NFDI4DataScience (NFDI4DS)
NFDI4DataScience (NFDI4DS) follows a vision: for Data Science and the advancements in Artificial Intelligence, it is essential to fully support all steps of the complex and interdisciplinary lifecycle for research data, i.e., the collection/creation, processing, analysis, publication, archiving, and reuse of various resources. The paradigm shift in recent years has meant that the most powerful computational methods are increasingly achieved through data-driven approaches, especially Deep Learning. This has led to the establishment of Data Science as an independent and ubiquitous scientific discipline, driven by advances in computer science, but drawing its great significance from the diverse results in almost all scientific disciplines.
The challenges for Data Science and AI lie in mastering modern Data Science methods by implementing the principles of transparency, reproducibility, and fairness for digital objects (i.e., for the combination of code, models, and data used for training). Due to the outstanding importance of Data Science and AI for computer science and for the broader spectrum of many scientific disciplines, NFDI4DS will open its research data infrastructures to bring all available resources such as code, models, data, or publications into the scientific communities.
NFDI4DS pursues the development, establishment, and maintenance of a national research data infrastructure for the Data Science and Artificial Intelligence communities in Germany. This also offers advantages for a broader community that relies on data analysis solutions (e.g., within the NFDI). The ultimate goal is that all digital artifacts are made available, linked together, and innovative tools and services are offered to enable new and innovative research through diverse reuse.
In the initial phase, NFDI4DS will focus on four application areas that are particularly prominent in data science: language technology, life sciences, information sciences, and social sciences.
Objectives
- Support the entire lifecycle of research data in Data Science and AI.
- Ensure transparency, reproducibility, and FAIRness of models, data, and code.
- Build a national research data infrastructure for the AI and Data Science communities.
- Foster collaboration across disciplines including language technology, life sciences, information, and social sciences.