Schimmler, SonjaHennig, ChristineKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43020The consortium NFDI4DS supports researchers along all stages of the research data lifecycle to conduct their research in line with the FAIR principles. By conducting interviews and surveys, NFDI4DS continuously identifies the needs and challenges of researchers from various disciplines regarding data science and artificial intelligence, keeping ethical, legal, and social aspects in mind. Those identified needs and challenges are continuously addressed by picking up existing services, developing new ones and integrating them into the NFDI4DS infrastructure. By systematically adding digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) to the NFDI4DS research knowledge graph within the infrastructure, transparency, reproducibility, and fairness are steadily improved. The process is continuously accompanied by providing resources such as educational videos and organizing events such as community challenges. In this short paper, we give an overview of NFDI4DS, and provide details about our approach to address the current challenges. We will report on how we plan to utilize FAIR digital objects (FDOs) and Research Knowledge Graphs (RKGs) as a basis for the infrastructure envisioned. We will also give an overview of the services planned, and how they are meant to interact.enNFDINFDI4DSData ScienceArtificial IntelligenceResearch Data InfrastructuresText/Conference Paper10.18420/inf2023_1001617-5468