The visualization of scientific data serves to make large amounts of data accessible to human perception. For this purpose, mappings of often spatially structured data to suitable visual representations have to be found. These can be geometric primitives such as lines or triangles or spatial objects such as spheres, rods, arrows, etc. Alternatively, semi-transparent representations of objects can be used to provide insights into soil layers, bodies, respectively cell structures or galaxies.
Data can for example originate from computer-based simulations or instruments such as microscopes, telescopes, sequencing devices, tomographers. They serve as intermediate carriers of information about the data sources. The visualization of scientific data forms the basis for gaining knowledge about the simulated systems, the measured objects, etc. Visual data analysis encompasses the entire process from data generation or collection, through conversion in visual representation, to gaining insight into the behaviour or properties of systems.
A so-called CAVE (Cave Automatic Virtual Environment) enables a particularly efficient use of human perceptual abilities when capturing visualizations. Through interaction in the CAVE with the visualizations of simulated systems, comprehension as a cognitive process is supported particularly efficiently.