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Contributing to Awesome Computational Primatology

Thank you for your interest in contributing! This document explains how to add papers and improve the project.

🚀 Quick Start

Preview Your Changes Locally 

# 1. Fork and clone the repository
git clone https://github.com/YOUR-USERNAME/awesome-computational-primatology.git
cd awesome-computational-primatology

# 2. Make your changes to README.md

# 3. Preview the website (auto-generates index.html and opens browser)
python scripts/dev-preview.py

# 4. Commit BOTH README.md and index.html, then create a pull request

Important: Always commit index.html along with your README.md changes. The CI will fail if they're out of sync.

Automatic PR Previews

When you submit a PR, our automation will:

  • ✅ Generate a preview website with your changes
  • ✅ Post a comment with the preview link
  • ✅ Validate table formatting and links
  • ✅ Check that index.html is in sync with README.md

1. Branch Protocol

  • Fork the repository
  • Create a branch with format: add-paper/YYYY-AuthorName (e.g., add-paper/2024-Smith)
  • For multiple papers or other changes: update/brief-description
  • Add your paper in the correct section following the format below
  • Verify all links are working

2. Pull Request Process

  1. Create a draft PR first
  2. Use title format: "Add: YYYY AuthorName paper" or "Update: brief description"
  3. Fill out the PR template
  4. Mark as ready for review when complete

3. Review Process

  • Maintainers will review within 1-2 weeks
  • Automated checks will verify table formatting and links
  • Reviews focus on:
    • Correct formatting
    • Working links
    • Appropriate categorization
    • Complete information

Eligibility Criteria

  • Papers must be at the intersection of deep learning and non-human primatology
  • Published from 2012 onwards (around AlexNet era)
  • Must provide novel approaches or applications in computational primatology
  • Cross-species datasets including primates are acceptable

Table Format

Add your paper to the appropriate table section using this format: | Year | Paper | Topic | Animal | Model? | Data? | Image/Video Count |

Where:

  • Year: Publication year
  • Paper: [Title](link) or just Title if preprint
  • Topic: Use abbreviations from Topic Legend (PD, BPE, FD, etc.)
  • Animal: Specific primate species or "Cross-species"
  • Model?:
    • [Yes](link) if code + pretrained models available
    • [Code only](link) if repository available but no pretrained models
    • [No](link) if repository with information but no functional code
    • "N/A" if neither available
  • Data?:
    • [Yes](link) if publicly available
    • "Upon request" if available through contact
    • "N/A" if not available
  • Image/Video Count: Number or "N/A" if not applicable

Topic Legend

Use these abbreviations for the Topic column:

  • PD: Primate Detection
  • BPE: Body Pose Estimation
  • FD: Face Detection
  • FLE: Facial Landmark Estimation
  • FR: Face Recognition and/or Re-Identification
  • FAC: Facial Action Coding / Units
  • HD: Hand Detection
  • HPE: Hand Pose Estimation
  • BR: Behavior Recognition / Understanding / Modeling
  • AM: Avatar / Mesh
  • SI: Species Identification
  • RL: Reinforcement Learning
  • O: Other

Verification Steps

Before submitting your PR:

  1. Verify all links are accessible
  2. Check table formatting matches existing entries
  3. Ensure topic abbreviations are correct
  4. Confirm model/data availability is accurately represented
  5. Test any code repository links

Questions or Issues?

  • Open an issue for:
    • Clarification on guidelines
    • Suggesting improvements
    • Reporting broken links
    • Discussing paper categorization
  • Expect response within 1 week

Additional Resources