Maastricht University

Egon Willighagen

[Ammar](FAIR%20assessment%20of%20nanosafety%20data%20reusability%20with%20community%20standards)
is finishing up his PhD thesis with his research on the use of FAIR
towards predictive toxicology. Or, "AI ready", as the term FAIR is now
sometimes explained. Any computational method needs good data, and just
FAIR is not enough. It needs to meet community standards, as formalized
in R1.3. To me, this includes meeting community standards like minimal
reporting standards. Indeed, in the [EU NanoSafety
Cluster](https://www.nanosafetycluster.eu/) the notion that FAIR data
also needs be scientifically good data is well noted.

In this paper
(doi:[10.1038/s41597-024-03324-x](https://doi.org/10.1038/s41597-024-03324-x)),
Ammar explores this notion and compiled more than 200 maturity
indicators in the category R1.3 resulting from 12 different community
standards. For example, this includes minimal reporting standards. There
is overlap in needs, but they often also have a different focus. The
conclusion here: different (re)use cases have different needs, and data
not usable to one use case can be sufficiently FAIR for another. Of
course, ideally, it would be FAIR enough for all use cases.

Ammar formalizes the maturity indicators and links the comming maturity
indicators to various use cases. That means that when you determine the
indicator values for your data, people can immediately lookup how this
data can be reused. And, the generator of the data can immediately see
how the data would need to be improved to widen the reusability. How
FAIR can we get?

His proposal has already been further explored in two other papers, one
around data sharing
(doi:[10.1038/s41596-024-00993-1](https://doi.org/10.1038/s41596-024-00993-1),
see also [this blog post](https://doi.org/10.59350/vfvwq-s0v13)) and one
around QSAR modelling
(doi:[10.1016/j.impact.2023.100475](https://doi.org/10.1016/j.impact.2023.100475),
see also [this blog post](https://doi.org/10.59350/7zf38-w9670)).

The below screenshot shows what an analysis using this approach can look
like:

![](/assets/images/41597_2024_3324_Fig3_HTML.png)

#### References

- [10.1038/S41597-024-03324-X](https://doi.org/10.1038/S41597-024-03324-X)
- [10.1016/J.IMPACT.2023.100475](https://doi.org/10.1016/J.IMPACT.2023.100475)
- [10.1038/S41596-024-00993-1](https://doi.org/10.1038/S41596-024-00993-1)


FAIR assessment of nanosafety data reusability with community standards

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials

Ammar is finishing up his PhD thesis with his research on the use of FAIR towards predictive toxicology. Or, “AI ready”, as the term FAIR is now sometimes explained. Any computational method needs good data, and just FAIR is not enough. It needs to meet community standards, as formalized in R1.3. To me, this includes meeting community standards like minimal reporting standards.

Rogue Scholar Posts

New paper: FAIR assessment of nanosafety data reusability with community standards