Information discovery is a powerful reduction technique whose significance becomes apparent when dealing with very large dynamic datasets. For such multiscale physics and statistics datasets as those required for our work in fluid dynamics of rotorcraft, plasma physics, and acoustic scattering, information discovery involves identifying features and patterns of interest over the underlying geometry, topology, and temporal events. Features that represent interesting objects or structures in the data not only reduce the memory bandwidth but also increase our qualitative understanding of the underlying patterns and relationships among the structures. Typical examples of such features and patterns in computational fluid dynamics include vortices, shock waves, boundary layers, wave fronts, separation, recirculation, and attachment zones. The Augmentarium is ideally suited to provide high resolution feature details with the necessary lower- resolution context.