A myriad of design rules for what constitutes a "good" colormap can be found in the literature. Some common rules include order, uniformity, and high discriminative power. However, the meaning of many of these terms is often ambiguous or open to interpretation. At times, different authors may use the same term to describe different concepts or the same rule is described by varying nomenclature. These ambiguities stand in the way of collaborative work, the design of experiments to assess the characteristics of colormaps, and automated colormap generation. In this paper, we review current and historical guidelines for colormap design. We propose a specified taxonomy and provide unambiguous mathematical definitions for the most common design rules.

Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.

Fig. 1. Using traditional physical artistic media as input to the digital visualization pipeline provides a richer visual vocabulary and opens the door for artists to participate in creating more expressive and engaging 3D scientific visualizations. This example helps scientists understand commercially viable macroalgae growth in the Gulf of Mexico by encoding temperature and salinity from remote sensing together with eddy direction and curvature and three nitrate concentrations from computational simulation.Abstract-We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.

Fig. 1. Left: Screenshot of our new interactive test suite implemented in the CCC-Tool. Right: Two visualizations show at b the valley shaped Six-Hump Camel Function [15] for the area [−5, 5] × [−5, 5] and at a the LittleBit test function (Sect. 4.2.2). Special features of the Six-Hump Camel function are irregularly shaped troughs in the center of the area that are more than two orders smaller than the data range difference between center and boundary. The uniformly spaced colormap 1 cannot reveal the troughs, while the last nonlinear colormap 2 clearly shows the topological structure of the irregularities in the center of the domain.

This article presents ColorMoves, an interactive tool that promotes exploration of scientific data through artist-driven color methods in a unique and transformative way. We discuss the power of contrast in scientific visualization, the design of the ColorMoves tool, and the tools application in several science domains.