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Felix Hoffstaedter 2feab132aa [DATALAD RUNCMD] run MRIQC classifier 
=== Do not change lines below ===
{
 "chain": [],
 "cmd": "./code/containers/scripts/singularity_cmd exec code/containers/images/bids/bids-mriqc--0.15.1.sing mriqc_clf --load-classifier -X group_T1w.tsv;   mv mclf_run*data-unseen_pred.csv mclf_data-unseen_pred.csv",
 "dsid": "b628df70-00cb-4da2-9898-333149c6d8e5",
 "exit": 0,
 "extra_inputs": [
  "code/containers/images/bids/bids-mriqc--0.15.1.sing"
 ],
 "inputs": [
  "group_T1w.tsv"
 ],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^
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.datalad [DATALAD RUNCMD] Remove datalad-get option and do singularity exec instead of run 2026-03-27 02:04:49 +01:00
code SLURM submission setup 2026-03-27 02:05:03 +01:00
duct-logs Merge results 2026-03-27 04:35:25 +01:00
sourcedata Register ds005050 BIDS dataset as input 2026-03-27 02:04:58 +01:00
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.gitignore SLURM submission setup 2026-03-27 02:05:03 +01:00
.gitmodules Register ds005050 BIDS dataset as input 2026-03-27 02:04:58 +01:00
CHANGELOG.md Apply YODA dataset setup 2026-03-27 02:04:38 +01:00
dataset_description.json move data to dataset root 2026-03-27 04:35:47 +01:00
group_bold.html [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 04:39:04 +01:00
group_bold.tsv [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 04:39:04 +01:00
group_T1w.html [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 04:39:04 +01:00
group_T1w.tsv [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 04:39:04 +01:00
mclf_data-unseen_pred.csv [DATALAD RUNCMD] run MRIQC classifier 2026-03-27 04:39:26 +01:00
README.md add README with Acknowledgements 2026-03-27 02:04:40 +01:00
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README.md

ds005050 - MRIQC derivatives

This dataset is a BIDS Derivatives dataset resulting from running MRIQC 24.0.2 on ds005050.

Methods

MRIQC documentation can be found on https://mriqc.readthedocs.io/.

This dataset was created using BOOTSTRAP - a high-throughput container workflow for MRIQC implementing the FAIRly Big Workflow using Datalad and Singularity containers for full reproducibility and provenance capture including resource consumption via con-duct.

Acknowledgements

The authors from the Institute of Neuroscience and Medicine (INM-7) at the Forschungszentrum Jülich, Germany gratefully acknowledge the FENIX Research Infrastructure for funding this project by providing cloud computing time on the FZJ Supercomputer JUSUF at Jülich Supercomputing Centre (JSC).