  The Gemini Planet Imager Exoplanet Survey (GPIES) is a direct imaging
campaign designed to search for young, self-luminous, giant exoplanets. To
date, GPIES has observed nearly 500 targets, and generated over 30,000
individual exposures using its integral field spectrograph (IFS) instrument.
The GPIES team has developed a campaign data system with a database
incorporating all of the metadata for all individual raw data products,
including environmental conditions and instrument performance metrics. The same
database also indexes metadata associated with multiple levels of reduced data
products, including contrast measures for individual images and combined image
sequences, which serve as the primary metric of performance for the final
science products. The database is also used to track telemetry products from
the adaptive optics subsystem, and associate these with corresponding IFS data.
Here, we discuss several data exploration and visualization projects enabled by
the GPIES database. Of particular interest are any correlations between
instrument performance and environmental or operating conditions. We show
single and multiple-parameter fits of single-image and observing sequence
contrast as functions of various seeing measures, and discuss automated outlier
rejection and other fitting concerns. Supervised learning techniques are
employed in order to partition the space of raw (single image) to final (full
sequence) contrast in order to better predict the value of the final data set
from the first few completed observations. Finally, we discuss the particular
features of the database design that aid in performing these analyses, and
suggest potential future upgrades and refinements.
