Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains





FAIR assessment, FAIRification, biomedicine, EOSC, COMBINE


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. 


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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

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]

FAIR Data Maturity Model Working Group, FAIR Data Maturity Model. Specification and Guidelines, Zenodo (2020), doi: https://doi.org/10.15497/rda00050

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

Neal et al., Harmonizing semantic annotations for computational models in biology, Briefings in Bioinformatics 20 (2019), doi: https://doi.org/10.1093/bib/bby087

Ramachandran et al., FAIR Sharing of Reproducible Models of Epidemic and Pandemic Forecast, Preprints (2022). doi: https://doi.org/10.20944/preprints202206.0137.v1

Mazein et al., Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms, NPJ Systems Biology and Applications (2018), doi: https://doi.org/10.1038/s41540-018-0059-y

Gawron et al., MINERVA—a platform for visualization and curation of molecular interaction networks, NPJ Systems Biology and Applications (2016), doi: https://doi.org/10.1038/npjsba.2016.20

Völzke et al., Cohort Profile Update: The Study of Health in Pomerania (SHIP), International Journal of Epidemiology 6 (2022). Doi: https://doi.org/10.1093/ije/dyac034

Michaelis et al., How FAIR is NUM? - Lessons learnt from a FAIR survey within the German Network University Medicine (NUM), Proceedings of the 2023 SWAT conference, Basel, Feb. 2023.

Bruno et al., FAIR and Open Data in Science: The Opportunity for IUPAC, Chemistry International (2021), doi: https://doi.org/10.1515/ci-2021-0304




How to Cite

Waltemath, D., Inau, E., Michaelis, L., Satagopam, V., & Balaur, I. (2023). Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains. Proceedings of the Conference on Research Data Infrastructure , 1. https://doi.org/10.52825/cordi.v1i.415
Received 2023-04-27
Accepted 2023-06-29
Published 2023-09-07

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