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NFDI4Ing
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NFDI4Ing represents the German engineering research landscape. The consortium brings together institutions and individuals from all areas of the engineering landscape, each contributing their specific expertise, experience, and contacts to the consortium’s work program. NFDI4Ing thus enables the close collaboration of engineering scientists, service providers, and users to work towards the common goal of realizing the FAIR principles of scientific data management in the engineering sciences. A central task of NFDI4Ing is to consolidate the wide variety of engineering research approaches and methods into a limited set of common standards and requirements for research data management (FDM). This is an ongoing process, which we keep advancing since 2017. In our work programme, we focus on three aspects: establishing education in data literacy in all phases of scientific careers, development and testing of a governance concept for research data management, and ensuring broad availability of technologies and services for machine-readable data and their metadata.
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Information package about Praktikum Digitalisierung
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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.
Learning Resource Type
Lecture
File Format
PDF
Languages
German
Media Type
Presentation, Text
Publication Date
2024-11-27
License
CCBYSA
Size
5.1 MB
Authors
Keywords
#research data management#programming#engineering science#physics
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NFDI4ING Basic RDM Trainings
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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.
Learning Resource Type
Other, Web Portal
File Format
MD, PNG, HTML, MP4, H5P
Languages
German
Media Type
Image, Text, Video
Publication Date
2024-12-31
License
CCBY
Authors
Keywords
#Research Data Management#Data Life Cycle#RDM#Forschungsdatenmanagement#FDM#DLC#Engineering Sciences#Ingenieurwissenschaften#wikidata:Q30089794
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How to Use Metadata4Ing - First Steps Tutorial
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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.
Learning Resource Type
Cookbook, Tutorial
File Format
MD
Languages
English
Media Type
Text
Publication Date
2023
License
CCBY
Authors
Keywords
#terminology
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NFDI4Chem Web Seminars - Joint Webinar Ontologies in Science and Technology
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NFDI4Chem Web Seminars - Joint Webinar Ontologies in Science and TechnologyIn this joint webinar by NFDI4Chem, NFDI4Cat and NFDI4Ing we introduce the concept of Ontologies and their use in Engineering Sciences, Catalysis and Chemistry. Ontologies can help describe research objects, processes and results in a way that not only humans, but also machines can understand and work with. This opens up amazing possibilities for finding, using and integrating research results. The webinar is aimed at scientists with an interest in, but no prior experience with Ontologies. It provides a have a brief introduction to Ontologies and interesting speakers from academia and industry.
File Format
Unknown
Languages
English
Media Type
Video
Publication Date
2021-01-29
License
Proprietary
Authors
Keywords
No keywords
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Data
Literacy
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