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Open Source Science

https://github.com/afni/apaper_highlight_narps

Scripts related to the paper

https://github.com/afni/apaper_highlight_narps

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: pubmed.ncbi, ncbi.nlm.nih.gov
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Scripts related to the paper

Basic Info
  • Host: GitHub
  • Owner: afni
  • Language: Shell
  • Default Branch: main
  • Size: 53.7 KB
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  • Watchers: 11
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Created over 3 years ago · Last pushed over 3 years ago

https://github.com/afni/apaper_highlight_narps/blob/main/ 

# apaper_highlight_narps
Scripts related to the following paper:

  **Highlight Results, Don't Hide Them: Enhance interpretation, reduce
  biases and improve reproducibility** \
  by Paul A Taylor, Richard C Reynolds, Vince Calhoun, Javier
  Gonzalez-Castillo, Daniel A Handwerker, Peter A Bandettini, Amanda F
  Mejia, Gang Chen (2023) \
  Neuroimage 274:120138. doi: 10.1016/j.neuroimage.2023.120138 \
  https://pubmed.ncbi.nlm.nih.gov/37116766/

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The input data comes from the NARPS project (Botvinik-Nezer et al., 2020): \
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771346/ \
This paper uses both the raw, unprocessed data as well as the
participating teams' results, which were uploaded to NeuroVault (see
the same paper for those details).

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Essentially all scripts here use AFNI; one also uses FreeSurfer.

The `scripts_biowulf` directory contains the main processing scripts,
including:
+ Checking the data
+ Estimating nonlinear alignment to template space and skullstripping
  with `@SSwarper`
+ Full FMRI time series processing through regression modeling and QC
  generation with `afni_proc.py`
+ Group level modeling: both voxelwise (with cluster calcs) and
  ROI-based (using `RBA`, in particular) 

... and more.

The `scripts_suppl_proc_vox` directory contains supplementary scripts
for making images of the above-processed data, mainly for figure
generation.

The `scripts_suppl_proc_teams` directory contains scripts for
processing the group-level results of the original participating Teams
in the NARPS project.  Those public datasets were downloaded from
NeuroVault.  The scripts make a lot of images and perform some simple
similarity analyses.

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  • Name: afni
  • Login: afni
  • Kind: organization

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