Berlin 2024 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 43: Data Driven Material Science: Big Data and Workflows V
MM 43.3: Vortrag
Mittwoch, 20. März 2024, 16:15–16:30, C 243
FAIR Data Management for Computational Materials Science using NOMAD — •Luca M. Ghiringhelli1, Joseph F. Rudzinski2, José M. Pizarro2, Nathan Daelman2, and Silvana Botti3 — 1Department of Materials Science and Engineering, Friedrich-Alexander-Universität, Erlangen-Nürnberg — 2Institut für Physik und IRIS-Adlershof, Humboldt-Universität zu Berlin, Berlin — 3RC-FEMS and Faculty of Physics, Ruhr University Bochum, Bochum
NOMAD [nomad-lab.eu][1, 2] is an open-source data infrastructure for materials science data. Originally built as a repository for DFT calculations, NOMAD has been extensively developed over the past 2 years to support a wide range of both computational and experimental data. Additionally, NOMAD now includes a general workflow support that not only streamlines data provenance and analysis but also facilitates the curation of AI-ready datasets. This talk will demonstrate how these features, along with NOMAD’s adherence to the FAIR principles (Findability, Accessibility, Interoperability, Reusability), provide a powerful framework for enhancing data utility and discovery. I will highlight how this FAIR-compliant perspective distinguishes NOMAD from other Big-Data infrastructures, e.g., allowing users to specify their data quality needs. Finally, I will provide an outlook of NOMAD’s potential for creating a cohesive, interconnected, and economical scientific data landscape.
[1] Scheidgen, M. et al., JOSS 8, 5388 (2023).
[2] Scheffler, M. et al., Nature 604, 635-642 (2022).
Keywords: FAIR data; data management; workflow; AI-ready data; datasets