This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.1038/s41597-023-02501-8 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-03524 in citations.
Shared metadata for data-centric materials science
Shared metadata for data-centric materials science
The expansive production of data in materials science, their widespread sharing andrepurposing requires educated support and stewardship. In order to ensure that this needhelps rather than hinders scientific work, the implementation of the FAIR-data principles(Findable, Accessible, Interoperable, an...
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Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-03524 in citations.
The expansive production of data in materials science, their widespread sharing andrepurposing requires educated support and stewardship. In order to ensure that this needhelps rather than hinders scientific work, the implementation of the FAIR-data principles(Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, thewider materials-science community ought to agree on the strategies to tackle the challengesthat are specific to its data, both from computations and experiments. In this paper, wepresent the result of the discussions held at the workshop on “Shared Metadata and DataFormats for Big-Data Driven Materials Science”. We start from an operative definition ofmetadata, and the features that a FAIR-compliant metadata schema should have. Wewill mainly focus on computational materials-science data and propose a constructiveapproach for the FAIRification of the (meta)data related to ground-state and excited-statescalculations, potential-energy sampling, and generalized workflows. Finally, challenges withthe FAIRification of experimental (meta)data and materials-science ontologies are presentedtogether with an outlook of how to meet them. |