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, "CAREER: Developing a Flexible Serverless Multimedia Streaming Cloud Platform."

Data management plan for the grant, "Collaborative Research: IRES Track I: Wireless Federated Fog Computing for Remote Industry 4.0 Applications."

Data management plan for the grant, "CAREER: Manufacturing of Mechanically Stable Nanoporous Ceramic Structures Via Selective Infiltration of Polymer Templates." This Faculty Early Career Development (CAREER) grant from the National Science Foundation supports fundamental research to elucidate a new strategy of manufacturing nanoporous ceramic structures with controllable structure and composition and programmable mechanical stability. The specific goal of this research is to discover processing-structure-property relationships in ceramic coatings and heterostructures by providing fundamental insights on the mechanism of liquid phase swelling-based infiltration of spin-coated polymer templates with inorganic precursors and defining the rules that control the resulting structure and, thus, access to various materials surfaces and interfaces.

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, "Mechanochemical Reconstruction Principles for Two-Dimensional Material Adaptation to Applied Stresses." This grant focuses on understanding how 2D materials change their structure and function under loading and shear stresses. For this, we will investigate the fundamental origins of solid-state interactions, unraveling the processes occurring at contacting surfaces during sliding, why some materials show better stability than others, and how one can use these novel 2D materials for mitigating friction and wear and enabling superlubricity.

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 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, "Variable Conductor Radiator for CO2 Deposition in Deep Space Transit"

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

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

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, "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, "CAREER: Shape Memory Polymers as Biomaterial." This CAREER projectaimsto elucidate the underlying mechanism of the plasticization-induced shape memory effect of thiol-enebased polymers. The model application for this material will be a heat shrink tubing that can shrink at bodily conditions (37° C and simulated body fluids) and can be used to seal colonic anastomosis. The specific three aims are to (1) Systematically investigate the effect of crosslink-density and chain extender length on theplasticization-induced shape memory effect of thiol-enebased polymers. Mechanical and thermomechanical measurements inside simulated body fluids will be used to assess shape memory properties and structure-property relationships. (2) Understand the relationship between material thickness, degree of shape-programming, and radial recovery forces of tube-shaped SMPs to determine optimal design parameters for sufficient shape recovery using the heat shrink tube model. (3) Demonstrate the functionality of a biomedical heat shrink tube that utilizes the plasticization-induced shape recovery through an ex vivo colon anastomosis model and quantify mechanical and sealing properties.

Data management plan for the grant "IUCRC Planning Grant University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT)." Research concentrating on interdisciplinary research, aiming to initiate and accelerate the transformation of mobility methods from conventional vehicles to electric, connected and autonomous vehicles by creating innovative electric, connected and autonomous technologies. The grant will create the Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT). A partnership between Wayne State University (WSU), University of North Texas (UNT), and Clarkson University (Clarkson), the center not only serves as an apparatus of academic researchers collaborating with industry on important problems, but also provides industry partners opportunities to access advanced synergic research produced from a diverse group of researchers.

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, "CICI: UCSS: Secure Containers in High-Performance Computing Infrastructure." Ensuring the security and privacy of high-performance computing (HPC) infrastructures is of utmost importance due to their handling of sensitive data and critical scientific computations. HPC infrastructures commonly employ containers, which provide lightweight and isolated environments for running applications. Nevertheless, containers in HPC infrastructures encounter security challenges, including insecure container images and vulnerabilities related to isolation. Existing container image scanners face a major challenge of low coverage, while current container runtimes struggle to ensure both security and performance for HPC workloads simultaneously. This project addresses these challenges by developing secure containers specifically tailored for HPC infrastructures. The project introduces innovative solutions, including the development of an efficient image vulnerability scanner and a secure container runtime.

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.

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

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, "Collaborative Research: Topological Defects and Dynamic Motion of Symmetry-breaking Tadpole Particles in Liquid Crystal Medium."

Data management plan for the grant, "Collaborative Research: Engaging Blind and Visually Impaired Youth in Computer Science through Music Programming."

Data management plan for the grant, "Increasing Skills in Accessibility through Integration in the Undergraduate Computer Science and Information Technology Curricula."