FAIR principles

Following the FAIR principles, opens an external URL in a new window, research objects should be findable, accessible, interoperable and re-usable. These principles form the basis of a trusted environment where researchers, innovators, companies, and citizens can publish, find, and re-use each other’s data and tools for research, innovation, and educational purposes. The FAIR principles refer to any digital object evolving from the research process, that is, quantitative as well as qualitative data, metadata or algorithms, tools, software and services.

Data that meet the FAIR principles can - but need not - be Open Data and thus available to everyone. The FAIR principles also allow a restriction of data access, which is useful or even necessary in certain cases. On the other hand, if Open Data is well documented and machine-readable, has an open license, uses independent formats and open standards, it will also conform to the FAIR concept.

In the news article The FAIR principles for research data, opens an external URL in a new window we have compiled how the FAIR principles, which are explained in detail below, can be applied in practice.


The first step in (re)using data is to find them. Globally unique and persistent identifiers and rich metadata are essential for automatic discovery of datasets and services.  Furthermore, the (meta-)data must be registered or indexed on a searchable platform like a repository.

To ensure long-term accessibility, data need to be archived in a way that they (or at least their metadata) are retrievable via standardised, universally implementable communication protocols. The exact conditions under which the data is accessible should be clearly described by the researchers using standardised licence agreements.

Data are considered interoperable if they can be exchanged, interpreted and combined with other data sets in a (semi-)automated way.
Computer systems must therefore be able to determine whether your data is comparable with other data in terms of content. The use of metadata based on controlled vocabularies, classifications, ontologies or thesauruses, which in turn are subject to the FAIR principles, helps to achieve this.

In order to ensure that research data can be reused in future research projects, your metadata, together with supplementary documentation, should provide a comprehensive and detailed description of the research context. In this way, researchers who wish to reuse research data can better assess whether it is really suitable for their research project. Furthermore, the conditions under which the data can be reused must be clearly and comprehensibly presented by researchers on the basis of standardised licensing contracts.

FAIR networks and projects

There are numerous initiatives and projects worldwide that deal with the application, dissemination and discipline-specific implementation of the FAIR principles. You can find a selection here:

Take the FAIR-Test

Would you like to find out whether your data complies with the FAIR principles?

With these tests it is easily done:

Video: Let's make our data FAIR!

In the webinar "Let`s make our data FAIR!", opens an external URL in a new window we present the basics of the FAIR principles and give hands-on tips for their implementation.


Further reading

Turning FAIR into reality, opens an external URL in a new window
Final report and action plan from the European Commission expert group on FAIR data, European Commission, 26.11.2018.


Three camps, one destination, opens an external URL in a new window:
the intersections of research data management, FAIR and Open,
Higman R., Bangert D. and Jones S., Insights, 2019.