Hands on Workshop on „Reserach Data management on Wednesday, 11 February 2026 from 10 am to 4 pm.
We look forward to broad participation from different groups and perspectives: lab technicians, group data officers, PhD students, postdocs, group leaders — everyone is welcome, and registration is needed until Feb 6, 2026!
Afterwards, everyone will receive a certificate of participation!
What is it about?
The FAIR Research Data Management Workshop provides practical guidance on how to make research data FAIR: Findable, Accessible, Interoperable, Reusable.
The focus is on concrete steps along the research cycle – from publication (data availability statements, licences, DOIs, repositories) to documentation (metadata, README, codebooks) and organisation (structures, formats) to data management plans (including RDMO).
Legal aspects (copyright, data protection/GDPR), PIDs (DOI, ORCID, ROR) and the selection of suitable repositories are also covered, as are FAIR assessment tools.
Why participate?
- Addresses funding and journal requirements (DFG, EU, Data Availability Statements).
- Increases the reproducibility and visibility of your own research.
- Saves time by establishing good practices early on.
- Creates legal certainty (licences, personal data).
- Is interdisciplinary – from theory to experiment, from code to measurement data.
In short: those who publish their data cleanly will be found, cited and reused more often – a clear advantage for projects, third-party funding and careers.
The workshop is being held based on recommendations from our Open Science Ambassadors Jasmin (ARB) and Ruben (PSE), among others. Having participated in a similar workshop themselves, they highly recommend taking part!
Further information on Love Data Week 2026:
https://forschungsdaten.info/fdm-im-deutschsprachigen-raum/love-data-week-2026/
Further information on RDM tools @ MPI DCTS:
https://www.mpi-magdeburg.mpg.de/researchdatamanagement
A few takeaways from the 2025 Data-Handling workshop:
✅ A dedicated FAQ on Research Software Engineering helps navigate common pitfalls (https://gwp-rse-ff130b.pages.gwdg.de/en/).
✅ There are (more or less effective) FAIR-checkers (https://fair-checker.france-bioinformatique.fr/).
✅ Labfolder can be used to maintain an overview table of all experiments (including IDs!) . Labforward
✅ And let’s not forget—data is the gold of our time (just look at the Super Bowl, https://operations.nfl.com/gameday/analytics/big-data-bowl/).
Looking forward to more discussions during #LoveDataWeek
NEW: Here you can find the teaching material.
