Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=5&sort=end_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=end_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=end_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=6&sort=end_date", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=end_date" }, "data": [ { "type": "Grant", "id": "14339", "attributes": { "award_id": "2128707", "title": "Doctoral Dissertation Improvement Award: Modeling Precolonial and Colonial Niche Construction", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Archaeology DDRI" ], "program_reference_codes": [], "program_officials": [ { "id": 7884, "first_name": "John", "last_name": "Yellen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": null, "award_amount": 13948, "principal_investigator": { "id": 30932, "first_name": "Elic", "last_name": "Weitzel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30931, "first_name": "Elic M", "last_name": "Weitzel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 257, "ror": "https://ror.org/02der9h97", "name": "University of Connecticut", "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).<br/><br/>This project will undertake research to better understand how and why humans modify their natural environments, and what happens when they stop doing so. Humans have had far-reaching effects on the environment across the planet, so it is important to research the specific demographic and economic drivers of these impacts. Archaeology can provide a useful means to address these issues, as the past is full of case studies which can be analyzed to understand such activities and trace the history of environmental impacts. Such research can also inform solutions to environmental degradation and contribute to understanding the environmental impacts of human population changes and how different economic systems can lead to either ecological destruction or long-term health.<br/><br/>This project consists of both computational modeling and archaeological data analysis to better understand human environmental impacts. The modeling will contribute to theory about environmental modification by building computer simulations to investigate the impacts of human population size and economic forces on the environment. The analysis will evaluate environmental data from a time of colonial settlement and integration into a new global economy. This analysis will test the predictions of the model and attempt to determine the ecological impacts of colonization and how the environment was impacted by resulting changes in forest management, farming, and hunting. The results of this research will contribute to knowledge of environmental modification and natural resource management, both in theory and in terms of the history of environmental changes that have led us to the present. This information can help researchers to better understand how to improve the ecology of our planet today.<br/><br/>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": "14850", "attributes": { "award_id": "2345178", "title": "Partnering to Recruit, Engage, Prepare, and Support New STEM Teachers for North Dallas Area Schools", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Robert Noyce Scholarship Pgm" ], "program_reference_codes": [], "program_officials": [ { "id": 31530, "first_name": "Patrice", "last_name": "Waller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 1199934, "principal_investigator": { "id": 16099, "first_name": "Mary", "last_name": "Urquhart", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 16094, "first_name": "John", "last_name": "Zweck", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16097, "first_name": "Kate", "last_name": "York", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31531, "first_name": "Katherine", "last_name": "Donaldson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to respond to the national need of preparing and retaining high-quality teachers. Teacher shortages in science, technology, engineering, and mathematics (STEM) are becoming increasingly dire in the aftermath of the Covid-19 pandemic and its impact on attitudes regarding teaching. New strategies are needed to rise to the challenges of recruiting, preparing, and retaining K–12 teachers. This program seeks to partner to recruit, engage, prepare, and support (PREPS) new teachers in the north Dallas area to increase the number of well-prepared science and mathematics teachers and retain them in the teaching profession. The PREPS project intends to investigate and disseminate effective strategies for recruitment of STEM majors into the teaching profession. PREPS also intends to investigate and disseminate effective strategies for support and retention of these newly prepared STEM teachers. <br/><br/>This project at the University of Texas at Dallas (UT Dallas) School of Natural Sciences and Mathematics includes partnerships with at least three high-need independent school districts (ISDs) in the north Dallas area of the Dallas-Fort Worth Metroplex (DFW): Garland ISD, Mesquite ISD, and Richardson ISD. The PREPS project is part of the UTeach Dallas STEM teacher certification preparation program. Project goals include strengthening collaborations with partner ISDs for 1) quality field experiences for preservice teachers, 2) effective induction support for new teachers, 3) investigation of new pathways for recruitment into and/or completion of teacher preparation, and 4) identification of barriers to new teacher retention and strategies to address these barriers. At the university, project goals include 1) targeted recruitment into teacher preparation of STEM majors in critical teacher shortages in mathematics and the sciences, 2) identification and dissemination of effective post-pandemic messaging for teacher recruitment from a pool of undergraduate STEM majors, 3) investigation of a potential partnership with the UT Dallas School of Science and Engineering for recruitment into the teaching profession of STEM majors in additional critical shortage areas such as computer science, and 4) systematic use of data for continuous improvement. UTeach Dallas PREPS also intends to explore direct recruitment of students in local, diverse, high-needs high schools into mathematics and science majors at UT Dallas and into UTeach Dallas. The project plans to disseminate findings to multiple university-based teacher preparation national and statewide networks. UTeach Dallas PREPS intends to provide up to 54 scholarships and 40 internships, with recipients anticipated to directly impact STEM learning for up to 27,000 students in their first five years of teaching in the Dallas-Fort Worth Metroplex. Participants are expected to positively impact K–12 students through internships and in their field experiences beginning as early as their first university semester. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts.<br/><br/>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": "7682", "attributes": { "award_id": "1ZIEBC011384-10", "title": "Anatomic Pathology Residency Program", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [], "start_date": null, "end_date": null, "award_amount": 852774, "principal_investigator": { "id": 23485, "first_name": "Frederic", "last_name": "Barr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1601, "ror": "", "name": "DIVISION OF BASIC SCIENCES - NCI", "address": "", "city": "", "state": "", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1601, "ror": "", "name": "DIVISION OF BASIC SCIENCES - NCI", "address": "", "city": "", "state": "", "zip": "", "country": "United States", "approved": true }, "abstract": "Residents in the Anatomic Pathology Residency Program contribute to the clinical research program of the NCI and the NIH. Through their efforts as anatomic pathology residents in training, they help illuminate the pathological changes associated initial presentation and therapy of both neoplastic and non-neoplastic diseases and explore new techniques to improve diagnosis of these diseases. These residents are critical to the patient care activities of the NCI and the NIH, and contribute to the diagnosis and management of disease, especially COVID-19 this year. Residents have also contributed to publications dealing with characterization, diagnosis, and pathogenesis of a number of disease entities.", "keywords": [ "Accreditation", "Anatomy", "Area", "Autopsy", "Biological Assay", "COVID-19", "Case Study", "Clinical", "Clinical Research", "Clinical Trials", "Clonality", "Communicable Diseases", "Consult", "Consultations", "Cytopathology", "DNA Methylation", "DNA analysis", "Diagnosis", "Diagnostic", "Digestive System Disorders", "Disease", "Disease Management", "Educational process of instructing", "Enrollment", "Etiology", "Exposure to", "Extramural Activities", "Flow Cytometry", "Fluorescent in Situ Hybridization", "Functional disorder", "Funding", "Gene Mutation", "Genes", "Genetic study", "Hematopathology", "Institutes", "Investigation", "Kidney Diseases", "Laboratories", "Learning", "Lymphoid", "Malignant Neoplasms", "Medicine", "Mission", "Molecular Genetics", "Mutation", "Oncology", "Outcome", "Pathogenesis", "Pathologic", "Pathology", "Patient Care", "Patients", "Philosophy", "Play", "Protocols documentation", "Publications", "Research", "Research Activity", "Research Training", "Residencies", "Role", "Services", "Surgical Pathology", "Techniques", "Tissues", "Training", "Training Programs", "United States National Institutes of Health", "base", "cancer therapy", "clinical center", "disease diagnosis", "epidemiology study", "experience", "improved", "insight", "molecular diagnostics", "neoplastic", "nervous system disorder", "neuropathology", "next generation sequencing", "novel", "programs", "recruit", "research clinical testing", "soft tissue", "urologic" ], "approved": true } }, { "type": "Grant", "id": "13572", "attributes": { "award_id": "2144339", "title": "CAREER: Integrating Western Science and Traditional Ecological Knowledge (TEK) to Understand Aphonopelma Diversity Across the Madrean Sky Islands and Educate K-12 Tribal Students", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cross-BIO Activities" ], "program_reference_codes": [], "program_officials": [ { "id": 923, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": null, "award_amount": 1016312, "principal_investigator": { "id": 29734, "first_name": "Chris", "last_name": "Hamilton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 627, "ror": "", "name": "Regents of the University of Idaho", "address": "", "city": "", "state": "ID", "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).<br/><br/>Adapting to climate change is a fundamental challenge for life on Earth. As organisms are forced to move in search of hospitable habitat, species leave areas no longer favorable and expand into new areas, or they go extinct. These events are playing out across North America’s Madrean Pine-Oak woodlands, a biodiversity hotspot, as increasing temperatures and decreasing precipitation push the endemic organisms further up the mountains. The sky islands are natural laboratories perfect for evolutionary studies because each sky island can be thought of as a replicate, with each representing a time point in an ongoing natural experiment. With one-fifth of the world’s invertebrates at risk for extinction, species with a limited ability to move, like the long-lived Aphonopelma – the only tarantula genus in the United States - are of particular concern. If these species are lost to extinction before discovery, this biodiversity knowledge will be lost forever. This research will inform our understanding of island biogeography theory and the evolutionary history of the Madrean Pine-Oak Woodlands biodiversity hotspot. Importantly, this biodiversity hotspot is found within the sovereign land of the Apache and Tohono O’odham peoples. This land, its geology, and the flora and fauna holds significant scientific and cultural knowledge (Traditional Ecological Knowledge – TEK) for these tribes. Gained through thousands of years of living with the land, tribes have an intimate understanding of the interconnections between people and the environment (e.g., how climate change has altered the seasonal patterns and distributions of sky island biodiversity). The goal of this research is to integrate Western science and TEK to better understand the evolutionary patterns and processes that led to the remarkable radiation of Aphonopelma spiders throughout the sky islands, and how climate change is going to impact this diversity in the future. The education component of this project will develop a unique STEM program for San Carlos Apache K-12 students that integrates their TEK with modern research to show them how they can become the next generation of protectors of their tribe’s natural resources.<br/><br/>The overarching question this research looks to answer is whether the Madrean Archipelago has been a generator for North American tarantula diversity. Specifically, this research will use genome data to understand if the sky island Aphonopelma species evolved once or many times. This will allow the researchers to determine what advantageous changes in the genome occurred when species moved into the sky islands and diversified by adapting to new habitats, and whether sky island species will be able to adapt to global climate change. Lastly, the researchers will work with San Carlos Apache elders to show tribal K-12 students how TEK and Western science can be utilized together to better understand the world around them. This combined effort will take students into the different sky island ranges to collect specimens and link the organisms with the land and their TEK. In the classroom, this collaboration will use a “LEGO DNA sequencer” to teach the students about DNA and genetics and introduce students to genome sequencing and bioinformatics. This project will produce transformative results with high scientific impact by informing our understanding of how climate change has and will affect the genetic diversity of a biodiversity hotspot, as well as by including the most underrepresented group in the sciences, Native peoples, in this process.<br/><br/>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": "13571", "attributes": { "award_id": "2130259", "title": "AIM Scholars Program– Accomplish-Innovate-Motivate", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [ { "id": 2077, "first_name": "Connie", "last_name": "Della-Piana", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": null, "award_amount": 1368264, "principal_investigator": { "id": 29733, "first_name": "Hamza", "last_name": "Raheel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 29729, "first_name": "Virginia C", "last_name": "Saiki", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29730, "first_name": "Anne H", "last_name": "Seidler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29731, "first_name": "Taryn", "last_name": "Chase", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29732, "first_name": "Marko", "last_name": "Davinic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2301, "ror": "https://ror.org/042fn4q48", "name": "Bismarck State College", "address": "", "city": "", "state": "ND", "zip": "", "country": "United States", "approved": true }, "abstract": "The project, AIM Scholars – Accomplish-Innovate-Motivate, will contribute to meeting the national need for well-educated scientists, engineers, and technicians in science, technology, engineering, and mathematics (STEM) at Bismarck State College. Bismarck State College in North Dakota will award scholarships to 138 unique scholars/250 scholarships to academically talented students with demonstrated financial need, who are pursuing associate and baccalaureate degrees in Applied Science. Both full-time and part-time students are eligible to apply for scholarships. In addition, the project will foster an ecosystem of academic activities and co-curricular student supports that scaffold student success. The project builds on a prior successful S-STEM award and the research literature by leveraging existing student-centered institutional infrastructure and developing and implementing additional activities and supports based on lessons learned.<br/><br/>The overall purpose of the project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Specifically, the project’s goals are to (1) recruit, enroll, and award scholarships to low-income, talented students into STEM programs; (2) provide student support services to increase retention and graduation rate of AIM Scholars to degree achievement; and (3) build relationships with industry to enhance experiential learning and internship opportunities for AIM Scholars. Activities include pre-enrollment assessment; advising, counseling, and career development; individualized action plans; faculty and peer mentoring; internships; a one-credit student success course; and training and professional development for faculty and peer mentors. These activities will create a system of curricular and co-curricular supports for students in applied science fields, a majority of which are focused on STEM-oriented technician areas. Academic and career pathways will be facilitated by established articulation agreements between Bismarck and other four-year programs at colleges and universities in North Dakota. Importantly, the knowledge generating component of the effort will investigate the relationship between project components and expected outcomes, such as student outcomes (e.g., retention and graduation, time to completion, and grade point average) and institutional outcomes (e.g., institutional commitment to continuing successful project activities). This project is funded by NSF/s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.<br/><br/>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": "13826", "attributes": { "award_id": "2225665", "title": "CAREER: Bolstering Food System Resilience to Reduce the Human Impacts of Disasters", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CAREER: FACULTY EARLY CAR DEV" ], "program_reference_codes": [], "program_officials": [ { "id": 587, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-15", "end_date": null, "award_amount": 521938, "principal_investigator": { "id": 3593, "first_name": "Lauren", "last_name": "Clay", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 676, "ror": "", "name": "University of Maryland Baltimore County", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The focus of this Faculty Early Career Development (CAREER) Program award is to advance the science of food environments and enhance the mitigation and adaptation of social and built environment systems to disasters by bolstering food security and the resilience of food systems. Food is a basic need for human survival and the ability of social systems to meet this need in disaster situations is compromised when our homes, businesses and other structures are damaged and lifelines disrupted. While elements of the various social and built environmental systems that make up the broader food environment as well as food security issues have been studied by various disciplines, a comprehensive, systematic approach has yet to be applied and tested in disaster settings. The overall objectives of this research are to develop a model of the Food Environment in Disasters (FED) along with theory-based tools to support food system resilience. The development of this model and associated tools facilitates a clearer understanding and monitoring of food availability, acceptability, and accessibility to enhance our understanding of the causes, consequences, and health effects of food environment disruption in disasters. This work contributes to NSF’s mission to promote the process of science by developing and validating a new theoretical model and associated metrics on food environment disruption and food security following disasters. The products of this research will advance national health, prosperity, and welfare by supporting improved food security and food system functioning following disasters. <br/><br/>The purpose of this CAREER project is to transform our understanding of disruptions to the social and built food environment and food insecurity in disaster impacted communities. The overall objective of this research is to develop a socio-ecological model of the Food Environment in Disasters (FED), associated metrics, and theory-based tools to generate findings that can bolster food system resilience to hazards. This research departs from the status quo of relying on food system theory and metrics developed in non-disaster contexts to developing an understanding of disaster specific processes and outcomes, thereby enhancing the depth and utility of our knowledge of food environments and security in disasters. Developing a FED theoretical model of food accessibility, availability, and acceptability in disasters will support improved food response and bolsters food system resilience (Aim 1). A new Disaster Research Lab training program based on the Taxonomy of Significant Learning will integrate theory development and empirical testing activities (Aim 2). A portfolio of food system environmental audit tools (EAT) for monitoring disaster preparedness, impact, response, and recovery (Aim 3) will promote improved food environment functioning in disasters. This contribution is expected to help address acute food insecurity issues that can double or triple chronic food security problems following disasters. A public engagement strategic plan for the project will guide meaningful engagement with public stakeholders and audiences to maximize the impact of research activities.<br/><br/>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": "15104", "attributes": { "award_id": "2406129", "title": "SCH: AI-Enhanced Risk Assessment for Mitigating Indoor Viral Transmission in Public Schools", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "OE Operations Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 2155, "first_name": "Georgia-Ann", "last_name": "Klutke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 642608, "principal_investigator": { "id": 27368, "first_name": "Yu", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31650, "first_name": "Chenang", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 387, "ror": "https://ror.org/01g9vbr38", "name": "Oklahoma State University", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true }, "abstract": "This Smart and Connected Health (SCH) award will support research that advances national health, prosperity, and welfare by investigating the potential of Heating, Ventilation, and Air Conditioning (HVAC) systems to mitigate the spread of airborne viruses such as SARS-CoV-2 and other pollutants in school classrooms. Due to the nature of the indoor classroom environment, school-aged children are particularly vulnerable to infectious diseases, and most current HVAC systems are not optimized to effectively prevent cross-infections. This research project combines a computational model that captures the effects of airflow on viral transport, uptake, and immune response with a generative Artificial Intelligence (AI) model trained by laboratory data and simulation experiments to improve design and real-time control of air handling technology. By optimizing HVAC systems to minimize infection risks, the project plans to contribute to healthier indoor environments, reducing the incidence of disease transmission and improving overall public health outcomes. <br/><br/>The goal of this research project is to develop a robust, multiscale computational model to understand the relationship between HVAC design, indoor airflow, virus emission, transmission, and infection risks among children in representative indoor environments. Specifically, the research objectives are to: (1) determine the spatiotemporal concentration distribution of pollutant- and virus-laden aerosols in classrooms with various layouts and children’s respiratory systems, using a model that combines Computational Fluid Dynamics (CFD) and Host Cell Dynamics (HCD) to generate infection risk indices that guide HVAC system design optimization; and (2) develop a generative AI-empowered tool for efficient HVAC design and real-time control to mitigate infection risks. The computational model aims to predict virus-laden aerosol transport, distribution, and infection risks from emission sites to children’s respiratory system under multiple HVAC configurations. The generative AI model plans to deploy generative adversarial networks (GAN) and diffusion models for the design and optimization of HVAC systems, reducing computational costs and enhancing design efficiency. This project leverages the interdisciplinary expertise of the research team to with the intent of creating a transformative tool for public health enhancement. The project includes outreach to engage K-12 students, educators, and the broader community, raising awareness about the importance of indoor air quality and the role of advanced technologies in public health. Additionally, the project provides interdisciplinary training opportunities for students and researchers in engineering, computer science, data science, and public health, promoting diversity and inclusion in these fields.<br/><br/>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": "12803", "attributes": { "award_id": "2327447", "title": "When Teachers \"Aren't There\": Detecting, Evaluating, and Learning from Rote Teaching Across Development", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [ { "id": 6454, "first_name": "Eric", "last_name": "Knuth", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-15", "end_date": null, "award_amount": 338170, "principal_investigator": { "id": 28723, "first_name": "Ilona", "last_name": "Bass", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project explores the effects of automated teaching on children’s learning and development. In contrast to live, engaged teaching, automated teaching occurs when a teacher is not with the student, such as in cases of asynchronous learning, pre-recorded lectures, and virtual classrooms. Automated teaching can also include in-person cases when the teacher is not actively engaged or thinking about the individual learner’s needs and beliefs. Recently, and particularly since the COVID-19 pandemic, the use of asynchronous learning, pre-recorded lectures, and virtual classrooms in education has been on the rise. Given this, it is crucial to understand how and why automated approaches affect children’s learning. This project takes a first step in explaining why young children might learn differently from teachers who are “not really there”. There are many broader impacts of this work. First, results from this research will help explain how to continue to leverage technology in education while making sure children’s learning outcomes do not suffer as a result. Second, through science communication and dissemination efforts, the project will spread the word about its findings to a diverse audience of educators, parents, and researchers. Third, this project will provide research opportunities for students from backgrounds that are typically underrepresented in STEM fields. Finally, the project’s research approach will draw from and integrate across many different disciplines, including early childhood education, cognitive development, neuroscience, and computational modeling. By using a multidisciplinary approach, the project will answer questions about children’s learning from automated teaching from multiple different perspectives and with implications for multiple different fields.<br/><br/>The increasing use of automated approaches in education makes it imperative to understand their impact on children’s learning. Past work in education and developmental psychology raises one cause for concern: Effective teaching requires engaging with students’ real-time learning goals and individual needs, which may be difficult in large-scale automatic teaching. Put together, this leads to a troubling dynamic: Students who detect that a teacher or source of information is “not really there”, engaging with them in the moment, may be more likely to disengage from it, ignore it, and generally learn less from it. Very little is known about how children reason about automaticity in teaching; even the more basic question of whether children understand that social partners in general can either be more automatic and scripted, versus reflective and engaged, is not well understood. In order to design future educational experiences that effectively utilize automated teaching approaches, how children reason about automatic behavior when learning from others must first be understood. Therefore, this project has three specific aims. Aim 1 (three studies, N = 430), will investigate whether learners notice when teachers are acting automatically and how this affects evaluations of their teaching. Aim 2 (two studies, N = 60) will ask how learning differs between automatic versus reflective teaching, leveraging behavioral and neurological methods. Aim 3 (two studies, N = 180) will test whether differences in learning between automatic and reflective teaching could be mitigated with minimal intervention. These questions will be answered using behavioral experiments and neurological measures, while also drawing influence from research in education and cognitive science. The project will recruit participants from a broad target age range (5- to 10-year-olds), in order to understand how these processes change with development during the formative years in early- to middle-childhood.<br/><br/>This project is funded by the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.<br/><br/>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": "14848", "attributes": { "award_id": "2406488", "title": "Implementation Project: Leveraging Innovation and Discovery for STEM Success (LIDSS)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Hist Black Colleges and Univ" ], "program_reference_codes": [], "program_officials": [ { "id": 27246, "first_name": "Alfred", "last_name": "Hall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-15", "end_date": null, "award_amount": 3000000, "principal_investigator": { "id": 31528, "first_name": "Connie", "last_name": "Walton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31527, "first_name": "Stacey", "last_name": "Duhon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2502, "ror": "https://ror.org/05mnb6484", "name": "Grambling State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP) provides support to strengthen STEM undergraduate education and research at HBCUs. This Implementation Project: Leveraging Innovation and Discovery for STEM Success (LIDSS) is a comprehensive effort at Grambling State University to prepare highly competitive STEM graduates to meet the challenges of an ever-changing world. Discovery and Innovation are the core of the design for each strategic activity. This project aligns with the goals of HBCU-UP in its work to foster STEM student success via its support of faculty research experiences, student support programs, and outreach initiatives for K-12 students and teachers.<br/><br/>The overarching goal of this project is to enhance the ability of Grambling State University to train highly prepared STEM majors to meet workforce needs, while reversing the effects that the pandemic has had on education at all levels. The components of this project were identified using a challenge-based learning approach. STEM faculty and students identified the problems and provided possible solutions. The overall premise is STEM education must not remain static but constantly evolve to meet a changing world. The model used to design each component of this project has discovery and innovation as the core for STEM Learning. The LIDSS project aims to improve the recruitment, retention and graduation of STEM students. A priority will be given to the recruitment of veterans as STEM majors. A STEM Entrepreneurship Academy and a Makers Space will support faculty being able to integrate entrepreneurship within curricula to further nurture the creativity of STEM majors. A Student Success Initiative will be established that will create a judgement free zone where students can enhance skills with assistance from faculty/student leader teams. This project aims to establish partnerships with research intensive institutions to expand the research capacity of STEM faculty through collaboration and mentoring opportunities. The results of this project should be of great interest to educators who also face challenges related to recruiting, retaining and graduating STEM students who are prepared to be innovative leaders. A Biennial Symposium that will focus on the use of innovative educational practices to promote STEM learning will be hosted on campus. Data collected in this project, including the symposia, will advance the knowledge of best practices that will lead to improved STEM programs that are nimble and able to utilize innovative strategies to respond to ever changing needs.<br/><br/>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": "14851", "attributes": { "award_id": "2341932", "title": "HNDS-I: Developing a Large-Scale Data Platform for Processing Algorithms for Epidemic Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Human Networks & Data Sci Infr" ], "program_reference_codes": [], "program_officials": [ { "id": 31532, "first_name": "May", "last_name": "Yuan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 998028, "principal_investigator": { "id": 31534, "first_name": "Duncan", "last_name": "Watts", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1043, "first_name": "VICTOR M", "last_name": "PRECIADO", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, { "id": 31533, "first_name": "Hamed", "last_name": "Hassani", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project develops a data platform that collects data and tools for analysis that can be used to study, prevent and respond to pandemics. The name of this platform is the Data Access Platform for Human Mobility in Epidemiology (DAPHME). The DAPHME platform contains global positioning satellite (GPS) datasets that are privatized, so that data can be made available to researchers and policy-makers at minimal cost and without the need for extensive computing resources. Because more people will have access to the data and to repositories of tools for processing and analyzing the data, more people will be able to contribute to the understanding of the spread and prevention of disease. DAPHME promises to significantly improve the nation's capacity to model and mitigate the effects of future pandemics and is a strategic investment in public health infrastructure.<br/><br/>The DAPHME project improves access to mobility data through several coordinated actions. First, it creates and arranges data sharing agreements with various location data providers for platform users. Second, it reduces data access costs by providing analytical tools and servers, along with a subscription model that helps keep the platform financially self-sustaining. Third, it develops methods and mobility metrics specifically for epidemiological research to make analyses more efficient. Finally, it builds a community of researchers through networking to enhance their research projects and encourages the widespread development of new code and methods. The project will positively impact the replication of research results, the validation and broader use of data, and the effectiveness of tools designed to combat public health emergencies.<br/><br/>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": 5, "pages": 1405, "count": 14046 } } }