The field of materials characterisation encompasses a wide range of methods and related research communities. This has led to a proliferation of terminologies and data management approaches, hindering collaboration and interoperability. In this work, a domain ontology designed to model the common aspects across the different characterisation methodologies is presented. This ontology, called the CHAMEO ontology, is based on a recent CEN Workshop Agreement (CWA 17815) which introduced a standardised terminology and the Characterisation Data (CHADA) documentation scheme. The goal of CHAMEO is to provide a framework for harmonising the underlying method-specific ontologies, which can be developed by reusing and specialising the generic constructs of the CHAMEO ontology. This work is part of a broader initiative under the umbrella of the European Materials Modelling Council (EMMC), for the development of interconnected materials modelling ontologies based on a common root that is the Elementary Multiperspective Material Ontology (EMMO). The CHAMEO ontology was developed within the NanoMECommons European project that has the goal of harmonising characterisation protocols. The CHAMEO ontology has also been aligned with a number of recently developed, EMMO-based domain ontologies for the classification of materials, models, manufacturing processes and software products related to Materials Modelling. Availability. The axiomatization of the ontology is stored in a GitHub repository available at: https://github.com/emmo-repo/domain-characterisation-methodology, and is published at the following URL: http://emmo.info/emmo/domain/chameo/chameo.
[1]
F. Berman,et al.
The Research Data Alliance: Benefits and Challenges of Building a Community Organization
,
2020,
2.1.
[2]
E. Koumoulos,et al.
Innovative Data Management in advanced characterization: Implications for materials design
,
2019,
Materials Today Communications.
[3]
Daniele Toti,et al.
Experimentation of a smart learning system for law based on knowledge discovery and cognitive computing
,
2019,
Comput. Hum. Behav..
[4]
Erik Schultes,et al.
The FAIR Guiding Principles for scientific data management and stewardship
,
2016,
Scientific Data.
[5]
Asunción Gómez-Pérez,et al.
Scenarios for building ontology networks within the NeOn methodology
,
2009,
K-CAP '09.