20.500.12556/RUL-185559 QuNex recipes executable, human- readable workflows for reproducible neuroimaging research 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. neuroimaging data processing data analysis and reproducibility nevrološko slikanje procesiranje podatkov analiza podatkov ponovljivost true false true Angleški jezik Slovenski jezik Članek v reviji 2026-08-10 09:20:02 2026-08-10 09:20:03 2026-08-11 04:01:55 0000-00-00 00:00:00 2026 0 0 15 str. art. ǂ1274 Vol. 4 Jun. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.6:616.8 2837-6056 10.1162/IMAG.a.1274 283171331 RAZ_Demsar_Jure_2026.pdf RAZ_Demsar_Jure_2026.pdf 1 D9042AF6C827B01D8670D85B73463858 2a94efb7c5814d9487ee9786ba4b85f04bec50e4f159fffb6bcc05e377614c1e 9fc83b17-948c-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=244198 https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.1274/136907/QuNex-recipes-Executable-human-readable-workflows 1 8ffee41f-948b-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=244197 Fakulteta za računalništvo in informatiko Filozofska fakulteta 0 0 0