The data transfer federation aims to increase mobility of data by providing a low-level service that allows asynchronous bulk transfer of large quantities of files from one storage cluster to another.
“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.
The Data Collections Explorer is an information system for the engineering community. It facilitates sharing of and searching for discipline specific repositories, archives, and databases, as well as for datasets published individually by research groups. Scientists can get a quick overview of the most important facts about services and datasets, such as access rights or usage restrictions. A SPARQL endpoint ensures programmatic access for integration with third-party services.
Coscine is a RDM platform for the active phase of research projects that enables NFDI4ING researchers to access storage space on DataStorage.nrw while guaranteeing the FAIR principles. Projects created in Coscine enable role management, metadata management, public sharing of data, referencing with PIDs and archiving of research and metadata for 10 years. In addition, project-related GitLab repositories can be integrated and external files linked. Registration takes place via your own organization (using the NFDI4ING community AAI) or ORCiD.
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.
SciKGTeX is a LaTeX package that allows researchers to annotate specific research contributions in their scientific documents. By embedding these annotations into the PDF metadata, SciKGTeX makes it easier for search engines and knowledge graphs to find and understand the research. This enhances the discoverability and usability of scientific work, making it a valuable tool for anyone looking to improve the visibility and impact of their research.
