NFDI4DataScience aims to build a community-driven research data infrastructure for data science and Artificial Intelligence. Data science is establishing itself as a discipline driven by the technological progress in the field of informatics and has enormous relevance for many other scientific disciplines. The consortium focuses on various types of data and artifacts established within the community, including publications, data, models and code.
New calculation methods are increasingly based on data-driven approaches. The role of Deep Learning is ever more important. Modern data science methods are characterised by the combination of codes, models and data. This complexity turns transparency, reproducibility and fairness into crucial challenges for modern science.
The ZBW develops a FAIR specification for NFDI4DataScience resources. The ZBW addresses the FAIRness of artifacts in data science. Among these are research datasets, benchmarks, models for machine learning or research software (code and executable files). With its contribution, the ZBW supports the development of FAIR quality measures for digital objects.
- FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
- Fraunhofer FIT
- Fraunhofer FOKUS (Lead)
- German Research Center for Artificial Intelligence (DFKI)
- GESIS – Leibniz Institute for the Social Sciences
- Hamburger Informatik Technologie-Center e.V. (HITeC)
- Leibniz University Hannover
- Leipzig University
- RWTH Aachen University
- Schloss Dagstuhl – Leibniz Center for Informatics
- TIB Leibniz Information Centre for Science and Technology
- TU Berlin
- TU Dresden
- ZB MED Information Centre for Life Sciences