National research data infrastructure for data sciences and artificial intelligence (NFDI4DataScience)

In recent years, a paradigm shift has taken place: Computational methods increasingly work with data-driven and often deep-learning based approaches. This leads to an increasing establishment and visibility of data science as a discipline that is driven by technical progress in the field of computer science and at the same time of great relevance for many other scientific disciplines. Transparency, reproducibility, and fairness have become critical challenges for data science and artificial intelligence due to the complexity of modern data science methods, which often rely on a combination of code, models, and data for training.

NFDI4DataScience has the goal to build a community-driven research data infrastructure for data science and artificial intelligence. The consortium focuses on different types of data and artifacts that have become established within the scientific community. These include publications, data, models, and code. Transparency, reproducibility, and fairness are key challenges. NFDI4DataScience will support all steps of the interdisciplinary research data lifecycle, including collection/creation, processing, analysis, publication, archiving, and reuse of data science and AI resources.

A total of 15 institutions are involved in the consortium, including FIZ Karlsruhe. It is coordinated by the Fraunhofer Institute for Open Communication Systems FOKUS.

FIZ Karlsruhe’s research unit Information Service Engineering (ISE) contributes to this consortium in the area of ontology development as well as with the construction and implementation of a knowledge graph.

The contribution of FIZ Karlsruhe’s research unit Intellectual Property Rights (IGR) focuses on legal and ethical aspects of data science and artificial intelligence, among other things, through training measures such as workshops and by publishing guidelines and recommendations. Furthermore, FIZ Karlsruhe will contribute its expertise in consultations and statements on regulatory issues at an early stage of AI legislation at German and EU level. The relevant legal areas include data protection and AI regulatory law.

Term: October 01, 2021 – September 30, 2026

Gesese, G.A., Chen, Z., Zoubia, O., Limani, F., Silva, K., Suryani, M.A., Zapilko, B., Castro, L.J., Kutafina, E., Solanki, D., Fliegl, H., Schimmler, S., Boukhers, Z. and Sack, H., 2025. A Survey on Metadata for Machine Learning Models and Datasets: Standards, Practices, and Harmonization Challenges. In CEUR Workshop Proceedings (Vol. 4065). https://ceur-ws.org/Vol-4065/paper05.pdf [bibTeX]

Silva, K., Ackermann, M.R., Fliegl, H., Gesese, G.A., Limani, F., Mayr, P., Mutschke, P., Oelen, A., Suryani, M.A., Upadhyaya, S., Zapilko, B., Sack, H. and Dietze, S. 2025. Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions. arXiv preprint arXiv:2508.02300. [bibTeX]

Ahmad, R.A., Abdulla, R., Taffa, T.A., Auer, S., Giglou, H.B., Borisova, E., Chen, Z., Dietze, S., DSouza, J., Elwes, M. and Gesese, G.A., Jiang, S., Kutafina, E., Mayr, P., Rehm, G., Sadruddin, S., Schimmler, S., Schneider, D., Silva, K., Upadhyaya, S. and Usbeck, R., 2025. NFDI4DS Shared Tasks for Scholarly Document Processing. arXiv preprint arXiv:2509.22141.  [bibTeX]

G. A. Gesese, J. Waitelonis, Z. Chen, S. Schimmler, H. Sack, NFDI4DSO: Towards a BFO Compliant Ontology for Data Science, in Proc. of 20th Int. Conf. on Semantic Systems (SEMANTICS 2024), poster & demo track [local copy(PDF)|bibtex]

H. Sack, T. Schrade, O. Bruns, E. Posthumus, T. Tietz, E. Norouzi, J. Waitelonis, H. Fliegl, L. Söhn, J. Tolksdorf, J. Jalle Steller, A. Azócar Guzmán, S. Fathalla, A. Zainul Ihsan, V. Hofmann, S. Sandfeld, F. Fritzen, A. Laadhar, S. Schimmler, and P. Mutschke, ‘Knowledge Graph Based RDM Solutions: NFDI4Culture - NFDI-MatWerk - NFDI4DataScience’, Proc Conf Res Data Infrastr, vol. 1, 2023, DOI: 10.52825/cordi.v1i.371.

S. Karmakar, M. Zloch, F. Limani, B. Zapilko, S. Upadhyaya, J. D’Souza, L. J. Castro, G. Rehm, M. R. Ackermann, H. Sack, Z. Boukhers, S. Schimmler, D. Dessí, P. Mutschke, and S. Dietze, ‘Research Knowledge Graphs in NFDI4DS’, 2023, DOI: 10.18420/INF2023_102.