Skip to content

FAIR Data Principles

What is FAIR?

As more datasets for research and innovation are published openly, there is a need to have well-described, accessible data that conforms to community standards in order to discover and reuse relevant data, perform machine analysis at scale, or employ techniques such as artificial intelligence to identify patterns and correlations not visible to the human eye.

The FAIR principles define the characteristics that data must have to be reused by humans and machines1. The FAIR data principles provide a set of guidelines to help make research data F-indable, A-ccessible, I-nteroperable, and R-eusable. These principles ensure that research objects are reusable and will indeed be reused, and thus become as valuable as possible. We base this toolkit on these fundamental principles of sustainable datasets.

Findable Ensuring that data is easily discoverable, identifiable and citeable.
Accessible Providing access to data in a format that is easy to download, use, and reuse i.e the technical means for accessing the data is available for widespread use.
Interoperable Ensuring that data can be integrated and combined with other data sources i.e. that data attributes across various datasets are consistent and understandable.
Reusable Ensuring that data can be reused for future research and analysis e.g. through clear documentation and licensing frameworks

  1. European Commission, Directorate-General for Research and Innovation, (2018). Turning FAIR into reality : final report and action plan from the European Commission expert group on FAIR data, Publications Office. https://data.europa.eu/doi/10.2777/1524 ↩