8  FAIR principles

Estimated time: 30 minutes

In this module, we will discover the FAIR data principles and their main elements. At the end of this module, you should be able to:

Activities

8.1 What are the FAIR principles?

The FAIR principles were created in order to maximize the reuse of scientific data, to promote best practices on Research Data Management and to enable Open Science. Applying the FAIR principles means to make research data Findable, Accessible, Interoperable and Reusable (Wilkinson et al. 2016).

  • Findable means that others (both human and machines) can discover the data
  • Accessible means that the data can be made available to others
  • Interoperable means that the data can be integrated with other data and can be easily used by machines or in data analysis workflows.
  • Reusable means that the data can be used for new research

These four principles should be applied (as much as possible) throughout the entire research cycle and they are closely interconnected with each other.

The Turing Way illustration by Scriberia. CC-BY 4.0. DOI: 10.5281/zenodo.3332807

The FAIR Data principles are NOT:

  • A standard. The FAIR principles need to be adapted and followed as much as possible by considering the research practices in your field. FAIR principles should be rather seen as progressive steps that help you make your data re-usable.
  • Equivalent to Open Data. FAIR data does not necessarily mean openly available: it should be clear to others that the data exists and which steps they could take to potentially access the data.
  • Applied using a particular technology or tool. There might be different tools that enable FAIR data within different disciplines or research workflows.

There are important elements to consider within your research workflows if you aim to make the data of your project FAIR:

Scripts of the videos copied or adapted from (Holmstrand et al. 2019).

See also Leiden University’s video on Metadata.

8.2 Improve the interoperability of the data using metadata standards

You can use FAIRsharing.org to check if a metadata standard exists for your discipline or type of data.

Examples of possible relevant metadata standards are listed on the Open Science support website.

8.3 Test how aware of FAIR you are (optional)

Use the FAIR-Aware tool to test your knowledge about the FAIR principles

8.4 Data and Code Licences

  • To choose a licenses for data (and presentations and articles!): Use the CC license chooser.
  • To choose a license for software: Use Choose a License. (For more detailed information, see ‘How to choose a software licence’).
  • Note that apart from CC0, the licenses for data and code are different. Software licenses are more complex as software differs from data in the ways you can reuse it, as unlike data, software is executable.
  • If in doubt, talk to Esther.
  • Indicate your license preference in GitHub discussion #126 Licenses

8.5 FAIR software

Only read this section if you’re working with code in your project, or if Research Software is one of the main outputs of your projects!

Many of the FAIR Guiding Principles for research data can be directly applied to research software by treating software and data as similar digital research objects. However, specific characteristics of software — such as its executability, composite nature, and continuous evolution and versioning — make it necessary to revise and extend the principles. When you need to share the code/software, you can follow the FAIR software checklist developed by TU Delft’s Digital Competence Center.

Let’s hear from Maurits, one of TU Delft research software engineers, how the FAIR principle apply to Research Software and the distinction between FAIR software and open source:

Video: FAIR and Open Software (4 min)

8.6 FAIR in the literature

(Scheffler et al. 2022)

(Kievits et al. 2022)

References

FAIR and Open Software. Video recording from TU Delft MOOC Open Science: Sharing Your Research with the World. Presenter: Dr. Maurits Kok. Credits: TU Delft Extension School, TU Delft New Media Center, TU Delft Digital Competence Center. Licence: CC-BY-NC-SA

Holmstrand, K. F., S. P. A. den Boer, E. Vlachos, P. M. Martínez-Lavanchy, K. K. Hansen, A. V. Larsen, S. Zurcher, et al. 2019. “Research Data Management (eLearning Course).” https://doi.org/10.11581/DTU:00000047.
Kievits, Arent J., Ryan Lane, Elizabeth C. Carroll, and Jacob P. Hoogenboom. 2022. “How Innovations in Methodology Offer New Prospects for Volume Electron Microscopy.” Journal of Microscopy 287 (3): 114–37. https://doi.org/10.1111/jmi.13134.
Scheffler, Matthias, Martin Aeschlimann, Martin Albrecht, Tristan Bereau, Hans-Joachim Bungartz, Claudia Felser, Mark Greiner, et al. 2022. “FAIR Data Enabling New Horizons for Materials Research.” Nature 604 (7907): 635–42. https://doi.org/10.1038/s41586-022-04501-x.
Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, 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.