Speaker
Description
As a researcher, do you often postpone thinking about data management until the very end of your project? Do you sometimes feel that the research data management (RDM) infrastructure provided by your institution does not fully support the realities of your daily research practice?
If these questions resonate with you, you are not alone. Researchers across disciplines face increasing challenges throughout the entire data lifecycle: from data collection and documentation to preprocessing, versioning, long‑term archiving, sharing within teams, re-using, and metadata creation. While many institutions offer RDM tools and services, gaps frequently emerge between infrastructural intentions and researchers’ actual needs. As a result, data management is often perceived as an additional burden rather than an integral part of the research process.
This discussion session invites researchers, academic experts, and professors to collectively explore these challenges using the Lightning Decision Jam method—a structured yet highly participatory format that helps groups identify key problems, prioritize them, and develop actionable solution ideas within a limited time frame.
Together, we will reflect on questions such as:
- In which ways does existing RDM infrastructure in academia fall short of supporting real research workflows?
- What are the most pressing challenges in archiving research data for the long term and sharing it effectively within and across research teams?
- Where do issues such as data loss, overwriting, lack of version control, or insufficient metadata most frequently arise?
- Can we envision more collaborative, researcher‑centered RDM solutions that align better with disciplinary practices?
The goal of this session is not only to voice frustrations, but to move toward constructive insights and feasible directions for improving RDM practices and services. By combining diverse perspectives, we aim to identify concrete starting points for a more sustainable, usable, and collaborative research data management culture.