Data set for "Contextual gating of whisker-evoked responses by frontal cortex supports flexible decision making"

Data set for: Ghaderi P, Crochet S, Petersen CCH (2026) Contextual gating of whisker-evoked responses by frontal cortex supports flexible decision making. Nature Communications 17: 5982. https://doi.org/10.1038/s41467-026-73622-y There are 2 files in this upload: 1. The file named "2026_Ghaderi_NCOMMS.pdf" is the Open Access pdf of the online publication in Nature Communications. 2. The file named "2026_Ghaderi_NCOMMS_Zenodo_data_code.zip" (~21 GB) is a zipped version of a folder "2026_Ghaderi_NCOMMS_Zenodo_data_code" (~24 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file to reveal the following subfolders: 1. data_electrophysiology : High-density extracellular recordings with Neuropixels probes - one NWB file for each session2. data_optogenetics : Optogenetic inactivation sessions - one NWB file for each session3. data_helpers : small data structures used in the analyses4. functions : functions and toolboxes used in the main codes5. processed_data_code : codes used to generate the analyses from the NWB files and saving processed data in the 'processed_data' subfolder 6. processed_data : data structures generated by the processed_data_codes and used to plot the figures7. Main_figures_codes: code plotting the main figures of the publication and saving each panel in the 'Main_figures_pdf' subfolder8. Main_figures_pdf : subfolder containing the plots for the main figures in .pdf or .svg format9. Supplementary_figures_codes: code plotting the supplementary figures of the publication and saving each panel in the 'Supplementary_figures_pdf' subfolder10. Supplementary_figures_pdf : subfolder containing the plots for the supplementary figures in .pdf or .svg format Follow the instructions in the 'README.txt' file in order to run the code. The full data set is also available via DANDI at: https://doi.org/10.48324/dandi.001530/0.260605.0601 The MATLAB and Python codes are also available via Github: https://github.com/LSENS-BMI-EPFL/Ghaderi_et_al_NCOMMS_2026/