Color sequences, ordered sets of colors for data visualization, that balance aesthetics with accessibility considerations are presented. In order to model aesthetic preference, data were collected with an online survey, and the results were used to train a machine-learning model. To ensure accessibility, this model was combined with minimum-perceptual-distance constraints, including for simulated color-vision deficiencies, as well as with minimum-lightness-distance constraints for grayscale printing, maximum-lightness constraints for maintaining contrast with a white background, and scores from a color-saliency model for ease of use of the colors in verbal and written descriptions. Optimal color sequences containing six, eight, and ten colors were generated using the data-driven aesthetic-preference model and accessibility constraints. Due to the balance of aesthetics and accessibility considerations, the resulting color sequences can serve as reasonable defaults in data-plotting codes, e.g., for use in scatter plots and line plots.

翻译：本文提出了一种平衡美学与无障碍性的色序（用于数据可视化的有序颜色集）。为建模美学偏好，通过在线调查收集数据，并利用其结果训练机器学习模型。为确保无障碍性，该模型融入了最小感知距离约束（包括针对模拟色觉缺陷的约束）、用于灰度打印的最小亮度距离约束、维持与白色背景对比度的最大亮度约束，以及基于颜色显著性模型的评分以提升颜色在口头及书面描述中的易用性。通过数据驱动的美学偏好模型与无障碍约束，生成了包含六种、八种及十种颜色的最优色序。由于兼顾了美学与无障碍性考量，所得色序可作为数据绘图代码（例如散点图与线图）的合理默认方案。