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Research Data Management

A comprehensive guide to the best practices for planning, collecting, working with, sharing and reusing research data

FAIR Principles

FAIR Principles (Findable, Accessible, Interoperable and Reusable) are a set of guiding principles describing how research data should be managed to make them more easily accessed, understood, exchanged and reused. The FAIR Principles are the foundation of good data management which help to optimize the discovery and reuse of data. It is important for researchers to understand and follow the FAIR Principles in data management in order to make their data as FAIR as possible!

How to make data FAIR?


Data should be discoverable by both humans and machinesRich metadata should be available online in a searchable resource, e.g. a data repository, and the data should be assigned a persistent identifier, e.g. a DOI.


Accessible does not mean that data need to be open to public. In case the access to the data is restricted, access conditions should be made clear to users who wish to reuse the data. Also, metadata should be accessible even when the data is not available.


Data should be able to integrate with other data, applications and workflows. Hence, open and common formats, standards, and controlled vocabularies should be used where possible.


Data should be well-documented and assigned with a clear license to govern the conditions on reuse, and provenance information to describe how, why and by whom the data have been created and processed.

Learning Resources

The FAIR principles explained

This video explains the principles of FAIR, which helps you maximising your research output and impact, and enhancing your recognition as a researcher.

Source: Maastricht University. (2020, May 28).The FAIR principles explained [Video]. YouTube.