This tutorial demonstrate how to build and use data pipelines using DataJoint for Python. As an example, we use the ALM-1 dataset from http://crcns.org/data-sets/motor-cortex/alm-1/
For any questions and requests, please subscribe to https://mesoscaleactivitymap.slack.com and post them there.
You may also submit issues through the repo issue tracker https://github.com/mesoscale-activity-map/ALM-tutorial/issues
General DataJoint documentatin is available here:
- DataJoint documentation http://docs.datajoint.io
- DataJoint tutorials http://tutorials.datajoint.io
This tutorial comprises a series of Jupyter notebooks. These can be viewed online publicly at http://nbviewer.jupyter.org/github/mesoscale-activity-map/
All MAP pipelines are hosted at mesoscale-activity.datajoint.io
To work with the data interactively, please obtain a username and password and keep them secure. Please contact support through Slack.
The instructions for downloading the DataJoint library are available here: http://docs.datajoint.io/setup/Install-and-connect.html
Please contact support through Slack if you run into any trouble.
