Karmakar, SauravZloch, MatthäusLimani, FidanZapilko, BenjaminUpadhyaya, SharmilaD’Souza, JenniferCastro, Leyla J.Rehm, GeorgAckermann, Marcel R.Sack, HaraldBoukhers, ZeydSchimmler, SonjaDessí, DaniloMutschke, PeterDietze, StefanKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43022The ever-increasing amount of research output through scientific articles requires means to enable transparency and a better understanding of key entities of the research lifecycle, referred to as research artifacts, such as methods, software, datasets, etc. Research Knowledge Graphs (RKG) make research artifacts findable, accessible, interoperable, and reusable (FAIR) and facilitate their interpretability. In this article, we describe the role of RKGs, from their construction to the expected benefits, including an overview and a vision of their use within the German National Research Data Infrastructure (NFDI) consortium NFDI4DataScience (NFDI4DS). This paper includes insights about the existing RKGs, how to formally represent research artifacts, and how this supports better transparency and reproducibility in data science and artificial intelligence. We also discuss key challenges, such as RKG construction, and integration, and give an outlook on future work.enNFDINFDI4DSData ScienceResearch Knowledge GraphScholarly DataKnowledge Graph IntegrationKnowledge Graph FederationText/Conference Paper10.18420/inf2023_1021617-5468