InteroperAble Descriptions of Observable Property Terminologies (I-ADOPT) WG Outputs and Recommendations

The InteroperAble Description of Observable Property Terminologies Working Group (I-ADOPT WG) was formed in June 2019 under the auspices of the Research Data Alliance’s RDA Vocabulary Services Interest Group (VSSIG). Its objective was to develop a framework to harmonise the way observable properties are named and conceptualised, in various communities within and across scientific domains. There was a realisation that the rapid demand for controlled vocabularies specialised in describing observed properties (i.e. measured, simulated, counted quantities, or qualitative observations) was presenting a risk of proliferation of semantic resources that were poorly aligned. This, in turn, was becoming a source of confusion for the end-users and a hindrance to data interoperability.

The development of the I-ADOPT Framework proceeded in multiple phases. Following the initial phase dedicated to the collection of user stories primarily from the environmental domain, the identification of key requirements, and an in-depth analysis of existing semantic representations of scientific variables and of terminologies in use, the group focused on identifying the essential components of the conceptual framework, reusing as much as possible concepts that were common to existing operational resources. The proposed framework was then tested against a variety of examples to ensure that it could be used as a sound basis for the creation of new variable names as needed. The results were formalised into the I-ADOPT ontology and subsequently extended with usage guidelines to form the I-ADOPT Framework presented in this document. The output can now be used to facilitate interoperability between existing semantic resources and to support the provision of machine-actionable variable descriptions whose components are mapped to FAIR vocabulary concepts. The easier integration of datasets annotated with I-ADOPT-enabled variable descriptions will not only enable more comprehensive analyses, but also provides the corpora for machine learning and other artificial intelligence applications.