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Collaborate to Share


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Presentation by Susan Reilly at RDA National event in Florence, Italy, November 2016

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Collaborate to Share

  1. 1. LIBER: Collaborate to Share RDA Florence, 14 November, 2016 Susan Reilly, Executive Director, LIBER
  2. 2. Overview  Open data in the LIBER vision  Benefits of open data • FAIR data • Supporting FAIR Data
  3. 3. LIBER is Europe’s largest research library network…
  4. 4. Mission Enable world class research… = Collaborative – Growth in collaboration from 13% (2003)- 17% (2011) = International – 40% of French & German research outputs a result of international collaboration – Rate of citation grows as geographic extent of collaboration increases =Interdisciplinary – Foundation of frontiers research =Data intensive • supports interdisciplinary exploration … and open
  5. 5. 2022 Libraries powering sustainable knowledge in the digital age… Vision
  6. 6. Vision • Open Access is the default • Research data is FAIR • Digital skills underpin open, transparent research lifecycle • Research infra is participatory and tailored to different disciplines • Cutural heritage build on today’s digital info 2022
  7. 7. Knowledge as a Public Good  Non rivalrous -sharing it doesn’t deplete it as a resource  Non excludable -it’s impossible stop the supply of knowledge  Copyright reconises this by only exerting control over the “expression of an idea” not the idea itself  In the digital age data can be infinately accessible
  8. 8. Benefits of Open Data For society Solves global challenges e.g. hunger, pollution For researchers: Data re-use, avoiding costly duplication Data re-use,facilitate complex interdisciplinary enquiry Validation of results – quality control For policy: Inform decision making For industry: In development of new products & services
  9. 9. Fake data!
  10. 10. Barriers  Cultural differences  Definition of research data  Lack of skills/education  Poorly defined roles and responsibilities  Lack of infrastructure  Lack of career incentives
  11. 11. European Member States Commitment  All member states to transition towards Open Science (council conclusion May 2016)  Open access the default by 2020  Research data from publically funded projects a public good  Data management standard scientific practice  DMPs obilgatory  Follow FAIR principles
  12. 12. EU Horizon 2020 Mandates  Open Access Mandatory (2015)  Open Data Pilot (7 funding areas, 2015)  Open Data pilot extended to all funding areas from 2017
  13. 13. H2020 Open Data Pilot  Opt out at any stage (1/3 opted out so far)  All research data, including metadata, needed to validate the results in a peer-reviewed publication  Other curated or raw data, and its associated metadata, specified in the DMP even if it did not result in a publication  Documentation, software, hardware or tools required to enable reuse of the data  DMP obilgatory
  14. 14.
  15. 15. The European Open Science Cloud  A virtual environment to store and process large volumes of information 
  16. 16. The Challenge Research data are the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form…
  17. 17. Research Data is… ODE data publication pyramid
  18. 18. Research Data is… Findable  Metadata  Persistent identifiers  Indexed in a searchable resource Accessible (openly)  Open and standardised communication protocols Interoperable  Shared language for knowledge representation Reusable  Clear provenance and licences  Detailed provenance
  19. 19. Supporting FAIR Data • Active – Offering and planning RDS service – Consultative (Discussion e.g. metadata, policy, training, outreach) – 38% provide tech support • On the horizon – 42% plannig tech support – 48% planning ID support – 43% planning metadata services – West and north more involved in discussions
  20. 20. Findable… Data management planning support (46%) Identifiers Support for citation and finding datasets Identification of datasets for repositories
  21. 21. Accessible… Consulting on data standards and methods Preparing datasets for deposit 25% Web guides Data storage 78%
  22. 22. Interoperable… Consulting on data standards and methods (44%) Partnering with researchers (32%) ID Datasets Collaborating with disciplinary departments Collaborting with other institutions and infra
  23. 23. Reusable… Policy development and planning (66%) Gudiance and training e.g. re copyright (54%) Tools for data analysis (23%)
  24. 24. Regional differences in consultative RDS availability
  25. 25. Regional differences in technical RDS availability
  26. 26. Why collaborate? • No one size fits all approach (work across disciplines) • Need to work across services (libraries, IT, research) • Need to work across infrastructures • Potential for interdiciplinary research • Shared responsibility!
  27. 27. Ways to collaborate • Get involved in the RDA – Libraries in RDM Interest Group – Repositories Interest Group – …start a group! – Start a discussion
  28. 28. Roadmap RDM!Roadmap RDM!  Policies  Costs  Infrastructure  Skills  Advocacy and engagement Barcelona, 26 Jan 2017! Self assessment Dialogue RDM Templates
  29. 29. ARE YOU WITH US? @skreilly Thank you!