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:
- A clinical research lab requires new data analysis skills in scalp
EEG. Lab members complete notebooks describing analysis of brain
rhythms and rhythmic coupling of field data (Notebooks #3-5).
- A postdoctoral researcher requires modern spike-field coherence
analysis techniques as part of her ongoing research. An expert in
Python, she completes a single notebook (Notebook #11).
- An instructor plans to update his undergraduate laboratory course in
basic neuroscience. In the lab, students record spike train data. The
instructor augments this lab by requiring students complete a notebook
introducing spike train analysis (Notebook #8) and apply basic
visualizations to the spike train data recorded in lab.
- An instructor must suddenly teach her neuroscience course remotely.
She selectively chooses content from a subset of notebooks to
aggregate into a coherent curriculum. Students complete notebooks
outside of class, and discuss material during (virtual) classroom time
[3].
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).