This case study is written by Sina Krottmaier. The page is designed by Felix Bui. 

What is FAIR data and why does it matter?

The FAIR Guiding Principles for scientific data management and stewardship, published in 2016, provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets^[1]. They can serve to guide data producers and publishers helping to maximize the added value gained by scholarly digital publishing.^[2]

Although originally developed in the life sciences, the FAIR principles are applicable to all research disciplines that deal with data, as they emphasise the importance of computational systems being able to find, access, interoperate and reuse data with minimal human intervention. Since their publication, the FAIR principles have been endorsed by the European Union, national funders and universities, leading to the development of data management policies, tools and infrastructures. Some projects adhere closely to the original FAIR definitions, while others are inspired by the spirit of the principles. Ensuring that data is FAIR is critical in the digital transformation space. FAIR data access is essential to enable researchers to find and access relevant data, leading to new insights and discoveries that would otherwise remain out of reach. Each letter of FAIR stands for one of the four basic principles

• F - Findable
• A - Accessible
• I - Interoperable
• R - Reusable

the principles can be summed up in four points:

Both humans and machines are intended as digesters of data. This will lead to the creation of an ecosystem that is fast to respond to change and automatically adapts to new findings or changes: the Internet of FAIR Data and Services. This is the reason for focusing on standards for data, identification mechanisms, data availability, etc.

The FAIR principles apply to both data and metadata.

The principles are not necessarily about open data. You can and should work in a FAIR manner also with data that is not intended for public availability.

The FAIR principles are not rules or standards. The FAIR principles must not be mistaken for rules or standards that you can use to evaluate tools, data, policies, etc. This would soon make the principles out-of-date and inapplicable across research disciplines. Adopting the FAIR principles will often be a gradual adaptation of work routines – but it could also be a huge leap, where you replace one type of infrastructure with another. It will be up to the different research areas and research communities to make the FAIR principles work in their respective contexts.

FAIR data principles and how you can integrate them into your research

F - Make your data Findable

Findable^[1][3] means that data can be easily discovered by both humans and machines. This is achieved through the use of machine-actionable metadata and keywords that can be indexed by search engines and research data catalogs. Additionally, unique and persistent identifiers, such as DOIs or Handles, are used to reference the data, and the metadata includes the identifier of the data it describes. Easy discoverability of metadata and data is crucial for enabling their (re)use, and machine-readable metadata plays a vital role in automating the process of discovering datasets and services. The Findable principle is divided into 4 'sub-principles" ^[1]

As you can see, the quintessence of this principle is to make data discoverable for everyone through persistent identifiers and metadata. You can do so by ^[3]

• publishing your data in searchable repositories, e.g. Zenodo, as they assign persistent identifiers to resources, and by
• adding machine-readable metadata (e.g. Dublin Core).

A - Make your data Accessible

Accessibility^[1][3], as part of the FAIR principles, ensures that users are given guidance on how to access the desired data, which may involve authentication and authorization. It means that the data is stored and can be accessed using standard procedures. While it doesn't require open availability, information on data retrieval should be provided, such as labeling data as "Access only with explicit permission" and including contact details. Ideally, this accessibility information should be machine-readable.

Similar to the Findable principle, the Accessible principle is also subdivided further ^[1]:

To make your data accessible, you simple have to do the following^[3]:

• have openly available administrative metadata and attach a data licence or statement to it, which informs users how they can access the data
• use a long-term storage solution for archiving, which ensures the data is retrievable by their persistant identifier using a standard protocol
• give access to metadata (always)

I - Make your data Interoperable

Interoperability^[1][3] guarantees the ability to exchange and utilize data across different applications and systems, even in the long term, by employing open file formats. It also entails the integration of data from various research fields, accomplished through the implementation of metadata standards, ontologies, controlled vocabularies, and purposeful connections between data and associated digital research objects. Interoperability further necessitates the smooth interaction of data with analysis, storage, and processing applications or workflows.

The Interoperability principle is divided in 3 sub-principles ^[1]:

To make your data interoperable, you should ^[3]

• use standardised structural metadata in an sufficient amount
• use common standards, ontologies, terminologies, etc.
• use open, and long-term viable file formats.

R - Make your data Reusable

Reusable^[1][3] data in context of the FAIR principle is data, that are well-documented, curated, and provide contextual information. They adhere to community standards and have clear terms for access and reuse, ideally through machine-readable licenses. This promotes reproducibility and enables the design of new projects based on the original results. The FAIR principle aims to maximize data reuse by ensuring comprehensive descriptions of metadata and data.

The Reusability principle is divided into 4 sub-principles ^[1]:

In order to make your data reusable for others, you have to^[3]

• add the relevant contextual information
• use standardized structural metadata (incl. controlled vocabularies and ontologies used in your resp. research field)
• use open, long-term viable file formats, and
• add a machine-readable date license.

Of course, it is not always possible to comply with all the principles - also because other laws often do not always allow the principles to be applied in their entirety - but you should try to be as FAIR as possible with your data. If you want to learn more about research data management, here is a eLearning Course, which might be interesting for you.

Furthermore, in the "Tools and Tutorials" section of the Toolkit, you can find the "FAIR Data Maturity Model" - an interactive tool that helps you assess your data's FAIRness level. 

Author Bio*: 

Sina Krottmaier is a research assistant at the Centre for Information Modelling – Austrian Centre for Digital Humanities, University of Graz. She is interested in video game studies, 3D modeling and ethical and legal topics relating to the use of tools and data. Her research interests primarily focus on the representation of the past and of different cultures, alternative timelines, and horror in video games.

Designer Bio*:

Felix Bui is currently a junior lecturer at the Faculty of Arts & Social Sciences at Maastricht University. She teaches courses about the history and development of AI, the philosophy of technology, and research skills. She holds a master’s degree in Media Studies: Digital Cultures and a background in Marketing & Communication. Her research interest involves AI and creativity, mediatization and media representation of queer communities, data and media ethics with a focus on diversity and inclusivity.

*Bios and affiliations are accurate at the time of writing. 

References

1. GO FAIR, n.d., „FAIR PRINCIPLES”, FAIR Principles, https://www.go-fair.org/fair-principles/.
2. Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J.G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A.C ’t Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, and Barend Mons, 2016, “The FAIR Guiding Principles for scientific data management and stewardship”, in Sci Data 3, 160018, https://www.nature.com/articles/sdata201618.
3. Daniella Bayle Deutz, Mareike Christina Harms Buss, Jitka Stilung Hansen, Karsten Kryger Hansen, Kristian G. Kjelmann, Asger Væring Larsen, Evgenios Vlachos, Katrine Flindt Holmstrand, 2020, How to FAIR: a Danish website to guide researchers on making research data more FAIR, https://doi.org/10.5281/zenodo.3712065, https://howtofair.dk.