 Community-engaged research, SENR, has become increasingly popular among academic institutions. To better understand how SENR is being implemented, researchers have been developing methods for identifying and categorizing SENR projects. This study examined existing IRB applications to identify SENR projects and compared them to three additional questions added to the IRB application. The results showed that the algorithm was able to categorize studies at a higher level of engagement than investigators recorded on their own. Additionally, the algorithm was more likely to categorize studies as SENR than the investigators themselves. With further refinement, universities can use these findings to track progress in meeting community needs and coordinate efforts across programs and departments. This article was authored by Emily B. Zimmerman, Sarah Eraskin, Brian Ferrell, and others.