diff --git a/.github/ISSUE_TEMPLATE/issue.yml b/.github/ISSUE_TEMPLATE/issue.yml
new file mode 100644
index 0000000..eff3c20
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/issue.yml
@@ -0,0 +1,49 @@
+# GitHub Issue template for bug reports
+name: Open a GitHub issue
+description: >
+  Please use this form to send along new ideas for content or
+  changes that might be helpful!
+
+body:
+  - type: checkboxes
+    attributes:
+      label: Is this a duplicate of an existing idea for this project?
+      description: >
+        Please make sure to search in the
+        [issues](https://github.com/WayScience/CytoDataFrame/issues) first
+        to see whether the same issue was reported already.
+        If you find an existing issue, please don't hesitate to comment
+        on it or add a reaction to existing content!
+      options:
+        - label: >
+            I found no existing
+            [issues](https://github.com/WayScience/CytoDataFrame/issues)
+            covering this topic.
+          required: true
+
+  - type: textarea
+    id: description
+    attributes:
+      label: What is your idea?
+      description: >
+        Please provide a specific description of what you'd like to see
+        including the context and what the result might look like.
+      placeholder: >
+        For example: "When x happens I see y.
+        The following might be a good way to address this ..."
+    validations:
+      required: true
+
+  - type: checkboxes
+    attributes:
+      label: Would you like to work on a solution for this?
+      description: >
+        This is a community-driven project and we
+        love new contributors (including through opening or adding to issues)!
+        This is an optional check to help us understand your interest to be
+        involved (especially if you already have a good understanding
+        of how to implement it).
+        We are happy to guide you in the contribution process and please
+        don't hesitate to reach out for help along the way.
+      options:
+        - label: Yes I am willing to submit a PR for this!
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000..9f03218
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,37 @@
+<!-- _modified from [EmbeddedArtistry](https://embeddedartistry.com/blog/2017/08/04/a-github-pull-request-template-for-your-projects/)_
+_referenced with modifications from [pycytominer](https://github.com/cytomining/pycytominer/blob/master/.github/PULL_REQUEST_TEMPLATE.md)_ -->
+
+# Description
+
+<!--
+Thank you so much for your contribution to CytoDataFrame!
+
+Please _succinctly_ summarize your proposed change.
+Namely, consider addressing the following questions:
+
+- What motivated you to open this pull request?
+- Were there any special adjustments you had to make to complete the work?
+- Are there any issues which are related to this pull request (you may used a `#<digit>` to reference GitHub issues as links within this description)?
+
+Also, if you haven't already, please use `pre-commit run --all-files` to help check your files using this project's pre-commit configuration.
+Pre-commit checks will automatically run as part of opening this pull request and we seek to ensure all checks pass before merging changes.
+-->
+
+## What kind of change(s) are included?
+
+- [ ] Documentation (changes docs or other related content)
+- [ ] Bug fix (fixes an issue).
+- [ ] Enhancement (adds functionality).
+- [ ] Breaking change (these changes would cause existing functionality to not work as expected).
+
+# Checklist
+
+Please ensure that all boxes are checked before indicating that this pull request is ready for review.
+
+- [ ] I have read and followed the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines.
+- [ ] I have searched for existing content to ensure this is not a duplicate.
+- [ ] I have performed a self-review of these additions (including spelling, grammar, and related).
+- [ ] These changes pass all pre-commit checks.
+- [ ] I have added comments to my code to help provide understanding
+- [ ] I have added a test which covers the code changes found within this PR
+- [ ] I have deleted all non-relevant text in this pull request template.
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..9c752b0
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,16 @@
+# GitHub Dependabot configuration
+# Note: there is no interaction between this
+# configuration and dependabot security updates.
+# See here for more information:
+# https://docs.github.com/en/code-security/dependabot/dependabot-security-updates/about-dependabot-security-updates#about-dependabot-security-updates
+
+version: 2
+updates:
+  # GitHub Actions checks
+  # See here for more information:
+  # https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuring-dependabot-version-updates
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      # Check for updates to GitHub Actions every week
+      interval: "weekly"
diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml
new file mode 100644
index 0000000..3d8f4ed
--- /dev/null
+++ b/.github/release-drafter.yml
@@ -0,0 +1,21 @@
+---
+# template configuration for release-drafter
+# see: https://github.com/release-drafter/release-drafter
+name-template: 'v$RESOLVED_VERSION'
+tag-template: 'v$RESOLVED_VERSION'
+version-resolver:
+  major:
+    labels:
+      - 'release-major'
+  minor:
+    labels:
+      - 'release-minor'
+  patch:
+    labels:
+      - 'release-patch'
+  default: patch
+change-template: '- $TITLE (@$AUTHOR via #$NUMBER)'
+template: |
+  ## Changes
+
+  $CHANGES
diff --git a/.github/workflows/draft-release.yml b/.github/workflows/draft-release.yml
new file mode 100644
index 0000000..4847e3e
--- /dev/null
+++ b/.github/workflows/draft-release.yml
@@ -0,0 +1,23 @@
+---
+# workflow for drafting releases on GitHub
+# see: https://github.com/release-drafter/release-drafter
+name: release drafter
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  draft_release:
+    permissions:
+      # write permission is required to create a github release
+      contents: write
+      # write permission is required for autolabeler
+      # otherwise, read permission is required at least
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      - uses: release-drafter/release-drafter@v6
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/publish-docs.yml b/.github/workflows/publish-docs.yml
new file mode 100644
index 0000000..133c00f
--- /dev/null
+++ b/.github/workflows/publish-docs.yml
@@ -0,0 +1,50 @@
+---
+# used for publishing documentation on push to main or published release
+name: publish docs
+
+on:
+  push:
+    branches:
+      - main
+  release:
+    types:
+      - published
+
+jobs:
+  build:
+    # only build and deploy docs if the actor is not dependabot
+    if: ${{ github.actor != 'dependabot[bot]' }}
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Setup for poetry
+        run: |
+          python -m pip install poetry poetry-dynamic-versioning
+      - name: poetry deps
+        run: poetry install
+      - name: Build documentation
+        run: |
+          mkdir pages
+          touch pages/.nojekyll
+          cd docs
+          poetry run sphinx-build src build
+          # remove any doctrees dirs which aren't needed for publishing
+          find ./build -type d -name '.doctrees' -exec rm -rf {} +
+          cp -r build/* ../pages/
+      - name: Add index redirector to latest docs
+        run: |
+          cp docs/redirector.html pages/redirector.html
+      - name: Add media folder to latest docs
+        run: |
+          cp -r docs/src/media pages/media
+      - name: Deploy documentation
+        uses: JamesIves/github-pages-deploy-action@v4
+        with:
+          branch: pages
+          folder: pages
diff --git a/.github/workflows/publish-pypi.yml b/.github/workflows/publish-pypi.yml
new file mode 100644
index 0000000..6dd028a
--- /dev/null
+++ b/.github/workflows/publish-pypi.yml
@@ -0,0 +1,34 @@
+---
+# used for publishing packages to pypi on release
+name: publish pypi release
+
+on:
+  release:
+    types:
+      - published
+
+jobs:
+  publish_pypi:
+    runs-on: ubuntu-latest
+    environment: release
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Python setup
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Setup for poetry
+        run: |
+          python -m pip install poetry poetry-dynamic-versioning
+      - name: Install environment
+        run: poetry install --no-interaction --no-ansi
+      - name: poetry build distribution content
+        run: poetry build
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml
new file mode 100644
index 0000000..e03a3ce
--- /dev/null
+++ b/.github/workflows/run-tests.yml
@@ -0,0 +1,50 @@
+---
+# used for running tests
+name: tests
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  pre_commit_checks:
+    runs-on: ubuntu-22.04
+    steps:
+      # checks out the repo
+      - uses: actions/checkout@v4
+      # run pre-commit
+      - name: Python setup
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Setup for poetry
+        run: |
+          python -m pip install poetry
+      - name: Install environment
+        run: poetry install --no-interaction --no-ansi
+      - uses: pre-commit/action@v3.0.1
+  run_tests:
+    strategy:
+      matrix:
+        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        os: [ubuntu-22.04, macos-13]
+    runs-on: ${{ matrix.os }}
+    env:
+      OS: ${{ matrix.os }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Python setup
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python_version }}
+      - name: Setup for poetry
+        run: |
+          python -m pip install poetry
+      - name: Install environment
+        run: poetry install --no-interaction --no-ansi
+      - name: Run pytest
+        # run all tests except those marked generate_report_image
+        run: poetry run pytest -m "not generate_report_image"
diff --git a/.gitignore b/.gitignore
index 82f9275..770b919 100644
--- a/.gitignore
+++ b/.gitignore
@@ -82,34 +82,13 @@ target/
 profile_default/
 ipython_config.py
 
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
 # pdm
 #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
 #pdm.lock
 #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
 #   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+#   https://pdm.fming.dev/#use-with-ide
 .pdm.toml
-.pdm-python
-.pdm-build/
 
 # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
 __pypackages__/
@@ -154,9 +133,15 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+# test data ignores
+*.tif
+*.tiff
+*.sqlite
+*.parquet
+*.zip
+*.csv
+
+.DS_Store
+
+# jupyter notebook build files from myst-nb
+docs/jupyter_execute
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..0131be9
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,73 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+default_language_version:
+  python: python3.11
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+        exclude: |
+          (?x)^(
+              .*\.svg
+          )$
+      - id: check-yaml
+      - id: check-added-large-files
+      - id: detect-private-key
+  - repo: https://github.com/python-poetry/poetry
+    rev: "1.8.0"
+    hooks:
+      - id: poetry-check
+  - repo: https://github.com/tox-dev/pyproject-fmt
+    rev: "2.4.3"
+    hooks:
+      - id: pyproject-fmt
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.3.0
+    hooks:
+      - id: codespell
+        exclude: |
+          (?x)^(
+              .*\.lock |
+              .*\.json |
+              .*\.ipynb |
+              .*\.cppipe
+          )$
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.18
+    hooks:
+      - id: mdformat
+        additional_dependencies:
+          - mdformat-gfm
+  - repo: https://github.com/citation-file-format/cffconvert
+    rev: b6045d78aac9e02b039703b030588d54d53262ac
+    hooks:
+      - id: validate-cff
+  - repo: https://github.com/adrienverge/yamllint
+    rev: v1.35.1
+    hooks:
+      - id: yamllint
+  - repo: https://github.com/rhysd/actionlint
+    rev: v1.7.3
+    hooks:
+      - id: actionlint
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.7.0"
+    hooks:
+      - id: ruff-format
+      - id: ruff
+  - repo: local
+    hooks:
+      - id: code-cov-gen
+        name: Generate code coverage
+        language: system
+        entry: poetry run coverage run -m pytest
+        pass_filenames: false
+        always_run: true
+  - repo: https://github.com/Weird-Sheep-Labs/coverage-pre-commit
+    rev: 0.1.1
+    hooks:
+      - id: coverage-xml
+      - id: coverage-badge
+        args: ["-o", "media/coverage-badge.svg"]
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..2fb5a5c
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,163 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+---
+cff-version: 1.2.0
+title: CytoDataFrame
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: David
+    family-names: Bunten
+    orcid: 'https://orcid.org/0000-0001-6041-3665'
+  - given-names: Jenna
+    family-names: Tomkinson
+    orcid: 'https://orcid.org/0000-0003-2676-5813'
+  - given-names: Gregory
+    family-names: Way
+    orcid: 'https://orcid.org/0000-0002-0503-9348'
+repository-code: 'https://github.com/WayScience/CytoDataFrame'
+abstract: >-
+  An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks.
+keywords:
+  - python
+  - single-cell-analysis
+  - profiling
+  - dataframes
+  - data-analysis
+  - way-lab
+license: BSD-3-Clause
+references:
+  - authors:
+      - name: "Way Lab CFReT_data Team"
+    date-accessed: "2024-05-13"
+    title: Way Lab CFReT_data CytoTable Data
+    type: data
+    repository-code: "https://github.com/WayScience/CFReT_data"
+    url: "https://github.com/WayScience/CFReT_data/blob/main/3.process_cfret_features/data/converted_profiles/localhost231120090001_converted.parquet"
+    scope: "localhost231120090001_converted.parquet"
+    notes: >-
+      Data from CFReT_data project is used to help validate
+      expected results. Data is generated from CellProfiler
+      and CytoTable.
+    identifiers:
+      - description: "Github Link with Contributors"
+        type: url
+        value: "https://github.com/WayScience/CFReT_data/graphs/contributors"
+  - authors:
+      - name: "Way Lab NF1_cellpainting_data Team"
+    date-accessed: "2024-06-28"
+    title: Way Lab NF1_cellpainting_data CytoTable Data
+    type: data
+    repository-code: "https://github.com/WayScience/nf1_cellpainting_data"
+    notes: >-
+      Data from NF1_cellpainting_data project is used to help validate
+      expected results. Data is generated from CellProfiler
+      and CytoTable. We use the following files from the repository:
+      - "Plate_2_nf1_analysis.sqlite"
+      - "Plate_2.parquet"
+    identifiers:
+      - description: "Github Link with Contributors"
+        type: url
+        value: "https://github.com/WayScience/nf1_cellpainting_data/graphs/contributors"
+  - title: >-
+      Plate 2 (Cell Painting images from Plate 2 for NF1_cellpainting_data project)
+    type: data
+    url: https://figshare.com/articles/dataset/Plate_2/22233700
+    notes: >-
+      Image data for related NF1_cellpainting_data parquet sqlite.
+    authors:
+    - family-names: Tomkinson
+      given-names: Jenna
+    - family-names: Mattson-Hoss
+      given-names: Michelle
+    - family-names: Sarnoff
+      given-names: Herb
+    - family-names: Way
+      given-names: Gregory
+    date-published: "2023-04-12"
+    identifiers:
+    - type: doi
+      value: 10.6084/m9.figshare.22233700.v4
+  - authors:
+      - name: "Way Lab and Alexander Lab Nuclear Speckles Collaboration"
+    date-accessed: "2024-09-04"
+    title: Way Lab and Alexander Lab Nuclear Speckles Collaboration Data
+    type: data
+    repository-code: https://github.com/WayScience/nuclear_speckle_image_profiling
+    notes: >-
+      Data from a collaborative project focusing on nuclear speckles
+      with the Way Lab and Alexander Lab s used to help validate
+      expected results. Parquet data is generated from CellProfiler
+      and CytoTable. Images courtesy of Katherine Alexander
+      and the Alexander Lab.
+    identifiers:
+      - description: "Github Link with Contributors"
+        type: url
+        value: "https://github.com/WayScience/nuclear_speckle_image_profiling/graphs/contributors"
+  - authors:
+      - family-names: Chandrasekaran
+        given-names: Srinivas Niranj
+      - family-names: Cimini
+        given-names: Beth A.
+      - family-names: Goodale
+        given-names: Amy
+      - family-names: Miller
+        given-names: Lisa
+      - family-names: Kost-Alimova
+        given-names: Maria
+      - family-names: Jamali
+        given-names: Nasim
+      - family-names: Doench
+        given-names: John G.
+      - family-names: Fritchman
+        given-names: Briana
+      - family-names: Skepner
+        given-names: Adam
+      - family-names: Melanson
+        given-names: Michelle
+      - family-names: Kalinin
+        given-names: Alexandr A.
+      - family-names: Arevalo
+        given-names: John
+      - family-names: Haghighi
+        given-names: Marzieh
+      - family-names: Caicedo
+        given-names: Juan C.
+      - family-names: Kuhn
+        given-names: Daniel
+      - family-names: Hernandez
+        given-names: Desiree
+      - family-names: Berstler
+        given-names: James
+      - family-names: Shafqat-Abbasi
+        given-names: Hamdah
+      - family-names: Root
+        given-names: David E.
+      - family-names: Swalley
+        given-names: Susanne E.
+      - family-names: Garg
+        given-names: Sakshi
+      - family-names: Singh
+        given-names: Shantanu
+      - family-names: Carpenter
+        given-names: Anne E.
+    date-accessed: "2024-08-21"
+    title: >-
+      Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations
+    type: article
+    issn: 1548-7105
+    issue: 6
+    journal: Nature Methods
+    pages: 1114-1121
+    volume: 21
+    url: https://doi.org/10.1038/s41592-024-02241-6
+    date-published: "2024-06-01"
+    identifiers:
+      - type: doi
+        value: 10.1038/s41592-024-02241-6
+    notes: >-
+      JUMP (cpg0000-jump-pilot) was used to help demonstrate CytoDataFrame performance
+      with large data. See here for more information:
+      https://github.com/broadinstitute/cellpainting-gallery
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..6611dd1
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,3 @@
+# Contributor Covenant Code of Conduct
+
+Please see our full code of conduct at https://WayScience.github.io/CytoDataFrame/main/code_of_conduct
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..51e5e5e
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,3 @@
+# Contributing
+
+Please see our [contributing](https://WayScience.github.io/CytoDataFrame/main/contributing) documentation for more details on contributions, development, and testing.
diff --git a/LICENSE b/LICENSE
index 66cabec..92cf4cb 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2024, The Way Lab
+Copyright (c) 2024, Way Science
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
diff --git a/README.md b/README.md
index 7a9e432..dc416d5 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,39 @@
 # CytoDataFrame
-An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks.
+
+[![PyPI - Version](https://img.shields.io/pypi/v/cytodataframe)](https://pypi.org/project/CytoDataFrame/)
+[![Build Status](https://github.com/WayScience/CytoDataFrame/actions/workflows/run-tests.yml/badge.svg?branch=main)](https://github.com/WayScience/CytoDataFrame/actions/workflows/run-tests.yml?query=branch%3Amain)
+![Coverage Status](https://raw.githubusercontent.com/WayScience/CytoDataFrame/main/media/coverage-badge.svg)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/)
+
+![](https://raw.githubusercontent.com/WayScience/coSMicQC/refs/heads/main/docs/presentations/2024-09-18-SBI2-Conference/images/cosmicqc-example-cytodataframe.png)
+_CytoDataFrame extends Pandas functionality to help display single-cell profile data alongside related images._
+
+CytoDataFrame is an advanced in-memory data analysis format designed for single-cell profiling, integrating not only the data profiles but also their corresponding microscopy images and segmentation masks.
+Traditional single-cell profiling often excludes the associated images from analysis, limiting the scope of research.
+CytoDataFrame bridges this gap, offering a purpose-built solution for comprehensive analysis that incorporates both the data and images, empowering more detailed and visual insights in single-cell research.
+
+CytoDataFrame development began within [coSMicQC](https://github.com/WayScience/coSMicQC) - a single-cell profile quality control package.
+
+## Installation
+
+Install CytoDataFrame from source using the following:
+
+```shell
+# install from pypi
+pip install cytodataframe
+
+# or install directly from source
+pip install git+https://github.com/WayScience/CytoDataFrame.git
+```
+
+## Contributing, Development, and Testing
+
+Please see our [contributing](https://WayScience.github.io/CytoDataFrame/main/contributing) documentation for more details on contributions, development, and testing.
+
+## References
+
+- [coSMicQC](https://github.com/WayScience/coSMicQC)
+- [pycytominer](https://github.com/cytomining/pycytominer)
+- [CellProfiler](https://github.com/CellProfiler/CellProfiler)
+- [CytoTable](https://github.com/cytomining/CytoTable)
diff --git a/docs/src/_static/cosmicqc-example-cytodataframe.png b/docs/src/_static/cosmicqc-example-cytodataframe.png
new file mode 100644
index 0000000..1643d7b
Binary files /dev/null and b/docs/src/_static/cosmicqc-example-cytodataframe.png differ
diff --git a/docs/src/_static/custom.css b/docs/src/_static/custom.css
new file mode 100644
index 0000000..0d87ac3
--- /dev/null
+++ b/docs/src/_static/custom.css
@@ -0,0 +1,39 @@
+div.cell_output table {
+    border: none;
+    border-collapse: collapse;
+    border-spacing: 0;
+    color: black;
+    font-size: 1em;
+    table-layout: fixed;
+    background: white;
+    overflow: scroll;
+    max-width: 600px;
+}
+
+.pt-5, .py-5 {
+    padding-top: 1rem !important;
+}
+
+section {
+    margin-top: 0rem;
+    margin-bottom: 2rem;
+}
+
+.cell_input div.highlight, .cell_output pre, .cell_input pre, .cell_output .output {
+    border: none;
+    box-shadow: none;
+    overflow: auto;
+}
+
+#main img{
+    max-width:600px;
+}
+
+.page-toc {
+    font-size: .875rem;
+    padding-top: 0 !important;
+    color: #494949;
+    max-height: 100vh;
+    overflow: hidden auto;
+    word-break: break-all;
+}
diff --git a/docs/src/code_of_conduct.md b/docs/src/code_of_conduct.md
new file mode 100644
index 0000000..14f473b
--- /dev/null
+++ b/docs/src/code_of_conduct.md
@@ -0,0 +1,132 @@
+# Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socioeconomic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+- Demonstrating empathy and kindness toward other people
+- Being respectful of differing opinions, viewpoints, and experiences
+- Giving and gracefully accepting constructive feedback
+- Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+- Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+- The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+- Trolling, insulting or derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official email address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to community leaders responsible for enforcement.
+Please open a [new security advisory notice](https://github.com/WayScience/CytoDataFrame/security/advisories/new) (using defaults or "n/a" where unable to fill in the form) to privately notify us of any incidents of this nature.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][mozilla coc].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][faq]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[faq]: https://www.contributor-covenant.org/faq
+[homepage]: https://www.contributor-covenant.org
+[mozilla coc]: https://github.com/mozilla/diversity
+[translations]: https://www.contributor-covenant.org/translations
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
diff --git a/docs/src/conf.py b/docs/src/conf.py
new file mode 100644
index 0000000..6e50b48
--- /dev/null
+++ b/docs/src/conf.py
@@ -0,0 +1,100 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import pathlib
+import sys
+
+basedir = str(pathlib.Path(__file__).parent.parent.parent.resolve())
+
+sys.path.insert(0, basedir)
+
+# -- Project information -----------------------------------------------------
+
+project = "CytoDataFrame"
+# is used here due to sphinx decision-making: https://github.com/sphinx-doc/sphinx/issues/8132
+copyright = "2024, WayScience Community"  # noqa: A001
+author = "WayScience Community"
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "myst_nb",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx_multiversion",
+    "sphinx_wagtail_theme",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []  # type: ignore
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = "pydata_sphinx_theme"
+
+html_theme_options = {
+    "external_links": [
+        {
+            "url": "https://github.com/WayScience/coSMicQC",
+            "name": "coSMicQC",
+        },
+    ],
+    "header_links_before_dropdown": 5,
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/WayScience/CytoDataFrame",
+            "icon": "fa-brands fa-github",
+        },
+    ],
+    "logo": {"text": "CytoDataFrame"},
+    "use_edit_page_button": False,
+    "show_toc_level": 1,
+    "navbar_align": "left",
+    "navbar_center": ["navbar-nav"],
+    "footer_start": ["copyright"],
+    "footer_center": ["sphinx-version"],
+    "secondary_sidebar_items": {
+        "**/*": ["page-toc", "edit-this-page", "sourcelink"],
+    },
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+html_css_files = ["custom.css"]
+
+# set theme options
+
+# Options for myst-nb
+# turn off notebook execution for docs builds
+# (we rely on the notebook already being processed
+# prior to the publish to help navigate compute needs)
+nb_execution_mode = "off"
+
+# set option to avoid rendering default variables
+autodoc_preserve_defaults = True
+
+# enable anchor creation
+myst_heading_anchors = 3
diff --git a/docs/src/contributing.md b/docs/src/contributing.md
new file mode 100644
index 0000000..7277af9
--- /dev/null
+++ b/docs/src/contributing.md
@@ -0,0 +1,135 @@
+# Contributing
+
+First of all, thank you so much for contributing! 🎉 💯
+
+This document contains guidelines on how to most effectively contribute within this repository.
+
+If you are stuck, please feel free to ask any questions or ask for help.
+
+## Code of conduct
+
+This project is governed by our [code of conduct](code_of_conduct.md). By participating, you are expected to uphold this code.
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to community leaders responsible for enforcement.
+Please open a [new security advisory notice](https://github.com/WayScience/CytoDataFrame/security/advisories/new) (using defaults or "n/a" where unable to fill in the form) to privately notify us of any incidents of this nature.
+
+## Development
+
+This project leverages development environments managed by Python [Poetry](https://python-poetry.org/).
+We leverage interactions with the Docker through Python to achieve reproducible results through containers.
+We use [pytest](https://docs.pytest.org/) for testing and [GitHub actions](https://docs.github.com/en/actions) for automated tests.
+
+### Development setup
+
+Perform the following steps to setup a Python development environment.
+
+1. [Install Python](https://www.python.org/downloads/) (we recommend using [`pyenv`](https://github.com/pyenv/pyenv) or similar)
+1. [Install Poetry](https://python-poetry.org/docs/#installation)
+1. [Install Poetry Environment](https://python-poetry.org/docs/basic-usage/#installing-dependencies): `poetry install`
+
+### Linting
+
+Work added to this project is automatically checked using [pre-commit](https://pre-commit.com/) via [GitHub Actions](https://docs.github.com/en/actions).
+Pre-commit can work alongside your local [git with git-hooks](https://pre-commit.com/index.html#3-install-the-git-hook-scripts)
+
+After [installing pre-commit](https://pre-commit.com/#installation) within your development environment, the following command also can perform the same checks within your local development environment:
+
+```sh
+% pre-commit run --all-files
+```
+
+We use these same checks within our automated tests which are managed by [GitHub Actions workflows](https://docs.github.com/en/actions/using-workflows).
+These automated tests generally must pass in order to merge work into this repository.
+
+### Testing
+
+Work added to this project is automatically tested using [pytest](https://docs.pytest.org/) via [GitHub Actions](https://docs.github.com/en/actions).
+Pytest is installed through the Poetry environment for this project.
+We recommend testing your work before opening pull requests with proposed changes.
+
+You can run pytest on your work using the following example:
+
+```sh
+% poetry run pytest
+```
+
+## 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.
+
+Specifically, there are several ways to suggest or make changes to this repository:
+
+1. Open a GitHub issue: https://github.com/WayScience/CytoDataFrame/issues
+1. Create a pull request from a forked branch of the repository
+
+### Creating a pull request
+
+### Pull requests
+
+After you’ve decided to contribute code and have written it up, please file a pull request.
+We specifically follow a [forked pull request model](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork).
+Please create a fork of this repository, clone the fork, and then create a new, feature-specific branch.
+Once you make the necessary changes on this branch, you should file a pull request to incorporate your changes into this (fork upstream) repository.
+
+The content and description of your pull request are directly related to the speed at which we are able to review, approve, and merge your contribution.
+To ensure an efficient review process please perform the following steps:
+
+1. Follow all instructions in the [pull request template](https://github.com/WayScience/CytoDataFrame/blob/main/.github/PULL_REQUEST_TEMPLATE.md)
+1. Triple check that your pull request is adding _one_ specific feature or additional group of content.
+   Small, bite-sized pull requests move so much faster than large pull requests.
+1. After submitting your pull request, ensure that your contribution passes all status checks (e.g. passes all tests)
+
+Pull request review and approval is required by at least one project maintainer to merge.
+We will do our best to review the code addition in a timely fashion.
+Ensuring that you follow all steps above will increase our speed and ability to review.
+We will check for accuracy, style, code coverage, and scope.
+
+## Versioning
+
+We use [`poetry-dynamic-versioning`](https://github.com/mtkennerly/poetry-dynamic-versioning) to help version this software through [`PEP 440`](https://peps.python.org/pep-0440/) standards.
+Configuration for versioning is found within the `pyproject.toml` file.
+All builds for packages include dynamic version data to help label distinct versions of the software.
+`poetry-dynamic-versioning` uses `git` tags to help distinguish version data.
+We also use the `__init__.py` file as a place to persist the version data for occaissions where the `git` history is unavailable or unwanted.
+
+The following command is used to add `poetry-dynamic-versioning` to Poetry for use with this project: `poetry self add "poetry-dynamic-versioning[plugin]"`.
+Versioning for the project is intended to align with GitHub Releases which provide `git` tag capabilities.
+
+### Releases
+
+We publish source code by using [GitHub Releases](https://docs.github.com/en/repositories/releasing-projects-on-github/about-releases) available [here](https://github.com/wayscience/CytoDataFrame/releases).
+We publish a related Python package through the [Python Packaging Index (PyPI)](https://pypi.org/) available [here](https://pypi.org/project/CytoDataFrame/).
+
+#### Release Publishing Process
+
+Several manual and automated steps are involved with publishing CytoDataFrame releases.
+See below for an overview of how this works.
+
+Notes about [semantic version](https://en.wikipedia.org/wiki/Software_versioning#Semantic_versioning) (semver) specifications:
+CytoDataFrame version specifications are controlled through [`poetry-dynamic-versioning`](https://github.com/mtkennerly/poetry-dynamic-versioning) which leverages [`dunamai`](https://github.com/mtkennerly/dunamai) to create version data based on [git tags](https://git-scm.com/book/en/v2/Git-Basics-Tagging) and commits.
+CytoDataFrame release git tags are automatically applied through [GitHub Releases](https://docs.github.com/en/repositories/releasing-projects-on-github/about-releases) and related inferred changes from [`release-drafter`](https://github.com/release-drafter/release-drafter).
+
+1. Open a pull request and use a repository label for `release-<semver release type>` to label the pull request for visibility with [`release-drafter`](https://github.com/release-drafter/release-drafter) (for example, see [CytoDataFrame#24](https://github.com/wayscience/CytoDataFrame/pull/24) as a reference of a semver patch update).
+1. On merging the pull request for the release, a [GitHub Actions workflow](https://docs.github.com/en/actions/using-workflows) defined in `draft-release.yml` leveraging [`release-drafter`](https://github.com/release-drafter/release-drafter) will draft a release for maintainers.
+1. The draft GitHub release will include a version tag based on the GitHub PR label applied and `release-drafter`.
+1. Make modifications as necessary to the draft GitHub release, then publish the release (the draft release does not normally need additional modifications).
+1. On publishing the release, another GitHub Actions workflow defined in `publish-pypi.yml` will run to build and deploy the Python package to PyPI (utilizing the earlier modified `pyproject.toml` semantic version reference for labeling the release).
+
+## Documentation
+
+Documentation for this project is published using [Sphinx](https://www.sphinx-doc.org) with markdown and Jupyter notebook file compatibility provided by [myst-parser](https://myst-parser.readthedocs.io/en/latest/) and [myst-nb](https://myst-nb.readthedocs.io/en/latest/) to create a "documentation website" (also known as "docsite").
+The docsite is hosted through [GitHub Pages](https://pages.github.com/) and deployed through automated [GitHub Actions](https://docs.github.com/en/actions) jobs which trigger on pushes to the main branch or the publishing of a new release on GitHub.
+Documentation is versioned as outlined earlier sections covering versioning details to help ensure users are able to understand each release independently of one another.
+
+It can sometimes be useful to test documentation builds locally before proposing changes within a pull request.
+See below for some examples of how to build documentation locally.
+
+```shell
+# build single-version sphinx documentation
+# (useful for troubleshooting potential issues)
+poetry run sphinx-build docs/src docs/build
+
+# build multi-version sphinx documentation
+# (used in production)
+poetry run sphinx-multiversion docs/src docs/build
+```
diff --git a/docs/src/index.md b/docs/src/index.md
new file mode 100644
index 0000000..18c343a
--- /dev/null
+++ b/docs/src/index.md
@@ -0,0 +1,19 @@
+<!-- include contents from the root readme -->
+
+```{include} ../../README.md
+---
+relative-docs: docs/src/
+relative-images:
+---
+```
+
+```{toctree}
+---
+caption: 'Contents:'
+maxdepth: 3
+---
+python-api
+presentations
+contributing
+code_of_conduct
+```
diff --git a/docs/src/presentations.md b/docs/src/presentations.md
new file mode 100644
index 0000000..651e42e
--- /dev/null
+++ b/docs/src/presentations.md
@@ -0,0 +1,10 @@
+# Presentations
+
+```{toctree}
+---
+caption: "List of presentations"
+maxdepth: 2
+glob:
+---
+presentations/*
+```
diff --git a/docs/src/presentations/sbi2-2024.md b/docs/src/presentations/sbi2-2024.md
new file mode 100644
index 0000000..f58ade3
--- /dev/null
+++ b/docs/src/presentations/sbi2-2024.md
@@ -0,0 +1,7 @@
+# SBI2 Annual Confrerence 2024 Poster
+
+We presented `CytoDataFrame` within the context of `coSMicQC` during the [SBI2 annual conference (2024)](https://sbi2.org/conference/) poster sessions.
+
+See below for the [poster](https://wayscience.github.io/coSMicQC/media/sbi2-2024-cosmicqc-poster.pdf) or reference our Zenodo record here: [https://zenodo.org/records/13829960](https://zenodo.org/records/13829960)
+
+<iframe src="https://wayscience.github.io/coSMicQC/media/sbi2-2024-cosmicqc-poster.pdf" width="100%" height="600px"></iframe>
diff --git a/docs/src/python-api.md b/docs/src/python-api.md
new file mode 100644
index 0000000..5616923
--- /dev/null
+++ b/docs/src/python-api.md
@@ -0,0 +1,22 @@
+# Python API
+
+```{eval-rst}
+
+cytodataframe.frame
+-------------------
+
+.. automodule:: src.cytodataframe.frame
+   :members:
+   :private-members:
+   :undoc-members:
+   :show-inheritance:
+
+cytodataframe.image
+-------------------
+
+.. automodule:: src.cytodataframe.image
+   :members:
+   :private-members:
+   :undoc-members:
+   :show-inheritance:
+```
diff --git a/media/coverage-badge.svg b/media/coverage-badge.svg
new file mode 100644
index 0000000..f02f0ef
--- /dev/null
+++ b/media/coverage-badge.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="coverage: 80.93%"><title>coverage: 80.93%</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="61" height="20" fill="#555"/><rect x="61" width="53" height="20" fill="#97ca00"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="315" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">coverage</text><text x="315" y="140" transform="scale(.1)" fill="#fff" textLength="510">coverage</text><text aria-hidden="true" x="865" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="430">80.93%</text><text x="865" y="140" transform="scale(.1)" fill="#fff" textLength="430">80.93%</text></g></svg>
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..2004ac5
--- /dev/null
+++ b/poetry.lock
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+]
+
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+description = "Traitlets Python configuration system"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
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+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.2.2"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
+]
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+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
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+]
+
+[[package]]
+name = "websocket-client"
+version = "1.8.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
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+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "zipp"
+version = "3.19.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+]
+
+[package.extras]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.9,<3.13"
+content-hash = "7bf0a68da199cec360a9a70580fb005ea005403f32f4955e58fc673e7d9fa3d9"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..3f51348
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,130 @@
+[build-system]
+build-backend = "setuptools.build_meta"
+requires = [ "setuptools>=64", "setuptools-scm>=8" ]
+
+[tool.poetry]
+name = "CytoDataFrame"
+version = "0.0.0"
+description = "An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks."
+authors = [ "Way Science Community" ]
+license = "BSD-3-Clause"
+readme = "README.md"
+packages = [ { include = "cytodataframe", from = "src" } ]
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.13"
+# used for data management
+pandas = [
+  { version = "<2.2.2", python = "<3.9" },
+  { version = "^2.2.2", python = ">=3.9" },
+]
+# used for data ingest and export
+pyarrow = "^16.0.0"
+# used for report generation capabilities
+plotly = "^5.22.0"
+# used for plotly dependencies
+kaleido = "0.2.1"
+# used for environment detection
+ipython = "^8.12.3"
+# used for image processing
+scikit-image = [
+  { version = "^0.19.3", python = "<3.9" },
+  { version = ">0.19.3", python = ">=3.9" },
+]
+# dependency of scikit-image
+pywavelets = [
+  { version = "^1.4.1", python = "<3.9" },
+  { version = ">1.4.1", python = ">=3.9" },
+]
+# used for image modifications in cytodataframe
+opencv-python = "^4.10.0.84"
+# used for report visualizations from within cytodataframe
+cosmicqc = "^0.0.11"
+
+[tool.poetry.group.dev.dependencies]
+# provides testing capabilities for project
+pytest = "^8.2.0"
+# used for report html export to image for tests
+html2image = "^2.0.4.3"
+# used for test data generation
+sqlalchemy = ">=1.3.6,<2"
+# added to generate test coverage reports
+coverage = "^7.6.0"
+
+[tool.poetry.group.docs.dependencies]
+# used for rendering docs into docsite
+sphinx = "^7.0.0"
+# used for rendering markdown through sphinx
+myst-parser = "^3.0.0"
+# used for rendering notebooks through myst parser
+myst-nb = "^1.1.1"
+# used for extension sphinx-multiversion and to fix git-based challenges with default branch handling
+sphinx-multiversion = { git = "https://github.com/J-RN/sphinx-multiversion", rev = "a77f0c862dace3a62c18fc866da60ef7dde3873d" }
+# used for gathering version data for docsite
+dunamai = "^1.22.0"
+# used for theming the docsite
+pydata-sphinx-theme = "^0.16.0"
+
+[tool.poetry-dynamic-versioning]
+enable = true
+style = "pep440"
+vcs = "git"
+
+# specify where version replacement is performed
+[tool.poetry-dynamic-versioning.substitution]
+files = [ "src/CytoDataFrame/__init__.py" ]
+
+# defines various development tasks
+
+[tool.setuptools_scm]
+root = "."
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+fix = true
+
+lint.select = [
+  # flake8-builtins
+  "A",
+  # flake8-annotations
+  "ANN",
+  # flake8-comprehensions
+  "C4",
+  # mccabe
+  "C90",
+  # pycodestyle
+  "E",
+  # pyflakes
+  "F",
+  # isort
+  "I",
+  # pylint
+  "PL",
+  # ruff
+  "RUF",
+  # flake8-simplify
+  "SIM",
+  "W",
+]
+# Ignore `E402` and `F401` (unused imports) in all `__init__.py` files
+lint.per-file-ignores."__init__.py" = [ "E402", "F401" ]
+# ignore typing rules for tests
+lint.per-file-ignores."tests/*" = [ "ANN201", "PLR0913" ]
+
+[tool.pytest.ini_options]
+markers = [
+  "generate_report_image: tests which involve the creation of report images.",
+]
+
+[tool.coverage.run]
+# settings to avoid errors with cv2 and coverage
+# see here for more: https://github.com/nedbat/coveragepy/issues/1653
+omit = [
+  "config.py",
+  "config-3.py",
+]
+
+[tool.bandit]
+exclude_dirs = [ "tests" ]
+
diff --git a/src/cytodataframe/__init__.py b/src/cytodataframe/__init__.py
new file mode 100644
index 0000000..81154a8
--- /dev/null
+++ b/src/cytodataframe/__init__.py
@@ -0,0 +1,9 @@
+"""
+Initialization for cytodataframe package
+"""
+
+from .frame import CytoDataFrame
+
+# note: version placeholder is updated during build
+# by poetry-dynamic-versioning.
+__version__ = "0.0.0"
diff --git a/src/cytodataframe/frame.py b/src/cytodataframe/frame.py
new file mode 100644
index 0000000..5ffe79b
--- /dev/null
+++ b/src/cytodataframe/frame.py
@@ -0,0 +1,866 @@
+"""
+Defines a CytoDataFrame class.
+"""
+
+import base64
+import pathlib
+import random
+import re
+import webbrowser
+from io import BytesIO, StringIO
+from typing import (
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+import numpy as np
+import pandas as pd
+import plotly
+import plotly.colors as pc
+import plotly.express as px
+import plotly.graph_objects as go
+import skimage
+import skimage.io
+import skimage.measure
+from IPython import get_ipython
+from jinja2 import Environment, FileSystemLoader
+from pandas._config import (
+    get_option,
+)
+from pandas.io.formats import (
+    format as fmt,
+)
+from PIL import Image, ImageDraw
+
+from .image import adjust_image_brightness, is_image_too_dark
+
+# provide backwards compatibility for Self type in earlier Python versions.
+# see: https://peps.python.org/pep-0484/#annotating-instance-and-class-methods
+CytoDataFrame_type = TypeVar("CytoDataFrame_type", bound="CytoDataFrame")
+
+
+class CytoDataFrame(pd.DataFrame):
+    """
+    A class designed to enhance single-cell data handling by wrapping
+    pandas DataFrame capabilities, providing advanced methods for quality control,
+    comprehensive analysis, and image-based data processing.
+
+    This class can initialize with either a pandas DataFrame or a file path (CSV, TSV,
+    TXT, or Parquet). When initialized with a file path, it reads the data into a
+    pandas DataFrame. It also includes capabilities to export data.
+
+    Attributes:
+        _metadata (ClassVar[list[str]]):
+            A class-level attribute that includes custom attributes.
+        _custom_attrs (dict):
+            A dictionary to store custom attributes, such as data source,
+            context directory, and bounding box information.
+    """
+
+    _metadata: ClassVar = ["_custom_attrs"]
+
+    def __init__(  # noqa: PLR0912
+        self: CytoDataFrame_type,
+        data: Union[CytoDataFrame_type, pd.DataFrame, str, pathlib.Path],
+        data_context_dir: Optional[str] = None,
+        data_bounding_box: Optional[pd.DataFrame] = None,
+        data_mask_context_dir: Optional[str] = None,
+        **kwargs: Dict[str, Any],
+    ) -> None:
+        """
+        Initializes the CytoDataFrame with either a DataFrame or a file path.
+
+        Args:
+            data (Union[CytoDataFrame_type, pd.DataFrame, str, pathlib.Path]):
+                The data source, either a pandas DataFrame or a file path.
+            data_context_dir (Optional[str]):
+                Directory context for the image data within the DataFrame.
+            data_bounding_box (Optional[pd.DataFrame]):
+                Bounding box data for the DataFrame images.
+            data_mask_context_dir: Optional[str]:
+                Directory context for the mask data for images.
+            **kwargs:
+                Additional keyword arguments to pass to the pandas read functions.
+        """
+
+        self._custom_attrs = {
+            "data_source": None,
+            "data_context_dir": None,
+            "data_bounding_box": None,
+            "data_mask_context_dir": None,
+        }
+
+        if data_context_dir is not None:
+            self._custom_attrs["data_context_dir"] = data_context_dir
+
+        if data_mask_context_dir is not None:
+            self._custom_attrs["data_mask_context_dir"] = data_mask_context_dir
+
+        if isinstance(data, CytoDataFrame):
+            self._custom_attrs["data_source"] = data._custom_attrs["data_source"]
+            self._custom_attrs["data_context_dir"] = data._custom_attrs[
+                "data_context_dir"
+            ]
+            self._custom_attrs["data_mask_context_dir"] = data._custom_attrs[
+                "data_mask_context_dir"
+            ]
+            super().__init__(data)
+        elif isinstance(data, (pd.DataFrame, pd.Series)):
+            self._custom_attrs["data_source"] = (
+                "pandas.DataFrame"
+                if isinstance(data, pd.DataFrame)
+                else "pandas.Series"
+            )
+            super().__init__(data)
+        elif isinstance(data, (str, pathlib.Path)):
+            data_path = pathlib.Path(data)
+            self._custom_attrs["data_source"] = str(data_path)
+
+            if data_context_dir is None:
+                self._custom_attrs["data_context_dir"] = str(data_path.parent)
+            else:
+                self._custom_attrs["data_context_dir"] = data_context_dir
+
+            if data_path.suffix in {".csv", ".tsv", ".txt"} or data_path.suffixes == [
+                ".csv",
+                ".gz",
+            ]:
+                data = pd.read_csv(data_path, **kwargs)
+            elif data_path.suffix == ".parquet":
+                data = pd.read_parquet(data_path, **kwargs)
+            else:
+                raise ValueError("Unsupported file format for CytoDataFrame.")
+
+            super().__init__(data)
+
+        else:
+            super().__init__(data)
+
+        if data_bounding_box is None:
+            self._custom_attrs["data_bounding_box"] = self.get_bounding_box_from_data()
+
+        else:
+            self._custom_attrs["data_bounding_box"] = data_bounding_box
+
+    def __getitem__(self: CytoDataFrame_type, key: Union[int, str]) -> Any:  # noqa: ANN401
+        """
+        Returns an element or a slice of the underlying pandas DataFrame.
+
+        Args:
+            key:
+                The key or slice to access the data.
+
+        Returns:
+            pd.DataFrame or any:
+                The selected element or slice of data.
+        """
+
+        result = super().__getitem__(key)
+
+        if isinstance(result, pd.Series):
+            return result
+
+        elif isinstance(result, pd.DataFrame):
+            return CytoDataFrame(
+                super().__getitem__(key),
+                data_context_dir=self._custom_attrs["data_context_dir"],
+                data_bounding_box=self._custom_attrs["data_bounding_box"],
+                data_mask_context_dir=self._custom_attrs["data_mask_context_dir"],
+            )
+
+    def _wrap_method(
+        self: CytoDataFrame_type,
+        method: Callable,
+        *args: List[Any],
+        **kwargs: Dict[str, Any],
+    ) -> Any:  # noqa: ANN401
+        """
+        Wraps a given method to ensure that the returned result
+        is an CytoDataFrame if applicable.
+
+        Args:
+            method (Callable):
+                The method to be called and wrapped.
+            *args (List[Any]):
+                Positional arguments to be passed to the method.
+            **kwargs (Dict[str, Any]):
+                Keyword arguments to be passed to the method.
+
+        Returns:
+            Any:
+                The result of the method call. If the result is a pandas DataFrame,
+                it is wrapped in an CytoDataFrame instance with additional context
+                information (data context directory and data bounding box).
+
+        """
+        result = method(*args, **kwargs)
+        if isinstance(result, pd.DataFrame):
+            result = CytoDataFrame(
+                result,
+                data_context_dir=self._custom_attrs["data_context_dir"],
+                data_bounding_box=self._custom_attrs["data_bounding_box"],
+                data_mask_context_dir=self._custom_attrs["data_mask_context_dir"],
+            )
+        return result
+
+    def sort_values(
+        self: CytoDataFrame_type, *args: List[Any], **kwargs: Dict[str, Any]
+    ) -> CytoDataFrame_type:
+        """
+        Sorts the DataFrame by the specified column(s) and returns a
+        new CytoDataFrame instance.
+
+        Note: we wrap this method within CytoDataFrame to help ensure the consistent
+        return of CytoDataFrames in the context of pd.Series (which are
+        treated separately but have specialized processing within the
+        context of sort_values).
+
+        Args:
+            *args (List[Any]):
+                Positional arguments to be passed to the pandas
+                DataFrame's `sort_values` method.
+            **kwargs (Dict[str, Any]):
+                Keyword arguments to be passed to the pandas
+                DataFrame's `sort_values` method.
+
+        Returns:
+            CytoDataFrame_type:
+                A new instance of CytoDataFrame sorted by the specified column(s).
+
+        """
+
+        return self._wrap_method(super().sort_values, *args, **kwargs)
+
+    def get_bounding_box_from_data(
+        self: CytoDataFrame_type,
+    ) -> Optional[CytoDataFrame_type]:
+        """
+        Retrieves bounding box data from the DataFrame based
+        on predefined column groups.
+
+        This method identifies specific groups of columns representing bounding box
+        coordinates for different cellular components (cytoplasm, nuclei, cells) and
+        checks for their presence in the DataFrame. If all required columns are present,
+        it filters and returns a new CytoDataFrame instance containing only these
+        columns.
+
+        Returns:
+            Optional[CytoDataFrame_type]:
+                A new instance of CytoDataFrame containing the bounding box columns if
+                they exist in the DataFrame. Returns None if the required columns
+                are not found.
+
+        """
+        # Define column groups and their corresponding conditions
+        column_groups = {
+            "cyto": [
+                "Cytoplasm_AreaShape_BoundingBoxMaximum_X",
+                "Cytoplasm_AreaShape_BoundingBoxMaximum_Y",
+                "Cytoplasm_AreaShape_BoundingBoxMinimum_X",
+                "Cytoplasm_AreaShape_BoundingBoxMinimum_Y",
+            ],
+            "nuclei": [
+                "Nuclei_AreaShape_BoundingBoxMaximum_X",
+                "Nuclei_AreaShape_BoundingBoxMaximum_Y",
+                "Nuclei_AreaShape_BoundingBoxMinimum_X",
+                "Nuclei_AreaShape_BoundingBoxMinimum_Y",
+            ],
+            "cells": [
+                "Cells_AreaShape_BoundingBoxMaximum_X",
+                "Cells_AreaShape_BoundingBoxMaximum_Y",
+                "Cells_AreaShape_BoundingBoxMinimum_X",
+                "Cells_AreaShape_BoundingBoxMinimum_Y",
+            ],
+        }
+
+        # Determine which group of columns to select based on availability in self.data
+        selected_group = None
+        for group, cols in column_groups.items():
+            if all(col in self.columns.tolist() for col in cols):
+                selected_group = group
+                break
+
+        # Assign the selected columns to self.bounding_box_df
+        if selected_group:
+            return self.filter(items=column_groups[selected_group])
+
+        return None
+
+    def export(
+        self: CytoDataFrame_type, file_path: str, **kwargs: Dict[str, Any]
+    ) -> None:
+        """
+        Exports the underlying pandas DataFrame to a file.
+
+        Args:
+            file_path (str):
+                The path where the DataFrame should be saved.
+            **kwargs:
+                Additional keyword arguments to pass to the pandas to_* methods.
+        """
+
+        data_path = pathlib.Path(file_path)
+
+        # export to csv
+        if ".csv" in data_path.suffixes:
+            self.to_csv(file_path, **kwargs)
+        # export to tsv
+        elif any(elem in data_path.suffixes for elem in (".tsv", ".txt")):
+            self.to_csv(file_path, sep="\t", **kwargs)
+        # export to parquet
+        elif data_path.suffix == ".parquet":
+            self.to_parquet(file_path, **kwargs)
+        else:
+            raise ValueError("Unsupported file format for export.")
+
+    @staticmethod
+    def is_notebook_or_lab() -> bool:
+        """
+        Determines if the code is being executed in a Jupyter notebook (.ipynb)
+        returning false if it is not.
+
+        This method attempts to detect the interactive shell environment
+        using IPython's `get_ipython` function. It checks the class name of the current
+        IPython shell to distinguish between different execution environments.
+
+        Returns:
+            bool:
+                - `True`
+                    if the code is being executed in a Jupyter notebook (.ipynb).
+                - `False`
+                    otherwise (e.g., standard Python shell, terminal IPython shell,
+                    or scripts).
+        """
+        try:
+            # check for type of session via ipython
+            shell = get_ipython().__class__.__name__
+            if "ZMQInteractiveShell" in shell:
+                return True
+            elif "TerminalInteractiveShell" in shell:
+                return False
+            else:
+                return False
+        except NameError:
+            return False
+
+    def show_report(
+        self: CytoDataFrame_type,
+        report_path: Optional[str] = None,
+        auto_open: bool = True,
+        color_palette: Optional[List[str]] = None,
+    ) -> None:
+        """
+        Generates and displays a report based on the current DataFrame's data
+        quality control (DQC) columns.
+
+        This method organizes the DQC columns from the DataFrame, creates
+        visualizations for each threshold set, and then either displays the
+        visualizations inline (if running in a Jupyter notebook or lab) or
+        opens an HTML report in the default web browser.
+
+        Args:
+            report_path (Optional[str]):
+                The file path where the HTML report should be saved and displayed.
+                If `None`, the report will be displayed inline if in a notebook
+                or lab environment.
+            auto_open: bool:
+                Whether to automatically open the report.
+            color_palette Optional(List[str]):
+                Optional list for color palette to use.
+
+        Raises:
+            ValueError: If the DataFrame does not contain any DQC columns.
+        """
+
+        # find all cosmicqc columns in the data using the prefix `cqc.`
+        cqc_cols = [col for col in self.columns.tolist() if "cqc." in col]
+        # organize column data into the threshold set name, threshold is_outlier col,
+        # and the threshold score columns as list
+        organized_columns = [
+            [
+                # name of the threshold set
+                threshold_set,
+                # column which includes boolean is_outlier data for threshold set
+                next(
+                    (
+                        col
+                        for col in cqc_cols
+                        if f"cqc.{threshold_set}.is_outlier" in col
+                    ),
+                    None,
+                ),
+                # columns which show the data associated with thresholds
+                [col for col in cqc_cols if f"cqc.{threshold_set}.Z_Score." in col],
+            ]
+            for threshold_set in sorted({col.split(".")[1] for col in cqc_cols})
+        ]
+
+        # create figures for visualization based on the name, outlier status,
+        # and threshold scores
+        figures = [
+            self.create_threshold_set_outlier_visualization(
+                df=self,
+                threshold_set_name=set_name,
+                col_outlier=col_outlier,
+                cols_threshold_scores=cols_threshold_scores,
+                color_palette=color_palette,
+            )
+            for set_name, col_outlier, cols_threshold_scores in organized_columns
+        ]
+
+        # if we're running in a notebook or jupyter lab, show the figures as-is
+        if self.is_notebook_or_lab() or report_path is None:
+            # if we should automatically open, show the figures
+            if auto_open:
+                for figure in figures:
+                    figure.show()
+
+            return figures
+
+        # otherwise, create an html file with figures and open it with default browser
+        else:
+            html_path = self.create_figure_group_html(
+                figures=figures, report_path=report_path
+            )
+
+            # if we should auto open, show the html file in default web browser
+            if auto_open:
+                webbrowser.open(f"file://{pathlib.Path(html_path).resolve()}")
+
+            print(f"Opened default web browser for report {html_path}")
+
+            return html_path
+
+    @staticmethod
+    def create_figure_group_html(
+        figures: List[plotly.graph_objs._figure.Figure],
+        report_path: Optional[str] = None,
+    ) -> str:
+        """
+        Generates an HTML file containing multiple Plotly figures.
+
+        This method takes a list of Plotly figure objects, converts them to HTML,
+        and embeds them into a template HTML file. The resulting HTML file is then
+        saved to the specified path.
+
+        Args:
+            figures (List[plotly.graph_objs._figure.Figure]):
+                A list of Plotly figure objects to be included in the HTML report.
+            report_path (str):
+                The file path where the HTML report will be saved.
+                Defaults to "cosmicqc_outlier_report.html" when None.
+
+        Returns:
+            str: The path to the saved HTML report.
+        """
+
+        # if we have none for the report path, use a default name.
+        if report_path is None:
+            report_path = "cosmicqc_outlier_report.html"
+
+        # create wrapped html for figures
+        figure_html = "".join(
+            [
+                f"<div class='fig_wrapper'>{fig.to_html(full_html=False)}</div>"
+                for fig in figures
+            ]
+        )
+
+        # configure jinja environment
+        env = Environment(
+            loader=FileSystemLoader(f"{pathlib.Path(__file__).parent!s}/data")
+        )
+        # load a jinja template
+        template = env.get_template("report_template.html")
+
+        # Render the template with Plotly figure HTML
+        rendered_html = template.render(figure_html=figure_html)
+
+        # write the html to file
+        with open(report_path, "w") as f:
+            f.write(rendered_html)
+
+        # return the path of the file
+        return report_path
+
+    def create_threshold_set_outlier_visualization(
+        self: CytoDataFrame_type,
+        df: pd.DataFrame,
+        threshold_set_name: str,
+        col_outlier: str,
+        cols_threshold_scores: List[str],
+        color_palette: Optional[List[str]] = None,
+    ) -> plotly.graph_objs._figure.Figure:
+        """
+        Creates a Plotly figure visualizing the Z-score distributions and outliers
+        for a given threshold set.
+
+        This method generates histograms for each Z-score column in the given DataFrame,
+        colors them based on outlier status, and overlays them into a single figure.
+
+        Args:
+            df (pd.DataFrame):
+                The DataFrame containing the data to be visualized.
+            threshold_set_name (str):
+                The name of the threshold set being visualized.
+            col_outlier (str):
+                The column name indicating outlier status.
+            cols_threshold_scores (List[str]):
+                A list of column names representing the Z-scores to be visualized.
+            color_palette Optional(List[str]):
+                Optional list for color palette to use.
+                Defaults to use Dark24 color palette from Plotly.
+
+        Returns:
+            plotly.graph_objs._figure.Figure:
+                A Plotly figure object containing the visualization.
+        """
+
+        # Create histograms using plotly.express with pattern_shape and random color
+        figures = [
+            px.histogram(
+                df,
+                x=col,
+                color=col_outlier,
+                nbins=50,
+                pattern_shape=col_outlier,
+                opacity=0.7,
+            )
+            for col in cols_threshold_scores
+        ]
+
+        # Create a combined figure
+        fig = go.Figure()
+
+        # check that we have enough colors for figures if provided
+        if color_palette is not None and len(color_palette) < len(figures):
+            raise ReferenceError(
+                f"Color palette length must match figure length of {len(figures)}."
+            )
+
+        # Add traces from each histogram and modify colors, names, and pattern shapes
+        for idx, fig_hist in enumerate(figures):
+            if color_palette is None:
+                # Create a default list of colors from a Plotly color palette
+                fig_color = random.choice(pc.qualitative.Dark24)
+            else:
+                # otherwise, use static color palette based on the number of figures
+                fig_color = color_palette[idx]
+
+            for trace in fig_hist.data:
+                trace.marker.color = fig_color
+                trace.marker.pattern.shape = (
+                    "x" if trace.name == "True" else ""
+                )  # Use pattern shapes
+                renamed_col = cols_threshold_scores[idx].replace(
+                    f"cqc.{threshold_set_name}.Z_Score.", ""
+                )
+                trace.name = (
+                    f"{renamed_col} ({'outlier' if trace.name == 'True' else 'inlier'})"
+                )
+                # Update hovertemplate to match the name in the key
+                trace.hovertemplate = (
+                    f"<b>{renamed_col}</b><br>"
+                    + "Z-Score: %{x}<br>"
+                    + "Single-cell Count (log): %{y}<br>"
+                    + "<extra></extra>"
+                )
+                fig.add_trace(trace)
+
+        # Update layout
+        fig.update_layout(
+            title=f"{threshold_set_name.replace('_', ' ').title()} Z-Score Outliers",
+            xaxis_title="Z-Score",
+            yaxis_title="Single-cell Count (log)",
+            yaxis_type="log",
+            # ensures that histograms are overlapping
+            barmode="overlay",
+            legend_title_text="Measurement Type and QC Status",
+            legend={
+                "orientation": "v",
+                "yanchor": "top",
+                "y": 0.95,
+                "xanchor": "left",
+                "x": 1.02,
+            },
+        )
+
+        return fig
+
+    def find_image_columns(self: CytoDataFrame_type) -> bool:
+        pattern = r".*\.(tif|tiff)$"
+        return [
+            column
+            for column in self.columns
+            if self[column]
+            .apply(
+                lambda value: isinstance(value, str)
+                and re.match(pattern, value, flags=re.IGNORECASE)
+            )
+            .any()
+        ]
+
+    @staticmethod
+    def draw_outline_on_image(actual_image_path: str, mask_image_path: str) -> Image:
+        """
+        Draws outlines on a TIFF image based on a mask image and returns
+        the combined result.
+
+        This method takes the path to a TIFF image and a mask image, creates
+        an outline from the mask, and overlays it on the TIFF image. The resulting
+        image, which combines the TIFF image with the mask outline, is returned.
+
+        Args:
+            actual_image_path (str): Path to the TIFF image file.
+            mask_image_path (str): Path to the mask image file.
+
+        Returns:
+            PIL.Image.Image: A PIL Image object that is the result of
+            combining the TIFF image with the mask outline.
+
+        Raises:
+            FileNotFoundError: If the specified image or mask file does not exist.
+            ValueError: If the images are not in compatible formats or sizes.
+        """
+        # Load the TIFF image
+        tiff_image_array = skimage.io.imread(actual_image_path)
+
+        # Check if the image is 16-bit and grayscale
+        if tiff_image_array.dtype == np.uint16:
+            # Normalize the image to 8-bit for display purposes
+            tiff_image_array = (tiff_image_array / 256).astype(np.uint8)
+
+        # Convert to PIL Image and then to 'RGBA'
+        tiff_image = Image.fromarray(tiff_image_array).convert("RGBA")
+
+        # Check if the image is too dark and adjust brightness if needed
+        if is_image_too_dark(tiff_image):
+            tiff_image = adjust_image_brightness(tiff_image)
+
+        # Load the mask image and convert it to grayscale
+        mask_image = Image.open(mask_image_path).convert("L")
+        mask_array = np.array(mask_image)
+        mask_array[mask_array > 0] = 255  # Ensure non-zero values are 255 (white)
+
+        # Find contours of the mask
+        contours = skimage.measure.find_contours(mask_array, level=128)
+
+        # Create an outline image with transparent background
+        outline_image = Image.new("RGBA", tiff_image.size, (0, 0, 0, 0))
+        draw = ImageDraw.Draw(outline_image)
+
+        for contour in contours:
+            # Swap x and y to match image coordinates
+            draw.line(
+                [(x, y) for y, x in np.round(contour).astype(int)],
+                fill=(0, 255, 0, 200),
+                width=2,
+            )
+
+        # Combine the TIFF image with the outline image
+        return Image.alpha_composite(tiff_image, outline_image)
+
+    def process_image_data_as_html_display(
+        self: CytoDataFrame_type,
+        data_value: Any,  # noqa: ANN401
+        bounding_box: Tuple[int, int, int, int],
+    ) -> str:
+        if not pathlib.Path(data_value).is_file():
+            # Use rglob to recursively search for a matching file
+            if candidate_paths := list(
+                pathlib.Path(self._custom_attrs["data_context_dir"]).rglob(data_value)
+            ):
+                # if we find a candidate, return the first one
+                candidate_path = candidate_paths[0]
+            else:
+                # we don't have any candidate paths so return the unmodified value
+                return data_value
+
+        try:
+            if self._custom_attrs["data_mask_context_dir"] is not None and (
+                matching_mask_file := list(
+                    pathlib.Path(self._custom_attrs["data_mask_context_dir"]).rglob(
+                        f"{pathlib.Path(candidate_path).stem}*"
+                    )
+                )
+            ):
+                pil_image = self.draw_outline_on_image(
+                    actual_image_path=candidate_path,
+                    mask_image_path=matching_mask_file[0],
+                )
+
+            else:
+                # Read the TIFF image from the byte array
+                tiff_image = skimage.io.imread(candidate_path)
+
+                # Convert the image array to a PIL Image
+                pil_image = Image.fromarray(tiff_image)
+
+            cropped_img = pil_image.crop(bounding_box)
+
+            # Save the PIL Image as PNG to a BytesIO object
+            png_bytes_io = BytesIO()
+            cropped_img.save(png_bytes_io, format="PNG")
+
+            # Get the PNG byte data
+            png_bytes = png_bytes_io.getvalue()
+
+        except (FileNotFoundError, ValueError):
+            # return the raw data value if we run into an exception of some kind
+            print("Unable to process image from {candidate_path}")
+            return data_value
+
+        return (
+            '<img src="data:image/png;base64,'
+            f'{base64.b64encode(png_bytes).decode("utf-8")}" style="width:300px;"/>'
+        )
+
+    def get_displayed_rows(self: CytoDataFrame_type) -> List[int]:
+        # Get the current display settings
+        max_rows = pd.get_option("display.max_rows")
+        min_rows = pd.get_option("display.min_rows")
+
+        if len(self) <= max_rows:
+            # If the DataFrame has fewer rows than max_rows, all rows will be displayed
+            return self.index.tolist()
+        else:
+            # Calculate how many rows will be displayed at the beginning and end
+            half_min_rows = min_rows // 2
+            start_display = self.index[:half_min_rows].tolist()
+            end_display = self.index[-half_min_rows:].tolist()
+            return start_display + end_display
+
+    def _repr_html_(
+        self: CytoDataFrame_type, key: Optional[Union[int, str]] = None
+    ) -> str:
+        """
+        Returns HTML representation of the underlying pandas DataFrame
+        for use within Juypyter notebook environments and similar.
+
+        Referenced with modifications from:
+        https://github.com/pandas-dev/pandas/blob/v2.2.2/pandas/core/frame.py#L1216
+
+        Modifications added to help achieve image-based output for single-cell data
+        within the context of CytoDataFrame and coSMicQC.
+
+        Mainly for Jupyter notebooks.
+
+        Returns:
+            str: The data in a pandas DataFrame.
+        """
+
+        if self._info_repr():
+            buf = StringIO()
+            self.info(buf=buf)
+            # need to escape the <class>, should be the first line.
+            val = buf.getvalue().replace("<", r"&lt;", 1)
+            val = val.replace(">", r"&gt;", 1)
+            return f"<pre>{val}</pre>"
+
+        if get_option("display.notebook_repr_html"):
+            max_rows = get_option("display.max_rows")
+            min_rows = get_option("display.min_rows")
+            max_cols = get_option("display.max_columns")
+            show_dimensions = get_option("display.show_dimensions")
+
+            # determine if we have image_cols to display
+        if image_cols := self.find_image_columns():
+            # re-add bounding box cols if they are no longer available as in cases
+            # of masking or accessing various pandas attr's
+            bounding_box_externally_joined = False
+
+            if self._custom_attrs["data_bounding_box"] is not None and not all(
+                col in self.columns.tolist()
+                for col in self._custom_attrs["data_bounding_box"].columns.tolist()
+            ):
+                data = self.join(other=self._custom_attrs["data_bounding_box"])
+                bounding_box_externally_joined = True
+            else:
+                data = self.copy()
+
+            # gather indices which will be displayed based on pandas configuration
+            display_indices = self.get_displayed_rows()
+
+            # gather bounding box columns for use below
+            bounding_box_cols = self._custom_attrs["data_bounding_box"].columns.tolist()
+
+            for image_col in image_cols:
+                data.loc[display_indices, image_col] = data.loc[display_indices].apply(
+                    lambda row: self.process_image_data_as_html_display(
+                        data_value=row[image_col],
+                        bounding_box=(
+                            # rows below are specified using the column name to
+                            # determine which part of the bounding box the columns
+                            # relate to (the list of column names could be in
+                            # various order).
+                            row[
+                                next(
+                                    col
+                                    for col in bounding_box_cols
+                                    if "Minimum_X" in col
+                                )
+                            ],
+                            row[
+                                next(
+                                    col
+                                    for col in bounding_box_cols
+                                    if "Minimum_Y" in col
+                                )
+                            ],
+                            row[
+                                next(
+                                    col
+                                    for col in bounding_box_cols
+                                    if "Maximum_X" in col
+                                )
+                            ],
+                            row[
+                                next(
+                                    col
+                                    for col in bounding_box_cols
+                                    if "Maximum_Y" in col
+                                )
+                            ],
+                        ),
+                    ),
+                    axis=1,
+                )
+
+            if bounding_box_externally_joined:
+                data = data.drop(
+                    self._custom_attrs["data_bounding_box"].columns.tolist(), axis=1
+                )
+
+            formatter = fmt.DataFrameFormatter(
+                data,
+                columns=None,
+                col_space=None,
+                na_rep="NaN",
+                formatters=None,
+                float_format=None,
+                sparsify=None,
+                justify=None,
+                index_names=True,
+                header=True,
+                index=True,
+                bold_rows=True,
+                # note: we avoid escapes to allow HTML rendering for images
+                escape=False,
+                max_rows=max_rows,
+                min_rows=min_rows,
+                max_cols=max_cols,
+                show_dimensions=show_dimensions,
+                decimal=".",
+            )
+
+            return fmt.DataFrameRenderer(formatter).to_html()
+
+        else:
+            return None
diff --git a/src/cytodataframe/image.py b/src/cytodataframe/image.py
new file mode 100644
index 0000000..799b995
--- /dev/null
+++ b/src/cytodataframe/image.py
@@ -0,0 +1,66 @@
+"""
+Helper functions for working with images in the context of CytoDataFrames.
+"""
+
+import cv2
+import numpy as np
+from PIL import Image, ImageEnhance
+
+
+def is_image_too_dark(image: Image, pixel_brightness_threshold: float = 10.0) -> bool:
+    """
+    Check if the image is too dark based on the mean brightness.
+    By "too dark" we mean not as visible to the human eye.
+
+    Args:
+        image (Image):
+            The input PIL Image.
+        threshold (float):
+            The brightness threshold below which the image is considered too dark.
+
+    Returns:
+        bool:
+            True if the image is too dark, False otherwise.
+    """
+    # Convert the image to a numpy array and then to grayscale
+    img_array = np.array(image)
+    gray_image = cv2.cvtColor(img_array, cv2.COLOR_RGBA2GRAY)
+
+    # Calculate the mean brightness
+    mean_brightness = np.mean(gray_image)
+
+    return mean_brightness < pixel_brightness_threshold
+
+
+def adjust_image_brightness(image: Image) -> Image:
+    """
+    Adjust the brightness of an image using histogram equalization.
+
+    Args:
+        image (Image):
+            The input PIL Image.
+
+    Returns:
+        Image:
+            The brightness-adjusted PIL Image.
+    """
+    # Convert the image to numpy array and then to grayscale
+    img_array = np.array(image)
+    gray_image = cv2.cvtColor(img_array, cv2.COLOR_RGBA2GRAY)
+
+    # Apply histogram equalization to improve the contrast
+    equalized_image = cv2.equalizeHist(gray_image)
+
+    # Convert back to RGBA
+    img_array[:, :, 0] = equalized_image  # Update only the R channel
+    img_array[:, :, 1] = equalized_image  # Update only the G channel
+    img_array[:, :, 2] = equalized_image  # Update only the B channel
+
+    # Convert back to PIL Image
+    enhanced_image = Image.fromarray(img_array)
+
+    # Slightly reduce the brightness
+    enhancer = ImageEnhance.Brightness(enhanced_image)
+    reduced_brightness_image = enhancer.enhance(0.7)
+
+    return reduced_brightness_image
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..a312b25
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,144 @@
+"""
+Fixtures for testing via pytest.
+See here for more information:
+https://docs.pytest.org/en/7.1.x/explanation/fixtures.html
+"""
+
+import pathlib
+
+import numpy as np
+import pandas as pd
+import pytest
+import skimage
+from PIL import Image
+
+
+@pytest.fixture(name="cytotable_CFReT_data_df")
+def fixture_cytotable_CFReT_df():
+    """
+    Return df to test CytoTable CFReT_data
+    """
+    return pd.read_parquet(
+        "tests/data/cytotable/CFRet_data/test_localhost231120090001_converted.parquet"
+    )
+
+
+@pytest.fixture(name="cytotable_NF1_data_parquet_shrunken")
+def fixture_cytotable_NF1_data_parquet_shrunken():
+    """
+    Return df to test CytoTable NF1 data through shrunken parquet file
+    """
+    return (
+        "tests/data/cytotable/NF1_cellpainting_data_shrunken/"
+        "Plate_2_with_image_data_shrunken.parquet"
+    )
+
+
+@pytest.fixture(name="cytotable_nuclear_speckles_data_parquet")
+def fixture_cytotable_nuclear_speckle_data_parquet():
+    """
+    Return df to test CytoTable nuclear speckles data through shrunken parquet file
+    """
+    return "tests/data/cytotable/nuclear_speckles/test_slide1_converted.parquet"
+
+
+@pytest.fixture(name="basic_outlier_dataframe")
+def fixture_basic_outlier_dataframe():
+    """
+    Creates basic example data for use in tests
+    """
+    return pd.DataFrame({"example_feature": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
+
+
+@pytest.fixture(name="basic_outlier_csv")
+def fixture_basic_outlier_csv(
+    tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame
+):
+    """
+    Creates basic example data csv for use in tests
+    """
+
+    basic_outlier_dataframe.to_csv(
+        csv_path := tmp_path / "basic_example.csv", index=False
+    )
+
+    return csv_path
+
+
+@pytest.fixture(name="basic_outlier_csv_gz")
+def fixture_basic_outlier_csv_gz(
+    tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame
+):
+    """
+    Creates basic example data csv for use in tests
+    """
+
+    basic_outlier_dataframe.to_csv(
+        csv_gz_path := tmp_path / "example.csv.gz", index=False, compression="gzip"
+    )
+
+    return csv_gz_path
+
+
+@pytest.fixture(name="basic_outlier_tsv")
+def fixture_basic_outlier_tsv(
+    tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame
+):
+    """
+    Creates basic example data tsv for use in tests
+    """
+
+    basic_outlier_dataframe.to_csv(
+        tsv_path := tmp_path / "example.tsv", sep="\t", index=False
+    )
+
+    return tsv_path
+
+
+@pytest.fixture(name="basic_outlier_parquet")
+def fixture_basic_outlier_parquet(
+    tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame
+):
+    """
+    Creates basic example data parquet for use in tests
+    """
+
+    basic_outlier_dataframe.to_parquet(
+        parquet_path := tmp_path / "example.parquet", index=False
+    )
+
+    return parquet_path
+
+
+@pytest.fixture
+def fixture_dark_image():
+    # Create a dark image (50x50 pixels, almost black)
+    dark_img_array = np.zeros((50, 50, 3), dtype=np.uint8)
+    return Image.fromarray(dark_img_array)
+
+
+@pytest.fixture
+def fixture_mid_brightness_image():
+    # Create an image with medium brightness (50x50 pixels, mid gray)
+    mid_brightness_img_array = np.full((50, 50, 3), 128, dtype=np.uint8)
+    return Image.fromarray(mid_brightness_img_array)
+
+
+@pytest.fixture
+def fixture_bright_image():
+    # Create a bright image (50x50 pixels, almost white)
+    bright_img_array = np.full((50, 50, 3), 255, dtype=np.uint8)
+    return Image.fromarray(bright_img_array)
+
+
+@pytest.fixture
+def fixture_nuclear_speckle_example_image():
+    # create an image array from example nuclear speckle data
+    return Image.fromarray(
+        (
+            skimage.io.imread(
+                "tests/data/cytotable/nuclear_speckles/images/plate1/slide1_A1_M10_CH0_Z09_illumcorrect.tiff"
+            )
+            / 256
+        ).astype(np.uint8)
+    ).convert("RGBA")
diff --git a/tests/data/coSMicQC/show_report/cosmicqc_example_report.png b/tests/data/coSMicQC/show_report/cosmicqc_example_report.png
new file mode 100644
index 0000000..ce2fbdc
Binary files /dev/null and b/tests/data/coSMicQC/show_report/cosmicqc_example_report.png differ
diff --git a/tests/data/cytotable/CFRet_data/shrink_source_data.py b/tests/data/cytotable/CFRet_data/shrink_source_data.py
new file mode 100644
index 0000000..0a39f12
--- /dev/null
+++ b/tests/data/cytotable/CFRet_data/shrink_source_data.py
@@ -0,0 +1,24 @@
+"""
+Module to shrink source data for testing.
+
+Original source of data:
+https://github.com/WayScience/CFReT_data/blob/
+main/3.process_cfret_features/data/
+converted_profiles/localhost231120090001_converted.parquet
+"""
+
+import os
+
+import pandas as pd
+
+# note: we assume the dataset has been manually added to the
+# directory containing this module.
+filename = f"{os.path.dirname(__file__)}/localhost231120090001_converted.parquet"
+
+# read the data from parquet, sample a fraction of the data
+df = pd.read_parquet(filename).sample(frac=0.03, replace=True, random_state=1)
+
+# export to a new file
+df.to_parquet(
+    f"{os.path.dirname(__file__)}/test_localhost231120090001_converted.parquet"
+)
diff --git a/tests/data/cytotable/NF1_cellpainting_data/NF1_plate2_export_masks.cppipe b/tests/data/cytotable/NF1_cellpainting_data/NF1_plate2_export_masks.cppipe
new file mode 100644
index 0000000..b9c4567
--- /dev/null
+++ b/tests/data/cytotable/NF1_cellpainting_data/NF1_plate2_export_masks.cppipe
@@ -0,0 +1,227 @@
+CellProfiler Pipeline: http://www.cellprofiler.org
+Version:5
+DateRevision:424
+GitHash:
+ModuleCount:13
+HasImagePlaneDetails:False
+
+Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is left blank as we are giving the path to the corrected images in the CLI']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    :
+    Filter images?:Images only
+    Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.")
+
+Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Extract metadata from file names and folder names using regular expressions.', '', 'The only metadata that will be outputed in the SQLite DB file are:', '', 'Plate', 'Well', 'Site', '', 'The rest of the information is useful to make sure that the expression is working, but can be removed/not necessary.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Extract metadata?:Yes
+    Metadata data type:Text
+    Metadata types:{"Channel": "integer", "FileLocation": "text", "Frame": "text", "Plate": "text", "Series": "text", "Site": "integer", "Stain": "float", "Well": "text"}
+    Extraction method count:2
+    Metadata extraction method:Extract from file/folder names
+    Metadata source:File name
+    Regular expression to extract from file name:(?P<Well>[A-Z]{1}[0-9]{1,2})_01_(?P<Channel>[1-3]{1})_(?P<Site>[1-4]{1})_(?P<Stain>DAPI|GFP|RFP)
+    Regular expression to extract from folder name:(?P<Date>[0-9]{4}_[0-9]{2}_[0-9]{2})$
+    Extract metadata from:All images
+    Select the filtering criteria:and (file does contain "")
+    Metadata file location:Elsewhere...|
+    Match file and image metadata:[]
+    Use case insensitive matching?:No
+    Metadata file name:None
+    Does cached metadata exist?:No
+    Metadata extraction method:Extract from file/folder names
+    Metadata source:Folder name
+    Regular expression to extract from file name:^(?P<Plate>.*)_(?P<Well>[A-P][0-9]{2})_s(?P<Site>[0-9])_w(?P<ChannelNumber>[0-9])
+    Regular expression to extract from folder name:Corrected_(?P<Plate>Plate_[0-9]{1})
+    Extract metadata from:All images
+    Select the filtering criteria:and (file does contain "")
+    Metadata file location:Elsewhere...|
+    Match file and image metadata:[]
+    Use case insensitive matching?:No
+    Metadata file name:None
+    Does cached metadata exist?:No
+
+NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['Assign files to their respective channel (only 3):', '', 'DAPI', 'GFP', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Assign a name to:Images matching rules
+    Select the image type:Grayscale image
+    Name to assign these images:DNA
+    Match metadata:[]
+    Image set matching method:Order
+    Set intensity range from:Image metadata
+    Assignments count:3
+    Single images count:0
+    Maximum intensity:255.0
+    Process as 3D?:No
+    Relative pixel spacing in X:1.0
+    Relative pixel spacing in Y:1.0
+    Relative pixel spacing in Z:1.0
+    Select the rule criteria:and (file does contain "DAPI")
+    Name to assign these images:DAPI
+    Name to assign these objects:Cell
+    Select the image type:Grayscale image
+    Set intensity range from:Image metadata
+    Maximum intensity:255.0
+    Select the rule criteria:and (file does contain "GFP")
+    Name to assign these images:GFP
+    Name to assign these objects:Nucleus
+    Select the image type:Grayscale image
+    Set intensity range from:Image metadata
+    Maximum intensity:255.0
+    Select the rule criteria:and (file does contain "RFP")
+    Name to assign these images:RFP
+    Name to assign these objects:Cytoplasm
+    Select the image type:Grayscale image
+    Set intensity range from:Image metadata
+    Maximum intensity:255.0
+
+Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Do you want to group your images?:No
+    grouping metadata count:1
+    Metadata category:None
+
+IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['These are the current best parameters to segment nuclei from the DAPI channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input image:DAPI
+    Name the primary objects to be identified:Nuclei
+    Typical diameter of objects, in pixel units (Min,Max):30,90
+    Discard objects outside the diameter range?:Yes
+    Discard objects touching the border of the image?:Yes
+    Method to distinguish clumped objects:None
+    Method to draw dividing lines between clumped objects:Shape
+    Size of smoothing filter:10
+    Suppress local maxima that are closer than this minimum allowed distance:7.0
+    Speed up by using lower-resolution image to find local maxima?:Yes
+    Fill holes in identified objects?:After both thresholding and declumping
+    Automatically calculate size of smoothing filter for declumping?:Yes
+    Automatically calculate minimum allowed distance between local maxima?:Yes
+    Handling of objects if excessive number of objects identified:Continue
+    Maximum number of objects:500
+    Use advanced settings?:Yes
+    Threshold setting version:12
+    Threshold strategy:Global
+    Thresholding method:Otsu
+    Threshold smoothing scale:1.3488
+    Threshold correction factor:1.0
+    Lower and upper bounds on threshold:0.0,1.0
+    Manual threshold:0.0
+    Select the measurement to threshold with:None
+    Two-class or three-class thresholding?:Three classes
+    Log transform before thresholding?:No
+    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
+    Size of adaptive window:50
+    Lower outlier fraction:0.05
+    Upper outlier fraction:0.05
+    Averaging method:Mean
+    Variance method:Standard deviation
+    # of deviations:2.0
+    Thresholding method:Minimum Cross-Entropy
+
+IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['These are the current best parameters to segment whole cells using the RFP (actin) channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Nuclei
+    Name the objects to be identified:Cells
+    Select the method to identify the secondary objects:Propagation
+    Select the input image:RFP
+    Number of pixels by which to expand the primary objects:10
+    Regularization factor:0.05
+    Discard secondary objects touching the border of the image?:Yes
+    Discard the associated primary objects?:No
+    Name the new primary objects:Nuclei
+    Fill holes in identified objects?:Yes
+    Threshold setting version:12
+    Threshold strategy:Global
+    Thresholding method:Otsu
+    Threshold smoothing scale:1.3488
+    Threshold correction factor:1.0
+    Lower and upper bounds on threshold:0.0,1.0
+    Manual threshold:0.0
+    Select the measurement to threshold with:None
+    Two-class or three-class thresholding?:Three classes
+    Log transform before thresholding?:No
+    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
+    Size of adaptive window:50
+    Lower outlier fraction:0.05
+    Upper outlier fraction:0.05
+    Averaging method:Mean
+    Variance method:Standard deviation
+    # of deviations:2.0
+    Thresholding method:Otsu
+
+IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['This module creates a third object from the first two where the nuclei is subtracted from the cells to create cytoplasm']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the larger identified objects:Cells
+    Select the smaller identified objects:Nuclei
+    Name the tertiary objects to be identified:Cytoplasm
+    Shrink smaller object prior to subtraction?:Yes
+
+ConvertObjectsToImage:[module_num:8|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Nuclei
+    Name the output image:MaskNuclei
+    Select the color format:Binary (black & white)
+    Select the colormap:Default
+
+ConvertObjectsToImage:[module_num:9|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Cells
+    Name the output image:MaskCells
+    Select the color format:Binary (black & white)
+    Select the colormap:Default
+
+ConvertObjectsToImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Cytoplasm
+    Name the output image:MaskCytoplasm
+    Select the color format:Binary (black & white)
+    Select the colormap:Default
+
+SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the type of image to save:Image
+    Select the image to save:MaskNuclei
+    Select method for constructing file names:From image filename
+    Select image name for file prefix:DAPI
+    Enter single file name:OrigBlue
+    Number of digits:4
+    Append a suffix to the image file name?:Yes
+    Text to append to the image name:_MaskNuclei
+    Saved file format:tiff
+    Output file location:Default Output Folder|
+    Image bit depth:8-bit integer
+    Overwrite existing files without warning?:No
+    When to save:Every cycle
+    Record the file and path information to the saved image?:No
+    Create subfolders in the output folder?:No
+    Base image folder:Elsewhere...|
+    How to save the series:T (Time)
+    Save with lossless compression?:Yes
+
+SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the type of image to save:Image
+    Select the image to save:MaskCells
+    Select method for constructing file names:From image filename
+    Select image name for file prefix:RFP
+    Enter single file name:OrigBlue
+    Number of digits:4
+    Append a suffix to the image file name?:Yes
+    Text to append to the image name:_MaskCells
+    Saved file format:tiff
+    Output file location:Default Output Folder|
+    Image bit depth:8-bit integer
+    Overwrite existing files without warning?:No
+    When to save:Every cycle
+    Record the file and path information to the saved image?:No
+    Create subfolders in the output folder?:No
+    Base image folder:Elsewhere...|
+    How to save the series:T (Time)
+    Save with lossless compression?:Yes
+
+SaveImages:[module_num:13|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the type of image to save:Image
+    Select the image to save:MaskCytoplasm
+    Select method for constructing file names:From image filename
+    Select image name for file prefix:RFP
+    Enter single file name:OrigBlue
+    Number of digits:4
+    Append a suffix to the image file name?:Yes
+    Text to append to the image name:_MaskCytoplasm
+    Saved file format:tiff
+    Output file location:Default Output Folder|
+    Image bit depth:8-bit integer
+    Overwrite existing files without warning?:No
+    When to save:Every cycle
+    Record the file and path information to the saved image?:No
+    Create subfolders in the output folder?:No
+    Base image folder:Elsewhere...|
+    How to save the series:T (Time)
+    Save with lossless compression?:Yes
diff --git a/tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py b/tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py
new file mode 100644
index 0000000..12d46eb
--- /dev/null
+++ b/tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py
@@ -0,0 +1,83 @@
+"""
+Used for downloading and preparing data for use
+with coSMicQC tests.
+
+This file may be processed using the following command from the root
+of the project repository:
+`poetry run python tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py`
+"""
+
+import pathlib
+import subprocess
+import zipfile
+
+import pandas as pd
+
+# Define the paths
+test_data_path = "tests/data/cytotable/NF1_cellpainting_data/"
+sqlite_url = "https://github.com/WayScience/nf1_cellpainting_data/raw/main/2.cellprofiler_analysis/analysis_output/Plate_2/Plate_2_nf1_analysis.sqlite"
+sqlite_file_path = test_data_path + "Plate_2_nf1_analysis.sqlite"
+parquet_url = "https://github.com/WayScience/nf1_cellpainting_data/raw/main/3.processing_features/data/converted_data/Plate_2.parquet"
+parquet_file_path = test_data_path + "Plate_2.parquet"
+image_zip_url = "https://figshare.com/ndownloader/articles/22233700/versions/4"
+image_zip_file_path = test_data_path + "Plate_2_images.zip"
+image_extract_dir = test_data_path + "Plate_2_images"
+joined_data_path = test_data_path + "Plate_2_with_image_data.parquet"
+
+# for url and filepath, download the url as the filepath on local system
+for url, file_path in zip(
+    [sqlite_url, parquet_url, image_zip_url],
+    [sqlite_file_path, parquet_file_path, image_zip_file_path],
+):
+    if not pathlib.Path(file_path).is_file():
+        print(f"Downloading {file_path}...")
+        subprocess.run(["wget", "-O", file_path, url], check=True)
+        print(f"Downloaded {file_path}")
+
+# Check if the zip file exists
+if not pathlib.Path(image_zip_file_path).is_file():
+    print(f"{image_zip_file_path} does not exist.")
+else:
+    # Create the extraction directory if it doesn't exist
+    if not pathlib.Path(image_extract_dir).is_dir():
+        pathlib.Path(image_extract_dir).mkdir(parents=True)
+
+    # Extract the zip file
+    with zipfile.ZipFile(image_zip_file_path, "r") as zip_ref:
+        zip_ref.extractall(image_extract_dir)
+
+    print(f"Extracted {image_zip_file_path} to {image_extract_dir}")
+
+# form parquet table which includes image paths
+df_cytotable = pd.read_parquet(parquet_file_path)
+
+# read image data from the CellProfiler SQlite database
+df_image = pd.read_sql(
+    sql="SELECT * FROM per_image;", con=f"sqlite:///{sqlite_file_path}"
+)
+
+# merge the image data from the CellProfiler SQLite database
+df_full = pd.merge(
+    left=df_cytotable,
+    right=df_image,
+    how="left",
+    left_on="Metadata_ImageNumber",
+    right_on="ImageNumber",
+)
+
+
+# modify the filename to match what was gathered from figshare
+def modify_filename(file_path: str):
+    return file_path.replace("_illumcorrect.tiff", ".tif")
+
+
+# apply the filename rename to several columns
+df_full["Image_URL_DAPI"] = df_full["Image_URL_DAPI"].apply(modify_filename)
+df_full["Image_URL_GFP"] = df_full["Image_URL_GFP"].apply(modify_filename)
+df_full["Image_URL_RFP"] = df_full["Image_URL_RFP"].apply(modify_filename)
+df_full["Image_FileName_DAPI"] = df_full["Image_FileName_DAPI"].apply(modify_filename)
+df_full["Image_FileName_GFP"] = df_full["Image_FileName_GFP"].apply(modify_filename)
+df_full["Image_FileName_RFP"] = df_full["Image_FileName_RFP"].apply(modify_filename)
+
+# export the result to parquet file
+df_full.to_parquet(joined_data_path)
diff --git a/tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe b/tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe
new file mode 100644
index 0000000..b9c4567
--- /dev/null
+++ b/tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe
@@ -0,0 +1,227 @@
+CellProfiler Pipeline: http://www.cellprofiler.org
+Version:5
+DateRevision:424
+GitHash:
+ModuleCount:13
+HasImagePlaneDetails:False
+
+Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is left blank as we are giving the path to the corrected images in the CLI']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    :
+    Filter images?:Images only
+    Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.")
+
+Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Extract metadata from file names and folder names using regular expressions.', '', 'The only metadata that will be outputed in the SQLite DB file are:', '', 'Plate', 'Well', 'Site', '', 'The rest of the information is useful to make sure that the expression is working, but can be removed/not necessary.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Extract metadata?:Yes
+    Metadata data type:Text
+    Metadata types:{"Channel": "integer", "FileLocation": "text", "Frame": "text", "Plate": "text", "Series": "text", "Site": "integer", "Stain": "float", "Well": "text"}
+    Extraction method count:2
+    Metadata extraction method:Extract from file/folder names
+    Metadata source:File name
+    Regular expression to extract from file name:(?P<Well>[A-Z]{1}[0-9]{1,2})_01_(?P<Channel>[1-3]{1})_(?P<Site>[1-4]{1})_(?P<Stain>DAPI|GFP|RFP)
+    Regular expression to extract from folder name:(?P<Date>[0-9]{4}_[0-9]{2}_[0-9]{2})$
+    Extract metadata from:All images
+    Select the filtering criteria:and (file does contain "")
+    Metadata file location:Elsewhere...|
+    Match file and image metadata:[]
+    Use case insensitive matching?:No
+    Metadata file name:None
+    Does cached metadata exist?:No
+    Metadata extraction method:Extract from file/folder names
+    Metadata source:Folder name
+    Regular expression to extract from file name:^(?P<Plate>.*)_(?P<Well>[A-P][0-9]{2})_s(?P<Site>[0-9])_w(?P<ChannelNumber>[0-9])
+    Regular expression to extract from folder name:Corrected_(?P<Plate>Plate_[0-9]{1})
+    Extract metadata from:All images
+    Select the filtering criteria:and (file does contain "")
+    Metadata file location:Elsewhere...|
+    Match file and image metadata:[]
+    Use case insensitive matching?:No
+    Metadata file name:None
+    Does cached metadata exist?:No
+
+NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['Assign files to their respective channel (only 3):', '', 'DAPI', 'GFP', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Assign a name to:Images matching rules
+    Select the image type:Grayscale image
+    Name to assign these images:DNA
+    Match metadata:[]
+    Image set matching method:Order
+    Set intensity range from:Image metadata
+    Assignments count:3
+    Single images count:0
+    Maximum intensity:255.0
+    Process as 3D?:No
+    Relative pixel spacing in X:1.0
+    Relative pixel spacing in Y:1.0
+    Relative pixel spacing in Z:1.0
+    Select the rule criteria:and (file does contain "DAPI")
+    Name to assign these images:DAPI
+    Name to assign these objects:Cell
+    Select the image type:Grayscale image
+    Set intensity range from:Image metadata
+    Maximum intensity:255.0
+    Select the rule criteria:and (file does contain "GFP")
+    Name to assign these images:GFP
+    Name to assign these objects:Nucleus
+    Select the image type:Grayscale image
+    Set intensity range from:Image metadata
+    Maximum intensity:255.0
+    Select the rule criteria:and (file does contain "RFP")
+    Name to assign these images:RFP
+    Name to assign these objects:Cytoplasm
+    Select the image type:Grayscale image
+    Set intensity range from:Image metadata
+    Maximum intensity:255.0
+
+Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Do you want to group your images?:No
+    grouping metadata count:1
+    Metadata category:None
+
+IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['These are the current best parameters to segment nuclei from the DAPI channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input image:DAPI
+    Name the primary objects to be identified:Nuclei
+    Typical diameter of objects, in pixel units (Min,Max):30,90
+    Discard objects outside the diameter range?:Yes
+    Discard objects touching the border of the image?:Yes
+    Method to distinguish clumped objects:None
+    Method to draw dividing lines between clumped objects:Shape
+    Size of smoothing filter:10
+    Suppress local maxima that are closer than this minimum allowed distance:7.0
+    Speed up by using lower-resolution image to find local maxima?:Yes
+    Fill holes in identified objects?:After both thresholding and declumping
+    Automatically calculate size of smoothing filter for declumping?:Yes
+    Automatically calculate minimum allowed distance between local maxima?:Yes
+    Handling of objects if excessive number of objects identified:Continue
+    Maximum number of objects:500
+    Use advanced settings?:Yes
+    Threshold setting version:12
+    Threshold strategy:Global
+    Thresholding method:Otsu
+    Threshold smoothing scale:1.3488
+    Threshold correction factor:1.0
+    Lower and upper bounds on threshold:0.0,1.0
+    Manual threshold:0.0
+    Select the measurement to threshold with:None
+    Two-class or three-class thresholding?:Three classes
+    Log transform before thresholding?:No
+    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
+    Size of adaptive window:50
+    Lower outlier fraction:0.05
+    Upper outlier fraction:0.05
+    Averaging method:Mean
+    Variance method:Standard deviation
+    # of deviations:2.0
+    Thresholding method:Minimum Cross-Entropy
+
+IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['These are the current best parameters to segment whole cells using the RFP (actin) channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Nuclei
+    Name the objects to be identified:Cells
+    Select the method to identify the secondary objects:Propagation
+    Select the input image:RFP
+    Number of pixels by which to expand the primary objects:10
+    Regularization factor:0.05
+    Discard secondary objects touching the border of the image?:Yes
+    Discard the associated primary objects?:No
+    Name the new primary objects:Nuclei
+    Fill holes in identified objects?:Yes
+    Threshold setting version:12
+    Threshold strategy:Global
+    Thresholding method:Otsu
+    Threshold smoothing scale:1.3488
+    Threshold correction factor:1.0
+    Lower and upper bounds on threshold:0.0,1.0
+    Manual threshold:0.0
+    Select the measurement to threshold with:None
+    Two-class or three-class thresholding?:Three classes
+    Log transform before thresholding?:No
+    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
+    Size of adaptive window:50
+    Lower outlier fraction:0.05
+    Upper outlier fraction:0.05
+    Averaging method:Mean
+    Variance method:Standard deviation
+    # of deviations:2.0
+    Thresholding method:Otsu
+
+IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['This module creates a third object from the first two where the nuclei is subtracted from the cells to create cytoplasm']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the larger identified objects:Cells
+    Select the smaller identified objects:Nuclei
+    Name the tertiary objects to be identified:Cytoplasm
+    Shrink smaller object prior to subtraction?:Yes
+
+ConvertObjectsToImage:[module_num:8|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Nuclei
+    Name the output image:MaskNuclei
+    Select the color format:Binary (black & white)
+    Select the colormap:Default
+
+ConvertObjectsToImage:[module_num:9|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Cells
+    Name the output image:MaskCells
+    Select the color format:Binary (black & white)
+    Select the colormap:Default
+
+ConvertObjectsToImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input objects:Cytoplasm
+    Name the output image:MaskCytoplasm
+    Select the color format:Binary (black & white)
+    Select the colormap:Default
+
+SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the type of image to save:Image
+    Select the image to save:MaskNuclei
+    Select method for constructing file names:From image filename
+    Select image name for file prefix:DAPI
+    Enter single file name:OrigBlue
+    Number of digits:4
+    Append a suffix to the image file name?:Yes
+    Text to append to the image name:_MaskNuclei
+    Saved file format:tiff
+    Output file location:Default Output Folder|
+    Image bit depth:8-bit integer
+    Overwrite existing files without warning?:No
+    When to save:Every cycle
+    Record the file and path information to the saved image?:No
+    Create subfolders in the output folder?:No
+    Base image folder:Elsewhere...|
+    How to save the series:T (Time)
+    Save with lossless compression?:Yes
+
+SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the type of image to save:Image
+    Select the image to save:MaskCells
+    Select method for constructing file names:From image filename
+    Select image name for file prefix:RFP
+    Enter single file name:OrigBlue
+    Number of digits:4
+    Append a suffix to the image file name?:Yes
+    Text to append to the image name:_MaskCells
+    Saved file format:tiff
+    Output file location:Default Output Folder|
+    Image bit depth:8-bit integer
+    Overwrite existing files without warning?:No
+    When to save:Every cycle
+    Record the file and path information to the saved image?:No
+    Create subfolders in the output folder?:No
+    Base image folder:Elsewhere...|
+    How to save the series:T (Time)
+    Save with lossless compression?:Yes
+
+SaveImages:[module_num:13|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+    Select the type of image to save:Image
+    Select the image to save:MaskCytoplasm
+    Select method for constructing file names:From image filename
+    Select image name for file prefix:RFP
+    Enter single file name:OrigBlue
+    Number of digits:4
+    Append a suffix to the image file name?:Yes
+    Text to append to the image name:_MaskCytoplasm
+    Saved file format:tiff
+    Output file location:Default Output Folder|
+    Image bit depth:8-bit integer
+    Overwrite existing files without warning?:No
+    When to save:Every cycle
+    Record the file and path information to the saved image?:No
+    Create subfolders in the output folder?:No
+    Base image folder:Elsewhere...|
+    How to save the series:T (Time)
+    Save with lossless compression?:Yes
diff --git a/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py
new file mode 100644
index 0000000..a63eec1
--- /dev/null
+++ b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py
@@ -0,0 +1,73 @@
+"""
+Creates and shrunken dataset for testing puproses
+based on coSMicQC/tests/data/cytotable/NF1_cellpainting_data (Plate 2)
+
+This file may be processed using the following command from the root
+of the project repository:
+`poetry run python tests/data/cytotable/NF1_cellpainting_data_shrunken/create.py`
+"""
+
+import pathlib
+import shutil
+
+import pandas as pd
+
+source_data_path = "tests/data/cytotable/NF1_cellpainting_data/"
+target_data_path = "tests/data/cytotable/NF1_cellpainting_data_shrunken/"
+source_image_data_path = source_data_path + "Plate_2_images"
+source_parquet_path = source_data_path + "Plate_2_with_image_data.parquet"
+target_image_data_path = target_data_path + "Plate_2_images"
+target_parquet_path = target_data_path + "Plate_2_with_image_data_shrunken.parquet"
+
+# create target image dir
+pathlib.Path(target_image_data_path).mkdir(exist_ok=True)
+
+# read source data
+source_df = pd.read_parquet(source_parquet_path)
+
+# get a sample of 5 from the source data
+sampled_df = source_df.sample(n=5)
+
+# send the sampled df to parquet file
+sampled_df.to_parquet(target_parquet_path)
+
+
+def check_and_copy_file(filename: str):
+    """
+    Checks for files in target dir and copies them if they don't already exist
+    """
+    source_path = f"{source_image_data_path}/{filename}"
+    target_path = f"{target_image_data_path}/{filename}"
+
+    # Check if the file already exists in the target directory
+    if not pathlib.Path(target_path).is_file():
+        # Copy the file if it doesn't exist
+        shutil.copy(source_path, target_path)
+        return f"Copied {filename} to {target_image_data_path}"
+    else:
+        return f"{filename} already exists in {target_image_data_path}"
+
+
+# apply the file copy and collect status information
+sampled_df["Image_FileName_DAPI_status"] = sampled_df["Image_FileName_DAPI"].apply(
+    check_and_copy_file
+)
+sampled_df["Image_FileName_GFP_status"] = sampled_df["Image_FileName_GFP"].apply(
+    check_and_copy_file
+)
+sampled_df["Image_FileName_RFP_status"] = sampled_df["Image_FileName_RFP"].apply(
+    check_and_copy_file
+)
+
+sampled_df
+
+# show the results using a mask on the dataframe for status
+print(
+    sampled_df[
+        [
+            "Image_FileName_DAPI_status",
+            "Image_FileName_GFP_status",
+            "Image_FileName_RFP_status",
+        ]
+    ]
+)
diff --git a/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py
new file mode 100644
index 0000000..d360508
--- /dev/null
+++ b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py
@@ -0,0 +1,51 @@
+"""
+Creates image masks for images in
+coSMicQC/tests/data/cytotable/NF1_cellpainting_data (Plate 2)
+
+Note: expects Docker to be installed as a CLI on the system.
+
+This file may be processed using the following command from the root
+of the project repository:
+`poetry run python \
+tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py`
+"""
+
+import os
+import pathlib
+import subprocess
+
+# create a dir for segmentation masks
+pathlib.Path("tests/data/cytotable/NF1_cellpainting_data_shrunken/Plate_2_masks").mkdir(
+    exist_ok=True
+)
+
+# define docker command for CellProfiler use with provided pipeline file
+command = [
+    "docker",
+    "run",
+    "--platform",
+    "linux/amd64",
+    "--rm",
+    "-v",
+    f"{os.getcwd()}/tests/data/cytotable/NF1_cellpainting_data_shrunken:/app",
+    "cellprofiler/cellprofiler:4.2.4",
+    "cellprofiler",
+    "-c",
+    "-r",
+    "-p",
+    "/app/NF1_plate2_export_masks.cppipe",
+    "-o",
+    "/app/Plate_2_masks",
+    "-i",
+    "/app/Plate_2_images",
+]
+
+# Run the command and show output
+try:
+    result = subprocess.run(command, check=True, text=True, capture_output=True)
+    print("Command Output:\n", result.stdout)
+    print("Command Error:\n", result.stderr)
+except subprocess.CalledProcessError as e:
+    print("Error:", e)
+    print("Command Output:\n", e.stdout)
+    print("Command Error:\n", e.stderr)
diff --git a/tests/data/cytotable/nuclear_speckles/shrink_source_data.py b/tests/data/cytotable/nuclear_speckles/shrink_source_data.py
new file mode 100644
index 0000000..210843f
--- /dev/null
+++ b/tests/data/cytotable/nuclear_speckles/shrink_source_data.py
@@ -0,0 +1,31 @@
+"""
+Module to shrink source data for testing.
+
+Original source of data (processing):
+https://github.com/WayScience/nuclear_speckle_image_profiling
+"""
+
+import os
+
+import pandas as pd
+
+# note: we assume the dataset has been manually added to the
+# directory containing this module.
+filename = f"{os.path.dirname(__file__)}/slide1_converted.parquet"
+
+# read the data from parquet, sample a fraction of the data
+df = pd.read_parquet(filename)
+
+# filter to only those data which include slide1_A1_M10_CH0_Z09_illumcorrect
+df = df[
+    (
+        df["Image_FileName_A647"].str.contains(
+            img_str := "slide1_A1_M10_CH0_Z09_illumcorrect"
+        )
+    )
+    | (df["Image_FileName_DAPI"].str.contains(img_str))
+    | (df["Image_FileName_GOLD"].str.contains(img_str))
+]
+
+# export to a new file
+df.to_parquet(f"{os.path.dirname(__file__)}/test_slide1_converted.parquet")
diff --git a/tests/test_frame.py b/tests/test_frame.py
new file mode 100644
index 0000000..332089c
--- /dev/null
+++ b/tests/test_frame.py
@@ -0,0 +1,193 @@
+"""
+Tests cosmicqc CytoDataFrame module
+"""
+
+import pathlib
+
+import cosmicqc
+import pandas as pd
+import plotly
+import plotly.colors as pc
+import pytest
+from pyarrow import parquet
+
+from cytodataframe.frame import CytoDataFrame
+from tests.utils import cytodataframe_image_display_contains_green_pixels
+
+
+def test_cytodataframe_input(
+    tmp_path: pathlib.Path,
+    basic_outlier_dataframe: pd.DataFrame,
+    basic_outlier_csv: str,
+    basic_outlier_csv_gz: str,
+    basic_outlier_tsv: str,
+    basic_outlier_parquet: str,
+):
+    # Tests CytoDataFrame with pd.DataFrame input.
+    sc_df = CytoDataFrame(data=basic_outlier_dataframe)
+
+    # test that we ingested the data properly
+    assert sc_df._custom_attrs["data_source"] == "pandas.DataFrame"
+    assert sc_df.equals(basic_outlier_dataframe)
+
+    # test export
+    basic_outlier_dataframe.to_parquet(
+        control_path := f"{tmp_path}/df_input_example.parquet"
+    )
+    sc_df.export(test_path := f"{tmp_path}/df_input_example1.parquet")
+
+    assert parquet.read_table(control_path).equals(parquet.read_table(test_path))
+
+    # Tests CytoDataFrame with pd.Series input.
+    sc_df = CytoDataFrame(data=basic_outlier_dataframe.loc[0])
+
+    # test that we ingested the data properly
+    assert sc_df._custom_attrs["data_source"] == "pandas.Series"
+    assert sc_df.equals(pd.DataFrame(basic_outlier_dataframe.loc[0]))
+
+    # Tests CytoDataFrame with CSV input.
+    sc_df = CytoDataFrame(data=basic_outlier_csv)
+    expected_df = pd.read_csv(basic_outlier_csv)
+
+    # test that we ingested the data properly
+    assert sc_df._custom_attrs["data_source"] == str(basic_outlier_csv)
+    assert sc_df.equals(expected_df)
+
+    # test export
+    sc_df.export(test_path := f"{tmp_path}/df_input_example.csv", index=False)
+
+    pd.testing.assert_frame_equal(expected_df, pd.read_csv(test_path))
+
+    # Tests CytoDataFrame with CSV input.
+    sc_df = CytoDataFrame(data=basic_outlier_csv_gz)
+    expected_df = pd.read_csv(basic_outlier_csv_gz)
+
+    # test that we ingested the data properly
+    assert sc_df._custom_attrs["data_source"] == str(basic_outlier_csv_gz)
+    assert sc_df.equals(expected_df)
+
+    # test export
+    sc_df.export(test_path := f"{tmp_path}/df_input_example.csv.gz", index=False)
+
+    pd.testing.assert_frame_equal(
+        expected_df, pd.read_csv(test_path, compression="gzip")
+    )
+
+    # Tests CytoDataFrame with TSV input.
+    sc_df = CytoDataFrame(data=basic_outlier_tsv)
+    expected_df = pd.read_csv(basic_outlier_tsv, delimiter="\t")
+
+    # test that we ingested the data properly
+    assert sc_df._custom_attrs["data_source"] == str(basic_outlier_tsv)
+    assert sc_df.equals(expected_df)
+
+    # test export
+    sc_df.export(test_path := f"{tmp_path}/df_input_example.tsv", index=False)
+
+    pd.testing.assert_frame_equal(expected_df, pd.read_csv(test_path, sep="\t"))
+
+    # Tests CytoDataFrame with parquet input.
+    sc_df = CytoDataFrame(data=basic_outlier_parquet)
+    expected_df = pd.read_parquet(basic_outlier_parquet)
+
+    # test that we ingested the data properly
+    assert sc_df._custom_attrs["data_source"] == str(basic_outlier_parquet)
+    assert sc_df.equals(expected_df)
+
+    # test export
+    sc_df.export(test_path := f"{tmp_path}/df_input_example2.parquet")
+
+    assert parquet.read_table(basic_outlier_parquet).equals(
+        parquet.read_table(test_path)
+    )
+
+    # test CytoDataFrame with CytoDataFrame input
+    copy_sc_df = CytoDataFrame(data=sc_df)
+
+    pd.testing.assert_frame_equal(copy_sc_df, sc_df)
+
+
+def test_show_report(cytotable_CFReT_data_df: pd.DataFrame):
+    """
+    Used for testing show report capabilities
+    """
+
+    df = cosmicqc.analyze.label_outliers(
+        df=cytotable_CFReT_data_df,
+        include_threshold_scores=True,
+    )
+
+    figures = df.show_report(auto_open=False)
+
+    expected_number_figures = 3
+    assert len(figures) == expected_number_figures
+    assert (
+        next(iter({type(figure) for figure in figures}))
+        == plotly.graph_objs._figure.Figure
+    )
+
+    df.show_report(
+        report_path=(report_path := pathlib.Path("cosmicqc_example_report.html")),
+        auto_open=False,
+    )
+
+    assert report_path.is_file()
+
+
+def test_repr_html(
+    cytotable_NF1_data_parquet_shrunken: str,
+    cytotable_nuclear_speckles_data_parquet: str,
+):
+    """
+    Tests how images are rendered through customized repr_html in CytoDataFrame.
+    """
+
+    # Ensure there's at least one greenish pixel in the image
+    assert cytodataframe_image_display_contains_green_pixels(
+        frame=CytoDataFrame(
+            data=cytotable_NF1_data_parquet_shrunken,
+            data_context_dir=f"{pathlib.Path(cytotable_NF1_data_parquet_shrunken).parent}/Plate_2_images",
+            data_mask_context_dir=f"{pathlib.Path(cytotable_NF1_data_parquet_shrunken).parent}/Plate_2_masks",
+        ),
+        image_cols=["Image_FileName_DAPI", "Image_FileName_GFP", "Image_FileName_RFP"],
+    ), "The NF1 images do not contain green outlines."
+    assert cytodataframe_image_display_contains_green_pixels(
+        frame=CytoDataFrame(
+            data=cytotable_nuclear_speckles_data_parquet,
+            data_context_dir=f"{pathlib.Path(cytotable_nuclear_speckles_data_parquet).parent}/images",
+            data_mask_context_dir=f"{pathlib.Path(cytotable_nuclear_speckles_data_parquet).parent}/masks",
+        ),
+        image_cols=[
+            "Image_FileName_A647",
+            "Image_FileName_DAPI",
+            "Image_FileName_GOLD",
+        ],
+    ), "The nuclear speckles images do not contain green outlines."
+
+
+@pytest.mark.generate_report_image
+def fixture_generate_show_report_html_output(cytotable_CFReT_data_df: pd.DataFrame):
+    """
+    Used for generating report output for use with other tests.
+    """
+
+    # create outliers dataframe
+    df = cosmicqc.analyze.label_outliers(
+        df=cytotable_CFReT_data_df,
+        include_threshold_scores=True,
+    )
+
+    # show a report
+    df.show_report(
+        report_path=(
+            report_path := pathlib.Path(__file__).parent
+            / "data"
+            / "coSMicQC"
+            / "show_report"
+            / "cosmicqc_example_report.html"
+        ),
+        color_palette=pc.qualitative.Dark24[0:2],
+        auto_open=False,
+    )
+
+    return report_path
diff --git a/tests/test_image.py b/tests/test_image.py
new file mode 100644
index 0000000..349eae8
--- /dev/null
+++ b/tests/test_image.py
@@ -0,0 +1,53 @@
+"""
+Tests cosmicqc image module
+"""
+
+from PIL import Image
+
+from cytodataframe.image import adjust_image_brightness, is_image_too_dark
+
+
+def test_is_image_too_dark_with_dark_image(fixture_dark_image: Image):
+    assert is_image_too_dark(fixture_dark_image, pixel_brightness_threshold=10.0)
+
+
+def test_is_image_too_dark_with_bright_image(fixture_bright_image: Image):
+    assert not is_image_too_dark(fixture_bright_image, pixel_brightness_threshold=10.0)
+
+
+def test_is_image_too_dark_with_mid_brightness_image(
+    fixture_mid_brightness_image: Image,
+):
+    assert not is_image_too_dark(
+        fixture_mid_brightness_image, pixel_brightness_threshold=10.0
+    )
+
+
+def test_adjust_image_brightness_with_dark_image(fixture_dark_image: Image):
+    adjusted_image = adjust_image_brightness(fixture_dark_image)
+    # we expect that image to be too dark (it's all dark, so there's no adjustments)
+    assert is_image_too_dark(adjusted_image, pixel_brightness_threshold=10.0)
+
+
+def test_adjust_image_brightness_with_bright_image(fixture_bright_image: Image):
+    adjusted_image = adjust_image_brightness(fixture_bright_image)
+    # Since the image was already bright, it should remain bright
+    assert not is_image_too_dark(adjusted_image, pixel_brightness_threshold=10.0)
+
+
+def test_adjust_image_brightness_with_mid_brightness_image(
+    fixture_mid_brightness_image: Image,
+):
+    adjusted_image = adjust_image_brightness(fixture_mid_brightness_image)
+    # The image should still not be too dark after adjustment
+    assert not is_image_too_dark(adjusted_image, pixel_brightness_threshold=10.0)
+
+
+def test_adjust_nuclear_speckle_image_brightness(
+    fixture_nuclear_speckle_example_image: Image,
+):
+    assert is_image_too_dark(fixture_nuclear_speckle_example_image)
+    assert not is_image_too_dark(
+        adjust_image_brightness(fixture_nuclear_speckle_example_image),
+        pixel_brightness_threshold=3.0,
+    )
diff --git a/tests/utils.py b/tests/utils.py
new file mode 100644
index 0000000..a1f7887
--- /dev/null
+++ b/tests/utils.py
@@ -0,0 +1,77 @@
+"""
+Utilities for running pytest tests in CytoDataFrame
+"""
+
+import base64
+import re
+from io import BytesIO
+from typing import List
+
+import numpy as np
+from PIL import Image
+
+from cytodataframe import CytoDataFrame
+
+
+def cytodataframe_image_display_contains_green_pixels(
+    frame: CytoDataFrame, image_cols: List[str]
+) -> bool:
+    """
+    Determines if relevant image from the CytoDataFrame HTML
+    contains green pixels.
+
+    Args:
+        frame (CytoDataFrame):
+            A custom `CytoDataFrame` object which includes image paths.
+        image_cols (List[str]):
+            A list of column names in the `CytoDataFrame`
+            that contain images paths.
+
+    Returns:
+        bool:
+            True if any greenish pixels are found in relevant
+            image within the HTML, otherwise False.
+
+    Raises:
+        ValueError:
+            If no base64-encoded image data is found in the
+            HTML representation of the given columns.
+    """
+
+    # gather HTML output from CytoDataFrame
+    html_output = frame[image_cols]._repr_html_()
+
+    # Extract all base64 image data from the HTML
+    matches = re.findall(r'data:image/png;base64,([^"]+)', html_output)
+
+    # check that we have matches
+    if not len(matches) > 0:
+        raise ValueError("No base64 image data found in HTML")
+
+    # Select the third base64 image data (indexing starts from 0)
+    # (we expect the first ones to not contain outlines based on the
+    # html and example data)
+    base64_data = matches[2]
+
+    # Decode the base64 image data
+    image_data = base64.b64decode(base64_data)
+    image = Image.open(BytesIO(image_data)).convert("RGB")
+
+    # Check for the presence of green pixels in the image
+    image_array = np.array(image)
+
+    # gather color channels from image
+    red_channel = image_array[:, :, 0]
+    green_channel = image_array[:, :, 1]
+    blue_channel = image_array[:, :, 2]
+
+    # Define a threshold to identify greenish pixels
+    green_threshold = 50
+    green_pixels = (
+        (green_channel > green_threshold)
+        & (green_channel > red_channel)
+        & (green_channel > blue_channel)
+    )
+
+    # return true/false if there's at least one greenish pixel in the image
+    return np.any(green_pixels)