A consortium of researchers dedicated to improving the understanding of the human causes and consequences of terrorism

DoD awards START nearly $3 million to enhance critical terrorism datasets

New suite of projects aims to inform efficient counterterrorism decision-making

The Department of Defense (DoD) Combating Terrorism Technical Support Office (CTTSO) has awarded nearly $3 million to START to update and enhance key terrorism-related datasets that serve as inputs into DoD and intelligence community modeling and simulation efforts; threat, vulnerability and risk assessments; and visualization and decision-support platforms.

With this new funding – Enhancing Datastreams to Inform Counter-Terrorism (EDICT) – START will help inform DoD research, development test and evaluation (RDT&E) efforts by updating relevant datasets and enhancing the speed, accuracy, comprehensiveness and interoperability of the data collection, dissemination and visualization efforts used to curate and transition the datasets. Data generated through these projects will be shared with DoD to inform efficient counterterrorism decision-making.

“START datasets are among the most robust sources of terrorism-related data in the world. Through the partnership with CTTSO, START will be able to provide more timely data to decision makers and policy experts, while also facilitating the research of other scholars through increased collaboration,” said Dr. Amy Pate, START research director and acting executive director.

The funding will support updates and expansion for four ongoing research efforts:

The Big Allied And Dangerous (BAAD) 2 dataset tracks terrorist organizations over time, examining how group behaviors (e.g., lethality, pursuit of WMD) are impacted by factors such as collaboration, competition, size, ideology and counter terrorism strategies. With data on violent non-state actors who have perpetrated at least one event between 1998 and 2016, the dataset will be updated through 2017 and steps will be taken to accelerate its data collection to a yearly basis and the ability to fast-track information on specific organizations of interest. The dataset was founded and is spearheaded by Dr. Victor Asal and Dr. R. Karl Rethemeyer of the Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York.

The Leadership of the Extreme and Dangerous for Innovative Results (LEADIR) dataset examines the organizational psychology of terrorist groups by providing quantitative assessments of their structure, organizational practices, human capital, and leaders’ decision-making style. Using an internal strategic organizational approach, the dataset has shown that leadership, organizational structure, and innovation vary across the various terrorism “industries," which has implications for how government resources should be allocated for monitoring and analysis. The dataset can be used to provide advanced indicator and warning signals of which groups will emerge as the most strategically differentiated and capable of malevolent innovation in coming years. The dataset will be updated through 2018 for strategically important organizations and their leaderships. It was founded and is led by Dr. Gina Ligon, associate professor of management and director of R&D at the Center for Collaboration Science at the University of Nebraska Omaha.  

The Transnational Illicit Trafficking (TransIT) tool was originally developed to analyze possible routes for smuggling radiological and/or nuclear materials into the United States. However, it can be adapted to examine other illicit trafficking contexts. To date, the model has been built out for Central America, North and West Africa, Europe, and Central Asia. The model calculates optimized routes of transnational criminal organizations (TCOs) based on a variety of risk indices and accounting for 12 modes of transportation: road, tunnel, foot, commercial and passenger aviation, Cessna, ultra-light aircraft, shipping, go-fast boats, pangas, full and semi-submersibles, sailboats, and rail. For this program, TransIT will be expanded to include Afghanistan and Pakistan under the leadership of START Senior Researcher Marcus Boyd.

The Global Terrorism Database (GTD), the world’s most comprehensive, open-source terrorism database. It includes information on more than 180,000 terrorist attacks that have occurred worldwide since 1970. It provides a more complete understanding of the dynamics, causes and consequences of terrorism around the world, by allowing its users to analyze patterns such as the frequency of terrorist attacks, geo-spatial patterns of terrorist attacks, the lethality of terrorist attacks, patterns of casualties including injured persons and hostages, the emergence and prevalence of particular tactics and targeting strategies used in terrorist attacks, and the evolution of perpetrators of terrorist attacks. Led by START Senior Researcher Dr. Erin Miller, the GTD team will collect six months of data and begin work on a new API (application programming interface) and other tools to accelerate data collection and delivery.  

The new funding will also be used to study and develop data collection frameworks that will eventually enable the collection of data on “blue” (e.g., counterterrorism) actions and capabilities, as well as additional data on “green” (e.g., relevant contextual data), in a way that is optimized for integrated analysis alongside data from existing START databases on terrorism (e.g., GTD, BAAD2, LEADIR). Dr. Barnett Koven, START Senior Researcher, will lead the development of these new frameworks, which can be used to systematize data collection occurring at different levels of analysis, whether at the incident level, group level or individual level.

“Our goal is to enable more accurate and useful analysis, not just of the threat, but also of the counterterrorism effectiveness of the United States and allied nations,” said William Braniff, START director. “These projects will allow policymakers and practitioners to make more effective and cost-efficient counterterrorism decisions using multiple different types of datasets.”