diff --git a/.gitignore b/.gitignore
index bfc06a0..5e026de 100644
--- a/.gitignore
+++ b/.gitignore
@@ -172,4 +172,5 @@ cython_debug/
 
 # PyPI configuration file
 .pypirc
+data/*
 testing.ipynb
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4ef5b53..4dc64fb 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -51,6 +51,32 @@ You can run pytest on your work using the following example:
 % uv run pytest
 ```
 
+### Linting, testing, coverage, and updating coverage badges
+
+We apply a custom script to update our coverage badge in the README during our CI workflow.
+When running `scripts/update_coverage_badge.py` the script will parse the `coverage.xml` file generated from pytest and update the badge in the README with the new coverage percentage.
+When running `just all` locally, the coverage badge will not be updated since it is intended to be updated as part of the CI workflow.
+
+```sh
+just all
+```
+
+Where `all` is runnins all defined steps in the `justfile` which includes running tests and generating coverage reports.
+To see all of the steps defined in the `justfile`, you can run:
+
+```sh
+just --list
+```
+
+the coverage badge will be automatically updated as part of the workflow.
+If you want to update the coverage, run:
+
+```sh
+just coverage
+```
+
+This will run the tests and update the coverage badge in the README.
+
 ## Making changes to this repository
 
 We welcome anyone to use [GitHub issues](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues) (requires a GitHub login) or create [pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) (to directly make changes within this repository) to modify content found within this repository.
diff --git a/README.md b/README.md
index 7ad5bd7..c49e40c 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ZedProfiler
 
-[![Coverage](https://img.shields.io/badge/coverage-89%25-green)](#quality-gates)
+[![Coverage](https://img.shields.io/badge/coverage-96%25-brightgreen)](#quality-gates)
 
 CPU-first 3D image feature extraction toolkit for high-content and high-throughput image-based profiling.
 
diff --git a/ROADMAP.md b/ROADMAP.md
index 2c0ef42..2032efa 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -36,13 +36,13 @@ The roadmap is intended to be a living document and may be updated as needed.
 
 1. PR 1: Packaging and environment baseline
 
-- [ ] Python package scaffold, uv dependency management, version metadata 0.0.1, lint/test tooling, CI skeleton.
-- [ ] Linux support and CPU-only scope statements in metadata and docs.
+- [x] Python package scaffold, uv dependency management, version metadata 0.0.1, lint/test tooling, CI skeleton.
+- [x] Linux support and CPU-only scope statements in metadata and docs.
 
 2. PR 2: Core data model and API contracts
 
-- [ ] Canonical input contracts, loader interfaces, common error types.
-- [ ] Return schema contract (required keys, types, deterministic ordering).
+- [x] Canonical input contracts, loader interfaces, common error types.
+- [x] Return schema contract (required keys, types, deterministic ordering).
 
 3. PR 3: RFC2119 naming specification and validators
 
diff --git a/pyproject.toml b/pyproject.toml
index 2ee42e4..9b9b352 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,8 +24,11 @@ dependencies = [
   "jinja2>=3.1.6",
   "matplotlib>=3.10.8",
   "pandas>=3.0.2",
-  "pyarrow>=24",
+  "pandera>=0.31.1",
+  "pyarrow>=23.0.1",
+  "pydantic>=2.10",
   "scikit-image>=0.26",
+  "tomli>=2.4.1",
 ]
 scripts.ZedProfiler = "ZedProfiler.cli:trigger"
 
diff --git a/src/zedprofiler/contracts.py b/src/zedprofiler/contracts.py
index ea4a654..bca5604 100644
--- a/src/zedprofiler/contracts.py
+++ b/src/zedprofiler/contracts.py
@@ -1,31 +1,609 @@
 """Core data contracts shared across featurizers.
 
-The package accepts either:
+We have contracts for the data inputs and outputs of featurizers,
+as well as for column names in the output DataFrame.
+These contracts are enforced through a combination of Pydantic models,
+Beartypes Pandera schemas.
+
+Where inputs are the image path or arrays.
+Outputs are the feature DataFrame and metadata dictionary/Dfs.
+
+The package accepts:
 - Single-channel 3D arrays shaped (z, y, x)
-- Multi-channel 4D arrays shaped (c, z, y, x)
 """
 
 from __future__ import annotations
 
-from dataclasses import dataclass
-from typing import Literal
+import pathlib
+from typing import Any
 
 import numpy as np
-import numpy.typing as npt
+import pandas as pd
+import pandera as pa
+import tomli
+from beartype import beartype
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    field_validator,
+    model_validator,
+)
+
+from zedprofiler.exceptions import ContractError
+
+EXPECTED_SPATIAL_DIMS = 3
+TWO_DIMENSIONAL = 2
+FOUR_DIMENSIONAL = 4
+FIVE_OR_MORE_DIMENSIONS = 5
+NON_METADATA_UNDERSCORE_SEPARATED_PARTS = 4
+METADATA_UNDERSCORE_SEPARATED_PARTS = 3
+REQUIRED_RETURN_KEYS = ("image_array", "features", "metadata")
+
+# Pandera schema for validating numpy arrays with expected dimensionality
+ImageArraySchema = pa.DataFrameSchema(
+    columns={},
+    checks=[
+        pa.Check(
+            lambda x: len(x.shape) in [3, 4, 5],
+            error="Array must have 3, 4, or 5 dimensions",
+        ),
+    ],
+    name="ImageArray",
+)
+
+
+class ImageArrayModel(BaseModel):
+    """Pydantic model for validating image arrays."""
+
+    array: np.ndarray
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    @field_validator("array", mode="before")
+    @classmethod
+    def validate_array_type(_cls, v: object) -> np.ndarray:
+        """Ensure array is a numpy array."""
+        if not isinstance(v, np.ndarray):
+            raise ValueError(f"Expected numpy array, got {type(v)}")
+        return v
+
+    @field_validator("array", mode="after")
+    @classmethod
+    def validate_array_dtype_and_shape(_cls, arr: np.ndarray) -> np.ndarray:
+        """Validate array dtype is numeric and shape is valid."""
+        if not np.issubdtype(arr.dtype, np.number):
+            raise ValueError(f"Array dtype must be numeric, got {arr.dtype}")
+
+        arr_shape = arr.shape
+        # Check dimensionality
+        if len(arr_shape) == TWO_DIMENSIONAL:
+            raise ValueError(
+                f"Input array has shape {arr_shape} with {TWO_DIMENSIONAL} "
+                f"dimensions. Expected {EXPECTED_SPATIAL_DIMS} dimensions."
+            )
+        elif len(arr_shape) == FOUR_DIMENSIONAL and arr_shape[0] > 1:
+            raise ValueError(
+                f"Input array has shape {arr_shape} with {FOUR_DIMENSIONAL} "
+                "dimensions, but the first dimension (channels) has size "
+                f"{arr_shape[0]}. Expected a single-channel 3D array."
+            )
+        elif (
+            len(arr_shape) >= FIVE_OR_MORE_DIMENSIONS
+            and arr_shape[0] > 1
+            and arr_shape[1] > 1
+        ):
+            raise ValueError(
+                f"Input array has shape {arr_shape} with {len(arr_shape)} "
+                f"dimensions. Expected {EXPECTED_SPATIAL_DIMS} dimensions."
+            )
+
+        # Check all dimensions are positive
+        for dim_size in arr_shape:
+            if dim_size <= 0:
+                raise ValueError(
+                    f"Input array has shape {arr_shape} with non-positive "
+                    "dimension size. All dimensions must have size greater than 0."
+                )
+
+        # Check not all dimensions are 1
+        if sum(arr_shape) == len(arr_shape):
+            raise ValueError(
+                f"Input array has shape {arr_shape} with one or more "
+                "dimensions of size 1. Expected all three dimensions to "
+                "have size greater than 1."
+            )
+
+        return arr
+
+
+class FeatureDictModel(BaseModel):
+    """Pydantic model for validating feature dictionaries."""
+
+    features: dict[str, Any]
+
+    @field_validator("features", mode="before")
+    @classmethod
+    def validate_is_dict(_cls, v: object) -> dict[str, Any]:
+        """Ensure features is a dictionary."""
+        if not isinstance(v, dict):
+            raise ValueError(f"Expected dict, got {type(v)}")
+        return v
+
+
+class MetadataDictModel(BaseModel):
+    """Pydantic model for validating metadata dictionaries."""
+
+    metadata: dict[str, Any]
+
+    @field_validator("metadata", mode="before")
+    @classmethod
+    def validate_is_dict(_cls, v: object) -> dict[str, Any]:
+        """Ensure metadata is a dictionary."""
+        if not isinstance(v, dict):
+            raise ValueError(f"Expected dict, got {type(v)}")
+        return v
+
+
+class ReturnSchemaModel(BaseModel):
+    """Pydantic model for validating return schema."""
+
+    result: dict[str, Any]
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    @field_validator("result", mode="before")
+    @classmethod
+    def validate_is_dict(_cls, v: object) -> dict[str, Any]:
+        """Ensure result is a dictionary."""
+        if not isinstance(v, dict):
+            raise ValueError(f"Expected dict, got {type(v)}")
+        return v
+
+    @field_validator("result", mode="after")
+    @classmethod
+    def validate_keys_and_types(_cls, result: dict[str, Any]) -> dict[str, Any]:
+        """Validate result has correct keys in correct order and correct types."""
+        actual_keys = tuple(result.keys())
+        if actual_keys != REQUIRED_RETURN_KEYS:
+            raise ValueError(
+                "Return result keys must match required deterministic order "
+                f"{REQUIRED_RETURN_KEYS}, got {actual_keys}."
+            )
+
+        if not isinstance(result["image_array"], np.ndarray):
+            raise ValueError(
+                f"Return result key 'image_array' must be a numpy array, "
+                f"got {type(result['image_array'])}"
+            )
+        if not isinstance(result["features"], dict):
+            raise ValueError(
+                f"Return result key 'features' must be a dict, "
+                f"got {type(result['features'])}"
+            )
+        if not isinstance(result["metadata"], dict):
+            raise ValueError(
+                f"Return result key 'metadata' must be a dict, "
+                f"got {type(result['metadata'])}"
+            )
+
+        return result
+
+
+class ColumnNameModel(BaseModel):
+    """Pydantic model for parsing and validating column names."""
+
+    column_name: str
+    compartment: str | None = None
+    channel: str | None = None
+    feature: str | None = None
+
+    @field_validator("column_name", mode="before")
+    @classmethod
+    def validate_is_string(_cls, v: object) -> str:
+        """Ensure column_name is a string."""
+        if not isinstance(v, str):
+            raise ValueError(f"Expected string, got {type(v)}")
+        return v
+
+    @model_validator(mode="after")
+    def parse_column_name(self) -> ColumnNameModel:
+        """Parse column name into components."""
+        parts = self.column_name.split("_")
+
+        if "Metadata" in self.column_name:
+            if len(parts) < METADATA_UNDERSCORE_SEPARATED_PARTS:
+                raise ValueError(
+                    "Metadata column name must have at least "
+                    f"{METADATA_UNDERSCORE_SEPARATED_PARTS} "
+                    "parts separated by underscores, "
+                    f"got {len(parts)} parts in '{self.column_name}'"
+                )
+            # Don't parse compartment/channel/feature for metadata columns
+            return self
+
+        if len(parts) < NON_METADATA_UNDERSCORE_SEPARATED_PARTS:
+            raise ValueError(
+                "Column name must have at least "
+                f"{NON_METADATA_UNDERSCORE_SEPARATED_PARTS} "
+                "parts separated by underscores, "
+                f"got {len(parts)} parts in '{self.column_name}'"
+            )
+
+        self.compartment = parts[0]
+        self.channel = parts[1]
+        self.feature = parts[2]
+
+        return self
+
+
+@beartype
+def validate_image_array_shape_contracts(
+    arr: np.ndarray,
+) -> bool:
+    """
+    Validate the input array for dimensionality
+
+    Parameters
+    ----------
+    arr : np.ndarray
+        Input array to validate
+
+    Returns
+    -------
+    bool
+        The status of the validation
+
+    Raises
+    ------
+    ContractError
+        If the input array does not meet the expected contract
+    """
+
+    arr_shape = arr.shape
+    if len(arr_shape) == TWO_DIMENSIONAL:
+        raise ContractError(
+            f"Input array has shape {arr_shape} with {TWO_DIMENSIONAL} dimensions. "
+            f"Expected {EXPECTED_SPATIAL_DIMS} dimensions."
+        )
+    elif len(arr_shape) == FOUR_DIMENSIONAL and arr_shape[0] > 1:
+        raise ContractError(
+            f"Input array has shape {arr_shape} with {FOUR_DIMENSIONAL} dimensions, "
+            "but the first dimension (channels) has size "
+            f"{arr_shape[0]}. Expected a single-channel 3D array."
+        )
+    elif (
+        len(arr_shape) >= FIVE_OR_MORE_DIMENSIONS
+        and arr_shape[0] > 1
+        and arr_shape[1] > 1
+    ):
+        raise ContractError(
+            f"Input array has shape {arr_shape} with {len(arr_shape)} dimensions. "
+            f"Expected {EXPECTED_SPATIAL_DIMS} dimensions."
+        )
+
+    for dim_size in arr_shape:
+        if dim_size <= 0:
+            raise ContractError(
+                f"Input array has shape {arr_shape} with non-positive dimension size. "
+                "All dimensions must have size greater than 0."
+            )
+    if sum(arr_shape) == len(arr_shape):
+        raise ContractError(
+            f"Input array has shape {arr_shape} with one or more dimensions of size 1. "
+            "Expected all three dimensions to have size greater than 1."
+        )
+    return True
+
+
+@beartype
+def validate_image_array_type_contracts(
+    arr: np.ndarray,
+) -> bool:
+    """
+    Validate the input array for type
+
+    Parameters
+    ----------
+    arr : np.ndarray
+        Input array to validate
+
+    Returns
+    -------
+    bool
+        The status of the validation
+
+    Raises
+    ------
+    ContractError
+        If the input array does not meet the expected contract
+    """
+    if not isinstance(arr, np.ndarray):
+        raise ContractError(f"Input is of type {type(arr)}, expected a numpy array.")
+    # check for numeric dtype (int or float) in the array
+    if not np.issubdtype(arr.dtype, np.number):
+        raise ContractError(
+            f"Input array has dtype {arr.dtype}, expected a numeric dtype "
+            "(int or float)."
+        )
+    return True
+
+
+@beartype
+def validate_image_with_pydantic(arr: np.ndarray) -> ImageArrayModel:
+    """
+    Validate image array using Pydantic model.
+
+    Parameters
+    ----------
+    arr : np.ndarray
+        Input array to validate
+
+    Returns
+    -------
+    ImageArrayModel
+        Validated image array model
+
+    Raises
+    ------
+    ContractError
+        If validation fails
+    """
+    try:
+        return ImageArrayModel(array=arr)
+    except Exception as e:
+        raise ContractError(f"Image array validation failed: {e}")
+
+
+@beartype
+def validate_return_with_pydantic(
+    result: dict[str, object],
+) -> ReturnSchemaModel:
+    """
+    Validate return schema using Pydantic model.
+
+    Parameters
+    ----------
+    result : dict[str, object]
+        Return result to validate
+
+    Returns
+    -------
+    ReturnSchemaModel
+        Validated return schema model
+
+    Raises
+    ------
+    ContractError
+        If validation fails
+    """
+    try:
+        return ReturnSchemaModel(result=result)
+    except Exception as e:
+        msg = (
+            "Return schema validation failed. Please ensure that the data "
+            f"fit the expected schema: {e}"
+        )
+        raise ContractError(msg)
+
+
+@beartype
+def validate_column_name_with_pydantic(column_name: str) -> ColumnNameModel:
+    """
+    Validate column name using Pydantic model.
+
+    Parameters
+    ----------
+    column_name : str
+        Column name to validate
+
+    Returns
+    -------
+    ColumnNameModel
+        Validated column name model with parsed components
+
+    Raises
+    ------
+    ContractError
+        If validation fails
+    """
+    try:
+        return ColumnNameModel(column_name=column_name)
+    except Exception as e:
+        raise ContractError(f"Column name validation failed: {e}")
+
+
+def create_image_array_schema() -> pa.SeriesSchema:
+    """
+    Create a Pandera schema for image array validation.
+
+    Returns
+    -------
+    pa.SeriesSchema
+        Pandera schema for numeric arrays
+    """
+    # Use a single numeric dtype for the series schema; Pandera dtype
+    # objects should be instantiated rather than combined with `|`.
+    return pa.SeriesSchema(
+        dtype=pa.Float64(),
+        name="image_array",
+        checks=[pa.Check(lambda x: x is not None, error="Value cannot be None")],
+    )
+
+
+@beartype
+def validate_return_schema_contract(
+    result: dict[str, object],
+) -> bool:
+    """Validate return schema keys, types, and deterministic key ordering."""
+    if not isinstance(result, dict):
+        raise ContractError(f"Return result must be a dict, got {type(result)}.")
+
+    actual_keys = tuple(result.keys())
+    if actual_keys != REQUIRED_RETURN_KEYS:
+        raise ContractError(
+            "Return result keys must match required deterministic order "
+            f"{REQUIRED_RETURN_KEYS}, got {actual_keys}."
+        )
+
+    try:
+        ReturnSchemaModel(result=result)
+    except Exception as e:
+        raise ContractError(f"Return result validation failed: {e}")
+
+    return True
+
+
+class ExpectedFeatureNameValues(BaseModel):
+    """Pydantic model for expected values in feature naming validation."""
+
+    config_file_path: pathlib.Path
+    compartments: list[str] = Field(default_factory=list)
+    channels: list[str] = Field(default_factory=list)
+    features: list[str] = Field(default_factory=list)
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    @field_validator("config_file_path", mode="before")
+    @classmethod
+    def validate_config_path(_cls, v: object) -> pathlib.Path:
+        """Ensure config_file_path is a valid Path object."""
+        if not isinstance(v, pathlib.Path):
+            v = pathlib.Path(v)
+        return v
+
+    def model_post_init(self, _context: object) -> None:
+        """Load expected values from a TOML configuration file."""
+        config = tomli.loads(self.config_file_path.read_text())
+        self.compartments = list(set(config["expected_values"]["compartments"]))
+        self.channels = list(set(config["expected_values"]["channels"]))
+        # add "NoChannel" as a valid channel for metadata columns
+        # This is automatically added in the ZedProfiler
+        # regardless of input channel we want this added
+        self.channels.append("NoChannel")
+        self.features = [
+            "AreaSizeShape",
+            "Correlation",
+            "Granularity",
+            "Intensity",
+            "Neighbors",
+            "Texture",
+            "SAMMed3D",
+            "CHAMMI-75",
+        ]
+
+    def to_dict(self) -> dict[str, list[str]]:
+        """Return expected values as a dictionary."""
+        return {
+            "compartments": self.compartments,
+            "channels": self.channels,
+            "features": self.features,
+        }
+
+    def __init__(self, *args: object, **data: object) -> None:
+        """Support positional `config_file_path` for backward compatibility.
+
+        Tests and existing code may instantiate ExpectedValues(path) using a
+        positional argument. Pydantic BaseModel requires keyword arguments, so
+        accept a single positional argument and forward it as
+        `config_file_path=` to the BaseModel initializer.
+        """
+        if args and "config_file_path" not in data:
+            # take the first positional arg as config_file_path
+            data["config_file_path"] = args[0]
+        super().__init__(**data)
+
 
-DimensionOrder = Literal["zyx", "czyx"]
+@beartype
+def validate_column_name_schema(
+    column_name: str,
+    expected_values_config_path: pathlib.Path,
+) -> bool:
+    """
+    Validate the column name schema for required fields and types
 
+    Parameters
+    ----------
+    column_name : str
+        The column name to validate
+    expected_values_config_path : pathlib.Path
+        Path to the configuration file containing expected values for validation
+    Returns
+    -------
+    bool
+        The status of the validation
+    Raises
+    ------
+    ContractError
+        If the column name does not meet the expected schema
+    """
+    non_metadata_underscore_separated_parts = NON_METADATA_UNDERSCORE_SEPARATED_PARTS
+    metadata_underscore_separated_parts = METADATA_UNDERSCORE_SEPARATED_PARTS
 
-@dataclass(frozen=True)
-class ImageArrayContract:
-    """Document expected dimensionality and ordering for input arrays."""
+    expected_values = ExpectedFeatureNameValues(expected_values_config_path).to_dict()
+    # check if the column name is a string
+    if not isinstance(column_name, str):
+        raise ContractError(f"Column name must be a string, got {type(column_name)}")
 
-    dimensions: int
-    order: DimensionOrder
+    # check if the column name has at least 4 parts separated by underscores
+    parts = column_name.split("_")
+    if (
+        len(parts) < non_metadata_underscore_separated_parts
+        and "Metadata" not in column_name
+    ):
+        msg = (
+            "Column name must have at least "
+            f"{non_metadata_underscore_separated_parts} "
+            "parts separated by underscores, "
+            f"got {len(parts)} parts in '{column_name}'"
+        )
+        raise ContractError(msg)
 
+    if "Metadata" in column_name:
+        if len(parts) < metadata_underscore_separated_parts:
+            raise ContractError(
+                "Metadata column name must have at least "
+                f"{metadata_underscore_separated_parts} "
+                "parts separated by "
+                f"underscores, got {len(parts)} parts in '{column_name}'"
+            )
+        return True
 
-FloatArray = npt.NDArray[np.floating]
-IntArray = npt.NDArray[np.integer]
+    feature_components = pd.DataFrame(
+        [
+            {
+                "compartment": parts[0],
+                "channel": parts[1],
+                "feature": parts[2],
+            }
+        ]
+    )
+    feature_component_schema = pa.DataFrameSchema(
+        {
+            "compartment": pa.Column(
+                str,
+                checks=pa.Check.isin(expected_values.get("compartments", [])),
+                nullable=False,
+                coerce=True,
+            ),
+            "channel": pa.Column(
+                str,
+                checks=pa.Check.isin(expected_values.get("channels", [])),
+                nullable=False,
+                coerce=True,
+            ),
+            "feature": pa.Column(
+                str,
+                checks=pa.Check.isin(expected_values.get("features", [])),
+                nullable=False,
+                coerce=True,
+            ),
+        },
+        strict=True,
+    )
+    try:
+        feature_component_schema.validate(feature_components)
+    except (pa.errors.SchemaError, pa.errors.SchemaErrors) as e:
+        raise ContractError(f"Column name schema validation failed: {e}") from e
 
-SINGLE_CHANNEL_CONTRACT = ImageArrayContract(dimensions=3, order="zyx")
-MULTI_CHANNEL_CONTRACT = ImageArrayContract(dimensions=4, order="czyx")
+    return True
diff --git a/tests/conftest.py b/tests/conftest.py
index 72658c5..9519594 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,10 +1,471 @@
+"""Pytest fixtures for reusable test profiles and image-size parameters.
+
+This module defines a set of `Profile` fixtures with different image shapes,
+feature dictionaries, and metadata patterns used across the test suite.
 """
-conftest.py for pytest configuration.
-"""
 
+from __future__ import annotations
+
+import numpy as np
 import pytest
 
+# Import dataclass from test_data_profiles
+from test_data_profiles import Profile
+
 
 @pytest.fixture
 def my_data() -> str:
+    """Provide a basic string fixture.
+
+    Returns
+    -------
+    str
+        A deterministic string value used by tests that require simple text
+        input.
+    """
     return "Hello, differently!"
+
+
+@pytest.fixture
+def minimal_profile() -> Profile:
+    """Create the smallest valid test profile.
+
+    Returns
+    -------
+    Profile
+        A profile containing a valid 3D image array and empty feature and
+        metadata mappings.
+    """
+    return Profile(
+        image_array=np.random.rand(8, 16, 16),
+        features={},
+        metadata={},
+    )
+
+
+@pytest.fixture
+def small_image_profile() -> Profile:
+    """Create a profile with a small 3D image.
+
+    Returns
+    -------
+    Profile
+        A profile with image shape ``(4, 8, 8)`` plus representative
+        intensity features and metadata fields.
+    """
+    return Profile(
+        image_array=np.random.rand(4, 8, 8).astype(np.float32),
+        features={
+            "Nuclei_DNA_Intensity_MeanIntensity": 0.512,
+            "Nuclei_DNA_Intensity_MaxIntensity": 0.987,
+            "Nuclei_DNA_Intensity_MinIntensity": 0.015,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 1,
+            "Metadata_Imaging_ExposureTime": 100.0,
+        },
+    )
+
+
+@pytest.fixture
+def medium_image_profile() -> Profile:
+    """Create a profile with a medium 3D image.
+
+    Returns
+    -------
+    Profile
+        A profile with image shape ``(16, 32, 32)`` and a mixed set of
+        intensity, texture, and morphology feature values.
+    """
+    return Profile(
+        image_array=np.random.rand(16, 32, 32).astype(np.float32),
+        features={
+            "Nuclei_DNA_Intensity_MeanIntensity": 0.528,
+            "Nuclei_DNA_Intensity_MaxIntensity": 0.992,
+            "Nuclei_DNA_Intensity_StdIntensity": 0.125,
+            "Nuclei_DNA_Texture_Entropy-256-3": 5.234,
+            "Cell_DNA_Areasizeshape_Volume": 512.0,
+            "Cell_DNA_Areasizeshape_SurfaceArea": 256.0,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 42,
+            "Metadata_Imaging_ExposureTime": 50.0,
+            "Metadata_Microscopy_Magnification": 60.0,
+        },
+    )
+
+
+@pytest.fixture
+def large_image_profile() -> Profile:
+    """Create a profile with a large 3D image.
+
+    Returns
+    -------
+    Profile
+        A profile with image shape ``(32, 64, 64)`` and a richer collection of
+        features and metadata fields.
+    """
+    return Profile(
+        image_array=np.random.rand(32, 64, 64).astype(np.float32),
+        features={
+            "Nuclei_DNA_Intensity_MeanIntensity": 0.435,
+            "Nuclei_DNA_Intensity_MaxIntensity": 0.998,
+            "Nuclei_DNA_Intensity_MinIntensity": 0.001,
+            "Nuclei_DNA_Intensity_StdIntensity": 0.187,
+            "Nuclei_DNA_Intensity_MedianIntensity": 0.442,
+            "Nuclei_DNA_Texture_Entropy-256-3": 6.145,
+            "Nuclei_DNA_Texture_Gabor-3-0": 0.234,
+            "Cell_DNA_Areasizeshape_Volume": 2048.0,
+            "Cell_DNA_Areasizeshape_SurfaceArea": 1024.0,
+            "Cell_DNA_Areasizeshape_Sphericity": 0.856,
+            "Cytoplasm_DNA_Intensity_MeanIntensity": 0.312,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 123,
+            "Metadata_Storage_FilePath": "/data/images/cell_001.tif",
+            "Metadata_Imaging_ExposureTime": 25.0,
+            "Metadata_Microscopy_Magnification": 60.0,
+            "Metadata_Biology_CellType": "NPC",
+            "Metadata_Experiment_Treatment": "Control",
+        },
+    )
+
+
+@pytest.fixture
+def intensity_profile() -> Profile:
+    """Create a profile focused on intensity features.
+
+    Returns
+    -------
+    Profile
+        A profile emphasizing intensity-derived measurements across multiple
+        channels and compartments.
+    """
+    return Profile(
+        image_array=np.random.rand(16, 48, 48).astype(np.float32),
+        features={
+            "Nuclei_DNA_Intensity_MeanIntensity": 0.654,
+            "Nuclei_DNA_Intensity_MaxIntensity": 0.989,
+            "Nuclei_DNA_Intensity_StdIntensity": 0.156,
+            "Nuclei_Mito_Intensity_MeanIntensity": 0.412,
+            "Nuclei_Mito_Intensity_MaxIntensity": 0.956,
+            "Nuclei_ER_Intensity_MeanIntensity": 0.378,
+            "Cell_DNA_Intensity_MeanIntensity": 0.523,
+            "Cell_Mito_Intensity_MeanIntensity": 0.387,
+            "Cell_ER_Intensity_MeanIntensity": 0.445,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 456,
+            "Metadata_Imaging_ExposureTime": 30.0,
+            "Metadata_Microscopy_Magnification": 100.0,
+        },
+    )
+
+
+@pytest.fixture
+def texture_profile() -> Profile:
+    """Create a profile focused on texture features.
+
+    Returns
+    -------
+    Profile
+        A profile containing entropy, gabor, and contrast style texture
+        measurements.
+    """
+    return Profile(
+        image_array=np.random.rand(12, 40, 40).astype(np.float32),
+        features={
+            "Nuclei_DNA_Texture_Entropy-256-3": 5.678,
+            "Nuclei_DNA_Texture_Gabor-3-0": 0.234,
+            "Nuclei_DNA_Texture_Gabor-3-45": 0.198,
+            "Nuclei_DNA_Texture_Gabor-3-90": 0.212,
+            "Nuclei_DNA_Texture_Gabor-3-135": 0.205,
+            "Cytoplasm_Mito_Texture_Entropy-256-3": 4.892,
+            "Cytoplasm_Mito_Texture_Contrast-3": 0.456,
+            "Cell_DNA_Texture_Entropy-256-3": 6.123,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 789,
+            "Metadata_Imaging_ExposureTime": 50.0,
+        },
+    )
+
+
+@pytest.fixture
+def morphology_profile() -> Profile:
+    """Create a profile focused on area-size-shape features.
+
+    Returns
+    -------
+    Profile
+        A profile emphasizing morphology metrics such as volume, surface area,
+        and sphericity.
+    """
+    return Profile(
+        image_array=np.random.rand(20, 56, 56).astype(np.float32),
+        features={
+            "Nuclei_DNA_Areasizeshape_Volume": 1024.0,
+            "Nuclei_DNA_Areasizeshape_SurfaceArea": 512.0,
+            "Nuclei_DNA_Areasizeshape_Sphericity": 0.892,
+            "Nuclei_DNA_Areasizeshape_Solidity": 0.945,
+            "Nuclei_DNA_Areasizeshape_Eccentricity": 0.342,
+            "Nuclei_DNA_Areasizeshape_EulerCharacteristic": 1.0,
+            "Cell_DNA_Areasizeshape_Volume": 4096.0,
+            "Cell_DNA_Areasizeshape_SurfaceArea": 2048.0,
+            "Cell_DNA_Areasizeshape_Sphericity": 0.756,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 321,
+            "Metadata_Storage_FilePath": "/data/images/cell_002.tif",
+            "Metadata_Imaging_ExposureTime": 40.0,
+        },
+    )
+
+
+@pytest.fixture
+def colocalization_profile() -> Profile:
+    """Create a profile focused on colocalization features.
+
+    Returns
+    -------
+    Profile
+        A profile containing correlation and overlap metrics for paired channel
+        combinations.
+    """
+    return Profile(
+        image_array=np.random.rand(14, 44, 44).astype(np.float32),
+        features={
+            "Cell_DNA-Mito_Colocalization_Correlation": 0.623,
+            "Cell_DNA-Mito_Colocalization_Overlap": 0.456,
+            "Cell_DNA-ER_Colocalization_Correlation": 0.234,
+            "Cell_DNA-ER_Colocalization_Overlap": 0.178,
+            "Cell_Mito-ER_Colocalization_Correlation": 0.567,
+            "Cell_Mito-ER_Colocalization_Overlap": 0.389,
+            "Nuclei_DNA-Mito_Colocalization_Correlation": 0.345,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 654,
+            "Metadata_Imaging_ExposureTime": 60.0,
+        },
+    )
+
+
+@pytest.fixture
+def granularity_profile() -> Profile:
+    """Create a profile focused on granularity features.
+
+    Returns
+    -------
+    Profile
+        A profile with a sequence of granularity spectrum measurements.
+    """
+    return Profile(
+        image_array=np.random.rand(16, 48, 48).astype(np.float32),
+        features={
+            "Nuclei_DNA_Granularity_Spectrum-1": 0.234,
+            "Nuclei_DNA_Granularity_Spectrum-2": 0.256,
+            "Nuclei_DNA_Granularity_Spectrum-3": 0.289,
+            "Nuclei_DNA_Granularity_Spectrum-4": 0.312,
+            "Nuclei_DNA_Granularity_Spectrum-5": 0.334,
+            "Nuclei_DNA_Granularity_Spectrum-6": 0.345,
+            "Nuclei_DNA_Granularity_Spectrum-7": 0.356,
+            "Nuclei_DNA_Granularity_Spectrum-8": 0.362,
+            "Nuclei_DNA_Granularity_Spectrum-9": 0.365,
+            "Nuclei_DNA_Granularity_Spectrum-10": 0.367,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 987,
+            "Metadata_Imaging_ExposureTime": 35.0,
+        },
+    )
+
+
+@pytest.fixture
+def neighbors_profile() -> Profile:
+    """Create a profile focused on neighbor-based features.
+
+    Returns
+    -------
+    Profile
+        A profile with adjacency, neighbor count, and nearest-distance style
+        neighborhood features.
+    """
+    return Profile(
+        image_array=np.random.rand(10, 32, 32).astype(np.float32),
+        features={
+            "Nuclei_NoChannel_Neighbors_AdjacentCount": 6.0,
+            "Nuclei_NoChannel_Neighbors_NumberOfNeighbors": 8.0,
+            "Nuclei_NoChannel_Neighbors_DistanceClosestNeighbor": 15.234,
+            "Nuclei_NoChannel_Neighbors_PercentTouching": 0.35,
+            "Cell_NoChannel_Neighbors_AdjacentCount": 12.0,
+            "Cell_NoChannel_Neighbors_NumberOfNeighbors": 15.0,
+        },
+        metadata={
+            "Metadata_Object_ObjectID": 110,
+            "Metadata_Neighbors_AdjacentCount": 6,
+        },
+    )
+
+
+@pytest.fixture
+def complete_profile() -> Profile:
+    """Create a comprehensive profile spanning feature categories.
+
+    Returns
+    -------
+    Profile
+        A profile that mixes intensity, texture, morphology, granularity,
+        colocalization, and neighbor-derived measurements.
+    """
+    return Profile(
+        image_array=np.random.rand(24, 64, 64).astype(np.float32),
+        features={
+            # Intensity
+            "Nuclei_DNA_Intensity_MeanIntensity": 0.543,
+            "Nuclei_DNA_Intensity_MaxIntensity": 0.987,
+            "Cell_DNA_Intensity_MeanIntensity": 0.421,
+            # Texture
+            "Nuclei_DNA_Texture_Entropy-256-3": 5.892,
+            "Nuclei_DNA_Texture_Gabor-3-0": 0.234,
+            # Morphology
+            "Nuclei_DNA_Areasizeshape_Volume": 1536.0,
+            "Nuclei_DNA_Areasizeshape_Sphericity": 0.865,
+            # Granularity
+            "Nuclei_DNA_Granularity_Spectrum-5": 0.334,
+            # Colocalization
+            "Cell_DNA-Mito_Colocalization_Correlation": 0.512,
+            # Neighbors
+            "Nuclei_NoChannel_Neighbors_AdjacentCount": 5.0,
+        },
+        metadata={
+            "Metadata_Storage_FilePath": "/data/images/sample_001.tif",
+            "Metadata_Object_ObjectID": 100,
+            "Metadata_Biology_CellType": "Neuron",
+            "Metadata_Imaging_ExposureTime": 45.0,
+            "Metadata_Microscopy_Magnification": 63.0,
+            "Metadata_Experiment_Treatment": "Drug_A",
+            "Metadata_Location_CentroidX": 156.5,
+            "Metadata_Location_CentroidY": 234.2,
+            "Metadata_Location_CentroidZ": 12.8,
+        },
+    )
+
+
+# COLLECTION FIXTURES: Groups of profiles
+
+
+@pytest.fixture
+def all_profiles(
+    minimal_profile: Profile,
+    small_image_profile: Profile,
+    medium_image_profile: Profile,
+    large_image_profile: Profile,
+) -> list[Profile]:
+    """Collect profiles spanning multiple image sizes.
+
+    Parameters
+    ----------
+    minimal_profile : Profile
+        Fixture that provides the minimal valid profile.
+    small_image_profile : Profile
+        Fixture that provides a small image profile.
+    medium_image_profile : Profile
+        Fixture that provides a medium image profile.
+    large_image_profile : Profile
+        Fixture that provides a large image profile.
+
+    Returns
+    -------
+    list[Profile]
+        A list of profiles ordered to provide size diversity for parameterized
+        tests.
+    """
+    return [
+        small_image_profile,
+        minimal_profile,
+        medium_image_profile,
+        large_image_profile,
+    ]
+
+
+@pytest.fixture
+def all_feature_type_profiles(
+    request: pytest.FixtureRequest,
+) -> list[Profile]:
+    """Collect profiles grouped by feature category.
+
+    Parameters
+    ----------
+    request : pytest.FixtureRequest
+        Pytest fixture request object used to resolve fixtures dynamically by
+        name.
+
+    Returns
+    -------
+    list[Profile]
+        Profiles specialized for intensity, texture, morphology,
+        colocalization, granularity, and neighbor-based tests.
+    """
+    fixture_names = [
+        "intensity_profile",
+        "texture_profile",
+        "morphology_profile",
+        "colocalization_profile",
+        "granularity_profile",
+        "neighbors_profile",
+    ]
+    return [request.getfixturevalue(fixture_name) for fixture_name in fixture_names]
+
+
+# PARAMETERIZED FIXTURE DATA
+
+
+@pytest.fixture(
+    params=[
+        (4, 8, 8),
+        (8, 16, 16),
+        (12, 24, 24),
+        (16, 32, 32),
+        (20, 40, 40),
+    ]
+)
+def varying_image_sizes(request: pytest.FixtureRequest) -> tuple[int, int, int]:
+    """Provide parameterized image dimensions for profile generation.
+
+    Parameters
+    ----------
+    request : pytest.FixtureRequest
+        Pytest fixture request containing the current ``params`` tuple.
+
+    Returns
+    -------
+    tuple[int, int, int]
+        A 3-tuple ``(z, y, x)`` describing image dimensions for test cases.
+    """
+    return request.param
+
+
+@pytest.fixture
+def profile_with_varying_size(
+    varying_image_sizes: tuple[int, int, int],
+) -> Profile:
+    """Create a profile from the current parameterized image size.
+
+    Parameters
+    ----------
+    varying_image_sizes : tuple[int, int, int]
+        Image dimensions as ``(z, y, x)`` supplied by
+        ``varying_image_sizes``.
+
+    Returns
+    -------
+    Profile
+        A profile containing a random 3D float32 image of the requested shape,
+        a minimal feature set, and minimal metadata.
+    """
+    z, y, x = varying_image_sizes
+    return Profile(
+        image_array=np.random.rand(z, y, x).astype(np.float32),
+        features={"Nuclei_DNA_Intensity_MeanIntensity": 0.512},
+        metadata={"Metadata_Object_ObjectID": 1},
+    )
diff --git a/tests/test_contracts.py b/tests/test_contracts.py
index b454645..e569c31 100644
--- a/tests/test_contracts.py
+++ b/tests/test_contracts.py
@@ -1,39 +1,620 @@
-"""Tests for core data contracts."""
+"""Tests for data contract validation in zedprofiler.contracts."""
 
 from __future__ import annotations
 
-from dataclasses import FrozenInstanceError
+from pathlib import Path
 
 import numpy as np
 import pytest
+from beartype.roar import BeartypeCallHintParamViolation
+
+# Skip tests if core optional dependencies aren't installed in this environment
+pytest.importorskip("pydantic")
+pytest.importorskip("pandera")
+from pydantic import ValidationError
 
 from zedprofiler.contracts import (
-    MULTI_CHANNEL_CONTRACT,
-    SINGLE_CHANNEL_CONTRACT,
-    FloatArray,
-    ImageArrayContract,
-    IntArray,
+    ColumnNameModel,
+    ExpectedFeatureNameValues,
+    FeatureDictModel,
+    ImageArrayModel,
+    MetadataDictModel,
+    ReturnSchemaModel,
+    create_image_array_schema,
+    validate_column_name_schema,
+    validate_column_name_with_pydantic,
+    validate_image_array_shape_contracts,
+    validate_image_array_type_contracts,
+    validate_image_with_pydantic,
+    validate_return_schema_contract,
+    validate_return_with_pydantic,
 )
+from zedprofiler.exceptions import ContractError
+
+
+@pytest.fixture
+def expected_values_config_path(tmp_path: Path) -> Path:
+    """Create a temporary expected-values TOML configuration file."""
+    config_path = tmp_path / "config.toml"
+    config_path.write_text(
+        """
+[expected_values]
+compartments = ["Nuclei", "Cytoplasm", "Cell", "Organoid"]
+channels = ["DNA", "AGP", "ER", "Mito"]
+""".strip()
+    )
+    return config_path
+
+
+def test_validate_image_array_shape_contracts_accepts_valid_3d_array() -> None:
+    arr = np.zeros((8, 16, 16), dtype=float)
+
+    assert validate_image_array_shape_contracts(arr) is True
+
+
+def test_validate_image_array_shape_contracts_accepts_single_channel_4d_array() -> None:
+    arr = np.zeros((1, 8, 16, 16), dtype=float)
+
+    assert validate_image_array_shape_contracts(arr) is True
+
+
+def test_validate_image_array_shape_contracts_rejects_2d_array() -> None:
+    arr = np.zeros((16, 16), dtype=float)
+
+    with pytest.raises(ContractError):
+        validate_image_array_shape_contracts(arr)
+
+
+def test_validate_image_array_shape_contracts_rejects_multichannel_4d_array() -> None:
+    arr = np.zeros((2, 8, 16, 16), dtype=float)
+
+    with pytest.raises(ContractError):
+        validate_image_array_shape_contracts(arr)
+
+
+def test_validate_image_array_shape_contracts_rejects_multichannel_5d_array() -> None:
+    arr = np.zeros((2, 2, 4, 8, 8), dtype=float)
+
+    with pytest.raises(ContractError):
+        validate_image_array_shape_contracts(arr)
+
+
+def test_validate_image_array_shape_contracts_rejects_all_singleton_3d_array() -> None:
+    arr = np.zeros((1, 1, 1), dtype=float)
+
+    with pytest.raises(ContractError):
+        validate_image_array_shape_contracts(arr)
+
+
+def test_validate_image_array_type_contracts_accepts_numeric_array() -> None:
+    arr = np.zeros((8, 8, 8), dtype=np.float32)
+
+    assert validate_image_array_type_contracts(arr) is True
+
+
+def test_validate_image_array_type_contracts_rejects_non_numpy_array() -> None:
+    arr = [[1, 2], [3, 4]]
+
+    # Beartype catches type errors before function execution
+    with pytest.raises(BeartypeCallHintParamViolation):
+        validate_image_array_type_contracts(arr)  # type: ignore[arg-type]
+
+
+def test_validate_image_array_type_contracts_rejects_non_numeric_dtype() -> None:
+    arr = np.array([["a", "b"], ["c", "d"]], dtype=str)
+
+    with pytest.raises(ContractError):
+        validate_image_array_type_contracts(arr)
+
+
+def test_expected_values_loads_config_and_adds_nochannel(
+    expected_values_config_path: Path,
+) -> None:
+    values = ExpectedFeatureNameValues(expected_values_config_path)
+
+    assert "Nuclei" in values.compartments
+    assert "DNA" in values.channels
+    assert "NoChannel" in values.channels
+    assert "Intensity" in values.features
+
+
+def test_expected_values_to_dict_returns_expected_keys(
+    expected_values_config_path: Path,
+) -> None:
+    values = ExpectedFeatureNameValues(expected_values_config_path).to_dict()
+
+    assert set(values.keys()) == {"compartments", "channels", "features"}
+
+
+def test_validate_column_name_schema_accepts_valid_feature_column(
+    expected_values_config_path: Path,
+) -> None:
+    valid_name = "Nuclei_DNA_Intensity_MeanIntensity"
+
+    assert validate_column_name_schema(valid_name, expected_values_config_path) is True
+
+
+def test_validate_column_name_schema_accepts_valid_metadata_column(
+    expected_values_config_path: Path,
+) -> None:
+    valid_name = "Metadata_Storage_FilePath"
+
+    assert validate_column_name_schema(valid_name, expected_values_config_path) is True
+
+
+def test_validate_column_name_schema_rejects_non_string_column_name(
+    expected_values_config_path: Path,
+) -> None:
+    # Beartype catches type errors before function execution
+    with pytest.raises(BeartypeCallHintParamViolation):
+        validate_column_name_schema(123, expected_values_config_path)  # type: ignore[arg-type]
+
+
+def test_validate_column_name_schema_rejects_non_metadata_with_too_few_parts(
+    expected_values_config_path: Path,
+) -> None:
+    invalid_name = "Nuclei_DNA_Intensity"
+
+    with pytest.raises(ContractError):
+        validate_column_name_schema(invalid_name, expected_values_config_path)
+
+
+def test_validate_column_name_schema_rejects_metadata_with_too_few_parts(
+    expected_values_config_path: Path,
+) -> None:
+    invalid_name = "Metadata_Storage"
+
+    with pytest.raises(ContractError):
+        validate_column_name_schema(invalid_name, expected_values_config_path)
+
+
+def test_validate_column_name_schema_rejects_unknown_compartment(
+    expected_values_config_path: Path,
+) -> None:
+    invalid_name = "Nucleus_DNA_Intensity_MeanIntensity"
+
+    with pytest.raises(ContractError):
+        validate_column_name_schema(invalid_name, expected_values_config_path)
+
+
+def test_validate_column_name_schema_rejects_unknown_channel(
+    expected_values_config_path: Path,
+) -> None:
+    invalid_name = "Nuclei_GFP_Intensity_MeanIntensity"
+
+    with pytest.raises(ContractError):
+        validate_column_name_schema(invalid_name, expected_values_config_path)
+
+
+def test_validate_column_name_schema_rejects_unknown_feature(
+    expected_values_config_path: Path,
+) -> None:
+    invalid_name = "Nuclei_DNA_UnknownFeature_MeanIntensity"
+
+    with pytest.raises(ContractError):
+        validate_column_name_schema(invalid_name, expected_values_config_path)
+
+
+def test_validate_return_schema_contract_accepts_valid_result() -> None:
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"Nuclei_DNA_Intensity_MeanIntensity": 0.5},
+        "metadata": {"Metadata_Object_ObjectID": 1},
+    }
+
+    assert validate_return_schema_contract(result) is True
+
+
+def test_validate_return_schema_contract_rejects_non_dict_result() -> None:
+    # Beartype catches type errors before function execution
+    with pytest.raises(BeartypeCallHintParamViolation):
+        validate_return_schema_contract(["not", "a", "dict"])  # type: ignore[arg-type]
+
+
+def test_validate_return_schema_contract_rejects_missing_required_key() -> None:
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"Nuclei_DNA_Intensity_MeanIntensity": 0.5},
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_schema_contract(result)  # type: ignore[arg-type]
+
+
+def test_validate_return_schema_contract_rejects_extra_key() -> None:
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"Nuclei_DNA_Intensity_MeanIntensity": 0.5},
+        "metadata": {"Metadata_Object_ObjectID": 1},
+        "extra": 123,
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_schema_contract(result)
+
+
+def test_validate_return_schema_contract_rejects_wrong_key_order() -> None:
+    # Dict insertion order is deterministic in Python 3.7+; this order is intentional.
+    result = {
+        "features": {"Nuclei_DNA_Intensity_MeanIntensity": 0.5},
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "metadata": {"Metadata_Object_ObjectID": 1},
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_schema_contract(result)
+
+
+def test_validate_return_schema_contract_rejects_invalid_image_array_type() -> None:
+    result = {
+        "image_array": [[1, 2], [3, 4]],
+        "features": {"Nuclei_DNA_Intensity_MeanIntensity": 0.5},
+        "metadata": {"Metadata_Object_ObjectID": 1},
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_schema_contract(result)
+
+
+def test_validate_return_schema_contract_rejects_invalid_features_type() -> None:
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": ["not", "a", "dict"],
+        "metadata": {"Metadata_Object_ObjectID": 1},
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_schema_contract(result)
+
+
+def test_validate_return_schema_contract_rejects_invalid_metadata_type() -> None:
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"Nuclei_DNA_Intensity_MeanIntensity": 0.5},
+        "metadata": ["not", "a", "dict"],
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_schema_contract(result)
+
+
+# ============================================================================
+# Tests for Pydantic Models
+# ============================================================================
+
+
+def test_pydantic_image_array_model_accepts_valid_array() -> None:
+    """Test ImageArrayModel validates correct numpy arrays."""
+    arr = np.zeros((10, 20, 30), dtype=np.float32)
+    model = ImageArrayModel(array=arr)
+
+    assert isinstance(model.array, np.ndarray)
+    assert model.array.shape == (10, 20, 30)
+
+
+def test_pydantic_image_array_model_rejects_non_numpy_array() -> None:
+    """Test ImageArrayModel rejects non-numpy objects."""
+    with pytest.raises(ValidationError):
+        ImageArrayModel(array=[1, 2, 3])  # type: ignore[arg-type]
+
+
+def test_pydantic_image_array_model_rejects_non_numeric_dtype() -> None:
+    """Test ImageArrayModel rejects non-numeric dtypes."""
+    arr = np.array(["a", "b", "c"], dtype=str)
+    with pytest.raises(ValidationError):
+        ImageArrayModel(array=arr)
+
+
+def test_pydantic_image_array_model_rejects_2d_array() -> None:
+    """Test ImageArrayModel rejects 2D arrays."""
+    arr = np.zeros((16, 16), dtype=np.float32)
+    with pytest.raises(ValidationError):
+        ImageArrayModel(array=arr)
+
+
+def test_pydantic_image_array_model_rejects_multichannel_4d_array() -> None:
+    """Test ImageArrayModel rejects multi-channel 4D arrays."""
+    arr = np.zeros((2, 8, 16, 16), dtype=np.float32)
+    with pytest.raises(ValidationError):
+        ImageArrayModel(array=arr)
+
+
+def test_pydantic_feature_dict_model_accepts_valid_dict() -> None:
+    """Test FeatureDictModel validates correct dictionaries."""
+    features = {"feature_1": 0.5, "feature_2": 1.2}
+    model = FeatureDictModel(features=features)
+
+    assert model.features == features
+
+
+def test_pydantic_feature_dict_model_accepts_empty_dict() -> None:
+    """Test FeatureDictModel accepts empty dictionaries."""
+    model = FeatureDictModel(features={})
+
+    assert model.features == {}
+
+
+def test_pydantic_feature_dict_model_rejects_non_dict() -> None:
+    """Test FeatureDictModel rejects non-dictionary objects."""
+    with pytest.raises(ValidationError):
+        FeatureDictModel(features=["not", "a", "dict"])  # type: ignore[arg-type]
+
+
+def test_pydantic_metadata_dict_model_accepts_valid_dict() -> None:
+    """Test MetadataDictModel validates correct dictionaries."""
+    metadata = {"key_1": "value_1", "key_2": "value_2"}
+    model = MetadataDictModel(metadata=metadata)
+
+    assert model.metadata == metadata
+
+
+def test_pydantic_metadata_dict_model_rejects_non_dict() -> None:
+    """Test MetadataDictModel rejects non-dictionary objects."""
+    with pytest.raises(ValidationError):
+        MetadataDictModel(metadata="not a dict")  # type: ignore[arg-type]
+
+
+def test_pydantic_return_schema_model_accepts_valid_result() -> None:
+    """Test ReturnSchemaModel validates correct return dictionaries."""
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"feature_1": 0.5},
+        "metadata": {"key": "value"},
+    }
+    model = ReturnSchemaModel(result=result)
+
+    assert isinstance(model.result["image_array"], np.ndarray)
+    assert isinstance(model.result["features"], dict)
+    assert isinstance(model.result["metadata"], dict)
+
+
+def test_pydantic_return_schema_model_rejects_wrong_key_order() -> None:
+    """Test ReturnSchemaModel enforces key order."""
+    result = {
+        "features": {"feature_1": 0.5},
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "metadata": {"key": "value"},
+    }
+
+    with pytest.raises(ValidationError):
+        ReturnSchemaModel(result=result)
+
+
+def test_pydantic_return_schema_model_rejects_missing_key() -> None:
+    """Test ReturnSchemaModel requires all keys."""
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"feature_1": 0.5},
+    }
+
+    with pytest.raises(ValidationError):
+        ReturnSchemaModel(result=result)
+
+
+def test_pydantic_return_schema_model_rejects_extra_key() -> None:
+    """Test ReturnSchemaModel rejects extra keys."""
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"feature_1": 0.5},
+        "metadata": {"key": "value"},
+        "extra": "not allowed",
+    }
+
+    with pytest.raises(ValidationError):
+        ReturnSchemaModel(result=result)
+
+
+def test_pydantic_column_name_model_parses_valid_feature_column() -> None:
+    """Test ColumnNameModel parses feature column names."""
+    model = ColumnNameModel(column_name="Nuclei_DNA_Intensity_MeanIntensity")
+
+    assert model.column_name == "Nuclei_DNA_Intensity_MeanIntensity"
+    assert model.compartment == "Nuclei"
+    assert model.channel == "DNA"
+    assert model.feature == "Intensity"
+
+
+def test_pydantic_column_name_model_parses_metadata_column() -> None:
+    """Test ColumnNameModel parses metadata column names."""
+    model = ColumnNameModel(column_name="Metadata_Storage_FilePath")
+
+    assert model.column_name == "Metadata_Storage_FilePath"
+    # Metadata columns don't parse compartment/channel/feature
+    assert model.compartment is None
+
+
+def test_pydantic_column_name_model_rejects_non_string() -> None:
+    """Test ColumnNameModel rejects non-string column names."""
+    with pytest.raises(ValidationError):
+        ColumnNameModel(column_name=123)  # type: ignore[arg-type]
+
+
+def test_pydantic_column_name_model_rejects_too_few_parts() -> None:
+    """Test ColumnNameModel rejects names with too few parts."""
+    with pytest.raises(ValidationError):
+        ColumnNameModel(column_name="Nuclei_DNA_Intensity")
+
+
+def test_pydantic_column_name_model_rejects_metadata_too_few_parts() -> None:
+    """Test ColumnNameModel rejects metadata names with too few parts."""
+    with pytest.raises(ValidationError):
+        ColumnNameModel(column_name="Metadata_Storage")
+
+
+# ============================================================================
+# Tests for Helper Functions with Pydantic
+# ============================================================================
+
+
+def test_validate_image_with_pydantic_accepts_valid_array() -> None:
+    """Test validate_image_with_pydantic helper."""
+    arr = np.zeros((10, 20, 30), dtype=np.float32)
+    model = validate_image_with_pydantic(arr)
+
+    assert isinstance(model, ImageArrayModel)
+    assert model.array.shape == (10, 20, 30)
+
+
+def test_validate_image_with_pydantic_raises_contract_error() -> None:
+    """Test validate_image_with_pydantic raises ContractError on invalid input."""
+    arr = np.zeros((16, 16), dtype=np.float32)
+
+    with pytest.raises(ContractError):
+        validate_image_with_pydantic(arr)
+
+
+def test_validate_return_with_pydantic_accepts_valid_result() -> None:
+    """Test validate_return_with_pydantic helper."""
+    result = {
+        "image_array": np.zeros((4, 8, 8), dtype=np.float32),
+        "features": {"feature": 0.5},
+        "metadata": {"key": "value"},
+    }
+    model = validate_return_with_pydantic(result)
+
+    assert isinstance(model, ReturnSchemaModel)
+
+
+def test_validate_return_with_pydantic_raises_contract_error() -> None:
+    """Test validate_return_with_pydantic raises ContractError on invalid input."""
+    result = {
+        "image_array": [[1, 2], [3, 4]],
+        "features": {"feature": 0.5},
+        "metadata": {"key": "value"},
+    }
+
+    with pytest.raises(ContractError):
+        validate_return_with_pydantic(result)
+
+
+def test_validate_column_name_with_pydantic_accepts_valid_name() -> None:
+    """Test validate_column_name_with_pydantic helper."""
+    model = validate_column_name_with_pydantic("Nuclei_DNA_Intensity_Mean")
+
+    assert isinstance(model, ColumnNameModel)
+    assert model.compartment == "Nuclei"
+
+
+def test_validate_column_name_with_pydantic_raises_contract_error() -> None:
+    """Test validate_column_name_with_pydantic raises ContractError."""
+    with pytest.raises(ContractError):
+        validate_column_name_with_pydantic("Nuclei_DNA_Intensity")
+
+
+# ============================================================================
+# Tests for Pandera Schemas
+# ============================================================================
+
+
+def test_create_image_array_schema_returns_series_schema() -> None:
+    """Test create_image_array_schema returns a valid Pandera schema."""
+    schema = create_image_array_schema()
+
+    assert schema is not None
+    assert hasattr(schema, "validate")
+
+
+def test_pandera_schema_rejects_non_numeric_array() -> None:
+    """Test Pandera schema rejects non-numeric arrays."""
+    schema = create_image_array_schema()
+
+    arr = np.array(["a", "b", "c"], dtype=str)
+
+    # Should raise SchemaError
+    with pytest.raises(Exception):  # SchemaError from pandera
+        schema.validate(arr)
+
+
+# ============================================================================
+# Tests for Beartype Runtime Type Checking
+# ============================================================================
+
+
+def test_beartype_enforces_correct_array_type() -> None:
+    """Test beartype validates function argument types."""
+    arr = np.zeros((8, 16, 16), dtype=np.float32)
+
+    # Should not raise
+    assert validate_image_array_type_contracts(arr) is True
+
+
+def test_beartype_catches_wrong_return_dict_type() -> None:
+    """Test beartype catches wrong dict type in validate_return_schema_contract."""
+    # Beartype enforces type hints at runtime, so invalid types are caught
+    # before execution
+    with pytest.raises(BeartypeCallHintParamViolation):
+        validate_return_schema_contract(["not", "a", "dict"])  # type: ignore[arg-type]
+
+
+def test_beartype_enforces_pathlib_path_type(
+    expected_values_config_path: Path,
+) -> None:
+    """Test beartype validates pathlib.Path argument."""
+    # Beartype would reject non-Path objects
+    with pytest.raises(BeartypeCallHintParamViolation):
+        validate_column_name_schema(
+            "Nuclei_DNA_Intensity_Mean",
+            "not a path",  # type: ignore[arg-type]
+        )
+
+
+# ============================================================================
+# Integration Tests
+# ============================================================================
+
+
+def test_full_validation_pipeline_passes() -> None:
+    """Test complete validation pipeline with all components."""
+    # Create valid array
+    arr = np.zeros((10, 20, 30), dtype=np.float32)
+
+    # Validate shape and type
+    assert validate_image_array_shape_contracts(arr) is True
+    assert validate_image_array_type_contracts(arr) is True
+
+    # Validate with Pydantic
+    img_model = validate_image_with_pydantic(arr)
+    assert img_model.array.shape == (10, 20, 30)
+
+    # Create valid return
+    result = {
+        "image_array": arr,
+        "features": {"feature_1": 0.5, "feature_2": 1.2},
+        "metadata": {"processor": "test"},
+    }
+
+    # Validate return schema
+    assert validate_return_schema_contract(result) is True
 
+    # Validate with Pydantic
+    return_model = validate_return_with_pydantic(result)
+    assert "image_array" in return_model.result
+    assert "features" in return_model.result
+    assert "metadata" in return_model.result
 
-def test_contract_constants_have_expected_values() -> None:
-    """Single- and multi-channel constants should match documented shapes."""
-    assert ImageArrayContract(dimensions=3, order="zyx") == SINGLE_CHANNEL_CONTRACT
-    assert ImageArrayContract(dimensions=4, order="czyx") == MULTI_CHANNEL_CONTRACT
 
+def test_validation_error_messages_are_descriptive() -> None:
+    """Test that validation errors provide clear messages."""
+    # Test with 2D array
+    arr = np.zeros((16, 16), dtype=np.float32)
 
-def test_image_array_contract_is_frozen() -> None:
-    """Contracts are immutable to prevent accidental mutation in pipelines."""
-    contract = ImageArrayContract(dimensions=3, order="zyx")
+    try:
+        validate_image_array_shape_contracts(arr)
+        pytest.fail("Expected ContractError")
+    except ContractError as e:
+        assert "dimensions" in str(e)
+        assert "2" in str(e)
 
-    with pytest.raises(FrozenInstanceError):
-        contract.dimensions = 4  # type: ignore[misc]
 
+def test_column_name_validation_with_pydantic_and_schema(
+    expected_values_config_path: Path,
+) -> None:
+    """Test column name validation combines Pydantic and schema checking."""
+    valid_name = "Nuclei_DNA_Intensity_MeanIntensity"
 
-def test_array_type_aliases_accept_numpy_ndarray_runtime_values() -> None:
-    """Type aliases should correspond to ndarray values at runtime."""
-    float_values: FloatArray = np.array([1.0, 2.0, 3.0], dtype=float)
-    int_values: IntArray = np.array([1, 2, 3], dtype=int)
+    # Pydantic model should parse successfully
+    col_model = validate_column_name_with_pydantic(valid_name)
+    assert col_model.compartment == "Nuclei"
 
-    assert isinstance(float_values, np.ndarray)
-    assert isinstance(int_values, np.ndarray)
+    # Full schema validation should pass
+    assert validate_column_name_schema(valid_name, expected_values_config_path) is True
diff --git a/tests/test_data_profiles.py b/tests/test_data_profiles.py
new file mode 100644
index 0000000..2445afd
--- /dev/null
+++ b/tests/test_data_profiles.py
@@ -0,0 +1,28 @@
+"""Test data profiles utilities for ZedProfiler feature extraction.
+
+This module provides the TestProfile dataclass for working with test data.
+All fixture definitions are in conftest.py for easy pytest discovery and reuse.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass
+class Profile:
+    """A complete test profile with image data, features, and metadata."""
+
+    image_array: np.ndarray
+    features: dict
+    metadata: dict
+
+    def to_dict(self) -> dict:
+        """Convert profile to dictionary format."""
+        return {
+            "image_array": self.image_array,
+            "features": self.features,
+            "metadata": self.metadata,
+        }
diff --git a/tests/test_profile_fixtures.py b/tests/test_profile_fixtures.py
new file mode 100644
index 0000000..986e20e
--- /dev/null
+++ b/tests/test_profile_fixtures.py
@@ -0,0 +1,160 @@
+"""Test suite demonstrating the usage of test data profile fixtures.
+
+This module shows how to use the available test data profiles in your tests.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from test_data_profiles import Profile
+
+EXPECTED_IMAGE_NDIM = 3
+EXPECTED_ALL_PROFILES_COUNT = 4
+EXPECTED_FEATURE_TYPE_PROFILES_COUNT = 6
+
+
+class ProfileFixtures:
+    """Tests demonstrating profile fixture usage."""
+
+    def test_minimal_profile(self, minimal_profile: Profile) -> None:
+        """Verify minimal profile has correct structure."""
+        assert isinstance(minimal_profile.image_array, np.ndarray)
+        assert minimal_profile.image_array.ndim == EXPECTED_IMAGE_NDIM
+        assert len(minimal_profile.features) == 0
+        assert len(minimal_profile.metadata) == 0
+
+    @pytest.mark.parametrize(
+        ("fixture_name", "expected_shape", "required_feature", "required_metadata_key"),
+        [
+            (
+                "small_image_profile",
+                (4, 8, 8),
+                "Nuclei_DNA_Intensity_MeanIntensity",
+                "Metadata_Object_ObjectID",
+            ),
+            (
+                "medium_image_profile",
+                (16, 32, 32),
+                "Nuclei_DNA_Intensity_MeanIntensity",
+                "Metadata_Object_ObjectID",
+            ),
+            (
+                "large_image_profile",
+                (32, 64, 64),
+                "Nuclei_DNA_Intensity_MeanIntensity",
+                "Metadata_Object_ObjectID",
+            ),
+        ],
+    )
+    def test_image_profiles_by_size(
+        self,
+        request: pytest.FixtureRequest,
+        fixture_name: str,
+        expected_shape: tuple[int, int, int],
+        required_feature: str,
+        required_metadata_key: str,
+    ) -> None:
+        """Verify size-specific profiles via parametrized fixture lookup."""
+        profile: Profile = request.getfixturevalue(fixture_name)
+        assert profile.image_array.shape == expected_shape
+        assert required_feature in profile.features
+        assert required_metadata_key in profile.metadata
+
+    @pytest.mark.parametrize(
+        ("feature_kind", "expected_ratio", "fixture_name"),
+        [
+            ("Intensity", 0.7, "intensity_profile"),
+            ("Texture", 0.7, "texture_profile"),
+            ("Areasizeshape", 0.7, "morphology_profile"),
+        ],
+    )
+    def test_feature_dominance_profiles(
+        self,
+        request: pytest.FixtureRequest,
+        feature_kind: str,
+        expected_ratio: float,
+        fixture_name: str,
+    ) -> None:
+        """Verify feature-focused profiles are dominated by their feature kind."""
+        profile: Profile = request.getfixturevalue(fixture_name)
+        features = profile.features
+        feature_count = sum(
+            1 for feature_name in features if feature_kind in feature_name
+        )
+        total_count = len(features)
+        assert feature_count > total_count * expected_ratio
+
+    @pytest.mark.parametrize(
+        ("fixture_name", "required_token"),
+        [
+            ("colocalization_profile", "Colocalization"),
+            ("granularity_profile", "Granularity"),
+            ("neighbors_profile", "Neighbors"),
+        ],
+    )
+    def test_single_kind_feature_profiles(
+        self,
+        request: pytest.FixtureRequest,
+        fixture_name: str,
+        required_token: str,
+    ) -> None:
+        """Verify token-specific profiles contain only their expected token."""
+        profile: Profile = request.getfixturevalue(fixture_name)
+        features = profile.features
+        assert all(required_token in feature_name for feature_name in features)
+
+    def test_complete_profile(self, complete_profile: Profile) -> None:
+        """Verify complete profile has comprehensive feature coverage."""
+        assert complete_profile.image_array.shape == (24, 64, 64)
+        features = complete_profile.features
+        assert any("Intensity" in feature_name for feature_name in features)
+        assert any("Texture" in feature_name for feature_name in features)
+        assert any("Areasizeshape" in feature_name for feature_name in features)
+        assert any("Colocalization" in feature_name for feature_name in features)
+        assert any("Granularity" in feature_name for feature_name in features)
+        assert any("Neighbors" in feature_name for feature_name in features)
+
+    def test_all_profiles_collection(self, all_profiles: list[Profile]) -> None:
+        """Verify collection fixture contains expected profiles."""
+        assert len(all_profiles) == EXPECTED_ALL_PROFILES_COUNT
+        assert all(isinstance(p, Profile) for p in all_profiles)
+        # Verify increasing sizes
+        sizes = [p.image_array.size for p in all_profiles]
+        assert sizes == sorted(sizes)
+
+    def test_all_feature_type_profiles(
+        self, all_feature_type_profiles: list[Profile]
+    ) -> None:
+        """Verify feature type collection has all feature types."""
+        assert len(all_feature_type_profiles) == EXPECTED_FEATURE_TYPE_PROFILES_COUNT
+        assert all(isinstance(p, Profile) for p in all_feature_type_profiles)
+
+
+class ProfileWithVaryingSize:
+    """Tests demonstrating parameterized profile fixtures."""
+
+    def test_varying_profile_sizes(self, profile_with_varying_size: Profile) -> None:
+        """Verify profile_with_varying_size fixture produces valid profiles."""
+        assert isinstance(profile_with_varying_size.image_array, np.ndarray)
+        assert profile_with_varying_size.image_array.ndim == EXPECTED_IMAGE_NDIM
+        assert (
+            "Nuclei_DNA_Intensity_MeanIntensity" in profile_with_varying_size.features
+        )
+        assert profile_with_varying_size.metadata["Metadata_Object_ObjectID"] == 1
+
+
+def test_profile_to_dict() -> None:
+    """Verify Profile.to_dict() method works correctly."""
+    profile = Profile(
+        image_array=np.ones((4, 8, 8)),
+        features={"test_feature": 0.5},
+        metadata={"test_meta": "value"},
+    )
+
+    profile_dict = profile.to_dict()
+    assert isinstance(profile_dict, dict)
+    assert "image_array" in profile_dict
+    assert "features" in profile_dict
+    assert "metadata" in profile_dict
+    assert np.array_equal(profile_dict["image_array"], profile.image_array)
diff --git a/tests/test_update_coverage_badge.py b/tests/test_update_coverage_badge.py
new file mode 100644
index 0000000..8cb3c8b
--- /dev/null
+++ b/tests/test_update_coverage_badge.py
@@ -0,0 +1,78 @@
+import importlib.util
+from pathlib import Path
+
+import pytest
+
+# Load the module relative to this test file
+_spec = importlib.util.spec_from_file_location(
+    "update_coverage_badge",
+    Path(__file__).parent.parent / "scripts" / "update_coverage_badge.py",
+)
+mod = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(mod)
+
+
+def test_replaces_existing_badge(tmp_path: Path) -> None:
+    readme = tmp_path / "README.md"
+    original = (
+        "# Project\n\n"
+        "[![Coverage](https://img.shields.io/badge/coverage-42%25-red)](#quality-gates)\n\n"
+        "Some text\n"
+    )
+    readme.write_text(original, encoding="utf-8")
+
+    changed = mod.update_readme_badge(readme, 85)
+    assert changed is True
+
+    updated = readme.read_text(encoding="utf-8")
+    assert "coverage-85%25-green" in updated
+    assert updated.count("[![Coverage]") == 1
+    assert "coverage-42%25-red" not in updated
+
+
+def test_inserts_badge_when_missing(tmp_path: Path) -> None:
+    readme = tmp_path / "README.md"
+    original = "# Project\nDescription line\n"
+    readme.write_text(original, encoding="utf-8")
+
+    changed = mod.update_readme_badge(readme, 60)
+    assert changed is True
+
+    updated = readme.read_text(encoding="utf-8")
+    lines = updated.splitlines()
+    # After insertion: title at 0, blank line at 1, badge at 2
+    assert lines[0] == "# Project"
+    assert lines[1] == ""
+    assert lines[2].startswith("[![Coverage]")
+    assert "coverage-60%25-yellow" in lines[2]
+
+
+def test_preserves_trailing_newline_on_insert(tmp_path: Path) -> None:
+    readme = tmp_path / "README.md"
+    original = "# Project\nDescription line\n"  # ends with newline
+    readme.write_text(original, encoding="utf-8")
+
+    changed = mod.update_readme_badge(readme, 70)
+    assert changed is True
+
+    updated = readme.read_text(encoding="utf-8")
+    assert updated.endswith("\n")
+
+
+def test_no_change_returns_false_when_badge_already_exact(tmp_path: Path) -> None:
+    readme = tmp_path / "README.md"
+    # percent 75 -> color "yellowgreen" (per thresholds)
+    badge = "[![Coverage](https://img.shields.io/badge/coverage-75%25-yellowgreen)](#quality-gates)"
+    original = f"# Project\n\n{badge}\n\nMore\n"
+    readme.write_text(original, encoding="utf-8")
+
+    changed = mod.update_readme_badge(readme, 75)
+    assert changed is False
+    assert readme.read_text(encoding="utf-8") == original
+
+
+def test_empty_readme_raises_value_error(tmp_path: Path) -> None:
+    readme = tmp_path / "README.md"
+    readme.write_text("", encoding="utf-8")
+    with pytest.raises(ValueError):
+        mod.update_readme_badge(readme, 50)
diff --git a/uv.lock b/uv.lock
index c955306..ef69623 100644
--- a/uv.lock
+++ b/uv.lock
@@ -181,8 +181,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "bioio-base" },
     { name = "dask", extra = ["array"] },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "numpy" },
     { name = "ome-types", extra = ["lxml"] },
     { name = "semver" },
 ]
@@ -198,8 +197,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "dask", extra = ["array", "distributed"] },
     { name = "fsspec" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "numpy" },
     { name = "ome-types", extra = ["pint"] },
     { name = "pint" },
     { name = "xarray" },
@@ -218,8 +216,7 @@ dependencies = [
     { name = "dask", extra = ["array"] },
     { name = "fsspec" },
     { name = "imagecodecs" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "numpy" },
     { name = "tifffile", extra = ["zarr"] },
     { name = "xarray" },
 ]
@@ -274,11 +271,11 @@ css = [
 
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+version = "2026.4.22"
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