
In higher education, “You don’t know what you don’t know” is often the axiom that spurs research to shine a light on a subject not thoroughly illuminated. For Emily Green, music historian and faculty member in George Mason University’s College of Visual and Performing Arts, this maxim served as a motivator to launch a 4-VA@Mason research project titled Music of Early Black Virginians. Green, whose previous research focused on music publishing and marketing in eighteenth- and nineteenth-century western Europe and America, became especially interested in the backstory of this area of music history while teaching Nineteenth-Century African American Music, a graduate course at Mason
Green recognized that much of this important music history needed to be cataloged and made publicly available. “It was a long-standing wish of mine to create a resource for educators to help them understand the variety of genres in Black-American music,” she explained. “K-12 music educators do not always have access to library databases or peer-reviewed journals. I wanted to create an open-access landing point to help teachers navigate the rich resources they can use to learn and quickly enhance their knowledge—and build lesson plans for students of a variety of ages.”
To that end, Green sought the involvement of scholars in the field at several 4-VA partner schools to help her put the project into motion: Mary Caton Lingold at VCU and Bonnie Gordon at UVA. Both readily volunteered their time to bring this multi-level and multi-faceted research to fruition. Michael Nickens (a.k.a. Doc Nix — most recognized as the leader of George Mason University’s “Green Machine,”) also eagerly joined the team. Additionally, Maria Ryan, at Florida State University, came on to collaborate on the project.
4-VA funding provided UVA graduate students Laura Carrington OBrion and Sergio Manuel Silva and George Mason undergrads Crystal D. Williams and Jaelin Mitchell the time and space to build a website of nineteenth-century sources that reference Black-American music making: https://masonlibraries.gmu.edu/blackmusic/s/music-black-va/page/home.
From there, the team then published a wide-ranging website Early Black Music in Performance https://earlyblackmusic.4va.gmu.edu/.

A zoom conference helped introduce and promote the site: Presenters included 4-VA cohort, Kayondra Reid, music educator at Oak Street Elementary School in Falls Church, VA, and Tyler Diaz, Hunter College, NY. Artist Shodekeh Talifero also gave a presentation for the Dewberry School of Music entitled “Breaths along the Potomac.”

As an added bonus resulting from the effort, Green, Lingold, and Ryan have been contracted to develop a related anthology for Oxford University Press, Sources in Early Black Atlantic Music, which is expected to be published in 2026.
“Most music educators are not taught much about the variety of Black American music in bachelor’s or master’s degrees in our field. Between the database and the web resource, and soon the anthology, our hope is that educators can learn more about reliable print and online resources to use in the future,” says Green.







Professor in George Mason’s College of Science, Department of Biology, saw an opportunity to combine the experience and knowledge of other experts in the field to develop a novel genomic tool to address this issue.
remain In the wild, there are approximately 160 bongos in U.S. zoos and several hundred more on private ranches. As such, breeding and transfer strategies that increase genetic diversity and reduce inbreeding of captive animals are critical for the survival of the species.
GMU researchers, Lim and his PhD student, Karen Holm, assembled their team — Aakrosh Ratan, Assistant Professor, University of Virginia; Klaus-Peter Koepfli, Senior Research Scientist, Smithsonian Mason School of Conservation; and Budhan Pukazhenthi, Research Physiologist, Smithsonian Conservation Biology Institute. 4VA funding was used to purchase the necessary supplies, reagents and equipment including DNA tests and probe sets.
centers and zoos were collected to begin the project. This resulted in a total of 207 samples from both current and historical ex situ populations (populations outside of the native ranges). They evaluated the effectiveness of the probe-set in generating high-quality genetic data from different sample types and for different genetic marker types. The data obtained are now being accessed for their ability to produce accurate information on animal kinship, genetic diversity and population structure, which will ultimately be used to guide precise captive breeding programs.

Today, to Luther’s great delight, the results have proved far more successful than he could have ever anticipated. Tens of thousands of animal images from camera traps and audio recordings have already been collected.
Hassan, Jordan Seidmeyer, Katie Russell, Carolian Sanabria, Adrian Em, Alix Upchurch, Piper Robinson, Tristan Silva-Montoya, and Estefany Umana spent hours creating this treasure trove of records. Emilia Roberts, a MS student in ESP, managed these undergraduate researchers.
The project continues to gain traction. The team has created a website featuring the results of the acoustic portion of the research,
This was achieved following 4-VA’s approval of a proposal by George Mason’s Yun Yu, an Assistant Professor in Chemistry and Biochemistry Department, for a grant entitled

Angeline Lillard, a widely respected developmental psychologist and researcher in the Department of Psychology at UVA. Explains Doebel, “Our collaboration would not have taken off the way it did if it hadn’t been for Angeline, because she was the one who sent me an email about 4-VA. She said, ‘Have you heard of 4-VA? We should consider this option.’”


Led by Myeong Lee, Mason’s Assistant Professor of Information Science and the Director of the Community Informatics Lab, the researchers also included former College of Science faculty members Olga Gkountouna, who assisted with machine learning model development, and Ron Mahabir who provided insight on geographical data analysis. Amr Hilal of Virginia Tech helped with data analytics from a machine learning perspective.
in their geographical area, it tends to attract more participants. In a second finding, the team implemented three advanced machine learning models to predict the success of local Meetup groups, finding that the performances of these prediction models vary across different categories and cities, with some outperforming the state-of-the-art models.