DOC improve readme rendering

Author: TomDLT

doc/_static/custom.css

@@ -7,7 +7,7 @@
 }
 .sphx-glr-thumbcontainer img {
     max-height: 112px !important;       /*default = 112 */
-    width: 210px !important;            /*default = 160 */
+    max-width: 210px !important;        /*default = 160 */
 }
 .sphx-glr-thumbcontainer a.internal {
     padding: 150px 10px 0 !important;   /*default = 150px 10px 0 */

tutorials/movies_3T/README.rst

@@ -4,21 +4,23 @@ Movies 3T tutorial
 This tutorial describes how to perform voxelwise modeling on a visual
 imaging experiment.
 
-This tutorial is based on publicly available data `published on CRCNS <TBD>`_
-[4]_.
-The data is briefly described in the dataset `description PDF <TBD>`_.
-The experiment is described in more details in the original publication
-[1]_.
+**Data set:**
+This tutorial is based on publicly available data
+`published on CRCNS <TBD>`_ [4]_.
+The data is briefly described in the dataset `description PDF <TBD>`_,
+and in more details in the original publication [1]_.
+If you publish work using this data set, please cite the original
+publication [1]_, and the CRCNS data set [4]_.
 
+**Models:**
 This tutorial implements different voxelwise models:
 
-- the wordnet model described in [1]_
-- the motion-energy model described in [2]_
-- the banded-ridge model described in [3]_
-
+- a ridge model with wordnet semantic features as described in [1]_.
+- a ridge model with motion-energy features as described in [2]_.
+- a banded-ridge model with both feature spaces as described in [3]_.
 
-**Requirements**
 
+**Requirements:**
 This tutorial requires the following Python packages:
 
 - numpy  (for the data array)
@@ -30,10 +32,7 @@ This tutorial requires the following Python packages:
 - voxelwise  (this repository)
 
 
-**References**
-
-If you publish work using this data set, please cite the original
-publication [1]_, and the CRCNS data set [4]_.
+**References:**
 
 .. [1] Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012).
     A continuous semantic space describes the representation of thousands of

tutorials/movies_4T/README.rst

@@ -7,22 +7,23 @@ Movies 4T tutorial
 This tutorial describes how to perform voxelwise modeling on a visual
 imaging experiment.
 
-This tutorial is based on publicly available data
-published on `CRCNS <https://crcns.org/data-sets/vc/vim-2/about-vim-2>`_
-[6]_.
-The data is briefly described in the dataset descriptio
-`PDF <https://crcns.org/files/data/vim-2/crcns-vim-2-data-description.pdf>`_.
-The experiment is described in more details in the original publication
-[5]_.
+**Data set:**
+This tutorial is based on publicly available data published on
+`CRCNS <https://crcns.org/data-sets/vc/vim-2/about-vim-2>`_ [6]_.
+The data is briefly described in the dataset description
+`PDF <https://crcns.org/files/data/vim-2/crcns-vim-2-data-description.pdf>`_,
+and in more details in the original publication [5]_.
+If you publish work using this data set, please cite the original
+publication [5]_, and the CRCNS data set [6]_.
 
 .. Note::
-    This tutorial is redundant with the "Movies 3T" tutorial. It uses a
-    different data set, with brain responses limited to the occipital lobe.
-    Using the "Movies 3T" tutorial with full brain responses is recommended.
-
+    This tutorial is redundant with the "Movies 3T tutorial". It uses a
+    different data set, with brain responses limited to the occipital lobe,
+    and with no mappers to plot the data on flatmaps.
+    Using the "Movies 3T tutorial" with full brain responses is recommended.
 
-**Requirements**
 
+**Requirements:**
 This tutorial requires the following Python packages:
 
 - numpy  (for the data array)
@@ -33,10 +34,7 @@ This tutorial requires the following Python packages:
 - pymoten  (for extracting motion energy)
 - voxelwise  (this repository)
 
-**References**
-
-If you publish work using this data set, please cite the original
-publication [5]_, and the CRCNS data set [6]_.
+**References:**
 
 .. [5] Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu,
     B., & Gallant, J. L. (2011). Reconstructing visual experiences from brain