The NFDI4ING Education Platform provides self-paced RDM training tailored to engineering, featuring practical use cases and quizzes to help researchers manage, share, and reuse research data.
SciKGTeX is a LaTeX package that lets researchers annotate key research contributions in their papers and embed them in PDF metadata, improving discoverability for search engines and knowledge graphs.
The NFDI4Ing conference 2024 will take place on the 18th & 19th September as a virtual event. The call for proposals is now open!
NFDI4Ing’s Special Interest Groups (SIGs) are the place for exchange between NFDI4Ing and interested experts from the community. In the SIG “RDM Training”, we work together on reviewing and improving RDM trainings.
By utilizing large language models to provide an automated method for extracting knowledge from a vast amount of literature and integrating it with the research data management platform, users will be offered more intelligent data management and analysis methods.
GOLO’s Field Database Platform (FDP) redefines field data management with digital twin technology. It offers real-time visualization, interactive analysis, and efficient filtering, turning complex sensor data into actionable insights and enhancing research efficiency.
The transparent and sustainable scientific exchange of methods, results and findings along the FAIR principles is one of the greatest challenges facing science today. To show how these challenges can be met with innovative technologies, the most recent NFDI4Ing Community Meeting presenting the work of our archetype ELLEN took place on June 6th.
The next meeting of the NFDI4Ing SIG QA&metrics is on 26th July at 09:00.
On July 25th a NFDI4Ing Community Meeting will focus on the challenges, solutions and services of our archetype FRANK, who mainly works in Mechanical and Industrial Engineering and with heterogeneous data sources.
NFDI4Ing provides infrastructure, best practices and templates to make research software and its development more replicable and reproducible while improving the quality of the written code. For the further improvement of the our RDM knowledge base and our JupyterHub prototype, we are looking for your collaboration and input!