The case for establishing a global research meta-database

The field of terrorism studies has grown considerably in recent years, and our shared understanding of the phenomenon has grown with it. Put simply, we know a lot more about terrorism than we did 20, 10, or even five years ago. The maturation of the field owes a lot to the increased focus on generating findings through the development and use of large datasets, as well as an emphasis on case studies, field work, ethnographic studies, and program evaluations that are grounded in robust primary data collection. While empirical research and data collection have led to substantial scientific discovery, the field’s ability to generate new knowledge continues to be limited by an enduring problem: While we acknowledge that terrorism is a transnational and interdisciplinary concern, we continue to study it as if it were isolated to specific disciplinary, national, cultural, and linguistic contexts.

This is especially true of efforts to collect data on violent extremism, which, with few exceptions, rarely cross subnational, national, or regional boundaries. The lack of integrated, cross-national data on terrorism, counterterrorism, and violence prevention makes it difficult to assess how violent movements and emerging technologies spread, to compare national approaches to prevention, and simply, to learn from each other.

The lack of integrated, cross-national data on terrorism, counterterrorism, and violence prevention makes it difficult to assess how violent movements and emerging technologies spread.

Recently, there have been several efforts to bring scholars and practitioners together across national and disciplinary boundaries to share findings, experiences, and future research. IAPSS is one such effort. Considerable progress has been made in bringing diverse voices together, but true research collaboration, especially on data collection, is still in its infancy.

There are several reasons why this is the case. First, the production of cross-national datasets on topics like terrorism and targeted violence, radicalisation, disengagement, and program evaluation tend to be prohibitively expensive and require language skills that individual research teams and centers do not typically possess. Second, data often come from sources (e.g., court records, police files, intelligence assessments, etc.) that are not available in all geographic contexts or cannot be shared with researchers in other parts of the world. Third, there are legitimate concerns about sharing data due to ethical, privacy, and security concerns, as well as considerations for the safety of both researchers and research subjects. And finally, researchers often need to protect their data because it is their pathway to academic success.

While there are real challenges to working together on data collection, we argue that a key building block for future collaboration is within reach: a global meta-database of existing empirical research. As abstract as the development of such a database may seem, the resulting benefits would be very real, making knowledge systematically accessible and analysable. Only when researchers know (ideally at an early stage) what empirical data exist and how they were collected can we begin to understand the possibilities for data sharing, integration, and expansion.

Thus, we argue that the field should cultivate a shared understanding of what data have been collected, what sources were used to collect the data, how key concepts were operationalised during the data collection process, and how the data were analysed. Having this knowledge in a centralised meta-database will allow the research community to identify data synergies, gaps, and opportunities for collaboration.

Constructing a meta-database of existing empirical research is not a small undertaking and itself will require substantial collaboration. There are several points to consider before embarking on this type of effort.

First, it is important to have a clear incentive structure for adding one’s research designs and data to a meta-database. Researchers may be motivated to participate by simply seeing the benefits of enabling comparative research. However, it is more likely that widespread participation among researchers will depend on making funding available and the joint commitment by funders to make participation a condition of funding.

Second, a meta-database must avoid being perceived as biased towards certain disciplines or methods. The more biased the database is perceived to be, the less it will be viewed as a necessary and useful tool for the research community.

Third, it is important to set practical expectations while having a long-term outlook. A plan should be established for the development of a comprehensive database, but it is important to remember that in the short-term, less than perfect data are better than no data at all.

Fourth, a meta-database should include projects based on all types of data—qualitative, quantitative, experimental and so on.

The use of AI-based tools would not remove the need for a long-term commitment to fund and support the database.

Finally, maintaining and curating a meta-database would be time consuming, and thus the field should consider the benefits and drawbacks of using AI-based tools to improve the efficiency of data collection. Importantly, however, the community must recognise that the use of AI-based tools would not remove the need for a long-term commitment to fund and support the database.

International and interdisciplinary partnerships on data collection are difficult but not impossible. The construction of a meta-database can serve as the foundation for future collaboration. In our opinion, IAPSS is the perfect platform for beginning to explore these possibilities.