Reproducibility is at the heart of science. Nevertheless, with the advent of computer-based data processing and analysis, most spectroscopists have a hard time fully reproducing a figure from last year's publication starting from the raw data. Unfortunately, this renders their work eventually unscientific. To change this, we need to develop analysis tools that relieve their users from having to trace each processing and analysis step. Furthermore, these tools need to be modular, extendible, and easy to use in order to get used. To this end, we present here the open-source Python package cwepr based on the ASpecD framework for reproducible analysis of spectroscopic data. This package follows best practices of both, science and software development. Key features include an automatically generated gap-less record of each individual processing and analysis step from the raw data to the final published figure. Additionally, it provides a powerful user interface requiring no programming skills of the user. Due to its code quality, modularity, and extensive documentation, it can be easily extended and is actively developed by spectroscopists working in the field. We expect this approach to have a high impact in the field and to help fighting the looming reproducibility crisis in spectroscopy.
Keywords: Data analysis; Electron paramagnetic resonance spectroscopy; Reproducible research; Software.
Copyright © 2021 Elsevier Inc. All rights reserved.