Dresden 2020 – wissenschaftliches Programm

Die DPG-Frühjahrstagung in Dresden musste abgesagt werden! Lesen Sie mehr ...

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

CPP: Fachverband Chemische Physik und Polymerphysik

CPP 66: Focus Session: Big Data in Aquisition in ARPES (joint session O/CPP)

CPP 66.1: Hauptvortrag

Mittwoch, 18. März 2020, 10:30–11:00, REC C 213

Towards FAIR experimental data — •Claudia Draxl — Humboldt-Universität zu Berlin — Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany

Knowledge and understanding of materials is based on their characterization in terms of a variety of properties and functions. Surprisingly though, for only a very small number of materials this information exists. Making materials data available, opens avenues for data-driven research in terms of re-purposing (using materials for a different purpose than intended by the original work), detecting candidate materials for a given application, and finding descriptors by approaches of artificial intelligence. Prerequisite for all this is a FAIR (findable, accessible, interoperable, reusable) data infrastructure. In computational materials science, the NOMAD Laboratory (https://nomad-coe.eu) has set the stage for FAIR data [1], by offering services like free upload to the NOMAD Repository, the NOMAD Archive, the NOMAD Encyclopedia, and the NOMAD Analytics Toolkit. In this talk, I will address our concepts and first steps towards extension of this open-science platform towards experimental data and sample synthesis. Here, for instance, data volume and velocity are big issues for many measurement techniques, while large uncertainties may come from (often incompletely known) sample quality, instrumental resolution, or measurement conditions. These challenges are tackled within the non-profit association FAIR-DI (https://fairdi.eu) and FAIRmat (https://fairdi.eu/fairmat), a proposed consortium for the NFDI.

[1] C. Draxl and M. Scheffler, MRS Bulletin 43, 676 (2018).