Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&sort=-approved
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The primary objective of the this award is to advance the understanding of the fundamental processes that control the development and evolution of extreme geomagnetically induced currents (GICs). The horizontal geoelectric field induced at the Earth's surface is the primary quantity that determines the magnitude of GIC that flow on high-voltage power transmission networks. However, knowledge on many aspects of the dynamic solar wind-magnetosphere-ionosphere interactions that control the generation and evolution of the geoelectric fields is lacking, especially during extreme geomagnetic storms. The award would carry out a detailed characterization of the dynamic solar wind-magnetosphere-ionosphere processes that produce extreme geoelectric fields. Specifically, the award would address the science objectives: 1) What are the key high-latitude processes associated with the development and evolution of extreme geoelectric fields? (2) What is the role of field-aligned current meso-scale structures in the development of extreme geoelectric fields? (3) What are the key physical parameters/processes that control the auroral equatorward boundary expansion to lower latitudes? The award investigation is aligned with the White House-led National Science and Technology Council (National Space Weather Strategy and Action Plan, 2015/2019) that identified GICs as one of the top national threats. This award also contains a STEM education component in-which undergraduate students would be supported and mentored to advance knowledge and understanding while promoting teaching, training and learning. The award research plan features a comprehensive data analysis and modeling effort that would connect new multiscale understanding of the fundamental drivers of GICs with GIC modeling. Observations from AMPERE/Swarm, SuperMAG, THEMIS, DMSP, and SuperDARN would be combined with global magnetohydrodynamic (MHD) simulations to address the science questions. The University of Michigan, SWMF, a global MHD application available at the CCMC operating at NASA/GSFC, would carry out a tightly integrated analysis of numerical modeling in combination with observations. The magnetosphere and ionosphere processes would be examined to determine when, why, and how changes in magnetosphere, magnetotail, magnetopause and in ionosphere currents affect the electric field characteristics. An in-depth analysis would be performed to understand what in the magnetosphere causes the geoelectric field latitude boundary effect, and their relationship to the boundary location in the simulations and observations.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12621", "attributes": { "award_id": "2313351", "title": "CRII: CPS: Cooperative Neuro-Inspired Actor Critic Model for Anomaly Detection in Connected Vehicles", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CPS-Cyber-Physical Systems" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28542, "first_name": "Heena", "last_name": "Rathore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Connected vehicles are an integral part of the future of intelligent transportation systems. They use wireless and sensing technologies to enable communication and cooperation between vehicles and infrastructure. Nonetheless, sensor reliability and data integrity play a crucial role in these vehicles. As vehicles and infrastructures grow increasingly networked and automated, there is a pressing need to identify sensor-related anomalies and mitigate potential safety hazards they might pose. The overarching goal of this project is to protect the connected vehicular network against anomalous sensor readings from any cause to ensure the safety of vehicles and passengers. The research aims to (1) provide new capabilities to broadly address safety concerns in connected vehicles to meet emerging future needs of intelligent transportation systems, and (2) enable a diverse and inclusive community of scientists and engineers to work in multidisciplinary areas such as cognitive machine learning and cybersecurity.With the ever increasing complexity of connected vehicles operating in a more complicated cyber-physical social environment, conventional anomaly detection methods will likely not be able to keep pace with the demands of these challenges and function safely in a tomorrow's smart and connected communities. This project will explore (1) novel algorithmic methods that will enable the vehicles to quickly classify different types of sensor failures, learn new emerging anomalous patterns of sensor activity, and assess their risks relative to vehicle safety, and (2) designs for efficient scalable safe multi-agent models to build reputational trust among the connected vehicles in order to facilitate V2V information sharing, learning, and cooperative decision-making, and (3) new consensus-based protocols for connected vehicles that provide for resilience and adaptivity in the presence of disruptions, interruptions, and changes to vehicle participation. Initial test and evaluations are conducted by computer simulations with publicly-available data sets on connected vehicles and autonomous systems.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12622", "attributes": { "award_id": "2213261", "title": "MPS-Ascend: Structure-Preserving Algorithms and Their Applications in Plasma Physics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "ASCEND - MPS" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28543, "first_name": "Allen", "last_name": "Alvarez Loya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2122, "ror": "", "name": "Alvarez Loya, Allen", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). PI Allen Alvarez Loya is awarded a National Science Foundation Mathematical and Physical Sciences Ascending Postdoctoral Research Fellowship (NSF MPS-Ascend) to conduct a program of research and activities related to broadening participation by groups underrepresented in STEM. This fellowship to Dr. Alvarez Loya supports the research project entitled \"MPS-Ascend: Structure-preserving algorithms and their applications in plasma physics\", under the mentorship of a sponsoring scientist. The host institution for the fellowship is the Los Alamos National Laboratory, and the sponsoring scientist is Dr. Qi Tang.In this project PI Alvarez Loya will consider two approaches to developing highly efficient and accurate algorithms which preserve important physical or mathematical structures in numerical simulations. The first approach is a divergence-free scalable MHD solver. The second approach is based on structure-preserving NNs for parameterized Hamiltonian systems. The critical piece of the proposed work is a parameterized symplectic neural network that has a provable approximation property significantly generalizing previous work in the area. To broaden participation in MPS fields, the PI brings his own experience as a participating student in programs such as University of Colorado's BOLD (Broadening Opportunity through Leadership and Diversity) program and California State University's PUMP (Preparing Undergraduates Through Mentoring towards PhDs) as well as a substantial track record in mentoring students from under-represented minority groups. Along with the sponsoring scientist Dr. Tang, the PI will co-mentor undergraduate and graduate students at LANL.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12623", "attributes": { "award_id": "2304710", "title": "EAGER: Plant pathogenic Streptomyces encode components for genetic code mistranslation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Plant-Biotic Interactions" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28544, "first_name": "Oscar", "last_name": "Vargas-Rodriguez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 582, "ror": "https://ror.org/02kzs4y22", "name": "University of Connecticut Health Center", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117- 2). Plant-pathogenic microorganisms are ubiquitous in soils across the U.S. and negatively affect the production of vital food crops and the country’s agricultural economy. Understanding the biological mechanisms to infect and colonize plants employed by these microbes is fundamental for the development of effective management approaches. The present project will investigate novel protein factors that are uniquely encoded by a group of plant-associated microbes, including several pathogens responsible for the common scab disease in potatoes and other tubers. Early research indicates that the protein factors endow these pathogens with the ability to deliberately alter their genomic information without changing their DNA, and ultimately can result in the production of multiple protein mutants from a single gene. Why these pathogens encode these dedicated factors and how they function is largely unknown. The goals of this project are to determine the function of these factors and to explore their role in plant pathogenicity of their host species. Importantly, the implementation of this work will involve the training of two postbaccalaureate associates from underrepresented groups in STEM and will offer opportunities for undergraduate students to gain a first-hand research experience.The genetic code assigns each of the twenty canonical amino acids to one of the 61 sense codons. One of the only two exceptions to the rule of one codon specifying one amino acid was recently discovered in a small group of plant pathogens from the bacterial Streptomyces family, including Streptomyces turgidiscabies and Streptomyces scabiei. These organisms, which are the causative agents of distinct tuber diseases that cause substantial economic losses to U.S. farmers, encode an anomalous aminoacyl-tRNA synthetase and a corresponding tRNA. Expression of these factors causes translation of alanine codons as proline. This dual use of alanine codons enables the diversification and expansion of the host’s proteome due to stochastic Ala→Pro mutations, which can generate a multiplicity of protein variants from a single gene. This may provide a rapid response against stress and environmental changes as well as a mechanism for virulence and infection. This project will combine biochemical, biophysical, multi-omics, comparative genomics, genetics, and molecular biology approaches to elucidate the molecular interactions and features that enabled the emergence of these translation factors and to explore their biological function in the pathogenic Streptomyces hosts.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12624", "attributes": { "award_id": "2328619", "title": "CRII: RI: Sub-mm 3D Scanning of Real-World Scenes with Active Multi-View Event Sensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28545, "first_name": "Florian", "last_name": "Willomitzer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 438, "ror": "https://ror.org/03m2x1q45", "name": "University of Arizona", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).State-of-the-art 3D imaging methods are not able to measure all possible classes of objects at once and still need to be tailored to a specific application. This is one of the main reasons why 3D imaging is still not omnipresent in our society, and still solely trained experts with task-specific equipment are able to capture high-quality 3D models. This project seeks to build a “one fits all” system that has the potential to change this. The targeted system enables precise 3D measurements of complicated surfaces/scenes in today’s billion-dollar industries, such as virtual reality, industrial inspection, autonomous navigation, or medical imaging. Many of these industries routinely run into particularly challenging scenarios for 3D scanning systems. Moreover, a scene-independent and precise 3D sensing system can have many applications. The produced sets of high-quality 3D data can usher the next wave in vision-related artificial intelligence research, leading to algorithms with unprecedented detection quality, prediction accuracy, or navigation precision. Given the current dissimilation of related techniques in all sectors of our modern society, everyone can profit. The project is accompanied by a comprehensive education program incorporating 3D imaging principles in a curriculum for Chicago afterschool programs to introduce at-risk youth to basic concepts in optics, image processing, and electronics.The focus of this research is to solve a long-standing problem in Computer Vision: high-resolution active 3D scanning of scenes cluttered with objects of mixed specularity and polluted by undesirable light contributions such as ambient illumination or strong inter-reflections. Existing approaches for this challenging task deliver rather sobering results or rely on large training datasets or other extensive prior knowledge, such as the geometry and reflectance of objects in the scene. An easy and flexible solution that delivers high-quality data is of significant interest for researchers in the broader computer vision community. This research distills the past decade’s research of the investigator and his colleagues. It combines previous experience in active multi-view 3D imaging concepts for different object classes with the novel detection modality of biologically inspired event sensors (which operate on a fundamentally different principle than conventional sensors). By properly facilitating the existing tradeoffs in 3D imaging and event sensing, the team will develop theory, hardware, and algorithms that lead to a fundamentally new type of 3D camera. The developed technique significantly advances the state-of-the-art and our fundamental understanding of limits.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12625", "attributes": { "award_id": "2304291", "title": "NNA Research: The Greenland Hazards Project", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "NNA-Navigating the New Arctic" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28546, "first_name": "Michael", "last_name": "Willis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. This Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region, and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by addressing interactions among the natural environment, social systems, and the built environment in the following NNA focus areas: Arctic Residents, Data and Observation, Education, and Resilient Infrastructure.As the Arctic warms, the environment is rapidly changing; ice sheets, ice caps, and glaciers are thinning and receding; and permafrost is degrading. In some areas, such as southeastern Alaska and Greenland, these changes can trigger landslides, which in turn can generate localized tsunami-like waves when the landslides flow into fjords and other coastal waters. Since 1995, several coastal landslides have triggered tsunami-like waves, tragically resulting in the loss of lives, damage to infrastructure, and the abandonment of communities in western Greenland. This project is carefully mapping how Greenland is changing in response to ice thinning and is exploring the developing risk of landslides and other hazards. The research integrates local observations made by Greenlandic people in their communities with data collected through advanced remote sensing to learn how hazards evolve over time. The collaboration between US and Greenlandic scientists and Greenlandic residents will be critical to ensure that the research addresses community needs. This project is providing the first Greenland-wide analysis of unstable land and how hazards affect infrastructure and society, while prototyping a monitoring system that could provide warning of approaching large waves. Researchers are examining and modeling high energy events such as rockslides and avalanches to ascertain their potential tsunami threats to communities around the country. The project team hypothesizes that the known distribution of recorded landslides is controlled by rock type, slope and aspect and then by proximity to retreating glaciers, changing permafrost, temperatures and precipitation. Satellite radar, optical imagery and topographic differencing are being used to investigate geophysical changes and how they alter hazards on regional scales. On local scales, drone surveys are examining permafrost changes and rock instabilities on seasonal to sub-daily timescales. Machine learning and modeling are being applied at all scales to identify patterns of change. In addition, a qualitative study is advancing our understanding of the communication processes through which scientific and Greenlandic communities give meaning to environmental changes and hazards through diverse ways of knowing and multiple forms of expertise. The project is building capacity for geodesy, remote sensing, and machine learning through a series of workshops in Greenland. A multilingual website is providing a source of open, intuitively understandable, and easily accessible information that municipalities can use to inform decision making and policy. The project is developing a communication-theory based virtual workshop on community engagement at annual NNA meetings that is being shared with program managers at NSF. The project data are useful to a wide range of disciplines, with work relevant to solid earth geophysics, glaciology, oceanography, natural hazards, and the field of communication.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12626", "attributes": { "award_id": "2142344", "title": "Collaborative Research: Exploring Connections Between Instructional Practice and Student Learning in Inorganic Chemistry Learning Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28547, "first_name": "Sam", "last_name": "Pazicni", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding the complex relationships among teaching, course environments, and student learning is important to inform improvements in science, technology, engineering, and mathematics (STEM) education. This project aims to serve the national interest by studying the connections between several aspects of inorganic chemistry instruction and college students’ learning about molecular-level symmetry, a concept that is essential for understanding how three-dimensional structures influence the properties of substances. Currently, little is known about how instructors teach about symmetry or how students use their understanding of it to solve problems. This project plans to collect and analyze data from inorganic chemistry courses around the country to characterize teaching practices, course environments, and their connections to student learning. The results of this work will be used to develop evidence-based professional development materials for inorganic chemistry instructors to enhance student learning about molecular-level symmetry.The project will be conducted by a collaborative research team from the University of North Carolina at Greensboro and the University of Wisconsin–Madison. The project team will employ the Consensus Model of Teacher Professional Knowledge, which positions the relationship between instruction and learning within a framework of multiple factors, such as instructor and student beliefs, behaviors, and knowledge. Faculty and student research participants will be recruited from twenty-six inorganic chemistry courses from a variety of institution types across the nation. The research questions to be addressed include: (1) How do the teaching beliefs of inorganic chemistry instructors impact their classroom practices when teaching symmetry? (2) How do classroom practices for teaching symmetry emerge from the mutual bootstrapping of personal pedagogical content knowledge (PCK), PCK & skill, and classroom context? (3) How can student outcomes for symmetry be traced to classroom practices? (4) How can student amplifiers and filters explain different student outcomes across different sets of classroom practices? and (5) What constitutes topic-specific professional knowledge for teaching symmetry? To address these research questions, the project team plans to conduct multiple embedded case studies using convergent mixed methods. A variety of quantitative and qualitative data streams will inform each case. Data sources will include interviews with faculty members and students, video observations of symmetry instruction, course artifacts, and student learning and skills assessments. In constructing each case, the project team will use a combination of inductive and deductive coding methods to analyze qualitative data prior to integrating with quantitative data. An expert advisory panel will monitor the success of the project and provide important feedback at specific decision-making points. The results of this project will be disseminated to researchers and educators via workshops, conference presentations, and journal publications. The project is expected to advance our understanding of the learning and teaching of symmetry, as well as various instruction-learning relationships. Thus, the findings will have potential to improve undergraduate chemistry education and will be of interest to education researchers from a variety of STEM disciplines. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12627", "attributes": { "award_id": "2142214", "title": "Collaborative Research: Exploring Connections Between Instructional Practice and Student Learning in Inorganic Chemistry Learning Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28548, "first_name": "Maia", "last_name": "Popova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 728, "ror": "", "name": "University of North Carolina Greensboro", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding the complex relationships among teaching, course environments, and student learning is important to inform improvements in science, technology, engineering, and mathematics (STEM) education. This project aims to serve the national interest by studying the connections between several aspects of inorganic chemistry instruction and college students’ learning about molecular-level symmetry, a concept that is essential for understanding how three-dimensional structures influence the properties of substances. Currently, little is known about how instructors teach about symmetry or how students use their understanding of it to solve problems. This project plans to collect and analyze data from inorganic chemistry courses around the country to characterize teaching practices, course environments, and their connections to student learning. The results of this work will be used to develop evidence-based professional development materials for inorganic chemistry instructors to enhance student learning about molecular-level symmetry.The project will be conducted by a collaborative research team from the University of North Carolina at Greensboro and the University of Wisconsin–Madison. The project team will employ the Consensus Model of Teacher Professional Knowledge, which positions the relationship between instruction and learning within a framework of multiple factors, such as instructor and student beliefs, behaviors, and knowledge. Faculty and student research participants will be recruited from twenty-six inorganic chemistry courses from a variety of institution types across the nation. The research questions to be addressed include: (1) How do the teaching beliefs of inorganic chemistry instructors impact their classroom practices when teaching symmetry? (2) How do classroom practices for teaching symmetry emerge from the mutual bootstrapping of personal pedagogical content knowledge (PCK), PCK & skill, and classroom context? (3) How can student outcomes for symmetry be traced to classroom practices? (4) How can student amplifiers and filters explain different student outcomes across different sets of classroom practices? and (5) What constitutes topic-specific professional knowledge for teaching symmetry? To address these research questions, the project team plans to conduct multiple embedded case studies using convergent mixed methods. A variety of quantitative and qualitative data streams will inform each case. Data sources will include interviews with faculty members and students, video observations of symmetry instruction, course artifacts, and student learning and skills assessments. In constructing each case, the project team will use a combination of inductive and deductive coding methods to analyze qualitative data prior to integrating with quantitative data. An expert advisory panel will monitor the success of the project and provide important feedback at specific decision-making points. The results of this project will be disseminated to researchers and educators via workshops, conference presentations, and journal publications. The project is expected to advance our understanding of the learning and teaching of symmetry, as well as various instruction-learning relationships. Thus, the findings will have potential to improve undergraduate chemistry education and will be of interest to education researchers from a variety of STEM disciplines. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12628", "attributes": { "award_id": "2143529", "title": "CAREER: Fostering Prosocial Behavior and Well-Being in Online Communities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HCC-Human-Centered Computing" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28549, "first_name": "David", "last_name": "Jurgens", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Social media platforms are a common part of daily life that connect people and aid in sharing information. While society has learned how these platforms can negatively impact individuals, little is known comparatively about how our interactions on the platforms benefit us and how to encourage positive behaviors of people on these platforms. Being able to measure the potential benefits of social media can help understand its cumulative effects on people and help identify both ways individuals can change their own behavior and inform the design of social media platforms themselves. This project aims to develop computational tools to identify and measure a variety of ways in which positive behaviors such as compassion or empathy occur online. This research will study the effects of experiencing positive and negative behaviors in social media on people’s psychological well-being to create a holistic picture of social media’s impact. Furthermore, the project will identify strategies for how to phrase positive reinforcement and develop tools to support individuals in behaving positively, both of which should encourage future positive behavior in social media. The project team will also release educational material, courses, and videos for the public and students at multiple levels, as well as provide practical and informative public-facing tools that educate people about the impact of social media on well-being at a personal level.This project studies prosocial behavior in social media and quantifies the impact of interactions, content, and interventions there on specific dimensions of psychological well-being. To accomplish this goal, the project will develop datasets, models, and technologies to identify and encourage positive behaviors. The computational techniques will combine insights from natural language processing and computational social science to recognize subtle social signals in language, which will enable moving from just understanding the meaning of a text to understanding the text’s effects on the audience. The project will address three core technical challenges: (1) recognizing prosocial behavior in online spaces in ways that are sensitive to the norms of the community; (2) quantifying the impact of social media interactions and content on dimensions of psychological well-being and developing computational methods for analyzing this impact at scale; and (3) creating proactive technologies that promote prosocial behavior via behavioral nudges and tools to help people write more prosocially.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12629", "attributes": { "award_id": "2203898", "title": "Spatial Distribution and Drivers of Forest Restoration Reversals and Successes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "(SPRF-FR) SBE Postdoctoral Res" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 6960, "first_name": "Megan", "last_name": "Mills-Novoa", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2123, "ror": "", "name": "Benzeev, Rayna", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Meg Mills-Novoa, at University of California Berkeley, this postdoctoral fellowship award supports an early career scientist to investigate spatial distribution and drivers of forest restoration. Forest restoration is one of the most effective strategies to capture carbon and counteract the emissions causing global environmental change. Despite their great promise and opportunity, many forest restoration projects to date have failed to achieve their stated objectives, particularly for the long term. This project determines the drivers of short-term versus long-term restoration. By providing feedback on the monitoring of forest restoration goals, this research will provide recommendations to improve the longevity of forest restoration efforts, with implications for forest-based environmental solutions in the US and across the globe. Focusing on restoration reversals—restored forests that were later deforested (within 5-10 years)—and restoration successes—restored forests that remained forested for the long term (a minimum of 10 years), this research examines the long-term outcomes of forest restoration efforts at the local scale. By conducting biome- and property-scale geospatial analyses, this research determines the spatial hotspots of forest restoration reversals and successes. Drawing upon spatial, political, and socio-environmental literature of forest change, this project evaluates the drivers of restoration reversals and successes across the biome, community, and property scales. This project advances scholarly knowledge in three key ways. First, the substantial literature on forest restoration has remained focused on where or why forest restoration occurs with less attention to the long-term outcomes of such initiatives. This research project will generate much needed knowledge on the drivers of restoration reversals and successes. Second, by integrating data across multiple scales, this project analyzes how the drivers of forest restoration vary across local to regional scales in a socio-environmental system. The property-scale analysis has been particularly neglected in the geospatial understanding of forest restoration and local dynamics have been neglected in understanding restoration reversals and successes. Third, this project will integrate quantitative and qualitative approaches by combining geospatial science, political ecology, and integrated socio-environmental systems theory. A geospatial analysis will enable the identification of forest restoration hotspots to focus on for qualitative data collection. The interpretation of the patterns of geospatial analysis will be enriched by a better understanding of the underlying processes (i.e., socio-political and economic drivers) of forest restoration.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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1419, "pages": 1424, "count": 14236 } } }