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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.
“FAIR-by-Design” Publications with SciKGTeX – A LaTeX Package for Enriching Publications with FAIR Scientific Information at the Time of Creation
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.
New sub-ontology within Metadata4Ing for workflows in high performance measurement and computing (HPMC)
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
We’re excited to introduce SOFIRpy, a Python package that lets you co-simulate functional mock-up units (FMUs) with custom models.
Are you interested in data models and their industrial applications? Join the NFDI4Ing Community Meeting on August 10th!
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
Final Report: Seed Fund “ReCIPE” – Metadata and Provenance tracking for Conductive Inks in Printed Electronics
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.
ield experiments mostly take place under difficult conditions. Due to the large number of sensors and computers, large amounts of data are usually generated, and data acquisition can be affected by small errors or external events. For further processing and analysis of field data, verification of data quality is essential. To assist researchers, TA Golo has documented a website – the Data Quality Metrics Website.
The recently published “Guidelines zum Text und Data Mining für Forschungszwecke in Deutschland” describe under which conditions text and data mining may be carried out for scientific purposes and what risks exist. An online workshop, “Urheberrechtliche Fragestellungen bei der Gestaltung von Dienstleistungen zum Text und Data Mining” will take place on April 25th to address copyright issues in designing services to support text and data mining.
NFDI4Ing cordially invites you to participate with a contribution on this years conference! The motto of the NFDI4Ing conference 2023 is “Innovation in Research Data Management: Bridging the gaps between disciplines and opening new perspectives for research in engineering science”.
The international online journal ing.grid is now accepting submissions addressing FAIR data management in engineering sciences. With an open access policy, the journal bridges a gap in the field, offering a platform and recognition for sound scientific practice in generating research data, developing reusable tools for processing that data and curating the data to make it findable, accessible, interoperable, and reusable (FAIR).
Getting and staying in touch with the community is the essence of NFDI4Ing. That’s why the consortium offers a broad variety of exchange formats like community meetings, conferences, and workshops. To meet the needs of the community, we continuously work to improve our engagement processes.