Research reproducibility remains elusive despite the widespread adoption of FAIR data principles, as data and code lifecycles disconnect, computational environments vanish after publication, and, without explicit environment specifications, reproducing analyses demands expert knowledge even when code is shared. The open-source Reproducible Research Platform (RRP) bridges this by unifying research data management (via openBIS RDMS) with Git-managed code, Docker/repo2docker environments, and Kubernetes scaling into shareable, executable projects. Users mount datasets via traceable permanent IDs into JupyterLab, VS Code, RStudio, MATLAB, or full desktops, enabling instant execution anywhere, from local machines to institutional clusters, without setup hurdles. In this presentation, we demonstrate RRP across diverse research domains and open-source tools. In engineering, RRP facilitates integration of CAD and PCB design workflows into collaborative, shareable projects, enabling open-hardware development with open-source tools. Machine learning practitioners leverage RRP for reproducible training and inference on large microscopy datasets. Molecular biologists use RRP to perform reproducible DNA editing experiments with CRISPR tools linked to DNA materials stored in the RDMS. Software engineers and bioinformaticians benefit from RRP’s support for established IDEs like VS Code and RStudio, preserving user workflows while ensuring reproducibility. Finally, RRP streamlines collaborative manuscript writing by linking research data and analysis code directly to the publishing process, enhancing reproducible scholarship. We built RRP with a modular architecture and open-source ethos, inviting developers to extend its capabilities, integrate new tools, and customize workflows, fostering an evolving ecosystem for reproducible research across diverse scientific domains.