Regensburg 2025 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
MM: Fachverband Metall- und Materialphysik
MM 31: Data-driven Materials Science: Big Data and Worksflows
MM 31.7: Vortrag
Donnerstag, 20. März 2025, 16:45–17:00, H10
Enhancing FAIR Data Management with Automated Visualization of Calculations — •N. Daelman1, E. Boydas1, B. Mohr1, J.M. Pizarro1, T. Bereau2, C. Draxl1, L.M. Ghiringhelli3, M. Girard4, D. Usvyat5, R. Valenti6, S. Botti7, and J.F. Rudzinski1 — 1CSMB, HU Berlin — 2ITP, Heidelberg Uni. — 3Dept. of Mater. Sci. and Eng., FAU Erlangen — 4Max Planck Inst. for Poly. Res., Mainz — 5Inst. für Chem., HU Berlin — 6Inst. für Theor. Phys., GU Frankfurt/M — 7RC-FEMS, Ruhr Uni. Bochum
In contrast to data science packages, first-degree data post-processing tends to lock people into silos built around a particular simulation software. NOMAD [nomad-lab.eu][1] is an open-source and community-driven data infrastructure that breaks open these silos by extracting scientific data from over 60 code packages into a code-agnostic schema within a research data management (RDM) ecosystem [2]. This talk showcases NOMAD*s new visualization features at various levels of RDM. At the level of individual calculations, NOMAD provides now more detailed electronic structure visualizations and fast, dynamic rendering of heavy files. Automated visualization does not imply, however, a lack of customizability. NOMAD provides support for tailored figures and larger-scale specialization via an accessible plugin-based system. At the level of research projects, NOMAD allows for quick monitoring of the data coverage via a fully customizable dashboard.
[1] Scheidgen, M. et al., JOSS 8, 5388 (2023).
[2] Scheffler, M. et al., Nature 604, 635-642 (2022).
Keywords: FAIR data; research data management; scientific visualization; highthroughput; ab initio