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QuNex recipes: Executable, human-readable workflows for reproducible neuroimaging research.

Jure Demšar^1,2, Aleksij Kraljič², Andraž Matkovič^2,3,4

¹Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.

Imaging Neuroscience (Cambridge, Mass.)

|June 18, 2026

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This summary is machine-generated.

QuNex recipes standardize neuroimaging workflows for enhanced transparency and reproducibility. This framework allows complex data analysis pipelines to be shared and replicated with a single command.

Area of Science:

Neuroimaging
Computational Neuroscience
Data Science

Background:

Neuroimaging data preprocessing and analysis are complex, requiring multiple tools and parameter tuning.
Current methods often lack sufficient detail for complete transparency and reproducibility.

Purpose of the Study:

Introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows.
Enhance transparency, standardization, and reproducibility in neuroimaging research.

Main Methods:

Developed QuNex recipes as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex).
Recipes define workflows in a transparent, machine- and human-readable format.
Enable seamless integration of QuNex commands, custom scripts, and external tools.

Keywords:

data analysis and reproducibility data processing neuroimaging

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Main Results:

QuNex recipes capture every processing step and parameter setting.
Facilitate one-command replication of complex neuroimaging analyses.
A reproducible study can be shared by providing QuNex version, recipe file, and data.

Conclusions:

The recipes framework standardizes workflow specification and enhances transparency.
Enables one-command replication of complex neuroimaging analyses.
Promotes open exchange of best practices and reproducible methods in the neuroimaging community.