Alex’ Knowledge Base offers a clear and practical entry point into the work of the NFDI4ING task area addressing challenges connected to one-of-a-kind, highly-variable experiments. It not only showcases the developed tools and services but also provides helpful guides and workflow recommendations. It’s a useful starting point for researchers fitting the Archetype Alex looking for support in navigating their research data management tasks.
The Research Data Management Organiser (RDMO) guides researchers through the planning, implementation and administration of all research data management tasks.
It offers structured questionnaires with detailed guidance, enabling efficient and comprehensive data management planning for research projects, ensuring compliance with funding requirements.
The NFDI4ING Q&A platform is here to empower researchers in the engineering sciences with a collaborative space to ask and answer questions about their research data management. Whether you’re a seasoned expert or just starting out, this platform is designed to foster knowledge exchange and support your research journey.
The NFDI-RFC (Request For Comments) process is adopted from the Internet Engineering Task Force (IETF) standard. This process ensures that NFDI community submitted standards are practical, effective, and widely used within the research community. A review process for the publication of NFDI-RFCs was also designed to ensure that they meet the highest quality and integrity standards.
The NFDI4Ing Jupyter Service provides Jupyterlab servers to the NFDI4Ing community. Jupyter allows interactive programming via the browser in more than 40 programming languages. Via computational notebooks, the code can be enriched with text descriptions, images, videos and more (literate programming).
Ing.grid is a scholarly-led diamond open access journal for FAIR data management in engineering sciences. It uses an open peer review process and accepts data and software submissions in addition to regular manuscripts.
“Data Quality Metrics Webpage” is a knowledge platform offering in-depth resources on data quality, FAIR principles, and image and machine learning metrics. Built on ReadTheDocs with GitHub, it supports decentralized editing and easy updates using reStructuredText, requiring no specialized software. The platform provides, practical guidance with examples, images, and code snippets, making it accessible to users for applying data concepts, optimizing models, and enhancing understanding.
Basic RDM Training for Engineers provides essential resources for self-paced trainings in RDM tailored specifically to engineering disciplines. Driven by the needs and characteristics of the engineering domain, these trainings provide basic RDM topics adapted for engineering, including use cases and interactive quiz elements. Based on that, users can start learning and enhancing their skills in managing research data effectively, improving collaboration in their projects, and reuse research data.
