Abstract
FAIR data is considered good data. However, it can be difficult to quantify data FAIRness objectively, without appropriate tooling. To address this issue, FAIR metrics were developed in the early days of the FAIR era. However, to be truly informative, these metrics must be carefully interpreted in the context of a specific domain, and sometimes even of a project. Here, we share our experience with FAIR assessments and FAIRification processes in the biomedical domain. We aim to raise the awareness that “being FAIR” is not an easy goal, neither the principles are easily implemented. FAIR goes far beyond technical implementations: it requires time, expertise, communication and a shift in mindset.
Funder
Horizon 2020
Bundesministerium für Bildung und Forschung
Reference11 articles.
1. M.D. Wilkinson et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data 3 (2016) 160018. doi: https://doi.org/10.1038/sdata.2016.18
2. RDA COVID-19 Working Group, RDA COVID 19 Case Statement. Research Data Alliance. 2020. URL: https://www.rd-alliance.org/group/rda-covid19-rda-covid19-omics-rda-covid19-epidemiology-rda-covid19-clinical-rda-covid19-social [accessed 2023-04-27]
3. FAIR Data Maturity Model Working Group, FAIR Data Maturity Model. Specification and Guidelines, Zenodo (2020), doi: https://doi.org/10.15497/rda00050
4. Waltemath et al., The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE), Journal of Integrative Bioinformatics (2020) doi: https://doi.org/10.1515/jib-2020-0005
5. Neal et al., Harmonizing semantic annotations for computational models in biology, Briefings in Bioinformatics 20 (2019), doi: https://doi.org/10.1093/bib/bby087