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Title: Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

Abstract

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. In this paper, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identicalmore » visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.« less

Authors:
 [1];  [1]; ORCiD logo [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Earth and Biological Sciences Directorate
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1468616
Alternate Identifier(s):
OSTI ID: 1573024
Report Number(s):
PNNL-SA-134716; PNNL-SA-130546
Journal ID: ISSN 1932-6203
Grant/Contract Number:  
AC0576RL01830; AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 13; Journal Issue: 7; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; color vision; sensory perception; fluid flow; vision; data processing; linear regression analysis; secondary ion mass spectrometry; sine waves; color blind; deficiency; colormap; data visualization; Python; NanoSIMS; visual; perception

Citation Formats

Nuñez, Jamie R., Anderton, Christopher R., Renslow, Ryan S., and Malo, Jesús. Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data. United States: N. p., 2018. Web. doi:10.1371/JOURNAL.PONE.0199239.
Nuñez, Jamie R., Anderton, Christopher R., Renslow, Ryan S., & Malo, Jesús. Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data. United States. doi:10.1371/JOURNAL.PONE.0199239.
Nuñez, Jamie R., Anderton, Christopher R., Renslow, Ryan S., and Malo, Jesús. Wed . "Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data". United States. doi:10.1371/JOURNAL.PONE.0199239. https://www.osti.gov/servlets/purl/1468616.
@article{osti_1468616,
title = {Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data},
author = {Nuñez, Jamie R. and Anderton, Christopher R. and Renslow, Ryan S. and Malo, Jesús},
abstractNote = {Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. In this paper, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.},
doi = {10.1371/JOURNAL.PONE.0199239},
journal = {PLoS ONE},
number = 7,
volume = 13,
place = {United States},
year = {2018},
month = {8}
}

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