diff --git a/docs/notebooks/00_loading_and_fetching.ipynb b/docs/notebooks/00_loading_and_fetching.ipynb
index 3a4f456..f0809db 100644
--- a/docs/notebooks/00_loading_and_fetching.ipynb
+++ b/docs/notebooks/00_loading_and_fetching.ipynb
@@ -6,8 +6,25 @@
    "source": [
     "# Fetching and Loading Data\n",
     "\n",
-    "This is a short tutorial showing how to fetch and load data in context.\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srlearn/relational-datasets/blob/main/docs/notebooks/00_loading_and_fetching.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/srlearn/relational-datasets/HEAD?filepath=docs%2Fnotebooks%2F00_loading_and_fetching.ipynb)\n",
     "\n",
+    "This is a short tutorial showing how to fetch and load data in context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install relational-datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## `fetch()`\n",
     "\n",
     "`fetch()` always returns the path to a specific version of a dataset.\n",
@@ -96,7 +113,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/01_converting_propositional_datasets.ipynb b/docs/notebooks/01_converting_propositional_datasets.ipynb
index b6468b5..f807c3c 100644
--- a/docs/notebooks/01_converting_propositional_datasets.ipynb
+++ b/docs/notebooks/01_converting_propositional_datasets.ipynb
@@ -6,6 +6,9 @@
    "source": [
     "# Converting machine learning benchmark datasets\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srlearn/relational-datasets/blob/main/docs/notebooks/01_converting_propositional_datasets.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/srlearn/relational-datasets/HEAD?filepath=docs%2Fnotebooks%2F01_converting_propositional_datasets.ipynb)\n",
+    "\n",
     "[Alexander L. Hayes](https://hayesall.com): *Ph.D. Student, Indiana University*.\n",
     "\n",
     "**Abstract**: Most benchmark machine learning datasets have a *vector-based representation*, where we have a single type of object (people, images, houses) and we learn an *attribute* of those objects (disease risk, cat/dog, median price). This tutorial bridges the gap between vector-based machine learning and relational machine learning, and shows how to view the former in terms of the latter.\n",
@@ -13,6 +16,15 @@
     "Examples in this notebook are provided as documentation, and are available under the terms of the Apache 2.0 License."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install numpy relational-datasets"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -389,6 +401,15 @@
     "Here we: (**1**) load the data and class labels, (**2**) split into training and test sets, (**3**) bin the continuous features to discrete, and (**4**) convert to the relational format."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install scikit-learn"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -678,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,