Section outline

    • 1. Definition

      It’s recommended to respect the 4 FAIR principles in order to ensure an optimal use of research data and associated metadata, both by people and by machines.

      Findable, Accessible, Interoperable, Reusable
      By SangyaPundir — Personnal work, CC BY-SA 4.0

      FAIR data are those that are Findable, Accessible, Interoperable and Reusable.
      What do each of these terms mean in a practical sense and how can you tell if your own research data is FAIR?

    • 2. Explaining the FAIR Principles

      A FAIR data is a data...
      F - Findable

      Principle F is implemented through the use of persistent identifiers (for example: DOI), rich metadata, by listing in catalogs, in repositories...

      Principle A means implementing long-term storage of data and metadata, with facilitated access and/or download (standardized and open communication protocols), and specification of access and use conditions.

      Principle I means that the data is downloadable, usable, intelligible and combinable with other data, by humans and machines, through the use of standard formats, vocabularies and ontologies.

      Principle R relies on characteristics that make data reusable for future research or other purposes (teaching, innovation, reproduction/scientific transparency). This is made possible by a rich description that specifies the data provenance, the use of community standards, and the addition of licenses.


      The gradual adoption of these FAIR principles will make data easier to share and reusable by both humans and computer systems.


      Examples of implementation of FAIR principles

      Many recommended actions for the management and sharing of research data are fully or partially compliant with FAIR. Some examples are:

      As a researcher in Social and Human Sciences: I securely save and store my data throughout the project using SHAREDOCS and Huma-Num Box.
      I work in the field of ecology: I document the metadata associated with my data according to the EML (Ecological Metadata Language) standard.
      I organize and name my files in the same way as all project partners.
      As an archaeologist: I use a disciplinary controlled vocabulary, the PACTOLS thesaurus.

      I apply an Etalab license to my datasets.
      I deposit my genomics data in the GenBank data repository.
      My datasets are uniquely and persistently identified by a DOI.
      I communicate my source codes.
      I make my files available in .csv rather than .xls, that is, in an open and non-proprietary format.
      As an ethnologist: during my research project, I conducted interviews that have significant heritage value. I deposit my datasets in the CINES permanent archiving platform.


      These various actions contribute to making my data FAIR!

    • 3. Test your data

      Test your data with this checklist created by Sarah Jones & Marjan Grootveld, EUDAT (2017):

    • 4. Play with FAIR principles

      How do you think the FAIR principles benefits the researcher and the scientific community?

      Instructions: A researcher has produced data within a research project in accordance with FAIR principles. This offers immediate benefits within the framework of their project and career, but can also benefit the scientific community later. Place each card on one of the two zones identified "For the Researcher" and "For the Scientific Community".

    • External Resources


      Study Materials