  In recent years, the potential applications of machine learning to Minimally
Invasive Surgery (MIS) have spurred interest in data sets that can be used to
develop data-driven tools. This paper introduces a novel dataset recorded
during ex vivo pseudo-cholecystectomy procedures on pig livers, utilizing the
da Vinci Research Kit (dVRK). Unlike current datasets, ours bridges a critical
gap by offering not only full kinematic data but also capturing all pedal
inputs used during the procedure and providing a time-stamped record of the
endoscope's movements. Contributed by seven surgeons, this data set introduces
a new dimension to surgical robotics research, allowing the creation of
advanced models for automating console functionalities. Our work addresses the
existing limitation of incomplete recordings and imprecise kinematic data,
common in other datasets. By introducing two models, dedicated to predicting
clutch usage and camera activation, we highlight the dataset's potential for
advancing automation in surgical robotics. The comparison of methodologies and
time windows provides insights into the models' boundaries and limitations.
