Materials Data Infrastructure and Ontology Development for Exemplary Reference Dataset

The development of new engineered materials relies on high-quality reference data and material standards. Especially mechanical properties at high temperatures, such as creep properties, are crucial for potential structural applications of new alloys. Within NFDI-MatWerk, the example reference dataset uses data from creep experiments on single crystal Ni-based superalloy [1] with benchmark material properties, along with a detailed experimental description and relevant material information.

To distribute reference datasets along with their metadata, we apply the FAIR (Findable, Accessible, Interoperable, and Reusable) principles [2]. FAIR Digital Objects (FDOs) are created to support machine readability and accessibility using Persistent Identifiers (PIDs). Moreover, we develop semantic description in the form of an ontology and metadata schema to make the information explicit, and to enable interoperability between databases, automated analysis, and integration into simulation workflows. The reference dataset ontology (RDO) provides a formal description of concepts, which define reference data, e.g., standard, validation, curation, etc. ... mehrand are based on the exemplary dataset for creep reference data. Thus, classifies the RDO a top-level ontology with universal concepts. The experimental workflow and the material characteristics, which are important for the interpretation of the research data, have to be described in an application-level ontology or mapped to other related ontologies, e.g., PMD (Platform Material Digital) Application Ontologies [3].

We summarize our data journey in Figure 1, highlighting the two aspects, namely “data description and storage” and “machine actionable representation as FDO”. The research data management platform Coscine [4] is utilized for data and metadata storage, along with services such as AIMS [5] and MetaStore [6] for the 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 such as the FDO Maker, FAIR-DOscope [7], and a part of Coscine are employed to create, resolve, and visualize FDOs, making the reference dataset and its metadata interpretable for the external material scientists’ community.

This approach ensures the FAIRness of the data and accelerates research in the field of Material Science and Engineering by providing high-quality reference datasets and material standards for the development of new alloys for high-temperature applications. The workflow has been successfully implemented for an example use case and will be supported by easy-to-use tools in the future.

References
[1] B. Skrotzki, & S. Schriever, BAM reference data: results of ASTM E139 -11 creep tests on a reference material of Nimonic 75 nickel-base alloy [Data set]. Zenodo 2021, DOI: 10.5281/zenodo.5106606
[2] M. Wilkinson, M. Dumontier, I. Aalbersberg, I. et al. Sci Data 2016. 3, 160018, https://doi.org/10.1038/sdata.2016.18
[3] https://github.com/materialdigital/application-ontologies
[4] https://about.coscine.de/
[5] https://www.aims-projekt.de/
[6] https://matwerk.datamanager.kit.edu/schema-management.html
[7] https://demo.datamanager.kit.edu/fairdoscope/

Acknowledgments:
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524.