GitLab as a tool for Research Data Management Candidate for a Base4NFDI general service Dirk von Suchodoletz1[https://orcid.org/0000-0002-4382-5104], Dominik Brilhaus, 2[https://orcid.org/0000-0001-9021- 3197], Marcel Tschöpe1[https://orcid.org/0000-0002-3731-7664], and Jonathan Bauer1[https://orcid.org/0000-0002-5624- 2055] 1 Computer Center University of Freiburg, Freiburg, Germany 2 Cluster of Excellence on Plant Sciences (CEPLAS) Heinrich-Heine-University Düsseldorf, Germany Abstract. The collaboration of researchers locally or worldwide ranging from single research- ers over a group or a lab up to cross institutional and disciplinary cooperation requires suitable working environments. Data hubs in the form of science gateways--usually abstracting from just locally shared storage resources--are discussed and explored for quite some time. While it was en vogue to propose and develop discipline specific gateways, we suggest to rely on well-established standard software frameworks instead. Research data considered over the entire data life cycle and closely related activities like annotation, versioning and sharing has a lot in common with (open source) software development. Git and GitLab--well established tools in software development--could play a major role in general research data management. From DataPLANT's point of view, GitLab as a science gateway would be a valuable addition to the NFDI [5] software landscape. It would be beneficial to address it as a joint service in cross-domain activities of all interested consortia. Keywords: Git, GitLab, versioning, sharing of data, science gateway, LFS, DataPLANT Software development processes match central RDM requirements Research data management (RDM) typically follows a cycle: data is gathered, analyzed, and published, leading to new research questions which often leads to a new start of the cycle or iterative loops. Anticipating the need for publication earlier in the process is beneficial, partic- ularly in data gathering and analysis stages. For example, metadata annotation is crucial for data interpretation and reuse. Instead of annotating data just before publication, we propose continuous annotation throughout the RDM life cycle. Similarly, reproducibility should be con- tinuous, ensuring that analyses on a data set are always reproducible. Rather than focusing on publication, research data can be managed and curated using pro- cesses similar to software development. This approach involves analyzing problems, design- ing solutions, and implementing and releasing iterations. Techniques like unit testing and con- tinuous integration ensure the correctness of the data throughout the development phase and enable flexible release schedules. Many considerations in software development match the expectations in RDM quite well. The goal of each iteration of a cycle, i.e., producing a release, is anticipated throughout all steps. This has led to the development of techniques such as unit testing and continuous integration. [1] Scientific research data is often seen as static and unchanging, but it should be viewed as dynamic and evolving. Version control can be a valuable tool for collaboration in RDM, allowing researchers to work together seamlessly while maintaining consistency, proper annotation, and reproducibility. By adopting version control as a systematic approach, ad hoc collaboration methods can be replaced, ensuring an atomic and unambiguous history of changes without