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DataLad extension for raw data capturing and conversion workflows

Project description

# Datalad-Hirni

[![Travis tests status](]( [![](]( [![Documentation](]( [![License: MIT](]( [![GitHub release](]( [![PyPI version](]( [![Average time to resolve an issue](]( “Average time to resolve an issue”) [![Percentage of issues still open](]( “Percentage of issues still open”)

This extension enhances DataLad ( with support for (semi-)automated, reproducible processing of (medical/neuro)imaging data. 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-hirni 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/datalad . ~/env/datalad/bin/activate

# install from PyPi pip install datalad_hirni

# alternative: install the latest development version from GitHub pip install git+

## Support

The documentation of this project is found here: The documentation is built from this very repository’s files under docs/source and thus you can contribute to the docs by opening a pull request just like you’d contribute to the code itself.

All bugs, concerns and enhancement requests for this software can be submitted here:

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

All previous DataLad questions are available here:

## 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.

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