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 (see documentation for more information).

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

You will also need to download the ukbfetch utility provided by the UK Biobank. See the ukbfetch documentation for specifics.

Use

To track UKB data for a single participant (example ID: 1234), start by creating and initializing a new dataset:

% datalad create 1234
% cd 1234
% datalad ukb-init --bids 1234 20227_2_0 20227_3_0 25755_2_0 25755_3_0

In this example only two data records with two instances each are selected. However, any other selection is supported too. The --bids flag enables an additional dataset layout with a BIDS-like structure.

After initialization, run ukb-update at any time to (re-)download data from UKB, and update the dataset in order to track changes longitudinally.

datalad -c datalad.ukbiobank.keyfile=<pathtoaccesstoken> ukb-update

This will maintain two or three branches:

• incoming: tracking the pristine UKB downloads
• incoming-native: a "native" representation of the extracted downloads for single file access using UKB naming conventions
• incoming-bids: an alternative dataset layout using BIDS conventions (if enabled with ukb-init --bids)

Changes can then be merged manually into the main branch. Alternatively, ukb-update --merge merges incoming-native (or incoming-bids if enabled) automatically.

Use with pre-downloaded data

Re-download can be avoided (while maintaining all other functionality), if the ukbfetch utility is replaced by a shim that obtains the relevant files from where they have been downloaded to. An example script is provided at tools/ukbfetch_surrogate.sh.

One simple way to use this script is to add a symlink at ~/env/datalad/bin/ for example:

ln -s tools/ukbfetch_surrogate.sh ~/env/datalad/bin/ukbfetch`

Use on non-UNIX-like operating systems

This code relies on a number of POSIX filesystem features that may make it somewhat hard to get working on Windows. Contributions to port this extension to non-POSIX platforms are welcome, but presently this is not supported.

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).