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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 |
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Alexogiannopoulos, E., McKenney S., & Pickton M.
(2010). Research Data Management Project: a DAF investigation of research data management practices at The University of Northampton.
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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 |
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Averkamp, S., Gu X., & Rogers B.
(2014). Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs.
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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 |
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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 |
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Beile, P.
(2014). The UCF Research Data Management Survey: A report of faculty practices and needs.
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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 |
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Bergin, M. Banach
(2013). Sabbatical Report: Summary of Survey Results on Digital Preservation Practices at 148 Institutions.
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Investigate how digital preservation programs were implemented in institutions with established programs |
Conducted an online survey and follow-up interviews with 12 librarians and archivists from various institutions. The survey received 148 responses [from libraries and archives]. 100 people finished the survey.
"...I received responses from all types of institutions including national libraries, state libraries, academic libraries, public libraries, church and corporate archives, national parks archives, historical societies, research data centers, and presidential libraries. Roughly a third of the respondents were from large academic institutions with more than 20,000 students, another third were from smaller academic institutions with less than 20,000 students, and the remaining third were from non-academic institutions." |
Measurement, Wider |
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Borgman, C. L., Darch P. T., Sands A. E., Wallis J. C., & Traweek S.
(2014). The Ups and Downs of Knowledge Infrastructures in Science: Implications for Data Management.
Proceedings of the Joint Conference on Digital Libraries, 2014 (DL2014). |
Compared data management activities of four large, distributed, multidisciplinary scientific endeavors to gain insight into the domain expertise and expertise in organizing and retrieving complex data objects necessary for successful infrastructures for research data |
Findings are based on interviews (n=113 to date), ethnography, and other analyses of four cases (two big science and two small science), studied since 2002 |
Measurement, Targeted |
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Fearon, D., Gunia B., Pralle B., Lake S., & Sallans A.
(2013). ARL Spec Kit 334: Research data management services.
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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 |
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Gibbs, H.
(2009). Southampton Data Survey: Our Experience and Lessons Learned.
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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 |
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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 |
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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 |
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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 |
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Jerrome, N., & Breeze J.
(2009). Imperial College Data Audit Framework Implementation: Final Report.
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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 |
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Open Exeter Project Team
(2012). Summary Findings of the Open Exeter Data Asset Framework Survey.
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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 |
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Parsons, T., Grimshaw S., & Williamson L.
(2013). Research Data Management Survey.
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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 |
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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 |
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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 |
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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 |
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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 |
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UNC-CH
(2012). Research Data Stewardship at UNC: Recommendations for Scholarly Practice and Leadership.
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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 |
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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 |
