Rainbow color scheme is popularly used across the board in many scientific disciplines for
visualizing data, yet heavily debated in visualization literature. In this paper, we first report …

Color map** is a foundational technique for visualizing scalar data. Prior literature offers
guidelines for effective colormap design, such as emphasizing luminance variation while …

Visualizations often encode numeric data using sequential and diverging color ramps.
Effective ramps use colors that are sufficiently discriminable, align well with the data, and are …

Guidelines for color use in quantitative visualizations have strongly discouraged the use of
rainbow colormaps, arguing instead for smooth designs that do not induce visual …

Applying lossy data compression to climate model output is an attractive means of reducing
the enormous volumes of data generated by climate models. However, because lossy data …

In recent years, considerable work has been devoted to explaining predictive, deep learning-
based models, and in turn how to evaluate explanations. An important class of evaluation …

Color encoding is foundational to visualizing quantitative data. Guidelines for colormap
design have traditionally emphasized perceptual principles, such as order and uniformity …

Two of the primary reasons rainbow color maps are considered ineffective trace back to the
idea that they implicitly discretize encoded data into hue‐based bands, yet no research …

Color palette selection is an essential aspect of visualization design, influencing data
interpretation and evoking emotions in the viewer. Rules of thumb grounded in perceptual …

Probabilistic seismic hazard estimates are a key ingredient of earthquake risk mitigation
strategies and are often communicated through seismic hazard maps. Though the literature …