Software: Neurophysiological analytics for all! Free open-source software tools for documenting, analyzing, visualizing, and sharing using electronic notebooks
Internal Dataset

UID: 27

Author(s): Rosenberg, David M.; Horn, Charles C.* * Corresponding Author + University of Pittsburgh Author

Description
This Github repository contains raw data and code to explore open-source neurophysiology data analysis tools within an included Jupyter notebook. Software dependencies are listed within each data supplement folder, and can be downloaded with Docker via the associated Github repository described in the related data catalog record: Software: Docker image with JupyterLab, Python 3, Python 2, and R. The supplementary data were collected from electrophysiological recordings of the musk shrew vagus, a model system to investigate gut-brain communication.
Keywords
Access via Github

Data files, full list of dependencies, and author-supplied instructions and information.

Access Restrictions
Free to all
Access Instructions
Download data files or access Binder link through Github. More information and instructions are available through the associated publication.
Associated Publications
Data Type
Electrophysiological
Software Used
ez
ggplot2
Jupyter Notebook
Matplotlib
Neo
NumPy
OpenElectrophy
pandas
plyr
Python
quantities
R
reshape2
rpy2
scikit-learn
SciPy
Spike2
STAR
Dataset Format(s)
CSV (.csv), TXT (.txt), PY (.py), Spike2 data (.smr, .smrx), IPYNB (.ipynb), Spike2 resource (.s2r, .s2rx)
Dataset Size
8.61 MB
Grant Support
Related Datasets
Data Catalog Record Updated
2018-12-04

Notice and Disclaimer: Please note that the information in this catalog is provided as a courtesy, as is, and with no representations or warranties of any kind. When you contact the responsible individual(s) listed in each record, or, where applicable, access a data repository listed, you will be subject to terms and conditions required by the data custodian/data repository. The University of Pittsburgh does not attempt to judge the scholarly quality of the data referenced and relies on the judgment and research expertise of those who created and/or deposited it.