Update 'README.md'

README.md

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