The LéXPLORE platform (LP) is a cutting-edge open-water infrastructure on Lake Geneva, delivering high-frequency environmental data for multidisciplinary research. To streamline data access, Datalakes (DL) serves as an open-access, FAIR-compliant data platform, providing real-time sensor data, visualization, and extraction tools. However, two key challenges impact data reliability and usability: (1) the LP data transfer pipeline’s reliance on an on-site computer, and (2) the need for a more interactive, community-driven data quality assessment and quality control (QA/QC). The ORD project FLAKE is strengthening LP’s data pipeline by shifting storage and processing to a cloud-based infrastructure for improved reliability. All data pipelines have now been successfully implemented on a secure file transfer protocol server at Eawag, where their status is continuously monitored online to detect and address failures more rapidly. A key innovation of the project has been the development of a collaborative QA/QC framework within DL. We have built a community of data cleaners and clarified the roles of each participant: each dataset is now assigned to a data curator and a data advisor to ensure data quality. While the project initially proposed using an external tool, we successfully integrated the QA/QC functionality directly within the DL system, improving reliability and ensuring long-term maintenance. Co-created with the LP community during two workshops, the tool allows researchers to assess, flag, and refine data interactively, leveraging their domain expertise. The tool was successfully launched during dedicated data cleanup days, which also served to test it and begin cleaning the data. By improving data integrity, accessibility, and collaborative data stewardship, this project reinforces LP’s long-term impact and further strengthens DL’s role in advancing Open Research Data practices.