliberation among report panel members. Measurement, Targeted
Averkamp, S., Gu X., & Rogers B. (2014).  Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs. This data management report was commissioned by the University of Iowa Libraries with the intention of performing a survey of the campus landscape and identifying gaps in data management services The first stage of data collection consisted of a survey conducted during summer 2012 to which 784 responses were received. The second phase of data collection consisted of approximately 40 in-depth interviews with individuals from the campus and were completed during summer 2013. The individuals engaged during the data collection phase spanned a diverse set of campus programs but should not be considered comprehensive. Information Technology Services was invited to participate in the interview process and has also contributed to this report. Measurement, Wider
Bardyn, T., Resnick T., & Camina S. (2012).  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), 274 - 287. Investigated the digital curation needs of translational researchers Conducted focus groups with eight faculty members in departments within the David Geffen School of Medicine, UCLA Measurement, Targeted
Bates, J. J., Privette J. L., & Hills A. D. (2012).  NOAA’s National Climatic Data Center’s Maturity Model for Climate Data Records. Curating for Quality: Ensuring Data Quality to Enable New Science. 32 - 33. Metrics, Targeted
Beagrie, N., Houghton J., Palaiologk A., & Williams P. (2012).  Economic Evaluation of Research Data Infrastructure. Investigated the economic benefits of investments of the Economic and Social Research Council (ESRC) in the Economic and Social Data Service (ESDS), a service that promotes use of research data and teaching in social sciences to ensure data availability. Performed analysis of existing evaluation literature and reports, looking at both methods and findings; examined results of KRDS and other studies; examined management and internal data collected by ESRC and ESDS such as user statistics, internal reports, and the ESDS Mid-Term Review; performed semi-structured interviews, case studies, and an online survey of ESDS users and depositors Measurement, Targeted
Beagrie, N., & Houghton J. (2013).  The Value and Impact of the Archaeology Data Service: A Study and Methods for Enhancing Sustainability. Investigated and attempted to measure the value and impact of the Archaeology Data Service (ADS) Reviewed value and impact evaluation literature; analyzed ADS reports and documentation; conducted 15 interviews with ADS stakeholders; conducted 2 online surveys, one of ADS data depositors and one of ADS users Note: This study and the similar study of the British Atmospheric Data Centre (Node 34) both use the same value metrics framework Measurement, Metrics, Targeted
Beagrie, N., & Houghton J. (2013).  The Value and Impact of the British Atmospheric Data Centre. Surveyed and analyzed perceptions of the value of the digital collections held by the British Atmospheric Data Centre (BADC), and quantified the value and impact of those collections for BADC’s user community using a range of economic approaches; investigated the extension of the methodology used in Beagrie et al. 2012 and Beagrie and Houghton 2013a to the BADC. Note: The results of Beagrie et al. 2012, Beagrie and Houghton 2013a and Beagrie and Houghton 2013b were summarized and collated in Beagrie and Houghton 2014. Similar to Beagrie et al. 2012 and Beagrie and Houghton 2013, methods included a combination of literature and documentation review, review of reports from BADC, 13 interviews of BADC users and depositors, and two online surveys, one of BADC data depositors and one of BADC users. Note: This study and the similar study of the Archaeology Data Service (Node 33) both use the same value metrics framework Measurement, Targeted
Beile, P. (2014).  The UCF Research Data Management Survey: A report of faculty practices and needs. Investigated faculty data management needs and practices at the University of Central Florida Conducted an online survey containing 33 questions. There were 534 valid recipients and 97 who partially or fully completed the survey. Measurement, Wider
Conway, P., & Bronicki J. (2012).  Error Metrics for Large-Scale Digitization. Prepared for the NSF III #1247471 workshop, Curating for Quality: Ensuring Data Quality to Enable New Science. Metrics, Targeted
Digital Curation Centre (2014).  Five steps to decide what data to keep: a checklist for appraising research data v.1. A guide written by the DCC Metrics, Targeted
Fearon, D., Gunia B., Pralle B., Lake S., & Sallans A. (2013).  ARL Spec Kit 334: Research data management services. To assess early endeavors in research data services and benchmark future growth in ARL member libraries. Conducted a survey of ARL member libraries. 73 of 125 responded. Measurement, Wider
Fecher, B., Friesike S., & Hebing M. (2015).  What Drives Academic Data Sharing?. PLoS ONE. 10(2), e0118053. Investigated the creation of a framework that explains the process of data sharing from the researcher’s point of view Performed a systematic review of 98 scholarly papers and empirical survey among 603 secondary data users Measurement, Metrics, Targeted
Federer, L., Lu Y-L., Joubert D., Welsh J., & Brandys B. (2015).  Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff. PLoS ONE. 10(6),  Investigated differences in experiences with and perceptions about sharing data, as well as barriers to sharing among clinical and basic science researchers Distributed a survey to Clinical and basic science researchers in the Intramural Research Program at the National Institutes of Health. The survey was publicized through various NIH email lists, including NIH library and NIH special interest groups. Of 190 respondents, 135 who identified as clinical or basic science researchers were included in analysis.
 Asked: Reuse (how relevant was their work and level of expertise); Relevance and expertise regarding depositing data in a repository; uploading data to a repository; sharing practices (metadata, codebook, processing); acknowledgement for sharing; reasons for not sharing Measurement, Targeted
Finholt, T. A., & Birnholtz J. P. (2006).  If We Build It, Will They Come? The Cultural Challenges of Cyberinfrastructure Development. 89 - 101. Reflected on dysfunction in collaboration in early stages of the Network for Earthquake Engineering Simulation (NEES), a large-scale deployment of cyberinfrastructure Analyzed differences in three professional cultures in light of Hofstede’s cultural constructs [Hofstede, G. 1980. Culture’s Consequences. Newbury Park, Calif.: Sage Publications and Hofstede, G. 1991. Cultures and Organizations: Software of the Mind. London: McGraw-Hill] Measurement, Targeted
Fry, J., Lockyer S.., Oppenheim C.., Houghton J.W.., & Rasmussen B.. (2008).  Identifying benefits arising from the curation and open sharing of research data produced within UK Higher Education and research institutes: exploring costs and benefits. Investigated the benefits of the curation and open sharing of research data and the development of a methodology and model for estimating the benefits of data curation and sharing in UK higher education Performed a literature review to provide illustrative examples of reuse and the views of stakeholders in various disciplines towards data curation and sharing; conducted two case studies to identify and illustrate benefits and costs in these areas Measurement, Metrics, Wider
Giarlo, M. J. (2012).  Academic Libraries as Data Quality Hubs. Prepared for the NSF III #1247471 workshop, Curating for Quality: Ensuring Data Quality to Enable New Science. Metrics, Targeted
Gibbs, H. (2009).  Southampton Data Survey: Our Experience and Lessons Learned. To pilot the Digital Asset Framework (or Digital Audit Framework) methodology Used a modified version of the Digital Asset Framework; modified mainly due to time considerations; distributed an online questionnaire and follow-up interviews with researchers at the University of Southampton Measurement, Wider
Guindon, A. (2014).  Research Data Management at Concordia University: A Survey of Current Practices.. Feliciter. 60(2), 15 - 17. Assess what researchers were doing with the data they generated and whether they were interested in sharing it with the academic community and determine what types of research data management services the library could offer Conducted a survey of full-time faculty in four departments (Geography, Planning and Environment, Political Science, Psychology, and Sociology and Anthropology); received 41 responses out of 11. Conducted post-survey interviews. Both the survey and interviews were based on the DCC Data Asset Framework. Measurement, Wider
Gutmann, M., Schürer K.., Donakowski D., & Beedham H. (2004).  The Selection, Appraisal, and Retention of Digital Social Science Data. Data Science Journal. 3, 209 - 221. Metrics, Targeted
Harvey, R. (2008).  Appraisal and Selection. Briefing Papers: Introduction to Curation. Describes selection and appraisal criteria for scientific data and records Metrics, Targeted
Hedstrom, M., Niu J., & Marz K. (2006).  Producing Archive-Ready Datasets: Compliance, Incentives, and Motivation. IASSIST. Investigated effort researchers are willing to put into preparing data for deposit into an archive and incentives to induce researchers to improve the quality of data and metadata deposited Surveyed 170 researchers funded by the National Institute of Justice, which requires deposit of data in an established archive (National Archive of Criminal Justice Data at the Inter-university Consortium for Political and Social Research-NAJCD) Measurement, Metrics, Targeted
Hedstrom, M., & Niu J. (2008).  Incentives for Data Producers to Create “Archive-Ready” Data: Implications for Archives and Records Management. Society of American Archivists Research Forum. Investigated researcher behavior and attitudes about depositing data from sponsored research Conducted a survey of 55 graduates of the National Institute of Justice Measurement, Targeted
Huang, X., Hawkins B. A., Lei F., Miller G. L., Favret C., Zhang R., et al. (2012).  Willing or unwilling to share primary biodiversity data: results and implications of an international survey. Conservation Letters. 5(5), 399 - 406. Investigated attitudes, experiences, and expectations of researchers sharing and archiving of regarding biodiversity data Conducted an online survey asking about the respondents’ demographics and research background, their attitudes and experiences regarding biodiversity data sharing, and their expectations regarding future data archiving practices; invitations were sent to specific researchers, and then distributed by communications officers of select scientific societies; there were 372 valid responses (where ¾ of the survey was completed) Measurement, Targeted
Jerrome, N., & Breeze J. (2009).  Imperial College Data Audit Framework Implementation: Final Report. To pilot the Digital Asset Framework Methodology; evaluate the scale and scope of research data; and make recommendations accordingly Used a modified form of the Digital Asset Framework in multiple departments: used the audit framework in a first phase of investigation, then conducted an online survey and follow up interviews. Measurement, Wider
Kejser, U. Bøgvad, Johansen K. Hougaard E., Thirifays A., Nielsen A. Bo, Wang D., Strodl S., et al. (2014).  4C Project: Evaluation of Cost Models and Needs & Gaps Analysis. 4C Project: Analyzed research related to the economics of digital curation and cost and benefit modelling; investigated how well current models and tools meet stakeholders’ needs for calculating and comparing financial information; pointed out gaps to be bridged to increase the uptake of cost & benefit modelling and practices that will enable costing and comparison of the costs of alternative scenarios Evaluated and compared ten current and emerging cost and benefit models; performed consultations (in the form of a questionnaire) with 4C project stakeholders; 296 contacts were made and 164 responded (55% response rate) Measurement, Targeted
Knight, S-A., & Burn J. (2005).  Developing a framework for assessing information quality on the World Wide Web. Informing Science Journal. 8(3), 159 - 172. Metrics, Targeted
Kroll, S., & Forsman R. (2010).  A Slice of Research Life: Information Support for Research in the United States. Investigated use of tools and services that support of all stages of the research life cycle in institutions of higher education in the U.S. Conducted a literature review and interviews with researchers, research assistants, graduate students, grant and other research administration specialists, and university administrators at four prominent US research institutions Measurement, Wider
Kuipers, T., & van der Hoeven J. (2009).  PARSE.Insight: Insight into Digital Preservation of Research Output in Europe: Survey Report. Sought to gain insight into issues surrounding the preservation of digital research data; investigated amount of data stored by researchers in Europe in 2008 or 2009 and amounts projected two and five years into the future Data was obtained from a question in a larger survey designed to gain insight into infrastructure needed for digital preservation Measurement, Wider
Martinez-Uribe, L. (2009).  Using the Data Audit Framework: An Oxford Case Study. Piloted the Digital Asset Framework methodology in work to scope digital repository services for research data management Adapted the Digital Asset Framework methodology Measurement, Wider
McLure, M., Level A., Cranston C., Oehlerts B., & Culbertson M. (2014).  Data Curation: A Study of Researcher Practices and Needs. portal: Libraries and the Academy. 14(2), 139 - 164. Investigated (1) the nature of data sets that researchers create or maintain; (2) How participants manage their data; (3) Needs for support that the participants identify in relation to sharing, curating, and preserving their data; and (4) The feasibility of adapting the Purdue University Libraries’ Data Curation Profiles Toolkit1 interview protocol for use in focus groups with researchers Conducted five focus groups with 31 faculty, research scientists, and research associates Measurement, Wider
Noor, M. A. F., Zimmerman K. J., & Teeter K. C. (2006).  Data Sharing: How Much Doesn't Get Submitted to GenBank?. PLoS Biol. 4(7), e228. Investigated frequency of researcher submission of DNA sequences to journals where their research was published Searched 290 papers in six journals with explicit policies requiring submission of DNA sequences to “GenBank” [Note: “GenBank” here refers to GenBank, the European Molecular Biology Laboratory, and the DNA Databank of Japan] Measurement, Targeted
Open Exeter Project Team (2012).  Summary Findings of the Open Exeter Data Asset Framework Survey. Investigated how researchers at the University of Exeter created data, where they stored their data, whether they backed up their data and what happened to their data when the project was finished Adapted from the Data Curation Centre’s Data Asset Framework methodology, an online survey was created and follow up interviews were conducted with respondents. Measurement, Wider
Parsons, T., Grimshaw S., & Williamson L. (2013).  Research Data Management Survey. Sought to understand the baseline of RDM practices, gather researcher requirements for RDM, and raise awareness of and gauge interest in a proposed service After testing on a smaller population, conducted an online survey of career researchers and post-doctoral researchers at the University of Nottingham using targeted email Measurement, Wider
Peng, G., Privette J. L., Kearns E. J., Ritchey N. A., & Ansari S. (2015).  A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets. Data Science Journal. 13, Metrics, Targeted
Pepe, A., Goodman A., Muench A., Crosas M., & Erdmann C. (2014).  How Do Astronomers Share Data? Reliability and Persistence of Datasets Linked in AAS Publications and a Qualitative Study of Data Practices among US Astronomers. PLoS ONE. 9(8), e104798. Investigated data sharing practices of astronomers over the last 15 years Analyzed URL links embedded in papers published by the American Astronomical Society; performed interviews with 12 scientists and online surveys with 173 scientists at the Harvard-Smithsonian Center for Astrophysics Measurement, Targeted
Perry, C. (2008).  Archiving of publicly funded research data: A survey of Canadian researchers.. Government Information Quarterly. 25(1), 133 - 148. To assess researchers’ attitudes and behaviours in relation to archiving research data and to determine researchers’ views about policies relating to data archiving. Investigated how much of the data being produced in the course of SSHRC-funded research is being archived. Surveyed social sciences and humanities researchers from universities across Canada. A questionnaire comprising 15 questions was mailed to 175 researchers randomly sampled from a publicly available list of 5,821 individuals who had received grants and awards from the Social Sciences and Humanities Research Council of Canada (SSHRC). From this sample, 75 (43.4%) responded within the five week time-frame stipulated. The questionnaire was constructed using four existing surveys and asked researchers for information about: geographical location, years of research experience, research funding sources, current plans to archive research data, awareness of archiving policies, attitude to mandated research data archiving, effect of mandatory data archiving policies on grant-seeking, attitude to making archived research data accessible, and use of research data collected by others. The questionnaire also included space for respondents to make comments. Measurement, Wider
Peters, C., & Dryden A. (2011).  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), 387 - 403. Interviewed 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 Conducted interviews with PIs of 10 projects (14 contacted), as well as one Co-PI, one post-doctorate and one graduate student associated with one of the projects) Measurement, Wider
Pienta, A. M., Alter G. C., & Lyle J. A. (2010).  The Enduring Value of Social Science Research: The Use and Reuse of Primary Research Data. Investigated the extent to which social science research data are shared and whether data sharing affected research productivity of the research data themselves. Searched NSF and NIH databases to create a database of 7,040 research projects in the social and behavioral sciences funded by NSF and NIH from 1985-2001; surveyed the 4,883 unique PIs for these projects (there was a 24.9% response rate) about research data collected, methods of sharing data, attitudes about data sharing, and demographic information Measurement, Targeted
Piwowar, H. A., & Chapman W. W. (2008).  Identifying Data Sharing in Biomedical Literature. AMIA Annual Symposium Proceedings. 2008, 596 - 600. Investigated extent of data sharing in biomedical research Used national language processing (NLP) techniques to find evidence of dataset sharing within 1,028 open access research that mentioned one or more of five databases Measurement, Targeted
Piwowar, H. A., & Chapman W. W. (2010).  Public sharing of research datasets: a pilot study of associations. Journal of informetrics. 4(2), 148 - 156. Investigated whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact Used a previously-created set of 397 articles in 20 journals describing studies using gene expression microarray data; identified which studies had made their raw datasets available; used multivariate logistic regression to evaluate the association between authorship, grant, and journal attributes of a study and the public availability of its microarray data Measurement, Targeted
Piwowar, H. A. (2011).  Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data. PLoS ONE. 6(7), e18657. Investigated patterns in the frequency with which researchers openly archive raw gene expression microarray datasets after research publication Performed a full-text query of 5 databases to identify 11,603 articles published between 2000 and 2009 that describe the creation of gene expression microarray data; performed multivariate regression on 124 bibliometric attributes of the articles, which revealed 15 factors describing authorship, funding, institution, publication, and domain environments. Measurement, Targeted
Pronk, T. E., Wiersma P. H., van Weerden A., & Schieving F. (2015).  A game theoretic analysis of research data sharing. PeerJ. 3, Used a game theory model to investigate the costs and benefits of sharing data to researchers Created a model and ran simulations using different parameters to analyze implications for sharing in a variety of scenarios Measurement, Metrics, Targeted
Ramapriyan, H.., Moses J. F., & Duerr R.. (2012).  Preservation of data for Earth system science - Towards a content standard. 5304 - 5307. Presents the need for a preservation content specification for earth science data, and proposes content items to be captured Metrics, Targeted
Read, K. B., Sheehan J. R., Huerta M. F., Knecht L. S., Mork J. G., Humphreys B. L., et al. (2015).  Sizing the Problem of Improving Discovery and Access to NIH-Funded Data: A Preliminary Study. PLoS ONE. 10(7), e0132735. Investigated the discovery of and access to biomedical datasets to provide a preliminary estimate of the number and type of datasets generated annually by research funded by the U.S. National Institutes of Health (NIH); specifically those that are “invisible” or not deposited in a known repository Analyzed NIH-funded journal articles that were published in 2011, cited in PubMed and deposited in PubMed Central (PMC) to identify articles where data were submitted to a known repository; excluded these and analyzed a random sample of the remaining articles to estimate how many and what types of invisible datasets were used in each article Measurement, Targeted
Scaramozzino, J., Ramírez M., & McGaughey K. (2012).  A Study of Faculty Data Curation Behaviors and Attitudes at a Teaching-Centered University. College & Research Libraries. 73(4), 349 - 365. Investigated science researchers’ data curation awareness, behaviors, and attitudes, as well as what needs they exhibited for services and education regarding maintenance and management of data Distributed survey via email to 331 College of Science and Mathematics faculty at California Polytechnic State University, San Luis Obispo (Cal Poly), a master’s-granting, teaching-centered institution. Filtered results to include only science faculty from the Biology, Chemistry, Kinesiology, Mathematics, Physics, and Statistics departments who engaged in data collection in the course of their research (131 tenure-track faculty; 82 responded (62.6%) Measurement, Wider
Science Staff (2011).  Challenges and Opportunities. Science. 331(6018), 692 - 693. Investigated issues surrounding the growing amounts of research data that exist Performed a survey of Science peer reviewers, receiving 1,700 responses; asked about frequency of use of datasets from published literature and archival databases, the size of the largest dataset used or generated, where most of the data they generate is archived, whether they have asked colleagues for research data, whether the data were provided, whether there is adequate expertise in their lab or group to analyze their data in the way desired, and whether there is sufficient funding for data curation in their group Measurement, Targeted
J. Sticco, C. (2012).  Towards Data Quality Metrics Based on Functional Requirements for Scientific Records. Prepared for the NSF III #1247471 workshop, Curating for Quality: Ensuring Data Quality to Enable New Science. Metrics, Targeted
Sturges, P., Bamkin M., Anders J. H. S., Hubbard B., Hussain A., & Heeley M. (2015).  Research data sharing: developing a stakeholder-driven model for journal policies. Journal of the Association for Information Science and Technology. Investigated the state of journal data sharing policies the views and practices of stakeholders to data sharing in order to outline a model journal research data sharing policy Reviewed the web pages of 371 journals including the most and least cited journals internationally and nationally and extracted categories of policy based on Piwowar and Chapman 2008b definitions of strong and weak policies; conducted 13 interviews with key stakeholders selected on the basis of their expertise in data sharing issues Measurement, Targeted
Tenopir, C., Allard S., Douglass K., Aydinoglu A., Wu L., Read E., et al. (2011).  Data Sharing by Scientists: Practices and Perceptions. PLoS ONE. 6(6), e21101. Investigated scientists’ data sharing practices and their perceptions of the barriers and enablers of data sharing Conducted an internet survey including questions about demographics and questions about scientists’ relationship with data; the survey was distributed initially using a snowball approach (contacting specific individuals who could promote the survey) and then by targeting universities in states with a low response rate. In all 1,329 respondents answered at least one question (an estimated response rate of 9%) Measurement, Targeted
Tenopir, C., Dalton E. D., Allard S., Frame M., Pjesivac I., Birch B., et al. (2015).  Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide. PLoS ONE. 10(8), e0134826. Examined the state of data sharing and reuse perceptions and practices among research scientists as compared to the 2009/2010 baseline study (reported in Tenopir et al. 2011); examined differences in practices and perceptions across age groups, geographic regions, and subject disciplines Used snowball and volunteer sampling approaches to recruit respondents to an online survey; the survey was also distributed via a variety of listservs Measurement, Targeted
Thornhill, K., & Palmer L. (2014).  An Assessment of Doctoral Biomedical Student Research Data Management Needs. Explored institutional repository data management needs at the University of Massachusetts Medical School Conducted literature review; sent a data needs assessment survey based on the DCC lifecycle model and NSF requirements for data management to 470 students on an email discussion list.
 Measurement, Targeted
Turner, V., Reinsel D., Gantz J. F., & Minton S. (2014).  The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things. Investigated size and rate of expansion of the digital universe worldwide in 2013; investigated business opportunities to use data in new ways and extract value from the digital universe Analyzed 60 streams of data comprising the digital universe (11 streams were added to the 49 in the 2007 study); analyzed ways in particular that the Internet of Things is creating business opportunities; developed 5 criteria to identify “target rich” or valuable data in the digital universe; identified information technology prerequisites to being able to take advantage of the value of data; identified business steps enterprises must take to leverage data value Measurement, Metrics, Targeted
UNC-CH (2012).  Research Data Stewardship at UNC: Recommendations for Scholarly Practice and Leadership. Sought to identify policy options for digital research data stewardship at UNC; further understanding of the full-breadth of activities, concerns, and opinions surrounding research data stewardship among researchers at UNC-CH Conducted semi-structured interviews with 23 faculty researchers representing several disciplines at UNC-CH; conducted an online survey of all faculty members, graduate students, and staff assigned to departments that engage in research Measurement, Wider
Waller, M., & Sharpe R. (2006).  Mind the Gap: Assessing Digital Preservation Needs in the UK. A study carried out for the Digital Preservation Coalition (DPC) to reveal the extent of the risk of loss or degradation to digital material held in the UK's public and private sectors Surveyed 900 individuals from a wide range of organisations in different sectors. The selected individuals all had an assumed interest in digital preservation as part of their professional responsibilities, and included a range of roles including records managers, archivists, librarians, but also IT managers and data producers. 104 responses were received, giving a good response rate of over 10%. These included respondents from education, libraries, archives, museums, local and central government bodies, scientific research institutions, and from organisations in the pharmaceutical, financial, manufacturing and engineering, media, energy and chemical, and publishing sectors. Note: Discusses duration for keeping data. Measurement, Wider
Wallis, J. C., Rolando E., & Borgman C. L. (2013).  If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE. 8(7), e67332. Investigated data sharing practices among scientists and technology researchers in CENS, a National Science Foundation Science and Technology Center. This was done as part of efforts to identify infrastructure needs for research data produced in long tail science. Conducted two rounds of interviews with researchers, students, and staff in CENS in the fourth and eighth years of the study, and ten years of ethnographic observation Measurement, Targeted
Wang, R. Y., & Strong D. M. (1996).  Beyond Accuracy: What Data Quality Means to Data Consumers. J. Manage. Inf. Syst.. 12(4), 5 - 33. Metrics, Targeted
Wicherts, J. M., Bakker M., & Molenaar D. (2011).  Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results. PLoS ONE. 6(11), e26828. Investigated reasons for researchers’ reluctance to share data from published research Related the willingness to share data (as experienced by requesting data from the authors of 49 papers published in two high-ranked APA journals) to the internal consistency of the statistical results in the papers and the distribution of significantly reported (p<.05) p-values Measurement, Targeted
Wynholds, L., Fearon, Jr. D. S., Borgman C. L., & Traweek S. (2011).  When Use Cases Are Not Useful: Data Practices, Astronomy, and Digital Libraries. 383 - 386. Sought to understand issues in developing the institutions and practices needed to provide access to research data Conducted interviews of users of the SDSS dataset covering their type of research, participation in sky survey projects, data challenges, conceptions of data, data sources, data analysis tools, walk-throughs, end of project curation, and funding structures for data Measurement, Wider
Zins, C. (2007).  Conceptual Approaches for Defining Data, Information, and Knowledge. Journal of the American Society for Information Science and Technology. 58(4), 479 - 493. Undertook a study, “Knowledge Map of Information Science,” to explore the foundations of information science. Used a panel discussion-based methodology called Critical Delphi. The panel for the study was comprised of leading scholars who represent nearly all the major subfields and important as- pects of the field (see Appendix A). The indirect discussions were anonymous and were conducted in three successive rounds of structured questionnaires. The first questionnaire contained 24 detailed and open-ended questions covering 16 pages. The second questionnaire contained 18 questions in 16 pages. The third questionnaire contained 13 questions in 28 pages (see relevant excerpts from the three question- naires in Appendix B). Measurement, Metrics, Targeted
Bigagli, L., Sveinsdottir T., Wessels B., Smallwood R., Linde P., Tsoukala V., et al. (2014).  Infrastructural and technological challenges and potential solutions. Investigated infrastructural and technological barriers to Open Access and preservation of research data in Europe. This work was conducted within the EU FP7 funded project RECODE, which focuses on developing policy recommendations for Open Access to Research Data in Europe. In particular, this work is coordinated by RECODE Work Package 2 (WP2), Infrastructure and technology. It distinguishes between different categories of stakeholders in terms of how the experience and respond to these challenges Conducted desk research, an online survey, interviews, and a validation workshop Measurement, Metrics, Wider
Noorman, M., Kalaitzi V., Angelaki M., Tsoukala V., Linde P., Sveinsdottir T., et al. (2014).  Institutional barriers and good practice solutions. Investigated challenges faced by institutions, such as archives, libraries, universities, data centres and funding bodies, in making open access to research data possible. This work was conducted within the EU FP7 funded project RECODE, which focuses on developing policy recommendations for Open Access to Research Data in Europe. Conducted desk research, case study interviews, and a validation workshop Measurement, Wider
Finn, R., Wadhwa K., Taylor M. J., Sveinsdottir T., Noorman M., & Sondervan J. (2014).  Legal and ethical barriers and good practice solutions. Identify legal and ethical issues relevant to open access to research data in Europe, identify examples that illuminate these issues, and identify potential solutions currently being used to address these issues Conducted a literature review, five disciplinary case studies, and a validation workshop Measurement, Wider
Sveinsdottir, T., Wessels B., Smallwood R., Linde P., Kala V., Tsoukala V., et al. (2013).  Stakeholder values and relationships within open access and data dissemination and preservation ecosystems. Identify and map the diverse range of stakeholder values in Open Access data and data dissemination and preservation; map stakeholder values on to research ecosystems using case studies from different disciplinary perspectives; conduct a workshop to evaluate and identify good practice in addressing conflicting value chains and stakeholder fragmentation. This work was conducted within the EU FP7 funded project RECODE, which focuses on developing policy recommendations for Open Access to Research Data in Europe. Conducted desk research, case study interviews, and a validation workshop Measurement, Metrics, Wider
Beagrie, N., & Houghton J. (2012).  Economic Impact Evaluation of the Economic and Social Data Service. Sought to (i) evaluate the economic benefits and impact of ESDS; and (ii) contribute to the further development of impact evaluation methods that can provide ESRC with robust estimates of the economic benefits of its data service infrastructure investments Conducted (i) desk-based analysis of existing evaluation literature and reports, looking at both methods and findings; (ii) existing data from KRDS and other studies; (iii) existing management and internal data collected by ESRC and ESDS such as user statistics, internal reports, and the ESDS Mid-Term Review; and (iv) original data collection in the form of semi-structured interviews, case studies, and an online survey of ESDS users and depositors Measurement, Metrics, Targeted
Mayo, C., Vision T. J., & Hull E. A. (2015).  The location of the citation: changing practices in how publications cite original data in the Dryad Digital Repository. Zenodo. Measurement, Targeted
Roche, D. G., Kruuk L. E. B., Lanfear R., & Binning S. A. (2015).  Public Data Archiving in Ecology and Evolution: How Well Are We Doing?. PLOS Biol. 13(11), e1002295. Investigated the quality of 100 datasets deposited in Dryad and "associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy" Evaluated the completeness and reusability of datasets based on criteria described in the paper Measurement, Metrics, Targeted
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