What is FAIR Data?

The FAIR principles are a fundamental part of open science, and describe some of the central guidelines to good data management and open access to research data.

 

FAIR

 

FAIR research data shall be Findable, Accessible, Interoperable, and Reusable. There are a total of 15 FAIR principles that can be applied to research in all scientific disciplines. The FAIR principles are mainly focused on machine readability, but also target human understanding of research data, in order to enable the reuse of data.

FAIR in Short

The FAIR principles were first published in 2016. Since then, they have been adopted by for instance the European Union and a large number of research funding organisations, universities, and research infrastructures. In Sweden, the Swedish Research Council and the National Library of Sweden have a government mandate to select criteria for how to evaluate to which extent scientific research data and publications meet with the FAIR principles.

A brief overview of what the FAIR principles mean for research data:

  • Findable research data:
    • they are assigned a unique and persistent identifier
    • they are described with rich and machine readable metadata
    • metadata specify the data identifier for the described data
    • (meta)data are searchable and easy to find on the Internet
       
  • Accessible research data:
    •  (meta)data can be retrieved by their identifier, read and accessed via a standardised communications protocol (e.g. http or ftp)
    • the communications protocol is open, free, and universally implementable
    • it is possible to create different user roles and mechanisms for user verification and controlled data access. The access to research data should be as open as possible and as restricted as necessary for sensitive data
    • metadata are accessible even after the data are no longer available
       
  • Interoperable research data:
    • (meta)data are presented with standardised, documented, and accessible semantic descriptions
    • (meta)data use standardised vocabularies, terminologies, and ontologies
    • (meta)data are described with references to other (meta)data, so that it is possible to understand the relations between data
       
  • Reusable research data:
    • (meta)data contain multiple types of contextual information, such as scientific purpose, collection context, and what kind of equipment and software has been used
    • there are clear conditions for data usage
    • (meta)data are associated with detailed provenance information
    • (meta)data are structured and documented in accordance with applicable domain-relevant standards and formats
       

If you want to delve deeper into the FAIR principles, follow the links below:

FORCE 11 – the FAIR principles from 2016: https://www.force11.org/group/fairgroup/fairprinciples

The Swedish Research Council’s evaluation criteria for FAIR research data (in Swedish only): https://www.vr.se/analys-och-uppdrag/vi-analyserar-och-utvarderar/alla-publikationer/publikationer/2018-12-07-kriterier-for-fair-forskningsdata.html

The Swedish Research Council’s coordination work on open access to research data: https://www.vr.se/english/analysis-and-assignments/open-science/open-access-to-research-data.html

The Swedish National Library’s evaluation criteria for FAIR publications (in Swedish): http://www.mynewsdesk.com/se/kungliga_biblioteket/documents/vetenskapliga-publikationer-och-fair-principerna-bedoemningskriterier-och-metod-foer-att-kunna-foelja-utvecklingen-mot-ett-oeppet-vetenskapssystem-86176

The EU project Fostering Fair Data Practices in Europe (FAIRsFAIR): https://www.fairsfair.eu/

The GO FAIR initiative: https://www.go-fair.org/

Final Report and Action Plan from the European Commission Expert Group on FAIR Data: https://ec.europa.eu/info/sites/info/files/turning_fair_into_reality_1.pdf