Abstract
Calcium signaling data analysis has become increasing complex as the size of acquired datasets increases. In this paper we present a Ca(2+) signaling data analysis method that employs custom written software scripts deployed in a collection of Jupyter-Lab "notebooks" which were designed to cope with this complexity. The notebook contents are organized to optimize data analysis workflow and efficiency. The method is demonstrated through application to several different Ca(2+) signaling experiment types.