Advances in multimodal characterization methods fuel the generation of increasing immense hyper-dimensional data sets. Color mapping is employed for conveying higher dimensional data in two-dimensional (2D) representations for human consumption without relying on multiple projections. How one constructs these color maps, however, critically affects how accurately one perceives data. For simple scalar fields, perceptually uniform color maps and color selection have been shown to improve data readability and interpretation across research fields. Here we review core concepts underlying the design of perceptually uniform color maps and extend the concepts from scalar fields to two-dimensional vector fields and three-component composition fields frequently found in materials-chemistry research to enable high-fidelity visualization. We develop the software tools PAPUC and CMPUC to enable researchers to utilize these colorimetry principles and employ perceptually uniform color spaces for rigorously meaningful color mapping of higher dimensional data representations. Last, we demonstrate how these approaches deliver immediate improvements in data readability and interpretation in microscopies and spectroscopies routinely used in discerning materials structure, chemistry, and properties.

中文翻译：

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利用比色法确保数据可视化

多峰表征方法的进步推动了超大量超维数据集的产生。颜色映射用于在不依赖多个投影的情况下以二维（2D）表示形式传递更高维度的数据以供人类消费。但是，人们如何构造这些颜色图会严重影响人们感知数据的准确性。对于简单的标量场，已显示出感知上统一的颜色图和颜色选择可提高整个研究领域的数据可读性和解释性。在这里，我们回顾了感知均匀颜色图设计的核心概念，并将其概念从标量场扩展到材料化学研究中经常发现的二维矢量场和三成分组成场，以实现高保真可视化。我们开发了软件工具PAPUC和CMPUC，以使研究人员能够利用这些比色原理，并在感知上统一的色彩空间中使用严格有意义的高维数据表示形式进行色彩映射。最后，我们演示了这些方法如何在通常用于识别材料结构，化学性质和特性的微观和光谱学中，立即提高数据可读性和解释性。