Data management plan for the grant, "EAGER: Experimental Methods and Measurements of Anomalous Properties of Supercritical Fluids and their Mixtures"

Data management plan for the grant, "The University of North Texas Industrial Assessment Center."

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 for the grant, "Collaborative Research: Framework Implementations: CSSI: CANDY: Cyberinfrastructure for Accelerating Innovation in Network Dynamics." Research addressing the lack of a comprehensive cyberinfrastructure that supports innovative research challenges in large-scale, complex, dynamic networks by developing a novel platform, called CANDY (Cyberinfrastructure for Accelerating Innovation in Network Dynamics), based on efficient, scalable parallel algorithm design for dynamic networks and high-performance software development with performance optimization.

Data management plan for the grant "Developing a cylindrical-type wireless high temperature pressure sensor for rocket propulsion system test."

Data management plan for the grant "Process and Materials Development for Additive Manufacturing (AM) of Tungsten-Based Alloys with Superior High Temperature Performance and Good Room Temperature Ductility." Research on CALPHAD simulations will be conducted to optimize the chemical composition of tungsten based alloys towards 3D printability while boosting mechanical performance through solid solution and dispersion strengthening. This will followed by powder prototyping to validate the simulation results experimentally and to finalize the chemical composition of interest. Process parameter optimization and post-process heat treatment design will help to achieve optimal low-to-high temperature mechanical properties. Mechanical testing and microstructural characterization will be performed to establish process-microstructure-property relationships.

Data management plan for the grant "Modulating 3D Cellular Connectivity Via Spatially-Controlled Programmable Bonding." This project seeks to demonstrate proof-of-concept for technology that allows one to systematically place cells on substrates to create complex 3D assemblies with precise control over individual cellular interactions. The technology generated within this proposal will open new avenues for studying multicellular communication pathways, stem cell differentiation, and understanding developmental processes. Spatially-defined cell-cell communication plays a critical role in numerous disease and developmental processes that include osteoarthritic degeneration, cancer metastasis, and organ regeneration.

Data management plan for the grant, "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach." This Research Experiences for Undergraduates (REU) Site Program at the University of North Texas will enhance the knowledge and research skills of a diverse cohort of undergraduate students through empowering, innovative, and interdisciplinary research experiences in developing Deep Learning applications and systems. The program aims to 1) expose undergraduate students to real-world and cutting-edge research focused on accelerated deep learning through combined hardware and software development; 2) encourage more undergraduate students to continue their academic careers and seek graduate degrees in computer science, computer engineering, and related disciplines; 3) develop research skills and improve communication and collaborative skills in undergraduate students.

Data management plan for the grant, "CAREER: Reinventing Network-on-Chips of GPU-Accelerated Systems." Research seeking to reinvent on-chip networks for GPU-accelerated systems to remove a communication bottleneck. A major outcome of the project is a set of techniques that enable the development of effective and efficient network-on-chip architectures. Graphics processing units (GPUs) have rapidly evolved to become high-performance accelerators for data-parallel computing. To fully take advantage of the computing power of GPUs, on-chip networks need to provide timely data movement to satisfy the requests of data by the processing cores. Currently, there exists a big gap between the fast-growing processing power of the GPU processing cores and the slow-increasing on-chip network bandwidth. Because of this, GPU-accelerated systems are interconnect-dominated and the on-chip network becomes their performance bottleneck.

Data management plan for the grant, "In-Situ Monitoring for Quality Assurance and Machine Learning in Direct-Write Additive Manufacturing of 5G RF Electronic Ceramics."

Data management plan for the grant "Collaborative Research: Microneedle‐mediated Adaptive Phototherapy (MAP) for Wound Healing."

Data management plan for the grant, "SaTC: CORE: Small: Customizable Geo-Obfuscation to Protect Users' Location Privacy in Mobile Crowdsourcing."

Data management plan for the grant, "Targeted Systemic Delivery of SDF-1 DNA for the Treatment of Chronic Heart Disease."

Data management plan for the grant "Collaborative Research: Smart Stent for Post-Endovascular Aneurysm Repair." The overall objective of this proposal is to develop a Smart Stent for post-endovascular aneurysm repair (EVAR) surveillance that combines a flexible, hybrid, seamless, battery-less bioelectronic system with a deep-learning algorithm that offers an automated diagnosis of complications such as endoleak.

Data management plan for the grant "High-throughput Screening and Quality Assessment Testing and an AI-based In-Situ Process Monitoring and Quality Control Framework for Additive Manufacturing New Builds and Repaired Parts."

Data management plan for the grant, "Collaborative Research: Combined Tribological and Bactericidal Effect of Bioinjectable Nanodiamonds on Biological Joints."

Data management plan for the grant "CAREER: A Prosthetic Elbow with Network of Soft and Modular Thermo-Active Actuators for Mobility Impaired Patients." This NSF Faculty Early Career Development (CAREER) project will develop novel networks of flexible Peltier-based soft actuators designed for rehabilitation.

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 grant "Travel: NSF Student Travel Grant for 2023 Great Lakes Bioinformatics Conference (GLBIO)"

Data management plan for the grant "Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response." Research to develop open source integrative computational tools that perform secondary analysis of publicly available multi-omics biological, clinical and environmental exposure datasets to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response.

Data management plan for the grant, "CAREER: Fundamental Limits of Cryptographic Primitives Through Network Information Theory." Research project studying the fundamental limits of a diverse array of cryptographic primitives through network information theory and coding tools. The project takes an information theoretic view of the investigation of the fundamental limits of cryptographic primitives. The project is expected to unveil theoretical and practical insights into cryptographic primitives, and enhance the understanding on their fundamental limits.

Data management plan for research material for the grant "Multi-modal Surface Acoustic Wave Sensing System for Pressure and Temperature monitoring of Spent Fuel Canisters." This project is a collaboration of ​University of North Texas, Oak Ridge National Laboratory, and National Energy Technology Laboratory to develop a multi-modal wireless passive SAW (Surface Acoustic Wave) sensor array, which are deployed on the outside surface of stainless steel canisters used for nuclear waste, to monitor the strain of the canister and thus determine the inside pressure to ensure safety and security. In addition, the SAW strain sensor could also measure the surface temperature and potentially monitor helium gas leaks.​

Data management plan for the grant "Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE)." Research relating to creating a community infrastructure for researchers using multilayer networks (MLN). This project uses a formally established network decoupling approach to perform various aggregate analysis (community, centrality, substructure detection, etc.) using individual layers and composing them. The broader impact of this planning project is to provide meaningful and appropriate analysis tools that are grounded in theory to a broad range of applications from different domains. The focus is on facilitating the mainstream use of multilayer network analysis in data analysis, research and teaching.

Data management plan for the grant, "Collaborative Research: SaTC: CORE: Small: Privacy protection of Vehicles location in Spatial Crowdsourcing under realistic adversarial models." Research to develop new location privacy protection techniques by considering vehicles’ mobility features in the road network, and consequently lead to a more secure and trustworthy computing environment in location-based services (LBSs). As privacy concerns are still among the main obstacles for mobile users to participate in many advanced LBSs, this project is poised to contribute to the wider adoption of LBSs for many applications (e.g. navigation systems and location-based recommendation systems). The project will also provide a set of diverse and interesting topics for undergraduate and graduate students and outreach activities for the community.