  We present sMolBoxes, a dataflow representation for the exploration and
analysis of long molecular dynamics (MD) simulations. When MD simulations reach
millions of snapshots, a frame-by-frame observation is not feasible anymore.
Thus, biochemists rely to a large extent only on quantitative analysis of
geometric and physico-chemical properties. However, the usage of abstract
methods to study inherently spatial data hinders the exploration and poses a
considerable workload. sMolBoxes link quantitative analysis of a user-defined
set of properties with interactive 3D visualizations. They enable visual
explanations of molecular behaviors, which lead to an efficient discovery of
biochemically significant parts of the MD simulation. sMolBoxes follow a
node-based model for flexible definition, combination, and immediate evaluation
of properties to be investigated. Progressive analytics enable fluid switching
between multiple properties, which facilitates hypothesis generation. Each
sMolBox provides quick insight to an observed property or function, available
in more detail in the bigBox View. The case study illustrates that even with
relatively few sMolBoxes, it is possible to express complex analyses tasks, and
their use in exploratory analysis is perceived as more efficient than
traditional scripting-based methods.
