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# studyforrest.org Dataset
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[](https://datalad.org)
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[](http://opendatacommons.org/licenses/pddl/summary)
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[]()
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[](http://dx.doi.org/)
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## Retinotopic Mapping
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All participants in the phase2 extension of the studyforrest dataset underwent
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retinotopic mapping with standard flickering checkerboard stimulus (ring and
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wedges). More information on the procedure and the results can be found in:
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Ayan Sengupta, Falko R. Kaule, J. Swaroop Guntupalli, Michael B. Hoffmann,
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Christian Häusler, Jörg Stadler, Michael Hanke. [An extension of the
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studyforrest dataset for vision research](http://biorxiv.org/content/early/2016/03/31/046573).
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(submitted for publication)
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For further information about the project visit: http://studyforrest.org
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## Content
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``code/``:
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source code for retinotopic mapping analysis.
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- The main script is *process_retmap* and a Python based GUI *easyret_gui* to
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call it from an easy to use front end. The *process_retmap* script calls the
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Python scripts *RetMap_phaseshift* for post processing phase shift (if
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required) and *combine_volumes* for combining the clw/ccw maps and ecc/con
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maps together.
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``src/``:
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links to repositories containing all inputs for the analysis
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``sub-??/``:
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analysis results per participant
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``surface_maps/``:
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contains eccentricity and polar angle maps of left and right hemispheres
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of a particular participant's cortical surface in *MGH* format
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``post_processing/``:
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contains the post-processed/combined compressed *NIfTI* files in a
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participant's bold3Tp2 image template space
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(see ``src/templatetransforms``), before it is aligned to the
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*T1 structural* and represented on cortical surfaces.
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``qa/``:
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contains the *pyretmap_subjQuali.ods* file which details the quality of the
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participant-wise retinotopic maps produced by the *processing pipeline*.
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## How to obtain the data files
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This repository is a [DataLad](https://www.datalad.org/) dataset. It provides
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fine-grained data access down to the level of individual files, and allows for
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tracking future updates. In order to use this repository for data retrieval,
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[DataLad](https://www.datalad.org/) is required. It is a free and
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open source command line tool, available for all major operating
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systems, and builds up on Git and [git-annex](https://git-annex.branchable.com/)
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to allow sharing, synchronizing, and version controlling collections of
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large files. You can find information on how to install DataLad at
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[handbook.datalad.org/en/latest/intro/installation.html](http://handbook.datalad.org/en/latest/intro/installation.html).
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### Get the dataset
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A DataLad dataset can be `cloned` by running
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```
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datalad clone <url>
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```
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Once a dataset is cloned, it is a light-weight directory on your local machine.
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At this point, it contains only small metadata and information on the
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identity of the files in the dataset, but not actual *content* of the
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(sometimes large) data files.
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### Retrieve dataset content
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After cloning a dataset, you can retrieve file contents by running
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```
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datalad get <path/to/directory/or/file>
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```
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This command will trigger a download of the files, directories, or
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subdatasets you have specified.
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DataLad datasets can contain other datasets, so called *subdatasets*.
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If you clone the top-level dataset, subdatasets do not yet contain
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metadata and information on the identity of files, but appear to be
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empty directories. In order to retrieve file availability metadata in
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subdatasets, run
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```
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datalad get -n <path/to/subdataset>
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```
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Afterwards, you can browse the retrieved metadata to find out about
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subdataset contents, and retrieve individual files with `datalad get`.
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If you use `datalad get <path/to/subdataset>`, all contents of the
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subdataset will be downloaded at once.
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### Stay up-to-date
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DataLad datasets can be updated. The command `datalad update` will
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*fetch* updates and store them on a different branch (by default
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`remotes/origin/master`). Running
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|
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```
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datalad update --merge
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```
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will *pull* available updates and integrate them in one go.
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### More information
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More information on DataLad and how to use it can be found in the DataLad Handbook at
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[handbook.datalad.org](http://handbook.datalad.org/en/latest/index.html). The chapter
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"DataLad datasets" can help you to familiarize yourself with the concept of a dataset.
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137
README.rst
137
README.rst
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@ -1,137 +0,0 @@
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studyforrest.org Dataset
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************************
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|license| |access| |doi|
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Retinotopic Mapping
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===================
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|
||||
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
|
||||
<http://biorxiv.org/content/early/2016/03/31/046573>`_. (submitted for
|
||||
publication)
|
||||
|
||||
For further information about the project visit: http://studyforrest.org
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||||
|
||||
Content
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-------
|
||||
|
||||
``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 <https://www.datalad.org/>`__ dataset. It provides
|
||||
fine-grained data access down to the level of individual files, and allows for
|
||||
tracking future updates up to the level of single files. In order to use
|
||||
this repository for data retrieval, `DataLad <https://www.datalad.org>`_ 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
|
||||
<https://git-annex.branchable.com>`__ 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
|
||||
<http://handbook.datalad.org/en/latest/intro/installation.html>`_.
|
||||
|
||||
Get the dataset
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^^^^^^^^^^^^^^^
|
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|
||||
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 <http://handbook.datalad.org/en/latest/index.html>`_. The
|
||||
chapter "DataLad datasets" can help you to familiarize yourself with the
|
||||
concept of a dataset.
|
||||
|
||||
.. _Git: http://www.git-scm.com
|
||||
|
||||
.. _git-annex: http://git-annex.branchable.com/
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||||
|
||||
.. |license|
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||||
image:: https://img.shields.io/badge/license-PDDL-blue.svg
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||||
:target: http://opendatacommons.org/licenses/pddl/summary
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||||
:alt: PDDL-licensed
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||||
.. |access|
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image:: https://img.shields.io/badge/data_access-unrestricted-green.svg
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:alt: No registration or authentication required
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.. |doi|
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image:: https://img.shields.io/badge/doi-missing-lightgrey.svg
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:target: http://dx.doi.org/
|
||||
:alt: DOI
|
||||
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