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Trust and Accountability: experiences from the FAIRDOM Commons Initiative.

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Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session.
In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers.
The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme.
2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes.
In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust.
[1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
[2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032

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Trust and Accountability: experiences from the FAIRDOM Commons Initiative.

  1. 1. Trust and Accountability: experiences from the FAIRDOM Commons Initiative. Professor Carole Goble, carole.goble@manchester.ac.uk The University of Manchester, UK The FAIRDOM Association Coordinator ELIXIR-UK Head of Node Co-lead ELIXIR Interoperability Platform Digital Life 2018, Bergen, Norway 21-22 March 2018
  2. 2. Open Science Open Data Reproducible Science Personally Productive Science
  3. 3. Norway’s Contribution
  4. 4. Systems and Synthetic Biology Projects Practically address (Open) Assets Management Support transparency, reproducibility, personal productivity In an ecosystem of platforms and an egosystem of research projects 10Year Anniversary!
  5. 5. Why? Programmes • Foster stewardship & skills • Stimulate sharing • Ensure retention • Capitalise on investments • Audit & Compliance • Respect global community, local project resources Synthetic Biology for Growth Programme
  6. 6. … FAIR model reuse and reproducibility … Stanford et alThe evolution of standards and data management practices in systems biology, Molecular Systems Biology (2015) 11: 851 DOI 10.15252/msb.20156053
  7. 7. 1. Ecosystem • public collections & archives • data centres • journals • Institutional repositories • most researchers • labs & universities • my resources *Meso too – but to complicated for 20 minutes! See http://www.knowledge-exchange.info/event/ke-approach-open-scholarship
  8. 8. 2. Egosystem • SME multi-disciplinary teams • Multi-site collaborations • Competing • Experimentalists, dry modellers • Self-deposit, self-curating • Variable stewardship skills
  9. 9. Capitalising on investments Retaining results post-project Pooling, transfer, sharing results Public collections Skilling workforce Compliance audit/metrics New publishable assets Business models Reproducibility Doing science with collaborators Publishing & getting credit Productivity Access to resources, results, collections Retention of my results post student Repeatability - reviewer wants more  Competitiveness, protecting assets Managing costs Compliance StakeholderAccountabilityValues overlaps, mismatches?
  10. 10. “The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, 160018 (2016) doi:10.1038/sdata.2016.18 Metadata Identifiers Access policies Licences Technical: Political Social Economic: Rally 1. Make everything FAIR
  11. 11. 2. Improve Knowledge Flow A FAIRDOM Commons & Catalogue • Draw together scattered resources, platforms, people • Coordination, collaboration • Reproducible, transparent [original figure: Josh Sommer] Commons Goble, De Roure, Bechhofer, Accelerating KnowledgeTurns, I3CK, 2013, isbn: 978-3-642-37186-8 “Resources collectively created, owned or shared and used between or among a community (with Governance)”
  12. 12. Fair-dom.org FAIRDOMHub.org
  13. 13. What is FAIRDOM? multi-institution collaboration, FAIRDOm Association e.V. Social SW Platforms Processes Stewardship Support Tech Solns & Support Public Commons FAIRDOMHub.org Policy, Advocacy Community Work 90+ Projects on Hub 30+ Private installations Standards
  14. 14. FAIRDOM Platform collecting metadata Web-based portal Project spaces Metadata catalogue Yellow pages Results repository Collaboration Archives Gateway Sharing Organisation Front end Projects Hub Entry Point On site Tracking Pipelines LIMS, Instruments Large data Samples Auto-archiving Back end Onsite storage & analytics
  15. 15. Two Flavours Trust vs Responsibility I’ll run my own – when the project ends can you host it for me … Service hosted at HITS InstitutionalGuarantee 2029
  16. 16. Projects, People, Assets • Project spaces – Upload or link to data • One place catalogue – Regardless of physical store – Standards-compliant metadata • Linked with other systems – Project repositories – Public deposition archives – Tools
  17. 17. Champions…
  18. 18. 20 Programme Overarching research theme (The Digital Salmon) Project Research grant (DigiSal, GenoSysFat) Investigation A particular biological process, phenomenon or thing (typically corresponds to [plans for] one or more closely related papers) Study Experiment whose design reflects a specific biological research question Assay Standardized measurement or diagnostic experiment using a specific protocol (applied to material from a study)Jon OlavVik, Norwegian University of Life Science Investigation Study Assay
  19. 19. Jon OlavVik, Norwegian University of Life Science Structured organisation Research objects &Versions Investigation Study Assay
  20. 20. Jon OlavVik, Norwegian University of Life Science Retain Context Upload or reference Over different (trusted?) resources Investigation Study Assay
  21. 21. FAIRDOM + NeLS Structure metadata Link to NeLS sample dataset
  22. 22. Schema Dublin core Datacite DCAT Bioschemas Catalogue Level Investigation Studies Assay/Analysis Content level Persistent Identifiers Content level subject thematic standards Content level Linked Metadata
  23. 23. Designed by PALs What methods are been used to determine enzyme activity? What SOP was used for this sample? Where is the validation data for this model? Is there any group generating kinetic data? Is this data available? Track versions of my model Whats the relationship between the data and model? Which data belong to which publications?
  24. 24. Transparent Publication 16 datafiles (kinetic, flux inhibition, runout) 19 models (kinetics, validation) 13 SOPs 3 studies (model analysis, construction, validation) 24 assays/analyses (simulations, model characterisations) Penkler, G., du Toit, F., Adams, W., Rautenbach, M., Palm, D. C., van Niekerk, D. D. and Snoep, J. L. (2015), Construction and validation of a detailed kinetic model of glycolysis in Plasmodium falciparum. FEBS J, 282: 1481–1511. doi:10.1111/febs.13237
  25. 25. Investigation Study Analysis Data Model SOP(Assay) https://fairdomhub.org/investigations/56
  26. 26. Citation, credit Living Entry Published Snapshot Entry
  27. 27. Process… People Credit Process Transparency
  28. 28. Reproducibility, Standards [Jacky Snoep, Dagmar Waltemath, Martin Peters, Martin Scharm]
  29. 29. NotVisible
  30. 30. Trust,Accountability TheTragedy of the Commons FAIR Play 1. resourcing 2. behaviours 3. adoption
  31. 31. 1. Resourcing • Software and data are free, like free puppies • Puppies are not a one-off cost “we want FAIR data but we will only fund research” The economics of data infrastructures needs new brave funding models …
  32. 32. 2. Behaviours Self-controlled spaces • enclave sharing • tribal sharing / reuse • models vs data • hoarding, flirting, voyeurs • dowries • “on date” publishing • gang rules & consortia pressures • credits • asymmetrical reciprocity
  33. 33. FAIR Play Sensitivities Open science applies to you but not me … not available = not citable Jurgen Hannstra Vrije Universiteit, Amsterdam Using FAIRDOM my own lab colleagues saw what I was doing and called to collaborate! • Licenses • Negotiated access • Embargos • Permission controls • Staged sharing • Private walled gardens
  34. 34. me ME my team close colleagues peers Staged Spiral – Data Lifecycle organisation – collaboration - dissemination The number of assets reduces The richness of metadata needed increases As reach of sharing increases Staged sharing
  35. 35. Tragedy of the Commons metadata & identifier quality https://ncip.nci.nih.gov/blog/face-new-tragedy-commons-remedy-better-metadata/ https://metadatacenter.org “The challenge for all the data-commons initiatives — is that many online datasets are annotated with metadata that are simply terrible…. Creating good metadata takes considerable work …. When investigators act in their own self- interest, taking short cuts to generate metadata as quickly as possible, we should expect that the overall utility of the resource will decline. The creation of a data commons requires the ability to deal with extremely varied — and often unanticipated — metadata patterns and data types …. a need for easy-to-use solutions that are generic to provide guidance over the entire life cycle of metadata — streamlining metadata creation, discovery, and access, as well as supporting metadata publication to third-party repositories” Mark Musen
  36. 36. TheTragedy of the Commons community socialisation Value Systems • of assets, of reproducibility, of metadata • economics of infrastructure • priorities • public vs personal good Sweatshops • collaborating but competing • burden - time, skills • short term, shortcuts • leadership sets the tone
  37. 37. rightfield.org.uk templates spreadsheets, notebooks seamless system join-ups automated metadata “Last / First Mile” “Born FAIR” “FAIR Ramps” “FAIR by Design” Knowledge Exchange Report: http://www.knowledge-exchange.info/event/ke-approach-open-scholarship The ‘last mile’ challenge for European research e-infrastructures https://doi.org/10.3897/rio.2.e9933 Semantic Annotation by Stealth
  38. 38. 3. Adoption - stewards, champions, PALs respected, embedded not tolerated, external • 500,000 stewards needed in Europe* • Specialist skills • Career pathways Curation and management • Supported, resourced • Recognised, rewarded • By the Projects, Programmes and PIs Building trust between FAIRDOM and the projects * Realising the Open European Science Cloud (2016)
  39. 39. Upstream Professionalism self-stewardship Maksim Zakhartsev Alexander Wentzel MOSES ExtremoPharm, ZucAt DigiSal GenoSysFat Fatemeh Zamanzad Ghavidel
  40. 40. Social Technology Process Stewardship Professionalisation Metadata Ramps Defeating Cultural Inertia Summary: Overcoming TheTragedy of the Commons Properly Resourcing Embedding
  41. 41. By Side Effect Commons Production Incentives
  43. 43. Systems and Synthetic Biology Projects Practically address (Open) Assets Management Support transparency, reproducibility, personal productivity In an ecosystem of platforms and an egosystem of research projects