Template images and image space transformations
This repository contains data derived from the raw data releases of the
studyforrest.org project. In particular these are:
- participant/scan-specific template images
- transformation between these respective image spaces
For more information about the project visit: http://studyforrest.org
File name conventions
Each directory in the subject directories and the "templates" directory
corresponds to one image template. Templates in
sub* directories are
participant-specific (not aligned across participants). However, templates with
the same name have corresponding input data. Templates in the
directory have been derived from all participants, and there are typically
transformation from participant specific templates into the group template
space provided. Group template images carry a
grp prefix in their label.
All transformations are the output if FSL tools: either MAT files with
4x4 affine transformation matrices from FLIRT, or FNIRT warp files.
Here is an example of how transformations can be located. The transformation
of the template image created from all 3T BOLD images of participant
acquired in phase 2 of the project into the group template space for 3T BOLD
scans can be found in:
Each template directory contains one or more image files with more-or-less
self-explanatory names, such as "head", "brain", or "brain_mask". File with
such a name in the one of the
in_* folders represent the image in the parent
folder, aligned and resliced to the target space for this transformation.
These images can be used to inspect the quality of the transformation.
code/ directory contains the source code for computing template
images and transformation between them.
How to obtain the dataset
This repository is a DataLad dataset. It provides
fine-grained data access down to the level of individual files, and allows for
tracking future updates. In order to use this repository for data retrieval,
DataLad is required. It is a free and
open source command line tool, available for all major operating
systems, and builds up on Git and git-annex
to allow sharing, synchronizing, and version controlling collections of
large files. You can find information on how to install DataLad at
Get the dataset
A DataLad dataset can be
cloned by running
datalad clone <url>
Once a dataset is cloned, it is a light-weight directory on your local machine.
At this point, it contains only small metadata and information on the
identity of the files in the dataset, but not actual content of the
(sometimes large) data files.
Retrieve dataset content
After cloning a dataset, you can retrieve file contents by running
datalad get <path/to/directory/or/file>
This command will trigger a download of the files, directories, or
subdatasets you have specified.
DataLad datasets can contain other datasets, so called subdatasets.
If you clone the top-level dataset, subdatasets do not yet contain
metadata and information on the identity of files, but appear to be
empty directories. In order to retrieve file availability metadata in
datalad get -n <path/to/subdataset>
Afterwards, you can browse the retrieved metadata to find out about
subdataset contents, and retrieve individual files with
If you use
datalad get <path/to/subdataset>, all contents of the
subdataset will be downloaded at once.
DataLad datasets can be updated. The command
datalad update will
fetch updates and store them on a different branch (by default
datalad update --merge
will pull available updates and integrate them in one go.
More information on DataLad and how to use it can be found in the DataLad Handbook at
handbook.datalad.org. The chapter
"DataLad datasets" can help you to familiarize yourself with the concept of a dataset.