  Advances in multimodal characterization methods fuel a generation of
increasing immense hyper-dimensional datasets. 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 map 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.
