- Project description and purpose - Dataset description and data source - Technology stack and framework choices - System architecture - Database structure and relationships - GraphQL API documentation - Application functionality - Installation and setup instructions - Environment variables and configuration - VM deployment (backend, database and Apache) - Accessibility and universal design - Sustainable web development choices - Testing (component, API and end-to-end) - Code quality (linting, Prettier and TypeScript) - AI usage and evaluation of AI-generated contributions - Development process (issues, pull requests and code reviews) - Repository structure and important files - Individual contribution summary (separate Canvas submission)