  From a data perspective, the field of materials mechanics is characterized by
a sparsity of available data, mainly due to the strong
microstructure-sensitivity of properties such as strength, fracture toughness,
and fatigue limit. Consequently, individual tests are needed for specimens with
various thermo-mechanical process histories, even if their chemical composition
remains the same. Experimental data on the mechanical behavior of materials is
usually rare, as mechanical testing is typically a destructive method requiring
large amounts of material and effort for specimen preparation and testing.
Furthermore, mechanical behavior is typically characterized in simplified tests
under uniaxial loading conditions, whereas a complete characterization of
mechanical material behavior requires multiaxial testing conditions. To address
this data sparsity, different simulation methods, such as micromechanical
modeling or even atomistic simulations, can contribute to
microstructure-sensitive data collections. These methods cover a wide range of
materials with different microstructures characterized under multiaxial loading
conditions. In the present work, we describe a novel data schema that
integrates metadata and mechanical data itself, following the workflows of the
material modeling processes by which the data has been generated. Each run of
this workflow results in unique data objects due to the incorporation of
various elements such as user, system, and job-specific information in
correlation with the resulting mechanical properties. Hence, this integrated
data format provides a sustainable way of generating data objects that are
Findable, Accessible, Interoperable, and Reusable (FAIR). The choice of
metadata elements has centered on necessary features required to characterize
microstructure-specific data objects, simplifying how purpose-specific datasets
are collected by search algorithms.
