Reproducibility is a key aspect of science, and both, publishers and funders increasingly ask for the raw data and details of the data processing to be available. However, still most papers lack the details necessary to fully reproduce the data analysis. This can in part be attributed to a lack of appropriate software tools helping the researchers to document both, data acquisition and analysis automatically and with the required details. Here, we present strategies and tools for more reproducible research. After a general introduction to "research data management", solutions are introduced that everyone can use by themselves, not relying on external infrastructure.