A pipeline for an Anterior Lateral Motor Cortex study
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This pipeline is based on the study described in Li, Nuo, et al. "Amotor cortex circuit for motor planning and movement." Nature 519.7541 (2015): 51 with the data shared at http://crcns.org/data-sets/motor-cortex/alm-1/

Online viewing

All Jupyter notebooks in this catalog can be better viewed online through the Jupyter.org viewer at http://nbviewer.jupyter.org/github/datajoint-catalog

Obtain credentials

If you need a database account to try these examples, you can get a free tutorial account by subscribing through https://datajoint.io.

Before you start working with the pipeline, please obtain the following credentials:

  • host address
  • user name
  • password


The instructions for downloading the DataJoint library are available here: http://docs.datajoint.io/setup/Install-and-connect.html

Additionally, the common 'djcat-lab' module for general experiment data needs to be cloned from:


since it is referenced from this project. Be sure to setup your PYTHONPATH variable accordingly so that djcat-lab is included in sys.path. For example, your djcat-lab source code is stored in the same folder as this experiment, you can add it from within python as follows:

>>> import sys
>>> import os
>>> sys.path.insert(0, os.path.join('..','djcat-lab'))


Please submit issues and questions through the Issues tab above

The pipeline design

The [ALM1-erd.ipynb] notebook plots the entity-relationships diagrams (ERDs) for the ALM1. It comprises two schemas: lab for common elements across various studies and alm1 for data specific to the ALM-1 study.

Schema lab

lab erd

Schema alm1

lab erd

The entire pipeline

entire pipeline

Source Data for ingest

Source data for this pipeline are available via CRCNS.org: http://crcns.org/data-sets/motor-cortex/alm-1

Data downloads require an account registration; Details are provided here: http://crcns.org/download

The example import scripts use the NWBv1 format files available within the datafiles/nwb_files subdirectory of the download area, and require the h5py library to be read.

More information concerning the NWB file format is available here: https://github.com/NeurodataWithoutBorders/specification

With the h5py library available via pip (pip install h5py). More details concerning h5py are available here: http://www.h5py.org/

The example export scripts are written using the NWB v1 API, available from: https://github.com/NeurodataWithoutBorders/api-python