Ing. Jana Martínková

Článek

10.5334/dsj-2025-016

Katedra softwarového inženýrství

Semantic annotation has emerged as a key technique for transforming human-readable data into machine-actionable formats. It corresponds with the growing emphasis on data reusability and research reproducibility. This paper examines tools for semantic annotation using ontologies and controlled vocabularies, with a focus on their application in data management planning. A systematic review identified 34 relevant tools, which show potential for adaptation to the data management plan (DMP) domain. While these tools meet many requirements, they do not fully address all DMP-specific needs. The paper provides an overview of current tools and suggests directions for future research to adapt them for DMP use.

Stať ve sborníku

10.5220/0012940000003838

Katedra softwarového inženýrství

The growing significance of Data Management Plans (DMPs) has highlighted the need for standardized and accurate data management practices. Current DMPs often suffer from inconsistent terminology, leading to misunderstandings and reducing their effectiveness. This study proposes the development of a DMP OntoUML conceptual model to address these issues. The model aims to clearly define all relevant concepts and their relationships, ensuring consistency and interoperability, particularly by connecting with the FAIR principles OntoUML model. The research follows a structured approach: specifying necessary concepts using existing templates and ontologies, defining terms and their relationships within the OntoUML model, and verifying the model’s syntax. The resulting conceptual model will standardize terminology, promote interoperability, and support future DMP development and education.

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.