datalad-xnat 0.2.1

DataLad support for XNAT server access

DataLad extension tracking data in an XNAT server

This software is a DataLad extension that equips DataLad with a set of commands to track XNAT projects.

XNAT is an open source imaging informatics platform developed by the Neuroinformatics Research Group at Washington University. It facilitates common management, productivity, and quality assurance tasks for imaging and associated data. XNAT can be used to support a wide range of neuro/medical imaging-based projects.

Command(s) provided by this extension

• xnat-init -- Initialize an existing dataset to track an XNAT project
• xnat-update -- Update an existing dataset of an XNAT project; retrieve data from the tracked project
• xnat-query-files -- Query available files on an XNAT instance or project

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-xnat 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_xnat

Support

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.

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

This development was supported by European Union’s Horizon 2020 research and innovation programme under grant agreement VirtualBrainCloud (H2020-EU.3.1.5.3, grant no. 826421).

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Michael Hanke 💻 🐛 📖 🤔 🚧	Laura Waite 💻 🐛 🤔 🚧	Adina Wagner 💻 🐛 📖 🤔 🚧	John T. Wodder II 💻 🤔 🚧	Yaroslav Halchenko 💻	Janvi Raina 📖	Stephan Heunis 🚇 🎨 🧑‍🏫
tsankeuodelfa 📖	Michał Szczepanik 📖 💻	Benjamin Poldrack 💻	Alex Waite 💬 📖 ✅	oportoles 📓 📖 🐛 ✅

This project follows the all-contributors specification. Contributions of any kind welcome!