|
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 |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
Bates, J. J., Privette J. L., & Hills A. D.
(2012). NOAA’s National Climatic Data Center’s Maturity Model for Climate Data Records.
Curating for Quality: Ensuring Data Quality to Enable New Science. 32 - 33. |
|
|
Metrics, Targeted |
|
Beagrie, N., 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. |
Measurement, Metrics, Targeted |
|
Beagrie, N., & Houghton J.
(2013). The Value and Impact of the Archaeology Data Service: A Study and Methods for Enhancing Sustainability.
|
Investigated and attempted to measure the value and impact of the Archaeology Data Service (ADS) |
Reviewed value and impact evaluation literature; analyzed ADS reports and documentation; conducted 15 interviews with ADS stakeholders; conducted 2 online surveys, one of ADS data depositors and one of ADS users
Note: This study and the similar study of the British Atmospheric Data Centre (Node 34) both use the same value metrics framework |
Measurement, Metrics, Targeted |
|
Beagrie, N., 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 |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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). |
|
|
Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
Borgman, C. L.
(2015). Big data, little data, no data: scholarship in the networked world.
|
|
|
Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
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. |
Measurement, Metrics, Targeted |
|
Conway, P., & Bronicki J.
(2012). Error Metrics for Large-Scale Digitization.
Prepared for the NSF III #1247471 workshop, Curating for Quality: Ensuring Data Quality to Enable New Science. |
|
|
Metrics, Targeted |
|
Digital Curation Centre
(2014). Five steps to decide what data to keep: a checklist for appraising research data v.1.
|
A guide written by the DCC |
|
Metrics, Targeted |
|
Digital Curation Centre, DigitalPreservationEurope
(2007). DRAMBORA.
|
|
|
Metrics, Targeted |
|
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. |
Measurement, Metrics, Targeted |
|
DSA Group
(n.d.). Data Seal of Approval.
|
A framework for evaluating digital preservation repositories |
|
Metrics, Targeted |
|
Earth Science Information Partners
(2011). Interagency Data Stewardship/Principles.
ESIP. |
Federation of Earth Science Information Partners (ESIP Federation) statement of data stewardship principles |
|
Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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. |
|
Measurement, Metrics, Targeted |
|
Fecher, B., Friesike S., & Hebing M.
(2015). What Drives Academic Data Sharing?.
PLoS ONE. 10(2), e0118053. |
Investigated the creation of a framework that explains the process of data sharing from the researcher’s point of view |
Performed a systematic review of 98 scholarly papers and empirical survey among 603 secondary data users |
Measurement, Metrics, Targeted |
|
FORCE11
(n.d.). Guiding Principles for Findable, Accessible, Interoperable and Re-usable Data Publishing.
|
|
|
Metrics, Targeted |
|
Fry, J., Lockyer S.., Oppenheim C.., Houghton J.W.., & Rasmussen B..
(2008). Identifying benefits arising from the curation and open sharing of research data produced within UK Higher Education and research institutes: exploring costs and benefits.
|
Investigated the benefits of the curation and open sharing of research data and the development of a methodology and model for estimating the benefits of data curation and sharing in UK higher education |
Performed a literature review to provide illustrative examples of reuse and the views of stakeholders in various disciplines towards data curation and sharing; conducted two case studies to identify and illustrate benefits and costs in these areas |
Measurement, Metrics, Wider |
|
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 |
Measurement, Metrics, Targeted |
|
Giarlo, M. J.
(2012). Academic Libraries as Data Quality Hubs.
Prepared for the NSF III #1247471 workshop, Curating for Quality: Ensuring Data Quality to Enable New Science. |
|
|
Metrics, Targeted |
|
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; |
Metrics, Targeted |
|
Gutmann, M., Schürer K.., Donakowski D., & Beedham H.
(2004). The Selection, Appraisal, and Retention of Digital Social Science Data.
Data Science Journal. 3, 209 - 221. |
|
|
Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
Harvey, R.
(2008). Appraisal and Selection.
Briefing Papers: Introduction to Curation. |
Describes selection and appraisal criteria for scientific data and records |
|
Metrics, Targeted |
|
Hedstrom, M., Niu J., & Marz K.
(2006). Producing Archive-Ready Datasets: Compliance, Incentives, and Motivation.
IASSIST. |
Investigated effort researchers are willing to put into preparing data for deposit into an archive and incentives to induce researchers to improve the quality of data and metadata deposited |
Surveyed 170 researchers funded by the National Institute of Justice, which requires deposit of data in an established archive (National Archive of Criminal Justice Data at the Inter-university Consortium for Political and Social Research-NAJCD) |
Measurement, Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
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) |
Measurement, Metrics, Targeted |
|
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. |
Measurement, Metrics, Targeted |
|
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 |
|
Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
Knight, S-A., & Burn J.
(2005). Developing a framework for assessing information quality on the World Wide Web.
Informing Science Journal. 8(3), 159 - 172. |
|
|
Metrics, Targeted |
|
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 |
|
Metrics, Targeted |
|
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. |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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.
|
|
|
Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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) |
|
Metrics, Targeted |
|
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. |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
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. |
|
Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
National Science Foundation, National Cyber Infrastructure Foundation
(2007). Cyberinfrastructure Vision for 21st Century Discovery.
Director. |
|
|
Metrics, Targeted |
|
Open Data Initiative
(n.d.). Open Data Certificate.
Open Data Initiative. |
|
|
Metrics, Targeted |
|
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. |
|
Metrics, Targeted |
|
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 |
|
Metrics, Targeted |
|
Peng, G., Privette J. L., Kearns E. J., Ritchey N. A., & Ansari S.
(2015). A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets.
Data Science Journal. 13, |
|
|
Metrics, Targeted |
|
Pronk, T. E., Wiersma P. H., van Weerden A., & Schieving F.
(2015). A game theoretic analysis of research data sharing.
PeerJ. 3, |
Used a game theory model to investigate the costs and benefits of sharing data to researchers |
Created a model and ran simulations using different parameters to analyze implications for sharing in a variety of scenarios |
Measurement, Metrics, Targeted |
|
Ramapriyan, H.., Moses J. F., & Duerr R..
(2012). Preservation of data for Earth system science - Towards a content standard.
5304 - 5307. |
Presents the need for a preservation content specification for earth science data, and proposes content items to be captured |
|
Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
J. Sticco, C.
(2012). Towards Data Quality Metrics Based on Functional Requirements for Scientific Records.
Prepared for the NSF III #1247471 workshop, Curating for Quality: Ensuring Data Quality to Enable New Science. |
|
|
Metrics, Targeted |
|
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 |
Metrics, Targeted |
|
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 |
|
Metrics, Targeted |
|
Turner, V., Reinsel D., Gantz J. F., & Minton S.
(2014). The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things.
|
Investigated size and rate of expansion of the digital universe worldwide in 2013; investigated business opportunities to use data in new ways and extract value from the digital universe |
Analyzed 60 streams of data comprising the digital universe (11 streams were added to the 49 in the 2007 study); analyzed ways in particular that the Internet of Things is creating business opportunities; developed 5 criteria to identify “target rich” or valuable data in the digital universe; identified information technology prerequisites to being able to take advantage of the value of data; identified business steps enterprises must take to leverage data value |
Measurement, Metrics, Targeted |
|
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 |
Measurement, Metrics, Targeted |
|
Wang, R. Y., & Strong D. M.
(1996). Beyond Accuracy: What Data Quality Means to Data Consumers.
J. Manage. Inf. Syst.. 12(4), 5 - 33. |
|
|
Metrics, Targeted |
|
Zins, C.
(2007). Conceptual Approaches for Defining Data, Information, and Knowledge.
Journal of the American Society for Information Science and Technology. 58(4), 479 - 493. |
Undertook a study, “Knowledge Map of Information Science,” to explore the foundations of information science. |
Used a panel discussion-based methodology called Critical Delphi. The panel for the study was comprised of leading scholars who represent nearly all the major subfields and important as- pects of the field (see Appendix A). The indirect discussions were anonymous and were conducted in three successive rounds of structured questionnaires. The first questionnaire contained 24 detailed and open-ended questions covering 16 pages. The second questionnaire contained 18 questions in 16 pages. The third questionnaire contained 13 questions in 28 pages (see relevant excerpts from the three question- naires in Appendix B). |
Measurement, Metrics, Targeted |
|
Bigagli, L., Sveinsdottir T., Wessels B., Smallwood R., Linde P., Tsoukala V., et al.
(2014). Infrastructural and technological challenges and potential solutions.
|
Investigated infrastructural and technological barriers to Open Access and preservation of research data in Europe. This work was conducted within the EU FP7 funded project RECODE, which focuses on developing policy recommendations for Open Access to Research Data in Europe. In particular, this work is coordinated by RECODE Work Package 2 (WP2), Infrastructure and technology. It distinguishes between different categories of stakeholders in terms of how the experience and respond to these challenges |
Conducted desk research, an online survey, interviews, and a validation workshop |
Measurement, Metrics, Wider |
|
Sveinsdottir, T., Wessels B., Smallwood R., Linde P., Kala V., Tsoukala V., et al.
(2013). Stakeholder values and relationships within open access and data dissemination and preservation ecosystems.
|
Identify and map the diverse range of stakeholder values in Open Access data and data dissemination and preservation; map stakeholder values on to research ecosystems using case studies from different disciplinary perspectives; conduct a workshop to evaluate and identify good practice in addressing conflicting value chains and stakeholder fragmentation. This work was conducted within the EU FP7 funded project RECODE, which focuses on developing policy recommendations for Open Access to Research Data in Europe. |
Conducted desk research, case study interviews, and a validation workshop |
Measurement, Metrics, Wider |
|
ICU World Data System
(0). World Data System Certification.
|
A repository certification framework |
|
Metrics, Targeted |
|
Beagrie, N., & Houghton J.
(2012). Economic Impact Evaluation of the Economic and Social Data Service.
|
Sought to (i) evaluate the economic benefits and impact of ESDS; and (ii) contribute to the further development of impact evaluation methods that can provide ESRC with robust estimates of the economic benefits of its data service infrastructure investments |
Conducted (i) desk-based analysis of existing evaluation literature and reports, looking at both methods and findings; (ii) existing data from KRDS and other studies; (iii) existing management and internal data collected by ESRC and ESDS such as user statistics, internal reports, and the ESDS Mid-Term Review; and (iv) original data collection in the form of semi-structured interviews, case studies, and an online survey of ESDS users and depositors |
Measurement, Metrics, Targeted |
|
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 |
Metrics, Targeted |
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Roche, D. G., Kruuk L. E. B., Lanfear R., & Binning S. A.
(2015). Public Data Archiving in Ecology and Evolution: How Well Are We Doing?.
PLOS Biol. 13(11), e1002295. |
Investigated the quality of 100 datasets deposited in Dryad and "associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy" |
Evaluated the completeness and reusability of datasets based on criteria described in the paper |
Measurement, Metrics, Targeted |
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Russell, K., & Weinberger E.
(2000). Cost elements of digital preservation.
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"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" |
|
Metrics, Targeted |
