Research data management (RDM) has become an important discipline that enables researchers to effectively organise, preserve and share their research results.

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.

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.

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.

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.