Analyzing Functional Requirements of Data Curation Activities Support in Research Data Platforms with Domain Ontology Approach

In recent years, there has been international progress in developing research data platforms that support the reproducibility and reusability of research data. In developing the functionality of research data platforms, designing them to connect highly versatile service domains such as research data management, publication, and discovery is crucial. When considering the research data platform functions to support data curation activities in various fields, data curation activities practiced in various fields are often incompatible, with fragmented tasks and procedures and tacit knowledge. In order to develop data curation functions applicable to each field, it is necessary to integrate and clarify the fragmented tasks and procedures as a step.

As an approach for integrating fragmented data curation tasks and procedures, the authors have developed the data curation process ontology to represent data curation activities in various fields. By assessing existing research data platforms using the data curation process ontology, the missing elements of functions to support data curation activities could be extracted and lead to the design of functions to support data curation activities. In this study, the authors report on a comprehensive functional development method based on the NII Research Data Cloud. The authors first analyzed the functional requirements collected by the NII Research Data Cloud developers based on the data curation process ontology. Furthermore, the authors identify features missing from the NII Research Data Cloud in supporting data curation activities by assessing consistency between the functional groups obtained from the analysis and the domain model used in the NII Research Data Cloud. Additionally, the authors discuss the open-source software corresponding to the functional groups obtained from the analysis. This approach makes it possible to integrate and articulate fragmented tasks and procedures while maintaining the generality of the service domain. In turn, it is expected to improve the reproducibility and reusability of research data.