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At the core of good science and engineering is the careful and respectful treatment of data. We calibrate our instruments, scrutinize the algorithms we use to process the data, and study the behavior of the models we use to interpret the data or simulate the phenomena we may be observing. Surprisingly, this careful treatment of data often breaks down when we visualize our 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. We will also establish demonstrate principles we have discovered which can help people avoid these artifacts and build more effective representations of their data. These principles explicitly consider characteristics of the data, how the data are to be used, and how the human visual system processes color information.