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.
SM4RO-C is a specification that defines how to use SciMesh graphs in RO-Crates in order to describe the raw data files stored within the crate. It enables researchers to publish and/or share their research results in the richest way possible, i.e. raw data, intermediate data, and workflow graphs combined in one ZIP file.
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.
To evaluate the implementation of research data management, maturity models are provided for individual phases, which researchers can use for self-evaluation. The models are aimed at a standardised and optimised implementation of RDM in engineering research projects.
Are you an engineer in a project to (re-)program scientific code? Or are you a student in a likewise project? Or maybe you are a project/data manager looking for some inspiration and advice? Then this Knowledge Base might be just for you!
We cover multiple topics regarding the development of scientific software, e.g. Version Control and Continuous Integration.
Are you an engineer in a project to (re-)program scientific code? Or are you a student in a likewise project? Or maybe you are a project/data manager looking for some inspiration and advice? Then this Knowledge Base might be just for you!
We cover multiple topics regarding the development of scientific software, e.g. Version Control and Continuous Integration.
A python-written metadata crawler that allows to automatically retrieve relevant research metadata from script-based workflows on HPC systems. The tool offers a flexible approach to metadata collection, as the metadata scheme can be read out from an ontology file. Through minimal user input, the crawler can be adapted to the user’s needs and easily implemented within the workflow, enabling to retrieve relevant metadata.
“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.
