Data and code underlying the publication: Color Strategies in AI Service Interfaces : Standardized Trust, Calculated Differentiation, and Design Isomorphism

This dataset and source code provide a comprehensive analysis of the color strategies employed by generative AI service interfaces. The study explores the visual trends of "Standardized Trust" and "Calculated Differentiation" within the AI industry through a quantitative lens.<strong>The repository includes:</strong><strong>Source Code-</strong>Python-based scripts for automated data collection (web crawling), color metadata extraction, and statistical analysis including Color Entropy and Principal Component Analysis (PCA).<strong>Dataset-</strong>Color data and metadata extracted from 82 representative generative AI services (selected from an initial pool of 100 global services).<strong>Analytical Outputs-</strong>Scripts to reproduce hue distribution graphs and PCA scatter plots as presented in the associated paper.This project offers empirical evidence of how AI services utilize specific color palettes to establish reliability and brand identity. It serves as a technical foundation for researchers in HCI (Human-Computer Interaction), UI/UX design, and AI-driven visual communication.