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    <identifier>10.57760/sciencedb.26480</identifier>
    <datestamp>2025-07-18T14:48:19Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2025-07-18</dc:date>
  <dc:title>Dataset required for CMM and GC-Net simulations</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.26480</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Instructions for Using the Dataset to Reproduce the Paper&amp;rsquo;s Results.Paper: Human and artificial visual systems share a computational principle for transforming binocular disparity into depth representationhttps://www.nature.com/articles/s42003-025-08474-1Dataset Contents.The dataset comprises pre-simulated data files necessary for reproducing the figures presented in the paper. The contents of the dataset and their corresponding roles are as follows:&amp;middot;&amp;nbsp;BEM_canonical: Contains cross-correlation weights based on the binocular energy model (Supplementary Figure 4).&amp;middot;&amp;nbsp;checkpoint: Includes the pretrained GC-Net model.&amp;middot;&amp;nbsp;CMM: Stores cross-correlation and cross-matching responses to random dot stereograms (RDSs), used in Figure 3.&amp;middot;&amp;nbsp;epoch_7_iter_22601: Outputs from GC-Net used in Figures 4 and 5.&amp;middot;&amp;nbsp;MVPA: Provides decoding results for Figure 2 and Supplementary Figures 1 and 2.&amp;middot;&amp;nbsp;rds: Contains the generated RDS stimuli used for simulations (general use).&amp;middot;&amp;nbsp;S2N: fMRI percent signal change data used in Supplementary Figure 3.&amp;middot;&amp;nbsp;VTC_extract_smoothed: Smoothed VTC data extracted from BrainVoyager (general use).&amp;middot;&amp;nbsp;VTC_normalized: Normalized VTC data (general use).&amp;middot;&amp;nbsp;VTC_stimID: Parameters describing the stimulus presentation corresponding to the VTC data (general use).&amp;middot;&amp;nbsp;wavelet: Contains wavelet analysis data (Supplementary Figure 5).Python Code Repository.The Python code for generating the figures in the paper is available on GitHub and can be cloned from the following link:https://github.com/wundari/CMM_modelRepository Folder Structure Overview.The top-level structure of the cloned repository includes the following primary folders:&amp;middot;&amp;nbsp;Codes/&amp;middot;&amp;nbsp;Data/&amp;middot;&amp;nbsp;Plots/Please refer to the file folder_tree_structure.txt for the detailed folder hierarchy and organization.Organizing Dataset Files.After cloning the repository, organize the dataset as follows:Step 1: Move the following folders into the CMM_model/Data/ directory:&amp;middot;&amp;nbsp;BEM_canonical&amp;middot;&amp;nbsp;CMM&amp;middot;&amp;nbsp;MVPA&amp;middot;&amp;nbsp;rds&amp;middot;&amp;nbsp;S2N&amp;middot;&amp;nbsp;VTC_extract_smoothed&amp;middot;&amp;nbsp;VTC_normalized&amp;middot;&amp;nbsp;VTC_stimID&amp;middot;&amp;nbsp;waveletStep 2: Create the following directory path:CMM_model/Codes/Python/gcnet/results/sceneflow/monkaa/shift_1.5_median_wrt_leftThen move the following folders into that directory:&amp;middot;&amp;nbsp;checkpoint&amp;middot;&amp;nbsp;epoch_7_iter_22601For additional details and step-by-step instructions on running the Python scripts to reproduce the paper&amp;rsquo;s plots, please visit the GitHub repository: https://github.com/wundari/CMM_model</dc:description>
  <dc:subject>CMM;  MVPA; DNN</dc:subject>
  <dc:creator>Wundari Bayu Gautama</dc:creator>
  <dc:creator>Fujita Ichiro</dc:creator>
  <dc:creator>Ban Hiroshi</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1038/s42003-025-08474-1</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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