Use cases for learners and educators. The material was developed for investigators of any ability and at all career levels - spanning from the beginning undergraduate researcher to the established PI - to analyze and understand neural data. For those new to Python, Notebook #1 is dedicated to a thorough but rapid and practical introduction to the use of Python 3. We anticipate many scenarios for participation, for example:
We expect interested researchers, students, and educators in the neuroscience community will develop many other uses beyond the examples listed here.
Notebooks are dynamic and available for further development by the community. The collaborative nature of GitHub allows for dynamic evolution of the material. A modification (e.g., an error correction) suggested by any learner can be flagged as an issue and incorporated into the material; versioning allows a complete record of attributable changes. Moreover, the repository is expandable. For example, a tutorial that implements and applies a new data analysis method to an example data set may be contributed as a standalone notebook. In this way, the community’s expertise allows the material to grow and include important topics not yet presented (e.g., analysis of imaging data).