Saturday, October 24, 2020

new paper: "A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences"

Figure 1 from the Nanomaterials article.
In a collaboration orchestrated via the NanoSafety Cluster (NSC) Work Group F on Data Management (WGF is formerly known as WG4) between a few H2020 projects (NanoCommons, Gov4Nano, RiskGONE, and NanoSolveIT), we just published an article about the using of Jupyter Notebooks to assess how FAIR (see doi:10.1162/dint_r_00024) several databases are.

It is important to realize this is not to judge these database, but to provide them with a map of how they can make there databases more FAIR. After all, the notebook explains in detail how the level of FAIR was assessed and what they database can do become from "mature". This is what the maturity indicators are about. In doing so, we also discovered that existing sets of maturity indicators do not always benefit the community, often because they are currently focusing more the F and the A, than the I and the R (see What is wrong with FAIR today.). Another really neat feature is the visual representation of the map, proposed by Serena (now at Transparent Musculoskeletal Research) in this paper (shown on the top right).

I like to thank everyone involved in the project, the NSC projects involved in the discussions (Joris Quik, Martine Bakker, Dieter Maier, Iseult Lynch), Serena for starting this work (see this preprint), Laurent for reviewing the notebook, and Ammar and Jeaphianne for their hard work on finishing the paper into a this, now published, revision (the original paper was rejected).

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