diff --git a/docs/notebooks/convolutions.ipynb b/docs/notebooks/convolutions.ipynb
index 9d91804b6..01b8a2be0 100644
--- a/docs/notebooks/convolutions.ipynb
+++ b/docs/notebooks/convolutions.ipynb
@@ -121,13 +121,13 @@
     }
    ],
    "source": [
-    "from scipy import misc\n",
+    "from scipy import datasets\n",
     "import jax.scipy as jsp\n",
     "\n",
     "fig, ax = plt.subplots(1, 3, figsize=(12, 5))\n",
     "\n",
     "# Load a sample image; compute mean() to convert from RGB to grayscale.\n",
-    "image = jnp.array(misc.face().mean(-1))\n",
+    "image = jnp.array(datasets.face().mean(-1))\n",
     "ax[0].imshow(image, cmap='binary_r')\n",
     "ax[0].set_title('original')\n",
     "\n",
diff --git a/docs/notebooks/convolutions.md b/docs/notebooks/convolutions.md
index b98099aa9..7d58cc2b4 100644
--- a/docs/notebooks/convolutions.md
+++ b/docs/notebooks/convolutions.md
@@ -75,13 +75,13 @@ For example, here is a simple approach to de-noising an image based on convoluti
 :id: Jk5qdnbv6QgT
 :outputId: 292205eb-aa09-446f-eec2-af8c23cfc718
 
-from scipy import misc
+from scipy import datasets
 import jax.scipy as jsp
 
 fig, ax = plt.subplots(1, 3, figsize=(12, 5))
 
 # Load a sample image; compute mean() to convert from RGB to grayscale.
-image = jnp.array(misc.face().mean(-1))
+image = jnp.array(datasets.face().mean(-1))
 ax[0].imshow(image, cmap='binary_r')
 ax[0].set_title('original')
 
diff --git a/docs/requirements.txt b/docs/requirements.txt
index bfbb4e271..5d49222bb 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -17,6 +17,7 @@ pytest-xdist
 # Packages used for notebook execution
 matplotlib
 scikit-learn
+pooch
 numpy
 rich[jupyter]
 cmake