Large Language Models for Research Data Management?! 2025 (LLMs4RDM 2025)

Research data management (RDM) has become an important discipline that enables researchers to effectively organise, preserve and share their research results.<br> RDM is a new development that aims to prepare researchers for the future by building on the principles of open science. It utilises innovative approaches such as generative artificial intelligence (genAI), which is powered by large language models (LLMs), to complement traditional research methods.<br> As data-driven research becomes increasingly complex, researchers often have to spend a lot of time learning how to manage, analyse and interpret large amounts of information. Traditional data literacy training can be time-consuming and doesn't always keep pace with evolving technologies and methods of analysis.<br> Foundation models based on generative AI offer the potential to streamline this learning process. By automating data pre-processing, pattern recognition and even hypothesis generation, these models can lower the technical barriers to entry, allowing researchers to focus more on insights and discovery rather than spending excessive amounts of time mastering data skills.<br> The objective of this workshop is an exchange of perspectives regarding the implementation of novel RDM approaches using LLMs or not, both past and prospective, in research and practice. The presentations of the Large Language Models for Research Data Management?! workshop were as follows: Magnus Bender<br> <em>Aarhus University, Denmark</em><br> <strong>Welcome</strong> Ralf Möller<br> <em>University of Hamburg, Germany</em><br> Keynote: <strong>Advancing RDM: From Immersion to Argumentation in Science</strong><br> (download pdf) Jens Dörpinghaus^1,2,3, Michael Tiemann^1,2<br> <em>¹University of Koblenz, Germany; ²Federal Institute for Vocational Education and Training (BIBB), Germany; ³Linnaeus University, Sweden</em><br> <strong>Large Language Models in Labor Market Research Data Management: Potentials and Limitations</strong> Edyta Jurkiewicz-Rohrbacher^1,2, Thomas Asselborn²<br> <em>¹University of Regensburg, Germany; ²University of Hamburg, Germany</em><br> <strong>Challenges in Automatic Speech Recognition in the Research on Multilingualism</strong><br> (download pdf) Florian Marwitz, Marcel Gehrke<br> <em>University of Hamburg, Germany</em><br> <strong>Improving Accessibility and Reproducibility by Guiding Large Language Models</strong><br> (download pdf) Maximilian Plazotta, Meike Klettke<br> <em>University of Regensburg, Germany</em><br> <strong>Talk to your database: An open-source in-context learning approach to interact with relational databases through LLMs</strong><br> (download pdf) Thomas Asselborn, Magnus Bender, Florian Marwitz, Ralf Möller, Sylvia Melzer<br> <em>University of Hamburg, Germany</em><br> <strong>Verbalisation Process of a RAG-Based Chatbot to Support Tabular Data Evaluation for Humanities Researchers</strong><br> (download pdf) Magnus Bender<br> <em>Aarhus University, Denmark</em><br> <strong>Farewell</strong>