High-resolution data in neurocritical care is enormous, especially for patients that are monitored for days to weeks at a time. This type of data is ideal for machine learning applications, but certain barriers have traditionally thwarted this type of work. Data acquired from medical devices are often stored in proprietary company formats, making it difficult to use for research. Additionally, this data often contains PHI and cannot be moved outside of the hospital without an intermediate anonymization step. Our work highlights a solution to both of these problems. With the help of Dr. Eddy Amorim at the Zuckerberg San Francisco General Hospital, we successfully deployed software to anonymize and convert data from the Moberg CNS Monitor with the aim to use the hospital’s dataset for research.