diff --git a/tests/conftest.py b/tests/conftest.py
index 949a2535..d267f911 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -23,6 +23,9 @@ from lerobot import available_cameras, available_motors, available_robots
 from lerobot.common.utils.utils import init_hydra_config
 from tests.utils import DEVICE, ROBOT_CONFIG_PATH_TEMPLATE, make_camera, make_motors_bus
 
+# Import fixture modules as plugins
+pytest_plugins = ["tests.fixtures.dataset"]
+
 
 def pytest_collection_finish():
     print(f"\nTesting with {DEVICE=}")
diff --git a/tests/fixtures/dataset.py b/tests/fixtures/dataset.py
new file mode 100644
index 00000000..ad70ff66
--- /dev/null
+++ b/tests/fixtures/dataset.py
@@ -0,0 +1,152 @@
+import datasets
+import numpy as np
+import pytest
+
+from lerobot.common.datasets.utils import get_episode_data_index, hf_transform_to_torch
+
+
+@pytest.fixture(scope="session")
+def img_array_factory():
+    def _create_img_array(width=100, height=100) -> np.ndarray:
+        return np.random.randint(0, 256, size=(height, width, 3), dtype=np.uint8)
+
+    return _create_img_array
+
+
+@pytest.fixture(scope="session")
+def tasks():
+    return [
+        {"task_index": 0, "task": "Pick up the block."},
+        {"task_index": 1, "task": "Open the box."},
+        {"task_index": 2, "task": "Make paperclips."},
+    ]
+
+
+@pytest.fixture(scope="session")
+def episode_dicts():
+    return [
+        {"episode_index": 0, "tasks": ["Pick up the block."], "length": 100},
+        {"episode_index": 1, "tasks": ["Open the box."], "length": 80},
+        {"episode_index": 2, "tasks": ["Pick up the block."], "length": 90},
+        {"episode_index": 3, "tasks": ["Make paperclips."], "length": 150},
+    ]
+
+
+@pytest.fixture(scope="session")
+def episode_data_index(episode_dicts):
+    return get_episode_data_index(episode_dicts)
+
+
+@pytest.fixture(scope="session")
+def hf_dataset(hf_dataset_factory, episode_dicts, tasks):
+    keys = ["state", "action"]
+    shapes = {
+        "state": 10,
+        "action": 10,
+    }
+    return hf_dataset_factory(episode_dicts, tasks, keys, shapes)
+
+
+@pytest.fixture(scope="session")
+def hf_dataset_image(hf_dataset_factory, episode_dicts, tasks):
+    keys = ["state", "action"]
+    image_keys = ["image"]
+    shapes = {
+        "state": 10,
+        "action": 10,
+        "image": {
+            "width": 100,
+            "height": 70,
+            "channels": 3,
+        },
+    }
+    return hf_dataset_factory(episode_dicts, tasks, keys, shapes, image_keys=image_keys)
+
+
+def get_task_index(tasks_dicts: dict, task: str) -> int:
+    """
+    Given a task in natural language, returns its task_index if the task already exists in the dataset,
+    otherwise creates a new task_index.
+    """
+    tasks = {d["task_index"]: d["task"] for d in tasks_dicts}
+    task_to_task_index = {task: task_idx for task_idx, task in tasks.items()}
+    return task_to_task_index[task]
+
+
+@pytest.fixture(scope="session")
+def hf_dataset_factory(img_array_factory):
+    def _create_hf_dataset(
+        episode_dicts: list[dict],
+        tasks: list[dict],
+        keys: list[str],
+        shapes: dict,
+        fps: int = 30,
+        image_keys: list[str] | None = None,
+    ):
+        key_features = {
+            key: datasets.Sequence(length=shapes[key], feature=datasets.Value(dtype="float32"))
+            for key in keys
+        }
+        image_features = {key: datasets.Image() for key in image_keys} if image_keys else {}
+        common_features = {
+            "episode_index": datasets.Value(dtype="int64"),
+            "frame_index": datasets.Value(dtype="int64"),
+            "timestamp": datasets.Value(dtype="float32"),
+            "next.done": datasets.Value(dtype="bool"),
+            "index": datasets.Value(dtype="int64"),
+            "task_index": datasets.Value(dtype="int64"),
+        }
+        features = datasets.Features(
+            {
+                **key_features,
+                **image_features,
+                **common_features,
+            }
+        )
+
+        episode_index_col = np.array([], dtype=np.int64)
+        frame_index_col = np.array([], dtype=np.int64)
+        timestamp_col = np.array([], dtype=np.float32)
+        next_done_col = np.array([], dtype=bool)
+        task_index = np.array([], dtype=np.int64)
+
+        for ep_dict in episode_dicts:
+            episode_index_col = np.concatenate(
+                (episode_index_col, np.full(ep_dict["length"], ep_dict["episode_index"], dtype=int))
+            )
+            frame_index_col = np.concatenate((frame_index_col, np.arange(ep_dict["length"], dtype=int)))
+            timestamp_col = np.concatenate((timestamp_col, np.arange(ep_dict["length"]) / fps))
+            next_done_ep = np.full(ep_dict["length"], False, dtype=bool)
+            next_done_ep[-1] = True
+            next_done_col = np.concatenate((next_done_col, next_done_ep))
+            ep_task_index = get_task_index(tasks, ep_dict["tasks"][0])
+            task_index = np.concatenate((task_index, np.full(ep_dict["length"], ep_task_index, dtype=int)))
+
+        index_col = np.arange(len(episode_index_col))
+        key_cols = {key: np.random.random((len(index_col), shapes[key])).astype(np.float32) for key in keys}
+
+        image_cols = {}
+        if image_keys:
+            for key in image_keys:
+                image_cols[key] = [
+                    img_array_factory(width=shapes[key]["width"], height=shapes[key]["height"])
+                    for _ in range(len(index_col))
+                ]
+
+        dataset = datasets.Dataset.from_dict(
+            {
+                **key_cols,
+                **image_cols,
+                "episode_index": episode_index_col,
+                "frame_index": frame_index_col,
+                "timestamp": timestamp_col,
+                "next.done": next_done_col,
+                "index": index_col,
+                "task_index": task_index,
+            },
+            features=features,
+        )
+        dataset.set_transform(hf_transform_to_torch)
+        return dataset
+
+    return _create_hf_dataset