Data management plan for the grant, "CAS: Highly Interacting Panchromatic Push-Pull Systems: Symmetry Breaking and Quantum Coherence in Electron Transfer."

Data management plan for the grant "Charge Transport in Two-Dimensional Materials Based Integrated Flexible Energy System."

Data management plan for the grant, "Chemical-Guided Identification of Primary Metabolic Targets for Improvement of Hydroxy Fatty Acid Synthesis in Physaria fendleri." Research on the identification of primary metabolic targets using chemical-guided identification. The first objective of this research is to conduct metabolomics analysis on P. fendleri embryos cultured with two identified chemical regulators of fatty acid metabolism. The second objective of this research is to generate a metabolic flux map of embryos treated with these regulatory compounds in order to determine how metabolic rates and carbon flow can be manipulated to improve HFA production in this species and increase its commercial viability. With properties that could replace imported castor oil, research on the crop in discussion is situated directly in the scope of the USDA-AFRI Education and Workforce Development goals.

Data management plan for the grant, "Child Health and Human Development Extramural Research." This study will use the genome-edited human induced pluripotent stem cell (hiPSC) with NOTCH1 knockout to recapitulate the genetic variants of NOTCH1 mutation in the Hypoplastic left heart syndrome (HLHS). It will use advances in the vascularized cardiac organoid directly differentiated from hiPSCs to replay the development and function of cardiomyocytes, endothelial cells, smooth muscle cells, and other cardiac cells in a defined 3D cell culture model by stencil-based micropatterning. It will elucidate the pathogenesis of cardiovascular underdevelopment and dysfunction found in HLHS with NOTCH1 mutation via the NOTCHDELTA/JAG ligand-receptor binding and multicellular crosstalk by single-cell RNA-seq and proteomics analysis.

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: A Systematic and Comprehensive Study of Black Hole-Driven Turbulence in Massive Galactic Systems." This research team has developed a technique to measure gas turbulence in systems hosting giant central black holes and will directly evaluate the viability of this mechanism using a large sample of galaxies. This project will deliver a more complete view of the “feedback” provided by accreting SMBHs, leading to a better understanding of the black hole-host galaxy relation. More specifically, it will directly probe the energetics of the intra-cluster, circum-galactic, and interstellar media of massive early-type galaxies.

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: CIF: Medium: Fundamental Limits of Cache-aided Multi-user Private Function Retrieval." This project is motivated by the need to efficiently execute complex queries on massive databases in a way that minimizes the use of communication resources while preserving the privacy of the entity that initiated the query. Such queries are functions of the data points that are stored at remote servers. This project develops a principled and holistic framework for the problem of privately retrieving, at distributed cache-aided nodes, the output of functions based on both data that is locally computed and data that is received from multiple servers.

Data management plan for the grant "Collaborative Research: CISE-MSI: RCBP-RF: CPS, CNS: Emergency Response and Evacuation Training for Active Shooter Events."

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, "Collaborative Research: Conference: Texas-Oklahoma Representations and Automorphic forms (TORA)."

Data management plan for the grant, "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure." This project aims to develop a novel set of interactive training materials, including hands-on lecture modules, invited research talks from renowned researchers, and an interdisciplinary collaborative project in an intensive workshop, integrating a wide variety of advanced and inter-connected techniques employed by research workforce for deep learning (DL) systems in advanced GPU cyberinfrastructure (CI). Specifically, this project focuses on training seniors, graduate students, and researchers on how advanced GPU CI can be efficiently utilized and improved to enable high-performance DL systems for data-intensive DL applications in geoscience (GS) and computer science and engineering (CSE) research. The goal is to foster future CI users and contributors to adopt, develop, and improve advanced GPU CI for DL systems in their research.

Data management plan for the grant, "Collaborative Research: Diversifying Human-Centered Data Science through the Research and Design of Ethical Games." This project studies collaborative learning in a virtual setting for groups of peers with conflicting methodological cultures and diverse backgrounds. An additional artifact of this two-year, interdisciplinary project will be a prototype of an educational game, targeted towards the underrepresented communities and the greater public, that will bring more people into critical conversations about the roles of AI and data science in our society. The primary aim of this research is the production of novel theories in learning sciences regarding collaborative learning in the face of diverse backgrounds and cultural norms.

Data management plan for the grant, "Collaborative Research: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys"

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, "Collaborative Research: Equitable Science Sensemaking: Helping Teacher Candidates Support Multiple Pathways for Learning"

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, "Collaborative Research: Increasing Inclusion and Equity of Minoritized STEM Faculty: Examining the Role of Epistemic Exclusion in Scholar(ly) Evaluation Practices."

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, "Collaborative Research: LTREB Renewal - River ecosystem responses to floodplain restoration."

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

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.

Data management plan for the grant, "Collaborative Research: PIC: Slow Wave Enhanced Electrooptically Tuned Michelson Interferometer Biosensor for On-Chip Polarization Interferometry."

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.