FIX dataloader when dataset already present

Author: TomDLT

README.rst

@@ -99,7 +99,7 @@ corresponding publications:
   preparation*.
 
 .. [2] Dupré La Tour, T., Eickenberg, M., & Gallant, J. L. (2022).
-	Variance decomposition with banded ridge regression. *In preparation*.
+	Feature-space selection with banded ridge regression. *In preparation*.
 
 .. [3] Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015).
     Pycortex: an interactive surface visualizer for fMRI. Frontiers in

doc/voxelwise_modeling.rst

@@ -141,8 +141,8 @@ Datasets
 
 .. _hut2012data:
 
-[4b] 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--
+[4b] Huth, A. G., Nishimoto, S., Vu, A. T., Dupré la Tour, T., & Gallant, J. L. (2022).
+    Gallant Lab Natural Short Clips 3T fMRI Data. http://dx.doi.org/10.12751/g-node.vy1zjd
 
 Packages
 --------
@@ -156,7 +156,7 @@ Packages
 .. _dup2022:
 
 [14] Dupré La Tour, T., Eickenberg, M., & Gallant, J. L. (2022).
-	Variance decomposition with banded ridge regression. *In preparation*.
+	Feature-space selection with banded ridge regression. *In preparation*.
 
 .. _gao2015:
 

tutorials/notebooks/shortclips/00_download_shortclips.ipynb

@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## References\n\n.. [1] Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A\n    continuous semantic space describes the representation of thousands of\n    object and action categories across the human brain. Neuron, 76(6),\n    1210-1224.\n\n.. [2] Huth, A. G., Nishimoto, S., Vu, A. T., Dupre la Tour, T., & Gallant, J. L. (2022).\n    Gallant Lab Natural Short Clips 3T fMRI Data. http://dx.doi.org/--TBD--\n\n"
+        "## References\n\n.. [1] Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A\n    continuous semantic space describes the representation of thousands of\n    object and action categories across the human brain. Neuron, 76(6),\n    1210-1224.\n\n.. [2] Huth, A. G., Nishimoto, S., Vu, A. T., Dupr\u00e9 la Tour, T., & Gallant, J. L. (2022).\n    Gallant Lab Natural Short Clips 3T fMRI Data. http://dx.doi.org/10.12751/g-node.vy1zjd\n\n"
       ]
     }
   ],
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/00_setup_colab.ipynb

@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/01_plot_explainable_variance.ipynb

@@ -337,7 +337,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/02_plot_ridge_regression.ipynb

@@ -386,7 +386,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To summarize, to select the best hyperparameter $\\alpha$, the standard\nmethod is to perform a grid search:\n  - Split the training set into two subsets: one subset used to fit the\n    models, and one subset to estimate the prediction accuracy (*validation\n    set*)\n  - Define a number of hyperparameter candidates, for example [0.1, 1, 10,\n    100].\n  - Fit a separate ridge model with each hyperparameter candidate\n    $\\alpha$.\n  - Compute the prediction accuracy on the validation set.\n  - Select the hyperparameter candidate leading to the best validation\n    accuracy.\n\nTo make the grid search less sensitive to the choice of how the training data\nwas split, the process can be repeated for multiple splits. Then, the\ndifferent prediction accuracies can be averaged over splits before the\nhyperparameter selection. Thus, the process is called a *cross-validation*.\n\nLearn more about hyperparameter selection and cross-validation on the\n`scikit-learn documentation\n<https://scikit-learn.org/stable/modules/cross_validation.html>`_.\n\n"
+        "To summarize, to select the best hyperparameter $\\alpha$, the standard\nmethod is to perform a grid search:\n\n  - Split the training set into two subsets: one subset used to fit the\n    models, and one subset to estimate the prediction accuracy (*validation\n    set*)\n  - Define a number of hyperparameter candidates, for example [0.1, 1, 10,\n    100].\n  - Fit a separate ridge model with each hyperparameter candidate\n    $\\alpha$.\n  - Compute the prediction accuracy on the validation set.\n  - Select the hyperparameter candidate leading to the best validation\n    accuracy.\n\nTo make the grid search less sensitive to the choice of how the training data\nwas split, the process can be repeated for multiple splits. Then, the\ndifferent prediction accuracies can be averaged over splits before the\nhyperparameter selection. Thus, the process is called a *cross-validation*.\n\nLearn more about hyperparameter selection and cross-validation on the\n`scikit-learn documentation\n<https://scikit-learn.org/stable/modules/cross_validation.html>`_.\n\n"
       ]
     }
   ],
@@ -406,7 +406,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/03_plot_wordnet_model.ipynb

@@ -653,7 +653,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/04_plot_hemodynamic_response.ipynb

@@ -352,7 +352,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/05_plot_motion_energy_model.ipynb

@@ -341,7 +341,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/06_plot_banded_ridge_model.ipynb

@@ -510,7 +510,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.0"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,