While an important goal in science is the reproducibility of results, reproducibility can have two levels of meaning. Rerunning the same analysis (code) on the same dataset using the same machine should always be possible. However, we should really be aiming for a deeper level of reproducability that helps foster new scientific discoveries, namely where rerunning the same analysis on data collected on an equivalent task in another laboratory. In other words, analysis pipelines should ideally be reusable across laboratories. Although it is often overlooked by users (and some developers), care must be taken regarding the license governing use of a given software package in order to maximize its impact. The MNE-Python code is thus provided under the very permissive open source new BSD license. This license allows anybody to reuse and redistribute the code, even in commercial applications.
