diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 55a3454d..f5345e0e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -32,6 +32,8 @@ repos:
                 - types-setuptools
                 - pandas-stubs
                 - types-attrs
+                - types-PyYAML
+                - types-requests
     - repo: https://github.com/mgedmin/check-manifest
       rev: "0.49"
       hooks:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 95ece7ad..27161124 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -259,9 +259,9 @@ by the [German Neuroinformatics Node](https://www.g-node.org/).
 GIN has a GitHub-like interface and git-like
 [CLI](gin:G-Node/Info/wiki/GIN+CLI+Setup#quickstart) functionalities.
 
-Currently the data repository contains sample pose estimation data files
-stored in the `poses` folder. Each file name starts with either "DLC" or "SLEAP",
-depending on the pose estimation software used to generate the data.
+Currently, the data repository contains sample pose estimation data files
+stored in the `poses` folder. Metadata for these files, including information
+about their provenance, is stored in the `poses_files_metadata.yaml` file.
 
 ### Fetching data
 To fetch the data from GIN, we use the [pooch](https://www.fatiando.org/pooch/latest/index.html)
@@ -269,15 +269,16 @@ Python package, which can download data from pre-specified URLs and store them
 locally for all subsequent uses. It also provides some nice utilities,
 like verification of sha256 hashes and decompression of archives.
 
-The relevant functionality is implemented in the `movement.datasets.py` module.
+The relevant functionality is implemented in the `movement.sample_data.py` module.
 The most important parts of this module are:
 
-1. The `POSE_DATA` download manager object, which contains a list of stored files and their known hashes.
-2. The `list_pose_data()` function, which returns a list of the available files in the data repository.
-3. The `fetch_pose_data_path()` function, which downloads a file (if not already cached locally) and returns the local path to it.
+1. The `SAMPLE_DATA` download manager object.
+2. The `list_sample_data()` function, which returns a list of the available files in the data repository.
+3. The `fetch_sample_data_path()` function, which downloads a file (if not already cached locally) and returns the local path to it.
+4. The `fetch_sample_data()` function, which downloads a file and loads it into movement directly, returning an `xarray.Dataset` object.
 
 By default, the downloaded files are stored in the `~/.movement/data` folder.
-This can be changed by setting the `DATA_DIR` variable in the `movement.datasets.py` module.
+This can be changed by setting the `DATA_DIR` variable in the `movement.sample_data.py` module.
 
 ### Adding new data
 Only core movement developers may add new files to the external data repository.
@@ -287,9 +288,8 @@ To add a new file, you will need to:
 2. Ask to be added as a collaborator on the [movement data repository](gin:neuroinformatics/movement-test-data) (if not already)
 3. Download the [GIN CLI](gin:G-Node/Info/wiki/GIN+CLI+Setup#quickstart) and set it up with your GIN credentials, by running `gin login` in a terminal.
 4. Clone the movement data repository to your local machine, by running `gin get neuroinformatics/movement-test-data` in a terminal.
-5. Add your new files and commit them with `gin commit -m <message> <filename>`.
-6. Upload the commited changes to the GIN repository, by running `gin upload`. Latest changes to the repository can be pulled via `gin download`. `gin sync` will synchronise the latest changes bidirectionally.
-7. Determine the sha256 checksum hash of each new file, by running `sha256sum <filename>` in a terminal. Alternatively, you can use `pooch` to do this for you: `python -c "import pooch; pooch.file_hash('/path/to/file')"`. If you wish to generate a text file containing the hashes of all the files in a given folder, you can use `python -c "import pooch; pooch.make_registry('/path/to/folder', 'sha256_registry.txt')`.
-8. Update the `movement.datasets.py` module on the [movement GitHub repository](movement-github:) by adding the new files to the `POSE_DATA` registry. Make sure to include the correct sha256 hash, as determined in the previous step. Follow all the usual [guidelines for contributing code](#contributing-code). Make sure to test whether the new files can be fetched successfully (see [fetching data](#fetching-data) above) before submitting your pull request.
-
-You can also perform steps 3-6 via the GIN web interface, if you prefer to avoid using the CLI.
+5. Add your new files to `/movement-test-data/poses/`.
+6. Determine the sha256 checksum hash of each new file by running `sha256sum <filename>` in a terminal. Alternatively, you can use `pooch` to do this for you: `python -c "import pooch; hash = pooch.file_hash('/path/to/file'); print(hash)"`. If you wish to generate a text file containing the hashes of all the files in a given folder, you can use `python -c "import pooch; pooch.make_registry('/path/to/folder', 'sha256_registry.txt')`.
+7. Add metadata for your new files to `poses_files_metadata.yaml`, including their sha256 hashes.
+8. Commit your changes using `gin commit -m <message> <filename>`.
+9. Upload the committed changes to the GIN repository by running `gin upload`. Latest changes to the repository can be pulled via `gin download`. `gin sync` will synchronise the latest changes bidirectionally.
diff --git a/docs/source/api_index.rst b/docs/source/api_index.rst
index c191e086..293e98b5 100644
--- a/docs/source/api_index.rst
+++ b/docs/source/api_index.rst
@@ -33,14 +33,15 @@ Input/Output
     ValidPosesCSV
     ValidPoseTracks
 
-Datasets
---------
-.. currentmodule:: movement.datasets
+Sample Data
+-----------
+.. currentmodule:: movement.sample_data
 .. autosummary::
     :toctree: api
 
-    list_pose_data
-    fetch_pose_data_path
+    list_sample_data
+    fetch_sample_data_path
+    fetch_sample_data
 
 Logging
 -------
diff --git a/docs/source/getting_started.md b/docs/source/getting_started.md
index bf7b7a26..7b7d6e52 100644
--- a/docs/source/getting_started.md
+++ b/docs/source/getting_started.md
@@ -53,7 +53,7 @@ Please see the [contributing guide](target-contributing) for more information.
 
 ## Loading data
 You can load predicted pose tracks from the pose estimation software packages
-[DeepLabCut](dlc:) or [SLEAP](sleap:).
+[DeepLabCut](dlc:), [SLEAP](sleap:), or [LightingPose](lp:).
 
 First import the `movement.io.load_poses` module:
 
@@ -114,27 +114,36 @@ You can also try movement out on some sample data included in the package.
 You can view the available sample data files with:
 
 ```python
-from movement import datasets
+from movement import sample_data
 
-file_names = datasets.list_pose_data()
+file_names = sample_data.list_sample_data()
 print(file_names)
 ```
+
 This will print a list of file names containing sample pose data.
-The files are prefixed with the name of the pose estimation software package,
-either "DLC" or "SLEAP".
+Each file is prefixed with the name of the pose estimation software package
+that was used to generate it - either "DLC", "SLEAP", or "LP".
 
 To get the path to one of the sample files,
 you can use the `fetch_pose_data_path` function:
 
 ```python
-file_path = datasets.fetch_pose_data_path("DLC_two-mice.predictions.csv")
+file_path = sample_data.fetch_sample_data_path("DLC_two-mice.predictions.csv")
 ```
 The first time you call this function, it will download the corresponding file
 to your local machine and save it in the `~/.movement/data` directory. On
 subsequent calls, it will simply return the path to that local file.
 
-You can feed the path to the `from_dlc_file` or `from_sleap_file` functions
-and load the data, as shown above.
+You can feed the path to the `from_dlc_file`, `from_sleap_file`, or
+`from_lp_file` functions and load the data, as shown above.
+
+Alternatively, you can skip the `fetch_sample_data_path()` step and load the
+data directly using the `fetch_sample_data()` function:
+
+```python
+ds = sample_data.fetch_sample_data("DLC_two-mice.predictions.csv")
+```
+
 :::
 
 ## Working with movement datasets
diff --git a/examples/load_and_explore_poses.py b/examples/load_and_explore_poses.py
index de5f5196..4a68ee7e 100644
--- a/examples/load_and_explore_poses.py
+++ b/examples/load_and_explore_poses.py
@@ -10,7 +10,7 @@
 # -------
 from matplotlib import pyplot as plt
 
-from movement import datasets
+from movement import sample_data
 from movement.io import load_poses
 
 # %%
@@ -18,14 +18,14 @@
 # ------------------------
 # Print a list of available datasets:
 
-for file_name in datasets.list_pose_data():
+for file_name in sample_data.list_sample_data():
     print(file_name)
 
 # %%
 # Fetch the path to an example dataset.
 # Feel free to replace this with the path to your own dataset.
 # e.g., ``file_path = "/path/to/my/data.h5"``)
-file_path = datasets.fetch_pose_data_path(
+file_path = sample_data.fetch_sample_data_path(
     "SLEAP_three-mice_Aeon_proofread.analysis.h5"
 )
 
diff --git a/movement/datasets.py b/movement/datasets.py
deleted file mode 100644
index 6853d952..00000000
--- a/movement/datasets.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""Module for fetching and loading datasets.
-
-This module provides functions for fetching and loading data used in tests,
-examples, and tutorials. The data are stored in a remote repository on GIN
-and are downloaded to the user's local machine the first time they are used.
-"""
-
-from pathlib import Path
-
-import pooch
-
-# URL to the remote data repository on GIN
-# noinspection PyInterpreter
-DATA_URL = (
-    "https://gin.g-node.org/neuroinformatics/movement-test-data/raw/master"
-)
-
-# Save data in ¬/.movement/data
-DATA_DIR = Path("~", ".movement", "data").expanduser()
-# Create the folder if it doesn't exist
-DATA_DIR.mkdir(parents=True, exist_ok=True)
-
-# Create a download manager for the pose data
-POSE_DATA = pooch.create(
-    path=DATA_DIR / "poses",
-    base_url=f"{DATA_URL}/poses/",
-    retry_if_failed=0,
-    registry={
-        "DLC_single-wasp.predictions.h5": "931dddb6ef5e08db6054d3757a441ee31dd0d9ff5a10802ad8405d6c4e7e274e",  # noqa: E501
-        "DLC_single-wasp.predictions.csv": "9b194cc930c2e2e0d33c816320d029f889b306d53ff2fe95ff408e99c9cdea23",  # noqa: E501
-        "DLC_single-mouse_EPM.predictions.h5": "0ddc2b08c9401435929783b22ea31b3673ceb80c3a02c5f3531bb1cfd78deea5",  # noqa: E501
-        "DLC_two-mice.predictions.csv": "6e891ab4e14a3ad74451ed0899f770b47aa3eb959d9824d3782827297d7c75e0",  # noqa: E501
-        "LP_mouse-face_AIND.predictions.csv": "13620f20a3fbb20c9ef0ad6fd8898944d2cfe3e83c5fe18946b7592e0a718da2",  # noqa: E501
-        "LP_mouse-twoview_AIND.predictions.csv": "22cded593ff77226dffab3c10f87874e597d86d70d62a81e5c4c690a96fd1e49",  # noqa: E501
-        "SLEAP_single-mouse_EPM.analysis.h5": "0df0a09c2493a1d9964ba98cbf751eda62743f1d688ae82b6df7b0f77169ed47",  # noqa: E501
-        "SLEAP_single-mouse_EPM.predictions.slp": "ca620db6123635761ddf69947f72f653d14a59137b355bd2d8f7c2f1be67e474",  # noqa: E501
-        "SLEAP_two-mice_social-interaction.analysis.h5": "f7f1e59d4b2c34712089f8aaf2390272291d93e6991c1abe32d9ce798a6234f9",  # noqa: E501
-        "SLEAP_two-mice_social-interaction.predictions.slp": "45881affde9704c045e70b8d4b3f6bbb8d9bd8ef9f4cdea6d173cfe35857549b",  # noqa: E501
-        "SLEAP_three-mice_Aeon_proofread.analysis.h5": "82ebd281c406a61536092863bc51d1a5c7c10316275119f7daf01c1ff33eac2a",  # noqa: E501
-        "SLEAP_three-mice_Aeon_proofread.predictions.slp": "7b7436a52dfd5f4d80d7c66919ad1a1732e5435fe33faf9011ec5f7b7074e788",  # noqa: E501
-        "SLEAP_three-mice_Aeon_mixed-labels.analysis.h5": "899651ec027eb8fd6181246f89142ad1c0a40f14394fc8144d44ea093c0e137d",  # noqa: E501
-        "SLEAP_three-mice_Aeon_mixed-labels.predictions.slp": "6d3f2c5446e9c12aabf28d5a9470835736b0419dcefebba89c305114f83b82d1",  # noqa: E501
-    },
-)
-
-
-def list_pose_data() -> list[str]:
-    """Find available sample pose data in the *movement* data repository.
-
-    Returns
-    -------
-    filenames : list of str
-        List of filenames for available pose data."""
-    return list(POSE_DATA.registry.keys())
-
-
-def fetch_pose_data_path(filename: str) -> Path:
-    """Fetch sample pose data from the *movement* data repository.
-
-    The data are downloaded to the user's local machine the first time they are
-    used and are stored in a local cache directory. The function returns the
-    path to the downloaded file, not the contents of the file itself.
-
-    Parameters
-    ----------
-    filename : str
-        Name of the file to fetch.
-
-    Returns
-    -------
-    path : pathlib.Path
-        Path to the downloaded file.
-    """
-    return Path(POSE_DATA.fetch(filename, progressbar=True))
diff --git a/movement/sample_data.py b/movement/sample_data.py
new file mode 100644
index 00000000..03b59d35
--- /dev/null
+++ b/movement/sample_data.py
@@ -0,0 +1,188 @@
+"""Module for fetching and loading sample datasets.
+
+This module provides functions for fetching and loading sample data used in
+tests, examples, and tutorials. The data are stored in a remote repository
+on GIN and are downloaded to the user's local machine the first time they
+are used.
+"""
+
+import logging
+from pathlib import Path
+
+import pooch
+import xarray
+import yaml
+from requests.exceptions import RequestException
+
+from movement.io import load_poses
+from movement.logging import log_error, log_warning
+
+logger = logging.getLogger(__name__)
+
+# URL to the remote data repository on GIN
+# noinspection PyInterpreter
+DATA_URL = (
+    "https://gin.g-node.org/neuroinformatics/movement-test-data/raw/master"
+)
+
+# Save data in ~/.movement/data
+DATA_DIR = Path("~", ".movement", "data").expanduser()
+# Create the folder if it doesn't exist
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def _download_metadata_file(file_name: str, data_dir: Path = DATA_DIR) -> Path:
+    """Download the yaml file containing sample metadata from the *movement*
+    data repository and save it in the specified directory with a temporary
+    filename - temp_{file_name} - to avoid overwriting any existing files.
+
+    Parameters
+    ----------
+    file_name : str
+        Name of the metadata file to fetch.
+    data_dir : pathlib.Path, optional
+        Directory to store the metadata file in. Defaults to the constant
+        ``DATA_DIR``. Can be overridden for testing purposes.
+
+    Returns
+    -------
+    path : pathlib.Path
+        Path to the downloaded file.
+    """
+    local_file_path = pooch.retrieve(
+        url=f"{DATA_URL}/{file_name}",
+        known_hash=None,
+        path=data_dir,
+        fname=f"temp_{file_name}",
+        progressbar=False,
+    )
+    logger.debug(
+        f"Successfully downloaded sample metadata file {file_name} "
+        f"from {DATA_URL} to {data_dir}"
+    )
+    return Path(local_file_path)
+
+
+def _fetch_metadata(file_name: str, data_dir: Path = DATA_DIR) -> list[dict]:
+    """Download the yaml file containing metadata from the *movement* sample
+    data repository and load it as a list of dictionaries.
+
+    Parameters
+    ----------
+    file_name : str
+        Name of the metadata file to fetch.
+    data_dir : pathlib.Path, optional
+        Directory to store the metadata file in. Defaults to
+        the constant ``DATA_DIR``. Can be overridden for testing purposes.
+
+    Returns
+    -------
+    list[dict]
+        A list of dictionaries containing metadata for each sample file.
+    """
+
+    local_file_path = Path(data_dir / file_name)
+    failed_msg = "Failed to download the newest sample metadata file."
+
+    # try downloading the newest metadata file
+    try:
+        downloaded_file_path = _download_metadata_file(file_name, data_dir)
+        # if download succeeds, replace any existing local metadata file
+        downloaded_file_path.replace(local_file_path)
+    # if download fails, try loading an existing local metadata file,
+    # otherwise raise an error
+    except RequestException as exc_info:
+        if local_file_path.is_file():
+            log_warning(
+                f"{failed_msg} Will use the existing local version instead."
+            )
+        else:
+            raise log_error(RequestException, failed_msg) from exc_info
+
+    with open(local_file_path, "r") as metadata_file:
+        metadata = yaml.safe_load(metadata_file)
+    return metadata
+
+
+metadata = _fetch_metadata("poses_files_metadata.yaml")
+
+# Create a download manager for the pose data
+SAMPLE_DATA = pooch.create(
+    path=DATA_DIR / "poses",
+    base_url=f"{DATA_URL}/poses/",
+    retry_if_failed=0,
+    registry={file["file_name"]: file["sha256sum"] for file in metadata},
+)
+
+
+def list_sample_data() -> list[str]:
+    """Find available sample pose data in the *movement* data repository.
+
+    Returns
+    -------
+    filenames : list of str
+        List of filenames for available pose data."""
+    return list(SAMPLE_DATA.registry.keys())
+
+
+def fetch_sample_data_path(filename: str) -> Path:
+    """Download sample pose data from the *movement* data repository and return
+    its local filepath.
+
+    The data are downloaded to the user's local machine the first time they are
+    used and are stored in a local cache directory. The function returns the
+    path to the downloaded file, not the contents of the file itself.
+
+    Parameters
+    ----------
+    filename : str
+        Name of the file to fetch.
+
+    Returns
+    -------
+    path : pathlib.Path
+        Path to the downloaded file.
+    """
+    try:
+        return Path(SAMPLE_DATA.fetch(filename, progressbar=True))
+    except ValueError:
+        raise log_error(
+            ValueError,
+            f"File '{filename}' is not in the registry. Valid "
+            f"filenames are: {list_sample_data()}",
+        )
+
+
+def fetch_sample_data(
+    filename: str,
+) -> xarray.Dataset:
+    """Download and return sample pose data from the *movement* data
+    repository.
+
+    The data are downloaded to the user's local machine the first time they are
+    used and are stored in a local cache directory. Returns sample pose data as
+    an xarray Dataset.
+
+    Parameters
+    ----------
+    filename : str
+        Name of the file to fetch.
+
+    Returns
+    -------
+    ds : xarray.Dataset
+        Pose data contained in the fetched sample file.
+    """
+
+    file_path = fetch_sample_data_path(filename)
+    file_metadata = next(
+        file for file in metadata if file["file_name"] == filename
+    )
+
+    if file_metadata["source_software"] == "SLEAP":
+        ds = load_poses.from_sleap_file(file_path, fps=file_metadata["fps"])
+    elif file_metadata["source_software"] == "DeepLabCut":
+        ds = load_poses.from_dlc_file(file_path, fps=file_metadata["fps"])
+    elif file_metadata["source_software"] == "LightningPose":
+        ds = load_poses.from_lp_file(file_path, fps=file_metadata["fps"])
+    return ds
diff --git a/pyproject.toml b/pyproject.toml
index c41c4e99..dfebb7d4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ dependencies = [
     "tqdm",
     "sleap-io",
     "xarray",
+    "PyYAML",
 ]
 
 classifiers = [
@@ -54,6 +55,8 @@ dev = [
   "pandas-stubs",
   "types-attrs",
   "check-manifest",
+  "types-PyYAML",
+  "types-requests",
 ]
 
 [build-system]
diff --git a/tests/conftest.py b/tests/conftest.py
index e4baa670..411c9679 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,29 +8,17 @@
 import pytest
 import xarray as xr
 
-from movement.datasets import fetch_pose_data_path
 from movement.io import PosesAccessor
 from movement.logging import configure_logging
+from movement.sample_data import fetch_sample_data_path, list_sample_data
 
 
 def pytest_configure():
     """Perform initial configuration for pytest.
     Fetches pose data file paths as a dictionary for tests."""
-    pytest.POSE_DATA = {
-        file_name: fetch_pose_data_path(file_name)
-        for file_name in [
-            "DLC_single-wasp.predictions.h5",
-            "DLC_single-wasp.predictions.csv",
-            "DLC_two-mice.predictions.csv",
-            "SLEAP_single-mouse_EPM.analysis.h5",
-            "SLEAP_single-mouse_EPM.predictions.slp",
-            "SLEAP_three-mice_Aeon_proofread.analysis.h5",
-            "SLEAP_three-mice_Aeon_proofread.predictions.slp",
-            "SLEAP_three-mice_Aeon_mixed-labels.analysis.h5",
-            "SLEAP_three-mice_Aeon_mixed-labels.predictions.slp",
-            "LP_mouse-face_AIND.predictions.csv",
-            "LP_mouse-twoview_AIND.predictions.csv",
-        ]
+    pytest.POSE_DATA_PATHS = {
+        file_name: fetch_sample_data_path(file_name)
+        for file_name in list_sample_data()
     }
 
 
@@ -186,7 +174,9 @@ def new_csv_file(tmp_path):
 @pytest.fixture
 def dlc_style_df():
     """Return a valid DLC-style DataFrame."""
-    return pd.read_hdf(pytest.POSE_DATA.get("DLC_single-wasp.predictions.h5"))
+    return pd.read_hdf(
+        pytest.POSE_DATA_PATHS.get("DLC_single-wasp.predictions.h5")
+    )
 
 
 @pytest.fixture(
@@ -201,7 +191,7 @@ def dlc_style_df():
 )
 def sleap_file(request):
     """Return the file path for a SLEAP .h5 or .slp file."""
-    return pytest.POSE_DATA.get(request.param)
+    return pytest.POSE_DATA_PATHS.get(request.param)
 
 
 @pytest.fixture
diff --git a/tests/test_integration/test_io.py b/tests/test_integration/test_io.py
index 260ef7b1..316485c4 100644
--- a/tests/test_integration/test_io.py
+++ b/tests/test_integration/test_io.py
@@ -2,7 +2,7 @@
 import numpy as np
 import pytest
 import xarray as xr
-from pytest import POSE_DATA
+from pytest import POSE_DATA_PATHS
 
 from movement.io import load_poses, save_poses
 
@@ -56,7 +56,7 @@ def test_to_sleap_analysis_file_returns_same_h5_file_content(
         """Test that saving pose tracks (loaded from a SLEAP analysis
         file) to a SLEAP-style .h5 analysis file returns the same file
         contents."""
-        sleap_h5_file_path = POSE_DATA.get(sleap_h5_file)
+        sleap_h5_file_path = POSE_DATA_PATHS.get(sleap_h5_file)
         ds = load_poses.from_sleap_file(sleap_h5_file_path, fps=fps)
         save_poses.to_sleap_analysis_file(ds, new_h5_file)
 
@@ -85,7 +85,7 @@ def test_to_sleap_analysis_file_source_file(self, file, new_h5_file):
         """Test that saving pose tracks (loaded from valid source files)
         to a SLEAP-style .h5 analysis file stores the .slp labels path
         only when the source file is a .slp file."""
-        file_path = POSE_DATA.get(file)
+        file_path = POSE_DATA_PATHS.get(file)
         if file.startswith("DLC"):
             ds = load_poses.from_dlc_file(file_path)
         else:
diff --git a/tests/test_unit/test_load_poses.py b/tests/test_unit/test_load_poses.py
index dedc5b5b..37f76f6a 100644
--- a/tests/test_unit/test_load_poses.py
+++ b/tests/test_unit/test_load_poses.py
@@ -2,7 +2,7 @@
 import numpy as np
 import pytest
 import xarray as xr
-from pytest import POSE_DATA
+from pytest import POSE_DATA_PATHS
 from sleap_io.io.slp import read_labels, write_labels
 from sleap_io.model.labels import LabeledFrame, Labels
 
@@ -15,7 +15,9 @@ class TestLoadPoses:
     @pytest.fixture
     def sleap_slp_file_without_tracks(self, tmp_path):
         """Mock and return the path to a SLEAP .slp file without tracks."""
-        sleap_file = POSE_DATA.get("SLEAP_single-mouse_EPM.predictions.slp")
+        sleap_file = POSE_DATA_PATHS.get(
+            "SLEAP_single-mouse_EPM.predictions.slp"
+        )
         labels = read_labels(sleap_file)
         file_path = tmp_path / "track_is_none.slp"
         lfs = []
@@ -43,7 +45,7 @@ def sleap_slp_file_without_tracks(self, tmp_path):
     @pytest.fixture
     def sleap_h5_file_without_tracks(self, tmp_path):
         """Mock and return the path to a SLEAP .h5 file without tracks."""
-        sleap_file = POSE_DATA.get("SLEAP_single-mouse_EPM.analysis.h5")
+        sleap_file = POSE_DATA_PATHS.get("SLEAP_single-mouse_EPM.analysis.h5")
         file_path = tmp_path / "track_is_none.h5"
         with h5py.File(sleap_file, "r") as f1, h5py.File(file_path, "w") as f2:
             for key in list(f1.keys()):
@@ -112,7 +114,7 @@ def test_load_from_sleap_file_without_tracks(
             sleap_file_without_tracks
         )
         ds_from_tracked = load_poses.from_sleap_file(
-            POSE_DATA.get("SLEAP_single-mouse_EPM.analysis.h5")
+            POSE_DATA_PATHS.get("SLEAP_single-mouse_EPM.analysis.h5")
         )
         # Check if the "individuals" coordinate matches
         # the assigned default "individuals_0"
@@ -144,8 +146,8 @@ def test_load_from_sleap_slp_file_or_h5_file_returns_same(
     ):
         """Test that loading pose tracks from SLEAP .slp and .h5 files
         return the same Dataset."""
-        slp_file_path = POSE_DATA.get(slp_file)
-        h5_file_path = POSE_DATA.get(h5_file)
+        slp_file_path = POSE_DATA_PATHS.get(slp_file)
+        h5_file_path = POSE_DATA_PATHS.get(h5_file)
         ds_from_slp = load_poses.from_sleap_file(slp_file_path)
         ds_from_h5 = load_poses.from_sleap_file(h5_file_path)
         xr.testing.assert_allclose(ds_from_h5, ds_from_slp)
@@ -161,7 +163,7 @@ def test_load_from_sleap_slp_file_or_h5_file_returns_same(
     def test_load_from_dlc_file(self, file_name):
         """Test that loading pose tracks from valid DLC files
         returns a proper Dataset."""
-        file_path = POSE_DATA.get(file_name)
+        file_path = POSE_DATA_PATHS.get(file_name)
         ds = load_poses.from_dlc_file(file_path)
         self.assert_dataset(ds, file_path, "DeepLabCut")
 
@@ -174,8 +176,8 @@ def test_load_from_dlc_df(self, dlc_style_df):
     def test_load_from_dlc_file_csv_or_h5_file_returns_same(self):
         """Test that loading pose tracks from DLC .csv and .h5 files
         return the same Dataset."""
-        csv_file_path = POSE_DATA.get("DLC_single-wasp.predictions.csv")
-        h5_file_path = POSE_DATA.get("DLC_single-wasp.predictions.h5")
+        csv_file_path = POSE_DATA_PATHS.get("DLC_single-wasp.predictions.csv")
+        h5_file_path = POSE_DATA_PATHS.get("DLC_single-wasp.predictions.h5")
         ds_from_csv = load_poses.from_dlc_file(csv_file_path)
         ds_from_h5 = load_poses.from_dlc_file(h5_file_path)
         xr.testing.assert_allclose(ds_from_h5, ds_from_csv)
@@ -193,7 +195,7 @@ def test_load_from_dlc_file_csv_or_h5_file_returns_same(self):
     def test_fps_and_time_coords(self, fps, expected_fps, expected_time_unit):
         """Test that time coordinates are set according to the provided fps."""
         ds = load_poses.from_sleap_file(
-            POSE_DATA.get("SLEAP_three-mice_Aeon_proofread.analysis.h5"),
+            POSE_DATA_PATHS.get("SLEAP_three-mice_Aeon_proofread.analysis.h5"),
             fps=fps,
         )
         assert ds.time_unit == expected_time_unit
@@ -216,7 +218,7 @@ def test_fps_and_time_coords(self, fps, expected_fps, expected_time_unit):
     def test_load_from_lp_file(self, file_name):
         """Test that loading pose tracks from valid LightningPose (LP) files
         returns a proper Dataset."""
-        file_path = POSE_DATA.get(file_name)
+        file_path = POSE_DATA_PATHS.get(file_name)
         ds = load_poses.from_lp_file(file_path)
         self.assert_dataset(ds, file_path, "LightningPose")
 
@@ -224,7 +226,7 @@ def test_load_from_lp_or_dlc_file_returns_same(self):
         """Test that loading a single-animal DeepLabCut-style .csv file
         using either the `from_lp_file` or `from_dlc_file` function
         returns the same Dataset (except for the source_software)."""
-        file_path = POSE_DATA.get("LP_mouse-face_AIND.predictions.csv")
+        file_path = POSE_DATA_PATHS.get("LP_mouse-face_AIND.predictions.csv")
         ds_drom_lp = load_poses.from_lp_file(file_path)
         ds_from_dlc = load_poses.from_dlc_file(file_path)
         xr.testing.assert_allclose(ds_from_dlc, ds_drom_lp)
@@ -234,6 +236,6 @@ def test_load_from_lp_or_dlc_file_returns_same(self):
     def test_load_multi_animal_from_lp_file_raises(self):
         """Test that loading a multi-animal .csv file using the
         `from_lp_file` function raises a ValueError."""
-        file_path = POSE_DATA.get("DLC_two-mice.predictions.csv")
+        file_path = POSE_DATA_PATHS.get("DLC_two-mice.predictions.csv")
         with pytest.raises(ValueError):
             load_poses.from_lp_file(file_path)
diff --git a/tests/test_unit/test_sample_data.py b/tests/test_unit/test_sample_data.py
new file mode 100644
index 00000000..09bcafad
--- /dev/null
+++ b/tests/test_unit/test_sample_data.py
@@ -0,0 +1,121 @@
+"""Test suite for the sample_data module."""
+
+from unittest.mock import MagicMock, patch
+
+import pooch
+import pytest
+from requests.exceptions import RequestException
+from xarray import Dataset
+
+from movement.sample_data import (
+    _fetch_metadata,
+    fetch_sample_data,
+    list_sample_data,
+)
+
+
+@pytest.fixture(scope="module")
+def valid_file_names_with_fps():
+    """Return a dict containing one valid file name and the corresponding fps
+    for each supported pose estimation tool."""
+    return {
+        "SLEAP_single-mouse_EPM.analysis.h5": 30,
+        "DLC_single-wasp.predictions.h5": 40,
+        "LP_mouse-face_AIND.predictions.csv": 60,
+    }
+
+
+def validate_metadata(metadata: list[dict]) -> None:
+    """Assert that the metadata is in the expected format."""
+    metadata_fields = [
+        "file_name",
+        "sha256sum",
+        "source_software",
+        "fps",
+        "species",
+        "number_of_individuals",
+        "shared_by",
+        "video_frame_file",
+        "note",
+    ]
+    check_yaml_msg = "Check the format of the metadata yaml file."
+    assert isinstance(
+        metadata, list
+    ), f"Expected metadata to be a list. {check_yaml_msg}"
+    assert all(
+        isinstance(file, dict) for file in metadata
+    ), f"Expected metadata entries to be dicts. {check_yaml_msg}"
+    assert all(
+        set(file.keys()) == set(metadata_fields) for file in metadata
+    ), f"Expected all metadata entries to have the same keys. {check_yaml_msg}"
+
+    # check that filenames are unique
+    file_names = [file["file_name"] for file in metadata]
+    assert len(file_names) == len(set(file_names))
+
+    # check that the first 3 fields are present and are strings
+    required_fields = metadata_fields[:3]
+    assert all(
+        (isinstance(file[field], str))
+        for file in metadata
+        for field in required_fields
+    )
+
+
+# Mock pooch.retrieve with RequestException as side_effect
+mock_retrieve = MagicMock(pooch.retrieve, side_effect=RequestException)
+
+
+@pytest.mark.parametrize("download_fails", [True, False])
+@pytest.mark.parametrize("local_exists", [True, False])
+def test_fetch_metadata(tmp_path, caplog, download_fails, local_exists):
+    """Test the fetch_metadata function with different combinations of
+    failed download and pre-existing local file. The expected behavior is
+    that the function will try to download the metadata file, and if that
+    fails, it will try to load an existing local file. If neither succeeds,
+    an error is raised."""
+    metadata_file_name = "poses_files_metadata.yaml"
+    local_file_path = tmp_path / metadata_file_name
+
+    with patch("movement.sample_data.DATA_DIR", tmp_path):
+        # simulate the existence of a local metadata file
+        if local_exists:
+            local_file_path.touch()
+
+        if download_fails:
+            # simulate a failed download
+            with patch("movement.sample_data.pooch.retrieve", mock_retrieve):
+                if local_exists:
+                    _fetch_metadata(metadata_file_name)
+                    # check that a warning was logged
+                    assert (
+                        "Will use the existing local version instead"
+                        in caplog.records[-1].getMessage()
+                    )
+                else:
+                    with pytest.raises(
+                        RequestException, match="Failed to download"
+                    ):
+                        _fetch_metadata(metadata_file_name, data_dir=tmp_path)
+        else:
+            metadata = _fetch_metadata(metadata_file_name, data_dir=tmp_path)
+            assert local_file_path.is_file()
+            validate_metadata(metadata)
+
+
+def test_list_sample_data(valid_file_names_with_fps):
+    assert isinstance(list_sample_data(), list)
+    assert all(
+        file in list_sample_data() for file in valid_file_names_with_fps
+    )
+
+
+def test_fetch_sample_data(valid_file_names_with_fps):
+    # test with valid files
+    for file, fps in valid_file_names_with_fps.items():
+        ds = fetch_sample_data(file)
+        assert isinstance(ds, Dataset) and ds.fps == fps
+
+    # Test with an invalid file
+    with pytest.raises(ValueError):
+        fetch_sample_data("nonexistent_file")
diff --git a/tests/test_unit/test_save_poses.py b/tests/test_unit/test_save_poses.py
index c65a88d8..e2a4f43e 100644
--- a/tests/test_unit/test_save_poses.py
+++ b/tests/test_unit/test_save_poses.py
@@ -5,7 +5,7 @@
 import pandas as pd
 import pytest
 import xarray as xr
-from pytest import POSE_DATA
+from pytest import POSE_DATA_PATHS
 
 from movement.io import load_poses, save_poses
 
@@ -65,25 +65,25 @@ def output_file_params(self, request):
             (np.array([1, 2, 3]), pytest.raises(ValueError)),  # incorrect type
             (
                 load_poses.from_dlc_file(
-                    POSE_DATA.get("DLC_single-wasp.predictions.h5")
+                    POSE_DATA_PATHS.get("DLC_single-wasp.predictions.h5")
                 ),
                 does_not_raise(),
             ),  # valid dataset
             (
                 load_poses.from_dlc_file(
-                    POSE_DATA.get("DLC_two-mice.predictions.csv")
+                    POSE_DATA_PATHS.get("DLC_two-mice.predictions.csv")
                 ),
                 does_not_raise(),
             ),  # valid dataset
             (
                 load_poses.from_sleap_file(
-                    POSE_DATA.get("SLEAP_single-mouse_EPM.analysis.h5")
+                    POSE_DATA_PATHS.get("SLEAP_single-mouse_EPM.analysis.h5")
                 ),
                 does_not_raise(),
             ),  # valid dataset
             (
                 load_poses.from_sleap_file(
-                    POSE_DATA.get(
+                    POSE_DATA_PATHS.get(
                         "SLEAP_three-mice_Aeon_proofread.predictions.slp"
                     )
                 ),
@@ -91,7 +91,7 @@ def output_file_params(self, request):
             ),  # valid dataset
             (
                 load_poses.from_lp_file(
-                    POSE_DATA.get("LP_mouse-face_AIND.predictions.csv")
+                    POSE_DATA_PATHS.get("LP_mouse-face_AIND.predictions.csv")
                 ),
                 does_not_raise(),
             ),  # valid dataset