The aim of the NeuroElectro Project is to represent structured electrophysiological information from diverse neuron types. Currently, this information is obtained by text-mining and manually curating the neuroscience literature (see the NeuroElectro Publications for details).
This repository encapsulates the majority of the features of the project, including text-mining (located in the folder labelled "article_text_mining"), the models specifying the relational database (implemented in Django), and the website front end and back end code (implemented in Python's Django web framework).
Interested collaborators and contributors should contact @stripathy or @rgerkin, post in the Gitter chat, or post to the Google Groups Mailing List.
- Information Management
- Integrate and structure data related to neuron type electrophysiology, like resting membrane potentials or spike amplitudes
- Develop and maintain APIs for accessing and downloading data
- Keep data up-to-date
- Text-mining
- Apply algorithms for downloading neuroscience articles
- Identify electrophysiological parameters, neuron types and subtypes, and methodological information
- Identify experimental factors like use of genetically modified animals
- Data Curation
- Provide a curation interface for use by human curators to check and fix the text-mined content
- Web Interface
- Provide an interactive web interface where extracted data is viewable and searchable
- Ensure that all extracted data is trace-able back to its source
- I want to know the average value of an electrophysiological parameter in a neuron type
- I want to find publications reporting electrophysiological data about my favorite neuron type
- I need realistic parameters to constrain my computational model of a neuron type
- I want to see how variable an electrophysiological property (like resting membrane potential) is across neuron types and different publications within a neuron type.
- I want to see how experimental parameters and metadata affect electrophysiological measurements
- I want to compare electrophysiological variability across neuron types with gene expression variability