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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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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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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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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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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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Waller, M., & Sharpe R.
(2006). Mind the Gap: Assessing Digital Preservation Needs in the UK.
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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.
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Measurement, Wider |
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Finn, R., Wadhwa K., Taylor M. J., Sveinsdottir T., Noorman M., & Sondervan J.
(2014). Legal and ethical barriers and good practice solutions.
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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 |
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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.
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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 |
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Thompson, S. Day
(2016). Preserving Transactional Data.
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Investigated "the requirements for preserving transactional data and the accompanying challenges facing companies and institutions that aim to re-use these data for analysis or research." The report was commissioned to support the long-term preservation issues faced by UK ESRC-funded centres (Big Data Network Support (BDNS), which includes the Administrative Data Research Network (ADRN) and research centres that form the Business and Local Government Data initiative). |
Examined three use cases: Energy Demand Research Project: Early Smart Meter Trials at the UK Data Service (UKDS); Output Area Classification Data at the Consumer Data Research Centre (CDRC); Higher Education Data at the Administrative Data Research Network (ADRN) |
Measurement, Targeted |
