QuNex recipes: Executable, human-readable workflows for reproducible neuroimaging research.
The 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Methods › Executing a QuNex recipe ↔ example_2/execute.sh, the whole file · a weak match · score 0.53 · qunex_suite, qx_containers, sif, Executing
- [2] § Methods › Executing a QuNex recipe ↔ example_2/execute.sh, the whole file · a weak match · score 0.53 · qunex_suite, qx_containers, sif, Executing
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
Shell · 7 lines · 282 B · MIT · 2 matches
- # execute the recipe
- qunex_container run_recipe \
- --recipe_file="/data/qunex_run_recipe/example_2/dtifit_noddi.yaml" \
- --recipe="dtifit_noddi" \
- --nv \
- --scheduler="SLURM,time=01-00,mem=32G,gres=gpus:1,jobname=qx" \
- --container="/data/qx_containers/qunex_suite-1.3.1.sif"
execute.sh at commit b3058e8, under MIT · at the source
Overview
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Mind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Medical Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- McLean Hospital, Harvard Medical School, Belmont, MA, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
- Child Study Center, Yale University School of Medicine, New Haven, CT, United States
Abstract
Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows—encompassing data onboarding, preprocessing, and analysis—in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.
ULJ-Yale/qunex_run_recipe
b3058e8997ccf6ac6dd6786c19777161827c2f04, 16 February 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
4 files
- example_2/
execute.sh , Shell, 7 lines, 2 matches - example_2/
hcya_aws.sh , Shell, 89 lines - LICENSE, License, 21 lines
- README.md, Text, 31 lines
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 2 scripts, each with its path and the digest of its content;
- 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Data and Code Availability
The code and materials needed to reproduce the results and run the recipes are given in the official open repository of this manuscript: https://
QuNex is open source. To access the code, register at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 17 authors, 3 keywords, 1 funder, 43 references.
Cite
This paper
Demšar, J., Kraljič, A., Matkovič, A., Brege, S., Pan, L., Tamayo, Z., Fonteneau, C., Helmer, M., Ji, J. L., Anticevic, A., Korponay, C., Salavrakos, M., Drucker, D., Glasser, M. F., Nickerson, L. D., Cho, Y. T., & Repovš, G. (2026). QuNex recipes: Executable, human-readable workflows for reproducible neuroimaging research. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1274. https://
BibTeX
@article{demsar2026qunex
author = {Demšar, Jure and Kraljič, Aleksij and Matkovič, Andraž and Brege, Samuel and Pan, Lining and Tamayo, Zailyn and Fonteneau, Clara and Helmer, Markus and Ji, Jie Lisa and Anticevic, Alan and Korponay, Cole and Salavrakos, Melissa and Drucker, Daniel and Glasser, Matthew F and Nickerson, Lisa D and Cho, Youngsun T and Repovš, Grega},
title = {{QuNex recipes: Executable, human-readable workflows for reproducible neuroimaging research}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1274},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/
url = {https://
pmid = {42312086},
pmcid = {PMC13271150}
}
RIS
TY - JOUR
AU - Demšar, Jure
AU - Kraljič, Aleksij
AU - Matkovič, Andraž
AU - Brege, Samuel
AU - Pan, Lining
AU - Tamayo, Zailyn
AU - Fonteneau, Clara
AU - Helmer, Markus
AU - Ji, Jie Lisa
AU - Anticevic, Alan
AU - Korponay, Cole
AU - Salavrakos, Melissa
AU - Drucker, Daniel
AU - Glasser, Matthew F
AU - Nickerson, Lisa D
AU - Cho, Youngsun T
AU - Repovš, Grega
TI - QuNex recipes: Executable, human-readable workflows for reproducible neuroimaging research
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/
VL - 4
SP - IMAG.a.1274
SN - 2837-6056
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
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