Scientists in many disciplines are working with large and complex datasets that are challenging to store, manage, analyse, preserve, and share. Corresponding analytical workflows for the interpretation of data and generation of results are also increasing in complexity. As a result, the reusability of data and (computational) reproducibility of results becomes a major challenge, if the analytical pathway from data to the results is not documented in detail. This includes the analysis code but also information on the computational environment in which analyses are executed. The Reproducible Research Platform (RRP) has been developed to address this challenge. RRP builds on established open-source tools to enable a seamless connection between data and metadata management (with openBIS), tools for code management (Git), management of computational environments (repo2docker) and interactive computational notebooks (Jupyterlab). Finally, researchers can use RRP to share computational projects with colleagues, reviewers or other interested parties. In summary, the RRP platform enables researchers to achieve full reproducibility of their computational work with minimal additional effort.