The Beautiful World of SciVis

In the academic world, data visualization is broken into three main categories: Information Visualization (InfoVis), Visual Analytics, and Scientific Visualization (SciVis). InfoVis deals with smaller datasets, typically demographic or financial data. Visual analytics involves a cycle of rapidly creating visualizations to answer and generate new questions about a dataset. Infographics are typically in the realm of InfoVis, and often they show the results of the visual analytics process, but SciVis is not really a part of most infographics. SciVis is interesting because it deals with really large quantities of data that often has a spatial and/or temporal component. These are cool qualities to have in a dataset because they sometimes allow for the appropriate use of 3D visualization techniques. With the huge quantities of data involved, this often leads to extremely beautiful results that often look as if they are photographs or pieces of art. In many cases, they actually are photographs that are taken using advanced devices to show us either a scale we could not see, the inside of an object, or something so far away that to the naked eye it looks as if it is only a single point of light. Sometimes, scientific visualization also uses rainbow color scales, or inappropriately uses 3D techniques. This still produces beautiful images, but the usefulness of these images can be harmed by these techniques. The people who do this kind of work are extremely intelligent, so you might ask, why are they doing something wrong? The nature of SciVis requires people who are experts at some extremely niche subjects, and they spend the majority of their time learning about and working on these problems, unaware that there are better techniques for showing their data. Overall, these problems are minor in comparison to the issues that have been and are being solved with help from SciVis. People who work on these problems have developed the software that shows CAT scans and MRIs. They work on systems to show and predict weather patterns, both here on Earth, and on other planets. They build tools for seeing the molecular structure of substances we want to study or create. They show us what it looks like when tiny sub-atomic particles slam into matter, breaking it into other sub-atomic particles. They enable us to see inside the human body to heal people and keep them healthy. So, a big Thank You to all the scientists out there dealing with truly “Big Data,” and here’s to hoping your results are as functional as they are beautiful. All of the images and videos in this post are winners of the 2012 International Science & Engineering Visualization Challenge.   Drew Skau is Visualization Architect at and a PhD Computer Science Visualization student at UNCC with an undergraduate degree in Architecture. You can follow him on twitter @SeeingStructure

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