Introducing Sharing Information to Stop Mass Shootings (SISMS): A Short Report on a Behavioral and Event-Level Dataset for the Study of Mass Public Shootings

Abstract Mass public shooting research has been hindered by inconsistencies in case definitions, limited behavioral data, and variation in methodological transparency across existing datasets. This short report introduces Sharing Information to Stop Mass Shootings (SISMS), a behavioral and event-level dataset designed to support research on mass public shootings in the United States. SISMS currently includes 171 incidents and 175 perpetrators occurring between 1999 and 2024 for which pre-attack warning behaviors or leakage were confirmed through available source materials and incorporates data derived from public records, official documents, and triangulated open-source reporting. In addition to demographic and incident characteristics, the dataset includes information related to grievances, concerning behaviors, planning and preparation, leakage, and other pre-attack indicators associated with pathways to intended violence. This report describes the dataset structure, inclusion criteria, source collection procedures, and presence-based coding methodology. Potential applications for research and violence prevention also are discussed.