DataLad extension for working with the UKbiobank

This software is a DataLad extension that equips DataLad with a set of commands to obtain (and monitor) imaging data releases of the UKbiobank.

UKbiobank is a national and international health resource with unparalleled research opportunities, open to all bona fide health researchers. UK Biobank aims to improve the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. It is following the health and well-being of 500,000 volunteer participants and provides health information, which does not identify them, to approved researchers in the UK and overseas, from academia and industry.

Command(s) provided by this extension

• ukb-init -- Initialize an existing dataset to track a UKBiobank participant
• ukb-update -- Update an existing dataset of a UKbiobank participant

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-ukbiobank 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_ukbiobank

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/ukbiobank/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).