The development of advanced materials relies on high-quality reference data and standards, particularly for mechanical properties at elevated temperatures, essential for potential structural applications of new alloys. NDFI-MatWerk is establishing a framework for curating and distributing reference datasets, utilizing a Ni-based superalloy creep experiment dataset as an exemplary reference data set.

To ensure Findability, Accessibility, Interoperability, and Reusability (FAIR) of reference datasets, FAIR Digital Objects (FDOs) are generated, supporting machine readability and accessibility through Persistent Identifiers (PIDs). Semantic descriptions, using ontologies and metadata schemas, enhance explicit information and enable interoperability, automated analysis, and integration into simulation workflows.

The Reference Dataset Ontology (RDO) formalizes universal concepts defining reference data, including standards, calibration, and curation, based on the exemplary creep dataset. Descriptions of experimental workflows and material characteristics have to be described in an application-level ontology or mapped to related ontologies like PMD (Platform Material Digital) Application Ontologies.

The research data management platform Coscine is utilized for data and metadata storage, along with services such as AIMS and MetaStore for FDO management. Furthermore, the metadata schema is formulated enabling compatibility with ontologies. The dataset can be transferred as JSON Schema to the MetaStore for metadata registration and validation. Services like FDO Maker, FAIR-DOscope, and Coscine aid in creating, resolving, and visualizing FDOs, enhancing interpretability for external material scientists.

This approach ensures FAIRness, expediting research in Material Science and Engineering by delivering high-quality reference datasets and material standards for developing new engineered materials.

Dr. Angelika Gedsun