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Are we FAIR yet?

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The FAIR Principles propose key characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by people and machines. The Principles act as a guide that researchers should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”. This talk will elaborate on what FAIR is, why we need it, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.

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Are we FAIR yet?

  1. 1. 1 Michel Dumontier, Ph.D. Distinguished Professor of Data Science Director, Institute of Data Science @micheldumontier::RDA:2018-01-31
  2. 2. An increasing number of discoveries are made using other people’s data @micheldumontier::RDA:2018-01-312
  3. 3. 3 A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation Khatri et al. JEM. 210 (11): 2205 DOI: 10.1084/jem.20122709 @micheldumontier::RDA:2018-01-31 1. CRM genes correlated with the extent of graft injury and predicted future injury to a graft 2. Mice treated with drugs against the CRM genes extended graft survival
  4. 4. However, significant effort was needed to find the right datasets, put them together, and use them @micheldumontier::RDA:2018-01-314
  5. 5. @micheldumontier::RDA:2018-01-315
  6. 6. If we are ever to realize the full potential of content we create then we must find ways to reduce the barrier to (automatically) find and reuse that content @micheldumontier::RDA:2018-01-316
  7. 7. To achieve this objective we must build a social and technological infrastructure for the discovery and assessment of digital resources @micheldumontier::RDA:2018-01-317
  8. 8. Principles to enhance the value of all digital resources data, images, software, web services, repositories,… Developed and endorsed by researchers, publishers, funding agencies, industry partners. @micheldumontier::RDA:2018-01-318
  9. 9. @micheldumontier::RDA:2018-01-319 http://www.nature.com/articles/sdata201618 Dec 2017
  10. 10. Rapid Adoption of Principles @micheldumontier::RDA:2018-01-3110
  11. 11. @micheldumontier::RDA:2018-01-3111 4 Principles (F,A,I,R) and 15 sub-principles.
  12. 12. FAIR Principles - summarized Findable • Globally unique, resolvable, and persistent identifiers • Machine-readable descriptions to support structured search Accessible • Clearly defined access and security protocols • Metadata is always accessible beyond the lifetime of the digital resource Interoperable • Extensible machine interpretable formats for data + metadata • Vocabularies themselves must be FAIR • Linked to other resources Reusable • Provide licensing, provenance, and use community-standards @micheldumontier::RDA:2018-01-3112
  13. 13. FAIR Principles are FAIR: published as a Trusty Nanopublication in the nanopub server network @micheldumontier::RDA:2018-01-3113 http://purl.org/fair-ontology#FAIR
  14. 14. Improving the FAIRness of digital resources will increase their quality and their potential for reuse. @micheldumontier::RDA:2018-01-3114
  15. 15. What is FAIRness? FAIRness reflects the extent to which a digital resource addresses the FAIR principles as per the expectations defined by a community. @micheldumontier::RDA:2018-01-3115
  16. 16. How it might look at DANS @micheldumontier::RDA:2018-01-3116
  17. 17. Measuring FAIRness • A metric is a standard of measurement. • It must provide clear definition of what is being measured, why one wants to measure it. • It must describe the process by which you obtain a valid measurement result, so that it can be reproduced by others. It needs to specify what a valid result is. @micheldumontier::RDA:2018-01-3117
  18. 18. Qualities of a Good Metric • Clear: anyone can understand the purpose of the metric • Realistic: compliance should not be unduly complicated • Discriminating: the measure can distinguish between those that meet and those that do not meet the objective • Measurable: the assessment can be made in an objective, quantitative, machine-interpretable, scalable and reproducible manner • Universal: The metric should be applicable to all digital resources @micheldumontier::RDA:2018-01-3118
  19. 19. • 14 universal metrics covering each of the FAIR sub-principles. • The metrics demand evidence from the community, some of which may require specific new actions. • Digital resource providers must provide a web-accessible document with machine-readable metadata (FM-F2, FM-F3), detail identifier management (FM-F1B), metadata longevity (FM-A2), and any additional authorization procedures (FM-A1.2). • They must ensure the public registration of their identifier schemes (FM- F1A), (secure) access protocols (FM-A1.1), knowledge representation languages (FM-I1), licenses (FM-R1.1), provenance specifications (FM- R1.2). • They must provide evidence of ability to find the digital resource in search results (FM-F4), linking to other resources (FM-I3), FAIRness of linked resources (FM-I2), and meeting community standards (FM-R1.3) @micheldumontier::RDA:2018-01-3119
  20. 20. @micheldumontier::RDA:2018-01-3120
  21. 21. @micheldumontier::RDA:2018-01-3121 http://www.w3.org/TR/hcls-dataset/ Evidence: standard is also registered in FAIRsharing https://fairsharing.org
  22. 22. smartAPI @micheldumontier::RDA:2018-01-3122 http://smart-api.info
  23. 23. @micheldumontier::RDA:2018-01-3123
  24. 24. @micheldumontier::RDA:2018-01-3124
  25. 25. Availability of Metrics • The current metrics are available for public discussion at the FAIR Metrics GitHub, with suggestions and comments being made through the GitHub comment submission system (https://github.com/FAIRMetrics). • They are represented as i) nanopublications and ii) latex and iii) PDF documents • They are free to use for any purpose under the CC0 license. • Versioned releases will be made to Zenodo as the metrics evolve, with the first release already available for download @micheldumontier::RDA:2018-01-3125
  26. 26. @micheldumontier::RDA:2018-01-3126
  27. 27. @micheldumontier::RDA:2018-01-3127
  28. 28. @micheldumontier::RDA:2018-01-3128
  29. 29. @micheldumontier::RDA:2018-01-3129
  30. 30. Next steps • Open development of universal & resource-specific metrics (stay tuned) • Development of shared infrastructure to support metric-based FAIR assessments • Applications to create and publish FAIR assessments • Development of training, and support for implementation and adoption. • Measuring the impact of FAIR for research and innovation @micheldumontier::RDA:2018-01-3130
  31. 31. Acknowledgements @micheldumontier::RDA:2018-01-3131 FAIR FAIR metrics Myles Axton, Jennifer Boyd, Helena Cousijn, Scott Edmunds, Emma Ganley, Andrew Hufton, Rebecca Lawrence, Thomas Lemberger, Varsha Khodiyar, Robert Kiley, Michael Markie and Jonathan Tedds for their prospective on the metrics as journal editors and publishers, and their contribution to FAIRsharing RDA/Force 11 WG.
  32. 32. michel.dumontier@maastrichtuniversity.nl Website: http://maastrichtuniversity.nl/ids 32 @micheldumontier::RDA:2018-01-31 How are you contributing to the FAIR initiative?

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