FAIR Data Interim Report and Action Plan
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The document is an interim report from the European Commission Expert Group on FAIR Data. It provides recommendations and an action plan to make data FAIR (Findable, Accessible, Interoperable, Re-usable). The report defines key concepts of FAIR, recommends developing standards and components like identifiers, metadata and repositories to create a sustainable FAIR data ecosystem. It also recommends ensuring FAIR data and services, embedding a culture of FAIR practices, and developing metrics to assess progress. The action plan outlines next steps like consulting stakeholders on the recommendations and revising the report.
FAIR Data Interim Report and Action Plan
- 1. Turning FAIR Data into Reality Interim Report and Action Plan EOSC Summit 2018 European Commission Expert Group on FAIR Data Sarah Jones, Rapporteur Digital Curation Centre sarah.jones@glasgow.ac.uk @sjDCC Simon Hodson, Chair CODATA simon@codata.org @simonhodson99
- 2. Report framework 1. Concepts – why FAIR? 2. Creating a culture of FAIR data 3. Creating a technical ecosystem for FAIR data 4. Skills and capacity building 5. Measuring change 6. Funding and sustaining FAIR data 7. FAIR Data Action Plan
- 3. Primary recommendations and actions Step 1: Define and apply FAIR appropriately Step 2: Develop and support a sustainable FAIR data ecosystem Step 3: Ensure FAIR data and certified services to support FAIR Step 4: Embed a culture of FAIR in research practice
- 4. 1. Definitions of FAIR: FAIR is not limited to its four constituent elements: it must also comprise appropriate openness, the assessability of data, long- term stewardship, and other relevant features. To make FAIR data a reality, it is necessary to incorporate these concepts into the definition of FAIR. 2. Mandates and boundaries for Open: The Open Data mandate for publicly funded research should be made explicit in all policy. It is important that the maxim ‘as Open as possible, as closed as necessary’ be applied proportionately with genuine best efforts to share. Step 1: Define and apply FAIR appropriately
- 5. FAIR Data Objects DATA The core bits IDENTIFIERS Persistent and unique (PIDs) STANDARDS & CODE Open, documented formats METADATA Contextual documentation 3. A model for FAIR Data Objects: Implementing FAIR requires a model for FAIR Data Objects which by definition have a PID linked to different types of essential metadata, including provenance and licencing. The use of community standards and sharing of code is also fundamental for interoperability and reuse.
- 6. Standards Skills Rewards Metrics Investment FAIR Data Objects stored in Trusted repositories & Cloud Services Data policies People: researchers, funders, publishers, data stewards… PIDs Define & regulate Provide hub of info on FAIR data objects Assigned to Create & use FAIR components Motivated by outside drivers DMP Step 2: Develop and support a sustainable FAIR data ecosystem 4. Components of a FAIR data ecosystem: The realisation of FAIR data relies on, at minimum, the following essential components: policies, DMPs, identifiers, standards and repositories. There need to be registries cataloguing each component of the ecosystem and automated workflows between them. Supported by cultural aspects: skills, metrics, rewards, investment.
- 7. Primary recommendations and actions Step 1: Define and apply FAIR appropriately Rec. 1: Definitions of FAIR Rec. 2: Mandates and boundaries for Open Rec. 3: A model for FAIR Data Objects Step 2: Develop and support a sustainable FAIR data ecosystem Rec. 4: Components of a FAIR data ecosystem Rec. 5: Sustainable funding for FAIR components Rec. 6: Strategic and evidence-based funding Step 3: Step 3: Ensure FAIR data and certified services to support FAIR Rec. 7: Disciplinary interoperability frameworks Rec. 8: Cross-disciplinary FAIRness Rec. 9: Develop robust FAIR data metrics Rec. 10: Trusted Digital Repositories Rec. 11: Develop metrics to assess and certify data services Step 4: Embed a culture of FAIR in research practice Rec. 12: Data Management via DMPs Rec. 13: Professionalise data science and stewardship roles Rec. 14: Recognise and reward FAIR data and FAIR stewardship
- 8. FAIR Data EG: Timescale June Interim report due early June Launch at EOSC Summit on 11th June in Brussels June - August Consultation period to 5 August Workshop arranged for EOSC Summit Webinars for consultation and online feedback August - October Revision of interim Action Plan Refinement and focussing of report. November Final report and FAIR Data Action Plan due Official launch and formal communications at Austrian Presidency event in Vienna
- 9. Next steps… 9 • Interim FAIR Data Action Plan: https://doi.org/10.5281/zenodo.1285290 • Interim FAIR Data Report: https://doi.org/10.5281/zenodo.1285272 • Comment on Report: http://bit.ly/interim_FAIR_report • Comment on Action Plan: https://github.com/FAIR-Data-EG/Action-Plan Groups for Afternoon Session • Groups by Name: http://bit.ly/FAIR-Groups-Name • Groups by Group: http://bit.ly/FAIR-Groups-Groups
- 10. Simon Hodson, CODATA Chair of FAIR Data EG Rūta Petrauskaité, Vytautas Magnus University Peter Wittenburg, Max Planck Computing & Data Facility Sarah Jones, Digital Curation Centre (DCC), Rapporteur Daniel Mietchen, Data Science Institute, University of Virginia Françoise Genova, Observatoire Astronomique de Strasbourg Leif Laaksonen, CSC- IT Centre for Science Natalie Harrower, Digital Repository of Ireland – year 2 only Sandra Collins, National Library of Ireland – year 1 only FAIR Data EG: membership
- 11. Thank you! Questions? Sarah Jones, Rapporteur Digital Curation Centre sarah.jones@glasgow.ac.uk @sjDCC Simon Hodson, Chair CODATA simon@codata.org @simonhodson99