Author: Thorsten Schwetje

The availability of terminologies is a critical component of research data management. Without terminologies, meaningful descriptions of research data would not be possible, and the reusability of these data and associated information would be compromised. Accordingly, researchers and funding agencies have a vested interest in the availability of sophisticated and stable terminologies.

The NFDI4Ing task area BETTY envisions a future in which the engineering sciences produce verified, high-quality software that can be reused and extended. Betty wants to identify and provide the missing tools, teaching material and recommendations to make that vision reality.

For most researchers, working with code is common practice. However, many researchers lack knowledge and experience in how to write good code. In the Task Area Alex, we addressed this problem and developed a set of guidelines currently being tested at the Chair of Fluid Systems, TU Darmstadt.

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.