  This paper introduces a scholarly Question Answering (QA) system on top of
the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based
(RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a
unified and intuitive interface for querying various scientific databases using
federated search. The RAG-based scholarly QA, powered by a Large Language Model
(LLM), facilitates dynamic interaction with search results, enhancing filtering
capabilities and fostering a conversational engagement with the Gateway search.
The effectiveness of both the Gateway and the scholarly QA system is
demonstrated through experimental analysis.
