Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

In the age of scientific digitization, ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for machine-processable data. Ontologies play a vital role in achieving data FAIRness by representing knowledge in a machine-understandable format. Catalysis research data is often complex and diverse, requiring a broad collection of ontologies. While existing ontology portals aid in ontology discovery, they lack deep classification, and quality metrics specific to catalysis research ontologies are absent.

This work from Alexander Behr, Hendrik Borgelt and Prof. Dr. Norbert Kockmann from our partner TU Dortmund proposes a systematic approach for collecting ontology metadata focused on the catalysis research data value chain. Ontologies are classified by subdomains of catalysis research, enabling efficient comparison. A workflow and codebase are provided to represent metadata on GitHub, along with a method to automatically map ontology classes, offering insights into relatedness. The methodology is designed for reusability and can be adapted to other ontology collections or knowledge domains. The ontology metadata and code developed are available in a GitHub repository: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat.

If the summary has piqued your interest, you can find the paper here.