DCSO: Towards an Ontology for Machine-actionable Data Management Plans

Abstract

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a core set of universal concepts characterising a DMP in the pursuit of producing a DMP as a machine-actionable information artefact, i.e.,machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible, but has no clear mechanism to distinguish between the core specification and its extensions. Currently, the maDMP specification provides a JSON serialisation and schema to operationalise the approach. Such approach, however, does not address the concerns above. This paper reports on the community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

“…The article also focuses on the problems which can originate from the application of such ontologies and their future improvement. Three selected articles are referenced as "Medical Data" [61]- [63]. The first article reports on the community effort to create the Data Management Plan (DMP) Common Standard Ontology (DCSO), with a particular focus on a detailed description of the components of the ontology.…”

“…Although this review's primary objective is not to analyse the FAIRness of the ontologies, it is interesting to observe that only seven out of the 32 identified papers make reference to the FAIR principles [51], [52], [56], [58], [61], [67], [74]. Despite all articles agreed on the fact that ontologies actually lead to reproducible research and may improve the adoption of FAIR principles by supporting data integration, analysis, facilitating data interpretation, interoperability, and data mining, it clearly emerges that appropriate metrics to evaluate the FAIRness are still developing.…”