Data management plan for Eugene Martin and Jacqueline Vickery's National Endowment for the Arts grant. This project will support a series of collaborative media production and storytelling programs serving North Texas. Through partnerships between the University of North Texas' Youth Media Lab and local organizations, the program will offer training for university students and community participants to produce media works such as short films, podcasts, and documentaries that tell underrepresented stories of the local community.

Data management plan for the grant, "An Ethnic Spring in the Food Desert? How State Policy Affects Food Environments and Business Entrepreneurships." In food system research, the reduced quality of one's food environment (the combination of food security and food access) is a direct symptom of social, political, and economic disparities. Our research has three primary objectives: Aim 1: To determine the role of state policy in the food environment of immigrant communities and communities of color in the United States, Aim 2: To dissect how state and local policies affect the participation of these communities in the U.S. food system, local food environments, and food business ownership, and Aim 3: To explain the current role and the future role of local policy in the food environment of immigrant communities and communities of color in the United States.

Data management plan for the grant, "Collaborative Research: Conference: Texas-Oklahoma Representations and Automorphic forms (TORA)."

Data management plan for the grant, "Applying Game Theory for Improving and Optimizing the US Political Asylum Process"

Data management plan for the grant, "Exploiting machine learning for cybersecurity tasks to talent mapping and exploring credentialing ecosystem"

Data management plan for the grant "RAPID: Collaborative Research: Examining Household Movements and Evacuation Decision-Making in a Compounding Risk Event." The purpose of this Grants for Rapid Response Research (RAPID) collaborative project is to examine household protective action decisions during two simultaneous events: Hurricane Ida and the ongoing COVID-19 pandemic. Time-sensitive data gathered through online, phone, and mail surveys and supplemented with GPS/mobile phone data will be used to examine household protective action decision-making and mobility patterns before, during, and after Hurricane Ida. The findings from this project are expected to save lives and minimize stress during evacuations and return trips. Additionally, the findings of the research will benefit the emergency management community and society as new knowledge related to protective action decisions during simultaneous hazard events can help maximize safety and efficiency in coordinating and managing the movements of displaced residents.

Data management plan for the grant, "CPS: Medium: Integrating sensors, controls, and ecotoxicology with decoupled aquaponics using brackish groundwater and desalination concentrate for sustainable food production." This project aims to develop a cyber-physical system (CPS) testbed of integrated sensors, controls, machine learning, and ecotoxicology tools to engineer sustainable food production systems based on aquaculture using brackish water. The testbed includes an automated recirculating aquaculture system integrated with a real-time ecotoxicology system, implementing the system using desalination concentrate to demonstrate that brackish groundwater desalination costs can be offset by using its byproducts for profitable food production. This project will engage students of the local communities in three major ways: developing an exhibit and activity emphasizing interdisciplinary CPS research conducted during the academic year, offering summer research experiences to students from underrepresented groups, and participating in STEM outreach events targeting underrepresented groups.

Data management plan for the grant, "Research: The Formation of Engineers in the Research Lab: A Cognitive Ethnographic Study."

Data management plan for the grant, "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale." This project aims to improve the computation efficiency of graph neural networks (GNNs), which are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. This project aims to to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs.