Why Should Engineers and Scientists Be Worried About Color? Bernice E. Rogowitzrogowtz@watson.ibm.com Lloyd A. Treinish lloydt@watson.ibm.com IBM Thomas J. Watson Research CenterYorktown Heights, NY Introduction At the core of good science and engineering is the careful and respectful treatment of data. Using examples from a wide range of application areas in science and engineering, we will demonstrate how standard uses of color can distort the meaning of the underlying data, and can lead the analyst to incorrect evaluations, conclusions or decisions. Figure 1 shows two views of the same data set. Figure 1. While comparing these two representations of the same data, the first thing you may notice is the importance of capturing zero-crossings or other thresholds in the data.
Misleading Use of Color in Your Data It is easy to recognize and laugh at the artifacts produced by the rainbow colormap in well-known data. Figure 2 shows five different data sets spanning a range of scientific and engineering disciplines. Figure 2. Perceptually-Based Colormaps.
Welcome to RKWard. SciPy.org — SciPy.org. SciPy Central. Matplotlib: python plotting — Matplotlib 1.3.0 documentation. Untitled. Untitled. Ofeli KDE. LabPlot KDE. Veusz KDE. Gnuplot. Sage: Open Source Mathematics Software.