NFDI4Ing is running a community survey on the state of research data management (RDM) in the engineering sciences. One of the goals of the survey is to tailor the consortium's services precisely to the needs of engineers. To obtain a

Analysing large collections of documents using text-and-data-mining methods can yield entirely new scientific insights. In the NFDI4Ing task area Automated data and knowledge discovery in engineering literature (S-7), services are developed to enable researchers to more easily apply these innovative

In NFDI4Ing, we develop engineering-specific RDM trainings and educational material and make them publicly available. Learn more about the formats, contents, and our platform.

Field experiments are characterized by environmental conditions that cannot be fully controlled by the experimenter. Examples for field experiments include testing driver assistance systems in traffic, submarine robots in open water, or monitoring traffic in cities. Task Area GOLO is

NFDI4Ing is yielding results, and these and the documentation of our work want to be organized. But what can FAIR data management look like for research data management work? Maybe like this.

In the NFDI4Ing Base Service S-4, the Karlsruhe Institute of Technology, RWTH Aachen University, Technical University of Darmstadt and University of Stuttgart are working together on curating, facilitating and improving access to research data repositories and storage systems

Many researchers struggle with the complexity of RDM. The NFDI4Ing Task Area Frank aims to simplify RDM for researchers by supporting them with needed RDM knowledge, specific decision support along the RDM process and tooltips for their current