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TT: Fachverband Tiefe Temperaturen

TT 56: Focus Session: Making Experimental Data F.A.I.R. – New Concepts for Research Data Management I (joint session O/TT)

TT 56.6: Vortrag

Donnerstag, 30. März 2023, 16:30–16:45, WIL A317

Ontology for Experimental Data — •Sandor Brockhauser — Center for Materials Science Data, Humboldt-Universität zu Berlin, Germany

Ontology is the scientific field of formal knowledge representation. This field contributes to Data Science and helps Experimentalist to properly annotate their data and metadata on a FAIR way. During the last decades several different ways have been developed in the field of Ontology for describing knowledge as a set of information and their relationships. These include Information mapping, Concept maps, Topic maps, Mind maps, Knowledge graphs, BORO (Business Objects Reference Ontology), RDF (Resource Description Framework), OWL (Web Ontology Language), ORO (Object-Role Modeling), UML (Unified Modeling Language), ISO 15926 (standard for data sharing), OLOG (mathematical framework for knowledge representation), GELLISH (ontology language for data storage and communication), etc. For describing experimental facts, we suggest using an ontology in OWL which is derived from the NeXus community standard. It represents all the concepts developed for explaining experiments and experimental data, just like the relationships between them. Such representation allows connecting the concepts defined in NeXus also to other ontologies. Additionally, any data management systems, like NOMAD which accepts experiment data provided in the NeXus standard, can immediately link the data and metadata to the ontology and make them interoperable.