diff --git a/examples/06_pytorch_oxe_dataloader.py b/examples/06_pytorch_oxe_dataloader.py
index 046d0802..ca68c676 100644
--- a/examples/06_pytorch_oxe_dataloader.py
+++ b/examples/06_pytorch_oxe_dataloader.py
@@ -2,16 +2,22 @@
 This example shows how to use the `octo.data` dataloader with PyTorch by wrapping it in a simple PyTorch
 dataloader. The config below also happens to be our exact pretraining config (except for the batch size and
 shuffle buffer size, which are reduced for demonstration purposes).
+
+Before running the script, please download the OXE data (e.g. via the script here:
+https://github.com/kpertsch/rlds_dataset_mod/blob/main/prepare_open_x.sh)
+
+Then modify the `DATA_PATH` variable below to point to the directory where you downloaded the data to.
 """
 import numpy as np
-from octo.data.dataset import make_interleaved_dataset
-from octo.data.oxe import make_oxe_dataset_kwargs_and_weights
 import tensorflow as tf
 import torch
 from torch.utils.data import DataLoader
 import tqdm
 
-DATA_PATH = "gs://rail-orca-central2/resize_256_256"
+from octo.data.dataset import make_interleaved_dataset
+from octo.data.oxe import make_oxe_dataset_kwargs_and_weights
+
+DATA_PATH = "/path/to/your/oxe/directory"
 
 tf.config.set_visible_devices([], "GPU")