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MRIQC derivatives of the gallery of horror

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README.md

MRIQC derivatives of the gallery of horror

Summary

The so called "gallery of horror" is a collection of very bad quality functional and structural T1-weighted MRI images corrupted by clear artifacts. This collection has been put together for educating researchers on the quality control process at the occasion of the OHBM educational courses 2018 "Reusing Public Neuroimaging Datasets". Here we provide the derivatives of MRIQC 21.0.0rc2 (the latest pre-release at the moment of writing) run on this collection of data (Esteban et al., 2017). The derivatives contain both the MRIQC individual visual reports as well as the automatically computed image quality metrics (IQMs). Those IQMs can be found in the JSON files in the subject folders.

Downloading the data

Using DataLad

Make sure that datalad is installed on your computer. Create an account on gin and upload your public SSH key to your gin profile. Then clone the data repository using

datalad install -g git@gin.g-node.org:/cprovins/mriqc-horror-galery-derivatives

See the DataLad documentation for details.

Using gin

Create an account on gin and download the gin client. On your computer, log in using

gin login

Clone the repository using:

gin get cprovins/mriqc-horror-galery-derivatives

Large data files will not be downloaded automatically. To get them, use

gin get-content <filename>

Downloaded large files will be locked (read-only). You must unlock the files using

gin unlock <filename>

To remove the contents of a large file again, use

gin lock <filename>
gin remove-content <filename>

See here for detailed information on how to use gin.

Using git annex

Make sure git and git-annex are installed on your computer. Create an account on gin and upload your public SSH key to your gin profile. Then clone the repository using

git clone git@gin.g-node.org:/cprovins/mriqc-horror-galery-derivatives

Large data files will not be downloaded automatically. To get them, use

git annex get <filename>

Downloaded large files will be locked (read-only). You must unlock the files using

git annex unlock <filename>

To remove the contents of a large file again, use

git annex --force lock <filename>
git annex drop <filename>

See the git annex documentation for details.

Using the web browser

Download the latest release as a zip file by clicking on Releases on the main page at https://gin.g-node.org/cprovins/mriqc-horror-galery-derivatives. This zip file will contain all small (text) files only, while large data files will not be downloaded automatically and an empty placeholder will be put in their place. To get the full content of such a large file , download these files individually as needed from the web interface by clicking on them in the repository browser.

Repository structure

The individual MRIQC reports are html-format files in the root directory. These can be opened using any web-browser. There is also a folder for each participant containing a json file listing the IQMs and their values for each scan. Overall, the derivatives are organised following the BIDS standard.

Related Publications

  • Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One. 2017 Sep 25;12(9):e0184661. http://doi.org/10.1371/journal.pone.0184661. PMID: 28945803; PMCID: PMC5612458.

Licensing

Creative Commons License
MRIQC derivatives of the gallery of horror by Lausanne University Hospital, Rue centrale 7,1003 Lausanne, Switzerland is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.

See the LICENSE.txt or LICENSE files for the full license.

datacite.yml
Title MRIQC derivatives of the gallery of horror
Authors Provins,Céline;Lausanne University Hospital and University of Lausanne;ORCID:0000-0002-1668-9629
Esteban,Oscar;Lausanne University Hospital and University of Lausanne;ORCID:0000-0001-8435-6191
Hagen,McKenzie;University of Washington;ORCID:0000-0002-7454-8189
Description The so called gallery of horror is a collection of very bad quality functional and structural T1-weighted MRI images corrupted by clear artifacts. This collection has been put together for educating researchers on the quality control process at the occasion of the OHBM educational courses 2018 "Reusing Public Neuroimaging Datasets". Here we provide the derivatives of MRIQC 21.0.0rc2 (the latest pre-release at the moment of writing) run on this collection of data ([Esteban et al., 2017], (http://doi.org/10.1371/journal.pone.0184661)). The derivatives contain both the MRIQC individual visual reports as well as the automatically computed image quality metrics (IQMs). Those IQMs can be found in the JSON files in the subject folders.
License CC0 1.0 Universal (CC0 1.0) Public Domain Dedication (http://creativecommons.org/publicdomain/zero/1.0/)
References Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One. 2017 Sep 25;12(9):e0184661. http://doi.org/10.1371/journal.pone.0184661. PMID: 28945803; PMCID: PMC5612458. [doi:10.1371/journal.pone.0184661] (IsDescribedBy)
Funding FNS, 185872
Keywords Neuroscience
Quality control
Artifacts
Bad quality MRI
fMRI
T1w MRI
structural MRI
MRIQC
derivatives
education
openscience
reproducible science
image quality metrics
IQMs
Resource Type Dataset