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Nature of the Problem. While counterterrorism (CT) professionals prioritize the development of national security measures, many off-the-shelf datasets may not be suited for the unique work that Homeland Security professionals do. As such, this project is designed to identify and assess the most up-to-date datasets that the Department of Homeland Security (DHS) can provide its counterterrorism workforce. By assessing the specific needs of current counterterrorism professionals, academics will have a clearer picture of the types of data most helpful in addressing the threats of domestic terrorism (DT) and targeted violence (TV). Method. We adopted an interdisciplinary, multi-method approach that included (1) a scoping review and (2) roundtable discussions. Our scoping review identified and screened over 1,500 articles related to terrorism, violence, threat assessment, and national security published between 2001 and 2023. We also conducted roundtable discussions with CT analysts from DHS and its federal partners to better understand the data types most helpful in understanding the evolving DT/TV threat environment and any barriers or limitations regarding data access or usability. Findings. Based on our scoping review, we identified 97 datasets focused on one of six topics: (1) domestic terrorism (n = 33), (2) transnational terrorism (n = 29), (3) school shootings (n = 10), (4) mass shootings (n = 10), (5) other targeted violence (n = 14), and (6) cyber threats (n = 1). These findings suggest there is a robust corpus of data on terrorism, both domestic and transnational. Current datasets on targeted violence appear to emphasize school shootings or mass shootings. Based on the insights from the roundtable discussions, analysts commented on the importance of clarity and openness in describing and documenting the methods used in constructing a dataset. Analysts also discussed the importance of compliance with data protection, privacy, and intellectual property laws. Lastly, analysts discussed limited access to data as a significant barrier to dataset usage. Recommendations. Based on the insights from the roundtable discussions, we suggest the following recommendations. First, researchers should prioritize methodological transparency by clearly documenting the research design, purpose, objectives, and limitations of their datasets. Second, researchers should collect the minimum amount of personally identifiable information necessary to address their research questions. Finally, researchers should implement strategies such as data integration initiatives, standardization efforts, data governance frameworks, improved data sharing protocols, and investments in modern data management technologies.