FAIR data

The FAIR principles describe attributes that enable and enhance the reuse of data (and other digital objects) by both humans and machines.

Image by Patrick Hochstenbach, CC0 1.0
Image by Patrick Hochstenbach, CC0 1.0

The FAIR principles originated in the life sciences, but are now gaining much traction beyond. For example, they are central to the Horizon 2020 guidelines on RDM.  The European Code of Conduct for Research Integrity (2017), to which Ghent University subscribes, also expects that access to research data is in line with the FAIR principles where appropriate.

Want to know more? Watch our 'What are the FAIR data principles' knowledge clip.

How to make your data FAIR?

Just like there are various degrees of data sharing, FAIR is also a spectrum. In other words, data can be FAIR to a greater or lesser extent.

How to make your data FAIR in practice is different for different domains, but there are some broad guidelines:

Data should be assigned a persistent identifier (PID) and described with rich metadata that are available online in a searchable resource.

(Meta)data should be retrievable via their persistent identifier (PID) using a standard protocol such as http(s). Also, access restrictions and conditions should be clear, and metadata should be accessible even if the data themselves no longer available.

(Meta)data should use recognized standards (formats, languages, vocabularies, ontologies, metadata schemas…) to allow them to be exchanged and combined, and include references to other relevant (meta)data.


Data should be sufficiently described and documented in accordance with community standards (including information about how, why, by whom and when the data were created), and clearly licensed.

For a more detailed overview, see the FAIR Data Principles as published by FORCE 11.

Note that using a trusted data repository can do much of the work in making your research data FAIR.

Want to assess how FAIR your data are? Check out S. Jones & M. Grootveld (2017), How FAIR are your data? or ARDC, FAIR self-assessment tool.

FAIR vs. Open research data

Open and FAIR are both about making data available for reuse. However, they are not synonyms!

Research data can in principle be managed without a view to data sharing, in which case they are neither open nor FAIR. Nevertheless, there are increasing expectations to share research data.

When shared, data can be open or FAIR, or both:

  • FAIR does not mean that data have to be open, in the sense of data that can be 'freely used, modified and shared by anyone for any purpose' (opendefinition.org).
  • Rather, the 'A' in FAIR means that it is clear and transparent how data can be accessed, and - if applicable - under which conditions. In other words, data shared under restrictions can still be FAIR (also see degrees of data sharing).
  • Open data are not necessarily FAIR (or even managed) data.
  • Ideally, the aim is to increase the amount of data that are open as well as FAIR.


FAIR vs open data

Making data FAIR in any case requires well-managed data, starting with proper planning for data management before the start of data collection.

More information