Summary: FAIR data Management aims to ensure sustainable research data management. What exactly is meant by this acronym, and what is the difference between FAIR and open data?
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What is FAIR data?


The principles aim to ensure sustainable research data management by preparing and storing data in ways that others can reuse. FAIR stands for Findable, Accessible, Interoperable, Reusable. The principles apply to data management itself as well as to infrastructures and services.

Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
Accessible Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.
Interoperable The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
Reusable The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
Creative Commons licence information Go FAIR Initiative

Additional information

FAIR and open data

FAIR data management does not necessarily mean open data. For example, some data must not be published by legal reasons. Restrictions on access are consistent with the FAIR Principles as long as the conditions and ways of access are apparent.