FAIR in a nutshell

Creative Commons License: CC-BY Questions: Objectives:
  • Learn the FAIR principles

  • Recognise the relationship between FAIR and Open data

Time estimation: 10 minutes
Supporting Materials:
Published: May 30, 2023
Last modification: Apr 29, 2024
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT
purl PURL: https://gxy.io/GTN:T00351
version Revision: 19

The FAIR (Findable, Accessible, Interoperable, Reusable) principles emphasize machine-actionability. The main objective of FAIR is to increase data reuse by researchers. The core concepts of the FAIR principles are based on good scientific practice and intuitively grounded.

This tutorial is a short introduction to the FAIR principles and their origin. You can find out more at the FAIR Pointers module at the FAIR Data Management learning path.


In this tutorial, we will cover:

  1. FAIR and its origins
    1. Open data and FAIR
    2. FAIRification and FAIRness of data
  2. Conclusion

FAIR and its origins

The FAIR Guiding Principles aid in designing data publishing platforms for easier manual and automated deposition, exploration, sharing, and reuse Wilkinson et al. 2016. FAIR stands for specific improvements in data management and archival practices as it outlines clear, high-level, domain-independent principles that can be used to create a variety of scholarly outputs:

The FAIR Guiding Principles  
To be Findable: F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
F3. metadata clearly and explicitly include the identifier of the data it describes
F4. (meta)data are registered or indexed in a searchable resource
To be Accessible: A1. (meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where necessary
A2. metadata are accessible, even when the data are no longer available
To be Interoperable: I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles
I3. (meta)data include qualified references to other (meta)data
To be Reusable: R1. meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1. (meta)data are released with a clear and accessible data usage license
R1.2. (meta)data are associated with detailed provenance
R1.3. (meta)data meet domain-relevant community standards

Table 1: The FAIR guiding principles as described in Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship Wilkinson et al. 2016.

A report from the European Commission Expert Group on FAIR data describes the origins of FAIR and its development in 2014-2015 by a FORCE11 Working Group. The following exercise dips into this report and asks you to investigate some of FAIR’s history and foundation.

Open data and FAIR

The level of accessibility and usability criteria distinguish open from FAIR data. Open data is accessible without limitations, whereas FAIR data specifies certain requirements for access and use.

text reading fair does not equal open.

Open data can be modified, and distributed for any reason. Although extensively used and accessible, FAIR data additionally includes the following usability standards that go beyond permission alone:

  • In order to be found and cited, FAIR data must be identified and deposited into online public records.

  • It is necessary to make FAIR data available so that it may be accessed, read, and processed.

  • FAIR data needs to be captured and presented in a form that can be used.

The published FAIR Guiding Principles: Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship Wilkinson et al. 2016.

FAIRification and FAIRness of data

Making your data FAIR compatible involves adopting the 15 guiding principles listed in Table 1, which is the process known as “FAIRification.” The degree to which you adhere to these criteria determines how FAIR your data are. In other words, FAIRness refers to how FAIR your data is to a certain level and indicates a tacit method of evaluating its compliance.

Documentation and frameworks for data FAIRification. Each of the 15 FAIR principles is put into context with real data examples: GO FAIR, FAIR Cookbook.


The FAIR Principles place a strong focus on encouraging individual data reuse while also supporting the capacity of machines to automatically discover and utilise the data. This short introduction aims to develop and disseminate guidance and processes needed to make and keep data FAIR.