  The expansive production of data in materials science, their widespread
sharing and repurposing requires educated support and stewardship. In order to
ensure that this need helps rather than hinders scientific work, the
implementation of the FAIR-data principles (Findable, Accessible,
Interoperable, and Reusable) must not be too narrow. Besides, the wider
materials-science community ought to agree on the strategies to tackle the
challenges that are specific to its data, both from computations and
experiments. In this paper, we present the result of the discussions held at
the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials
Science". We start from an operative definition of metadata, and what features
a FAIR-compliant metadata schema should have. We will mainly focus on
computational materials-science data and propose a constructive approach for
the FAIRification of the (meta)data related to ground-state and excited-states
calculations, potential-energy sampling, and generalized workflows. Finally,
challenges with the FAIRification of experimental (meta)data and
materials-science ontologies are presented together with an outlook of how to
meet them.
