Published in chem-bla-ics

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.


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

Published in Nature Protocols
Authors Nina Jeliazkova, Eleonora Longhin, Naouale El Yamani, Elise Rundén-Pran, Elisa Moschini, Tommaso Serchi, Ivana Vinković Vrček, Michael J. Burgum, Shareen H. Doak, Mihaela Roxana Cimpan, Ivan Rios-Mondragon, Emil Cimpan, Chiara L. Battistelli, Cecilia Bossa, Rositsa Tsekovska, Damjana Drobne, Sara Novak, Neža Repar, Ammar Ammar, Penny Nymark, Veronica Di Battista, Anita Sosnowska, Tomasz Puzyn, Nikolay Kochev, Luchesar Iliev, Vedrin Jeliazkov, Katie Reilly, Iseult Lynch, Martine Bakker, Camila Delpivo, Araceli Sánchez Jiménez, Ana Sofia Fonseca, Nicolas Manier, María Luisa Fernandez-Cruz, Shahzad Rashid, Egon Willighagen, Margarita D Apostolova, Maria Dusinska

FAIR assessment of nanosafety data reusability with community standards

Published in Scientific Data

AbstractNanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets’ reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.