diff --git a/notebooks/09a_Object_recognition_with_CIFAR-10_and_CIFAR-100.ipynb b/notebooks/09a_Object_recognition_with_CIFAR-10_and_CIFAR-100.ipynb
index 475ade7..f002676 100644
--- a/notebooks/09a_Object_recognition_with_CIFAR-10_and_CIFAR-100.ipynb
+++ b/notebooks/09a_Object_recognition_with_CIFAR-10_and_CIFAR-100.ipynb
@@ -128,7 +128,16 @@
     "\n",
     "Both data provider classes provide a `label_map` attribute which is a list of strings which are the class labels corresponding to the integer targets (i.e. prior to conversion to a 1-of-K encoded binary vector).\n",
     "\n",
-    "Below example code is given for creating instances of the CIFAR-10 and CIFAR-100 data provider objects and using them to train simple two-layer feedforward network models with rectified linear activations in TensorFlow. You may wish to use this code as a starting point for your own experiments."
+    "Below example code is given for creating instances of the CIFAR-10 and CIFAR-100 data provider objects and using them to train simple two-layer feedforward network models with rectified linear activations in TensorFlow. You may wish to use this code as a starting point for your own experiments.\n",
+    "\n",
+    "\n",
+    "### Accessing the CIFAR-10 and CIFAR-100 data\n",
+    "\n",
+    "Before using the data provider objects you will need to copy the associated data files in to your local `mlp/data` directory (or wherever your `MLP_DATA_DIR` environment variable points to if different). The data is available as six compressed NumPy `.npz` files, (`cifar-10-train.npz, cifar-10-valid.npz, cifar-10-test-inputs.npz` and `cifar-100-train.npz, cifar-100-valid.npz, cifar-100-test.npz`) in the AFS directory `/afs/inf.ed.ac.uk/group/teaching/mlp/data`. Assuming your local `mlpractical` repository is in your home directory you should be able to copy the required files by running\n",
+    "\n",
+    "```\n",
+    "cp /afs/inf.ed.ac.uk/group/teaching/mlp/data/cifar*.npz ~/mlpractical/data\n",
+    "```"
    ]
   },
   {