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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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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.
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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 |
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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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Beagrie, N., Houghton J., Palaiologk A., & Williams P.
(2012). Economic Evaluation of Research Data Infrastructure.
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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 |
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Beagrie, N., & Houghton J.
(2013). The Value and Impact of the Archaeology Data Service: A Study and Methods for Enhancing Sustainability.
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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 |
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Beagrie, N., & Houghton J.
(2013). The Value and Impact of the British Atmospheric Data Centre.
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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 |
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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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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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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.
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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 |
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Kroll, S., & Forsman R.
(2010). A Slice of Research Life: Information Support for Research in the United States.
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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 |
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Martinez-Uribe, L.
(2009). Using the Data Audit Framework: An Oxford Case Study.
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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 |
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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 |
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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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Bigagli, L., Sveinsdottir T., Wessels B., Smallwood R., Linde P., Tsoukala V., et al.
(2014). Infrastructural and technological challenges and potential solutions.
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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 |
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Dallmeier-Tiessen, S., Guercio M., Helin H., Herterich P., Kaur K., Lavasa A., et al.
(2014). Exemplar Good Governance Structures and Data Policies.
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Investigated the level of preparedness for interoperable governance and data policies for different groups (memory institutions, universities and research institutions, funders and policy-makers) in Europe "and beyond" |
Performed desktop research and conducted an online survey |
Measurement, Targeted |
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Beagrie, N., & Houghton J.
(2012). Economic Impact Evaluation of the Economic and Social Data Service.
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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 |
