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F. A . I . R The opinions expressed in this presentation are my - PowerPoint PPT Presentation

Mar 22, 2024 •424 likes •741 views

Barend Mons Biosemantics Group LUMC and EMC LS integrator Netherlands eScience Center Chair of DTL-data Head of ELIXIR node NL EC member of Open PHACTS Chair of High Level Expert Group EOSC F. A . I . R The opinions expressed in this

  1. Barend Mons Biosemantics Group LUMC and EMC LS integrator Netherlands eScience Center Chair of DTL-data Head of ELIXIR node NL EC member of Open PHACTS Chair of High Level Expert Group EOSC F. A . I . R The opinions expressed in this presentation are my personal opinions and do not necessarily reflect the draft report of the High Level Expert Group for the European Open Science Cloud.

  2. Cloud https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/2054-infradev-04-2016.html y a o d t e n c e e r f o n C s s e P r

  4. https://vimeo.com/162062013

  5. First GO-FAIR lab in DTL (Health-RI) (2016-17) First (sister) GO-FAIR lab in SD Stated aim to create GO- (NIH/NHS/NSF) (2016) FAIR labs in other MS (DE, SWE, UK, FI, CZ) 2017 Stated aim to create Second (sister) FOIL in GO-FAIR labs in other Sao Paulo (Scielo)(2016) domains (2017-20) Third (sister) GO-FAIR lab in Arusha (2017)

  6. https://vimeo.com/162062013

  7. Science1.0 Data loss is real and significant, while data growth is staggering Nature news, 19 December 2013 • Computer speed and storage capacity is doubling every 18 months and this rate is steady • DNA sequence data is doubling every 6-8 months over the last 3 years and looks to continue for this decade ‘Oops, that link was the laptop of my PhD student’

  8. 90% of world's data generated over last two years ( http://www.sciencedaily.com/releases/2013/05/130522085217.htm ) US$28B/year (50%) spent on preclinical research is not reproducible Freedman et al. http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165 Only 12% of NIH funded datasets are demonstrably deposited in recognized repositories. So: approximately 200,000 to 235,000 invisible datasets cannot be effectively reused Read et al. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132735 What about US? (as in us, MS)?

  9. The Data Stewardship Cycle 5% 9

  10. something to refer to http://www.nature.com/articles/sdata201618

  11. Cloud European Open Science Cloud

  12. Cloud European Open Science Cloud

  14. Strong stakeholder support o Public consultation and validation workshops on Open Science (July-December 2014); o Final report on Open Science (February 2015); o HLEG EOSC stakeholder workshop (30 November 2015); o DSM Consultation on platforms, data and cloud (closed January 2016); o Research funders' workshop (15 March 2016). + PC meetings, EAG meetings, e-IRG meetings, concertation meetings, info days, conferences, events, ...

  15. Cloud Community emerging standards EOSC stop driving on the left

  16. Cloud EOSC: Framing Trusted access to services & systems Re-use of shared data Across disciplinary, social and geographical borders Federated environment, across Member States

  17. Cloud EOSC: ‘Internet approach’ Minimal international guidance and governance Maximum freedom to implement. Globally interoperable and accessible Globally embedded in a ‘Commons’

  18. Cloud EOSC: Scope Human expertise Core resources Standards, Best Practices Underpinning technical infrastructures A web of Data and Services

  19. Cloud EOSC: Supports Open Science Open Innovation Systematic and professional data management Long term data stewardship

  20. Cloud EOSC: Challenges and Observations The majority of the challenges are social rather than technical Not just t he size of data, but in particular complex data and analytics across domains . Shortage of data experts globally and in the European Union Archaic system of rewards and funding of science and innovation ‘ Valley of death ’ between (e-)infrastructure providers and domain specialists. Short funding cycles of core research infrastructures are not fit for purpose Fragmentation between domains causes repetitive and isolated solutions Distributed data sets increasingly do not move ( size & privacy reasons) Centralised HPC is insufficient to support distributed meta-analysis and learning . However, the major components for a first generation EOSC are largely ‘there’ But ‘ lost in fragmentation ’ and spread over 28 Member States.

  21. Cloud EOSC: Key requirements New modes [4] of scholarly communication Modern reward and recognition practices [3] need to support data sharing and re-use Innovative , fit for purpose funding schemes for sustainable underpinning infrastructures Core data experts [7] need to be trained and their career perspective significantly improved Cross-disciplinary collaboration-specific measures for review, funding and infrastructure Support for the transition from scientific insights towards societal innovation [8] The EOSC needs to be developed as an eco-system of infrastructures [2] Key Performance Indicators should be developed for the EOSC [2] The EOSC should enable automation of data processing: machine actionability [1]is key. FAIR principles [1, 6] (http://www.nature.com/articles/sdata201618) Research Integrity [6]

  22. Cloud EOSC: Policy Recommendations P1: Take immediate, affirmative action in close concert with Member States P2: Close discussions about the ‘perceived need’ P3: Build on existing capacity and expertise where possible P4: Frame the EOSC as supporting Internet based protocols & applications

  23. Cloud EOSC: Governance Recommendations G1: Aim at the lightest possible, internationally effective governance G2: Guidance only where guidance is due G3: Define Rules of Engagement for formal participation in the EOSC G4: Federate the Gems across Member States 


  24. Cloud EOSC: Implementation Recommendations I1: Turn this report into an EC approved document to guide EOSC initiative I2: Develop, Endorse and implement a Rules of Engagement scheme I3: Fund a concentrated effort to locate and develop Data Expertise in Europe I4: Install a highly innovative guided funding scheme for the preparatory phase I5: Make adequate data stewardship mandatory for all research proposals I6: Install an executive team to deal with international coherence of the EOSC I7: Install an executive team to deal with the preparatory phase of the EOSC

  25. Cloud EOSC: 7 Immediate actions based on feed back II1: Publish the report (final draft available) II2: Develop, Pilot and implement a Rules of Engagement scheme II3: Detail the transition and sustainability model (and pilot it) II4: Train the data experts to bridge between ‘e-INFRA’ and ‘ESFRI’ II5: Assist data stewardship planning and exec. tools for all researchers II6 Develop the plan for what ‘ minimal essential governance ’ means in practice II7 Federate Interoperability standards and best practices (key RDA-role)

  26. II7 II5 II3 II3 II3 II5 II3 II2 II3 II2 II3 II4 II4 II5 II4 II5 II7 II7 executive group(s) II3 Impactstory 
 II6 FDG’s RDA ORCID, FORCE 11 deliverables

  27. endorsed Repository EOSC Approves DS Plan before submission of grant Data Publisher In DS plan Review panel (DS = Tickbox) HPA service Monthly invoice to Cloud Coin Authority? core resource Cloud Coin Authority FUNDER

  28. GO-FAIR-

  29. FAIR Applications Workflows & e-Infra providers FOILS FAIR exchange FAIR Other Peoples’ Research Data Data

  30. Open Science Open Access FAIR data

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