Berlin 2024 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 44: Developement of Calculation Methods II
MM 44.1: Vortrag
Mittwoch, 20. März 2024, 15:45–16:00, C 264
FAIR Data Quality Metrics in NOMAD — •Nathan Daelman1, Joseph F. Rudzinski1, José M. Pizarro1, Luca M. Ghiringhelli2, and Silvana Botti3 — 1Institut für Physik und IRIS-Adlershof, Humboldt-Universität zu Berlin, Berlin — 2Department of Materials Science and Engineering, Friedrich-Alexander-Universität, Erlangen-Nürnberg — 3RC-FEMS and Faculty of Physics, Ruhr University Bochum, Bochum
The FAIR principles (Findable, Accessible, Interoperable, Reusable) serve as a reference for assessing the quality of data storage and publication [1]. NOMAD [nomad-lab.eu][2, 3] is an open-source data infrastructure for materials science data that is built upon these principles.
In this presentation, I will demonstrate the interplay between high-quality data and knowledge using the functionalities provided by NOMAD and with DFT as an example case. In particular, I will showcase the dynamic and flexible metadata framework, designed for a clearer, more customizable navigation of the zoo of density functionals. I will then show how precision and accuracy metrics are represented within this framework, and how they can be linked to benchmark datasets. Finally, I will present a brief outlook on the future of NOMAD as a platform that fosters an interconnected research community and engaged scientific discourse.
[1] Wilkinson, M. D. et al., Sci. Data 3, 160018 (2016).
[2] Scheffler, M. et al., Nature 604, 635-642 (2022).
[3] Scheidgen, M. et al., JOSS 8, 5388 (2023).
Keywords: FAIR data; data management; data quality; accuracy metrics; density functionals