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@@ -13,90 +13,94 @@ This data set contains full brain responses recorded every two seconds with a 3T
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The data set described in [2] contains responses from the occipital lobe only,
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recorded every second with a 4T scanner.
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-[1] Huth, Alexander G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012).
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+> **[1]** Huth, Alexander G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012).
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A continuous semantic space describes the representation of thousands of object
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and action categories across the human brain. Neuron, 76(6), 1210-1224.
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-[2] Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant,
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+> **[2]** Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant,
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J. L. (2011). Reconstructing visual experiences from brain activity evoked by
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natural movies. Current Biology, 21(19), 1641-1646.
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-## Conditions for using the data
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+## Cite this dataset
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-If you publish any work using the data, please cite the original publication [1] above
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-and also cite the data set in the following recommended format:
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+If you publish any work using the data, please cite the original publication [1] above,
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+and cite the dataset in the following recommended format:
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-[3] Huth, A. G., Nishimoto, S., Vu, A. T., Dupre la Tour, T., & Gallant, J. L. (2020).
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+> **[3]** Huth, A. G., Nishimoto, S., Vu, A. T., Dupre la Tour, T., & Gallant, J. L. (2022).
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Gallant Lab Natural Short Clips 3T fMRI Data. http://dx.doi.org/--TBD--
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## Data files organization
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- features/ → feature spaces used for voxelwise modeling
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- motion_energy.hdf → visual motion energy, as described in [2]
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- wordnet.hdf → visual semantic labels, as described in [1]
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- mappers/ → plotting mappers for each subject
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- S01_mapper.hdf
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- ...
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- S05_mapper.hdf
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- responses/ → functional responses for each subject
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- S01_responses.hdf
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- ...
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- S05_responses.hdf
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- stimuli/ → natural movie stimuli, for each fMRI run
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- test.hdf
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- train_00.hdf
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- ...
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- train_11.hdf
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- utils/
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- example.py → Python functions to analyze the data
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- wordnet_categories.txt → names of the wordnet labels
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- wordnet_graph.dot → wordnet graph to plot as in [1]
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+```text
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+features/ → feature spaces used for voxelwise modeling
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+ motion_energy.hdf → visual motion energy, as described in [2]
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+ wordnet.hdf → visual semantic labels, as described in [1]
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+mappers/ → plotting mappers for each subject
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+ S01_mapper.hdf
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+ ...
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+ S05_mapper.hdf
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+responses/ → functional responses for each subject
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+ S01_responses.hdf
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+ ...
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+ S05_responses.hdf
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+stimuli/ → natural movie stimuli, for each fMRI run
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+ test.hdf
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+ train_00.hdf
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+ ...
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+ train_11.hdf
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+utils/
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+ example.py → Python functions to analyze the data
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+ wordnet_categories.txt → names of the wordnet labels
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+ wordnet_graph.dot → wordnet graph to plot as in [1]
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+```
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## Data format
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-All files are hdf5 files, with multiple arrays stored inside. The names, shapes,
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-and descriptions of each array are listed below. The `utils.py` file contains
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-helpers to load the data in Python.
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-
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- Each file in `features` contains:
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- X_train: array of shape (3600, n_features)
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- Training features.
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- X_test: array of shape (270, n_features)
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- Testing features.
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- run_onsets: array of shape (12, )
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- Indices of each run onset.
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- where (n_features = 6555) for `motion_energy.hdf`
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- and (n_features = 1705) for `wordnet.hdf`.
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-
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- Each file in `mappers` contains:
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- voxel_to_flatmap: CSR sparse array of shape (n_pixels, n_voxels)
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- Mapper from voxels to flatmap image. The sparse array is stored with
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- four dense arrays: (data, indices, indptr, shape).
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- voxel_to_fsaverage: CSR sparse array of shape (n_vertices, n_voxels)
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- Mapper from voxels to FreeSurfer surface. The sparse array is stored
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- with four dense arrays: (data, indices, indptr, shape).
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- flatmap_mask: array of shape (width, height)
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- Pixels of the flatmap image associated with a voxel.
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- flatmap_rois: array of shape (width, height, 4)
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- Transparent image with annotated ROIs (for subjects S01, S02, and S03).
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- flatmap_curvature: array of shape (width, height)
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- Transparent image with binarized curvature to locate sulci/gyri.
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- roi_mask_xxx: array of shape (n_voxels, )
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- Mask indicating which voxels are in the ROI `xxx`.
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- ROI list is different on each subject. SO4 and S05 have no ROIs.
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-
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- Each file in `responses` contains:
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- Y_train: array of shape (3600, n_voxels)
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- Training responses.
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- Y_test: array of shape (270, n_voxels)
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- Testing responses.
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- run_onsets: array of shape (12, )
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- Indices of each run onset.
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-
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- Each file in `stimuli` contains:
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- stimuli: array of shape (n_images, 512, 512, 3)
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- Each training run contains 9000 images total.
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- The test run contains 8100 images total.
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+All files are hdf5 files, with multiple arrays stored inside.
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+The names, shapes, and descriptions of each array are listed below.
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+
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+```text
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+Each file in `features` contains:
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+ X_train: array of shape (3600, n_features)
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+ Training features.
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+ X_test: array of shape (270, n_features)
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+ Testing features.
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+ run_onsets: array of shape (12, )
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+ Indices of each run onset.
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+where (n_features = 6555) for `motion_energy.hdf`
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+and (n_features = 1705) for `wordnet.hdf`.
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+
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+Each file in `mappers` contains:
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+ voxel_to_flatmap: CSR sparse array of shape (n_pixels, n_voxels)
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+ Mapper from voxels to flatmap image. The sparse array is stored with
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+ four dense arrays: (data, indices, indptr, shape).
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+ voxel_to_fsaverage: CSR sparse array of shape (n_vertices, n_voxels)
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+ Mapper from voxels to FreeSurfer surface. The sparse array is stored
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+ with four dense arrays: (data, indices, indptr, shape).
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+ flatmap_mask: array of shape (width, height)
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+ Pixels of the flatmap image associated with a voxel.
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+ flatmap_rois: array of shape (width, height, 4)
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+ Transparent image with annotated ROIs (for subjects S01, S02, and S03).
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+ flatmap_curvature: array of shape (width, height)
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+ Transparent image with binarized curvature to locate sulci/gyri.
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+ roi_mask_xxx: array of shape (n_voxels, )
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+ Mask indicating which voxels are in the ROI `xxx`.
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+ ROI list is different on each subject. SO4 and S05 have no ROIs.
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+
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+Each file in `responses` contains:
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+ Y_train: array of shape (3600, n_voxels)
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+ Training responses.
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+ Y_test: array of shape (270, n_voxels)
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+ Testing responses.
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+ run_onsets: array of shape (12, )
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+ Indices of each run onset.
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+
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+Each file in `stimuli` contains:
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+ stimuli: array of shape (n_images, 512, 512, 3)
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+ Each training run contains 9000 images total.
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+ The test run contains 8100 images total.
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+```
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+The `utils.py` file contains helpers to load the data in Python.
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## How to get started
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