How reproducible are your EPR results? Tools for fully reproducible data analysis

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. We present a Python framework for the fully reproducible Analysis of Spectroscopic Data (AspecD) requiring no programming skills of the users thanks to a unique user interface in form of recipes formatted as YAML file. Based on this framework, a number of packages has been developed, most notably for the analysis of cw-EPR and tr-EPR data. Furthermore, we show a simple yet powerful file format for recording metadata during data acquisition that is both, human-writable and machine-readable. Together, these software tools tremendously enhance the reproducibility of our data analysis and hence the overall quality of the reported science