In NFDI4Ing, various services and solutions are being developed to improve and simplify (research) data management in engineering. To bring these solutions into practice and validate them based on the subject-specific requirements of researchers, NFDI4Ing regularly organizes Community Meetings. Researchers, infrastructure operators and industry partners from various fields of engineering meet to discuss ideas and to network.
You are interested in (research) data management, its current and future challenges, and topics like terminologies, metadata, ontologies, data repositories? Then join us on the NFDI4Ing conference on Wednesday and Thursday, October 26 and 27, 2022!
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 detailed picture of the state of RDM in the individual engineering disciplines, we hope for numerous participants from all fields of engineering.
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 methods in practice.
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 developing services and solutions to support engineers conducting field experiments in their work.
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.