NFDI4Ing conducted its second community survey on the topic of research data management (RDM) in the engineering sciences. The findings will help tailor the consortium’s services precisely to the needs of engineers. Counting on your support, the third survey has just started.
Image: “Research Data Diversity” by Heinz-Vale, CC BY-SA 4.0
The NFDI4Ing task area Doris designs transferable research data management concepts and tools for data from high-performance measurement and computation (HPMC).
Discover ing.grid, the journal for FAIR data management in engineering sciences.
We cordially invite you to this year’s NFDI4Ing Conference 2023 on 27th and 28th September 2023!
We’re excited to introduce SOFIRpy, a Python package that lets you co-simulate functional mock-up units (FMUs) with custom models.
This year’s meeting of the mechanical and industrial engineering community (CC-41) focuses on the application of data models. Guests from the field of production engineering will discuss their importance as well as common difficulties in their design, adaption and usage on the basis of their own experiences and use cases. Modelling complex information and its … Continued
Betty is the NFDI4Ing task area covering the research data management challenges faced by engineers who develop their own software solutions. Betty envisions a future in which every engineer produces verified, high-quality software that can be reused and extended. To that end, Betty wants to identify and provide the missing tools, teaching material and recommendations. … Continued
The latest release of the metadata4ing ontology (m4i) comes with a practical hands-on guide: Get to know basic classes and properties of m4i and see how a JSON-LD file is built up step by step applying the ontology to a real-world example. What is Metadata4Ing – a short recap In 2022, Metadata4Ing (m4i) was released … Continued
Providing Research Data Management (RDM) support and education is a central goal of NFDI4Ing. Together, the Base Service measure S-6, the Community Cluster measure CC-2, and the Special Interest Group “RDM Training and Education” are introducing new trainings on RDM with special focus on engineers. Illustration: Available learning materials The general objective of Base Service … Continued
While experimental data and methods are commonly reported as “best outcome” figure of merits, the systematic process parameters (while being crucial for achieved outcome, in particular in combination and chains of different process steps) are often neglected or buried in hard-to-access lab books and similar sources. This complicates and prolongs process optimization, especially when different printing and post-processing steps need to be combined for a specific application. The project ReCIPE aimed at setting up a tool for researchers and users of printed electronics that can grow into a broad open database on process parameters and connected outcomes for the wider academic and industry-based community.