Guide for technologies, techniques, and best practices for data visualization.
Introduction to Scientific Visualization
Scientific visualization is the process of representing numerical spatial data as images in order to better understand scientific phenomena described by the data. Some examples might include:
Volume rendering in medical images, where 3D volumes are generated by interpolating over sequential 2D slices from MRI or CT-Scans.
Surface rendering, such as a protein molecule, in which an isosurface is computed over the 3D volume.
Color in Scientific Visualizations
Choosing a Color Map
When pseudocoloring a scalar field for scientific visualization, the choice of color map is essential to conveying patterns and trends within your data or models.
Elements of an effective color map include:
|Perceptually Uniform||We do not perceive color uniformly by hue. We have an easier time differentiating some colors than others, thus color maps such as the rainbow map end up overemphasizing some differences in data while hiding others.|
|High Discrimination||Maximize discrimination by making colors change as much as possible over the range of the data you are visualizing, to ensure you can see changes in the data.|
|Implicit Order||Colors should have a natural ordering. If someone is viewing a visualization it should be apparent which colors are low and which are high without additional assistance.|
|3D-Shading Friendly||Dark colors hide shadows rendered to make an object appear 3D, so if you are using shading, ensure you select a lighter colormap that does not obscure shading.|
|Color Blind Friendly||Select a colormap that allows with color-blindness to effectively differentiate colors.|
|Aesthetically Pleasing||While subjective, aesthetically pleasing color maps will help make your visualizations more attractive to your intended audience.|