Examining Radicalization's Risk and Protective Factors: A Case-Control Study of Violent Extremists, Non-Violent Criminal Extremists, Non-Offending Extremists, and Regular Violent Offenders, United States, 1990-2020 (ICPSR 39026)
This project examined the multidimensional pathways that led to extremism and extremist behaviors. The focus of the research was to examine risk and protective factors that either directly, or in combination, interact to increase or mitigate the risk of radicalization and terrorism.
This project comparatively examined the presence/absence of risk and protective factors across three groups:
- extremist individuals who committed ideologically motivated violent (fatal and non-fatal violent attacks) and nonviolent (financial) crimes
- extremists who did not break the law and only engaged in legal extremist activities
- persons who committed non-ideological motivated homicides and other violent attacks
Thus, researchers accomplished four major goals/objectives in this project. First, although there has been a good amount of radicalization and risk assessment-related research on risk and protective factors, researchers expanded this work with comparative analyses that have not been previously explored. Second, few studies compared violent or nonviolent criminal extremists to nonoffending extremists or other types of violent offenders. Third, researchers used a case-control approach to provide an empirically robust understanding of categorical differences across groups that have not yet been achieved. Fourth, researchers examined differences in participation of warning behaviors across the groups studied.
The data file includes 971 cases.
Software Tool and Methodology for Enhancement of Unidentified Decedent Systems With Post-Mortem Automatic Iris Recognition, New York, 2019-2021 (ICPSR 38259)
The research team sought to create a methodology and software that allows for identification of deceased individuals based on iris patterns, with computer- and human-driven components. Using a dataset of post-mortem and peri-mortem iris images (acquired in near infrared and visible light) representing 259 cases, the research team engineered a software package, PMExpert, that incorporated three post-mortem specific iris matching algorithms. To understand what features humans believe to be useful in post-mortem iris matching, participants analyzed pairs of post-mortem samples, classified them as those originating from the same or different eyes, and annotated features supporting the decision.
Iris Images:
After the curation of all data collected by the Dutchess County Medical Examiner's Office, NY, iris images from 259 cases were selected for the final dataset release, and for analyses carried out in this project. This data corpus consists of 5,770 NIR and 4,643 RGB images, including images for one peri-mortem case with corresponding post-mortem samples after demise.
Human Examination Data:
The researchers conducted an experiment to collect annotation data on what humans believe to be distinctive features useful for post-mortem iris matching. Initial participants were recruited through the University of Notre Dame to complete study tasks in-person on-site. Due to the COVID-19 pandemic, the study design was later modified to be an online experiment recruiting participants through Amazon Mechanical Turk.
This data acquisition took place in two rounds:
- The first round was the initial collection of annotation data wherein participants had no prior knowledge of the task or previous decisions.
- The second round, called the verification step, is where the annotations collected in the first round were presented to future participants for them to either agree with or disagree with along with supporting annotations.
Software Package:
A software tool called PMExpert was created to provide a simple unified interface for all recognition methods, allowing them to be used in an operational setting.
PMExpert consists of two main components: a command line interface (CLI) and a graphical user interface (GUI). Both components are meant to allow examiners to use post-mortem iris recognition methods on images that are collected in their routine operations, offering not only similarity scores and decisions, but also additional information to equip examiners to make their final decision.