1.1 FAIR Principles

The FAIR principles were proposed to guide researchers to describe and share their data to increase data reuse and research reproducibility. FAIR stands for Findable, Accessible, Interoperable, and Reusable data. The different principles can be explained in the following way:

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 is essential for the automatic discovery of datasets and services.

Accessible: Once the user finds the required data, she/he needs to know how it can be accessed, including authentication and authorization.

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 goal of FAIR is to optimize 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.