Ten simple rules for achieving computational reproducibility in cognitive neuroscience
Abstract
Computational research depends on complex, linked operations. In cognitive neuroscience, these operations often combine multiple data types, specialized toolboxes, custom scripts, and changing computing environments. Open science practices, Findable, Accessible, Interoperable, and Reusable (FAIR) principles, and data standardization have improved how research objects are shared and described. However, they do not guarantee that the same analysis can be rerun to reconstruct the original result. This limitation becomes more consequential as AI-assisted coding tools are accelerating code development, with the potential to strengthen good practices, but also to amplify weak ones. Related problems have long been addressed in software engineering and machine learning operations through practices that make code, data, environments, and execution steps traceable. Drawing from these practices, we present ten simple rules for achieving computational reproducibility in cognitive neuroscience. We emphasize simple, low-cost decisions that can be implemented early and scaled as projects become more complex.
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BibTeX
@misc{mahajan2026simple,
title = {{Ten simple rules for achieving computational reproducibility in cognitive neuroscience}},
author = {Gaurav Mahajan and Caroline J Charpentier and Valentin Guigon},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/w7xpq_v2},
url = {https://osf.io/preprints/psyarxiv/w7xpq_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Ten simple rules for achieving computational reproducibility in cognitive neuroscience
AU - Gaurav Mahajan
AU - Caroline J Charpentier
AU - Valentin Guigon
PY - 2026
DA - 2026/09/17
PB - PsyArXiv
DO - 10.31234/osf.io/w7xpq_v2
UR - https://osf.io/preprints/psyarxiv/w7xpq_v2/
AB - Computational research depends on complex, linked operations. In cognitive neuroscience, these operations often combine multiple data types, specialized toolboxes, custom scripts, and changing computing environments. Open science practices, Findable, Accessible, Interoperable, and Reusable (FAIR) principles, and data standardization have improved how research objects are shared and described. However, they do not guarantee that the same analysis can be rerun to reconstruct the original result. This limitation becomes more consequential as AI-assisted coding tools are accelerating code development, with the potential to strengthen good practices, but also to amplify weak ones. Related problems have long been addressed in software engineering and machine learning operations through practices that make code, data, environments, and execution steps traceable. Drawing from these practices, we present ten simple rules for achieving computational reproducibility in cognitive neuroscience. We emphasize simple, low-cost decisions that can be implemented early and scaled as projects become more complex.
ER -