The da-Vinci Surgical System (dVSS) is a tele-robotic surgical system that provides surgeons with enhance dexterity, precision, and control in performing minimally invasive surgical procedures like laparoscopic surgery. To be able to use this technology, surgeons have to receive accreditation and proper technical training for these procedures. Previous studies have shown that these accreditation procedures are complex due to the lack of efficient and objective tactile feedback. To improve the training curriculum, we analyze data from JIGSAWS, a database containing results from experiments performed using the dVSS. The JIGSAWS database contains video and kinematic data of surgeons with various experience and expertise performing three tasks of Suturing, Knot-Tying, and Needle-Passing. Each task is divided up into different subtasks, called gestures. Each test was viewed by a group of professional surgeons and given a Global Rating Score (GRS) based on certain checklist standard. We use this data to generate anomaly data associated with how safe or unsafe a gesture was performed to identify the relationship between the presence of anomalies and the surgeons self-identified experience or GRS. We found that surgeons, regardless of experience and GRS, experienced a large number of anomalies with certain gestures. This data can be used to better understand human robot interaction and how humans obtain dexterity using this system. The data also has the potential to improve the safety and effectiveness of surgical patient care by improving surgical technical skill training.