5.2 Enable FAIR in your organisation

Now that you have been introduced to the concept of FAIR and the various ways in which it can be applied to the work of data archives, it is important to note that the FAIR principles can be approached in a variety of ways, both internally within an archive (more in this section) as well as externally to users (more in section 5.3). 

Archives differ, whether by domain, country, or even concerning their approach to FAIR principles. An organisation can be FAIR-enabling in a number of ways, including through policy, infrastructure, mission statements, assessments, training protocols, or simply the work culture. Broadly speaking, several archives use external guides such as the Data Management Expert Guide or the PARTHENOS Training Suite as an internal training tool, while other archives maintain (or are currently developing) internal training related to FAIR principles. 

This section will introduce a variety of tools available to assess FAIR qualities of archives, followed by some examples from different CESSDA Service Providers.


5.2.1 Determine the ways in which your organisation is FAIR-enabling

In order to know what could be improved, it is important to understand what is planned for in the future and what is currently in place in your own organisation. The following assessment frameworks are best performed holistically at an organisation and discussed with representatives of different teams. The first two frameworks would be performed manually, while the latter two frameworks are automated processes. Practical examples of these automated assessments in use are found below. 

Assessment frameworks

Manual Assessments

  • The EU Horizon 2020 project “Fostering FAIR Data Practices in Europe” (FAIRsFAIR) released their framework for assessing how research data infrastructure services support FAIR data practices alongside suggestions for improvement (Ramezani et al. 2021): https://doi.org/10.5281/zenodo.6656431 

  • The sustainable research framework from Science Europe provides maturity matrices and designates three types of organisations, each with their own framework (Boccali et al. 2021). Most archives would find the framework for Research Data Infrastructures (RDI) the most applicable: https://doi.org/10.5281/zenodo.4769703

Automated Assessments

  • From the FAIRsFAIR project, the F-UJI tool for assessing data holdings works both online or as an installed instance (Devaraju and Huber 2022). F-UJI attempts to assess the FAIR-ness of a published dataset using its PID; while the process is automated, interpretation of the results by the archive and how it impacts its practices will require additional time. https://www.f-uji.net/ and https://doi.org/10.5281/zenodo.6552689 

  • The FAIR Maturity Evaluation Services provide a number of automated assessments for collections (‘The FAIR Maturity Evaluation Service’ n.d.). Additional Maturity Indicators can be submitted to the community and assessed. As the service is automated for a wide range of archives, the output and its  interpretation will require additional time to determine how the score can impact practices. https://fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/#%2F


5.2.2 Examples from archives

Internal policies related to FAIR principles are key to long-term success of an organisation, and it can be worthwhile to make these internal policies known to users and designated communities to encourage trust in the archive - this will be addressed further in Section 5.4. 

Let’s look at two examples of CESSDA Service Providers that actively assessed the FAIRness of their holdings internally, either for metric tracking or through larger projects on enabling FAIR.

Croatian Social Science Data Archive (CROSSDA) using the F-UJI tool

Using the F-UJI tool allowed the relatively smaller CROSSDA to make initial tests on their published datasets; they were able to test all of their datasets one at a time. 

The team at CROSSDA picked F-UJI to better assess FAIR criteria of their data holdings in a more objective manner - as one staff member states, “FAIR should be tested only programmatically [...] findability, accessibility, interoperability and reusability, as defined by FAIR principles, [should] be strictly technical constructs.” The F-UJI tool can take care of this, as it runs on predetermined criteria, though there is a risk of losing nuance specific to social sciences if the results of the assessment aren’t interpreted critically. 

“I am afraid that simplification of criteria, and presentation of FAIR as an ultimate goal, will lead to hiding and diminishing some efforts. For instance, F-UJI is satisfied with DC [Dublin Core] and DataCite - but, if I understood it correctly, you don't get additional points if you are in social sciences and use DDI [Data Documentation Initiative]. Another example is the case where open data is not possible (as it is often the case for social sciences). Efforts of data sharing under restricted conditions can be unfairly penalised because you get more points on accessibility and reusability if your data is completely open. But, in reality, you should really be praised if you manage, with additional effort, to share data which are in essence hard to share.” (Marijana Glavica, CROSSDA)


Finnish Social Science Data Archive (FSD) using the FAIR Evaluator tool

For this example, we refer to a news bulletin posted by the FSD on their website in June of 2020, chronicling their participation in EOSC-Nordic and drawing up recommendations for implementing FAIR principles. This detailed account goes through the process of the archive using the FAIR Maturity Evaluator tool. This article also points to some stumbling blocks with result interpretation as well as re-assessing how appropriate the tool can be for more specific archives, similar to CROSSDA’s experience with the F-UJI tool. Some highlights include:

“In the evaluator, the FAIR principles have been broken down into component tests for findability, accessibility, interoperability and reusability. They emulate how a computer approaches an object. The tests look for machine-readable (meta)data and characteristics that support the qualities mentioned in the principles. For example, do the (meta)data have a unique and permanent identifier, can they be accessed using well-known retrieval protocols, are open and machine-readable vocabularies used to describe (meta)data, and are licenses and terms of use explicitly defined.

“[It] was a disappointment that the first maturity level tests gave results that only barely exceeded the minimum level.”

“[After assessing the results,] we found that the machine had difficulty interpreting our high-quality data descriptions. Based on this discovery, the solution was obvious - we enriched our metadata by embedding linked data that described the dataset. We used JSON-LD format and schema.org datatypes. The change is not visible in any way to a researcher who visits Aila to browse or search the holdings. This information is there only for the machine.

This resulted in a considerable improvement in the maturity evaluation.” (Alaterä 2020)


Increase your understanding

Find out more about your archive

Here are some questions that you can ask yourself to determine in what ways your archive implements FAIR principles

  • Who is the best person or people within your internal structure to discuss implementing FAIR?

  • Are there internal policies related to FAIR, such as for internal record-keeping or shared storage?

  • How does your archive approach assessment frameworks?

    • Does your archive use tools such as F-UJI or the FAIR Evaluator? Why or why not?

  • What resources are made available for assessing the FAIR maturity of the datasets in your archive?


Expert tips

Assess your archive using the DoIPASS checklist (Bruin et al. 2020). This one-page list is a good start or a regular check-up for any organisation that works with data, including archives. 

For a more holistic approach to curating FAIR data, check out the Research Data Alliance’s “10 Things for Curating Reproducible and FAIR Research” (Arguillas et al. 2022). Focusing primarily on research compendia produced by quantitative data-driven social science, it lists standards-based guidelines for ten practices.