studyforrest.org Dataset
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Retinotopic Mapping
All participants in the phase2 extension of the studyforrest dataset underwent
retinotopic mapping with standard flickering checkerboard stimulus (ring and
wedges). More information on the procedure and the results can be found in:
Ayan Sengupta, Falko R. Kaule, J. Swaroop Guntupalli, Michael B. Hoffmann,
Christian Häusler, Jörg Stadler, Michael Hanke. An extension of the
studyforrest dataset for vision research.
(submitted for publication)
For further information about the project visit: http://studyforrest.org
Content
code/:
source code for retinotopic mapping analysis.
- The main script is process_retmap and a Python based GUI easyret_gui to
call it from an easy to use front end. The process_retmap script calls the
Python scripts RetMap_phaseshift for post processing phase shift (if
required) and combine_volumes for combining the clw/ccw maps and ecc/con
maps together.
src/:
links to repositories containing all inputs for the analysis
sub-??/:
analysis results per participant
surface_maps/:
contains eccentricity and polar angle maps of left and right hemispheres
of a particular participant's cortical surface in *MGH* format
post_processing/:
contains the post-processed/combined compressed *NIfTI* files in a
participant's bold3Tp2 image template space
(see ``src/templatetransforms``), before it is aligned to the
*T1 structural* and represented on cortical surfaces.
qa/:
contains the pyretmap_subjQuali.ods file which details the quality of the
participant-wise retinotopic maps produced by the processing pipeline.
How to obtain the data files
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
handbook.datalad.org/en/latest/intro/installation.html.
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
subdatasets, run
datalad get -n <path/to/subdataset>
Afterwards, you can browse the retrieved metadata to find out about
subdataset contents, and retrieve individual files with datalad get.
If you use datalad get <path/to/subdataset>, all contents of the
subdataset will be downloaded at once.
Stay up-to-date
DataLad datasets can be updated. The command datalad update will
fetch updates and store them on a different branch (by default
remotes/origin/master). Running
datalad update --merge
will pull available updates and integrate them in one go.
More information
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.
