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 "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, "Enhancing Cybersecurity TKSs (Tasks, Knowledge, and Skills) for Cybersecurity Education and Workforce Development."

Data management plan for the grant, "Enhancing Techniques for Ontologies Mapping for the Cybersecurity Workforce Development Applications."

Data management plan for the grant, "Predicting Criminal Activities in Darknet Markets: Advancing Knowledge and Tools for Activities Monitoring."

Data management plan for the grant, "REU Site: TaMaLe - Testing and Machine Learning for Context-Driven Systems: Research Experience for Undergraduates." TaMaLe (Testing and Machine Learning for Context-Driven Systems), a renewal Research Experience for Undergraduates (REU) Site at University of North Texas, engages 10 undergraduate students for 10 weeks with problems in the context-driven system domain. The students explore research problems to improve the reliability and security of context-driven systems. Context-driven systems, such as mobile apps, face constant streams of input from both users and context changes in their environments. Users interact with apps through touch and speech interfaces. These systems also respond to context events that occur in their environments such as changes to network connection, battery level, screen orientation, and more. The combined explosion of possible user events and context event sequences pose new challenges that require cost effective testing solutions. Students and mentors in this REU program work in small teams to develop and empirically evaluate new software testing techniques for context-driven systems using strategies such as reinforcement learning and combinatorial-based techniques.

Data management plan for the research grant, "Using uncertainty quantification and machine learning techniques to study the evolution of odor capture." This research proposes the application of uncertainty quantification (UQ) and machine learning (ML) to a CFD model of odor capture to understand the role of hair-array morphology, kinematics, and fluid environment in odor capture. The combination of CFD modeling and UQ&ML techniques can map out the performance space under which these chemosensory hair arrays operate and the relative sensitivity of each parameter of odor capture to construct a global, quantitative understanding of how parameters control odor-capture performance. Furthermore, this analysis can eliminate parameters that have no influence on odor capture, extracting the root principles of odor capture and providing a more efficient way to construct bioinspired devices for chemical detection. This work is of interest to the Army for extracting design principles that can be used for biomimetic and/or bioinspired devices for sensing hazardous chemicals in the environment (e.g. explosives).

Data management plan for the grant, "Ultra-High Temperature Stable Morphing Materials for Hypersonic Applications"

Data management plan for the research grant, "Collaborative Research: Using Uncertainty Quantification and Validated Computational Models to Analyze Pumping Performance of Valveless, Tubular Hearts." This project will develop a computational model of the essential features of the circulatory system: the electrical activity of the heart, muscle contractions of the tube walls, and the fluid-structure interactions of the heart walls and blood within. This computational framework aims to be faithful to that of a real, model animal (tunicate, or sea squirt). The model will then be analyzed with mathematical tools to determine the physical limits of the pumping system. Results of this project will improve the understanding of human heart development at the earliest stages. Also, it will point to how the large, multi-chambered hearts of vertebrates could have evolved from smaller structures.

Data management plan for the grant "RINGS: Mobility-driven Spectrum-Agile Resilient mmWave Communication Links for Unmanned Aerial Vehicle Traffic Management in the Sky." As the unmanned aviation industry moves towards Advanced Air Mobility (AAM) services, air taxis and air ambulances are expected to become a reality in near future. Handling such a high volume of unmanned air traffic requires innovative solutions for enhanced situational awareness in the airspace. The US Federal Aviation Federal Aviation Administration envisions air tracks specifically reserved for AAM vehicles at altitudes ranging from 500 ft to 2000 ft. Air tracks cross one another at intersections and vehicles may need to share the airspace with other manned and unmanned aerial vehicles. This requires coordination among the vehicles especially during close encounters. Such a coordination requires highly reliable communication links which serve as a substitute for traffic signals on the roads. This proposal addresses this key knowledge gap by investigating strategies for establishing reliable and robust Vehicle-to-Vehicle (V2V) communication links to support AAM services. This project investigates spectrum-agile millimeter wave-based tunable beamforming strategies needed for establishing reliable and robust V2V communications links to support autonomous flight operations in air corridors along with the supporting radio frequency and mixed signal …

Data management plan for the research grant, "Development of Combinatorial Processing Techniques for Accelerated Discovery of Complex Concentrated Alloy (or Multi-Principal Element Alloys) for Structural Applications"

Data management plan for the grant, "Resilient Supply Chain Ecosystem for Agile Manufacturing of Unmanned Aircraft Systems."

Data management plan for the grant, "Capacity Building: Creating and Sustaining a Pathway for Engineering Majors to Become STEM Teachers." The project aims to serve a national need for building pathways for engineering students to become grade 6-12 STEM teachers in high-need schools. The project will develop a framework for recruitment, retention, and reshaping of graduate teacher certification specifically for engineering students to become STEM teachers. The effort is a collaboration between the College of Engineering and the College of Education at the University of North Texas, a Hispanic Serving Institution.

Data management plan for the grant, "Collaborative Research: Place Attachment in Mitigation and Recovery: A Mixed Methods Study of Residential Adjustment Following Wildfires." This study focuses on understanding how place attachment influences the decisions people make after disasters, specifically residential decisions about where they live and whether they invest in measures that reduce their risk to future disasters. Using three California counties as study sites, and building on a pilot study in these same, wildfire-affected communities, the research team uses photovoice method and phenomenological analyses to develop a deep, contextually grounded understanding of the role of place attachment in residential decisions.

Data management plan for the grant, "Piloting Deposit Contracts to Increase Accessibility of a Contingency Management Intervention to Reduce Problematic Drinking". The purpose of this study is to demonstrate the feasibility and/or effectiveness of deposit contracts as an intervention for problematic drinking.

Data management plan for the grant, "UNT Gencyber Academy."

Data management plan for the grant, "Collaborative Research: Equitable Science Sensemaking: Helping Teacher Candidates Support Multiple Pathways for Learning"

Data management plan for the grant, "Connecting Galaxies and Supermassive Black Holes: Meso-scale Simulations of Multiphase Accretion Flows." It proposes to study how gas flows onto the supermassive black holes in massive galaxies and galaxy clusters. They will perform numerical simulations with a nested zoom-in technique, focusing on the mesoscale accretion flows from the Bondi radius to hundreds of Schwarzschild radii. They will predict the mass flux of different phases, as well as their angular momentum and magnetic flux.

Data management plan for the grant, "HRSA Center of Excellence". The North Texas Center for Excellence (COE) will serve as an innovative resource for recruitment, training, and retention of health service psychology (HSP) Hispanic students and faculty, will improve clinical education and cultural competence associated with minority health issues and social determinates of health, and will facilitate research on minority health and health care delivery.

Data management plan for the grant, "Multi-Source Irrigation Status and Type Mapping." This grant will quantify the long-term irrigation status and system type changes in the Mississippi Alluvial Valley for an advanced understanding of irrigation-induced water resource changes by integrating multi-source earth observation data, machine learning techniques, artificial intelligence and cloud computing.

Data management plan for the grant, "GEOS Visualization And Lagrangian dynamics Immersive eXtended Reality Tool (VALIXR) for Scientific Discovery."

Data management plan for the grant, "New approach for identification pHFO networks to predict epilleptogenesis." About 40% of epilepsy patients fail to control seizures after treatment, and currently there is no treatment that can prevent epilepsy. The goal of this study is to perform multi-scale electrophysiological investigations combine with advanced computational algorithm development to understand the characteristics of the pathological brain networks during the latent period of epilepsy. The findings of this project will lead to a better understanding of the network mechanisms of epileptogenesis and suggest novel approaches to prevent the process of epileptogenesis.

Data management plan for the grant, "Cancer Detection and Treatment Based on Low-Level Light Interaction with Biological Cells: Augmentation to Existing Human Performance Research Capabilities."

Data management plan for the grant, "Collaborative Research: MRA: Particulates in canopy flowpaths: A missing mass flux at the macrosystem scale?" This research will investigate a vital, but currently uncharacterized, macrosystem biogeochemical function within forest canopies at the very start of the terrestrial hydrologic cycle: particulate concentration, flux, and composition in rainwaters draining from forest canopies. We will address three major objectives: (1) Estimate the net rainfall (TF+ SF) water and particulate mass flux across forest types; (2) Characterize the particulate composition (C:N:P, including C components, like total C, organic C, black C, and microplastic C) of TF and SF; and (3) Identify major drivers of macrosystem variability in net rainfall particulate flux and composition.