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.

Agenda

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:

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.

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.

Conclusion

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.

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Key points

• FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability (FAIR).

• FAIR data are as open as possible, and as closed as necessary.

• The main objective of FAIR data is to increase data reuse by researchers.

Frequently Asked Questions

References

1. Wilkinson, M. D., M. Dumontier, I. J. J. Aalbersberg, G. Appleton, M. Axton et al., 2016 The FAIR Guiding Principles for scientific data management and stewardship. Scientific data 3: 1–9. 10.1038/sdata.2016.18

Glossary

FAIR

Findable, Accessible, Interoperable, Reusable

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Citing this Tutorial

1. Katarzyna Kamieniecka, Krzysztof Poterlowicz, FAIR in a nutshell (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/fair/tutorials/fair-intro/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{fair-fair-intro,
author = "Katarzyna Kamieniecka and Krzysztof Poterlowicz",
	title = "FAIR in a nutshell (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/fair/tutorials/fair-intro/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

DASH UK

This Fellowship was funded through the ELIXIR-UK DaSH project as part of the UKRI Innovation Scholars: Data Science Training in Health and Bioscience call (DaSH). (MR/V038966/1). The project aims to embed Research Data Management (RDM) know-how into UK universities and institutes by producing and delivering training in FAIR data stewardship using ELIXIR-UK knowledge and resources.

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/fair/tutorials/fair-intro/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools: []

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