____          _           _                 _
|  _ \   __ _ | |_   __ _ | |      __ _   __| |
| | | | / _` || __| / _` || |     / _` | / _` |
| |_| || (_| || |_ | (_| || |___ | (_| || (_| |
|____/  \__,_| \__| \__,_||_____| \__,_| \__,_|
                                   Neuroimaging

This extension enhances DataLad (http://datalad.org) for working with neuroimaging data and workflows. Please see the extension documentation for a description on additional commands and functionality.

For general information on how to use or contribute to DataLad (and this extension), please see the DataLad website or the main GitHub project page.

Installation

Before you install this package, please make sure that you install a recent version of git-annex. Afterwards, install the latest version of datalad-neuroimaging from PyPi. It is recommended to use a dedicated virtualenv:

# create and enter a new virtual environment (optional)
virtualenv --system-site-packages --python=python3 ~/env/dataladni
. ~/env/dataladni/bin/activate

# install from PyPi
pip install datalad_neuroimaging

There is also a Singularity container available. The latest release version can be obtained by running:

singularity pull shub://datalad/datalad-neuroimaging

Support

The documentation of this project is found here: http://docs.datalad.org/projects/neuroimaging

All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/datalad/datalad-neuroimaging/issues

If you have a problem or would like to ask a question about how to use DataLad, please submit a question to NeuroStars.org with a datalad tag. NeuroStars.org is a platform similar to StackOverflow but dedicated to neuroinformatics.

All previous DataLad questions are available here: http://neurostars.org/tags/datalad/

Acknowledgements

DataLad development is supported by a US-German collaboration in computational neuroscience (CRCNS) project "DataGit: converging catalogues, warehouses, and deployment logistics into a federated 'data distribution'" (Halchenko/Hanke), co-funded by the US National Science Foundation (NSF 1429999) and the German Federal Ministry of Education and Research (BMBF 01GQ1411). Additional support is provided by the German federal state of Saxony-Anhalt and the European Regional Development Fund (ERDF), Project: Center for Behavioral Brain Sciences, Imaging Platform. This work is further facilitated by the ReproNim project (NIH 1P41EB019936-01A1).