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Presentatie Lianne Ippel

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Presentatie van het Datacongres ''data science voor maatschappelijke uitdagingen'' op 22 november 2018

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Presentatie Lianne Ippel

  1. 1. FAIRness of research (data) Lianne Ippel, PhD November, 22nd, 2018 Lianne.ippel@maastrichtuniversity.nl
  2. 2. Outline • What is FAIR? • Why is it important? • How to be FAIR
  3. 3. Lambin et al. Radiother Oncol. 2013. 109(1):159-64. doi: 10.1016/j.radonc.2013.07.007 Secondary use is nearly impossible ● Data are stored somewhere on a local drive ● Researchers don’t want to share their data ● Data are badly documented
  4. 4. Lambin et al. Radiother Oncol. 2013. 109(1):159-64. doi: 10.1016/j.radonc.2013.07.007
  5. 5. An international, bottom-up paradigm for the discovery and reuse of digital content by and for people and machines
  6. 6. Why FAIR? • Reproducibility of research • More efficient use of research products • Accelerate scientific discovery
  7. 7. Linking data The blind men and the elephant. Poem by John Godfrey Saxe Cartoon originally copyrighted by the authors; G. Renee Guzlas, artist)
  8. 8. FairHealth: Analyzing partitioned FAIR health data responsible This project is funded by: NWA Big Data route grant number: 400.17.605
  9. 9. How to be FAIR? 4 Principles (F,A,I,R) and 15 sub-principles. http://www.nature.com/articles/sdata201618
  10. 10. Uptake of FAIR principles • Funding agencies • Repositories (i.e., DANS) • Universities - e.g., data management agents
  11. 11. Note • FAIR ≠ open access/science • Legal/Ethical/Social issues: - GDPR (...obviously) - Ownership
  12. 12. @micheldumontier::IDS-TRAINING:2018-10-3012
  13. 13. Interested? • lianne.ippel@maastrichtuniversity.nl • researchgate: Lianne_Ippel • linkedin: lianneippel • github: /L-Ippel • info-ids@maastrichtuniversity.nl • facebook: @IDSatMU • LinkedIn: Maastricht University Institute of Data Science • twitter: UMIDS • github: /MaastrichtU-IDS

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