Scientists in many disciplines are working with large and complex datasets that are challenging to store, manage, analyze, preserve, and share. Furthermore, corresponding analytical workflows for the interpretation of data and generation of publishable results are also increasing in complexity. As a result, the reusability of data and (computational) reproducibility of results becomes a major challenge, if the full analytical pathway from data to the final result is not documented in detail. This includes not only the analysis code but also information on the computational environment in which analyses are executed, because a single missing library or an updated package can break a published workflow (in the future). In this project, we further developed the Reproducible Research Platform (RRP) which has been designed to address this challenge. RRP provides user-friendly access to established open-source tools for management of data and metadata (https://openbis.ch/), scientific code (https://git-scm.com/), computational environments (https://repo2docker.readthedocs.io/en/latest/index.html) and interactive computational notebooks (https://jupyter.org/). In the current project, we contributed an extension to RRP that allows to easily share projects with all required data and computational components from RRP with third parties (other research groups, reviewers and public). Sharing of RRP projects finally enables full reusability by allowing others to reproduce, but also modify and extend previous work.