The IAM4NFDI project team invites you to a workshop that will provide the technical and organisational basics for the operation of a Community AAI. Read more…
The next meeting of the NFDI4Ing Special Interest Group (SIG) Training & Education is on Friday, September 1st, starting at 10 PM. Read more…
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
Am 26. May 2023 veranstaltet die NFDI4Ing das zweite Community Meeting des Jahres mit Themen wie Datenschutz und dem richtigen Umgang mit Forschungsdaten… Read more…
Am 25. April 2023 veranstaltet die NFDI4Ing einen Workshop zu urheberrechtlichen Fragestellungen im Zusammenhang mit der Gestaltung von Dienstleistungen zum Text und Data Mining. Read more…
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.
Data processing is usually not a single task, but in general relies on a chain of tools. To achieve transparency, adaptability, and reproducibility of (computational) research, the FAIR principles must therefore be applied to all components of the research process. This includes the tools (i.e. any research software) used to analyze the data, but also … Continued