This report provides summary of the project "Exploration of methods for using SACADA data to estimate HEPs." The goal of the project was to conduct exploratory research on how to use the U.S. Nuclear Regulatory Commission's SACADA (Scenario, Authoring, Characterization, and Debriefing Application) database to develop an algorithm for estimating human error probabilities (HEPs). The approach used by the University of Maryland SyRRA lab uses a combination of Bayesian statistical methods and Bayesian Network models to conduct data analysis on SACADA data and to construct hybrid models informed by both data and engineering models. The end results provided various algorithms for mapping and binning SACADA data to be used within HEP estimation, and demonstrated a variety of options which create a framework for streamlined updating of HEPs as the amount and variety of SACADA data increases. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication submissions and reports that were prepared as part of this work.