Speaker
Description
The emergence of big data in science underscores the need for FAIR (Findable, Accessible, Interoperable, Reusable) [1] data management. NOMAD [nomad-lab.eu] [2, 3] is an open-source data infrastructure that meets this demand in materials science, enabling cross-disciplinary data sharing and annotation for both computational and experimental users. In this contribution, we will present our recent work in extending NOMAD to support a range of many-body and excited state calculations, including GW, BSE, and DMFT, among others. We will demonstrate how NOMAD captures these workflows in an automated but flexible fashion, enabling findability and clear, visual overviews. Finally, we will present an outlook on NOMAD′s potential for large-scale interoperability and harmonization between computational and experimental data in the field of spectroscopy.
[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).