All Studies

This page allows studies in the different gap areas to be filtered by 1) Measurement studies 2) Metrics studies 3) Targeted and Wider studies. It works best to filter by either Measurement or Metrics gap areas, not both (as both yields unreliable results). "Other" study areas are included. These are areas that were not identified as gaps in reviewed literature, but that studies seek to measure or develop metrics for nonetheless.

Count of Titles Biblio Citationsort ascending Purpose Method
145
1 Yakel, E., & Faniel I. (2013).  Dissemination Information Packages for Information Reuse. Studied "data reuse in three academic disciplines to identify how contextual information about the data that supports reuse can best be created and preserved. The project focuses on research data produced and used by quantitative social scientists, archaeologists, and zoologists." (http://www.oclc.org/research/themes/user-studies/dipir.html) Used a variety of methods including a survey, analysis of online behavior, and user observations
1 Russell, K., & Weinberger E. (2000).  Cost elements of digital preservation. "Provides an introduction and overview of some of the general issues associated with the costs of digital preservation and...a detailed breakdown of specific cost elements"
1 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
1 Kitchin, R., Collins S., & Frost D. (2015).  Funding Models for Open Access Repositories. Investigated how open access repositories are funded Examined 14 different funding models, grouped into six classes (institutional, philanthropy, research, audience, service, volunteer), that might be used to provide revenue streams to support open access repositories
1 Thompson, S. Day (2016).  Preserving Transactional Data. 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)
1 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.
1 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
1 Kaur, K., Darby R., Herterich P., Schmitt K., Schrimpf S., Tjalsma H., et al. (2013).  Report on Testing of Cost Models and Further Analysis of Cost Parameters. Test existing cost models and cost/benefit analyses for digital preservation against real-world examples; analyzed cost models in relation to ISO 16363 to identify gap areas not covered by the models Tested the cost models with data that had already been collected (e.g., as part of the development of the DANS cost model)
1 Dallmeier-Tiessen, S., Guercio M., Helin H., Herterich P., Kaur K., Lavasa A., et al. (2014).  Exemplar Good Governance Structures and Data Policies. 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
1 ICU World Data System (0).  World Data System Certification. A repository certification framework
1 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
1 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
1 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
1 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
1 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).
1 Wynholds, L. A., Wallis J. C., Borgman C. L., Sands A., & Traweek S. (2012).  Data, Data Use, and Scientific Inquiry: Two Case Studies of Data Practices. 19 - 22. Investigated the characteristics of data use and reuse within specific research communities, and how characteristics of data use and reuse vary within and between those communities Conducted semi-structured interviews and field observations in environmental sciences, marine biology, ecology, seismology, computer science, engineering, and astronomy
1 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
1 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
1 Wang, R. Y., & Strong D. M. (1996).  Beyond Accuracy: What Data Quality Means to Data Consumers. J. Manage. Inf. Syst.. 12(4), 5 - 33.
1 Wang, D., Strodl S., Kejser U. Bøgvad, Ferreira M., Borbinha J., Proença D., et al. (2015).  4C Project: From Costs to Business Models. Examined existing business models for digital curation from a business model canvas approach; outlined key aspects in terms of activity, customers, finance and unique selling points Evaluation was done primarily through review and analysis of literature and desk-based research on existing business models and initiatives
1 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
1 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.
1 Vickery, G. (2011).  Review of Recent Studies on PSI Re-use and Related Market Developments. Investigated the value of Public Sector Information (PSI) reuse in Europe Conducted a review of the findings of studies on PSI reuse and assess and changes or development since 2006
1 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
1 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
1 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.

1 The Governance Lab New York University (0).  Open Data 500. Open Data 500. An ongoing study of U.S. companies that use open government data to generate new business and develop new products and services (http://www.opendata500.com/us/about/) Compiles a list of companies through outreach, research and advice; gathers information about companies’ use of open data via an online survey; conducts roundtables that bring together federal agencies and businesses with organizations that use their data to help identify and facilitate access to high value data
1 The Consultative Committee for Space Data Systems (2012).  Reference Model for an Open Archival Information System (OAIS), Magenta Book. Provides a framework for an open archival information system
1 Berman, F., Lavoie B., Ayris P., G. Choudhury S., Cohen E., Courant P. N., et al. (2008).  Sustaining the Digital Investment: Issues and Challenges of Economically Sustainable Digital Preservation. 72. "To sample and understand best and current practices for digital preservation and access, and to begin to synthesize major themes and identify systemic challenges." Focused on two questions: How much does it cost? and Who should pay? Conducted a literature review and invited 16 speakers "representing a variety of domains and areas of expertise" to address five questions: 1) What is the nature of the materials being preserved; 2) Who are the stakeholders for these materials?; 3) What is the "value proposition" for this preservation effort?; 4) What are the key features of long-term preservation for these materials?; 5) What are the "economic aspects" of digital preservation?
1 van der Hoeven, J. (2010).  PARSE.Insight: Insight into issues of Permanent Access to the Records of Science in Europe. Final Report. Final Report of the PARSE.Insight study, which sought to gain insight into issues surrounding the preservation of research data in Europe Conducted a literature review, an online survey, and case study interviews
1 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
1 Tenopir, C., Birch B., & Allard S. (2012).  Academic Libraries and Research Data Services: Current Practices and Future Plans. Surveyed a cross section of academic library members of the Association of College and Research Libraries (ACRL) in the United States and Canada to provide a baseline assessment of the current state of and future plans for research data services in academic libraries in these countries Conducted an online survey of ACRL library directors
1 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%)
1 Sunlight Foundation, & Keserű J. (2015).  We're still looking for open data social impact stories!. Sunlight Foundation. Built a list of examples of how open data and transparency projects are having an impact on society Gathered approximately 150 stories through an open Google spreadsheet.
1 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
1 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.
1 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
1 Science and Technology Council (2007).  The Digital Dilemma: Strategic Issues in Archiving and Accessing Digital Motion Picture Materials. Academy of Motion Picture Arts and Sciences. Investigated size of picture and sound elements created during production and post production for two motion pictures in 2006 or 2007 Conducted case studies where motion picture studios provided information about the amounts of data for a pre-determined list of materials
1 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%)
1 Rusbridge, C., & Lavoie B. (2011).  Draft economic sustainability reference model. Economic Sustainability Reference Model (ESRM): Defined the notion of a sustainable strategy, highlighted key components that should be taken into account when designing a strategy; enumerated risks that sustainability strategies should guard against; presented an economic lifecycle model to assist in planning throughout the curation lifecycle Translated the concepts, findings, and recommendations of Berman et al. 2010 into a practical resource for sustainability planning; assessed several digital curation lifecycle models to inform the creation of an economic lifecycle model
1 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
1 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
1 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
1 Plietzsch, B. (2013).  How much (more) research data do we have, and where do we store it?. Investigated storage requirements for research data within their respective school Conducted a survey of Campus Officers in different schools and research centers at the University of St. Andrews
1 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.
1 Piwowar, H. A., & Vision T. J. (2013).  Data reuse and the open data citation advantage. PeerJ. 1, e175. Investigated the extent to which the article citation is affected by the availability of research data. Performed multi-variate regression on 10,555 studies that produced gene expression microarray data using date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic as covariates; examined patterns of reuse of data in the GEO database based on mentions in articles in PubMed Central; extrapolated from patterns of reuse of data in the GEO database in PubMed Central to estimate GEO data reuse in all of PubMed between 2000 and 2010
1 Piwowar, H. A., Vision T. J., & Whitlock M. C. (2011).  Data archiving is a good investment. Nature. 473(7347), 285. Estimated the cost of archiving data using Dryad; estimated reuse of data Cost: method not given; Reuse: searched the full text of articles in PubMed Central for mention of datasets in the Gene Expression Omnibus (GEO) database
1 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
1 Piwowar, H. A., & Chapman W. W. (2008).  A review of journal policies for sharing research data. Conference on Electronic Publishing. Investigated the state of data sharing policies among journals, features of journals associated with strength in data policy, and whether the strength of the policy affected extent of data sharing Identified journals that most often publish studies about gene expression microarray data and classified their policies for data sharing as none, weak, or strong; conducted univariate and linear multivariate regressions to understand the relationship between the strength of data sharing policy and journal impact factor, journal sub discipline, journal publisher (academic societies vs. commercial), and publishing model (open vs. closed access); measured through queries of PubMed how many recently published articles submitted data to the NCBI Gene Expression Omnibus (GEO) database
1 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
1 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
1 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)
1 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.
1 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
1 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,
1 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
1 Pampel, H., Vierkant P., Scholze F., Bertelmann R., Kindling M., Klump J., et al. (2013).  Making Research Data Repositories Visible: The re3data.org Registry. PLoS ONE. 8(11), e78080. Investigated the global landscape of research data repositories; presented a typology of institutional, disciplinary; outlined the features of re3data.org, and showed how this registry helps to identify appropriate repositories for storage and search of research data Analyzed 400 research data repositories and requested comments on a project website and various email lists on an appropriate vocabulary to describe and present information (such as policies, responsibilities, and technical and quality standards for different repositories); analyzed criteria for repository certification and audit and developed a low barrier of entry to inclusion in in the repository registry.
1 Owens, T., Goethals A., Grotke A., Kirchoff A., Klein K., Mandelbaum J., et al. (2012).  NDSA Levels of Digital Preservation. Provides a model of levels of preservation for digital materials
1 Organization for Economic Co-operation and Development (2015).  Making Open Science A Reality. Reviews the progress in OECD countries in making the results of publicly funded research, namely scientific publications and research data openly accessible to researchers and innovators alike. The report i) reviews the policy rationale behind open science and open data; ii) discusses and presents evidence on the impacts of policies to promote open science and open data; iii) explores the legal barriers and solutions to greater access to research data; iv) provides a description of the key actors involved in open science and their roles; and finally v) assesses progress in OECD and selected non-member countries based a survey of recent policy trends.
1 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.
1 Open Data Initiative (n.d.).  Open Data Certificate. Open Data Initiative.
1 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]
1 National Science Foundation, National Cyber Infrastructure Foundation (2007).  Cyberinfrastructure Vision for 21st Century Discovery. Director.
1 National Research Council (2003).  Sharing Publication-Related Data and Materials: Responsibilities of Authorship in the Life Sciences. A study to evaluate the responsibilities of authors of scientific papers in the life sciences to share data and materials referenced in their publications Held a workshop attended by more than 70 participants
1 National Academy of Sciences (2009).  Ensuring the integrity, accessibility, and stewardship of research data in the digital age. 325(5939), 368. An ad hoc committee will conduct a study of issues that have arisen from the evolution of practices in the collection, processing, oversight, publishing, ownership, accessing, and archiving of research data. The key questions to be addressed are: 1. What are the growing varieties of research data? In addition to issues concerned with the direct products of research, what issues are involved in the treatment of raw data, prepublication data, materials, algorithms, and computer codes? 2. Who owns research data, particularly that which results from federally funded research? Is it the public? The research institution? The lab? The researcher? 3. To what extent is a scientist responsible for supplying research data to other scientists (including those who seek to reproduce the research) and to other parties who request them? Is a scientist responsible for supplying data, algorithms, and computer codes to other scientists who request them? 4. What challenges do the science and technology community face arising from actions that would compromise the integrity of research data? What steps should be taken by the science and technology community, research institutions, journal publishers, and funders of research in response to these challenges? 5. What are the current standards for accessing and maintaining research data, and how should these evolve in the future? How might such standards differ for federally funded and privately funded research, and for research conducted in academia, government, nongovernmental organizations, and industry? The study will not address privacy issues and other issues related to human subjects.
1 Mitcham, J., Awre C., Allinson J., Green R., & Wilson S. (2015).  Filling the Digital Preservation Gap. A JISC Research Data Spring Project. Phase One Report. Investigated the use of Archivematica, a system designed to prepare data for long-term storage and access, to help preserve research data Reviewed funder obligations for data management and requirements for digital preservation and analyzed how Archivematica measures against them; conducted online surveys of research staff and students at York University to understand the landscape of research data management at York, and to gain insight into the software packages and top applications used; tested Archivematica with a range of file sizes, types, directory structures, descriptive information, workflows within Archivematica, and different places Archivematica could occupy in a broader research data management workflow.
1 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
1 McLeod, R., Wheatley P., Ayris P., & Girling H. (2006).  The LIFE Project: Bringing digital preservation to LIFE - A summary from the LIFE project Report Produced for the LIFE conference 20 April 2006. Investigated the development of a life cycle-based cost model for digital preservation Performed comprehensive review of life cycle models and digital preservation; broke a digital object’s lifecycle into six main lifecycle stages and identified the costs of elements in these stages over a specified time; performed case studies to identify costs for each stage of the life cycle; LIFE2 added cases studies for two institutional repositories and an analog collection; LIFE3 included a survey of digital preservation repositories and additional case studies
1 McCain, K. W. (1995).  Mandating Sharing Journal Policies in the Natural Sciences. Science Communication. 16(4), 403 - 431. Created an initial characterization of “research-related information” (RRI) types* and journal policies in the natural sciences and engineering * McCain includes physical research products and craft knowledge in this category, as well as raw data on which results are based. Examined 850 recent journals in the physical and biological sciences and developed a three-part categorization of RRI policies and practices, including regarding sharing and deposit of data, and penalties for non-compliance.
1 Mayer, R., Rauber A., Neumann M. Alexander, Thomson J., & Antunes G. (2012).  Preserving Scientific Processes from Design to Publications. Theory and Practice of Digital Libraries. 7489, 113 - 124. Provides a model for capturing contextual details (including provenance) for scientific processes (e.g., software and surrounding processes)
1 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
1 Manyika, J., Chui M., Groves P., Farrell D., Van Kuiken S., Doshi E. Almasi, et al. (2013).  Open data: Unlocking innovation and performance with liquid information. 103. Identified ways open data can create economic value in terms of revenue and savings and economic surplus (e.g., time savings that commuters gain when they avoid congestion); estimated potential annual value that use of open data could bring in seven domains: education, transportation, consumer products, electric power, oil and gas, health care, and consumer finance Quantified (in monetary terms) the potential value of using open data in seven “domains” of the global economy: education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance; identified “levers” through which open data can create economic value and explored the barriers to adoption and “enablers” for capturing value by making data more open; provided examples of uses of open data that have a significant impact
1 Manyika, J., Byers A. Hung, Chui M., Brown B., Bughin J., Dobbs R., et al. (2011).  Big data: The next frontier for innovation, competition, and productivity. 156. Examined the potential value that big data can create for organizations and sectors of the economy; sought to illustrate and quantify that value; explored what leaders of organizations and policy makers need to do to capture it; investigated amount of data stored by enterprises and consumers in 2010 Examined types and amounts of data generated, compute resources, and trends that will drive data growth in different sectors and regions throughout the world; examined the impact of IT on labor productivity, techniques and technologies for analyzing big data, and the transformative potential of big data in terms of efficiency, productivity, trust, profit, and other factors in five domains: Healthcare, Public Sector, Retail, Manufacturing, and Telecommunications; also examined the changes necessary (investments, incentives, skills development, policy development and others) to attain these benefits; specific methodologies to gather supporting data are listed in the paper
1 Malamud, C., O'Reilly T., Elin G., Sifry M., Holovaty A., O'Neil D. X., et al. (2007).  The Annotated 8 Principles of Open Government Data.
1 Lyman, P., & Varian H. R. (2003).  How Much Information? 2003. Investigated amount of new information created each year in the US and world in 2002 Estimated size based on research into the production of data stored on four storage media: print, film, magnetic, optical
1 Lyman, P., & Varian H. R. (2000).  How Much Information?. Journal of Electronic Publishing. 6(2),  Investigated amount of new information created each year in the US and world in 1999 Estimated size based on research into the production of data stored on four storage media: print, film, magnetic, optical
1 Lesk, M. (1997).  How Much Information Is There In the World?. Investigated amount of information in the world in 1997 Estimated size based on extrapolations from select examples of data storage
1 Lee, C. A., & Tibbo H. R. (2012).  Preparing for Digital Curation Governance: Educating Stewards of Public Information. 171 - 174. 2 projects (ESOPI and ESOPI2) undertaken to redesign and enhanced a dual degree program that was earlier developed by the University of North Carolina’s School of Information and Library Science (SILS) and School of Government (SOG) Performed a comprehensive literature review of information and library science and public administration masters-level programs; conducted interviews with project advisory board members and, public sector information experts; conducted a focus group study of current and alumni Fellows from the project.
1 Lavoie, B. (2003).  The Incentives to Preserve Digital Materials: Roles, Scenarios, and Economic Decision-Making. Based on three key economic decision-makers, identifies five organizational models, or scenarios, under which digital preservation activities might take place
1 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
1 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
1 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.
1 Kejser, U. Bøgvad, Nielsen A. Bo, & Thirifays A. (2011).  Cost Model for Digital Preservation: Cost of Digital Migration. International Journal of Digital Curation. 6(1),  Investigated the development of a framework for costing digital preservation, including a methodology with sufficient detail to outline required resources, a set of equations to transform the resources into cost data, and a description of the accounting principles applied Performed a literature review; used the OAIS reference model to structure the functional breakdown of costs, initially for preservation planning and migration; examined two case studies dealing with migrations of data
1 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)
1 Jones, S. (2012).  Developments in Research Funder Data Policy. International Journal of Digital Curation. 7(1), 114 - 125. Reviewed developments in funders’ data management and sharing policies, and explored the extent to which they have affected practice Policies are believed to have been obtained through desk research
1 Jones, S. (2009).  A report on the range of policies required for and related to digital curation. Compared policies of funders in the UK according to policy coverage, curation stipulations, and support provided Policies were obtained through desk research
1 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.
1 International Standards Organization (2012).  Space Data and Information Transfer Systems- Audit and Certification of Trustworthy Digital Repositories (ISO 16363:2012). Provides a framework for evaluating digital repositories
1 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)
1 Houghton, J., & Gruen N. (2014).  Open Research Data Report. Report to the Australian National Data Service (ANDS). Estimated the value and benefits to Australia of making publicly-funded research data freely available; examined the role and contribution of data repositories and associated infrastructure; explored the policy settings required to optimize research data sharing, and thereby increase the return on public investment in research Used and modified Solow-Swan model and extrapolated from results of other studies, primarily in the UK, to estimate the implied value of increased access to Australian public research data
1 Horton, L., & DCC (2014).  Overview of UK Institution RDM Policies. Compared policies across UK institutions of higher education according to criteria adapted from DCC 2014 and Erway 2013 With the exception of one, all policies were found online.
1 Hilbert, M., & López P. (2011).  The World’s Technological Capacity to Store, Communicate, and Compute Information. Science. 332(6025), 60 - 65. Investigated amounts of total information (not unique) stored, communicated, and computed from 1986 to 2007 Used worldwide estimates in 1,120 sources for data in 60 categories (analog and digital)
1 Hendley, T. (1998).  Comparison of Methods & Costs of Digital Preservation. Developed a matrix of data types and categories of digital resources, a decision model to assess these categories and determine the most appropriate preservation strategy, and a cost model for comparing costs of preferred preservation methods Conducted an extensive review of literature and work to develop cost models; visited a number of digital libraries and data centres
1 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
1 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)
1 Harvey, R. (2008).  Appraisal and Selection. Briefing Papers: Introduction to Curation. Describes selection and appraisal criteria for scientific data and records
1 Hank, C., Tibbo H. R., & Lee C. A. (2010).  DigCCurr I Final Report, 2006-09. DigCCurr Project: Sought to develop an openly accessible, graduate-level curricular framework, course modules, and experiential and enrichment components and exemplars necessary to prepare students to work in the 21st century environment of trusted digital and data repositories. About DigCCurr I: http://www.ils.unc.edu/digccurr/aboutI.html Reviewed and analyzed relevant literature, syllabi, job advertisements, workshops, standards, tools, services, and research projects; conducted interviews with advisory board members; conducted an paper-based and an online survey, held two symposia on digital curation
1 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.
1 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.
1 Grindley, N., Ruusalepp R., L'Hours H., Kejser U. Bøgvad, Thirifays A., & Stokes P. (2015).  4C Project: Assessment of Community Validation of the Economic Sustainability Reference Model. Sought to highlight key concepts, relationships and decision points for planning how to sustain digital assets into the future, and complement and enhance guidance about sustainability planning that was available to the community and provide those with responsibility for digital assets and curation services (practitioners) a systematic way of considering and discussing sustainability issues with senior managers and funders/investors (strategists) Conducted a variety of engagement meetings and received feedback at numerous events and venues on the Rusbridge and Lavoie ESRM model; received responses to and feedback also on an ESRM self-assessment questionnaire;
1 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
1 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.
1 Gantz, J. F., Minton S., Reinsel D., Chute C., Schlichting W., Toncheva A., et al. (2008).  The Diverse and Exploding Digital Universe [White Paper]. Investigated size and rate of expansion of the digital universe worldwide in 2006 and 2007 Estimated how much data was created in a year by a base of 49 classes of devices or applications that could capture or store information; estimated number of times the data is replicated
1 Gantz, J. F., McArthur J., Minton S., Reinsel D., Chute C., Schlichting W., et al. (2007).  The Expanding Digital Universe [White Paper]. Investigated size and rate of expansion of the digital universe worldwide in 2006 and 2007 Estimated how much data was created in a year by a base of 49 classes of devices or applications that could capture or store information; estimated number of times the data is replicated
1 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
1 FORCE11 (n.d.).  Guiding Principles for Findable, Accessible, Interoperable and Re-usable Data Publishing.
1 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]
1 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
1 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
1 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.
1 K. Fear, M. (2013).  Measuring and Anticipating the Impact of Data Reuse. Identified citation patterns among data reusers; developed and demonstrated a suite of data reuse impact metrics; and explored factors that influence whether or not a dataset is reused, as well as what impact its reuse has.
1 Ember, C., & Hanisch R. (2013).  Sustaining Domain Repositories for Digital Data. Explored the funding challenges faced by domain repositories in the United States Reviewed the different functions of domain repositories, the reuse of data stored in domain repositories, and the issues domain repositories face; reviewed different funding models for domain repositories and scored them based on several criteria
1 Earth Science Information Partners (2011).  Interagency Data Stewardship/Principles. ESIP. Federation of Earth Science Information Partners (ESIP Federation) statement of data stewardship principles
1 DSA Group (n.d.).  Data Seal of Approval. A framework for evaluating digital preservation repositories
1 Downs, R. R., & Chen R. S. (2013).  Towards Sustainable Stewardship of Digital Collections of Scientific Data. GSDI World Conference (GSDI 13) Proceedings. Described an experimental economic strategy being undertaken by Columbia University In the process of describing the model, reviewed a variety of business models and literature that discussed their benefits and drawbacks.
1 Digital Curation Centre, DigitalPreservationEurope (2007).  DRAMBORA.
1 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
1 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.
1 Cirrinnà, C., Fernie K., & Lunghi M. (2013).  Digital Curator Vocational Education Europe (DigCurV): Final report and Conference Proceedings. DigCurV Project: Sought to understand the need for training in the cultural sector for long-term management of digital collections and establish a curriculum framework for vocational education in digital curation Conducted an online survey (receiving more than 450 responses from 44 countries) of stakeholders on training needs in digital preservation and curation; conducted focus groups and workshops in project partner countries; analyzed job advertisements from the UK, Germany, USA, New Zealand, and Australia. Note: the project included a final conference where a number of papers relevant to skills were delivered.
1 Brown, S., Bruce R., & Kernohan D. (2015).  Directions for Research Data Management in UK Universities. Investigated development needed in five key areas related to research data management: policy development and implementation; skills and capabilities; infrastructure and interoperability, incentives for researchers and support stakeholders, business case and sustainability Drew on selected recent publications, stakeholder interviews, the outcomes of a JISC Research at Risk consultation, and a two day workshop
1 Borgman, C. L. (2015).  Big data, little data, no data: scholarship in the networked world.
1 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
1 Bohn, R. E., & Short J. E. (2010).  How Much Information? 2009 Report on American Consumers. Investigated amount of information consumed and rates of increase in consumption in the US from 1980 to 2008 Analyzed 20 sources of data flows (such as video, television, radio, internet browsing) consumed by people
1 Berman, F., Lavoie B., Ayris P., G. Choudhury S., Cohen E., Courant P., et al. (2010).  Sustainable Economics for a Digital Planet: Ensuring Long-Term Access to Digital Information. 110. Developed an economic framework for analyzing digital preservation as an economic problem. Employed the framework to analyze the "economic conditions and implications intrinsic to four key digital preservation contexts: scholarly discourse, research data, collectively produced Web content, and commercially owned cultural content." Drew on findings reported in the interim Blue Ribbon Task Force report: Sustaining the Digital Investment: Issues and Challenges of Economically Sustainable Digital Preservation
1 Bergin, M. Banach (2013).  Sabbatical Report: Summary of Survey Results on Digital Preservation Practices at 148 Institutions. 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."
1 Belter, C. W. (2014).  Measuring the Value of Research Data: A Citation Analysis of Oceanographic Data Sets. PLoS ONE. 9(3), e92590. Investigated the value of data curation as evidenced by the bibliometric impact of curated and openly accessible data sets at the National Oceanographic Data Center Compiled citation counts for three highly-used datasets in Web of Science, several journal publisher websites, and Google Scholar; performed a more detailed investigation into citations counts in the same sources of all versions of one dataset in particular
1 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.
1 Becker, C., Antunes G., Barateiro J., & Vieira R. (2011).  A Capability Model for Digital Preservation: Analysing Concerns, Drivers, Constraints, Capabilities and Maturities. 8th International Conference on Preservation of Digital Objects (IPRES 2011).
1 Beagrie, N., Semple N., Williams P., & Wright R. (2008).  Digital Preservation Study Policies. Studied digital preservation policies to provide a model for policy development in Higher and Further Education Institutions; analyzed the role that digital preservation can play in supporting and delivering key strategies for these institutions Examined preservation policies, case studies, strategies and resources from a variety of sources; examined a sample of policies for research, teaching, and learning, and other relevant digital preservation literature and resources
1 Beagrie, N., Lavoie B., & Woollard M. (2010).  Keeping Research Data Safe 2. Continued the work of Beagrie et al. 2008 to develop a cost model for digital preservation Updated the previous literature review; conducted a cost survey; developed a taxonomy of the benefits of digital preservation; analyzed national and disciplinary digital archives that have existing historic cost information for preservation of digital research data collections and interacted with additional digital archives and research universities to determine the validity of developed cost model and how the cost model might be used
1 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
1 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
1 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
1 Beagrie, N., Chruszcz J., & Lavoie B. (2008).  Keeping Research Data Safe: A Cost Model and Guidance for UK Universities. Investigated the medium to long term costs to Higher Education Institutions (HEIs) of the preservation of research data and developed guidance on these issues, including a framework for determining costs Mapped the OAIS reference model against the LIFE cost model and NASA’s Cost Estimation Toolkit; evaluated transferable practice and relative strengths and weaknesses for each; aligned the resulting model with the TRAC model; researched literature on preservation costs and reports and documentation from UK data services and funders; conducted 12 interviews to supplement and validate researched information; conducted three case studies to validate the developed methodology and illustrate the variety of costs and community and service requirements for research data.
1 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.
1 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
1 Ball, A., & Duke M. (2015).  How to Track the Impact of Research Data with Metrics. DCC How-to Guides.. Investigated and discussed a wide range of means of determining the impact of research data using a variety of different metrics and measurement services Performed an extensive literature review
1 Ayris, P., Wheatley P., Davies R., Shenton H., Miao R., & McLeod R. (2008).  The LIFE2 Final Project Report. Investigated the development of a life cycle-based cost model for digital preservation Performed comprehensive review of life cycle models and digital preservation; broke a digital object’s lifecycle into six main lifecycle stages and identified the costs of elements in these stages over a specified time; performed case studies to identify costs for each stage of the life cycle; LIFE2 added cases studies for two institutional repositories and an analog collection; LIFE3 included a survey of digital preservation repositories and additional case studies
1 Ayris, P., Wheatley P., Aitken B., Hole B., McCann P., Peach C., et al. (2010).  The LIFE3 Project: Bringing digital preservation to LIFE. Investigated the development of a life cycle-based cost model for digital preservation Performed comprehensive review of life cycle models and digital preservation; broke a digital object’s lifecycle into six main lifecycle stages and identified the costs of elements in these stages over a specified time; performed case studies to identify costs for each stage of the life cycle; LIFE2 added cases studies for two institutional repositories and an analog collection; LIFE3 included a survey of digital preservation repositories and additional case studies
1 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.
1 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.
1 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
1 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
1 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
1 Addis, M. (2015).  Estimating Research Data Volumes in UK HEI. Investigated amount of data in UK Higher Education Institutions (HEI) Used existing surveys about research data to estimate an average per researcher data size in terabytes, then used data on the number of researchers at each HEI institution to estimate an overall amount