The event series highlighting pragmatic measures developed by the community towards the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
- FAIR is a set of guiding principles, do you have examples for practical implementation choices that are being made within your community of practice?
- FAIRification efforts in the materials science community: a common API for serving data: https://www.optimade.org/, data repositories: https://nomad-lab.eu/, https://archive.materialscloud.org/, https://mpcontribs.org/
- Domain-specific implementation profiles and examples: https://www.go-fair.org/how-to-go-fair/fair-implementation-profile/ , https://www.go-fair.org/implementation-networks/overview/
- How to be FAIR with your data: A teaching and training handbook for higher education institutions (https://doi.org/10.5281/zenodo.5665492).
- Article: https://crl.acrl.org/index.php/crl/article/view/23610/30923
- FAIR not necessarily open: https://citrine.io/why-data-fairness-is-important-in-the-corporate-world/ , (chemical and materials informatics company)
- How to facilitate the implementation of open science principles by scientists with limited expertise and or time: https://github.com/FellowsFreiesWissen/computational_notebooks
Date Published: 2022-03-14