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Reproducibility of computational research is a challenge with overwhelming complexity. The research project "Opening Reproducible Research" (DFG-funded with six person-years) builds a platform on top of simple, focused and powerful building blocks: a BagIt bag carries a Docker image as well as the corresponding Dockerfile. The image executes an analysis when it is started and validates the generated output based on a single working directory with data and code. This gives us two levels of reproducibility: the first builds the software environment based on a complete scripted definition, the second falls back to the original self-contained run-time image created at time of submission of the research paper. We'll also add some levers to the bag to allow users to interact with the contained research by manipulating parameters, and be able to validate the result of an execution of the container. The more challenging goals of the projects, which include switching out data and code between archived papers and well-documented licenses for all of data, text and code, are approached with a mixture of semantic metadata and conventions. We will implement open-source prototypes for a cloud based infrastructure to create, validate, and interact with reproducible open-access publications. The architecture and implementations will not reinvent publication or archival processes, but integrate with existing open-source platforms. We focus on the actual users and their needs in the domain where we are most active: geospatial analysis using R. The technical challenges are simple compared to the required shift in scientists mind sets, the required education, and adjustments of workflows. Therefore the efforts in the project are equally divided between the perspectives archival and preservation, usability and evaluation, as well as architecture and specification. @o2r_project