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ARIADNE is funded by the European Commission's Seventh Framework Programme Open Data Publication Requirements, Good Practices, and Benefits EAA 2014 -

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Presentation on theme: "ARIADNE is funded by the European Commission's Seventh Framework Programme Open Data Publication Requirements, Good Practices, and Benefits EAA 2014 -"— Presentation transcript:

1 ARIADNE is funded by the European Commission's Seventh Framework Programme Open Data Publication Requirements, Good Practices, and Benefits EAA Istanbul Session: Barriers and Opportunities: Open Access and Open Data in Archaeology 13 September 2014 Guntram Geser Salzburg Research EAA Istanbul Session: Barriers and Opportunities: Open Access and Open Data in Archaeology 13 September 2014 Guntram Geser Salzburg Research

2 ARIADNE Advanced Research Infrastructure for archaeological Dataset Networking in Europe – EU FP7-Infrastructures project, type „Integrating Activity“ – Runs 4 years, 02/ /2017 – Focus on archaeological datasets – help overcome data fragmentation and foster a culture of sharing and re-using – 23 partners of 18 European countries – Open to other participants, e.g. Transnational Access Programme Special Interest Groups Website: Advanced Research Infrastructure for archaeological Dataset Networking in Europe – EU FP7-Infrastructures project, type „Integrating Activity“ – Runs 4 years, 02/ /2017 – Focus on archaeological datasets – help overcome data fragmentation and foster a culture of sharing and re-using – 23 partners of 18 European countries – Open to other participants, e.g. Transnational Access Programme Special Interest Groups Website:

3 Main topics of this presentation Open Data – expectations, criteria, drivers Current data (non-)sharing behaviour Reasons for lack of open data sharing ARIADNE survey results on data publication How to benefit from open data publication Issue of actual data re-use Takeaway points Open Data – expectations, criteria, drivers Current data (non-)sharing behaviour Reasons for lack of open data sharing ARIADNE survey results on data publication How to benefit from open data publication Issue of actual data re-use Takeaway points

4 Open Data – expectations The scientific method: provide evidence for knowledge claims („show us your data“) Output of publicly funded research should be openly available, and properly preserved More open data sharing => better analysis => better decision-making / solutions for critical issues Better return-on-investment, e.g. – no duplication of data collection – better exploitation of available data – therefore emphasis on re-use Innovation through „data-intensive“, „data-driven“, „big data“ (incl. archaeology?) The scientific method: provide evidence for knowledge claims („show us your data“) Output of publicly funded research should be openly available, and properly preserved More open data sharing => better analysis => better decision-making / solutions for critical issues Better return-on-investment, e.g. – no duplication of data collection – better exploitation of available data – therefore emphasis on re-use Innovation through „data-intensive“, „data-driven“, „big data“ (incl. archaeology?)

5 Open Data – criteria Accessible online – not necessarily without registration Reusable – not summarized data (i.e. figures, charts, etc.) canned in publications – state: raw, cleaned, normalized,… (accord. to practice) – open format (e.g. not PDF documents) Openly licensed (e.g. CC-BY, if other no NonDerivative!) For free – yes, but somebody has to pay to ensure sustainability Accessible online – not necessarily without registration Reusable – not summarized data (i.e. figures, charts, etc.) canned in publications – state: raw, cleaned, normalized,… (accord. to practice) – open format (e.g. not PDF documents) Openly licensed (e.g. CC-BY, if other no NonDerivative!) For free – yes, but somebody has to pay to ensure sustainability “Publishing data in a reusable form to support findings must be mandatory” – one of six key areas for action highlighted in the The Royal Society’s report Science as an Open Enterprise (2012)

6 Open Data – drivers /1 High-level policies & initiatives – OECD Declaration on Access to Research Data from Public Funding (2004; Principles and Guidelines, 2007) – EC Communications, Open data (2011), Towards better access to scientific information (2012) – Many others, e.g. Research Data Alliance – international initiative (launched in March 2013), various working & interest groups (archaeology not represented yet) High-level policies & initiatives – OECD Declaration on Access to Research Data from Public Funding (2004; Principles and Guidelines, 2007) – EC Communications, Open data (2011), Towards better access to scientific information (2012) – Many others, e.g. Research Data Alliance – international initiative (launched in March 2013), various working & interest groups (archaeology not represented yet) Neelie Kroes, EC Vice-President, 2012: “Taxpayers should not have to pay twice for scientific research and they need seam- less access to raw data. We want to bring dissemination and exploitation of scientific research results to the next level.”

7 Open Data – drivers /2 Research funding agencies – Open Access mandates extended to data – Mandatory data management plans, i.e. data sharing must be considered already at application stage Data archiving & access infrastructures put in place – Data centres / repositories General : zenodo (related to OpenAIRE), Figshare, … Archaeology: e.g. ADS (UK), eDNA (NL), IANUS (Germany, in preparation), MAPPA (IT), OpenContext and tDAR (USA) – Data registries / catalogues – Data citation standard, e.g. DataCite Research funding agencies – Open Access mandates extended to data – Mandatory data management plans, i.e. data sharing must be considered already at application stage Data archiving & access infrastructures put in place – Data centres / repositories General : zenodo (related to OpenAIRE), Figshare, … Archaeology: e.g. ADS (UK), eDNA (NL), IANUS (Germany, in preparation), MAPPA (IT), OpenContext and tDAR (USA) – Data registries / catalogues – Data citation standard, e.g. DataCite

8 Open Data – drivers /3 New publication format of „data papers“ – Describe a dataset/database, kind of extensive metadata record or documentation, also including examples of use / usefulness for research – Examples of data journals in archaeology Journal of Open Archaeology Data, started 2012 Internet Archaeology, started a series of data papers in 2013 New publication format of „data papers“ – Describe a dataset/database, kind of extensive metadata record or documentation, also including examples of use / usefulness for research – Examples of data journals in archaeology Journal of Open Archaeology Data, started 2012 Internet Archaeology, started a series of data papers in 2013

9 Barriers to open data sharing Many obstacles to providing open access to reusable data – Priority of published papers – Little academic reward for development and sharing of datasets/DB – Required effort to share re-usable data (incl. formatting, metadata creation, licensing etc.) – Existing copyrights, confidential and sensitive data – Concerns that data could be scooped, misused or misinterpreted – Potential reputational risk (e.g. data quality, errors,…) Overall a bad ratio of additional effort & risks to potential benefits

10 Current data (non-)sharing behaviour Contrary to what advocates of proper management and sharing of data would like them to do According to representative surveys (PARSE.Insight 2009, Science 2011): Most data remains locked away – On personal computers – Portable storage carriers – Restricted access servers – … Eventually discarded as “obsolete” or lost otherwise Mostly not considered: Potential value of the data for alternate and new uses by others Only 6-8% of researchers sometimes deposit data in a community archive

11 Archiving/storing data for future use /1 PARSE.Insight survey 2009: 1202 respondents from different research domains and countries

12 Archiving/storing data for future use /2 “Science” journal 2011 survey of peer reviewers: 1700 responses, (international and multi-disciplinary Where do you archive most of the data generated in your lab or for your research?” Note: archived ≠ curated 50.2% in our lab 38.5% university server 7.6% community repository 3.2% “other” 0.5% not stored

13 ARIADNE online survey on user needs Participants (881 questionnaires received, 692 with sufficient inform.) o 586 archaeological researchers o 54 directors of research institutes o 52 repository managers Organisational context (640 responses) o 54% Universities o 16% Governmental organisations o 12% Private companies or institutes o 13% Self-employed Gender (482 responses) o 57% male o 43% female Geographic distribution (482 resp.) o 83% EU (UK: 79, France: 51, Italy: 47% NL: 35,…) o 5% other European o 12% non-European November-December 20 13

14 Data publication

15 Barriers to data deposit/publication

16 „How would you rate the importance of the following potential barriers to enhancing access to research data?” European Commission: Online survey on scientific information in the digital age (2012); 1140 participants from around Europe. Need to make clear benefits of open data publication!

17 Authors‘ benefits focus Goal = recognition and academic reward for data providers – at least same as for other publications Core mechanism = citation of published data/set – Confirms value of the data contributed – Indicates providers of good data – Promotes further use of the data (i.e. more citations) – Allows the use and impact of the data to be tracked and measured But data citation metrics not implemented yet Some indications of higher citation rates of publications that make underlying data available Goal = recognition and academic reward for data providers – at least same as for other publications Core mechanism = citation of published data/set – Confirms value of the data contributed – Indicates providers of good data – Promotes further use of the data (i.e. more citations) – Allows the use and impact of the data to be tracked and measured But data citation metrics not implemented yet Some indications of higher citation rates of publications that make underlying data available

18 How to reap the benefits? / 1 Deposit data that underpins your research results in a reliable, community recognised repository – See: Data Seal of Approval; Trusted Repositories Audit & Certification (TRAC) and other checklists – Should provide unique persistent identifiers (e.g. DOIs) – Require following citation standard as part of user agreement (e.g. DataCite; citation in reference list) Provide good metadata – “no pain, no gain” – Key for data re-use without direct contact with creator – Costs of preparing data and metadata for publication should be included in project funding Deposit data that underpins your research results in a reliable, community recognised repository – See: Data Seal of Approval; Trusted Repositories Audit & Certification (TRAC) and other checklists – Should provide unique persistent identifiers (e.g. DOIs) – Require following citation standard as part of user agreement (e.g. DataCite; citation in reference list) Provide good metadata – “no pain, no gain” – Key for data re-use without direct contact with creator – Costs of preparing data and metadata for publication should be included in project funding

19 How to reap the benefits? /2 Apply a license not impeding reuse (e.g. CC-BY, ODC- BY), i.e. that that should be re-usable Demand proper citation by others who use your published data Publish a “data paper” (about your data) Promote/cite your data when appropriate Seek collaboration and co-authoring of papers with data re-users Apply a license not impeding reuse (e.g. CC-BY, ODC- BY), i.e. that that should be re-usable Demand proper citation by others who use your published data Publish a “data paper” (about your data) Promote/cite your data when appropriate Seek collaboration and co-authoring of papers with data re-users

20 Issue of actual data re-use /1 No re-use, no citation, no recognition/rewards Few studies on re-use outside of science and engineering disciplines DIPIR project included archaeology (Faniel et al. 2013); main results: – Key importance of contextual information – Especially research design and data collection procedures – Other criteria: institutional background of data producers (training, reputation) good practices of the data repository No re-use, no citation, no recognition/rewards Few studies on re-use outside of science and engineering disciplines DIPIR project included archaeology (Faniel et al. 2013); main results: – Key importance of contextual information – Especially research design and data collection procedures – Other criteria: institutional background of data producers (training, reputation) good practices of the data repository

21 Issue of actual data re-use /2 Unclear level of actual re-use in the domain Will there be more re-use if more data is openly available? Re-use for what – comparative analysis, meta- analysis,…? Main forms of re-use in the domain: – Re-use of whole datasets as is? – Build a derivative dataset, incorporating data from others as well as own data? – Extract specific „data points“? Unclear level of actual re-use in the domain Will there be more re-use if more data is openly available? Re-use for what – comparative analysis, meta- analysis,…? Main forms of re-use in the domain: – Re-use of whole datasets as is? – Build a derivative dataset, incorporating data from others as well as own data? – Extract specific „data points“?

22 Takeaway points /1 Researchers as open data publishers and consumers – Publish open data to reap benefits – individually and as research community – Recognise colleagues who share data, cite their datasets properly Research institutions – Reward researchers who publish data – Change mind-sets by doing (not teethless mandates) – Offer skills development and support Data archives/repositories – Important research infrastructure – need sustained funding, but also demonstrate usage/impact – Make open data sharing as easy as possible Researchers as open data publishers and consumers – Publish open data to reap benefits – individually and as research community – Recognise colleagues who share data, cite their datasets properly Research institutions – Reward researchers who publish data – Change mind-sets by doing (not teethless mandates) – Offer skills development and support Data archives/repositories – Important research infrastructure – need sustained funding, but also demonstrate usage/impact – Make open data sharing as easy as possible

23 Takeaway points /2 Research funders – Open Data mandates should come with financial coverage of extra effort for open data publication All: It‘s not about data management (plans) to comply with policies It‘s about … – making published data part of the scholarly record – persistent, citable, rewarded – demonstrating tangible benefits of open data publication Research funders – Open Data mandates should come with financial coverage of extra effort for open data publication All: It‘s not about data management (plans) to comply with policies It‘s about … – making published data part of the scholarly record – persistent, citable, rewarded – demonstrating tangible benefits of open data publication

24 References and other literature /1 ADS – Archaeology Data Service, ARIADNE First report on users needs: D2.1, April 2014, infrastructure.eu/Resources/D2.1-First-report-on-users-needshttp://www.ariadne- infrastructure.eu/Resources/D2.1-First-report-on-users-needs Charles Beagrie Ltd.: Keeping Research Data Safe (KRDS) Benefits Framework, Borgman, C.L: Research Data: Who will share what, with whom, when, and why? Fifth China – North America Library Conference 2010, Beijing, 8-12 September 2010, CODATA - ICSTI Task Group on Data Citation Standards and Practices (2013): Out of cite, out of mind: The current state of practice, policy, and technology for the citation of data. In: Data Science Journal, vol.12, https://www.jstage.jst.go.jp/article/dsj/12/0/12_OSOM /_articlehttps://www.jstage.jst.go.jp/article/dsj/12/0/12_OSOM /_article Costas R., Meijer I., Zahedi Z. & Wouters P. (2013): The Value of Research Data. Metrics for datasets from a cultural and technical point of view. Center for Science and Technology Studies, Leiden University, Data Seal of Approval, DataCite, DataCite Metadata Schema for the Publication and Citation of Research Data, V3.0, July 2013, ADS – Archaeology Data Service, ARIADNE First report on users needs: D2.1, April 2014, infrastructure.eu/Resources/D2.1-First-report-on-users-needshttp://www.ariadne- infrastructure.eu/Resources/D2.1-First-report-on-users-needs Charles Beagrie Ltd.: Keeping Research Data Safe (KRDS) Benefits Framework, Borgman, C.L: Research Data: Who will share what, with whom, when, and why? Fifth China – North America Library Conference 2010, Beijing, 8-12 September 2010, CODATA - ICSTI Task Group on Data Citation Standards and Practices (2013): Out of cite, out of mind: The current state of practice, policy, and technology for the citation of data. In: Data Science Journal, vol.12, https://www.jstage.jst.go.jp/article/dsj/12/0/12_OSOM /_articlehttps://www.jstage.jst.go.jp/article/dsj/12/0/12_OSOM /_article Costas R., Meijer I., Zahedi Z. & Wouters P. (2013): The Value of Research Data. Metrics for datasets from a cultural and technical point of view. Center for Science and Technology Studies, Leiden University, Data Seal of Approval, DataCite, DataCite Metadata Schema for the Publication and Citation of Research Data, V3.0, July 2013,

25 References and other literature /2 Data Curation Profiles – Witt M., Carlson J., Brandt D.S & Cragin M.H. (2009): Constructing Data Curation Profiles. In: International Journal of Digital Curation, Vol. 4, No. 3, 2009, Data Curation Profiles – website, Digital Object Identifier (DOI), DIPIR - Dissemination Information Packages for Information Reuse, DRYAD, EC – European Commission: Online survey on scientific information in the digital age, Brussels, 2012, scientific-information-digital-age_en.pdfhttp://ec.europa.eu/research/science-society/document_library/pdf_06/survey-on- scientific-information-digital-age_en.pdf EC Communication: Open data. An engine for innovation, growth and transparent governance ( ), mmunication/en.pdf mmunication/en.pdf EC Communication: Towards better access to scientific information: Boosting the benefits of public investments in research ( ), society/document_library/pdf_06/era-communication-towards-better-access-to-scientific- information_en.pdfhttp://ec.europa.eu/research/science- society/document_library/pdf_06/era-communication-towards-better-access-to-scientific- information_en.pdf EDNA – e-depot Nederlandse archeologie, Data Curation Profiles – Witt M., Carlson J., Brandt D.S & Cragin M.H. (2009): Constructing Data Curation Profiles. In: International Journal of Digital Curation, Vol. 4, No. 3, 2009, Data Curation Profiles – website, Digital Object Identifier (DOI), DIPIR - Dissemination Information Packages for Information Reuse, DRYAD, EC – European Commission: Online survey on scientific information in the digital age, Brussels, 2012, scientific-information-digital-age_en.pdfhttp://ec.europa.eu/research/science-society/document_library/pdf_06/survey-on- scientific-information-digital-age_en.pdf EC Communication: Open data. An engine for innovation, growth and transparent governance ( ), mmunication/en.pdf mmunication/en.pdf EC Communication: Towards better access to scientific information: Boosting the benefits of public investments in research ( ), society/document_library/pdf_06/era-communication-towards-better-access-to-scientific- information_en.pdfhttp://ec.europa.eu/research/science- society/document_library/pdf_06/era-communication-towards-better-access-to-scientific- information_en.pdf EDNA – e-depot Nederlandse archeologie,

26 References and other literature /3 European High-level Expert Group on Scientific Data (2010): Riding the wave. How Europe can gain from the rising tide of scientific data. A submission to the European Commission, October 2010, Faniel I., Kansa E., Whitcher-Kansa S., Barrera-Gomez J. & Yakel E. (2013): The Challenges of Digging Data: A Study of Context in Archaeological Data Reuse. JCDL 2013 Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, (preprint), Force 11: Joint Declaration of Data Citation Principles, https://www.force11.org/datacitation/https://www.force11.org/datacitation/ Gibney E. & Van Noorden R. (2013): Scientists losing data at a rapid rate. Nature, , Goodman A., Pepe A., Blocker A. W. et al. (2014): Ten Simple Rules for the Care and Feeding of Scientific Data. In: PLoS Computational Biology 10(4): e , Harley, Diane et al. (2010b): Assessing the Future Landscape of Scholarly Communication: An Exploration of Faculty Values and Needs in Seven Disciplines. – Archaeology Case Study. University of California Berkeley, Hedstrom M., Niu J., & Marz K. (2008): Incentives for data producers to create “archive/ready” data: Implications for archives and records management. Proceedings from the Society of American Archivists Research Forum. San Francisco, CA: SAA, ResearchPaper-2008.pdf ResearchPaper-2008.pdf European High-level Expert Group on Scientific Data (2010): Riding the wave. How Europe can gain from the rising tide of scientific data. A submission to the European Commission, October 2010, Faniel I., Kansa E., Whitcher-Kansa S., Barrera-Gomez J. & Yakel E. (2013): The Challenges of Digging Data: A Study of Context in Archaeological Data Reuse. JCDL 2013 Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, (preprint), Force 11: Joint Declaration of Data Citation Principles, https://www.force11.org/datacitation/https://www.force11.org/datacitation/ Gibney E. & Van Noorden R. (2013): Scientists losing data at a rapid rate. Nature, , Goodman A., Pepe A., Blocker A. W. et al. (2014): Ten Simple Rules for the Care and Feeding of Scientific Data. In: PLoS Computational Biology 10(4): e , Harley, Diane et al. (2010b): Assessing the Future Landscape of Scholarly Communication: An Exploration of Faculty Values and Needs in Seven Disciplines. – Archaeology Case Study. University of California Berkeley, Hedstrom M., Niu J., & Marz K. (2008): Incentives for data producers to create “archive/ready” data: Implications for archives and records management. Proceedings from the Society of American Archivists Research Forum. San Francisco, CA: SAA, ResearchPaper-2008.pdf ResearchPaper-2008.pdf

27 References and other literature /4 Heidorn, P.B: Shedding Light on the Dark Data in the Long Tail of Science. Library Trends 57(2), 2008, IANUS - Research Data Centre for Archaeology and Ancient Studies (Germany), Knowledge Exchange (2013): "Making Data Count: Research data availability and research assessment", workshop, Berlin, Internet Archaeology: Data Papers, Journal of Open Archaeology Data, JISC / Curtis G., Hammond M. & Oppenheim C. (2013): Access to Citation Data: Cost-benefit and Risk Review and Forward Look. JISC, September 2013, Key Perspectives (2010): Data Dimensions: Disciplinary Differences in Research Data Sharing, Reuse and Long Term Viability. Digital Curation Center, Kvalheim V. & Kvamme T. (2014): Policies for Sharing Research Data in Social Sciences and Humanities. A survey about research funders’ data policies. International Federation of Data Organisations (IFDO), sharinghttp://www.ada.edu.au/documents/ifdo-report-on-policies-for-data- sharing Lawrence B., Jones C., Matthews B., Pepler S. & Callaghan S. (2011): Citation and peer review of data: Moving towards formal data publication. International Journal of Digital Curation, 6(2): 4-37, Heidorn, P.B: Shedding Light on the Dark Data in the Long Tail of Science. Library Trends 57(2), 2008, IANUS - Research Data Centre for Archaeology and Ancient Studies (Germany), Knowledge Exchange (2013): "Making Data Count: Research data availability and research assessment", workshop, Berlin, Internet Archaeology: Data Papers, Journal of Open Archaeology Data, JISC / Curtis G., Hammond M. & Oppenheim C. (2013): Access to Citation Data: Cost-benefit and Risk Review and Forward Look. JISC, September 2013, Key Perspectives (2010): Data Dimensions: Disciplinary Differences in Research Data Sharing, Reuse and Long Term Viability. Digital Curation Center, Kvalheim V. & Kvamme T. (2014): Policies for Sharing Research Data in Social Sciences and Humanities. A survey about research funders’ data policies. International Federation of Data Organisations (IFDO), sharinghttp://www.ada.edu.au/documents/ifdo-report-on-policies-for-data- sharing Lawrence B., Jones C., Matthews B., Pepler S. & Callaghan S. (2011): Citation and peer review of data: Moving towards formal data publication. International Journal of Digital Curation, 6(2): 4-37,

28 References and other literature /5 MAPPA Open Data (University of Pisa, Italy), OECD: Declaration on Access to Research Data from Public Funding ( ), Book=False Book=False OECD: Principles and Guidelines for Access to Research Data from Public Funding (2007), Open Context (Alexandria Archive Institute, USA), Opportunities for Data Exchange (ODE) project / Kotarski R. et al. (2012). Report on best practices for citability of data and on evolving roles in scholarly communication, projects/ode/outputs/ projects/ode/outputs/ PARSE.Insight: Insight into digital preservation of research output in Europe. Project deliverable D3.4: Survey Report, 9 December 2009, insight.eu/downloads/PARSE-Insight_D3-4_SurveyReport_final_hq.pdfhttp://www.parse- insight.eu/downloads/PARSE-Insight_D3-4_SurveyReport_final_hq.pdf Parsons M.A. & Fox P.A. (2013): Is data publication the right metaphor? In: Data Science Journal, vol.12, February 2013, https://www.jstage.jst.go.jp/article/dsj/12/0/12_WDS- 042/_pdfhttps://www.jstage.jst.go.jp/article/dsj/12/0/12_WDS- 042/_pdf Piwowar H.A. & Vision T.J. (2013): Data reuse and the open data citation advantage. In: PeerJ, 1:e175 MAPPA Open Data (University of Pisa, Italy), OECD: Declaration on Access to Research Data from Public Funding ( ), Book=False Book=False OECD: Principles and Guidelines for Access to Research Data from Public Funding (2007), Open Context (Alexandria Archive Institute, USA), Opportunities for Data Exchange (ODE) project / Kotarski R. et al. (2012). Report on best practices for citability of data and on evolving roles in scholarly communication, projects/ode/outputs/ projects/ode/outputs/ PARSE.Insight: Insight into digital preservation of research output in Europe. Project deliverable D3.4: Survey Report, 9 December 2009, insight.eu/downloads/PARSE-Insight_D3-4_SurveyReport_final_hq.pdfhttp://www.parse- insight.eu/downloads/PARSE-Insight_D3-4_SurveyReport_final_hq.pdf Parsons M.A. & Fox P.A. (2013): Is data publication the right metaphor? In: Data Science Journal, vol.12, February 2013, https://www.jstage.jst.go.jp/article/dsj/12/0/12_WDS- 042/_pdfhttps://www.jstage.jst.go.jp/article/dsj/12/0/12_WDS- 042/_pdf Piwowar H.A. & Vision T.J. (2013): Data reuse and the open data citation advantage. In: PeerJ, 1:e175

29 References and other literature /6 Pryor, Graham (2009): Multi-scale Data Sharing in the Life Sciences: Some Lessons for Policy Makers. International Journal of Digital Curation, Vol. 4, No 3, Research Data Alliance, https://www.rd-alliance.orghttps://www.rd-alliance.org RIN - Research Information Network & British Library (2010): Patterns of information use and exchange: case studies of researchers in the life sciences, work/using-and-accessing-information-resources/patterns-information-use-and-exchange- case-studiehttp://www.rin.ac.uk/our- work/using-and-accessing-information-resources/patterns-information-use-and-exchange- case-studie RIN - Research Information Network & Key Perspectives (2008): To Share or not to Share: Publication and Quality Assurance of Research Data Outputs. Main Report and Annex. RIN: London, share-research-data-outputshttp://www.rin.ac.uk/our-work/data-management-and-curation/share-or-not- share-research-data-outputs RIN & NESTA - National Endowment for Science, Technology, and the Arts (2010): Open to All? Case Studies of Openness in Research. Report prepared by the Digital Curation Centre. London: RIN, science-case-studieshttp://www.rin.ac.uk/our-work/data-management-and-curation/open- science-case-studies Science magazine: Science Staff introduction to the Special Issue “Dealing with Data”, Science, Vol. 331 no. 6018, 11 February 2011, pp , tDAR - The Digital Archaeological Record (Digital Antiquity consortium, USA), Pryor, Graham (2009): Multi-scale Data Sharing in the Life Sciences: Some Lessons for Policy Makers. International Journal of Digital Curation, Vol. 4, No 3, Research Data Alliance, https://www.rd-alliance.orghttps://www.rd-alliance.org RIN - Research Information Network & British Library (2010): Patterns of information use and exchange: case studies of researchers in the life sciences, work/using-and-accessing-information-resources/patterns-information-use-and-exchange- case-studiehttp://www.rin.ac.uk/our- work/using-and-accessing-information-resources/patterns-information-use-and-exchange- case-studie RIN - Research Information Network & Key Perspectives (2008): To Share or not to Share: Publication and Quality Assurance of Research Data Outputs. Main Report and Annex. RIN: London, share-research-data-outputshttp://www.rin.ac.uk/our-work/data-management-and-curation/share-or-not- share-research-data-outputs RIN & NESTA - National Endowment for Science, Technology, and the Arts (2010): Open to All? Case Studies of Openness in Research. Report prepared by the Digital Curation Centre. London: RIN, science-case-studieshttp://www.rin.ac.uk/our-work/data-management-and-curation/open- science-case-studies Science magazine: Science Staff introduction to the Special Issue “Dealing with Data”, Science, Vol. 331 no. 6018, 11 February 2011, pp , tDAR - The Digital Archaeological Record (Digital Antiquity consortium, USA),

30 References and other literature /7 Tenopir C., Allard S., Douglass K. et al. (2011): Data Sharing by Scientists: Practices and Perceptions. PLoS ONE 6(6): e21101, The Royal Society, Science as an Open Enterprise, June 2012, Thessen, A.E & Patterson, D.J (2011) Data issues in the life sciences. In: ZooKeys 150: 15–51, Uhlir, Paul F. (ed.): For attribution: Developing scientific data attribution and citation practices and standards: Summary of an international workshop. Washington, D.C.: National Academies Press, 2012, Vines T.H., Andrew R.L., Bock D.G. et al. (2013): Mandated data archiving greatly improves access to research data. FASEB Journal, 27(4), Wright S.J., Kozlowski W.A., Dietrich D. et al. (2013): Using Data Curation Profiles to Design the Datastar Dataset Registry. D-Lib Magazine, Volume 19, Number 7/8, July/August 2013, zenodo, (CERN, related to OpenAIRE)http://www.zenodo.org Tenopir C., Allard S., Douglass K. et al. (2011): Data Sharing by Scientists: Practices and Perceptions. PLoS ONE 6(6): e21101, The Royal Society, Science as an Open Enterprise, June 2012, Thessen, A.E & Patterson, D.J (2011) Data issues in the life sciences. In: ZooKeys 150: 15–51, Uhlir, Paul F. (ed.): For attribution: Developing scientific data attribution and citation practices and standards: Summary of an international workshop. Washington, D.C.: National Academies Press, 2012, Vines T.H., Andrew R.L., Bock D.G. et al. (2013): Mandated data archiving greatly improves access to research data. FASEB Journal, 27(4), Wright S.J., Kozlowski W.A., Dietrich D. et al. (2013): Using Data Curation Profiles to Design the Datastar Dataset Registry. D-Lib Magazine, Volume 19, Number 7/8, July/August 2013, zenodo, (CERN, related to OpenAIRE)http://www.zenodo.org

31 Disclaimer ARIADNE is a project funded by the European Commission under the Community’s Seventh Framework Programme, contract no. FP7- INFRASTRUCTURES The views and opinions expressed in this presentation are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. ARIADNE is a project funded by the European Commission under the Community’s Seventh Framework Programme, contract no. FP7- INFRASTRUCTURES The views and opinions expressed in this presentation are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission.


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