Critical steps towards large-scale implementation of the FAIR data principles

Bruna dos Santos Vieira; César Henrique Bernabé; Ines Henriques; Shuxin Zhang; Alberto Ballasteros Camara; Jose Antonio Ramírez García; Joeri van der Velde; Philip van Damme; Pablo Alarcón Moreno; Nirupama Benis; Jolanda Strubel; Fieke Schoots; Pauline L'Henaff; P.A.C. 't Hoen; Marco Roos; Annika Jacobsen; Ronald Cornet; Mark D. Wilkinson; Franz Schaefer; Morris Swertz; Mijke Jetten

doi:10.5281/zenodo.7867293

Published March 15, 2023 | Version 1.0

Poster Open

Critical steps towards large-scale implementation of the FAIR data principles

1. Radboudumc, Nijmegen, The Netherlands
2. Leiden University Medical Center, Leiden, The Netherlands
3. Amsterdam UMC, Amsterdam, The Netherlands
4. Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-CSIC (INIA-CSIC), Madrid, Spain
5. University of Heidelberg, Heidelberg, Germany.
6. University Medical Center Groningen, Groningen, The Netherlands
7. Health-RI, Utrecht, The Netherlands

The process of making data Findable, Accessible, Interoperable and Reusable (FAIR - FAIRification) varies across projects, depending on their objectives and domain needs. However, such variation can complicate identifying the best workflow for an efficient large-scale national implementation. It is therefore necessary to align the different FAIRification processes and workflows. With this aim in mind, we mapped a set of FAIRification workflows to understand the divergence among them and identify critical common FAIRification and domain-specific steps. This work was executed in the context of the Dutch Health-RI and the European Joint Programme on Rare Diseases (EJP RD), which both rely on the FAIR principles to enable sustainable data reuse in the healthcare domain.

First, we gathered a group of FAIR experts amongst the collaborators in the two initiatives. Next, we identified a set of relevant workflows used within the rare disease domain and the Dutch health research infrastructure context. A total of seven workflows were selected, including the EJP RD FAIRopoly, given its compilation of the De Novo and Generic workflows. Through several hands-on meetings, we discussed the interpretation and best mapping of the workflows’ steps. Finally, we considered any steps that were mapped in more than four workflows (>50%) as critical.

Preliminary results show three critical steps: a) identification of FAIRification objectives and expertise, b) (meta)data assessment, definition and semantic modelling, and c) defining data licensing (or consent), access and use conditions. The first can impact the success of the FAIRification of projects, whereas (meta)data assessment and modelling are intrinsically related to the core of the FAIR principles: semantic interoperability and machine actionability of (meta)data. Finally, licensing (consenting), accessing and reusing conditions are equally crucial for healthcare data reuse as they protect sensitive information.

Future work includes identifying and analysing domain-specific steps and detailing each critical step into recommended resources, checklists and needed expertise. Any training needs regarding the FAIR expertise capacity can then be identified from such descriptions. We expect that embedding critical steps in the national research infrastructure can improve FAIR data availability.

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