diff --git a/doc/about.rst b/doc/about.rst
index dc6172f23e17b..d5b1430d73e21 100644
--- a/doc/about.rst
+++ b/doc/about.rst
@@ -176,6 +176,8 @@ program.
 - 2015 - `Raghav RV <https://github.com/raghavrv>`_, Wei Xue
 - 2016 - `Nelson Liu <http://nelsonliu.me>`_, `YenChen Lin <https://yclin.me/>`_
 
+.. _Vlad Niculae: https://vene.ro/
+
 It also provided funding for sprints and events around scikit-learn. If
 you would like to participate in the next Google Summer of code
 program, please see `this page
diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst
index 5e1a19af189b2..77ecc05c710d0 100644
--- a/doc/datasets/index.rst
+++ b/doc/datasets/index.rst
@@ -81,18 +81,6 @@ These datasets are useful to quickly illustrate the behavior of the
 various algorithms implemented in scikit-learn. They are however often too
 small to be representative of real world machine learning tasks.
 
-.. toctree::
-    :maxdepth: 2
-    :hidden:
-
-    boston_house_prices
-    iris
-    diabetes
-    digits
-    linnerud
-    wine_data
-    breast_cancer
-
 .. include:: ../../sklearn/datasets/descr/boston_house_prices.rst
 
 .. include:: ../../sklearn/datasets/descr/iris.rst
@@ -132,18 +120,6 @@ They can be loaded using the following functions:
    fetch_kddcup99
    fetch_california_housing
 
-.. toctree::
-    :maxdepth: 2
-    :hidden:
-
-    olivetti_faces
-    twenty_newsgroups
-    labeled_faces
-    covtype
-    rcv1
-    kddcup99
-    california_housing
-
 .. include:: ../../sklearn/datasets/descr/olivetti_faces.rst
 
 .. include:: ../../sklearn/datasets/descr/twenty_newsgroups.rst
diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py
index 329584e5cc5b2..d1cee0345d5e6 100644
--- a/sklearn/decomposition/pca.py
+++ b/sklearn/decomposition/pca.py
@@ -251,7 +251,7 @@ class PCA(_BasePCA):
     "Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604*
 
     Implements the probabilistic PCA model from:
-    `Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
+    Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
     component analysis". Journal of the Royal Statistical Society:
     Series B (Statistical Methodology), 61(3), 611-622.
     via the score and score_samples methods.