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

Authors

DOI:

https://doi.org/10.52825/cordi.v1i.415

Keywords:

FAIR assessment, FAIRification, biomedicine, EOSC, COMBINE

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. 

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References

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Published

2023-09-07

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

Conference Proceedings Volume

Vol. 1 (2023): 1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities

Section

Spreading RDM

License

Copyright (c) 2023 Dagmar Waltemath, Esther Inau, Lea Michaelis, Venkata Satagopam, Irina Balaur

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Received 2023-04-27
Accepted 2023-06-29
Published 2023-09-07

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