Ideally, researchers can incorporate existing, established and tested research software into their own work. BETTY tries to help researchers in finding existing software suitable for their needs, developing new software, and publishing software artifacts in a sustainable and reproducible way.

Text and data mining methods offer the potential to gain new insights from publications. The NFDI4Ing base service automated data and knowledge discovery in engineering literature is developing the legal and technical basis for making engineering literature accessible for this purpose.

Providing Research Data Management (RDM) support and education is a central goal of NFDI4Ing. That is why we are developing a learning platform for RDM with a special focus on engineering sciences. The challenge is to design it subject-specific and attractive for motivated, self-paced learning. To meet this requirement, we need your input!

Explore Golo‘s work in field data management and Digital Twin Frameworks, aimed at optimizing data reusability in field environments.

The most recent NFDI4Ing Community Meeting 2023 took place online on 27 October. Talks and presentations were focused on giving an introduction to research data management and data management plans. The meeting was rounded off with interactive workshop sessions in which current research projects served as case studies. The workshops laid the foundation for NFDI4Ing to initiate application-related harmonisation and standardisation efforts.

In our base service “Repositories and Storage” (S-4) we have reached a couple of milestones during the last year, with many new developments in the storage and repositories domains.

The base service “research software development” provides infrastructure, best practices and templates to make research software and its development more replicable and reproducible while improving the quality of the written code. Currently, our main projects are a JupyterHub server for the NFDI4Ing-community, a knowledge base with best practices and examples for sustainable code development, and training courses for, e.g., GitLab.

The base service “Quality assurance in RDM processes and metrics for FAIR data” in NFDI4Ing includes the support of data management plans with RDMO, the development of maturity models for research data management processes and the provision and maintenance of FAIR data metrics. The common goal is to ensure the quality of research data management in engineering research through each individual service.

By ordering the RDM activities based on their occurrence in the engineering research process, Jarves provides a structure for RDM in engineering. Based on the research’s boundaries, Jarves offers information on the next steps and available tools.

SciKGTeX enables authors to create “FAIR-by-Design” publications by enriching them with FAIR information once and in parallel to the time of creation. This information is embedded into the PDF’s XMP metadata for persistent and long-term availability.

You want to document your engineering workflows from simulations or post-processing on HPC systems in a controlled vocabulary? You want to contribute to our user-based ontology development? Then please try out our alpha-release and share your needs and feedback in the NFDI4Ing GitLab platform.

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!

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

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

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,

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)

From its inception, members of NFDI4Ing have been working on a concept for the publication and organization of standards in the field of research data. By now, the concept is quite advanced and ready for the next step. The working group “Standardisation” (CC-5) is looking for interested persons from the community to support the work and to get the implementation underway.