Grants

NSF

About 12% of U.S. adults have difficulty with mobility, including walking or climbing stairs. Current technologies for helping people manage these issues are often expensive, heavy, and hard to use. The goal of this research is to develop a new paradigm for assistive robotics that will make it possible to take such systems from the laboratory to widely accessible tools for people. The research team will combine advances in robotic exoskeletons for human joints, simulation enabled by artificial intelligence, and other approaches to design lightweight, wearable robotic systems that can be personally controlled. The investigators will also test the systems in different settings to improve their usability. The project will develop new science and technology that have the potential to help people to perform daily activities, along with their quality of life and overall well-being. The project will design and test new assistive robotic systems by integrating artificial intelligence, robotics, biosensors, rehabilitation medicine, gerontology, and neurorehabilitation. The research will use a modular design approach, that can adapt to individual needs and daily activities without extensive calibration. By leveraging computational modeling and physics-informed deep reinforcement learning, the systems will learn control strategies from computer simulations and user feedback to deliver personalized support that addresses an individual’s mobility challenges and needs. The objective is to broaden the reach of robotic mobility support to a much larger population in a wide variety of settings. The resulting enhanced mobility and function can lead to broader benefits, including promoting independent living, employment, and well-being. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Many solar energy projects are installed in agricultural lands, creating land competition with crops, orchards, vineyards, and pastures. However, relatively little is known about how these solar installations affect surrounding communities, landscapes, and agriculture. Moreover, careful design and siting of these installations can yield a variety of benefits, including increasing farm income, enhancing water resources, improving plant and animal habitat, and enriching soil. To address these issues, this project will bring together an interdisciplinary team of scientists and engineers with agricultural extension specialists, landscape designers, community members, and industry and nonprofit partners. The project will focus on two questions: 1) how is solar energy affecting the landscape and surrounding communities?; and 2) how can the U.S. build a stronger, more productive, and more resilient agroenergy landscape? The project explores practices that will improve outcomes of solar energy in agricultural landscapes. To do so, the project will collect novel data at existing solar facilities and launch a first-of-its-kind scientific research facility to collect data on how solar installations affect agricultural land and communities. Using these data, the research team will study how solar facilities change soil and habitat conditions, the water cycle, crop production, economic returns, and surrounding communities. Throughout the project, an advisory team of farmers, stakeholders, policymakers, and community members will help shape the research and focus the project’s efforts on the needs of farmers, utilities, and the public. This approach will bring together new forms of biogeophysical data collection, modeling, and life cycle assessment with community co-creation. The project’s findings will be used to create decision-support tools, design new solar installations, conduct workforce training, and develop educational workshops and programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Many solar energy projects are installed in agricultural lands, creating land competition with crops, orchards, vineyards, and pastures. However, relatively little is known about how these solar installations affect surrounding communities, landscapes, and agriculture. Moreover, careful design and siting of these installations can yield a variety of benefits, including increasing farm income, enhancing water resources, improving plant and animal habitat, and enriching soil. To address these issues, this project will bring together an interdisciplinary team of scientists and engineers with agricultural extension specialists, landscape designers, community members, and industry and nonprofit partners. The project will focus on two questions: 1) how is solar energy affecting the landscape and surrounding communities?; and 2) how can the U.S. build a stronger, more productive, and more resilient agroenergy landscape? The project explores practices that will improve outcomes of solar energy in agricultural landscapes. To do so, the project will collect novel data at existing solar facilities and launch a first-of-its-kind scientific research facility to collect data on how solar installations affect agricultural land and communities. Using these data, the research team will study how solar facilities change soil and habitat conditions, the water cycle, crop production, economic returns, and surrounding communities. Throughout the project, an advisory team of farmers, stakeholders, policymakers, and community members will help shape the research and focus the project’s efforts on the needs of farmers, utilities, and the public. This approach will bring together new forms of biogeophysical data collection, modeling, and life cycle assessment with community co-creation. The project’s findings will be used to create decision-support tools, design new solar installations, conduct workforce training, and develop educational workshops and programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Achieving national sustainability goals will require rapid adoption of more sustainable practices in many areas of society but transitions to sustainable practices are often slow. This project tests whether these transitions can be accelerated by (1) creating innovative ecological forecasts that predict where and when more sustainable practices would have the greatest benefits and (2) engaging impacted communities in the process of co-implementing forecasts and advocating for sustainability transitions. The study system is the proliferation of artificial lights at night (ALAN) and its impacts on migrant birds. ALAN is increasing rapidly worldwide, and its benefits are countered by pervasive negative consequences for biodiversity, ecosystems, and human health. A major ecological consequence of ALAN is disruption of bird migration – millions of birds die annually in collisions with well-lit buildings – which contributes to widespread bird population declines. The ALAN-bird migration system is ideal for this study because, like many wicked environmental problems, environmental concerns emerge as a product of complex social and cultural processes that have proven difficult to resolve using traditional approaches. This project employs a transdisciplinary convergence approach to integrating advances in ecological forecasting with those in the social and political science of community engaged scholarship. Experiments testing sustainability impacts of innovations in ecological forecasting will be co-designed and implemented with a coalition of convergence research partners. The project will generate an understanding of pathways by which sustainable practices are adopted for ALAN, this new knowledge can be used to help address other societal-environmental conflicts. The project focuses on testing a key prediction of sustainability transformations science theory – that innovations originate within advocacy coalitions then accumulate at the subsystem level to drive sustainability transformations (e.g., new policies). During phase one the investigation gathers detailed national survey information on the ALAN system and creates transformational technological improvements in existing bird migration forecasts specific to impacts of ALAN. This new social and ecological knowledge will then be used to engage with advocacy coalitions in specific urban testbed sites to co-implement sustainability transformation experiments during phase two. These experiments will use targeted messaging campaigns to foster ALAN mitigation. Experiments will be focused on sustainability-oriented coalitions because these advocates are predicted to have high leverage to affect radical transformation toward sustainability across the ALAN subsystem. Impacts of the experiments on ALAN, impacts of ALAN on migrant birds, and human behaviors and attitudes toward ALAN will be quantified. Through this two-phase approach this project will produce a new understanding of how innovations derived from a convergence research approach can be employed in a sustainability science and policy framework to accelerate transformations. These outcomes will contribute understanding of how communities and researchers can co-engage with wicked environmental problems more broadly to drive transformations toward sustainability. Results will create new, and potentially transformative, understanding of how ecological forecasting contributes to sustainability transformations. This project is jointly funded by the Growing Convergence Research Program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The interaction of the solar wind and the Earth's magnetic field created a collisionless shock called a bow shock in front of the Earth's magnetosphere. The incident particles can be accelerated and reflected at the bow shock, resulting in their counter-streaming along field lines and interacting with the local plasma. Earth's bow shock is a natural laboratory to study the complex interactions between the incident solar wind and the counter-streaming foreshock populations. This study aims to build a predictive model of the foreshock backstreaming ions, which can be integrated into a space weather prediction model to help accurately forecast and mitigate space weather hazards. An early-career female scientist leads this project and involves the participation of multiple early-career scientists. The related science materials will be presented to k-12 students, parents, and the general public audience through various outreach events to promote STEM education. This study focuses on the foreshock ion properties and addresses the following questions: (1) What are the properties of the freshly reflected ions near the bow shock, including reflection rate, velocity, perpendicular and parallel temperatures and Velocity Distribution Function (VDF) types (e.g., field-aligned beam, ring distributions, etc), as a function of upstream solar wind conditions (e.g., speed, interplanetary magnetic field (IMF), Mach number, beta, etc) and shock normal angles? (2) How do the properties of the freshly reflected ions evolve spatially and temporally as they travel away from the bow shock as suggested by global simulations? (3) Can the obtained properties be explained by existing acceleration and reflection theories, such as adiabatic and specular reflections? The project combines analysis of satellite measurements and numerical simulations to reveal how the ion distributions are determined by the upstream solar wind conditions. This project will improve our understanding of the properties of the bow shock reflected ions and the related reflection, acceleration, and scattering processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The radiation belts are a region of high-energy particles that orbit around the Earth. These energetic particles present a hazard to human exploration and technology in space, especially for satellites in geostationary orbit. Understanding the timing and dominant mechanism of radiation belt enhancement events (sudden, system-wide energization of radiation belt particles) is critical to nation security. This project will determine how the plasmapause (the outer boundary of a region of cold, dense plasma surrounding the Earth) controls radiation belt enhancements. One major acceleration mechanism of radiation belt particles (local acceleration) is driven by plasma waves that only occur outside the plasmapause, so we expect that the radiation belt enhancements that are driven by local acceleration would occur outside the plasmapause while other acceleration mechanisms may drive enhancements inside the plasmasphere. This study will systematically evaluate the long-term behavior of radiation belt enhancements and conduct in-depth case studies to evaluate how plasmapause controls radiation belt enhancement events. Radiation belt enhancement events occur when electrons in near-Earth space are accelerated close to the speed of light. Local acceleration is a major acceleration mechanism, which occurs when 10’s – 100’s keV electrons interact with chorus waves, resulting in particle energization to multiple MeV. Chorus waves can only happen outside the plasmapause, so radiation belt enhancements tend to occur outside the plasmasphere. This study will use multi-spacecraft observations provided by the Global Positioning System (GPS) constellation to study the control of the plasmapause on radiation belt enhancements on timescales over a solar cycle. We will study data from 2008 (when 8 GPS with combined X-ray dosimeters, CXDs, were active) until 2023 (25 active GPS with CXD) and combine these data with plasmapause models to statistically analyze the offset of enhancement locations from the plasmapause and energy-dependence of radiation belt enhancements. Case studies will then be performed to examine why the radiation belt enhancements tend to be offset from the plasmapause, determine the spatial extent of the region of electron energization, and evaluate why some radiation belt enhancements occur inside the plasmasphere. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The radiation belts are a region of high-energy particles that orbit around the Earth. These energetic particles present a hazard to human exploration and technology in space, especially for satellites in geostationary orbit. Understanding the timing and dominant mechanism of radiation belt enhancement events (sudden, system-wide energization of radiation belt particles) is critical to nation security. This project will determine how the plasmapause (the outer boundary of a region of cold, dense plasma surrounding the Earth) controls radiation belt enhancements. One major acceleration mechanism of radiation belt particles (local acceleration) is driven by plasma waves that only occur outside the plasmapause, so we expect that the radiation belt enhancements that are driven by local acceleration would occur outside the plasmapause while other acceleration mechanisms may drive enhancements inside the plasmasphere. This study will systematically evaluate the long-term behavior of radiation belt enhancements and conduct in-depth case studies to evaluate how plasmapause controls radiation belt enhancement events. Radiation belt enhancement events occur when electrons in near-Earth space are accelerated close to the speed of light. Local acceleration is a major acceleration mechanism, which occurs when 10’s – 100’s keV electrons interact with chorus waves, resulting in particle energization to multiple MeV. Chorus waves can only happen outside the plasmapause, so radiation belt enhancements tend to occur outside the plasmasphere. This study will use multi-spacecraft observations provided by the Global Positioning System (GPS) constellation to study the control of the plasmapause on radiation belt enhancements on timescales over a solar cycle. We will study data from 2008 (when 8 GPS with combined X-ray dosimeters, CXDs, were active) until 2023 (25 active GPS with CXD) and combine these data with plasmapause models to statistically analyze the offset of enhancement locations from the plasmapause and energy-dependence of radiation belt enhancements. Case studies will then be performed to examine why the radiation belt enhancements tend to be offset from the plasmapause, determine the spatial extent of the region of electron energization, and evaluate why some radiation belt enhancements occur inside the plasmasphere. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Electromagnetic waves with frequencies in the ultra-low frequency (ULF) range are known to be among the leading causes of radial diffusion and transport of energetic electrons in Earth's radiation belts. The frequencies of ULF waves overlap with the range of drift frequencies of energetic electrons as they circle the Earth, leading to resonant interactions. Numerous expressions have been derived to quantitatively describe radial diffusion so that they can be incorporated into global models of radiation belt electrons. However, most expressions of the radial diffusion rates are derived only for equatorially mirroring electrons and are based on estimates of the power of ULF waves that are obtained either from spacecraft close to the equatorial plane or from the ground. Recent studies using the Van Allen Probes and Arase have shown that the wave power in magnetic fluctuations is significantly enhanced away from the magnetic equator, consistent with models simulating the natural modes of oscillation of magnetospheric field lines. This has significant implications for the estimation of radial diffusion rates, as higher pitch angle electrons will experience considerably higher ULF wave fluctuations than equatorial electrons. This project will derive the magnetic and electric field wave powers and incorporate them into the 3D test particle simulations to estimate the diffusion coefficient. The novel, pitch-angle-dependent diffusion rates will be introduced to a global model of radiation belt electrons to evaluate the effect of the pitch-angle dependence of the diffusion coefficient on radiation belt dynamics. The result could have significant implications for the radial diffusion rates as currently estimated. It will pave the way for incorporating pitch-angle-dependent radial diffusion coefficients in global models to predict the near-Earth radiation environment better. The main goal of this project is to quantify the role of off-equatorial Ultra-Low Frequency (ULF) waves on the radial transport and diffusion of relativistic electrons (100s keV to few MeV) in the outer radiation belt (L~4 to 7), investigating the effect of pitch-angle-dependent radial transport of energetic particles on global dynamics of the radiation belts. The following science questions will be answered: How are ULF electric and magnetic field fluctuations distributed in magnetic latitude and magnetic local time under varying solar and geomagnetic conditions? What is the role of off-equatorial ULF wave fluctuations on the radial diffusion and transport of relativistic electrons in the outer radiation belt (L~4 to 7)? How are off-equatorial ULF waves expected to impact current radiation belt models, and what is their contribution to the global dynamics of the radiation belts? The team will use multiple satellite datasets (THEMIS, Van Allen Probes, Cluster, and Arase), test particle tracing simulations, and a global radiation belt model to quantify the contribution of off-equatorial ULF waves on radial diffusion in the radiation belts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project develops an innovative radio frequency (RF) signal propagation modeling framework to advance next-generation wireless technologies, including Wi-Fi 7 and sixth-generation cellular networks. These technologies enable critical applications in smart cities, precision agriculture, and smart healthcare by supporting efficient wireless communication and sensing tasks such as human activity recognition and environmental monitoring. A key challenge in data-driven wireless systems is the labor-intensive process of collecting large-scale RF signal datasets for training deep learning models. This project addresses that challenge by generating high-fidelity synthetic RF datasets using advanced propagation modeling. These synthetic datasets support improved network planning, resource allocation, and sensing accuracy, ultimately leading to more efficient and scalable wireless infrastructures. The outcomes of this research contribute to societal benefits such as economic development, cost-effective network deployment, and enhanced connectivity in dynamic and infrastructure-limited environments. The project also integrates educational activities at the University of California, Merced and the University of California, Los Angeles, incorporating wireless communications and generative artificial intelligence into undergraduate and graduate curricula. In addition, the research team trains graduate students and postdoctoral scholars to support workforce development in RF modeling and next-generation wireless systems. The research investigates the use of Neural Radiance Fields for RF signal propagation modeling, with the goal of synthesizing received signals at arbitrary transmitter and receiver positions in complex three-dimensional environments. The scientific problem centers on overcoming key limitations of existing models, including high data requirements, computational inefficiencies, and poor adaptability to dynamic scenes and spatial variations. To address these issues, the research team develops a scalable approach that combines Gaussian-distribution-based representations, Graph Neural Network-guided scene modeling, and accelerated neural ray tracing to reduce data needs, training duration, and inference latency. Temporal adaptability is introduced through the use of deformation fields that capture dynamic environmental changes, while spatial generalization allows for applications across varying receiver positions and new environments without extensive retraining. The approach integrates techniques such as multi-head deformation decoders, neural-driven ray tracing, and contextual scene embeddings. The resulting models are evaluated using both simulation and experimental testbeds at UC Merced and UCLA. Evaluation metrics include fidelity of signal reconstruction, computational efficiency, and task performance in key applications such as device localization, activity recognition, network design, and resource allocation for diverse wireless technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project develops an innovative radio frequency (RF) signal propagation modeling framework to advance next-generation wireless technologies, including Wi-Fi 7 and sixth-generation cellular networks. These technologies enable critical applications in smart cities, precision agriculture, and smart healthcare by supporting efficient wireless communication and sensing tasks such as human activity recognition and environmental monitoring. A key challenge in data-driven wireless systems is the labor-intensive process of collecting large-scale RF signal datasets for training deep learning models. This project addresses that challenge by generating high-fidelity synthetic RF datasets using advanced propagation modeling. These synthetic datasets support improved network planning, resource allocation, and sensing accuracy, ultimately leading to more efficient and scalable wireless infrastructures. The outcomes of this research contribute to societal benefits such as economic development, cost-effective network deployment, and enhanced connectivity in dynamic and infrastructure-limited environments. The project also integrates educational activities at the University of California, Merced and the University of California, Los Angeles, incorporating wireless communications and generative artificial intelligence into undergraduate and graduate curricula. In addition, the research team trains graduate students and postdoctoral scholars to support workforce development in RF modeling and next-generation wireless systems. The research investigates the use of Neural Radiance Fields for RF signal propagation modeling, with the goal of synthesizing received signals at arbitrary transmitter and receiver positions in complex three-dimensional environments. The scientific problem centers on overcoming key limitations of existing models, including high data requirements, computational inefficiencies, and poor adaptability to dynamic scenes and spatial variations. To address these issues, the research team develops a scalable approach that combines Gaussian-distribution-based representations, Graph Neural Network-guided scene modeling, and accelerated neural ray tracing to reduce data needs, training duration, and inference latency. Temporal adaptability is introduced through the use of deformation fields that capture dynamic environmental changes, while spatial generalization allows for applications across varying receiver positions and new environments without extensive retraining. The approach integrates techniques such as multi-head deformation decoders, neural-driven ray tracing, and contextual scene embeddings. The resulting models are evaluated using both simulation and experimental testbeds at UC Merced and UCLA. Evaluation metrics include fidelity of signal reconstruction, computational efficiency, and task performance in key applications such as device localization, activity recognition, network design, and resource allocation for diverse wireless technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Accurate statistical inference is essential for making reliable decisions in various fields, such as forensic science, medicine, economics, and machine learning. This project develops and advances generalized fiducial inference (GFI), an innovative statistical method that quantifies uncertainty without requiring subjective assumptions. By addressing complex real-world problems, such as evaluating evidence in criminal cases, understanding causal relationships in economics and health, and improving reliability in machine learning, the project will significantly enhance decision-making processes. Additionally, the project provides valuable research training opportunities for graduate students in science, technology, engineering, and mathematics (STEM), thereby contributing directly to national goals of promoting scientific advancement, health, prosperity, and welfare. This collaborative research aims to advance generalized fiducial inference (GFI), building upon Fisher’s original fiducial argument and recent developments in modern statistics. The primary objectives include extending GFI methods to causal inference models, particularly instrumental variable models, and redefining GFI through normalizing flows to manage computational complexity in non-analytic scenarios. The project will also apply these methodological innovations to pressing real-world problems in forensic science, specifically addressing the accurate calibration of likelihood ratios from machine learning models, as well as, resources permitting, investigations into uncertainty quantification for social network learning and sports analytics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Polar bears have numerous traits that not only give them their iconic appearance but are also adaptations to living in the Arctic environment. These include their large body size with thick fat layer, streamlined head to help dive after prey, and most-noticeable their white fur. While other species of bears live in tropical or temperate environments, polar bears adapted to the extreme low temperatures of the Arctic with novel ways to absorb solar radiation or retain internal body heat. Three of these thermoregulatory adaptations within the skin or hair include: light hair color, dark skin color, and insulating hollow hair. To identify the genes responsible for these traits, DNA, RNA, proteins, and tissue structure of polar bears will be compared to both the brown/grizzly bear and American black bear. Further, the rate of hair heating and cooling, and energy transfer at the hair-skin boundary will be measured and modeled with the aim of developing novel nanoscale materials. Thus, the project will yield new insights into how mammals remain warm in extreme cold environments. Similar pigmentation and hair structural variation have evolved in other Arctic mammals; thus, understanding the molecular and cellular mechanisms of these traits will provide a starting point to investigate convergence. Research results will be translated into educational materials appropriate for K-12 students and distributed to zoos with polar bears, as zoos are often the only opportunity people have to observe these animals. Finally, the project will train scientists in effective science communication and reach non-expert audiences through public outreach. Arctic species must adapt to the extreme temperatures of their environment. This research focuses on skin and hair traits associated with thermoregulatory adaptation. Comparative genomics, transcriptomics, and histological staining of skin tissue between polar bears and two temperate species, brown and American black bears, will determine the cellular basis for light hair and dark skin. Specifically, the abundance and location of melanocytes will be characterized, then changes in gene expression among bear species that can account for differences in cellular location will be identified. Beyond pigmentation differences, polar bear hair has a hollow center thought to increase the insulating capacity of the fur. To identify the genes that underly this trait, protein abundance within hair will be compared among species. Ancestral recombination graphs will be constructed to quantify when during the evolutionary history of polar bears this adaptation arose. As the hollow hair has distinct applications in creating novel heat retaining nanomaterials, this research will quantify and model thermodynamic properties of the hair and hair-skin interface over a range of environmentally relevant temperatures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Polar bears have numerous traits that not only give them their iconic appearance but are also adaptations to living in the Arctic environment. These include their large body size with thick fat layer, streamlined head to help dive after prey, and most-noticeable their white fur. While other species of bears live in tropical or temperate environments, polar bears adapted to the extreme low temperatures of the Arctic with novel ways to absorb solar radiation or retain internal body heat. Three of these thermoregulatory adaptations within the skin or hair include: light hair color, dark skin color, and insulating hollow hair. To identify the genes responsible for these traits, DNA, RNA, proteins, and tissue structure of polar bears will be compared to both the brown/grizzly bear and American black bear. Further, the rate of hair heating and cooling, and energy transfer at the hair-skin boundary will be measured and modeled with the aim of developing novel nanoscale materials. Thus, the project will yield new insights into how mammals remain warm in extreme cold environments. Similar pigmentation and hair structural variation have evolved in other Arctic mammals; thus, understanding the molecular and cellular mechanisms of these traits will provide a starting point to investigate convergence. Research results will be translated into educational materials appropriate for K-12 students and distributed to zoos with polar bears, as zoos are often the only opportunity people have to observe these animals. Finally, the project will train scientists in effective science communication and reach non-expert audiences through public outreach. Arctic species must adapt to the extreme temperatures of their environment. This research focuses on skin and hair traits associated with thermoregulatory adaptation. Comparative genomics, transcriptomics, and histological staining of skin tissue between polar bears and two temperate species, brown and American black bears, will determine the cellular basis for light hair and dark skin. Specifically, the abundance and location of melanocytes will be characterized, then changes in gene expression among bear species that can account for differences in cellular location will be identified. Beyond pigmentation differences, polar bear hair has a hollow center thought to increase the insulating capacity of the fur. To identify the genes that underly this trait, protein abundance within hair will be compared among species. Ancestral recombination graphs will be constructed to quantify when during the evolutionary history of polar bears this adaptation arose. As the hollow hair has distinct applications in creating novel heat retaining nanomaterials, this research will quantify and model thermodynamic properties of the hair and hair-skin interface over a range of environmentally relevant temperatures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Studies of human population genetics can reveal patterns of human movements and interactions, advancing knowledge about how individual and group behaviors shape human societies. In this project, the investigators use genomic and isotopic methods and data to reconstruct population histories and evaluate changes in mobility patterns through time. The project advances science outreach as well as graduate and postdoctoral training in STEM fields. The project generates genomic, paleogenomic, and isotopic data to reconstruct population histories and address the following three objectives: 1) evaluate genomic variation and population dynamics, 2) investigate changes in mobility patterns, and 3) assess genomic and isotopic variation and evaluate genomic continuities/discontinuities within groups over time. The research can inform our understanding of the scale of interactions between various populations before and after major events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Geoscientific computational models are used to predict a wide range of natural processes such as weather, water supply, air pollution, eruptions, floods, and tsunamis. Progress in geo-modeling is held back by the need for geoscientists to also acquire expertise in computer science. The project will introduce a novel modeling workflow to overcome this hurdle. This project supports the development and community adoption of a symbolic equation-based modeling system. This system separates automates the numerical processing optimization so geoscientists can focus on the equations that describe natural processes. This system also advances the use of artificial intelligence in geosciences for simplifying model reduction and parameterization. The proposed work consists of three thrusts involving 1) community organization, 2) model development, and 3) de-centralized model management and education. A series of workshops at major international geoscientific meetings domestically and abroad will help define project priorities. Models will be documented on a dedicated website, run by a decentralized governance system and supported with interactive educational experiences to transition to a user-supported network for long-term growth. Geoscientific computational models simulate natural processes such as weather, water supply, and air pollution; for analyzing risks of volcanoes, floods, and tsunamis. The proposed project will introduce a new process of geoscientific model development, where model components and their interrelationships are specified as a system of equations that a compiler automatically transforms into a computer model. By separating the model design (the equations) from model implementation (the code compiler), geoscientists can focus on building equation systems that represent their areas of expertise, greatly increasing the participation in geoscientific modeling. This system will also provide an ideal base for integrating AI model reduction and parameterization into the geosciences. Project activities are divided into three thrusts. Thrust 1 will convene a series of workshops to create a shared roadmap for model development at major international geoscientific meetings domestically and abroad. Thrust 2 will expand on the types of systems that can be studied with equation-based models by implementing model components and capabilities as prioritized by project members and workshop participants. Model capabilities will be documented on a dedicated website. Thrust 3 will implement a decentralized governance system and interactive educational experiences to transition to a user-supported network of equation-based modelers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Over 10 million fossil specimen records are publicly available via online portals, such as GBIF (Global Biodiversity Information Facility). However, researchers are currently unable to realize the full potential of these data because they are difficult to find and use due to the bio-focused nature of the portals. This project aims to: (1) leverage the Paleo Data Working Group's (PDWG) decade-long leadership to connect existing open science cyberinfrastructure resources in a network that will better integrate data from fossil and biological collections; (2) democratize access to collections-based, digitized paleontological data for research; and (3) expand PDWG's successful community-of-practice model to further build capacity for improving and mobilizing data from fossil collections. Paleontology, situated at the intersection of geo- and bioscience, is an inherently interdisciplinary field with impactful research that has the potential to significantly contribute to better understanding the current biodiversity crisis in relation to rapidly changing global climatic conditions. This project will support transformational and translational research in these fields by driving development in the open data landscape, and improving discoverability and use of millions of fossil specimen records. It will target known cyberinfrastructure gaps related to taxonomy and stratigraphy by engaging with the paleontological collections and research user communities, in collaboration with cyberinfrastructure partners, to evaluate the existing technical landscape. These efforts will ultimately lead to the development of a roadmap for establishing an enhanced network of FAIR and research-ready data accessible via TRUSTed digital repositories (the “Paleo Roadmap”). The project goals will be accomplished through a community-based approach that centers the role of humans in open science cyberinfrastructure. Engagement of a diverse group of stakeholders - ranging from small collections to early career individuals - is central to all parts of the project and development of the Paleo Roadmap. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The Earth’s rock record preserves a substantial fraction of its history, attesting to our planet’s unique and complex systems from which tectonics, minerals, climate, and life have emerged. The Geological Time Scale (GTS) is the authoritative calendar of Earth’s history that has resulted from centuries of observation and quantification of this rock record, and which serves as a foundation for research and innovation in Earth systems science. However, the process of GTS construction and revision has yet to evolve into an open science structure that can harness the full energy and potential of the scientific community. This project will build robust connections among disciplinary scientists, data resources, and community organizations to create a new framework of open science GTS construction. The goals of this project are to bring together scientists to: a) discover and translate the existing data, metadata, and algorithms behind the GTS, in order to establish future community-sourced best practices for dynamic time scale construction; b) plan the design of a findable, accessible, interoperable, and reusable (FAIR) data system that leverages and adapts existing standard data formats and open source software into a community-accessible system; and c) explore how these data and algorithms can be best exposed through public-facing online tools that allow visualization and experimentation with the GTS. By engaging and coordinating an open community to lay the groundwork for production of the GTS within a public-facing system, the Open Science GTS will enable input and participation from all scientists, thus accelerating the amount and quality of data that can be compiled into the GTS and increasing its responsiveness to community needs across geoscience disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The Earth’s rock record preserves a substantial fraction of its history, attesting to our planet’s unique and complex systems from which tectonics, minerals, climate, and life have emerged. The Geological Time Scale (GTS) is the authoritative calendar of Earth’s history that has resulted from centuries of observation and quantification of this rock record, and which serves as a foundation for research and innovation in Earth systems science. However, the process of GTS construction and revision has yet to evolve into an open science structure that can harness the full energy and potential of the scientific community. This project will build robust connections among disciplinary scientists, data resources, and community organizations to create a new framework of open science GTS construction. The goals of this project are to bring together scientists to: a) discover and translate the existing data, metadata, and algorithms behind the GTS, in order to establish future community-sourced best practices for dynamic time scale construction; b) plan the design of a findable, accessible, interoperable, and reusable (FAIR) data system that leverages and adapts existing standard data formats and open source software into a community-accessible system; and c) explore how these data and algorithms can be best exposed through public-facing online tools that allow visualization and experimentation with the GTS. By engaging and coordinating an open community to lay the groundwork for production of the GTS within a public-facing system, the Open Science GTS will enable input and participation from all scientists, thus accelerating the amount and quality of data that can be compiled into the GTS and increasing its responsiveness to community needs across geoscience disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The Earth’s rock record preserves a substantial fraction of its history, attesting to our planet’s unique and complex systems from which tectonics, minerals, climate, and life have emerged. The Geological Time Scale (GTS) is the authoritative calendar of Earth’s history that has resulted from centuries of observation and quantification of this rock record, and which serves as a foundation for research and innovation in Earth systems science. However, the process of GTS construction and revision has yet to evolve into an open science structure that can harness the full energy and potential of the scientific community. This project will build robust connections among disciplinary scientists, data resources, and community organizations to create a new framework of open science GTS construction. The goals of this project are to bring together scientists to: a) discover and translate the existing data, metadata, and algorithms behind the GTS, in order to establish future community-sourced best practices for dynamic time scale construction; b) plan the design of a findable, accessible, interoperable, and reusable (FAIR) data system that leverages and adapts existing standard data formats and open source software into a community-accessible system; and c) explore how these data and algorithms can be best exposed through public-facing online tools that allow visualization and experimentation with the GTS. By engaging and coordinating an open community to lay the groundwork for production of the GTS within a public-facing system, the Open Science GTS will enable input and participation from all scientists, thus accelerating the amount and quality of data that can be compiled into the GTS and increasing its responsiveness to community needs across geoscience disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project uses advances in artificial intelligence, computer science, and statistics to develop the GeoRDyn toolkit to allow researchers to identify the spatial and relational complexities in more fine-grained data. This will permit new approaches to investigating longstanding questions in the field, such as if and how networks emerge and influence collective action and the conditions under which events diffuse across time and space. This project is jointly funded by the Security and Preparedness program and the Established Program to Stimulate Competitive Research (EPSCoR). Events are often observed at different temporal and spatial scales, involving complex interactions among actors at the micro, macro, and meso levels. Methodological tools commonly used in the study of collective-action events are often less dynamic and/or do not account for complex dependencies. This project develops the GeoRDyn toolkit to help researchers dissect the spatial and relational complexities in events and draw rigorous causal inferences. The toolkit incorporates recent advances in artificial intelligence (AI), computer science, and statistics. The project develops new methods and software for studying complex and dynamic processes, such as diffusion, that can be used by researchers as well as those outside of the academic community who need to understand the causes and potential implications of events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project uses advances in artificial intelligence, computer science, and statistics to develop the GeoRDyn toolkit to allow researchers to identify the spatial and relational complexities in more fine-grained data. This will permit new approaches to investigating longstanding questions in the field, such as if and how networks emerge and influence collective action and the conditions under which events diffuse across time and space. Events are often observed at different temporal and spatial scales, involving complex interactions among actors at the micro, macro, and meso levels. Methodological tools commonly used in the study of collective-action events are often less dynamic and/or do not account for complex dependencies. This project develops the GeoRDyn toolkit to help researchers dissect the spatial and relational complexities in events and draw rigorous causal inferences. The toolkit incorporates recent advances in artificial intelligence (AI), computer science, and statistics. The project develops new methods and software for studying complex and dynamic processes, such as diffusion, that can be used by researchers as well as those outside of the academic community who need to understand the causes and potential implications of events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Low oxygen, as is common at high altitudes, poses major challenges to the body. These effects are well-described with respect to activities like exercise; however, low oxygen also challenges reproduction. Across mammals, low oxygen found at high altitudes leads to greater risk for pregnancy complications, especially low birth weight. Even though understanding how this happens could help us predict and prevent these poor outcomes, the processes by which low oxygen influences pregnancy and fetal growth trajectories remains poorly understood. This research will test the hypothesis that maternal metabolism and its response to low oxygen are important factors that determine pregnancy outcomes and fetal growth at high altitudes. The research uses a rodent model, the North American deer mouse, for which some populations have resided at high altitude for many generations and no longer experience birth weight reductions at high altitude. The research aims will compare how low oxygen alters the maternal physiology of high-altitude deer mice (that don’t experience birth weight reductions) versus low altitude-resident deer (which do experience birth weight reductions). The proposed experiments will also use advanced genetic and genomic tools to determine how the expression of key genes in the placenta influence maternal metabolism. This work will advance our basic understanding of how reproductive biology contributes to mammalian adaptation while also providing new perspectives on some of the most common pregnancy complications that are linked to low oxygen, like pre-eclampsia. This research applies experimental approaches to determine how reproductive physiologies evolve and adapt to cope with challenging environments (specifically, low oxygen). The experiments aim to test two main hypotheses: (a) that adaptation to high elevations modifies or prevents hypoxia-dependent adjustments to maternal metabolism and glucose regulation, and (b) that these protective maternal metabolic changes are directed by adaptions in imprinted gene networks found in the placenta. To test these hypotheses, North American deer mice of high- and low-altitude ancestry will be experimentally acclimated to high elevation during pregnancy. The maternal and fetal contributions to the physiologies that shape gestational metabolism will be parsed by measuring maternal energy allocation, fuel use, and the metabolic gene networks that interact with genomic imprinting in the. The integrated, whole-organism approach will enable the identification of links between specific gene expression patterns in the placenta and the fetal and maternal metabolic traits that are associated with growth outcomes under environmental hypoxia. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Low oxygen, as is common at high altitudes, poses major challenges to the body. These effects are well-described with respect to activities like exercise; however, low oxygen also challenges reproduction. Across mammals, low oxygen found at high altitudes leads to greater risk for pregnancy complications, especially low birth weight. Even though understanding how this happens could help us predict and prevent these poor outcomes, the processes by which low oxygen influences pregnancy and fetal growth trajectories remains poorly understood. This research will test the hypothesis that maternal metabolism and its response to low oxygen are important factors that determine pregnancy outcomes and fetal growth at high altitudes. The research uses a rodent model, the North American deer mouse, for which some populations have resided at high altitude for many generations and no longer experience birth weight reductions at high altitude. The research aims will compare how low oxygen alters the maternal physiology of high-altitude deer mice (that don’t experience birth weight reductions) versus low altitude-resident deer (which do experience birth weight reductions). The proposed experiments will also use advanced genetic and genomic tools to determine how the expression of key genes in the placenta influence maternal metabolism. This work will advance our basic understanding of how reproductive biology contributes to mammalian adaptation while also providing new perspectives on some of the most common pregnancy complications that are linked to low oxygen, like pre-eclampsia. This research applies experimental approaches to determine how reproductive physiologies evolve and adapt to cope with challenging environments (specifically, low oxygen). The experiments aim to test two main hypotheses: (a) that adaptation to high elevations modifies or prevents hypoxia-dependent adjustments to maternal metabolism and glucose regulation, and (b) that these protective maternal metabolic changes are directed by adaptions in imprinted gene networks found in the placenta. To test these hypotheses, North American deer mice of high- and low-altitude ancestry will be experimentally acclimated to high elevation during pregnancy. The maternal and fetal contributions to the physiologies that shape gestational metabolism will be parsed by measuring maternal energy allocation, fuel use, and the metabolic gene networks that interact with genomic imprinting in the. The integrated, whole-organism approach will enable the identification of links between specific gene expression patterns in the placenta and the fetal and maternal metabolic traits that are associated with growth outcomes under environmental hypoxia. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Successful solutions to complex global challenges transcend disciplinary boundaries and necessitate integrating diverse fields of knowledge, making interdisciplinary education an urgent need to train our national workforce. Few sciences rely on integration more than geosciences, which play critical, foundational roles in nearly every sector of the U.S. economy, from energy exploration and environmental conservation to disaster preparedness. Experiences such as field trips, summer programs, and hands-on outdoor activities have been repeatedly shown to increase retention, maximize learning gains, and improve students’ attitudes, perceptions, and feelings toward STEM topics. Unfortunately, field trips can be costly, and physical disabilities and logistical and administrative constraints can restrict access to field-based learning opportunities for many students, potentially hindering their interest in geoscience. This project addresses this important impediment to inclusivity by developing the first immersive, online Virtual Field Trip (VFT) that capitalizes on the exciting imagery and experience of paleontologic fieldwork. The novel VFT will help address issues in exposure to geoscience content by providing high school and early-college teachers with freely available, geoscience-inclusive resources which can be easily incorporated into their existing classes. This project will capitalize on the broad appeal of both dinosaur paleontology and virtual field trips (VFT) by combining them into an innovative new resource for educators. The VFT will cover core principles of geography, math, biology, and physics within the context of a virtual paleontology expedition, which will function as an open-ended, self-paced, ‘capstone’ educational program embedded with a plethora of educational ‘training’ materials and media from real fieldwork. The primary goals of this project are to: (1) increase exposure to geosciences in high schools and early-college classrooms by incorporating lessons utilizing geoscience examples and problems into existing courses; (2) enhance educational content for traditional courses such as geography, math, physics, and biology by integrating these disciplines, and; (3) identify factors contributing to effective recruitment and retention in the geoscience pipeline so that they may be employed by educators to successfully broaden participation in these exciting and impactful fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.