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Felix Hoffstaedter bee198e6e6 [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": "e36e5ee0-1b5f-474e-ad97-a8a69aaa6010",
 "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 ^^^
2026-03-27 00:46:51 +01:00
.datalad [DATALAD RUNCMD] Remove datalad-get option and do singularity exec instead of run 2026-03-27 00:04:28 +01:00
code SLURM submission setup 2026-03-27 00:04:42 +01:00
duct-logs Merge results 2026-03-27 00:38:23 +01:00
sourcedata Register ds006893 BIDS dataset as input 2026-03-27 00:04:37 +01:00
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.gitmodules Register ds006893 BIDS dataset as input 2026-03-27 00:04:37 +01:00
CHANGELOG.md Apply YODA dataset setup 2026-03-27 00:04:16 +01:00
dataset_description.json move data to dataset root 2026-03-27 00:38:40 +01:00
group_bold.html [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 00:46:32 +01:00
group_bold.tsv [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 00:46:32 +01:00
group_T1w.html [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 00:46:32 +01:00
group_T1w.tsv [DATALAD RUNCMD] Compute MRIQC group stats 2026-03-27 00:46:32 +01:00
mclf_data-unseen_pred.csv [DATALAD RUNCMD] run MRIQC classifier 2026-03-27 00:46:51 +01:00
README.md add README with Acknowledgements 2026-03-27 00:04:18 +01:00
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README.md

ds006893 - MRIQC derivatives

This dataset is a BIDS Derivatives dataset resulting from running MRIQC 24.0.2 on ds006893.

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).