diff --git a/datascienceintro/Classification_DecisionTree.ipynb b/datascienceintro/Classification_DecisionTree.ipynb
index 7505fde4fd24aa7e0c93016434c5b409c3c8781e..1401a85592d94a124ecd82d9957a9c8840594ef4 100644
--- a/datascienceintro/Classification_DecisionTree.ipynb
+++ b/datascienceintro/Classification_DecisionTree.ipynb
@@ -11,7 +11,7 @@
         "\n",
         "In this example, we look at the performance of a simple [decision tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) from the Scikit-Learn package.\n",
         "\n",
-        "We will use the [adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) that focuses on a (binary) classification task whether or not a person makes more than 50k USD per year. The data are taken from a 1994 census and were first discussed in the paper [Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid\", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996](https://www.academia.edu/download/40088603/Scaling_Up_the_Accuracy_of_Naive-Bayes_C20151116-5477-1fw84ob.pdf)\n",
+        "We will use the [adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) that focuses on a (binary) classification task whether or not a person makes more than 50k USD per year. The data are taken from a 1994 census and were first discussed in the paper [Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid\", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996](https://staff.icar.cnr.it/manco/Teaching/2005/datamining/articoli/nbtree.pdf)\n",
         "\n",
         "The data have a number of categorial and numerical features.\n",
         "We can access the data directly from the archive (or use a local copy)."
diff --git a/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb b/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb
index ecd87192a72b3f9cdfd9ca39def25e1f8828f332..662f2e9941bd1e813c405a35d89219ae237ee430 100644
--- a/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb
+++ b/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb
@@ -307,7 +307,8 @@
       "name": "python3"
     },
     "language_info": {
-      "name": "python"
+      "name": "python",
+      "version": "3.10.6"
     }
   },
   "nbformat": 4,
diff --git a/datascienceintro/cluster.ipynb b/datascienceintro/cluster.ipynb
index 60d546f6c53d3dedd5457803c98fd2ffc6bf3467..d18c2cb4e8199214734bd8f38cee47f408109928 100644
--- a/datascienceintro/cluster.ipynb
+++ b/datascienceintro/cluster.ipynb
@@ -707,7 +707,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
+   "version": "3.10.6"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/datascienceintro/index.ipynb b/datascienceintro/index.ipynb
index 49e0b58a2ada5efffc6e0f258254c4507d592bf8..ae1958ad1b83965eb685232e4b052733ba0d09da 100644
--- a/datascienceintro/index.ipynb
+++ b/datascienceintro/index.ipynb
@@ -21,7 +21,7 @@
   },
   "language_info": {
    "name": "python",
-   "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
+   "version": "3.10.6"
   },
   "orig_nbformat": 4,
   "vscode": {