NFDI for Data Science and Artificial Intelligence
On July 2, the Joint Science Conference (Gemeinsame Wissenschaftskonferenz – GWK) decided to fund the NFDI for Data Science and Artificial Intelligence proposal (NFDI4DataScience). The consortium is coordinated by the Fraunhofer Institute for Open Communication Systems.
NFDI4DataScience aims to establish a community-driven research data infrastructure for Data Science and Artificial Intelligence. In this regard, it focuses on several types of data and artifacts established within the community, including publications, data, models and code.
Transparency, reproducibility and fairness have become crucial challenges
Prof. Reimund Neugebauer, President of the Fraunhofer-Gesellschaft, says: »Data is a crucial commodityraw material for scientific work and serves as the basis for new developments and solutions. The National Research Data Infrastructure provides the entire German science system with access to valuable scientific and research data resources. This sustainably strengthens the innovative power of Germany as a research location and our position in international competition. The Joint Science Conference’s decision to fund the NFDI4DataScience consortium will advance the fields of artificial intelligence and data science, which are strategically important to our future, in a targeted and visionary manner.«
Dr. Sonja Schimmler, Spokesperson of the consortium, says: »We are very happy about the acceptance of the consortium. With NFDI4DataScience we intend to put forward the Data Science and Artificial Intelligence community in academia, which is an interdisciplinary field rooted in Computer Science. Our aim is to reuse existing solutions, and to collaborate closely with the other NFDI consortia and beyond.«
NFDI4DataScience will support all steps of the interdisciplinary research data lifecycle, including collecting/creating, processing, analysing, publishing, archiving and reusing resources in Data Science and Artificial Intelligence. The past years have seen a paradigm shift, with computational methods increasingly relying on data-driven and often deep learning-based approaches, leading to the establishment and ubiquity of Data Science as a discipline driven by advances in the field of Computer Science but being of relevance to most scientific disciplines. Transparency, reproducibility and fairness have become crucial challenges for Data Science and Artificial Intelligence due to the complexity of contemporary Data Science methods, often relying on a combination of code, models and data used for training.
HITeC is a supporter of this project and initiative and gratulates to this start!