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Use this checklist to reflect on how well your data meet the FAIR principles: Findable, Accessible, Interoperable, and Reusable.

Findable

  • Does the dataset have a clear title and description?
  • Is rich metadata provided?
  • Are keywords, subject terms, and disciplinary categories included?
  • Has the dataset been assigned a persistent identifier, such as a DOI?
  • Is the dataset deposited in a searchable repository or catalogue?

Accessible

  • Can users access the data or metadata through a clear landing page?
  • Are access conditions clearly stated?
  • Is the licence or reuse condition visible?
  • If the data cannot be open, is the restriction explained?
  • Will metadata remain available even if access to the data is restricted?

Interoperable

  • Are open, standard, and machine-readable formats used where possible?
  • Are community standards, vocabularies, or ontologies used?
  • Are variables, units, methods, and relationships clearly documented?
  • Can the data be combined with other datasets or systems?
  • Are links provided to related publications, software, protocols, or datasets?

Reusable

  • Is the dataset accompanied by enough documentation for others to understand and reuse it?
  • Are methods, provenance, processing steps, and quality checks described?
  • Are versioning and file naming clear?
  • Is a clear licence applied?
  • Are ethical, legal, or confidentiality restrictions explained?

Jones, S. & Grootveld, M. (2017, November). How FAIR are your data? Zenodo. http://doi.org/10.5281/zenodo.1065991

 

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Key Message

FAIR does not always mean fully open. Data should be as open as possible and as closed as necessary, but the metadata, documentation, access conditions, and preservation plan should make the research output understandable and reusable over time.

 

 

FAIR HOW TO

Source: U.S. National Library of Medicine (NLM), Common Data Elements (CDE) Tutorial.