diff --git a/renumics/spotlight_plugins/core/huggingface_datasource.py b/renumics/spotlight_plugins/core/huggingface_datasource.py
index 0206160c..14e921bf 100644
--- a/renumics/spotlight_plugins/core/huggingface_datasource.py
+++ b/renumics/spotlight_plugins/core/huggingface_datasource.py
@@ -161,6 +161,15 @@ def get_column_values(
         if isinstance(feature, datasets.Translation):
             return np.array([str(value) for value in raw_values])
 
+        if isinstance(feature, datasets.Value):
+            hf_dtype = feature.dtype
+            if hf_dtype.startswith("duration"):
+                return raw_values.to_numpy().astype(int)
+            if hf_dtype.startswith("time32") or hf_dtype.startswith("time64"):
+                return raw_values.to_numpy().astype(str)
+            if hf_dtype.startswith("timestamp[ns"):
+                return raw_values.to_numpy().astype(int)
+
         return raw_values.to_numpy()
 
     def get_column_metadata(self, _: str) -> ColumnMetadata:
@@ -187,17 +196,19 @@ def _get_intermediate_dtype(feature: _FeatureType) -> DType:
         elif hf_dtype.startswith("float"):
             return float_dtype
         elif hf_dtype.startswith("time32"):
-            return datetime_dtype
+            return str_dtype
         elif hf_dtype.startswith("time64"):
-            return datetime_dtype
+            return str_dtype
         elif hf_dtype.startswith("timestamp"):
+            if hf_dtype.startswith("timestamp[ns"):
+                return int_dtype
             return datetime_dtype
         elif hf_dtype.startswith("date32"):
             return datetime_dtype
         elif hf_dtype.startswith("date64"):
             return datetime_dtype
         elif hf_dtype.startswith("duration"):
-            return float_dtype
+            return int_dtype
         elif hf_dtype.startswith("decimal"):
             return float_dtype
         elif hf_dtype == "binary":
diff --git a/scripts/create_hf_dataset.py b/scripts/create_hf_dataset.py
deleted file mode 100755
index db3006fa..00000000
--- a/scripts/create_hf_dataset.py
+++ /dev/null
@@ -1,161 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-This script creates multimodal Hugging Face dataset to test Spotlight on.
-"""
-
-import datasets
-import numpy as np
-from renumics import spotlight
-
-from renumics.spotlight import dtypes
-
-
-def random_values(
-    dtype: dtypes.DType, num_rows: int, optional: bool = False
-) -> np.ndarray:
-    if dtypes.is_bool_dtype(dtype):
-        values = np.random.randint(0, 2, num_rows, bool)
-    elif dtypes.is_int_dtype(dtype):
-        values = np.random.randint(0, 2, num_rows, bool)
-    elif dtypes.is_float_dtype(dtype):
-        values = np.random.normal(0, 100, num_rows)
-    # elif dtypes.is_str_dtype(dtype):
-    #     str_lengths = np.random.randint(0, 100, num_rows)
-    #     null_indices = np.random.randint(0, num_rows, num_rows // 10)
-    #     str_lengths[null_indices] = 0
-    #     all_letters = np.array(
-    #         list(string.ascii_letters + string.digits + string.punctuation)
-    #     )
-    else:
-        raise NotImplementedError
-
-    if not optional:
-        return values
-
-    null_indices = np.random.randint(0, num_rows, num_rows // 10)
-    if np.issubdtype(values.dtype, np.floating):
-        values[null_indices] = np.nan
-    else:
-        values = values.astype(object)
-        values[null_indices] = None
-    return values
-
-
-def create_hf_dataset(num_rows: int) -> None:
-    ds = datasets.Dataset.from_dict(
-        {
-            "bool": [True, False, False],
-            "int": [-1, 1, 100000],
-            "uint": [1, 1, 30000],
-            "float": [1.0, float("nan"), 1000],
-            "string": ["foo", "barbaz", ""],
-            "label": ["foo", "bar", "foo"],
-            # "int": random_values(dtypes.int_dtype, num_rows),
-            # "float": random_values(dtypes.float_dtype, num_rows),
-            "embedding": [[1, 2, 3, 4], [1, 6, 3, 7], [-1, -2, -3, -4]],
-            # HF sequence as Spotlight sequence
-            "sequence_1d": [[1, 2, 3, 4], [1, 6, 3], [-1, -2, float("nan"), -4, 10]],
-            "sequence_2d": [
-                [[1, 2, 3, 4], [-1, 3, 1, 6]],
-                [[1, -3, 10], [1, 6, 3]],
-                [[-10, 0, 10], [-1, -2, -3]],
-            ],
-            "sequence_2d_t": [[[5, 3], [2, 5], [10, 8]], [], [[-1, 1], [1, 10]]],
-            # HF sequence as Spotlight array
-            "sequence_2d_array": [
-                [[1, 2, 3, 4], [-1, 3, 1, 6], [1, 2, 4, 4]],
-                [[1, -3, 10], [1, 6, 3], [1, float("nan"), 4]],
-                [[-10, 0, 10], [-1, -2, -3], [1, 2, 4]],
-            ],
-            "sequence_3d_array": [
-                [[[1, 2, 3, 4], [-1, 3, 1, 6], [1, 2, 4, 4]]],
-                [[[1, -3, 10], [1, 6, 3], [1, float("nan"), 4]]],
-                [[[-10, 0, 10], [-1, -2, -3], [1, 2, 4]]],
-            ],
-            # HF 2D array as Spotlight sequence
-            "array_2d_sequence": [
-                [[1, 2, 3], [-1, 3, 1]],
-                [[1, -3, 10], [1, 6, 3]],
-                [[-10, 0, 10], [-1, -2, -3]],
-            ],
-            "array_2d_t_sequence": [
-                [[5, 3], [2, 5], [10, 8]],
-                [[float("nan"), 1], [1, 1], [2, 2]],
-                [[-1, 1], [1, 10], [10, 1]],
-            ],
-            "array_2d_vlen_sequence": [
-                [[5, 3], [2, 5], [10, 8]],
-                [],
-                [[-1, 1], [1, 10]],
-            ],
-            # HF 4D array as Spotlight array
-            "array_4d": [
-                [[[[1.0, 1.0, -10.0]]], [[[-1.0, 1.0, -1.0]]], [[[2.0, 1.0, 1.0]]]],
-                [
-                    [[[2.0, -3.0, 0.0]]],
-                    [[[3.0, 6.0, -2.0]]],
-                    [[[4.0, float("nan"), 2.0]]],
-                    [[[4.0, float("nan"), 2.0]]],
-                ],
-                [[[[3.0, 10.0, 10.0]]], [[[6.0, 3.0, -3.0]]], [[[4.0, 4.0, 4.0]]]],
-            ],
-            # HF list as Spotlight embedding
-            "list_sequence": [[1, 2, 3, 4], [1, 6, 3, 7], [-1, -2, -3, -4]],
-        },
-        features=datasets.Features(
-            {
-                "bool": datasets.Value("bool"),
-                "int": datasets.Value("int32"),
-                "uint": datasets.Value("uint16"),
-                "float": datasets.Value("float64"),
-                "string": datasets.Value("string"),
-                "label": datasets.ClassLabel(
-                    num_classes=4, names=["foo", "bar", "baz", "barbaz"]
-                ),
-                "embedding": datasets.Sequence(
-                    feature=datasets.Value("float64"), length=4
-                ),
-                "sequence_1d": datasets.Sequence(feature=datasets.Value("float64")),
-                "sequence_2d": datasets.Sequence(
-                    feature=datasets.Sequence(feature=datasets.Value("float64")),
-                    length=2,
-                ),
-                "sequence_2d_t": datasets.Sequence(
-                    feature=datasets.Sequence(
-                        feature=datasets.Value("float64"), length=2
-                    ),
-                ),
-                "sequence_2d_array": datasets.Sequence(
-                    feature=datasets.Sequence(feature=datasets.Value("float64")),
-                    length=3,
-                ),
-                "sequence_3d_array": datasets.Sequence(
-                    feature=datasets.Sequence(
-                        feature=datasets.Sequence(feature=datasets.Value("float64")),
-                        length=3,
-                    ),
-                    length=1,
-                ),
-                "array_2d_sequence": datasets.Array2D(shape=(2, 3), dtype="float64"),
-                "array_2d_t_sequence": datasets.Array2D(shape=(3, 2), dtype="float64"),
-                "array_2d_vlen_sequence": datasets.Array2D(
-                    shape=(None, 2), dtype="float64"
-                ),
-                "array_4d": datasets.Array4D(shape=(None, 1, 1, 3), dtype="float64"),
-                "list_sequence": [datasets.Value("float64")],
-            }
-        ),
-        # info=datasets.DatasetInfo(),
-        # split=datasets.NamedSplit,
-    )
-    ds.save_to_disk("./build/datasets/hf")
-    print(ds.features)
-
-
-if __name__ == "__main__":
-    np.random.seed(42)
-    create_hf_dataset(100)
-
-    ds = datasets.load_from_disk("./build/datasets/hf")
-    spotlight.show(ds)
diff --git a/tests/integration/huggingface/__init__.py b/tests/integration/huggingface/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/integration/huggingface/conftest.py b/tests/integration/huggingface/conftest.py
new file mode 100644
index 00000000..5f6f089b
--- /dev/null
+++ b/tests/integration/huggingface/conftest.py
@@ -0,0 +1,17 @@
+"""
+Pytest Fixtures for Hugging Face tests
+"""
+
+import datasets
+import pytest
+
+from .dataset import create_hf_dataset
+
+
+@pytest.fixture
+def dataset() -> datasets.Dataset:
+    """
+    H5 Dataset for tests
+    """
+
+    return create_hf_dataset()
diff --git a/tests/integration/huggingface/dataset.py b/tests/integration/huggingface/dataset.py
new file mode 100644
index 00000000..3b81abb6
--- /dev/null
+++ b/tests/integration/huggingface/dataset.py
@@ -0,0 +1,146 @@
+"""
+Data for Hugging Face tests
+"""
+
+import datetime
+
+import datasets
+
+
+DATA = {
+    "bool": [True, False, False],
+    "int": [-1, 1, 100000],
+    "uint": [1, 1, 30000],
+    "float": [1.0, float("nan"), 1000],
+    "string": ["foo", "barbaz", ""],
+    "label": ["foo", "bar", "foo"],
+    "binary": [b"foo", b"bar", b""],
+    "duration": [-1, 2, 10],
+    "decimal": [1.0, 3.0, 1000],
+    "date": [datetime.date.min, datetime.date(2001, 2, 15), datetime.date.max],
+    "time": [
+        datetime.time.min,
+        datetime.time(14, 24, 15, 2672),
+        datetime.time.max,
+    ],
+    "timestamp": [
+        datetime.datetime(1970, 2, 15, 14, 24, 15, 2672),
+        datetime.datetime(2001, 2, 15, 14, 24, 15, 2672),
+        datetime.datetime(2170, 2, 15, 14, 24, 15, 2672),
+    ],
+    "timestamp_ns": [
+        datetime.datetime(1970, 2, 15, 14, 24, 15, 2672),
+        datetime.datetime(2001, 2, 15, 14, 24, 15, 2672),
+        datetime.datetime(2170, 2, 15, 14, 24, 15, 2672),
+    ],
+    "embedding": [[1, 2, 3, 4], [1, 6, 3, 7], [-1, -2, -3, -4]],
+    "audio": [
+        "data/audio/mono/gs-16b-1c-44100hz.mp3",
+        "data/audio/1.wav",
+        "data/audio/stereo/gs-16b-2c-44100hz.ogg",
+    ],
+    "image": [
+        "data/images/nature-256p.ico",
+        "data/images/sea-360p.gif",
+        "data/images/nature-360p.jpg",
+    ],
+    # HF sequence as Spotlight sequence
+    "sequence_1d": [[1, 2, 3, 4], [1, 6, 3], [-1, -2, float("nan"), -4, 10]],
+    "sequence_2d": [
+        [[1, 2, 3, 4], [-1, 3, 1, 6]],
+        [[1, -3, 10], [1, 6, 3]],
+        [[-10, 0, 10], [-1, -2, -3]],
+    ],
+    "sequence_2d_t": [[[5, 3], [2, 5], [10, 8]], [], [[-1, 1], [1, 10]]],
+    # HF sequence as Spotlight array
+    "sequence_2d_array": [
+        [[1, 2, 3, 4], [-1, 3, 1, 6], [1, 2, 4, 4]],
+        [[1, -3, 10], [1, 6, 3], [1, float("nan"), 4]],
+        [[-10, 0, 10], [-1, -2, -3], [1, 2, 4]],
+    ],
+    "sequence_3d_array": [
+        [[[1, 2, 3, 4], [-1, 3, 1, 6], [1, 2, 4, 4]]],
+        [[[1, -3, 10], [1, 6, 3], [1, float("nan"), 4]]],
+        [[[-10, 0, 10], [-1, -2, -3], [1, 2, 4]]],
+    ],
+    # HF 2D array as Spotlight sequence
+    "array_2d_sequence": [
+        [[1, 2, 3], [-1, 3, 1]],
+        [[1, -3, 10], [1, 6, 3]],
+        [[-10, 0, 10], [-1, -2, -3]],
+    ],
+    "array_2d_t_sequence": [
+        [[5, 3], [2, 5], [10, 8]],
+        [[float("nan"), 1], [1, 1], [2, 2]],
+        [[-1, 1], [1, 10], [10, 1]],
+    ],
+    "array_2d_vlen_sequence": [
+        [[5, 3], [2, 5], [10, 8]],
+        [],
+        [[-1, 1], [1, 10]],
+    ],
+    # HF 4D array as Spotlight array
+    "array_4d": [
+        [[[[1.0, 1.0, -10.0]]], [[[-1.0, 1.0, -1.0]]], [[[2.0, 1.0, 1.0]]]],
+        [
+            [[[2.0, -3.0, 0.0]]],
+            [[[3.0, 6.0, -2.0]]],
+            [[[4.0, float("nan"), 2.0]]],
+            [[[4.0, float("nan"), 2.0]]],
+        ],
+        [[[[3.0, 10.0, 10.0]]], [[[6.0, 3.0, -3.0]]], [[[4.0, 4.0, 4.0]]]],
+    ],
+    # HF list as Spotlight embedding
+    "list_sequence": [[1, 2, 3], [1, 6, 3, 7, 8], [-1, -2, -3, -4]],
+}
+
+FEATURES = {
+    "bool": datasets.Value("bool"),
+    "int": datasets.Value("int32"),
+    "uint": datasets.Value("uint16"),
+    "float": datasets.Value("float64"),
+    "string": datasets.Value("string"),
+    "label": datasets.ClassLabel(num_classes=4, names=["foo", "bar", "baz", "barbaz"]),
+    "binary": datasets.Value("binary"),
+    "duration": datasets.Value("duration[s]"),
+    "decimal": datasets.Value("decimal128(10, 2)"),
+    "date": datasets.Value("date32"),
+    "time": datasets.Value("time64[us]"),
+    "timestamp": datasets.Value("timestamp[us]"),
+    "timestamp_ns": datasets.Value("timestamp[ns]"),
+    "audio": datasets.Audio(),
+    "image": datasets.Image(),
+    "embedding": datasets.Sequence(feature=datasets.Value("float64"), length=4),
+    "sequence_1d": datasets.Sequence(feature=datasets.Value("float64")),
+    "sequence_2d": datasets.Sequence(
+        feature=datasets.Sequence(feature=datasets.Value("float64")),
+        length=2,
+    ),
+    "sequence_2d_t": datasets.Sequence(
+        feature=datasets.Sequence(feature=datasets.Value("float64"), length=2),
+    ),
+    "sequence_2d_array": datasets.Sequence(
+        feature=datasets.Sequence(feature=datasets.Value("float64")),
+        length=3,
+    ),
+    "sequence_3d_array": datasets.Sequence(
+        feature=datasets.Sequence(
+            feature=datasets.Sequence(feature=datasets.Value("float64")),
+            length=3,
+        ),
+        length=1,
+    ),
+    "array_2d_sequence": datasets.Array2D(shape=(2, 3), dtype="float64"),
+    "array_2d_t_sequence": datasets.Array2D(shape=(3, 2), dtype="float64"),
+    "array_2d_vlen_sequence": datasets.Array2D(shape=(None, 2), dtype="float64"),
+    "array_4d": datasets.Array4D(shape=(None, 1, 1, 3), dtype="float64"),
+    "list_sequence": [datasets.Value("float64")],
+}
+
+
+def create_hf_dataset() -> datasets.Dataset:
+    ds = datasets.Dataset.from_dict(
+        DATA,
+        features=datasets.Features(FEATURES),
+    )
+    return ds
diff --git a/tests/integration/huggingface/test_hf.py b/tests/integration/huggingface/test_hf.py
new file mode 100644
index 00000000..9380380f
--- /dev/null
+++ b/tests/integration/huggingface/test_hf.py
@@ -0,0 +1,37 @@
+"""
+Integration Test on API level for h5 data sources
+"""
+import pytest
+import httpx
+
+import datasets
+
+from renumics import spotlight
+
+from .dataset import DATA
+
+
+def test_get_table_returns_http_ok(dataset: datasets.Dataset) -> None:
+    """
+    Ensure /api/table/ returns a valid response
+    """
+    viewer = spotlight.show(dataset, no_browser=True, wait=False)
+    response = httpx.Client(base_url=viewer.url).get("/api/table/")
+    viewer.close()
+    assert response.status_code == 200
+
+
+@pytest.mark.parametrize("col", DATA.keys())
+def test_get_cell_returns_http_ok(dataset: str, col: str) -> None:
+    """
+    Serve h5 dataset and get cell data for dtype
+    """
+    viewer = spotlight.show(dataset, no_browser=True, wait=False)
+    gen_id = (
+        httpx.Client(base_url=viewer.url).get("/api/table/").json()["generation_id"]
+    )
+    response = httpx.Client(base_url=viewer.url).get(
+        f"/api/table/{col}/0?generation_id={gen_id}"
+    )
+    viewer.close()
+    assert response.status_code == 200