This guide demonstrates how to model metadata for research processes and resulting datasets in engineering with the ontology Metadata4Ing 1.2.1. The guide will enable you to create machine-understandable metadata for your own research data, following Semantic Web standards like RDF, RDFS, OWL, and JSON-LD.
Information package about Praktikum DigitalisierungDuring the course, students will explore fundamental digitalization concepts and FAIR research data management within the context of mechanical engineering in a practice-oriented manner. This will be achieved through selected experiments in mechanical engineering, utilizing digital measurement technology. By integrating research data management (aligned with FAIR principles, the data life cycle, and data quality), the course imparts both subject-specific and interdisciplinary data literacy.
Data Management Training Clearinghouse (DMTC)DMTC is a registry for excellent online learning resources focusing on data skills and capacity building for research data management, data stewardship and data education.
How to Use Metadata4Ing - First Steps TutorialThis guide demonstrates how to model metadata for research processes and resulting datasets in engineering with the ontology Metadata4Ing 1.2.1. The guide will enable you to create machine-understandable metadata for your own research data, following Semantic Web standards like RDF, RDFS, OWL, and JSON-LD.
NFDI4ING Basic RDM TrainingsThe NFDI consortium for Engineering Sciences (NFDI4ING) is providing a collection of basic trainings on research data management (RDM), covering training modules along the research data lifecycle (DLC) phases as well as cross-topic modules like licenses and metadata. Trainings are provided as self-paced materials for individual learning, but can be used in classes as well.