Cyber-based scientific investigations involve complex problems with massive multifaceted data from distributed sources including computational simulations, high dimensional sensing/data collections, and meta-data analysis. Examples include: large scale scientific computational simulations used in cosmology, mechanical engineering, civil engineering, climate studies, oceanography, geology, and aeronautics; geological exploration; biological system analysis (bioinformatics); security information analysis; and multimedia analysis (video, text, image processing). Visualization and computer-based interaction combined with analytical reasoning are crucial components enabling us to tackle these immense large scale problems. Little is known, however, about how people access and interpret current visualization techniques. As the graphics capabilities of commodity PC's become more powerful, more sophisticated techniques are increasingly available to the novice user. Anyone can download programs to view 3D MRI/CT or view satellite maps and other observation data. Currently, there is no general scientific curriculum teaching "scientific visualization literacy" for more than simple graphs and plots, and there is an implicit expectation that students can inherently understand such figures/images on their own. However, it has been observed in a variety of scientific fields that a subset of students has difficulty in understanding the representations used to explain concepts in these fields. The goal of this project is to investigate how to train students in visualization literacy, based on one class of scientific visualizations commonly used in many scientific disciplines to understand 3D space-filling figures, namely, slice or cutaway views. The project involves determining the generic characteristics of these types of visualizations, understanding the underlying strategies that need to be acquired to solve problems involving these figures, and creating a structured training program to teach students how to solve problems involving these visualizations. To evaluate how well the generic training facilitates the acquisition of a specific discipline-based scientific visualization, the training will then be introduced and tested in an introductory level geology course. Geology is a natural test-bed choice, as it attracts a diverse representation of students, and it already utilizes many 3D visualizations, for example, to understand subsurface geology. However, the results of this research can be used to train students in all fields utilizing space-filling visualization.
