This approach documents all steps of analysis, which can be shared in easily readable formats including PDF, HTML, and a web-based computer environment (an example provided) capable of regenerating the analysis directly from raw data. These notebooks use Python and R programming languages, with real experimental data, in the analysis of neural signals and generation of publication-quality graphics. To boost reproducible research, neurophysiology analysis is performed with free, open-source software within the Jupyter electronic notebook. In an age of diverse, high-throughput computational workflows, this methodology can increase efficiency, transparency, and the collaborative potential of neurophysiological research. This demonstrates the promise of combining disparate analyses into one platform, along with the ease of sharing this work. Raw data and code are available from notebooks in data supplements or from an executable online version, which replicates all analyses without installing software-an implementation of reproducible research. We show methods for spike sorting (including statistical validation), spike train analysis, and analysis of compound action potentials in notebooks. The present report illustrates these methods using data from electrophysiological recordings of the musk shrew vagus-a model system to investigate gut-brain communication, for example, in cancer chemotherapy-induced emesis. An electronic notebook like Jupyter allows these analyses, along with documentation of procedures, to display locally or remotely in an internet browser, which can be saved as an HTML, PDF, or other file format for sharing with team members and the scientific community.
This tool chain can largely replace current routines by importing data, producing analyses, and generating publication-quality graphics.
An ecosystem of free open-source software exists to fill these gaps, including thousands of analysis and plotting packages written in Python and R, which can be implemented in a sharable and reproducible format, such as the Jupyter electronic notebook. Neurophysiology requires an extensive workflow of information analysis routines, which often includes incompatible proprietary software, introducing limitations based on financial costs, transfer of data between platforms, and the ability to share.
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