Grants

NSF

This project creates a database and dashboard of ecosystem guidelines built from expert assessments for 80 countries. Such guidelines have proliferated worldwide, yet no systematic means exists for the comparative study of a nation state's pledges in relation to whether and how these are executed. The data produced allow scholars and analysts to compare ecosystem guidelines and their effects across multiple dimensions in a wide range of nation states. In addition to releasing the data set for scholarly and expert analysis, graduate students are trained to use the data set. Research findings can be used to inform the development of guidelines that advance national welfare and security. To deepen understanding, the project assesses national guidelines with respect to practices across the sectors prioritized in international agreements, namely, energy, industry, transportation, agriculture, land use and forestry, buildings, and waste and wastewater. Moreover, the researchers measure dozens of indicators of adaptation in two broad areas: short-term reactions to weather events and longer-term planning for societal resilience. A survey of at least 10 experts in each of 80 countries chosen from the world’s major regions yields data that allows measurement of nations’ ambitions and claims against expert evaluations. Survey findings are corroborated where possible by extant complementary data. Using this data set, the project seeks answers to multiple research questions, including: 1) What are the determinants of robust guidelines within nation states? 2) What roles do robust guidelines play in determining practices of adaptation and resilience? 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 creates a database and dashboard of ecosystem guidelines built from expert assessments for 80 countries. Such guidelines have proliferated worldwide, yet no systematic means exists for the comparative study of a nation state's pledges in relation to whether and how these are executed. The data produced allow scholars and analysts to compare ecosystem guidelines and their effects across multiple dimensions in a wide range of nation states. In addition to releasing the data set for scholarly and expert analysis, graduate students are trained to use the data set. Research findings can be used to inform the development of guidelines that advance national welfare and security. To deepen understanding, the project assesses national guidelines with respect to practices across the sectors prioritized in international agreements, namely, energy, industry, transportation, agriculture, land use and forestry, buildings, and waste and wastewater. Moreover, the researchers measure dozens of indicators of adaptation in two broad areas: short-term reactions to weather events and longer-term planning for societal resilience. A survey of at least 10 experts in each of 80 countries chosen from the world’s major regions yields data that allows measurement of nations’ ambitions and claims against expert evaluations. Survey findings are corroborated where possible by extant complementary data. Using this data set, the project seeks answers to multiple research questions, including: 1) What are the determinants of robust guidelines within nation states? 2) What roles do robust guidelines play in determining practices of adaptation and resilience? 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.

NIA - National Institute on Aging

PROJECT SUMMARY Alzheimer's Disease (AD) has enormous physical, financial, and social consequences for patients and families, and its US prevalence will rise to ~12.7 million cases by 2050. Since 2012, the AD Sequencing Project (ADSP) has played a key role advancing AD genetics, specifically identifying mechanisms that will enhance diagnosis and drive effective AD therapies. Recent expansions to include projects focused on phenotype harmonization, functional genomics, and artificial intelligence/machine learning provide great potential for further advances, but also call for a refocusing and updating of ADSP structure and operations, which we will support as the Collaboration, Communication, and Coordination Center for ADSP (ADSP-4C). Our ADSP-4C brings together subject matter experts in AD/ADRD research, neuropathology, data infrastructure, cloud computing, genetic and phenotypic data management and analysis, statistical genetics and genetic epidemiology, bioethics, and consortium organization and governance to provide scientific and logistical support for the ADSP across three main areas. (1) For Collaborative Leadership and Strategic Planning, we will lead a collaborative reshaping of ADSP organization and governance to optimize productivity; establish new fora and projects to promote synergy and teamwork across ADSP components; strengthen partnerships with the global AD research community; and coordinate with data generators and repositories to improve the utility, accessibility, and interoperability of ADSP data and related resources. (2) For Administration, Coordination, and Dissemination, we will provide comprehensive administrative and logistical support for ADSP working groups and committees program-wide, establish an internal and public-facing ADSP website to support communications and outreach, build comprehensive reports and metrics to track program progress, and plan annual hybrid ADSP meetings. (3) For Education and Training, we will increase ADSP’s impact by expanding the pool of engaged scientists across career stage and discipline through developing and disseminating distance learning modules, engaging trainees and early stage investigators in a virtual training and mentorship cohort, and providing further education and training outreach at conferences and existing short courses. We will also provide support for small pilot projects to innovate approaches for integrating genetics, artificial intelligence/machine learning, and functional studies to accelerate AD research. By contributing in a number of key areas, and prioritizing Research Dissemination from all of them, our ADSP-4C will support the ADSP in its robust characterization of AD genetics, which is essential for a clear understanding of the molecular mechanisms underlying AD and ultimately the development of effective interventions and therapies.

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

PROJECT SUMMARY Stillbirth is far too common in the U.S. with 20,000 stillborn babies every year with large racial and ethnic disparities. Black birthing individuals have twice the risk of stillbirth compared to White individuals. The Collaborative Action for Research to End Stillbirth Research Center (CARES) will conduct innovative, integrated, multilayered research - with and for people with lived experience of stillbirth squarely at the center- to develop and implement actionable strategies to improve the prediction of stillbirth early in pregnancy and to understand reasons for the excess risk for Black birthing individuals. We propose to address a critical area in stillbirth care – early identification of patients at highest risk for stillbirth to facilitate prevention. Screening patients early in pregnancy is important as 63% of stillbirths occur before 32 weeks of gestation. Joining efforts from researchers across essential disciplines, people with lived experience of stillbirth, and community, public health, and health care stakeholders, CARES will address the following Specific Aims: Aim 1. Establish a strong infrastructure to support stillbirth research and collaboration across the consortium. With input from the CARES Stakeholder Advisory Board and the CARES Parent Advisory Board, we will develop and maintain a core team of investigators, collaborators and staff with expertise essential for a broad range of stillbirth-related research. We will also build shared resources to promote interdisciplinary team science and ensure access to a diverse patient population to facilitate stillbirth research. Aim 2. Develop a scalable, electronic health record (EHR)-based, dynamic risk prediction model for stillbirth. Inadequate data sources have been identified as a major barrier to advancing knowledge on stillbirth and its prevention. We will (2a) Ascertain patients’ and health care professionals’ perspectives on risk factors for stillbirth (overall and for Black birthing individuals specifically), data needs for stillbirth research, and the acceptability and design of a mobile app-based patient data reporting tool to support pregnancy monitoring and stillbirth prediction; (2b) Develop and validate EHR-based and artificial intelligence-powered algorithms for automated identification and characterization of stillbirth, measurement of stillbirth risk factors, and prediction of stillbirth risk scalable across institutions; (2c) Develop and test a mobile app for patient-reporting of pregnancy information to augment EHR data and support pregnancy care. Aim 3. Develop novel markers of underlying incipient placental dysfunction early in pregnancy for predicting stillbirth. Fetal growth restriction (FGR) is a major antecedent to stillbirth with the most common underlying pathogenesis being placental dysfunction. Accurate identification early in pregnancy and management of FGR could potentially make an impact on stillbirth prevention. We will conduct a prospective cohort study to investigate innovative (3a) placental imaging before 14 weeks’ gestation, (3b) placental biomarkers, and (3c) fetal genetic markers for predicting fetuses at greatest risk for stillbirth. CARES will use multifaceted approaches applicable to the entire population of birthing people to prevent stillbirth.

NSF

The Collaborative and Operational Leadership (COLead) project aims to increase the participation of faculty from teaching-intensive institutions (TIIs) in the national research enterprise. Distinct from research-intensive institutions which often have divisions devoted to research (i.e. an Office of Research) and/or senior administrators (i.e. a Vice President of Research) solely dedicated to advancing research activity, research leadership at TIIs is often led by an administrator with multiple duties such as a Provost or Vice Provost. This fragmentation of duties, combined with limited resources to create and support a research environment, may result in lower research productivity among faculty at TIIs. This project addresses this absence of dedicated research leadership through a collaborative leadership model led by the institution’s office of research and sponsored projects (or equivalent) and a guiding body of campus leaders. To increase the participation of faculty from teaching-intensive institutions (TIIs) in the nation’s research enterprise, the goal of COLead is to build capacity and research support infrastructure using a replicable, systematic approach guided by collaborative leadership. The project includes three aims: Aim 1 – Build institutional capacity using a structural collaborative leadership model that provides sustained infrastructure for strategic research and sponsored project leadership, resulting in increased capacity for developing, maintaining, and executing a strategic vision for research; Aim 2 – Driven by the collaborative leadership model, increase the capacity of faculty and staff and streamline processes for research development and grant management through a coordinated and sustainable procedural research infrastructure model; and Aim 3 - Build a systematic, replicable approach based upon the collaborative leadership and research infrastructure model, resulting in an organizational change toolkit that TIIs can use to grow and sustain research and research development by leveraging existing resources. The toolkit will be tested and iteratively refined in partnership with two peer institutions. The proposed collaborative leadership model will be informed by institutional external assessments, peer research, selected peer advisors, and guided by a faculty with expertise in organizational change, teaming, and strategic planning. The model will be documented in the toolkit and disseminated widely through national networks. 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.

The Little Orchestra Society/Orpheon, Inc.

Collaborative Arts-in-Education Program

National Park Service

The project objective is for the NPS Inventory and Monitoring Program, Southeast Coast Network (SECN) and the University of Georgia, Department of Computer Science (UGA) to work jointly to organize, and make available natural resource data for the public, for researchers and for the NPS through database development.

NIDCR - National Institute of Dental and Craniofacial Research

Temporomandibular disorders (TMDs) involve complex and poorly understood pathophysiological processes, limiting prevention, diagnosis, prognosis and the development or application of effective, evidence-based treatments. The Collaborative for REsearch to Advance TMD Evidence (CREATE) aims to address these critical gaps through a bidirectional approach that integrates clinical and preclinical research to identify underlying mechanisms, develop personalized therapies, and improve patient outcomes. The Administrative Core (AC) will manage CREATE’s scientific, administrative, and operational functions, ensuring alignment with NIH goals, standardizing data collection across sites, and adhering to FAIR principles for data and biospecimen management. The Bioinformatics and Data Science Core (BDSC) will employ the latest AI and machine learning techniques to analyze multi-modal data, identify TMD subtypes, implicate therapeutic targets, and establish frameworks such as common data elements (CDEs) and ontologies to enhance data integration. The Outreach, Training, and Education Core (OTEC) will expand the TMD research and clinical workforce, foster collaboration with stakeholders, and organize biannual public workshops to disseminate findings and guide the Collaborative’s efforts. At the heart of CREATE is the Deep Pain Phenotyping Project, a large-scale longitudinal study involving 1,000 TMD patients and 300 age-matched controls across multiple US clinical centers, designed to enable identification of TMD subtypes through standardized assessments and mechanistic deep phenotyping. The identification of TMD subtypes with high reliability and selectivity will facilitate diagnosis and prognosis and future clinical trials, and may suggest more effective preventive and therapeutic strategies. The clinical dataset will also inform reverse translation to preclinical studies to further elucidate mechanisms and validate therapeutic targets. By leveraging the expertise of a diverse, multidisciplinary team, CREATE seeks to bridge the gap between clinical data and preclinical insights, paving the way for improved diagnostics, treatments, and patient care in TMDs.

NIAID - National Institute of Allergy and Infectious Diseases

As part of the Collaborative Influenza Vaccine Innovation Centers (CIVICs) program, the Statistical, Data Management and Coordination Center will provides the required support to the CIVICs Vaccine Centers, Clinical Cores and Manufacturing and Toxicology Core.

NSF

Ecotron facilities, crucial for studying ecosystem responses to urban change under controlled conditions, suffer from fragmented data practices. Varying data formats, metadata standards, and access protocols hinder data sharing and comparative analysis across institutions. This project addresses these challenges by developing a unified national cyberinfrastructure specifically designed for ecotron-generated data. The Collaborative Open Framework for Accessible Interoperable Research (CO-FAIR) project establishes a collaborative network of ecotron facilities. The project focuses on developing standardized metadata schemata, common ontologies by leveraging and extending existing standards, and robust data access protocols, adhering to principles that make data Findable, Accessible, Interoperable, and Reusable (FAIR). The technical architecture includes a centralized metadata discovery portal, distributed data storage solutions, and Application Programming Interfaces to enable seamless data integration and analysis. Data ingestion pipelines will automate data flow from participating ecotrons, with the University of Idaho's Deep Soil Ecotron serving as a primary development and pilot implementation site. The system is built on open-source technologies to ensure scalability and portability. By standardizing data management and fostering collaboration, CO-FAIR enhances the value and utility of ecotron research. This project will transform disparate ecotron datasets into a cohesive, accessible national resource, accelerating discoveries in soil science, ecosystem ecology, and sustainable agriculture. It will enable large-scale comparative studies, enhance the reproducibility of ecological research, expand access to valuable experimental data, and serve as a model for open science practices in other scientific domains. 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 tremendous growth and widespread deployment of sensor networks in advanced technologies like robotics, autonomous vehicles, environmental monitoring, and digital farming underscore the historic new industry revolution of the 21st century. As data measurement and processing increasingly take place collaboratively across networks of distributed agents, there is an urgent need to innovate networked systems for collaborative sensing and decision-making while maintaining high spectrum efficiency. Collaborative sensing and decision-making based on edge devices and agents typically features low-cost, modest-power, and limited-computation-capacity wireless devices and sensors. Current wireless protocols, such as IEEE 802.11 Wi-Fi and 3GPP Cellular networks, tend to be inefficient for short data bursts due to their relatively large control and signaling overhead. This project represents a comprehensive effort to develop innovative solutions for collaborative decision-making based on wireless networks of distributed nodes, aiming to jointly achieve high decision accuracy and spectrum efficiency. The project's goal is to develop innovative, simple, and low-cost networks that are easy to deploy for future large-scale applications in smart farming, disaster or hazard detection, air quality monitoring, and security. Leveraging a collaborative framework based on collaborative over-the-air sensing (COTAS), the project focuses on the most common and specific learning tasks: event detection and parameter estimation. Among its major research thrusts, we specifically tackle critical and practical challenges including COTAS detection and estimation under channel uncertainty, robustness against passive eavesdropping attacks, secure and reliable COTAS detection against jamming and Byzantine attacks, as well as deep learning solutions against physical uncertainties. Successes from the proposed research tasks can significantly impact collaborative decision-making, federated learning, sensor networks, and network spectrum management. Broadly, this work promotes reliable and efficient decision-making based on wirelessly-networked agents in cooperative systems ranging from smart agriculture, seismic detection, environmental monitoring, safety monitoring, and national security, thereby providing immense potential societal impacts. 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 Magnetics Information Consortium (MagIC) provides a web-based research facility that allows researchers and other users free access to archive, search, visualize, manipulate, download, and reuse data that address many grand challenges in the geosciences. Over the past six years, the number of contributors uploading data more than tripled and MagIC usage and dataset downloads increased more than sixfold. These large increases in contributors, contributions, and data usage are possible because of a community-driven unified MagIC data model and because the MagIC Facility deploys highly scalable databases and a highly adaptable web interface. Although there is still a noticeable sparsity of data on the internet that can be characterized as being easily findable, accessible, interoperable, and reusable—these FAIR data principles lie at the heart of MagIC. In addition, training of the next generation of researchers and members of underrepresented groups in the development and use of modern data analysis methods form another central component in the MagIC Facility. In the next three years, the MagIC Facility will focus its efforts on making enhancements to ensure efficient access to data in a wide range of subdomain science disciplines, transparency in data processing, and reliable documentation—altogether to allow questions of reproducibility and standardization to be readily addressed by researcher and student users. The MagIC Facility will develop several novel geoinformatics products including an Integrated Contribution Development Environment using template notebooks on https://jupyterhub.earthref.org with guided steps for programmatically editing, visualizing, reproducing data analysis results, and updating user contributions without leaving the browser; two specialized Subdomain Views with featured filters, relevant data summaries, and subdomain-specific calculations and visualizations; a Dataset Enrichment tool to further reduce efforts to prepare an online data contribution for publication; and Superuser Private Workspaces to support large facility and laboratory data management needs within MagIC. In particular, the new team from Oregon State University, the Institute for Rock Magnetism (IRM) at the University of Minnesota, University of California, Berkeley, and the Scripps Institution of Oceanography at University of California, San Diego, will work collaboratively to integrate PmagPy (an open-source and cross-platform software package for curating and analyzing paleomagnetic data) and preexisting IRM rock magnetic computational tools into the MagIC back-end server to allow for advanced online calculations, interactive visualizations, and integration on JupyterHub and EarthCube’s Pangeo; to make MagIC interoperable with other online data repositories that also follow FAIR principles; and to keep strengthening MagIC as a community facility by engaging researchers from historically underrepresented groups and varied institution types. Over the next three years, the MagIC Facility will be transferred to FIESTA (Framework for Integrated Earth Science and Technology Applications) technology that is being finalized under funding from EarthCube. 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 objective of this research project is to establish and operate a research network for enhancing airport resilience. It brings together experts from academia, industry, and government to develop a novel framework for analyzing airport resilience to natural hazards and operational disruptions. The network aims to quantify the resilience of airport operations, synthesize best practices for recovery, and explore the integration of emerging technologies to simulate and optimize airport responses to external shocks. Through an interdisciplinary approach, the network intends to addresses complex resilience challenges in the aviation sector, enabling data-driven decision-making processes, anticipating evolving threats, thereby advancing both the science and practice of infrastructure resilience. Airports are highly complex systems with the primary function of maintaining flight schedules while ensuring safety, sustainability, and economic viability. The research network looks to produce conceptual models informed by resilience science to help airport managers identify critical functions across interconnected sub-systems, quantify the impacts of disruptions, evaluate resilience against both known and emerging threats, and optimize resource allocation for performance improvements. This goal intends to be achieved through three key objectives: 1) evaluating how resilience indicators translate into airport operational impacts and vary across different types of disruption; 2) identifying challenges related to data access, management, privacy, security, and interoperability while exploring pathways to improve data integration; and 3) proposing new paradigms for infrastructure resilience research by applying resilience science concepts to airport operations. Using Dallas Fort Worth International Airport as testbed, the project seeks to address significant gaps in resilience measurement and evaluation and generate robust and generalizable knowledge for transportation systems. Most importantly, it looks to forge a self-sustaining, collaborative platform for expert engineers, scientists, and practitioners to share insights that will improve airport resilience strategies, inform broader efforts in standardizing aviation resilience metrics, and set the foundation for long-term competitiveness of US transportation sector. 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 award supports the study of cloud electrification and lightning using phased-array weather radar. The research team will deploy multiple mobile radars to the field to observe key aspects of thunderstorms. Phased-array radars provide extremely fast sampling compared to traditional radars, allowing measurement of lightning features that were previously difficult to observe. Improved knowledge of lightning processes supports better hazard prediction and helps reduce risks to people and infrastructure. Students will be trained at the intersection of meteorology, radar engineering, and data science. The research in this award will focus on two core scientific uncertainties in lightning and atmospheric electricity: 1) flash-scale plasma physics, and 2) the meteorological setting of lightning initiation via electric-field changes. These topics will be studied using data collected during thunderstorms in the U.S. Great Plains from the University of Oklahoma’s Horus S-band radar and the X-band RaXPol radar. The research will provide new insight into when and where lightning channels heat and how they interact with the storm’s electric field as observed through ice crystal alignment. The study will also clarify how storm-scale microphysical and kinematic conditions relate to the probability of observing ice crystal alignment and direct plasma scattering. 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 aims to build an inclusive research ecosystem to serve as a cornerstone of the Vermont economy. Project goals are to establish administrative support to build and sustain a diverse, integrated science and technology (S&T) research ecosystem in southern Vermont. Additionally, the project seeks to broaden the participation of underrepresented groups in S&T research and employment. Through the project's responsiveness to the S&T needs of Vermont, the jurisdiction has potential to increase its S&T research and employment; have a strongly educated S&T population; position the community to understand various aspects of S&T research and its role in the community; and increase participation of underrepresented groups in S&T research including neurodivergent, first-generation, low-income, and multilingual students. The project will build S&T research capacity through training, increasing research opportunities, and creating new S&T jobs. It will contribute to expanding the S&T workforce locally and nationally. The Science and Technology Research Initiative for the Vermont Economy (STRIVE) is a collaboration between Landmark College (lead institution), Bennington College, and the Community College of Vermont. STRIVE will examine how an equitable research infrastructure can catalyze the civic value of science in undergraduate education and rural economies. The project will invest in achieving a holistic, jurisdiction-wide research ecosystem attuned to the needs of Vermont while monitoring the impact they have on STEM participation at an institutional, community, and jurisdictional wide level. Investments will broaden participation in S&T driven research and economic development, and bridge longstanding S&T research divides in the state. STRIVE will invest in the following research administrative infrastructure: Research Administrators, Research Scholars, and improvements to a research farm; experiential learning opportunities; S&T research grants; professional development; and outreach activities. STRIVE will engage S&T research entities within and outside of the education system. This will lead to community-responsive partnerships that will leverage STRIVE’s investments to secure funding for S&T research expansion in Vermont. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Collaborations for Optimizing Research Ecosystems (E-CORE). E-CORE supports jurisdictions in strengthening their jurisdiction-wide research ecosystems through fostering interconnected networks, building research infrastructure, and growing research capacity and competitiveness aligned with jurisdictional priorities. 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 eyewall in mature hurricanes contains small vortices, which have been seen in satellite and weather radar data. While the presence of these vortices is well-known, their impact on the hurricane structure and intensity is still uncertain. In this award, the research team will use a recently derived mathematical framework to analyze hurricane vortices with a goal of better explaining how the small-scale vortices relate to the large-scale hurricane structure. The primary societal benefit of this project would be through the potential for improved forecasting of hurricanes. A graduate student and postdoctoral researcher would be involved in the project, allowing for the training of the next generation of scientists. Liutex is a recent methodological advancement in the field of mathematics and fluid dynamics that intends to better represent vortices in fluids, like the atmosphere. Liutex is a vector whose direction is aligned with the rotational axis and whose magnitude is twice the angular speed. It separates the pure rigid rotation from shear. Small-scale vortices within hurricanes provide a strong test case for Liutex. The research team plans to apply Liutex to observations and numerical modeling of three-dimensional winds in hurricanes to quantify the relationship between vortex structure and storm intensity and to identify mechanisms of vortex formation and mergers. The overarching goal of the project is to gain a deeper understanding both theoretically and numerically of how the eye-eyewall meso- and miso- vortices form and provide a sound method to identify them and quantify their structures. 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 eyewall in mature hurricanes contains small vortices, which have been seen in satellite and weather radar data. While the presence of these vortices is well-known, their impact on the hurricane structure and intensity is still uncertain. In this award, the research team will use a recently derived mathematical framework to analyze hurricane vortices with a goal of better explaining how the small-scale vortices relate to the large-scale hurricane structure. The primary societal benefit of this project would be through the potential for improved forecasting of hurricanes. A graduate student and postdoctoral researcher would be involved in the project, allowing for the training of the next generation of scientists. Liutex is a recent methodological advancement in the field of mathematics and fluid dynamics that intends to better represent vortices in fluids, like the atmosphere. Liutex is a vector whose direction is aligned with the rotational axis and whose magnitude is twice the angular speed. It separates the pure rigid rotation from shear. Small-scale vortices within hurricanes provide a strong test case for Liutex. The research team plans to apply Liutex to observations and numerical modeling of three-dimensional winds in hurricanes to quantify the relationship between vortex structure and storm intensity and to identify mechanisms of vortex formation and mergers. The overarching goal of the project is to gain a deeper understanding both theoretically and numerically of how the eye-eyewall meso- and miso- vortices form and provide a sound method to identify them and quantify their structures. 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 is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Earth’s energy budget is balanced between incoming solar radiation and outgoing thermal energy. Clouds exert a significant impact in both directions, but there has been significantly more study of the impact of clouds on incoming energy from the sun. This project will focus on the opposite route, the emission of longwave, or infrared, radiation from the Earth’s surface and how that radiation interacts with ice clouds in the high latitudes. The impact of this study will be to provide better information to the scientific community on processes that are important for understanding the warming Arctic region. The project will also enhance collaboration between US and UK scientists and provide training for early career researchers. This project addresses three primary research questions: (1) Do current representations of surface properties capture the longwave emission spectrum of snow and ice surfaces correctly? (2) Is a new light-scattering model able to reconcile ice cloud microphysics (ice crystal sizes, shapes) with energetic (radiative) impact across the longwave spectrum? (3) Can our radiative transfer models successfully match simultaneous observations of the longwave energy spectrum at the surface, within the atmosphere and at the top of the atmosphere under a variety of different atmospheric and surface conditions? The research team will address these questions through a multi-faceted plan. First, the team will use measurements from a new instrument, called the Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) that has been deployed in Norway and Canada in the past few years. The deployment in Canada was matched with airborne observations of clouds, providing both radiative and cloud properties to the research team. This data will be used to evaluate a new ice cloud optical property model, assess the impact of snow and ice emissivity models in an Earth System Model, and combine the ground and airborne data with satellite observations to achieve radiative closure from the surface to space across the infrared spectrum for the first time. 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 is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Earth’s energy budget is balanced between incoming solar radiation and outgoing thermal energy. Clouds exert a significant impact in both directions, but there has been significantly more study of the impact of clouds on incoming energy from the sun. This project will focus on the opposite route, the emission of longwave, or infrared, radiation from the Earth’s surface and how that radiation interacts with ice clouds in the high latitudes. The impact of this study will be to provide better information to the scientific community on processes that are important for understanding the warming Arctic region. The project will also enhance collaboration between US and UK scientists and provide training for early career researchers. This project addresses three primary research questions: (1) Do current representations of surface properties capture the longwave emission spectrum of snow and ice surfaces correctly? (2) Is a new light-scattering model able to reconcile ice cloud microphysics (ice crystal sizes, shapes) with energetic (radiative) impact across the longwave spectrum? (3) Can our radiative transfer models successfully match simultaneous observations of the longwave energy spectrum at the surface, within the atmosphere and at the top of the atmosphere under a variety of different atmospheric and surface conditions? The research team will address these questions through a multi-faceted plan. First, the team will use measurements from a new instrument, called the Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) that has been deployed in Norway and Canada in the past few years. The deployment in Canada was matched with airborne observations of clouds, providing both radiative and cloud properties to the research team. This data will be used to evaluate a new ice cloud optical property model, assess the impact of snow and ice emissivity models in an Earth System Model, and combine the ground and airborne data with satellite observations to achieve radiative closure from the surface to space across the infrared spectrum for the first time. 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 is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Earth’s energy budget is balanced between incoming solar radiation and outgoing thermal energy. Clouds exert a significant impact in both directions, but there has been significantly more study of the impact of clouds on incoming energy from the sun. This project will focus on the opposite route, the emission of longwave, or infrared, radiation from the Earth’s surface and how that radiation interacts with ice clouds in the high latitudes. The impact of this study will be to provide better information to the scientific community on processes that are important for understanding the warming Arctic region. The project will also enhance collaboration between US and UK scientists and provide training for early career researchers. This project addresses three primary research questions: (1) Do current representations of surface properties capture the longwave emission spectrum of snow and ice surfaces correctly? (2) Is a new light-scattering model able to reconcile ice cloud microphysics (ice crystal sizes, shapes) with energetic (radiative) impact across the longwave spectrum? (3) Can our radiative transfer models successfully match simultaneous observations of the longwave energy spectrum at the surface, within the atmosphere and at the top of the atmosphere under a variety of different atmospheric and surface conditions? The research team will address these questions through a multi-faceted plan. First, the team will use measurements from a new instrument, called the Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) that has been deployed in Norway and Canada in the past few years. The deployment in Canada was matched with airborne observations of clouds, providing both radiative and cloud properties to the research team. This data will be used to evaluate a new ice cloud optical property model, assess the impact of snow and ice emissivity models in an Earth System Model, and combine the ground and airborne data with satellite observations to achieve radiative closure from the surface to space across the infrared spectrum for the first time. 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 Super Dual Auroral Radar Network, or SuperDARN, is an international collaborative experiment for observations of plasma motions in Earth’s upper atmosphere. By observing ionospheric plasma motions, a multitude of geophysical processes are being studied. These processes range from fundamental plasma instabilities to the global-scale plasma response to changes in the solar-terrestrial environment. Each of these areas of study contributes to developing an understanding of the coupling of energy from the Sun into Earth’s upper atmosphere and its effects on humanity and technological systems. This project will support operations and maintenance of the U.S. SuperDARN radars in the northern hemisphere by the consortium of Penn State University, Virginia Tech, Dartmouth College, and the Johns Hopkins University Applied Physics Laboratory. The collaboration operates twelve radars that cover a vast region from Alaska to Iceland at high latitudes, and Oregon to Virginia at middle latitudes. In addition to operation and maintenance activities, the project will support a program of research that exploits new capabilities that have been developed over the last several years. This includes providing improved fidelity in measurements (plasma convection mapping and imaging), extending the area over which measurements are obtained (bistatic observations), and providing new types of measurements (sounding). SuperDARN has a long-standing commitment to including graduate students in all aspects of the program. The SuperDARN observations are also important for space weather applications since HF radio propagation is sensitive to perturbations in the bottomside ionosphere, e.g., solar flares. 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 Super Dual Auroral Radar Network, or SuperDARN, is an international collaborative experiment for observations of plasma motions in Earth’s upper atmosphere. By observing ionospheric plasma motions, a multitude of geophysical processes are being studied. These processes range from fundamental plasma instabilities to the global-scale plasma response to changes in the solar-terrestrial environment. Each of these areas of study contributes to developing an understanding of the coupling of energy from the Sun into Earth’s upper atmosphere and its effects on humanity and technological systems. This project will support operations and maintenance of the U.S. SuperDARN radars in the northern hemisphere by the consortium of Penn State University, Virginia Tech, Dartmouth College, and the Johns Hopkins University Applied Physics Laboratory. The collaboration operates twelve radars that cover a vast region from Alaska to Iceland at high latitudes, and Oregon to Virginia at middle latitudes. In addition to operation and maintenance activities, the project will support a program of research that exploits new capabilities that have been developed over the last several years. This includes providing improved fidelity in measurements (plasma convection mapping and imaging), extending the area over which measurements are obtained (bistatic observations), and providing new types of measurements (sounding). SuperDARN has a long-standing commitment to including graduate students in all aspects of the program. The SuperDARN observations are also important for space weather applications since HF radio propagation is sensitive to perturbations in the bottomside ionosphere, e.g., solar flares. 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

Research into ceramic production and exchange in past societies has led to wide-ranging understanding of social, economic, and political organization in these ancient systems as well as implications for contemporary societies. Much of this work has utilized chemical analysis of ceramics using neutron activation analysis (NAA) and the American Southwest has been one area of intensive research. Existing NAA data represent significant research investment, but the combined power of this investment remains unrealized largely due to two impediments: 1) existing databases require synthesis and standardization; and 2) analytical methods that can systematically compare compositional data at these macroregional scales needs more development. This project produces a standardized, expanded, and broadly accessible database of more than 30,000 ceramic compositional analyses that will allow new big-data approaches (AI and machine learning) to large-scale interactions. The resulting databases are updated and readily available for a broad range of future research. Recent developments in artificial intelligence and machine learning allow exploration of large-scale multivariate data at scales that facilitate the exploration of regional-scale interaction. This project develops new methods for analyzing NAA data using social network analysis (SNA) tools that allow one to evaluate models and expectations derived from social network theory using findings of past networks based on typological similarities. Shifting Southwestern NAA studies from narrowly focused individual projects to regionally informed large-scale investigations allows researchers to more effectively sample sites or regions for NAA, making small samples considerably more valuable while not duplicating existing data. The methods developed in this project are directly applicable to other regions with comparable NAA databases. 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

Research into ceramic production and exchange in past societies has led to wide-ranging understanding of social, economic, and political organization in these ancient systems as well as implications for contemporary societies. Much of this work has utilized chemical analysis of ceramics using neutron activation analysis (NAA) and the American Southwest has been one area of intensive research. Existing NAA data represent significant research investment, but the combined power of this investment remains unrealized largely due to two impediments: 1) existing databases require synthesis and standardization; and 2) analytical methods that can systematically compare compositional data at these macroregional scales needs more development. This project produces a standardized, expanded, and broadly accessible database of more than 30,000 ceramic compositional analyses that will allow new big-data approaches (AI and machine learning) to large-scale interactions. The resulting databases are updated and readily available for a broad range of future research. Recent developments in artificial intelligence and machine learning allow exploration of large-scale multivariate data at scales that facilitate the exploration of regional-scale interaction. This project develops new methods for analyzing NAA data using social network analysis (SNA) tools that allow one to evaluate models and expectations derived from social network theory using findings of past networks based on typological similarities. Shifting Southwestern NAA studies from narrowly focused individual projects to regionally informed large-scale investigations allows researchers to more effectively sample sites or regions for NAA, making small samples considerably more valuable while not duplicating existing data. The methods developed in this project are directly applicable to other regions with comparable NAA databases. 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.