  Appropriate evaluation is a key component in visualization research. It is
typically based on empirical studies that assess visualization components or
complete systems. While such studies often include the user of the
visualization, empirical research is not necessarily restricted to user studies
but may also address the technical performance of a visualization system such
as its computational speed or memory consumption. Any such empirical experiment
faces the issue that the underlying visualization is becoming increasingly
sophisticated, leading to an increasingly difficult evaluation in complex
environments. Therefore, many of the established methods of empirical studies
can no longer capture the full complexity of the evaluation. One promising
solution is the use of data-rich observations that we can acquire during
studies to obtain more reliable interpretations of empirical research. For
example, we have been witnessing an increasing availability and use of
physiological sensor information from eye tracking, electrodermal activity
sensors, electroencephalography, etc. Other examples are various kinds of logs
of user activities such as mouse, keyboard, or touch interaction. Such
data-rich empirical studies promise to be especially useful for studies in the
wild and similar scenarios outside of the controlled laboratory environment.
However, with the growing availability of large, complex, time-dependent,
heterogeneous, and unstructured observational data, we are facing the new
challenge of how we can analyze such data. This challenge can be addressed by
establishing the subfield of visualization for visualization (Vis4Vis):
visualization as a means of analyzing and communicating data from empirical
studies to advance visualization research.
