Introduction

The FAIR data principles are a concise and measurable set of principles that may act as a guideline for those wishing to enhance the reusability of their data holdings. FAIR stands for Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016). The FAIR data principles aims at (Wilkinson et al., 2016; Lowenberg, 2021):

• Improving the infrastructure supporting the reuse of scholarly data.
• Enhancing the ability of machines to automatically find and use data.
• Supporting the reuse of data by individuals, which “is essential to the advancement of research and clinical practice”.

The principles (Wilkinson et al., 2016; “Fair Principles,” 2022) are reproduced below:

To be Findable

• (Meta)data are assigned a globally unique and persistent identifier.
• Data are described with rich metadata.
• Metadata clearly and explicitly include the identifier of the data it describes.
• (Meta)data are registered or indexed in a searchable resource (e.g. data repository).

To be Accessible

• (Meta)data are retrievable by their identifier using a standardized communications protocol (e.g. http(s)).
• The protocol is open, free, and universally implementable.
• The protocol allows for an authentication and authorization procedure, where necessary.
• Metadata are accessible, even when the data are no longer available.

To be Interoperable

Data interoperability is the ability of a dataset to work with other datasets or systems without special effort on the part of the user (Action, 2019).

• (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation (e.g. controlled vocabularies/ontologies/thesauri, a good data model).
• (Meta)data use vocabularies that follow the FAIR principles (e.g. using FAIR Data Point).
• (Meta)data include qualified references to other (meta)data (e.g. specifying if one dataset builds on another one, properly citing all datasets).

To be Reusable

• Meta(data) are richly described with a plurality of accurate and relevant attributes (i.e. metadata that richly describes the context under which the data was generated such as the experimental protocols, the species used).
• (Meta)data are released with a clear and accessible data usage license.
• (Meta)data are associated with detailed provenance.

Further resources

• Introducing the FAIR Principles for research software: Barker et al. 2022

Learning resources

• Course on FAIR in (biological) practice: The Carpentries Incubator
• How to be FAIR with your data. A teaching and training handbook for higher education institutions: Engelhardt et al. 2022
• Unit on the benefits and challenges associated with sharing research data openly: The University of Edinburgh

How to make data FAIR?

• Guidelines to FAIRify data management and make data reusable: PARTHENOS
• Preparing data for sharing: Knight 2015
• Recipes that help you to make and keep data FAIR: FAIR Cookbook
• Top 10 FAIR Data & Software Things: Martinez et al. 2019

How to assess the FAIRness of your datasets?

• FAIR data maturity model indicators: Bahim et al. 2020, Table 1
• FAIR evaluator service: Fraunhofer FIT
• How FAIR are your data? Jones and Grootveld 2017
• Self-Assessment Tool to Improve the FAIRness of Your Dataset (SATIFYD)

References

1. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., & et al. (2016). The Fair Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3(1). https://doi.org/10.1038/sdata.2016.18
2. Lowenberg, D. (2021). COVID Tracking Project Data Now Available in Dryad. In Dryad news. https://blog.datadryad.org/2021/08/04/
3. Fair principles. (2022). In GO FAIR. https://www.go-fair.org/fair-principles/
4. Action, G. O. D. A. N. (2019). GODAN Action Online Course on Open Data Management in Agriculture and Nutrition (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.3588148