FAIR data vs. open data

FAIR data are data that are findable (F), accessible (A), interoperable (I) and reusable (R).

Research data that are well managed are essential to qualitative and efficient research. You can go one step further and make your data FAIR, if you apply machine-readable, standardized (meta)data, persistent identifiers, data usage licenses and open formats.

Open data are data that are openly available with an open license.

FAIR data and open data are not the same. On the one hand, FAIR data does not necessarily have to be open, and on the other, open data are not always FAIR, or even properly managed. However, all funders recommend or require that research data are FAIR and as open as possible.

FAIR vs open data Image from 'Open data, FAIR data and RDM: the ugly duckling', CC BY S. Jones

How can you make your data FAIR?


Data are discoverable via search engines and catalogues, have machine-readable metadata and a unique persistent identifier.

  • Assign a globally unique and persistent identifier to your data, e.g. a DOI, an ORCiD, etc.
  • Describe your data with rich metadata, e.g. creator, title, keywords, abstract, etc.


Data does not have to be openly available, but the access protocol should be clear and preferably machine-readable.

  • Apply a free and standardized access protocol. (e.g. a download link, an authentication/authorization procedure, a phone number, email address, …)
  • The metadata should always remain accessible, even if the data are not (anymore).


(meta)Data are interoperable when they can be combined and exchanged with other (meta)data.

  • Use standardized language in your (meta)data: controlled vocabularies, ontologies and thesauri that are common in your discipline and well documented.
  • Use a (meta)data model that is common in your discipline and well documented.
  • Use standard, open file formats instead of proprietary ones.


Data are reusable when they are clearly structured, documented and provided with a data usage license.

  • Depositing your data in a data repository will help you to fulfill most of these requirements.
  • UHasselt does not have an institutional data repository, but does have an institutional metadata repository. Datasets that are deposited in an external data repository can be described in the institutional metadata repository, and will consequently appear on your researcher profile together with your publications.
  • The use of a data repository and the institutional metadata repository will help you fulfill the funder requirements, especially those of the European Commission.

Want to know more?

More information on the FAIR principles can be found on the websites of GO FAIR and FAIRsFAIR.

Contact the RDM team if you need help making your dataset FAIR.