Data availability

The data, documentation and code can be accessed with DataLad by cloning the dataset repository from Github (https://github.com/courtois-neuromod/cneuromod-things), or by downloading an archived version (1.0.1) of the repository hosted on Zenodo1. The repository contains a nested set of submodules with symbolic links to larger data files hosted remotely on the Canadian Open Neuroscience Platform54 (CONP) servers. Data files can be pulled with DataLad via symbolic links without requiring registered access. Data are released under a liberal Creative Commons (CC0) license that authorizes the re-sharing of derivatives.

Code availability

BOLD data were acquired with the PsychoPy library (https://www.psychopy.org/) and preprocessed with the fMRIprep pipeline38,39 (versions 20.2.3 and 20.2.5; https://fmriprep.org/en/stable/). All CNeuroMod data acquisition and data preprocessing scripts are available on the CNeuroMod GitHub (https://github.com/courtois-neuromod).

• Data acquisition scripts: https://github.com/courtois-neuromod/task_stimuli

• BOLD data preprocessing scripts: https://github.com/courtois-neuromod/ds_prep

The code used to generate derivatives from the preprocessed CNeuroMod-THINGS data is integrated throughout the https://github.com/courtois-neuromod/cneuromod-things repository and its submodules. This repository includes scripts to:

• extract trial-wise and image-wise beta scores per voxel from preprocessed BOLD data (‘THINGS/glmsingle/code/glmsingle’)

• generate maps of temporal signal-to-noise ratio from preprocessed BOLD data (‘THINGS/tsnr/code‘, ‘retinotopy/tsnr/code’, ‘fLoc/tsnr/code’,)

• perform t-tests and GLM fixed-effects analyses to assess memory recognition effects on the BOLD data (‘THINGS/glm-memory/code’)

• quantify in-scan head motion (‘THINGS/glmsingle/code/qc’)

• organize trial-wise metrics (stimulus image annotations, task conditions, task accuracy, reaction time, gaze fixation compliance; ‘THINGS/fmriprep/sourcedata/things/code’)

• analyze behavioural performance on the image recognition task (‘THINGS/behaviour/code’)

• process eye-tracking data (‘THINGS/fmriprep/sourcedata/things/code’)

• perform data-driven analyses to characterize stimulus representation in the brain signal (‘THINGS/glmsingle/code/descriptive’)

• derive ROI masks from two functional localizer tasks (‘retinotopy/prf/code’ and ‘fLoc/rois/code’)

• visualize results onto flattened cortical maps of the subjects’ brains (‘anatomical/pycortex’)

The cneuromod-things repository also includes a collection of Jupyter Notebooks with step-by-step instructions and code to pull data files directly from the DataLad collection and reproduce the figures included in the current manuscript. The content of these Notebooks can be visualized directly on Github (https://github.com/courtois-neuromod/cneuromod-things/tree/main/datapaper), and provides concrete examples of analyses that can be conducted with the current dataset.

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Acknowledgements

This work was supported by the Courtois Foundation and an NSERC discovery grant awarded to LB (RGPIN-2025-06022), and by a Max Planck Research Group Grant (M.TN.A.NEPF0009) and an ERC Starting Grant COREDIM (StG-2021-101039712) awarded to MNH.

Funding

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations

Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Marie St-Laurent, Oliver Contier, Katja Seeliger & Martin N. Hebart 1.

Centre de recherche de l’Institut universitaire de gériatrie de Montréal, Montréal, Canada

Marie St-Laurent, Basile Pinsard, Elizabeth DuPre, Valentina Borghesani, Julie A. Boyle & Lune Bellec 1.

Département de psychologie, Université de Montréal, Montréal, Canada

Elizabeth DuPre & Lune Bellec 1.

Martin Luther University Halle-Wittenberg, Medical Faculty, Halle, Germany

Katja Seeliger 1.

Faculté de psychologie et des sciences de l’éducation, Université de Genève, Genève, Switzerland

Valentina Borghesani 1.

Department of Medicine, Justus Liebig University Giessen, Giessen, Germany

Martin N. Hebart 1.

Center for Mind, Brain and Behavior (CMBB), Universities of Marburg, Giessen, Darmstadt, Germany

Martin N. Hebart

Authors

1. Marie St-Laurent
2. Basile Pinsard
3. Oliver Contier
4. Elizabeth DuPre
5. Katja Seeliger
6. Valentina Borghesani
7. Julie A. Boyle
8. Lune Bellec
9. Martin N. Hebart

Contributions

M.S.L.: Methodology, Software, Data curation, Formal analysis, Visualization, Writing – original draft, review & editing B.P.: Conceptualization, Project administration, Investigation, Methodology, Software, Data curation, Formal analysis, Visualization O.C.: Software, Formal analysis, Visualization, Writing – review & editing E.D.: Software, Formal analysis, Data curation, Writing – review & editing K.S.: Software, Writing – review & editing V.B.: Conceptualization J.B.: Conceptualization, Funding acquisition, Resources, Project administration, Investigation, Data curation, Writing – review & editing L.B.: Conceptualization, Funding acquisition, Resources, Project administration, Investigation, Data curation, Formal analysis, Visualization, Writing – review & editing M.N.H.: Conceptualization, Funding acquisition, Supervision, Methodology, Software, Data curation, Formal analysis, Visualization, Writing – original draft, review & editing.

Corresponding author

Correspondence to Marie St-Laurent.

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St-Laurent, M., Pinsard, B., Contier, O. et al. CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience. Sci Data (2026). https://doi.org/10.1038/s41597-026-06591-y

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Received: 25 August 2025

Accepted: 08 January 2026

Published: 29 January 2026

DOI: https://doi.org/10.1038/s41597-026-06591-y