Abstract
An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional,
and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing
a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter
choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and
between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential
animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method
that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques,
first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both
techniques are capable of supporting meaningful comparisons of ensemble data.
