Ing. Jana Martínková

Stať ve sborníku

10.5220/0012837900003756

Katedra softwarového inženýrství

In recent times, Data Management Planning has become increasingly crucial. Effective practices in data management ensure more precise data collection, secure storage, proper handling, and utilization beyond the primary project. However, existing DMPs often suffer from complex structures that impede accessibility for humans and machines. This project aims to address these challenges by converting DMPs into formats that are both machine-actionable and human-readable. Leveraging established DMP templates and relevant ontologies, our methodology involves analyzing diverse approaches to achieve this dual functionality. We assess machine-actionability through comparative evaluations using AI and NLP tools. Furthermore, we identify gaps in ontologies, laying the groundwork for future enhancements in this critical area of research.

Stať ve sborníku

10.3233/FAIA230229

Katedra softwarového inženýrství

Effective management of research data is crucial in modern scientific research, and ontologies and vocabularies play a significant role in describing and organizing such data. However, the abundance of available ontologies and vocabularies for various aspects of research data management (RDM) poses challenges in selecting the most suitable ones. This work aims to comprehensively analyze the key ontologies relevant to data stewardship and RDM. By investigating concepts, properties, interlinks, and potential overlaps, we establish and describe the relationships between these selected ontologies. Our analysis not only enhances understanding of existing ontologies and vocabularies used in RDM but also suggests practical applications for the outcomes of this study. For instance, we propose leveraging the findings to develop semantic data management plans in RDF, thereby improving the organization and accessibility of research data. Moreover, we identify potential ontologies for future extensions of this work.