Ontologies are widely used in the domain of materials science for the purpose of describing experiments, processes, properties of materials, and experimental and computational workflows. There are several online platforms for accessing and sharing ontologies related to materials science and engineering available. However, the in-depth analysis and evaluation of these ontologies with respect to quality-control metrics are missing. This work presents an overview of ontologies used in Materials Science and Engineering in order to guide the domain experts to decide which of the already available ontologies would be best suited for a particular purpose. We have identified 55 ontologies that are evaluated and assessed by adopting the well-known criteria of FAIR data principles [1]. Furthermore, statistical results about the reuse of these ontologies as well as essential ontology metrics are provided. Our analysis also provides information about whether the ontologies investigated are actively and openly maintained. With an overview of the identified ontologies, domain experts will be able to determine the intended ontologies and import and reuse terms from existing ontologies.


[1] Wilkinson, Mark D., et al. "The FAIR Guiding Principles for scientific data management and stewardship." Scientific data 3.1 (2016): 1-9.