FAIR principles

The FAIR principles, launched in 2014, aim to improve discovery, access, integration and usability of data, both by machines and by human beings.

FAIR stands for:

  • Findable
  • Accessible
  • Interoperable
  • Reusable

Each letter representing a category of principles. These 4 categories encompass a set of 14 principles.  

To sum up the 14 principles:

Findable.png Accessible.png Interoperable.png Reusable.png
  • persistent identifier
  • enriched metadata
  • (meta)data searchable and findable online
  • data retrievable using standard communication protocols 
  • possibilily to define access rights
  • standard formats
  • controlled vocabulary to describe data
  • well-described & documented data (eg. in a README file)
  • clear conditions to cite and reuse data (e.g. CC licenses)

 If the dataset cannot be made openly accessible to all, at least its description should be publicly accessible online.

In a repository that ensures long-term preservation

To be compatible and combinable with other datasets

So data can be correctly interpreted and reused

Image by Patrick Hochstenbach CC0:


Why is it important?

These principles are often mentioned by funding agencies, institutions (including the University of Geneva) or publishers, who recommend their adoption. The more FAIR a dataset is, the easier it will be to identify, access, understand and reuse it. By following the FAIR principles, you enrich your research data.


How do I know if my data is FAIR compliant?

There are tools to help you do a self-diagnosis and identify possible areas for improvement.
We recommend in particular the use of the self-diagnosis tool proposed by the Australian Research Data Commons


FAIR = Open ?

Not necessarily.

  • Open data may or may not comply with the FAIR principles. It all depends if the about principles are followed or not.
  • On the other hand, data that is closed and/or can be shared on demand may be FAIR compliant.