Making something FAIR is hard, particularly when you do more than making something findable. We’ve seen before that making something usefully findable requires deep indexing, and already that continues to be difficult, because we are not seeing it enough. So, when I thought convert a paper led by Hoet’s lab in Leuven into machine-actionable RDF to make it FAIR, I gravely underestimated the amount of work.

My research is about the interaction of (machine) representation and the impact on the success of data analysis (matchine learning, chemometrics, AI, etc). See the posts about molecular chemometrics. This got me into FAIR: making data interoperable and being able to (really) reuse data is the starting point of doing research.

This paper got published in July already, but I had not had the time yet to blog about this exciting work by Irini Furxhi and Ammar Ammar: 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 (doi:10.1016/j.impact.2023.100475) The study has two sides to it: first, it looks into how far we