In order to characterize battery performance, electrodes are typically cycled for weeks or months at a time, resulting in an extremely large dataset of charge-discharge curves, a subset of which are often analyzed individually in the form of total differential capacity plots. Due to the difficulty in analyzing these datasets in their entirety, qualitative interpretations based on a subset of cycling data prevail in the literature. Here, we present software that quantitatively analyzes cycling datasets in their entirety by extracting peak characteristics from every cycle through a pseudo-Voight distribution fit. Initial results demonstrate that our system can differentiate between cycling data for two different battery chemistries, and we have implemented a database backend, allowing users to perform analyses and return to their data at a later point. We hope that this will lay the framework for an open source database, fostering collaboration and advancement within the electrochemistry community.